@article {pmid41529886,
year = {2026},
author = {Zhu, H and Gan, Y and Ye, J and Li, Y and Yu, JZ and Li, X},
title = {Effectiveness of brain-computer interface interventions in autism spectrum disorder rehabilitation: a systematic review and meta-analysis protocol.},
journal = {BMJ open},
volume = {16},
number = {1},
pages = {e102277},
doi = {10.1136/bmjopen-2025-102277},
pmid = {41529886},
issn = {2044-6055},
mesh = {Humans ; *Autism Spectrum Disorder/rehabilitation ; *Brain-Computer Interfaces ; Systematic Reviews as Topic ; Meta-Analysis as Topic ; Research Design ; },
abstract = {BACKGROUND: Autism spectrum disorder (ASD) is a neurodevelopmental condition characterised by impairments in social interaction, communication and the presence of repetitive behaviours. Recent advancements in brain-computer interface (BCI) technologies have demonstrated potential benefits in enhancing cognitive, social and communication skills in individuals with ASD. However, the effectiveness of BCI-based interventions in ASD rehabilitation remains inconsistent across studies. Therefore, this protocol outlines a systematic review and meta-analysis to synthesise the evidence on the effectiveness of BCI-based interventions for ASD rehabilitation.<br><br>METHODS: We will conduct a comprehensive literature search across multiple databases, including MEDLINE Ovid, Embase Ovid, Cochrane Central Register of Controlled Trials (CENTRAL), Conference Proceedings Citation Index-Science (CPCI-S), Science Citation Index Expanded (SCI-EXPANDED) and so on, to identify relevant studies published from inception to the present. The search will be supplemented by screening the reference lists of included studies and relevant systematic reviews. Two independent reviewers will screen the titles, abstracts and full texts of identified studies for eligibility based on predefined criteria. Data extraction will be performed using a standardised form, and the risk of bias (RoB) will be assessed using the Cochrane RoB tool. Heterogeneity will be evaluated using the I&#xb2; statistic, and a random-effects or fixed-effects model will be selected for meta-analysis based on the degree of heterogeneity. Subgroup analyses will be conducted to explore potential sources of heterogeneity, including participant age, ASD severity, type of BCI intervention and duration of the intervention. The review will be conducted from January 2026 to April 2026.<br><br>ETHICS AND DISSEMINATION: Ethical approval is not required for this study, as it does not involve the collection of primary data from individual patients. Findings will be disseminated through peer-reviewed publication and conference presentations.<br><br>PROSPERO REGISTRATION NUMBER: CRD420251010496.},
}
@article {pmid41528907,
year = {2026},
author = {Zhao, Y and Cao, D and Yu, H and Liang, G and Chen, Z},
title = {MSHANet: A Multiscale Hybrid Attention Network for Motor Imagery EEG Decoding.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2026.3653824},
pmid = {41528907},
issn = {1558-2531},
abstract = {Brain-computer interface (BCI) technology has significant applications in neuro rehabilitation and motor function restoration, especially for patients with stroke or spinal cord injury. Motor imagery electroencephalog-raphy (MI-EEG) is widely used in BCIs, but its nonlinear dynamics and inter-subject variability limit decoding accuracy. In this paper, a multiscale hybrid attention network (MSHANet) for MI-EEG decoding, which consists of spatiotemporal feature extraction (STFE), talking head self-attention (THSA), dynamic squeeze-and-excitation attention (DSEA), and a temporal convolutional network (TCN), is proposed. MSHANet was evaluated via within-subject experiments using BCI Competition IV Datasets 2a and 2b, as well as EEGMMID, achieving decoding accuracies of 83.56%, 89.75%, and 75.66%, respectively. In cross-subject experiments on the three datasets, the mode lattained accuracies of 69.93% on BCI-2a, 81.85% on BCI-2b, and 79.67% on EEGMMID. In addition, we propose an electrode spatial structure-aware encoder. This technique encodes the spatial positions of electrodes in the original data, enabling the model to obtain richer spatial electrode information at the input stage. In within-subject and cross-subject tasks on BCI-2a, this encoding improved the decoding performance by 2.83% and 2.91%, respectively. Visualization was also employed to elucidate feature distributions and the effec tiveness of its attention mechanisms. Experimental results demonstrate that MSHANet performs exceptionally well in MI-EEG decoding tasks and has high potential for clinical applications, particularly in neurorehabilitation and motor function reconstruction.},
}
@article {pmid41528455,
year = {2026},
author = {Becker, L and Krüger, L and Wolf, M and Alfen, K and Theysohn, J and Lefering, R and Dudda, M and Kamp, O and , },
title = {The necessity of CT scans on pediatric carotid injury after blunt trauma - An analysis of the traumaregister DGU[®].},
journal = {European journal of trauma and emergency surgery : official publication of the European Trauma Society},
volume = {52},
number = {1},
pages = {13},
pmid = {41528455},
issn = {1863-9941},
mesh = {Humans ; Child ; *Wounds, Nonpenetrating/diagnostic imaging/epidemiology ; Child, Preschool ; Adolescent ; Male ; Registries ; Female ; Infant ; Germany/epidemiology ; *Carotid Artery Injuries/diagnostic imaging/epidemiology ; *Tomography, X-Ray Computed ; Injury Severity Score ; Infant, Newborn ; Prevalence ; Risk Factors ; },
abstract = {PURPOSE: Blunt carotid injuries (BCI) in pediatric trauma patients are rare. Using data from the TraumaRegister DGU[®][,] this study aims to identify screening parameters and calculate the prevalence of pediatric BCI. By proposing potential risk factors for a BCI, this research seeks to reduce unnecessary radiation exposure in pediatric trauma cases. These findings may enhance understanding of pediatric BCI and highlight the necessity of cautious diagnostic approaches that balance clinical needs with radiation risks.<br><br>METHODS: The TraumaRegister DGU[&#xae;] is a multicenter database established in 1993 to document the treatment of severely injured patients from initial injury to hospital discharge. Data are collected in four phases: demographics, injury patterns, treatments, and outcomes. Almost 700 hospitals, primarily from Germany, contribute to the registry annually. Statistical analysis was conducted using SPSS. For analysis, the dataset was divided into two groups: trauma patients diagnosed with BCI and trauma patients without BCI. The complete dataset from the TraumaRegister DGU[&#xae;] for 2006-2020 was screened for relevant cases. The dataset was limited to patients between 0 and 15 years old.<br><br>RESULTS: Out of 9070 severely injured pediatric trauma patients analysed, 50 cases of pediatric BCI were identified, representing a prevalence of 0.6%. Patients with BCI presented with higher injury severity scores (ISS), lower Glasgow Coma Scale (GCS) scores, and a greater prevalence of head injuries, as well as thoracic, abdominal, and extremity injuries. These patients also experienced higher in-hospital mortality rates (34%) and required more frequent blood transfusions. Full-body CT scans were more commonly performed in patients with BCI.<br><br>CONCLUSION: This study highlights the rarity and severity of BCI in pediatric trauma patients, with a prevalence of 0.6%. Significant risk factors for a BCI include high injury severity, head trauma, neurological deficits, and pre-hospital hypotension. The findings emphasise the importance of early diagnosis and targeted diagnostic strategies to balance the need for prompt intervention with reducing unnecessary radiation exposure.},
}
@article {pmid41527472,
year = {2026},
author = {Niu, J and Xia, J and He, Y and Li, W and Chen, K and Liu, Q and Li, W and Qiu, J and Chen, H and Li, J and Liao, W},
title = {Controllability of morphometric network colocalize with underlying neurobiology in major depression.},
journal = {Psychological medicine},
volume = {56},
number = {},
pages = {e15},
doi = {10.1017/S0033291725103140},
pmid = {41527472},
issn = {1469-8978},
support = {62473082, 62571105, 82121003, 62036003, 62333003//National Natural Science Foundation of China/ ; ZYGX2022YGRH008, ZYGX2024XJ054//Fundamental Research Funds for the Central Universities/ ; },
mesh = {Humans ; *Depressive Disorder, Major/diagnostic imaging/physiopathology/metabolism/pathology ; Female ; Adult ; Male ; Middle Aged ; Case-Control Studies ; *Brain/diagnostic imaging/metabolism/pathology ; *Nerve Net/diagnostic imaging ; Magnetic Resonance Imaging ; Diffusion Tensor Imaging ; Young Adult ; },
abstract = {BACKGROUND: Cognitive and behavioral symptoms of major depressive disorder (MDD) are linked to aberrant changes in the controllability of brain networks. However, previous studies examined network controllability using white matter tractography, neglecting the contributions of gray matter. We aimed to examine differences in the controllability of morphometric networks between patients with MDD and demographic-matched healthy controls and identify the associated neurobiological signatures.<br><br>METHODS: Based on the structural and diffusion MRI data from two independent cohorts, we calculated the controllability of morphometric similarity networks for each participant. A generalized additive model was used to investigate the case-control differences in regional controllability and their cognitive and behavioral associations. We investigated the associations between imaging-derived controllability and neurotransmitters, brain metabolism, and gene transcription profiles using multivariate linear regression and partial least squares regression analyses.<br><br>RESULTS: In both cohorts, depression-related abnormalities of morphometric network controllability were primarily located in the prefrontal, cingulate, and visual cortices, contributing to memory, sensation, and perception processes. These abnormalities in network controllability were spatially aligned with the distributions of serotonergic transmission pathways as well as with altered oxygen and glucose metabolism. In addition, these abnormalities spatially overlapped with differentially expressed genes enriched in annotations related to protein catabolism and mitochondria in neuronal cells and were disproportionately located on chromosome 22.<br><br>CONCLUSIONS: Collectively, neuroimaging evidence revealed aberrant morphometric network controllability underlying MDD-related cognitive and behavioral deficits, and the associated genetic and molecular signatures may help identify the neurobiological mechanisms underlying MDD and provide feasible therapeutic targets.},
}
@article {pmid41526383,
year = {2026},
author = {Wang, D and Shi, Y and Pang, J and Zhu, X and Meng, L and Ming, D},
title = {Data-driven subtyping of early Parkinson's disease via mutual cross-attention fusion of EEG and dual-task gait features.},
journal = {NPJ Parkinson's disease},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41531-026-01258-2},
pmid = {41526383},
issn = {2373-8057},
support = {82372083//National Natural Science Foundation of China/ ; },
abstract = {Parkinson's disease (PD) exhibits marked clinical heterogeneity, which poses challenges for diagnosis, prognosis, and therapeutic precision, especially for early-stage PD patients. Existing subtyping approaches often rely on subjective clinical scales and single-modality data, which limits their sensitivity in capturing subtle but clinically relevant differences across patients. To reveal clinically meaningful PD subtypes, we propose a data-driven multimodal framework that integrates resting-state electroencephalography (EEG) and dual-task gait features using mutual cross-attention (MCA) fusion. Forty idiopathic early-stage PD patients were enrolled in a prospective study. EEG biomarkers were encoded via a convolutional neural network for the prediction of motor severity (MDS-UPDRS-III), while dual-task gait features were derived to capture subtle motor dysfunctions. The MCA enabled bidirectional attention-guided integration of EEG and gait features, which were then clustered using an unsupervised method. The analysis revealed three distinct subtypes, with dual-task-based fusion providing superior clinical separation. Subtype I was characterized by pronounced motor deficits; Subtype II showed moderate symptoms with relatively preserved quality of life; and Subtype III presented mild motor impairments but exhibited poorer cognitive and psychosocial outcomes. Feature contribution analyses highlighted central beta and theta EEG activity, along with dual-task gait metrics (e.g., stride length during turning), as key drivers of subtype differentiation. Longitudinal follow-up demonstrated subtype-specific rehabilitation responses, with Subtype II showing an insufficient response compared to other subtypes. In conclusion, this study enables digital phenotyping of PD with prognostic implications for personalized rehabilitation strategies and accelerates precision medicine.},
}
@article {pmid41525762,
year = {2026},
author = {Ding, W and Chen, X and Liu, A},
title = {Breaking the performance barrier in deep learning-based SSVEP-BCIs: A joint frequency-phase training strategy.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae36f6},
pmid = {41525762},
issn = {1741-2552},
abstract = {OBJECTIVE: Deep learning exhibits considerable potential for steady-state visual evoked potential (SSVEP) classification in electroencephalography (EEG)-based brain-computer interfaces (BCIs). SSVEP signals contain both frequency and phase characteristics that correspond to the visual stimuli. However, existing deep learning training strategies typically focus on either frequency or phase information alone, thus failing to fully exploit these dual inherent properties and substantially limiting classification accuracy.<br><br>APPROACH: To tackle this limitation, this study proposes a Joint Frequency-Phase Training Strategy (JFPTS), which comprises two complementary stages with distinct time-window sampling schemes. The first stage adopts a frequency prior-driven sampling scheme to improve frequency component utilization, whereas the second stage employs a phase-locked sampling scheme to enhance intra-category phase consistency. This design enables JFPTS to effectively leverage both frequency and phase properties of SSVEP signals.<br><br>MAIN RESULTS: Comprehensive experiments on two well-established public datasets validate the effectiveness of JFPTS. The results demonstrate that the JFPTS-enhanced model achieves a marked superiority over the current state-of-the-art classification approaches, notably surpassing the long-standing performance benchmark set by task discriminative component analysis (TDCA).<br><br>SIGNIFICANCE: Overall, JFPTS establishes a new training paradigm that advances deep learning approaches for SSVEP classification and promotes the broader adoption of SSVEP-BCIs.},
}
@article {pmid41525614,
year = {2026},
author = {Jin, J and Wang, C and Xu, R and He, X and Wu, X and Li, J and Chen, W and Wang, X and Cichocki, A},
title = {RUNet: A Zero-Calibration Framework for Cross-Domain EEG Decoding via Riemannian and Unsupervised Representation Learning.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2026.3653024},
pmid = {41525614},
issn = {1558-2531},
abstract = {OBJECTIVE: Inter-session and inter-subject variability in electroencephalography (EEG) signals, resulting from individual differences and environmental factors, poses a major challenge for neural decoding in brain-computer interface (BCI) applications.<br><br>METHODS: To address this issue, we propose RUNet, a zero-calibration motor imagery EEG decoding framework based on Riemannian manifold learning and unsupervised representation learning. RUNet incorporates a multi-scale spatiotemporal convolutional module that jointly captures local global spatial and multi-resolution temporal dynamics features. To enhance the robustness of EEG features against non stationarity, a polysynergistic covariance optimization module is employed, which strengthens the covariance matrix representation through multiple regularizations and adaptive fusion. In addition, RUNet integrates the Riemannian Affine Log Mapping layer, based on Affine-Invariant Transformation and Log-Euclidean Mapping, in an end-to-end manner to mitigate cross-domain covariance drift and enhance domain-invariant feature learning. A transfer learning framework is further integrated into RUNet: during pre-training, an unsupervised contrastive loss is applied to resting-state EEG data to learn domain-invariant spatiotemporal features; during retraining, task-specific data are used to enhance discriminability and feature disentanglement.<br><br>CONCLUSION: Experimental results on the BCI Competition IV 2a, 2b datasets and a self-collected laboratory dataset show that RUNet achieves average cross-session accuracies of 87.19%, 88.03% and 85.45%, and cross-subject accuracies of 68.09%, 78.29% and 87.25%, respectively. On the PhysioNet dataset, a cross-subject accuracy of 78.14% is achieved. These results demonstrate the effectiveness of RUNet's unified pipeline and its robust cross-domain generalization.},
}
@article {pmid41525559,
year = {2026},
author = {Guan, S and Li, Y and Gao, Y and Yin, R and Luo, Y and Liang, J and Zhang, J and Zhang, Y and Li, R},
title = {Enhanced Mapping of Finger Movement Representations Using Diffuse Optical Tomography: A Systematic Comparison with fNIRS.},
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.2026.3652812},
pmid = {41525559},
issn = {1558-0210},
abstract = {Advancing neuroimaging modalities for motor cortex analysis is critical for understanding the neural mechanisms underlying fine motor tasks and for expanding clinical applications. Functional Near-Infrared Spectroscopy (fNIRS) is widely used for measuring cortical hemodynamic activity due to its portability and accessibility, but its inherent limitations in spatial resolution and noise sensitivity reduce its utility for precise neural mapping. Diffuse Optical Tomography (DOT) has emerged as a promising alternative with superior spatial resolution and sensitivity. In this study, we performed a systematic comparison of DOT and fNIRS in detecting task-evoked neural activation during a finger-tapping paradigm including four conditions varying by finger type (thumb vs. little finger) and frequency (high vs. low). Our results demonstrated that DOT consistently captured robust activation in motor-related brain regions, even during less demanding conditions, while fNIRS exhibited limited sensitivity. Temporal trace analyses revealed that DOT achieved higher contrast-to-noise ratio (CNR) and contrast-to-background ratio (CBR), validating its enhanced signal quality and ability to distinguish subtle hemodynamic responses. Furthermore, statistical comparisons highlighted significant differences in task-related activations detected by the two modalities, particularly in low-effort conditions. These findings underscore the advantages of DOT over fNIRS, particularly in applications requiring high spatial resolution and sensitivity to subtle neural processes. The results contribute to ongoing efforts to refine optical imaging techniques for motor neuroscience and reinforce DOT's potential for clinical translation in motor deficit diagnosis, rehabilitation monitoring, and brain-computer interface development.},
}
@article {pmid41525552,
year = {2026},
author = {Zhu, J and Li, K and Chen, S and Huang, H and Zhang, Y and Hu, L and Li, Y},
title = {Smart Ward Control Based on a Wearable Multimodal Brain-Computer Interface Mouse.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2026.3653138},
pmid = {41525552},
issn = {1558-0210},
abstract = {For patients with severe extremity motor function impairment, traditional smart ward control methods, such as those using joysticks and touchscreens, are frequently unsuitable due to their limited physical abilities. Consequently, developing an effective brain-computer interface (BCI) suitable for their operation has become an immediate concern. This paper presents a wearable multimodal BCI system for smart ward control, which employs a self-designed wearable headband to capture head rotation and blinking movement. By wearing the headband, users can control a computer cursor on the screen only with head rotation and blinking, and further control devices in a smart ward with self-designed graphical user interfaces (GUIs). The system decodes signals from an inertial measurement unit (IMU) to map the head posture to the position of the cursor on the screen and decodes electrooculography (EOG) and electroencephalography (EEG) signals to detect valid blinks for selecting and activating function buttons. Ten participants were recruited to perform two experimental tasks that simulate the daily needs of patients with extremity motor function issues. To our satisfaction, all the participants fully accomplished the simulated tasks, and an average accuracy of 97.0±3.9 % and an average response time of 2.39±0.53 s were achieved. Different from traditional step-controlled BCI nursing beds, we designed a continuous-controlled nursing bed and achieved satisfactory results. Furthermore, workload evaluation using NASA Task Load Index (NASA-TLX) revealed that the participants experienced a low workload when using the system. The experimental results demonstrate the effectiveness of our proposed system, indicating significant potential for practical applications.},
}
@article {pmid41525550,
year = {2026},
author = {Padmaja, GKR and Bhagat, NA and Balasubramani, PP},
title = {Assessing the utility of Fronto-Parietal and Cingulo-Opercular networks in predicting the trial success of brain-machine interfaces for upper extremity stroke rehabilitation.},
journal = {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.2026.3653049},
pmid = {41525550},
issn = {1558-0210},
abstract = {Brain-machine interfaces (BMIs) have the potential to improve stroke rehabilitation by actively facilitating sensory-cognitive-motor connections to restore movement. However, individuals with cognitive impairments are often excluded from BMI-based neurorehabilitation due to concerns about impaired cognition, specifically reduced attention and executive control. We propose leveraging the trial-wise dynamics of large-scale cognitive control networks-specifically, the frontoparietal (FPN) and cingulo-opercular (CON) networks-to build neural markers of cognitive control. Using existing BMI datasets, we demonstrate that trial-wise activity within these networks predicts motor task performance, suggesting that cognitive control signals in these networks could serve as adaptive modulations for BMI-based rehabilitation. Our system is able to predict unsuccessful BMI trials at the population level about 84.2% of the time on average, with an overall mean accuracy of 72.2% in a 3-fold cross-validation. Additionally, in a leave-one-subject-out validation, our system achieved 71% specificity on average, with an overall mean accuracy of 68.3%. Notably, model performance varies across subjects, with some individuals showing up to 92% specificity and 100% sensitivity. Unlike previous studies that primarily focus on resting-state data, our findings point toward the untapped potential of incorporating cognitive network state monitoring into BMI systems to optimize online performance through trials. Specifically, we suggest that our pre-trained models can be fine-tuned with subject-specific information to design more targeted rehabilitation programs that enhance motor performance by identifying precise attention and learning tasks to improve the successful response of the network model in patients with significant cognitive impairment.},
}
@article {pmid41525004,
year = {2026},
author = {Yan, Y and Zhang, Y and Zhao, X and Chen, R and Fang, S and Zhou, Y and Huang, J and Wang, F and Chen, C and Lin, Z and Xu, X},
title = {Life-course body shape trajectories and cerebral oxygen metabolism in community-dwelling older adults.},
journal = {GeroScience},
volume = {},
number = {},
pages = {},
pmid = {41525004},
issn = {2509-2723},
support = {NSFC/72274170//Natural Science Foundation of China/ ; NSFC/82201733//Natural Science Foundation of China/ ; },
abstract = {Obesity and lifelong body-shape fluctuation are associated with late-life structural brain damage, suggesting the involvement of metabolic pathways. The cerebral metabolic rate of oxygen (CMRO2) reflects hemodynamic and oxidative stress and precedes structural atrophy, but its role in adiposity-related brain change remains unclear. We examined whether current and life-course adiposity relate to CMRO2 and to structural change. A total of 303 community-dwelling adults aged 50 years and older were included. Body shape was assessed using Body Mass Index (BMI) and Body Roundness Index (BRI). Global CMRO2 was derived from TRUST and phase-contrast MRI. T1-weighted MPRAGE provided volumetry, and medial temporal atrophy (MTA) grading. General linear models estimated associations of BMI and BRI with CMRO2, including age interactions. Age-stratified mediation tested CMRO2 as a mediator of adiposity to MTA associations. Body-shape trajectories at ages 25, 40, 60, and current age were modeled and related to CMRO2 and metabolism-related regions. Adiposity was associated with lower CMRO2: with overweight (β = -1.12 μmol/100 g/min, 95%CI = (-1.96, -0.28)) and higher BRI (β = -1.31, 95%CI = (-2.36, -0.27)) showing stronger effects with advancing age. Among participants aged 70 years, CMRO2 mediated the association between BMI and MTA (indirect β = 0.06, 95%CI = (0.01, 0.14)). Three adulthood body-shape patterns emerged, and CMRO2 was lower in moderate increasing (β = -11.40; 95%CI = (-20.90, -1.90)) and high-rising (β = - 12.23; 95%CI = (-23.56, -0.90)) groups. Metabolism-related regions were larger in higher-risk patterns, particularly the left hypothalamus. Greater and prolonged adiposity is linked to reduced CMRO2 and related structural differences in older adults.},
}
@article {pmid41523970,
year = {2025},
author = {Xu, C and Kong, L and Mou, T and Tang, A and Hu, S and Lai, J},
title = {Vitamin B12 and Affective Disorders: A Focus on the Gut-Brain Axis.},
journal = {Alpha psychiatry},
volume = {26},
number = {6},
pages = {49138},
pmid = {41523970},
issn = {2757-8038},
abstract = {Accumulating evidence highlights the role of Vitamin B12 (VitB12) in the pathophysiology of affective disorders. However, its influence on brain function and the underlying mechanisms remain incompletely understood. In humans, VitB12 is obtained solely from dietary sources, primarily animal-based foods. VitB12 deficiency leads to the accumulation of homocysteine, a known contributor to emotional and behavioral dysregulation. VitB12 plays a critical role in maintaining neuron stability, synapsis plasticity, and regulating neuroinflammation by modulating key bioactive factors. These processes help to alleviate hippocampal damage, mitigate blood-brain barrier disruption, reduce oxidative stress, and enhance both structural and functional connectivity-collectively contributing to resilience against affective disorders. VitB12 from both diet and microbial sources is essential to gut homeostasis. Within the gut lumen, it stabilizes gut microbial communities, promotes short-chain fatty acid (SCFA) production, and supports neurotransmitter metabolism (e.g., serotonin and dopamine) via its role in S-adenosyl-l-methionine biosynthesis. Crucially, VitB12, gut microbiota, SCFAs, intestinal mucosa, and vagal nerve signaling interact synergistically within the gut-brain axis (GBA) to maintain gut microenvironment stability, protect the gut-blood barrier, and suppress neuroinflammatory cascades, eventually reducing the susceptibility to affective disorders. This review synthesizes current evidence on the involvement of VitB12 in the GBA, its association with mood regulation, and its potential as a nutritional adjunct in managing affective disorders. By elucidating this integrative mechanism, we provide new insights into targeting the GBA to improve clinical outcomes in affective disorders.},
}
@article {pmid41523966,
year = {2025},
author = {Wang, R and Hou, X and Li, R and Cheng, B and Zhou, C and Xue, C and Li, K and Deng, W},
title = {Maintenance of Noninvasive Brain Stimulation for Preventing Relapse in Depression: A Systematic Review and Meta-Analysis.},
journal = {Alpha psychiatry},
volume = {26},
number = {6},
pages = {49140},
pmid = {41523966},
issn = {2757-8038},
abstract = {BACKGROUND: Depression relapse rates remain high after acute treatment; this study evaluates the efficacy of maintenance noninvasive brain stimulation in preventing relapse and identifies optimal treatment parameters.<br><br>METHODS: This meta-analysis was conducted following PRISMA guidelines. We conducted a systematic search of PubMed, Embase, Web of Science, Cochrane Library, and PsycINFO databases up to January 5, 2025. The primary outcome was relapse rate.<br><br>RESULTS: A total of nine randomized controlled trials with 837 participants were included, six studies used electroconvulsive therapy (ECT) and three studies used repetitive transcranial magnetic stimulation (rTMS). Our findings indicate that ECT combined with pharmacotherapy or rTMS alone demonstrated superiority over pharmacotherapy alone in reducing the relapse of depression during 6, 9, 12-month maintenance treatment periods. Interestingly, ECT alone did not show significant results. In terms of stimulation parameters, the ECT combined with pharmacotherapy group mainly received right unilateral stimulation, while the ECT alone group had bitemporal stimulation. The stimulation frequency was similar between the two groups. In contrast, the rTMS-alone group had significantly higher stimulation frequencies than the ECT groups. We did not find any eligible studies on transcranial direct current stimulation, transcranial alternating current stimulation or magnetic seizure therapy, but they also showed potential in the maintenance treatment of depression, which warrants further investigation.<br><br>CONCLUSIONS: ECT combined with pharmacotherapy, or rTMS alone, is more effective than pharmacotherapy alone in preventing relapse of depression during 6 to 12 months of maintenance treatment. Future research should prioritize identifying the optimal treatment regimen and exploring the potential of combination therapies.<br><br>THE PROSPERO REGISTRATION: CRD42023490546, https://www.crd.york.ac.uk/PROSPERO/view/CRD42023490546.},
}
@article {pmid41523191,
year = {2026},
author = {van Balen, B and Ramsey, NF and Vansteensel, MJ},
title = {Relational personhood: the missing link for evaluating clinical impact of brain-computer interfaces.},
journal = {Brain communications},
volume = {8},
number = {1},
pages = {fcaf470},
pmid = {41523191},
issn = {2632-1297},
}
@article {pmid41521389,
year = {2026},
author = {Yilmaz Kars, M and Akkar, I and Dogan, MH and Turgut, ZI and Cicek, O and Dikmeer, A and Kollu, K and Cakir Ozden, EC and Kizilarslanoglu, MC},
title = {EXPRESS: The CRP/Albumin Ratio (CAR) may be more strongly linked to delirium than other indices derived from laboratory parameters in older patients in an intensive care unit.},
journal = {Journal of investigative medicine : the official publication of the American Federation for Clinical Research},
volume = {},
number = {},
pages = {10815589261415891},
doi = {10.1177/10815589261415891},
pmid = {41521389},
issn = {1708-8267},
abstract = {The aim of this study is to investigate the association of delirium with laboratory-derived indices and ratios in patients staying in an intensive care unit (ICU). Delirium was diagnosed according to DSM-5 criteria, and laboratory data obtained at the time of diagnosis were retrospectively analyzed. The following indices were calculated: C-reactive protein(CRP)/albumin ratio(CAR), CRP-albumin-lymphocyte(CALLY), B12-CRP(BCI), Systemic Immune-Inflammation(SII), Prognostic Nutritional Index(PNI), Advanced Lung Cancer Inflammation (ALI), Systemic Inflammation Response indices (SIRI) and Glasgow Prognostic Score (GPS). In addition, inflammation markers derived from the complete blood count were also analyzed. They were compared between patients with and without delirium. The study included 215 patients, of whom 104 had delirium (median age 76 years, 51.6% female). Patients with delirium were older than those without delirium(p=0.008). The median CAR index was higher in patients with delirium (3.4 mg/g, 0.02-28.23) compared to those without delirium (2.19 mg/g,0.02-16.74), with borderline statistical significance(p=0.071). No statistically significant differences were found in other indices and laboratory parameters between patients with delirium and those without it (p>0.05 for all). When patients were stratified into tertiles based on CAR levels, the occurrence of delirium was significantly higher in the third tertile than in the other two tertiles (p=0.020). Even after adjusting for all significant confounding factors, CAR remained independently associated with delirium [Odds ratio(OR):1.099, 95% confidence interval(CI):1.002-1.205, p=0.046]. The findings of this study suggest that the CAR index may serve as an independent associated factor for delirium compared to other laboratory-derived markers in critically ill patients.},
}
@article {pmid41521257,
year = {2026},
author = {Wang, S and Song, X and Song, X and Gu, Y and Cong, Z and Shen, Y and Yu, L},
title = {Non-Invasive Brain-Computer Interfaces: Converging Frontiers in Neural Signal Decoding and Flexible Bioelectronics Integration.},
journal = {Nano-micro letters},
volume = {18},
number = {1},
pages = {193},
pmid = {41521257},
issn = {2150-5551},
abstract = {The development of non-invasive brain-computer interfaces (BCIs) relies on multidisciplinary integration across neuroscience, artificial intelligence, flexible electronics, and systems engineering. Recent advances in deep learning have significantly improved the accuracy and robustness of neural signal decoding. Parallel progress in electrode design-particularly through the use of flexible and stretchable materials like nanostructured conductors and novel fabrication strategies-has enhanced wearability and operational stability. Nevertheless, key challenges persist, including individual variability, biocompatibility limitations, and susceptibility to interference in complex environments. Further validation and optimization are needed to address gaps in generalization capability, long-term reliability, and real-world operational robustness. This review systematically examines the representative progress in neural decoding algorithms and flexible bioelectronic platforms over the past decade, highlighting key design principles, material innovations, and integration strategies that are poised to advance non-invasive BCI capabilities. It also discusses the importance of multimodal data fusion, hardware-software co-optimization, and closed-loop control strategies. Furthermore, the review discusses the application potential and associated engineering challenges of this technology in clinical rehabilitation and industrial translation, aiming to provide a reference for advancing non-invasive BCIs toward practical and scalable deployment.},
}
@article {pmid41521019,
year = {2026},
author = {Lv, Z and Li, X and Zhang, X},
title = {Commentary on He et al.: From static association to dynamic causation - a methodological leap in understanding and addressing addiction.},
journal = {Addiction (Abingdon, England)},
volume = {},
number = {},
pages = {},
doi = {10.1111/add.70312},
pmid = {41521019},
issn = {1360-0443},
support = {2024YFF0507600//National Key R&amp;D Program of China/ ; 2021ZD0202101//Chinese National Programs for Brain Science and Brain-like Intelligence Technology/ ; 32571266//National Natural Science Foundation of China/ ; 32171080//National Natural Science Foundation of China/ ; 32400919//National Natural Science Foundation of China/ ; 32200914//National Natural Science Foundation of China/ ; ZSYS(2024)001//Project of Guizhou Key Laboratory of Artificial Intelligence and Brain-inspired Computing QianKeHe Platform/ ; 2408085QC081//Natural Science Foundation of Anhui Province/ ; 24YJCZH014//Ministry of Education of China/ ; //Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior/ ; },
}
@article {pmid41518865,
year = {2026},
author = {Hu, W and Xiao, J and Li, L and Zhao, W and Feng, Y and Shan, X and Chen, H and Duan, X},
title = {Developmental organization of neural dynamics supporting social processing: Evidence from naturalistic fMRI in children and adults.},
journal = {Developmental cognitive neuroscience},
volume = {78},
number = {},
pages = {101670},
doi = {10.1016/j.dcn.2026.101670},
pmid = {41518865},
issn = {1878-9307},
abstract = {The development of social cognition underpins significant implications for diagnosing and treating neurodevelopmental disorders such as autism spectrum disorder. This study investigates the dynamic neural organization of social cognition in children (n = 60, ages 3-10) and adults (n = 55) using a naturalistic fMRI paradigm that tracks continuous brain activity during real-world social interactions. We identify four distinct co-activation patterns (CAP) that reflect a functional hierarchy, ranging from basic sensory processing to complex social-cognitive integration. These brain state dynamics reveal significant developmental differences: children exhibit immature transitions, often bypassing intermediate states (e.g., salience-driven filtering, State 3) and prematurely shifting from early sensory encoding (State 1) to internally-directed integration (State 2). Moreover, during mentalizing and pain events, children show reduced modulation of sensory and perceptual brain states, indicating limited cognitive flexibility that is essential for social interaction. Structural equation modeling reveals a developmental cascade linking the maturation of sensory (State 1), perceptual filtering (State 3), and social-cognitive (State 2) processing states. This pathway is mediated by individual differences in Theory of Mind (ToM) development and further predicts empathic abilities. These findings advance our understanding of how brain state reorganization supports social cognitive maturation and offer new insights into neurodevelopmental disorders.},
}
@article {pmid41518463,
year = {2026},
author = {Fernández-Rodríguez, &#xc1; and Velasco-Álvarez, F and Vizcaíno-Martín, FJ and Ron-Angevin, R},
title = {Evaluation of video background and stimulus transparency in a visual ERP-based BCI under RSVP.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {},
number = {},
pages = {},
pmid = {41518463},
issn = {1741-0444},
abstract = {Rapid serial visual presentation (RSVP) is a promising paradigm for visual brain-computer interfaces (BCIs) based on event-related potentials (ERPs) for patients with limited muscle and eye movement. This study explores the impact of video background and stimulus transparency on BCI control, factors that have not been previously examined together under RSVP. Two experimental sessions were conducted with 12 participants each. Four BCI conditions were tested: opaque pictograms, and white background (A255W); opaque pictograms, and video background (A255V); intermediate transparent pictograms, and video background (A085); and highly transparent pictograms, and video background (A028V). The results indicated that the video background had a negative impact on BCI performance. In addition, the intermediate transparent pictograms (A085V) proved to be balanced, as it did not show significant performance differences compared to opaque pictograms (A255V) but was rated significantly better by users on subjective measures related to attending to the video background. Therefore, in applications where users must shift attention between BCI control and their surroundings, balancing stimulus transparency is a suitable option for enhancing system usability. These findings are particularly relevant for designing asynchronous ERP-BCIs using RSVP for patients with impaired oculomotor control.},
}
@article {pmid41518099,
year = {2026},
author = {Berwal, U and Kumar, V},
title = {Exploring assistive technology in adaptive sports: a bibliometric analysis.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {},
number = {},
pages = {1-13},
doi = {10.1080/17483107.2025.2612557},
pmid = {41518099},
issn = {1748-3115},
abstract = {Assistive technology in adaptive sports has become a transformative force for individuals with disabilities. It helps disabled athletes to overcome physical and cognitive barriers to participate in sports. This study presents a bibliometric analysis of assistive technology in adaptive sports to examine its development, key themes, and emerging trends. The analysis used data from 8,660 documents across 2,137 sources retrieved from the Scopus database from 1987 to 2025. The result shows that due to advancements in technology and increased awareness of inclusivity in sports, the research output grows exponentially after 2010. Among these research outputs, the most used theme was rehabilitation. The other emerging topics incorporated into adaptive sports are virtual reality, brain-computer interfaces, wearable technologies. Further, the co-occurrence network analysis reveals that there are strong interdisciplinary connections between rehabilitation, assistive technology, and physical activity. However, several areas remain unexplored such as digital health and telehealth applications in adaptive sports. Thus, bibliometric analysis provides a roadmap for future research by identifying critical trends and gaps. This study highlights the interdisciplinary collaboration and technological innovation in advancing accessibility and inclusivity for athletes with disabilities.},
}
@article {pmid41516662,
year = {2025},
author = {Gomez-Rivera, A and Collazos-Huertas, DF and Cárdenas-Peña, D and Álvarez-Meza, AM and Castellanos-Dominguez, G},
title = {Gaussian Connectivity-Driven EEG Imaging for Deep Learning-Based Motor Imagery Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {1},
pages = {},
doi = {10.3390/s26010227},
pmid = {41516662},
issn = {1424-8220},
support = {91908//Ministerio de Ciencia, Tecnolog&#xed;a e Innovaci&#xf3;n/ ; 57661//Universidad Nacional de Colombia/ ; },
mesh = {*Electroencephalography/methods ; Humans ; Brain-Computer Interfaces ; *Deep Learning ; Normal Distribution ; Neural Networks, Computer ; Brain/physiology ; *Imagination/physiology ; Algorithms ; },
abstract = {Electroencephalography (EEG)-based motor imagery (MI) brain-computer interfaces (BCIs) hold considerable potential for applications in neuro-rehabilitation and assistive technologies. Yet, their development remains constrained by challenges such as low spatial resolution, vulnerability to noise and artifacts, and pronounced inter-subject variability. Conventional approaches, including common spatial patterns (CSP) and convolutional neural networks (CNNs), often exhibit limited robustness, weak generalization, and reduced interpretability. To overcome these limitations, we introduce EEG-GCIRNet, a Gaussian connectivity-driven EEG imaging representation network coupled with a regularized LeNet architecture for MI classification. Our method integrates raw EEG signals with topographic maps derived from functional connectivity into a unified variational autoencoder framework. The network is trained with a multi-objective loss that jointly optimizes reconstruction fidelity, classification accuracy, and latent space regularization. The model's interpretability is enhanced through its variational autoencoder design, allowing for qualitative validation of its learned representations. Experimental evaluations demonstrate that EEG-GCIRNet outperforms state-of-the-art methods, achieving the highest average accuracy (81.82%) and lowest variability (±10.15) in binary classification. Most notably, it effectively mitigates BCI illiteracy by completely eliminating the "Bad" performance group (<60% accuracy), yielding substantial gains of ∼22% for these challenging users. Furthermore, the framework demonstrates good scalability in complex 5-class scenarios, performing competitive classification accuracy (75.20% ± 4.63) with notable statistical superiority (p = 0.002) against advanced baselines. Extensive interpretability analyses, including analysis of the reconstructed connectivity maps, latent space visualizations, Grad-CAM++ and functional connectivity patterns, confirm that the model captures genuine neurophysiological mechanisms, correctly identifying integrated fronto-centro-parietal networks in high performers and compensatory midline circuits in mid-performers. These findings suggest that EEG-GCIRNet provides a robust and interpretable end-to-end framework for EEG-based BCIs, advancing the development of reliable neurotechnology for rehabilitation and assistive applications.},
}
@article {pmid41516650,
year = {2025},
author = {Li, J and Yang, H and Xu, M and Wu, Y and Shou, X and Huang, Z and Hao, Y and Wu, F and Ruan, W and Zhang, Y and Cui, Z and Wei, Y},
title = {Task-Dependent Cortical Oscillatory Dynamics in Functional Constipation.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {1},
pages = {},
doi = {10.3390/s26010211},
pmid = {41516650},
issn = {1424-8220},
support = {62373326//National Natural Science Foundation of China/ ; 32471148//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Constipation/physiopathology ; Male ; Electroencephalography/methods ; Female ; Adult ; Middle Aged ; *Cerebral Cortex/physiopathology ; Cognition/physiology ; Defecation/physiology ; Brain/physiopathology ; },
abstract = {Functional constipation (FC) is a common functional gastrointestinal disorder thought to arise from the brain-gut axis dysfunction, yet direct human neurophysiological evidence is lacking. We recorded high-density electroencephalography (EEG) data in 21 FC patients and 37 healthy controls across resting, cognitive, and defecation-related tasks. We observed that FC patients displayed a consistent, task-dependent signature compared with healthy controls. At the regional level, FC patients exhibited increased alpha during both resting and defecation-related tasks, reduced temporal gamma during defecation-related tasks, as well as elevated temporal theta during the cognitive task. At the global level, we found altered network properties, such as global efficiency in the delta and beta band networks during resting and defecation-related tasks. These findings establish a direct neurophysiological link between specific, condition-dependent perturbations in cortical rhythm activity and FC pathophysiology. Our work implicates the brain-gut axis in symptom generation and opens a path toward EEG-based biomarkers and targeted neuromodulatory therapies.},
}
@article {pmid41514692,
year = {2025},
author = {Marques, L and Rodrigues, DP and Duarte, RC and Calado, R},
title = {Thermal Limits of the Estuarine Amphipod Melita palmata Under Different Salinities and Its Relevance for Aquaculture Production.},
journal = {Animals : an open access journal from MDPI},
volume = {16},
number = {1},
pages = {},
doi = {10.3390/ani16010004},
pmid = {41514692},
issn = {2076-2615},
support = {10.54499/2022.01620.PTDC//Funda&#xe7;&#xe3;o para a Ci&#xea;ncia e Tecnologia/ ; 10.54499/UID/50017/2025//Funda&#xe7;&#xe3;o para a Ci&#xea;ncia e Tecnologia/ ; 10.54499/LA/P/0094/2020//Funda&#xe7;&#xe3;o para a Ci&#xea;ncia e Tecnologia/ ; },
abstract = {Estuarine organisms experience frequent fluctuations in salinity and temperature, facing major challenges to their physiological homeostasis. Such variability can promote high energetic costs for osmoregulation, potentially reducing tolerance to additional stressors. We investigated the effect of salinity on the thermal tolerance of the estuarine amphipod Melita palmata (Montagu, 1804), a species of growing interest for aquaculture, either as live feed or as a potential source for essential fatty acids in formulated diets. The critical thermal maximum (CTmax) was determined for males and females collected from three sites within a temperate coastal lagoon (Ria de Aveiro, Portugal) characterized by different salinity regimes (15, 20, and 30). Individuals from lower-salinity environments exhibited significantly lower CTmax values than those from higher salinities, indicating that osmoregulatory costs may restrict thermal resistance. No significant sex-based differences in CTmax were detected. However, thermal safety margins (TSMs) increased with salinity, indicating greater thermal tolerance under higher salinity conditions, and differences in body condition index (BCI) between sites suggest salinity-related effects on growth performance. These results highlight that the elevated energetic demands of osmoregulation under hypo-osmotic conditions can constrain the thermal limits of M. palmata, underscoring the complex trade-offs between environmental variability and physiological performance in estuarine habitats. Beyond its ecological implications, understanding the physiological responses of M. palmata to salinity and temperature is key, optimising its use in aquaculture. The species' physiological plasticity under such variable conditions reinforces its suitability for aquaculture production, particularly in earthen ponds in estuarine environments.},
}
@article {pmid41512946,
year = {2026},
author = {Xia, S and Zhao, X and Lv, B and Gan, Y and Kang, Y and Long, J and Liu, F and Hu, X and He, G and Xing, H and Cheng, B},
title = {Functional Gradient Alteration and Structural Remodeling in Postpartum Women.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121702},
doi = {10.1016/j.neuroimage.2026.121702},
pmid = {41512946},
issn = {1095-9572},
abstract = {Postpartum women (PW) undergo profound brain functional and structural reorganization to support maternal adaptation. However, the specific large-scale neural adaptation mechanisms remain unclear. The current study employed a multimodal MRI approach integrating functional gradient analysis, graph-theoretical network metrics, and morphometry to explore the brain connectome reorganization across the postpartum period and its clinical correlates in 209 participants (134 PW and 75 healthy nulliparous women (HNW)). Compared to HNW, PW exhibited a significant contraction of the first two principal functional gradients, reduced local network segregation and less efficient information processing, accompanied by matter volume (GMV) reductions. Mediation analysis revealed that GMV alterations in PW modulate functional gradient reorganization by influencing network integration and segregation. These neural changes were closely linked to clinical symptoms including sleep quality and anxiety. Our findings revealed a large-scale network reconfiguration in PW, simultaneously elucidating neurobiological mechanisms of adaptive plasticity in postpartum period.},
}
@article {pmid41512029,
year = {2026},
author = {Liu, P and Zhou, L and Xu, D and An, D and Lu, Y and Hu, B and Shao, Y and Huang, N and Guo, C and Chen, L and Li, J and Li, J and Liang, F and Liu, J and Huang, G and Mei, Y and Li, R and Song, E},
title = {A self-wrapping, bioresorbable neural interface for wireless multimodal therapy of localized peripheral nerve injury.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {123},
number = {2},
pages = {e2521817123},
doi = {10.1073/pnas.2521817123},
pmid = {41512029},
issn = {1091-6490},
support = {2022ZD0209900//Ministry of Science and Technology of the People's Republic of China (MOST)/ ; 62204057 62304044//MOST | National Natural Science Foundation of China (NSFC)/ ; 22ZR1406400//Science and Technology Commission of Shanghai Municipality (STCSM)/ ; },
mesh = {Animals ; *Peripheral Nerve Injuries/therapy ; Rats ; *Wireless Technology/instrumentation ; Rats, Sprague-Dawley ; Combined Modality Therapy ; *Absorbable Implants ; Photothermal Therapy/methods ; Drug Delivery Systems/methods ; },
abstract = {High-precision in vivo therapeutic technologies that establish three-dimensional (3D), multimodal neural interfaces with targeted biotissues offer significant clinical potential for the timely treatments of localized peripheral nerve injury (PNI). Current approaches for this purpose such as implantable devices face challenges in terms of percutaneous wires and/or nondegradable designs, and support only single-mode operation that lack microscale spatial resolution. Here, we develop a miniaturized, self-wrapping system that yields wireless, multimodal neural interfaces with 3D adaptation across localized peripheral nerves at scales ranging from tens of micrometers (15 μm) to millimeters. Such platform integrates multilayer architectures that include SiNx layers as the mechanically triggered substrate for 3D wrapping, with multimodal treatments via MXene and drug-loaded layers for photothermal stimulation and pharmacological release. Experimental and computational studies establish operational principle as the basis for the combination of long-term photothermal therapy and transient drug delivery at high spatiotemporal resolution. In vivo tests on living rat models demonstrate that the implantable neural interface can roll up across the localized, dynamic surface of injured nerves, providing sustained treatments over 1 mo in a fully bioresorbable design after the healing process. These findings create future opportunities of such wireless, multimodal system with 3D self-wrapping techniques for precise PNI therapeutic strategies.},
}
@article {pmid41510853,
year = {2026},
author = {Zhang, X and Liu, X and Liu, M and Li, Y and Yan, X and Zhang, X and Xu, J},
title = {The Integrated Application and Future Trends of Multimodal Neuromodulation Techniques in Spinal Cord Injury Rehabilitation.},
journal = {Neurology India},
volume = {74},
number = {1},
pages = {3-11},
pmid = {41510853},
issn = {1998-4022},
mesh = {Humans ; *Spinal Cord Injuries/rehabilitation ; *Transcranial Magnetic Stimulation/methods ; *Transcranial Direct Current Stimulation/methods ; Brain-Computer Interfaces ; *Spinal Cord Stimulation/methods ; Recovery of Function/physiology ; Neuronal Plasticity/physiology ; Combined Modality Therapy ; },
abstract = {Spinal cord injury (SCI) remains a severe condition that leads to permanent motor and sensory impairments, significantly affecting patients' quality of life. In recent years, neuromodulation techniques such as spinal cord stimulation (SCS), transcranial magnetic stimulation (TMS), and transcranial direct current stimulation (tDCS) have shown promising results in promoting neural plasticity and functional recovery. However, the limitations of single-modality approaches have spurred the development of multimodal neuromodulation strategies. This review systematically analyzes the integrated application of multimodal neuromodulation techniques in SCI rehabilitation. We first provide an overview of current neuromodulation methods, including SCS, TMS, tDCS, and brain-computer interface (BCI), highlighting their individual mechanisms and clinical outcomes. Next, we discuss the synergistic effects of combining these techniques, such as SCS with TMS or BCI, which act on multiple levels of the nervous system to enhance neuroplasticity, reconstruct neural networks, and modulate neurotransmitter release. Additionally, we explore the mechanisms underlying multimodal neuromodulation, emphasizing its role in promoting axonal regeneration, synaptic reconnection, and adaptive functional recovery. Despite the promising advancements, challenges remain, including technical complexity, safety concerns, and the heterogeneity of SCI patients. Addressing these limitations requires standardized treatment protocols and further clinical validation. Future trends, such as the development of closed-loop systems, artificial intelligence-driven precision rehabilitation, and personalized therapies, will likely drive innovations in this field. In conclusion, multimodal neuromodulation techniques offer a synergistic and integrative approach for SCI rehabilitation, providing new avenues for clinical intervention. This review underscores the importance of combining complementary techniques to optimize neural recovery and highlights the potential for future breakthroughs in neurorehabilitation.},
}
@article {pmid41506085,
year = {2026},
author = {Wang, Y and Xu, S},
title = {Relationship between artificial intelligence tool usage experience and academic stress among college students: Mediating role of loneliness and moderating role of academic self-efficacy.},
journal = {Acta psychologica},
volume = {263},
number = {},
pages = {106220},
doi = {10.1016/j.actpsy.2026.106220},
pmid = {41506085},
issn = {1873-6297},
abstract = {As artificial intelligence (AI) rapidly integrates into higher education, AI tools are increasingly being utilized to support student learning. Although these tools offer efficiency and convenience, their psychological implications-particularly vis-à-vis academic stress-remain unclear. This study investigated the relationship between AI tool usage experience and academic stress among college students, focusing on the potential mediating role of loneliness and the moderating role of academic self-efficacy. Overall, 624 university students were surveyed using the AI Tool Usage Experience Scale, UCLA Loneliness Scale, Academic Stress Scale, and Academic Self-Efficacy Scale. The following three key findings were observed: (1) AI tool usage experience significantly positively predicted students' academic stress. (2) Loneliness partially mediated this relationship. (3) Academic self-efficacy significantly moderated the mediation pathway's first stage. Specifically, AI usage's positive predictive effect on loneliness was stronger (weaker) for students with higher (lower) academic self-efficacy levels. These findings suggest that AI tool usage not only directly influences academic stress but also contributes indirectly through heightened feelings of loneliness, particularly among students with strong self-efficacy beliefs. This study underscores the complex psychological mechanisms underlying students' interactions with AI in educational settings.},
}
@article {pmid41505837,
year = {2026},
author = {Zhang, K and Dong, S and Shi, P and Hu, D and Gao, G and Yang, J and Gan, T and Rao, N},
title = {GenoPath-MCA: Multimodal masked cross-attention between genomics and pathology for survival prediction.},
journal = {Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society},
volume = {128},
number = {},
pages = {102699},
doi = {10.1016/j.compmedimag.2026.102699},
pmid = {41505837},
issn = {1879-0771},
abstract = {Survival prediction using whole slide images (WSIs) and bulk genes is a key task in computational pathology, essential for automated risk assessment and personalized treatment planning. While integrating WSIs with genomic features presents challenges due to inconsistent modality granularity, semantic disparity, and the lack of personalized fusion. We propose GenoPath-MCA, a novel multimodal framework that models dense cross-modal interactions between histopathology and gene expression data. A masked co-attention mechanism aligns features across modalities, and the Multimodal Masked Cross-Attention Module (M2CAM) jointly captures high-order image-gene and gene-gene relationships for enhanced semantic fusion. To address patient-level heterogeneity, we develop a Dynamic Modality Weight Adjustment Strategy (DMWAS) that adaptively modulates fusion weights based on the discriminative relevance of each modality. Additionally, an importance-guided patch selection strategy effectively filters redundant visual inputs, reducing computational cost while preserving critical context. Experiments on public multimodal cancer survival datasets demonstrate that GenoPath-MCA significantly outperforms existing methods in terms of concordance index and robustness. Visualizations of multimodal attention maps validate the biological interpretability and clinical potential of our approach.},
}
@article {pmid41501731,
year = {2026},
author = {Zhang, W and Xiong, B and Shen, D and Wang, W},
title = {Characteristics of resting-state EEG after deep brain stimulation in nucleus accumbens and anterior limb of internal capsule: a pilot study.},
journal = {BMC psychiatry},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12888-025-07681-8},
pmid = {41501731},
issn = {1471-244X},
}
@article {pmid41103211,
year = {2026},
author = {Martín, I and Zamora-López, G and Fousek, J and Schirner, M and Ritter, P and Jirsa, V and Deco, G and Patow, G},
title = {TVB C++: A Fast and Flexible Back-End for The Virtual Brain.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {13},
number = {2},
pages = {e06440},
doi = {10.1002/advs.202406440},
pmid = {41103211},
issn = {2198-3844},
support = {PID2021-122136OB-C22//Ministerio de Ciencia, Innovaci&#xf3;n y Universidades/ ; PID2022-136216NB-I00//Ministerio de Ciencia, Innovaci&#xf3;n y Universidades/ ; 785907 (HBP SGA2)//H2020 Excellent Science/ ; Horizon EBRAINS2.0 (101147319)//HORIZON EUROPE Framework Programme/ ; Virtual Brain Twin (101137289)//HORIZON EUROPE Framework Programme/ ; EBRAINS-PREP 101079717//HORIZON EUROPE Framework Programme/ ; AISN - 101057655//HORIZON EUROPE Framework Programme/ ; EBRAIN-Health 101058516//HORIZON EUROPE Framework Programme/ ; EICgrantPHRASE(101058240)//HORIZON EUROPE Framework Programme/ ; DigitalEuropeTEF-Health(101100700)//HORIZON EUROPE Framework Programme/ ; BRIDGE(101219311)//HORIZON EUROPE Framework Programme/ ; SHAIPED(101195135)//HORIZON EUROPE Framework Programme/ ; CoordinaTEF(101168074)//HORIZON EUROPE Framework Programme/ ; SFB 1436 (project ID 425899996)//Kinderherzen F&#xf6;rdergemeinschaft Deutsche Kinderherzzentren/ ; SFB 1315 (project ID 327654276)//Kinderherzen F&#xf6;rdergemeinschaft Deutsche Kinderherzzentren/ ; SFB 936 (project ID 178316478//Kinderherzen F&#xf6;rdergemeinschaft Deutsche Kinderherzzentren/ ; SFB-TRR 295 (project ID 424778381)//Kinderherzen F&#xf6;rdergemeinschaft Deutsche Kinderherzzentren/ ; SPP Computational Connectomics RI 2073/6-1//Kinderherzen F&#xf6;rdergemeinschaft Deutsche Kinderherzzentren/ ; RI 2073/10-2//Kinderherzen F&#xf6;rdergemeinschaft Deutsche Kinderherzzentren/ ; RI 2073/9-1//Kinderherzen F&#xf6;rdergemeinschaft Deutsche Kinderherzzentren/ ; BECAUSE-Y 504745852//DFG Clinical Research Group/ ; CZ.02.01.01/00/22\_008/0004643//ERDF/ ; 2021SGR00917//AGAUR/ ; 2021SGR01035//AGAUR/ ; 945539(HBPSGA3)//Horizon 2020/ ; 425899996//German Research Foundation/ ; 327654276//German Research Foundation/ ; 178316478//German Research Foundation/ ; 424778381//German Research Foundation/ ; RI2073/6-1//SPP Computational Connectomics/ ; RI2073/10-2//SPP Computational Connectomics/ ; RI2073/9-1//SPP Computational Connectomics/ ; ANR-22-PESN-0012//Agence Nationale de la Recherche/ ; NEMESIS(101071900)/ERC_/European Research Council/International ; },
mesh = {Humans ; *Brain/physiology ; *Computer Simulation ; *Brain-Computer Interfaces ; *User-Computer Interface ; *Software ; },
abstract = {This study introduces TVB C++, a streamlined and fast C++ Back-End for The Virtual Brain (TVB), a renowned platform and a benchmark tool for full-brain simulation. TVB C++ is engineered with speed as a primary focus while retaining the flexibility and ease of use characteristic of the original TVB platform. Positioned as a complementary tool, TVB serves as a prototyping platform, whereas TVB C++ becomes indispensable when performance is paramount, particularly for large-scale simulations and leveraging advanced computation facilities like supercomputers. Developed as a TVB-compatible Back-End, TVB C++ seamlessly integrates with the original TVB implementation, facilitating effortless usage. Users can easily configure TVB C++ to execute the same code as in TVB but with enhanced performance and parallelism capabilities. As a consequence, TVB C++ will enable the widespread use of individualized models that will open the possibility of designed tailored solutions at the individual patient level.},
}
@article {pmid41499961,
year = {2026},
author = {Pan, L and Wang, K and Yi, W and Zhang, Y and Xu, M and Ming, D},
title = {CTSSP: A temporal-spectral-spatial joint optimization algorithm for motor imagery EEG decoding.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae34ea},
pmid = {41499961},
issn = {1741-2552},
abstract = {OBJECTIVE: Motor imagery brain-computer interfaces (MI-BCIs) hold significant promise for neurorehabilitation, yet their performance is often compromised by EEG non-stationarity, low signal-to-noise ratios, and severe cross-session variability. Current decoding methods typically suffer from fragmented optimization, treating temporal, spectral, and spatial features in isolation.<br><br>APPROACH: We propose common temporal-spectral-spatial patterns (CTSSP), a unified framework that jointly optimizes filters across all three domains. The algorithm integrates: 1) multi-scale temporal segmentation to capture dynamic neural evolution, 2) channel-adaptive finite impulse response (FIR) filters to enhance task-relevant rhythms, and 3) low-rank regularization to improve generalization.<br><br>MAIN RESULTS: Evaluated across five public datasets, CTSSP achieves state-of-the-art performance. It yielded mean accuracies of 76.9% (within-subject), 68.8% (cross-session), and 69.8% (cross-subject). In within-subject and cross-session scenarios, CTSSP significantly outperformed competing baselines by margins of 2.6-14.6% (p < 0.001) and 2.3-13.8% (p < 0.05), respectively. In cross-subject tasks, it achieved the highest average accuracy, proving competitive against deep learning models. Neurophysiological visualization confirms that the learned filters align closely with motor cortex activation mechanisms.<br><br>SIGNIFICANCE: CTSSP effectively overcomes the limitations of decoupled feature extraction by extracting robust, interpretable, and coupled temporal-spectral-spatial patterns. It offers a powerful, data-efficient solution for decoding MI EEG in noisy, non-stationary environments. The code is available at https://github.com/PLC-TJU/CTSSP.},
}
@article {pmid41499837,
year = {2026},
author = {Ge, H and Feng, T and Wu, H and Hu, H and Li, J and Wu, X},
title = {Uncovering the Cognitive Mechanisms of Risk Decision-Making among ICU Nurses in Complex Clinical Contexts.},
journal = {Intensive &amp; critical care nursing},
volume = {93},
number = {},
pages = {104329},
doi = {10.1016/j.iccn.2025.104329},
pmid = {41499837},
issn = {1532-4036},
abstract = {OBJECTIVES: The intensive care unit is a high-stakes, information-intensive environment requiring nurses to make rapid and accurate decisions. This study aimed to elucidate the cognitive and neural mechanisms underlying nurses' risk decision-making under time pressure and complex clinical demands.<br><br>METHODS: Thirty ICU nurses participated in a computer-based multitasking experiment simulating concurrent medical multitasking scenarios, with twenty-one valid datasets analyzed. Participants performed priority judgments under high- and low-risk conditions while EEG signals were continuously recorded. Event-related potential components and oscillatory activities across &#x3b4;, &#x3b8;, &#x3b1;, and &#x3b2; frequency bands were analyzed. Gaussian Hidden Markov Models were used to characterize cognitive state transition dynamics aligned to task events.<br><br>RESULTS: Risk decision-making emerged as a multi-stage, dynamically coordinated process involving four distinct cognitive patterns: monolithic stability progression, compulsory path lock-in, multi-path flexible convergence, and flow separation and premature convergence. Correct decisions were associated with enhanced low-frequency oscillations (&#x3b4;, &#x3b8;) and stable HMM transitions, reflecting efficient integration and adaptive cognitive control. In contrast, incorrect decisions exhibited early perceptual inefficiency, unstable state transitions, and premature cognitive closure under high-risk conditions.<br><br>CONCLUSIONS: This study is the first to identify four distinct dynamic cognitive patterns of risk decision-making in a simulated ICU multitasking context. The findings indicate that decision accuracy is closely linked to coordinated state-transition dynamics rather than isolated neural activations, highlighting the importance of adaptive cognitive control in clinical judgment.<br><br>Although the present findings are exploratory, they may provide a preliminary reference for future research on brain-machine collaboration in clinical nursing contexts. In particular, future work could examine how EEG-decoded cognitive states might be incorporated as input information for robot-assisted systems to characterize nurses' cognitive intentions during risk tasks. Further studies with larger samples and in more realistic clinical settings are needed to validate the model's robustness and generalizability.},
}
@article {pmid41499809,
year = {2026},
author = {Aktaş, FA and Eken, A and Erogul, O},
title = {Explainable AI for Pain Perception: Subject-Independent EEG Decoding Using DeepSHAP and CNNs.},
journal = {Biomedical physics &amp; engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/ae34b4},
pmid = {41499809},
issn = {2057-1976},
abstract = {Accurate classification of pain levels is essential for clinical monitoring, particularly in clinical populations with limited verbal communication. This study explores the feasibility of decoding pain from EEG using explainable deep learning. Approach: EEG signals from 50 subjects exposed to low and high pain stimuli were analyzed. A 1D convolutional neural network (CNN) was trained using leave-one-subject-out (LOSO) cross-validation. To enhance interpretability, DeepSHAP was applied to identify frequency-specific contributions of EEG features to the model's decisions. Main Results: The CNN achieved a classification accuracy of 95.85%, outperforming traditional classifiers (SVM, LDA, RF, etc.) on the same dataset. Explainability analysis showed that increased beta activity (14-15 Hz) was associated with high pain, while alpha (11-12 Hz) theta and delta bands correlated with lower pain states. Significance: This work demonstrates the potential of explainable deep learning in real-time, subject-independent pain decoding. The results support the integration of XAI techniques into EEG-based brain-computer interface (BCI) systems for objective pain monitoring.},
}
@article {pmid41499216,
year = {2026},
author = {Zhai, W and Sun, L and Fang, W and Dong, Y and Cheng, C and Liu, Y and Zhou, Y and Ji, J and Wu, L and Pan, A and Gamazon, ER and Pan, XF and Zhou, D},
title = {Cross-ancestry information transfer framework improves protein abundance prediction and protein-trait association identification.},
journal = {Briefings in bioinformatics},
volume = {27},
number = {1},
pages = {},
doi = {10.1093/bib/bbaf707},
pmid = {41499216},
issn = {1477-4054},
support = {82204118//National Natural Sciences Foundation of China/ ; K-20230085//Healthy Zhejiang One Million People Cohort/ ; SN-ZJU-SIAS-0021//Starry Night Science Fund of Zhejiang University Shanghai Institute for Advanced Study/ ; 82473646//National Natural Science Foundation of China/ ; 2024NSFSC0578//Sichuan Provincial Natural Science Foundation/ ; 2024YFC2707602//National Key Research and Development Program of China/ ; YJ202346//Fundamental Research Funds for the Central Universities/ ; },
mesh = {Humans ; *Genome-Wide Association Study ; *Proteomics/methods ; *Proteome/genetics ; *Quantitative Trait Loci ; },
abstract = {Genetics-informed proteome-wide association studies (PWASs) provide an effective way to uncover proteomic mechanisms underlying complex diseases. PWAS relies on an ancestry-matched reference panel to model the impact of genetically determined protein expression on phenotype. However, reference panels from underrepresented populations remain relatively limited. We developed a multi-ancestry framework to enhance protein prediction in these populations by integrating diverse information-sharing strategies into a Multi-Ancestry Best-performing Model (MABM). Results indicated that MABM increased the prediction performance with higher performance observed in both cross-validation and an external dataset. Leveraging the Biobank Japan, we identified three times as many significant PWAS associations using MABM as using Lasso model. Notably, 47.5% of the MABM specific associations were reproduced in independent East Asian datasets with concordant effect sizes. Furthermore, MABM enhanced decision-making in gene/protein prioritization for functional validation for complex traits by validating well-established associations and uncovering novel trait-related candidates. The benefits of MABM were further validated in additional ancestries and demonstrated in brain tissue-based PWAS, underscoring its broad applicability. Our findings close critical gaps in multi-omics research among underrepresented populations and facilitate trait-relevant protein discovery in underrepresented populations.},
}
@article {pmid41497491,
year = {2026},
author = {Huang, Y and Ding, Q and Chen, Z and Chen, J and Li, Y and Chen, L and Yao, S and Lan, Y and Xu, G},
title = {Brain-Computer Interface Training Enhances Attention Function via Modulating Frontoparietal Connectivity: Evidence From Functional Near-Infrared Spectroscopy.},
journal = {Neural plasticity},
volume = {2026},
number = {},
pages = {8133428},
pmid = {41497491},
issn = {1687-5443},
mesh = {Humans ; *Brain-Computer Interfaces ; Spectroscopy, Near-Infrared/methods ; Male ; Female ; *Parietal Lobe/physiology/diagnostic imaging ; Young Adult ; *Attention/physiology ; Adult ; *Frontal Lobe/physiology ; Prefrontal Cortex/physiology ; Executive Function/physiology ; *Nerve Net/physiology ; },
abstract = {OBJECTIVE: Attention is a critical cognitive function impaired in various neurological disorders, and brain-computer interface (BCI) training shows potential for cognitive improvement. However, the neural mechanisms of BCI training on attention networks remain unclear. This study investigated the effects of BCI training on attention and the underlying neural mechanisms in healthy young adults.<br><br>METHODS: Thirty healthy young adults participated in this study. Attention function was assessed using the attention network test (ANT), while brain activation and connectivity were measured using functional near-infrared spectroscopy (fNIRS). Participants underwent the ANT and fNIRS assessments before and after BCI training.<br><br>RESULTS: BCI training significantly improved the efficiency of the executive control network (p = 0.016). Nodal efficiency in the right posterior parietal cortex (PPC) was decreased (p&#x2009; = 0.044). In the resting state, effective connectivity (EC) analysis showed decreased connectivity from the right PPC to the left PPC in the resting state (p&#x2009; = 0.047). In the task state, the EC from the right prefrontal cortex (PFC) to the right PPC was significantly increased (p&#x2009; = 0.016), and the connectivity from the left PFC to the right PFC was significantly decreased (p&#x2009; = 0.023).<br><br>CONCLUSION: BCI training optimized connectivity within frontoparietal networks (FPNs), leading to enhanced executive control function. These findings suggest that BCI training could be an effective cognitive intervention for improving the function of FPNs. Future studies should explore the long-term effects of BCI training and its potential application in clinical populations, such as patients with attention deficit hyperactivity disorder and stroke.},
}
@article {pmid41495620,
year = {2026},
author = {Li, Y and Feng, Y and Liu, X and Yuan, R and Chen, S and Wang, J and Pan, C and Li, G and Tang, Z},
title = {Functional near-infrared spectroscopy: Systematic mapping of abnormal brain function features in neurological disorders.},
journal = {Neural regeneration research},
volume = {},
number = {},
pages = {},
doi = {10.4103/NRR.NRR-D-25-00595},
pmid = {41495620},
issn = {1673-5374},
abstract = {Functional near-infrared spectroscopy quantifies cerebral hemodynamic signals by capturing oxygenation-dependent changes in hemoglobin in a noninvasive, portable, and ecologically valid manner, providing a unique insight into neurovascular coupling. However, functional imaging biomarkers with high ecological validity for neurological disorders such as stroke, Parkinson's disease, dementia, amyotrophic lateral sclerosis, epilepsy, spinal cord injury, and traumatic brain injury are lacking, limiting the mechanistic understanding, treatment evaluations, and individualized interventions. The aim of this review is to systematically summarize evidence from the past decade on the use of functional near-infrared spectroscopy under the aforementioned conditions, synthesize its value for revealing neural mechanisms and assessing therapeutic responses, and identify current technical bottlenecks and future directions for advancement. Collectively, the findings demonstrate that functional near-infrared spectroscopy possesses substantial and far-reaching potential for uncovering the neural mechanisms underlying disease and for evaluating treatment-induced changes in brain function. Equipped with wearable probes, functional near-infrared spectroscopy can continuously and noninvasively monitor brain activity in naturalistic environments for extended periods, thereby overcoming the limitations of conventional imaging modalities that can only acquire data under restricted settings. This capability can furnish unprecedented objective neuroimaging evidence for neuroregenerative therapy research. Moreover, the portability of functional near-infrared spectroscopy allows it to be integrated into neurofeedback training systems: hemoglobin signals can be fed back to participants within milliseconds, enabling targeted, individualized, closed-loop modulation of brain function and considerably expanding the scope of hemodynamics-based neurofeedback. When combined with other brain function assays (such as electroencephalography) and intervention techniques (such as transcranial magnetic stimulation and transcranial direct current stimulation), functional near-infrared spectroscopy also supplies high-temporal-resolution hemodynamic information, laying a critical foundation for the construction of high-precision noninvasive brain-computer interfaces, real-time cognitive-state decoding, and adaptive neuromodulation. Admittedly, almost all existing functional near-infrared spectroscopy studies are still observational and have small sample sizes, short follow-ups, and insufficient controls-shortcomings that together produce low-grade evidence. Therefore, there is still a significant gap before clinical translation can be achieved. Technically, the limited penetration depth of functional near-infrared spectroscopy restricts sampling to the superficial cortex, leaving deep nuclei largely unreachable. In addition, no consensus exists across devices regarding optode layout, light-source choice, motion-artifact correction, or analytical pipelines, creating pronounced heterogeneity that undermines reproducibility. With artificial intelligence and big data analytics advancing rapidly, functional near-infrared spectroscopy embedded within multimodal fusion frameworks is now poised to systematically map aberrant brain function signatures of neurological disorders, identify pathological regions suitable for targeted intervention, and provide real-time assessments of functional changes produced by neuroregenerative therapies.},
}
@article {pmid41495200,
year = {2026},
author = {Li, D and Cui, G and Yang, K and Lu, C and Jiang, Y and Zhang, L and Wu, Q and Dixit, D and Zhu, Z and Gimple, RC and Gu, D and Gao, J and Lin, Q and Yu, H and Shi, Z and Chen, Y and Wang, Q and Jin, G and Lin, F and Shao, J and Zhou, Q and Liu, C and Li, C and You, Y and Zhang, N and Zhang, J and Qian, X and Zhang, Q and Rich, JN and Wang, X},
title = {Inhibiting macrophage-derived lactate transport restores cGAS-STING signalling and enhances antitumour immunity in glioblastoma.},
journal = {Nature cell biology},
volume = {},
number = {},
pages = {},
pmid = {41495200},
issn = {1476-4679},
support = {82525047//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82573312//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Glioblastoma (GBM) is a malignancy with a complex tumour microenvironment (TME) dominated by GBM stem cells (GSCs) and infiltrated by tumour-associated macrophages (TAMs) and exhibits aberrant metabolic pathways. Lactate is a critical glycolytic metabolite that promotes tumour progression; however, the mechanisms of lactate transport and lactylation in the TME of GBM remain elusive. Here we show that lactate is transported from TAMs to GSCs via MCT4-MCT1. TAMs provide lactate to GSCs, promoting GSC proliferation and inducing lactylation of the non-homologous end joining protein KU70 at lysine 317 (K317), which inhibits cGAS-STING signalling and remodels the immunosuppressive TME. Inhibition of lactate transport or targeting the lactylation of KU70, in combination with the immune checkpoint blockade, demonstrates additive therapeutic benefits in immunocompetent xenograft models. This study unveils TAM-derived lactate and lactylation as critical regulators in GSCs to enforce an immunosuppressive microenvironment, opening avenues for developing combinatorial therapy for GBM.},
}
@article {pmid41494647,
year = {2026},
author = {Luckie, DB and Green, MA and Hami, DW and Zawisa, HL},
title = {CURE lecture too: MCAT, BCI &amp; tracking data show students who regularly discussed research data in lecture learned more than peers using traditional textbooks.},
journal = {Advances in physiology education},
volume = {},
number = {},
pages = {},
doi = {10.1152/advan.00002.2025},
pmid = {41494647},
issn = {1522-1229},
support = {//Cystic Fibrosis Foundation (CFF)/ ; //Pennsylvania Cystic Fibrosis Inc./ ; //National Science Foundation (NSF)/ ; },
abstract = {The purpose of this study was to examine the impact of an intervention, a "CURE lecture" approach, which introduced course-based undergraduate research experience (CURE) strategies into the lecture setting. Rather than learning biological explanations from a traditional textbook, instead students studied primary literature curated in a reformed research-focused textbook and had discussions of data and experimental design. In control cohorts, reformed active and cooperative pedagogies were used in lecture to engage students in learning traditional textbook content. In experimental cohorts, "lecture" format was replaced with active and cooperative "journal club" discussions of published experiments. Prior studies examined use of research-focused Integrating Concepts in Biology (ICB) textbook readings in two sequential introductory biology courses. In this study assessments focused on student learning gains after a single semester. Klymkowsky's Biology Concept Inventory with known misconceptions as distractors, and Loznak's MCAT instrument used for over a decade prior, joined longitudinal tracking to evaluate impact of intervention. The ICB student cohort had higher scores (46.3% versus 34.3%) than controls on the Concept Inventory, and on the MCAT questions performed comparably in the range achieved by peer controls since the year 2000. Longitudinal tracking revealed ICB students immediately outperformed peers in their next biology course the following semester. The literature suggested a two-semester ICB experience helped students better succeed, and these findings support even a shorter exposure, of just a single semester, to the "CURE Lecture" strategy is impactful to students.},
}
@article {pmid41494544,
year = {2026},
author = {Jiang, H and He, J and Zhou, B and Guo, Y and Gan, X and Fan, X and Wang, X and Ferraro, S and Vatansever, D and Kendrick, KM and Li, L and Becker, B},
title = {Adolescents with non-suicidal self-injury exhibit increased pain empathic neural reactivity and personal distress to physical but not affective pain.},
journal = {Journal of affective disorders},
volume = {},
number = {},
pages = {121145},
doi = {10.1016/j.jad.2025.121145},
pmid = {41494544},
issn = {1573-2517},
abstract = {BACKGROUND: Non-suicidal self-injury (NSSI) in adolescents represents a critical public health issue. While symptomatic links between NSSI and alterations in pain and social processing have been established, changes in neural responses and everyday reactivity to others' pain remain unknown.<br><br>METHODS: This pre-registered study examined pain empathic processing in unmedicated adolescents with NSSI (n&#x202f;=&#x202f;29) and healthy controls (n&#x202f;=&#x202f;33) using functional magnetic resonance imaging (fMRI). A validated paradigm assessed neural responses to physical pain versus affective pain observation and was combined with both univariate and machine learning analytic approaches.<br><br>RESULTS: NSSI participants exhibited significantly increased neural reactivity during physical pain empathy in lateral prefrontal, insular, temporal, and the somatomotor network regions (all p&#x202f;<&#x202f;0.05, FDR-corrected), while affective pain processing remained intact. Machine learning analysis revealed distinguishable whole-brain signatures, with a physical pain empathic pattern achieving superior discrimination in NSSI. NSSI participants reported elevated personal distress to others' negative experiences in everyday life, which was associated with enhanced limbic reactivity during physical pain empathy.<br><br>CONCLUSIONS: Findings identify domain-specific neural hyperreactivity to others' physical pain in NSSI adolescents and elevated personal distress in daily life. These characteristics may represent predisposing alterations that facilitate engagement in self-harm or consequences of repeated engagement in NSSI that impact everyday social behavior.},
}
@article {pmid41494206,
year = {2026},
author = {Qi, W and Wang, X and Yang, W and Wang, W},
title = {ACFSENet: an adaptive cross-frequency global sparse encoding network for end-to-end EEG emotion recognition.},
journal = {Biomedical physics &amp; engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/ae33c7},
pmid = {41494206},
issn = {2057-1976},
abstract = {End-to-end EEG-based emotion recognition is attracting increasing attention due to its potential in human-computer interaction, mental health, and affective brain-computer interfaces (aBCIs). However, most existing methods overlook cross-frequency interactions in neural oscillations and suffer from high computational complexity, limiting their applicability in real-time or resource-constrained scenarios. To this end, we propose ACFSENet, a novel end-to-end neural architecture that integrates adaptive cross-frequency modeling with global sparse encoding. ACFSENet employs an adaptive frequency-aware mechanism to dynamically focus on subject- and task-specific local brain dynamics, thereby enhancing the flexibility of emotional representation. In parallel, it incorporates a sparse attention mechanism with a temporal distillation structure to reduce computational complexity while preserving the ability to model long-range temporal dependencies. We evaluate ACFSENet using cross-block validation on three benchmark datasets: DEAP, SEED, and SEED-IV. Results demonstrate that ACFSENet outperforms state-of-the-art methods and achieves a favorable balance between recognition performance and computational efficiency.},
}
@article {pmid41494035,
year = {2026},
author = {Chia, R and Lin, CT},
title = {Biologically-constrained spiking neural network for neuromodulation in locomotor recovery after spinal cord injury.},
journal = {PLoS computational biology},
volume = {22},
number = {1},
pages = {e1013866},
doi = {10.1371/journal.pcbi.1013866},
pmid = {41494035},
issn = {1553-7358},
abstract = {Presynaptic inhibition after spinal cord injury (SCI) has been hypothesised to disproportionately affect flexion reflex loops in locomotor spinal circuitry. Reducing gamma-aminobutyric acid (GABA) inhibitory activity increases the excitation of flexion circuits, restoring muscle activation and stepping ability. Conversely, nociceptive sensitisation and muscular spasticity can emerge from insufficient GABAergic inhibition. To investigate the effects of neuromodulation and proprioceptive sensory afferents in the spinal cord, a biologically constrained spiking neural network (SNN) was developed. The network describes the flexor motoneuron (MN) reflex loop with inputs from ipsilateral Ia- and II-fibres and tonically firing interneurons. The model was tuned to a Baseline level of locomotive activity before simulating an inhibitory-dominant and body-weight supported (BWS) SCI state. Electrical stimulation (ES) and serotonergic agonists were simulated by the excitation of dorsal fibres and reduced conductance in excitatory neurons. ES was applied across all afferent fibres without phase- or muscle-specific protocols. The present computational findings suggest that reducing stance-phase GABAergic inhibition on flexor motoneurons could facilitate more physiological flexor activation during locomotion. The model further predicts that neuromodulatory therapy, together with body-weight support, modulates the balance of synaptic excitation and inhibition in ankle flexor motoneurons to mitigate excessive inhibitory drive in the ankle flexor circuitry.},
}
@article {pmid41493973,
year = {2026},
author = {Gao, M and Zang, S and Zhu, Y and Xi, K and Du, Y and Cheng, S and Miao, L and Lu, Y and Mao, C and Zhang, Y and Ma, X},
title = {Structural insights into the activation mechanism of the human metabolite receptor HCAR1.},
journal = {Science signaling},
volume = {19},
number = {919},
pages = {eadw1483},
doi = {10.1126/scisignal.adw1483},
pmid = {41493973},
issn = {1937-9145},
mesh = {Humans ; Cryoelectron Microscopy ; *Receptors, G-Protein-Coupled/chemistry/metabolism/genetics/agonists ; *Lactic Acid/metabolism/chemistry ; Ligands ; Binding Sites ; Protein Binding ; Signal Transduction ; },
abstract = {Hydroxycarboxylic acid receptor 1 (HCAR1) is a class A G protein-coupled receptor (GPCR) that is activated by the endogenous metabolite l-lactate and that plays an important role in various metabolic and inflammatory disorders. HCAR1 uses distinct ligand recognition and self-activation mechanisms to mediate specific pathophysiological functions through Gαi/o and β-arrestin signaling pathways. To support effective drug development targeting HCAR1, we investigated ligand recognition and activation mechanisms through cryo-electron microscopy (cryo-EM) structures of the HCAR1-Gαi1 complex in the apo state or with l-lactate or with the synthetic agonist CHBA. Compared with other HCARs, HCAR1 has a more compact binding pocket, which is stabilized by three unique disulfide bonds. l-lactate exhibited a flexible binding mode and relatively weak intermolecular interactions, thus requiring millimolar concentrations for receptor activation. In contrast, the binding of CHBA was more stable because of its chlorinated benzene ring, thus resulting in improved agonist potency. Structural comparisons with HCAR2 identified critical residues that restrict the size of the binding pocket of HCAR1 and influence ligand selectivity. Self-activation of HCAR1 is driven by conformational rearrangements within extracellular loop 2, with Phe168[ECL2] playing a pivotal role as the key agonist. Together, these results clarify the mechanisms underlying HCAR1 activation, self-activation, and ligand selectivity, providing a structural framework for the design of high-affinity, selective agonists and inverse agonists with minimized off-target effects.},
}
@article {pmid41493559,
year = {2026},
author = {Andrade, P and Mercado, R and Jimenez, F and Visser-Vandewalle, V},
title = {[Neuroprosthetics].},
journal = {Chirurgie (Heidelberg, Germany)},
volume = {},
number = {},
pages = {},
pmid = {41493559},
issn = {2731-698X},
abstract = {Neuroprosthetics represents a dynamic field at the interface of neurosciences, engineering and neurosurgery that is based on implanted devices for restoration or extension of neurological functions. Important advances involve brain-computer and brain-spine interfaces that enable communication, motor and sensory feedback in paralyzed or anarthric patients. Intracortical arrays, subdural electrocorticographic lattices and endovascular electrodes provide different access routes, supplemented by strategies, such as spinal neuromodulation and functional electrostimulation. Recent studies confirmed the restoration of grasping movements, standing and walking as well as fluid speech and text communication, sometimes via avatars. Bidirectional systems with sensory feedback enhance the naturalness and precision. There are challenges in signal stability, longevity and minimally invasive access routes. With interdisciplinary cooperation and technical maturity neuroprostheses can enrich the routine neurosurgical care in the future.},
}
@article {pmid41490776,
year = {2026},
author = {Bao, M and Feng, S and Wang, J and Ye, J and Wang, J and Li, W and Jiang, K and Yao, L},
title = {Efficacy and Safety of a Video Game-Like Digital Therapy Intervention for Chinese Children With Attention-Deficit/Hyperactivity Disorder: Single-Arm, Open-Label Pre-Post Study.},
journal = {JMIR serious games},
volume = {14},
number = {},
pages = {e76114},
doi = {10.2196/76114},
pmid = {41490776},
issn = {2291-9279},
abstract = {BACKGROUND: The digital therapy of attention-deficit/hyperactivity disorder (ADHD) based on a "self-adaptive multitasking training paradigm" has been developed to improve the cognitive functional impairments and attention deficits of children with ADHD. However, the efficacy and safety of such treatment for Chinese patients remain untested.<br><br>OBJECTIVE: This study aimed to preliminarily evaluate the actual intervention effects of a video game-like training software (ADHD-DTx) for children with ADHD aged 6-12 years as the first nationally certified digital therapeutics medical device for ADHD in China. We performed a single-arm, open-label efficacy and safety study.<br><br>METHODS: This is a single-arm, open-label, pre-post efficacy and safety study. A total of 97 participants were included in the analysis. Participants received digital therapy (ADHD-DTx) and basic behavioral parent training for 4 weeks (25 min/day, &#x2265;5 times/week) without medication. The efficacy outcomes included the Test of Variables of Attention (TOVA), Swanson, Nolan, and Pelham Questionnaire, version 4 (SNAP-IV), Weiss Functional Impairment Rating Scale (WFIRS), and Conner's Parent Symptom Questionnaire (PSQ). Safety-related events were monitored during and after the trial.<br><br>RESULTS: From day 0 (baseline) to day 28, the population TOVA Attention Performance Index exhibited statistically significant improvement (from mean -4.15, SE of the mean [SEM] 0.32 to mean -1.70, SEM 0.30; t94=-8.78; n=95; P<.001); the population total, inattention (AD), hyperactivity/impulsivity (HD), and oppositional defiant disorder (ODD) scores of SNAP-IV all significantly improved (total: from mean 1.33, SEM 0.05 to mean 1.09, SEM 0.05; t96=5.32; P<.001; AD: from mean 1.71, SEM 0.06 to mean 1.44, SEM 0.06; t96=4.44; P<.001; HD: from mean 1.38, SEM 0.07 to mean 1.05, SEM 0.06; t96=5.96; P<.001; ODD: mean 0.84, SEM 0.05 to mean 0.75, SEM 0.05; Z=2.47; P=.03; n=97); for WFIRS results, domains of "family" and "social activities" showed significant population improvement (family: from mean 0.75, SEM 0.05 to mean 0.65, SEM 0.04; Z=2.80; P=.01; social activities: from mean 0.56, SEM 0.05 to mean 0.45, SEM 0.05; Z=2.91; P=.01; n=97); for PSQ results, domains of "learning problem," "psychosomatic problem," "impulsivity-hyperactivity," and "hyperactivity index" showed significant improvement (learning problem: from mean 1.72, SEM 0.06 to mean 1.57, SEM 0.06; Z=2.42; P=.03; psychosomatic problem: from mean 0.40, SEM 0.03 to mean 0.32, SEM 0.03; Z=2.66; P=.02; impulsivity-hyperactivity: from mean 0.94, SEM 0.06 to mean 0.80, SEM 0.06; Z=2.49; P=.03; hyperactivity index: from mean 1.06, SEM 0.05 to mean 0.92, SEM 0.05; Z=2.90; P=.01; n=97). No device-related adverse event or severe adverse event was observed or reported during or after the intervention.<br><br>CONCLUSIONS: This study preliminarily suggested the significant improvements of ADHD symptoms and attention function after 4 weeks of ADHD-DTx digital therapy combining basic behavioral parent training with satisfying safety outcomes.},
}
@article {pmid41489950,
year = {2026},
author = {Chung, CM and Tsai, CH and Chu, YL and Hsu, CH and Lu, JB and Hsu, YC and Su, YJ and Wu, Y and Hung, CF and Wang, YT},
title = {3D printed watermill-like semi-dry electrodes for BCI applications.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2026.3650950},
pmid = {41489950},
issn = {1558-0210},
abstract = {Wet electrodes with conductive gel are widely applied as the gold standard for recording EEG signals due to their low impedance between the scalp and the electrode. However, their extensive preparation time before data collection and the required cleaning afterward make them impractical for real-world Brain-Computer Interface (BCI) applications. Recent advancements in semi-dry electrodes, which use a minimal amount of conductive material and achieve a comparable signal-to-noise quality to wet electrodes, present an alternative approach for continuous EEG monitoring when comparing to dry electrodes. Our prior study introduced a potential solution for overcoming challenges related to hair-layer penetration and dose control through 3D-printed, watermill-shaped EEG electrodes. Based on those promising results, this study prototypes three designs of watermill-shaped EEG electrodes and refines the fabrication process to scale production and accommodate diverse hairstyles in real-world scenarios. Eight different wig styles which were made of either human or synthetic hair were tested in offline experiments to evaluate hair-layer penetration performance and gel-applying application efficiency. In the real-world experiment, 15 participants with varying hairstyles were recruited in neurophysiological experiments. Statistical analysis revealed that the watermill electrodes consumed significantly less gel than wet electrodes (p<0.001), with the star electrode requiring the fewest mean rolls to achieve target impedance (1.94 rolls). The results demonstrate that the watermill-shaped electrode effectively works across different hairstyles, ensuring consistent hair-layer penetration and controlled application of conductive material. These findings establish the proposed electrode as a viable semi-dry solution for real-world BCI applications.},
}
@article {pmid41489217,
year = {2026},
author = {Lu, J and Zhan, G and Jia, J and Zhang, L and Kang, X},
title = {Automated source domain EEG analysis based on graph theory for healthy controls and stroke patients in different tasks.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-19},
doi = {10.1080/10255842.2025.2609653},
pmid = {41489217},
issn = {1476-8259},
abstract = {This study aimed to compare functional brain networks and identify recovery markers in 12 stroke patients (SG) and 14 healthy controls (HG) using EEG during three fist-task paradigms. Analyzing clustering coefficient (CC), characteristic path length (CPL), small-world index (SWI), and frontal node strength across frequency bands, passive task revealed significant alpha band differences in CC/CPL/SWI between groups. Lower SG strength in alpha/mu vs. controls predicted better recovery. An automated source imaging pipeline reduced volume conduction effects, providing new insights into stroke rehabilitation outcomes. Large-scale source imaging shows promise for broader disease applications.},
}
@article {pmid41488688,
year = {2025},
author = {Cao, Y and Ding, J and Zhao, Z and He, Y and Fu, M and Liu, X and Lyv, X},
title = {Improved filter bank common spatial pattern algorithm based on the sparrow search algorithm.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1679329},
pmid = {41488688},
issn = {1662-5161},
abstract = {INTRODUCTION: The application of motor imagery in human-computer interaction and rehabilitative medicine has attracted growing attention due to recent advances in brain-computer interface technologies. However, traditional EEG decoding paradigms based on fixed frequency-band segmentation often exhibit limited performance because they fail to capture individual variability in brain rhythms.<br><br>METHODS: This work proposes an adaptive method that integrates the sparrow search algorithm (SSA) with Filter Bank Common Spatial Pattern (FBCSP) to optimize sub-band segmentation for motor imagery EEG decoding. SSA adaptively searches for optimal sub-band boundaries, enabling individualized frequency-band selection.<br><br>RESULTS: Experiments on the BCI Competition IV 2a dataset under a cross-session evaluation protocol (training on session T, testing on session E) demonstrated that SSA-FBCSP effectively improves frequency-band adaptability. The SSA-FBCSP approach was further combined with Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and k-Nearest Neighbor (KNN) classifiers to evaluate the influence of different downstream classifiers.<br><br>CONCLUSION: Among them, SSA-FBCSP-LDA achieved the best performance, outperforming the conventional uniform sub-band approach by 21.76% and reaching an average accuracy of 89.92%. The adaptively selected sub-bands closely matched the ERD/ERS distribution, confirming the method's effectiveness in frequency-band optimization. Compared with recent deep-learning-based MI-EEG models, the proposed technique offers a balance of accuracy, interpretability, and computational efficiency, providing a promising direction for personalized brain-computer interface systems.},
}
@article {pmid41486740,
year = {2026},
author = {Zhang, T and Ngetich, RK and Zhang, J and Jin, Z and Li, L},
title = {The role of emotion in economic decision making: behavioral and neurophysiological evidence from the Wheel of Fortune Gambling Task.},
journal = {Reviews in the neurosciences},
volume = {},
number = {},
pages = {},
pmid = {41486740},
issn = {2191-0200},
abstract = {Decision making is frequently influenced by factors such as an individual's emotional state, cognitive biases, social influences, and environmental constraints. Understanding how these factors influence the way decisions are made is essential for optimizing and improving this cognitive process. Therefore, this review examines the theoretical basis of emotion-influenced decision making. Here, we integrate insights from eye-tracking, electroencephalography (EEG), and magnetic resonance imaging (MRI) evidence, as well as behavioral findings. We specifically review evidence from studies applying the Wheel of Fortune Gambling Task paradigm. Through critical and reflective synthesis, we (1) present suggestions for distinguishing between emotion types in decision-making theoretical models, (2) identify key research gaps, and (3) explore innovative applications of emerging technologies. In essence, our review highlights the role of diverse emotions in decision making across theoretical models and neural mechanisms, utilizing the Wheel of Fortune Gambling Task paradigm to link clinical disorders with decision-making impairments. This knowledge may have implications for predicting and intervening in behavioral addictions and cognitive disorders through strategies such as the neuromodulation. Additionally, by synthesizing existing knowledge and proposing new avenues for research, this review aims to deepen understanding of emotion-driven decision making and inspire further exploration into this vital area of cognitive science.},
}
@article {pmid41486339,
year = {2026},
author = {Li, S and Wang, X and Zheng, J and Xu, H},
title = {Subparafascicular Thalamic Nucleus: An Integration Center for Sexual Motivation and Physical Contact in Mating Behaviour.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {41486339},
issn = {1995-8218},
}
@article {pmid41486068,
year = {2026},
author = {Yamada, S and Sato, M and Osawa, T and Harabayashi, T and Miki, J and Kobayashi, T and Hashine, K and Kawashima, A and Matsumoto, T and Mochizuki, T and Taoka, R and Urabe, F and Tatarano, S and Sawada, A and Kojima, T and Takahashi, A and Yokomizo, A and Suekane, S and Hashimoto, K and Hashimoto, Y and Yatsuda, J and Morita, K and Kobayashi, K and Satake, Y and Sazawa, A and Matsui, Y and Ito, YM and Shimizu, S and Fukuhara, S and Nishiyama, H and Kitamura, H and Shinohara, N and , },
title = {Longitudinal Impact of Urinary Diversion on Health-Related Quality of Life After Radical Cystectomy: A Multicenter Study in Japan.},
journal = {Cancer science},
volume = {},
number = {},
pages = {},
doi = {10.1111/cas.70289},
pmid = {41486068},
issn = {1349-7006},
support = {2019-67//Japan Urological Association, Young Researcher Promotion Grant/ ; },
abstract = {This multicenter longitudinal study was conducted across 24 institutions in Japan to examine the impact of urinary diversion on health-related quality of life (HRQOL) among bladder cancer patients who underwent radical cystectomy (RC). We evaluated bladder cancer-specific HRQOL and general HRQOL via the bladder cancer index (BCI) and the QOL General (QGEN-8), respectively, before the operation and at 3, 6, and 12 months postoperatively. The scores were compared across urinary diversion groups as well as across different time points within each urinary diversion group with linear mixed-effects models. Data from 227 patients were analyzed (151 with ileal conduits, 45 with ureterostomy, and 31 with neobladders). Neobladder patients were more likely to experience longitudinal impacts of their urinary diversion on urinary function than ileal conduit or ureterostomy patients were. Compared with that at baseline, the bowel function of neobladder patients remained impaired 12 months after surgery. All urinary diversion groups had worse sexual function scores at 3 and 6 months than at baseline, and the ileal conduit and neobladder groups had significantly worse sexual function scores at 12 months than at baseline. On the other hand, there was no significant difference in bother scores in the urinary, bowel, or sexual domain. The generic HRQOL was maintained from the preoperative to the postoperative period in all urinary diversion groups. This study explored longitudinal changes in HRQOL after RC, and the findings may help inform patient counseling regarding possible QOL trajectories.},
}
@article {pmid41485025,
year = {2026},
author = {Ying, W and Wang, X and Yu, J and Wang, J and He, Q and Yang, B and Chen, Y and Ying, M},
title = {Fusion oncoproteins orchestrate tumorigenesis and sustain malignant progression via a positive feedback mechanism.},
journal = {Cell &amp; bioscience},
volume = {},
number = {},
pages = {},
doi = {10.1186/s13578-025-01523-6},
pmid = {41485025},
issn = {2045-3701},
support = {No. 82272677//National Natural Science Foundation of China/ ; No. LR23H310001//Natural Science Fund for Distinguished Young Scholars of Zhejiang Province/ ; No. GZC20232321//Postdoctoral Fellowship Program of CPSF/ ; No. 2024C03181//Pioneer and Leading Goose R&amp;D Program of Zhejiang Province/ ; No. 226-2025-00136//Fundamental Research Funds for the Central Universities/ ; },
abstract = {Chromosomal translocations are prevalent genetic events across multiple pediatric cancers, notably in CNS tumors, solid tumors, and leukemias. For decades, Fusion oncoproteins resulting from chromosomal translocations have been proposed as a hallmark of cancers, some of which can drive the process of cancers as the initial event of the disease. In addition, studies have shown that some tumor cells become addicted to the activity of fusion proteins, and cell death occurs when the fusion proteins are depleted. These researches suggest that fusion oncoproteins are one of the most promising targets for cancer treatment. Although fusion proteins are already recognized as critical oncogenic drivers, increasing evidence suggests that they can also form positive feedback loops with other proteins. In cancer patients, positive feedback loops have been shown to activate various oncogenic signals to drive tumor development, and influencing tumor cells' sensitivity to different therapies. Therefore, these loops not only amplify the functions of the fusion proteins but also render single-agent targeting of the fusion protein insufficient to suppress tumor growth, highlighting the therapeutic potential of combination strategies in treating fusion-positive tumors. This review highlights the oncogenic roles of fusion protein-driven positive feedback loops in tumor initiation and progression, outline the molecular mechanisms underlying their formation and function, and summarize emerging therapeutic strategies targeting these circuits, offering new insights into the treatment of fusion-positive cancers.},
}
@article {pmid41484139,
year = {2026},
author = {Gao, J and Liu, Y and Li, Z and Huang, K and Wang, F and Xu, J and Zhao, L and Li, T and Fu, Y},
title = {An EEG Dataset for Visual Imagery-Based Brain-Computer Interface.},
journal = {Scientific data},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41597-025-06512-5},
pmid = {41484139},
issn = {2052-4463},
support = {No.62366026, No.62376112, No.82172058, No.81771926, No.61763022, No. 62006246//National Natural Science Foundation of China (National Science Foundation of China)/ ; No.62366026, No.62376112, No.82172058, No.81771926, No.61763022, and No. 62006246//National Natural Science Foundation of China (National Science Foundation of China)/ ; No.62366026, No.62376112, No.82172058, No.81771926, No.61763022, and No. 62006246//National Natural Science Foundation of China (National Science Foundation of China)/ ; No.62366026, No.62376112, No.82172058, No.81771926, No.61763022, and No. 62006246//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {With the advancement of non-invasive brain-computer interface (BCI) technologies, decoding high-level cognitive activity has become pivotal for expanding human-machine interaction. Visual imagery-based BCI (VI-BCI) enable voluntary activation of specific brain regions without external cue, offering novel pathways for immersive applications. However, research on the neural representation of such complex cognitive tasks is still limited, and most existing electroencephalogram (EEG) datasets primarily target motor imagery, hindering the development of robust VI decoding models. Here we present an EEG dataset recorded from 22 participants performing visual imagery tasks involving ten commonly recognized images across three categories: figures, animals, and objects. Each participant completed two sessions, with EEG recorded from 32-channels at 1000 Hz. This resource helps overcome data homogeneity issues in VI studies and provides a foundation for exploring neuroplasticity, adaptive decoding algorithms, and cross-subject generalization, facilitating the transition from controlled experiments to real-world applications.},
}
@article {pmid41483149,
year = {2026},
author = {Zhu, L and Hong, H and Qian, M and Cao, W and Luo, Z and Gong, J and Zou, W and Kang, L},
title = {Hierarchical Channel System Drives Stimulus Specificity and Polymodal Encoding in A Mechano-Cold Sensory Neuron.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {41483149},
issn = {1995-8218},
abstract = {Polymodal sensory neurons integrate diverse stimuli for environmental perception, but their modality discrimination mechanisms remain unclear. We focused on Caenorhabditis elegans inner labial type 1 (IL1) neurons, key polymodal neurons mediating mechanical and cold responses, and identified a hierarchical channel system supporting their multimodal function. Specifically, DEG-1 sodium channels are dedicated mechanotransduction receptors; GLR-3 glutamate receptors are the main rapid cold sensors, driving cold-induced calcium signals and behaviors; TRPA-1 bidirectionally modulates mechanical adaptation via calcium signaling and promotes cold-related longevity. This framework reveals a polymodal design logic: dedicated channels (DEG-1/GLR-3) process discrete modalities in parallel for specificity, while TRPA-1 regulates both. Our work provides a molecular blueprint for IL1's precise stimulus processing, offering insights into conserved multimodal integration mechanisms across lineages.},
}
@article {pmid41482611,
year = {2026},
author = {Nochalabadi, A and Khazaei, M and Kadivarian, S and Rezakhani, L},
title = {Innovative Herbal-Based Decellularization of Pericardium for Advanced Polymeric Skin Substitutes.},
journal = {Artificial organs},
volume = {},
number = {},
pages = {},
doi = {10.1111/aor.70087},
pmid = {41482611},
issn = {1525-1594},
support = {//Kermanshah University of Medical Sciences/ ; },
abstract = {INTRODUCTION: Tissue engineering has opened new horizons with the introduction of biological scaffolds obtained by decellularization techniques as novel tools in regenerative medicine. Chemical agents such as SDS, although effective in cell removal, can cause cytotoxicity. Herbal agents can be a safer and more biocompatible alternative. This study aimed to investigate the efficacy of Acanthophelium extract (ACP) as a herbal agent in decellularization of sheep pericardium and compare it with SDS for use in skin engineering.<br><br>METHODS: Pericardial tissues were decellularized with different concentrations of ACP (5, 7.5% and 10%) and SDS (1%), as well as the combination of ACP&#x2009;+&#x2009;SDS. Tissue staining, biocompatibility (MTT), hemolysis, blood clotting index (BCI), scanning electron microscopy (SEM), ATR-FTIR spectroscopy, mechanical testing, contact angle, and antibacterial activity were performed.<br><br>RESULTS: Complete cell removal was observed in the ACP&#x2009;+&#x2009;SDS combination groups, while the ECM structure was preserved. Biocompatibility was more than 90% in all groups. ACP-based scaffolds had less hemolysis, a more favorable coagulation index, preserved protein structure, higher porosity, and higher hydrophilicity. Although the mechanical properties were slightly reduced, they remained acceptable. The 10% ACP&#x2009;+&#x2009;0.1% SDS group reported the highest antibacterial effect.<br><br>CONCLUSIONS: ACP extract, as a plant agent in pericardial decellularization, has an effective and biocompatible function, and in combination with a small amount of SDS, it can provide a balanced scaffold with desirable properties for skin engineering.},
}
@article {pmid41481676,
year = {2026},
author = {Proverbio, AM and Zanetti, A},
title = {Reinstating motivational states: Electrical signatures of craving and neural mind reading.},
journal = {PloS one},
volume = {21},
number = {1},
pages = {e0315068},
pmid = {41481676},
issn = {1932-6203},
mesh = {Humans ; Male ; Female ; Electroencephalography ; Adult ; *Motivation/physiology ; Young Adult ; Evoked Potentials/physiology ; *Craving/physiology ; *Brain/physiology ; Brain Mapping ; },
abstract = {The aim of this electroencephalogram (EEG) study was to identify electrical neuro-markers of 12 different motivational and physiological states such as visceral craves, affective and somatosensory states, and secondary needs. Event-related potentials (ERPs) were recorded in 30 right-handed participants while recalling a specific state upon the presentation of an auditory verbal command incorporating an evocative sound background consistent with that state (e.g., the chirping of cicadas associated with the verbal complaint about feeling hot). ERP data showed larger amplitude N400 responses in the affective and somatosensory states, while the P400 component displayed greater amplitudes for the secondary and visceral states. Furthermore, the two components were also discernibly responsive to the 12 micro-categories (e.g., joy vs. pain or hunger), by providing a distinctive electric pattern for mostly all microstates. The reconstruction of the intracranial generators of surface signals revealed common imagery-related activations, including the middle and superior frontal gyri, the fusiform and lingual gyri, supramarginal, and middle occipital regions, as well as the middle temporal region. Additionally, specific regions were identified that were active for distinct mentally represented content, such as that visceral needs were associated with activations in the medial and inferior frontal gyri, uncus, precuneus, and cingulate gyrus. Affective states were associated with activations in the medial frontal, superior temporal, and middle temporal gyri. Somatosensory states (e.g., pain or cold) activated regions in the parietal cortex and the crave for music was linked to activations in the auditory and motor regions. These findings support the use of ERP markers for BCI applications.},
}
@article {pmid41480666,
year = {2026},
author = {Hu, X and Li, N and Pang, M and Bai, S and Mo, J and Yao, S and Lu, Y and Huang, M and Di, J and Kang, Y and Tang, J and Zhang, H and Zhao, T and He, J and He, L and Xie, R and Liu, B and Xu, G and Hu, X and Rong, L},
title = {Brain-Computer Interface-Controlled Exoskeleton Training for Lower-Limb Rehabilitation in Spinal Cord Injury: A Pilot Randomized Clinical Trial.},
journal = {Annals of neurology},
volume = {},
number = {},
pages = {},
doi = {10.1002/ana.78144},
pmid = {41480666},
issn = {1531-8249},
support = {U22A20297//National Natural Science Foundation of China/ ; 202206060003//Key Research and Development Program of Guangzhou/ ; GZC20251372//Postdoctoral Fellowship Program and China Postdoctoral Science Foundation/ ; },
abstract = {OBJECTIVE: This study aimed to evaluate the efficacy of brain-computer interface (BCI)-controlled exoskeleton training on lower-limb functional recovery, psychological outcomes, and neural plasticity in patients with spinal cord injury (SCI).<br><br>METHODS: We conducted a single-center, prospective, randomized, single-blind pilot trial (ChiCTR2300074503) including 21 patients with SCI. Participants were randomized to a BCI-exoskeleton group (B&#x2009;+&#x2009;E, n&#x2009;=&#x2009;10) or an exoskeleton-only group (E, n&#x2009;=&#x2009;11) for lower-limb training. Both groups received conventional rehabilitation plus 30&#x2009;minutes of training, 6&#x2009;days per week, for 4&#x2009;weeks. The primary outcomes were Walking Index for Spinal Cord Injury II (WISCI II) scoring. Secondary outcomes included Lambert-Eaton myasthenic syndrome (LEMS), Spinal Cord Independence Measure version III (SCIM III), International Association of Neurorestoratology Spinal Cord Injury Functional Rating Scale (IANR-SCIFRS), 10-Meter Walk Test (10MWT), 6-Minute Walk Test (6MWT), and Hospital Anxiety and Depression Scale (HADS). Cortical plasticity was assessed by electroencephalography (EEG) and magnetic resonance imaging (MRI).<br><br>RESULTS: The B&#x2009;+&#x2009;E group showed a significant improvement in LEMS (p&#x2009;=&#x2009;0.003), whereas both groups improved in IANR-SCIFRS (p&#x2009;<&#x2009;0.05). The B&#x2009;+&#x2009;E group demonstrated significant within-group gains in walking speed (10MWT, p&#x2009;<&#x2009;0.001) and endurance (6MWT, p&#x2009;=&#x2009;0.031), although between-group differences were not significant. Compared with the E group, the B&#x2009;+&#x2009;E group had larger reductions in HADS scores (p&#x2009;=&#x2009;0.003). EEG analyses revealed stronger &#x3bc;/&#x3b2; desynchronization and increased network efficiency, whereas MRI showed no structural changes.<br><br>INTERPRETATION: BCI-controlled exoskeleton training enhanced motor function, walking performance, and depressive symptoms more than exoskeleton training alone, likely through cortical reorganization. Extended training may further consolidate these benefits, supporting BCI-exoskeleton integration as a promising rehabilitation strategy for SCI. ANN NEUROL 2026.},
}
@article {pmid41476655,
year = {2025},
author = {Li, Y and Miao, Y and Wei, L and Li, W and Shan, M and Jiang, Q and Wang, F and Wang, L and Zhang, Z and Song, J and Zhu, Y and Mao, J},
title = {An Anisotropic and Stable-Conductance Patch for Mechanical-Electrical Coupling With Infarcted Myocardium.},
journal = {Exploration (Beijing, China)},
volume = {5},
number = {6},
pages = {20250021},
pmid = {41476655},
issn = {2766-2098},
abstract = {Polymeric conductive patches have conventionally been employed to facilitate the repair of infarcted myocardium by enhancing myocardial electrical conduction and providing mechanical support. However, it remains a challenge to restore the electrical conduction and diastolic-systolic functions with stable and anisotropic mechanical and electrical cues in the dynamic physiological environment. Herein, inspired by the hierarchical myocardial fiber microscopic striated structure, we established a weaving-based processing method to compound a striated polypyrrole conductive coating on the surface of highly oriented elastic fiber bundles. This unique design endows the patch with exceptional stretchability (elongation at break > 400%), stable conductance (ΔR/R 0 = 0.04 within 20% strain), and excellent fatigue resistance (ΔR/R 0 = 0.01 after 1,000,000 cycles). In addition, the precision process grounded on woven molding accomplished the tunable mechanical and electrical properties of the patch, which facilitates the achievement of long-term, stable, and anisotropic mechanical-electrical coupling with the infarcted myocardium. The rat MI model experiments demonstrated that this anisotropic conductive patch can not only improve cardiac function and electrical activity over an extended period, but also effectively inhibit myocardial inflammation and fibrosis and promote angiogenesis. This study proposes a promising MI-treatment patch and highlights the potential of woven technology in processing biomaterials composed of both rigid and elastic materials.},
}
@article {pmid41474622,
year = {2025},
author = {Chen, J and Xu, T and Xiong, X and Yang, X and Wang, Y and Qi, Y},
title = {Surrogate deep neural networks reveal hierarchical handwriting encoding in the human motor cortex.},
journal = {Cell reports},
volume = {45},
number = {1},
pages = {116837},
doi = {10.1016/j.celrep.2025.116837},
pmid = {41474622},
issn = {2211-1247},
abstract = {Skilled fine movements are essential for daily life. Although prior work has identified motor cortical tuning to low-level kinematic features like velocity and position, these findings fall short of explaining the precision underlying complex motor behaviors. Critically, it remains unclear whether and how the motor cortex (MC) represents higher-level features of movement. Using single-unit recordings from the human MC during handwriting, we employed surrogate deep neural networks (DNNs) as a tool to investigate these mechanisms. We found that surrogate DNNs capture key aspects of neural activity at both single-unit and population levels. Through this approach, we demonstrate that the MC encodes hierarchical information of movement, including both low-level kinematics and high-level features related to the written content. These results uncover neural encoding behind dexterous motor execution and provide a framework for studying the neural basis of complex behavior.},
}
@article {pmid41472918,
year = {2025},
author = {Sedi Nzakuna, P and D'Auria, E and Paciello, V and Gallo, V and Kamavuako, EN and Lay-Ekuakille, A and Kyamakya, K},
title = {Real-world evaluation of deep learning decoders for motor imagery EEG-based BCIs.},
journal = {Frontiers in systems neuroscience},
volume = {19},
number = {},
pages = {1718390},
pmid = {41472918},
issn = {1662-5137},
abstract = {INTRODUCTION: Motor Imagery (MI) Electroencephalography (EEG)-based control in online Brain-Computer Interfaces requires decisions to be made within short temporal windows. However, the majority of published Deep Learning (DL) EEG decoders are developed and validated offline on public datasets using longer window lengths, leaving their real-time applicability unclear.<br><br>METHODS: To address this gap, we evaluate 10 representative DL decoders, including convolutional neural networks (CNNs), filter-bank CNNs, temporal convolutional networks (TCNs), and attention- and Transformer-based hybrids-under a soft real-time protocol using 2-s windows. We quantify performance using accuracy, sensitivity, precision, miss-as-neutral rate (MANR), false-alarm rate (FAR), information-transfer rate (ITR), and workload. To relate decoder behavior to physiological markers, we examine lateralization indices, mu-band power at C3 vs. C4, and topographical contrasts between MI and neutral conditions.<br><br>RESULTS: Results show shifts in performance ranking between offline and online BCI settings, along with a pronounced increase in inter-subject variability. Best online means were FBLight ConvNet 71.7% (&#xb1;2.1) and EEG-TCNet 70.0% (&#xb1;5.3), with attention/Transformer designs less stable. Errors were mainly Left-Right swaps while Neutral was comparatively stable. Lateralization indices/topomaps revealed subject-specific &#x3bc;/&#x3b2; patterns consistent with class-wise precision/sensitivity.<br><br>DISCUSSION: Compact spectro-temporal CNN backbones combined with lightweight temporal context (such as TCNs or dilated convolutions) deliver more stable performance under short-time windows, whereas deeper attention and Transformer architectures are more susceptible to variation across subjects and sessions. This study establishes a reproducible benchmark and provides actionable guidance for designing and calibrating online-first EEG decoders that remain robust under real-world, short-time constraints.},
}
@article {pmid41471452,
year = {2025},
author = {Gao, D and Zhao, Y and Zhou, J and Zhang, H and Li, H},
title = {MCRBM-CNN: A Hybrid Deep Learning Framework for Robust SSVEP Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {24},
pages = {},
pmid = {41471452},
issn = {1424-8220},
abstract = {The steady-state visual evoked potential (SSVEP), a non-invasive EEG modality, is a prominent approach for brain-computer interfaces (BCIs) due to its high signal-to-noise ratio and minimal user training. However, its practical utility is often hampered by susceptibility to noise, artifacts, and concurrent brain activities, complicating signal decoding. To address this, we propose a novel hybrid deep learning model that integrates a multi-channel restricted Boltzmann machine (RBM) with a convolutional neural network (CNN). The framework comprises two main modules: a feature extraction module and a classification module. The former employs a multi-channel RBM to unsupervisedly learn latent feature representations from multi-channel EEG data, effectively capturing inter-channel correlations to enhance feature discriminability. The latter leverages convolutional operations to further extract spatiotemporal features, constructing a deep discriminative model for the automatic recognition of SSVEP signals. Comprehensive evaluations on multiple public datasets demonstrate that our proposed method achieves competitive performance compared to various benchmarks, particularly exhibiting superior effectiveness and robustness in short-time window scenarios.},
}
@article {pmid41471422,
year = {2025},
author = {Ammar, S and Triki, N and Karray, M and Ksantini, M},
title = {A Multidimensional Benchmark of Public EEG Datasets for Driver State Monitoring in Brain-Computer Interfaces.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {24},
pages = {},
doi = {10.3390/s25247426},
pmid = {41471422},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Automobile Driving ; Benchmarking ; Male ; Female ; Adult ; },
abstract = {Electroencephalography (EEG)-based brain-computer interfaces (BCIs) hold significant potential for enhancing driver safety through real-time monitoring of cognitive and affective states. However, the development of reliable BCI systems for Advanced Driver Assistance Systems (ADAS) depends on the availability of high-quality, publicly accessible EEG datasets collected during driving tasks. Existing datasets lack standardized parameters and contain demographic biases, which undermine their reliability and prevent the development of robust systems. This study presents a multidimensional benchmark analysis of seven publicly available EEG driving datasets. We compare these datasets across multiple dimensions, including task design, modality integration, demographic representation, accessibility, and reported model performance. This benchmark synthesizes existing literature without conducting new experiments. Our analysis reveals critical gaps, including significant age and gender biases, overreliance on simulated environments, insufficient affective monitoring, and restricted data accessibility. These limitations hinder real-world applicability and reduce ADAS performance. To address these gaps and facilitate the development of generalizable BCI systems, this study provides a structured, quantitative benchmark analysis of publicly available driving EEG datasets, suggesting criteria and recommendations for future dataset design and use. Additionally, we emphasize the need for balanced participant distributions, standardized emotional annotation, and open data practices.},
}
@article {pmid41471369,
year = {2025},
author = {Ga, YJ and Yeh, JY},
title = {Does Coxsackievirus B3 Require Autophagosome Formation for Replication? Evidence for an Autophagosome-Independent Mechanism: Insights into Its Limited Potential as a Therapeutic Target.},
journal = {Pharmaceuticals (Basel, Switzerland)},
volume = {18},
number = {12},
pages = {},
doi = {10.3390/ph18121880},
pmid = {41471369},
issn = {1424-8247},
support = {RS-2025-02304897//Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries/ ; },
abstract = {Background/Objectives: Coxsackievirus B3 (CVB3), a neurotropic enterovirus, is a major causative agent of viral encephalitis and myocarditis, yet no protective vaccine or effective antiviral therapy is currently available. Autophagy plays a dual role in viral infections, acting as both an antiviral defense and a process that can be exploited by certain viruses. Although CVB3 has been proposed to utilize autophagosomes as replication platforms, the underlying mechanisms remain controversial. Methods: In this study, we investigated the relationship between CVB3 replication and autophagosome formation under starvation-induced conditions and in ATG5 knockout cells. Results: While nutrient deprivation robustly induced autophagy, CVB3 infection did not trigger autophagosome formation. Moreover, viral replication proceeded efficiently in ATG5-deficient cells lacking autophagosomes. Pharmacological modulation of autophagy using rapamycin, a potent autophagy inducer, did not alter intracellular viral titers or protein expression, although extracellular viral release was modestly reduced. These results indicate that CVB3 replication occurs independently of autophagosome formation, suggesting that pharmacological targeting of autophagy provides limited therapeutic benefit. Conclusions: This study refines our understanding of autophagy as an antiviral target and highlights the need to identify alternative host-directed pathways for antiviral drug development.},
}
@article {pmid41470530,
year = {2025},
author = {Wang, C and Cheng, B and Tang, Q and Wu, R and Li, H},
title = {Design and Validation of a Brain-Controlled Hip Exoskeleton for Assisted Gait Rehabilitation Training.},
journal = {Micromachines},
volume = {16},
number = {12},
pages = {},
pmid = {41470530},
issn = {2072-666X},
support = {GXXT2022053//the Collaborative Innovation Program for Universities in Anhui Province/ ; 2023A3112//Huainan City Science and Technology Plan Project/ ; 2023AHIMB05//Base for Innovative Methods Promotion Application and Demonstration of Anhui Province/ ; },
abstract = {This study presents an integrated micro-system solution to address the challenges of gait instability in patients with impaired hip motor function. We developed a novel wearable hip exoskeleton, where a flexible support unit and a parallel drive mechanism achieve self-alignment with the biological hip joint to minimize parasitic forces. The system is driven by an active brain-computer interface (BCI) that synergizes an augmented reality visual stimulation (AR-VS) paradigm for enhanced motor intent recognition with a high-performance decoding algorithm, all implemented on a real-time embedded processor. This integration of micro-sensors, control algorithms, and actuation enables the establishment of a gait phase-dependent hybrid controller that optimizes assistance. Online experiments demonstrated that the system assisted subjects in completing 10 gait cycles with an average task time of 37.94 s, a correlated instantaneous rate of 0.0428, and an effective output ratio of 82.17%. Compared to traditional models, the system achieved an 18.64% reduction in task time, a 28.31% decrease in instantaneous rate, and a 7.36% improvement in output ratio. This work demonstrates a significant advancement in intelligent micro-system platforms for human-centric rehabilitation robotics.},
}
@article {pmid41469392,
year = {2025},
author = {Jilderda, MF and Bartlett, JMS and Liefers, GJ and Zhang, Y and Dunn-Davies, H and Rebattu, V and Salunga, R and Meershoek-Klein Kranenbarg, E and de Munck, L and Hasenburg, A and Markopoulos, C and Dirix, L and van de Velde, CJH and Rea, D and Anderson, AKL and Bastiaannet, E and Treuner, K and Taylor, KJ},
title = {Validation of minimal risk of recurrence classification by the Breast Cancer Index in early stage breast cancer.},
journal = {NPJ breast cancer},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41523-025-00885-x},
pmid = {41469392},
issn = {2374-4677},
abstract = {The Breast Cancer Index (BCI) was previously shown to identify ~20% of postmenopausal patients with early stage, hormone receptor positive (HR+), node negative (N0) breast cancer with minimal (<5%) risk of 10-year distant recurrence (DR) even without receiving adjuvant endocrine therapy (ET). This prospective-retrospective study further validated the BCI minimal risk classification in postmenopausal patients with early-stage, HR + HER2- N0 breast cancer from the Netherlands Cancer Registry (NCR) and the Tamoxifen and Exemestane Adjuvant Multinational (TEAM, NCT00279448, NCT00032136) randomized trial who received 5 years of primary adjuvant ET. BCI classified approximately 15% of patients as minimal risk. In the NCR cohort (n = 1264 out of 15,053 HR+ patients in the registry), risks of DR in the minimal, low, intermediate, and high groups were 4.8%, 3.3%, 8.0%, and 12.4%, respectively (P < 0.001). In the TEAM cohort (n = 978 out of 3544 in the BCI study), DR risks were 3.8%, 8.3%, 12.6% and 22.7% (P < 0.001). In multivariate analyses, BCI risk scores provided independent information over standard prognostic factors (P < 0.001). This study confirmed the ability of the adjusted BCI model to identify postmenopausal women with HR + HER2- N0 breast cancer who are at minimal risk of DR and may consider de-escalating adjuvant ET.},
}
@article {pmid41469234,
year = {2026},
author = {Ge, H and Gu, X and Wang, Z and Tan, S and Jiao, B and Zhang, L and Yang, Y and Li, W and Xie, J and Bai, R},
title = {Anesthetics Modulate Cerebrospinal Fluid Efflux Pathways in Mice by Altering Perineural and Perivascular Spaces.},
journal = {NMR in biomedicine},
volume = {39},
number = {2},
pages = {e70222},
doi = {10.1002/nbm.70222},
pmid = {41469234},
issn = {1099-1492},
support = {2022ZD0206000//STI2030-Major Projects of China/ ; 92359303//National Natural Science Foundation of China/ ; 82222032//National Natural Science Foundation of China/ ; 2025ZD0215000//Brain Science and Brain-like Intelligence Technology-National Science and Technology Major Project/ ; },
mesh = {Animals ; *Cerebrospinal Fluid/drug effects/metabolism ; *Anesthetics/pharmacology ; Male ; Mice ; Magnetic Resonance Imaging ; *Glymphatic System/drug effects ; Mice, Inbred C57BL ; },
abstract = {The brain-wide glymphatic transport system facilitates cerebrospinal fluid (CSF) circulation and the clearance of metabolic waste, processes largely influenced by sleep and sleep-like anesthesia. Recent research indicates that different anesthetic agents modulate CSF dynamics in distinct ways; however, their effects on CSF efflux pathways remain unclear. This study utilized dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and structural MRI to investigate CSF efflux pathways in mice under three anesthesia protocols (n = 6 per group): isoflurane alone (ISO), isoflurane combined with dexmedetomidine (DEXI), and ketamine/xylazine (K/X). Additionally, blood vessel diameters and CSF volume fractions were quantified. Our results demonstrate that ISO induced vasodilation in the anterior brain, slowing CSF flow to the dorsal brain while substantially accelerating CSF efflux across the cribriform plate and nasal mucosa toward the nasopharyngeal lymphatic plexus compared with DEXI and K/X (p < 0.001). However, ISO reduced CSF outflow through the spinal subarachnoid space primarily due to a decreased spinal subarachnoid CSF volume (ISO vs. DEXI, p = 0.0373; ISO vs. K/X, p = 0.0436). K/X considerably impaired CSF efflux via the cervical ganglia relative to DEXI and ISO, likely resulting from a lower CSF volume fraction within the peri-cranial nerve space (ISO vs. K/X, p = 0.0328, K/X vs. DEXI, p = 0.023). In conclusion, different anesthesia protocols modulate CSF efflux pathways by altering perineural and perivascular CSF spaces. These findings suggest that anesthetic agents influence glymphatic function by modulating distinct CSF efflux routes.},
}
@article {pmid41468722,
year = {2025},
author = {Ye, QY and Zhang, SY and He, XL and Yang, YQ and Ni, K and Yang, HX and Wei, W and Preece, DA and Chan, RCK and Li, BM and Cai, XL},
title = {Interrelationships between childhood trauma, alexithymia, and depressive symptoms: A network analysis and replication.},
journal = {Child abuse &amp; neglect},
volume = {172},
number = {},
pages = {107877},
doi = {10.1016/j.chiabu.2025.107877},
pmid = {41468722},
issn = {1873-7757},
abstract = {BACKGROUND: Childhood trauma has been found to increase the risk of developing alexithymia and depressive symptoms. However, the complex interplay between childhood trauma, alexithymia, and depressive symptoms remains unclear.<br><br>OBJECTIVE: To understand how different facets of childhood trauma, alexithymia across positive and negative emotions, and depressive symptoms interact with each other, this study adopted the network analysis approaches to examine this complex relationship.<br><br>PARTICIPANTS AND SETTING: An initial sample of 2918 Chinese college students completed a set of psychometric questionnaires measuring childhood trauma, alexithymia and depressive symptoms. Another independent sample (n&#xa0;=&#xa0;858) was used to investigate the replicability of our results.<br><br>METHODS: Undirected networks were estimated to explore the most relevant connections between the above variables. Bayesian network analysis was further used to explore the potential causal directions between the variables.<br><br>RESULTS: Findings from the initial dataset showed that childhood trauma was positively correlated with both alexithymia and depressive symptoms in the undirected networks. Physical abuse was the most central node. The Bayesian network analysis indicated that externally orientated thinking and depressed mood may be key drivers for activating other symptoms. Physical abuse might affect suicide ideation through difficulties in describing negative emotions. The replication dataset showed similar network structures as the initial dataset.<br><br>CONCLUSIONS: The findings suggest that childhood trauma, especially physical abuse, plays an important role in developing later depressive symptoms via valenced components of alexithymia. This study clarifies how early adversities link to depressive symptoms through emotional functioning and informs clinical interventions targeting influential symptoms in trauma-exposed populations.},
}
@article {pmid41467724,
year = {2025},
author = {Wang, C and Allison, BZ and Wu, X and Li, J and Zhao, R and Chen, W and Wang, X and Cichocki, A and Jin, J},
title = {Multi-Domain Dynamic Weighting Network for Motor Imagery Decoding.},
journal = {International journal of neural systems},
volume = {},
number = {},
pages = {2650005},
doi = {10.1142/S012906572650005X},
pmid = {41467724},
issn = {1793-6462},
abstract = {In motor imagery (MI)-based brain-computer interfaces (BCIs), convolutional neural networks (CNNs) are widely employed to decode electroencephalogram (EEG) signals. However, due to their fixed kernel sizes and uniform attention to features, CNNs struggle to fully capture the time-frequency features of EEG signals. To address this limitation, this paper proposes the Multi-Domain Dynamic Weighted Network (MD-DWNet), which integrates multimodal complementary feature information across time, frequency, and spatial domains through a branch structure to enhance decoding performance. Specifically, MD-DWNet combines multi-band filtering, spatial convolution, and temporal variance calculation to extract spatial-spectral features, while a dual-scale CNN captures local spatiotemporal features at different time scales. A dynamic global filter is designed to optimize fused features, improving the adaptive modeling capability for dynamic changes in frequency band energy. A lightweight mixed attention mechanism selectively enhances salient channel and spatial features. The dual-branch joint loss function adaptively balances contributions through a task uncertainty mechanism, thereby enhancing optimization efficiency and generalization capability. Experimental results on the BCI Competition IV 2a, IV 2b, OpenBMI, and a self-collected laboratory dataset demonstrate that MD-DWNet achieves classification accuracies of 83.86%, 88.67%, 75.25% and 84.85%, respectively, outperforming several advanced methods and validating its superior performance in MI signal decoding.},
}
@article {pmid41467584,
year = {2026},
author = {Wang, M and He, Q and Zhu, S and Cao, T and Wang, N and Jia, Y and Wu, H and Liang, J and Niu, H and Xu, Z and Cui, Z and Yang, Y and Zhao, J},
title = {Global White Matter Damage in Focal Brainstem Injury Patients With Disorders of Consciousness: A Diffusion Tensor Tractography Study.},
journal = {European journal of neurology},
volume = {33},
number = {1},
pages = {e70476},
doi = {10.1111/ene.70476},
pmid = {41467584},
issn = {1468-1331},
support = {2022ZD0205300//Science and Technology Innovation 2030/ ; Z221100002722014//International (Hong Kong, Macao, and Taiwan) Science and Technology Cooperation Project/ ; 2022-NKX-XM-02//Chinese Institute for Brain Research Youth Scholar Program/ ; 82371197//National Natural Science Foundation of China/ ; 7232049//Natural Science Foundation of Beijing Municipality/ ; },
mesh = {Humans ; Diffusion Tensor Imaging ; Female ; Male ; *White Matter/pathology/diagnostic imaging ; Adult ; *Consciousness Disorders/etiology/pathology/diagnostic imaging ; *Brain Stem/pathology/diagnostic imaging/injuries ; Middle Aged ; Retrospective Studies ; Young Adult ; Neural Pathways/pathology/diagnostic imaging ; Aged ; },
abstract = {BACKGROUND: Disorders of consciousness (DoC) pose significant challenges in clinical diagnosis and treatment. This study aims to investigate the relationship between consciousness levels and the brainstem-cortical white matter tracts in DoC patients resulting from focal brainstem injury using diffusion tensor imaging (DTI).<br><br>METHODS: DTI data of DoC patients with focal brainstem injury and healthy volunteers were retrospectively collected. White matter tractography was performed to reconstruct brainstem-cortical projections. The number of streamlines, total volume, and fractional anisotropy (FA) were analyzed from the perspective of global brain, physiological pathways, and functional networks. The relationship between these measurements and consciousness levels was investigated.<br><br>RESULTS: A cohort of 28 DoC patients and 32 healthy controls were included in the analysis. DoC patients exhibited significant reductions in the number of streamlines in global brainstem-cortical projections compared to controls. However, the total volume and FA of these fibers were relatively preserved. Specific pathways such as the corticospinal tract and frontoparietal tract showed marked reductions in streamline counts. Significant reductions in streamline counts were also observed in the somatomotor and frontoparietal networks. No significant changes in mean FA were observed across different physiological pathways and brain networks. Correlation analyses revealed significant associations between consciousness levels and structural connections in the frontoparietal tract and frontoparietal network.<br><br>CONCLUSION: This study highlights the impact of focal brainstem injury on global brain structural connectivity in DoC patients. Despite significant reductions in streamline counts, the preservation of FA suggests maintained microstructural integrity in surviving fibers.},
}
@article {pmid41466537,
year = {2025},
author = {Selcuk, C and Boulgouris, NV},
title = {Dynamic graph representation of EEG signals for speech imagery recognition.},
journal = {Journal of neural engineering},
volume = {22},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ae2ccb},
pmid = {41466537},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; *Imagination/physiology ; *Brain-Computer Interfaces ; *Speech/physiology ; Algorithms ; *Pattern Recognition, Automated/methods ; Male ; Female ; },
abstract = {Objective. Speech imagery recognition from electroencephalography (EEG) signals is an emerging challenge in brain-computer interfaces, and has important applications, such as in the interaction with locked-in patients. In this work, we use graph signal processing for developing a more effective representation of EEG signals in speech imagery recognition.Approach. We propose a dynamic graph representation that uses multiple graphs constructed based on the time-varying correlations between EEG channels. Our methodology is particularly suitable for signals that exhibit fluctuating correlations, which cannot be adequately modeled through a static (single graph) model. The resultant representation provides graph frequency features that compactly capture the spatial patterns of the underlying multidimensional EEG signal as well as the evolution of spatial relationships over time. These dynamic graph features are fed into an attention-based long short-term memory network for speech imagery recognition. A novel EEG data augmentation method is also proposed for improving training robustness.Main results. Experimental evaluation using a range of experiments shows that the proposed dynamic graph features are more effective than conventional time-frequency features for speech imagery recognition. The overall system outperforms current state-of-the-art approaches, yielding accuracy gains of up to 10%.Significance. The dynamic graph representation captures time-varying spatial relationships in EEG signals, overcoming limitations of static graph models and conventional feature extraction. Combined with data augmentation and attention-based classification, it demonstrates substantial improvements over existing methods in speech imagery recognition.},
}
@article {pmid41464697,
year = {2025},
author = {Kimmeyer, M and Buijze, GA and Soares, MN and Rab, P and Colombini, AG and Diot, R and Macken, A and Lafosse, T},
title = {Arthroscopic Bioinductive Collagen Scaffold Augmentation in High-Risk Posterosuperior Rotator Cuff Tears: Clinical and Radiological Outcomes.},
journal = {Journal of clinical medicine},
volume = {14},
number = {24},
pages = {},
doi = {10.3390/jcm14248797},
pmid = {41464697},
issn = {2077-0383},
abstract = {Background/Objectives: Bioinductive bovine collagen implants (BCI) have been introduced to enhance tendon biology and promote tissue regeneration in rotator cuff (RC) repairs. This study aimed to assess the clinical and radiological outcomes of arthroscopic posterosuperior rotator cuff (psRC) repair with BCI augmentation in full-thickness tears at increased risk of retear. Methods: This case series analyzed 30 patients with psRC tears who were classified as being at high risk of failure according to a predefined set of parameters, including patient history, radiological findings and intraoperative assessments, and the presence of psRC retears. All patients subsequently underwent arthroscopic psRC repair with BCI augmentation, compromising 21 primary and 9 secondary repairs. Clinical outcomes were assessed using Subjective Shoulder Value (SSV), American Shoulder and Elbow Surgeons (ASES) shoulder score, and Constant score at 6 and 12 months postoperatively. Tendon integrity was assessed using the Sugaya classification. Results: At 12 months, magnetic resonance imaging revealed complete tendon healing in 56.7%, partial healing in 16.7%, and insufficient healing in 26.7%. Significant improvements in SSV (45.3 to 83.5), ASES (40.6 to 77.8), and Constant score (36.6 to 71.7) were observed at 12 months postoperatively, with all outcome measures exceeding their respective minimally clinically important differences. Two patients (6.7%) developed secondary shoulder stiffness, and 1 patient (3.3%) required revision surgery for bicipital groove pain. Conclusions: Augmentation with a BCI in arthroscopic repair of high-risk psRC tears demonstrate promising short-term results. Patients achieve significant improvements in pain and shoulder function, accompanied by satisfactory tendon healing on MRI.},
}
@article {pmid41461597,
year = {2025},
author = {Di Nicola, MR and Colla, L and Mulder, KP and Storniolo, F and Verbrugghe, E and Esposito, G and Grasso, DA and Pasmans, F and Martel, A},
title = {Ophidiomycosis Prevalence and Disease Ecology in a Natrix tessellata (Laurenti, 1768) Population From Northern Italy.},
journal = {Journal of experimental zoology. Part A, Ecological and integrative physiology},
volume = {},
number = {},
pages = {},
doi = {10.1002/jez.70061},
pmid = {41461597},
issn = {2471-5646},
abstract = {Fungal pathogens pose a growing threat to vertebrate biodiversity. In snakes, Ophidiomyces ophidiicola (Oo) has garnered particular concern, although its impact in Europe remains poorly understood. We conducted a season-long, standardized survey of dice snakes (Natrix tessellata) along the northern shore of Lake Como (Italy) to quantify Oo and ophidiomycosis prevalence, identify the circulating strain, and explore the association with environmental, morphological and behavioral traits. Between March and October 2024, we collected 96 N. tessellata samples (23 sheds and swabs from 73 live individuals; scale clips were also collected from 60 out of the 73 live individuals). These samples were analyzed through qPCR, histopathology, and direct field observations. After excluding four recaptures, the dataset comprised 92 N. tessellata samples (23 sheds and swabs from 69 individuals), of which 49 tested positive for Oo (53.3%). Among live individuals, 26 tested positive (37.7%). Of these, 21 showed clinical signs (i.e., skin lesions; 80.8%), and histology confirmed ophidiomycosis in 10 of 20 tested Oo-positive samples (47.6%). Among the five Oo-positive snakes without skin lesions, only one showed histological evidence of ophidiomycosis. This resulted in "at least apparent" ophidiomycosis (i.e., pooling the case-classification categories "Apparent ophidiomycosis", "Ophidiomycosis" and "Ophidiomycosis and Oo shedder") being confirmed in 22 out of 69 live snakes (31.9%), corresponding to an overall disease prevalence of 23.9% (22 out of 92) across the full sample set. All sequenced samples belonged to clade II. Bayesian models revealed that skin lesions predicted both Oo detection and ophidiomycosis, while snout-vent length was inversely related to both pathogen presence and disease, suggesting age-linked susceptibility. Both Oo-positive and diseased snakes had lower body temperatures but showed no clear preference for warmer substrates, suggesting limited or absent behavioral fever. Body-condition index (BCI) did not differ between Oo/disease-positive and Oo/disease-negative snakes, suggesting possible host tolerance. An assessment of antipredator behavior revealed a marked reduction in musking among Oo-positive snakes, potentially compromising antipredator defenses. Our findings identify N. tessellata as a possible model for European ophidiomycosis research and highlight the need for multi-season capture-recapture studies.},
}
@article {pmid41460615,
year = {2025},
author = {Liu, H and Bai, Y and Guo, M and Zhao, R and Zhu, J and Ni, G},
title = {Dynamic brain functional connectivity in age-related hearing loss during auditory selective spatial attention.},
journal = {GeroScience},
volume = {},
number = {},
pages = {},
pmid = {41460615},
issn = {2509-2723},
support = {824B2056//National Natural Science Foundation of China/ ; 2023YFF1203500//Key Technologies Research and Development Program/ ; 2025XJ1-0006//Seed Foundation of Tianjin University/ ; },
abstract = {Age-related hearing loss (ARHL) is a common health problem that impairs auditory perception. However, the dynamic patterns of brain functional connectivity in ARHL during auditory spatial selective attention have not been thoroughly investigated. In this study, 32 older adults were recruited to investigate the dynamic brain functional connectivity in ARHL. First, an experimental paradigm for auditory spatial selective attention was designed, and neural electrical signals were recorded using electroencephalography. Then, a multilayer time-varying brain network was constructed based on multiple time windows, equally dividing each epoch signal to capture dynamic functional connectivity across time scales. Finally, the core layer brain network was identified by the multilayer time-varying brain network properties to investigate the changing patterns of network topology. Behavioral analysis revealed a significant negative correlation between the severity of hearing loss and auditory spatial selective attention performance. Multilayer time-varying brain network analysis revealed that worsening hearing loss was found to lead to increased inter-layer connectivity strength, decreased multilayer modularity and a higher participation coefficient. This suggests that the brain compensates by weakening the independence of local functional modules and enhancing cross-interaction. Core layer analysis further highlighted the critical role of the right parietal lobe in auditory spatial selective attention. It also suggested that connectivity between the right prefrontal and frontal lobes may play a compensatory role in ARHL. In conclusion, these findings provide important neuroscientific insights into the dynamic brain functional connectivity of ARHL, and potential biomarkers and time windows for the development of precision auditory rehabilitation strategies.},
}
@article {pmid41459740,
year = {2026},
author = {Kwon, J and Shin, Y},
title = {Foundation Models for Neural Signal Decoding: EEG-Centered Perspectives Toward Unified Representations.},
journal = {The European journal of neuroscience},
volume = {63},
number = {1},
pages = {e70376},
doi = {10.1111/ejn.70376},
pmid = {41459740},
issn = {1460-9568},
mesh = {Humans ; *Electroencephalography/methods ; *Brain/physiology ; *Models, Neurological ; Machine Learning ; Animals ; },
abstract = {Neural signals such as EEG, ECoG, and intracortical recordings offer a valuable window into brain dynamics but remain difficult to decode due to high dimensionality, nonstationarity, and substantial interindividual variability. Traditional machine learning and deep learning models often show limited generalizability and insufficient interpretability in these settings. Foundation models (FMs)-large-scale architectures pretrained on diverse datasets-have recently emerged as a promising paradigm for building robust, transferable, and physiologically grounded neural representations. Among these modalities, EEG currently serves as the most practical and representative platform for FM development due to its large-scale open datasets, standardized protocols, and broad clinical applicability, while the same conceptual framework remains generalizable to other neural recording types. This review synthesizes emerging FM approaches for neural decoding and critically examines representative EEG-based architectures. We highlight three essential design principles: physiology-aware representation learning that captures oscillatory and dynamic structure, structure-aware architectures that incorporate spatial and anatomical priors, and interpretability mechanisms that ensure neuroscientific and clinical validity. Although models such as the Patched Brain Transformer, CBraMod, and BrainGPT demonstrate encouraging adaptability, many still inherit objectives from non-neural domains and underutilize spatial priors such as electrode topology or functional connectivity. While this review focuses on EEG as the most data-rich and scalable testbed, the same framework can extend to ECoG and intracortical recordings to support unified neural representations across spatial and temporal scales. Fully realizing the potential of neural FMs will require biologically informed objectives, structure-aware architectures, interpretable representations, and standardized data ecosystems.},
}
@article {pmid41467019,
year = {2025},
author = {Yang, L and Zhen, H and Li, L and Li, Y and Zhang, H and Xie, X and Zhang, RY},
title = {Functional diversity of visual cortex improves constraint-free natural image reconstruction from human brain activity.},
journal = {Fundamental research},
volume = {5},
number = {6},
pages = {2639-2648},
pmid = {41467019},
issn = {2667-3258},
abstract = {Previous brain decoding studies using functional magnetic resonance imaging (fMRI) have greatly advanced our understanding of human visual coding and non-invasive brain-machine interfaces. However, most of these studies focus on classifying a limited number of image categories or reconstructing visual images with additional information, e.g., semantic categories and textual cues. Constraint-free visual reconstruction remains scarce. Here, we propose a generative network based on the functional diversity of the human visual cortex (FDGen) that takes multivariate brain activity as input and directly reconstructs natural images perceived by observers without any additional cues (semantic categories or textual description). Our FDGen is augmented by two bio-inspired computational modules. Based on the functional specializations of the human visual cortex, we propose a new function-based input module (FIM) that projects responses from different brain regions into separate feature spaces. Second, inspired by human attention, we construct a computational module to derive attentive feature weights at the function level to refine the feature map. These function-selection modules (FSMs) allow the network to dynamically select multiscale visual information during the generation process. We test FDGen on the popular fMRI datasets of natural images and achieve highly robust performance. Our work represents an important step forward in the development of fMRI-based brain decoding algorithms and highlights the utility of neuroscience theories in the design of deep learning models.},
}
@article {pmid41459561,
year = {2025},
author = {Pham, DT and Titkanlou, MK and Mouček, R},
title = {A hybrid Spiking Neural Network-Transformer architecture for motor imagery and sleep apnea detection.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1716204},
pmid = {41459561},
issn = {1662-4548},
abstract = {INTRODUCTION: Motor imagery (MI) classification and sleep apnea (SA) detection are two critical tasks in brain-computer interface (BCI) and biomedical signal analysis. Traditional deep learning models have shown promise in these domains, but often struggle with temporal sparsity and energy efficiency, especially in real-time or embedded applications.<br><br>METHODS: In this study, we propose SpiTranNet, a novel architecture that deeply integrates Spiking Neural Networks (SNNs) with Transformers through Spiking Multi-Head Attention (SMHA), where spiking neurons replace standard activation functions within the attention mechanism. This integration enables biologically plausible temporal processing and energy-efficient computations while maintaining global contextual modeling capabilities. The model is evaluated across three physiological datasets, including one electroencephalography (EEG) dataset for MI classification and two electrocardiography (ECG) datasets for SA detection.<br><br>RESULTS: Experimental results demonstrate that the hybrid SNN-Transformer model achieves competitive accuracy compared to conventional machine learning and deep learning models.<br><br>DISCUSSION: This work highlights the potential of neuromorphic-inspired architectures for robust and efficient biomedical signal processing across diverse physiological tasks.},
}
@article {pmid41459239,
year = {2025},
author = {Radu, R},
title = {Cognitive frontiers: neurotechnology and global internet governance.},
journal = {Frontiers in digital health},
volume = {7},
number = {},
pages = {1690489},
pmid = {41459239},
issn = {2673-253X},
abstract = {This article explores the largely uncharted intersection of neurotechnology and Internet governance on the international policy agenda. Neurotechnologies encompass a broad spectrum of functions and applications, from the direct recording or alteration of brain activity to the analysis of emotions and mental states through data collected from wearable devices, applications, and AI-based tools. Innovations such as cochlear implants, sleep optimisation technologies, and immersive educational tools are already available, and significant investments are made in the next generation of devices that blur the lines between mind, machine, and action, posing unprecedented challenges. While some international organisations have begun addressing the ethical and human rights implications of neurotechnology, there remains significant fragmentation and a lack of clarity regarding its integration into Internet governance. Critical issues related to neural infrastructure, standards, access to technologies, and protections for neural data have been overlooked in the 2024 Global Digital Compact and might remain off the agenda for the upcoming 20th review of the World Summit on the Information Society. This contribution underscores the urgent need to analyse the profound implications of neurotechnology, advocating for proactive measures that align with progress made across Internet governance fora, with respect to legal safeguards, multistakeholder consultations and institutional pillars.},
}
@article {pmid41457672,
year = {2025},
author = {Cai, Z and Zhang, S and Wang, J and Luo, Y and Zhu, M and Lv, Z and Li, X and Chen, Y and Song, Y and Gao, X and Guan, C and Chen, X},
title = {Bioinspired Heat-Induced Viscoelasticity-Switchable Electrodes for Conformal Brain-Computer Interfaces.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {},
number = {},
pages = {e17936},
doi = {10.1002/adma.202517936},
pmid = {41457672},
issn = {1521-4095},
support = {//Prime Minister's Office/ ; //Campus of Research Excellence and Technological Enterprise (CREATE) programme/ ; //Agency for Science, Technology and Research (A*STAR)/ ; //Scent Digitalization and Computation (SDC) programme/ ; U2241208//National Natural Science Foundation of China/ ; M23L8b0049//Agency for Science, Technology and Research/ ; SHINE//National Research Foundation Singapore/ ; CREATE-SGSR//National Research Foundation Singapore/ ; },
abstract = {Electroencephalography is a promising noninvasive modality for brain-computer interfaces (BCIs), yet its widespread adoption is constrained by electrode limitations: dry electrodes yield unstable signals, whereas wet electrodes require laborious setup and are ill-suited to wearable devices. Inspired by honeybees that locally heat beeswax to reversibly switch it between rigid and moldable states for comb construction, this work introduces a heat-induced viscoelasticity-switchable electrode (HIVE) that enables conformal contact on hairy scalps and user-friendly operation in wearable systems. HIVE integrates a thermoresponsive gelatin gel confined in a sponge matrix with an on-electrode microheater. Its temperature is actively modulated on demand, enabling autonomous switching between the gel and sol states. As a flowable sol, it permeates hair, conforms to the skin. At body temperature, it remains in a viscoelastic state, providing strong adhesion. Moreover, heating duration is closed-loop controlled using real-time electrode-skin impedance. In steady-state visual evoked potential paradigm, HIVE delivers high classification accuracy comparable to gold-standard wet electrodes while supporting wearable BCI devices for vision-based wheelchair navigation and high-speed text entry. By translating honeybee viscoelasticity-modulation strategy into bioelectronic interfaces, this work provides a practical solution for wearable BCI devices and a new design paradigm for conformal biointerfaces on hairy or piliferous surfaces.},
}
@article {pmid41456250,
year = {2025},
author = {Vincenzo, R and Marianna, C and Rossella, C and Gianluca, DF and Andrea, G and Daniele, G and Gianluca, B and Fabio, B and Pietro, A},
title = {Beyond the lab: real-world benchmarking of wearable EEGs for passive brain-computer interfaces.},
journal = {Brain informatics},
volume = {},
number = {},
pages = {},
doi = {10.1186/s40708-025-00290-x},
pmid = {41456250},
issn = {2198-4018},
support = {SAP_RICERCA_2024_TCI_ARIC&#xd2;_P_01//Sapienza Universit&#xe0; di Roma/ ; B83C24006240005//Istituto Nazionale per l'Assicurazione Contro Gli Infortuni sul Lavoro/ ; },
abstract = {PURPOSE: Wearable EEG systems are increasingly used for brain-computer interface (BCI) applications beyond controlled laboratory environments. However, there is still limited evidence on their reliability in real-world cognitive monitoring, especially for deriving robust mental-state indicators. This study investigates the signal quality, computational stability, and neurometric consistency of two widely used consumer-grade EEG devices (Emotiv EPOC X and Muse S) compared to a validated research-grade system (Mindtooth Touch) during naturalistic tasks relevant to passive BCIs and brain-machine intelligence.<br><br>METHOD: Twenty-four participants completed a multimodal protocol including video observation, multitasking under varying cognitive loads, and a simulated driving task. Each participant used all three EEG systems in a counterbalanced order to avoid any bias induced by the order. Signal quality was assessed through artefact analysis and Power Spectral Density (PSD) stability. Neurometrics, i.e., metrics related to specific mental and emotional states that can be extracted from EEG signal processing (workload, attention, vigilance, and approach-withdrawal) were extracted and compared across devices, conditions, and subjective reports of effort and comfort.<br><br>FINDING: The research grade system demonstrated higher signal stability, fewer artefacts, and more consistent neurometric responses to cognitive variations, with high significant correlation with subjective measures. Post-processing improved data continuity in consumer devices, but neurometrics remained less sensitive to task demands and less aligned with subjective ratings. Each device reflected different trade-offs between data quality, usability, and cost.<br><br>CONCLUSION: Research-grade systems remain more reliable for passive BCI applications requiring high-resolution cognitive state monitoring. Nevertheless, consumer-grade headsets may still be appropriate for exploratory studies or non-critical applications. This work highlights key trade-offs between signal quality, usability, and application goals, contributing to the broader integration of wearable neurotechnologies into brain-machine intelligence frameworks.},
}
@article {pmid41456194,
year = {2025},
author = {Kotov, SV and Isakova, EV and Borisova, VA},
title = {[Spectrum of tolerability and safety of the use of brain-computer interfaces with biofeedback in cognitive rehabilitation after a stroke].},
journal = {Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova},
volume = {125},
number = {12. Vyp. 2},
pages = {86-93},
doi = {10.17116/jnevro202512512286},
pmid = {41456194},
issn = {1997-7298},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; Middle Aged ; *Stroke Rehabilitation/methods ; *Biofeedback, Psychology ; *Stroke/complications/psychology ; Aged ; Electroencephalography ; Adult ; Event-Related Potentials, P300 ; Cognitive Training ; },
abstract = {OBJECTIVE: To assess the tolerability and safety of using high-tech software complexes with biofeedback (BF) via a brain-computer interface (BCI) in the recovery of patients after a stroke, based on an analysis of neuropsychological examination data.<br><br>MATERIAL AND METHODS: The study included 100 stroke patients: 40 patients in the main group, 40 patients in the comparison group, and 20 patients in the control group. The Hospital Anxiety and Depression Scale (HADS), the Beck Depression Inventory (BDI), the Hamilton Anxiety Rating Scale (HARS), the Hamilton Depression Rating Scale (HDRS), the Montreal Cognitive Assessment (MoCA), and the Mini-Mental State Examination (MMSE) were used. In the main group, sessions were conducted using BCI-BF1 based on the P300 potential; in the comparison group, sessions were conducted using BCI-BF2 based on the mu-rhythm of electroencephalography (EEG); control group patients received standard of care.<br><br>RESULTS: Improvement of the symptoms was reported; no &#xab;aggravation/increase&#xbb; of the existing symptoms or the occurrence of new symptoms was observed, which indicated good tolerance of using BCI-BF1 and BCI-BF2. The results of the assessment on the BDI, HARS, and HDRS scales showed a statistically significant improvement, indicating the regression of existing affective disorders corresponding to the level of minor disorders, namely &#xab;subclinical anxiety/depression&#xbb; (p<0.001). When assessing the BDI and HDRS scales, a statistically significant decrease in the scores for the subscale of affective-cognitive disorders was found in the main group (p=0.002) and in the comparison group (p<0.001). MoCA score showed no decrease from the baseline score of 25 or more: in the main group, there was an increase in the median total score (p=0.014); in the comparison group, there was no change (p=0.683).<br><br>CONCLUSION: Treatment with BCI-BF1 based on P300 and BCI-BF2 based on the EEG mu-rhythm was safe in patients in the recovery period of stroke, showed good tolerance, did not cause the occurrence or increase of affective disorders, and did not reduce the MoCA score.},
}
@article {pmid41455765,
year = {2025},
author = {Huang, X and Zhou, W and Hou, W and Zhou, Y and Li, M and Zhang, Y and Zhang, Q and Yan, W and Zhang, D and Lee, HJ},
title = {Tumor-secreted factors induce aberrant accumulation of vitamin A-enriched lipid droplets in the liver.},
journal = {Communications biology},
volume = {},
number = {},
pages = {},
doi = {10.1038/s42003-025-09404-x},
pmid = {41455765},
issn = {2399-3642},
support = {LZ25H180001//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
abstract = {Cancer is increasingly recognized as a systemic disease, extending beyond local alterations to systemic alterations in distant organs through the release of various factors that promote tumor progression and metastasis. Here, we applied hyperspectral stimulated Raman scattering (hSRS) microscopy to study metabolic alterations in the liver driven by distant tumors, revealing unprecedented accumulation of vitamin A-enriched lipid droplets. Quantitative spectral analysis uncovered increased unsaturation levels and abnormal vitamin A ester. Notably, inhibition of secretory pathways in remote tumors effectively abrogated these metabolic alterations, with FABP5 in tumor-derived extracellular vesicles identified as a key mediator. These findings uncover a unique aspect of cancer progression mechanisms, implicating tumor-driven systemic lipid metabolic remodeling and vitamin A dysregulation in metastatic progression and therapeutic response.},
}
@article {pmid41455146,
year = {2025},
author = {Zhang, H and Che, J},
title = {From Hierarchical Decoding to State Dependent Computation: "Comment on Neural decoding in brain computer interfaces Hierarchical representations, complexity measures, and dynamical perspectives" by Li et al.},
journal = {Physics of life reviews},
volume = {56},
number = {},
pages = {202-203},
doi = {10.1016/j.plrev.2025.12.014},
pmid = {41455146},
issn = {1873-1457},
}
@article {pmid41454830,
year = {2025},
author = {Wen, X and Xue, P and Ma, S and Zhu, M and Liu, Y and Liu, P and Jing, B and Ge, R and Yang, M and Mo, X and Zhang, D},
title = {Sex effects on cortical alterations in infants with complex congenital heart disease.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {35},
number = {12},
pages = {},
doi = {10.1093/cercor/bhaf339},
pmid = {41454830},
issn = {1460-2199},
support = {62476129//National Natural Science Foundation of China/ ; 81970265//National Natural Science Foundation of China/ ; 82270310//National Natural Science Foundation of China/ ; NZ2024040//Fundamental Research Funds for the Central Universities/ ; },
mesh = {Humans ; Male ; Female ; *Heart Defects, Congenital/diagnostic imaging/complications/physiopathology ; Infant ; Magnetic Resonance Imaging ; *Cerebral Cortex/diagnostic imaging/growth &amp; development/pathology ; *Sex Characteristics ; Child, Preschool ; Gray Matter/diagnostic imaging/growth &amp; development ; },
abstract = {Congenital heart disease is linked to substantial variability in neurodevelopmental outcomes, with sex being a key contributing factor. Compared with females, male congenital heart disease infants often show greater impairments in motor, cognitive, and language development. However, studies on sex differences in early brain development among congenital heart disease patients remain limited. To fill these gaps, this study included 79 infants with complex congenital heart disease (42 males, 37 females) and 87 healthy controls (47 males, 40 females), collecting magnetic resonance imaging data, clinical information, and neurodevelopmental assessments. We examined sex-specific effects on global and regional brain development in congenital heart disease infants aged 1 to 2 yr using imaging and statistical analysis. Male congenital heart disease infants showed global brain volume reduction and regional cortical delays, including increased cortical thickness and gray matter volume. In contrast, female congenital heart disease infants had no significant global volume change but exhibited localized structural abnormalities, such as reduced surface area and increased cortical thickness. Notably, reduced global brain volume in congenital heart disease males was associated with poorer gross motor skills. Distinct sex differences in brain development exist among congenital heart disease infants during early life. Recognizing these differences is critical for developing sex-specific treatment and neuroprotective strategies.},
}
@article {pmid41454419,
year = {2025},
author = {Stump, T and Baker, B and Caldwell, R and Sharma, R and Negi, S and Rieth, L},
title = {Improved electrode stimulation stability of Utah arrays.},
journal = {Bioelectronic medicine},
volume = {11},
number = {1},
pages = {30},
pmid = {41454419},
issn = {2332-8886},
support = {UG3NS107688//BRAIN Initiative/ ; N66001-15-C-4017//DARPA HAPTIX/ ; },
}
@article {pmid41453502,
year = {2025},
author = {Niu, S and Han, X and Cao, L and Tian, Y and Yuan, D and Cheng, L},
title = {Fusion framework: Conditional-aware one-stage nested event extraction model.},
journal = {Journal of biomedical informatics},
volume = {},
number = {},
pages = {104972},
doi = {10.1016/j.jbi.2025.104972},
pmid = {41453502},
issn = {1532-0480},
abstract = {We present CA-NEE, a Conditional-Aware one-stage model for overlapping and nested biomedical event extraction. CA-NEE integrates an event-type-aware conditioning mechanism with token-pair relation modeling to jointly identify triggers, argument spans, and roles. A Conditional Layer Normalization (CLN) dynamically adapts token representations to candidate event types, and a parallel word-pair scorer predicts span boundaries and roles in a single pass. Evaluations on GENIA11 and GENIA13 show consistent gains in Trigger Classification (TC) and Argument Classification (AC) over strong baselines, particularly on complex overlapping and nested structures. These results demonstrate that CA-NEE offers an effective and efficient solution for biomedical event extraction.},
}
@article {pmid41452697,
year = {2025},
author = {Zhang, L and Luo, X and Cao, P and Cheng, K and Liu, H and Zhao, R and Zan, X and Ma, J and Cheng, R and Wang, R and Hou, X and Chou, X and He, J},
title = {A Novel Rat Robot: Multi Degree of Freedom Motion Control.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3648651},
pmid = {41452697},
issn = {1558-2531},
abstract = {OBJECTIVE: The development of brain-computer interface (BCI) technology has enabled animals to execute movements in accordance with human intent. The rat robot represents a novel robotic system based on BCI technology. However, due to limitations in electrode fabrication techniques and the use of simplistic control strategies, current rat robots are restricted to limited movement patterns, which hinder their applicability in real-world scenarios. To address these challenges, we have developed a portable wireless neural stimulator and a novel 3D integrated stimulating electrode. By refining the locomotion control strategy, we aim to achieve complex, high-degree-of-freedom movement in rat robot systems.<br><br>METHODS: 3D integrated electrodes were implanted into the rats' head, with no reward-based training required. By utilizing a wearable wireless stimulation backpack to connect the electrodes and deliver electrical stimulation to multiple brain regions, thereby enabling the rat to perform forward movement, turning, and stopping behaviors.<br><br>RESULTS: The experimental results demonstrate that under optimized stimulation parameters, the forward speed of the rat robot can be controlled to achieve 31.06 &#xb1; 1.21 m/min, the turning angle can reach up to 150 &#xb1; 1.22&#xb0;, and the stopping duration can be flexibly adjusted. Furthermore, we presented a practical scenario in which the rat robot successfully executed a predefined navigation task in a real-world environment, thereby validating its high degree of movement flexibility and control precision.<br><br>CONCLUSION: This study achieved high-degree-of-freedom motion control of rat robots without the need for reward-based training, which was previously unattainable.<br><br>SIGNIFICANCE: This research establishes a crucial foundation and provides valuable technical references for the application of animal robots in fields such as information reconnaissance and wreckage search and rescue operations.},
}
@article {pmid41451424,
year = {2025},
author = {Huang, X and Zhang, Y and Xiao, H and Chen, J and Jiang, Y},
title = {The evolution of cervical spine trauma classification: a paradigm shift from morphological description to clinical decision-making.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1728720},
pmid = {41451424},
issn = {1664-2295},
abstract = {OBJECTIVE: This review systematically traces the evolution of subaxial cervical spine classification, highlighting the paradigm shift from morphological description to decision-oriented functional assessment and exploring future technological directions.<br><br>METHODS: A comprehensive narrative literature review was conducted, analyzing key classification systems, their underlying principles, and the technological advancements shaping the field.<br><br>RESULTS: Early mechanistic classifications were limited by poor interobserver reliability. The Subaxial Injury Classification (SLIC) system was a pivotal advance, integrating morphology, disco-ligamentous complex (DLC) integrity, and neurological status into a treatment-guiding score. However, its inconsistent reliability, particularly in DLC assessment, limited its adoption. The subsequent AO spine classification resolved these issues by introducing a more rigorous, hierarchical framework that achieved excellent, validated interobserver reliability. Crucially, the AO spine system also provides significant prognostic value by correlating morphological subtypes with long-term neurological recovery.<br><br>CONCLUSION: The classification of cervical trauma has transitioned from a descriptive to an applied science. Future developments promise to resolve remaining challenges: artificial intelligence (AI) offers a definitive solution to interobserver variability, advanced imaging like diffusion tensor imaging (DTI) will refine prognostication, and brain-computer interfaces (BCI) provide new hope for functional reconstruction in severe injuries, heralding an era of precision medicine.},
}
@article {pmid41450957,
year = {2025},
author = {Sondh, I and Johnson, LA and Ghose, GM and Loveland, A and Larson, L and Lim, HH and Adams, ME},
title = {Development of a non-human primate model for preclinical research of a novel auditory nerve implant.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1669116},
pmid = {41450957},
issn = {1662-4548},
abstract = {The cochlear implant is a widely available hearing restoration technology that can provide speech understanding in quiet environments. This technology struggles however, in noisy settings or situations involving multiple speakers. The primary cause of these performance limitations is a poor neural interface, in which the bony wall of the cochlea separates the electrode surface from the auditory nerve fibers, thus causing unwanted current spread and non-specific frequency activation. This study utilizes an alternative auditory prosthetic technology (auditory nerve implant, ANI) that enables direct auditory nerve stimulation, which provides a potentially superior neural interface and enables more precise targeting of auditory nerve fibers than traditional cochlear implants. As auditory nerve implants progress towards clinical translation, new implant designs and stimulation strategies will be created. Animal models to efficiently test and iterate through these new designs will be useful for the continued development of ANI technology. We present a viable surgical approach in the non-human primate (rhesus macaque) along with electrophysiological results that demonstrate robust activation of the auditory system at low current levels via intraneural stimulation. Our findings indicate that the rhesus macaque, which possesses an inner ear anatomy more similar to the human compared to other animal models used in the hearing field (e.g., rodents, felines and ferrets), has strong potential as a useful preclinical testbed involving an upright head model for future ANI prototypes and stimulation strategy development.},
}
@article {pmid41450855,
year = {2025},
author = {Cui, J},
title = {An adaptive hand exoskeleton rehabilitation training system integrating virtual reality and an AI-based assessment engine.},
journal = {Frontiers in sports and active living},
volume = {7},
number = {},
pages = {1724021},
pmid = {41450855},
issn = {2624-9367},
abstract = {INTRODUCTION: Post-stroke hand motor impairment is a major cause of long-term functional disability and reduced quality of life, with approximately 70% of stroke survivors experiencing persistent limitations in fine motor control. Conventional rehabilitation is constrained by low adherence, subjective assessment, and insufficient individualization, which limits exploitation of the neuroplasticity window for motor relearning. To address these challenges, we propose a bio-AI-VR integrated hand rehabilitation system that fuses biosignal sensing (bio), AI-based analysis, and virtual reality (VR) interaction to realize an efficient, adaptive, and quantifiable closed-loop training process. The integration rationale is grounded in three theoretical pillars: (i) multimodal data fusion theory-combining heterogeneous biosignal and behavioral data through AI to overcome single-modality limitations; (ii) closed-loop adaptive control theory-dynamically balancing challenge and capability via real-time feedback; (iii) neuroplasticity multisensory enhancement theory-coordinating visual, proprioceptive, and motor pathways to strengthen cortical reorganization. This work addresses three testable hypotheses: (RQ1) Can multimodal biosignal fusion achieve real-time assessment with R 2 ≥ 0.65 and latency < 50 ms? (RQ2) Does bio-AI-VR integration yield FMA-UE improvement ≥ 6 points (minimal clinically important difference) with effect size d ≥ 0.8 ? (RQ3) Are all three components (bio, AI, VR) necessary, with ablation causing ≥ 15 % performance degradation?<br><br>METHODS: A lightweight hand exoskeleton (< 400 g, 3 DoF/finger) integrates a 6-axis IMU (100 Hz) and 16-channel sEMG (1 kHz) to synchronously acquire kinematics and muscle activation. Extended Kalman filtering fuses sensor streams before AI processing. Features include range of motion (ROM), smoothness metrics (SPARC, LDLJ), sEMG root-mean-square (RMS), median frequency (MDF), and co-contraction index (CCI). A hybrid model combining random forests (200 trees, depth 8) and support vector regression (RBF kernel, &#x3b3; = 0.01 , C = 10) outputs a real-time composite score S t &#x2208; [ 0 , 1 ] via multi-task learning with GroupKFold cross-validation, mapped to clinical scales through Sigmoid normalization. FMA-UE proxy labels for window-level training were constructed via linear interpolation (80%), biomechanical anchoring (15%), and expert annotation (5%, inter-rater &#x3ba; = 0.78). A cloud AI engine communicates bidirectionally with Unity-based VR over MQTT to close the perception-assessment-assistance loop. The assistance-as-needed (AAN) algorithm adjusts exoskeleton torque (u t) and VR difficulty (d t) using S t as control input with hysteresis, dead zone, and rate limiting to ensure smooth adaptation. Twenty-four stroke survivors (3-12 months post-stroke, FMA-UE 15-50) underwent 4-week training (5 sessions/week, 20 min/session). Outcomes included FMA-UE (primary), ARAT, grip strength, normalized ROM, task success rate, and System Usability Scale (SUS). Statistical analysis employed paired t -tests with Hedges' correction for effect sizes, Bonferroni adjustment for multiple comparisons, and leave-one-subject-out cross-validation (LOSOCV) to assess model generalization.<br><br>RESULTS: All 24 participants completed the study with one missed session (479 of 480 scheduled sessions, 8,946 annotated segments); end-to-end latency median 38 ms (IQR 33-42 ms), decomposed as: sampling 8 &#xb1; 2 ms, preprocessing 4 &#xb1; 1 ms, network 12 &#xb1; 3 ms, AI inference 5 &#xb1; 1 ms, control 2 &#xb1; 0.5 ms, command return 7 &#xb1; 2 ms. Offline model performance: R 2 = 0.72 (GroupKFold), MAE = 3.2 points, Spearman &#x3c1; = 0.68 with FMA-UE (p < 0.001); LOSOCV: R 2 = 0.68 &#xb1; 0.09 ; test-retest ICC(2,1) = 0.84 [0.76, 0.91]. AAN algorithm reduced assist torque 62 % &#x2192; 45 % (- 27.4 %), increased VR difficulty 0.42 &#x2192; 0.69 (+ 64.3 %), improved task success 61.3 % &#x2192; 82.1 % (+ 20.8 pp). Clinical outcomes (paired t -test): FMA-UE + 9.1 [6.7, 11.5], d = 0.98 ; ARAT + 7.6 [5.2, 10.0], d = 0.93 ; grip + 4.1 kg [2.5, 5.7], d = 0.72 ; ROM n + 0.14 ; SPARC - 0.16 . Subgroup analysis: moderate-to-severe (n = 13) showed greater FMA-UE gain (+ 10.7 vs + 7.2 in mild-to-moderate, p < 0.05). Ablation experiments confirmed synergistic necessity: Bio only (R 2 = 0.45 , FMA-UE + 4.3), VR only (R 2 = 0.38 , + 3.9), Bio-AI (R 2 = 0.70 , + 7.2 , compliance 68%), complete system (R 2 = 0.72 , + 9.1 , compliance 88%). SUS 84 &#xb1; 6 ; no serious adverse events.<br><br>DISCUSSION: Results validate all three hypotheses: (i) multimodal fusion exceeded technical targets (R 2 = 0.72 > 0.65 , latency 38 ms < 50 ms); (ii) clinical efficacy surpassed MCID with large effect sizes (FMA-UE + 9.1 > 6 points, d = 0.98 > 0.8), exceeding published spontaneous recovery rates (2-4 points); (iii) ablation experiments demonstrated &#x2265; 15 % degradation when removing any component, confirming non-additive synergistic effects of bio-AI-VR integration. Compared to recent brain-computer interface systems using EEG-based motor imagery, this approach achieves paradigm shift toward execution-based rehabilitation with direct motor intent capture and real-time physical feedback. The AAN control law elevates from low-level motion control to high-level rehabilitation strategy, spanning multiple temporal scales (window to course-level) and dual channels (physical assistance + cognitive challenge). Limitations include single-arm design limiting causal inference, small sample size (n = 24), short intervention period (4 weeks), FMA-UE proxy construction via linear interpolation, and controlled clinical setting vs. real-world deployment. Future work requires larger RCTs with active control arms, extended follow-up (3-6 months), dense longitudinal assessments, exploration of deep learning architectures for temporal modeling, and validation in home-based telerehabilitation settings. The bio-AI-VR system demonstrates feasibility of data-driven, multimodal closed-loop rehabilitation, offering a wearable, low-latency, and personalized solution for post-stroke hand recovery that bridges the gap between laboratory innovation and clinical translation.},
}
@article {pmid41450502,
year = {2025},
author = {Li, J and Hu, M and Wang, T and Xie, Y and Liu, Y and Yao, J and Hua, G and Yan, X and Fan, H},
title = {Temporal trends in chronic diseases among offshore oil workers and the interaction effect of age with body mass index.},
journal = {Frontiers in public health},
volume = {13},
number = {},
pages = {1738126},
pmid = {41450502},
issn = {2296-2565},
mesh = {Humans ; *Body Mass Index ; Middle Aged ; Male ; Adult ; Female ; *Hypertension/epidemiology ; Chronic Disease/epidemiology ; Prevalence ; China/epidemiology ; Age Factors ; *Diabetes Mellitus/epidemiology ; *Dyslipidemias/epidemiology ; Risk Factors ; *Oil and Gas Industry ; Aged ; },
abstract = {OBJECTIVE: To analyze trends in hypertension, diabetes, and dyslipidemia prevalence among Chinese offshore oil workers and explore the independent and interaction effects of age and BMI.<br><br>METHODS: Using health examination data of this population (2014-2024), we calculated the crude prevalence rate (CPR, the prevalence rate without age-structure adjustment) and the age-standardized prevalence rate (ASPR, adjusted to a standard population structure). Joinpoint regression assessed ASPR trends, and multivariable logistic regression analyzed age and BMI effects.<br><br>RESULTS: The overall mean CPR for hypertension, diabetes, and dyslipidemia from 2014 to 2024 were 22.41, 2.53, and 29.64%, respectively. Trend analysis revealed that ASPR for diabetes [Annual Percent Change (APC): 24.08, 95% CI: 12.93-35.23] and Dyslipidemia (APC: 21.83, 95% CI: 10.45-33.21) increased significantly (both p&#x202f;<&#x202f;0.001), while hypertension trend was non-significant. In the risk factor analysis, both age (OR for hypertension&#x202f;=&#x202f;1.03, 95% CI: 1.03-1.04; OR for diabetes&#x202f;=&#x202f;1.11, 95% CI: 1.08-1.13; OR for Dyslipidemia&#x202f;=&#x202f;1.02, 95% CI: 1.01-1.03) and BMI (OR for hypertension&#x202f;=&#x202f;1.13, 95% CI: 1.11-1.15; OR for diabetes&#x202f;=&#x202f;1.18, 95% CI: 1.12-1.22; OR for Dyslipidemia&#x202f;=&#x202f;1.18, 95% CI: 1.16-1.20) were independent risk factors for all three conditions (all p&#x202f;<&#x202f;0.001). A significant multiplicative interaction effect was observed among age and BMI, the group "Age >40&#x202f;years and BMI&#x202f;&#x2265;&#x202f;24&#x202f;kg/m[2]" had the highest risk for hypertension (OR&#x202f;=&#x202f;2.98, 95% CI: 2.37-3.74, p&#x202f;<&#x202f;0.05), diabetes (OR&#x202f;=&#x202f;16.11, 95% CI: 6.95-37.31), and Dyslipidemia (OR&#x202f;=&#x202f;4.01, 95% CI: 3.30-4.88).<br><br>CONCLUSION: The prevalence of chronic diseases among Chinese offshore oil workers is high, with diabetes and Dyslipidemia showing significant upward trends. Age and BMI are important influencing factors and exhibit an interaction effect. This population should be prioritized in occupational health surveillance, and comprehensive interventions focusing on weight management and metabolic screening should be implemented, particularly targeting middle-aged individuals with elevated BMI.},
}
@article {pmid41266142,
year = {2025},
author = {Chu, JP and Coulter, ME and Denovellis, EL and Nguyen, TTK and Liu, DF and Deng, X and Eden, UT and Kemere, CT and Frank, LM},
title = {RealtimeDecoder: A Fast Software Module for Online Clusterless Decoding.},
journal = {eNeuro},
volume = {12},
number = {12},
pages = {},
doi = {10.1523/ENEURO.0252-24.2025},
pmid = {41266142},
issn = {2373-2822},
mesh = {Animals ; *Software ; *Hippocampus/physiology ; Algorithms ; Rats ; Action Potentials/physiology ; *Neurons/physiology ; Male ; *Signal Processing, Computer-Assisted ; },
abstract = {Decoding algorithms provide a powerful tool for understanding the firing patterns that underlie cognitive processes such as motor control, learning, and recall. When implemented in the context of a real-time system, decoders also make it possible to deliver feedback based on the representational content of ongoing neural activity. That, in turn, allows experimenters to test hypotheses about the role of that content in driving downstream activity patterns and behaviors. While multiple real-time systems have been developed, they are typically implemented with a compiled programming language, making them more difficult for users to quickly adapt for new experiments. Here we present a software system written in the widely used Python programming language to facilitate rapid experimentation. Our solution implements the state space based clusterless decoding algorithm for an online, real-time environment. The parallelized application processes neural data with temporal resolution of 6 ms and median computational latency <50 ms for medium- to large-scale (32+ tetrodes) rodent hippocampus recordings without the need for spike sorting. It also executes auxiliary functions such as detecting sharp wave ripples from local field potential data. Even with an interpreted language, the performance is similar to state-of-the-art solutions that use compiled programming languages. We demonstrate this real-time decoder in a rat behavior experiment in which the decoder allowed closed-loop neurofeedback based on decoded hippocampal spatial representations. Overall this system provides a powerful and easy-to-modify tool for real-time feedback experiments.},
}
@article {pmid41448755,
year = {2025},
author = {Li, A and Mei, J and Chen, W and Tao, L and Xu, M and Ming, D},
title = {[Brain-controlled unmanned aerial vehicle system based on meta brain computer interface open-source software platform].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {6},
pages = {1139-1147},
doi = {10.7507/1001-5515.202506031},
pmid = {41448755},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; Humans ; *Software ; *Unmanned Aerial Devices ; Electroencephalography ; Signal Processing, Computer-Assisted ; *Brain/physiology ; },
abstract = {Brain computer interface (BCI) system includes multiple links such as stimulus presentation, data acquisition, signal processing, external device control and command feedback. As an open-source software platform which covers all links of BCI chain, meta brain computer interface (MetaBCI) has provided flexible solutions for effectively encoding, decoding and feeding back brain activities, but has not yet provided an integrated tool that can support the implementation of a complete BCI system. In view of the above shortcoming, this paper designed and constructed a brain-controlled unmanned aerial vehicle system by using MetaBCI, which realized the online control of the physical unmanned aerial vehicle. The results of the experiment involving 10 subjects indicated that the average online classification accuracy and information transfer rate (ITR) of this system could reach 93.83% and 38.57 bits/min, respectively, which verified the feasibility of constructing a practical BCI system for external device control by using MetaBCI. Meanwhile, this paper elaborated the design idea, implementation process and the usage logic of MetaBCI toolkit involved in this brain-controlled unmanned aerial vehicle system in detail, hoping to provide guidance for subsequent developers to design and construct BCI systems that can meet individual needs by using MetaBCI independently.},
}
@article {pmid41448752,
year = {2025},
author = {Dong, Z and Bao, X and Yang, Y and Wu, J},
title = {[A motor imagery decoding study integrating differential attention with a multi-scale adaptive temporal convolutional network].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {6},
pages = {1115-1122},
doi = {10.7507/1001-5515.202507012},
pmid = {41448752},
issn = {1001-5515},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Algorithms ; *Attention/physiology ; *Neural Networks, Computer ; *Imagination/physiology ; *Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; },
abstract = {Motor imagery electroencephalogram (MI-EEG) decoding algorithms face multiple challenges. These include incomplete feature extraction, susceptibility of attention mechanisms to distraction under low signal-to-noise ratios, and limited capture of long-range temporal dependencies. To address these issues, this paper proposes a multi-branch differential attention temporal network (MDAT-Net). First, the method constructed a multi-branch feature fusion module to extract and fuse diverse spatio-temporal features from different scales. Next, to suppress noise and stabilize attention, a novel multi-head differential attention mechanism was introduced to enhance key signal dynamics by calculating the difference between attention maps. Finally, an adaptive residual separable temporal convolutional network was designed to efficiently capture long-range dependencies within the feature sequence for precise classification. Experimental results showed that the proposed method achieved average classification accuracies of 85.73%, 90.04%, and 96.30% on the public datasets BCI-IV-2a, BCI-IV-2b, and HGD, respectively, significantly outperforming several baseline models. This research provides an effective new solution for developing high-precision motor imagery brain-computer interface systems.},
}
@article {pmid41448732,
year = {2026},
author = {Wei, Z and Zhang, X},
title = {Refining Accelerated Intermittent Theta Burst Stimulation for Depression.},
journal = {Biological psychiatry},
volume = {99},
number = {3},
pages = {182-183},
doi = {10.1016/j.biopsych.2025.10.027},
pmid = {41448732},
issn = {1873-2402},
}
@article {pmid41448222,
year = {2025},
author = {Bourhis, J and Aupérin, A and Borel, C and Lefebvre, G and Racadot, S and Geoffrois, L and Sun, XS and Saada, E and Cirauqui, B and Rutkowski, T and Henry, S and Modesto, A and Johnson, A and Chapet, S and Calderon, B and Sire, C and Malard, O and Bainaud, M and Da Silva Motta, A and Thureau, S and Pointreau, Y and Blanchard, P and Buiret, G and Bozec, L and Lopez, S and Vanbockstael, J and Bosset, M and Greilsamer, C and Daste, A and Bruna, A and N'Guyen, F and Plana, M and Iruarrizaga, E and Temam, S and Even, C and Ruiz, EP and Bert, M and Karamouza, E and Thariat, J and Kazmierska, J and Psyrri, A and Mesia, R and Tao, Y},
title = {Nivolumab added to cisplatin and radiotherapy versus cisplatin and radiotherapy alone after surgery for people with squamous cell carcinoma of the head and neck at a high risk of relapse (GORTEC 2018-01 NIVOPOST-OP): a randomised, open-label, phase 3 trial.},
journal = {Lancet (London, England)},
volume = {},
number = {},
pages = {},
doi = {10.1016/S0140-6736(25)01850-1},
pmid = {41448222},
issn = {1474-547X},
abstract = {BACKGROUND: Postoperative cisplatin and radiotherapy is the standard of care for high-risk resected locally advanced squamous cell carcinoma of the head and neck (LA-SCCHN). The NIVOPOST-OP trial aimed to assess the efficacy and safety of programmed death 1 blockade by nivolumab added to cisplatin and radiotherapy in this setting.<br><br>METHODS: This open-label, phase 3 trial evaluated adding nivolumab to cisplatin and radiotherapy after surgery for LA-SCCHN with high-risk pathological features. The main inclusion criteria were age 19-74 years, an Eastern Cooperative Oncology Group performance status 0-1, squamous cell carcinoma of the oral cavity, oropharynx, larynx, or hypopharynx resected with macroscopic complete resection, and at least one high-risk pathological feature: nodal extracapsular extension, microscopically positive margins, four or more cervical nodal involvements without extracapsular extension, and multiple perineural invasions. 680 participants recruited in 82 sites across six countries (France, Spain, Poland, Belgium, Greece, and Switzerland) were randomly assigned 1:1 to receive cisplatin and radiotherapy (66 Gy, cisplatin 100 mg/m[2] intravenously once every 3 weeks, for three cycles); or nivolumab 240 mg intravenously, followed by cisplatin and radiotherapy with three cycles of concomitant nivolumab 360 mg once every 3 weeks, and six cycles of adjuvant nivolumab 480 mg once every 4 weeks. The primary endpoint was disease-free survival as per investigator assessment in the intention-to-treat population. 230 disease-free survival events (relapses or deaths) were required to detect a hazard ratio of 0&#xb7;65 with 0&#xb7;05 two-sided &#x3b1; error, with 90% power. The trial is registered at ClinicalTrials.gov (NCT03576417) and is active, but not recruiting.<br><br>FINDINGS: The 680 patients were recruited from Oct 15, 2018, to July 3, 2024. The analysis was based on 666 participants randomly assigned until the cutoff date (April 30, 2024), at which point the required number of events was reached (median follow-up 30&#xb7;3 months). Disease-free survival was significantly improved with nivolumab, cisplatin, and radiotherapy versus cisplatin and radiotherapy alone, irrespective of programmed death ligand 1 expression (HR 0&#xb7;76; 95% CI 0&#xb7;60-0&#xb7;98; stratified log-rank test p value=0&#xb7;034). There was an increase in the rate of participants with treatment-related grade 4 adverse events with nivolumab, cisplatin, and radiotherapy compared with cisplatin and radiotherapy (30 [10%] of 312 vs 16 [5%] of 306). Treatment-related deaths occurred in two participants in each group.<br><br>INTERPRETATION: Nivolumab added to cisplatin and radiotherapy in high-risk resected LA-SCCHN improves disease-free survival with moderate toxic effect increase, and can be proposed as a new standard treatment.<br><br>FUNDING: Groupe Oncologie Radiotherapie Tete Et Cou (GORTEC) and Bristol Myers Squibb.},
}
@article {pmid41447233,
year = {2025},
author = {Wahid, SR and Khan, AA},
title = {Basic Science and Pathogenesis.},
journal = {Alzheimer's &amp; dementia : the journal of the Alzheimer's Association},
volume = {21 Suppl 1},
number = {},
pages = {e106434},
doi = {10.1002/alz70855_106434},
pmid = {41447233},
issn = {1552-5279},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; *Alzheimer Disease/diagnosis/physiopathology ; *Dementia/diagnosis/physiopathology ; *Cognitive Dysfunction/diagnosis/physiopathology ; *Brain/physiopathology ; Biomarkers ; },
abstract = {BACKGROUND: Dementia, a syndrome characterized by progressive cognitive decline, affects over 55 million people globally, with Alzheimer's disease accounting for 60-70% of cases. Traditional interventions, such as medication and cognitive therapies, have limited success in slowing down dementia progress. In this abstract, we propose Brain-Machine Interfaces (BMIs), which enable direct communication between the brain and external devices, present a novel opportunity to detect early stage dementia and help in rehabilitation of dementia patients. BMIs can detect early neural biomarkers of cognitive decline, such as altered theta-gamma oscillations or diminished functional connectivity, while offering real-time therapeutic interventions like neurofeedback. This exploratory study investigates the feasibility of non-invasive BMIs as tools for monitoring cognitive health and enhancing resilience in dementia patients, focusing on usability, neural correlates of decline, and patient engagement.<br><br>METHODS: Proposed Framework for Detection: BMIs could use non-invasive sensors (e.g., EEG, fNIRS) to monitor biomarkers linked to early dementia, such as: Theta-gamma phase-amplitude coupling: Reduced coupling correlates with memory deficits. Event-related potentials (ERPs): Delayed P300 responses during attention tasks could indicate processing speed decline. Machine learning algorithms can be employed to analyze these patterns to classify dementia risk before clinical symptoms show. Passive Monitoring:Wearable BMIs could track neural activity during daily activities (e.g., reading, social interactions) to detect anomalies in real time. For example, erratic frontal alpha oscillations during problem-solving might signal executive dysfunction. Proposed Framework for Rehabilitation: BMIs could provide real-time feedback during cognitive exercises. For instance: A memory game adjusts difficulty based on hippocampal theta activity. Personalized Engagement: Gamified interfaces, tailored to user preferences (e.g., music, visual themes), could improve adherence.<br><br>RESULTS: Detecting dementia using EEG and machine learning classifiers have shown promising results. For example, Joshi et al. used EEG signals and a BiLSTM model which achieved an accuracy of 97.27%.<br><br>CONCLUSION: This exploratory study proposes that BMIs hold transformative potential for dementia care, bridging early detection with dynamic, personalized rehabilitation. By utilizing neural biomarkers, BMIs could identify at-risk individuals years before symptom onset, while adaptive neurofeedback systems might slow decline by strengthening cognitive reserve.},
}
@article {pmid41446876,
year = {2025},
author = {Sun, Y and Chen, D and Ye, Q and You, Z and Zhao, Z and Shi, J and Sun, H and Li, S and Xu, X and Xu, Y and Zhang, P and Tang, Z},
title = {Applications of Endovascular Brain-Computer Interface in Patients with Alzheimer's Disease.},
journal = {Research (Washington, D.C.)},
volume = {8},
number = {},
pages = {1049},
pmid = {41446876},
issn = {2639-5274},
abstract = {Alzheimer's disease (AD) is a prevalent neurodegenerative disorder affecting the elderly, leading to important impairments in cognitive function and the ability to live independently. This results in substantial disability and places an increasing burden on families and society. Currently, the therapeutic approaches adopted in clinical practice predominantly hinge upon cholinesterase inhibitors and the N-methyl-d-aspartate (NMDA) receptor antagonist memantine. Nevertheless, these medications merely alleviate symptoms and fail to tackle the pathological characteristics of AD. In recent years, monoclonal antibodies such as lecanemab and donanemab against β-amyloid (Aβ) have shown good efficacy in clinical practice for early-stage AD patients. However, the early diagnosis of AD remains a challenge. Against this backdrop, endovascular brain-computer interface (EBCI) offers an integrated solution for the early diagnosis and neuroregulatory treatment of AD patients, with minimal invasiveness. This review comprehensively examines the safety and feasibility of EBCI for AD patients, focusing on 3 major application areas: early diagnosis, deep brain stimulation targeting specific brain regions, such as the fornix and the basal nuclei of Meynert, and the use of external neurofeedback devices. Furthermore, we explore future development trends in this field, including miniaturization, integration, and the exploration of deep brain regions.},
}
@article {pmid41446611,
year = {2025},
author = {Duarte-Mendes, P and Ramalho, A and Bertollo, M and Neiva, HP and Marinho, DA},
title = {To move without moving: a perspective article on motor imagery.},
journal = {Frontiers in psychology},
volume = {16},
number = {},
pages = {1697086},
pmid = {41446611},
issn = {1664-1078},
abstract = {Motor imagery - the mental simulation of movement without execution - activates motor networks with near-physical fidelity. Once considered ancillary, it is now central to neuroplasticity, enhancing skill acquisition, accelerating rehabilitation, and sustaining motor function across the lifespan. From stroke recovery to elite performance, motor imagery demonstrates that movement begins in cognition. As neurofeedback, brain-computer interfaces and virtual reality integrate with mental rehearsal, the boundary between thought and action becomes narrower. This perspective argues that motor imagery is not a cognitive accessory but the neurocognitive foundation of movement - a rehearsal mechanism through which the brain reshapes the body. In doing so, it supports the view that action is cognitively prepared before it is expressed.},
}
@article {pmid41444995,
year = {2025},
author = {Zhu, Z and Wang, X and Xu, Y and Chen, W and Zheng, J and Chen, S and Chen, H},
title = {A heart rate variability-driven framework for depression screening leveraging emotion-elicited autonomic divergence.},
journal = {Journal of physiological anthropology},
volume = {44},
number = {1},
pages = {33},
pmid = {41444995},
issn = {1880-6805},
mesh = {Humans ; *Heart Rate/physiology ; Male ; *Depression/diagnosis/physiopathology ; Female ; Adult ; *Emotions/physiology ; Young Adult ; *Autonomic Nervous System/physiopathology/physiology ; Middle Aged ; Machine Learning ; },
abstract = {OBJECTIVE: Depression manifests significant emotional dysregulation, characterized by heightened sadness susceptibility and attenuated happiness responsiveness in individuals with depression (IWD). This study employs structured emotion induction protocols to analyze physiological response disparities between IWD and healthy controls (HC) across multiple affective states, establishing empirical foundations for optimizing affective computing-based depression screening.<br><br>METHODS: Dual-phase statistical identification was conducted using Mann-Whitney U tests: initially verifying emotion elicitation validity by comparing HRV features between emotional states and resting conditions, subsequently detecting IWD/HC response differences within each emotion. Machine learning frameworks were then constructed leveraging HRV features and intergroup differential response patterns.<br><br>RESULTS: Comparative analysis revealed generally consistent directional patterns and response magnitudes across groups for most features, while critical divergences emerged characterized by heightened sadness reactivity in IWD alongside attenuated happiness responsiveness. Implemented models achieved 76.8% accuracy (AUC&#x2009;=&#x2009;0.772, 95% CI 0.699-0.841) under sadness-specific conditions, outperforming anger/happiness-induced models (&#x2248;&#xa0;70% accuracy) and substantially surpassing resting-state baselines.<br><br>CONCLUSION: Systematic investigation of HRV-mediated elicitation patterns through discrete emotion induction confirms clinically significant differential responsiveness between groups, empirically validating heightened sadness susceptibility in IWDs.<br><br>SIGNIFICANCE: These findings offer valuable guidance for refining affective computing-based depression screening algorithms, while contributing to the mechanistic understanding of disorder-specific physiological responses to emotional stimuli.},
}
@article {pmid41444014,
year = {2025},
author = {Knopman, J and Davies, JM and Mokin, M and Hassan, AE and Harbaugh, RE and Khalessi, A and Fiehler, J and Levy, EI and Gross, BA and Grandhi, R and Tarpley, J and Sivakumar, W and Bain, M and Crowley, RW and Link, TW and Fraser, JF and Levitt, MR and Chen, PR and Hanel, RA and Bernard, JD and Jumaa, M and Youssef, PP and Cress, MC and Chaudry, MI and Shakir, HJ and Lesley, WS and Billingsley, J and Jones, J and Koch, MJ and Paul, AR and Mack, WJ and Osbun, JW and Dlouhy, KM and Grossberg, JA and Kellner, CP and Sahlein, DH and Santarelli, J and Schirmer, CM and Mazaris, P and Liu, JJ and Majjhoo, AQ and Wolfe, T and Patel, NV and Roark, CD and Siddiqui, AH and , },
title = {EMBOLISE randomized surgical trial for subdural hematoma: clinical benefits beyond reoperation with middle meningeal artery embolization.},
journal = {Journal of neurointerventional surgery},
volume = {},
number = {},
pages = {},
doi = {10.1136/jnis-2025-024587},
pmid = {41444014},
issn = {1759-8486},
abstract = {BACKGROUND: Randomized clinical trials have demonstrated that middle meningeal artery embolization (MMAe) reduces reoperation rates in surgically treated patients with subacute/chronic subdural hematoma (SDH). The effect of embolization on outcomes beyond reoperation remains to be determined. We analyzed the impact of reoperation and healthcare encounters among patients enrolled in the EMBOLISE trial.<br><br>METHODS: Symptomatic subacute/chronic SDH patients were randomized to surgical evacuation alone (control) or surgical evacuation plus Onyx MMAe (treatment). Changes in modified Rankin Scale (mRS) scores, frequency of unscheduled follow-up visits, and radiographic evolution of hematomas in patients with versus without reoperation were analyzed.<br><br>RESULTS: A total of 197 patients were randomly assigned to the treatment group and 203 to the control group. Patients who required reoperation compared with those who did not exhibited a ~threefold higher incidence of mRS >2 (37.0% vs 12.9%, P=0.0025) and an ~2.5&#x2009;fold increase in mRS worsening (22.2% vs 9.5%, P=0.0503) at 180 days. In patients who did not receive MMAe, there was a ~threefold&#x2009;fold increase in rate of SDH recurrence/progression even among those who did not require reoperation (14.3% vs 5.3%, P=0.0045) and a ~twofold&#x2009;increase in unscheduled physician follow-up visits (27.1% vs 14.7%, P=0.0031).<br><br>CONCLUSION: Among patients with symptomatic subacute/chronic SDH, reoperation was associated with increased rates of mRS worsening and higher mRS scores at follow-up. Adjunctive Onyx MMAe resulted in lower rates of hematoma recurrence/progression and fewer unscheduled physician follow-up visits. Thus, in addition to reducing surgical reoperation rates, adjunctive MMAe led to improved clinical outcomes and reduced healthcare encounters.},
}
@article {pmid41443766,
year = {2025},
author = {Zhang, H and Liao, Y and Wen, H and Pang, T and Zhao, X and Zhang, W and Lou, X and Chen, C and Liu, Z and Hu, S and Xu, X},
title = {Clinical Manifestations.},
journal = {Alzheimer's &amp; dementia : the journal of the Alzheimer's Association},
volume = {21 Suppl 3},
number = {},
pages = {e097364},
doi = {10.1002/alz70857_097364},
pmid = {41443766},
issn = {1552-5279},
mesh = {Humans ; Male ; Female ; *Dementia/epidemiology ; Middle Aged ; United Kingdom/epidemiology ; Aged ; *Bipolar Disorder/epidemiology ; Incidence ; Comorbidity ; Adult ; *Mood Disorders/epidemiology ; Depression/epidemiology ; },
abstract = {BACKGROUND: Mood disorders including depression and bipolar disorders have been linked to dementia. However, early manifestation of bipolar disorder, especially manic symptom, were easily overlooked. The present study aimed to investigate the association of midlife and late-life mood symptoms, especially their comorbidity, with long-term dementia incidence among multi-regional and ethnic adults.<br><br>METHOD: The study used UK Biobank as a discovery dataset and three Asian studies as validation datasets. Participants aged > 35 were included in the analysis. Individuals with diagnosed mood disorders and dementia were excluded at baseline. Baseline mood symptoms were classified as: normal, manic symptoms, depressive symptoms, and comorbidity of depressive and manic symptoms. Long-term (12 years) incident mood disorders (depression, mania and bipolar) and dementia were diagnosed and recorded. Primary outcome was dementia incidence. Secondary outcomes were domain-specific cognitive function and metabolomics. Fine-Gray sub-distribution hazard models and linear regression were used to estimate the associations of mood symptoms with dementia risk, cognitive function and selected metabolites.<br><br>RESULT: The study included 142,670 UK and 1,610 Asian participants (mean [SD] age, 57.2 [8.2] and 70.5 [7.3] years, respectively). Mood symptoms were prevalent (11.4% and 31.2%) among 1462 (1.0%) and 74 (19.4%) who developed dementia during a mean follow-up of 11.0 and 4.4 years in community and clinical settings, respectively. The average durations from mood symptoms and disorders to dementia onset were 7.5 and 1.7 years, respectively. Comorbidity of depressive and manic symptoms was associated with an earlier onset and a higher risk of developing dementia (sub-distribution hazard ratios [sHR]=9.46, 95% confidence interval [CI]=4.07-21.97; and sHR=4.32, 95%CI=2.10-8.88; respectively), as compared to single symptom or none (on average 0.9 and 1.6 year earlier). Comorbidity of symptoms were associated with worse cognition (B=-0.32; 95% CI=-0.38--0.25), especially in reasoning and numeric memory, and an exacerbation of metabolic dysfunction, especially in fatty acids, lipoproteins and triglycerides.<br><br>CONCLUSION: Mood symptoms were prevalent among incident dementia patients. Comorbidity of mood symptoms in midlife and late-life could lead to a higher cumulative risk of dementia. Future studies warrant in-depth investigation of distinct pathophysiological mechanisms.},
}
@article {pmid41443376,
year = {2025},
author = {Xia, XY and Huang, ZQ and Lin, HH and Liu, ZY and Zhang, L and Li, MC and Tu, YQ and Chen, NP and Ni, J and Chen, QL and Hu, JP and Gan, SR and Chen, XY},
title = {Diffusion along perivascular spaces as a marker for Glymphatic system impairment in spinocerebellar Ataxia type 3.},
journal = {Neurobiology of disease},
volume = {},
number = {},
pages = {107232},
doi = {10.1016/j.nbd.2025.107232},
pmid = {41443376},
issn = {1095-953X},
abstract = {Spinocerebellar ataxia type 3 (SCA3) is a neurodegenerative disorder characterized by the accumulation of polyglutamylated ATXN3 protein within neurons, which can potentially compromise the integrity of the brain's glymphatic system. Our objective is to investigate whether glymphatic function is impaired in patients with SCA3 and its clinical relevance. This study recruited 129 SCA3 subjects, including 98 symptomatic (ataxic SCA3) and 31 presymptomatic (preataxic SCA3) individuals, along with 67 healthy controls (HCs). We calculated the index for diffusion tensor image analysis along the perivascular space (DTI-ALPS) across groups and examined its correlation with SCA3 clinical features. Except for the left cerebral hemisphere DTI-ALPS index showing no statistically significant difference between HC and preataxic SCA3, statistically significant differences in ALPS index were observed among the remaining three groups. The DTI-ALPS index decreased in the order HC group > preataxic SCA3 group > ataxic SCA3 group. The Ataxic SCA3 group exhibited a significantly lower DTI-ALPS index than the HC group. The mean DTI-ALPS index showed negative correlations with the Scale for the Assessment and Rating of Ataxia (SARA) scores and International Cooperative Ataxia Rating Scale (ICARS) scores. In this study, we demonstrate that glymphatic waste clearance is impaired in SCA3 and that the magnitude of ALPS-detected dysfunction parallels clinical burden. DTI-ALPS may serve as a potential indicator for evaluating glymphatic system alterations and disease.},
}
@article {pmid41442763,
year = {2025},
author = {Okoye, C and Cuffaro, L and Pozzi, FE and Ferrara, MC and Noale, M and Calciolari, S and Chicco, D and Cincotti, F and Daini, R and Finazzi, A and Francioso, L and Gasparini, F and Pagan, E and Ribino, P and Romeo, Z and Sala, G and Solfrizzi, V and Zambon, A and Maggi, S and Bellelli, G and Ferrarese, C and , },
title = {Multicomponent interventions and technologies to reduce the burden of frailty, functional, and cognitive decline: insights from the Age-It Research Program.},
journal = {The journals of gerontology. Series B, Psychological sciences and social sciences},
volume = {80},
number = {Supplement_2},
pages = {S180-S188},
doi = {10.1093/geronb/gbaf186},
pmid = {41442763},
issn = {1758-5368},
support = {DM 1557//Next Generation EU/ ; //National Recovery and Resilience Plan/ ; //Ageing Well in an Ageing Society/ ; },
mesh = {Humans ; *Frailty/prevention &amp; control ; *Cognitive Dysfunction/prevention &amp; control ; Aged ; Aged, 80 and over ; *Frail Elderly ; Aging ; Cost-Benefit Analysis ; Male ; },
abstract = {OBJECTIVES: Preventing age-related complications is a critical priority for health systems. Within the Age-It program, Spoke 8 aims to evaluate scalable, multicomponent, technology-assisted interventions to prevent frailty and mitigate functional and cognitive decline in older adults across different care settings.<br><br>METHODS: Spoke 8 includes three clinical studies conducted in community, hospital, and long-term care settings, supported by cross-cutting work packages on digital infrastructure, technology development, and economic evaluation. The intervention model integrates physical, cognitive, nutritional, and psychosocial components, supported by digital tools, biomarkers of aging, and a centralized data platform.<br><br>RESULTS: The project is expected to generate evidence on the effectiveness, feasibility, and cost-effectiveness of multidomain interventions implemented across diverse real-world settings, including community, hospital, and long-term care. Technology-assisted strategies-such as wearable sensors and digital cognitive tools-may enhance adherence and enable remote monitoring, while also supporting more personalized care delivery. The integration of artificial intelligence will facilitate the interpretation of complex clinical and biological data, improving risk stratification and the early identification of individuals most likely to benefit from targeted interventions. Together, these approaches may help reduce hospitalizations, delay functional decline, and promote aging in place.<br><br>DISCUSSION: This initiative supports the transition toward more integrated and equitable care models for older adults. Through the implementation of scalable, person-centered interventions within routine services, the project offers policy-relevant strategies to address frailty and functional decline-contributing to the redesign of aging care in Italy and providing insights applicable across diverse health systems facing the challenges of population aging countries.},
}
@article {pmid41442279,
year = {2025},
author = {Zhao, Y and Yang, Z and Shi, S and Hao, H and Li, X and Ma, D and Su, N and Zhao, W and Shao, J and An, Y and Wang, K and Liu, Y and Zou, L and Qi, J and Zhang, H and Guo, J and Du, X},
title = {Structure basis for the activation of KCNQ2 by endogenous and exogenous ligands.},
journal = {Cell reports},
volume = {45},
number = {1},
pages = {116771},
doi = {10.1016/j.celrep.2025.116771},
pmid = {41442279},
issn = {2211-1247},
abstract = {The voltage-gated potassium channel KCNQ2 is crucial for stabilizing neuronal membrane potential, and its mutations can cause various epilepsies. KCNQ2 is activated by endogenous ligand phosphatidylinositol-4,5-bisphosphate (PIP2) and exogenous ligands, yet the structural mechanisms underlying these activations remain unclear. Here, we report the cryo-electron microscopy structures of human KCNQ2 in complex with exogenous ligands QO-58 and QO-83 in the absence or presence of PIP2 in either closed or open conformation. While QO-83 binds in the classical fenestration pocket of the pore domain, QO-58 mainly binds at the flank of S4 in the voltage-sensing domain. These structures, along with electrophysiological assays and computational studies, provide mechanistic insights into the ligand activation of KCNQ2 and may guide the development of anti-epileptic drugs targeting KCNQ2.},
}
@article {pmid41441860,
year = {2025},
author = {He, C and Ding, Y and Rabczuk, T and Ding, C},
title = {Reliable AI Platform for Monitoring BCI Caused Brain Injury and Providing Real-Time Protection.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e06747},
doi = {10.1002/advs.202506747},
pmid = {41441860},
issn = {2198-3844},
support = {TZ2025006//Science Challenge Project/ ; 2023YFA1008902//National Key R and D Program of China/ ; 12472191//National Natural Science Foundation of China/ ; //Fundamental Research Funds for the Central Universities, Peking University/ ; },
abstract = {Invasive brain-computer interface (BCI) holds great promise for restoring motor, sensory, and cognitive functions in patients with disabilities, yet chronic implantation induces neuroinflammation and degeneration at the electrode-tissue interface, impairing neural connectivity and device long-term stability. Current brain injury assessment approaches cannot simultaneously meet the requirements of efficiency and interpretability in healthcare with high-risk diagnoses and treatment. Meanwhile, limited and expensive biomechanics data pose significant challenges in AI training. Herein, feature-based Gaussian process emulators are proposed to enable interpretable data-driven modeling with limited biomechanics data under noise. Furthermore, a reliable AI platform, BrainGuard is developed, for efficiently providing a reliable and quantitative patient-specific basis and real-time monitoring of BCI caused brain injury. These results demonstrate exceptional performance of BrainGuard in rapidly and accurately predicting and monitoring the full-field von Mises strain revealing the brain injury even under challenging noise conditions. By constructing interpretable digital brain twins to offer reliable digital healthcare solutions, the platform enhances real-time patient protection and improves the security and durability of long-term BCI-based measurement and treatment strategies.},
}
@article {pmid41440053,
year = {2025},
author = {Zhang, N and Jian, H and Li, X and Jiang, G and Tang, X},
title = {LPGGNet: Learning from Local-Partition-Global Graph Representations for Motor Imagery EEG Recognition.},
journal = {Brain sciences},
volume = {15},
number = {12},
pages = {},
doi = {10.3390/brainsci15121257},
pmid = {41440053},
issn = {2076-3425},
abstract = {Objectives: Existing motor imagery electroencephalography (MI-EEG) decoding approaches are constrained by their reliance on sole representations of brain connectivity graphs, insufficient utilization of multi-scale information, and lack of adaptability. Methods: To address these constraints, we propose a novel Local-Partition-Global Graph learning Network (LPGGNet). The Local Learning module first constructs functional adjacency matrices using partial directed coherence (PDC), effectively capturing causal dynamic interactions among electrodes. It then employs two layers of temporal convolutions to capture high-level temporal features, followed by Graph Convolutional Networks (GCNs) to capture local topological features. In the Partition Learning module, EEG electrodes are divided into four partitions through a task-driven strategy. For each partition, a novel Gaussian median distance is used to construct adjacency matrices, and Gaussian graph filtering is applied to enhance feature consistency within each partition. After merging the local and partitioned features, the model proceeds to the Global Learning module. In this module, a global adjacency matrix is dynamically computed based on cosine similarity, and residual graph convolutions are then applied to extract highly task-relevant global representations. Finally, two fully connected layers perform the classification. Results: Experiments were conducted on both the BCI Competition IV-2a dataset and a laboratory-recorded dataset, achieving classification accuracies of 82.9% and 87.5%, respectively, which surpass several state-of-the-art models. The contribution of each module was further validated through ablation studies. Conclusions: This study demonstrates the superiority of integrating multi-view brain connectivities with dynamically constructed graph structures for MI-EEG decoding. Moreover, the proposed model offers a novel and efficient solution for EEG signal decoding.},
}
@article {pmid41439901,
year = {2025},
author = {Lee, DG and Lee, SB},
title = {Robust Motor Imagery-Brain-Computer Interface Classification in Signal Degradation: A Multi-Window Ensemble Approach.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {10},
number = {12},
pages = {},
doi = {10.3390/biomimetics10120832},
pmid = {41439901},
issn = {2313-7673},
support = {Bisa Research Grant: project number 20240421//Keimyung University/ ; },
abstract = {Electroencephalography (EEG)-based brain-computer interface (BCI) mimics the brain's intrinsic information-processing mechanisms by translating neural oscillations into actionable commands. In motor imagery (MI) BCI, imagined movements evoke characteristic patterns over the sensorimotor cortex, forming a biomimetic channel through which internal motor intentions are decoded. However, this biomimetic interaction is highly vulnerable to signal degradation, particularly in mobile or low-resource environments where low sampling frequencies obscure these MI-related oscillations. To address this limitation, we propose a robust MI classification framework that integrates spatial, spectral, and temporal dynamics through a filter bank common spatial pattern with time segmentation (FBCSP-TS). This framework classifies motor imagery tasks into four classes (left hand, right hand, foot, and tongue), segments EEG signals into overlapping time domains, and extracts frequency-specific spatial features across multiple subbands. Segment-level predictions are combined via soft voting, reflecting the brain's distributed integration of information and enhancing resilience to transient noise and localized artifacts. Experiments performed on BCI Competition IV datasets 2a (250 Hz) and 1 (100 Hz) demonstrate that FBCSP-TS outperforms CSP and FBCSP. A paired t-test confirms that accuracy at 110 Hz is not significantly different from that at 250 Hz (p < 0.05), supporting the robustness of the proposed framework. Optimal temporal parameters (window length = 3.5 s, moving length = 0.5 s) further stabilize transient-signal capture and improve SNR. External validation yielded a mean accuracy of 0.809 ± 0.092 and Cohen's kappa of 0.619 ± 0.184, confirming strong generalizability. By preserving MI-relevant neural patterns under degraded conditions, this framework advances practical, biomimetic BCI suitable for wearable and real-world deployment.},
}
@article {pmid41439390,
year = {2025},
author = {Gupta, D and Brangaccio, JA and Hill, NJ},
title = {Methodological optimization for eliciting robust median nerve somatosensory evoked potentials for realtime single trial applications.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae30ac},
pmid = {41439390},
issn = {1741-2552},
abstract = {OBJECTIVE: Single-trial measurement of median nerve Somatosensory Evoked Potentials (SEPs) with noninvasive electroencephalography (EEG) is challenging due to low signal-to-noise ratio (SNR), limiting its use in real-time neurorehabilitation applications. We describe and evaluate methodological optimizations for eliciting reliable median nerve SEPs measurable in real time, with reduced reliance on post-processing.<br><br>METHODS: In twelve healthy participants, two sessions each, SEPs were assessed at three pulse widths (0.1, 0.5, 1 msec), at a low-frequency stimulation (0.5 Hz &#xb1; 10%), and at an intensity sufficient to evoke consistent and robust sensory nerve action potentials (SNAPs) and compound muscle action potentials (CMAPs). The Evoked Potential Operant Conditioning System platform was used to monitor responses in real time. Feasibility was also evaluated in a participant with incomplete spinal cord injury (iSCI).<br><br>RESULTS: SEP P50 and N70 were reliably elicited in healthy participants, and in individual with iSCI, across all tested pulse widths with minimal discomfort. N70 amplitude increased significantly with pulse width (&#x3c7;[2]= 17.64, p= 0.0001, w= 0.80), while P50 amplitude remained unchanged. SNR showed a significant pulse width-dependent increase (&#x3c7;[2]= 7.82, p= 0.02, w= 0.35) with improvements of 40% and 52% at 0.5 and 1 msec, respectively. N70 single-trial separability significantly improved at 1 msec (AUC of 0.83, &#x3c7;[2]= 8.17, p= 0.017), including the iSCI participant (0.84-less impaired hand, 0.79-more impaired hand). Test-retest reliability (ICC= 0.70-0.84, p< 0.05) was highest at 0.5 msec, indicating more consistent N70 and P50 measurements across sessions at a longer pulse width.<br><br>SIGNIFICANCE: Robust median nerve SEPs can be measured at single trials with methodological optimizations such as a longer pulse width (0.5-1ms), low frequency (0.5 Hz), a consistent afferent excitation guided by nerve and muscle responses, and a robust EEG acquisition system. This setup can be useful for real time SEP-based brain computer interface applications for rehabilitation.},
}
@article {pmid41438920,
year = {2025},
author = {Kundu, B and Pleitez, J},
title = {Brain Implants in the Age of Artificial Intelligence.},
journal = {Missouri medicine},
volume = {122},
number = {6},
pages = {517-524},
pmid = {41438920},
issn = {0026-6620},
mesh = {Humans ; *Artificial Intelligence/trends ; *Brain/physiology ; *Brain-Computer Interfaces ; },
abstract = {Brain implants are routinely used to treat movement disorders and other network disorders such as obsessive-compulsive disorder. Closed-loop intracranial brain stimulation systems can now detect neural biomarkers of disease in real-time and therapeutically stimulate the brain based on these signals. Research devices can measure neural data on the order of single neurons and transform these data, via machine learning algorithms, into cursor movements and keyboard clicks, so that a quadriplegic patient can control a robotic arm. It is still a challenge to find the important brain signals of interest, that encode a patient's intentions or needs. Furthermore, the ethics of developing devices that allow for human cognitive and physical enhancement should be a part of societal discussion. The hope is that artificial intelligence (AI) will continue to advance neurotechnology's role in human health.},
}
@article {pmid41438236,
year = {2025},
author = {Li, T and Gao, Y and Zhou, J and Chen, Y and Zhang, S and Gong, X and Liu, Y},
title = {Advancements in the application of brain-computer interfaces based on different paradigms in amyotrophic lateral sclerosis.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1658315},
pmid = {41438236},
issn = {1662-4548},
abstract = {Amyotrophic lateral sclerosis (ALS) is a progressive neurological condition that leads to the gradual loss of movement and communicative abilities, significantly diminishing the quality of life for affected individuals. Recent advancements in neuroscience and engineering have propelled the swift evolution of brain-computer interfaces (BCIs), which are now extensively utilised in medical rehabilitation, military applications, assistive technologies, and various other domains. As a communication medium facilitating direct interaction between the brain and the external world independent of the peripheral nervous system, BCI provides ALS patients with an innovative method for communication and control, offering unparalleled prospects for improving their quality of life. Recent collaborative endeavours among several specialists have markedly enhanced the precision and velocity of diverse BCI paradigms, signifying a breakthrough in BCI applications for ALS. Nonetheless, obstacles and constraints remain. This study methodically extracted pertinent literature from the Web of Science and PubMed databases in accordance with PRISMA guidelines. Following stringent inclusion and exclusion criteria, 23 studies were identified. This data allows us to summarise the application results and current limitations of several BCI paradigms in motor control and communication, while delineating prospects in multimodal fusion and adaptive calibration. This review presents evidence-based references for the effective translation and application of BCI technology in ALS rehabilitation.},
}
@article {pmid41437397,
year = {2025},
author = {Qian, MB and Wang, L and Huang, JL and Zhou, CH and Zhu, TJ and Zhou, XN and Lai, YS and Li, SZ},
title = {Disability-adjusted life years of clonorchiasis in China: a high-resolution spatial analysis.},
journal = {Infectious diseases of poverty},
volume = {14},
number = {1},
pages = {126},
pmid = {41437397},
issn = {2049-9957},
support = {82373645//National Natural Science Foundation of China/ ; 82073665//National Natural Science Foundation of China/ ; 2021YFC2300800//National Key Research and Development Program of China/ ; },
mesh = {Humans ; China/epidemiology ; *Clonorchiasis/epidemiology/parasitology ; Male ; Female ; *Disability-Adjusted Life Years ; Clonorchis sinensis/isolation &amp; purification/physiology ; Spatial Analysis ; Middle Aged ; Adult ; Animals ; Child ; Aged ; Young Adult ; Bayes Theorem ; Adolescent ; Prevalence ; Child, Preschool ; Incidence ; Quality-Adjusted Life Years ; },
abstract = {BACKGROUND: Clonorchiasis is caused by the ingestion of raw freshwater fish containing infective metacercariae of Clonorchis sinensis. This study aimed to fully evaluate disease burden in terms of disability-adjusted life years (DALYs) for clonorchiasis in China.<br><br>METHODS: Following our previous study which established the fine-scale prevalence distribution of C. sinensis infection in China, we further adopted Bayesian geostatistical models to estimate the infection intensity in terms of eggs per gram of feces (EPG) in infected individuals based on the national surveillance data of clonorchiasis between 2016 and 2021. Disability weight was then captured through its quantitative association with EPG, and used to estimate years of life living with a disability (YLDs). Incidence of cholangiocarcinoma attributed to C. sinensis infection was employed to calculate years of life lost (YLLs). DALYs was then estimated at 5&#x2009;&#xd7;&#x2009;5&#xa0;km[2] resolution, and aggregated by areas and populations.<br><br>RESULTS: In 2020, 431,009 [95% Bayesian credible interval (BCI): 370,427 to 500,553] DALYs were exerted due to clonorchiasis in China, of which 372,918 (95% BCI: 318,775-435,727) was due to YLDs and 57,998 (95% BCI: 50,816-66,069) due to YLLs. The DALYs, YLDs and YLLs per 1000 were 0.31 (95% BCI: 0.26-0.35), 0.26 (95% BCI: 0.23-0.31), and 0.04 (95% BCI: 0.04-0.05), respectively. The DALYs predominantly distributed in southern areas including Guangxi (201,029, 95% BCI: 157,589-248,287) and Guangdong (161,958, 95% BCI: 128,326-211,358). The DALYs was over doubled in male (302,678, 95% BCI: 262,028-348,300) than in female (127,970, 95% BCI: 106,834-151,699), and high in middle aged population.<br><br>CONCLUSIONS: Clonorchiasis causes significant disease burden in China especially in southern areas including Guangxi and Guangdong. Urgent control is needed for clonorchiasis in the endemic areas with high burden, and adult males need to be prioritized.},
}
@article {pmid41437118,
year = {2025},
author = {Chen, H and Dai, H and Zhang, L and Deng, Y and Zhang, K and Yu, J and Peng, G and Guo, Z and Zhang, J and Yuan, C and Xie, F and Luo, B},
title = {The biomarker and clinical changes across the Alzheimer's continuum study (BCAS): rationale, design, and baseline characteristics of the first 1,013 participants.},
journal = {Alzheimer's research &amp; therapy},
volume = {},
number = {},
pages = {},
doi = {10.1186/s13195-025-01937-x},
pmid = {41437118},
issn = {1758-9193},
support = {2022C03064//Key R&amp;D Program of Zhejiang/ ; },
abstract = {INTRODUCTION: Alzheimer's disease (AD) is the leading cause of dementia in China, but deeply phenotyped clinical cohorts remain limited. The Biomarker and Clinical changes across the Alzheimer's continuum Study (BCAS) was established at the First Affiliated Hospital, Zhejiang University School of Medicine to capture biological and clinical changes across the AD spectrum.<br><br>METHODS: BCAS is an ongoing, longitudinal memory clinic-based cohort initiated in 2016 in Zhejiang, one of China's most economically vigorous and rapidly aging regions. Individuals aged&#x2009;&#x2265;&#x2009;40&#xa0;years with cognitive concerns are recruited and undergo standardized clinical evaluation, comprehensive neuropsychological testing, biospecimen collection, and multimodal neuroimaging including MRI and amyloid and tau PET in subsets. Participants are followed every 1-2&#xa0;years with repeat assessments. This paper reports baseline characteristics and preliminary findings from the first 1,013 participants enrolled up to January 2025.<br><br>RESULTS: Participants had a mean age of 66.5&#xa0;years (SD 9.6), with 49.8% women and an average of 9.7&#xa0;years of education. Hypertension (41.4%), diabetes (14.6%), and hypercholesterolemia (12.0%) were the most prevalent comorbidities. The mean MoCA score was 19.2 (SD 6.1). Mean cognitive scores showed gradient decline across diagnostic groups from cognitively unimpaired, mild cognitive impairment to dementia, consistent with expected disease severity. Tau PET positivity showed a numerically larger cognitive z-score difference (-0.973 for T&#x2009;+&#x2009;vs. T-) compared with amyloid PET positivity (-0.530 for A&#x2009;+&#x2009;vs. A-). Among risk factors, higher age and diabetes were linked to lower scores, whereas higher education, tea consumption, and higher BMI were associated with better cognitive performance.<br><br>CONCLUSIONS: The BCAS served as a biomarker-rich and multimodal resource to study the clinical and biological progression of AD in China. Preliminary analyses demonstrate expected associations and support the data quality. BCAS will act as a platform for biomarker validation and precision approaches to AD diagnosis and intervention.},
}
@article {pmid41434442,
year = {2025},
author = {Zhao, X and Lin, Z and Zhang, H and Chen, C and Ji, H and Liu, Z and Hu, S and Xu, X},
title = {Public Health.},
journal = {Alzheimer's &amp; dementia : the journal of the Alzheimer's Association},
volume = {21 Suppl 6},
number = {},
pages = {e097185},
doi = {10.1002/alz70860_097185},
pmid = {41434442},
issn = {1552-5279},
mesh = {Humans ; Male ; Female ; Aged ; Middle Aged ; *Dementia/epidemiology ; *Mental Disorders/epidemiology ; *Public Health ; United Kingdom/epidemiology ; *Cardiovascular Diseases/epidemiology ; Risk Factors ; *Renal Insufficiency, Chronic/epidemiology ; },
abstract = {BACKGROUND: Cardiovascular-Kidney-Metabolic (CKM) syndrome describes pathological interactions among metabolic risk factors, chronic kidney disease, and cardiovascular dysfunction. These conditions are shared risk factors for psychiatric disorders and dementia. This study examined the associations of CKM syndrome with psychiatric disorders and dementia in middle-aged and older adults.<br><br>METHOD: Using data from the UK Biobank, we included participants free of psychiatric disorders and dementia at baseline. CKM syndrome was categorized into five stages (0 to 4) based on AHA definitions. Psychiatric disorders (psychotic, bipolar, depressive, and anxiety disorders) and dementia (Alzheimer's and vascular dementia) were identified using ICD-10 codes. Multi-state models analyzed the impact of CKM on transitions from healthy status to psychiatric disorders and dementia. Competing risk (death) models assessed the associations of CKM with specific psychiatric disorders and dementia. Additionally, Cox regression models and XGBoost classifiers were employed to identify key metabolomics associated with CKM stage-related outcomes.<br><br>RESULT: Among 389,314 participants, CKM stages were distributed as follows: stage 0 (10.0%), stage 1 (8.0%), stage 2 (57.6%), stage 3 (17.9%), and stage 4 (6.5%). Multi-state model results indicated that each one-stage increment in CKM stage was associated with higher hazards of psychiatric disorders (Healthy &#x2192; Psychiatric Disorder: HR=1.26, 95% CI: [1.24, 1.29]) and subsequent transition to dementia (Psychiatric Disorder &#x2192; Dementia: HR=1.30, 95% CI: [1.41, 1.49]). However, each CKM stage increment increased the hazards of directly developing dementia (Healthy &#x2192; Dementia: HR=1.26, 95% CI: [1.31, 1.49]) but was not linked to subsequent psychiatric disorders. Competing risk analyses revealed that worsening CKM stages were associated with greater hazards of developing pre-dementia psychiatric disorders, including bipolar disorder, depressive disorder, and anxiety disorder whilst only advanced CKM stages (CKM stage 3/4) were associated with all-cause, Alzheimer's and vascular dementia. We identified several key predictors of pre-dementia psychiatric disorders at different CKM stages (e.g., citrate at CKM stages 1 and 2; degree of unsaturation at CKM stages 3 and 4).<br><br>CONCLUSION: CKM syndrome is associated with pre-dementia psychiatric disorders and dementia, emphasizing the need for regular monitoring and early intervention to manage CKM progression and reduce geriatric neuropsychiatric disturbances.},
}
@article {pmid41433160,
year = {2025},
author = {Hamdan, E and Luo, Y and Forelli, R and Liufu, M and Zhou, N and Shridhar, S and Quattrocchi, E and Leveroni, Z and Ogrenci, S and Tran, N and Cetin, AE and Yu, JY},
title = {Real-time Instantaneous Phase Estimation Using a Deep Dual-Branch Complex Neural Network.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3647598},
pmid = {41433160},
issn = {1558-2531},
abstract = {Estimating the instantaneous phase of neural oscillations is crucial for technology that interfaces with the brain, such as brain-computer interfaces (BCIs) and neuromodulation systems. In these systems, phase information from the oscillating neural signal can be used to guide subsequent decisions to apply experimental perturbation. Traditional methods for phase estimation rely on the Hilbert transform computed using the Discrete Fourier Transform (DFT), which introduces a phase lag due to dependency on past and present signal values. This paper proposes a deep learning algorithm utilizing a dual-branch structure similar to the complex wavelet transform to generate a pseudo-complex valued signal for instantaneous phase estimation. The network has Discrete Cosine Transform (DCT) layers, which help to extract latent space representations for the real and imaginary signal components, respectively. An additional design goal was to make this Deep Learning (DL)-based algorithm suitable for deployment on portable edge devices with limited computing resources such as field-programmable gate arrays (FPGAs). This work demonstrates a proof-of-principle for real-time instantaneous phase estimation in neuromodulation applications. Our generalized model achieves an improvement of 40.3% in phase estimation accuracy over the endpoint-corrected Hilbert Transform (ecHT) method and an improvement of 9.2% over conventional deep learning model architectures.},
}
@article {pmid41432054,
year = {2025},
author = {Chen, Q and Wu, H and Xie, S and Zhu, F and Xu, F and Xu, Q and Sun, L and Yang, Y and Xie, L and Xie, J and Li, H and Dai, A and Zhang, W and Wang, L and Jiao, C and Zhang, H and Zhou, X and Xu, ZZ and Chen, X},
title = {GPR30 in spinal cholecystokinin-positive neurons modulates neuropathic pain.},
journal = {eLife},
volume = {13},
number = {},
pages = {},
doi = {10.7554/eLife.102874},
pmid = {41432054},
issn = {2050-084X},
support = {82371220//National Natural Science Foundation of China/ ; 82171206//National Natural Science Foundation of China/ ; ZDFY2022-4XA102//4+X Clinical Research Project of Women's Hospital, School of Medicine, Zhejiang University/ ; 2023ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 226-2022-00227//Fundamental Research Funds for the Central Universities/ ; },
mesh = {Animals ; *Receptors, G-Protein-Coupled/metabolism/genetics ; *Neuralgia/physiopathology/metabolism ; *Cholecystokinin/metabolism ; Mice ; *Neurons/metabolism ; *Receptors, Estrogen/metabolism/genetics ; Male ; Mice, Inbred C57BL ; Disease Models, Animal ; *Spinal Cord ; },
abstract = {Neuropathic pain, a major health problem affecting 7-10% of the global population, lacks effective treatment due to its elusive mechanisms. Cholecystokinin-positive (CCK[+]) neurons in the spinal dorsal horn (SDH) are critical for neuropathic pain, yet the underlying molecular mechanisms remain unclear. Here, we show that the membrane estrogen receptor G-protein coupled estrogen receptor (GPER/GPR30) in spinal neurons was significantly upregulated in chronic constriction injury (CCI) mice and that inhibition of GPR30 in CCK[+] neurons reversed CCI-induced neuropathic pain. Furthermore, GPR30 in spinal CCK[+] neurons was essential for the enhancement of AMPA-mediated excitatory synaptic transmission in CCI mice. Moreover, GPR30 was expressed in spinal CCK[+] neurons that received direct projection from the primary sensory cortex (S1-SDH). Chemogenetic inhibition of S1-SDH post-synaptic neurons alleviated CCI-induced neuropathic pain. Conversely, chemogenetic activation of these neurons mimicked neuropathic pain symptoms, which were attenuated by spinal inhibition of GPR30. Finally, we confirmed that GPR30 in S1-SDH post-synaptic neurons was required for CCI-induced neuropathic pain. Taken together, our findings suggest that GPR30 in spinal CCK[+] neurons and S1-SDH post-synaptic neurons is pivotal for neuropathic pain, thereby representing a promising therapeutic target for neuropathic pain.},
}
@article {pmid41431688,
year = {2024},
author = {Lee, J and Letner, JG and Lim, J and Atzeni, G and Liao, J and Kamboj, A and Mani, B and Jeong, S and Kim, Y and Sun, Y and Koo, B and Richie, J and Valle, ED and Patel, PR and Sylvester, D and Kim, HS and Jang, T and Phillips, JD and Chestek, CA and Weiland, J and Blaauw, D},
title = {A Sub-mm[3] Wireless Neural Stimulator IC for Visual Cortical Prosthesis With Optical Power Harvesting and 7.5-kb/s Data Telemetry.},
journal = {IEEE journal of solid-state circuits},
volume = {59},
number = {4},
pages = {1110-1122},
pmid = {41431688},
issn = {0018-9200},
abstract = {This article proposes StiMote, an untethered, free-floating and individually addressable stimulator mote designed for visual cortex stimulation, aimed at vision restoration. The system is optically powered by a custom photovoltaic (PV) layer. In addition, the photodiode (PD) layer captures the light modulation and forwards it to the optical receiver (ORX) including a tranimpedance amplifier. Translated instructions can assign a unique slot, up to 1024 available, to each mote within the time-division multiple access (TDMA) framework. In this work, we propose an automatic charge balance (CB) technique that monitors the injected charge to balance in bi-phasic switched-capacitor stimulation (SCS). The chip was confirmed fully functional when operated completely wirelessly using harvested light. Measurement results revealed a power consumption of 4.48 μ W with a 7.5-kb/s optical downlink data rate, corresponding to continuous updates at 2.5 Hz of 1024 motes to their individual 3-b stimulation intensity levels. The dc-dc converter, responsible for providing high voltage for stimulation, demonstrated 4.3-V output voltage when unloaded, with a maximum efficiency of 67.4%. The proposed CB circuit exhibited linear controllability of stimulation charge up to 16 nC, with a charge imbalance of less than 0.2 nC. Furthermore, in vitro testing confirmed the absence of chemical reactions at electrodes, and in vivo experiments conducted on live rats verified the effectiveness of the stimulation through StiMote.},
}
@article {pmid41429310,
year = {2025},
author = {Rizzuto, DS and Herrema, HG and Hu, Z and Utin, D and Kahn, J and Ho, C and Smiles, A and Gross, RE and Lega, BC and Das, SR and Kahana, MJ},
title = {A wireless, 60-channel, AI-enabled neurostimulation platform.},
journal = {Brain stimulation},
volume = {},
number = {},
pages = {103013},
doi = {10.1016/j.brs.2025.103013},
pmid = {41429310},
issn = {1876-4754},
abstract = {OBJECTIVE: Closed-loop neuromodulatory therapies require devices that can decode ongoing brain states and deliver multi-site stimulation.<br><br>METHODS: We describe the Smart Neurostimulation System (SNS), a cranially mounted implant with 60 configurable recording/stimulation channels, inductive power, and onboard spectral-feature classification. In three freely-moving sheep, we streamed local-field potentials and conducted two parameter-sweep experiments.<br><br>RESULTS: Cross-validated movement classifiers achieved an average AUC exceeding 0.95. Increasing stimulation amplitude and frequency produced post-stimulation elevations in &#x3b1;-band (8-12 Hz) and &#x3b3;-band (78-82 Hz) power at most target locations.<br><br>CONCLUSION: The SNS unifies high-density sensing, real-time brain state decoding, and programmable closed-loop stimulation in a single device, demonstrating behavioral-state prediction and parameter-dependent neuromodulation in vivo.<br><br>SIGNIFICANCE: These findings establish a preclinical foundation for biomarker-guided stimulation targeting distributed cortical networks underlying memory and cognition.},
}
@article {pmid41429054,
year = {2025},
author = {Radman, M and Podmore, JJ and Poli, R and Paulmann, S and Daly, I},
title = {Decoding semantic categories: Insights from an fMRI ALE meta analysis.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae302b},
pmid = {41429054},
issn = {1741-2552},
abstract = {The human brain organizes conceptual knowledge into semantic categories; however, the extent to which these categories share common or distinct neural representations remains unclear. This study aims to clarify this organizational structure by identifying consistent, modality-controlled activation patterns across several widely used and frequently investigated semantic domains in fMRI research. By quantifying the distinctiveness and overlap among these patterns, we provide a more precise foundation for understanding the brain's semantic architecture, as well as for applications such as semantic brain-computer interfaces (BCI). Approach: Following PRISMA guidelines, we conducted a systematic review and meta-analysis of 75 fMRI studies covering six semantic categories: animals, tools, food, music, body parts, and pain. Using Activation Likelihood Estimation (ALE), we identified convergent activation patterns for each category while controlling for stimulus modality (visual, auditory, tactile, and written). Subsequently, Jaccard-based overlap analyses were performed to quantify the degree of neural commonality and separability across concept-modality pairs, thereby revealing the underlying structure of representational similarity. Main Results: Distinct yet partially overlapping activation networks were identified for each semantic category. Tools and animals showed shared activity in the lateral occipital and ventral temporal regions, reflecting common object-based visual processing. In contrast, food-related stimuli primarily recruited limbic and subcortical structures associated with affective and motivational processing. Music and animal sounds overlapped within the superior temporal and insular cortices, whereas body parts and pain engaged occipito-parietal and cingulo-insular networks, respectively. Together, these findings reveal a hierarchically organized and modality-dependent semantic architecture in the human brain. Significance: This meta-analysis offers a quantitative and integrative characterization of how semantic knowledge is distributed and differentiated across cortical systems. By demonstrating how conceptual content and sensory modality jointly shape neural organization, the study refines theoretical models of semantic cognition and provides a methodological basis for evaluating conceptual separability. These insights have direct implications for semantic neural decoding and for the development of BCI systems grounded in meaning-based neural representations. .},
}
@article {pmid41428932,
year = {2025},
author = {Lu, R and Deng, W and Gao, T and Huang, S and Zhang, Z and Liu, Y and Zhong, SH},
title = {Mutual Generation for Cross-domain Challenge in Stroke Patients' Motor Imagery Classification and Functional Recovery Prediction.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3646871},
pmid = {41428932},
issn = {2168-2208},
abstract = {The accumulating body of research indicates that Motor Imagery (MI)-BCIs have the potential to enhance the quality of life for individuals with disabilities and to advance our understanding of brain function and rehabilitation strategies. Among these diseases, stroke is the leading cause of long-term motor disability across the globe, thereby underscoring the need for innovative rehabilitation strategies, such as MI-BCI technologies. In contrast with these expectations, the majority of existing research is built upon data obtained from healthy subjects. The construction of effective classification models for Motor Imagery tasks in patients with brain diseases, particularly stroke, remains a significant challenge. The lateralization of the left and right hemispheres is more pronounced in patients who have suffered a stroke than in healthy individuals. Moreover, the specific locations of lesions and the regions of influence result in significant variations in the electroencephalogram (EEG) data of patients with different hemiplegic sides. This paper explores the potential of generative models in addressing the issue of domain differences arising from different hemiplegic sides EEG data. Furthermore, this paper circumvents the potential adverse effects of rigorous optimization of low-quality samples on model performance through the utilization of label softening algorithm. Two MI-EEG datasets of stroke patients performing Motor Imagery tasks are used to validate our method. In comparison to both classical machine learning methods and those state-of-the-art models for MI classification, the classification model in this paper achieves a noticeable performance improvement in different data partitioning strategies, including subject-dependent and subject-independent scenarios. Each sub-module, and each designed loss function, contributes to the final performance growth. In addition, this paper also investigates the potential of the proposed framework for predicting a patient's level of functional recovery. Our findings indicate that the addition of a prediction layer to the proposed model enables the accurate prediction of functional recovery level in stroke patients. The source code is available at https://github.com/arrogant-R/MutualGeneration.},
}
@article {pmid41428911,
year = {2025},
author = {Fu, X and Liu, R and Wai, AAP and Pulferer, H and Robinson, N and Muller-Putz, GR and Guan, C},
title = {EEG2GAIT: A Hierarchical Graph Convolutional Network for EEG-based Gait Decoding.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3647101},
pmid = {41428911},
issn = {1558-0210},
abstract = {Decoding gait dynamics from EEG signals presents significant challenges due to the complex spatial dependencies of motor processes, the need for accurate temporal and spectral feature extraction, and the scarcity of high-quality gait EEG datasets. To address these issues, we propose EEG2GAIT, a novel hierarchical graph-based model that captures multi-level spatial embeddings of EEG channels using a Hierarchical Graph Convolutional Network (GCN) Pyramid. To further improve decoding performance, we introduce a Hybrid Temporal-Spectral Reward (HTSR) loss function, which integrates time-domain, frequency-domain, and reward-based loss components. In addition, we contribute a new Gait-EEG Dataset (GED), consisting of synchronized EEG and lower-limb joint angle data collected from 50 participants across two laboratory visits. Extensive experiments demonstrate that EEG2GAIT with HTSR achieves superior performance on the GED dataset, reaching a Pearson correlation coefficient (r) of 0.959, a coefficient of determination (R[2]) of 0.914, and a Mean Absolute Error (MAE) of 0.193. On the MoBI dataset, EEG2GAIT likewise consistently outperforms existing methods, achieving an r of 0.779, an R[2] of 0.597, and an MAE of 4.384. Statistical analyses confirm that these improvements are significant compared to all prior models. Ablation studies further validate the contributions of the hierarchical GCN modules and the proposed HTSR loss, while saliency analysis highlights the involvement of motor-related brain regions in decoding tasks. Collectively, these findings underscore EEG2GAIT's potential for advancing brain-computer interface applications, particularly in lower-limb rehabilitation and assistive technologies.},
}
@article {pmid41426299,
year = {2025},
author = {Huang, S and Chen, C and Mo, Y and Zhao, Y and Zhu, Y and Dong, K and Xu, T},
title = {Exploring the n-back task: insights, applications, and future directions.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1721330},
pmid = {41426299},
issn = {1662-5161},
abstract = {The n-back task has become a central paradigm for investigating the mechanisms of working memory (WM) and related executive functions. This review provides an integrative analysis of the n-back experiment, covering its cognitive operations, task variants, neuroimaging findings, and practical applications across multiple domains. We first delineate three core cognitive components-updating, maintenance, and attentional control-and summarize converging evidence that these functions rely on overlapping fronto-striatal and fronto-parietal networks. We then examine major task variants and review applications in: (1) cognitive training and transfer effects, particularly the proposed association between WM and fluid intelligence; (2) clinical contexts including attention deficit hyperactivity disorder (ADHD), depression, and neurological rehabilitation; (3) developmental and educational settings; and (4) emerging research on social cognition, stress, and emotional regulation. Critically, this review evaluates ongoing inconsistencies in how the n-back task is interpreted as a measure of WM and highlights methodological factors, such as task heterogeneity, multi-process interference, and mental fatigue, that complicate both behavioral and neural inferences. To address these issues, we outline methodological recommendations including adaptive task design, multimodal physiological monitoring, and standardized experimental protocols. We further discuss future directions involving virtual reality (VR), mobile platforms, and brain-computer interface (BCI) integration to improve ecological validity and translational relevance. By synthesizing behavioral and neural evidence, this review underscores the n-back task's versatility while emphasizing the need for improved construct clarity and methodological rigor.},
}
@article {pmid41426186,
year = {2025},
author = {Khan, H and Nazeer, H and Minhas, HS and Naseer, N and Mirtaheri, P},
title = {Open-access fNIRS dataset for motor imagery of lower-limb knee and ankle joint tasks.},
journal = {Frontiers in robotics and AI},
volume = {12},
number = {},
pages = {1695169},
pmid = {41426186},
issn = {2296-9144},
}
@article {pmid41424861,
year = {2026},
author = {Li, X and Zheng, C and Tian, Y and Ming, D},
title = {Channel-specific differential effects of bacterial mechanosensitive channels for ultrasound neuromodulation in precision sonogenetics.},
journal = {Theranostics},
volume = {16},
number = {5},
pages = {2447-2465},
pmid = {41424861},
issn = {1838-7640},
mesh = {Animals ; Rats ; *Ion Channels/genetics/metabolism ; *Hippocampus/metabolism/physiology/radiation effects ; Male ; Rats, Sprague-Dawley ; Ultrasonic Waves ; *Escherichia coli Proteins/genetics/metabolism ; Mechanotransduction, Cellular ; Dependovirus/genetics ; },
abstract = {Rationale: Ultrasound neuromodulation offers promising therapeutic potential, but its effectiveness is limited by imprecise targeting of neural circuits. Engineering mechanosensitive ion channels can enhance ultrasound sensitivity, providing a more precise approach for targeted neuromodulation. This study aimed to compare three bacterial mechanosensitive channels (MscL-G22S, MscL-G22N, and MscS) for mediating ultrasound-responsive hippocampal activity to identify optimal candidates for precision sonogenetics applications. Methods: We expressed MscL-G22S, MscL-G22N, and MscS in the rat hippocampus using AAV vectors and applied focused ultrasound stimulation at various intensities while recording local field potentials. Neural oscillatory patterns, ultrasound-evoked potentials, behavioral outcomes, immunohistology, and transcriptomic analyses were conducted to assess response consistency, efficacy, and biosafety. Results: Each channel conferred distinct neuromodulatory signatures: MscL-G22S exhibited remarkable ultrasound sensitivity with non-monotonic intensity-response amplification of evoked potentials (2.3-fold increase at maximum intensity), and accelerated response timing (latency reduction). Notably, MscL-G22N showed weaker ultrasound responses despite having a lower mechanical threshold than G22S, suggesting ultrasound sensitivity depends on factors beyond mechanical gating thresholds. Conversely, MscS displayed diminished responses at higher intensities. No statistically significant differences were detected in behavior assessments and histology evaluations. All channels maintained normal anxiety indices, spatial memory, and neuronal morphology, though MscS selectively increased depressive-like behaviors. Transcriptomic analysis revealed that MscS demonstrated exceptional genomic compatibility with minimal off-target gene alterations (9 vs. >400 in MscL variants). Conclusion: This characterization provides insights for potential precision sonogenetics applications: MscS offers a biosafety-optimized option with minimal genomic footprint, whereas MscL-G22S enables modulation of neural oscillations. These findings contribute to the development of customized neuromodulation approaches for targeting pathological circuits in neurological disorders.},
}
@article {pmid41357970,
year = {2025},
author = {Wei, Y and Wang, Y and Wei, T and Lu, X and Li, D and Sherwood, CC and Zhang, Y and Cheng, C and Jiang, T and Fan, L and Cheng, L},
title = {Cross-Species Cortical Geometry Reveals Conserved Gradients Across Primates and Human-Specific Expansion.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {41357970},
issn = {2692-8205},
support = {R24 NS092988/NS/NINDS NIH HHS/United States ; U54 MH091657/MH/NIMH NIH HHS/United States ; R01 AG067419/AG/NIA NIH HHS/United States ; R01 AG087945/AG/NIA NIH HHS/United States ; R01 HG011641/HG/NHGRI NIH HHS/United States ; },
abstract = {The primate cerebral cortex, characterized by its complex structural geometry, underlies advanced cognitive functions and represents a defining feature distinguishing primates from other mammals. However, cross-species patterns of cortical geometry and the links between human cortical geometry and transcriptional architecture remain poorly understood. We developed a geometry-based cross-species cortical alignment framework to systematically investigate the similarities and differences in structural connectivity and cortical expansion characteristics among macaques, chimpanzees, and humans, and additionally explored the transcriptional underpinnings of human cortical geometry. Our analysis revealed conserved spatial patterns of cortical geometric features across species, providing the foundation for constructing a cross-species structural common space to support the alignment framework. We found that primary sensory, somatomotor, and face-selective regions exhibited high structural connectivity similarity across species, whereas prefrontal and parietal association cortices displayed significant divergence. We also identified disproportionate cortical expansion in the default mode network, with a consistent expansion trend across different evolutionary lineages in primates. Furthermore, neuroimage-transcription analysis indicated that cortical geometric features were correlated with transcriptional profiles enriched in neurodevelopmental and connectivity-related pathways. These results highlight a conserved yet hierarchically differentiated organization of the cerebral cortex in primates, providing new insights into the biological basis of human brain evolution.},
}
@article {pmid41329575,
year = {2026},
author = {Zhang, W and Lai, J and Xu, B and Zeng, H and Wu, T and Hu, H and Song, A},
title = {The Role of Vibrotactile Stimulation in Soft Rehabilitation Glove-Assisted Hand Rehabilitation Training: A Pilot Study.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {34},
number = {},
pages = {149-162},
doi = {10.1109/TNSRE.2025.3639490},
pmid = {41329575},
issn = {1558-0210},
mesh = {Humans ; Male ; Pilot Projects ; Female ; *Vibration ; *Stroke Rehabilitation/methods/instrumentation ; *Hand/physiopathology ; Electroencephalography ; Adult ; Middle Aged ; Robotics/instrumentation ; Spectroscopy, Near-Infrared ; Young Adult ; Motor Cortex/physiology/physiopathology ; Aged ; Somatosensory Cortex/physiology ; Hand Strength ; Brain-Computer Interfaces ; Stroke/physiopathology ; },
abstract = {Brain-controlled robotic hand rehabilitation systems based on motor intention recognition have been used to promote recovery of hand function in stroke patients. However, the low decoding accuracy of motor imagery (MI) and unclear neural response mechanisms limit its widespread application. This study introduces a novel vibrotactile-assisted brain-controlled soft robotic hand rehabilitation system to validate its effectiveness in activating the motor sensory areas of the brain and to explore the neural response mechanisms of vibration stimulation in hand rehabilitation training. A total of 23 healthy subjects and 5 stroke patients were recruited to perform EEG and fNIRS-based experiments. Healthy subjects performed an EEG-based active rehabilitation task and an fNIRS-based passive rehabilitation task driven by the soft glove. Stroke patients only completed an EEG-based passive rehabilitation task. All experiments were conducted under two conditions: with and without vibrotactile stimulation. EEG results revealed that vibration stimulation significantly enhanced motor-sensory cortex activation during MI, and improved the online decoding performance of subjects with poor training outcomes. Grasping and stretching movements driven by the soft glove effectively activated the subjects' motorsensory cortex. Vibration stimulation boosted the event-related desynchronization (ERD) phenomenon in the contralateral somatosensory cortex of the healthy subjects, but was not significant in the motor cortex. Meanwhile, it strengthened bilateral sensorimotor activation in stroke patients. Moreover, fNIRS results indicated that vibration stimulation increased the concentration of HbO in the motor-sensory areas during passive movement and enhanced the bidirectional functional connectivity between the left and right hemispheres. These findings suggest that the proposed tactile-assisted hand rehabilitation system can effectively enhance neural activation in the motor-sensory cortex, potentially leading to improved hand function recovery in stroke patients.},
}
@article {pmid41325123,
year = {2026},
author = {Wei, R and Hua, C and Chen, J and Mu, D and Zhao, J},
title = {Improving Generalization in Federated Learning for Steady-State Visual Evoked Potential Classification and Its Application in Soft Gripper.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {34},
number = {},
pages = {126-136},
doi = {10.1109/TNSRE.2025.3639091},
pmid = {41325123},
issn = {1558-0210},
mesh = {Humans ; *Electroencephalography/methods ; *Evoked Potentials, Visual/physiology ; Male ; *Hand Strength/physiology ; Adult ; Algorithms ; Female ; Brain-Computer Interfaces ; *Machine Learning ; Young Adult ; Signal Processing, Computer-Assisted ; Databases, Factual ; Federated Learning ; },
abstract = {Conventional cross-subject electroencephalogram (EEG) signal identification frameworks require centralized aggregation of all subjects' data for feature extraction, which inherently poses substantial risks of data privacy breaches. In response to this critical issue, the present study delves into the classification of steady-state visual evoked potential (SSVEP) signals with an emphasis on data privacy preservation. First, we design a federated learning framework (FedGF) consisting of a central server and multiple clients, where the server generates global features and coordinates distributed training across clients, while retaining subject-specific raw data locally to ensure privacy protection. Then, to enhance model generalizability, FedGF employs data-free knowledge distillation (DFKD) to achieve knowledge transfer across clients through global feature learning. Extensive experiments on two public datasets (Dataset 1 'session01' and 2 'session02') and one private dataset (Dataset 3) demonstrate the superiority of the proposed method over baseline approaches, achieving performance improvements of 0.52%, 0.65%, and 0.53%, respectively. Finally, we develop a novel smart soft gripper with thermochromic capabilities and seamlessly integrate it with the trained network, demonstrating robust performance in daily grasping tasks. The source code is available at https://github.com/raow923/FedGF.},
}
@article {pmid41423674,
year = {2025},
author = {Van Den Kerchove, A and Meunier, J and de Moura, M and Willemssens, A and Geeurickx, D and Schiettecatte, E and Van Damme, P and Si-Mohammed, H and Cabestaing, F and Allart, E and Van Hulle, MM},
title = {Visual ERP-based brain-computer interface use with severe physical, speech and eye movement impairments: case studies.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12984-025-01836-0},
pmid = {41423674},
issn = {1743-0003},
support = {G0A4321N//Fonds Wetenschappelijk Onderzoek/ ; G0A4118N//Fonds Wetenschappelijk Onderzoek/ ; GPUDL/20/031//KU Leuven Special Research Fund/ ; C24/18/098//KU Leuven Special Research Fund/ ; RITMEA//European Regional Development Fund/ ; RITMEA//European Regional Development Fund/ ; 101118964//HORIZON EUROPE Marie Sklodowska-Curie Actions/ ; 857375//Horizon 2020/ ; AKUL 043//Herculesstichting/ ; },
abstract = {BACKGROUND: Individuals who experience severe speech and physical impairment face significant challenges in communication and daily interaction. Visual brain-computer interfaces (BCIs) offer a potential assistive solution, but their usability is limited when facing restrictions in eye motor control, gaze redirection and fixation. This study investigates the feasibility of a gaze-independent visual oddball BCI for use as an augmentative and alternative communication (AAC) device in the presence of limited eye motor control.<br><br>METHODS: Seven participants with varying degrees of eye motor control were recruited and their conditions were thoroughly assessed. Visual oddball BCI decoding accuracy was evaluated with multiple decoders in three visuospatial attention (VSA) conditions: overt VSA, with fixation cued on the target, covert VSA, with fixation cued on the center of the screen, and free VSA without gaze cue.<br><br>RESULTS: covert VSA with central fixation leads to decreased accuracy, whereas free VSA is comparable to overt VSA for some participants. Furthermore, cross-condition decoder training and evaluation suggests that training with overt VSA may improve performance in BCI users experiencing gaze control difficulties.<br><br>CONCLUSIONS: These findings highlight the need for adaptive decoding strategies and further validation in applied settings in the presence of gaze impairment.},
}
@article {pmid41421050,
year = {2025},
author = {Ming, W and Zheng, Y and Lian, Q and Shen, C and Zhang, Y and Wang, Z and Wang, S and Li, F and Zheng, Z and Qi, Y and Zhu, J and Wu, H},
title = {Brain connectivity predict surgical outcomes of low-grade epilepsy-associated neuroepithelial tumors.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {183},
number = {},
pages = {2111478},
doi = {10.1016/j.clinph.2025.2111478},
pmid = {41421050},
issn = {1872-8952},
abstract = {OBJECTIVE: Low-grade epilepsy-associated neuroepithelial tumors (LEATs) often cause drug-resistant epilepsy. Despite complete resection of these lesions, approximately 20% of patients continue to experience suboptimal seizure control. This study aims to investigate the predictive value of quantitative features in determining the surgical outcomes for LEAT patients.<br><br>METHODS: We retrospectively analyzed 44 temporal LEAT patients who underwent gross-total lesionectomy. EEG features, including power spectral density (PSD) and weighted phase lag index (wPLI), were compared between patients with good (Engel I) and poor (Engel II-IV) outcomes. Significant EEG features were identified through these analyses. Domain Adversarial Neural Network (DANN) was employed to assess the predictive value of these features for surgical outcomes.<br><br>RESULTS: No significant PSD differences were found, but patients with good outcomes had higher alpha-band wPLI (p&#xa0;=&#xa0;0.008). LEATnet, predicted outcomes with an AUC of 0.81and correctly classified 8 of 11 patients in the independent validation cohort.<br><br>CONCLUSIONS: Alpha-band functional connectivity is a key predictor of surgical outcomes in LEAT patients.<br><br>SIGNIFICANCE: EEG-based connectivity analysis may improve prognostic accuracy and aid clinical decision-making in LEAT epilepsy.},
}
@article {pmid41418936,
year = {2025},
author = {Zhou, Y and Jiang, R and Zhang, J},
title = {A multi-scale deep CNN based on attention mechanism for EEG emotion recognition.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110662},
doi = {10.1016/j.jneumeth.2025.110662},
pmid = {41418936},
issn = {1872-678X},
abstract = {BACKGROUND: Recognizing emotion is a crucial project within the domain of brain-computer interface technology. Recently, researchers have found that deep learning have been proven to be superior to machine learning, but how to obtain more discriminative features still faces great challenges.<br><br>NEW METHOD: We propose a multi-scale convolutional neural network (MSCNN) based on channel attention and spatial attention (CSA-MSCNN) for EEG emotion recognition. The channel attention enhances the feature extraction ability of critical channels by generating channel weights, while suppressing noise or interference from redundant channels. The spatial attention helps the model to more precisely locate key areas related to emotion by generating a spatial weight matrix. To extract more comprehensive features, CSA-MSCNN uses MSCNN for feature extraction, with smaller convolutional kernels capturing the local details of the signals, and larger convolutional kernels with a broader receptive field to obtain deeper signal information.<br><br>RESULTS: CSA-MSCNN achieves average accuracies of 95.75% and 95.39% for three-class classification of valence and arousal on DEAP, respectively, while achieving an average three-class classification accuracy of 90.48% on SEED.<br><br>The classification accuracy of CSA-MSCNN is not only significantly better than traditional machine learning models, but also shows strong competitiveness compared with mainstream deep learning models such as graph convolutional neural network (GCNN).<br><br>CONCLUSIONS: CSA-MSCNN addresses the issues of multiple EEG signal channels and complex regional information.},
}
@article {pmid41418897,
year = {2025},
author = {Zhang, T and Zhang, Q and Xiong, R and Zhang, J and Jin, Z and Li, L},
title = {Grey Matter Volume Predicts Decision Speed and Reveals Stage-Specific Contributions of Large-Scale Brain Networks in Gambling Tasks.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121659},
doi = {10.1016/j.neuroimage.2025.121659},
pmid = {41418897},
issn = {1095-9572},
abstract = {Large-scale brain networks are well-established in resting-state research and are increasingly being used in task-based functional magnetic resonance imaging (fMRI) studies. However, the mechanisms by which brain networks dynamically reorganize across the various stages of decision-making remain unclear. Here, we investigated the neural basis of decision-making by integrating voxel-based morphometry and fMRI within a modified "Wheel of Fortune" gambling task. Stage-specific brain activation was characterized using the Yeo-7 network atlas to delineate large-scale network dynamics across task stages. We found that: (1) Reaction time (RTs) were significantly longer during choose conditions compared to follow conditions; (2) Gray matter volume correlated with individual variability in RT and predicted RT during choose conditions using multivariate pattern analysis with a Kernel Ridge Regression model, effects absent during follow conditions; (3) A negative correlation was observed between RT and activation in the right superior temporal gyrus and left mid-cingulate cortex; (4) Choice stage involved more extensive network engagement than the result and rating stages, with the rating stage showing the lowest overall activation. Network-specific fractional contributions revealed dominant engagement of the ventral attention network, default mode network, and somato-motor network during the choice stage; the frontoparietal network (FPN), dorsal attention network (DAN), and visual network during the result stage; and the DAN and FPN during the rating stage. These findings provide structural and functional explanations for individual differences in decision speed within a gambling paradigm, revealing the distinct and dynamic roles of brain networks across decision stages and offering mechanistic insights into the neural architecture of this process.},
}
@article {pmid41417240,
year = {2025},
author = {Yakovlev, L and Miroshnikov, A and Syrov, N and Berkmush-Antipova, A and Kaplan, A},
title = {Sensorimotor event-related desynchronization and hemodynamic responses during motor and tactile imagery.},
journal = {Brain structure &amp; function},
volume = {231},
number = {1},
pages = {4},
pmid = {41417240},
issn = {1863-2661},
support = {24-75-00163//Russian Science Foundation/ ; },
mesh = {Humans ; Male ; *Imagination/physiology ; Female ; Adult ; Electroencephalography ; Young Adult ; *Hemodynamics/physiology ; Spectroscopy, Near-Infrared ; *Touch Perception/physiology ; *Touch/physiology ; Brain Mapping ; *Cortical Synchronization/physiology ; *Sensorimotor Cortex/physiology ; },
abstract = {Mental imagery is widely used in cognitive neuroscience and rehabilitation studies, yet their neural mechanisms remain not fully understood. In this study, we investigated neural correlates of motor and tactile imagery using simultaneous electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) recordings. A total of 16 healthy participants performed motor and tactile imagery tasks while brain activity was assessed. We analyzed event-related desynchronization (ERD) of the mu-rhythm and hemodynamic responses in sensory-motor regions. Similar spatio-temporal EEG patterns were observed for both motor and tactile imagery conditions (e.g., prominent contralateral ERD at C3). Hemodynamic responses differed: motor imagery elicited activation in both precentral and postcentral regions (p = 0.433), whereas tactile imagery predominantly engaged postcentral regions. The latter effect reached significance only in the functional channels of interest (fCOI) analysis (p = 0.003) and showed a non-significant trend across the full anatomical channel groups (p = 0.101). Correlation analysis revealed a strong across-subject correlation (r = 0.84; p < 0.001) between ERD values in motor and tactile imagery, but no correlation between ERD and hemodynamic responses. Linear mixed model analysis revealed significant (p < 0.001) associations between precentral and postcentral HRs for both MI and TI. These findings suggest that although motor and tactile imagery share common sensorimotor engagement at the electrophysiological level, their hemodynamic signatures are distinct. The absence of linear associations between modalities highlights the complexity of brain dynamics and the importance of multimodal assessments. The findings have implications for the design of brain-computer interfaces and rehabilitation protocols using mental imagery.},
}
@article {pmid41416623,
year = {2025},
author = {Adhikary, S and Dutta, S and Bose, A and Ranjan, R},
title = {Brain computer interface to recognize hand movements by magnification of subtle electroencephalogram patterns.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-15},
doi = {10.1080/10255842.2025.2602830},
pmid = {41416623},
issn = {1476-8259},
abstract = {Brain-computer interfacing facilitates usage of medical devices such as Electroencephalograms to study brain activities using signal processing techniques. Hand movements are motor activities which cause signature electrical signals in the electroencephalogram recordings. Signal processing and machine learning can be used to remove artefact contamination, amplify subtle features associated with hand movement and classify them. This paper experiments to utilize mathematical models to extract features and classify hand movement from electroencephalogram data up to 98% accuracy based on tests performed on an open-sourced dataset. The study, after further tests, can be used to build prosthetic limbs and mind-controlled robotic arms.},
}
@article {pmid41414721,
year = {2025},
author = {Zhang, Y and Huang, HF and Xie, JJ and Ni, W and Yu, H and Wu, ZY},
title = {Genetic and Clinical Characteristics of Chinese Adult Patients With Krabbe Disease.},
journal = {CNS neuroscience &amp; therapeutics},
volume = {31},
number = {12},
pages = {e70708},
doi = {10.1002/cns.70708},
pmid = {41414721},
issn = {1755-5949},
support = {82230062//National Natural Science Foundation of China/ ; 188020-193810101/089//Research Foundation for Distinguished Scholar of Zhejiang University/ ; },
mesh = {Adult ; Female ; Humans ; Male ; Middle Aged ; Young Adult ; China ; Exome Sequencing ; *Galactosylceramidase/genetics ; *Leukodystrophy, Globoid Cell/genetics/diagnosis ; East Asian People/genetics ; },
abstract = {AIM: This study aims to expand the clinical and genetic spectrum of Krabbe disease (KD) in Chinese adult patients and to improve diagnosis and understanding of its phenotypic diversity.<br><br>METHODS: Patients clinically suspected of leukodystrophy were recruited between 2015 and 2025. Clinical features were collected, and whole-exome sequencing (WES) was performed to identify potential variants. The pathogenicity of detected variants was classified according to the American College of Medical Genetics and Genomics (ACMG) standards and guidelines. Functional assays assessing protein expression, processing, secretion, subcellular localization, and enzymatic activity were conducted to further validate variant pathogenicity.<br><br>RESULTS: Fourteen unrelated patients were genetically diagnosed with KD, and their genetic and clinical features were summarized. Eleven variants in GALC were identified, including a novel missense variant c.1019C>T (p.P340L) which is not reported in the Human Gene Mutation Database (HGMD). Unlike most adult patients who typically present with spastic paraplegia, the patient carrying this variant exhibited initial symptoms of peripheral neuropathy. Functional experiments demonstrated that the variant led to impaired protein processing and localization, as well as reduced GALC enzymatic activity. Other variants including p.D56H, p.L377X, p.L441X, and p.L634S also affected GALC functions to varying degrees.<br><br>CONCLUSION: This study enhances the genotypic and phenotypic characterization of KD in China, aiding in differential diagnosis and genetic counseling. Functional data reinforce the pathogenicity of identified variants.},
}
@article {pmid41413923,
year = {2025},
author = {Xu, Y and Wei, Y and Xu, M and Zhou, H and Zheng, J and Chen, H and Chen, S and Chen, W},
title = {The relationship between heart rate variability and baseline state anxiety during stress and recovery.},
journal = {BMC psychology},
volume = {},
number = {},
pages = {},
doi = {10.1186/s40359-025-03823-5},
pmid = {41413923},
issn = {2050-7283},
support = {No. QD2025017//Scientific Research Foundation of Hang Zhou City University/ ; },
}
@article {pmid41413226,
year = {2025},
author = {Sun, Y and Si, X and He, R and Hu, X and Smielewski, P and Wang, W and Tong, X and Yue, W and Pang, M and Zhang, K and Song, X and Ming, D and Liu, X},
title = {An Automated Classifier of Harmful Brain Activities for Clinical Usage Based on a Vision-Inspired Pre-trained Framework.},
journal = {NPJ digital medicine},
volume = {8},
number = {1},
pages = {768},
pmid = {41413226},
issn = {2398-6352},
support = {ZYGXQNJSKYCXNLZCXM-H15//Scientific Research Innovation Capability Support Project for Young Faculty/ ; 0401260011//National Science Fund for Excellent Overseas Scholars/ ; 82472098, 32300704//National Natural Science Foundation of China/ ; 24JCJQJC00250//Tianjin Natural Science Foundation-Outstanding Youth Project/ ; 24ZXZSSS00510//Major Science and Technology Special Projects and Engineering-Major Project of National Key Laboratories/ ; 2021YFF1200602//Key Technologies Research and Development Program/ ; 2024-JKCS-16//Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences/ ; },
abstract = {Timely identification of harmful brain activities via electroencephalography (EEG) is critical for brain disease diagnosis and treatment, which remains limited in application due to inter-rater variability, resource constraints, and poor generalizability of existing artificial intelligence models. In this study, we describe an automated classifier, VIPEEGNet, which leverages the advantage of transfer learning from ImageNet-pretrained models to distinguish six types of brain activities. For the development cohort, the recall of VIPEEGNet ranges from 36.8% to 88.2%, and the precision ranges from 55.6% to 80.4%, with performance comparable to that of human experts. Notably, the external testing showed Kullback-Leibler divergence (KLD) values of 0.223 (public) and 0.273 (private), ranking second among the existing 2767 competing algorithms, while using only 0.7% of the parameters of the top-ranked algorithm. Its minimal parameter requirements and modular design offer a deployable solution for real-time brain monitoring, potentially expanding access to expert-level EEG interpretation in resource-limited settings.},
}
@article {pmid41412372,
year = {2025},
author = {Zhang, L and Li, B and Cao, M and Peng, C and Wang, H},
title = {Classification of EEG-fNIRS bimodal brain signals for motor imagery tasks based on wavelet transform and spatio-temporal domain processing.},
journal = {Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuroscience.2025.12.036},
pmid = {41412372},
issn = {1873-7544},
abstract = {The fusion of Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) provides richer neural information for brain-computer interface decoding. However, due to their distinct physiological mechanisms and heterogeneous temporal and statistical properties, EEG and fNIRS are difficult to temporally align and to project into a shared latent representation. To address this challenge, we propose BiCAT, a lightweight bimodal decoding framework that integrates wavelet-based preprocessing, artifact-aware time-domain refinement, and early feature-level fusion with a compact Transformer encoder. Wavelet transform is first applied to separate signal and noise components across frequency bands, after which spatio-temporal domain processing suppresses motion and physiological artifacts while preserving task-relevant patterns. The cleaned EEG and fNIRS features are concatenated and fed into a single-encoder Transformer, where joint self-attention captures salient temporal cues within each segment.BiCAT is evaluated on two publicly available EEG-fNIRS datasets covering motor imagery (MI), mental arithmetic (MA), and word generation (WG) tasks. The model achieves 93.41 % accuracy on MI, outperforming the strongest unimodal baseline (fNIRS) by 4.39 percentage points. On MA and WG, BiCAT attains 96.47 % and 96.41 % accuracy, corresponding to gains of 10.39 and 3.86 points over the best unimodal fNIRS and HbR baselines, respectively. Despite having only 111 k parameters, BiCAT performs competitively with representative multimodal fusion methods on the same benchmarks. These results demonstrate that BiCAT provides effective bimodal feature integration and robust performance across multiple EEG-fNIRS tasks while maintaining low computational complexity.},
}
@article {pmid41410819,
year = {2025},
author = {Hickman, J and Tsai, A and Fullard, M and Korsmo, M and Forbes, E and Aslam, S and Baumgartner, AJ and Feuerstein, JS and Bayram, E},
title = {Early-Onset Parkinson's Disease: Unique Features and Management Approaches.},
journal = {Current neurology and neuroscience reports},
volume = {26},
number = {1},
pages = {3},
pmid = {41410819},
issn = {1534-6293},
support = {R01NS120850/NS/NINDS NIH HHS/United States ; R00AG073453/AG/NIA NIH HHS/United States ; },
mesh = {Humans ; *Parkinson Disease/therapy/diagnosis/epidemiology ; Age of Onset ; *Disease Management ; Risk Factors ; Disease Progression ; },
abstract = {PURPOSE OF REVIEW: To highlight the unique clinical features, risk factors, and management strategies associated with early-onset Parkinson's disease (EOPD), and contrast these with late-onset Parkinson's disease (LOPD). We outline how these differences influence diagnostic and therapeutic approaches and identify key knowledge gaps critical to improving clinical care.<br><br>RECENT FINDINGS: Compared to LOPD, EOPD (onset age 21-50) has a higher prevalence of monogenic risk factors, focal dystonia, depression, anxiety; slower motor progression; lower rates of cognitive decline; higher risk for delayed diagnosis. Treatment is complicated by earlier and more frequent dyskinesias, motor fluctuations, and unique considerations such as pregnancy and career impact. Risk factors, clinical presentation, progression, and management needs of EOPD can differ from LOPD. Despite advances in characterizing and diagnosing EOPD, most research remains focused on LOPD. There is a critical need to tailor research and clinical trials to address the distinct needs of people with EOPD.},
}
@article {pmid41408409,
year = {2025},
author = {Zhen, X and Yu, Z and Shi, Y and Zhao, Y},
title = {Fusing LandTrendr BCI and machine learning for spoil dump mapping.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-025-32957-0},
pmid = {41408409},
issn = {2045-2322},
}
@article {pmid41408286,
year = {2025},
author = {Zhang, Y and Li, M and Guo, M and Xu, G and Wang, A},
title = {Decoding preparatory movement state-based motor imagery with multi layer energy decoder.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12984-025-01837-z},
pmid = {41408286},
issn = {1743-0003},
support = {F2024202019//Natural Science Foundation of Hebei Province/ ; 52320105008//International Cooperation and Exchange of the National Natural Science Foundation of China/ ; 2022YFC2402203//National Key Research and Development Program of China/ ; },
abstract = {BACKGROUND: Motor imagery (MI) is a widely used paradigm in brain-computer interface (BCI) research due to its potential applications in areas such as motor rehabilitation. As a purely cognitive process, MI produces low-amplitude, non-stationary EEG. Despite improving accuracies, cross-subject variability and limited generalization continue to motivate approaches that strengthen MI representations and enhance system robustness.<br><br>METHODS: We designed a task-guided preparatory movement state-based motor imagery (PMS-MI) paradigm that elicits a brief motor preparatory state before MI and captures EEG features from both the preparation and imagery phases. To decode the features effectively, we introduced a multilayer energy decoder (MLED) that integrates graph signal processing (GSP): EEG is modeled as intra- and cross-frequency multilayer brain networks, and a graph Fourier transform (GFT) projects the signals into network energy features before classification. We benchmarked the PMS-MI paradigm and the MLED method across multiple time window lengths using a panel of classical and deep-learning classifiers.<br><br>RESULTS: The PMS-MI paradigm elicited significant energy variations during the movement preparation phase and induced earlier event-related desynchronization (ERD) with broader frequency band activation during MI, compared to traditional MI paradigms. Classification performance using CSP in the PMS-MI paradigm surpassed that of the traditional paradigm at all time windows. Further accuracy improvements were achieved with the MLED method. Brain network analysis revealed distinct neural representations between the preparation and MI phases, and MLED effectively captured these differences. Feature fusion of preparation and MI stages resulted in classification accuracies exceeding 85% for both 1&#xa0;s and 4&#xa0;s windows. The results demonstrate that both algorithmic design and paradigm choice play important roles in MI EEG decoding, with their relative contributions varying across temporal windows and experimental conditions.<br><br>CONCLUSIONS: Integration of preparatory movement states into the movement imagery process can generate distinguishable features at different stages and improve the classification performance of BCI systems. The proposed PMS-MI paradigm, combined with the MLED decoding method, provides a promising direction for developing more accurate and robust BCIs, particularly in the context of neurorehabilitation.},
}
@article {pmid41407306,
year = {2025},
author = {Wang, J and Liu, H and Wu, W and Hu, X and Wu, Z and Zhu, S and Ma, G and Wan, H and Feng, C and Wang, H},
title = {Structure-Property Modulation in Pyrolytic Photoresist Films Enabled Size-Dependent Electrochemical Performance of Neural Interfaces.},
journal = {ACS applied materials &amp; interfaces},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsami.5c20142},
pmid = {41407306},
issn = {1944-8252},
abstract = {Neural probes are critical devices used to monitor and record brain activity, usually connected to neurons to measure neural activity. However, traditional metal electrodes face numerous challenges, including high Young's modulus, susceptibility to electromagnetic interference, insufficient biocompatibility, and the risk of corrosion and delamination. In this study, we explore a highly biocompatible carbon material, a pyrolytic photoresist film (PPF), developed through a photoresist pyrolysis process. The effects of the pyrolysis temperature and hold time on material properties were systematically studied. The optimal pyrolysis condition was identified as 1000 °C for 2 h. Furthermore, a quantitative model was established to link the electrode's geometric area with electrochemical performance and optimize the performance of PPF neural probes. Ultimately, we successfully fabricated a multichannel flexible neural probe with superior electrochemical performance.},
}
@article {pmid41406614,
year = {2025},
author = {Sultana, M and Matran-Fernandez, A and Halder, S and Nawaz, R and Jain, O and Scherer, R and Chavarriaga, R and Millan, JDR and Perdikis, S},
title = {An out-of-the-lab evaluation of dry EEG technology on a large-scale motor imagery brain-computer interface dataset.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae2e8a},
pmid = {41406614},
issn = {1741-2552},
abstract = {OBJECTIVE: This study assesses the signal quality of state-of-the-art dry electroencephalography (EEG) under highly challenging, uncontrolled, real-world conditions and compares it to conventional wet EEG.<br><br>APPROACH: EEG data from 530 participants recorded during a public exhibition were benchmarked against several established signal quality metrics, including spiking activity, kurtosis, Auto-Mutual Information (AMI), spectral entropy, gamma-band power, and parameters extracted using the Fitting Oscillations and One-Over F (FOOF) model. Additionally, ICLabel decomposition was applied to quantify artifact influences across EEG channels. Dry electrode results were compared with their equivalents extracted on two control datasets comprising 71 and 80 participants, respectively, recorded with wet EEG systems in laboratory, home, or clinical surroundings. Main Results The analysis revealed condition-specific susceptibility to artifacts for both EEG modalities. The dry EEG system exhibited substantial robustness in moderate-noise scenarios, with artifact profiles comparable to controlled wet EEG recordings. However, recordings obtained in highly dynamic conditions showed increased muscle artifacts and broadband activity, notably in frontal and temporal regions. Wet EEG systems, under controlled conditions, were overall less inflicted by artifacts, yet, fronto-central ocular and muscular artifacts were consistently present. ICLabel analysis further confirmed these findings, indicating similar proportions of brain-related activity across systems (approximately 31-49.5%), but highlighted increased vulnerability to muscular and environmental artifacts in dry EEG during dynamic tasks.<br><br>SIGNIFICANCE: In agreement with recent similar investigations, our findings demonstrate that dry EEG caps have significantly matured, achieving signal quality comparable to wet EEG systems even in challenging real-world conditions, provided appropriate artifact mitigation strategies are employed. These results affirm the practical readiness and broad feasibility of dry EEG technologies for diverse Brain-Computer Interface (BCI) applications in naturalistic environments.},
}
@article {pmid41406276,
year = {2025},
author = {Wang, F and Cao, F and Gao, J and An, N and Yang, J and Wang, Y and Yu, D and Ma, X and Xiang, M and Ning, X},
title = {Exploring the Potential of SSVER-BCI Based on Contactless Measurement Using Optically Pumped Magnetometers.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3644887},
pmid = {41406276},
issn = {2168-2208},
abstract = {Brain-computer interfaces (BCIs) based on electroencephalogram (EEG) have been widely applied in health monitoring and neurorehabilitation. However, EEG signals are often attenuated and distorted by tissues like the scalp and skull, limiting EEG-based BCI performance. In contrast, magnetoencephalography (MEG) with contactless measurement offers higher spatial resolution and immunity to volume conduction effects. Traditional MEG systems, based on superconducting quantum interference devices (SQUIDs), are hindered by their size and cost, while optically pumped magnetometers (OPMs) have made OPM-MEG-based BCIs more practical and accessible. Nevertheless, the performance potential of OPM-MEG in BCI applications remains underexplored. To address this, we developed an OPM-MEG BCI system based on steady-state visual evoked response (SSVER) and conducted a systematic evaluation of its performance, highlighting the practical advantages of OPM-MEG in this context. Furthermore, we proposed a fusion framework for OPM-MEG and EEG to further enhance system performance. Offline experiments conducted with 13 participants showed that the developed EEG-BCI achieved an average accuracy of 94.30% and an information transfer rate (ITR) of 122.76 bits/min, the developed OPM-MEG BCI achieved an average accuracy of 98.68% and an ITR of 138.20 bits/min, while the hybrid BCI achieved an average accuracy of 99.72% and an ITR of 159.4 bits/min. The findings highlight the advantages of OPM-MEG for BCI applications and validate the proposed fusion framework as a viable means to enhance decoding performance, thereby extending the potential use cases of OPM-MEG-based systems.},
}
@article {pmid41406275,
year = {2025},
author = {Carlino, MF and Gielen, G},
title = {An artifact-free 290$μ$m[2]/ch 610nW/ch neural readout frontend with hybrid EDO compensation for high-channel-count closed-loop neuromodulation.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBCAS.2025.3644137},
pmid = {41406275},
issn = {1940-9990},
abstract = {Next-generation neurorehabilitation implants demand high-channel-count closed-loop systems with ultra-low area and ultra-low-power readout and classification. This is essential in applications such as multi-type epileptic seizure detection, brain machine interfaces or brain-to-text conversion. Although recent designs achieve compactness and low power, they often cannot record neural signals during stimulation due to large, saturating artifacts. Conversely, artifact-tolerant solutions typically incur excessive area and power overhead to avoid saturation. We introduce a paradigm shift: enabling an ultra-compact, artifact-tolerant readout frontend by permitting brief saturation during stimulation pulses and applying backend interpolation to reconstruct the signals. High-fidelity neural features can thus be extracted with minimal error. To minimize the readout area footprint and to facilitate the routing from many electrodes, we reuse the whole frontend to read-out 64 inputs in a time-multiplexed fashion. Implemented in a 40nm CMOS process, our chip leverages the first published secondorder fully time-based incremental analog-to-digital converter, achieving a state-of-the-art 290-$μ$m[2]/ch area occupation and only 610-nW/ch of power consumption. The proposed hybrid electrode offset compensation further minimizes the area overhead without significantly compromising the noise or common-mode/power rejection across the full cancellation range. Artifact tolerance is validated in saline using an external stimulator chip. We demonstrate that the error on a broad set of features extracted from interpolated local-field-potential data remains below ±10%, even under harsh stimulation conditions.},
}
@article {pmid41404976,
year = {2025},
author = {Garg, M and Kaur, J and Prakash, NR},
title = {Ocular artifact from electroencephalogram - a comparative analysis of feature extraction, selection and classification.},
journal = {Journal of medical engineering &amp; technology},
volume = {},
number = {},
pages = {1-8},
doi = {10.1080/03091902.2025.2600336},
pmid = {41404976},
issn = {1464-522X},
abstract = {An electroencephalogram (EEG) is a record of signals that represent surface potentials varying whenever the brain performs any task and can be recorded by placing an arrangement of electrodes at the scalp of the brain. These recordings are often contaminated by unwanted movement near these electrodes, resulting in non-cerebral signals called artefacts. The presence of artefacts makes the study of EEG signals difficult. This work focuses on a comparative analysis of classification of ocular artefacts from EEG signal that mainly comprise of eye blinks. Various feature extraction, feature selection and classification techniques are used to compare the prediction performance of the system. Three different methods were used to extract features from the EEG recording done on eight subjects, performing two different tasks. Then the diagnostic performance of three feature selection and 30 classification methods were evaluated using 5-fold cross-validation. Performance of the system on various combinations has been calculated in terms of accuracy and results have been discussed. The maximum accuracy of 93.8% was yielded by classifiers: Kernel Naïve Bayes, Linear Support Vector Machine (SVM) and Ensemble Bagged Trees using wavelet-based features, principal component analysis as feature selection algorithm. By methodically assessing 360 feature-classifier combinations, this study is innovative and provides one of the most thorough benchmarks for ocular artefact identification with exceptional accuracy. It also has great potential for real-time EEG preprocessing in clinical and BCI applications.},
}
@article {pmid41402805,
year = {2025},
author = {Wu, Y and Zhao, X and Jiang, Y and Chen, C and Liu, L and Hou, X and Xian, Q and Guo, J and Sun, L},
title = {Microbubble-enhanced ultrasound stimulation of β-cells improves insulin release and glycemic control in mice.},
journal = {Journal of nanobiotechnology},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12951-025-03926-6},
pmid = {41402805},
issn = {1477-3155},
support = {C5053-22 GF//Hong Kong Research Grants Council Collaborative Research Fund/ ; 15126524//General Research Fund/ ; 2023YFC2410900//National Key Research and Development Program of Ministry of Science and Technology of China/ ; G-SACD//Hong Kong Polytechnic University/ ; 1-CE0M//Research Center for Non-invasive Brain Computer Interface/ ; 1-CDJM//Research Institute of Smart Ageing/ ; },
abstract = {Diabetes poses a significant global health burden, with complications such as cardiovascular disease, stroke, and kidney failure. While insulin therapy is central to type 2 diabetes (T2D) management, its limitations-including rapid degradation and the need for frequent injections-highlight the demand for non-invasive alternatives. Here, we present an ultrasound (US)-mediated approach to enhance insulin release by selectively stimulating pancreatic β-cells via targeted microbubbles (MBs). In vitro experiments using RINm5F β-cells demonstrated that US-MB stimulation induces significant calcium influx and subsequent insulin release. In addition, this method effectively decreased blood glucose levels in mice by promoting insulin release. Mechanistic studies revealed that mechanosensitive ion channels play a pivotal role, as their inhibition (via GdCl3) abolished the ultrasonic effect. Importantly, the approach exhibited high biosafety, with no detectable cell death or tissue damage. Our findings establish ultrasound-stimulated β-cell targeting as a promising non-invasive strategy for diabetes treatment, offering a potential alternative to conventional insulin therapy.},
}
@article {pmid41399831,
year = {2025},
author = {Sorokin N, I and Nesterova O, Y and Khokhlov M, A and Kamalov D, M and Dzitiev V, K and Strigunov A, A and Tereshina A, D and Veriaskina A, E and Kamalov A, A and Pshikhachev A, M and Mikhalchenko A, V},
title = {[Urodynamic risk factors for transient urinary incontinence after endoscopic enucleation of prostate hyperplasia].},
journal = {Urologiia (Moscow, Russia : 1999)},
volume = {},
number = {5},
pages = {104-112},
pmid = {41399831},
issn = {1728-2985},
mesh = {Humans ; Male ; *Prostatic Hyperplasia/surgery/physiopathology ; *Urinary Incontinence/etiology/physiopathology ; Aged ; *Urodynamics ; Risk Factors ; Middle Aged ; Prospective Studies ; *Postoperative Complications/etiology/physiopathology ; *Endoscopy/adverse effects ; *Prostatectomy/adverse effects/methods ; },
abstract = {INTRODUCTION: Urinary incontinence in men after endoscopic enucleation of benign prostate hyperplasia (BPH) can reach 55% and significantly impairing the quality of life and social rehabilitation of patients. A large number of individual patient parameters and features of surgical treatment are considered as potential risk factors. At the same time, the influence of urodynamic factors, including the external urethral sphincter function at the preoperative stage, fades into the background, and research on this issue is extremely limited.<br><br>OBJECTIVE: comprehensive assessment of urodynamic risk factors for urinary incontinence after endoscopic enucleation of BHP.<br><br>MATERIALS AND METHODS: This prospective study included 69 patients who underwent endoscopic enucleation of BPH (thulium fiber enucleation - 62 patients, bipolar enucleation - 7 patients) performed by single surgeon between October 2023 and August 2024. All patients underwent an invasive urodynamic study 1 day before the planned surgical treatment, including uroflowmetry, cystometry, flow/pressure study and profilometry performed by single urologist. In the postoperative period, the presence and duration of urinary incontinence were recorded in accordance with the definition of the International Continence Society. Statistical data processing was carried out using RStudio software in the R programming language.<br><br>RESULTS: Transient urinary incontinence after endoscopic enucleation was detected in 36.2% patients. In 100% cases, the duration of incontinence did not exceed a 3-month period. The independent urodynamic predictors of urinary incontinence were the bladder outlet obstruction index (BOOI), the bladder contractility index (BCI) and maximum intraurethral pressure (Pura max). Thus, with an increase in BOOI for 1 unit, the chance of urinary incontinence increased by 1,027 times or 2.7% (OR=1,027; 95%CI=1,003-1,052; p=0,027). With an increase in BCI for every 1, the chance of urinary incontinence increased by 1,020 times or 2.0% (OR=1,020; 95%CI=1,001-1,039; p=0,043). Large values of Pura max, on the contrary, led to a decrease in the chance of urinary incontinence, thereby acting as a protective factor. With an increase in Pura max for every 1 cm of H2O, the chance of urinary incontinence decreased by 1,087 times or by 8% (OR=0,920; 95%CI=0,876-0,966). The overall accuracy of the proposed model was 88,1% with sensitivity and specificity of 90,5 and 86,8% (ROC-AUC=0,897). The only independent intraoperative factor associated with urinary incontinence was the operation time: with an increase in the operation time for every 1 minute, the chance of urinary incontinence increased by 1,022 times or by 2,2%, regardless of the type of energy used and the early sphincter release (OR=1,022; 95%CI=1,005-1.040; p=0,011; ROC-AUC=0,721).<br><br>CONCLUSION: The chance of urinary incontinence at longer endoscopic enucleation, higher BOOI and BCI and low Pura max increases, which, thereby, can be used in predicting the functional results of endoscopic enucleation, taking into account individual urodynamic risk factors.},
}
@article {pmid41399276,
year = {2025},
author = {Wang, Z and Du, Y and Guo, D and Jiang, H and Li, Z and Wu, J and Yang, J and Li, H and Li, L and Fei, J and Li, Z},
title = {Brain-computer interface and functional electrical stimulation: a novel approach to motor rehabilitation in CNS injury patients.},
journal = {International journal of surgery (London, England)},
volume = {},
number = {},
pages = {},
doi = {10.1097/JS9.0000000000004392},
pmid = {41399276},
issn = {1743-9159},
abstract = {Central nervous system (CNS) injuries, such as stroke and spinal cord injury, often result in persistent motor impairments that conventional rehabilitation can only partially alleviate. Recent developments in brain-computer interfaces (BCIs) combined with functional electrical stimulation (FES) have introduced a novel approach to motor rehabilitation by directly linking cortical signals with specific muscle activation. This closed-loop system compensates for disrupted neural transmission and simultaneously promotes activity-dependent plasticity, thereby supporting functional reorganisation within the CNS. Findings from pilot trials and preclinical studies indicate that BCI-FES enhances motor recovery in both upper and lower limbs, increases patient engagement, and facilitates long-term cortical reorganisation. However, significant limitations persist, such as inconsistent neural decoding, stimulation-related fatigue, and the lack of standardised treatment protocols. Moreover, ethical challenges such as informed consent, neural data privacy, and equitable access must be resolved before broad clinical adoption can be achieved. Future research should focus on rigorous multicentre trials, tailored intervention strategies, and integration with emerging digital health technologies. This review synthesises current evidence on BCI-FES paradigms, stimulation parameters, underlying mechanisms, and ethical considerations, and outlines future directions to accelerate its clinical translation in CNS rehabilitation.},
}
@article {pmid41399243,
year = {2025},
author = {Lawrence, D and Avraham, G and Yao, J and Li, L and Shi, C and Starr, PA and Little, SJ},
title = {Cortico-basal oscillations index naturalistic movements during deep brain stimulation.},
journal = {Brain : a journal of neurology},
volume = {},
number = {},
pages = {},
doi = {10.1093/brain/awaf466},
pmid = {41399243},
issn = {1460-2156},
abstract = {The basal ganglia and sensorimotor cortex are essential nodes of a network that supports motor control. In Parkinson's disease, disruptions in this network lead to rigidity and slowness during movement execution. Deep brain stimulation (DBS) of the basal ganglia has proven effective in alleviating Parkinson's disease-related hypokinetic symptoms, and sensing-enabled neurostimulators now afford the opportunity to detect cortico-basal oscillations during motion. However, the specific contributions of these motor network nodes to chronic, naturalistic movement and the effects of DBS on circuit dynamics are not well understood. To address these gaps, we recorded over 530 hours of cortical and subcortical signals from 15 Parkinson's disease patients (27 hemispheres) during unsupervised, unconstrained daily activities and subthalamic or pallidal DBS. Synchronized wrist-worn accelerometers tracked forearm speeds, supporting the evaluation of neural biomarkers related to motion. Our study validated and extended the known relationship between cortical and subcortical beta power (13-30 Hz) and movement. We show that cortical low (13-20 Hz) and high (21-30 Hz) beta movement-related desynchronization (MRD) effectively distinguished between mobile and stationary states. In the subthalamic nucleus (STN) and globus pallidus interna (GPi), high beta MRD and gamma (40-80 Hz) movement-related synchronization (MRS) exhibited significant group-level correlations with movement kinematics. When stimulated at 130 Hz, cortical stimulation-entrained gamma oscillations at the half-harmonic (∼65 Hz) were observed. Further, cortical entrained gamma MRS was a stronger predictor of motion than broadband gamma MRS. We developed machine learning (ML) models to predict naturalistic movement over extended periods using spectral features from brief neural recordings (0.5-8 s epochs). Cortical models outperformed subcortical models, although combining cortico-basal signals yielded the highest model performance (AUC > 0.85 for binary movement state classifiers; Pearson r statistic > 0.68 for continuous forearm speed regressors). Higher DBS current amplitudes were associated with reduced beta MRD and low gamma (40-60 Hz) MRS in the STN/GPi. This negatively impacted the accuracy of the subcortical models, whereas cortical and cortico-basal model performance remained stable across stimulation amplitudes. Our study demonstrates that cortico-basal nodes of the motor network encode complementary kinematic information, which can be integrated to enhance the accuracy and stability of chronic, naturalistic movement decoding during deep brain stimulation. These insights support the development and integration of therapeutic brain-computer interfaces (BCIs) with closed-loop, adaptive DBS (aDBS) to leverage rapid and precise movement-predictive models for the treatment of motor network disorders.},
}
@article {pmid41398930,
year = {2025},
author = {Chen, Y and Ge, H and Deng, C},
title = {A novel method for EEG-based motor imagery classification using feature fusion.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-15},
doi = {10.1080/10255842.2025.2568700},
pmid = {41398930},
issn = {1476-8259},
abstract = {This paper introduces a multi-scale feature fusion framework for EEG-based motor imagery (MI) classification, designed to leverage the spectral-temporal-spatial structure of EEG data, its nonlinear intrinsic characteristics, and convolutional features. Several proposed feature fusion models surpass current state-of-the-art classification systems for MI tasks. A support vector machine (SVM) model achieves an accuracy of 86.92% on the BCIC-IV-2a dataset. To mitigate redundancy, the proposed models incorporate dimensionality reduction via factor analysis (FA) and channel selection using common spatial pattern (CSP). Selecting 12 channels yields superior classification performance compared to using all 22or only 8 selected channels, achieving an accuracy of 88.17%.},
}
@article {pmid41398422,
year = {2025},
author = {Qu, B and Tan, X and Tang, Z and Wang, H and Lan, L and Schriver, KE and Pan, G and Friedman, RM and Lai, HY},
title = {Unveiling interactions of spatial-temporal information in tactile motion perception.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {43838},
pmid = {41398422},
issn = {2045-2322},
support = {2021ZD0200401//STI 2030-Major/ ; 2021ZD0200401//STI 2030-Major Projects/ ; 2021ZD0200401//STI 2030-Major Projects/ ; 2021YFF0702200//National Key R&amp;D Program of China/ ; 82101323//National Natural Science Foundation of China/ ; 2021C03001//Key R&amp;D Program of Zhejiang Province/ ; 2019XZZX003-20//Fundamental Research Funds for the Central Universities/ ; },
abstract = {Tactile perception is inherently dynamic, relying on active manual exploration to extract information about motion and surface properties. Spatiotemporal inputs facilitate tactile motion perception by conveying information both direction and speed perception. Although previous studies have examined these features separately, the interactions between spatial and temporal features in shaping perceptual outcomes remain poorly understood. To address this gap, we conducted two psychophysical experiments in which tactile motion stimuli, varying in direction, speed and spatial frequency (wavelength), were delivered to the distal fingerpad of healthy participants, and then requested the participants to report their feedback directly. In Experiment I, we found that the anisotropic distortion of directional perceptual bias is quadrant-dependent, while variations in speed did not alter this general pattern. Experiment II revealed a dissociation between spatial and temporal contributions to perception. Spatial frequency primarily determined the overall pattern of perceptual bias, reflecting the structural properties of the stimulus. In contrast, speed modulates its dynamic expression by influencing the amplitude and phase of deviations. Additional psychometric function analyses indicated that tactile speed perception arises from a combination of linear and nonlinear processes. Collectively, these findings elucidate how the brain integrates spatiotemporal cues to construct a coherent tactile motion representation, thereby accounting for the systematic directional distortions and nonlinear speed estimation.},
}
@article {pmid41397373,
year = {2025},
author = {Jia, Y and Lian, Q and Wang, L and Wang, Y and Qi, Y},
title = {Learning discrete neural latent spaces for high-performance speech decoding.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae2ccd},
pmid = {41397373},
issn = {1741-2552},
abstract = {OBJECTIVE: Speech brain-computer interfaces (BCIs), which directly transform neural signals into intelligible voices, offer a promising avenue for people with aphasia. To decode speech information from brain signals, neural representation learning plays an important role.<br><br>APPROACH: Existing studies mainly explored continuous neural latent spaces for speech decoding and ignored the intrinsic discrete property in speech production. Here, we propose to learn a discrete neural latent spaces by constructing a quantized representation learning network for speech decoding.<br><br>MAIN RESULTS: Experiments with intracranial stereotactic EEG (sEEG) signals from 11 subjects demonstrated that our approach significantly improved the precision and robustness of speech decoding.<br><br>SIGNIFICANCE: These results underscore the potential of our method to improve the functionality and usability of speech BCIs for people with aphasia.},
}
@article {pmid41397350,
year = {2025},
author = {Xia, Y and Wei, Y and Li, S and Mai, X and Luo, R and Zhu, X and Meng, J},
title = {A potential field shared control approach for wheelchair navigation via brain‑computer interface.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae2ccc},
pmid = {41397350},
issn = {1741-2552},
abstract = {OBJECTIVE: Electroencephalography (EEG) -based brain-computer interfaces (BCIs) can help patients with disabilities control external devices directly without peripheral pathways. Due to the limitations in EEG signal quality, the performance of EEG-based BCIs may not be satisfactory. Shared control has become an important research direction in the field of brain-controlled wheelchairs (BCWs). However, most existing studies do not achieve the flexible movement of BCW in environments with narrow spaces. This study proposes a shared controller based on the potential field method to integrate environmental information and user commands intelligently.<br><br>APPROACH: Considering the flexibility of wheelchair movement, we incorporated EEG decoding results obtained through the motor imagery paradigm and fused them with environmental information to create a fusion field. We then used these components separately to construct the BCI and obstacle fields. Twelve subjects participated in the virtual wheelchair navigation experiment, while five subjects took part in the real-world wheelchair navigation experiment, aiming to evaluate the control performance in different scenarios under three control modes (keyboard, BCI-only, and shared control).<br><br>MAIN RESULTS: The experimental results show that the proposed shared controller: 1) significantly enhances navigation performance in both general and narrow environments compared with BCI-only control; 2) improves the total success rate from 8.33% to 83.33% in virtual complex environments and from 23.33% to 66.67% in real-world two-way navigation; 3) achieves success rates that are statistically comparable to keyboard control (p > 0.05). Moreover, the shared control reduced the average navigation time by nearly 100 seconds compared with BCI-only control in real-world experiments.<br><br>SIGNIFICANCE: This new shared control method improves the ability of BCWs to move flexibly in challenging, narrow environments.},
}
@article {pmid41397305,
year = {2025},
author = {Zhang, L and Li, B and Shi, X and Peng, C},
title = {Hybrid BCI-Based Instruction Set for Dual Robotic Arm Control Using EEG and Eye Movement Signals.},
journal = {Biomedical physics &amp; engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/ae2c8f},
pmid = {41397305},
issn = {2057-1976},
abstract = {A brain-computer interface (BCI) establishes a pathway for information transmission between a human (or animal) and an external device. It can be used to control devices such as prosthetic limbs and robotic arms, which in turn assist, rehabilitate, and enhance human limb function. At present, although most studies focus on brain signal acquisition, feature extraction and recognition, and further explore the use of brain signals to control external devices, the features obtained via noninvasive approaches are fewer and less robust, which makes it difficult to directly control devices with more degrees of freedom such as robotic arms. To address these issues, we propose an extended instruction set based on motor imagery that fuses eyemovement signals and electroencephalogram (EEG) signals for motion control of a dual collaborative robotic arm. The method incorporates spatio-temporal convolution and attention mechanisms for brain-signal classification. Starting from a small base of control commands, the hybrid BCI combining eye-movement signals and EEG expands the command set, enabling motion control of the dual cooperative manipulator. On the Webots simulation platform, we carried out kinematic control and three-dimensional motion simulation of a dual 6-degree-of-freedom collaborative robotic arm (UR3e). The experimental results demonstrate the feasibility of the proposed method. Our algorithm achieves an average accuracy of 83.8% with only 8.8k parameters, and the simulation results are within the expected range. The results demonstrate that the proposed extended instruction set based on motor imagery is effective not only for controlling dual collaborative robotic arms to perform grasping tasks in complex scenarios, but also for operating other multi-degree-of-freedom peripheral devices.},
}
@article {pmid41397035,
year = {2025},
author = {Zhang, L and Shi, W and Zhao, Z and Wang, Z and Chu, C and Zhao, B and Zhang, J and Liu, Q and Lan, Y and Jiang, T},
title = {Lysergic acid diethylamide-derived excitatory/inhibitory ratio change enhances global synchrony in functional brain dynamics.},
journal = {PLoS computational biology},
volume = {21},
number = {12},
pages = {e1013822},
doi = {10.1371/journal.pcbi.1013822},
pmid = {41397035},
issn = {1553-7358},
abstract = {Lysergic acid diethylamide (LSD) has shown remarkable potential in modulating brain functional organization and dynamics. However, the exact mechanisms underlying its effects remain unclear. In this study, we employed a data-driven approach to analyze recurrent functional connectivity patterns in resting-state fMRI data and developed a parameterized feedback inhibition model to characterize excitatory/inhibitory (E/I) balance. The findings demonstrate that LSD enhances global brain synchrony and dynamic complexity. This enhanced synchrony likely stems from LSD's preferential stabilization of a globally synchronized yet functionally non-modular brain state - a pattern showing higher occurrence probability and acts as an "attractor" that recruits transitions from cognitive control networks. Crucially, these phenomena appear underpinned by LSD-induced convergence of excitatory/inhibitory balance across cortical hierarchies, particularly through Sensorimotor (SOM) suppression coupled with transmodal potentiation, where the Sensorimotor cortices emerge as potential regulatory hubs driving this neurochemical rebalancing. These convergent effects are consistent with the emergence of a brain state characterized by weakened sensory anchoring and enhanced cognitive flexibility, where the typical separation between concrete perception and abstract cognition becomes blurred. This neurophysiological remodeling therefore suggests a potential mechanism that could contribute to LSD's hallucinatory effects and its therapeutic potential in mental disorders characterized by rigid thought patterns.},
}
@article {pmid41397031,
year = {2025},
author = {Xu, K and Li, W and Yin, Y and Li, F and Wang, H and Sui, H and Zou, J and Mu, J and Wang, S},
title = {Hemi-obturator Nerve Innervated Latissimus Dorsi Muscle for Restoring Voluntary Voiding: Anatomic Study and Clinical Application.},
journal = {Plastic and reconstructive surgery},
volume = {},
number = {},
pages = {},
doi = {10.1097/PRS.0000000000012721},
pmid = {41397031},
issn = {1529-4242},
abstract = {This study presents a modified latissimus dorsi detrusor myoplasty (LDDM) technique using the hemi-obturator nerve for neurogenic underactive bladder (NUAB) reconstruction. Anatomical studies (n=22 hemipelves) revealed that the diameters of the anterior (mean: 0.209 cm) and posterior branches (mean: 0.199 cm) matched the thoracodorsal nerve's diameter (one-way ANOVA, p = 0.557), confirming their ideal donor potential. LDDM by using posterior branch of intrapelvic obturator nerve as the donor nerve was performed in five patients with NUAB. 4/5 (80%) patients restored voluntary voiding postoperatively, with post-void residual volume (PVR) decreasing significantly from 308.5(187.5) mL to 62.0 (58.8) mL (P=0.042) and bladder contractility index (BCI) improving significantly from 12.8(5.7) to 151.9(46.5) (P=0.007). These results demonstrate that LDDM using the hemi-obturator nerve is an effective surgical approach for functional detrusor reconstruction in NUAB patients.},
}
@article {pmid41396750,
year = {2025},
author = {Chen, W and Mei, J and Xiao, X and Li, A and Tao, L and Wang, K and Xu, M and Ming, D},
title = {An Online Adaptation Framework for Enhancing Calibration-Free SSVEP-Based BCI Performance.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3644250},
pmid = {41396750},
issn = {2168-2208},
abstract = {Accomplishing a plug-and-play steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) remains a critical challenge, due to the unsatisfying performance of calibration-free decoding algorithms. A current method called online adaptive canonical correlation analysis (OACCA) has proved efficient in enhancing calibration-free performance by self-adaptation merely with online data. However, OACCA only concerns the adaptation of spatial filters and excludes other useful adaptive procedures like individual template estimation, hindering fully exploitable model decoding and adaptation. This study proposes a new online adaptation framework termed online adaptive extended correlation analysis (OAECA) to augment the calibration-free online adaptation loop. OAECA first recalls and cleans the online trials for reliable data learning, then tunes individual templates and spatial filters for complete model updating, and finally adopts extended feature matching to improve target recognition. The simulation results on two public SSVEP datasets revealed that OAECA significantly outperformed OACCA for almost all 105 subjects, and both offline and online experiments further confirmed the effectiveness of OAECA. Particularly, OAECA achieved the highest average information transfer rate (ITR) of 202.17 bits/min in the online experiment, significantly exceeding the state-of-the-art OACCA of 177.02 bits/min. This study enhanced the calibration-free performance through comprehensive online adaptation, hopefully advancing SSVEP-based BCIs toward practical plug-and-play real-world applications.},
}
@article {pmid41394963,
year = {2025},
author = {Wang, N and Si, J and He, Y and Song, J and Chai, X and Liu, D and Li, J and Zhang, T and Cao, T and He, Q and Zhu, S and Jia, Y and Ma, W and Yang, Y and Zhao, J},
title = {Cerebral Neurovascular Networks May Serve as Potential Targets for Identifying Disorders of Consciousness: A Synchronous Electroencephalography and Functional Near-Infrared Spectroscopy Study.},
journal = {MedComm},
volume = {6},
number = {12},
pages = {e70530},
pmid = {41394963},
issn = {2688-2663},
abstract = {The diagnosis and management of disorders of consciousness (DoC) remain a critical challenge in clinical medicine and neuroscience. The key bottleneck is the lack of reliable biomarkers and an incomplete understanding of the pathophysiological mechanisms that underlie DoC. In view of this, a bedside-compatible, multimodal technique based on electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) was utilized to simultaneously capture neuronal oscillations and accompanying hemodynamics, so as to explore neurovascular biomarkers that can effectively discriminate different states of DoC. Resting-state EEG-fNIRS data from 13 regions of interest (ROIs) were acquired and compared across healthy controls (HC), minimally conscious state (MCS), and unresponsive wakefulness syndrome (UWS) groups. Hemodynamics-based functional connectivity and the spectral power of neuronal activity were quantified and subsequently employed to interrogate neurovascular coupling. The results demonstrated significantly stronger neurovascular coupling and beta-band power in premotor and Broca's areas of the MCS group. A multimodal classifier achieved an accuracy of 87.9% in distinguishing between MCS and UWS. The noninvasive, bedside-suitable nature of this tool underscores its potential for routine monitoring and prognostic assessment in DoC, addressing a critical need for accessible and reliable biomarkers in both neurology and intensive-care practice.},
}
@article {pmid41394940,
year = {2025},
author = {Liu, P and Ge, Q and Dong, L and Jiao, L and Han, S and Kang, X and Wang, H and He, J and Zhang, H},
title = {Motor imagery-based brain-computer interface for differential diagnosis in prolonged disorders of consciousness.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1695730},
pmid = {41394940},
issn = {1662-5161},
abstract = {INTRODUCTION: Patients with prolonged disorders of consciousness (pDoC) present significant challenges to the assessment of consciousness. This study investigated the clinical utility of motor imagery-based brain-computer interface (MI-BCI) for discriminating consciousness levels in patients with pDoC.<br><br>METHODS: Thirty-one pDoC patients [12 with unresponsive wakefulness syndrome (UWS) and 19 in a minimally conscious state (MCS)] underwent EEG recordings during resting state and MI-BCI training. The analysis focused on relative power spectral density across five frequency bands (delta, theta, alpha, beta, gamma) in motor imagery-related regions (frontal and parietal cortices), along with BCI performance metrics (classification accuracy and attention indices).<br><br>RESULTS: We found that MCS patients exhibited multiband neural oscillation modulation during MI-BCI tasks, including slow-wave enhancement [(delta in frontal lobes (p = 0.003); theta in frontal (p = 0.026) and parietal lobes (p < 0.001)) and fast-wave suppression (alpha in frontal (p < 0.001) and parietal lobes (p = 0.049); beta in frontal (p = 0.014) and parietal lobes (p = 0.001); gamma in parietal lobes (p = 0.023)]. In contrast, UWS patients only showed localized parietal gamma enhancement (p = 0.042). Notably, the MCS group achieved significantly higher classification accuracy (55% vs. 38%, p = 0.02), and attention indices correlated moderately with CRS-R scores across all patients (Spearman's &#x3c1; = 0.43, p = 0.02).<br><br>CONCLUSION: The findings suggest that MI-BCI classification accuracy and attention indices may serve as auxiliary discriminators between UWS and MCS patients, with MCS patients demonstrating superior responsiveness to MI-BCI training.},
}
@article {pmid41293018,
year = {2025},
author = {Bougou, V and Gamez, J and Rosario, ER and Liu, C and Pejsa, K and Bari, A and Andersen, RA},
title = {Hierarchical and Context-Dependent Encoding of Actions in Human Posterior Parietal and Motor Cortex.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {41293018},
issn = {2692-8205},
support = {UG1 EY032039/EY/NEI NIH HHS/United States ; },
abstract = {Action understanding requires internal models that link vision to motor goals. In monkeys, mirror neurons demonstrate motor resonance during observation, but single-unit evidence in humans is limited, leaving open whether such representations rely solely on motor resonance. We recorded neural activity from motor cortex (MC) and superior parietal lobule (SPL) in two tetraplegic participants implanted with Utah arrays while they intended or observed hand actions. MC strongly encoded intention but showed only weak, feature-specific overlap during observation, evident primarily at the population level. SPL, in contrast, supported shared models across intended movement and observation formats at both single-unit and population levels. In variants with incongruent instructed and observed actions, SPL encoded observed actions only when behaviorally relevant, whereas MC remained intention-dominant. Our results identify a context-dependent gating mechanism in SPL and suggest a hierarchical organization in which MC maintains intention-specific codes while SPL flexibly links observed input with internal goals to support action understanding.},
}
@article {pmid41392156,
year = {2025},
author = {Xia, J and Zhang, L and Wang, S and Yu, Y and Ding, L and Zhang, F and Zhang, S and Luo, J and Huang, YYS and Occhipinti, L and Pan, G and Cao, Z and Ding, G and Dong, S},
title = {Implantable neural probes with monolithically integrated CNTFET arrays for multimodal monitoring.},
journal = {Nature communications},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41467-025-67535-5},
pmid = {41392156},
issn = {2041-1723},
abstract = {The implantable neural probe for simultaneous recording of various brain signals is one of the key technologies for neurological science and clinics that is yet to be broken through. Here, we introduce an implantable neural probe with integrated carbon nanotube field-effect transistors which is able to perform multimodal recording of electrical and chemical signals of the brain under magnetic resonance imaging (MRI). We demonstrate here a simultaneous measurement of an electrophysiological signal with high signal-to-noise ratio up to 40.34 dB and calcium concentration with a detection limit down to 0.47 nM. We use our neural probes to detect neural activity in rats and results reveal that changes in Ca[2+] concentration occur concurrently with the epileptiform local field potential events, providing an alternative method for accurate detection of epilepsy. Our work may provide a powerful means for the future studies of brain and holds great potential for practical diagnostic applications.},
}
@article {pmid41391167,
year = {2025},
author = {Pang, J and Sun, Y and Cheng, T and Wang, J and He, X and Xiang, Y and Zhu, W and Cao, Y and Wu, M and Pei, W and Pei, R and Cao, Y},
title = {Multifunctional Composite Coating-Enhanced Flexible Microelectrodes for Chronic, High-Fidelity Neural Signal Recording.},
journal = {Analytical chemistry},
volume = {},
number = {},
pages = {},
doi = {10.1021/acs.analchem.5c04799},
pmid = {41391167},
issn = {1520-6882},
abstract = {Implantable flexible neuroelectrodes are critical for brain-computer interface (BCI) applications. However, conventional flexible electrodes often face challenges such as increased electrochemical impedance upon miniaturization, mechanical mismatch with brain tissue, and implantation-induced damage, all of which compromise long-term signal stability and recording quality. Here, we present a multifunctional surface modification strategy to address these limitations. By integrating polycaprolactone/silk fibroin-methacrylate (PCL-SFMA) nanofibers loaded with anti-inflammatory minocycline hydrochloride (MH), nanostructured poly(3,4-ethylenedioxythiophene) (PEDOT) for impedance reduction, and a bioactive SFMA hydrogel layer for seamless neural integration, we developed a composite-coated flexible microelectrode (Au-PCLSFMA-PEDOT-GEL). Comprehensive in vitro and in vivo evaluations demonstrated that the modified electrode exhibited low impedance, enhanced biocompatibility, improved biointegration, and effective mitigation of both acute and chronic inflammation. Long-term electrophysiological recordings in freely moving mice revealed stable, high-fidelity neural signal acquisition for up to 8 months, maintaining a signal-to-noise ratio of approximately 20. This work establishes a durable and functionally stable neural interface, offering a promising platform for long-term neuroscience research and the development of next-generation BCIs.},
}
@article {pmid41389568,
year = {2025},
author = {Pfeffer, MA and Wong, JKW and Ling, SH},
title = {Transformer-based hybrid systems to combat BCI illiteracy.},
journal = {Computers in biology and medicine},
volume = {200},
number = {},
pages = {111378},
doi = {10.1016/j.compbiomed.2025.111378},
pmid = {41389568},
issn = {1879-0534},
abstract = {This study addresses the challenge of enhancing Brain-Computer Interfaces (BCIs), focusing on low Signal-to-Noise Ratios and "BCI illiteracy" often affecting up to 20% of users. Transformer-based models show promise but remain underexplored. Three experiments were conducted. Experiment A assessed the performance of architectures combining Convolutional and Transformer Blocks for binary Motor Imagery (MI) classification. Experiment B introduced a hybrid system, refining both block types and adding a Noise Focus Block to infuse Stochastic Noise, enhancing multi-class classification robustness. Experiment C evaluated the emerging architectures on 106 subjects, focusing on robustness across weak and strong learners. In Experiment A, the best networks achieved a validation accuracy of 0.914 and a loss of 0.146 (p=0.000967, F=12.675). In Experiment B, the proposed architecture improved multi-class MI classification to 84.5% on Dataset II, significantly improving performance for BCI-illiterate users. Experiment C showed a Kappa >83%, reduced standard deviation, and a highest validation accuracy of 88.69% across all individuals. The hybrid integration of Transformers, CNNs, and Noise-Resonance-based layers significantly enhances classification performance, particularly for weak BCI learners. Further research is recommended to optimize hybrid system architectures and hyperparameter settings to overcome current limitations in BCI performance.},
}
@article {pmid41389307,
year = {2025},
author = {Li, T and Zhao, ZH and Tang, HB and Chen, Z and Lu, ZW and Yang, XL and Zhao, LL and Li, Y and Dang, MJ and Chen, ZY and Zhang, GL and Liu, L and Fan, H},
title = {Advances in Bionic Therapies for Targeting Neural Circuit Reconstruction and Integration for Spinal Cord Injury.},
journal = {Cellular and molecular neurobiology},
volume = {},
number = {},
pages = {},
doi = {10.1007/s10571-025-01647-w},
pmid = {41389307},
issn = {1573-6830},
support = {82101551//National Natural Science Foundation of China/ ; 82471333//National Natural Science Foundation of China/ ; 82171361//National Natural Science Foundation of China/ ; 82171471//National Natural Science Foundation of China/ ; },
abstract = {Spinal cord injury (SCI) is one of the most common critical illnesses, which can cause neurological deficits and disabilities of motor, sensory and autonomic nervous system in mild cases, and lead to paralysis or even death following severe trauma. Although there are currently no effective and satisfactory clinical treatments, the efforts for repair SCI never stop. Besides the traditional strategies such as drugs, surgical interventions and rehabilitative care, the bionic therapies have attracted significant attention due to its considerable promise. The bionic therapies for SCI mainly included engineered biomaterials-based approaches aiming for reconstruction of internal neural circuit and brain machine interfaces (BMI)-based technologies to integrate extrinsic control and intrinsic circuit. This review provides an extensive overview of SCI research and bionic therapies, with focus on reconstruction and integration of neural circuit, which might provide promising insights on clinical treatment.},
}
@article {pmid41389026,
year = {2025},
author = {Zhou, T and Shang, K and Liu, C and Cui, Z and Liang, D},
title = {Deep equilibrium-adversarial robust unfolding network for MRI reconstruction.},
journal = {Medical physics},
volume = {52},
number = {12},
pages = {e70185},
doi = {10.1002/mp.70185},
pmid = {41389026},
issn = {2473-4209},
support = {JCYJ20240813155840052//Shenzhen Science and Technology&#xa0;Program/ ; 2022YFA1004203//National Key R&amp;D Program of China/ ; 2021YFF0501503//National Key R&amp;D Program of China/ ; 62125111//National Natural Science Foundation of China/ ; 62331028//National Natural Science Foundation of China/ ; 62476268//National Natural Science Foundation of China/ ; 62206273//National Natural Science Foundation of China/ ; },
mesh = {*Magnetic Resonance Imaging ; *Image Processing, Computer-Assisted/methods ; Artifacts ; Brain/diagnostic imaging ; Humans ; *Deep Learning ; Signal-To-Noise Ratio ; *Neural Networks, Computer ; Knee/diagnostic imaging ; },
abstract = {BACKGROUND: Deep unfolding neural networks have shown significant promise in magnetic resonance imaging (MRI) reconstruction by replacing traditional iterative prior modeling with more efficient and flexible network architectures. However, the iterative optimization process makes these methods susceptible to signal perturbations caused by noticeable artifacts in the reconstructed images.<br><br>PURPOSE: To develop a general framework that enhances the robustness of the reconstruction process against prominent artifacts and noise in k-space, while also improving the stability of the&#xa0;reconstruction.<br><br>METHODS: This paper proposes a deep equilibrium-adversarial robust unfolding network (DEAR-net), a novel framework that integrates adversarial learning with deep equilibrium architectures. In this design, adversarial learning enhances the capability of network to suppress perturbations during the reconstruction process, effectively addressing the issue of noise and artifacts amplification in deep equilibrium architectures. However, the modification of the learned mapping from clean k-space to MR images by adversarial learning may compromise the stability of the reconstruction. Fortunately, this problem can be mitigated through the application of deep equilibrium&#xa0;architectures.<br><br>RESULTS: Experimental results demonstrate that DEAR-net achieves superior reconstruction performance, delivering higher image quality and greater robustness to varying levels of noise and artifacts in k-space, as evidenced by tests on the fastMRI knee dataset and our private brain&#xa0;dataset.<br><br>CONCLUSIONS: DEAR-net enhances the robustness of the reconstruction process in the presence of mild noise and artifacts in under-sampled k-space. Furthermore, we provide a mathematical analysis of the reconstruction&#xa0;error.},
}
@article {pmid41388263,
year = {2025},
author = {Zhang, Y and Wang, Y and Guo, J and Fang, T and Wang, R and Liu, W and Zhao, X and Fan, Q and Chen, Y and Peng, Y},
title = {Age-dependent recovery of white matter integrity after surgical correction in children with infantile esotropia.},
journal = {BMC neurology},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12883-025-04578-7},
pmid = {41388263},
issn = {1471-2377},
support = {82071994//National Natural Science Foundation of China/ ; 82202249//National Natural Science Foundation of China/ ; 12171330//National Natural Science Foundation of China/ ; DFL20221002//the Beijing Hospitals Authority's Ascent Plan/ ; 2021ZD0200508//the STI 2030-Major Projects/ ; },
abstract = {BACKGROUND: Infantile esotropia may interfere with white matter maturation during early childhood, a critical period of brain development. Surgical correction not only restores ocular alignment but may also influence neurodevelopmental trajectories. However, the role of age in modulating white matter recovery after surgery remains unclear. This study aimed to investigate the effects of age on white matter rehabilitation following surgical intervention in children with infantile esotropia, with the goal of identifying the optimal therapeutic window to maximize both neurodevelopmental and clinical outcomes.<br><br>METHODS: We included 29 typically developing children (F/M&#x2009;=&#x2009;14/15) and 30 children with IE (F/M&#x2009;=&#x2009;13/17), 17 of whom provided longitudinal data following surgical intervention. All participants underwent MRI scanning and clinical assessments. Diffusion tensor imaging (DTI) was performed to quantify white matter integrity using fractional anisotropy (FA) and mean diffusivity (MD). Automated fiber quantification was applied to analyze microstructural properties across 20 major white matter tracts. Cross-sectional and longitudinal analyses were conducted to evaluate developmental trajectories in patients versus controls.<br><br>RESULTS: Preoperatively, IE patients exhibited significantly elevated MD across multiple tracts, including the thalamic radiation and forceps minor. Following surgery, MD values decreased significantly in most tracts. FA alterations were less pronounced, with preoperative reductions and postoperative improvement limited to only a few tracts. In controls, age was negatively correlated with MD and FA changes. Longitudinal analysis revealed that surgical intervention was associated with accelerated growth in white matter microstructure compared to typical development, particularly in younger children.<br><br>CONCLUSIONS: Surgical correction of IE facilitates white matter restoration through mechanisms that operate independently of, and synergistically with, typical neurodevelopment. Earlier intervention is associated with faster rates of microstructural recovery, suggesting a higher sensitive period during which surgery can maximize white matter repair and optimize functional outcomes.},
}
@article {pmid41387563,
year = {2025},
author = {Peplow, M},
title = {Brain-computer interfaces race to the clinic.},
journal = {Nature nanotechnology},
volume = {},
number = {},
pages = {},
pmid = {41387563},
issn = {1748-3395},
}
@article {pmid41387461,
year = {2025},
author = {Zhou, L and Liu, P and Liu, J and Yuan, W and Wu, Z and Xu, D and Hu, B and Shao, Y and Lu, Y and Huang, N and Li, J and Li, Z and Liang, F and Wu, X and Ma, L and Wang, M and Di, Z and Li, R and Bi, Y and Xu, F and Mei, Y and Song, E},
title = {Wireless battery-free ultrathin lithium-niobate resonator as wearable and implantable electronics for continuous monitoring of mechanical vital signs.},
journal = {Nature communications},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41467-025-67413-0},
pmid = {41387461},
issn = {2041-1723},
support = {62204057//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Continuous monitoring of physiological parameters associated with dynamic biomechanics, such as intracranial pressure (ICP) and vital signs, is important for clinical diagnosis of brain diseases and timely medical intervention. Current skin-interfaced and implant technologies face challenges in terms of bulky tethers and/or percutaneous wires with high infection risks. Here, we report the wireless, battery-free, and lightweight devices for both wearable and fully implantable applications. The devices incorporate an ultrathin piezoelectric resonator with suspended lithium niobate thin film (LNTF, 3 μm thick), enabling the wireless tracking of mechanophysiological signals by detecting variations in resonance frequency. We experimentally and computationally establish the operational principles of the resonator sensor and assess the device performance as wearables for dynamically monitoring artery pulse and apnea events during respiration. Implantable wireless pressure sensors adapted from this scheme allow for untethered, minimally invasive ICP sensing with a low detection limit of 0.15 mmHg over a wide range up to 240 mmHg. In vivo experiments performed on rat models validate the device capabilities of accurately capturing clinically relevant ICP variations and elevated levels of ICP under pathophysiological conditions of hydrocephalus, with excellent biocompatibility after long-term implantation periods. These findings create the clinical significance of such battery-less and wireless devices for precise characterization of dynamic biomechanics of living tissues.},
}
@article {pmid41386385,
year = {2025},
author = {Schippers, A and Freudenburg, ZV and Vansteensel, MJ and Raemaekers, M and Ramsey, NF},
title = {High-density electrocorticography reveals sensorimotor cortex engagement in two distinct sites with different roles during audiovisual, audio, and visual speech perception.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121645},
doi = {10.1016/j.neuroimage.2025.121645},
pmid = {41386385},
issn = {1095-9572},
abstract = {Recent neuroimaging studies have shown the involvement of the speech motor system in the sensorimotor cortex (SMC) in speech perception, but knowledge on the relative contributions of visual and auditory speech information on SMC engagement and the cortical representation thereof remains scarce. To further elucidate the representation of different components of perceived speech on the SMC, we recorded high-density ECoG during a passive speech perception task. We found that audiovisual, visual-only, and auditory-only speech perception increased high frequency band activity in the SMC. We discovered two distinct regions of the SMC that are differentially engaged depending on the perceptual input modality, being a dorsally located cluster of activity associated with both unimodal and bimodal perception of auditory and visual information and a ventral cluster that is involved specifically in auditory speech perception. Together, these results shine a new light on the engagement of the sensorimotor cortex during speech perception and suggest that auditory and visual information play different roles.},
}
@article {pmid41385954,
year = {2025},
author = {Kripalal, A and Sekar, C},
title = {Intelligent electroencephalogram feature engineering for rapid mental health diagnosis.},
journal = {Psychiatry research. Neuroimaging},
volume = {356},
number = {},
pages = {112103},
doi = {10.1016/j.pscychresns.2025.112103},
pmid = {41385954},
issn = {1872-7506},
abstract = {Schizophrenia is one of the serious disorders and, if left untreated, can result in a range of problems with cognition, behavior, and emotions that affect every area of life. Diagnosis based on behavioral and clinical investigations remains difficult with schizophrenia symptoms which are complex and heterogenic. Early detection of schizophrenia is essential for the timely treatment leading to betterment of the life of patients. In this study based on machine learning algorithms, we have identified the relevant set of features from the electroencephalogram (EEG) signal to improve the classification accuracy of patients with schizophrenia and healthy controls. Combinations of these identified relevant features have been used to diagnose schizophrenia.Furthermore, we validated this same feature set as the high performing feature subset on an independent dataset, confirming its robustness and generalizability. The results show that the selected features from the EEG signal achieve the highest accuracy of 94.7% and 96.4% for Logistic Regression (LR) and Support Vector Machines (SVM) respectively with reduced data. Reduction in training data with this feature selection enhances the performance of edge devices that are optimized for applications such as brain computer interfaces, neurological disorder detection, cognitive state monitoring, and neurofeedback training.},
}
@article {pmid41385812,
year = {2025},
author = {Tian, X and Zhang, X and Zhou, C and Jiang, Y and Ren, X and Li, T and Ni, P},
title = {Generation and characterization of a human-derived iPSC line (HZSMHCi003-A) from a male child with fragile X syndrome.},
journal = {Stem cell research},
volume = {90},
number = {},
pages = {103880},
doi = {10.1016/j.scr.2025.103880},
pmid = {41385812},
issn = {1876-7753},
abstract = {This study reports the successful establishment of induced pluripotent stem cells (iPSCs) derived from a pediatric patient with Fragile X Syndrome (FXS), representing a valuable cellular model for studying the most prevalent hereditary form of intellectual disability. Blood samples were collected from an 8-year-old Han Chinese male presenting with intellectual disability and carrying a full FMR1 gene mutation (>200 CGG repeat expansion). A stable iPSC line designated HZSMHCi003-A was generated using episomal vector-mediated reprogramming with seven transcription factors (OCT4, SOX2, NANOG, LIN28, c-MYC, KLF4, and SV40LT). Comprehensive characterization confirmed normal chromosomal integrity, robust expression of pluripotency-associated markers, and tri-lineage differentiation potential as evidenced by teratoma formation assays. This FXS patient-derived iPSC line provides a unique platform for investigating neurodevelopmental pathophysiology and screening potential therapeutic interventions for intellectual disability associated with FMR1 dysfunction.},
}
@article {pmid41385417,
year = {2025},
author = {Meng, M and Yu, P and She, Q and Xi, X and Kong, W},
title = {ASA-STGCN: Adaptive Sparse Awareness-Spatiotemporal Graph Convolutional Network for Multi-Class Motor Imagery EEG Classification.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3643173},
pmid = {41385417},
issn = {2168-2208},
abstract = {Graph Convolutional Networks (GCNs) have shown promise in motor imagery electroencephalogram (EEG) signals classification by modeling spatial dynamics and brain connectivity. However, over-smoothing remains a challenge, leading to homogenized node features and reduced discrimination. To address this, we propose an Adaptive Sparse Awareness-Spatiotemporal Graph Convolutional Network (ASA-STGCN) that combines adaptive sparse graph convolution with attention mechanisms. Notably, a Graph Sparse Convolutional Network (GSCN) in the Adaptive Sparse Awareness Spatial Module (ASAM) enhances brain region feature selection, while the Graph Node Neighborhood Awareness Layer (GNNAL) applies self-attention to reinforce critical topological relationships. The Multi-scale Temporal Convolution Module (MTCM) captures both transient and sustained temporal dependencies. Experimental results achieve accuracies of 97.2%±3.4% (binary) and 83.6%±4.9% (four-class) on BCIC-IV-2a, 96.6%±3.1% (binary) on BCIC-III IVa, and 83.41%±4.3 (binary) on OpenBMI. Discussion confirms the model's effectiveness and its potential to support EEG-based neurorehabilitation and clinical brain computer interface applications.},
}
@article {pmid41385296,
year = {2025},
author = {Sha, L and Li, H and Peng, A and Yang, H and Liu, X and Zhao, H and Ma, W and Hong, Q and Tang, Y and Zhang, M and Chen, L},
title = {Diagnostic value of saccades in mild cognitive impairment (MCI): a community-based study.},
journal = {The journals of gerontology. Series A, Biological sciences and medical sciences},
volume = {},
number = {},
pages = {},
doi = {10.1093/gerona/glaf264},
pmid = {41385296},
issn = {1758-535X},
abstract = {BACKGROUND: Accurate diagnosis and assessment of mild cognitive impairment (MCI) are essential. The efficacy of saccades in the detection of MCI lacks validation through large-scale clinical trials.<br><br>METHODS: All eligible participants underwent saccadic assessment in four tasks and cognitive assessment. MCI diagnoses were made on the basis of clinical indicators and MRI by experienced physicians. The physicians were blinded to the saccade experiments and the operators of saccade experiments were blind to the diagnosis of physicians. The classification models based on machine learning was constructed for assessing the diagnostic accuracy of MCI based on saccadic parameters.<br><br>RESULTS: Of the 559 residents who consented to participate, 383 (153 with MCI and 230 controls) were completely assessed. The classification model trained by saccadic parameters achieved high accuracy in dissociating MCI and control with AUROC of 0.945 (95% CI, 0.924-0.964), sensitivity of 0.824 (95% CI, 0.769-0.886) and specificity of 0.904 (95% CI, 0.867-0.935). The parameters of the memory-guided and antisaccade tasks demonstrated better diagnostic efficacy. The saccade model also exhibited a good diagnostic value in patients with borderline cognition being defined by the score of MoCA. When the borderline cognition was defined as 23-27 of MoCA score, the diagnosing accuracy of MCI based on saccadic parameters resulted with AUROC of 0.911 (95% CI: 0.836-0.972), sensitivity of 0.929 (95% CI, 0.762-1.000) and specificity of 0.796 (95% CI, 0.718-0.863).<br><br>CONCLUSIONS: Saccades can distinguish MCI from controls with great accuracy, offering a sensitive and objective diagnostic aid of MCI, especially in participants with borderline cognition.},
}
@article {pmid41385039,
year = {2025},
author = {Wu, YH and Chen, SF and Kuo, HC},
title = {Therapeutic outcomes and predictive factors of intradetrusor onabotulinumtoxinA for neurogenic detrusor overactivity (NDO) associated with spinal cord lesion.},
journal = {International urology and nephrology},
volume = {},
number = {},
pages = {},
pmid = {41385039},
issn = {1573-2584},
support = {TCMF-MP-114-02-01//Buddhist Tzu Chi Medical Foundation/ ; },
abstract = {PURPOSE: Intradetrusor onabotulinumtoxinA (Botox) is an established treatment for neurogenic detrusor overactivity (NDO), although predictors of success remain unclear. This study evaluated the therapeutic efficacy of Botox and identified predictors of response in patients with spinal cord lesion (SCL)-related NDO.<br><br>METHODS: We retrospectively reviewed 167 patients with SCL-related NDO who received intradetrusor 200 U Botox at a single center between January 1, 2002, and December 31, 2024. Treatment response was classified using the Global Response Assessment (GRA) as excellent (GRA&#x2009;=&#x2009;3), moderately improved (GRA&#x2009;=&#x2009;2), mildly improved (GRA&#x2009;=&#x2009;1), or no change (GRA&#x2009;=&#x2009;0). Success was defined as GRA&#x2009;=&#x2009;3 or 2. Baseline demographics, neurological level, and videourodynamic (VUDS) parameters, including detrusor pressure at maximum flow (Pdet), maximum flow rate, voided volume, post-void residual, voiding efficiency (VE), bladder outlet obstruction index (BOOI), and bladder contractility index (BCI), were analyzed as predictors.<br><br>RESULTS: VUDS confirmed detrusor overactivity in 92.8%. Overall, 51.5% (86/167) achieved an excellent response, 43.1% (72/167) improved, and 5.4% (9/167) showed no change. Outcomes did not differ by neurological level (P&#x2009;=&#x2009;0.665). Patients with successful outcomes had higher baseline Pdet (41.9&#x2009;&#xb1;&#x2009;20.3 vs 22.9&#x2009;&#xb1;&#x2009;13.4 cmH&#x2082;O, P&#x2009;<&#x2009;0.001), BOOI (33.2&#x2009;&#xb1;&#x2009;21.9 vs 12.7&#x2009;&#xb1;&#x2009;15.2, P&#x2009;<&#x2009;0.001), and BCI (63.6&#x2009;&#xb1;&#x2009;33.0 vs 48.4&#x2009;&#xb1;&#x2009;27.9, P&#x2009;=&#x2009;0.010), but lower VE (0.28&#x2009;&#xb1;&#x2009;0.31 vs 0.40&#x2009;&#xb1;&#x2009;0.35, P&#x2009;=&#x2009;0.045). Logistic regression analysis showed that higher Pdet, BOOI, and BCI predicted treatment success, while higher VE predicted nonresponse. Sex distribution differed across GRA groups (P&#x2009;=&#x2009;0.004), with more men in the failure group. Acute adverse events were similar among groups.<br><br>CONCLUSIONS: Intradetrusor Botox produced good efficacy in SCL-related NDO. Higher baseline detrusor pressure, bladder contractility, and bladder outlet resistance predicted better outcomes, whereas greater VE was associated with nonresponse. Neurological level did not influence treatment success.},
}
@article {pmid41382998,
year = {2025},
author = {Ju, Y and Liu, J and Li, Z and Liu, Y and He, H and Liu, J and Liu, B and Wang, M and Zhang, Y},
title = {[Prospects and technical challenges of non-invasive brain-computer interfaces in manned space missions].},
journal = {Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences},
volume = {50},
number = {8},
pages = {1363-1370},
doi = {10.11817/j.issn.1672-7347.2025.250389},
pmid = {41382998},
issn = {1672-7347},
mesh = {*Brain-Computer Interfaces ; Humans ; *Space Flight ; *Astronauts/psychology ; Neurofeedback ; Cognition ; Electroencephalography ; Man-Machine Systems ; },
abstract = {During long-duration manned space missions, the complex and extreme space environment exerts significant impacts on astronauts' physiological, psychological, and cognitive functions, thereby posing direct risks to mission safety and operational efficiency. As a key bridge between the brain and external devices, brain-computer interface (BCI) technology enables precise acquisition and interpretation of neural signals, offering a novel paradigm for human-machine collaboration in manned spaceflight. Non-invasive BCI technology shows broad application prospects across astronaut selection, mission training, in-orbit task execution, and post-mission rehabilitation. During mission preparation, multimodal signal assessment and neurofeedback training based on BCI can effectively enhance cognitive performance and psychological resilience. During mission execution, BCI can provide real-time monitoring of physiological and psychological states and enable intention-based device control, thereby improving operational efficiency and safety. In the post-mission rehabilitation phase, non-invasive BCI combined with neuromodulation may improve emotional and cognitive functions, support motor and cognitive recovery, and contribute to long-term health management. However, the application of BCI in space still faces challenges, including insufficient signal robustness, limited system adaptability, and suboptimal data processing efficiency. Looking forward, integrating multimodal physiological sensors with deep learning algorithms to achieve accurate monitoring and individualized intervention, and combining BCI with virtual reality and robotics to develop intelligent human-machine collaboration models, will provide more efficient support for space missions.},
}
@article {pmid41382997,
year = {2025},
author = {Li, Z and Liu, J and Liu, B and Wang, M and Ju, Y and Zhang, Y},
title = {[Potential biological mechanisms underlying spaceflight-induced depression symptoms in astronauts].},
journal = {Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences},
volume = {50},
number = {8},
pages = {1355-1362},
doi = {10.11817/j.issn.1672-7347.2025.250380},
pmid = {41382997},
issn = {1672-7347},
mesh = {*Space Flight ; Humans ; *Astronauts/psychology ; *Depression/etiology/physiopathology ; Gastrointestinal Microbiome ; *Weightlessness/adverse effects ; Oxidative Stress ; Brain/physiopathology ; Hypothalamo-Hypophyseal System ; Neuronal Plasticity ; Pituitary-Adrenal System ; },
abstract = {Long-term spaceflight exposes astronauts to multiple extreme environmental factors, such as cosmic radiation, microgravity, social isolation, and circadian rhythm disruption, that markedly increase the risk of depressive symptoms, posing a direct threat to mental health and mission safety. However, the underlying biological mechanisms remain complex and incompletely understood. The potential mechanisms of spaceflight-induced depressive symptoms involve multiple domains, including alterations in brain structure and function, dysregulation of neurotransmitters and neurotrophic factors, oxidative stress, neuroinflammation, neuroendocrine system imbalance, and gut microbiota disturbances. Collectively, these changes may constitute the biological foundation of depressive in astronauts during spaceflight. Space-related stressors may increase the risk of depressive symptoms through several pathways: impairing hippocampal neuroplasticity, suppressing dopaminergic and serotonergic system function, reducing neurotrophic factor expression, triggering oxidative stress and inflammatory responses, activating the hypothalamic-pituitary-adrenal axis, and disrupting gut microbiota homeostasis. Future research should integrate advanced technologies such as brain-computer interfaces to develop individualized monitoring and intervention strategies, enabling real-time detection and effective prevention of depressive symptoms to safeguard astronauts' psychological well-being and mission safety.},
}
@article {pmid41382980,
year = {2025},
author = {Kasper-Jędrzejewska, M and Ptaszkowski, K and Rutkowski, T and Halski, T},
title = {Surface Electromyography Characteristics of Pelvic Floor Muscles in Healthy Women, Pelvic Floor Dyssynergia, and Urinary Incontinence: A Retrospective Comparative Study.},
journal = {Medical science monitor : international medical journal of experimental and clinical research},
volume = {31},
number = {},
pages = {e950086},
doi = {10.12659/MSM.950086},
pmid = {41382980},
issn = {1643-3750},
mesh = {Humans ; Female ; *Electromyography/methods ; *Pelvic Floor/physiopathology/physiology ; Retrospective Studies ; *Urinary Incontinence/physiopathology ; Adult ; Middle Aged ; Muscle Contraction/physiology ; *Pelvic Floor Disorders/physiopathology ; *Ataxia/physiopathology ; },
abstract = {BACKGROUND Surface electromyography (sEMG) of pelvic floor muscles (PFM) offers insights into neuromuscular control but lacks standardized normative values. This study aimed to evaluate baseline and contractile sEMG signal characteristics - including root mean square (RMS) amplitude in microvolts and normalized to maximum voluntary contraction (%MVC) - in a healthy control (H) group, pelvic floor dyssynergia (DS) group, and urinary incontinence (UI) group. MATERIAL AND METHODS A retrospective analysis included 68 women (H=28, UI=22, DS=18). UI was confirmed by the International Consultation on Incontinence Questionnaire-Short Form, and DS diagnosed via anorectal manometry. sEMG was recorded with a intravaginal probe using the Glazer protocol. RMS and %MVC were analyzed using Bayesian multivariate regression adjusted for age and BMI. RESULTS No significant differences were found at baseline rest or rapid contractions (P>0.05). The DS group showed higher RMS during tonic contractions vs H group (Δ=4.20, 95% BCI [0.99, 7.29], P<0.05) and UI (Δ=3.44, 95% BCI [0.48, 6.20], P<0.05), and impaired post-tonic relaxation vs H group (Δ=1.13, 95% BCI [0.10, 2.15], P<0.05). Normalized to %MVC, DS group showed lower rapid contraction activity than H group (Δ=-10.49, 95% BCI [-19.46, -1.86], P<0.05). H group outperformed UI group in tonic contraction (P<0.05). CONCLUSIONS DS showed higher RMS amplitudes during tonic contractions, impaired relaxation, and reduced %MVC efficiency, indicating paradoxical activity. UI patterns were heterogeneous, highlighting its multifactorial nature. Reliance on raw RMS alone may misclassify dysfunctions; multiparametric assessment and validation in larger cohorts are needed.},
}
@article {pmid41381932,
year = {2025},
author = {Qin, X and Li, H and Zhao, H and Wang, X},
title = {Photobiomodulation and Addiction: Exploring Mechanisms, Therapeutic Potential, and Future Directions in Substance Use Disorders.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {41381932},
issn = {1995-8218},
}
@article {pmid41381864,
year = {2025},
author = {Tang, A and Chen, Y and Si, K and Lai, J and Gong, W and Hu, S},
title = {Gut microbiota modulates synaptic plasticity, connectivity, and dopamine transmission in the VTA-mPFC pathway in bipolar depression.},
journal = {Molecular psychiatry},
volume = {},
number = {},
pages = {},
pmid = {41381864},
issn = {1476-5578},
support = {LR20F050002//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; LR22F050007//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; 82201676//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82471542//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Adequate evidence has shown that gut microbial dysbiosis is an emerging disease phenotype of bipolar disorder (BD), and is closely related to clinical symptoms of this intractable disease. However, how gut microbiota affects the nervous system in BD remains largely unclear. In this study, we constructed a BD depression-like mouse model via fecal microbiota transplantation, and explored the changes of synaptic plasticity and connectivity in the medial prefrontal cortex (mPFC) of BD mice. We found that bipolar depression-like mice presented with a decrease in the density of dendritic spines in medial prefrontal neurons, and "Translation at postsynapse" as a key contributor to the changes in synaptic plasticity. In addition, analysis of synaptic connectivity in the mPFC revealed that compared to control mice, less connections were observed between ventral tegmental area and mPFC glutamate neurons and dopamine response was decreased in BD mice. These findings suggest that gut microbiota from BD depression patients induces the development of bipolar depression possibly by modulating aberrant synaptic connectivity and dopamine transmission in the VTA-mPFC pathway, which sheds light on the microbiota-gut-brain mechanisms underlying BD.},
}
@article {pmid41381863,
year = {2025},
author = {Liang, R and Meng, L and Liu, X and Zhu, J and Wang, L and Ren, J and Zhao, M and Liu, J and Zheng, C and Yang, J and Ming, D},
title = {Syn III participated in rTMS-modulated emotional rescue in the prefrontal cortex under simulated space composite environment.},
journal = {Molecular psychiatry},
volume = {},
number = {},
pages = {},
pmid = {41381863},
issn = {1476-5578},
abstract = {Emotional state is a critical indicator of astronaut performance during long-duration space missions, significantly impacting both mission efficiency and post-mission adaptation to life on Earth. In this context, transcranial magnetic stimulation (TMS) may serve as a valuable tool for studying the psychological changes induced by the space environment. By combining whole-brain imaging, finite element model, cerebral blood flow imaging, genomics, and molecular validation, we tried to identify potential regulatory targets and their cofactors involved in rTMS-mediated improvement of emotional abnormalities under simulated spaceflight conditions. We identified the activation patterns of brain-wide neurons in simulated space composite environment (SSCE), particularly the reduced neuronal activity in the prefrontal cortex (PFC). The rTMS could activate PFC neurons and, on a macro scale, alleviate abnormal cortical hemodynamics. Importantly, synapsin III (Syn III) is a key candidate for rTMS-mediated improvement of emotional abnormalities under SSCE, working together with proteins such as MAPK, PSD95, and NR2B. Our work not only advances the understanding of spaceflight-associated neuropsychiatric risks but also establishes a molecular framework for developing targeted neuromodulation strategies in stress-related psychiatric disorders.},
}
@article {pmid41381618,
year = {2025},
author = {Yang, H and Fukuma, R and Namima, T and Okuda, K and Nishi, A and Iwata, T and Reza, A and Sasaki, KS and Kaiju, T and Gill, G and Kishima, H and Nishimoto, S and Yanagisawa, T},
title = {Longitudinal multitask wireless electrocorticography data from two fully implanted nonhuman primates.},
journal = {Scientific data},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41597-025-06359-w},
pmid = {41381618},
issn = {2052-4463},
support = {JPMJER1801//MEXT | Japan Science and Technology Agency (JST)/ ; JPMJMS2012 (TY)//MEXT | Japan Science and Technology Agency (JST)/ ; JPMJCR24U2 (TY)//MEXT | Japan Science and Technology Agency (JST)/ ; JPMJCR18A5 (TY)//MEXT | JST | Core Research for Evolutional Science and Technology (CREST)/ ; },
abstract = {We present a unique dataset of chronic wireless electrocorticography (ECoG) recordings obtained from two fully implanted nonhuman primates (adult Japanese macaques, Macaca fuscata) spanning hundreds of days post implantation. Each animal was equipped with bilateral subdural ECoG arrays targeting the sensorimotor cortices and a fully implantable wireless transmission unit. The dataset involves multiple tasks, including resting-state measurements, auditory listening paradigms, voluntary button presses, reaching movements, and somatosensory evoked potentials, providing a broad range of behavioural and stimulus conditions. All raw signals, event annotations, and metadata are organized according to the Brain Imaging Data Structure (BIDS) extension for intracranial electrophysiology, ensuring ease of reuse and interoperability with common neurophysiological software. We verified the data quality and stability through impedance monitoring, power spectral analyses, and task-specific event-related measures across the recording period, confirming the reliability and consistency of the ECoG signals. By offering open access to these longitudinal wireless ECoG data, we aim to facilitate the acquisition of new insights into long-term cortical dynamics and advance brain-computer interface (BCI) research.},
}
@article {pmid41381487,
year = {2025},
author = {Mao, L and Liu, P and Li, J and Wang, X and Su, H and Zhang, X and Sun, J and Li, T},
title = {Tactile-evoked EEG Dataset for Natural Perception Using an Integrated Stimulation-Recording Framework.},
journal = {Scientific data},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41597-025-06250-8},
pmid = {41381487},
issn = {2052-4463},
abstract = {The increasing demand for assistive living and medical technologies in aging societies has driven advancements in tactile-evoked Brian Computer Interface (BCI) systems, offering an alternative to traditional visual and auditory-based BCI systems. However, the development of such systems is constrained by challenges in quantifying tactile sensations and a lack of diverse datasets. This study presents an integrated system enabling natural tactile perception during dynamic touch experience while simultaneously recording electroencephalographic (EEG) responses. EEG signals were collected from 10 healthy participants (64 channels, 1000 Hz) in natural tactile perception tasks involving contact with three distinct materials. Preliminary analysis revealed significant differences in the P300 peak latency and amplitude between tactile conditions, highlighting the unique characteristics of tactile-evoked EEG signals. A three-class classification using Common Spatial Pattern (CSP) and Support Vector Machine (SVM) models demonstrated above-chance accuracy. This tactile-evoked EEG dataset provides a valuable resource for seeking tactile-related neural mechanisms and driving the practical application of BCI systems, offering a pathway to improved user experiences and functionality in real-world scenarios.},
}
@article {pmid41380689,
year = {2025},
author = {Wei, Y and Ma, Z and Zhang, B and Fu, L and Sun, X and Li, K and Wang, Z and Wang, Y and Yu, Q and Yang, H and Tan, C and Duan, S and Ni, JD},
title = {Sympathetic functional units encoded by genetically defined postganglionic neurons.},
journal = {Neuron},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuron.2025.10.028},
pmid = {41380689},
issn = {1097-4199},
abstract = {The sympathetic system connects the brain with internal organs through distinct functional pathways; however, our understanding of their organization is limited. Here, we employed genetic labeling and single-cell transcriptomic analysis and identified two molecularly defined subpopulations of celiac-superior mesenteric ganglia (CG-SMG) neurons that implement different sympathetic functional pathways. Calb2-positive CG-SMG neurons project exclusively to the muscular layer of the gastrointestinal tract, forming endings associated with myenteric ganglia. Conversely, Nxph4-positive neurons innervate blood vessels within multiple organs, creating perivascular endings. Functional manipulations demonstrated that Calb2-labeled sympathetic neurons regulate gut motility without affecting blood flow, whereas Nxph4-positive neurons act as visceral vasoconstrictors, regulating blood flow independently of gut motility. The selectively induced autonomic responses by these two transcriptionally distinct subsets of postganglionic neurons suggest that the sympathetic nervous system uses a labeled line logic to control organ physiology.},
}
@article {pmid41379999,
year = {2025},
author = {Lyu, B and Qin, L and Wang, X and Ou, J and Nour, MM and Ding, N and Gao, JH and Liu, Y},
title = {Building hierarchically nested structure by rapid neural sequences.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {122},
number = {50},
pages = {e2507417122},
doi = {10.1073/pnas.2507417122},
pmid = {41379999},
issn = {1091-6490},
support = {82327806 W2431053//MOST | National Natural Science Foundation of China (NSFC)/ ; 32271093//MOST | National Natural Science Foundation of China (NSFC)/ ; 2021ZD0200500 2021ZD0200506//National science and technology innovation 2030 major program/ ; 2022ZD0205500//National science and technology innovation 2030 major program/ ; n/a//MOE | Fundamental Research Funds for the Central Universities (Fundamental Research Fund for the Central Universities)/ ; 7100604651//MOE | Fundamental Research Funds for the Central Universities (Fundamental Research Fund for the Central Universities)/ ; },
mesh = {Humans ; Magnetoencephalography ; *Cognition/physiology ; Male ; Female ; *Brain/physiology ; Adult ; Young Adult ; },
abstract = {Hierarchically nested structures are fundamental to human cognition, enabling complex behaviors across domains including language, planning, and mathematics. However, the neural mechanisms that enable the flexible construction of these hierarchical structures are poorly understood. Here, we designed a task where participants mentally built sequences with nested, multidepth structures by recursively applying a fixed set of rules. Using magnetoencephalography, we find that the brain constructs nested hierarchies through rapid neural sequences that perform two recurring generative operations. The first operation identifies the hierarchy depth of a symbol and is associated with increased ripple-band power; while the second arranges the symbol into its correct order at that level, a process that scales with the number of depths, also positively correlated with planning time. These results reveal a fundamental neural computation for transforming sensory information into structured representations, which is essential for higher-order cognition.},
}
@article {pmid41379898,
year = {2025},
author = {Chen, X and Li, Z and Shen, Y and Mahmud, M and Pham, H and Ng, MK and Pun, CM and Wang, S},
title = {High-Fidelity Functional Ultrasound Reconstruction via a Visual Auto-Regressive Framework.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3623196},
pmid = {41379898},
issn = {2168-2208},
abstract = {Functional ultrasound (fUS) imaging provides exceptional spatiotemporal resolution for neurovascular mapping, yet its practical application is significantly hampered by critical challenges. Foremost among these is data scarcity, arising from ethical considerations and signal degradation through the cranium, which collectively limit dataset diversity and compromise the fairness of downstream machine learning models. To address these limitations, we introduce UltraVAR (Ultrasound Visual Auto-Regressive model), the first data augmentation framework designed for fUS imaging that leverages a pre-trained visual auto-regressive generative model. UltraVAR is designed not only to mitigate data scarcity but also to enhance model fairness through the reconstruction of diverse and physiologically plausible fUS samples. The generated samples preserve essential neurovascular coupling features-specifically, the dynamic interplay between neural activity and microvascular hemodynamics. This capability distinguishes UltraVAR from conventional augmentation techniques, which often disrupt these vital physiological correlations and consequently fail to improve, or even degrade, downstream task performance. The proposed UltraVAR employs a scale-by-scale reconstruction mechanism that meticulously preserves the spatial topological relationships within vascular networks. The framework's fidelity is further enhanced by two integrated modules: the Smooth Scaling Layer, which ensures the preservation of critical image information during multi-scale feature propagation, and the Perception Enhancement Module, which actively suppresses artifact generation via a dynamic residual compensation mechanism. Comprehensive experimental validation demonstrates that datasets augmented with UltraVAR yield statistically significant improvements in downstream classification accuracy. This work establishes a robust foundation for advancing ultrasound-based neuromodulation techniques and brain-computer interface technologies by enabling the reconstruction of high-fidelity, diverse fUS data.},
}
@article {pmid41379894,
year = {2025},
author = {Ke, Y and Fu, Z and Yang, J and Shang, H and Basu, A},
title = {A 1024-Channel 0.8V 23.9-nW/Channel Event-based Compute In-memory Neural Spike Detector.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBCAS.2025.3642865},
pmid = {41379894},
issn = {1940-9990},
abstract = {The increasing data rate has become a major issue confronting next-generation intracortical brain-machine interfaces (iBMIs). The scaling number of recording sites requires complex analog wiring and lead to huge digitization power consumption. Compressive event-based neural frontends have been used in high-density neural implants to support the simultaneous recording of more channels. Event-based frontends (EBF) convert recorded signals into asynchronous digital events via delta modulation and can inherently achieve considerable compression. But EBFs are prone to false events that do not correspond to neural and may affect the output firing rate, which is the key feature for neural decoding. Spike detection (SPD) is a key process in the iBMI pipeline to detect neural spikes and further reduce the data rate. However, conventional digital SPD suffers from the increasing buffer size and frequent memory access power, and conventional spike emphasizers are not compatible with EBFs. In this work we introduced an event-based spike detection (Ev-SPD) algorithm for scalable compressive EBFs. To implement the algorithm effectively, we proposed a novel low-power 10-T eDRAM-SRAM hybrid random-access memory (HRAM) in-memory computing (IMC) bitcell for event processing. We fabricated the proposed 1024-channel IMC SPD macro in a 65nm process and tested the macro with both synthetic dataset and Neuropixel recordings. The proposed macro achieved a high spike detection accuracy of 96.06% on a synthetic dataset and 95.08% similarity and 0.05 firing pattern MAE on Neuropixel recordings. Our event-based IMC SPD macro achieved a high per channel spike detection energy efficiency of 23.9 nW per channel and an area efficiency of 375 μm[2] per channel. Our work presented a SPD scheme compatible with compressive EBFs for high-density iBMIs, achieving ultra-low power consumption with an IMC architecture while maintaining considerable accuracy.},
}
@article {pmid41377313,
year = {2025},
author = {Ali, MR and Talpur, Y and Irshad, NUN and Imran, SB},
title = {Brain-computer interfaces: a new horizon in communication for locked-in syndrome.},
journal = {Annals of medicine and surgery (2012)},
volume = {87},
number = {12},
pages = {9159-9160},
pmid = {41377313},
issn = {2049-0801},
}
@article {pmid41374720,
year = {2025},
author = {Jochumsen, M and Sulkjær, CS and Dalgaard, KS},
title = {Comparison of Classifier Calibration Schemes for Movement Intention Detection in Individuals with Cerebral Palsy for Inducing Plasticity with Brain-Computer Interfaces.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {23},
pages = {},
doi = {10.3390/s25237347},
pmid = {41374720},
issn = {1424-8220},
support = {22-B01-1432//Elsass Foundation/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Cerebral Palsy/physiopathology ; Electroencephalography/methods ; Movement/physiology ; Male ; Female ; Adult ; Calibration ; Intention ; Young Adult ; },
abstract = {Brain-computer interfaces (BCIs) have successfully been used for stroke rehabilitation by pairing movement intentions with, e.g., functional electrical stimulation. It has also been proposed that BCI training is beneficial for people with cerebral palsy (CP). To develop BCI training for CP patients, movement intentions must be detected from single-trial EEG. The study aim was to detect movement intentions in CP patients and able-bodied participants using different classification scenarios to show the technical feasibility of BCI training in CP patients. Five CP patients and fifteen able-bodied participants performed wrist extensions and ankle dorsiflexions while EEG was recorded. All but one participant repeated the experiment on 1-2 additional days. The EEG was divided into movement intention and idle epochs that were classified with a random forest classifier using temporal, spectral, and template matching features to estimate movement intention detection performance. When calibrating the classifier on data from the same day and participant, 75% and 85% classification accuracies were obtained for CP- and able-bodied participants, respectively. The performance dropped by 5-15 percentage points when training the classifier on data from other days and other participants. In conclusion, movement intentions can be detected from single-trial EEG, indicating the technical feasibility of using BCIs for motor training in people with CP.},
}
@article {pmid41374637,
year = {2025},
author = {Paredes Ocaranza, CR and Yun, B and Paredes Ocaranza, ED},
title = {Traditional Machine Learning Outperforms EEGNet for Consumer-Grade EEG Emotion Recognition: A Comprehensive Evaluation with Cross-Dataset Validation.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {23},
pages = {},
doi = {10.3390/s25237262},
pmid = {41374637},
issn = {1424-8220},
mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; *Machine Learning ; Brain-Computer Interfaces ; Deep Learning ; Signal Processing, Computer-Assisted ; Adult ; },
abstract = {OBJECTIVE: Consumer-grade EEG devices have the potential for widespread brain-computer interface deployment but pose significant challenges for emotion recognition due to reduced spatial coverage and the variable signal quality encountered in uncontrolled deployment environments. While deep learning approaches have employed increasingly complex architectures, their efficacy in noisy consumer-grade signals and cross-system generalizability remains unexplored. We present a comprehensive systematic comparison of EEGNet architecture, which has become a benchmark model for consumer-grade EEG analysis versus traditional machine learning, examining when and why domain-specific feature engineering outperforms end-to-end learning in resource constrained scenarios.<br><br>APPROACH: We conducted comprehensive within-dataset evaluation using the DREAMER dataset (23 subjects, Emotiv EPOC 14-channel) and challenging cross-dataset validation (DREAMER&#x2192;SEED-VII transfer). Traditional ML employed domain-specific feature engineering (statistical, frequency-domain, and connectivity features) with random forest classification. Deep learning employed both optimized and enhanced EEGNet architectures, specifically designed for low channel consumer EEG systems. For cross-dataset validation, we implemented progressive domain adaptation combining anatomical channel mapping, CORAL adaptation, and TCA subspace learning. Statistical validation included 345 comprehensive evaluations with fivefold cross-validation &#xd7; 3 seeds &#xd7; 23 subjects, Wilcoxon signed-rank tests, and Cohen's d effect size calculations.<br><br>MAIN RESULTS: Traditional ML achieved superior within-dataset performance (F1 = 0.945 &#xb1; 0.034 versus 0.567 for EEGNet architectures, p < 0.000001, Cohen's d = 3.863, 67% improvement) across 345 evaluations. Cross-dataset validation demonstrated good performance (F1 = 0.619 versus 0.007) through systematic domain adaptation. Progressive improvements included anatomical channel mapping (5.8&#xd7; improvement), CORAL domain adaptation (2.7&#xd7; improvement), and TCA subspace learning (4.5&#xd7; improvement). Feature analysis revealed inter-channel connectivity patterns contributed 61% of the discriminative power. Traditional ML demonstrated superior computational efficiency (95% faster training, 10&#xd7; faster inference) and excellent stability (CV = 0.036). Fairness validation experiments supported the advantage of traditional ML in its ability to persist even with minimal feature engineering (F1 = 0.842 vs. 0.646 for enhanced EEGNet), and robustness analysis revealed that deep learning degrades more under consumer-grade noise conditions (17% vs. <1% degradation).<br><br>SIGNIFICANCE: These findings challenge the assumption that architectural complexity universally improves biosignal processing performance in consumer-grade applications. Through the comparison of traditional ML against the EEGNet consumer-grade architecture, we highlight the potential that domain-specific feature engineering and lightweight adaptation techniques can provide superior accuracy, stability, and practical deployment capabilities for consumer-grade EEG emotion recognition. While our empirical comparison focused on EEGNet, the underlying principles regarding data efficiency, noise robustness, and the value of domain expertise could extend to comparisons with other complex architectures facing similar constraints in further research. This comprehensive domain adaptation framework enables robust cross-system deployment, addressing critical gaps in real-world BCI applications.},
}
@article {pmid41370926,
year = {2025},
author = {Qiu, X and Wang, ZY and Jiang, XH and Zhao, H and Yan, ZP and Li, KH and Zhang, L and Chen, L and Meng, L and Ni, J},
title = {Neural correlation between swallowing motor imagery and execution: An EEG analysis.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae2b37},
pmid = {41370926},
issn = {1741-2552},
abstract = {OBJECTIVE: The relationship between swallowing motor imagery and actual swallowing remains unclear, leading to a lack of physiological basis for the application of swallowing imagery-based brain-computer interface (BCI) paradigms in rehabilitation. This research explored the link between swallowing execution and imagery, aiming to optimize brain-computer interface applications for swallowing rehabilitation in patients with dysphagia.<br><br>APPROACH: Thirty healthy participants performed swallowing motor imagery and saliva swallowing tasks under video cues, and Electroencephalography (EEG) signals from 64 channels and electromyographic (EMG) signals from the suprahyoid muscles were recorded. This study investigates swallowing onset detection using EMG, and explores neural dynamics during swallowing imagery and execution through EEG-based time-frequency analysis, functional connectivity analysis, and nonlinear dynamic analysis (Sample Entropy).<br><br>MAIN RESULTS: The results revealed event-related desynchronization (ERD) in the central region (CPz, CP1-CP4) and parietal region (Pz, P1-P4) for both swallowing motor imagery and actual swallowing. Pearson's correlation analysis indicated a weak but significant correlation (P = 0.0102). The ERD phenomenon during swallowing imagery was more similar to that during the pharyngeal stage, with a weak but significant correlation (P = 0.0139). Functional connectivity analysis revealed greater activation of the central region during swallowing imagery than during actual swallowing. In terms of sample entropy, swallowing motor execution exhibited higher signal complexity and dynamic characteristics compared to imagery.<br><br>SIGNIFICANCE: This study highlights the similarity in neural activation between swallowing imagery and execution, particularly in the central and parietal regions, supporting the application of the swallowing imagery paradigm in these regions for rehabilitation. Further research is required to enhance BCI applications in swallowing disorders.},
}
@article {pmid41370855,
year = {2025},
author = {Karaiskou, AI and Varon, C and Musluoglu, CA and Alaerts, K and De Vos, M},
title = {EEG-Based meditation decoding: Tackling subject variability with spatial and temporal alignment.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae2b0f},
pmid = {41370855},
issn = {1741-2552},
abstract = {Objective. Meditation and mindfulness are increasingly recognized as important in improving mental well-being. However, Electroencephalography (EEG)-based neurofeedback systems supporting these practices typically fail to generalize to unseen subjects. This study investigates the application of both spatial and spectral alignment to EEG to improve the classification of meditation and rest states for new subjects without any model retraining.Approach. Two unsupervised domain adaptation techniques are employed to reduce differences between subjects in their EEG recordings. The first, Riemannian Space Data Alignment (RSDA), adjusts and brings together patterns of brain activity across electrodes (spatial domain). The second, Convolutional Monge Mapping Normalization (CMMN), aligns the distribution of brain rhythms across frequencies (spectral domain). Each method is evaluated separately, in combination, and in interaction with z-score normalization. Classification between meditation and rest is performed on the aligned time series using EEGNet, a compact convolutional neural network architecture, with leave-one-subject-out (LOSO) cross-validation to assess generalization across subjects. All experiments are based on a publicly available dataset of meditation EEG recordings from 53 subjects, including both novice and expert meditators.Main Results. The combined RSDA+CMMN approach significantly improved LOSO classification accuracy (66.6%) compared to non-aligned (55.7%) and z-score normalized (59.6%) baselines, even though it did not improve overall harmonization. Spectral analysis identified consistent classification contributions from the Theta (4-8 Hz), Alpha (8-14 Hz), and Beta (14-30 Hz) bands, while spatial analysis highlighted Frontopolar and Temporal regions as critical for distinguishing the mental states of meditation and rest.Significance. This work is the first to explore both spatial and spectral alignment in subject-independent meditation decoding for improved cross-subject generalization. Aligning EEG time series without retraining provides a practical solution for real-time neurofeedback, thereby reducing subject variability and paving the way toward calibration-free neurotechnology that supports mental well-being. .},
}
@article {pmid41369883,
year = {2025},
author = {Tan, Y and Li, B and Sun, Z and Duan, F and Solé-Casals, J},
title = {Multi-source self-guided domain adaptation framework for EEG-based emotion recognition.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {},
number = {},
pages = {},
pmid = {41369883},
issn = {1741-0444},
support = {No. 2025YFE0107700//National Key R&amp;D Program of China/ ; No. 24ZXYXSY00140//Tianjin Science and Technology Plan Project/ ; },
}
@article {pmid41369868,
year = {2025},
author = {Lin, C and Lai, Q and Fang, E and Luo, B and Chen, Z and Huang, J and Hu, F and Yao, E},
title = {Serum urate, cardiovascular mediators, and atrial fibrillation: genetic evidence for URAT1-targeted therapy.},
journal = {Clinical rheumatology},
volume = {},
number = {},
pages = {},
pmid = {41369868},
issn = {1434-9949},
support = {2018-CX-20//Medical Innovation Project of Fujian Province/ ; },
abstract = {BACKGROUND: Current evidence indicates that high serum urate levels are associated with an increased occurrence of atrial fibrillation (AF), and urate-lowering drugs could potentially reduce this risk. Nonetheless, the processes driving this relationship remain unclear.<br><br>OBJECTIVE: To identify key mediators linking urate to AF and assess the direct effects of potential drug targets on AF risk.<br><br>METHODS: Genetic variants associated with serum urate levels, potential mediators, and urate-lowering drug targets were identified from genome-wide association studies (GWAS). Univariable Mendelian randomization, multivariable Mendelian randomization, and two-step-cis-MR were conducted. The Bayesian horseshoe prior MR approach was used as the primary method, and Genomic SEM was employed to support the mediation model.<br><br>RESULTS: The study identified a genetic and causal relationship between serum urate levels and AF onset. Key mediators included systolic blood pressure (proportion mediated 56.23%), diastolic blood pressure (25.27%), hypertension (49.46%), hypercholesterolemia (4.83%), coronary atherosclerosis (12.24%), myocardial infarction (30.32%), coronary artery disease (29.74%), and heart failure (47.66%). Drug target MR analysis found strong evidence for URAT1 inhibition reducing AF risk (odds ratio [OR]&#x2009;=&#x2009;0.91, 95% Bayesian credible interval [BCI] 0.85 to 0.97; Bayesian posterior probability [BPP]&#x2009;=&#x2009;0.997), which persisted after mediator adjustment. Under stricter flanking regions, evidence weakened after adjustment for heart failure (OR&#x2009;=&#x2009;0.93, 95% BCI 0.84 to 1.04; BPP&#x2009;=&#x2009;0.907) but remained robust for other mediators.<br><br>CONCLUSION: This study highlights several cardiovascular conditions (hypertension, hypercholesterolemia, heart failure, coronary artery diseases) as key mediators between serum urate and AF and supports URAT1 inhibition as a potential therapeutic strategy. Key points &#x2022;Elevated serum urate increases the risk of atrial fibrillation, potentially through cardiovascular mediators such as hypertension, heart failure, and coronary artery diseases. &#x2022;Genetic evidence from drug-target Mendelian randomization supports URAT1 inhibition as a potential therapeutic strategy for reducing atrial fibrillation risk. &#x2022;The protective effect of URAT1 inhibition against atrial fibrillation persists after adjusting for key cardiovascular mediators, suggesting additional therapeutic pathways beyond those identified.},
}
@article {pmid41368697,
year = {2025},
author = {Huang, T and Yin, X and Jiang, E},
title = {EEG motor imagery classification through a two-dimensional CNN-LSTM deep architecture and fuzzy decision-making.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-16},
doi = {10.1080/10255842.2025.2554256},
pmid = {41368697},
issn = {1476-8259},
abstract = {This study presents a robust deep learning framework for automatic motor imagery detection from raw EEG signals. Six band-power features were extracted using STFT, and dedicated 2D CNN-LSTM models were trained for each band. Their outputs were fused using a Choquet fuzzy integral to enhance decision reliability under noisy EEG conditions. Alpha- and sigma-band models achieved 88% and 87.1% accuracy, respectively. The fused architecture reached 90.4% on BCI IV-2a and 92.21% on BCI IV-1, outperforming existing methods in motor imagery classification.},
}
@article {pmid41368519,
year = {2025},
author = {Wang, P and Xie, T and Zhou, Y and Gong, P and Chan, RHM},
title = {TCPL: task-conditioned prompt learning for few-shot cross-subject motor imagery EEG decoding.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1689286},
pmid = {41368519},
issn = {1662-4548},
abstract = {Motor imagery (MI) electroencephalogram (EEG) decoding plays a critical role in brain-computer interfaces but remains challenging due to large inter-subject variability and limited training data. Existing approaches often struggle with few-shot cross-subject adaptation, as they require either extensive fine-tuning or fail to capture individualized neural dynamics. To address this issue, we propose a Task-Conditioned Prompt Learning (TCPL), which integrates a Task-Conditioned Prompt (TCP) module with a hybrid Temporal Convolutional Network (TCN) and Transformer backbone under a meta-learning framework. Specifically, TCP encodes subject-specific variability as prompt tokens, TCN extracts local temporal patterns, Transformer captures global dependencies, and meta-learning enables rapid adaptation with minimal samples. The proposed TCPL model is validated on three widely used public datasets, GigaScience, Physionet, and BCI Competition IV 2a, demonstrating strong generalization and efficient adaptation across unseen subjects. These results highlight the feasibility of TCPL for practical few-shot EEG decoding and its potential to advance the development of personalized brain-computer interface systems.},
}
@article {pmid41367898,
year = {2025},
author = {Paveliev, M and Melnikova, A and Samigullin, DV and Egorchev, AA and Titova, AA and Kiyasov, AP and Popova, IY and Parpura, V and Aganov, AV},
title = {Second harmonic generation for brain imaging: pathology-related studies.},
journal = {Biophysical reviews},
volume = {},
number = {},
pages = {},
pmid = {41367898},
issn = {1867-2450},
abstract = {Microscopy of the brain has been facing problems of contrast and thick tissue imaging. Second harmonic generation (SHG) is a non-linear effect of the light interaction with the imaged material, resulting in photon emission at half the wavelength of the absorbed light. SHG microscopy provides an unprecedented opportunity for imaging collagen and other noncentrosymmetric protein fibrils in unstained thick tissue samples and in the live brain via a regular multiphoton setup. This opens a remarkable methodological window for imaging pathological processes of high importance, including brain trauma, fibrosis, tumorigenesis, and neuroimplant-induced foreign body response. Moreover, SHG is a valuable tool for imaging astrocytes and nerve fiber microtubules. Third harmonic generation enhanced by three-photon resonance with the Soret band of hemoglobin is combined with SHG to resolve the microstructure of blood vessel walls and astrocyte-process endfeet on gliovascular interfaces. Here, we review current state-of-the-art methods in the field of brain imaging applications of SHG, including research on brain and spinal cord injury, glioma, ischemia, Alzheimer's disease, neuroimplantation, and brain meninges. We then address the method development perspective in the broader context of other tissue pathologies. Finally, we account for recent progress in artificial intelligence applications for SHG microscopy data analysis.},
}
@article {pmid41365192,
year = {2025},
author = {Li, Y and Ye, M and He, Q and Yang, B and Luo, P and Yang, X},
title = {Novel dual AMPK/NRF2 activation by leucocyanidin from Hawthorn (Crataegus) for mitochondria repair-Targeted therapy of hepatic steatosis.},
journal = {Phytomedicine : international journal of phytotherapy and phytopharmacology},
volume = {150},
number = {},
pages = {157614},
doi = {10.1016/j.phymed.2025.157614},
pmid = {41365192},
issn = {1618-095X},
abstract = {BACKGROUND AND PURPOSE: Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a global health challenge with limited therapeutic options. This study identified leucocyanidin (Leuc), a bioactive flavonoid from the traditional herb Crataegus pinnatifida (hawthorn), as a novel dual-target therapeutic agent against MASLD.<br><br>METHODS AND RESULTS: We evaluated the effects of Leuc on a mouse model induced by a 60% high-fat diet and a cell model induced by free fatty acids (FFA). Compared to the model group, Leuc treatment dose-dependently significantly reduced liver weight, serum levels of TG and TC, hepatic inflammation markers (IL-6 and TNF-&#x3b1;), as well as cellular TG content. Histological and fluorescence analyses revealed a significant reduction in lipid droplet accumulation. Mechanistically, Leuc exerted its protective effects through two major pathways: (1) By activating the NRF2 antioxidant axis, Leuc attenuated oxidative stress-induced mitochondrial dysfunction and restored fatty acid &#x3b2;-oxidation capacity; (2) Through direct allosteric binding to AMPK, Leuc suppressed fatty acid uptake, inhibited lipogenesis, and enhanced mitochondrial fatty acid transport.<br><br>CONCLUSION: These coordinated mechanisms reestablished hepatic lipid homeostasis, positioning Leuc as a promising dual-target natural compound for MASLD intervention through simultaneous AMPK/NRF2 activation.},
}
@article {pmid41364937,
year = {2025},
author = {Patrick-Krueger, KM and Pavlidis, I and Contreras-Vidal, JL},
title = {The state of science convergence in implantable brain-computer interface clinical trials.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae2a6f},
pmid = {41364937},
issn = {1741-2552},
abstract = {Advances in implantable brain-computer interfaces (iBCI) have rapidly accelerated in the last decade that promises to improve the quality of life of patients with communications, sensory, and motor control disabilities (CSM). In this Perspective, we quantify the extent and nature of scientific convergence across 21 research groups conducting iBCI clinical trials worldwide. Using Medical Subject Headers (MeSH) and Classification of Instructional Programs (CIP) taxonomies, we analyze topical and disciplinary integration within 161 publications from 1998-2023 to assess how deeply team composition aligns with research themes and translational impact. Our findings indicate uneven patterns of convergence, with many teams combining engineering and clinical expertise yet omitting ethical, legal, and social dimensions. This represents what we term short-cut convergence. We propose an operational definition of this phenomenon and identify practical steps for researchers and funders to strengthen full convergence to accelerate iBCI translation and implementation.},
}
@article {pmid41363017,
year = {2025},
author = {Rayson, H and Moreau, Q and Gailhard, S and Szul, MJ and Bonaiuto, JJ},
title = {Beta Burst Waveform Diversity: A Window onto Cortical Computation.},
journal = {The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry},
volume = {},
number = {},
pages = {10738584251390779},
doi = {10.1177/10738584251390779},
pmid = {41363017},
issn = {1089-4098},
abstract = {Neural activity in the beta band is increasingly recognized to occur not as sustained oscillations but as transient burst-like events. These beta bursts are diverse in shape, timing, and spatial distribution, but their precise functional significance remains unclear. Here, we review emerging evidence on beta burst properties, functional roles, and developmental trajectories and propose a new framework in which beta bursts are not homogeneous events but reflect distinct patterns of synaptic input from different brain regions targeting different cortical layers. We argue that burst waveform shape carries mechanistic and computational significance, offering a window into the dynamic integration of specific combinations of cortical and subcortical signals. This perspective repositions beta bursts as transient computational primitives, rather than generic inhibitory signals or averaged rhythms. We conclude by outlining key open questions and research priorities, including the need for improved detection methods, investigation into developmental and clinical biomarkers, and translational applications in neuromodulation and brain-computer interfaces.},
}
@article {pmid41362972,
year = {2025},
author = {Labor, VV and Mokienko, OA and Cherkasova, AN and Ikonnikova, ES and Lyukmanov, RK and Suponeva, NA},
title = {[Movement image training and brain-computer interface in cognitive rehabilitation].},
journal = {Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova},
volume = {125},
number = {11},
pages = {27-35},
doi = {10.17116/jnevro202512511127},
pmid = {41362972},
issn = {1997-7298},
mesh = {Humans ; *Brain-Computer Interfaces ; Parkinson Disease/rehabilitation ; Cognition ; Movement ; Stroke Rehabilitation ; Multiple Sclerosis/rehabilitation ; *Neurological Rehabilitation/methods ; Cognitive Training ; },
abstract = {The paper provides an overview of studies on the use of movement image training and brain-computer interfaces (BCIs) for cognitive rehabilitation in patients with neurological diseases. Based on the analysis of studies published from 2004 to 2025, the effectiveness of these methods in recovering cognitive functions in patients with stroke (13 studies), Parkinson's disease (4 studies), and multiple sclerosis (2 studies) was evaluated. Most studies demonstrated a positive effect of movement image training on the cognitive functions of patients with neurological diseases and moderate cognitive deficits. The effectiveness of this approach is comparable to that of specialized cognitive training. In studies using BCI to control movement image training, an improvement in cognitive functions was also reported. Some studies showed a positive correlation between changes in cognitive indicators and the degree of motor recovery. In groups of patients with normal or near-normal baseline MoCA scores, no significant improvement in cognitive function was reported after a training course. The heterogeneity of the analyzed studies makes direct comparison between them difficult. The results of the analysis of published studies indicate the prospect of using the movement image training with BCI control in the cognitive rehabilitation of neurological patients. However, well-designed randomized controlled trials are necessary to study the mechanisms of the ideomotor training effects on cognitive functions and to develop standardized protocols for assessing their effectiveness.},
}
@article {pmid41362341,
year = {2025},
author = {Simistira Liwicki, F and Saini, R and Chakladar, DD and Rakesh, S and Gupta, V and Liwicki, M and Eriksson, J},
title = {Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data during an inner speech task.},
journal = {Data in brief},
volume = {63},
number = {},
pages = {112258},
pmid = {41362341},
issn = {2352-3409},
abstract = {Inner speech, or covert speech, refers to the internal generation of language without overt articulation. Decoding inner speech has significant implications for brain-computer interfaces (BCIs), particularly for assistive communication in individuals with speech and motor impairments. To facilitate research in this area, we introduce a publicly available dataset comprising simultaneously recorded electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data during inner speech production. Data were collected from three healthy, right-handed participants performing an inner speech task. The task involved silent repetition of visually presented words belonging to either a social or numerical category. The experiment consisted of 40 trials per word, with eight unique words and starts with a fixation period of two seconds. Stimuli were displayed for two seconds at the beginning of each session, followed by a 12-second rest period to allow hemodynamic responses to return to baseline. Participants were instructed to remain still and avoid movements to minimize artifacts. EEG was recorded using a 64-channel MR-compatible cap (BrainCap MR, EasyCap GmbH) at a 5 kHz sampling rate. Electrocardiogram (ECG) signals were simultaneously acquired using an additional electrode placed on the trapezius muscle to facilitate cardioballistic artifact correction. Gradient and cardioballistic artifacts were corrected using BrainVision Analyzer software. Functional MRI data were acquired using a 3T scanner with a 48-channel headcoil, and an echo-planar imaging (EPI) sequence optimized for whole-brain coverage. The repetition time (TR) was 2 s. High-resolution anatomical T1-weighted images were also acquired for structural reference. The dataset is publicly available in the OpenNeuro repository. The aim of this dataset is to provide a resource for studying inner speech processing, multimodal neuroimaging, EEG-fMRI fusion techniques, and BCI-driven speech prosthesis development.},
}
@article {pmid41361966,
year = {2025},
author = {Song, Y and An, S and Choi, Y and Shin, R and Song, KH and Doh, J},
title = {Jammed Foamed Microgel-based Bioprinting for Ex Vivo Reconstruction of 3D T Cell-Cancer Cell Interactions.},
journal = {Advanced healthcare materials},
volume = {},
number = {},
pages = {e05696},
doi = {10.1002/adhm.202505696},
pmid = {41361966},
issn = {2192-2659},
support = {//Korea Health Industry Development Institute (KHIDI)/ ; RS-2023-00208359//National Research Foundation of Korea (NRF)/ ; RS-2023-00218543//National Research Foundation of Korea (NRF)/ ; RS-2023-00217061//National Research Foundation of Korea (NRF)/ ; RS-2024-00512240//Korea Health Industry Development Institute/Republic of Korea ; RS-2024-00406325//Korea Health Industry Development Institute/Republic of Korea ; },
abstract = {T cells in solid tumors migrate through the tumor tissues to find cancer cells and eliminate them. Ex vivo reconstruction of T cell-cancer cell interactions is key for the rational design of cancer immunotherapy. Porous 3D structures essential for optimal T cell motility are challenging to fabricate by 3D printing using conventional bioinks: at high ink concentration, rheological properties are suitable for printing, but T cells are trapped in dense polymer networks, and vice versa. To overcome this limitation, a new bioink based on foamed microgels (FMGs) that facilitates T cell motility is devised, without compromising printability in extrusion 3D printing. Norbornene-functionalized gelatin is synthesized, foamed, cross-linked, and ground to generate FMGs. The FMGs exhibited rougher surfaces than non-foamed microgels (NFMGs), and generated finer pores when jammed. T cell motility is significantly higher in JFMGs than in JNFMGs. Using the JFMG, two compartment structures containing T cells in one compartment and cancer cells in the other compartment are printed. T cells rapidly migrated to the cancer cell compartment and killed the cancer cells. This new bioink enables the ex vivo fabrication of various tissues where immune cell migration is critical.},
}
@article {pmid41361599,
year = {2025},
author = {Wilson, GH and Stein, EA and Kamdar, F and Avansino, DT and Pun, TK and Gross, R and Hosman, T and Singer-Clark, T and Kapitonava, A and Hochberg, LR and Simeral, JD and Shenoy, KV and Druckmann, S and Henderson, JM and Willett, FR},
title = {Long-term unsupervised recalibration of cursor-based intracortical brain-computer interfaces using a hidden Markov model.},
journal = {Nature biomedical engineering},
volume = {},
number = {},
pages = {},
pmid = {41361599},
issn = {2157-846X},
support = {U01DC017844//U.S. Department of Health &amp; Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; U01DC017844//U.S. Department of Health &amp; Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; U01-NS098968//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; U01-NS098968//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; R01-EB028171//U.S. Department of Health &amp; Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB)/ ; 542969//Simons Foundation/ ; },
abstract = {Intracortical brain-computer interfaces (iBCIs) require frequent recalibration to maintain robust performance due to changes in neural activity that accumulate over time, which result in periods when users cannot use their device. Here we introduce a hidden Markov model to infer which targets users are moving towards during iBCI use and we retrain the system using these inferred targets, enabling unsupervised adaptation to changing neural activity. Our approach outperforms distribution alignment methods in large-scale, closed-loop simulations over two months, as well as in a closed loop with a human iBCI user over one month. Leveraging an offline dataset spanning five years of iBCI recordings, we show how target inference recalibration methods appear capable of long-term unsupervised recalibration, whereas recently proposed data-distribution-matching approaches appear to accumulate compounding errors over time. We show offline that our approach performs well on freeform datasets of a person using a home computer with an iBCI. Our results demonstrate the use of task structure to bootstrap a noisy decoder into a highly performant one, thereby overcoming one of the major barriers to clinically translating BCIs.},
}
@article {pmid41361479,
year = {2025},
author = {Vermehren, M and Colucci, A and Angerhöfer, C and Peekhaus, N and Kim, WS and Chang, WK and Kim, H and Hömberg, V and Paik, NJ and Soekadar, SR},
title = {The Berlin bimanual test for stroke survivors (BeBiT-S): evaluating exoskeleton-assisted bimanual motor function after stroke.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12984-025-01822-6},
pmid = {41361479},
issn = {1743-0003},
abstract = {BACKGROUND: Brain/neural hand exoskeletons (B/NHEs) can restore motor function after severe stroke, enabling bimanual tasks critical for various activities of daily living. Yet, reliable clinical tests for assessing bimanual function compatible with B/NHEs are lacking. Here, we introduce the Berlin Bimanual Test for Stroke (BeBiT-S), a 10-task assessment focused on everyday bimanual activities, and evaluate its psychometric properties as well as compatibility with assistive technologies such as B/NHEs.<br><br>METHODS: BeBiT-S tasks were selected based on their relevance to daily activities, representation of various grasp types, and compatibility with current (neuro-)prosthetic devices. A scoring system was developed to assess key aspects of bimanual function-including reaching, grasping, stabilizing, manipulating, and lifting-based on video recordings of task performance. The BeBiT-S was administered without support of assistive technology (unassisted condition) to 24 stroke survivors (mean age&#x2009;=&#x2009;56.5&#xa0;years; 9 female) with upper-limb hemiparesis. We evaluated interrater reliability through the intraclass correlation coefficient (ICC) and construct validity through correlations with the Chedoke Arm and Hand Activity Inventory (CAHAI), and Stroke Impact Scale (SIS). A subgroup of 15 stroke survivors (mean age 50.3&#xa0;years, 5 female) completed a second session supported by a B/NHE (B/NHE-assisted condition) to assess the BeBiT-S' sensitivity to change related to B/NHE-application.<br><br>RESULTS: The BeBiT-S demonstrated high interrater reliability in both the unassisted (ICC&#x2009;=&#x2009;0.985, P&#x2009;<&#x2009;.001) and B/NHE-assisted (ICC&#x2009;=&#x2009;0.862, P&#x2009;<&#x2009;.001) conditions. Unassisted BeBiT-S scores correlated with the CAHAI-8 (r(22)&#x2009;=&#x2009;0.95, P&#x2009;<&#x2009;.001) and the SIS subscales "strength" (r(20)&#x2009;=&#x2009;0.53, P&#x2009;=&#x2009;.012) and "hand function" (r(20)&#x2009;=&#x2009;0.50, P&#x2009;=&#x2009;.018), indicating construct validity. BeBiT-S scores improved significantly with B/NHE assistance (Mdn&#x2009;=&#x2009;60, P&#x2009;<&#x2009;.05), compared to when no assistance was provided (Mdn&#x2009;=&#x2009;38, P&#x2009;<&#x2009;.05), demonstrating the test's sensitivity to change following the application of a B/NHE.<br><br>CONCLUSIONS: The findings support that the BeBiT-S is a reliable and valid tool for evaluating bimanual task performance in stroke survivors and is compatible with the use of assistive technology such as B/NHEs. Trial registration NCT04440709, submitted June 18th, 2020.},
}
@article {pmid41361196,
year = {2025},
author = {Zhao, R and Bai, Y and Zhang, S and Zhu, J and Liu, H and Ni, G},
title = {An open dataset of multidimensional signals based on different speech patterns in pragmatic Mandarin.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {1934},
pmid = {41361196},
issn = {2052-4463},
mesh = {Humans ; *Speech ; Electroencephalography ; *Language ; Brain-Computer Interfaces ; Electromyography ; China ; Brain/physiology ; },
abstract = {Speech is essential for human communication, but millions of people lose the ability to speak due to conditions such as amyotrophic lateral sclerosis (ALS) or stroke. Assistive technologies like brain-computer interfaces (BCIs), can convert brain signals into speech. However, these technologies still face challenges in decoding accuracy. This issue is especially challenging for tonal languages like Mandarin Chinese. Furthermore, most existing speech datasets are based on Indo-European languages, which hinders our understanding of how tonal information is encoded in the brain. To address this, we introduce a comprehensive open dataset, which includes multimodal signals from 30 subjects using Mandarin Chinese across overt, silent, and imagined speech modes, covering electroencephalogram (EEG), surface electromyogram (sEMG), and speech recordings. This dataset lays a valuable groundwork for exploring the neural encoding of tonal languages, investigating tone-related brain dynamics, and improving assistive communication strategies. It supports cross-linguistic speech processing research and contributes to data-driven neural speech decoding technology innovations.},
}
@article {pmid41361138,
year = {2025},
author = {Wu, M and Yang, Y and Zhang, J and Efimov, AI and Li, X and Zhang, K and Wang, Y and Bodkin, KL and Riahi, M and Gu, J and Wang, G and Kim, M and Zeng, L and Liu, J and Yoon, LH and Zhang, H and Freda, SN and Lee, M and Kang, J and Ciatti, JL and Ting, K and Cheng, S and Zhang, X and Sun, H and Zhang, W and Zhang, Y and Banks, A and Good, CH and Cox, JM and Pinto, L and Vázquez-Guardado, A and Huang, Y and Kozorovitskiy, Y and Rogers, JA},
title = {Patterned wireless transcranial optogenetics generates artificial perception.},
journal = {Nature neuroscience},
volume = {},
number = {},
pages = {},
pmid = {41361138},
issn = {1546-1726},
support = {U01NS131406//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; R01NS107539//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; R01MH117111//U.S. Department of Health &amp; Human Services | NIH | National Institute of Mental Health (NIMH)/ ; 2T32MH06756//U.S. Department of Health &amp; Human Services | NIH | National Institute of Mental Health (NIMH)/ ; R00MH120047//U.S. Department of Health &amp; Human Services | NIH | National Institute of Mental Health (NIMH)/ ; SP-2022-19027//Alfred P. Sloan Foundation/ ; 872599SPI//Simons Foundation/ ; },
abstract = {Synthesizing perceivable artificial neural inputs independent of typical sensory channels remains a fundamental challenge in developing next-generation brain-machine interfaces. Establishing a minimally invasive, wirelessly effective and miniaturized platform with long-term stability is crucial for creating research methods and clinically meaningful biointerfaces capable of mediating artificial perceptual feedback. Here we demonstrate a miniaturized, fully implantable transcranial optogenetic neural stimulator designed to generate artificial perceptions by patterning large cortical ensembles wirelessly in real time. Experimentally validated numerical simulations characterized light and heat propagation, whereas neuronal responses were assessed by in vivo electrophysiology and molecular methods. Cue discrimination during operant learning demonstrated the wireless genesis of artificial percepts sensed by mice, where spatial distance across large cortical networks and sequential order-based analyses of discrimination predicted performance. These conceptual and technical advances expand understanding of artificially patterned neural activity and its perception by the brain to guide the evolution of next-generation all-optical brain-machine communication.},
}
@article {pmid41360829,
year = {2025},
author = {Matran-Fernandez, A and Halder, S},
title = {An EEG dataset to study neural correlates of audiovisual long-term memory retrieval.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {1933},
pmid = {41360829},
issn = {2052-4463},
mesh = {Humans ; *Electroencephalography ; *Memory, Long-Term ; *Mental Recall ; },
abstract = {Memory retrieval is a fundamental cognitive process that plays a critical role in our lives. Studying the neural correlates of this process has significant implications for numerous fields, such as education and health care. Advances in neuroimaging technologies have facilitated the use of neural data, such as electroencephalography (EEG), to decode cognitive states associated with memory tasks. However, most memory research is still conducted using simple stimuli, such as lists of words, and it is unclear how much the discoveries made with such stimuli generalise to more naturalistic scenarios. We introduce a dataset of EEG signals from 27 participants acquired while they watched 10-second long clips of movies (some of which they had previously seen), together with annotations that reflect whether they recognised or remembered the scenes and the time points of recognition. This dataset allows the study of neural correlates of long-term memory recall in a naturalistic task.},
}
@article {pmid41360823,
year = {2025},
author = {Ma, X and Jiang, Y and Jiang, N},
title = {3M-CPSEED, An EEG-based Dataset for Chinese Pinyin Production in Overt, Mouthed, and Imagined Speech.},
journal = {Scientific data},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41597-025-06346-1},
pmid = {41360823},
issn = {2052-4463},
abstract = {Speech brain-computer interfaces (BCIs) enable communication with the external world by decoding neural signals. However, language function as a higher-order brain function, the neural mechanisms underlying speech production remain incompletely understood. Currently most existing Chinese EEG datasets use sentences as stimuli, overlooking that Pinyin constitutes the phonetic foundation of Chinese characters, which limits research on decoding individual Chinese character components. Moreover, most datasets employ only one speech production paradigm, preventing exploration of the brain's diverse speech production modes. This study aims to construct the 3M-CPSEED Chinese Pinyin dataset for exploring neural activity during three distinct speech modes (overt speech, silently articulated speech, imagined speech)of syllables from distinct articulatory positions. The dataset comprises EEG recordings from 20 participants completing four experimental blocks within one day, yielding 1,800 validated trials. 3M-CPSEED holds significant implications for speech neurophysiology research, not only facilitating exploration of neural activity differences across pinyin articulations but also enabling robust transfer learning studies for other alphabetic languages.},
}
@article {pmid41360014,
year = {2025},
author = {Xiong, H and Chang, S and Liu, J},
title = {Dual-Channel TRCA-net based on cross-subject positive transfer for SSVEP-BCI.},
journal = {Biomedical physics &amp; engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/ae291c},
pmid = {41360014},
issn = {2057-1976},
abstract = {To enhance the decoding accuracy and information transfer rate of steady-state visual evoked potential-based brain-computer interface (SSVEP-BCI) systems and to reduce inter-subject variability for broader SSVEP-BCI applications, a dual-channel TRCA-net (DC-TRCA-net) method is proposed, based on cross-subject positive transfer. The proposed method incorporates an innovative Transfer-Accuracy-based Subject Selection (T-ASS) strategy and a deep learning network integrated with the SSVEP Domain Adaptation Network (SSVEP-DAN) to enhance SSVEP-BCI decoding performance. The T-ASS strategy constructs contribution scores by computing each subject's self-accuracy and transfer accuracy, and enables effective source subject selection while mitigating negative transfer risks. DC-TRCA-net is further developed to improve model generalization through cross-subject data augmentation. The effectiveness of the proposed method is validated on two large-scale public benchmark datasets. Experimental results demonstrate that DC-TRCA-net outperforms existing networks across both datasets, with particularly substantial performance gains observed in complex experimental scenarios.},
}
@article {pmid41360010,
year = {2025},
author = {Jensen, MA and Schalk, G and Ince, NF and Hermes, D and Worrell, GA and Brunner, P and Staff, NP and Miller, KJ},
title = {sEEG-Based brain-computer interfacing in a large adult and pediatric cohort.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae2955},
pmid = {41360010},
issn = {1741-2552},
abstract = {OBJECTIVE: Stereoelectroencephalography (sEEG) is a mesoscale intracranial monitoring technique that records from the brain volumetrically with depth electrodes. sEEG is typically used for monitoring of epileptic foci, but can also serve as a useful tool to study distributed brain dynamics. Herein, we detail the implementation of sEEG-based brain-computer interfacing (BCI) across a diverse and large patient cohort.<br><br>APPROACH: Across 27 subjects (15 female, 31 total feedback experiments), we identified channels with increases in broadband power during hand, tongue, or foot movements using a simple block-design screening task. Subsequently, broadband power in these channels was coupled to continuous movement of a cursor on a screen during both overt movement and kinesthetic imagery.<br><br>MAIN RESULTS: 26 subjects (29 out of 31 feedback conditions) established successful control, defined as more than 80 percent accuracy, during the overt movement BCI task, while only 12 (of the same 31 conditions) achieved control during the motor imagery BCI task. In successful imagery BCI, broadband power in the reinforced control channel(s) in the two target conditions separated into distinct subpopulations. Outside of the control channel(s), we demonstrate that imagery BCI engages unique subnetworks of the motor system compared to cued movement or kinesthetic imagery alone.<br><br>SIGNIFICANCE: Pericentral sEEG-based motor BCI utilizing overt movement and kinesthetic imagery is robust across a diverse patient cohort with inconsistent accuracy during imagined movement. Cued movement, kinesthetic imagery, and feedback engage the motor network uniquely, providing the opportunity to understand the network dynamics underlying BCI control and improve future BCIs.},
}
@article {pmid41360009,
year = {2025},
author = {Faisal, M and Sahoo, S and Hazarika, J},
title = {STeCANet: spatio-temporal cross attention network for brain computer interface systems using EEG-fNIRS signals.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae2954},
pmid = {41360009},
issn = {1741-2552},
abstract = {Background- Multimodal neuroimaging fusion has shown promise in enhancing brain-computer interface (BCI) performance by capturing complementary neural dynamics. However, most existing fusion frameworks inadequately model the temporal asynchrony and adaptive fusion between EEG and fNIRS, thereby limiting their ability to generalize across sessions and subjects. Objective- This work aims to develop an adaptive fusion framework that effectively aligns and integrates EEG and fNIRS representations to improve cross-session and cross-subject generalization in BCI applications. Approach- To address this, we propose STeCANet, a novel Spatiotemporal Cross-Attention Network that integrates EEG and fNIRS signals through hierarchical attention-based alignment. The model leverages fNIRS-guided spatial attention, EEG-fNIRS temporal alignment, adaptive fusion, and adversarial training to ensure robust cross-modal interaction and spatiotemporal consistency. Main results- Evaluations across three cognitive paradigms, namely motor imagery (MI), mental arithmetic (MA), and word generation (WG), demonstrate that STeCANet significantly outperforms unimodal and recent multimodal baselines under both session-independent and subject-independent settings. Ablation studies confirm the contribution of each sub-module and loss function, including the domain adaptation component, in boosting classification accuracy and robustness. Significance- These results suggest that STeCANet offers a robust and interpretable solution for next-generation BCI applications.},
}
@article {pmid41359836,
year = {2025},
author = {Wang, Y and Liu, F and Shan, Q and Wang, X and Liu, W and Chen, X and Teng, C and Lv, Y and Gu, X and Wang, X and Yu, B},
title = {Functional recovery induced by KCC2-enabled relay pathways in completely injured spinal cords in adult rats.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {122},
number = {50},
pages = {e2421823122},
doi = {10.1073/pnas.2421823122},
pmid = {41359836},
issn = {1091-6490},
mesh = {Animals ; *Spinal Cord Injuries/physiopathology/therapy/metabolism ; *Symporters/metabolism/agonists ; K Cl- Cotransporters ; Rats ; *Recovery of Function/drug effects/physiology ; Neural Stem Cells/transplantation/metabolism ; Rats, Sprague-Dawley ; Female ; Nerve Regeneration/drug effects/physiology ; Axons/physiology ; Locomotion/drug effects ; Insulin-Like Growth Factor I/metabolism ; Osteopontin/metabolism ; },
abstract = {Despite tremendous progress in promoting endogenous axon regeneration and engineering relay pathways by cell transplantation, the obtained functional recovery is still limited. We reason that these regenerated connections might not be able to integrate into the functional circuits in injured spinal cord. In this study, we tested whether modulating the neuronal excitability by pharmacological potassium-chloride cotransporter (KCC2) activation could enhance the functional outcomes of these regenerative treatments in a complete spinal cord injury (SCI) in adult rats. We found that while osteopontin/insulin-like growth factor 1 overexpression (to enhance axon regeneration) or neural stem cell (NSC) transplantation (to build a relay) alone failed to restore the interrupted spinal circuitry, the double treatment facilitated the integration of NSCs into the host spinal network, significantly promoting axonal regeneration and synapse formation. Behavioral assessments demonstrated that the addition of CLP290, a KCC2 agonist, to the combined treatment markedly improved hindlimb locomotion, as evidenced by higher Basso, Beattie and Bresnahan (BBB) scores and enhanced joint oscillation in fine locomotion analysis. Consistently, electrophysiological evaluations indicated partial restoration of electrical transmission through the reconstructed spinal network. Our findings highlight the synergistic effects of KCC2-mediated neuronal modulation on promoting functional recovery after complete SCI.},
}
@article {pmid41359725,
year = {2025},
author = {Sun, Y and Chahine, D and Wen, Q and Liu, T and Li, X and Yuan, Y and Calamante, F and Lv, J},
title = {Voxel-Level Brain States Prediction Using Swin Transformer.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {12},
pages = {8719-8726},
doi = {10.1109/JBHI.2025.3613793},
pmid = {41359725},
issn = {2168-2208},
mesh = {Humans ; *Magnetic Resonance Imaging/methods ; *Brain/diagnostic imaging/physiology ; *Connectome/methods ; Male ; Female ; Adult ; *Image Processing, Computer-Assisted/methods ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {Understanding brain dynamics is important for neuroscience and mental health. Functional magnetic resonance imaging (fMRI) enables the measurement of neural activities through blood-oxygen-level-dependent (BOLD) signals, which represent brain states. In this study, we aim to predict future human resting brain states with fMRI. Due to the 3D voxel-wise spatial organization and temporal dependencies of the fMRI data, we propose a novel architecture which employs a 4D Shifted Window (Swin) Transformer as encoder to efficiently learn spatio-temporal information and a convolutional decoder to enable brain state prediction at the same spatial and temporal resolution as the input fMRI data. We used 100 unrelated subjects from the Human Connectome Project (HCP) for model training and testing. Our novel model has shown high accuracy when predicting 7.2s resting-state brain activities based on the prior 23.04s fMRI time series. The predicted brain states highly resemble BOLD contrast and dynamics. This work shows promising evidence that the spatiotemporal organization of the human brain can be learned by a Swin Transformer model, at high resolution, which provides a potential for reducing the fMRI scan time and the development of brain-computer interfaces in the future.},
}
@article {pmid41358304,
year = {2025},
author = {Hsieh, TH and Song, H and Shallal, C and Levine, DV and Yeon, SH and Qiao, J and Shu, T and Carty, MJ and McCullough, J and Herr, HM},
title = {Continuous neural control of a 2-DOF ankle-foot prosthesis enables dynamic obstacle maneuvers after transtibial amputation.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.11.25.25340897},
pmid = {41358304},
abstract = {UNLABELLED: Bionic reconstruction techniques that employ surgical neuroprosthetic interfaces, biomimetic control systems, and powered mechatronics have enabled versatile and biomimetic legged gait without reliance on intrinsic gait controllers. However, relative emphasis has been placed on the emulation of sagittal plane biomechanics while neglecting to provide control over frontal plane mechanics critical for terrain adaptation. Here, we present a 2-degree-of-freedom (DOF) bionic reconstruction at the transtibial amputation level that enables continuous neural control of both sagittal and frontal ankle and subtalar joint mechanics. To demonstrate its capabilities in a case study design, we integrated a 2-DOF robotic ankle-foot device via surface electromyographic electrodes to an individual provisioned with a surgical neuroprosthetic interface that augments residual muscle afferents. The subject was able to neurally control both degrees of freedom to regain nominal gait mechanics during both level-ground walking and continuous cross-slope navigation. Furthermore, the subject strategically traversed an obstacle course by dynamically hopping between a series of discrete cross-slope blocks, adapting to the slopes, and responding to rapid foot slips. These preliminary findings suggest that bionic reconstruction techniques can restore continuous neural control over multi-DOF prostheses to achieve agile locomotion over complex terrain.<br><br>ONE-SENTENCE SUMMARY: A multi-DOF ankle-foot prosthesis under continuous neural control enables agile locomotion over complex terrain.},
}
@article {pmid41358277,
year = {2025},
author = {Baniasad, A and Chao, S and Nguyen, JA and Tian, E and Modongo, C and Minin, VM and Sebastian, JN and Shin, SS},
title = {HIV Remains a Risk Factor for Unfavorable Tuberculosis Treatment Outcomes in the Era of Universal Access to Antiretroviral Therapy in Botswana.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.11.26.25340699},
pmid = {41358277},
abstract = {Botswana implemented its universal "Treat All" antiretroviral therapy (ART) policy in 2016, expanding treatment eligibility to all people living with HIV (PLHIV). HIV has been known to be a leading risk factor for tuberculosis (TB) and poor TB treatment outcomes. The primary goal of this study is to assess whether HIV infection and HIV-associated immunosuppression remain risk factors for unfavorable TB treatment outcomes in the Post-Treat All era. We analyzed 636 TB patients treated in Gaborone (2017-2023), of whom 54.4% were HIV-positive. Unfavorable outcomes (death, failure, or loss to follow-up) occurred in 19.7% of HIV-positive and 8.5% of HIV-negative patients. We used logistic regression to estimate unadjusted and covariate-adjusted associations between TB treatment outcome and HIV status and between TB treatment outcome and CD4+ T-cell count. HIV-positive patients had 2.5-fold higher odds of unfavorable outcomes compared with HIV-negative patients [adjusted OR: 2.51, 95% CI: (1.48, 4.38)], controlling for age, sex, TB history, distance to clinic, substance use, and occupational status. PLHIV with CD4+ T-cell < 200 cells/ µ L was associated with approximately three-fold higher odds of unfavorable outcomes compared with HIV-negative participants [OR: 3.12, 95% CI: (1.65, 5.97)]. The secondary goal was to test whether the HIV effect changed following Treat All implementation. We combined the data from 2017-2023 with a Pre-Treat All cohort (2012-2016, n= 233, HIV prevalence 60.8%) and fit a frequentist logistic regression and Bayesian mixed-effects models with an interaction term that allows treatment era (Pre- vs. Post-Treat All) to modify the effect of HIV on TB treatment outcome. The estimated change in the HIV effect was uncertain [relative OR: 0.41; 95% CI: (0.11, 1.55)]. Combining the two Botswana data sets with 12 Pre- and Post-Treat All studies from neighboring Ethiopia showed that the pooled effect of HIV infection on unfavorable TB outcome has increased in the Post-Treat All period [relative OR: 2.39; 95% BCI: (1.36, 3.34)].},
}
@article {pmid41357676,
year = {2025},
author = {Akhoundi, A and Yan, P and Landbrug, Y and Hays, M and Murmann, B and Chichilnisky, EJ and Muratore, DG},
title = {A Scalable 1024-Channel Ultra-Low-Power Spike Sorting Chip with Event-Driven Detection and Spatial Clustering.},
journal = {IEEE journal of solid-state circuits},
volume = {60},
number = {11},
pages = {3985-4001},
pmid = {41357676},
issn = {0018-9200},
abstract = {This paper presents a 1024-channel ultra-low-power spike sorting chip featuring event-driven spike detection and spatial clustering for large-scale neural recording. To address power and scalability constraints in brain-computer interfaces, the design integrates a compressive ADC with a two-stage spike detector that significantly reduces memory and processing activity. Spatial features derived from high-density microelectrode array (MEA) enhance cluster separability, enabling robust performance even under neural signal distortion or probe drift, particularly when recordings are obtained using planar MEAs. A modified self-organizing map algorithm clusters spikes in the spatial domain with minimal memory access, supporting on-chip training and real-time operation with low latency. Fabricated in 40 nm CMOS, the chip achieves 0.00029 mm[2]/channel area and 74 nW/channel power consumption, with over 1000× data compression. Performance is validated across synthetic and ex vivo datasets containing up to 500 neurons, demonstrating competitive accuracy and robust drift tracking compared to state-of-the-art solutions with much lower data bandwidth, processing, and power demands.},
}
@article {pmid41356598,
year = {2025},
author = {Chen, S and Xie, N and Tang, Y and Ji, Y and He, Z and Wang, Y and Huang, X and Fu, J and Ge, M and Liu, Q and Li, M and Xiao, Q and Xu, Y and Wang, J and Jia, J and Xu, S},
title = {Long-Term Brain-Computer Interface Functional Electrical Stimulation Enhances Neuroplasticity and Functional Recovery in Elderly Stroke: A 4.5-Year Longitudinal Study Integrating Electroencephalography Biomarkers and Clinical Assessments.},
journal = {Research (Washington, D.C.)},
volume = {8},
number = {},
pages = {0984},
pmid = {41356598},
issn = {2639-5274},
abstract = {Stroke-induced motor and cognitive impairments substantially reduce the quality of life in elderly populations, driving the need for rehabilitation strategies that integrate neural plasticity and functional recovery. In this 4.5-year longitudinal study, we evaluated the efficacy of brain-computer interface combined with functional electrical stimulation (BCI-FES) versus FES only and conventional care (control) in 100 stroke survivors (60 to 90 years; 4,172 total screened, with 24 chronic-stage patients [>1 year post-onset] completing long-term follow-up). We integrated clinical metrics (Fugl-Meyer assessment [FMA], modified Barthel index [MBI], and Montreal Cognitive Assessment [MoCA]) with electroencephalography-based neurophysiological profiling to dissect recovery mechanisms. BCI-FES yielded superior and sustained improvements across all domains: motor function (FMA Δ = 4.5 ± 1.2 points, Cohen's d = 1.2) versus FES (Δ = 1.7 ± 0.8, d = 0.4) and control (Δ = 0.9 ± 0.6, d = 0.2), functional independence (MBI Δ = 5.4 ± 1.5, d = 1.1) exceeding FES (Δ = 2.2 ± 1.1, d = 0.4) and control (Δ = 1.3 ± 0.5, d = 0.5), and cognitive function (MoCA Δ = 1.6 ± 0.5, d = 0.8 at 4 months), although cognitive gains declined to near baseline by 4.5 years. Hemorrhagic stroke patients showed exceptional BCI-FES responses, while ischemic patients exhibited higher variability. Neurophysiologically, BCI-FES induced theta (Cz and C4) and alpha (FC3 and CP3) power increases, with theta power at Cz strongly predicting FMA gains (r = 0.68), and enhanced theta/alpha band functional connectivity (clustering coefficient +22%, local efficiency +18%, and small-world index +15%). Predictive modeling identified that an optimal treatment window (3 to 12 months post-onset with 10 to 15 weeks of therapy) maximizes recovery via peak neuroplasticity, and a responder profile (stroke duration <23 months) includes patients with residual plasticity (age <70, baseline MBI >40), predicting 76% of favorable outcomes. These findings establish BCI-FES as a transformative rehabilitation tool, driving dual-phase recovery via early cortical plasticity and sustained network coherence while highlighting the need for age-tailored cognitive maintenance strategies. This work redefines precision stroke care by merging clinical outcomes with mechanistic insights, positioning BCI-FES as the standard of care for diverse stroke subtypes.},
}
@article {pmid41356332,
year = {2025},
author = {Coutray, K and Barbel, W and Groth, Z and LaViola, JJ},
title = {NeuroGaze: a hybrid EEG and eye-tracking brain-computer interface for hands-free interaction in virtual reality.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1695446},
pmid = {41356332},
issn = {1662-5161},
abstract = {Brain-Computer Interfaces (BCIs) have traditionally been studied in clinical and laboratory contexts, but the rise of consumer-grade devices now allows exploration of their use in daily activities. Virtual reality (VR) provides a particularly relevant domain, where existing input methods often force trade-offs between speed, accuracy, and physical effort. This study introduces NeuroGaze, a hybrid interface combining electroencephalography (EEG) with eye tracking to enable hands-free interaction in immersive VR. Twenty participants completed a 360° cube-selection task using three different input methods: VR controllers, gaze combined with a pinch gesture, and NeuroGaze. Performance was measured by task completion time and error rate, while workload was evaluated using the NASA Task Load Index (NASA-TLX). NeuroGaze successfully supported target selection with off-the-shelf hardware, producing fewer errors than the alternative methods but requiring longer completion times, reflecting a classic speed-accuracy tradeoff. Workload analysis indicated reduced physical demand for NeuroGaze compared to controllers, though overall ratings and user preferences were mixed. While the differing confirmation pipelines limit direct comparison of throughput metrics, NeuroGaze is positioned as a feasibility study illustrating trade-offs between speed, accuracy, and accessibility. It highlights the potential of consumer-grade BCIs for long-duration use and emphasizes the need for improved EEG signal processing and adaptive multimodal integration to enhance future performance.},
}
@article {pmid41356065,
year = {2025},
author = {Nair, K and Cecotti, H},
title = {Deep Learning Architectures for Code-Modulated Visual Evoked Potentials Detection.},
journal = {ArXiv},
volume = {},
number = {},
pages = {},
pmid = {41356065},
issn = {2331-8422},
abstract = {Non-invasive Brain-Computer Interfaces (BCIs) based on Code-Modulated Visual Evoked Potentials (C-VEPs) require highly robust decoding methods to address temporal variability and session-dependent noise in EEG signals. This study proposes and evaluates several deep learning architectures, including convolutional neural networks (CNNs) for 63-bit m-sequence reconstruction and classification, and Siamese networks for similarity-based decoding, alongside canonical correlation analysis (CCA) baselines. EEG data were recorded from 13 healthy adults under single-target flicker stimulation. The proposed deep models significantly outperformed traditional approaches, with distance-based decoding using Earth Mover's Distance (EMD) and constrained EMD showing greater robustness to latency variations than Euclidean and Mahalanobis metrics. Temporal data augmentation with small shifts further improved generalization across sessions. Among all models, the multi-class Siamese network achieved the best overall performance with an average accuracy of 96.89%, demonstrating the potential of data-driven deep architectures for reliable, single-trial C-VEP decoding in adaptive non-invasive BCI systems.},
}
@article {pmid41355286,
year = {2025},
author = {King, SE and Waddell, JT and Jan, I and McDonald, A and Raymond, C and Corbin, WR},
title = {Solitary drinking as a day-level risk factor for unique negative consequences among college students.},
journal = {Alcohol, clinical &amp; experimental research},
volume = {},
number = {},
pages = {},
doi = {10.1111/acer.70216},
pmid = {41355286},
issn = {2993-7175},
support = {T32-DA039772/DA/NIDA NIH HHS/United States ; },
abstract = {BACKGROUND: Solitary drinking represents a high-risk pattern of drinking across individuals but when examined within individuals, solitary moments are associated with less risk. One possibility is that solitary drinking confers risk for specific negative consequences at the day level, but aggregate measures of negative consequences mask such relations. Thus, this study examined the extent to which solitary drinking increased the likelihood of reporting specific negative consequences, controlling for drinking quantity.<br><br>METHOD: College students (N&#x2009;=&#x2009;1043; 51.8% female) completed a 30-day Timeline Followback Interview in which they reported day-level drinking context, drinking quantity, and negative consequences. A total of 7340 drinking days were reported. Two-level multilevel probit regressions with Bayesian estimation tested whether drinking context (i.e., solitary vs. social) was associated with an increased likelihood of experiencing each of eight unique negative consequences (i.e., social/interpersonal, risky behavior, blackouts, occupational, impaired control, physical dependence, self-care, and self-perception), controlling for drinking quantity.<br><br>RESULTS: When controlling for drinking quantity, solitary (vs. social) drinking days were associated with a higher likelihood of occupational consequences [&#x3b2;&#x2009;=&#x2009;0.05, 95% BCI&#x2009;=&#x2009;(0.01, 0.08)] and diminished self-perception [&#x3b2;&#x2009;=&#x2009;0.06, 95% BCI&#x2009;=&#x2009;(0.03, 0.10)]. Solitary drinking days were also associated with a lower likelihood of interpersonal consequences (&#x3b2;&#x2009;=&#x2009;-0.06, 95% BCI&#x2009;=&#x2009;[-0.11, -0.03]) and blackout drinking (&#x3b2;&#x2009;=&#x2009;-0.06, 95% BCI&#x2009;=&#x2009;[-0.09, -0.03]). Person-level results suggest that those who more often drink alone experience greater blackout drinking, impaired control, dependence, occupational consequences, and diminished self-perception (all p's&#x2009;<&#x2009;0.001). When consequences were summed, solitary drinking days (vs. social) were associated with fewer negative consequences (&#x3b2;&#x2009;=&#x2009;-0.023, 95% BCI&#x2009;=&#x2009;[-0.049, -0.005]), whereas at the person level, those who more frequently drink alone experienced more negative consequences (&#x3b2;&#x2009;=&#x2009;0.10, 95% BCI&#x2009;=&#x2009;[0.04, 0.17]).<br><br>CONCLUSIONS: Results suggest that social and solitary drinking contexts confer risk for specific consequences and that risk for consequences differs if consequences are aggregated. Findings may inform future interventions by emphasizing certain protective behavioral strategies in specific drinking contexts to reduce the likelihood of negative outcomes.},
}
@article {pmid41353180,
year = {2025},
author = {Soriano-Segura, P and Ortiz, M and Polo-Hortigüela, C and Iáñez, E and Azorín, JM},
title = {Characterization of error-related potentials during the command of a lower-limb exoskeleton based on deep learning.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12984-025-01833-3},
pmid = {41353180},
issn = {1743-0003},
support = {PID2021-124111OB-C31//MICIU/AEI/10.13039/501100011033 and by ERDF, EU/ ; PRE2022-103336//MICIU/AEI/10.13039/501100011033 and by ERDF, EU/ ; },
}
@article {pmid41352637,
year = {2025},
author = {Roc, A and Kolodzienski, L and Dreyer, P and Appriou, A and Monseigne, T and Lotte, F},
title = {Evolution of users' subjective experience over three training sessions with an EEG Motor-Imagery Brain-Computer Interface (MI-BCI).},
journal = {Brain research},
volume = {},
number = {},
pages = {150085},
doi = {10.1016/j.brainres.2025.150085},
pmid = {41352637},
issn = {1872-6240},
abstract = {Motor Imagery-based Brain-Computer Interfaces (MI-BCIs) have been shown to be promising for numerous applications, including sport training and entertainment for healthy users, but also for improving or restoring functions in neurological and neuropsychiatric disorders, e.g., for motor rehabilitation post-stroke or for attention training in attention deficits. Reliable interactions with such MI-BCIs require a heavy training process for both the machine and the user. Yet, how User eXperience (UX) evolves during standard training is still largely unclear, both within and between sessions/days. Through an exploratory study, we investigated the variations of users' answers to a UX questionnaire when training with a standard left vs. right-hand MI-BCI. 24 healthy novice users engaged in 3 training sessions (with 12 runs each) on different days. Each short run was followed by six questions on screen measuring UX factors on scales from 1 to 10: mental demand, performance, mental effort, frustration, mental fatigue and anxiety. Interestingly, BCI performances did not correlate with any subjective UX measure in this study. However, a time effect was observed. Within session, the results suggested that mental demand, effort, and fatigue significantly augmented during BCI operation, and that frustration significantly fluctuated but did not differ pre- vs. post-session. Between sessions, the first session was rated significantly more challenging than the other two regarding frustration, anxiety, mental demand, mental effort and mental fatigue. This highlights the importance of conducting studies across sessions and of considering the users' mental states during BCI use, for improving UX and thus possibly BCI treatment outcome.},
}
@article {pmid41351188,
year = {2025},
author = {Wang, N and Chai, X and Song, J and He, Y and He, Q and Zhang, T and Liu, D and Li, J and Cao, T and Zhu, S and Jia, Y and Si, J and Ma, W and Yang, Y and Zhao, J},
title = {Motor Intention Quantization for Patients With Disorders of Consciousness by Multimodal BCI Combining Electroencephalography and Functional Near-Infrared Spectroscopy.},
journal = {CNS neuroscience &amp; therapeutics},
volume = {31},
number = {12},
pages = {e70679},
doi = {10.1002/cns.70679},
pmid = {41351188},
issn = {1755-5949},
support = {7232049//Natural Science Foundation of Beijing Municipality/ ; 7252004//Natural Science Foundation of Beijing Municipality/ ; 2019-I2M-5-021//CAMS Innovation Fund for Medical Sciences&#xff08;CIFMS&#xff09;/ ; Z221100002722014//International (Hong Kong, Macao, and Taiwan) Science and Technology Cooperation Project/ ; 2022GKZS0003//2022 Open Project of Key Laboratory and Engineering Technology Research Center in the Rehabilitation Field of the Ministry of Civil Affairs/ ; 2022ZD0205300//Science and Technology Innovation 2030/ ; 82371197//National Natural Science Foundation of China/ ; 82501457//National Natural Science Foundation of China/ ; 2025-PUMCH-D-004//National High Level Hospital Clinical Research Funding/ ; },
mesh = {Humans ; Spectroscopy, Near-Infrared/methods ; *Electroencephalography/methods ; Female ; Male ; *Brain-Computer Interfaces ; Middle Aged ; *Consciousness Disorders/physiopathology/diagnosis ; Adult ; *Intention ; Aged ; Young Adult ; },
abstract = {OBJECTIVE: The current application of single-modality electroencephalography (EEG) or functional near-infrared spectroscopy (fNIRS) to assess consciousness levels in patients with disorders of consciousness (DoC) has garnered significant attention. However, the diagnostic accuracy of unimodal approaches remains suboptimal. Therefore, this study aims to apply the multimodal fusion technology of EEG and fNIRS to the clinical diagnosis of DoC patients.<br><br>METHODS: Eleven patients with DoC (six with a minimally conscious state [MCS] and five with a vegetative state [VS]) were enrolled. The motor intention-based brain-computer interface (MI-BCI) paradigm was adopted. EEG and fNIRS were recorded simultaneously. The synchronous states of EEG and fNIRS were analyzed, including time-frequency analysis, event-related desynchronization (ERD), and changes in oxy-hemoglobin (HbO)/de-oxygenated (HbR)/total hemoglobin (HbT) content. A multimodal method combining EEG and fNIRS was used to classify DoC patients.<br><br>RESULTS: The machine-learning results of the MI-BCI model showed that the EEG-fNIRS multimodal approach was superior to single-modality techniques in the diagnosis of healthy controls (HC), MCS, and VS. The multimodal model achieved a mean AUC of 0.69&#x2009;&#xb1;&#x2009;0.10, significantly outperforming both unimodal EEG (0.43&#x2009;&#xb1;&#x2009;0.19; p&#x2009;<&#x2009;0.01) and standalone fNIRS (0.63&#x2009;&#xb1;&#x2009;0.10; p&#x2009;<&#x2009;0.05). The EEG_ERD index of left-handed MI-BCI significantly differentiated the MCS and VS groups. Meanwhile, for the classification tasks of HC, MCS, and VS, the importance ranking of the indicators was as follows: fNIRS_ACC, EEG_ACC, fNIRS_slope, fNIRS_centroid, EEG_ERD, fNIRS_integral, and fNIRS_mean.<br><br>CONCLUSION: The integration of multimodal MI-BCI paradigms demonstrates clinical potential in evaluating consciousness levels, while the synergistic combination of neurophysiological and hemodynamic biomarkers provides a robust framework for enhancing the precision of bedside diagnostic protocols.<br><br>TRIAL REGISTRATION: Clinical Trial Registry: ChiCTR2400085830.},
}
@article {pmid41351118,
year = {2025},
author = {Liu, Q and Zhang, X and Zhang, H and Chen, K and He, Y and Niu, J and Li, W and Chen, H and Zhang, D and Li, J and Liao, W},
title = {Same movies, different stories: aberrant brain state dynamics during naturalistic emotional stimuli in depression.},
journal = {Journal of translational medicine},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12967-025-07512-0},
pmid = {41351118},
issn = {1479-5876},
}
@article {pmid41350592,
year = {2025},
author = {Gao, X and Lin, H and Wu, X and Zhang, D},
title = {Integrating active brain-computer interfaces (aBCIs) with passive BCIs (pBCIs) under different frustration levels.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-025-30168-1},
pmid = {41350592},
issn = {2045-2322},
abstract = {The mental state of the users can significantly affect the performance of active brain-computer interfaces (aBCIs). In this work, we aim to adopt passive BCIs (pBCIs) to measure a typical mental state, frustration, which is much relevant to aBCIs. A novel paradigm has been developed that combines both aBCIs and pBCIs under different frustration levels of users. The aBCI in this work is based on classic binary motor imagery (MI). In experiments, a new strategy was implemented that uses visual feedback to induce different levels of frustration. The electroencephalography (EEG) data collected were used for both aBCIs and pBCIs. The pBCI was utilized to assess the frustration level during the aBCI tasks, and the aBCI classification models for different levels of frustration were trained. For pBCI, the filter bank common spatial pattern (FBCSP) feature extraction and support vector machine (SVM) classification were utilized to classify three (i.e., low, moderate, high) frustration levels. For aBCI, the same method (FBCSP+SVM) was used to classify left versus right MI. We also aim to improve the performance of aBCIs in such conditions, so we developed two new methods to incorporate the pBCI results to adapt three MI classifiers to the varying states of frustration. Compared to the conventional approach of directly classifying MI tasks without considering frustration, the two proposed methods increased the mean classification accuracy by 7.40% and 8.62%, respectively. (Compared with the commonly used non-emotional discrimination data, the results are improved by 4.56% and 5.87% respectively.) Within the scope of non-invasive EEG and MI-based aBCI, this study provides, to our knowledge, an initial integrated demonstration in which a frustration-level classifier (pBCI) is trained and then used to adapt MI decoding (aBCI). It should not be taken as a claim of originality beyond this context. Starting from "user subjective perception", this paper rises to the engineering level of "objective frustration recognition and classification model adaptation", and makes a contribution to the depth of EEG data analysis and methodological integrity.},
}
@article {pmid41350471,
year = {2025},
author = {Yamaguchi, T and Hashimoto, RI and Sato, H},
title = {Cortical Representation of Auditory Selective Attention in a Dichotic Listening Task: A Functional Near-Infrared Spectroscopy Study.},
journal = {Brain topography},
volume = {39},
number = {1},
pages = {8},
pmid = {41350471},
issn = {1573-6792},
mesh = {Humans ; Spectroscopy, Near-Infrared/methods ; *Attention/physiology ; Male ; Female ; Dichotic Listening Tests ; Young Adult ; *Auditory Perception/physiology ; Adult ; Acoustic Stimulation ; Brain-Computer Interfaces ; Brain Mapping ; Music ; Reading ; *Cerebral Cortex/physiology ; },
abstract = {To advance the application of functional near-infrared spectroscopy (fNIRS) in brain-computer interface (BCI) technology, we investigated cortical activation patterns associated with auditory selective attention. Using a dichotic listening paradigm, participants were presented with simultaneous music and reading sounds to the left or right ear. During fNIRS recordings, they were instructed to selectively attend to the sound attribute (music vs. reading) or the spatial location (left vs. right ear). Cortical activity differences related to attentional targets were analyzed using a two-way analysis of variance (ANOVA), with sound attribute and spatial information as factors. Our results revealed a significant main effect of the sound attribute factor across multiple measurement channels. Notably, the right parietal region exhibited consistently greater activation when attention was directed toward music compared to reading sounds. Conversely, bilateral dorsolateral prefrontal cortex (DLPFC) channels showed higher activation when participants attended to reading sounds than to music. These findings indicate that cortical activation patterns are modulated by auditory attentional states based on sound attributes. Furthermore, preliminary classification analyses achieved an accuracy of 73.7% in discriminating attentional targets (music vs. reading sounds), demonstrating the feasibility of fNIRS-based BCI applications.},
}
@article {pmid41350343,
year = {2025},
author = {Houmani, N and Yabouri, R and Garcia-Salicetti, S and Bedoin, M and Medani, T and Andrade, K},
title = {Individual neural dynamics of successful Gamma neuromodulation through EEG-neurofeedback in the aging brain.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-025-30212-0},
pmid = {41350343},
issn = {2045-2322},
abstract = {Gamma-band synchronization is a key mechanism for healthy cognitive function, yet it tends to decrease with age. EEG-based Neurofeedback (EEG-NF) is a promising tool enabling subjects to modulate their brain activity. However, its efficacy at the individual level remains unclear, which may partly explain the heterogeneity of neurofeedback outcomes. The primary objective of this study was to investigate individual neural dynamics of Gamma-band synchronization through EEG-NF training. We analyzed data from a double-blind, placebo-controlled trial using an EEG-based brain-computer interface, involving healthy older adults with subjective memory complaints, randomly assigned to a neurofeedback or a sham feedback group. Specifically, we employed a two-step unsupervised machine learning framework: first, epoch-based Agglomerative Hierarchical Clustering to identify individual-level response patterns, then Spectral Bi-Clustering to uncover higher-order structure at the population level. Results revealed a subgroup of individuals within the real neurofeedback condition who successfully enhanced their Gamma-band synchronization, with effects extending across the broader frequency spectrum. In contrast, the remaining participants in the neurofeedback group exhibited neural responses comparable to those observed in the sham group. This randomized controlled trial offers novel insights into the individual neural dynamics underlying successful Gamma EEG-NF training, highlighting its potential to promote healthy brain aging.},
}
@article {pmid41349820,
year = {2025},
author = {Solano-Suarez, KG and Arango-Sabogal, JC and Roy, JP and Molgat, E and Bédard, C and Gagnon, CA and Buczinski, S and Dufour, S},
title = {Bayesian diagnostic accuracy estimation of milk enzyme-linked immunosorbent assay, blood polymerase chain reaction, and peripheral blood lymphocyte count tests to determine bovine leukosis virus status in dairy cows.},
journal = {Journal of dairy science},
volume = {},
number = {},
pages = {},
doi = {10.3168/jds.2025-27485},
pmid = {41349820},
issn = {1525-3198},
abstract = {We assessed the diagnostic accuracy of an adapted antibody ELISA (ELISA-Ab) test, originally designed for bulk milk samples but applied on individual DHI-collected milk samples, to identify the bovine leukosis virus infection status of individual cows. Blood real-time PCR (qPCR) and blood lymphocyte count (LC) tests were used for comparison. For the milk ELISA-Ab, secondary objectives included identifying a fit-for-purpose threshold for result interpretation and evaluating whether the test's specificity could be influenced by the sampling technique (i.e., DHI-collected milk samples). Additionally, we evaluated whether the accuracy of each test varied with cow age, categorizing cows as young (2 to 4 yr old) or older (>4 yr old). In 2023, 8 dairy herds in Québec, Canada, were selected based on their historical within-herd leukosis prevalence, which was estimated to range from 10% to 75%. From all milking cows within these herds (n = 637), milk samples were collected during regular DHI, and blood samples were collected by the research team within one week of the DHI sampling. The indirect IDEXX Leukosis Milk Screening ELISA test was adapted to accommodate individual cow milk samples (as opposed to bulk tank milk samples), whereas an in-house qPCR assay targeting gag-pro-pol gene regions and LC determination were applied to blood samples. Bayesian latent class models were used to estimate the diagnostic accuracy of the tests. An optical density threshold of ≥0.5 for the ELISA-Ab provided an optimal control of the misclassification cost across various leukosis prevalence and, to a lesser extent, false negative to false positive cost ratio scenarios. With this threshold, the sensitivity and specificity estimates (95% Bayesian credible interval [BCI]) were 92% (BCI: 88%, 95%) and 99% (BCI: 96%, 100%), respectively. Sensitivity was higher in cows >4 yr old (99%, BCI: 96%, 100%) compared with cows 2 to 4 yr old (88%, BCI: 80%, 94%). We observed lower ELISA-Ab specificity in cows milked immediately after a positive cow (median: 82%, BCI: 72%, 97%) compared with those milked after a negative cow (median: 91%, BCI: 85%, 99%), suggesting a milk carryover effect due to the sampling technique. This carryover effect had a more pronounced impact on the false positive rate in herds with 30% to 50% leukosis prevalence, with the largest differences observed at the 30% prevalence scenario. However, the overall influence of the carryover effect remained limited. The qPCR test showed a sensitivity of 81% (BCI: 75%, 86%) and a specificity of 100% (98%, 100), whereas the LC test had a sensitivity of 55% (49%, 61%) and a specificity of 96% (93%, 98%). Both the qPCR and LC test accuracy parameters remained similar across age groups. In conclusion, the adapted ELISA-Ab test appears suitable for individual cow testing using DHI-collected milk samples, with higher sensitivity in cows >4 yr old. Its integration into existing milk recording programs provides a practical opportunity for herd-level leukosis monitoring.},
}
@article {pmid41349431,
year = {2025},
author = {Zou, T and Wang, X and Hu, X and Gao, Q and Han, H and Chen, H and Li, R},
title = {Distinct cortical morphometric inverse divergence changes in Parkinson's disease correlate with transcriptional expression patterns.},
journal = {NeuroImage. Clinical},
volume = {48},
number = {},
pages = {103916},
doi = {10.1016/j.nicl.2025.103916},
pmid = {41349431},
issn = {2213-1582},
abstract = {Growing evidence shows that parkinson's disease (PD) is a heterogeneous neurodegenerative disorder associated with region-specific changes in brain anatomy. However, the genetic mechanisms underlining these abnormalities are unclear. We aim to investigate PD neuroanatomical subtypes and uncover the specific brain-wide gene expression associated with morphometric abnormalities in each PD subtype. The morphometric inverse divergence (MIND) algorithm was used to quantify the morphological similarity based on multiple MRI features in 127 patients with PD and 101 healthy controls (HC). Then, heterogeneity through discriminant analysis (HYDRA) was employed to investigate the PD subtypes based on the MIND strength. Intergroup comparisons were conducted to assess MIND strength and clinical behavioral differences among PD subtypes and HC. Finally, we explored the associations between MIND network changes and gene expression in each PD subtype through partial least squares (PLS) regression, functional enrichment of PLS-weighted genes and transcriptional signature assessment of cell types. We identified two distinct subtypes of PD-related MIND changes, indicating that MIND decreased mainly in the frontal and cingulate cortices in subtype 1, and increased mainly in the occipital cortex and postcentral gyrus in subtype 2 (Bonferroni correction, p < 0.05). Both PD subtypes exhibited impaired cognitive function compared to HC, with subtype 2 showing lower Unified Parkinson's Disease Rating Scale Part III (UPDRS-III) and Hoehn and Yahr (H&amp;Y) scores than subtype 1. Moreover, genetic commonalities analysis were identified 5 shared negative genes in the PD subtypes. Subtype 1 PLS1 genes were functionally enriched in biological processes related to synaptic function, neurodevelopment and degeneration. In addition, subtype 2 PLS1 genes showed additional involvement of metabolic pathways alongside synaptic function. Moreover, we identified MIND-related genes involved in inhibitory and excitatory neurons in subtype 1. In subtype 2, MIND-related genes were involved in astrocytes besides excitatory and inhibitory neurons. Our findings suggest two distinct neuroanatomical subtypes in PD, deepening the understanding of the heterogeneity of PD by bridging the gap between the transcriptome and neuroimaging.},
}
@article {pmid41348969,
year = {2025},
author = {Liu, X and Li, F and Czosnyka, M and Czosnyka, Z and Yu, H and Tong, X and Xing, Y and Li, H and Pu, K and Feng, K and Zhang, K and Pang, M and Ming, D},
title = {Multi-Omics and High-Spatial-Resolution Omics: Deciphering Complexity in Neurological Disorders.},
journal = {GigaScience},
volume = {},
number = {},
pages = {},
doi = {10.1093/gigascience/giaf137},
pmid = {41348969},
issn = {2047-217X},
abstract = {BACKGROUND: The world has witnessed a steady rise in neurological diseases, which represent a heterogeneous group of disorders characterized by complex pathogenesis involving disruptions at multiple molecular levels, including genomic, transcriptomic, proteomic, and metabolomic levels. These disorders, often caused by genetic mutations, metabolic imbalances, immune dysregulation, and environmental factors, pose significant challenges to global public health due to their high prevalence, mortality, and disability burden.<br><br>RESULTS: The advent of high-throughput technologies, such as next-generation sequencing and mass spectrometry, has provided valuable insights into the underlying mechanisms of disease, especially the development of multi- and high-spatial-resolution omics technologies, enabling the interaction of multiple levels of biology and analysis of the complex molecular networks and pathophysiological processes.<br><br>CONCLUSIONS: This review provides a comprehensive analysis of the latest advancements in multi- and high-spatial-resolution omics, with a focus on their applications in precision diagnostics, biomarker discovery, and therapeutic target identification in brain diseases. The study also highlights the current challenges in the clinical implementation and discusses the future directions, with artificial intelligence being anticipated to enhance clinical translation and diagnostic accuracy significantly.},
}
@article {pmid41348794,
year = {2025},
author = {Fu, Z and Zhang, P and He, X and Wang, H and Guo, Y and Chen, X and Huang, J},
title = {Deep Transfer Learning in Intra-subject and Inter-subjects for Intracortical Brain Machine Interface Decoding.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3640764},
pmid = {41348794},
issn = {1558-2531},
abstract = {OBJECTIVE: This study proposes an Improved Deep Transfer Network (IDTN) to enhance decoding accuracy, calibration efficiency, and adaptability of intracortical brain machine interface (iBMI) systems while reducing the reliance on new labeled samples.<br><br>METHODS: IDTN integrates two core components: Structural Joint Discriminative Maximum Mean Discrepancy (SJDMMD) and Kernel Norm Improved Multi-Gaussian Kernel (KNK). SJDMMD extends the standard MMD framework by incorporating a structure-enhanced soft label weighting mechanism that simultaneously minimizes intra-class distributional shifts and maximizes inter-class margins for precise cross-domain alignment. KNK employs multi-Gaussian kernels with kernel norm regularization to enhance high-dimensional feature representations and sharpen inter-class boundaries, thereby improving the effectiveness of SJDMMD.<br><br>RESULTS: Evaluated on neural datasets from two rhesus macaques, IDTN achieved superior performance in both intra- subject and inter-subject transfer scenarios, consistently outperforming state-of-the-art methods in decoding accuracy. IDTN also exhibited consistent decoding stability across daily recording sessions. Ablation studies further confirm that SJDMMD improves inter-class separability and intra-class coherence, while KNK contributes to more effective kernel mapping in complex feature spaces.<br><br>CONCLUSION: These findings underscore the effectiveness of structure-aware transfer learning for neural decoding.<br><br>SIGNIFICANCE: They also highlight the potential of IDTN for deployment in real-world iBMI applications, particularly in data-limited or cross-subject environments.},
}
@article {pmid41346965,
year = {2025},
author = {Mariscal, DM and Driscoll, B and Huang, H and Fisher, LE},
title = {Somatosensory restoration and neural control strategies in lower-limb prostheses.},
journal = {npj biomedical innovations},
volume = {2},
number = {1},
pages = {44},
pmid = {41346965},
issn = {3005-1444},
abstract = {People with lower-limb amputation cannot directly control or receive feedback from existing prostheses, but emerging technologies aim to address this gap. Some approaches focus on restoring somatosensation in the missing limb, while others record signals from residual muscles for prosthetic control. This review provides an overview of the current state of neuroprosthetics for somatosensory restoration and prosthetic control in lower-limb amputation, offering perspectives on integrating these technologies for bidirectional neuroprostheses.},
}
@article {pmid41346464,
year = {2025},
author = {Guragai, B and Jin, Z and Amos, TJ and Zhang, Q and Zhang, J and Li, L},
title = {Genetic contribution to intrinsic functional connectivity underlying general intelligence: evidence from adult twin study.},
journal = {Brain communications},
volume = {7},
number = {6},
pages = {fcaf461},
pmid = {41346464},
issn = {2632-1297},
abstract = {Resting-state functional connectivity has been linked to intelligence, and twin studies suggest that these associations may be influenced by genetic factors. To investigate this relationship, we analysed behavioural and resting-state functional magnetic resonance imaging data from young adult twins in the Human Connectome Project. General intelligence was assessed based on ten cognitive task performances. The results showed a positive correlation in both identical and fraternal twins, indicating a similarity of general intelligence among twin pairs. For the resting-state functional connectivity analysis, we conducted two approaches. In the first approach, twins were randomly assigned to two separate groups, ensuring that each pair was split between the groups. We then applied a connectome-based predictive method separately for identical and fraternal twins to predict general intelligence. Specifically, a predictive model was trained using one group's functional connectivity and then applied to its co-twin group to predict their general intelligence. Significant prediction was recorded in identical twins but not in fraternal twins, suggesting a high level of similarity of intelligence-related functional connectivity among identical twins. In the second approach, we aimed to quantify the intelligence similarity using the resting-state functional connectivity. To implement this, we generated models to predict the difference in general intelligence in twin pairs, where a smaller difference indicates a greater degree of similarity. The results showed that only the intelligence difference in identical twins was successfully predicted, where the default mode network showed a significant contribution, suggesting a higher neural basis for intelligence similarity in identical twins. Together, these findings demonstrate that functional connectivity patterns associated with intelligence extend across genetically identical twins. More broadly, they highlight the default mode network role in intelligence similarity and illustrate the utility of predictive modelling as a complementary framework to classical twin analyses.},
}
@article {pmid41282873,
year = {2025},
author = {Chen, H and Wang, J and Lai, S and Peng, G and Zong, G and Yuan, C and Luo, B},
title = {Smoking Cessation, Weight Change, and Risk of Dementia: A Prospective Cohort Study.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
pmid = {41282873},
abstract = {OBJECTIVES: To assess the associations of smoking cessation and post-cessation weight gain with the risk of dementia and cognitive trajectories.<br><br>DESIGN: Prospective cohort study.<br><br>SETTING: The U.S. Health and Retirement Study (1995-2020).<br><br>PARTICIPANTS: A total of 32,802 dementia-free participants were included, with a mean age of 60.5 years (SD 10.7) and 57.1% female.<br><br>EXPOSURE: Smoking status and body weight were collected biennially via structural interviews.<br><br>MAIN OUTCOME MEASURES: Dementia was identified using the Langa-Weir algorithm. Cognitive function was assessed using a 27-unit scale. Cox proportional hazard models estimated hazard ratio (HR) of dementia by smoking cessation status, subsequent weight change, and duration of cessation. Among participants who quit during follow-up, linear mixed models assessed cognitive trajectories before and after cessation.<br><br>RESULTS: Over 25 years of follow-up, 5,868 dementia cases were documented. Compared with current smokers, individuals who quit during follow-up had a lower dementia risk after quitting (HR: 0.82, 95% confidence interval: 0.72-0.93), similar to those who had quit before baseline (0.76, 0.69-0.83) and to never smokers (0.72, 0.66-0.79). The benefits of cessation were largely limited to participants with no or modest weight gain (&#x2264;5 kg). By contrast, quitting accompanied by >10 kg weight gain was marginally associated with higher dementia risk (1.31, 0.95-1.80). Dementia risk declined steadily with increasing cessation duration, reaching the level of never smokers after approximately 5-7 years. Cognitive trajectory analyses showed that quitting was associated with long-term slower cognitive decline but no transient change, especially among those with no or modest weight gain.<br><br>CONCLUSIONS: Smoking cessation was associated with a sustained lower dementia risk and slower cognitive decline, comparable to benefits observed in never smokers and without evidence of a short-term risk increase. However, substantial post-cessation weight gain may attenuate these advantages. Smoking cessation programs should incorporate weight-management strategies to optimize long-term brain health.},
}
@article {pmid41345782,
year = {2025},
author = {Gebeyehu, TF and Sabbaghalvani, MA and Failla, G and Kabani, AS and Shah, Y and Kharichev, A and Dian, JA and Matsoukas, S and Vaccaro, AR and Schroeder, GD and Prasad, SK and Jallo, J and Heller, JE and Fehlings, MG and Harrop, JS},
title = {The application of artificial intelligence in the acute and sub-acute phases of spinal cord injury- a systematic review.},
journal = {Spinal cord},
volume = {},
number = {},
pages = {},
pmid = {41345782},
issn = {1476-5624},
abstract = {STUDY DESIGN: Systematic Review.<br><br>OBJECTIVE: To describe applications of AI for traumatic SCI management with focus on diagnostics, prognostication, and therapeutic interventions.<br><br>METHODS: PubMed, Scopus and Cochrane libraries were searched (March 2025). Studies published in English between January 1[st], 2020, and March 18, 2025, dealing with clinical aspects in the acute, post-injury rehabilitative and first year phases of SCI were included. Studies on brain computer interface, robotics and non-neurologic aspects of SCI were excluded. Extracted were country of study, study design, focus of study, total participants, American Spinal Injury Association (ASIA) Impairment Scale (AIS), machine learning (ML) models, inputs, outcomes and performance metrices.<br><br>RESULTS: A total of 23 studies with 120,931 individuals were identified. Classical Machine Learning Models, Ensemble Learning Models and Deep Learning Models were the most used ML families. Age, AIS, neurologic level of injury, sex, mechanism of injury and motor score were the most common inputs. Predictions of neurologic status, functionality status, Hospital/ICU utilizations, complications, survival, discharge destination and results of image segmentation and patient grouping were the outputs of interest. The performance metrices were satisfactory in most and higher than humans in some studies.<br><br>CONCLUSION: AI can facilitate personalized approach to diagnosis of SCI, prediction of outcomes like neurological improvement, complications, functionality indicators like walking, selfcare and independence, re-admissions, prolonged length of stays, discharge destination and mortality after injury. It was also useful to suggest specific MAP goals and time of surgical intervention. These functions complement clinical judgement.},
}
@article {pmid41345432,
year = {2025},
author = {Francis, N and Vadivu, G},
title = {ReHA-Net: a ReVIN-hybrid attention network with multiscale convolution for robust EEG artifact removal in brain-computer interfaces.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-025-28855-0},
pmid = {41345432},
issn = {2045-2322},
abstract = {Electroencephalography (EEG) is a non-invasive technique for monitoring brain activity, but its signal quality is frequently degraded by artifacts from ocular movements, muscle activity, and environmental noise. ReHA-Net is a deep learning framework for robust EEG denoising, combining a U-Net-based encoder-decoder with three core modules. (1) Hybrid Attention integrates temporal, spatial, and frequency attention to emphasize neural patterns while suppressing structured noise. (2) The Multiscale Separable Convolution (MSC) block employs dilated and parallel depth-wise separable convolutions with varying kernel sizes to capture both short-term and long-term temporal dependencies. (3) Reversible Instance Normalization (ReVIN) enhances cross-subject generalization while retaining subject-specific features. The model trains on an enhanced EEGdenoiseNet dataset with a wider signal-to-noise ratio range, combined artifact conditions, and tailored normalization strategies. ReHA-Net achieved strong denoising performance, with a PSNR of 27.10 dB, an SNR of about 17.06 dB, and a correlation coefficient of 0.976 with clean signals and a relative root mean square error (RRMSE) of 0.165. These outcomes demonstrate effective artifact reduction while maintaining neural activity, highlighting its suitability as a preprocessing step for tasks such as seizure detection, imagined speech decoding, and cognitive state monitoring.},
}
@article {pmid41345285,
year = {2025},
author = {Miao, T and Sha, L and Huang, K and Li, Y and Liu, B},
title = {SATrans-Net: Sparse Attention Transformer for EEG-based motor imagery decoding.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-025-30806-8},
pmid = {41345285},
issn = {2045-2322},
support = {SJC2022011//Basic Research Program of Suzhou/ ; SJC2022011//Basic Research Program of Suzhou/ ; SJC2022011//Basic Research Program of Suzhou/ ; SJC2022011//Basic Research Program of Suzhou/ ; SJC2022011//Basic Research Program of Suzhou/ ; BK20232008//Basic Research on Frontier Leading Technology in Jiangsu Province/ ; BK20232008//Basic Research on Frontier Leading Technology in Jiangsu Province/ ; BK20232008//Basic Research on Frontier Leading Technology in Jiangsu Province/ ; BK20232008//Basic Research on Frontier Leading Technology in Jiangsu Province/ ; BK20232008//Basic Research on Frontier Leading Technology in Jiangsu Province/ ; BE2021012-3 and BE2021012//Key Research and Development Program of Jiangsu/ ; BE2021012-3 and BE2021012//Key Research and Development Program of Jiangsu/ ; BE2021012-3 and BE2021012//Key Research and Development Program of Jiangsu/ ; BE2021012-3 and BE2021012//Key Research and Development Program of Jiangsu/ ; BE2021012-3 and BE2021012//Key Research and Development Program of Jiangsu/ ; },
abstract = {Brain-computer interface (BCI) technology decodes electroencephalography (EEG) signals to identify motor intentions associated with motor imagery (MI), offering assistive solutions for individuals with motor impairments. However, current deep learning methods often overlook the long-sequence nature of EEG-MI signals, leading to limited feature extraction and reduced decoding accuracy. To address this, we propose SATrans-Net, an end-to-end framework that models long-range dependencies in EEG-MI signals to enhance decoding performance. SATrans-Net uses two-dimensional depthwise separable convolution (2D-DSC) to extract spatiotemporal features and incorporates a Top-K Sparse Attention (TKSA) mechanism into the Transformer architecture, improving long-range modeling while reducing computational cost. By fusing local and global features, the model achieves accurate classification via a fully connected layer. For interpretability, Grad-CAM is applied to generate Class Activation Topography (CAT) maps, visualizing spatial attention over cortical regions. Cross-session evaluations show that SATrans-Net achieves average accuracies of 84.72%, 89.76%, and 96.79% on the BCI IV-2a, BCI IV-2b, and High-Gamma datasets, respectively, outperforming existing methods. Ablation studies further verify the critical role of the TKSA module. Overall, SATrans-Net demonstrates high decoding accuracy and strong interpretability, paving the way for the application of computational techniques in biomedical signal processing. Source Code:https://github.com/Jasmin-Tianhua/EEG-research_SATrans-Net.},
}
@article {pmid41345210,
year = {2025},
author = {Do, M and Evancho, A and Tyler, WJ},
title = {Bilateral transcutaneous auricular vagus nerve stimulation for the treatment of insomnia in breast cancer.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-025-30600-6},
pmid = {41345210},
issn = {2045-2322},
support = {PREP Award//University of Alabama at Birmingham/ ; },
abstract = {Substantial diagnostic and therapeutic advances have been made in medicine to address breast cancer. There remain unmet needs to translate solutions for addressing insomnia and mental health concerns in breast cancer patients. In this open-label, pilot clinical trial, we evaluated the safety and efficacy of nightly, bilateral, transcutaneous auricular vagus nerve stimulation (taVNS) on insomnia and mental health outcomes in breast cancer patients across a two-week treatment period. Our results demonstrate that noninvasive vagus nerve stimulation can significantly reduce insomnia severity, improve sleep quality, decrease sleep onset latency, and enhance sleep efficiency. Treatment with taVNS also significantly reduced the number of nightly awakenings, cancer-related fatigue, and depression scores while increasing heart rate variability. These observations demonstrate that auricular vagus nerve stimulation holds promise for improving sleep quality and mental health in patients diagnosed with breast cancer. Future investigations aimed at more thoroughly investigating the safety profile and clinical impacts of taVNS on the quality of life in patients with breast cancer are warranted.ClinicalTrials.gov Identifier: NCT06006299 23/08/2023.},
}
@article {pmid41345143,
year = {2025},
author = {Zhang, P and Yao, L and Yang, T and Lou, Y and Xu, W and Jiang, W and Li, W and Ji, X and Gao, F and Qian, Z},
title = {Revealing neural resonance in neuronal ensembles through frequency response tests.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-025-21252-7},
pmid = {41345143},
issn = {2045-2322},
support = {2024ZB661//Jiangsu Funding Program for Excellent Postdoctoral Talent/ ; ZKX24043//Key Project of Nanjing Health Science and Technology Development Special Fund/ ; BK20210531//Natural Science Foundation of Jiangsu Province/ ; NZ2024032//Fundamental Research Funds for the Central Universities/ ; 82151311//National Natural Science Foundation of China/ ; },
abstract = {Photobiomodulation emerges as a novel method to boost neuronal activities and brain function, with notable implications for treating brain disorders. Yet, the mechanisms and optimal frequency parameters of transcranial photobiomodulation are still unclear, which highlights a research gap in understanding how different stimulation frequencies affect neural responses. This study proposes a hypothesis that the nervous system exhibits resonance phenomena, suggesting that external stimuli near the system's resonant frequency trigger the strongest responses. We tested this by performing frequency response tests with pulsed transcranial near-infrared light (10-200 Hz) on mouse brains, monitoring neural responses across frequencies by analyzing cerebral blood flow, concentration of oxygenated hemoglobin, and neurophysiological activity in both cortical and deep brain regions. Our results reveal pronounced neural responses in cortical and deep brain areas at 60-80 Hz and 120-140 Hz, suggesting the potential existence of neural system resonance. Conceptually, the neural system appears to be modulatable by external stimuli, reaching maximal neural response when the stimulation frequency aligns with the system's resonant frequency, leading to neural resonance. These findings will expect to become guide new theoretical frameworks and strategies for neural modulation and therapeutic interventions.},
}
@article {pmid41344323,
year = {2025},
author = {Che, X and Zhao, H and Ye, X and Ye, S and Zhen, Z and Huang, Z and Li, Y and Zhang, S and Xu, P and Chen, X and Jiang, C and Pan, F and Luan, H and Chen, J and Shang, D and Hu, S and Tu, Y and Hu, L and Fitzgibbon, BM and Fitzgerald, PB and Cash, RFH and Huang, M},
title = {Frontoparietal network mediates the antidepressant effects of accelerated iTBS and cTBS: TMS-EEG study.},
journal = {Cell reports. Medicine},
volume = {},
number = {},
pages = {102470},
doi = {10.1016/j.xcrm.2025.102470},
pmid = {41344323},
issn = {2666-3791},
abstract = {Accelerated intermittent and continuous theta burst stimulation (a-iTBS and a-cTBS) show strong efficacy for treatment-resistant depression (TRD), yet their neural mechanisms remain unclear. This study uses concurrent transcranial magnetic stimulation (TMS) and electroencephalography (TMS-EEG) to examine these mechanisms in 40 TRD patients and 40 healthy controls (HCs). TRD individuals demonstrate abnormal local cortical excitability at baseline, characterized by left hypoactivity and right disinhibition. A-iTBS increases left excitability, and a-cTBS increases right inhibition, and both normalize it to the level of HCs. Network analyses reveal that a-iTBS improves current propagation to the left inferior parietal lobule (IPL), correlating with a better antidepressant effect. Contrastingly, a-cTBS induces a widespread inhibition as indicated by current propagation over parietal cortices, with the left IPL being most prominent, and this also correlates with a better antidepressant effect. These findings outline the frontoparietal circuitry in TMS antidepressant effects and provide insights for optimizing treatment efficacy. This study was registered at the Chinese Clinical Trial Registry (ChiCTR2200055320).},
}
@article {pmid41344290,
year = {2025},
author = {Liu, YJ and Wang, XD},
title = {Parallel supramammillary-hippocampal routes: Organization, dysregulation, and restoration.},
journal = {Neuron},
volume = {113},
number = {23},
pages = {3879-3881},
doi = {10.1016/j.neuron.2025.10.020},
pmid = {41344290},
issn = {1097-4199},
mesh = {Animals ; *Hippocampus/physiology ; Alzheimer Disease/physiopathology/pathology ; Neural Pathways/physiology ; Mice ; Humans ; },
abstract = {In this issue of Neuron, Luo et al.[1] report two supramammillary neuronal populations with segregated projections to the dorsal and ventral dentate gyrus that selectively modulate cognitive and emotional processes, respectively. Targeted activation of each pathway alleviates domain-specific behavioral deficits in an Alzheimer's disease mouse model.},
}
@article {pmid41341607,
year = {2025},
author = {Mahrouk, A},
title = {Symbolic feedback for transparent fault anticipation in neuroergonomic brain-machine interfaces.},
journal = {Frontiers in robotics and AI},
volume = {12},
number = {},
pages = {1656642},
pmid = {41341607},
issn = {2296-9144},
abstract = {BACKGROUND: Brain-Machine Interfaces (BMIs) increasingly mediate human interaction with assistive systems, yet remain sensitive to internal cognitive divergence. Subtle shifts in user intention-due to fatigue, overload, or schema conflict-may affect system reliability. While decoding accuracy has improved, most systems still lack mechanisms to communicate internal uncertainty or reasoning dynamics in real time.<br><br>OBJECTIVE: We present NECAP-Interaction, a neuro-symbolic architecture that explores the potential of symbolic feedback to support real-time human-AI alignment. The framework aims to improve neuroergonomic transparency by integrating symbolic trace generation into the BMI control pipeline.<br><br>METHODS: All evaluations were conducted using high-fidelity synthetic agents across three simulation tasks (motor control, visual attention, cognitive inhibition). NECAP-Interaction generates symbolic descriptors of epistemic shifts, supporting co-adaptive human-system communication. We report trace clarity, response latency, and symbolic coverage using structured replay analysis and interpretability metrics.<br><br>RESULTS: NECAP-Interaction anticipated behavioral divergence up to 2.3 &#xb1; 0.4 s before error onset and maintained over 90% symbolic trace interpretability across uncertainty tiers. In simulated overlays, symbolic feedback improved user comprehension of system states and reduced latency to trust collapse compared to baseline architectures (CNN, RNN).<br><br>CONCLUSION: Cognitive interpretability is not merely a technical concern-it is a design priority. By embedding symbolic introspection into BMI workflows, NECAP-Interaction supports user transparency and co-regulated interaction in cognitively demanding contexts. These findings contribute to the development of human-centered neurotechnologies where explainability is experienced in real time.},
}
@article {pmid41341241,
year = {2024},
author = {Kubben, P},
title = {Invasive Brain-Computer Interfaces: A Critical Assessment of Current Developments and Future Prospects.},
journal = {JMIR neurotechnology},
volume = {3},
number = {},
pages = {e60151},
pmid = {41341241},
issn = {2817-092X},
abstract = {Invasive brain-computer interfaces (BCIs) are gaining attention for their transformative potential in human-machine interaction. These devices, which connect directly to the brain, could revolutionize medical therapies and augmentative technologies. This viewpoint examines recent advancements, weighs benefits against risks, and explores ethical and regulatory considerations for the future of invasive BCIs.},
}
@article {pmid41338361,
year = {2025},
author = {Li, Y and Chen, S and Liu, YJ},
title = {Microglial phagoptosis in development, health, and disease.},
journal = {Neurobiology of disease},
volume = {},
number = {},
pages = {107211},
doi = {10.1016/j.nbd.2025.107211},
pmid = {41338361},
issn = {1095-953X},
abstract = {Microglial phagoptosis, defined as the phagocytosis of a viable cell by microglia that ultimately causes the death of the engulfed cell, has emerged as a pivotal process in sculpting neural circuits within the central nervous system (CNS). Essential for neurodevelopmental circuit refinement and ongoing tissue homeostasis, this process relies on dynamic molecular cues that direct microglia to specific cellular substrates. Physiologically, phagoptosis contributes to neural circuit refinement and cell number regulation during development; however, its dysregulation can drive neurodevelopmental and neurodegenerative disorders via aberrant cell removal. Recent advances have elucidated the distinct signaling pathways involved in target recognition and engulfment, revealing the dual roles of microglial phagoptosis in both CNS health and disease. Deeper mechanistic insight into this process offers new therapeutic opportunities for conditions characterized by defective or excessive cell clearance. This review summarizes current progress, highlights unresolved challenges, and discusses future perspectives on targeting microglial phagoptosis for intervention in CNS disorders.},
}
@article {pmid41337436,
year = {2025},
author = {Ding, Y and Wang, L and Wang, X and Chen, F},
title = {Developing Lightweight Models with Data Optimization for Attending Speaker Identity from EEG without Spatial Information.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11253106},
pmid = {41337436},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; Algorithms ; *Attention/physiology ; Signal Processing, Computer-Assisted ; Male ; Adult ; Artifacts ; },
abstract = {Spatial auditory attention decoding (Sp-AAD) holds great promise for brain-computer interfaces (BCIs). However, studies have shown that the high performance of Sp-AAD relies heavily on eye gaze artifacts rather than actual auditory attention features. For this reason, this study focuses on verifying whether EEG signals contain sufficient discriminative features for attending target speaker identity without eye gaze artifacts. In this study, we proposed an EEG-Mixup data optimization method to suppress trial-specific features in EEG data by adjusting the data distribution and generating soft labels through linear interpolation. In addition, a lightweight EEG-MLP model containing only 2.5k parameters was designed, which showed significant advantages over the latest SOTA model (DenseNet-3D) in cross-trial scenarios. It is shown that the model's generalization ability can be significantly improved by optimizing the data without increasing the data volume; meanwhile, the lightweight model demonstrates higher computational efficiency and inference speed in specific tasks. This study provides important theoretical and practical references for future optimization applications of BCI systems.Clinical Relevance- This study demonstrates the potential of lightweight EEG-based methods for attending target speaker identity without relying on eye gaze artifacts, providing a foundation for future auditory brain-computer interface systems.},
}
@article {pmid41337381,
year = {2025},
author = {Haqiqat, A and Karimi, N and Mirmahboub, B and Sobhaninia, Z and Shirani, S and Samavi, S},
title = {Tri-Model Integration: Advancing Breast Cancer Immunohistochemical Image Generation through Multi-Method Fusion.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-6},
doi = {10.1109/EMBC58623.2025.11252716},
pmid = {41337381},
issn = {2694-0604},
mesh = {Humans ; *Breast Neoplasms/diagnostic imaging/metabolism/diagnosis ; Female ; *Immunohistochemistry/methods ; *Image Processing, Computer-Assisted/methods ; Neural Networks, Computer ; Algorithms ; Reproducibility of Results ; },
abstract = {Immunohistochemical (IHC) staining is a crucial technique for diagnosing and formulating treatment plans for breast cancer, particularly by evaluating the expression of biomarkers like human epidermal growth factor receptor-2. However, the high cost and complexity of IHC staining procedures have driven research toward generating IHC-stained images directly from more readily available Hematoxylin and Eosin-stained images using image-to-image (I2I) translation methods. In this work, we propose a novel approach that combines the predictive capabilities of three state-of-the-art I2I models to enhance the quality and reliability of synthetic IHC images. Specifically, we designed a Convolutional Neural Network that takes as input a four-dimensional input comprising the outputs of three distinct models (each contributing an IHC prediction, which is an RGB three-dimensional output for each) and produces a final consensus image through a fusion mechanism. This ensemble method leverages the strengths of each model, leading to more robust and accurate IHC image generation. Extensive experiments on the BCI dataset demonstrate that our approach outperforms existing single-model methods, achieving superior Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics. All of our code is available at: https://github.com/arshamhaq/BCI-fusion.Clinical RelevanceImproving the quality of synthetic IHC images can potentially reduce costs and streamline the diagnostic process, ultimately benefiting patient outcomes.},
}
@article {pmid41337376,
year = {2025},
author = {Kim, H and Ahn, M and Jun, SC},
title = {A Brain Switch for SSVEP-Based BCI Speller Using an RNN-Based Detection Approach.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-5},
doi = {10.1109/EMBC58623.2025.11252734},
pmid = {41337376},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Electroencephalography/methods ; *Brain/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; Male ; Adult ; *Neural Networks, Computer ; Female ; },
abstract = {Steady-state visual evoked potentials (SSVEP)-based brain-computer interface (BCI) systems are used commonly as spellers because they have high information transfer rate and high accuracy relative to other BCI paradigms. Asynchronous BCI systems allow users to input commands whenever they wish to use them, which may make these systems more realistic and practical than synchronous systems. In contrast, asynchronous BCIs, known as the Brain Switch, require robust mechanisms to detect users' intentions accurately while maintaining classification performance. This highlights the need for a BCI system that distinguishes users' intentions reliably. SSVEP paradigms often show variability in their frequency designs. In this study, we propose a two-stage asynchronous BCI system that combines a robust brain switch model that uses autocorrelation and Long Short-Term Memory (LSTM)) for detection and an EEGNet-based classifier. Our proposed system was evaluated using a 40-class SSVEP dataset involving 40 subjects. It achieved an impressive detection performance with a sensitivity (SEN) of 98.24 ± 2.21% and specificity (SPC) of 82.28 ± 11.63% for even 1-second epochs. Further, the system attained a classification accuracy (ACC) of 77.05 ± 14.95%. This model demonstrates significant potential to help develop more realistic and practical asynchronous BCI systems.},
}
@article {pmid41337322,
year = {2025},
author = {Zhao, R and Zhang, S and Bai, Y and Ni, G},
title = {Neural Dynamics in Imagined Speech: A Spatiotemporal Analysis Based on EEG Source Localization and Functional Connectivity.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-5},
doi = {10.1109/EMBC58623.2025.11254701},
pmid = {41337322},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Speech/physiology ; Male ; *Imagination/physiology ; Brain-Computer Interfaces ; Female ; Adult ; Spatio-Temporal Analysis ; *Brain/physiology ; Brain Mapping/methods ; Young Adult ; },
abstract = {Communication is a crucial part of daily life. However, patients with speech disorders may have difficulty communicating with the outside world and, in severe cases, may even completely lose the ability to speak. Imagined speech is an intrinsic speech activity that does not explicitly move any vocal organs, which has emerged as a promising avenue for brain-computer interface (BCI) research. In this study, we developed a novel experimental paradigm tailored to imagined speech tasks based on Chinese characters and collected participants' high-temporal-resolution electroencephalogram (EEG) data. Using dynamic statistical parametric mapping (dSPM), we delineated the spatial distribution of neural activation, while functional connectivity was quantified through phase-locking value (PLV) analysis to capture the temporal interplay between distinct brain regions. We introduced a novel spatiotemporal feature representation, termed information flow (IF), by segmenting the imagined speech process into 10 continuous temporal windows, we systematically analyzed the evolution of global and local information flow dynamics. The results revealed distinct spatiotemporal patterns of neural activation and functional connectivity, underscoring the coordinated interaction of critical brain regions involved in the process of imagined speech, which help to elucidate the spatiotemporal dynamics of imagined speech and provide valuable insights into its underlying neural mechanisms. This work provides a foundation for advancing speech BCI applications and contributes to understanding the cognitive and neural bases of imagined speech in Chinese.},
}
@article {pmid41337318,
year = {2025},
author = {Yadav, A and Garcia, FC and Gonzalez, A and Trevisan, BE and Xu, A and Ugur, M and Bhattacharjee, A and Pothukuchi, RP},
title = {Foresee: A Modular and Open Framework to Explore Integrated Processing on Brain-Computer Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11254710},
pmid = {41337318},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; Algorithms ; *Signal Processing, Computer-Assisted ; Electroencephalography ; },
abstract = {Brain-computer interfaces (BCIs) with processing integrated on the device enable fast and autonomous closed-loop interaction with the brain. While such BCIs are rapidly gaining traction, they are also difficult to design due to the tight and conflicting power and performance needs of on-device processing. Meeting these specifications often requires the BCI processors to be co-designed with applications and algorithms, with processor designers and computational neuroscientists working closely to converge on the target hardware platform. But, this process has traditionally been cumbersome and ad hoc, due to the lack of systematic design space exploration frameworks. In response, we present Foresee, a new framework for fast exploration of BCI processors. Foresee offers a unified and modular interface for iteratively co-optimizing BCI processors with their algorithms, without sacrificing accuracy, speed, or ease of use. Foresee is publicly available, and comes with a library of hardware blocks for common signal processing functions that the community could contribute and build on. We demonstrate Foresee's utility and capability by analyzing on-device processing for two seizure detection methods from prior work, and validating our analysis on real hardware. We expect Foresee to be vital in designing next-generation BCIs.},
}
@article {pmid41337309,
year = {2025},
author = {Thapa, BR and Bae, J},
title = {A Window Analysis for the Decoding of Premovement and Movement Intentions in Freewill EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11253481},
pmid = {41337309},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; Movement/physiology ; Male ; Female ; Support Vector Machine ; *Brain-Computer Interfaces ; Adult ; *Intention ; Young Adult ; Signal Processing, Computer-Assisted ; },
abstract = {Decoding movement-related intentions from electroencephalogram (EEG) is important for developing real-time brain machine interfaces (BMIs). While most studies focus on cue-based tasks in EEG-based BMIs, freewill reaching and grasping tasks allow subjects to initiate movements of their own will, making them relevant to practical EEG-based BMIs. However, the investigation of EEG window size for decoding freewill movements remains unexplored. This study systematically analyzes the effect of different window sizes on decoding EEG premovement (prior to the movement onset) and movement (after movement initiation) intentions in freewill reaching and grasping tasks. We used 49 EEG recordings from 23 subjects, and EEG windows of 0.1-1s in 0.1s increments were analyzed within the range of -3 to 3s relative to the movement onset at 0. Decoding was performed using regularized linear support vector machine (LSVM) and regularized linear discriminant analysis (RLDA), and performance was evaluated in terms of accuracy. Larger window sizes consistently outperformed smaller ones, with peak accuracy occurring between 0-1s relative to the movement onset. LSVM outperformed RLDA across all 10 window sizes, with peak accuracy ranging from 86.98% with 0.1s window to 90.94% with 1s window. Using LSVM, the earliest peak accuracy (90.03%) was achieved with a 0.7s window starting at 0.35s after the movement onset. Notably, a 0.5s window provided a peak accuracy of 89.5% which is not statistically significant compared to the 0.7s window (p = 0.05). The start point of the 0.5s window was 0.5s after the onset. With LSVM, considering the trade-off between decoding accuracy and latency, the 0.5s window offers the optimal choice for decoding movement intention in freewill EEG.Clinical relevance- Identifying the optimal window size to decode movement-related intentions in freewill EEG can help improve strategies to develop real-time BMIs for individuals with motor impairments.},
}
@article {pmid41337275,
year = {2025},
author = {Rutkowski, TM and Kasprzak, H and Otake-Matsuura, M and Komendzinski, T},
title = {Classifying Awareness with a Lightweight CNN in an Olfactory Oddball Passive BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11253457},
pmid = {41337275},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; *Neural Networks, Computer ; *Awareness/physiology ; *Smell/physiology ; Algorithms ; Male ; Signal Processing, Computer-Assisted ; Adult ; Female ; },
abstract = {Olfaction, or the sense of smell, presents a promising avenue for enhancing brain-computer interface (BCI) usability and enabling passive cognitive state monitoring. In reactive BCI paradigms, odor cues can be associated with specific commands, facilitating more intuitive interaction. Furthermore, passive BCI applications can leverage olfactory stimuli to monitor cognitive processes. Despite this potential, challenges remain, notably the requirement for precise odor delivery mechanisms and robust algorithms capable of detecting and interpreting associated brain activity. This work proposes a novel approach, combining electroencephalography (EEG) and electrobulbogram (EBG) within an olfactory modality oddball paradigm, for predicting user awareness levels. A pilot study is presented, demonstrating improved user awareness classification performance with a newly developed multiclass, lightweight convolutional neural network (CNN) for this passive olfactory BCI modality, surpassing previously reported results.Clinical relevance- This research demonstrates the feasibility of inferring user awareness levels from concurrently acquired electroencephalographic (EEG) and electrobulbogram (EBG) neurophysiological data.},
}
@article {pmid41337269,
year = {2025},
author = {Dijkema, EB and Pennartz, CMA and Olcese, U},
title = {A Proof-of-Concept Spike Based Neuromorphic Brain-Computer Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11253485},
pmid = {41337269},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Animals ; Mice ; Signal Processing, Computer-Assisted ; *Neural Networks, Computer ; *Action Potentials/physiology ; Visual Cortex/physiology ; },
abstract = {Closed-loop brain-computer interfaces (BCIs) hold promise for restoring function after neurological damage by dynamically processing neural signals and delivering targeted brain stimulation. To achieve clinically meaningful outcomes, such systems must operate with high spatiotemporal precision. This work aims to demonstrate a proof-of-concept neuromorphic BCI that processes neural spike events in near-real time, without necessitating preprocessing besides signal filtering and spike detection. Methods - We developed a system that acquires neural signals and streams spike events into a spiking neural network (SNN) running on SpiNNaker neuromorphic hardware. We evaluated the system's performance using both in vivo recordings from mouse visual cortex and simulated neural waveforms. We measured the roundtrip latency, defined as the time from spike detection to an output spike generated by the SNN. Results - Under baseline conditions with no hidden SNN layers, mean roundtrip latency was 4.69 ms (±1.70 ms). Adding hidden layers increased latency by approximately 3.65 ms per layer, reflecting the computational overhead of deeper networks. The system successfully detected and processed spikes in near real-time, demonstrating that neuromorphic hardware can manage spike-based input at speeds suitable for closed-loop intervention. Discussion - These findings indicate that neuromorphic SNNs can rapidly process neural signals, providing a foundation for closed-loop BCIs capable of bypassing damaged neural pathways. Future efforts will involve implementing stimulation protocols and functional SNNs. Such developments may ultimately facilitate more effective, flexible, and power-efficient neuroprosthetic devices.},
}
@article {pmid41337259,
year = {2025},
author = {Daling, MH and Alonzo, J and Lee, J and Lee, AH and Durfee, D and Larson, L and Nurmikko, A and Leung, VW},
title = {Shielded Relay Coil design to Optimize WPT and SAR for Distributed Wireless Brain Implants.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11253961},
pmid = {41337259},
issn = {2694-0604},
mesh = {*Wireless Technology/instrumentation ; Humans ; *Brain-Computer Interfaces ; Equipment Design ; *Brain/physiology ; *Prostheses and Implants ; },
abstract = {This paper presents a shielded relay antenna to simultaneously enhance Wireless Power Transfer (WPT) and reduce Specific Absorption Rate (SAR) for a network of distributed brain microimplants. Through strategic placement of conductive features, Eddy currents are created to oppose high magnetic fields. This design advantageously equalizes and increases the field strength over the cortical surface area. This work has the potential to address the WPT/ SAR co-optimization challenges for biomedical implants in general. When applied to the target 2 × 2 cm[2] wireless brain-machine interface (BMI) system operating at 915 MHz, HFSS simulations show it provides 1.2 dB WPT enhancement and a 29% SAR reduction.},
}
@article {pmid41337212,
year = {2025},
author = {Arjona, L and Rosenthal, J and Azkarate, M},
title = {Wireless Communication Protocol for backscatter-based Neural Implants.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11253936},
pmid = {41337212},
issn = {2694-0604},
mesh = {*Wireless Technology/instrumentation ; Humans ; *Brain-Computer Interfaces ; *Prostheses and Implants ; },
abstract = {This work presents a novel protocol for bidirectional wireless communication with neural implants that contributes to the growing field of closed-loop brain-computer interfaces (BCIs). BCIs are an emerging technology for studying and treating neurological disorders, such as spinal cord injuries. Furthermore, BCI heavily rely on neural implants as a crucial element, because they hold the potential to restore functionality of paralyzed limbs. The proposed protocol presents an open configuration to enable neural implants to communicate wirelessly with an external reader. Because computation to extract movement intention is performed externally, computing power is nearly unlimited and the energy consumption of the implant is reduced drastically. To validate the proposed protocol, the downlink (reader to implant) was implemented on a software defined radio running GNU-Radio toolkit with custom communication blocks. The uplink (implant to reader) was implemented on an FPGA. Finally, to validate the movement intention decoding, pre-recorded neural data was backscattered from an FPGA-based implant and the decoding was executed successfully.},
}
@article {pmid41337189,
year = {2025},
author = {Bleuze, A and Martel, F and Aksenova, T and Struber, L},
title = {Modification of cortical activation pattern after long-term BCI training and its impact on decoding model performances.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11253801},
pmid = {41337189},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Models, Neurological ; Male ; *Cerebral Cortex/physiology ; },
abstract = {In brain-computer-interfaces (BCIs) variability usually appears in brain signals from one session to another. This inter-session-variability is of major importance for two reasons. On the one hand it poses an issue for a model learned on previous session, that does not always perform correctly on new sessions. On the other hand, it can also be a marker of long-term adaptation in the brain of patients, which may reflect learning or even rehabilitation. This study investigates the phenomenon of physiological drift in BCIs, focusing on the evolution of brain activity over sessions. In order to do so, we analyzed the spatial patterns of synchronization and desynchronization in a wide range of frequencies. A linear regression model was proposed to quantify drift and residual variability. In this article, we study the inter-session variability both physiologically and from the point of view of the decoder performance and compute the correlation between them to examine their coherence. This study provides valuable insights on the physiological drift and its impact on BCI performance, contributing to the development of more stable and reliable BCI systems for rehabilitation medicine.(p)(p)Clinical Relevance-The long-term modifications in the activation patterns after BCI training studied in this article is an additional evidence of potential for rehabilitation using BCI.},
}
@article {pmid41337178,
year = {2025},
author = {Wang, M and Wang, J and Zhao, J and Yao, L and Wang, Y},
title = {EIMNet: An EEG and iEEG-Fused Interactive Modality Network for Accurate Memory State Prediction during Working Memory Task.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-6},
doi = {10.1109/EMBC58623.2025.11253846},
pmid = {41337178},
issn = {2694-0604},
mesh = {Humans ; *Memory, Short-Term/physiology ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Algorithms ; Signal Processing, Computer-Assisted ; Male ; },
abstract = {Recent advancements in Brain-Computer Interface (BCI) research have increasingly highlighted the significance of multimodal integration for effectively extracting task-discriminative features. In the context of working memory (WM) task, we introduce EIMNet, a cross-modality fusion model inspired by the phase-amplitude coupling phenomenon. By enabling interaction between electroencephalography (EEG) and intracranial electroencephalography (iEEG), EIMNet enhances the representation of task-related features, improving the prediction of memory-related effects. Our ablation experiments demonstrate that EIMNet enhances decoding performance, with factors such as interaction factor selection, frequency band splitting, and data augmentation playing vital roles. We demonstrate the effectiveness of EIMNet in improving decoding accuracy by integrating EEG and iEEG for working memory task, with promising applications in memory and attention-related cognitive research.},
}
@article {pmid41337165,
year = {2025},
author = {Xu, Y and Otsuka, S and Nakagawa, S},
title = {Enhancing EEG-Based Emotion Classification by Refining the Spatial Precision of Brain Activity.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-6},
doi = {10.1109/EMBC58623.2025.11253823},
pmid = {41337165},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology/classification ; *Brain/physiology ; Signal Processing, Computer-Assisted ; Neural Networks, Computer ; Brain-Computer Interfaces ; Algorithms ; },
abstract = {Advancements in neuroscience and deep learning have significantly enhanced bio-signal-based emotion recognition, a critical component in Brain-Machine Interface (BMI) applications for healthcare, human-computer interaction, and human-AI assistant communication. Former studies have proposed Manual Mapping electrode matrices and employing Convolutional Neural Networks (CNNs) to recognize spatial EEG activities. However, this Manual Mapping of EEG electrodes onto matrix grids limits spatial precision and introduces inefficiencies. This study proposes automated channel mapping methods of Orthographic Projection and Stereographic Projection to address these challenges, using Differential Entropy and Power Spectral Density with Linear Dynamical Systems as features. A 3-branch multiscale CNN was trained on open-source dataset, employing a 5-fold cross-classification approach. Experimental results demonstrate that higher-resolution grids (16×16, 24×24) with automated projections significantly outperform Manual Mappings, achieving up to a 4.06% improvement in classification accuracy (p < 0.05). This result indicates that enhancing spatial precision of EEG data improves emotion classification, establishing automated spatial mapping as an advancement in EEG-based emotion recognition.Clinical Relevance-Advancement in emotion classification accuracy can facilitate more reliable diagnostic tools and personalized therapeutic interventions for mental health disorders, such as depression and anxiety.},
}
@article {pmid41337115,
year = {2025},
author = {Rivelli, F and Popov, M and Kouzinopoulos, CS and Tang, G},
title = {Adaptively Pruned Spiking Neural Networks for Energy-Efficient Intracortical Neural Decoding.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11254088},
pmid = {41337115},
issn = {2694-0604},
mesh = {Animals ; *Brain-Computer Interfaces ; Algorithms ; *Neural Networks, Computer ; Humans ; *Neurons/physiology ; *Action Potentials/physiology ; *Nerve Net/physiology ; },
abstract = {Intracortical brain-machine interfaces demand low-latency, energy-efficient solutions for neural decoding. Spiking Neural Networks (SNNs) deployed on neuromorphic hardware have demonstrated remarkable efficiency in neural decoding by leveraging sparse binary activations and efficient spatiotemporal processing. However, reducing the computational cost of SNNs remains a critical challenge for developing ultra-efficient intracortical neural implants. In this work, we introduce a novel adaptive pruning algorithm specifically designed for SNNs with high activation sparsity, targeting intracortical neural decoding. Our method dynamically adjusts pruning decisions and employs a rollback mechanism to selectively eliminate redundant synaptic connections without compromising decoding accuracy. Experimental evaluation on the NeuroBench Non-Human Primate (NHP) Motor Prediction benchmark shows that our pruned network achieves performance comparable to dense networks, with a maximum tenfold improvement in efficiency. Moreover, hardware simulation on the neuromorphic processor reveals that the pruned network operates at sub-μW power levels, underscoring its potential for energy-constrained neural implants. These results underscore the promise of our approach for advancing energy-efficient intracortical brain-machine interfaces with low-overhead on-device intelligence.},
}
@article {pmid41337108,
year = {2025},
author = {Song, Q and Kang, G},
title = {A Multi-Band Self-Attention Network for Motor Imagery Classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11254113},
pmid = {41337108},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; Signal Processing, Computer-Assisted ; *Imagination/physiology ; Algorithms ; },
abstract = {Brain-computer interface (BCI) systems create a novel communication method between humans and machines by translating human thoughts into actionable commands to control external devices. Motor imagery (MI) electroencephalogram (EEG) signals have significant applicability in various medical and non-medical industries, including stroke rehabilitation, wheelchair control, and drone operation. However, the practical application of EEG remains limited by the decoding performance and generalization ability of MI signalsThis study introduces a multi-branch self-attention network for motor imagery (MI) signal classification. Each branch independently processes EEG signals decomposed into distinct frequency bands through convolutional neural networks (CNNs) and multi-head self-attention (MHA) mechanisms, enabling the extraction of both fundamental and discriminative spatial-temporal features. To further capture dynamic temporal dependencies, long short-term memory (LSTM) networks are integrated. We systematically evaluate three signal decomposition ensemble empirical mode decomposition (EEMD), wavelet packet decomposition (WPD), and brain rhythm-based decomposition-to optimize feature representation. Extensive experiments on the BCI Competition IV 2a dataset demonstrate state-of-the-art performance, with subject-dependent and subject-independent accuracies of 84.04% and 71.67%, respectively. Comparative analyses against benchmark models (EEGNet, EEGTCNet, ShallowConvNet, etc.) validate the superiority of our approach in classification accuracy and generalization capabilityClinical relevance- This study investigates the methods for decoding motor imagery EEG signals and establishes the positive role of each module in classification. The improvement in accuracy can lead to better outcomes in medical applications such as controlling prosthetics, wheelchairs, and stroke rehabilitation.},
}
@article {pmid41337106,
year = {2025},
author = {Zhong, Y and Wen, H and Assam, M and Yao, L and Wang, Y},
title = {Motor-Sensory Coupled Learning for Motor Imagery Decoding.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-5},
doi = {10.1109/EMBC58623.2025.11254055},
pmid = {41337106},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; *Imagination/physiology ; Stroke Rehabilitation ; Signal Processing, Computer-Assisted ; *Learning ; Male ; },
abstract = {Brain-Computer Interface (BCI) technology has significant potential for advancing stroke rehabilitation by promoting motor recovery by decoding motor intentions from electroencephalogram (EEG) signals. However, the practical application of BCI in rehabilitation faces several challenges, particularly in decoding accuracy. This limitation often stems from an overemphasis on motor imagery signals, while sensory components, which are crucial for effective motor function recovery, are frequently overlooked. In this paper, we propose a novel framework to enhance BCI performance by integrating both sensory and motor modalities through a motor-sensory coupled learning approach. The model leverages EEG data induced by both motor imagery (MI) and tactile sensation (TS), using adversarial training to capture the coupled features of these two domains. By incorporating reliable sensory signals, the proposed approach aims to improve the robustness and accuracy of motor imagery decoding, offering particular benefits for stroke patients with impaired motor rhythms. Experimental results from BCI-naive subjects show a significant improvement in classification accuracy compared to traditional motor imagery-only models, suggesting that this approach holds promise as a potential solution for stroke rehabilitation. These findings indicate that integrating sensory signals into BCI systems could lead to more effective rehabilitation strategies, paving the way for the development of more robust and adaptive BCI technologies in the future.},
}
@article {pmid41337085,
year = {2025},
author = {Ong, JX and Premchand, B and Lim, RY and Chew, E and Jiang, M and Tang, N and Ang, KK},
title = {Inhibitory Effects of Individualized Transcranial Alternating Current Stimulation on Motor Imagery and Interhemispheric Symmetry: Implications for Stroke Rehabilitation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11254631},
pmid = {41337085},
issn = {2694-0604},
mesh = {Humans ; *Stroke Rehabilitation ; *Transcranial Direct Current Stimulation/methods ; Male ; Female ; Stroke/physiopathology ; *Imagination/physiology ; Adult ; Brain-Computer Interfaces ; },
abstract = {Transcranial alternating current stimulation (tACS) holds potential in stroke rehabilitation, but its effects when delivered at an individual's peak motor imagery (MI) frequency remain unclear. This study investigated the impact of tACS delivered at subject-specific peak MI frequencies on MI performance accuracy, quantified in terms of classification accuracy, and interhemispheric symmetry, measured via the brain symmetry index (BSI). Using a brain-computer-brain closed-loop system, each subject's peak MI performance frequency was first identified during the Pre-stimulation phase, after which tACS was delivered at this determined frequency. Our findings show that active individualized tACS decreased MI performance and increased BSI, suggesting inhibitory effects on motor-related neural processes.Clinical Relevance- The observed inhibitory effects of tACS highlight its potential for targeted neuromodulation in stroke recovery. Future research should explore how inhibitory effects can be harnessed therapeutically and investigate stimulation parameters that could optimize outcomes for functional recovery. The demonstrated ability of tACS to modulate brain activity, evidenced by increased BSI, underscores its promise as a neuromodulatory tool in clinical applications.},
}
@article {pmid41337074,
year = {2025},
author = {Carvallo, A and Struber, L and Costecalde, T and Souriau, R and Charvet, G and Aksenova, T},
title = {Decoding of Individual Fingers Attempted Movement from Epidural ECoG in a Patient with Tetraplegia.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11254592},
pmid = {41337074},
issn = {2694-0604},
mesh = {Humans ; *Quadriplegia/physiopathology ; *Brain-Computer Interfaces ; *Fingers/physiopathology/physiology ; Movement/physiology ; Algorithms ; *Electrocorticography/methods ; Male ; },
abstract = {Brain-Computer interfaces (BCIs) enable direct communication between the brain and external devices. This technology holds significant potential for restoring motor function in individuals with severe neurological impairments. Among others, restoration of fine hand motor functions allowing grasping and objects manipulation is a priority for enhancing patients' lifestyle. Decoding finger movements is crucial for the precise control of hand neuroprosthetics. In this article, we analyzed neural activity of a tetraplegic patient implanted with two WIMAGINE ECoG recording devices in front of the sensorimotor cortex of both hemispheres. ECoG was recorded over three sessions while the patient attempted to move individual fingers on the right hand. The attempted finger movements was decoded using a Hidden Markov Model, integrating Recursive Sample Weighted - N-Ways Partial Least Square algorithm addressing class imbalance. In the offline study, we obtained balanced accuracy 0.6603 ± 0.0087 in average for decoding activation of five individual fingers. Our results shows that decoding individual fingers movements attempts is possible in ECoG, paving the way for fine movement restoration using BCI.Clinical Relevance- Efficient decoding of individual fingers attempted movements using chronic ECoG recording devices in a tetraplegic patient, suggesting the feasibility of hand neuroprosthesis aimed at fine hand motor restoration in impaired individuals.},
}
@article {pmid41337062,
year = {2025},
author = {Zhu, Z and Han, J and Zhang, Z and Wannawas, N and Faisal, AA},
title = {Identifying the Nature of Grip Force Signals in EEG &amp; fNIRS with Multi-Modal Graph Fusion Network.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11254624},
pmid = {41337062},
issn = {2694-0604},
mesh = {Humans ; *Hand Strength/physiology ; *Electroencephalography/methods ; Brain-Computer Interfaces ; Spectroscopy, Near-Infrared/methods ; Male ; Adult ; Signal Processing, Computer-Assisted ; Female ; },
abstract = {Brain-Computer interfaces can assist motor rehabilitation for people with severe paralysis by directly decoding their brain signals into movement intention and executing with external devices without passing the impaired neural pathways. It is crucial to restore natural and smooth daily movements, and continuous force control is one of the most important kinaesthetic functions. However, the complex continuous force decoding and limited relevant public datasets greatly challenge this field. How the brain coordinates the motor command or sensory feedback during the force control behaviour also remains to be discussed. This work investigated these questions through a novel experimental setup by isolating the motor intention and sensory feedback and combining both components flexibly for hand grip. We applied functional electrical stimulation to induce passive gripping and collected grip force with multi-modal brain signals. Significant neural pattern differences were found in EEG time-frequency representation by comparing the brain responses under different task conditions, including voluntary movement, motor imagery, and passive perception status. Additionally, we present a multi-modal graph fusion model fusing both EEG and fNIRS for continuous bimanual grip force decoding. These contributions are beneficial to developing neural interfaces for rehabilitation and assistive devices that involve force manipulation or operate in isometric schemes.},
}
@article {pmid41337056,
year = {2025},
author = {Abdo, EA and Yakovlev, A and Degenaar, P},
title = {Multipolar Hybrid Stimulation for Visual Prostheses: Enhancing Resolution and Specificity.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11254606},
pmid = {41337056},
issn = {2694-0604},
mesh = {*Visual Prosthesis ; Humans ; *Electric Stimulation/methods ; Optogenetics ; Brain-Computer Interfaces ; *Visual Cortex/physiology ; Animals ; },
abstract = {Advancements in neural stimulation techniques are essential for improving the precision and efficiency of brain-machine interfaces, particularly in visual cortical prostheses. These prostheses aim to restore vision by stimulating the visual cortex, but current methods face challenges such as limited spatial resolution, high power consumption, and non-specific activation. This work proposes a multipolar hybrid stimulation approach that combines electrical and optical neuromodulation to mitigate these limitations. Unlike traditional monopolar and bipolar methods, which require numerous electrodes or suffer from crosstalk and timing issues, the proposed system employs polarity switching and selective electrode control, enabling customizable electric fields alongside optogenetics for precise neural targeting and enhanced resolution. By utilizing subthreshold electrical and optogenetic stimulation, this approach improves spatial selectivity, minimizes crosstalk, and reduces power consumption. The conceptual design for neural tissue stimulation is presented, with ongoing efforts focused on integrating this system into a microelectronic chip. By addressing key limitations in current prosthetic systems, this work contributes to the development of more efficient and scalable solutions for visual restoration.},
}
@article {pmid41337025,
year = {2025},
author = {Liu, G and Yan, Y and He, S and Cai, J and Cheok, AD and Qi Wu, E and Song, A},
title = {A Neuromorphic Approach for Brain-Machine Interface Using Spiking Neural Networks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11254255},
pmid = {41337025},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Animals ; *Neural Networks, Computer ; Algorithms ; *Action Potentials/physiology ; Motor Cortex/physiology ; Macaca mulatta ; Humans ; },
abstract = {Brain-machine interfaces (BMIs) have emerged as a promising technology for restoring motor function in paralyzed individuals through direct neural control of prosthetic devices. While conventional decoding algorithms have achieved considerable success, they often overlook the fundamental biological properties of neural information processing. This paper presents a novel approach using Spiking Neural Networks (SNNs), a neuromorphic computing paradigm that closely mimics biological neural dynamics through event-driven processing and spike-timing-dependent plasticity. A SNN-based decoder was implemented for offline decoding of intracortical neural recordings from the primary motor cortex (M1) and dorsal premotor cortex (PMd) to continuous 2D cursor movements in a macaque monkey. This approach leverages the temporal processing capabilities of SNNs to capture the complex, time-varying nature of neural representations, potentially enabling more naturalistic and adaptive BMI control.},
}
@article {pmid41337011,
year = {2025},
author = {Yao, R and Du, Z and Liang, F and Li, W and Hong, B},
title = {Dual-layer hand gestures decoding with wireless epidural braincomputer interface in a tetraplegia.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-6},
doi = {10.1109/EMBC58623.2025.11254206},
pmid = {41337011},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; *Quadriplegia/physiopathology ; *Gestures ; *Hand/physiopathology/physiology ; *Wireless Technology ; Algorithms ; Electroencephalography ; },
abstract = {Spinal cord injury disrupts the neural connections between the brain and limbs, resulting in tetraplegia. Brain-computer interface (BCI) hold promise for enabling voluntary limb movements in tetraplegic patients, yet achieving fine motor control of the hand remains a challenge. Invasive BCI based on intracortical electrode arrays have demonstrated real-time multi-gesture decoding. However, their long-term safety is a major barrier in clinical applications. In this study, a tetraplegic patient was implanted with our recently developed wireless minimally invasive BCI, which records epidural field potential from eight electrodes over the sensorimotor cortex to decode continuous hand movement intentions. Natural hand movements can be decomposed into dual layers: the high level movement states and the low level finger kinematics. Accordingly, we propose a dual-layer decoding algorithm for multi-gesture BCI decoding. The upper layer infers the movement state using a hidden Markov model, while the lower layer decodes finger motion variables through a mixture of experts and filters them with a state specific linear system. This approach enables the real-time decoding of six hand gestures, outperforming classical decoders and recurrent neural networks. The proposed dual-layer framework achieves multi-gesture decoding solely from epidural EEG signals, paving the way for the development of flexible and robust BCI control of hand movement.},
}
@article {pmid41336923,
year = {2025},
author = {Chen, X and Peng, Y and Li, C and Pan, Y and Ding, N and Zhang, S},
title = {MI-LTN: A Neurosymbolic Framework for Enhanced EEG Feature Extraction and Model Interpretability in MI-BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11252655},
pmid = {41336923},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; *Brain-Computer Interfaces ; Humans ; *Signal Processing, Computer-Assisted ; Algorithms ; },
abstract = {Brain-Computer Interface (BCI) is a cutting-edge technology that facilitates human-computer interaction. Motor Imagery Electroencephalogram (MI-EEG) decoding technology has emerged as a significant direction in BCI research. Despite the remarkable advancements in deep learning for EEG signal decoding in recent years, two major challenges persist: the comprehensive representation and extraction of features, and the lack of interpretability. To address these issues, we propose a novel neurosymbolic framework termed MI-LTN (Motor Imagery Logic Tensor Network), incorporate logical constraints into the training model using the Logic Tensor Network (LTN) and employ Shapley values to evaluate and adjust the importance of channels. Our experimental results show that MI-LTN achieves classification accuracies of 86.00% and 88.84% on the BCI IV 2a and BCI IV 2b datasets, respectively. These results demonstrate the great potential of LTN in MI-EEG decoding.},
}
@article {pmid41336899,
year = {2025},
author = {Bradshaw Bernacchi, JK and Lopez Valdes, A},
title = {Electrophysiological Characterisation of Commercial Ear-EEG Devices.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11252639},
pmid = {41336899},
issn = {2694-0604},
mesh = {*Electroencephalography/instrumentation ; Humans ; *Ear/physiology ; Adult ; Male ; Female ; Wearable Electronic Devices ; Electrodes ; Brain-Computer Interfaces ; Equipment Design ; Young Adult ; Signal Processing, Computer-Assisted ; },
abstract = {Ear-EEG devices are advanced wearables revolutionizing EEG technology by combining comfort and portability. With the increasing availability of commercial ear-EEG devices, there is a need for an independent characterisation of the electrophysiological performance to guide users and researchers. Here, we evaluate the performance of the IDUN Guardian Earbuds (IGEB, IDUN Technologies AG) by analysing electrophysiological responses to several well-established EEG paradigms, including event-related potentials (ERPs), auditory steady-state response (ASSR), steady-state visually evoked potential (SSVEP), and alpha block, and comparing them to standard scalp-based EEG recordings acquired simultaneously from eight participants utilizing a validation toolkit previously developed in our lab. Results indicate that the in-ear device is capable of detecting SSVEPs. However, we did not observe ERPs, ASSRs, or alpha blocking. Simulating in-ear EEG with electrode T8 referenced to T7 slightly improved the quality of the signal, which was further enhanced with midline reference electrodes.Clinical Relevance- Characterising this technology marks a step forward providing independent assessment of commercially available devices in view of expanding EEG applications, from long-term monitoring and wearable health solutions to advanced brain-machine interfaces (BMI).},
}
@article {pmid41336877,
year = {2025},
author = {Torgersen, EL and Ragnarson, I and Molinas, M},
title = {Decoding Attention through EEG: Paving the Way for BCI Applications in Attention-Related Disorders.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11252840},
pmid = {41336877},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Attention Deficit Disorder with Hyperactivity/physiopathology/diagnosis ; *Attention/physiology ; *Brain-Computer Interfaces ; Male ; Female ; Adult ; Machine Learning ; Young Adult ; Brain/physiopathology ; },
abstract = {This study investigates attention-related traits in EEG signals to assess the potential of Electroencephalography (EEG) as an objective diagnostic tool for attention-related disorders such as ADHD, anxiety, and learning disabilities. EEG data were collected from 31 participants, including individuals with ADHD, while they performed a Go/No-Go task designed to evaluate attention and impulsivity. The analysis focused on the spectral characteristics of brain activity, examining the relative power of theta, alpha, and beta frequency bands, along with the theta-to-beta ratio (TBR), to identify distinguishing patterns of attention-related brain activity. Results indicate that the ADHD group exhibited higher theta power and consistently elevated TBR, particularly in the Frontal, Temporal, and Occipital brain regions. Machine learning models, such as K-Nearest Neighbors, effectively classified ADHD and Control groups based on TBR with high accuracy. Additionally, the ADHD group demonstrated faster reaction times but made more errors on the Go/No-Go task, highlighting difficulties with sustained attention. These findings suggest that this approach holds promise for developing objective diagnostic tools for attention-related disorders. While some limitations exist, this study underscores the potential of integrating EEG with machine learning to create brain-computer interface (BCI) systems for assessing attention processes.},
}
@article {pmid41336846,
year = {2025},
author = {Pahuja, S and Ivucic, G and Cai, S and De Silva, D and Li, H and Schultz, T},
title = {XAGnet: Cross-Attention Graph Network for Detecting Auditory Attention in Ear-EEG Signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-6},
doi = {10.1109/EMBC58623.2025.11252872},
pmid = {41336846},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; Humans ; *Attention/physiology ; *Signal Processing, Computer-Assisted ; *Ear/physiology ; Algorithms ; *Neural Networks, Computer ; Brain-Computer Interfaces ; },
abstract = {Auditory Attention Detection (AAD) is essential for developing advanced brain-computer interfaces including neuro-steered hearing technologies capable of functioning in complex auditory environments. In this study, we propose XAGnet, a novel method that leverages ear-centered EEG (ear-EEG) data to model both intra-ear and inter-ear neural dependencies for detection of auditory attention to one of the spatial locations. Specifically, Graph Convolutional Networks (GCNs) are applied separately to left and right ear-EEG signals to extract spatial features from each side for intra-ear interactions. A cross-attention mechanism is then introduced to model inter-ear interactions between the left and right ears. The attended features are combined for multi-class classification, with each class representing a speaker or a speaking location. We evaluate our method on a publicly available ear-EEG dataset, involving AAD tasks with four speakers. Experimental results demonstrate that XAGnet outperforms baseline models, highlighting the effectiveness of modeling both intra-ear and inter-ear dependencies in AAD tasks.},
}
@article {pmid41336840,
year = {2025},
author = {Jahanjoo, A and Wei, Y and Haghi, M and Schorpf, P and TaheriNejad, N},
title = {Hybrid CNN-Transformer Model for Accurate Classification of Human Attention Levels Using Workplace EEG Data.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-6},
doi = {10.1109/EMBC58623.2025.11251604},
pmid = {41336840},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Attention/physiology ; *Neural Networks, Computer ; *Workplace ; Signal Processing, Computer-Assisted ; Algorithms ; Brain-Computer Interfaces ; Fourier Analysis ; },
abstract = {Accurately detecting human attention levels is a key challenge in cognitive neuroscience, with broad application value in improving productivity. Although Electroencephalography (EEG) signals are often used to study cognitive states, most studies still rely on data collected in controlled laboratory environments. This paper collects EEG data from employees during their daily work using a commercial single-channel EEG headband, making attention detection closer to real-world applications and increasing its feasibility and promotion value. We propose a new classification method based on a multi-head attention transformer to identify six different attention levels. We first perform a Short-Time Fourier Transform (STFT) on the EEG signal. Subsequently, we constructed a transformer architecture to effectively model long-range dependencies and subtle pattern changes in EEG data using self-attention and stacked encoder layers. Experimental results show that our proposed model achieves 87.37% classification accuracy in the six-level attention classification task, outperforming traditional high-performance methods and demonstrating superior performance compared to existing similar approaches. This achievement not only verifies the potential of the transformer architecture in EEG attention level classification but also provides new possibilities for developing advanced tools in fields such as brain-computer interface (BCI) and cognitive monitoring.},
}
@article {pmid41336806,
year = {2025},
author = {Quiles, V and Polo-Hortiguela, C and Soriano-Segura, P and Ortiz, M and Ianez, E and Azorin, JM},
title = {Design of an Asynchronous BMI with Interpretable Neural Networks for Exoskeleton Control: A Proof of Concept on Data Evolution and Scalability Over One Week.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11251541},
pmid = {41336806},
issn = {2694-0604},
mesh = {Humans ; *Exoskeleton Device ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; Robotics ; Equipment Design ; },
abstract = {This paper presents a concept study of a week-long experimental protocol for controlling a lower-limb exoskeleton via a brain-machine interface. The system employed a neural network adapted from EEGNet that distinguishes motor imagery and resting states in a two-dimensional space under both static and movement conditions. Each day, the model was fine-tuned with that day's training data as well as data from previous days. Daily closed-loop asynchronous evaluations were carried out to assess real-time exoskeleton control performance. The results indicate steady improvements in system accuracy over the week, likely due to the cumulative integration of additional data, which enhanced the neural network-based approach to cognitive state classification in a multi-day setting.Clinical relevance-Incorporating repetitive robotic therapies in which the patient can actively engage in rehabilitation is a core goal of neurorehabilitation. Developing non-invasive brain-machine interfaces that enable an increasingly effective mind-robot connection is of great importance. This work outlines a protocol for creating a brain-machine interface controlled by motor imagery.},
}
@article {pmid41336805,
year = {2025},
author = {Yuan, Z and Li, Y and Zhang, H and Liu, X and Li, S and Zhu, Y and Wang, H and Li, J and Wang, H},
title = {Decoding Hybrid EEG-fNIRS Upper Limb Motor Execution with Capsule Dynamic Graph Convolutional Neural Network.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11251587},
pmid = {41336805},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; Spectroscopy, Near-Infrared/methods ; *Neural Networks, Computer ; *Upper Extremity/physiology ; Signal Processing, Computer-Assisted ; Algorithms ; Brain-Computer Interfaces ; Convolutional Neural Networks ; },
abstract = {In this study, we proposed a capsule dynamic graph convolution network (EF-CapsDGCN) for accurate decoding of upper limb motor execution (ME) based on both electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals. In EF-CapsDGCN, EEG/fNIRS features are extracted using the same convolutional architecture but different parameter settings. The extracted features from both modalities are then dynamically routed to capsules. Afterwards, the single-modality capsules are concatenated to form EEG-fNIRS multimodal capsules. Each capsule is treated as a graph node, and hidden feature representations are learned through dynamic graph convolution. Finally, after concatenating the original capsules with the learned hidden features, the combined features are passed through multi-head self-attention and then flattened to feed into a fully connected layer for classification. Compared to current state-of-the-art methods such as ANN, DeepConvNet, DNN, and EF-Net, the proposed method demonstrated superior classification performance on the multimodal EEG-fNIRS dataset HYGRIP. Furthermore, our model achieves at least 8% higher classification accuracy in multimodal EEG-fNIRS compared to single modality EEG/fNIRS. These results demonstrate the potential of capsule dynamic graph convolution for the multimodal fusion of EEG and fNIRS. The proposed model is promising for accurately decoding motor execution-based brain computer interfaces with EEG-fNIRS multiple signals. Overall, this study provides an effective solution for multimodal-BCI decoding.Clinical Relevance- This study demonstrates that integrating EEG and fNIRS signals via a capsule dynamic graph convolution network (EF-CapsDGCN) improves upper limb motor execution decoding accuracy by at least 8% compared to single-modality approaches, offering clinicians a more reliable tool for developing brain-computer interface systems to enhance rehabilitation or assistive device control in patients with motor impairments.},
}
@article {pmid41336758,
year = {2025},
author = {Cueva, VM and Lotte, F and Bougrain, L and Rimbert, S},
title = {Quantifying Inter- and Intra-Subject Variability of Sensorimotor Desynchronization Induced by Median Nerve Stimulation and Motor Imagery for BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11254356},
pmid = {41336758},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Median Nerve/physiology ; Male ; *Imagination/physiology ; Adult ; Female ; Electric Stimulation ; *Sensorimotor Cortex/physiology ; },
abstract = {Motor Imagery-based Brain-Computer Interfaces (MI-BCIs) enable users to control external devices by interpreting sensorimotor activity recorded via ElectroEncephaloGraphy (EEG). Median Nerve Stimulation (MNS) has recently emerged as a promising alternative motor task for BCI applications. However, intra- and inter-subject EEG variability remains a major challenge, affecting BCI system reliability. While variability is a well-known issue, its precise sources and impact on different EEG patterns remain unclear, with a lack of formal and quantitative studies of BCI variability. Thus, this study quantifies intra- and inter-subject variability in MNS-induced sensorimotor desynchronization (ERD) and compares it with that of MI. Results show that MI elicits stronger ERD with lower intra-subject variability, suggesting more consistent activation patterns, while inter-subject variability is similar between tasks. Additionally, the variability of classification accuracies based on Riemannian geometry exhibits a similar trend. These findings provide insights into EEG variability and its implications for BCI design. Identifying stable neural patterns could improve MI- and MNS-based BCIs, particularly for applications such as intraoperative awareness monitoring.},
}
@article {pmid41336716,
year = {2025},
author = {Abid, U and Zulfiqar, O and Nazeer, H and Naseer, N and Bo, APL and Khan, H},
title = {fNIRS Based Comparative Study of Classifiers and Feature Selection Techniques for Finger Tapping.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-6},
doi = {10.1109/EMBC58623.2025.11254285},
pmid = {41336716},
issn = {2694-0604},
mesh = {Humans ; Spectroscopy, Near-Infrared/methods ; *Fingers/physiology ; Algorithms ; Support Vector Machine ; Male ; Movement/physiology ; Machine Learning ; Adult ; Female ; },
abstract = {This study seeks to classify five-finger movements using machine learning (ML) algorithms. It also examines how feature optimization methods affect classification performance. The signals of functional near-infrared spectroscopy (fNIRS) were acquired from 20 healthy participants as they performed five different finger movements. The recorded signals are represented by a total of 17 spatial features such as kurtosis, variance, mean, skewness and others. The ML classifiers used in the beginning are Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost). Their performance parameters including precision, accuracy, F1-score, recall and processing time are recorded initially for the dataset comprising of all the features. Afterwards, three population-based metaheuristic algorithms Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are used to determine the top features from the dataset. The same ML classifiers are then applied to the selected feature datasets. Classification performance is significantly improved by optimized features, with GA and PSO outperforming ACO. SVM is beaten by XGBoost, while its accuracy (94.94%) is greatest when adopting GA-optimized features. The study also shows the role played by feature selection in improving the efficiency and accuracy of ML models in neuroimaging applications. It also suggests optimized classification pipelines for brain-computer interface systems.},
}
@article {pmid41336656,
year = {2025},
author = {Memar, MO and Ziaei, N and Nazari, B and Yousefi, A},
title = {RISE-iEEG: Robust to Inter-Subject Electrodes Implantation Variability iEEG Classifier.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11252788},
pmid = {41336656},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods/instrumentation ; Neural Networks, Computer ; Brain-Computer Interfaces ; *Electrodes, Implanted ; Algorithms ; Signal Processing, Computer-Assisted ; },
abstract = {Intracranial electroencephalography (iEEG) is increasingly used for clinical and brain-computer interface applications due to its high spatial and temporal resolution. However, inter-subject variability in electrode implantation poses a challenge for developing generalized neural decoders. To address this, we introduce a novel decoder model that is robust to inter-subject electrode implantation variability. We call this model RISE-iEEG, which stands for Robust to Inter-Subject Electrode Implantation Variability iEEG Classifier. RISE-iEEG employs a deep neural network structure preceded by a participant-specific projection network. The projection network maps the neural data of individual participants onto a common low-dimensional space, compensating for the implantation variability. In other words, we developed an iEEG decoder model that can be applied across multiple participants' data without requiring the coordinates of electrode for each participant. The performance of RISE-iEEG across multiple datasets, including the Music Reconstruction dataset, and AJILE12 dataset, surpasses that of advanced iEEG decoder models such as HTNet and EEGNet. Our analysis shows that the performance of RISE-iEEG is about 7% higher than that of HTNet and EEGNet in terms of F1 score, with an average F1 score of 0.83, which is the highest result among the evaluation methods defined. Furthermore, Our analysis of the projection network weights reveals that the Superior Temporal and Postcentral lobes are key encoding nodes for the Music Reconstruction and AJILE12 datasets, which aligns with the primary physiological principles governing these regions. This model improves decoding accuracy while maintaining interpretability and generalization.},
}
@article {pmid41336644,
year = {2025},
author = {Si, Y and Wang, Z and Zhao, X and Xu, T and Zhou, T and Hu, H},
title = {Sub-Group Partition Strategy for RSVP-based Collaborative Brain-Computer Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-5},
doi = {10.1109/EMBC58623.2025.11252828},
pmid = {41336644},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Algorithms ; Signal Processing, Computer-Assisted ; Reproducibility of Results ; },
abstract = {Collaborative brain-computer interfaces (cBCIs) have demonstrated significant improvements in single-trial electroencephalogram (EEG) classification performance in rapid serial visual presentation (RSVP) tasks. However, it remains unclear how to effectively organize multiple collaborators into sub-groups to optimize system performance. This study introduces a novel sub-group partition strategy for RSVP-based cBCI systems. We first developed intra-individual and inter-individual neural response reproducibility (IINRR) as a metric to estimate subgroup capability in RSVP tasks. Based on this metric, we propose an IINRR-based partition strategy to optimize sub-group composition. Additionally, we introduce a metric called collaborative information processing rate (CIPR) to evaluate overall system performance. Our experiments verified the effectiveness of the proposed strategy on a public RSVP-based cBCI dataset. The results showed that our strategy consistently outperformed random partitioning in both within-session and cross-session scenarios, achieving higher classification performance and system efficiency. These findings suggest the strategy's potential for optimizing group mode in practical RSVP-based cBCI applications.},
}
@article {pmid41336643,
year = {2025},
author = {Merino, EC and Sun, Q and Dauwe, I and Carrette, E and Meurs, A and Van Roost, D and Boon, P and Van Hulle, MM},
title = {Medial Wall's Potential in Enhancing Finger Movement Decoding from Electrocorticography (ECoG): A Single-Subject Pilot Study.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-6},
doi = {10.1109/EMBC58623.2025.11252768},
pmid = {41336643},
issn = {2694-0604},
mesh = {Humans ; *Electrocorticography/methods ; *Fingers/physiology ; Pilot Projects ; Movement/physiology ; *Brain-Computer Interfaces ; *Motor Cortex/physiology ; Male ; Adult ; },
abstract = {The next generation of motor brain-computer interfaces (BCIs) will likely benefit from integrating recordings from multiple motor-related brain regions. Among these is the medial wall, yet it remains relatively understudied in the case of finger movement decoding. Using electrocorticographic (ECoG) recordings from a subject implanted both over medial and lateral cortical areas, we first assessed the medial wall's potential for multiclass classification (5 fingers + rest). We achieved a six-class accuracy of 0.46, significantly above chance, with rest trials classified most accurately, followed by thumb movement trials. Several frequency features contributed to decoding, with Local Motor Potentials (LMP) being the most influential one, with distinctive activity already prior to movement onset, and power in the α (8-12 Hz) band aiding in decoding rest trials over finger movement trials. Next, we explored whether combining the best medial wall channel with lateral cortical channels could improve decoding performance. We found a significant accuracy improvement for most lateral channels (from an average of 0.36 to 0.42), except for the channel closest to the finger primary motor region, whose accuracy was already high (0.77). These findings highlight the medial wall's potential for motor decoding and its value as a target region for future motor BCIs, especially for individuals with impaired hand motor areas.},
}
@article {pmid41336630,
year = {2025},
author = {Wen, Y and An, Y and Chu, M and Chen, S and Lu, X and Guo, H and Yu, J},
title = {Classification of Functional Near-Infrared Spectroscopy Based on Gramian Angular Difference Field and a Temporal-Spatial Feature Fusion Network.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-5},
doi = {10.1109/EMBC58623.2025.11254538},
pmid = {41336630},
issn = {2694-0604},
mesh = {Spectroscopy, Near-Infrared/methods ; Humans ; Algorithms ; Deep Learning ; Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Neural Networks, Computer ; },
abstract = {Functional near-infrared spectroscopy (fNIRS) is a non-invasive functional neuroimaging technique widely employed in brain-computer interface (BCI) research and diverse clinical applications. The key challenge in fNIRS applications lies in extracting nonlinear structures and complex patterns from one-dimensional time series data. Gramian angular difference field (GADF) transforms one-dimensional time series into two-dimensional images, providing effective feature representation for subsequent signal classification. However, most studies have not explored the combined effects of image features and time series features. In this paper, we propose a deep learning model, VisiTempNet, which integrates both time series and GADF image features in a temporal-spatial fusion approach. The model first performs convolution on time series data based on delayed hemodynamic responses to highlight key features. It then separates the feature extraction process into two parallel modules, and normalizes and fuses these features with learnable weights, assigning greater importance to the most relevant information for classification. Experimental results show that our model achieved an accuracy of 76.65±2.43% on the open access fNIRS2MW dataset, outperforming all baseline models. This validates the effectiveness of combining image and time series features and demonstrates the superiority of the proposed model.},
}
@article {pmid41336626,
year = {2025},
author = {Bao, X and Xu, K and Zhu, J and Huang, H and Li, K and Huang, Q and Li, Y},
title = {Seasickness Alleviation based on a Mindfulness Brain-Computer Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-6},
doi = {10.1109/EMBC58623.2025.11254567},
pmid = {41336626},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; Male ; *Mindfulness ; Adult ; *Motion Sickness/therapy/prevention &amp; control/physiopathology ; Female ; Young Adult ; Attention ; },
abstract = {Seasickness is a common condition that negatively affects both the experience of passengers and the operating performance of maritime personnel. Techniques aimed at redirecting attention have been proposed to alleviate motion sickness symptoms; however, their effectiveness has not yet been rigorously verified, especially in maritime environments, which present unique challenges due to the prolonged and severe motion conditions. This research introduces a mindfulness brain-computer interface (BCI) specifically designed to redirect attention and alleviate seasickness. The system employs a single-channel headband to record prefrontal electroencephalography (EEG) signals, which are wirelessly transmitted to computing devices for real-time mindfulness assessments. Participants receive feedback in the form of mindfulness scores and audiovisual cues, facilitating a redirection of attention from physical discomfort. In maritime experiments with 43 participants across three sessions, 81.39% reported the BCI's effectiveness, and a substantial reduction in seasickness severity was observed using the Misery Scale (MISC). Together, our work presents the first wearable and nonpharmacological solution for alleviating seasickness, and opens up a brand-new application domain for BCIs.},
}
@article {pmid41336622,
year = {2025},
author = {Ahmadi, K and Dong, L and Kok, RL and Findeisen, R},
title = {Gaussian Process-Based Surrogate Models for Optimizing Electrode Configurations in HD-tDCS.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11254512},
pmid = {41336622},
issn = {2694-0604},
mesh = {*Transcranial Direct Current Stimulation/instrumentation/methods ; Humans ; Electrodes ; Normal Distribution ; Brain/physiology ; Computer Simulation ; Algorithms ; },
abstract = {High-definition transcranial direct current stimulation (HD-tDCS) is a promising noninvasive neurostimulation technique used in therapeutic applications and brain-machine interfaces. It delivers direct current via multiple scalp electrodes, generating targeted electrical fields to modulate specific brain areas. In the context of HD-tDCS, optimizing electrode placements is challenging due to the complexity of brain anatomy and the vast number of possible configurations. While simulation models enable model-based optimization, continuous electrode positioning is generally computationally prohibitive. We propose Gaussian Process (GP)-based framework for optimizing HD-tDCS, allowing continuous prediction of electric field distributions. Unlike traditional leadfield-based methods, which restrict electrode placement, our approach expands the search space for greater precision. We employ a Sparse Gaussian Process (SGP) approximation, optimized using Block-Coordinate Descent and Subset of Data techniques, to efficiently handle large datasets. Results demonstrate that the SGP-based model significantly enhanced focality for superficial and mid-brain regions, achieving performance comparable to leadfield-based methods for deep brain targets. Overall, this framework offers enhanced stimulation precision and flexibility, supporting the advancement of tDCS in research and clinical contexts.},
}
@article {pmid41336584,
year = {2025},
author = {Caracci, V and Riccio, A and D'Ippolito, M and Galiotta, V and Quattrociocchi, I and Formisano, R and Cincotti, F and Toppi, J and Mattia, D},
title = {Impact of latency jitter correction on offline P300-based classification: a preliminary study for BCI applications in MCS patients.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-6},
doi = {10.1109/EMBC58623.2025.11253369},
pmid = {41336584},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; *Event-Related Potentials, P300 ; Male ; Female ; Adult ; Electroencephalography/methods ; *Persistent Vegetative State/physiopathology ; Algorithms ; Middle Aged ; },
abstract = {Disorders of Consciousness (DoC) are clinical conditions characterized by different levels of arousal and awareness, including coma, Unresponsive Wakefulness Syndrome and Minimally Conscious State (MCS). A Brain-Computer Interface (BCI) employs brain signals to establish a non-muscular outward channel, representing a key frontier in the clinical care of individuals in MCS, with high potential to enhance communication and quality of life. The P300-based BCIs, which use the P300 ERP as a control signal, are the most investigated to emulate communication in MCS. However, a reliable control by MCS patients of these BCIs still remains matter of question. One major challenge could be the across trials variability of P300 characteristics, possibly related to attentional fluctuations in this population. The trial-by-trial instability of the P300 peak latency, known as latency jitter, negatively impacts classification performance, and an approach to mitigating this issue involves template matching algorithms (e.g. the Adaptive Wavelet Filtering, AWF) which detect and realign the P300 latency at the single-trial level. This study investigated the offline classification performance using Stepwise Linear Discriminant Analysis (SWLDA) models trained with progressively larger training sets, to discriminate target from non-target stimuli during an active auditory oddball paradigm. Performance from raw and jitter-corrected data, collected from a control group and a group of patients diagnosed as MCS, were compared. Results highlighted the key role of latency jitter correction in the enhancement of performance and classification speed.Clinical Relevance- The findings suggest that jitter correction could improve real-world applicability of P300-BCI systems for individuals with DoC.},
}
@article {pmid41336583,
year = {2025},
author = {Orlandi, M and Rapa, PM and Baracat, F and Benini, L and Donati, E and Benatti, S},
title = {Neural Strategies for Upper Limb Movements: Motor Unit Control during Dynamic Contractions at Increasing Speeds.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11253409},
pmid = {41336583},
issn = {2694-0604},
mesh = {Humans ; Electromyography ; *Upper Extremity/physiology ; Male ; Movement/physiology ; *Muscle Contraction/physiology ; Adult ; *Motor Neurons/physiology ; Muscle, Skeletal/physiology ; Female ; Young Adult ; },
abstract = {Understanding motor unit (MU) behavior in dynamic movements remains a critical gap in neuro-rehabilitation, prosthetics, and human-machine interfaces (HMI). While machine learning applied to surface electromyography (sEMG) enables movement classification, it provides little insight into neural control, limiting the development of more precise and adaptive assistive technologies. Recent studies have demonstrated that MU activity can be accurately extracted using high-density sEMG decomposition under isometric conditions. However, extracting and tracking MUs during dynamic tasks remains challenging due to signal non-stationarity caused by changes in muscle length. This study investigates MU control in the forearm flexor muscles across different contraction velocities (5°/s, 10°/s, 20°/s) and force levels (15% and 25% of the maximum voluntary contraction [MVC]). We investigate whether increases in movement velocity are primarily achieved through MU recruitment strategies or by adjusting the discharge rates of already-recruited units. Our findings show that MU control in the upper limb follows a velocity-dependent modulation pattern (p-value < 0.05), favoring discharge rate adjustments over additional MUs recruitment at higher speeds. We also validate the feasibility of MU tracking in dynamic conditions, opening new opportunities for neurotechnology applications such as HMI.},
}
@article {pmid41336567,
year = {2025},
author = {Roy Chowdhury, M and Ding, Y and Sen, S},
title = {SSL-SE-EEG: A Framework for Robust Learning from Unlabeled EEG Data with Self-Supervised Learning and Squeeze-Excitation Networks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11253365},
pmid = {41336567},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; Humans ; Brain-Computer Interfaces ; *Supervised Machine Learning ; *Signal Processing, Computer-Assisted ; Algorithms ; Deep Learning ; Neural Networks, Computer ; },
abstract = {Electroencephalography (EEG) plays a crucial role in brain-computer interfaces (BCIs) and neurological diagnostics, but its real-world deployment faces challenges due to noise artifacts, missing data, and high annotation costs. We introduce SSL-SE-EEG, a framework that integrates Self-Supervised Learning (SSL) with Squeeze-and-Excitation Networks (SE-Nets) to enhance feature extraction, improve noise robustness, and reduce reliance on labeled data. Unlike conventional EEG processing techniques, SSL-SE-EEG transforms EEG signals into structured 2D image representations, suitable for deep learning. Experimental validation on MindBigData, TUH-AB, SEED-IV and BCI-IV datasets demonstrates state-of-the-art accuracy (91% in MindBigData, 85% in TUH-AB), making it well-suited for real-time BCI applications. By enabling low-power, scalable EEG processing, SSL-SE-EEG presents a promising solution for biomedical signal analysis, neural engineering, and next-generation BCIs. The code is available at https://github.com/roycmeghna/SSL_SE_EEG_EMBC25.},
}
@article {pmid41336566,
year = {2025},
author = {Guttmann-Flury, E and Wei, Y and Zhao, S},
title = {Automatic Blink-Based Bad EEG channels Detection for BCI Applications.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11253420},
pmid = {41336566},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Blinking/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; Artifacts ; Adult ; Male ; },
abstract = {In Brain-Computer Interface (BCI) applications, noise presents a persistent challenge, often compromising the quality of EEG signals essential for accurate data interpretation. This paper focuses on optimizing the signal-to-noise ratio (SNR) to improve BCI performance, with channel selection being a key method for achieving this enhancement. The Eye-Bci multimodal dataset is used to address the issue of detecting and eliminating faulty EEG channels caused by non-biological artifacts, such as malfunctioning electrodes and power line interference. The core of this research is the automatic detection of problematic channels through the Adaptive Blink-Correction and DeDrifting (ABCD) algorithm. This method utilizes blink propagation patterns to identify channels affected by artifacts or malfunctions. Additionally, segmented SNR topographies and source localization plots are employed to illustrate the impact of channel removal by comparing Left and Right hand grasp Motor Imagery (MI). Classification accuracy further supports the value of the ABCD algorithm, reaching an average classification accuracy of 93.81% [74.81%; 98.76%] (confidence interval at 95% confidence level) across 31 subjects (63 sessions), significantly surpassing traditional methods such as Independent Component Analysis (ICA) (79.29% [57.41%; 92.89%]) and Artifact Subspace Reconstruction (ASR) (84.05% [62.88%; 95.31%]). These results underscore the critical role of channel selection and the potential of using blink patterns for detecting bad EEG channels, offering valuable insights for improving real-time or offline BCI systems by reducing noise and enhancing signal quality.},
}
@article {pmid41336553,
year = {2025},
author = {Sen, O and Khalifa, A and Chatterjee, B},
title = {High-Speed Neural Signal Inferencing for Handwritten Character Recognition on a Portable Hardware Device.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11253375},
pmid = {41336553},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; *Handwriting ; *Signal Processing, Computer-Assisted/instrumentation ; Algorithms ; },
abstract = {Brain-computer interfaces (BCIs) hold immense potential in assisting individuals with severe motor and communication disabilities by enabling neural signal-based activity recognition, such as handwriting. This study presents the very first implementation of neural signal inference on a portable hardware device, facilitating efficient handwritten character recognition on resource-constrained platforms. Neural signals from a publicly available dataset are processed into neural spike-event data, facilitating the classification of 31 handwritten characters on an NVIDIA Jetson TX2. To enhance model generalization and mitigate overfitting, random noise injection and time-shifting-based data augmentation techniques are applied. The proposed approach utilizes EfficientNetB0 with neural spikes, and achieves 99.17% test accuracy, significantly outperforming previous model results. During high-speed inference, EfficientNetB0 achieved a Word Error Rate (WER) of 0.96% and a Character Error Rate (CER) of 0.2%, with a character decoding latency of 37.5 milliseconds on the Jetson TX2 while processing 100 sentences used in daily life. These results validate the feasibility of accurate high-speed neural decoding on portable edge hardware, highlighting the impact of lightweight machine learning models in BCI applications.},
}
@article {pmid41336530,
year = {2025},
author = {Li, S and Yang, M and Sun, J and Sun, J and Yu, G and Lin, L and Meng, J and Xu, M},
title = {EEG features and suitable decoding algorithm of RSVP-based brain-computer interface in continuous scenes.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11251802},
pmid = {41336530},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Algorithms ; Male ; Signal Processing, Computer-Assisted ; Adult ; Female ; Evoked Potentials ; Young Adult ; },
abstract = {Brain-computer interface (BCI) based on rapid serial visual presentation (RSVP) hold significant value for achieving robust target detection through the integration of human and machine. RSVP in continuous scenes presents video materials and is thus much closer to real-world applications, which greatly exceeds traditional discrete-scene RSVP in terms of practicality. However, the similarities and differences in electroencephalography (EEG) features between continuous and discrete scenes have not yet been clearly clarified. And there is a lack of research on decoding algorithms that are more suitable for continuous scenes, which seriously hinders the development of continuous-scene target detection. To solve these problems, this study designed a comparative experiment based on RSVP paradigm in continuous and discrete scenes. Event-related potential (ERP), event-related spectral perturbation (ERSP), and inter-trial coherence (ITC) were used to investigate EEG features induced by distinct scenes. Further, this study used sliding hierarchical discriminant component analysis (sHDCA), shrinkage discriminative canonical pattern matching (SKDCPM) and attention-based temporal convolutional network (ATCNet) to implement target/non-target trial classification. Consequently, continuous scenes exhibited fewer induced ERP components, a shorter latency of P300, and reduced neural oscillation activities in alpha and beta1 bands over the occipital region within 0~0.2s. As for classification, traditional machine learning algorithms obtained significantly lower accuracy in continuous scenes. While ATCNet achieved the best and same level of accuracy in both scenes, indicating its suitability for decoding continuous-scene RSVP. The results contributed to develop more practical RSVP-BCI target detection systems.},
}
@article {pmid41336489,
year = {2025},
author = {Song, Z and Wu, S and Zhou, T and Wang, Y},
title = {Extracting Preserved Neural Latent Dynamics Across Tasks using Convolutional Transformer-based Variational Autoendecoder.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11251780},
pmid = {41336489},
issn = {2694-0604},
mesh = {Rats ; Animals ; Algorithms ; *Neurons/physiology ; *Neural Networks, Computer ; Brain-Computer Interfaces ; },
abstract = {Understanding how neural systems drive behavior is a fundamental goal in neuroscience. Numerous studies have demonstrated that the activity of large neural populations is often governed by low-dimensional neural dynamics. While much of the current research has focused on extracting informative and interpretable latent dynamics from individual motor tasks, it remains unclear whether these dynamics are preserved across different motor tasks. This question is particularly critical, as prior experience with a related task can facilitate faster learning in a new task. In this paper, we propose a Convolutional Transformer-based Variational Autoencoder (Conformer-VAE) to extract preserved neural latent dynamics across tasks by leveraging the rich spatiotemporal patterns in neural activity. We validate our approach using neural recordings from a rat, which first performed a one-lever pressing task (old task) and subsequently a two-lever discrimination task (new task). By projecting the inferred latent dynamics from both tasks onto a common 2D PCA plane, our results demonstrate that Conformer-VAE effectively captures preserved neural dynamics across tasks, outperforming baseline methods. Moreover, these preserved dynamics enable faster decoder training for the new task by transferring the neural-to-movement mapping learned from the old task. This capability facilitates seamless real-time task switching, offering promising applications for brain-machine interface systems.Clinical Relevance-This work facilitates faster adaptation in brain-machine interfaces by preserving neural dynamics across tasks, offering potential benefits for neuroprosthetics and motor rehabilitation in patients with motor impairments.},
}
@article {pmid41336487,
year = {2025},
author = {Iacomi, F and Tiberio, P and Tonon, T and Perugini, S and Farabbi, A and Barbieri, R and Mainardi, L},
title = {Validation of a Novel Protocol for Whole-Sentence Imagined Speech Acquisition: Advancing Brain-Computer Interface Applications.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11251720},
pmid = {41336487},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; *Speech/physiology ; Male ; Female ; *Imagination/physiology ; Young Adult ; Adult ; Electroencephalography/methods ; },
abstract = {This study aims to validate a novel protocol for whole-sentence imagined speech acquisition, building upon and addressing limitations of a previous single-word acquisition protocol. Eight participants (gender-balanced, mean age 21.3±6 years) were recruited for this study. Participant attention indices, and session variations were evaluated across multiple sessions. The protocol successfully maintains participant engagement while effectively stimulating language imagination processes. The neurophysiological findings, particularly the activation patterns in specific frequency bands and cortical regions, align well with established literature on imagined speech processing. The enhanced delta band activation observed during second sessions, associated with memory mechanisms, provides valuable insight into the cognitive processes involved in repeated imagined speech tasks. These findings contribute to the broader field of Brain Computer Interface (BCI) development and suggest potential applications in clinical settings, particularly for individuals with speech impairments.},
}
@article {pmid41336480,
year = {2025},
author = {Ramiotis, G and Mania, K},
title = {Enhancing EEG Classification for Motor Imagery Control of a VR Game based on Deep Learning Techniques on Small Datasets.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11251707},
pmid = {41336480},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; *Deep Learning ; Algorithms ; *Virtual Reality ; Neural Networks, Computer ; *Video Games ; Signal Processing, Computer-Assisted ; *Imagination ; Movement ; },
abstract = {Motor imagery-based Brain-Computer Interfaces (BCIs) suffer from limited accuracy when the EEG dataset is recorded from naive BCI users due to noisy components. Neural networks capture more robust representations of EEG features, but require large amount of data which is challenging to collect, due to long motor imagery training sessions. On the other hand, linear- and Riemann-based machine learning algorithms achieve above chance-level accuracy on small scale datasets, but, performance degrades on noisy datasets. To address this issue, we implemented a Wasserstein Generative Adversarial Network (WGAN) for data augmentation to prevent overfitting for the deep classifier, while reaching training convergence faster than existing models. For classification, we developed a Convolutional Neural Network (CNN) to eliminate noisy components caused by BCI illiteracy and extract robust temporal representations of EEG features. To evaluate our system, we designed a VR maze game utilizing the proposed BCI system to translate the EEG signal into movement for a playable character. We achieve increased accuracy, compared to conventional machine learning models, with minimal overfitting, on our own dataset, recorded from 16 naive BCI users.},
}
@article {pmid41336461,
year = {2025},
author = {Soriano-Segura, P and Quiles, V and Ortiz, M and Ianez, E and Azorin, JM},
title = {Effect of Electrode Reduction on the Error-Related Potential Detection During the Start of the Gait.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-5},
doi = {10.1109/EMBC58623.2025.11251757},
pmid = {41336461},
issn = {2694-0604},
mesh = {Humans ; *Gait/physiology ; Electrodes ; *Brain-Computer Interfaces ; Male ; Adult ; },
abstract = {Self-correcting Brain-Machine Interfaces based on Motor Imagery (MI-BMIs) using Error-Related Potentials (ErrP) are a promising approach to improve the accuracy of the system and enhancing their feasibility for the neurorehabilitation of patients with spinal cord injuries (SCI). However, these technologies require extensive preparation time, which shortens the therapy session and causes fatigue in the patient even before starting, potentially reducing the therapy's effectiveness. To address this issue, this study evaluates five electrode configurations to determine the impact of electrode reduction on ErrP detection at the beginning of the gait with a lower-limb exoskeleton. The results indicate that reducing the number of electrodes does not significantly affect detection performance but does reduce false positive rates (FPR). Therefore, these findings support the feasibility of using a reduced electrode configuration of 11 electrodes to enhance BMI usability while maintaining detection reliability.Clinical relevance- The long preparation time required for MI-BMI therapies poses a significant challenge. As a result, patients may begin therapy fatigued or experience rapid exhaustion, limiting their engagement in the rehabilitation process. To address this issue, this study explores electrode reduction for ErrP detection as a strategy to minimize preparation time, enhancing the feasibility of MI-BMIs for clinical applications.},
}
@article {pmid41336460,
year = {2025},
author = {Wang, X and Lai, YH and Chen, F},
title = {EEG-based Syllable-Level Voice Activity Detection.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11251715},
pmid = {41336460},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Male ; *Voice/physiology ; Adult ; Female ; *Speech/physiology ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {Speech brain-computer interface (BCI), as an ideal means to achieve direct communication between the brain and the outside world, has become a research area of great interest. This work studied syllable-level voice activity detection (VAD) based on electroencephalogram (EEG) signals to help identify the presence or absence of speech-related EEG activity. We utilized EEG signals from 10 participants performing auditory (listening to stimuli) and speech (pronouncing syllables) tasks to measure brain activity. Speech-Based VAD was employed to label the auditory stimuli and voice recordings, generating corresponding brain activity labels, which were then used to classify resting and active (listening or pronouncing) EEG states, respectively. The experimental results showed that the EEG-based VAD model achieved accuracies of 90.93% and 69.57% for the speech production and auditory speech tasks, respectively. The accuracies were lower in the cross-subject classification, with accuracies of 72.63% and 61.15% for the two tasks. Additionally, the experiment further compared the model's performance under different time window conditions, but no significant correlation was found between window length and classification accuracy. This study provided new insights into the application of EEG based speech decoding, particularly in future self-paced speech BCI applications.},
}
@article {pmid41336448,
year = {2025},
author = {Liu, G and Yan, Y and Cai, J and Cheok, AD and Qi Wu, E and Song, A},
title = {A More Rational and Efficient Kalman Filter Design for Motor Brain-Machine Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-5},
doi = {10.1109/EMBC58623.2025.11251710},
pmid = {41336448},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; Algorithms ; },
abstract = {The Kalman Filter has long been one of the most widely used models in motor brain-machine interface (BMI) research due to its noise handling capabilities and real-time adaptability. However, as a model originally developed for traditional control systems, its underlying assumptions of Markov property and the designs of observation models may not always hold true in the context of BMI applications, potentially leading to oversimplifications. This paper examines the limitations that arise when applying the Kalman Filter to BMI, and proposes the Dilated Kalman Filter, which performs Gaussian multiplication between state transition distribution and observation-mapped state distribution in state space, thereby combining observation noise with BMI-specific observation model noise, and consequently incorporates historical information from both states and observations. The proposed method improves the accuracy of Kalman Filter while significantly enhancing computational efficiency, particularly when processing data from large numbers of neurons.},
}
@article {pmid41336444,
year = {2025},
author = {Lin, L and Lin, J and Pu, Q and Zhou, H and Wang, H and Sun, J and Luo, R and Yu, G and Meng, L and He, F and Meng, J and Xu, M},
title = {Regularization SAME Method can Enhance the Performance of SSVEP-BCI with Very Weak Stimulation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11251722},
pmid = {41336444},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; Electroencephalography/methods ; Algorithms ; Male ; Signal-To-Noise Ratio ; Photic Stimulation ; Adult ; Signal Processing, Computer-Assisted ; Female ; },
abstract = {The steady-state visual evoked potential-based brain-computer interface (SSVEP-BCI) has gained considerable attention due to its high information transfer rate (ITR) and stable performance. However, the comfort of SSVEP-BCI still needs to be improved, as strong flickering stimuli cause users' visual fatigue. Reducing the pixel density of the stimuli has been demonstrated as an effective method to improve its comfort. However, the signal-to-noise rate (SNR) of the SSVEP signal induced by such very weak stimuli is low, posing challenges for their decoding. Therefore, it is necessary to develop suitable strategy for better decoding the SSVEP induced by very weak stimuli. This study employed the source aliasing matrix estimation (SAME) method to enlarge the dataset and improve decoding accuracy for SSVEP induced by low-pixel density stimuli. Additionally, this study further optimized the SAME with a regularization method to achieve much higher decoding performance. A SSVEP experiment was designed with various pixel densities (100%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10% and 1%) and frequencies (low: 7Hz, 11Hz, and 15Hz; mid-to-high: 23Hz, 31Hz, and 39Hz) to verify our methods. The results indicated SAME significantly improved the classification accuracy compared to traditional method without the SAME, especially under very weak stimulation conditions (pixel densities ≤ 50%), with the maximum increase reaching 8.6%. Besides, regularization SAME further yielded a significant enhancement, achieved maximum improvements of 4.29% compared to SAME. The regularization SAME proposed in this study significantly improves SSVEP decoding performance under low-pixel density stimuli, paving the way for the development of comfortable and effective SSVEP-BCI.},
}
@article {pmid41336422,
year = {2025},
author = {Li, H and Zhang, M and Karkkainen, T and Meng, Z},
title = {Single Trial Classification of per-stimulus EEG between Different Speed Accuracy Tradeoffs Instruction.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11254002},
pmid = {41336422},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; Male ; Female ; *Signal Processing, Computer-Assisted ; Adult ; Deep Learning ; Neural Networks, Computer ; Young Adult ; },
abstract = {The speed-accuracy tradeoff represents a cornerstone concept in cognitive processing, highlighting the inherent trade-off between decision-making speed and accuracy. Patients may have different speed-accuracy strategies during their neurologic consultation due to differences in understanding of instructions or increased diagnostic time. Despite extensive investigations into the neural mechanisms underpinning speed-accuracy trade-off (SAT), the classification of neural data to differentiate between distinct SAT strategies remains largely unexplored. This study bridges this critical gap by implementing a deep learning framework to classify single-trial EEG signals based on participants' instructed response strategies-either prioritizing speed or accuracy and leveraging a dataset from 20 participants engaged in a mirror-image judgment task. The data underwent preprocessing and were subsequently transformed using continuous wavelet transformation to extract time-frequency features. Employing a channel-stacking technique, we organized the EEG data into RGB-like images, which were then input into a RegNet convolutional neural network for classification. Ten-fold cross-validation results demonstrated that the occipital region achieved the highest classification accuracy (85.37%), followed by the parietal (82.97%), frontal (80.46%), and central regions (78.57%). This study not only validates the feasibility of single-trial EEG classification in distinguishing between speed and accuracy strategies but also highlights its potential applications in adaptive brain-computer interfaces and cognitive neuroscience research.Clinical Relevance- This study provides a novel method for real-time identification of cognitive strategies (speed vs. accuracy prioritization) via EEG, offering clinicians a tool to tailor neurofeedback or rehabilitation protocols based on individualized neural signatures.},
}
@article {pmid41336408,
year = {2025},
author = {Hu, G and Zeng, F and Tang, H and Zhao, Y and Zhang, X},
title = {A Study of Brain-Computer Interface Recognition Performance Crossing Action Observation Paradigms.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11254040},
pmid = {41336408},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography/methods ; Male ; Adult ; Female ; Evoked Potentials, Visual/physiology ; Movement/physiology ; },
abstract = {Action observation-based brain-computer interface (AO-BCI) could induce visual motor imagery through biological motion while relying on its movement frequency to stimulate steady-state visual evoked potential. This hybrid BCI with dual-brain-region activation offers significant potential for stroke rehabilitation. Since varying AO paradigms are employed in the rehabilitation of different limb movements, a limited training dataset can compromise recognition performance. Thus, this study tried to investigate the BCI performance crossing different AO paradigms for the first time. Three AO paradigms, each containing four actions, were designed to establish an online BCI system. Task discriminant component analysis was utilized to analyze the online and offline EEG data. Three training schemes were developed to construct spatial filters including target session (TS) data, source session (SS) data, and a combination of both. Results indicated that the paradigm content significantly affected the recognition performance (F=7.65, p=0.0039). The recognition accuracies of the four actions for each AO paradigm were 71.86%, 89.71%, and 82.71%, respectively. Among the three training schemes, the combined TS and SS data approach notably enhanced recognition accuracy for the AO paradigm with poor performance using TS data alone (p=0.0319). This study demonstrated that EEG data from existing AO paradigms can be used to construct training sets for new paradigms. And combining a small amount of data from the new paradigm could improve the recognition performance. Future research should focus on developing data calibration methods specific to cross-AO paradigms to further enhance recognition accuracy. This work will provide valuable insights for advancing AO-BCI applications in rehabilitation.},
}
@article {pmid41336362,
year = {2025},
author = {McDorman, RA and Raj Thapa, B and Kim, J and Bae, J},
title = {Transfer Learning in EEG-based Reinforcement Learning Brain Machine Interfaces via Q-learning Kernel Temporal Differences.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11253000},
pmid = {41336362},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Machine Learning ; Male ; Adult ; Algorithms ; Female ; Young Adult ; Movement ; },
abstract = {Reinforcement learning based brain machine interfaces (RLBMIs) is an emerging technology with many possible real-time applications. Transfer learning (TL) has proved beneficial as it can improve performance of machine learning algorithms by reusing learned knowledge from similar tasks. However, its application in BMIs has mainly focused on supervised learning approaches. In this study, we investigate the effect of TL in RLBMIs to decode freewill movement related intentions using multichannel scalp electroencephalogram (EEG). We applied TL strategies to Q-learning Kernel Temporal Difference (Q-KTD), which is an algorithm to estimate the action value function, Q, by a nonlinear function approximator using kernel methods. A publicly available EEG dataset recorded while healthy adult participants conduct a key pressing task was used to decode premovement (before movement onset) and movement intention (after movement onset). Differently from most cue-based tasks, participants had freewill to choose the key being pressed, providing unique neural dynamics for decoding. TL was applied between and within subjects to decode the movement related intentions. Significant increase on success rates (p < 0.01) were observed in 96% cases. The success rate increases in each case ranged from 1.39 to 10.69%. These results support the use of TL as an effective way to improve the efficiency of RL-based neural decoder's learning.Clinical Relevance- The improved performance of the neural decoder using transfer learning provides efficient modeling strategy of RLBMIs that can assist patients with neurological disorders.},
}
@article {pmid41336341,
year = {2025},
author = {Germano, D and Ronca, V and Capotorto, R and Di Flumeri, G and Borghini, G and Giorgi, A and Babiloni, F and Arico, P},
title = {Towards the Correction of Covariate Shift in EEG-Based Passive Brain-Computer Interfaces for Out-of-Lab Applications.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11252974},
pmid = {41336341},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; Signal Processing, Computer-Assisted ; Algorithms ; },
abstract = {The increasing adoption of wearable EEG technology is enabling the development of passive Brain-Computer Interface (pBCI) systems for real-world applications, in the near future, such as Industry 5.0. However, one major challenge in classifying electroencephalographic (EEG) signals in these settings is covariate shift, which occurs when the distribution of the data changes between training and testing sessions due to variations in EEG headset positioning. This study investigates the effectiveness of a linear transformation approach to mitigate the negative effect of covariate shift. Simulations were conducted by using different shift conditions (i.e. deviation of the headset position from the original one), to evaluate (i) the performance of the transformation function used for mitigating the covariate shift occurrence and (ii) the importance that the change of reference and/or channels has on the classification performance. Results show that normalizing covariate shift-affected data (i.e., target) using shift-free data as a template (i.e., source) helps mitigate the negative impact of covariate shift, leading to improved classification performanceThe accuracy loss drops from 14% to 6% in the worst configuration and from 5% to 4% in the best configuration. This improvement is more pronounced when the shift is larger, i.e., when both the reference and channels change between the control dataset and the test dataset. These findings have significant implications for the development of robust and reliable pBCI models for out-of-the-lab contexts.},
}
@article {pmid41336339,
year = {2025},
author = {Hu, C and Liu, Q and Luo, J and Lu, Y and Jiang, N and Li, G and Huai, Y and Li, Y},
title = {Can ICA-Based Artifact Removal Affect Deep Learning Decoding Accuracy? Yes!.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11253033},
pmid = {41336339},
issn = {2694-0604},
mesh = {Humans ; *Deep Learning ; *Artifacts ; *Electroencephalography/methods ; Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; Male ; Female ; Adult ; Stroke/physiopathology ; },
abstract = {Regarding brain-computer interfaces (BCIs), the effectiveness of Independent Component Analysis (ICA) for artifact removal in traditional machine learning-based EEG decoding has been widely implemented. However, its utility in deep learning-based EEG decoding remains understudied. This paper investigated the impact of ICA-based artifact removal on the accuracy of deep learning models for decoding motor imagery and motor execution from EEG signals in short time windows. We employed an ICA-based artifact removal approach named ERASE for automatic artifact removal and evaluated the performance of three decoding approaches: CNN, LSTM, and CEBRA. Compared to before artifact removal, The F1-score improved by averages of 27.90% (CNN), 22.06% (LSTM), and 28.38% (CEBRA) after artifacts removal for motor execution tasks in healthy subjects. For motor imagery tasks in stroke patients,The F1-score improved by averages of 18.90% (CNN), 21.04% (LSTM), and 25.84% (CEBRA). Topographic maps and manifold visualizations further confirmed that ICA enhances the spatial specificity and interpretability of neural signals. These findings suggest that ICA-based artifact removal is a valuable preprocessing step for deep learning-based EEG decoding, particularly in scenarios with significant artifact contamination, offering potential benefits for clinical applications such as stroke rehabilitation.},
}
@article {pmid41336297,
year = {2025},
author = {Ding, Y and Lee, JH and Zhang, S and Luo, T and Guan, C},
title = {Decoding Human Attentive States from Spatial-temporal EEG Patches Using Transformers.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-5},
doi = {10.1109/EMBC58623.2025.11254148},
pmid = {41336297},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Attention/physiology ; Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; Deep Learning ; Algorithms ; Neural Networks, Computer ; ROC Curve ; },
abstract = {Learning the spatial topology of electroencephalogram (EEG) channels and their temporal dynamics is crucial for decoding attention states. This paper introduces EEG-PatchFormer, a transformer-based deep learning framework designed specifically for EEG attention classification in Brain-Computer Interface (BCI) applications. By integrating a Temporal CNN for frequency-based EEG feature extraction, a pointwise CNN for feature enhancement, and Spatial and Temporal Patching modules for organizing features into spatial-temporal patches, EEG-PatchFormer jointly learns spatial-temporal information from EEG data. Leveraging the global learning capabilities of the self-attention mechanism, it captures essential features across brain regions over time, thereby enhancing EEG data decoding performance. Demonstrating superior performance, EEG-PatchFormer surpasses existing benchmarks in accuracy, area under the ROC curve (AUC), and macro-F1 score on a public cognitive attention dataset. The code can be found via: https://github.com/yi-ding-cs/EEG-PatchFormer.},
}
@article {pmid41336280,
year = {2025},
author = {Hong, J and Rao, P and Wang, W and Chen, S and Najafizadeh, L},
title = {ChatBCI-4-ALS: A High-Performance, LLM-Driven, Intent-Based BCI Communication System for Individuals with ALS.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-6},
doi = {10.1109/EMBC58623.2025.11253329},
pmid = {41336280},
issn = {2694-0604},
mesh = {*Amyotrophic Lateral Sclerosis/physiopathology ; Humans ; *Brain-Computer Interfaces ; Algorithms ; *Communication Devices for People with Disabilities ; Electroencephalography ; Event-Related Potentials, P300 ; Language ; },
abstract = {Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease that leads to significant motor and speech impairments, increasing the need for alternative means of communication to support quality of life. P300 speller brain computer interfaces (BCIs) have shown promise in facilitating non-muscular communication by detecting P300 event-related potentials (ERPs) in response to visual stimuli. However, these systems are generally slow and can not fully address the communication needs of ALS patients, specially, when the primary goal is to convey intent with minimal cognitive load. In this paper, we present ChatBCI-4-ALS, the first intent-based BCI communication system designed for individuals with ALS. ChatBCI-4-ALS leverages large language models (LLMs) and employs a dynamic flash algorithm to enhance typing speed, and enable efficient communication of the user's intent beyond exact lexical matches. Additionally, we introduce new semantic-based quantitative performance metrics to evaluate the effectiveness of intent-based communication. Results from online experiments suggest that ChatBCI-4-ALS achieves record-breaking average spelling speed of 23.87 char/min (with the best case scenario of 42.16 char/min), and a best information transfer rate (ITR) of 128.85 bits/min, marking an advancement in P300 BCI-based communication systems.},
}
@article {pmid41336226,
year = {2025},
author = {Kaseler, RL and Andreasen Struijk, LNS},
title = {Harmonic Component Analysis: A novel training-free and asynchronous BCI classification method.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11253522},
pmid = {41336226},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Adult ; Algorithms ; Male ; Evoked Potentials, Visual/physiology ; Signal Processing, Computer-Assisted ; Female ; },
abstract = {Assistive technologies can provide people with locked-in syndrome independence and improve their quality of life. However, existing brain-computer interfaces (BCI) can be unreliable and require excessive training. Therefore, we investigate the possibility of a training-free BCI that can provide asynchronous and online control of assistive robotic technologies. We propose the harmonic component analysis (HCA), a new training-free classifier for signals with known harmonic characteristics, such as steady-state visually evoked potentials. To validate the HCA, it is compared to the well-known canonical correlation analysis (CCA), using an offline data set of 10 healthy participants who performed cue trials with 16 SSVEP-targets. The HCA achieved better performance than a three-component CCA with up to 74% lower computational cost. For asynchronous control, the HCA achieved a detection accuracy of 85% with an average activation time of 1.6s, against 77% after an average of 1.7s for the CCA. For continuous activation, the HCA achieved a true positive rate of 65% with a false positive rate of 0. 59% from 2 s after cue onset until 5 s after, while the CCA achieved a true positive rate of 59% with a false positive rate of 0. 27%. Thus, the HCA is shown to be a well-suited SSVEP-classifier for systems that require asynchronous classification without the need for a calibration or training-session.},
}
@article {pmid41336218,
year = {2025},
author = {Ciferri, M and Ferrante, M and Toschi, N},
title = {Optimal Transport and Contrastive Learning for Brain Decoding of Musical Perception.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11253498},
pmid = {41336218},
issn = {2694-0604},
mesh = {*Music ; Humans ; Magnetic Resonance Imaging ; *Brain/physiology/diagnostic imaging ; *Auditory Perception/physiology ; Brain Mapping/methods ; Brain-Computer Interfaces ; Algorithms ; Male ; *Learning ; },
abstract = {Brain decoding aims to reconstruct external stimuli from brain activity, providing insights into the neural representation of cognitive experiences. Music decoding from functional magnetic resonance imaging (fMRI) is particularly challenging due to the complexity of auditory processing and the temporal limitations of fMRI signals. In this study, we introduce a novel decoding framework that improves the alignment between fMRI activity and latent musical representations extracted using a pre-trained multimodal model (CLAP). We propose a dual-loss approach combining Optimal Transport and Contrastive Learning to enhance feature mapping and retrieval accuracy. The first loss ensures structural consistency between brain-predicted and true musical embeddings, while the contrastive loss refines the embedding space by maximizing similarities between corresponding pairs and minimizing non-correspondences. Using fMRI data from five subjects listening to music tracks from the GTZAN dataset, our method achieves improved decoding performance, surpassing traditional regression-based approaches from 22.1% top-1 accuracy to 29.3%. These results highlight the potential of integrating Optimal Transport and Contrastive Learning to improve brain decoding performance, paving the way for extending the approach to different sensory domains and applications in Brain-Computer Interfaces (BCI).Clinical relevance- This study could have clinical implications for understanding auditory processing disorders and developing neurorehabilitation strategies. By elucidating how the brain encodes complex auditory stimuli, this approach may contribute to BCI applications for speech and music perception restoration in individuals with hearing impairments or neurological conditions affecting auditory cognition.},
}
@article {pmid41336204,
year = {2025},
author = {Padfield, N and Turk, S and Mujahid, K and Camilleri, T and Peng, Y and Camilleri, K},
title = {A Spatio-Spectral Analysis of Decoding Imagined Speech from the Idle State.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11253510},
pmid = {41336204},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Speech/physiology ; *Imagination/physiology ; Brain-Computer Interfaces ; Male ; Adult ; Female ; },
abstract = {Studies into speech imagery (SI) classification from electroencephalogram (EEG) data have generally focused on distinguishing imagined words from each other, but accurate discrimination from the idle state, when the user is relaxed, is also necessary for asynchronous brain-computer interfaces (BCIs). In this study, frequency bands and scalp regions most important for distinguishing SI from the idle state were identified and related to underlying neural processes. Power spectral density (PSD) features were extracted from each channel, and a statistical analysis of the features, as well as a classification analysis involving six classifiers, was carried out. The parietal region was identified as the most important scalp region, whilst the delta, theta, and gamma bands were the most important frequency bands. Furthermore, the importance of the alpha band, and of the temporal, frontal-temporal, frontal-central, and parietal regions varied significantly between the SI vs Idle and SI vs SI classification problems, highlighting the importance of including the idle state in SI classification studies.Clinical Relevance-This study identifies frequency bands and scalp regions that are significantly important for the SI vs Idle classification problem, which is important for asynchronous SI BCIs.},
}
@article {pmid41336201,
year = {2025},
author = {Wimpff, M and Aristimunha, B and Chevallier, S and Yang, B},
title = {Fine-Tuning Strategies for Continual Online EEG Motor Imagery Decoding: Insights from a Large-Scale Longitudinal Study.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11253543},
pmid = {41336201},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; Humans ; Longitudinal Studies ; Brain-Computer Interfaces ; *Imagination/physiology ; Male ; Signal Processing, Computer-Assisted ; Deep Learning ; Adult ; Female ; Algorithms ; Movement ; },
abstract = {This study investigates continual fine-tuning strategies for deep learning in online longitudinal electroencephalography (EEG) motor imagery (MI) decoding within a causal setting involving a large user group and multiple sessions per participant. We are the first to explore such strategies across a large user group, as longitudinal adaptation is typically studied in the single-subject setting with a single adaptation strategy, which limits the ability to generalize findings. First, we examine the impact of different fine-tuning approaches on decoder performance and stability. Building on this, we integrate online test-time adaptation (OTTA) to adapt the model during deployment, complementing the effects of prior fine-tuning. Our findings demonstrate that fine-tuning that successively builds on prior subject-specific information improves both performance and stability, while OTTA effectively adapts the model to evolving data distributions across consecutive sessions, enabling calibration-free operation. These results offer valuable insights and recommendations for future research in longitudinal online MI decoding and highlight the importance of combining domain adaptation strategies for improving BCI performance in real-world applications.Clinical Relevance-Our investigation enables more stable and efficient long-term motor imagery decoding, which is critical for neurorehabilitation and assistive technologies.},
}
@article {pmid41336191,
year = {2025},
author = {Gonzalez-Mitjans, A and Salinas-Medina, A and Toussaint, PJ and Valdes-Sosa, P and Evans, A},
title = {AI-Driven Neurodiagnostics: A Scalable Framework for EEG Anomaly Detection Using a Distributed-Delay Neural Mass Model.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11253534},
pmid = {41336191},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Artificial Intelligence ; Epilepsy/diagnosis/physiopathology ; Machine Learning ; Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Algorithms ; Neural Networks, Computer ; },
abstract = {The integration of biophysically grounded neural simulations with Artificial Intelligence (AI) has the potential to transform clinical neurodiagnostics by overcoming the inherent challenges of limited pathological EEG datasets. We present a novel AI-driven framework that leverages a Distributed-Delay Neural Mass Model (DD-NMM) to generate synthetic EEG signals replicating both healthy and pathological brain states. Through systematic parameter tuning and domain-specific data augmentation, we enrich the diversity of simulated signals, enabling robust anomaly detection using machine learning techniques. Our approach integrates supervised classification and unsupervised one-class anomaly detection, achieving over 95% accuracy in synthetic tests and over 89% when applied to empirical EEG data from epilepsy patients and healthy volunteers. By providing an engineered solution that bridges computational neuroscience with AI, this framework enhances early seizure detection, adaptive neurofeedback, and brain-computer interface applications. Our results demonstrate that theory-driven simulation, combined with state-of-the-art machine learning, can address critical gaps in medical AI, significantly advancing clinical neuroengineering.Clinical relevance- This study provides a scalable and interpretable AI-driven method for EEG anomaly detection, which can support clinicians in identifying seizure patterns and other neurological disorders with high accuracy. The integration of computational neuroscience with AI-based diagnostics offers a potential pathway for early intervention and personalized neurotherapeutic strategies.},
}
@article {pmid41336140,
year = {2025},
author = {Huang, H and Chen, Z and You, Q and Pan, J and Xiao, J},
title = {Emotion Decoding and Consciousness Evaluation in patients with DOC through EEG Microstate analysis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11253041},
pmid = {41336140},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; *Consciousness Disorders/physiopathology/diagnosis ; Male ; Female ; Adult ; Brain-Computer Interfaces ; *Consciousness ; Middle Aged ; Signal Processing, Computer-Assisted ; },
abstract = {Clinicians commonly employ the Coma Recovery Scale-Revised (CRS-R) as a standard tool for assessing patients with disorders of consciousness (DOC). However, the assessment is easily affected by subjective judgment, and patients with DOC are usually unable to provide adequate behavioral responses. Previous studies have indicated that emotion recognition-based brain-computer interface (BCI) can assist in the assessment of DOC, yet they lack more specific and quantitative indicators. This study is the first to apply electroencephalography (EEG) microstates for emotion recognition in patients with DOC. Specifically, EEG microstates were utilized to capture crucial spatio-temporal features of EEG signals, simplifying the rapidly changing EEG signals into a series of prototype topoplots. In this study, EEG data was recorded from 9 patients with DOC and 11 healthy volunteers. Among healthy participants, our system achieved an average classification accuracy of 94.16%, effectively demonstrating its success in eliciting and recognizing emotions. When applied to patients with DOC, the system yielded an average classification accuracy of 77.94%. The results of this study indicate that EEG microstate dynamics are associated with conscious processing in patients with DOC. However, further validation in a larger patient dataset is required to confirm these preliminary findings.},
}
@article {pmid41336101,
year = {2025},
author = {Wang, X and Wang, L and Ding, Y and Chen, F},
title = {EEG-based Auditory Attention Switch Detection with Multi-scale Gated Attention and Multi-task Learning based Hierarchical Spatiotemporal Networks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-5},
doi = {10.1109/EMBC58623.2025.11253070},
pmid = {41336101},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; *Attention/physiology ; Humans ; Signal Processing, Computer-Assisted ; Algorithms ; Signal-To-Noise Ratio ; *Auditory Perception/physiology ; },
abstract = {Auditory attention switch detection (AASD) poses significant challenges for adaptive neurotechnologies, particularly under electroencephalogram (EEG) with low signal-to-noise ratios (SNRs). However, the performance of existing methods is limited due to insufficient feature discriminability and high detection delay. To solve the problem, this paper proposes a Hierarchical Spatiotemporal Network (HSTN) for detecting auditory attention switch from EEG signals. The model employs a hierarchical spatiotemporal encoder to extract spatiotemporal features of EEG signals, integrates short-term transient and long-term dependency information through a multi-scale gated attention mechanism, and synchronously optimizes auditory attention switch detection and auditory attention decoding tasks via a multi-task joint training strategy. Experimental results demonstrate that HSTN significantly outperforms baseline models in both auditory attention switch detection (AASD F1=0.89, accuracy 88.6%) and auditory attention decoding tasks (AAD accuracy 89.3%), with superior model parameter efficiency and inference time. Ablation experiments further validate the critical roles of multi-task learning, gated attention, and multi-scale convolutions. This study provides an efficient solution for auditory attention switch detection in complex auditory scenarios.Clinical Relevance-The study confirms that spatiotemporal feature encoding combined with multi-task joint training significantly enhances performance in EEG attention switch detection, providing a practical technical framework for enabling dynamic sound source enhancement in intelligent hearing aids and auditory brain-computer interface systems.},
}
@article {pmid41336083,
year = {2025},
author = {Parashiva, PK and Gangadharan K, S and Vinod, AP},
title = {EEGScaler: A Deep Learning Network to Scale EEG Electrode and Samples for Hand Motor Imagery Speed Decoding.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11251649},
pmid = {41336083},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/instrumentation ; *Deep Learning ; Brain-Computer Interfaces ; Electrodes ; *Hand/physiology ; *Imagination/physiology ; Signal Processing, Computer-Assisted ; Movement ; Algorithms ; Male ; },
abstract = {Motor Imagery (MI)-based Brain-Computer Interface (MI-BCI) systems induce neuroplasticity, promoting rehabilitation in stroke patients. Existing MI-BCI systems decode bilateral MI actions from Electroencephalogram (EEG) data to facilitate motor recovery. However, such systems offer limited degrees of freedom. Decoding kinematics information, such as movement speed can enhance control and provide a more natural interface with the environment. Decoding speed-related information from unilateral MI tasks is challenging due to the significant spatial overlap of neuronal sources and the inherently low spatial resolution of EEG. To address this, we propose EEGScaler, an end-to-end deep learning framework designed to decode slow v/s fast MI tasks by adaptively scaling EEG samples and electrodes with high discriminative value. EEGScaler leverages a Multi-Layer Perceptron (MLP) network to assign scale factors to both samples and electrodes. Spatiotemporal features are subsequently extracted using temporal and depth-wise convolution filters. The model is pre-trained on subject-independent data to learn filter weights, while subject-specific fine-tuning further optimizes the MLP-based scaling mechanism. The EEGScaler model performance is evaluated on 14 healthy subjects' data recorded while performing slow v/s fast unilateral MI tasks. The proposed model achieves an average cross-validated accuracy of 65. 98% for decoding fast v/s slow MI speed tasks, outperforming existing methods by approximately 6%. The subject-specific scaling of samples and electrodes using an end-to-end deep learning model for speed from unilateral MI tasks is novel. By effectively decoding movement speed, EEGScaler enhances the degree of freedom in MI-BCI systems, paving the way for more intuitive and efficient neurorehabilitation applications.Clinical Relevance- This advancement has the potential to improve motor rehabilitation strategies by enabling more precise and adaptive BCI-driven therapy tailored to individual recovery needs.},
}
@article {pmid41336067,
year = {2025},
author = {Ahmadi, H and Mesin, L},
title = {Decoding Visual Imagination and Perception from EEG via Topomap Sequences.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11251641},
pmid = {41336067},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; Humans ; *Imagination/physiology ; *Visual Perception/physiology ; Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Neural Networks, Computer ; Adult ; Male ; },
abstract = {We propose a Topomap-based EEG decoding framework for distinguishing pictorial Imagination from Perception. By converting each trial's EEG signals into dense sequences of scalp voltage maps at short time intervals, our approach captures crucial spatiotemporal patterns that standard methods may overlook. We then apply a CNN with squeeze-and-excitation (SE) blocks to these Topomap "frames," enabling direct learning of both spatial topographies and rapid temporal fluctuations. Despite using only one trial per subject to simulate a data-scarce scenario, our model achieves 95.1% accuracy under a leave-one-subject-out (LOSO) cross-validation scheme. Results indicate clear neural distinctions between Imagination and Perception states, reflecting focused brain-region engagement during visual recall. In addition to confirming the viability of Topomaps as EEG feature representations, this study underscores their potential generalizability. We anticipate future extensions incorporating other modalities (orthographic, audio) and more advanced deep architectures will further expand the utility and robustness of this approach for brain-computer interface (BCI) applications.Clinical relevance- This framework offers a robust method for accurately distinguishing visual Imagination from Perception, even in data-scarce scenarios. It holds potential for enhancing diagnostic tools in cognitive disorders and refining BCI applications in clinical settings.},
}
@article {pmid41336065,
year = {2025},
author = {Perley, AS and Coleman, TP},
title = {A Dynamic Mutual Information Measure of Phase Amplitude Coupling.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11251622},
pmid = {41336065},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; Algorithms ; *Signal Processing, Computer-Assisted ; Linear Models ; Sleep/physiology ; },
abstract = {Phase-amplitude coupling (PAC) is a fundamental neural phenomenon in which the phase of a slow oscillation modulates the amplitude of a faster oscillation. PAC has been implicated in various cognitive and clinical conditions, including Parkinson's disease, epilepsy, and depression. Traditional methods for quantifying PAC compute a single summary statistic over an entire time series, limiting their ability to capture dynamic fluctuations. Growing interest in time-varying PAC has led to methods that rely on windowed time-series analysis, but these approaches struggle to track rapid changes in coupling at single-sample resolution. To address this limitation, we propose a novel dynamic mutual information measure of PAC, leveraging a state-space modeling approach based on a Gamma generalized linear model (GLM). By introducing a Gauss-Markov process on the regression weights, our method enables dynamic, interpretable PAC estimation at each time point. We validate our approach using synthetic phase-amplitude coupled signals with time-varying coupling coefficients and demonstrate superior performance in smoothly tracking PAC over time and distinguishing coupled from uncoupled states. Additionally, we apply our technique to sleep EEG data, successfully identifying PAC during sleep spindles, which may serve as a biomarker for neurophysiological conditions such as Alzheimer's disease. Our findings suggest that this dynamic PAC measure is a powerful tool for neuroscientific and clinical research, with potential applications in real-time brain-computer interfaces and neurostimulation protocols.Clinical relevanceThis work demonstrates a new technique for quantifying time-varying electrophysiological coupling. This may allow for understanding transient neural dynamics in disease states and may help more robustly inform electrical stimulation protocols for patients with neurodegenerative disorders.},
}
@article {pmid41335991,
year = {2025},
author = {Li, H and Xu, G and Zhang, S and Xie, J and Han, C and Wu, Q and Zhang, S},
title = {Signal extension with SeU-net for boosting the decoding performance of short-time SSVEP-based brain-computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-6},
doi = {10.1109/EMBC58623.2025.11253264},
pmid = {41335991},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Signal Processing, Computer-Assisted ; Electroencephalography/methods ; Algorithms ; },
abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (SSVEP-BCIs) have greatly benefited the lives of patients. However, existing SSVEP recognition methods exhibit poor performance on short SSVEP signals. SSVEP recognition accuracy heavily depends on signal length, which increases as the signal length. From a novel data perspective, this study proposes a signal extension method called SeU-net without requiring calibration data from the target subject to improve the recognition performance of calibration-free methods for short-time SSVEP signals. SeU-net employs LSTM and contrastive learning to enhance feature extraction, converting signals from sample space to feature space, and then back to the sample space to realize signal extension. SeU-net is designed to focus only on signal extension in the temporal domain, without subject-specific feature extraction operations, resulting in strong cross-subject signal extension performance. The extensive experiments demonstrate that SeU-net significantly enhances the decoding performance of calibration-free methods for short-time SSVEP signals. By enabling more accurate decoding with shorter SSVEP signals, SeU-net holds the potential to advance the practical application of high-speed SSVEP-BCIs further.},
}
@article {pmid41335988,
year = {2025},
author = {Kulwa, F and Sarwatt, DS and Asogbon, MG and Huang, J and Khushaba, RN and Oyemakinde, TT and Li, G and Samuel, OW and Li, H and Li, Y},
title = {A Novel Levant's Differentiator-Based Descriptor for EEG-Based Motor Intent Decoding.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-6},
doi = {10.1109/EMBC58623.2025.11253249},
pmid = {41335988},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Stroke/physiopathology ; Male ; Signal Processing, Computer-Assisted ; Stroke Rehabilitation ; Algorithms ; Movement ; Female ; },
abstract = {Motor intent (MI)-based brain-computer interfaces (BCIs) have been extensively studied to improve the performance and clinical realization of assistive robots for motor recovery in stroke patients. However, challenges arise in their low decoding performance. This can be attributed to the low spatial resolution and signal-to-noise ratio of electroencephalography (EEG), particularly in accurately deciphering hand movements, which reduces classification performance. Therefore, we have developed a novel feature extraction technique that exploits Levant's differentiators to extract distinct patterns in EEG signals and employs symmetric positive definite matrices (SPD) to effectively leverage the spatial-temporal properties of the EEG signal. Results from nine post-stroke patients and fifteen normal subjects showed an improved decoding accuracy of 99.16±0.64% and 99.30±0.69%, respectively in classifying twenty-four hand motor intents, significantly outperforming existing related methods. Thus, the proposed technique has the potential to greatly enhance the reliability and effectiveness of EEG-based control systems for post-stroke rehabilitation.Clinical Relevance- The outcome of this study can lead to better control of rehabilitation robots and improve the recovery speed of the stroke patients.},
}
@article {pmid41335965,
year = {2025},
author = {Thomas, A and Cho, Y and Zhao, H and Carlson, T},
title = {MI-CES: An explainable weak labelling approach to example selection for Motor Imagery BCI classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11253265},
pmid = {41335965},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; *Imagination/physiology ; Electroencephalography/methods ; Algorithms ; Movement/physiology ; },
abstract = {Motor Imagery (MI) Brain Computer Interfaces (BCI) can be used to control assistive devices such as wheelchairs. These systems require a training period to get both the user and the machine to learn and adapt to each other, achieving an acceptable control accuracy. Previous systems have discovered that providing a form of feedback to the user about what the system thinks the user is thinking can increase the effect of training and increase both the control accuracy of the user and the classification accuracy of the BCI system. However, if this feedback is 'incorrect' due to the classifier behind the BCI system having a poor accuracy, this may cause the user to 'incorrectly' adapt to the feedback, providing the system with further poor examples of MI. In this paper, we propose MI-CES, an explainable 'example selection' approach based on the neuro-physiological principle of MI. We found that while using 2 classification techniques, we achieved a statistically significant increase in classification accuracy across 3 datasets that were comprised of both multi-participant and multi-session recordings.},
}
@article {pmid41335962,
year = {2025},
author = {Buda, C and Gambosi, B and Toschi, N and Astolfi, L},
title = {A Deep Learning Framework for Multi-Source EEG Localization.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11253252},
pmid = {41335962},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; *Deep Learning ; Humans ; Algorithms ; Signal Processing, Computer-Assisted ; Neural Networks, Computer ; Brain/physiology ; },
abstract = {Electroencephalography (EEG) provides millisecond-scale resolution of neural activity but struggles to accurately localize multiple concurrent sources, especially in spatially close regions. Classical linear inverse methods, such as MNE, sLORETA, and dSPM, address the ill-posed inverse problem through regularization but often exhibit a "single-source bias", suppressing smaller generators. This paper introduces a deep learning framework designed to robustly identify multiple sources of activity from short EEG segments. Our approach leverages a realistic simulation pipeline that systematically generates EEG recordings from physiologically plausible, distributed current sources. We train a convolutional neural network (ConvNET) on thousands of such simulations, ensuring generalization by using a forward model distinct from that of classical solvers, thereby minimizing the risk of an "inverse crime". We evaluate our ConvNet against nine well-established inverse solvers (MNE, dSPM, sLORETA, eLORETA, LORETA, LAURA, and depth-weighted variants). Benchmarking across multiple synthetic test scenarios demonstrates that our method consistently outperforms traditional solvers, particularly in resolving closely spaced sources, while maintaining or improving accuracy for single-source cases. These results highlight the potential of deep learning to overcome biases in EEG source imaging, offering a more reliable approach for multi-source localization.Clinical relevance- By leveraging deep learning, our approach improves localization accuracy, particularly in closely spaced or deep brain sources, potentially enhancing presurgical planning, brain-computer interfaces, and real-time neurofeed-back applications.},
}
@article {pmid41335905,
year = {2025},
author = {Ding, Y and Wang, X and Chen, F},
title = {Enhancing Cross-subject Auditory Attention Detection with Contrastive Learning for EEG Feature Refinement.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11252602},
pmid = {41335905},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; Humans ; *Attention/physiology ; Brain-Computer Interfaces ; Algorithms ; Signal Processing, Computer-Assisted ; *Machine Learning ; },
abstract = {Electroencephalography (EEG)-based auditory attention detection (AAD) plays a crucial role in recent auditory brain-computer interface applications. However, the performance of AAD models in cross-subject tasks tends to be significantly degraded due to the excessive differences in EEG features across subjects. To address this challenge, we proposed a novel framework, AAD-ContrastNet, that incorporated contrastive learning to refine the temporal features from EEG and reduce the variance of EEG features across subjects. AAD-ContrastNet consists of four main components: (a) an attention-based EEG encoder; (b) a contrastive-learning-based EEG encoder; (c) a feature refinement module; and (d) a classifier. T-SNE visualization results show that combining contrastive learning with cross-attention feature refinement significantly improves the generalization of extracted EEG features. By comparing with SOTA models (i.e., DenseNet-3D and DARNet), we validate the significant effect of AAD-ContrastNet in improving cross-subject decoding accuracy, highlighting its potential in enhancing the robustness and generalization of EEG-based AAD systems.Clinical Relevance- This study demonstrates the potential of contrastive learning in mitigating cross-subject performance degradation, providing a solid foundation for applying generalized auditory brain-computer interface systems.},
}
@article {pmid41335889,
year = {2025},
author = {Sun, Y and Zhang, Z and Qi, Q and Li, X and Sun, J and Zhang, K and Zhuang, J and Chen, X and Gao, X},
title = {Beyond Frequency: Leveraging Spatial Features in SSVEP-Based Brain-Computer Interfaces with Visual Animations.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-5},
doi = {10.1109/EMBC58623.2025.11254745},
pmid = {41335889},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; Male ; Female ; Electroencephalography/methods ; Adult ; Young Adult ; },
abstract = {Current research on steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) predominantly focuses on utilizing the frequency- and phase-locking characteristics of SSVEP for encoding purposes. In this study, we propose an innovative paradigm wherein SSVEP serves as a marker, integrated with different types of motion animations to identify distinct neural processing pathways associated with these animations. This approach enables the classification of SSVEP-based BCIs without relying on frequency features. We designed six distinct animations corresponding to six behaviors commonly observed in daily life. Each animation was tagged with a uniform 6 Hz stimulus frequency, forming a six-target classification task. Offline testing was conducted with 10 participants. Despite identical frequency components, significant differences in spatial distribution corresponding to the animations were observed, likely due to the behavioral variations in the animations. Classification analysis demonstrated an accuracy of 0.93 within a 6-second window, validating the practical feasibility of this approach. This paradigm offers a novel direction for the advancement of SSVEP-based BCIs, potentially enabling the integration of multi-sensory information.},
}
@article {pmid41335877,
year = {2025},
author = {Lee, SH and Lee, SH and Lee, SW},
title = {EEG-Translator: A Cross-Modality Framework for Subject-Specific EEG and Voice Reconstruction from Imagined Speech.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11254826},
pmid = {41335877},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Speech/physiology ; *Voice/physiology ; Signal Processing, Computer-Assisted ; *Imagination/physiology ; Algorithms ; },
abstract = {Non-invasive brain-computer interfaces (BCIs) offer the potential to enable communication for individuals with speech impairments by decoding neural signals through speech-related electroencephalography (EEG) signals. Beyond domain-specific speech EEG decoding, generative approaches that enable cross-domain reconstruction are needed to enhance the overall system performance. Here, we propose a cross-modal EEG translation framework that reconstructs overt speech EEG from imagined speech EEG, for subject-specific speech synthesis. Our approach integrates a diffusion-based model with GAN training to enhance cross-domain EEG reconstruction by preserving both EEG class information and its time-frequency domain properties. In classification tasks, the reconstructed EEG improves class decoding accuracy by 6.2% over the original imagined EEG. Additionally, EEG reconstruction was trained not only on the EEG signal itself but also by incorporating spectrogram-based features, leveraging a fusion of spatial and spectral losses to preserve EEG properties. Beyond EEG reconstruction, category-wise analysis across a multi-speech paradigm dataset reveals variations in decoding performance, offering linguistic insights crucial for the advancement of speech BCI systems. Our findings highlight the potential of diffusion-driven EEG translation in speech BCIs, emphasizing the importance of integrating deep learning methodologies with linguistic insights for improved neural signal reconstruction and interpretation.},
}
@article {pmid41335876,
year = {2025},
author = {Wang, A and Zhang, Y and Zhan, G and Zhang, L and Kang, X},
title = {Flexible-Rigid Bonding of Silicon Based Neural Interface for Deep Brain LFP Recording.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11254814},
pmid = {41335876},
issn = {2694-0604},
mesh = {*Silicon/chemistry ; *Brain-Computer Interfaces ; *Brain/physiology ; Electrodes, Implanted ; Humans ; Equipment Design ; Animals ; },
abstract = {Microfabricated silicon neural probes have become the dominant technology in the field of implantable brain-computer interfaces. Mechanical bonding, electroplating, template printing, flip-chip bonding, and welding are prevalent methods for electrode packaging in preparation; however, these techniques often present challenges such as complex processes, elevated temperatures, or increased electrode thickness. We proposed a novel flexible-rigid bonding method for the silicon based neural interface, which markedly reduced the bonding volume compared with the traditional board to board connector. It simplified the assembly process of silicon probes, increased the electrode integration density and facilitated the assembly of the probe and flexible cable. This approach enables the flexible implantation of silicon electrodes in deep brain regions for recording neural signals.},
}
@article {pmid41335863,
year = {2025},
author = {Li, M and Yao, Y and Dong, B and Wang, K and Yu, H and Xu, M and Ming, D},
title = {A Novel Approach to Improve SSVEP-BCI Performance Through Neurofeedback Training.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-6},
doi = {10.1109/EMBC58623.2025.11254803},
pmid = {41335863},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; *Neurofeedback/methods ; *Evoked Potentials, Visual/physiology ; Male ; Adult ; Electroencephalography ; Female ; },
abstract = {Brain-Computer interface (BCI), which translates neural activities into commands for external devices, holds significant promise for clinical rehabilitation and assisted movement for individuals with motor disabilities. Among various BCI paradigms, the steady-state visual evoked potential (SSVEP) based BCI garnered considerable attention due to its relatively stable and high-speed communication capabilities. However, a notable portion of the population, referred to as BCI illiteracy, struggles to effectively control BCI systems due to their inability to generate or modulate the neural patterns required for interaction. To address this issue, we proposed a user-centered approach using neurofeedback training (NFT) to improve individual's performance on SSVEP-BCI. As a result, after a five-day training period, significant improvements in SSVEP-BCI performance were only observed in the training group rather than the control group without training. Notably, some subjects initially determined as BCI-illiterate also gained effective control of the BCI system after training. Further analysis revealed that the improvement of SSVEP-BCI performance had a close link with increased power and inter-trial phase coherence of the SSVEP response, indicating that NFT successfully strengthened the user's task-related neural responses. These findings highlight the potential of NFT as a user-centered intervention to improve BCI control performance, offering a promising pathway to address BCI illiteracy and promote the broader application of BCI systems.Clinical Relevance- This study proposes an effective approach to enhancing the controllability of SSVEP-BCI systems, addressing the critical issue of individual control limitations. The developed method demonstrates significant clinical potential for promoting SSVEP-BCI applications, particularly in facilitating communication and device control for patients with severe motor impairments, such as amyotrophic lateral sclerosis (ALS) and locked-in syndrome (LIS).},
}
@article {pmid41335841,
year = {2025},
author = {Ramos, J and Silva, S and Marques, B and Pais-Vieira, M and Stevenson, A and Bras, S},
title = {Empowering Accessibility: Human-Centered Approach to a BCI Home Control for Impaired People.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11253641},
pmid = {41335841},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; User-Computer Interface ; *Locked-In Syndrome/physiopathology/rehabilitation ; Self-Help Devices ; Male ; },
abstract = {Brain-Computer Interfaces (BCIs) have shown significant potential for individuals with motor impairments, either by improving physiotherapy treatments or by enabling to perform simple tasks, autonomously. However, much of this progress remains confined to controlled laboratory environments. This study aims to develop a BCI-controlled interface, for real-life scenario, tailored to allow individuals with Locked-In Syndrome (LIS) to interact with their home environment. To ensure system usability, a Human-Centered Design (HCD) approach was adopted prioritizing end-user needs. The interface control system was tested using a BITalino for Electroencephalogram (EEG) acquisition. Preliminary results demonstrated that professionals recognize the system's potential, highlighting the importance of real-time feedback, and design simplicity features to minimize user fatigue and improve control accuracy.Clinical Relevance-This interdisciplinary methodology bridges the gap between assistive technologies and the user needs, promoting autonomy and communication with a BCI-controlled interface for real home interaction.},
}
@article {pmid41335820,
year = {2025},
author = {Luo, R and Zheng, C and Ding, R and Shi, T and Li, D and Xiao, X and Huang, Y and Xu, M and Ming, D},
title = {Boosting Spatial Properties of Single-Flicker SSVEP via Laplacian Electrodes.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11253662},
pmid = {41335820},
issn = {2694-0604},
mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/instrumentation/methods ; Electrodes ; Male ; Adult ; Female ; Brain-Computer Interfaces ; Photic Stimulation ; },
abstract = {Spatially-encoded steady-state visual evoked potentials (SSVEP) acquired by electroencephalography (EEG) are extensively utilized in brain-computer interface and neuroscience research. However, EEG suffers from low spatial resolution due to volume conduction effects. To tackle this problem, this study developed a bipolar concentric ring electrode (CRE) for collecting high-resolution Laplacian EEG (LEEG), which was validated through a tank simulation experiment and a human experiment. The tank simulation experiment confirmed its high spatial resolution, and the results showed that LEEG acquired by CRE achieved 2.35 times greater spatial attenuation than EEG. Meanwhile, the human experiment designed a single-flicker SSVEP paradigm with stimuli positioned at different visual field orientations. The results revealed that LEEG had lower inter-channel similarity than EEG, with average coefficients of 0.63 for EEG and 0.46 for LEEG (p<0.01). Topographical analysis further demonstrated that CRE sharpened the spatial features of spatially-encoded SSVEPs, and indicated a clear visual hemifield dominance phenomenon. This study effectively enhances the spatial properties of SSVEP and holds promise for advancing high-resolution LEEG.},
}
@article {pmid41335778,
year = {2025},
author = {Nguyen, MTD and Zhu, HY and Burnham, M and Sun, H and Zhu, Q and Nguyen, V and Brown, S and Wu, E and Jin, C and Lin, CT},
title = {Auditory Steady-State Responses and the Effects of Interaural Decoherence and Presence of Vision.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11254438},
pmid = {41335778},
issn = {2694-0604},
mesh = {Humans ; Male ; Female ; Adult ; Young Adult ; *Vision, Ocular/physiology ; *Auditory Perception/physiology ; Acoustic Stimulation ; *Evoked Potentials, Auditory/physiology ; Electroencephalography/methods ; },
abstract = {The Auditory Steady-State Response (ASSR) is a periodic neural response used to detect speech and hearing loss, and it is also used as a Brain-Computer Interface paradigm. Our paper identifies two key factors that impact the quality and consistency of the ASSR. First is the interaural decoherence, the timing and intensity of sounds arriving at two ears produced by speakers in reverberant environments. Second is the impact of vision on modulating auditory perception and spatial attention, which could potentially influence the neural synchronisation of the response. To demonstrate this, we conducted an experiment on 26 healthy participants to examine the effects of interaural decoherence, by comparing the frequency responses between speakers and earphones, and the presence of vision, by comparing being blindfolded and non-blindfolded, on the ASSR. This study demonstrates that earphones elicit more consistent and reliable ASSRs compared to speakers, emphasising the detrimental effects of interaural decoherence from speaker-based sound delivery on ASSRs. Furthermore, we found that the response is more biased to one side in the absence of vision compared to the presence of vision. This study highlights the importance of using rooms with anechoic properties or less reverberation when using speakers to ensure the consistency and clarity of the response. Future ASSR paradigms should also consider fixating on a target to elicit less bias in ASSR and more accurate spatial features.},
}
@article {pmid41335768,
year = {2025},
author = {Morales-Magallon, F and Bojorges-Valdez, E},
title = {Intended and Non-Volitional Knee Joint Movements Elicit Distinct Functional Brain Networks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-6},
doi = {10.1109/EMBC58623.2025.11254479},
pmid = {41335768},
issn = {2694-0604},
mesh = {Humans ; *Knee Joint/physiology ; Movement/physiology ; Electroencephalography/methods ; *Brain/physiology ; Male ; Adult ; Brain-Computer Interfaces ; *Nerve Net/physiology ; Female ; },
abstract = {Motor execution induces significant alterations in the dynamics of electroencephalography (EEG) signals, which are crucial for assessing rehabilitation, brain plasticity, and brain-computer interface (BCI) applications. While traditional analyses have primarily focused on power spectral changes, recent advancements incorporate non-linear indices to uncover previously undetected characteristics of brain dynamics.Network analysis provides a powerful framework to examine the structural organization and communication within complex systems composed of interconnected neural units. This study investigates the structural properties functional networks formed during both active and resting states under different knee joint flexion tasks. These movements were performed under three physical demand conditions, including an assisted, non-volitional movement.Functional networks were constructed from EEG analysis over 16 electrodes for the μ, β, and γ frequency bands, and key network metrics were estimated, including input and output node degree centrality, clustering coefficient, and betweenness centrality. Results indicate that motor execution leads to a reduction in overall network connectivity while enhancing communication efficiency. Additionally, networks in the γ and μ bands were more involved in voluntary movement, whereas the β band played a predominant role in assisted movements. The spatial distribution of electrodes contributing to these networks differed between voluntary and assisted conditions, suggesting distinct underlying neural mechanisms rather than a simple linear modulation of connectivity.},
}
@article {pmid41335749,
year = {2025},
author = {Sun, Y and You, Z and Sun, D and Huang, Y and Wu, Q and Pan, J},
title = {DC-FFNet: Dual Channel Feature Fusion Network for Real-Time Asynchronous Signal Analysis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11254486},
pmid = {41335749},
issn = {2694-0604},
mesh = {Humans ; *Evoked Potentials, Visual/physiology ; Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; *Electroencephalography/methods ; Algorithms ; Computer Systems ; },
abstract = {Steady-state visual evoked potentials (SSVEP) are widely used in brain-computer interface (BCI) systems due to their high accuracy and fast response performance and are commonly used for the control of a variety of external devices. However, existing SSVEP signal classification methods still face the problems of insufficient recognition accuracy and poor real-time performance in complex dynamic scenes. Therefore, this study proposes a new SSVEP signal classification model Dual Channel Feature Fusion Network (DC-FFNet), and constructs a real-time control framework by combining it with an asynchronous control mechanism. DC-FFNet is a novel model for SSVEP signal classification based on a dual channel architecture. It incorporates a multi-head self-attention mechanism to capture global features, enhance local features, and fuse multimodal information, significantly improving classification accuracy. The classification accuracy of DC-FFNet reaches 91.80% on the SSVEP_SANDIEGO Dataset and 90.93% on the Self-recorded Dataset, which both exceed the existing models. In addition, the real-time framework that incorporates an asynchronous control mechanism effectively reduces the response time and improves the information transfer rate of the system (up to 128.66 bits/min). This research is expected to provide an efficient and flexible SSVEP signal processing scheme for multi-device asynchronous collaborative control systems assisting people with disabilities, balancing performance and real-time, which is of great significance for BCI technology.},
}
@article {pmid41335716,
year = {2025},
author = {Turk, S and Padfield, N and Mujahid, K and Camilleri, T and Camilleri, K},
title = {Word-specific properties affect classification performance in Brain Computer Interfaces for decoding imagined speech from EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-5},
doi = {10.1109/EMBC58623.2025.11254906},
pmid = {41335716},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Speech/physiology ; *Imagination/physiology ; Male ; Adult ; Female ; Young Adult ; *Brain/physiology ; },
abstract = {Decoding imagined speech from brain signals has become one of the most significant fields for BCI applications. One of the current challenges that researchers face is an insufficient classification performance for real-world applications. In this study, we investigate for the first time the effect of word-specific properties known to modulate brain signals on classification performance. We chose 16 word prompts that vary in age of acquisition (AoA) and word frequency, two word-specific properties known to modulate speech processing, and investigated their classification performance for speech imagery (SI) trials compared to the idle state using a random forest classifier and 10-fold cross-validation. We found highly significant effects of AoA, word frequency and their interaction on classification performance. Our results yield evidence that the word frequency and AoA of word prompts used in SI paradigms significantly influence the classification accuracy in a BCI application when SI trials are compared to the idle state.Relevance - Choosing word prompts with optimal properties can significantly improve classification performance in BCI applications.},
}
@article {pmid41335707,
year = {2025},
author = {Ronca, V and Di Flumeri, G and Lungarini, L and Capotorto, R and Germano, D and Giorgi, A and Borghini, G and Babiloni, F and Arico, P},
title = {A Novel Multi-Stage Algorithm for Real-Time Detection and Correction of Ocular Artifacts in EEG: A Calibration-Free Approach.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11254864},
pmid = {41335707},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; Humans ; *Algorithms ; *Artifacts ; *Signal Processing, Computer-Assisted ; Calibration ; Electrooculography/methods ; Male ; Adult ; Female ; },
abstract = {Ocular artifacts, particularly blinks, significantly affect the integrity of electroencephalographic (EEG) signals, posing a challenge for real-time applications. Traditional correction methods often require a calibration phase or additional electrooculogram (EOG) channels, limiting their applicability in mobile and real-world settings. This study presents a novel detection and correction method, designed for online ocular artifact correction without the need for prior calibration: the CFo-CLEAN. The proposed method integrates an Enhanced Adaptive Data-driven Algorithm (eADA) for real-time identification and correction of ocular artifacts directly from EEG signals. Unlike conventional approaches, this implementation adapts dynamically to ongoing EEG variations, enhancing flexibility and performance. The study evaluates the CFo-CLEAN method using EEG data recorded from 38 participants during real-world driving scenarios. Performance comparisons were conducted against established correction techniques, including Independent Component Analysis (ICA), regression-based methods, and subspace reconstruction approaches. The evaluation considered both artifact removal efficiency and EEG signal preservation across different experimental conditions. Results demonstrated that the method effectively reduced ocular artifact contamination while preserving neurophysiological content. Specifically, two implementations of the method, utilizing 60-second and 90-second time windows, were analyzed, revealing that longer windows provided superior EEG signal preservation, particularly in higher frequency bands. These findings validate the effectiveness of the CFo-CLEAN method for real-time applications, making it a valuable tool for brain-computer interfaces (BCIs), neuroergonomics, and cognitive state monitoring. By avoiding the need for a calibration phase and incorporating adaptive processing, this method represents a significant advancement in real-time EEG artifact correction, facilitating its deployment in dynamic, real-world environments.},
}
@article {pmid41335679,
year = {2025},
author = {Farabbi, A and Ballabio, F and Rossi, M and Palmisciano, AC and Antonello, N and Trojaniello, D and Ongarello, T and Cerveri, P and Mainardi, L},
title = {A Two-Stage Deep Learning Approach for EEG Artifact Removal and Classification: Towards Reliable Wearable Applications.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-5},
doi = {10.1109/EMBC58623.2025.11254976},
pmid = {41335679},
issn = {2694-0604},
mesh = {*Electroencephalography/methods/instrumentation ; Humans ; *Artifacts ; *Deep Learning ; *Wearable Electronic Devices ; *Signal Processing, Computer-Assisted ; Algorithms ; Male ; Blinking/physiology ; },
abstract = {EEG artifact removal remains a critical challenge in neural signal processing. In this paper, we present a novel two-stage approach combining a modified IC-UNet architecture for artifact removal with a modified VGGNet for artifact type identification. The system automatically triggers the classification stage when the difference between original and denoised signals exceeds a learned threshold, enabling the classification of ocular artifacts (eye blinks and saccadic movements) in the original signals. The denoising stage employs parallel encoding paths with channel-specific feature extraction, followed by a shared bottleneck and decoder network. The system was evaluated using EEG data from subjects performing controlled eye blink and saccadic movement tasks. The denoising network achieves high correlation values between predicted and ground truth signals, particularly in temporal and specific frontal regions (T5: 0.86 ± 0.01, T6: 0.85 ± 0.01, F3: 0.83 ± 0.01). The classification network shows excellent performance, achieving 99.35% accuracy on the test set with only four misclassifications out of 620 cases.Clinical relevance- This study demonstrates the feasibility of accurate artifact removal and classification in temporal and behind-the-ear EEG recordings, which is particularly relevant for the development of wearable EEG devices for continuous monitoring and hybrid BCI systems.},
}
@article {pmid41335297,
year = {2025},
author = {Belwafi, K and Alsuwaidi, A and Mejri, S and Djemal, R},
title = {Brain-inspired signal processing for detecting stress during mental arithmetic tasks.},
journal = {Brain informatics},
volume = {},
number = {},
pages = {},
doi = {10.1186/s40708-025-00281-y},
pmid = {41335297},
issn = {2198-4018},
abstract = {Brain-Computer Interfaces provide promising alternatives for detecting stress and enhancing emotional resilience. This study introduces a lightweight, subject-independent method for detecting stress during arithmetic tasks, designed for low computational cost and real-time use. Stress detection is performed through ElectroEncephaloGraphy (EEG) signal analysis using a simplified processing pipeline. The method begins with preprocessing the EEG recordings to eliminate artifacts and focus on relevant frequency bands (α, β, and γ). Features are extracted by calculating band power and its deviation from a baseline. A statistical thresholding mechanism classifies stress and no-stress epochs without the need for subject-specific calibration. The approach was validated on a publicly available dataset of 36 subjects and achieved an average accuracy of 88.89%. The method effectively identifies stress-related brainwave patterns while maintaining efficiency, making it suitable for embedded and wearable devices. Unlike many existing systems, it does not require subject-specific training, enhancing its applicability in real-world environments.},
}
@article {pmid41335119,
year = {2025},
author = {Hu, K and Wang, Y and Tu, K and Guo, H and Yan, J},
title = {Cross-domain correlation analysis to improve SSVEP signals recognition in brain-computer interfaces.},
journal = {Biomedical physics &amp; engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/ae2772},
pmid = {41335119},
issn = {2057-1976},
abstract = {The recognition of steady-state visual evoked potential (SSVEP) signals in brain-computer interface (BCI) systems is challenging due to the lack of training data and significant inter-subject variability. To address this, we propose a novel unsupervised transfer learning framework that enhances SSVEP recognition without requiring any subject-specific calibration. Our method employs a three-stage pipeline: (1) preprocessing with similarity-aware subject selection and Euclidean alignment to mitigate domain shifts; (2) hybrid feature extraction combining canonical correlation analysis (CCA) and task-related component analysis (TRCA) to enhance signal-to-noise ratio and phase sensitivity; and (3) weighted correlation fusion for robust classification. Extensive evaluations on the Benchmark and BETA datasets demonstrate that our approach achieves state-of-the-art performance, with average accuracies of 83.20% and 69.08% at 1s data length, respectively-significantly outperforming existing methods like ttCCA and Ensemble-DNN. The highest information transfer rate reaches 157.53 bits/min, underscoring the framework's practical potential for plug-and-play SSVEP-based BCIs.},
}
@article {pmid41332552,
year = {2025},
author = {Chan, AYC and Stiles, NRB and Levitan, CA and Wu, DA and Tanguay, AR and Shimojo, S},
title = {Bayesian Causal Inference Accounts for Multisensory Filling-In at the Blind Spot.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2024.11.15.623713},
pmid = {41332552},
issn = {2692-8205},
abstract = {We asked three questions about multisensory perception across the physiological blind spot: (1) Does audiovisual integration persist without bottom-up visual input? (2) Does the brain adjust its sensory uncertainties and priors accordingly? (3) Are the underlying causal-inference computations preserved? Participants judged flashes and beeps in an audiovisual illusion presented across the blind spot or a matched control location. Responses were fit with a Bayesian Causal Inference (BCI) model, estimating sensory noise, numerosity priors, and causal-inference priors under multiple decision strategies evaluated using BIC. Illusions were robust at both locations, indicating preserved integration. Model fits showed higher visual uncertainty and broader prior expectations at the blind spot, while auditory precision and the causal prior remained stable. Thus, the computational architecture of causal inference is maintained, but its parameters flexibly adapt to local sensory reliability. These findings demonstrate that perceptual inference remains intact even in regions without retinal input, achieved by adjusting internal uncertainty rather than altering core multisensory computations.},
}
@article {pmid41332173,
year = {2025},
author = {Uwimbabazi, M and Muhanguzi, G and Eryenyu, D and Arua, P and Tweheyo, M and Patten, MA and Eycott, AE and Babweteera, F},
title = {A link between increased temperature and avian body condition in a logged tropical forest.},
journal = {Conservation biology : the journal of the Society for Conservation Biology},
volume = {},
number = {},
pages = {e70190},
doi = {10.1111/cobi.70190},
pmid = {41332173},
issn = {1523-1739},
support = {//Earthwatch Institute/ ; //Royal Zoological Society of Scotland/ ; },
abstract = {The combined effects of anthropogenic disturbances, such as logging and climate change, remain poorly understood; yet, they are the main threats to tropical biodiversity. Most tropical African countries lack long-term climate data, so climate impacts on biodiversity cannot be assessed. However, individuals experience weather, rather than climate, such that climate effects could be seen as the cumulative effects of weather over time. We used morphometric data collected in 1996-2000 and 2017-2021 on understory birds in the Budongo Forest, Uganda, to assess how logging history and short-term weather variations affected the body condition (body condition index [BCI]) of birds. Birds were captured in mist nets in logged and unlogged sites. We analyzed data with Bayesian mixed-effects models. The BCI values were lower in logged forests and decreased as maximum temperatures increased, irrespective of the sensitivity of the birds to logging. Birds responded quickly to increasing temperatures and precipitation (within 1 week), and the longer a hot period was, the worse the effect on birds in heavily logged forests, suggesting reduced thermal buffering. Contrary to our expectations, BCI values for 2017-2021 were higher than values for 1996-2000, indicating possible forest recovery. Our findings underscore the importance of short-term weather data to predict climate change impacts. Such predictions can inform tropical forest management and restoration measures.},
}
@article {pmid41330936,
year = {2025},
author = {Liu, Y and Fu, Y and Tang, E and Wu, H and Han, J and Xie, M and Zhang, Y and Peng, B and Huang, J and Liu, H and Chen, H and Qin, P},
title = {Neural dissociation of attention and working memory through inhibitory control.},
journal = {Nature communications},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41467-025-66553-7},
pmid = {41330936},
issn = {2041-1723},
support = {32171046//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32200844//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32371098//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31971032//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Attention and working memory (WM) have traditionally been considered closely linked processes with shared neural mechanisms. In information selection, attention is often conceptualized as a gatekeeper to WM, regulating which information is encoded and stored. Here, combining tasks specifically designed to separate attention from WM encoding with a multimodal approach, we provide converging neural and causal evidence that these processes are dissociable. Functional MRI identifies the supramarginal gyrus (SMG) as the key region enabling this dissociation, while dynamic causal modeling reveals the neural circuitry through which the SMG exerts inhibitory control over attentional representations, regulating their integration into WM. Furthermore, neuromodulation via transcranial direct current stimulation (tDCS) demonstrates that enhancing SMG activity strengthens this inhibitory control. A second tDCS experiment using varied stimuli confirms the generalizability of the effect. Finally, a transcranial magnetic stimulation (TMS) experiment provides further causal evidence with greater spatial precision. These findings challenge the long-standing view that attention and WM encoding form a continuous process, demonstrating instead that they constitute two dissociable neural processes of information selection.},
}
@article {pmid41330225,
year = {2025},
author = {Yuan, J and Xu, M and Qian, L and Gao, L and Sun, Y},
title = {Task-specific effects of sleep deprivation on cognitive function and EEG brain network in night-shift nurses.},
journal = {Brain research bulletin},
volume = {233},
number = {},
pages = {111661},
doi = {10.1016/j.brainresbull.2025.111661},
pmid = {41330225},
issn = {1873-2747},
abstract = {BACKGROUND: Night-shift nurses experience chronic sleep deprivation, which impairs cognitive functions crucial for patient safety. However, the underlying reorganization of brain functional networks remains poorly understood. This study aimed to investigate the task-specific effects of sleep deprivation on brain network topology during sustained attention and working memory in night-shift female nurses.<br><br>METHODS: In a within-subjects design, electroencephalography (EEG) data from 28 female nurses were recorded during a rested session (R-Session) and a sleep-deprived session (SD-Session) immediately following a night shift. Participants performed the psychomotor vigilance test (PVT) and 2-back tasks. Functional connectivity was estimated using the weighted phase lag index (wPLI), and brain network properties were quantified using graph theoretical analysis at both global and nodal levels.<br><br>RESULTS: Our findings revealed a clear behavioral dissociation: sleep deprivation significantly impaired PVT performance but had no effect on 2-back task performance. This dissociation was mirrored by distinct patterns of neural reorganization. During the PVT, the brain network exhibited a compensatory enhancement of global topology, characterized by a significant increase in clustering coefficient, global efficiency, local efficiency, and small-worldness, alongside a decrease in characteristic path length, particularly in the theta and beta bands. In contrast, the 2-back task showed only a localized increase in the theta-band clustering coefficient. Nodal analysis further revealed a critical topographical distinction: PVT-related efficiency changes were strongly right-lateralized, whereas 2-back changes were bilaterally distributed.<br><br>CONCLUSION: In conclusion, these results demonstrate that sleep deprivation elicits task-specific neurocognitive adaptations. Sustained attention appears highly vulnerable, prompting a broad compensatory reorganization of the right-hemispheric attention network. Conversely, working memory function remains behaviorally stable, underpinned by a more specific network reorganization, primarily involving increased local connectivity. This study deepens our understanding of the neural mechanisms underlying cognitive vulnerability and resilience in nurses group.},
}
@article {pmid41330041,
year = {2025},
author = {Li, Y and Chen, E and Xiao, X and Xu, M and Ming, D},
title = {Lightweight deep learning models for EEG decoding: A Review.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae2717},
pmid = {41330041},
issn = {1741-2552},
abstract = {Brain-computer interface (BCI) technology enables direct communication between the human brain and external devices by decoding electroencephalogram (EEG) signals into actionable commands. As a noninvasive and portable modality, EEG-based BCIs hold promise for applications ranging from neurorehabilitation to assistive technologies. However, their performance depends critically on the accurate extraction of relevant neural features and the reliable recognition of underlying patterns. Deep learning has transformed this process. By automatically learning complex, task-relevant representations from raw or minimally processed EEG data, deep neural networks have surpassed many traditional handcrafted feature approaches in both accuracy and adaptability. Yet, the substantial computational and memory demands of many deep learning architectures limit their deployment in portable or real-time BCI systems. This challenge has motivated a growing interest in lightweight models-architectures optimized to reduce complexity while preserving or even enhancing performance. This paper provides a systematic review of such lightweight deep learning models for EEG signal classification, with EEGNet serving as a representative baseline. To organize this landscape, existing approaches are categorized into three main strategies: (1) information integration through multi-scale feature fusion, (2) optimization of hidden layer design, and (3) hybrid strategies combining multiple structural enhancements. The review synthesizes recent advances, identifies emerging trends, and outlines potential directions for future research. These insights aim to inform the design of efficient and robust EEG classification architectures capable of meeting the practical demands of real-world BCI applications.},
}
@article {pmid41329786,
year = {2025},
author = {Luo, X and Zhang, L and Pan, Y},
title = {Do we advise as one likes? The alignment bias in social advice giving.},
journal = {PLoS computational biology},
volume = {21},
number = {12},
pages = {e1013732},
doi = {10.1371/journal.pcbi.1013732},
pmid = {41329786},
issn = {1553-7358},
abstract = {We often give advice to influence others, but could our own advice also be shaped by the very individuals we aim to influence (i.e., advisees)? This reverse flow of social influence-from those typically seen as being influenced to those who provide the influence-has been largely neglected, limiting our understanding of the reciprocal nature of human communications. Here, we conducted a series of experiments and applied computational modelling to systematically investigate how advisees' opinions shape the advice-giving process. In an investment game, participants (n = 346, across four studies) provided advice either independently or after observing advisees' opinions (Studies 1 &amp; 2), with feedback on their advice (acceptance or rejection) provided by advisees (Studies 3 &amp; 4). Our findings reveal that advisors tend to adjust their advice to align with the advisees' opinions (we refer to this as the alignment bias) (Study 1). This tendency, which reflects normative conformity, persists even when advisors were directly incentivized to provide accurate advice (Study 2). As feedback is introduced, advisors' behavior shifts in ways best captured by a reinforcement learning model, suggesting that advisees' feedback drives adaptations in advice giving that maximize acceptance and minimize rejection (Study 3). This adaptation persisted even when acceptance is rare, as bolstered by the model-based evidence (Study 4). Collectively, our findings highlight advisors' susceptibility to the consequence of giving advice, which can lead to counterproductive impacts on decision-making processes and misinformation exacerbation in social encounters.},
}
@article {pmid41329325,
year = {2025},
author = {Luo, S and Fan, Y and Yu, F and Zhou, X and Hu, K and Yi, H and Zhou, H and Li, T and Chen, JF and Zhang, L},
title = {The Secondary Motor Cortex-External Globus Pallidus Pathway Regulates Auditory Feedback of Volitional Control.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {41329325},
issn = {1995-8218},
abstract = {Effective use of brain-computer interfaces (BCIs) requires the ability to suppress a planned action (volitional inhibition) for adaptable control in real-world scenarios, but their mechanisms are unclear. Here, we used fiber photometry to monitor external globus pallidus (GPe) and subthalamic nucleus (STN) neurons' activity in mice during a volitional stop-signal task (67% GO, 33% NO-GO). GPe/STN neurons (receiving M2 projections) responded to auditory cues, feedback, and rewards in both trials. Importantly, chemogenetic activation of the M2-GPe pathway enhanced volitional inhibition by modulating auditory feedback response, yet inhibited GPe neurons' feedback response. Furthermore, time-locked optogenetic inhibition of M2-projecting GPe neurons at auditory feedback also enhanced volitional inhibition via prolonged GO trial response times. Collectively, these findings identified the M2-GPe pathway for auditory biofeedback to improve volitional control, offering novel avenues for the advancement of neural interfaces for biofeedback and enhancement of BCI efficacy.},
}
@article {pmid41328607,
year = {2025},
author = {Zhang, H and Liao, Y and Lin, Z and Wen, H and Pang, T and Zhao, X and Zhang, W and Lou, X and Chen, C and Hu, S and Liu, Z and Xu, X},
title = {Comorbidity of undiagnosed mood symptoms with dementia risk in multi-regional multi-ethnic adults: evidence from epidemiological findings and plasma metabolites.},
journal = {Epidemiology and psychiatric sciences},
volume = {34},
number = {},
pages = {e58},
doi = {10.1017/S2045796025100346},
pmid = {41328607},
issn = {2045-7979},
mesh = {Humans ; Female ; Male ; United Kingdom/epidemiology ; *Dementia/epidemiology/ethnology/blood ; Middle Aged ; Aged ; Comorbidity ; Prospective Studies ; *Mood Disorders/epidemiology/ethnology/blood ; *Bipolar Disorder/epidemiology/ethnology ; Risk Factors ; Ethnicity/statistics &amp; numerical data ; Prevalence ; *Depression/epidemiology/ethnology ; },
abstract = {AIMS: To investigate the association of midlife and late-life undiagnosed mood symptoms, especially their comorbidity, with long-term dementia risk among multi-regional and ethnic adults.<br><br>METHODS: The prospective study used data from the UK Biobank (N = 142,670; mean follow-up 11.0 years) and three Asian studies (N = 1,610; mean follow-up 4.4 years). Undiagnosed mood symptoms (manic symptoms, depressive symptoms and comorbidity of depressive and manic symptoms) and diagnosed mood disorders (depression, mania and bipolar disorders) were classified. Plasma levels of 168 metabolites were measured. The association between undiagnosed mood symptoms and 12-year dementia (including subtypes) risk and domain-specific cognitive function was examined. The contribution of metabolites in explaining the association between symptom comorbidity and dementia risk was estimated.<br><br>RESULTS: Undiagnosed mood symptoms were prevalent (11.4% in the UK cohort and 31.2% in Asian cohorts) among 1,462 (1.0%) and 74 (19.4%) participants who developed dementia. Comorbidity of undiagnosed mood symptoms was associated with higher dementia risk (sub-distribution hazard ratios = 9.46; 95% confidence interval = 4.07-21.97), especially Alzheimer's disease, and with worse reasoning ability, poorer numeric memory and metabolic dysfunction. Glucose and total Esterified Cholesterol explained 9.1% of the association between symptom comorbidity and dementia, with most of the contribution being from glucose (6.8%).<br><br>CONCLUSIONS: Comorbidity of undiagnosed mood symptoms was associated with a higher cumulative risk of dementia in the long term. Glucose metabolism could be implicated in the development of mood disorders and dementia. The distinctive pathophysiological mechanism between psychiatric and neurodegenerative disorders warrants further exploration.},
}
@article {pmid41328405,
year = {2025},
author = {Zhu, R and Zhao, Y and Li, Y},
title = {Paradigm Shift in Global Governance of Medical Brain-Computer Interface: Addressing Practical Challenges Through Institutional Innovation.},
journal = {Risk management and healthcare policy},
volume = {18},
number = {},
pages = {3755-3768},
pmid = {41328405},
issn = {1179-1594},
abstract = {The rapid advancement of medical brain-computer interface (BCI) technology necessitates the transformation and upgrading of traditional governance paradigms urgently. China, the United States, and the European Union hold prominent positions in the global medical BCI landscape and have developed three highly representative governance models. Existing research on medical BCI primarily focuses on specific countries or regions, but it has failed to conduct a comprehensive comparison of governance frameworks across different jurisdictions from a horizontal perspective. In this study, a horizontal policy text analysis was employed to comprehensively compare the divergent approaches of China, the United States, and the European Union in regulating medical BCI, focusing on regulatory frameworks, approval procedures, neural data governance, and ethical governance. China's medical BCI governance is state-led, prioritizing safety; the United States features innovation-driven flexibility; the European Union uses an empowerment model to strictly mitigate risks. Yet these three models have inherent drawbacks. To ensure the healthy development of medical BCI, we suggest China, the United States, the European Union and other jurisdictions establish a lifecycle regulatory mechanism, introduce the regulatory sandbox, promote collaborative governance among multiple subjects, build hierarchical informed consent rules, endow users with neurorights and refine BCI ethical governance.},
}
@article {pmid41328166,
year = {2025},
author = {Sun, H and Wang, Z and Qi, Y and Wang, Y},
title = {Decoding multi-joint hand movements from brain signals by learning a synergy-based neural manifold.},
journal = {Patterns (New York, N.Y.)},
volume = {6},
number = {11},
pages = {101394},
pmid = {41328166},
issn = {2666-3899},
abstract = {Brain-computer interfaces have shown great potential in the reconstruction of motor functions. However, decoding complex and natural movements, such as hand movements, remains challenging. Traditional approaches primarily decode the movement of multiple joints in the hand independently, while the inherent synergies underlying these movements have not been well explored. Here, we demonstrate that complex hand movements can be decomposed into a set of motor primitives, each involving a synergy of multi-joint movements. Motor cortical neural activities recruit the motor synergies through spatiotemporal parameters to accomplish the complex motor targets. By learning a joint neural-motor representation of these motor synergies and decoding spatiotemporal parameters rather than the joint-level kinematics, significant improvement could be obtained in hand movement decoding. We propose a neural decoding framework, SynergyNet, to effectively learn the neural-motor synergies for hand movement control. The proposed approach significantly outperforms benchmark methods and provides high interpretability with the hand movement neural decoding task.},
}
@article {pmid41326740,
year = {2025},
author = {Tiawongsuwan, L and Klomchitcharoen, S and Chumanee, W and Tangwattanasirikun, T and Saksittikorn, S and Chawaruechai, S and Jatupornpoonsub, T and Wongsawat, Y},
title = {Autism spectrum disorder disrupts brain network connectivity maturation during childhood development.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-025-30971-w},
pmid = {41326740},
issn = {2045-2322},
support = {B42G670043//National Higher Education Science Research and Innovation Policy Council (PMU B)/ ; },
abstract = {Understanding the developmental trajectory of autism spectrum disorder (ASD) remains a critical barrier for timely intervention in children. Here, we investigated the deficit brain maturation trajectory during childhood development in 35 ASD level 1 and 35 neurotypical children through an electroencephalography (EEG) approach. An empirical study of the potential EEG biomarkers was demonstrated in a comprehensive view of group difference and age-related group comparison using alpha power, peak alpha frequency and transfer entropy during resting. We found a significant disruption of directional brain network communication between regions in children with ASD compared to neurotypical children. Our results also suggested that the children with ASD had altered occipital alpha power and peak alpha frequency development. The present study revealed promising findings that underpinned the developmental disruption of autism spectrum disorder, which may provide a prevailing insight into the disease pathology mechanisms, paving the way for future intervention advancement.},
}
@article {pmid41326639,
year = {2025},
author = {Jo, H and Yang, Y and Han, J and Duan, Y and Xiong, H and Lee, WH},
title = {Evaluating EEG-to-text models through noise-based performance analysis.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-025-29587-x},
pmid = {41326639},
issn = {2045-2322},
support = {RS-2023-00226263//Korea Creative Content Agency/ ; RS-2024-00509257//Institute for Information and Communications Technology Promotion/ ; },
abstract = {Brain-computer interfaces (BCIs) have the potential to revolutionize communication for individuals with severe disabilities. EEG-to-text models, which translate brain signals into written language, offer a promising avenue for restoring communication abilities. Recent advancements in machine learning have improved the accuracy and speed of these models, but their true capabilities remain unclear due to limitations in evaluation methodologies. This study critically examines the performance of EEG-to-text models, focusing on their ability to learn from EEG signals rather than simply memorizing patterns. We introduce a novel methodology that compares model performance on EEG data with that on noise inputs. Our findings reveal that many EEG-to-text models perform similarly or even better on noise, suggesting that they may be memorizing patterns rather than truly learning from EEG signals. These results highlight the need for more rigorous benchmarking and evaluation practices in the field of EEG-to-text translation. By addressing the limitations of current methodologies, we can develop more reliable and trustworthy systems that truly harness the potential of brain-computer interfaces for communication.},
}
@article {pmid41325805,
year = {2025},
author = {Zhang, P and Xu, W and Jiang, W and Jin, X and Lou, Y and Yang, T and Li, W and Gao, K and Gao, F and Qian, Z},
title = {Automated Ladder Rung Test for Evaluating Motor Coordination in Parkinson's Disease Mouse Models.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110642},
doi = {10.1016/j.jneumeth.2025.110642},
pmid = {41325805},
issn = {1872-678X},
abstract = {BACKGROUND: The ladder rung walking test assesses fine motor coordination in Parkinson's disease (PD) mouse models but relies on labor-intensive, subjective manual scoring, necessitating an automated, objective system.<br><br>NEW METHOD: We developed a cost-effective automated ladder rung test system with a ladder featuring regular and irregular rung patterns, array through-beam optical sensors for foot-error detection, and an Arduino microcontroller. Custom Python software enables intuitive control, real-time visualization, dynamic sensor mapping, adjustable debounce, and CSV data export.<br><br>RESULTS: In an MPTP-induced PD mouse model, the system detected increased foot errors on irregular rungs (5.13 &#xb1; 1.04 vs. 1.78 &#xb1; 0.69 in controls, p < 0.0001) and longer traversal times (18.04 &#xb1; 2.64s vs. 13.38 &#xb1; 1.95s, p = 0.001), corroborated by open field and rotarod tests and a 68.7% reduction in substantia nigra neurons.<br><br>Unlike costly camera-based systems requiring complex algorithms, our system uses simple photoelectric sensors and costs approximately 127 USD for all components, achieving 96.4% precision and 99.3% recall, making it accessible and user-friendly.<br><br>CONCLUSIONS: This automated system offers a reproducible, high-throughput tool for objective motor assessment in PD and neurological models, enhancing preclinical research.},
}
@article {pmid41323223,
year = {2025},
author = {Zhang, L and Zhang, M and Zhang, Y and Li, N and Hu, J and Peng, X},
title = {Efficacy of brain-computer interface with functional electrical stimulation, transcranial direct current stimulation, and conventional therapy on upper limb recovery after stroke: a systematic review and network meta-analysis.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1643536},
pmid = {41323223},
issn = {1664-2295},
abstract = {OBJECTIVE: To systematically evaluate and rank the efficacy of brain-computer interface-based functional electrical stimulation (BCI-FES), transcranial direct current stimulation (tDCS), functional electrical stimulation (FES), conventional therapy (CT), and their combination (BCI-FES + tDCS) on upper limb functional recovery after stroke, and to compare the advantages of different intervention combinations through network meta-analysis, providing evidence-based medicine for clinical practice.<br><br>METHODS: A network meta-analysis method was used to comprehensively compare the efficacy of BCI-FES, tDCS and conventional motor rehabilitation in upper limb rehabilitation of stroke survivors. Statistical analysis was performed using R and Stata software, including direct meta-analysis and network meta-analysis. The direct meta-analysis used mean difference (MD) and its 95% confidence interval (CI) as effect size indicators. The network meta-analysis was performed within a Bayesian framework using the gemtc package in R.<br><br>RESULTS: A total of 13 relevant studies were finally included, comprising 11 two-arm studies and 2 three-arm studies, with a total of 777 subjects. Direct comparison meta-analysis showed: BCI-FES vs. CT MD&#x202f;=&#x202f;6.01 (95%CI: 2.19, 9.83); BCI-FES vs. FES MD&#x202f;=&#x202f;3.85 (95%CI: 2.17, 5.53); BCI-FES vs. tDCS MD&#x202f;=&#x202f;6.53 (95%CI: 5.57, 7.48); BCI-FES&#x202f;+&#x202f;tDCS vs. BCI-FES MD&#x202f;=&#x202f;3.25 (95%CI: -1.05, 7.55); BCI-FES&#x202f;+&#x202f;tDCS vs. tDCS MD&#x202f;=&#x202f;6.05 (95%CI: -2.72, 14.82). BCI-FES showed significantly better effects than CT, FES and tDCS in improving FMA. Network meta-analysis: The inconsistency model was not significant (p&#x202f;=&#x202f;0.060), so the consistency model was adopted. The efficacy ranking was BCI-FES&#x202f;+&#x202f;tDCS (98.9), BCI-FES (73.4), tDCS (33.3), FES (32.4), CT (12.0). BCI-FES and BCI-FES&#x202f;+&#x202f;tDCS were significantly better than CT, but there was no statistically significant difference compared with FES and tDCS.<br><br>CONCLUSION: The combined application of BCI-FES and tDCS appears promising for upper limb rehabilitation after stroke, with potential therapeutic advantages arising from multimodal promotion of neuroplasticity. However, given the small number of trials, methodological variability, and risk of bias, this conclusion should be considered exploratory and hypothesis-generating rather than definitive guidance. Future studies should further verify its clinical benefits through standardized stimulation protocols, individualized parameter optimization and multicenter long-term follow-up studies, to promote the translational application of brain-computer interface technology in the field of neurorehabilitation.<br><br>INPLASY202550066.},
}
@article {pmid41322937,
year = {2025},
author = {Baladaniya, M and Baldania, S and Gandhi, NV and Hait, A},
title = {Impact of Physical Therapy on Empowering Neurological Aging: A Narrative Review.},
journal = {Cureus},
volume = {17},
number = {10},
pages = {e95640},
pmid = {41322937},
issn = {2168-8184},
abstract = {Aging is an inevitable biological process that is frequently accompanied by neurological decline, which profoundly impacts gait, balance, and the ability to perform activities of daily living. Physical therapy (PT) plays a pivotal role in mitigating these deficits by enhancing mobility, strength, and independence through evidence-based interventions like resistance training, balance exercises, and functional mobility programs. This narrative review synthesizes current evidence on PT's effectiveness in managing age-related neurological changes, emphasizing its integration within interdisciplinary teams and the use of innovative technologies such as exoskeletons, telerehabilitation, and brain-computer interfaces. A combination of specific keywords and Boolean operators was utilized to identify peer-reviewed studies on the databases PubMed, Google Scholar, and ScienceDirect, focusing on the impact of PT in empowering neurological aging. PT encourages active engagement and enhances the quality of life for elderly people with neurological disorders. With an aging population, the demand for PT services is expected to continue rising, underscoring the importance of adequate resources and specialized training programs in this profession. Despite robust evidence supporting the benefits of PT, gaps persist in understanding its long-term efficacy, optimal intervention dosing, and the integration of emerging technologies into routine practice. Challenges such as limited access to specialized services and insufficient data on cost-effectiveness and patient adherence further complicate the delivery of care. This review advocates for future research to refine PT strategies, enhance interdisciplinary collaboration, and leverage technological advancements to optimize outcomes for older adults with neurological conditions, ultimately promoting successful aging and sustained independence.},
}
@article {pmid41322339,
year = {2026},
author = {Huang, X and Lu, W and Jiang, D and Fang, Z and Feng, B},
title = {DUSP6 inhibitor (E/Z)-BCI hydrochloride stimulates glucose clearance and adipose lipolysis in diet-induced obese mice.},
journal = {Genes &amp; diseases},
volume = {13},
number = {2},
pages = {101671},
pmid = {41322339},
issn = {2352-3042},
}
@article {pmid41321942,
year = {2025},
author = {Danso, A and Ehlert, M and Koehler, F and Kirk, R and Natarajan, N and Wright, SE and Timmers, R and Saarikallio, S},
title = {Patterns of pre-sleep music use and sleep quality: exploratory survey findings on state anxiety.},
journal = {PeerJ},
volume = {13},
number = {},
pages = {e20444},
pmid = {41321942},
issn = {2167-8359},
mesh = {Humans ; Female ; Male ; Cross-Sectional Studies ; Adult ; *Sleep Quality ; *Anxiety/psychology ; *Music/psychology ; Surveys and Questionnaires ; Young Adult ; Middle Aged ; Stress, Psychological/psychology ; Self Report ; },
abstract = {Music listening is a widely used self-help approach that may influence psychological and physiological processes associated with sleep. This cross-sectional study explored patterns of pre-sleep music use in relation to psychological distress (state anxiety, mood disturbance, stress) and subjective sleep quality. Adults (N = 269, 52.6% female; M age = 27.7, SD = 9.0) completed validated self-report measures of sleep quality (the Pittsburgh Sleep Quality Index (PSQI)) and psychological distress. Pre-sleep music use was modestly associated with poorer sleep quality (r = 0.23, p < 0.01). A borderline interaction between state anxiety and music use (β = -0.170, p = 0.050) suggested, but did not confirm, a possible buffering pattern in which the anxiety-sleep association appeared weaker among more frequent music users. No moderation effects were observed for mood or stress. These preliminary findings suggest that pre-sleep music use may reflect a coping-oriented effort among individuals experiencing anxiety. However, given the cross-sectional design, self-report measures, and borderline statistical support, the results should be viewed as descriptive and hypothesis-generating.},
}
@article {pmid41320900,
year = {2025},
author = {Zhu, H and Zhu, S and Zhao, M and Zhu, Z and Shao, Y and Lu, X and Liu, T and Zhu, H and Shu, N and Lin, H and Cheng, J},
title = {Cerebellar and Brainstem White Matter Geometric Alterations in Multiple System Atrophy: A DFA-Based Biomarker for Disease Staging.},
journal = {CNS neuroscience &amp; therapeutics},
volume = {31},
number = {12},
pages = {e70623},
doi = {10.1111/cns.70623},
pmid = {41320900},
issn = {1755-5949},
support = {4252004//Beijing Natural Science Foundation/ ; L242038//Beijing Natural Science Foundation/ ; CFH2022-2-2014//Capital's Funds for Health Improvement and Research/ ; 2022ZD0213300//the STI2030-Major Projects/ ; 2024YFE0100900//National Key Research and Development Program Intergovernmental Key Project of China/ ; BMI2400001//the Open Research Fund of the State Key Laboratory of Brain-Machine Intelligence, Zhejiang University/ ; CX25YQ11//Chinese Institutes for Medical Research Beijing/ ; BYSYZD2023016//Key Clinical Project of Peking University Third Hospital/ ; 2022YFC2402205//National Key Research and Development Program of China/ ; 82471498//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Multiple System Atrophy/diagnostic imaging/pathology ; Male ; *White Matter/diagnostic imaging/pathology ; Female ; *Brain Stem/diagnostic imaging/pathology ; Middle Aged ; *Cerebellum/diagnostic imaging/pathology ; Aged ; Diffusion Magnetic Resonance Imaging/methods ; Biomarkers ; Disease Progression ; },
abstract = {AIMS: To characterize white matter geometric pathology in cerebellar subtype of multiple system atrophy (MSA-C) using director field analysis (DFA) and identify stage-specific biomarkers.<br><br>METHODS: We analyzed single-shell diffusion MRI (b&#x2009;=&#x2009;1000) in 31 MSA-C patients (15 early-, 16 late-stage) and 33 controls. DFA quantified axonal geometry (splay/bend/twist), complemented by fixel-based analysis (FBA) and brainstem volumetry. Group comparisons used threshold free cluster enhancement (TFCE) (p&#x2009;<&#x2009;0.05 FWE-corrected). DFA-altered regions were correlated with clinical scores. AutoGluon evaluated classification performance using different feature sets.<br><br>RESULTS: MSA-C exhibited distinct geometric degeneration patterns: cerebellar pathways showed reduced splay, bend, and twist (reflecting Wallerian degeneration), whereas brainstem tracts demonstrated dissociated geometry (increased splay/bend but decreased twist). Brainstem twist reduction strongly differentiated early- and late-stage MSA-C (AUC&#x2009;=&#x2009;0.95). Clinically, middle cerebellar peduncle bend correlated with motor progression (UMSARS-II: r&#x2009;=&#x2009;0.48), while cerebellar splay reduction predicted ataxia severity (SARA: r&#x2009;=&#x2009;-0.43).<br><br>CONCLUSION: DFA captures circuit-specific white matter pathology in MSA-C, with brainstem twist emerging as a novel biomarker associated with disease stage. The integration of geometric metrics with automated machine learning provides a robust framework for early diagnosis and disease staging, highlighting distinct neurodegenerative mechanisms in cerebellar versus brainstem pathways.},
}
@article {pmid41320740,
year = {2025},
author = {Palanichamy, C and Thirumoorthi, SP and Lakshminarayanan, K and Madathil, D and Rahman, MH},
title = {Multimodal brain-computer interface for robotic control: integration of real-time gaze tracking and EEG-based motor imagery.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {},
number = {},
pages = {},
pmid = {41320740},
issn = {1741-0444},
abstract = {Individuals with upper limb dysfunction face significant challenges in performing everyday tasks, often depending on healthcare professionals, caregivers, or family members. Such reliance places a continuous burden on helpers who must remain available for assistance. To address these challenges, this study investigated a virtual hybrid brain-computer interface (BCI) system that integrates gaze tracking with motor imagery (MI) to control a robotic arm, potentially reducing the dependency on human support. Twenty healthy, right-handed participants took part in a virtual game environment where they controlled a robotic arm using both gaze tracking and MI. During an initial training phase, participants' electroencephalography (EEG) signals were recorded with an EEG cap. These signals were then processed and classified using the common spatial pattern (CSP) algorithm and linear discriminant analysis (LDA). In parallel, a webcam was used for real-time gaze calibration to enable accurate target selection. In the subsequent testing phase, MI commands directed the virtual robot toward predetermined targets in a Unity-based game. Training accuracy consistently outperformed online testing accuracy. The MI signal classification achieved a true positive (TP) rate of approximately 75.5%, while a significant negative correlation (r = - 0.45) was observed between MI classification accuracy and game completion times, suggesting that higher MI accuracy led to quicker task execution. These findings demonstrate the potential of combining gaze tracking with MI-based BCI for robotic control as an assistive technology for upper limb impairments. Despite its promise, technical limitations indicate that further improvements are needed to enhance system robustness, practicality, and usability for everyday activities.},
}
@article {pmid41320142,
year = {2025},
author = {Ran, H and Li, Q and Li, Y and Deng, F and Pan, Y},
title = {Online and in-person collaborative writing have similar benefits but different costs.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121629},
doi = {10.1016/j.neuroimage.2025.121629},
pmid = {41320142},
issn = {1095-9572},
abstract = {With the rapid rise of online education, collaborative learning is no longer confined to physical classrooms. Yet, it remains unclear whether online collaboration, especially with or without visual cues, can support the same cognitive and neural processes as in-person collaboration. This study used multimodal learning analytics to compare collaboration processes and inter-brain synchronization (IBS) under three conditions: in-person, online with camera on, and online with camera off. Seventy-seven learner dyads completed a 28-minute collaborative writing task while their brain activity was recorded simultaneously using functional near-infrared spectroscopy (fNIRS). Across all three conditions, collaborative learning significantly improved outcomes. In-person and online (camera on) learners showed comparable IBS in the middle temporal gyrus. However, camera-on learners displayed more frequent higher-order behaviors (e.g., monitoring, questioning, mutual understanding, argument building) and greater dorsolateral prefrontal cortex activation, reflecting increased executive control demands. In contrast, camera-off learners achieved learning gains but engaged in less information exchange, emphasized mutual understanding and collaborative planning, and exhibited markedly lower IBS. Together, these findings indicate that while both in-person and online collaboration can yield similar levels of achievement, their cognitive costs differ: in-person collaboration is more efficient, whereas online collaboration requires additional regulation and cognitive effort. The absence of visual cues further constrains information sharing and social interaction, undermining IBS. These insights help explain the mechanisms that shape collaborative learning across contexts and offer guidance for designing more effective online learning environments.},
}
@article {pmid41319418,
year = {2025},
author = {Lin, D and Shen, Q and An, Y and Fu, S and Xiao, Q and Wu, S and Song, X and Jiang, X and Klucharev, V and Cai, D and Wang, Y},
title = {Assessing the roles of subjective value and valence in outcome evaluation for consumer products: evidence from behavioral and electrophysiological experiments.},
journal = {Acta psychologica},
volume = {262},
number = {},
pages = {106011},
doi = {10.1016/j.actpsy.2025.106011},
pmid = {41319418},
issn = {1873-6297},
abstract = {Value-based decision-making is ubiquitous in our daily lives, yet most EEG studies focus on monetary outcomes, with limited attention to how the brain encodes the subjective value and valence of consumer products during outcome evaluation. To address these questions, we set up a novel three-stage task to investigate the behavioral regularities of recall of valence for food products with varying subjective values and their underlying electrophysiological mechanisms for subjective valuation and valence differentiation. With respect to the event-related potential results, we found that not receiving the food products (No-Gain) led to an increase in the FRN. Regarding the P300, we found that both higher subjective values and positive feedback elicited greater deflection of P300 at the outcome stage. Intriguingly, further single-trial analysis of EEG demonstrated that the magnitude of P300, rather than the FRN at the outcome stage, could predict subsequent behavioral performance as represented by memory accuracy. Therefore, these findings highlight the vital and dissociated roles of FRN and P300 in the subjective valuation of consumer products and suggest the possible role of P300 as a biomarker to predict subsequent choice behavior.},
}
@article {pmid41319286,
year = {2025},
author = {Li, CY and Huang, H and Shen, XF and Cao, KL and Zheng, D and Zhu, Y and Xie, SZ and Yu, XD and Wang, H and Chen, JD and Shi, J and Li, Y and Yan, M and Li, XM},
title = {Delta Opioid Receptors within the Cortico-Thalamic Circuitry Underlie Hyperactivity Induced by High-Dose Morphine.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e03831},
doi = {10.1002/advs.202503831},
pmid = {41319286},
issn = {2198-3844},
support = {82090031//National Natural Science Foundation of China/ ; 82288101//National Natural Science Foundation of China/ ; 3220081//National Natural Science Foundation of China/ ; 82071227//National Natural Science Foundation of China/ ; 82371217//National Natural Science Foundation of China/ ; U23A20433//National Natural Science Foundation of China/ ; 2021ZD0202700//STI2030-Major Projects/ ; 2023-PT310-01//Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences/ ; 010904013//Nanhu Brain-computer Interface Institute/ ; 2025ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 226-2024-00133//Fundamental Research Funds for the Central Universities/ ; LR25C090001//Zhejiang Provincial Natural Science Foundation of China/ ; 2024SSYS0017//Key Research and Development Program of Zhejiang Province/ ; 2024C03091//Key Research and Development Program of Zhejiang Province/ ; },
abstract = {Hyperactivity is a well-documented neurobehavioral effect of morphine and other opioid drugs, predominantly observed in rodent models, yet the neural circuits and molecular mechanisms underlying this effect remain elusive. In this study, an excitatory projection from the cingulate cortex (Cg) to the intermediate rostrocaudal division of zona incerta (ZIm) is revealed that is activated by morphine in mice. Chemogenetic inhibition of the Cg-ZIm pathway decreased high-dose (10-15mg kg[-1]) morphine-induced hyperlocomotion without affecting its analgesic effects. Activation of this pathway faithfully reproduced the motor effect of morphine. Furthermore, high-dose morphine-induced hyperlocomotion is quickly attenuated by microinjecting delta-opioid receptor (DOR) antagonists into the ZI, which is not observed following the targeted knockout of the DOR in Cg-projecting ZI neurons, indicating a postsynaptic DOR-mediated mechanism. In summary, these findings identify the critical role of the DOR within the Cg-ZIm circuit in the psychomotor properties of morphine. This work sheds light on potential targets within the Cg-ZIm pathway for mitigating the undesired psychomotor effects of morphine and thereby optimizing its clinical outcomes.},
}
@article {pmid41299012,
year = {2025},
author = {Díaz-Pérez, A and de Eulate, NA and Masvidal-Codina, E and Illa, X and Navarro, X and Guimerà-Brunet, A and Jiménez-Altayó, F and Penas, C},
title = {Cortical spreading depolarizations in stroke: Mechanisms, neuroprotective interventions, and monitoring techniques.},
journal = {GeroScience},
volume = {},
number = {},
pages = {},
pmid = {41299012},
issn = {2509-2723},
support = {PID2021-126117NA-I00//Ministerio de Ciencia e Innovaci&#xf3;n/ ; CNS2023-144492//Ministerio de Ciencia e Innovaci&#xf3;n/ ; PID2022-140655OB-I00//Ministerio de Ciencia e Innovaci&#xf3;n/ ; PID2020-113634RB-C22//Ministerio de Ciencia e Innovaci&#xf3;n/ ; PDC2023-145866-I0//Ministerio de Ciencia e Innovaci&#xf3;n/ ; 101130650 (META-BRAIN)//European Commission/ ; 101136541 (GphT-BCI)//European Commission/ ; CB06/01/0049//Centro de Investigaci&#xf3;n Biom&#xe9;dica en Red en Bioingenier&#xed;a, Biomateriales y Nanomedicina/ ; CB06/05/1105//Centro de Investigaci&#xf3;n Biom&#xe9;dica en Red sobre Enfermedades Neurodegenerativas/ ; 2021-SGR-00495//Generalitat de Catalunya/ ; 2021-SGR-004488//Generalitat de Catalunya/ ; 2021-SGR-00969//Generalitat de Catalunya/ ; CEX2023-001397-M//Agencia Estatal de Investigaci&#xf3;n/ ; },
abstract = {Cortical spreading depolarization (CSD) is a pathophysiological event critically implicated in ischemic stroke and other brain disorders. It consists of slowly propagating waves of massive neuronal and glial depolarization in cerebral gray matter, accompanied by spreading depression of cortical activity. CSD disrupts ion homeostasis, alters cerebral blood flow, and contributes to neuronal death in vulnerable tissue. This comprehensive review summarizes both classic and recent studies on CSD mechanisms and their role in brain damage progression after stroke. We also review potential neuroprotective strategies to mitigate CSD-induced damage and discuss available technologies for detecting CSD. Advancing our understanding of CSD mechanisms, combined with targeted neuroprotective strategies and improved monitoring techniques, holds promise for reducing stroke-related brain injury and guiding personalized recovery approaches.},
}
@article {pmid40338479,
year = {2025},
author = {Moreno-Alcayde, Y and Traver, VJ and Leiva, LA},
title = {Predicting fixations and gaze location from EEG.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {63},
number = {10},
pages = {2969-2981},
pmid = {40338479},
issn = {1741-0444},
support = {CHIST-ERA-20-BCI-001//HORIZON EUROPE Framework Programme/ ; 101071147//HORIZON EUROPE European Innovation Council/ ; PCI2021-122036-2A//Agencia Estatal de Investigaci&#xf3;n/ ; },
mesh = {*Electroencephalography/methods ; Humans ; *Fixation, Ocular/physiology ; Signal Processing, Computer-Assisted ; Deep Learning ; Neural Networks, Computer ; Brain/physiology ; },
abstract = {Brain signals carry cognitive information that can be relevant in downstream tasks, but what about eye-gaze? Although this can be estimated with eye-trackers, it can be very convenient in practice to do it without extra equipment. We consider the challenging tasks of fixation prediction and gaze estimation from electroencephalography (EEG) using deep learning models. We argue that there are three critical design criteria when designing neural architectures for EEG: (1) the spatial and temporal dimensions of the data, (2) the local vs global nature of the data processing, and (3) the overall structure and order with which the steps (1) and (2) are orchestrated. We propose two model architectures, based on Transformers and LSTMs, with different variants in this large design space, and compare them with recent state-of-the-art (SOTA) approaches under two constraints: reduced EEG signal length and reduced set of EEG channels. Our Transformer-based model outperforms the LSTM-only model, but it turns out to be more sensitive with short signal lengths and with less number of channels. Interestingly, our results are similar or slightly better than SOTA, and the models are trained from scratch (i.e., without pre-training or fine-tuning). Our findings provide useful insights for advancing in eye-from-EEG tasks.},
}
@article {pmid40025160,
year = {2025},
author = {Ye, Z and Ai, Q and Liu, Y and de Rijke, M and Zhang, M and Lioma, C and Ruotsalo, T},
title = {Generative language reconstruction from brain recordings.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {346},
pmid = {40025160},
issn = {2399-3642},
support = {CHIST-ERA-20-BCI-001//EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)/ ; },
mesh = {Humans ; *Language ; *Brain/physiology/diagnostic imaging ; *Magnetic Resonance Imaging/methods ; *Brain Mapping/methods ; Male ; Female ; Adult ; Young Adult ; },
abstract = {Language reconstruction from non-invasive brain recordings has been a long-standing challenge. Existing research has addressed this challenge with a classification setup, where a set of language candidates are pre-constructed and then matched with the representation decoded from brain recordings. Here, we propose a method that addresses language reconstruction through auto-regressive generation, which directly uses the representation decoded from functional magnetic resonance imaging (fMRI) as the input for a large language model (LLM), mitigating the need for pre-constructed candidates. While an LLM can already generate high-quality content, our approach produces results more closely aligned with the visual or auditory language stimuli in response to which brain recordings are sampled, especially for content deemed "surprising" for the LLM. Furthermore, we show that the proposed approach can be used in an auto-regressive manner to reconstruct a 10 min-long language stimulus. Our method outperforms or is comparable to previous classification-based methods under different task settings, with the added benefit of estimating the likelihood of generating any semantic content. Our findings demonstrate the effectiveness of employing brain language interfaces in a generative setup and delineate a powerful and efficient means for mapping functional representations of language perception in the brain.},
}
@article {pmid38830946,
year = {2024},
author = {Dubiel, M and Barghouti, Y and Kudryavtseva, K and Leiva, LA},
title = {On-device query intent prediction with lightweight LLMs to support ubiquitous conversations.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {12731},
pmid = {38830946},
issn = {2045-2322},
support = {101071147 (SYMBIOTIK)//European Innovation Council Pathfinder program/ ; CHIST-ERA-20-BCI-001//Horizon 2020 FET program/ ; },
abstract = {Conversational Agents (CAs) have made their way to providing interactive assistance to users. However, the current dialogue modelling techniques for CAs are predominantly based on hard-coded rules and rigid interaction flows, which negatively affects their flexibility and scalability. Large Language Models (LLMs) can be used as an alternative, but unfortunately they do not always provide good levels of privacy protection for end-users since most of them are running on cloud services. To address these problems, we leverage the potential of transfer learning and study how to best fine-tune lightweight pre-trained LLMs to predict the intent of user queries. Importantly, our LLMs allow for on-device deployment, making them suitable for personalised, ubiquitous, and privacy-preserving scenarios. Our experiments suggest that RoBERTa and XLNet offer the best trade-off considering these constraints. We also show that, after fine-tuning, these models perform on par with ChatGPT. We also discuss the implications of this research for relevant stakeholders, including researchers and practitioners. Taken together, this paper provides insights into LLM suitability for on-device CAs and highlights the middle ground between LLM performance and memory footprint while also considering privacy implications.},
}
@article {pmid41318198,
year = {2025},
author = {Fliti, T and Shhaytli, A and Serhal, A and Takesh, Z and Chokor, M},
title = {Technology-assisted interventions for neuropsychiatric disorders.},
journal = {Progress in brain research},
volume = {298},
number = {},
pages = {241-269},
doi = {10.1016/bs.pbr.2025.08.017},
pmid = {41318198},
issn = {1875-7855},
mesh = {Humans ; *Mental Disorders/therapy ; Telemedicine ; *Brain-Computer Interfaces ; },
abstract = {Neuropsychiatric disorders are chronic diseases present in the community and cause both personal and community burdens. Though therapeutically useful and beneficial, standard treatments and managements face some challenges such as social discrimination, concerns about treatments' side effects, and delay in the delivery of the healthcare services. To overcome these barriers, technology-assisted interventions have emerged and are nowadays increasingly used due to their potentials to offer accessible, personalized, and cost-efficient care in neuropsychiatric field. It is believed that these new advancements provide many advantages, such as accessibility, the direct follow-up of the patients, and the development of neuropsychiatric care in the low-income countries. In contrast, technology-assisted interventions in neuropsychiatric disorders encounter certain limitations, especially those related to ethical considerations such as patient privacy, equal access, and data security. This article reviews the role of digital health tools, neurostimulation techniques, and brain-computer interface in neuropsychiatric field. Also, it discusses the advantages and limitations of each technology.},
}
@article {pmid41318089,
year = {2025},
author = {Yamashita, T and Sawada, M and Demura, A and Aoyama, S and Yao, Y and Chihara, H and Ikedo, T and Hattori, EY and Sano, N and Takada, S and Tanji, M and Mineharu, Y and Kikuchi, T and Arakawa, Y},
title = {Usefulness of the Scalp EEG over Bone Defect for the Decoding of Muscle Activity.},
journal = {World neurosurgery},
volume = {},
number = {},
pages = {124670},
doi = {10.1016/j.wneu.2025.124670},
pmid = {41318089},
issn = {1878-8769},
abstract = {BACKGROUND: Artificial neural connections (ANCs) included in brain-machine interfaces (BMIs) translate neural activity into control commands for external stimulators to restore motor function in individuals with paralysis. While invasive methods such as stereotactic electroencephalography (sEEG), electrocorticography (ECoG) and intracortical microelectrodes provide high-bandwidth, information-rich signals capable of controlling ANCs, noninvasive techniques like electroencephalography (EEG) are often limited by the skull's attenuation of cortical activity. In this study, we tested whether EEG recorded over a bone defect following decompressive craniectomy for brain injury retains rich neural information sufficient for effective ANCs control.<br><br>METHODS: In this cross-sectional study, we recorded scalp EEG signals from patients who had undergone hemicraniectomy and analyzed neural activity, including high-gamma frequencies (65-300 Hz), during a simple hand-grip task. Using these EEG signals, we predicted hand muscle activity through regression-based decoding models. For comparison, ECoG signals were recorded from patients undergoing awake tumor resection, and muscle activity was similarly decoded. The prediction accuracy of EEG over the bone defect was then compared with that obtained from ECoG.<br><br>RESULTS: EEG over the craniectomy site captured substantial neural activity, enabling decoding of muscle activity with moderate to high accuracy (R = 0.74 &#xb1; 0.21, N = 5). In comparison, decoding accuracy from ECoG signals did not significantly differ (R = 0.55 &#xb1; 0.18, N = 4).<br><br>CONCLUSION: EEG recorded over a bone defect preserves rich cortical signals comparable to invasive ECoG, providing a minimally invasive platform for developing effective ANCs-based therapy in patients with brain injury.},
}
@article {pmid41318041,
year = {2025},
author = {Gao, L and Xu, M and Qian, L and Zhang, R and Chen, M and Hu, Y and Li, C and Sun, Y},
title = {Identifying individuals with high susceptibility to mental fatigue: A functional connectivity study.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121623},
doi = {10.1016/j.neuroimage.2025.121623},
pmid = {41318041},
issn = {1095-9572},
abstract = {Substantial inter-individual difference of behavioral performance was repeatedly revealed during prolonged time-on-task (TOT), indicating complex neural mechanisms underlying mental fatigue. In this work, we provided a comprehensive investigation to identify individuals with high susceptibility to mental fatigue and to reveal its influence on brain network reorganization. Specifically, behavioral data and EEG signals were collected from 95 participants when they performed a 20-min psychomotor vigilance task (PVT). A composite index (Findex) was introduced, based upon which the participants were categorized into the fatigue-susceptible (FS, corresponding to top third Findex value) and the fatigue-resistant (FR, corresponding to bottom third Findex value) groups (NFS/NFR = 30/30). Functional connectivity was then estimated and set as input for the following analyses. As expect, significant impairment of behavioral performance was showed in the FS group, while the performance of the FR group remained relatively stable. Following brain network analyses showed frequency-dependent reorganizations in both groups, whereas the FR group exhibited greater stability and higher integrity than the FS group. Further classification analyses revealed satisfactory accuracy for FS identification (95.61%) and the prominent centro-parietal distribution of contributed nodal features. In sum, this study provides further evidence to support the notion of substantial individual differences in fatigue susceptibility and provides a practical approach to identify the individuals whose performance is particularly prone to performance decline.},
}
@article {pmid41317873,
year = {2025},
author = {He, Y and Gong, Z},
title = {Muscular Regulation of Strategic Self-righting Behavior in Drosophila Larvae.},
journal = {Behavioural brain research},
volume = {},
number = {},
pages = {115964},
doi = {10.1016/j.bbr.2025.115964},
pmid = {41317873},
issn = {1872-7549},
abstract = {Adjusting posture is crucial for animals. When animals topple over, they attempt to restore their body posture to the default state. In the case of Drosophila larvae, they can restore their posture through self-righting (SR) behavior when placed side-up or ventral-up. However, the mechanisms of muscular regulation underlying SR behavior remains unknown. In this study, we reported that Drosophila larvae achieve postural reorientation through four strategies and their combinations for the first time, while exhibiting strategic bias. Among the four SR strategies, the most frequently used were the asymmetric SR-fwd, followed by the oblique muscle-powered SR-torsion as the second most frequently employed strategy, while SR-bwd and SR-roll exhibit significantly lower utilization frequencies. These findings not only provide a detailed characterization of larval SR behavior and its strategic diversity, but also elucidate critical muscular regulatory mechanisms underlying SR execution and strategy bias modulation. This research offers important implications for motion control system design and biomimetic robotics development, particularly regarding self-posture adjustment mechanisms.},
}
@article {pmid41316368,
year = {2025},
author = {Feng, S and Yu, X and Guo, Y and Zheng, B and Peng, J and Wang, P and Wu, W},
title = {Corticomuscular coupling study for post-stroke rehabilitation: a scoping review.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12984-025-01711-y},
pmid = {41316368},
issn = {1743-0003},
support = {No. SQ2023YFE0100690//National Key Research and Development Program of China/ ; No. SQ2023YFE0100690//National Key Research and Development Program of China/ ; No. SQ2023YFE0100690//National Key Research and Development Program of China/ ; No. SQ2023YFE0100690//National Key Research and Development Program of China/ ; No. SQ2023YFE0100690//National Key Research and Development Program of China/ ; No. SQ2023YFE0100690//National Key Research and Development Program of China/ ; U21A20479//National Natural Science Foundation of China, Joint Fund Project/ ; U21A20479//National Natural Science Foundation of China, Joint Fund Project/ ; U21A20479//National Natural Science Foundation of China, Joint Fund Project/ ; U21A20479//National Natural Science Foundation of China, Joint Fund Project/ ; JCYJ20240813150250045//Shenzhen Municipal Science, Technology and Innovation Commission, Basic Research General Program/ ; JCYJ20240813150250045//Shenzhen Municipal Science, Technology and Innovation Commission, Basic Research General Program/ ; JCYJ20240813150250045//Shenzhen Municipal Science, Technology and Innovation Commission, Basic Research General Program/ ; JCYJ20240813150250045//Shenzhen Municipal Science, Technology and Innovation Commission, Basic Research General Program/ ; 2024010//Sun Yat-sen University, Clinical Medicine 5010 Special Program/ ; 2024010//Sun Yat-sen University, Clinical Medicine 5010 Special Program/ ; 2024010//Sun Yat-sen University, Clinical Medicine 5010 Special Program/ ; 2024010//Sun Yat-sen University, Clinical Medicine 5010 Special Program/ ; KJZD20230923115114028//Shenzhen Municipal Science, Technology and Innovation Commission&#xff0c; Major Science and Technology Project/ ; KJZD20230923115114028//Shenzhen Municipal Science, Technology and Innovation Commission&#xff0c; Major Science and Technology Project/ ; KJZD20230923115114028//Shenzhen Municipal Science, Technology and Innovation Commission&#xff0c; Major Science and Technology Project/ ; KJZD20230923115114028//Shenzhen Municipal Science, Technology and Innovation Commission&#xff0c; Major Science and Technology Project/ ; },
abstract = {The challenge of post-stroke rehabilitation lies in the difficulty of quantifying the dynamic process of neural remodeling using traditional assessment methods. Corticomuscular coupling (CMC), as an emerging neurophysiological index, offers a novel perspective for quantifying this dynamic process of neural remodeling following a stroke and optimizing rehabilitation interventions. This paper systematically reviews the research advancements in CMC within stroke rehabilitation through a scoping review, focusing on four primary areas: mechanisms, analytical methods, experimental paradigms, and interventions. Studies indicate that CMC can assess the neural mechanisms underlying motor dysfunction and guide personalized rehabilitation strategies by analyzing the dynamic information transfer between the brain and muscles. However, current studies encounter challenges such as technical calibration difficulties, insufficient sample sizes, and the heterogeneity of experimental paradigms. Moving forward, it is essential to promote large-sample multicenter studies, standardize the analytical processes, and explore the synergistic application of CMC with brain-computer interfaces and other technologies to facilitate the paradigm shift from experience-driven to data-driven stroke rehabilitation.},
}
@article {pmid41315523,
year = {2025},
author = {Kalyuzhner, Z and Agdarov, S and Beiderman, Y and Beiderman, Y and Zalevsky, Z},
title = {Visual cortex speckle imaging for shape recognition.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {42690},
pmid = {41315523},
issn = {2045-2322},
abstract = {This study introduces a non‑invasive approach for neurovisual classification of geometric shapes by capturing and decoding laser‑speckle patterns reflected from the human striate cortex. Using a fast digital camera and deep neural networks (DNN), we demonstrate that each visual stimulus - rectangle, triangle, mixed shapes, or blank screen, arouses a detectably distinct speckle pattern. Our optimized DNN classifier achieved near perfect recall (98%) for rectangles and high recall (91%) for triangles in single‑shape trials and sustained robust performance (82% recall) when multiple shapes appeared simultaneously. Circular stimuli produced subtler and less reliable speckle dynamics and were not classified with consistent accuracy. By leveraging low‑cost optics and scalable AI processing, this technique paves the way for real‑time, portable monitoring of visual cortex activity, offering transformative potential for cognitive neuroscience, brain-machine interfaces, and clinical assessment of visual processing. Future work will expand stimulus complexity, optimize model architectures, and explore multimodal neurophotonic applications.},
}
@article {pmid41315347,
year = {2025},
author = {Chang, W and Kong, W and Yan, G and Lv, R and Du, K and Sadiq, MT and Guo, B and Yin, R and Liu, X},
title = {A multi-paradigm EEG dataset for studying upper limb rehabilitation exercises.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {1877},
pmid = {41315347},
issn = {2052-4463},
support = {62366028, 62466032, W2421090;//National Natural Science Foundation of China (National Science Foundation of China)/ ; 24JRRA256//Natural Science Foundation of Gansu Province/ ; },
mesh = {Humans ; *Electroencephalography ; *Upper Extremity/physiopathology ; Brain-Computer Interfaces ; *Stroke Rehabilitation ; Adult ; Male ; *Exercise Therapy ; },
abstract = {Most stroke survivors experience persistent upper limb motor dysfunction, and brain-computer interface (BCI) rehabilitation technologies have been widely explored to address this issue. However, systematic comparisons and analyses of differences among rehabilitation paradigms remain challenging due to the lack of multi-paradigm EEG datasets from the same subjects. This study aims to construct an EEG dataset that collects various rehabilitation paradigms for the same subjects. A total of 28 healthy subjects were recruited, and EEG data were collected under six types of upper limb rehabilitation paradigms. Each paradigm involves two or three actions, including grasping and releasing with the left, right, or both hands. The dataset includes both raw EEG signals and preprocessed versions with bandpass filtering and artifact removal. This resource will support studies comparing the neural mechanisms underlying different rehabilitation paradigms and aid in the development of optimized rehabilitation strategies.},
}
@article {pmid41314034,
year = {2025},
author = {Brouwer, H},
title = {Mapping meaning in the brain's language.},
journal = {Cortex; a journal devoted to the study of the nervous system and behavior},
volume = {194},
number = {},
pages = {12-21},
doi = {10.1016/j.cortex.2025.10.012},
pmid = {41314034},
issn = {1973-8102},
abstract = {Recent advances in neuroscience and artificial intelligence have pushed the state-of-the-art from being able to decode the meaning of individual words from non-invasive brain recordings, to the reconstruction of the meaning of continuous language. Beyond game changing practical implications of such "mind reading" mapping models, e.g., brain-computer interfaces that restore lost ability to speak, they also hold the promise to be instrumental in addressing a fundamental question in the cognitive sciences: How does the human brain represent the meaning of concepts, phrases, and sentences? In order to fulfil this promise, however, important methodological and theoretical challenges need to be overcome: (1) extant mapping results are inconsistent and difficult to reconcile with neurocognitive theory, (2) extant neural meaning representations do not model the compositional semantics capturing the meaning of multi-word utterances, and (3) extant mapping models fail to take into account the spatiotemporal dynamics of lexical and compositional semantic representation and computation. I argue that in order to overcome these challenges, we should ground mapping models in linguistic and neurocognitive theory, and develop neurocomputational models that explicate the spatiotemporal dynamics of meaning in the brain's language.},
}
@article {pmid41313574,
year = {2025},
author = {Lin, H and Qiu, Y and Hu, Z and Chen, L and Dai, Y and Jiang, T and Li, R and Wang, S and Cao, Y and Li, J and Liu, H and Ye, Y and Lin, J and Zheng, Y and Liang, S and Tao, J and Chen, L and Yang, M and Liu, W},
title = {Integrated Aerobic Exercise and Multisensory Environment Training for Age-related Cognitive Decline via Hippocampal-prefrontal Neural Circuit Modulation.},
journal = {Molecular neurobiology},
volume = {63},
number = {1},
pages = {196},
pmid = {41313574},
issn = {1559-1182},
support = {2023ZQNZD015//Health Service Research of Fujian Province/ ; 82274626//National Natural Science Foundation of China/ ; XQB202203//Youth Science and Technology Innovation Talent Cultivation Program of FJTCM/ ; },
mesh = {Animals ; *Hippocampus/physiopathology/metabolism/pathology ; *Prefrontal Cortex/physiopathology/metabolism ; *Physical Conditioning, Animal/physiology ; *Cognitive Dysfunction/physiopathology/therapy ; Male ; Mice, Inbred C57BL ; *Aging ; Mice ; Neuronal Plasticity ; Receptors, N-Methyl-D-Aspartate/metabolism ; *Environment ; Neurons/metabolism ; Neural Pathways/physiopathology ; },
abstract = {Age-associated cognitive decline, characterized by progressive memory and executive function impairment without dementia, poses challenges to elderly health. While aerobic exercise and environmental enrichment training may improve cognitive function, the underlying neural mechanisms remain unclear. In this study, we developed a novel intervention combines aerobic exercise (AE) with multisensory stimulation environment training (MSET). This combined training (CT) was more effective in mitigating cognitive decline in aged mice than either individual component or controls, aligning with increased neuronal activity and synaptic plasticity in the hippocampus (HPC) and prefrontal cortex (PFC). Using neural circuit tracing and chemogenetics, we explored the importance of the HPC-PFC circuit. Inhibiting the HPC-PFC circuit reduced the improvement effect of combined training (CT) on cognitive function, whereas activating this circuit enhanced cognitive function. We found candidate molecules responsive to CT in the HPC and PFC using single-cell sequencing. We identified that AE component modulated the expression levels of proprotein convertase subtilisin/kexin type 1 inhibitor (PCSK1N) and lymphocyte antigen 6 family member H (LY6H) in neurons in the HPC and PFC. At the same time, MSET component influenced the expression levels of dipeptidyl peptidase like 6 (DPP6) and glutamate ionotropic receptor NMDA type subunit associated protein 1 (GRINA) in neurons of the HPC and PFC. CT was linked to the upregulation of these molecular targets, which correlated with its beneficial effects. These findings provide insight into the mechanism underlying cognitive improvement associated with CT, suggesting a potential basis for exploring strategies aimed at mitigating cognitive decline through interventions like CT.},
}
@article {pmid41310746,
year = {2025},
author = {Niu, X and Yuan, M and Wang, D},
title = {Influence of age, cognitive function, attention, and mental state on the effectiveness of EEG-based brain-computer interface device use: a systematic review.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12984-025-01813-7},
pmid = {41310746},
issn = {1743-0003},
support = {No.24QNMP077//Health Commission of Sichuan Province Medical Science and Technology Program/ ; No.Q2024016//Sichuan Medical Association Medical Youth Innovation Project/ ; },
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) are increasingly used to support rehabilitation and assistive communication. Individual traits such as age, cognitive function, attention, and mental state have been linked to variability in BCI performance. However, these factors have not been comprehensively evaluated across paradigms and populations.<br><br>METHODS: A systematic review was conducted following PRISMA 2020 guidelines and registered in the International Prospective Register of Systematic Reviews (PROSPERO) (CRD42024600285). PubMed and Web of Science databases were searched through June 2025 for studies reporting electroencephalography (EEG)-based BCI performance metrics stratified by age, cognition, attention, or psychological state. Twenty-five human studies were included after screening. Risk of bias was assessed using validated appraisal tools.<br><br>RESULTS: Across the 25 included studies, visual paradigms such as&#xa0;P300 event-related potential&#xa0;and&#xa0;steady-state visual evoked potential (SSVEP)&#xa0;showed stable performance across age groups.&#xa0;Motor imagery (MI)-based systems demonstrated higher sensitivity to cognitive and developmental differences. Attention scores and mental rotation were positively associated with EEG signal clarity and classification accuracy. Fatigue, motivation, and training duration influenced user responsiveness.<br><br>CONCLUSION: Age and cognitive traits impact BCI performance and system adaptability. To optimize usability in diverse populations, future BCI applications should integrate individualized training strategies, real-time feedback mechanisms, and standardized evaluation metrics.},
}
@article {pmid41281231,
year = {2025},
author = {Patel, D and Tanveer, MS and Gonzalez-Ferrer, J and Loeffler, A and Kagan, BJ and Mostajo-Radji, MA and Wang, G},
title = {A Computational Perspective on NeuroAI and Synthetic Biological Intelligence.},
journal = {ArXiv},
volume = {},
number = {},
pages = {},
pmid = {41281231},
issn = {2331-8422},
abstract = {NeuroAI is an emerging field at the intersection of neuroscience and artificial intelligence, where insights from brain function guide the design of intelligent systems. A central area within this field is synthetic biological intelligence (SBI), which combines the adaptive learning properties of biological neural networks with engineered hardware and software. SBI systems provide a platform for modeling neural computation, developing biohybrid architectures, and enabling new forms of embodied intelligence. In this review, we organize the NeuroAI landscape into three interacting domains: hardware, software, and wetware. We outline computational frameworks that integrate biological and non-biological systems and highlight recent advances in organoid intelligence, neuromorphic computing, and neuro-symbolic learning. These developments collectively point toward a new class of systems that compute through interactions between living neural tissue and digital algorithms.},
}
@article {pmid41309636,
year = {2025},
author = {Yang, D and Meng, W and Wang, Z and Yu, T and Li, C and Zhang, Q and Zhang, Z and Li, H and Lin, Y and Xue, F and Lin, P and Sun, L},
title = {Polymorphic functionalization driven by ion displacement-induced antiferroelectric ordering in CuBiP2Se6.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {10666},
pmid = {41309636},
issn = {2041-1723},
abstract = {Antiferroelectric two-dimensional materials, with their unique physical mechanisms, exhibit tunable polarization dynamics and layered structural characteristics, enabling the synergistic implementation of synaptic plasticity, sensory-mimetic functionality, and in-memory computing within a unified device architecture. These capabilities meet the growing polymorphic requirements of neuromorphic systems and position such materials as strong candidates for next-generation neuromorphic computing platforms. Among them, CuBiP2Se6 stands out among 2D antiferroelectric materials due to its intrinsic antiferroelectric properties, featuring a stable interlayer antiparallel Cu[+] dipole configuration. This structure, combined with its relaxor-like behavior, enables a reversible transition between antiferroelectric and ferroelectric states under an applied electric field, along with gradual polarization tuning. This transition mechanism enables continuously tunable conductance states, providing essential physical support for the gradual modulation of synaptic weights and the hardware implementation of complex neural functions, making it particularly suited for high-precision emulation of multilevel synaptic plasticity in neuromorphic applications. In this work, memristor based on two-dimensional antiferroelectric CuBiP2Se6 exhibit stable multilevel conductance states, high endurance, and excellent device uniformity, thus supporting diverse neurosynaptic functions and advanced learning rules. These attributes highlight the immense potential of antiferroelectric 2D materials as a foundation for compact, energy-efficient, and highly integrated neuromorphic hardware.},
}
@article {pmid41308307,
year = {2025},
author = {Sharma, D and Krekelberg, B},
title = {Predicting spiking activity from scalp EEG.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae2541},
pmid = {41308307},
issn = {1741-2552},
abstract = {OBJECTIVE: Despite decades of electroencephalography (EEG) research, the relationship between EEG and underlying spiking dynamics remains unclear. This limits our ability to infer neural dynamics reflected in intracranial signals from EEG, a critical step to bridge electrophysiological findings across species and to develop non-invasive brain-machine interfaces (BMIs). In this study, we aimed to estimate spiking activity in the visual cortex using non-invasive scalp EEG.<br><br>APPROACH: We recorded spiking activity from a 32-channel floating microarray permanently implanted in parafoveal V1 and scalp-EEG in a male macaque monkey. While the animal fixated, the screen flickered at different temporal frequencies to induce steady-state visual evoked potentials (SSVEP). We analyzed the relationship between the V1 multi-unit spiking activity envelope (MUAe) and EEG frequency bands to predict MUAe at each time point from EEG. We extracted instantaneous spectrotemporal features of the EEG signal, including phase, amplitude, and phase-amplitude coupling of its frequency bands.<br><br>MAIN RESULTS: Although the relationship between these spectrotemporal features and the V1 MUAe was complex and frequency-dependent, they were reliably predictive of the MUAe. Specifically, in a linear regression predicting MUAe from EEG, each EEG feature (phase, amplitude, coupling) contributed to model predictions. In addition, we found that MUAe predictions were better in shallow than deep cortical layers, and that the phase of stimulus frequency further improved MUAe predictions.<br><br>SIGNIFICANCE: Our study shows that a comprehensive account of spectrotemporal features of non-invasive EEG provides information on underlying spiking activity beyond what is available when only the amplitude or phase of the EEG signal is considered. This demonstrates the richness of the EEG signal and its complex relationship with neural spiking activity and suggests that using more comprehensive spectrotemporal signatures could improve BMI applications.},
}
@article {pmid41308205,
year = {2025},
author = {Jianqiu, W and Yang, B and Chen, X and Chen, J and Kuang, S},
title = {Research on Combining Motor Imagery and Somatosensory Attentional Orientation to Enhance BCI Performance.},
journal = {Biomedical physics &amp; engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/ae2512},
pmid = {41308205},
issn = {2057-1976},
abstract = {In this study, we propose a motor imagery(MI) method based on Somatosensory Attentional Orientation(SAO) to enhance the performance of MI based brain-computer interfaces (BCI). In this BCI system, participants perform unilateral hand MI tasks while maintaining attention to the corresponding hand, as if the wrist skin is actually receiving tactile stimulation(TS). A total of 44 participants were recruited and randomly divided into the experimental group(SAO and MI joint group, SMI group) and control group(MI group). The MI group performed right hand MI tasks, and two sessions were conducted, the content of the two experiments was identical. Each session was divided into two stages: the first stage including 1 run was the right hand MI mental task with TS on the right wrist, and the second stage including 6 runs was the right hand MI mental task without TS . For SAO group, first session was the same with the MI group. However, the second stage for SAO group was the right hand MI mental task with SAO. Compared with the first session, the performance in the first session was comparable between the MI group and SMI group, indicating similar MI abilities in both set of participants. For SAO group, A 6.5% performance enhancement was observed in the second session relative to the first session(p<0.05). However, no significant improvement was observed in the MI group(p>0.05), indicating no evidence of learning effect. EEG topographic mapping demonstrated robust bilateral hemispheric engagement when right hand MI mental task was performed for MI group. While in the SAO mental task, EEG exhibited clear hemispheric lateralization. This paradigm combining attention mechanisms with MI restructures the bilateral control modality inherent in conventional MI paradigms. As SAO paradigm engages endogenous cognitive processes, this approach augments corticomotor excitability during MI task, thereby improving BCI control performance.},
}
@article {pmid41308097,
year = {2025},
author = {Ke, Y and Wang, Z and Liu, S and Ming, D},
title = {A High-speed 120-target SSVEP-BCI Employing Dual-Frequency and Phase Modulation with Minimal Calibration.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3638253},
pmid = {41308097},
issn = {2168-2208},
abstract = {Large instruction-set brain-computer interfaces (BCIs) allow users to issue many commands through a single interface, greatly expanding their application scope. Increasing the number of targets, however, raises encoding complexity and intensifies the trade-off between calibration time and decoding performance. We introduce a 120-target steady-state visual evoked potential (SSVEP)-BCI that pairs dual-frequency phase modulation (DFPM) with a lightweight global multi stimulus canonical correlation analysis-based spatiotemporal filtering (gmsCCA-st) method. DFPM encodes the 120 targets with only 23 low-frequency carriers by simultaneously flickering two frequency-phase tags in a checkerboard pattern, thereby mitigating the "frequency scarcity" problem and eliciting pronounced harmonic and intermodulation responses. Instead of training a separate filter for each target, gmsCCA-st learns a set of shared spatiotemporal filters from all targets. With just one calibration trial per target in the offline experiment, the system achieved a peak information transfer rate (ITR) of 326.49±55.13 bits/min. During online cue-guided spelling, the system attained 94.69±5.99% accuracy and 251.47±25.47 bits/min ITR; in free-spelling mode, accuracy was 91.72±6.89% at 176.64±23.75 bits/min. These findings demonstrate the feasibility of a high-performance 120 target SSVEP-BCI after only three minutes of calibration, overcoming the compromise among instruction-set size, calibration burden, and performance. This study therefore offers a practical pathway toward high-performance, minimal-calibration large instruction-set BCIs.},
}
@article {pmid41307947,
year = {2025},
author = {Yu, H and Chen, Y and Li, D and Liu, W and Dong, B and Pei, G},
title = {Dual Processing of Aberrant Data Perception: Evidence From EEG Oscillations.},
journal = {Annals of the New York Academy of Sciences},
volume = {},
number = {},
pages = {},
doi = {10.1111/nyas.70146},
pmid = {41307947},
issn = {1749-6632},
support = {LGG21G010002//Zhejiang Provincial Natural Science Foundation of China/ ; 72401263//National Natural Science Foundation of China/ ; 2025C25080(SYS)//Soft Science Research Program of Zhejiang Province/ ; ZSKT2402//Research Project of Zhejiang Laboratory of Philosophy and Social Sciences - Laboratory of Intelligent Society and Governance, Zhejiang Lab/ ; 2023KFKT003//Open Research Project of Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior, Shanghai International Studies University/ ; },
abstract = {The perception of aberrant data (PAD) is an essential cognitive ability in human socialization, yet the underlying dual processing mechanisms remain underexplored. Based on dual processing theory, this study uses electroencephalogram (EEG) time-frequency analysis to investigate the mediating role and representational patterns of neural oscillatory activity in automatic processes (APs) and controlled processes (CPs). The results indicated that during the PAD task, β oscillations in the frontal-parietal regions exhibited clear event-related desynchronization in the AP mode, whereas β oscillations displayed prominent event-related synchronization in the CP mode. The brain network excitation mediated by β oscillations was closely followed by brain network inhibition mediated by α oscillations, allowing for effective separation of the dual processing modes in PAD tasks through the β-kα index (p < 0.001). Moreover, in the PAD task, the AP mode was primarily attributed to the efficient communication mediated by cross-frequency phase coherence between β and α oscillations, as well as information integration mediated by intersite phase coherence in the frontal-parietal regions. This study provides a framework for a comprehensive understanding of the dual processing neural mechanisms behind PAD, with promising applications in the study of pathophysiological mechanisms in neurodegenerative diseases and clinical interventions.},
}
@article {pmid41306427,
year = {2025},
author = {Zhang, W and Shi, X and Li, M and Zhang, L and Zhang, R and Wu, X and Xin, M and Li, R and Zhang, H and Hu, Y},
title = {Assess the level of consciousness in patients with disorders of consciousness by combining resting-state and auditory-evoked EEG.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1613356},
pmid = {41306427},
issn = {1662-4548},
abstract = {INTRODUCTION: Electroencephalography (EEG) can provide objective neural marker for assessing the level of consciousness of patients with disorders of consciousness (DoC), but current research mainly focuses on the EEG features of a single modality, such as the resting-state or the evoked state, which results in less than ideal assessment accuracy. To accurately assess the level of consciousness of DoC patients, we proposed a new method by combine with resting-state and auditory-evoked EEG.<br><br>METHODS: The EEG data of resting-state and auditory-evoked potential were collected from 157 DoC patients. Then, nonlinear dynamics feature (NDF) include spatiotemporal correlation entropy and neuromodulation intensity of multimodal EEG were extracted. Next, the multi-form feature selection algorithm (MFFS) was adopted to optimize the extracted EEG features. Finally, a diagnosis model was constructed using support vector machine (SVM).<br><br>RESULTS: Among them, SC-Theta, SC-Alpha, NI-Alpha and ERP features were significantly (p < 0.05) correlated with the patient's level of consciousness, resulting in an average grouping accuracy of 92.4%.<br><br>DISCUSSION: The proposed diagnostic model has demonstrated its distinctive advantages, showcasing remarkable effectiveness and reliability in accurately assessing consciousness states. This method holds promise for improving the reliability of clinical awareness assessments.},
}
@article {pmid41305274,
year = {2025},
author = {Gomez-Rivera, A and Álvarez-Meza, AM and Cárdenas-Peña, D and Orozco-Gutierrez, A},
title = {Takens-Based Kernel Transfer Entropy Connectivity Network for Motor Imagery Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {22},
pages = {},
doi = {10.3390/s25227067},
pmid = {41305274},
issn = {1424-8220},
support = {111091991908//Ministerio de Ciencia, Tecnolog&#xed;a e Innovaci&#xf3;n/ ; },
mesh = {Humans ; Electroencephalography/methods ; Brain-Computer Interfaces ; Entropy ; Brain/physiology ; Signal Processing, Computer-Assisted ; Deep Learning ; Algorithms ; *Imagination/physiology ; Neural Networks, Computer ; },
abstract = {Reliable decoding of motor imagery (MI) from electroencephalographic signals remains a challenging problem due to their nonlinear, noisy, and non-stationary nature. To address this issue, this work proposes an end-to-end deep learning model, termed TEKTE-Net, that integrates time embeddings with a kernelized Transfer Entropy estimator to infer directed functional connectivity in MI-based brain-computer interface (BCI) systems. The proposed model incorporates a customized convolutional module that performs Takens' embedding, enabling the decoding of the underlying EEG activity without requiring explicit preprocessing. Further, the architecture estimates nonlinear and time-delayed interactions between cortical regions using Rational Quadratic kernels within a differentiable framework. Evaluation of TEKTE-Net on semi-synthetic causal benchmarks and the BCI Competition IV 2a dataset demonstrates robustness to low signal-to-noise conditions and interpretability through temporal, spatial, and spectral analyses of learned connectivity patterns. In particular, the model automatically highlights contralateral activations during MI and promotes spectral selectivity for the beta and gamma bands. Overall, TEKTE-Net offers a fully trainable estimator of functional brain connectivity for decoding EEG activity, supporting MI-BCI applications, and promoting interpretability of deep learning models.},
}
@article {pmid41305083,
year = {2025},
author = {Liyanagedera, ND and Bareham, CA and Kempton, H and Guesgen, HW},
title = {Machine Learning-Based Comparative Analysis of Subject-Independent EEG Data Classification Across Multiple Meditation and Non-Meditation Sessions.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {22},
pages = {},
doi = {10.3390/s25226876},
pmid = {41305083},
issn = {1424-8220},
mesh = {Humans ; *Electroencephalography/methods ; *Machine Learning ; *Meditation ; Male ; Algorithms ; Adult ; Female ; Brain-Computer Interfaces ; Brain/physiology ; },
abstract = {In this study, subject-independent (inter-subject), multiple-session electroencephalography (EEG) data classification was tested for loving-kindness meditation (LKM) and non-meditation. This is a novel study that extends our previous work on intra-subject, multiple-session classification. Here, two meditation techniques, LKM-Self and LKM-Other, were independently compared with non-meditation. For each mental task, five sessions of data collected from each of the twelve participants were placed in a common data pool, from which randomly selected session data were used for training and testing the machine learning algorithms. Three previously tested BCI pipelines were used. In each case, feature extraction was performed using common spatial patterns (CSPs), short-time Fourier transform (STFT), or a fusion of CSP and STFT, followed by classification using a neural network structure. This study was further divided into three cases, where two, three, or four session pairs were used to train the algorithms, and the remaining session pair was used for testing. For each individual instance, the test was repeated thirty times to generalize the results. Thus, a total of 9900 independent tests were conducted for the entire dataset. The mean classification accuracies obtained in this study were lower than those reported in our previous intra-subject classification study. For example, in LKM-Self/non-meditation classification using three session pairs with the CSP + STFT pipeline, the mean accuracy for all tests was 62.3%, with the bottom 50% at 46.0% and the top 50% at 78.3%, demonstrating variability across session selections. The corresponding intra-subject classification result for the same instance was 72.1%. For all other instances, a similar pattern was observed. Furthermore, when considering all mean accuracies obtained, in 83.3% of the instances, CSP + STFT produced better classification accuracies than CSP or STFT alone. At the same time, in 75.0% of the instances, increasing the number of training session pairs led to improved classification accuracies. This study demonstrates that the subject-independent, multiple-session EEG classification of meditation and non-meditation is feasible for specific session combinations. Further research is needed to confirm these findings across larger and more diverse participant groups. These findings provide a foundation for developing subject-independent algorithms that can guide long-term meditation practice.},
}
@article {pmid41303071,
year = {2025},
author = {Moskiewicz, D and Sarzyńska-Długosz, I},
title = {Modern Technologies Supporting Motor Rehabilitation After Stroke: A Narrative Review.},
journal = {Journal of clinical medicine},
volume = {14},
number = {22},
pages = {},
doi = {10.3390/jcm14228035},
pmid = {41303071},
issn = {2077-0383},
support = {Journal of Clinical Medicine//Invitation letter - *100% discount* for my submission/ ; },
abstract = {Introduction: Stroke remains one of the leading causes of long-term disability worldwide. Post-stroke motor recovery depends on neuroplasticity, which is stimulated by intensive, repetitive, and task-specific training. Modern technologies such as robotic rehabilitation (RR), virtual reality (VR), functional electrical stimulation (FES), brain-computer interfaces (BCIs), and non-invasive brain stimulation (NIBS) offer novel opportunities to enhance rehabilitation. They operate through sensory feedback, neuromodulation, and robotic assistance which promote neural reorganization and motor relearning. Neurobiological Basis of Motor Recovery: Mechanisms such as long-term potentiation, mirror neuron activation, and cerebellar modulation underpin functional reorganization after stroke. Literature Review Methodology: A narrative review was conducted of studies published between 2005 and 2025 using PubMed, Scopus, Web of Science, Cochrane Library, and Google Scholar. Randomized controlled trials, cohort studies, and systematic reviews assessing the efficacy of these modern technologies were analyzed. Literature Review: Evidence indicates that RR, VR, FES, BCIs, and NIBS improve upper and lower limb motor function and strength, and enhance activities of daily living, particularly when combined with conventional physiotherapy (CP). Furthermore, integrated rehabilitation technologies (IRT) demonstrate synergistic neuroplastic effects. Discussion: Modern technologies enhance therapy precision, intensity, and motivation but face challenges related to cost, standardization, and methodological heterogeneity. Conclusions: RR, VR, FES, BCIs, NIBS, and IRT are effective complements to CP. Early, individualized, and standardized implementation can optimize neuroplasticity and functional recovery.},
}
@article {pmid41302782,
year = {2025},
author = {Aydın, S and Melek, M and Gökrem, L},
title = {Intersession Robust Hybrid Brain-Computer Interface: Safe and User-Friendly Approach with LED Activation Mechanism.},
journal = {Micromachines},
volume = {16},
number = {11},
pages = {},
doi = {10.3390/mi16111264},
pmid = {41302782},
issn = {2072-666X},
support = {36126//T&#xfc;rkiye Sa&#x11f;l&#x131;k Enstit&#xfc;leri Ba&#x15f;kanl&#x131;&#x11f;&#x131;/ ; },
abstract = {This study introduces a hybrid Brain-Computer (BCI) system with a robust and secure activation mechanism between sessions, aiming to minimize the negative effects of visual stimulus-based BCI systems on user eye health. The system is based on the integration of Electroencephalography (EEG) signals and Electrooculography (EOG) artefacts, and includes an LED stimulus operating at a frequency of 7 Hz for safe activation and objects moving in different directions. While the LED functions as an activation switch that reduces visual fatigue caused by traditional visual stimuli, moving objects provide command generation depending on the user's intention. In order to evaluate the stability of the system against physiological and psychological conditions, data were collected from 15 participants in two different sessions. The Correlation Alignment (CORAL) method was applied to the data to reduce the variance between sessions and to increase stability. A Bootstrap Aggregating algorithm was used in the classification processes, and with the CORAL method, the system accuracy rate was increased from 81.54% to 94.29%. Compared to similar BCI approaches, the proposed system offers a safe activation mechanism that effectively adapts to users' changing cognitive states throughout the day by reducing visual fatigue, despite using a low number of EEG channels, and demonstrates its practicality and effectiveness by performing on par or superior to other systems in terms of high accuracy and robust stability.},
}
@article {pmid41301176,
year = {2025},
author = {Kim, HG and Kim, JY},
title = {EEG-Based Local-Global Dimensional Emotion Recognition Using Electrode Clusters, EEG Deformer, and Temporal Convolutional Network.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {12},
number = {11},
pages = {},
doi = {10.3390/bioengineering12111220},
pmid = {41301176},
issn = {2306-5354},
support = {NRF-2023R1A2C1006756//National Research Foundation of Korea(NRF)/ ; },
abstract = {Emotions are complex phenomena arising from cooperative interactions among multiple brain regions. Electroencephalography (EEG) provides a non-invasive means to observe such neural activity; however, as it captures only electrode-level signals from the scalp, accurately classifying dimensional emotions requires considering both local electrode activity and the global spatial distribution across the scalp. Motivated by this, we propose a brain-inspired EEG electrode-cluster-based framework for dimensional emotion classification. The model organizes EEG electrodes into nine clusters based on spatial and functional proximity, applying an EEG Deformer to each cluster to learn frequency characteristics, temporal dynamics, and local signal patterns. The features extracted from each cluster are then integrated using a bidirectional cross-attention (BCA) mechanism and a temporal convolutional network (TCN), effectively modeling long-term inter-cluster interactions and global signal dependencies. Finally, a multilayer perceptron (MLP) is used to classify valence and arousal levels. Experiments on three public EEG datasets demonstrate that the proposed model significantly outperforms existing EEG-based dimensional emotion recognition methods. Cluster-based learning, reflecting electrode proximity and signal distribution, effectively captures structural patterns at the electrode-cluster level, while inter-cluster information integration further captures global signal interactions, thereby enhancing the interpretability and physiological validity of EEG-based dimensional emotion analysis. This approach provides a scalable framework for future affective computing and brain-computer interface (BCI) applications.},
}
@article {pmid41300224,
year = {2025},
author = {Kuipers, JA and Hoffman, NH and Carrick, FR and Jemni, M},
title = {Reconnecting Brain Networks After Stroke: A Scoping Review of Conventional, Neuromodulatory, and Feedback-Driven Rehabilitation Approaches.},
journal = {Brain sciences},
volume = {15},
number = {11},
pages = {},
doi = {10.3390/brainsci15111217},
pmid = {41300224},
issn = {2076-3425},
abstract = {BACKGROUND: Stroke leads to lasting disability by disrupting the connectivity of functional brain networks. Although several rehabilitation methods are promising, our full understanding of how these strategies restore network function is still limited. Here, we map how non-invasive brain stimulation (NIBS), brain-computer interface (BCI)/neurofeedback, virtual reality (VR), and robot-assisted therapy restore connectivity within the sensorimotor network (SMN), default mode network (DMN), and salience network, and we contextualize these effects within the known temporal evolution of post-stroke motor network reorganization.<br><br>METHODS: This scoping review adhered to PRISMA guidelines and searched PubMed, Cochrane, and Medline from January 2015 to January 2025 for clinical trials focused on stroke rehabilitation with functional connectivity outcomes. Included studies used conventional therapy, neuromodulation, or feedback-based interventions.<br><br>RESULTS: Twenty-three studies fulfilled the inclusion criteria, covering interventions like robotic training, transcranial stimulation (tDCS/TMS), brain-computer interfaces, virtual reality, and cognitive training. Motor impairments were linked to disrupted interhemispheric sensorimotor connectivity, while cognitive issues reflected changes in frontoparietal and default mode networks. Combining neuromodulation with feedback-based methods showed better network recovery than standard therapy alone, with clinical improvements closely associated with connectivity alterations.<br><br>CONCLUSIONS: Effective stroke rehabilitation depends on targeting specific disrupted networks through various modalities. Robotic interventions focus on restoring structural motor pathways, feedback-enhanced methods improve temporal synchronization, and cognitive training aims to enhance higher-order network integration. Future research should work toward standardizing connectivity assessment protocols and conducting multicenter trials. This will help develop evidence-based, network-focused rehabilitation guidelines that effectively translate mechanistic insights into personalized clinical treatments.},
}
@article {pmid41300174,
year = {2025},
author = {Zhang, L and Zhang, X and Zhang, X and Yu, C and Liu, X},
title = {Objective Emotion Assessment Using a Triple Attention Network for an EEG-Based Brain-Computer Interface.},
journal = {Brain sciences},
volume = {15},
number = {11},
pages = {},
doi = {10.3390/brainsci15111167},
pmid = {41300174},
issn = {2076-3425},
abstract = {Background: The assessment of emotion recognition holds growing significance in research on the brain-computer interface and human-computer interaction. Among diverse physiological signals, electroencephalography (EEG) occupies a pivotal position in affective computing due to its exceptional temporal resolution and non-invasive acquisition. However, EEG signals are inherently complex, characterized by substantial noise contamination and high variability, posing considerable challenges to accurate assessment. Methods: To tackle these challenges, we propose a Triple Attention Network (TANet), a triple-attention EEG emotion recognition framework that integrates Conformer, Convolutional Block Attention Module (CBAM), and Mutual Cross-Modal Attention (MCA). The Conformer component captures temporal feature dependencies, CBAM refines spatial channel representations, and MCA performs cross-modal fusion of differential entropy and power spectral density features. Results: We evaluated TANet on two benchmark EEG emotion datasets, DEAP and SEED. On SEED, using a subject-specific cross-validation protocol, the model reached an average accuracy of 98.51 ± 1.40%. On DEAP, we deliberately adopted a segment-level splitting paradigm-in line with influential state-of-the-art methods-to ensure a direct and fair comparison of model architecture under an identical evaluation protocol. This approach, designed specifically to assess fine-grained within-trial pattern discrimination rather than cross-subject generalization, yielded accuracies of 99.69 ± 0.15% and 99.67 ± 0.13% for the valence and arousal dimensions, respectively. Compared with existing benchmark approaches under similar evaluation protocols, TANet delivers substantially better results, underscoring the strong complementary effects of its attention mechanisms in improving EEG-based emotion recognition performance. Conclusions: This work provides both theoretical insights into multi-dimensional attention for physiological signal processing and practical guidance for developing high-performance, robust EEG emotion assessment systems.},
}
@article {pmid41295260,
year = {2025},
author = {Di Liddo, R and Naso, F and Gandaglia, A and Sturaro, G and Spina, M and Melder, RJ},
title = {Enhanced Detection of αGal Using a Novel Monoclonal IgG1 Antibody: Comparative Evaluation with IgM Antibody [Clone M86].},
journal = {Journal of personalized medicine},
volume = {15},
number = {11},
pages = {},
pmid = {41295260},
issn = {2075-4426},
abstract = {Introduction. Over the past two decades, the αGal (Galα1-3Galβ1-4GlcNAc-R) epitope, a carbohydrate found in many non-primate mammals, has gained significant relevance in medicine due to its association with an increasing number of allergic reactions to animal-derived foods, drugs, and medical devices. Due to a mutated gene coding for α1,3-galactosyltransferase (α1-3GT), humans lack αGal and, therefore, naturally produce anti-α-Gal antibodies (IgM, IgA, and IgG), especially in the context of a xenotransplantation, which can lead to extreme immunological reactivity, including hyperacute rejection of the transplant. Recently, these uncontrollable immune reactions have driven demand for more accurate procedures to better detect αGal in animal-derived foods or bioprosthetics. The currently most widely used α-Gal-specific monoclonal antibody is an IgM antibody (clone M86), developed in Ggta1 KO mice and isolated from hybridoma tissue culture. As the IgM isotype has limited purification properties, specificity, and sensitivity, we aimed to produce a novel IgG antibody with high affinity and extensive applicability. Methods. An experimental murine IgG1 anti-αGal antibody (IgG-αGalomab) was developed by immunization of Ggta1 knockout (KO) mice, and its affinity was evaluated using ELISA, Western blot, flow cytometry, and immunohistochemistry/immunofluorescence. Results. Compared to IgM-M86, IgG-αGalomab demonstrated ~1200-fold higher binding potency and lower cross-reactivity with competitive molecules, i.e., bovine serum albumin, galactobiose, and lactose. Unlike IgM-M86, IgG-αGalomab showed an increasing affinity over time in the binding tests performed on xenogeneic tissues. Notably, high-affinity for αGal was detected by Western blot at high dilution [1:200,000] of IgG-αGalomab compared to IgM-M86 [1:1000]. By flow cytometry, specificity and dose-dependent response were confirmed using in vitro cultures of porcine and human fibroblasts. Finally, in immunofluorescence and immunohistochemistry analysis, αGal was demonstrated to be detectable by IgG-αGalomab at a dilution of [1:1000], while IgM-M86 was demonstrated to be detectable at [1:100]. Conclusions. Altogether, our newly developed antibody showed high sensitivity and specificity for α-Gal in various applications. Based on its potential binding capacity, IgG-αGalomab could have important applications in precision medicine for predicting, treating, and preventing immune-mediated phenomena of patients in different medical areas.},
}
@article {pmid41299162,
year = {2025},
author = {Meng, L and Song, Z and Lu, J},
title = {Brain-imager: a multimodal framework for image reconstruction and captioning from human brain activity.},
journal = {Brain informatics},
volume = {12},
number = {1},
pages = {32},
pmid = {41299162},
issn = {2198-4018},
support = {62073061//National Natural Science Foundation of China/ ; 2025A1515011602//Guangdong Basic and Applied Basic Research Foundation/ ; },
abstract = {OBJECTIVE: The reconstruction of visual stimuli and captions from brain activity offers a distinctive viewpoint on how perception reconstructs the external world within neural dynamics. Despite considerable advancements in deep generative models in recent years, simultaneously generating images and captions with both detailed accuracy and semantic consistency remains a significant challenge.<br><br>METHODS: We introduce panoptic segmentation and generative semantics for the first time, offering enhanced, multi-level data support and a novel perspective in the domain of brain decoding. Using multi-scale fusion techniques, we integrate pixel features from natural images with structural features from panoptic segmentation, creating a state-of-the-art "initial guess." Building upon the neural paradigm that we discovered, we propose an innovative semantic connection strategy to guide image reconstruction. Additionally, by fine-tuning visual semantics within the encoded space compressed by a language model and further combining our advanced retrieval module with the comprehension capabilities of large language models (LLMs), we generate high-quality brain captions.<br><br>RESULTS: Experimental results demonstrate that we surpass current methods in visual decoding and brain captioning tasks. We offer a webpage to showcase the results: www.neuai4science.cn:5001/brain_visual_decode .<br><br>CONCLUSION: Our proposed Brain-Imager framework, which incorporates multi-level data and semantic guidance, sets a new standard in the domain.<br><br>SIGNIFICANCE: This work provides a novel perspective on the relationship between text and image semantics and the visual pathways of the human brain, with potential applications in downstream tasks such as brain-computer interfaces. Additionally, our code is publicly available at https://github.com/songqianyi01/Brain-Imager .},
}
@article {pmid41298548,
year = {2025},
author = {Ciferri, M and Ferrante, M and Toschi, N},
title = {Reconstructing music perception from brain activity using a prior guided diffusion model.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {42108},
pmid = {41298548},
issn = {2045-2322},
support = {101070908//European Union's European Innovation Council/ ; 101017727//European Union's Horizon 2020 Research and Innovation Programme/ ; },
mesh = {Humans ; *Music ; *Auditory Perception/physiology ; *Brain/physiology/diagnostic imaging ; Magnetic Resonance Imaging ; Bayes Theorem ; Male ; Female ; Adult ; Brain Mapping/methods ; Young Adult ; Models, Neurological ; Acoustic Stimulation ; },
abstract = {Reconstructing music directly from brain activity provides insight into the neural representations underlying auditory processing and paves the way for future brain-computer interfaces. We introduce a fully data-driven pipeline that combines cross-subject functional alignment with bayesian decoding in the latent space of a diffusion-based audio generator. Functional alignment projects individual fMRI responses onto a shared representational manifold, increasing the performance of cross-participant accuracy with respect to anatomically normalized baselines. A bayesian search over latent trajectories then selects the most plausible waveform candidate, stabilizing reconstructions against neural noise. Crucially, we bridge CLAP's multi-modal embeddings to music-domain latents through a dedicated aligner, eliminating the need for hand-crafted captions and preserving the intrinsic structure of musical features. Evaluated on ten diverse genres, the model achieves a cross-subject-averaged identification accuracy of [Formula: see text], and produces audio that human listeners recognize above chance in 85.7% of trials. Voxel-wise analyses locate the predictive signal within a bilateral circuit spanning early auditory, inferior-frontal, and premotor cortices, consistent with hierarchical and sensorimotor theories of music perception. The framework establishes a principled bridge between generative audio models and cognitive neuroscience.},
}
@article {pmid41296974,
year = {2025},
author = {Zhang, J and Zhang, L and Mu, F and Huang, Z and Zou, C and Huang, R and Wang, C and Cheng, H},
title = {Spatiotemporal Dynamics Modeling of Brain Activity for Human-Robot Cognitive Interaction: ADistributed-Lumped Parameter System Framework.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNNLS.2025.3631130},
pmid = {41296974},
issn = {2162-2388},
abstract = {This article investigates the system modeling problem for the dynamical process of human brain activity in human-robot cognitive interaction (HRCI). An important novelty of the proposed approaches is to build a computational model of a human-distributed robot-lumped parameter system (HDRLPS) that describes the inherent dynamical principle of human brain activity (with spatiotemporal-varying characteristic) undergoing the interaction between the intrinsic cognitive dynamics and extrinsic robot stimuli. A deterministic learning (DL)-based spatiotemporal dynamics identification scheme is proposed to accurately identify the spatiotemporal dynamics of HDRLS and obtain the associated knowledge as a constant radial basis functional neural network (RBF NN) model. A spatiotemporal dynamics estimator is designed with this model, which can accurately evaluate and monitor the dynamical process of human brain activity in real-time HRCI by the generated dynamics-synchronized state. The effectiveness and practicability of the approaches in the dynamics identification and evaluation for the human brain activity in HRCI are validated by the thorough analysis, including the mathematical proof, the simulation study, and the brain-computer interface (BCI) experiment using publicly available datasets. Our method is compared with state-of-the-art (SOTA) methods, such as LGGNet, EEGNet, Tsception, EEG-Deformer, EEG-Transformer, and EEGViT. The results show that our method can outperform these methods with better recognition accuracy and macro- $F1$ scores. The source code can be found at: https://github.com/alonexing/source_code/tree/master.},
}
@article {pmid41296962,
year = {2025},
author = {Wang, J and Bi, L and Wei, Y and Fei, W and Liu, H and Miao, D},
title = {EEG-Based Movement Decoding in Motor-Impaired Patients by Extracting and Aligning Neural Patterns with Healthy Individuals.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3637053},
pmid = {41296962},
issn = {2168-2208},
abstract = {Decoding human movement intentions from electroencephalography (EEG) signals is critical for brain-computer interface (BCI) applications in motor neurorehabilitation, active assistance, and functional augmentation. However, current BCI models face two challenges for motor-impaired patients: 1) prolonged EEG data collection from patients is difficult; 2) differences in brain functional structures and motor behaviors between healthy individuals and patients limit the generalizability of models trained on healthy individuals' EEG data. To address these challenges, this study proposes a transfer learning-based model, TL-ME, to bridge the gap between healthy individuals' and patients' EEG data and improve movement decoding accuracy for patients. TL-ME integrates an attention-based feature extractor, adversarial domain discriminator, multi-source selection, and movement classifier to transfer knowledge from healthy individuals' EEG data (source domain) to patients' EEG data (target domain). Temporal and spectral visualizations are used to inspect brain activation patterns for shared motor tasks between healthy individuals and patients. Experimental results show a 10.8% improvement in upper-limb movement decoding's accuracy using TL-ME, with each module contributing to performance gains. Visualization analyses also demonstrate similar brain activation patterns across domains, validating the transferability of healthy individuals' EEG data to patient-specific models. This work introduces a novel cross-population transfer learning approach that leverages healthy individuals' EEG data to enhance neural decoding for motor-impaired patients, bridging the gap between experimental studies and real-world applications in BCI-based neurorehabilitation.},
}
@article {pmid41295901,
year = {2025},
author = {Turner, S and Yadav, P and Morrin, H and Bhat, A},
title = {The future of psychiatry: clinical practice, diagnosis, and treatment.},
journal = {International review of psychiatry (Abingdon, England)},
volume = {},
number = {},
pages = {1-22},
doi = {10.1080/09540261.2025.2594523},
pmid = {41295901},
issn = {1369-1627},
abstract = {This paper overviews the future of clinical practice in psychiatry, covering diagnosis, treatment, and public health. We consider recent advances and new controversies as psychiatry moves from a categorical to a dimensional approach to diagnosing and classifying mental illness; as well as the potential pitfalls of overdiagnosis, underdiagnosis, and misdiagnosis. We also review some of the most exciting new developments in treatment modalities, such as psychedelic treatments, ketamine, and new antipsychotics. The potential of interventional psychiatry using technology, and review techniques including neuromodulation, neurofeedback, brain-computer interfaces, AI-assisted psychotherapy, and virtual reality is also discussed in the context of future of public mental health strategy, including the important issue of online disinformation and how it can influence the public's understanding of mental health. Finally, we consider the evolving understanding of addiction, particularly behavioural and technological addictions. We conclude with a brief discussion of how best to influence the political leadership in using these new advances to develop evidence-based, scientifically-informed healthcare policy.},
}
@article {pmid41294772,
year = {2025},
author = {Qi, L and Wang, Y and Liang, X},
title = {Emerging Implantable Sensor Technologies at the Intersection of Engineering and Brain Science.},
journal = {Biosensors},
volume = {15},
number = {11},
pages = {},
doi = {10.3390/bios15110762},
pmid = {41294772},
issn = {2079-6374},
support = {32200882//National Natural Science Foundation of China/ ; 5250071402//National Natural Science Foundation of China/ ; 2024YQB048//Young Doctoral Program of Xinqiao Hspital/ ; 0//Chongqing Brain Science Key Project/ ; },
mesh = {Humans ; *Biosensing Techniques ; *Brain/physiology ; Brain-Computer Interfaces ; *Prostheses and Implants ; Transistors, Electronic ; Animals ; },
abstract = {Advances in implantable sensor technologies are revolutionizing the landscape of brain science by enabling chronic, precise, and multimodal interfacing with neural tissues. With the convergence of material science, electronics, and neurobiology, flexible, wireless, bioresorbable, and multimodal sensors are expanding the frontiers of diagnosis, therapy, and brain-machine interfacing. This review presents the latest breakthroughs in implantable neural sensor technologies, emphasizing bio-integration, signal fidelity, and functional adaptability. We highlight innovations such as CMOS-integrated flexible probes, internal ion-gated organic electrochemical transistors (IGTs), multimodal neurotransmitter-electrophysiology sensors, and wireless energy systems. Finally, we discuss the clinical potential, translational challenges, and future directions for brain science and neuroengineering. We further benchmark transduction and analytical performance in physiological media and outline in vivo calibration, antifouling/packaging, and on-node data-efficient processing for long-term stability.},
}
@article {pmid41294401,
year = {2025},
author = {Gkintoni, E and Halkiopoulos, C},
title = {Mapping EEG Metrics to Human Affective and Cognitive Models: An Interdisciplinary Scoping Review from a Cognitive Neuroscience Perspective.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {10},
number = {11},
pages = {},
doi = {10.3390/biomimetics10110730},
pmid = {41294401},
issn = {2313-7673},
abstract = {Background: Electroencephalography (EEG) offers millisecond-precision measurement of neural oscillations underlying human cognition and emotion. Despite extensive research, systematic frameworks mapping EEG metrics to psychological constructs remain fragmented. Objective: This interdisciplinary scoping review synthesizes current knowledge linking EEG signatures to affective and cognitive models from a neuroscience perspective. Methods: We examined empirical studies employing diverse EEG methodologies, from traditional spectral analysis to deep learning approaches, across laboratory and naturalistic settings. Results: Affective states manifest through distinct frequency-specific patterns: frontal alpha asymmetry (8-13 Hz) reliably indexes emotional valence with 75-85% classification accuracy, while arousal correlates with widespread beta/gamma power changes. Cognitive processes show characteristic signatures: frontal-midline theta (4-8 Hz) increases linearly with working memory load, alpha suppression marks attentional engagement, and theta/beta ratios provide robust cognitive load indices. Machine learning approaches achieve 85-98% accuracy for subject identification and 70-95% for state classification. However, significant challenges persist: spatial resolution remains limited (2-3 cm), inter-individual variability is substantial (alpha peak frequency: 7-14 Hz range), and overlapping signatures compromise diagnostic specificity across neuropsychiatric conditions. Evidence strongly supports integrated rather than segregated processing, with cross-frequency coupling mechanisms coordinating affective-cognitive interactions. Conclusions: While EEG-based assessment of mental states shows considerable promise for clinical diagnosis, brain-computer interfaces, and adaptive technologies, realizing this potential requires addressing technical limitations, standardizing methodologies, and establishing ethical frameworks for neural data privacy. Progress demands convergent approaches combining technological innovation with theoretical sophistication and ethical consideration.},
}
@article {pmid41293812,
year = {2025},
author = {Ivanov, N and Wong, M and Chau, T},
title = {A Multi-Class Intra-Trial Trajectory Analysis Technique to Visualize and Quantify Variability of Mental Imagery EEG Signals.},
journal = {International journal of neural systems},
volume = {},
number = {},
pages = {2550075},
doi = {10.1142/S0129065725500753},
pmid = {41293812},
issn = {1793-6462},
abstract = {High inter- and intra-individual variation is a prominent characteristic of electroencephalography (EEG) signals and a significant inhibitor to the practical implementation of brain-computer interfaces (BCIs) outside of research laboratories. However, a few methods exist to assess EEG signal variability. Here, a novel multi-class intra-trial trajectory (MITT) analysis to study EEG variability for mental imagery BCIs is presented. The methods yield insight into different aspects of signal variation, specifically (i) inter-individual, (ii) inter-task, (iii) inter-trial, and (iv) intra-trial. A novel representation of the time evolution of EEG signals was developed. Task trials were segmented into short temporal windows and represented in a feature space derived from unsupervised clustering of trial covariance matrices. Using this representation, temporal trajectories through the feature space were constructed. Two metrics were defined to assess user performance based on these trajectories: (1) InterTaskDiff, based on time-varying distances between the mean trajectories of different tasks, and (2) InterTrialVar, which measured the inter-trial variation of the temporal trajectories along the feature dimensions. Analysis of three-class BCI data from 14 adolescents revealed both metrics correlated significantly with classification results. Further analysis of intra-trial trajectories suggested the existence of characteristic task- and user-specific temporal dynamics. The participant-specific insights provided by MITT analysis could be used to overcome EEG-variability challenges impeding practical implementation of BCIs by elucidating avenues to improve user training feedback or selection of user-optimal classifiers and hyperparameters.},
}
@article {pmid41293116,
year = {2025},
author = {Zheng, M and Qian, Z and Zhao, T},
title = {Motor imagery EEG classification via wavelet-packet synthetic augmentation and entropy-based channel selection.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1689647},
pmid = {41293116},
issn = {1662-4548},
abstract = {INTRODUCTION: Motor-imagery (MI) brain-computer interfaces often suffer from limited EEG datasets and redundant channels, hampering both accuracy and clinical usability. We address these bottlenecks by presenting a unified framework that simultaneously boosts classification performance, reduces the number of required sensors, and eliminates the need for extra recordings.<br><br>METHODS: A three-stage pipeline is proposed. (1) Wavelet-packet decomposition (WPD) partitions each MI class into low-variance "stable" and high-variance "variant" trials; sub-band swapping between matched pairs generates synthetic trials that preserve event-related desynchronization/synchronization signatures. (2) Channel selection uses wavelet-packet energy entropy (WPEE) to quantify both spectral-energy complexity and class-separability; the top-ranked leads are retained. (3) A lightweight multi-branch network extracts multi-scale temporal features through parallel dilated convolutions, refines spatial patterns via depth-wise convolutions, and feeds the fused spatiotemporal tensor to a Transformer encoder with multi-head self-attention; soft-voted fully-connected layers deliver robust class labels.<br><br>RESULTS: On BCI Competition IV 2a and PhysioNet MI datasets the proposed method achieves 86.81 and 86.64% mean accuracies, respectively, while removing 27% of sensors. These results outperform the same network trained on all 22 channels, and paired t-tests confirm significant improvements (p&#x202f;<&#x202f;0.01).<br><br>DISCUSSION: Integrating WPD-based augmentation with WPEE-driven channel selection yields higher MI decoding accuracy with fewer channels and without extra recordings. The framework offers a computationally efficient, clinically viable paradigm for enhanced EEG classification in resource-constrained settings.},
}
@article {pmid41292958,
year = {2025},
author = {Lichenstein, SD and Weng, Y and Robinson, H and Rodriguez, L and Babaeianjelodar, M and Maynard, J and Metayer, M and Suneja, S and Horien, C and Greene, AS and Constable, RT and Moore, TM and Barzilay, R and Yip, SW and Keller, AS},
title = {Multivariate environmental exposures are reflected in whole-brain functional connectivity and cognition in youth.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.11.13.688261},
pmid = {41292958},
issn = {2692-8205},
abstract = {Each individual's complex, multidimensional environment, known as their 'exposome', plays an essential role in shaping cognitive neurodevelopment. Understanding the mechanisms whereby children's exposome influences their development is crucial to facilitate the design of interventions to foster positive developmental trajectories for all youth. Recent work has identified a general exposome factor associated with socio-economic inequality that is strongly related to cognition and individual differences in the spatial organization of functional brain networks in youth. Building on these findings, the current study explores whether alterations in functional connectivity may represent a potential mechanism linking variation in the exposome to cognitive performance. We apply a data-driven, cross-validated, whole-brain machine learning approach, connectome-based statistical inference, to identify patterns of functional connectivity associated with exposome scores among early adolescents enrolled in the Adolescent Brain Cognitive Development (ABCD) Study using data collected during three cognitive tasks and during rest. Additionally, we investigate whether the identified patterns of functional connectivity relate to individual differences in cognitive performance across three domains: General Cognition, Executive Functioning, and Learning/Memory. Models incorporating 10-fold cross-validation over 100 iterations identified consistent functional connections associated with the exposome across task and rest conditions (model performance: ns = 6,137-8,391, rs = 0.34 - 0.44, ps <.001). Results were robust across data collection sites and functional connections common across all significant models were associated with cognitive performance across domains (ps < 0.0009). Collectively, these findings reveal that multidimensional environmental exposures are reflected in patterns of functional connectivity and relate to cognitive functioning among youth.},
}
@article {pmid41248548,
year = {2025},
author = {Lian, K and Liu, H and Fang, Z and Peng, Y and Padfield, N and Yang, B and Kong, W and Cichocki, A},
title = {P[2]CSL: cross-subject EEG classification by subspace class prototype-based progressive confident target sample labeling.},
journal = {Journal of neural engineering},
volume = {22},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ae204c},
pmid = {41248548},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods/classification ; Male ; Adult ; *Brain/physiology ; Female ; Algorithms ; Young Adult ; },
abstract = {Objective.Domain adaptation (DA) has achieved remarkable performance in cross-subject electroencephalogram (EEG) decoding by mitigating the inter-subject data distribution discrepancies. However, when exploring the feature alignment subspace and performing self-supervised pseudo-labeling in an iterative way, two difficulties are often encountered: one is that unreliable target labeling results inevitably mislead the domain-free feature learning process in the early stage and the other is that the contribution of source and target samples should be balanced in the later stage.Approach.To address both issues, this paper proposes prototype-based progressive confident target sample labeling (P[2]CSL) method to use subspace class prototypes to assist in labeling target samples under the unified framework of domain-invariant EEG feature learning and the self-supervised target sample labeling, and progressively incorporate confident target samples into DA model fitting. The underlying rationality is that early-stage pseudo-labels from unconverged models are prone to error propagation, requiring auxiliary mechanisms to ensure their reliability and stabilize training. With the gradual alignment of cross-subject features, the estimated pseudo-label information of target domain will be more reliable, meaning that more target samples should be involved in model training.Main results.Experiments on emotion recognition and inner speech decoding demonstrate the competitive performance of P[2]CSL in cross-subject EEG classification in comparison with SOTA methods.Significance.Our study indicates the effectiveness of jointly considering the reliability of target samples and their contribution to model training in the context of DA. In addition, some fine-grained results including the sample confidence allocation strategy, the DA effects, and the dynamic model optimization process are provided to further illustrate the model execution details.},
}
@article {pmid41289136,
year = {2025},
author = {Liu, Y and Wu, W and Gui, Z and Yan, D and Wang, Z and Han, N and Gao, R and Zhang, Z and Cui, L and Wu, J and Ming, D},
title = {The Enhancement Efficacy of Motor Imagery Based on Gait Phase Encoding Sequential Electrical Stimulation 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.2025.3637128},
pmid = {41289136},
issn = {1558-0210},
abstract = {Motor imagery-based brain-computer interface (MI-BCI) has been widely used to promote stroke rehabilitation. However, the conventional lower limb MI paradigm can only induce weak brain activation in stroke patients and cannot effectively guide patients to generate pronounced features during MI tasks, limiting the widespread application of MI-BCI. In this study, we applied a novel walking MI paradigm based on gait phase encoding sequential sensory threshold electrical stimulation (SES-MI) in stroke patients, and systematically explored the efficacy of SES-MI in enhancing brain response patterns and improving classification accuracy, compared with the MI paradigm only with text cues (Non-MI) and with invariable electrical stimulation (IES-MI). Thirteen stroke patients were recruited for this experiment. Event-related spectral perturbation (ERSP) was utilized to supply details about the event-related desynchronization (ERD) phenomenon. Brain activation region, intensity and functional connectivity were compared among the three paradigms. SES-MI induced stronger and wider-area ERD activation than Non-MI and IES-MI. In the somatosensory cortex, the ERD amplitudes of SES-MI increased by a maximum of 115% in contrast to Non-MI. The enhancement of activation in bilateral sensorimotor cortex and prefrontal cortex was observed in SES-MI. The increased brain excitability only occurred in the alpha frequency band. Compared with Non-MI, decreased functional connectivity between different brain regions was found in SES-MI and IES-MI, especially in SES-MI. In the alpha+beta bands, the 2-class classification accuracy for SES-MI vs. SES-Idle (81.30%) was significantly improved compared with the other two paradigms. This work demonstrates that SES-MI is a more efficient paradigm for the modulation of the brain activation patterns, having the potential to promote the development of MI-BCI in stroke lower limb rehabilitation.},
}
@article {pmid41289134,
year = {2025},
author = {Cai, X and Xue, C and Cao, L and Guo, Z and Xu, H and Zhang, S and Fan, C and Jia, J},
title = {A Novel Brain-Computer Interface Application: Precise Decoding of Urination and Defecation Motor Attempts in Spinal Cord Injury 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.2025.3637066},
pmid = {41289134},
issn = {1558-0210},
abstract = {Patients with spinal cord injury (SCI) often face urinary and defecation dysfunction, and existing treatments have limited effectiveness. Brain-computer interface (BCI) technology has been shown to have positive effects on the rehabilitation of SCI patients, but its application in promoting the recovery of urinary and defecation functions has not been explored. This study proposes a new BCI application approach and develops an accurate decoding model targeted at urination and defecation motor attempt tasks. Specifically, we designed a Bidirectional Temporal Convolutional Network (UDCNN-BiTCN) to decode both the suppressed urination and defecation (S-UD) task and the urination and defecation (UD) task. Seventy-one participants (including 44 healthy controls and 27 SCI patients) were recruited for the experiment. The results showed that UDCNN-BiTCN achieved an average accuracy of 91.47% on the S-UD task and 91.81% on the UD task. The study also conducted within-subject cross-task transfer learning and cross-subject experiments, further validating the superiority of the model. In addition, we conducted a comprehensive analysis of this new paradigm from the perspective of classification performance. The research approach and findings in this study provide a valuable new perspective for BCI applications in the recovery of urinary and defecation functions.},
}
@article {pmid41288610,
year = {2025},
author = {Shu, L and Zhuang, D and Tang, J and Zhao, J and Shao, W and Guan, X and Zhang, D},
title = {DemuxTrans: Transformer and temporal convolution network for accurate barcode demultiplexing in nanopore sequencing.},
journal = {Bioinformatics (Oxford, England)},
volume = {41},
number = {11},
pages = {},
doi = {10.1093/bioinformatics/btaf612},
pmid = {41288610},
issn = {1367-4811},
support = {62136004//National Natural Science Foundation of China/ ; 62276130//National Natural Science Foundation of China/ ; 2023YFF1204803//National Key R&amp;D Program of China/ ; BE2022842//Key Research and Development Plan of Jiangsu Province/ ; },
mesh = {*Nanopore Sequencing/methods ; *Deep Learning ; *Sequence Analysis, RNA/methods ; *Software ; Nanopores ; },
abstract = {MOTIVATION: Oxford Nanopore Technologies (ONT) direct RNA sequencing (dRNA-seq) offers high-resolution, single-molecule analysis but is hindered by the lack of robust multiplex barcoding methods. Existing approaches struggle to accurately demultiplex raw nanopore signals, failing to capture both local patterns and long-range dependencies. This limitation underscores the requirement for advanced solutions to improve accuracy, efficiency, and adaptability in sequencing workflows. We present DemuxTrans, a hybrid deep learning framework that integrates Multi-Layer Feature Fusion, Transformers, and Temporal Convolutional Networks (TCN) for precise barcode demultiplexing.<br><br>RESULTS: DemuxTrans achieves state-of-the-art performance across multiple datasets by effectively balancing local feature extraction, global context modeling, and long-term dependency capture, excelling in metrics such as accuracy, recall and F1-score. These results demonstrate DemuxTrans as a scalable, efficient solution for barcode demultiplexing in nanopore sequencing, enabling precise identification of multiplexed RNA samples and improving throughput in transcriptomic and epigenomic analyses.<br><br>The code and datasets are publicly available on https://github.com/LiyuanShu116/Demuxtrans.},
}
@article {pmid41286920,
year = {2025},
author = {Sun, Z and Hu, S and Zhu, J and Ye, Z and Ma, M and Ma, G},
title = {The impact of non-invasive brain-computer interface technology on the therapeutic effect of patients with spinal cord injury: a summary of evidence based on meta-analysis.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {250},
pmid = {41286920},
issn = {1743-0003},
support = {No. YC2024022//Graduate Student Research and Innovation Fund of Jilin Sport University/ ; No. YC2024022//Graduate Student Research and Innovation Fund of Jilin Sport University/ ; No. 2019B122//Social Science Foundation of Jilin Province/ ; },
mesh = {Humans ; *Spinal Cord Injuries/rehabilitation ; *Brain-Computer Interfaces ; Activities of Daily Living ; },
abstract = {BACKGROUND: The objective of this study is to systematically evaluate the effects of non-invasive brain-computer interface technology on motor and sensory functions and daily living abilities of patients with spinal cord injuries. In addition, the study will investigate the related modifying factors. Ultimately, the study will provide evidence-based recommendations for clinical practice.<br><br>METHODS: A systematic search was conducted on PubMed, Web of Science, Scopus, Wiley Online Library, Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang Data Resource System, and VIP Database for relevant literature from database inception to February 2025. The quality of the studies was assessed using Review Manager 5.4, with the risk of bias visually represented. The presence of publication bias was assessed through the utilization of the "metafor" package (version 4.6-0) in R (version 4.4.1). The certainty of the evidence was evaluated using the GRADE framework.<br><br>RESULTS: A total of 9 papers were included, including 4 randomized controlled trials and 5 self-controlled trials with 109 spinal cord injury patients. Compared with the control group, the non-invasive brain-computer interface intervention had a significant impact on patients' motor function (SMD&#x2009;=&#x2009;0.72, 95% CI: [0.35,1.09], P&#x2009;<&#x2009;0.01, I[2]&#x2009;=&#x2009;0%, medium level of evidence), sensory function (SMD&#x2009;=&#x2009;0.95, 95% CI: [0.43,1.48], P&#x2009;<&#x2009;0.01, I[2]&#x2009;=&#x2009;0%, medium level of evidence), activities of daily living (SMD&#x2009;=&#x2009;0.85, 95% CI: [0.46,1.24], P&#x2009;<&#x2009;0.01, I[2]&#x2009;=&#x2009;0%, low level of evidence) reached statistical significance. Subgroup analyses showed that for the current summary of evidence, noninvasive brain-computer interface interventions in patients with subacute stage spinal cord injuries showed statistically stronger effects on motor function, sensory function, and ability to perform activities of daily living than in patients with slow chronic stage spinal cord injuries.<br><br>CONCLUSION: As far as the existing literature is concerned, non-invasive brain-computer interface technology shows the potential to improve motor and sensory functioning as well as the ability to perform activities of daily living in patients with spinal cord injury. However, the conclusions are preliminary and hypothetical, and as the current evidence for non-invasive BCI interventions for people with spinal cord injury remains limited, this paper does not recommend the application of the conclusions to clinical practice until future large-sample RCTs.},
}
@article {pmid41287227,
year = {2021},
author = {Glannon, W},
title = {Ethical and social aspects of neural prosthetics.},
journal = {Progress in biomedical engineering (Bristol, England)},
volume = {4},
number = {1},
pages = {},
doi = {10.1088/2516-1091/ac23e6},
pmid = {41287227},
issn = {2516-1091},
abstract = {Neural prosthetics are devices or systems that bypass, modulate, supplement, or replace regions of the brain and its connections to the body that are damaged and dysfunctional from congenital abnormalities, brain and spinal cord injuries, limb loss, and neuropsychiatric disorders. Some prosthetics are implanted in the brain. Others consist of implants and systems outside the brain to which they are connected. Still others are completely external to the brain. But they all send inputs to and receive outputs from neural networks to modulate or improve connections between the brain and body. As artificial systems, neural prosthetics can improve but not completely restore natural sensory, motor and cognitive functions. This review examines the main ethical and social issues generated by experimental and therapeutic uses of seven types of neural prosthetics: auditory and visual prosthetics for deafness and blindness; deep brain stimulation for prolonged disorders of consciousness; brain-computer and brain-to-brain interfaces to restore movement and communication; memory prosthetics to encode and retrieve information; and optogenetics to modulate or restore neural function. The review analyzes and discusses how recipients of neural prosthetics can benefit from them in restoring autonomous agency, how they can be harmed by trying and failing to use or adapt to them, how these systems affect their identities, how to protect people with prosthetics from external interference, and how to ensure fair access to them. The review concludes by emphasizing the control these systems provide for people and a brief exploration of the future of neural prosthetics.},
}
@article {pmid41285817,
year = {2025},
author = {Li, S and Chen, J and Zhang, C and Tang, S and Xie, Y and Wang, L},
title = {Flexible Use of Limited Resources for Sequence Working Memory in Macaque Prefrontal Cortex.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {10386},
pmid = {41285817},
issn = {2041-1723},
mesh = {Animals ; *Prefrontal Cortex/physiology ; *Memory, Short-Term/physiology ; Neurons/physiology ; Macaca mulatta/physiology ; Male ; Behavior, Animal/physiology ; },
abstract = {Our brain is remarkably limited in how many items it can hold simultaneously, but it can also represent unbounded novel items through generalization. How the brain rationally uses limited resources in working memory (WM) remains unexplored. We investigated mechanisms of WM resource allocation using calcium imaging and electrophysiological recording in the prefrontal cortex of monkeys performing sequence WM (SWM) tasks. We found that changes in the neural representation of SWM, including geometry, generalizable and separate rank subspaces, reflected WM load. SWM resources, represented by neurons' signal strength and spatial tuning projected onto each rank subspace, were shared flexibly between ranks. Crucially, the prefrontal cortex dynamically utilized shared tuning neurons to ensure generalization, while engaging disjoint and spatially shifted neurons to minimize interference, thus achieving a trade-off between behavioral and neural costs within capacity. The allocated resources can predict monkeys' behavior. Thus, the geometry of compositionality underlies the flexible use of limited resources in SWM.},
}
@article {pmid41285049,
year = {2025},
author = {Lampert, F and Baker, MR and Jensen, MA and Ayyoubi, AH and Bentler, C and Bowersock, JL and Esteller, R and Herron, JA and Johnson, GW and Kipke, DR and Kovach, CK and Kremen, V and Mivalt, F and Neimat, JS and Netoff, TI and Opri, E and Rockhill, AP and Rosenow, JM and Sellers, KK and Staff, NP and Swamy, CP and Viswanathan, A and Schalk, G and Denison, TJ and Hermes, D and Ince, NF and Brunner, P and Worrell, GA and Miller, KJ},
title = {Adaptive neuromodulation dialogues: navigating current challenges and emerging innovations in neuromodulation system development.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae2359},
pmid = {41285049},
issn = {1741-2552},
abstract = {Adaptive neuromodulation systems and implantable brain-computer interfaces have made notable strides in recent years, translating experimental prototypes into clinical applications and garnering substantial attention from the public. This surge in interest is accompanied by increased scrutiny related to the safety, efficacy, and ethical implications of these systems, all of which must be directly addressed as we introduce new neurotechnologies. In response, we have synthesized the insights resulting from discussions between groups of experts in the field and summarized them into five key domains essential to therapeutic device development: (1) analyzing current landscape of neuromodulation devices and translational platforms (2) identifying clinical need, (3) understanding neural mechanisms, (4) designing viable technologies, and (5) addressing ethical concerns. The role of translational research platforms that allow rapid, iterative testing of hypotheses in both preclinical and clinical settings is emphasized. These platforms must balance experimental flexibility with patient safety and clear clinical benefit. Furthermore, requirements for interoperability, modularity, and wireless communication protocols are explored to support long-term usability and scalability. The current regulatory processes and funding models are examined alongside the ethical responsibilities of researchers and device manufacturers. Special attention is given to the role of patients as active contributors to research and to the long-term obligations we have to them as the primary burden-bearers of the implanted neurotechnologies. This article represents a synthesis of scientific, engineering, and clinical viewpoints to inform key stakeholders in the neuromodulation and brain-computer interface spaces.},
}
@article {pmid41284455,
year = {2025},
author = {Baradaran, Y and Rojas, RF and Goecke, R and Ghahramani, M},
title = {Exploring Prefrontal Cortex Involvement in Postural Control Across Degraded Sensory Conditions Using fNIRS and Classification.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3636169},
pmid = {41284455},
issn = {2168-2208},
abstract = {The prefrontal cortex (PFC) of the brain is involved in processing visual, vestibular, and somatosensory inputs to stabilise postural balance. However, the PFC's activation map for a standing person and different sensory inputs remains unclear. This study aimed to explore the PFC activity map and distinct haemodynamic responses during postural control when sensory inputs change. To this end, functional near-infrared spectroscopy (fNIRS) was employed to capture the haemodynamic responses throughout the PFC from a group of young adults standing in four sensory conditions. The results revealed distinct PFC activation patterns supporting sensory processing, motor planning, and cognitive control to maintain balance under different degraded sensory conditions. Furthermore, by applying machine learning classifiers and multivariate feature selection, the PFC locations and haemodynamic responses indicative of different sensory conditions were identified. The findings of this study offer valuable insights for optimising rehabilitation approaches, enhancing the design of fNIRS studies, and advancing brain-computer interface technologies for balance assessment and training.},
}
@article {pmid41284444,
year = {2025},
author = {Liu, J and Li, M and Li, Z and Yang, Y and Qi, Q},
title = {DA-META: A Dual Attention Meta-Learning Framework for Unsupervised Motor Imagery Decoding.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3636462},
pmid = {41284444},
issn = {2168-2208},
abstract = {Motor imagery electroencephalography (MI-EEG) decoding demonstrates significant potential for paralysis rehabilitation, and its generalization capability is often compromised by intersubject variability and scarcity of labeled target domain data. Meta-learning has emerged as a promising approach for unsupervised domain adaptation problem. However, existing implementations suffer from two critical limitations: insufficient feature extraction and overlooking the guiding role of unlabeled target data. To overcome these challenges, we propose a dual-attention meta-learning framework (DA-META) with model-agnostic architecture in this paper. The framework comprises three stages: meta-task construction, guided meta-training, and fine-tuning-free meta-testing. In the guided meta-training stage, DA-META incorporates two key attention mechanisms: an enhanced temporal attention module for effective feature extraction, and a cosine similarity-based attention module to leverage the guidance of target domain. Using EEGNet as the backbone network, DA-META achieves mean classification accuracies of 68.04% and 76.61% on self-collected datasets from patients and healthy subjects, and 73.29% and 80.93% on the public BCI Competition IV 2a and 2b datasets, outperforming state-of-the-art methods. When employing EEGNet, DeepConvNet, and EEG Conformer as backbone networks respectively, the framework achieves accuracy improvements of 5.17%, 2.56%, and 0.85% on the 2a dataset, compared to the baseline. These results demonstrate the framework's superior ability to handle inter-subject variability and its significant potential to improve practical applicability.},
}
@article {pmid41282818,
year = {2025},
author = {Johnson, TR and Foli, C and Conlan, EC and Koenig, KA and Lowe, MJ and Memberg, WD and Kirsch, RF and Herring, EZ and Bazarek, SF and Graczyk, EL and Taylor, DM and Ajiboye, AB and Sweet, J},
title = {Targeting Optimal Grasp-Related Cortical Areas for Intracortical Brain-Machine Interfaces after Spinal Cord Injury.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.10.10.25337598},
pmid = {41282818},
abstract = {OBJECTIVE: This study aimed to optimize intracortical microelectrode array implantation sites for grasp-related motor decoding by integrating anatomical, functional, and vascular imaging with preoperative 3D modeling.<br><br>METHODS: A participant with C5 tetraplegia underwent anatomical magnetic resonance imaging (MRI), diffusion-weighted imaging, and task-based functional MRI (fMRI) to identify grasp-related cortical regions while avoiding vasculature and speech-critical areas. Quicktome software was used to refine target selection by integrating structural connectivity and functional activation data. A 3D-printed skull and cortical model enabled preoperative planning, including craniotomy and electrode positioning simulations. Electrode placement was validated post-operatively using neural data collected from the implanted arrays during attempted movements of the arm and hand.<br><br>RESULTS: Functional imaging identified distinct grasp-related activation in anterior intraparietal area (AIP), ventral premotor cortex (PMv), and inferior frontal gyrus (IFG). AIP was selected based on its strong connectivity with motor cortex and distinct functional activation. Subregions 6v and 6r of PMv, which exhibited robust grasp-related activity and were surgically accessible, were chosen over the posterior IFG region, which extended into a sulcus making implantation difficult. Post-surgically, the arrays enabled high-fidelity decoding of arm/hand movements, achieving a combined accuracy of 96%.<br><br>CONCLUSION: This study presents a multi-modal approach for optimizing intracortical electrode placement by combining MRI-based anatomical mapping, fMRI-guided functional localization, connectivity information, and 3D surgical modeling. These findings demonstrate an effective method for identifying surgically feasible grasp network implant locations in a paralyzed individual. This is an essential step for brain-machine interface (BMI) systems that use grasp-related brain activity to command devices, such as neuromuscular stimulation systems for restoring upper limb function in individuals with spinal cord injury (SCI).},
}
@article {pmid41281720,
year = {2025},
author = {Gan, L and Yuan, S and Guo, M and Wang, Q and Deng, Z and Jia, B},
title = {Triboelectric nanogenerators for neural data interpretation: bridging multi-sensing interfaces with neuromorphic and deep learning paradigms.},
journal = {Frontiers in computational neuroscience},
volume = {19},
number = {},
pages = {1691017},
pmid = {41281720},
issn = {1662-5188},
abstract = {The rapid growth of computational neuroscience and brain-computer interface (BCI) technologies require efficient, scalable, and biologically compatible approaches for neural data acquisition and interpretation. Traditional sensors and signal processing pipelines often struggle with the high dimensionality, temporal variability, and noise inherent in neural signals, particularly in elderly populations where continuous monitoring is essential. Triboelectric nanogenerators (TENGs), as self-powered and flexible multi-sensing devices, offer a promising avenue for capturing neural-related biophysical signals such as electroencephalography (EEG), electromyography (EMG), and cardiorespiratory dynamics. Their low-power and wearable characteristics make them suitable for long-term health and neurocognitive monitoring. When combined with deep learning models-including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and spiking neural networks (SNNs)-TENG-generated signals can be efficiently decoded, enabling insights into neural states, cognitive functions, and disease progression. Furthermore, neuromorphic computing paradigms provide an energy-efficient and biologically inspired framework that naturally aligns with the event-driven characteristics of TENG outputs. This mini review highlights the convergence of TENG-based sensing, deep learning algorithms, and neuromorphic systems for neural data interpretation. We discuss recent progress, challenges, and future perspectives, with an emphasis on applications in computational neuroscience, neurorehabilitation, and elderly health care.},
}
@article {pmid41281344,
year = {2025},
author = {Xu, M and He, Z and Zhou, J and Zhao, J and Tian, X and Cheng, Q and Lin, Y and Xin, H and Mou, C and Xue, Q and Luo, B},
title = {Altered oral microbiomes in patients with prolonged disorders of consciousness.},
journal = {Journal of oral microbiology},
volume = {17},
number = {1},
pages = {2577220},
pmid = {41281344},
issn = {2000-2297},
abstract = {BACKGROUND: The host microbiome is increasingly recognized as a key modulator of brain function and disease progression, yet the role of the oral microbiome in patients with prolonged disorders of consciousness remains underexplored.<br><br>METHODS: This study characterized oral microbiota differences among pDoC patients (n&#x2009;=&#x2009;89) in the vegetative state (VS), the minimally conscious state (MCS), and emerging from the MCS (EMCS), with a particular focus on the impact of antibiotic use. We used 16S ribosomal RNA sequencing to profile oral microbiota in patients with different levels of consciousness.<br><br>RESULTS: &#x3b2;-diversity was significantly reduced in the VS group compared to the EMCS group. Differential abundance analysis identified five taxa (i.e., species Streptococcus danieliae, species Corynebacterium durum, family Lachnospiraceae, species Phocaeicola abscessus, and species Campylobacter showae) that exhibited significant differences between VS and EMCS, suggesting they were potentially involved in regulating oral microbial dysbiosis and brain-microbiome interactions. Antibiotic treatment induced pronounced microbial shifts in the VS group, while no such effect was observed in the MCS or EMCS groups. Prognostic models built using differential and dominant microbiota panels demonstrated strong predictive performance, achieving areas under the curve of 0.820 and 0.920, respectively.<br><br>CONCLUSIONS: These findings highlight oral microbiome alterations in pDoC and their potential relevance to disease progression, emphasizing the importance of microbiome-informed clinical strategies.},
}
@article {pmid41280332,
year = {2025},
author = {Wu, Z and Yu, S and Tian, D and Cheng, L and Jing, J},
title = {Microglial TREM2 and cognitive impairment: insights from Alzheimer's disease with implications for spinal cord injury and AI-assisted therapeutics.},
journal = {Frontiers in cellular neuroscience},
volume = {19},
number = {},
pages = {1705069},
pmid = {41280332},
issn = {1662-5102},
abstract = {Cognitive impairment is a frequent but underrecognized complication of neurodegenerative and traumatic central nervous system disorders. Although research on Alzheimer's disease (AD) revealed that microglial triggering receptor expressed on myeloid cells 2 (TREM2) plays a critical role in inhibiting neuroinflammation and improving cognition, its contribution to cognitive impairment following spinal cord injury (SCI) is unclear. Evidence from AD shows that TREM2 drives microglial activation, promotes pathological protein clearance, and disease-associated microglia (DAM) formation. SCI patients also experience declines in attention, memory, and other functions, yet the specific mechanism of these processes remains unclear. In SCI, microglia and TREM2 are involved in inflammation and repair, but their relationship with higher cognitive functions has not been systematically examined. We infer that TREM2 might connect injury-induced neuroinflammation in the SCI with cognitive deficits, providing a new treatment target. Artificial intelligence (AI) offers an opportunity to accelerate this endeavor by incorporating single-cell transcriptomics, neuroimaging, and clinical data for the identification of TREM2-related disorders, prediction of cognitive trajectories, and applications to precision medicine. Novel approaches or modalities of AI-driven drug discovery and personalized rehabilitation (e.g., VR, brain-computer interface) can more precisely steer these interventions. The interface between lessons learned from AD and SCI for generating new hypotheses and opportunities for translation.},
}
@article {pmid41279978,
year = {2025},
author = {Canfield, RA and Ouchi, T and Fang, H and Macagno, B and Smith, LI and Scholl, LR and Orsborn, AL},
title = {The spatiotemporal structure of neural activity in motor cortex during reaching.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {41279978},
issn = {2692-8205},
abstract = {UNLABELLED: Intracortical brain-computer interfaces (BCI) leverage knowledge about neural representations to translate movement-related neural activity into actions. BCI implants have targeted broad cortical regions known to have relevant motor representations, but emerging technologies will allow flexible targeting to specific neural populations. The structure of motor representations at this scale, however, has not been well characterized across frontal motor cortices. Here, we investigate how motor representations and population dynamics (temporal coordination) vary across a large expanse of frontal motor cortices. We used high-density, laminar, microelectrode arrays to simultaneously record many neurons and then sampled neural populations across frontal motor cortex in two monkeys while they performed a reaching task. Our experiments allowed us to map neuronal activity across three spatial dimensions and relate them to movement. Target decoding analysis revealed that task information was heterogeneously distributed across the cortical surface and in depth. Similarly, we found that the temporal dynamics of different neural populations were heterogeneous, but that the amount of task information predicted which neural populations had similar dynamics. The neural populations with the most similar dynamics were composed of neurons with high task information regardless of spatial location. Our results highlight the spatiotemporal complexity of motor representations across frontal motor cortex at the level of neurons and neural populations, where well-learned movements consistently recruit a spatially distributed subset of neurons. Further insights into the spatiotemporal structure of neural activity patterns across frontal motor cortex will be critical to guide future implants for improved BCI performance.<br><br>SIGNIFICANCE STATEMENT: Motor brain-computer interfaces (BCI) translate neural activity into movement, but how to target implants within motor cortices to maximize performance remains unclear. We used high-density recordings of neural activity spanning a large cortical area and related them to movement to map the spatial distribution of task information and the evolution of neural population activity over time. Our measurements revealed that neurons with the most task information were heterogeneously distributed across cortex yet also evolved coherently in time, suggesting that spatially distributed neurons coordinate to control movements. Our results provide new links between neuron- and population-level maps of motor representations, and highlight the complex spatiotemporal structure of activity that may need to be considered when designing next-generation BCIs.},
}
@article {pmid41278188,
year = {2025},
author = {Li, Z and Kambara, H and Koike, Y},
title = {Neural signatures of engagement in driving: comparing active control and passive observation.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1698625},
pmid = {41278188},
issn = {1662-4548},
abstract = {Understanding how the human brain differentiates between active engagement and passive observation is a fundamental question in cognitive neuroscience. Using a matched-stimulus driving paradigm to isolate engagement from sensory input, we recorded whole-brain EEG while participants performed a manual control task and passively viewed a replay of their own performance. Manual control elicited distinct spectral signatures, including stronger frontal midline theta power and, paradoxically, greater occipital alpha power, consistent with heightened cognitive control and active attentional filtering. While a classifier could distinguish these states with high within-subject accuracy, performance declined in cross-subject validation, highlighting inter-individual variability. These findings delineate the distinct neural signatures of active versus passive engagement under controlled conditions. This work establishes a foundational neurophysiological baseline that can inform research on cognitive state monitoring and the design of neuroadaptive systems in complex human-machine interaction.},
}
@article {pmid40936379,
year = {2025},
author = {Shin, CJ and Lee, K and Langford, L and Bai, W},
title = {Conductive and Semiconductive 2D Materials for Neural Interfaces, Biosensors, and Therapeutic Modulation.},
journal = {Small methods},
volume = {9},
number = {11},
pages = {e01330},
doi = {10.1002/smtd.202501330},
pmid = {40936379},
issn = {2366-9608},
support = {CCSS-2443105//Division of Electrical, Communications and Cyber Systems/ ; 1R01EB034332/EB/NIBIB NIH HHS/United States ; 1R01EB034332/EB/NIBIB NIH HHS/United States ; },
mesh = {Humans ; *Biosensing Techniques/methods/instrumentation ; *Semiconductors ; Graphite/chemistry ; Electric Conductivity ; Animals ; *Brain-Computer Interfaces ; },
abstract = {Due to population aging, the surge in chronic diseases, and recent pandemics, healthcare is increasingly shifting from hospital-centered models toward digital care. However, widespread adoption is impeded by signal degradation under physiological motion, biofouling, stringent power and data constraints. Effectively overcoming these challenges will require clinically robust devices providing precise, reliable, and reproducible performance. 2D materials address these demands through high carrier mobility that can improve signal-to-noise ratios, low-defect lattices for uniformity, and mechanical pliability that maintains intimate tissue contact and stable impedance during motion. These traits have fueled the rapid growth of 2D-material bioelectronics for remote care in lightweight, stretchable devices. This review surveys flexible, low-impedance neural electrodes of graphene, transition-metal dichalcogenides, and MXenes that integrate electrophysiological recording with optical imaging to provide high-resolution brain interfaces. It then examines their roles in biosensing and autonomous therapy, including sub-picomolar biomarker detection in complex fluids and photothermal, genetic, and antibacterial interventions. Open questions regarding long-term biocompatibility, scalable manufacturing, and protocol harmonization are highlighted. By aligning recent breakthroughs with persistent challenges, the review outlines the prospects of conductive and semiconductive 2D materials for neural interfacing, biosensing, and therapeutic delivery, and maps a pathway toward practical clinical translation.},
}
@article {pmid41275665,
year = {2025},
author = {Li, T and An, X and Di, Y and Wang, H and Yan, Y and Liu, S and Dong, Y and Ming, D},
title = {Fuzzy symbolic convergent cross mapping: A causal coupling measure for EEG signals in disorders of consciousness patients.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {195},
number = {},
pages = {108318},
doi = {10.1016/j.neunet.2025.108318},
pmid = {41275665},
issn = {1879-2782},
abstract = {Accurate and timely diagnosis in disorders of consciousness (DOC) patients remains a core clinical challenge. Electroencephalography (EEG) shows strong potential for detecting physiological biomarkers of consciousness, and brain network analysis serves as an effective technique. Therefore, a robust approach to brain network construction is of great significance. The convergent cross mapping (CCM) is a powerful tool for capturing the coupling relationship between two signals. However, a major drawback of CCM is its sensitivity to noise. To address this problem, we proposed a symbolic method that combines fuzzy membership functions called fuzzy symbolic convergent cross mapping (FuzzSCCM). Through the simulation results, we verified its robustness to noise, sensitivity to coupling, and data length. Building on this coupling measure, we constructed EEG brain networks and validated the approach on real DOC EEG datasets. In patients with DOC, FuzzSCCM identified distinct network features between vegetative state/unresponsive wakefulness syndrome (VS/UWS) and minimally conscious state (MCS). Specifically, compared with the MCS group, the VS group showed greater asymmetry between the left hemisphere and the right hemisphere in the α band, and was relatively less active in the anterior in the θ band. Moreover, our results demonstrate spontaneous transitions between distinct brain network states, suggesting these dynamic reconfigurations may constitute a fundamental mechanism underlying consciousness modulation. These findings provide novel insights into the dynamic neural signatures of DOC, while establishing a potential diagnostic tool.},
}
@article {pmid41274911,
year = {2025},
author = {Hardstone, R and Ostrowski, LM and Dusang, AN and López-Larraz, E and Jesser, J and Cash, SS and Cramer, SC and Hochberg, LR and Ramos-Murguialday, A and Lin, DJ},
title = {Extension of voxel-based lesion mapping to multidimensional neurophysiological data.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-025-17247-z},
pmid = {41274911},
issn = {2045-2322},
support = {FMD clinical research fellowship//MGH ECOR/ ; Clinical research Training Scholar//American Academy of Neurology/ ; 1IK2RX004237//U.S. Department of Veterans Affairs/ ; },
abstract = {Focal brain lesions cause neurophysiological changes in local and distributed neural systems. While electroencephalography (EEG) has a long history in post-stroke neurophysiological assessment, the observed changes have rarely been linked to specific lesion locations, leaving neuroanatomical-neurophysiological relationships after stroke unclear. Current data-driven methods, such as voxel-based lesion symptom mapping (VLSM), relate lesion locations to single-feature "symptoms" but currently cannot associate anatomical injury with multidimensional data such as EEG, with its rich spatiotemporal information. To overcome this limitation, we introduce MD-VLM, an extension of VLSM to multidimensional "symptoms" that identifies relationships between lesion locations and neurophysiology. MD-VLM is data-agnostic, compatible with various lesion (e.g., lesion maps, lesion network maps) and neurophysiological (e.g., channel-level or source-localized EEG) inputs, and uses robust statistics to test for the existence of significant neuroanatomical-neurophysiological relationships. We demonstrate MD-VLM's feasibility by applying it to EEG from chronic stroke patients performing a cued-movement task. MD-VLM revealed significant associations between frontal white-matter lesions and reduced ipsilesional parietal cue-evoked responses, consistent with damage to known fronto-parietal networks. MD-VLM is a novel data-driven extension to VLSM for multidimensional "symptoms". Applying MD-VLM to link lesions to neurophysiological data can improve mechanistic understanding of post-stroke neurological impairments and guide future biomarker development.},
}
@article {pmid41274087,
year = {2025},
author = {Xu, H and Lin, N},
title = {Neurovista: A bidirectional masked cross-Modal fusion network for robust EEG-to-Image decoding.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {195},
number = {},
pages = {108297},
doi = {10.1016/j.neunet.2025.108297},
pmid = {41274087},
issn = {1879-2782},
abstract = {Electroencephalography (EEG)-based visual decoding has significant potential in brain-computer interfaces but faces substantial challenges due to noise, inter-subject variability, and limited fine-grained alignment between neural signals and visual representations. Existing approaches predominantly utilize global EEG embeddings and static fusion methods, restricting their capability to capture nuanced cross-modal interactions. To address these limitations, We propose NeuroVista, a novel framework that integrates localized EEG masking with dynamic bidirectional cross-modal attention, achieving state-of-the-art EEG-to-image decoding performance. Specifically, NeuroVista employs a channel-level EEG masking strategy during training, encouraging the model to learn robust, context-sensitive neural features, thus significantly improving generalization and noise resistance. Simultaneously, our bidirectional cross-modal attention module dynamically aligns EEG embeddings with corresponding visual features, enhancing semantic coherence across modalities. Extensive experiments on standard EEG-to-image benchmarks demonstrate that NeuroVista consistently outperforms state-of-the-art methods, achieving up to +16.0 % top-1 accuracy improvement in both subject-dependent and subject-independent settings. Our results validate the effectiveness of combining localized masking and interactive cross-modal attention, establishing NeuroVista as a robust, interpretable, and highly generalizable approach for EEG-based visual decoding tasks.},
}
@article {pmid41273580,
year = {2026},
author = {Miloulis, ST and Kakkos, I and Zorzos, I and Karampasi, A and Anastasiou, A and Asvestas, P and Ventouras, EC and Kalatzis, I and Matsopoulos, GK},
title = {Deep Learning Discrimination for BCI Implementation Using 3D Convolutional Neural Network and EEG Topographic Maps.},
journal = {Advances in experimental medicine and biology},
volume = {1487},
number = {},
pages = {405-413},
pmid = {41273580},
issn = {0065-2598},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; *Deep Learning ; *Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Adult ; Male ; Convolutional Neural Networks ; },
abstract = {The growing interest in improved rehabilitation systems and assistive technologies for individuals with motor impairments necessitates the need for new applications of Deep Learning approaches for Brain-Computer Interface (BCI) implementation. This study investigates the application of Deep Learning techniques, specifically the Hierarchical 3D Convolutional Neural Network (H3DCNN) model, for enhancing classification systems utilizing electroencephalography (EEG) data. As such, topographic maps were extracted from EEG signals in a real motion task experiment integrating 4 different motions. The H3DCNN model was then employed in a step-wise fashion to classify and decode the EEG signals, demonstrating its effectiveness in distinguishing between different movement intentions. Moreover, three different optimizers were implemented, including RMSprop, Adam, and Stochastic Gradient Descent (SGD), to further assess and enhance the model performance. The findings indicate that the integration of advanced deep learning techniques can significantly enhance the accuracy and reliability of BCI systems, with RMSprop and SGD showing superior results in terms of accuracy. Moreover, our results illustrate the possibility of decoding neural mechanisms via deep learning paradigms, paving the way for future developments in BCI applications, thus aiming to improve the quality of life for individuals with motor impairments.},
}
@article {pmid41272829,
year = {2025},
author = {Shi, Y and Ma, J and Zhao, X and Zhao, H and Wang, D and Zhang, X and Zhu, X and Meng, L and Ming, D},
title = {Bilateral intermittent theta-burst stimulation as a priming strategy to enhance action observation and imitation training in early parkinson's disease: a proof-of-concept study.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {247},
pmid = {41272829},
issn = {1743-0003},
support = {82372083//National Natural Science Foundation of China/ ; 2022YFF1202500//National Key Research and Development Program of China/ ; },
mesh = {Humans ; *Parkinson Disease/rehabilitation/physiopathology ; *Transcranial Magnetic Stimulation/methods ; Male ; Female ; Double-Blind Method ; Middle Aged ; Aged ; Cross-Over Studies ; Proof of Concept Study ; Evoked Potentials, Motor/physiology ; *Gait Disorders, Neurologic/rehabilitation/physiopathology/etiology ; Motor Cortex/physiopathology ; *Imitative Behavior/physiology ; Postural Balance/physiology ; Theta Rhythm/physiology ; },
abstract = {BACKGROUND: Action observation and imitation training (AOIT) is an evidence-based cognitive-motor rehabilitation strategy for Parkinson's disease (PD), particularly for the postural instability and gait disorder (PIGD) subtype. However, its effectiveness may decline with disease-related impairments in neuroplasticity. Intermittent theta burst stimulation (iTBS), a patterned repetitive transcranial magnetic stimulation protocol, can induce LTP-like plasticity and may enhance responsiveness to rehabilitation. This study investigated whether iTBS priming augments AOIT effects on gait and cognition in early-stage PIGD and explored underlying neurophysiological mechanisms.<br><br>METHODS: Fifteen patients with early-stage PIGD participated in a randomized, double-blind, sham-controlled crossover trial. Each phase included five consecutive days of AOIT preceded by either real or sham iTBS applied over the bilateral leg region of the primary motor cortex, separated by a washout period of more than four weeks. Pre- and post-intervention assessments included dual-task gait analysis, cognitive tests, clinical scales, neurophysiological measures (motor evoked potentials, cortical silent period), and resting-state EEG power spectral density.<br><br>RESULTS: Both conditions improved balance and gait measures. However, real iTBS significantly enhanced dual-task gait automaticity (F&#x2009;=&#x2009;5.558, P&#x2009;=&#x2009;0.026) and global cognition (F&#x2009;=&#x2009;5.294, P&#x2009;=&#x2009;0.026) compared to sham. Real iTBS also increased cortical silent period (F&#x2009;=&#x2009;4.655, P&#x2009;=&#x2009;0.040) and MEP-based cortical plasticity response (F&#x2009;=&#x2009;6.131, P&#x2009;=&#x2009;0.020). Improvements in cortical plasticity were significantly correlated with better gait performance (r = -&#x2009;0.429, P&#x2009;=&#x2009;0.020) and motor scores (r = -&#x2009;0.463, P&#x2009;=&#x2009;0.011). No adverse events were reported.<br><br>CONCLUSIONS: Bilateral iTBS targeting the leg representation of the primary motor cortex can potentiate AOIT effects in early-stage PIGD by enhancing cortical plasticity and motor learning. These findings support the integration of iTBS as a priming strategy within cognitive-motor rehabilitation protocols for PD. Trial registration Chinese Clinical Trial Registry, ChiCTR2300067657. Registered January 17, 2023.},
}
@article {pmid41268354,
year = {2025},
author = {Haro, S and Beauchene, C and Quatieri, TF and Smalt, CJ},
title = {A Brain-Computer Interface for Improving Auditory Attention in Multi-Talker Environments.},
journal = {IEEE access : practical innovations, open solutions},
volume = {13},
number = {},
pages = {189903-189914},
pmid = {41268354},
issn = {2169-3536},
abstract = {There is significant research in accurately determining the focus of a listener's attention in a multi-talker environment using auditory attention decoding (AAD) algorithms. These algorithms rely on neural signals to identify the intended speaker, assuming that these signals consistently reflect the listener's focus. However, some listeners struggle with this competing talkers task, leading to suboptimal tracking of the desired speaker due to potential interference from distractors. The goal of this study was to enhance a listener's attention to the target speaker in real time and investigate the underlying neural bases of this improvement. This paper describes a closed-loop neurofeedback system that decodes the auditory attention of the listener in real time, utilizing data from a non-invasive, wet electroencephalography (EEG) brain-computer interface (BCI). Fluctuations in the listener's real-time attention decoding accuracy were used to provide acoustic feedback. As accuracy improved, the ignored talker in the two-talker listening scenario was attenuated; making the desired talker easier to attend to due to the improved attended talker signal-to-noise ratio (SNR). A one-hour session was divided into a 10-minute decoder training phase, with the rest of the session allocated to observing changes in neural decoding. In this study, we found evidence of suppression of (i.e., reduction in) net neural tracking and decoding of the unattended talker when comparing the first and second half of the neurofeedback session (p = 0.02, Cohen's d = -1.29, 95% CI [-0.02, -0.01] and p = 0.01, Cohen's d = -1.56, 95% CI [-7.25, -3.44], respectively). We did not find a statistically significant increase in the neural tracking or decoding of the attended talker. These results establish a single session performance benchmark for a time-invariant, non-adaptive attended talker linear decoder utilized to extract attention from a listener integrated within a closed-loop neurofeedback system. This research lays the engineering and scientific foundation for prospective multi-session clinical trials of an auditory attention training paradigm.},
}
@article {pmid41266624,
year = {2025},
author = {Sen, O and Soni, R and Virmani, D and Parekh, A and Lehman, P and Jena, S and Katikhaneni, A and Khalifa, A and Chatterjee, B},
title = {A low-latency neural inference framework for real-time handwriting recognition from EEG signals on an edge device.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {41040},
pmid = {41266624},
issn = {2045-2322},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Handwriting ; Male ; Female ; Adult ; Signal-To-Noise Ratio ; Machine Learning ; Young Adult ; Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interfaces (BCIs) hold significant promise for restoring communication in individuals with severe motor or speech impairments. Imagined handwriting, as a form of motor imagery, offers an intuitive paradigm for character-level neural decoding. While invasive techniques such as electrocorticography (ECoG) offer high decoding accuracy, their surgical requirements pose clinical risks and hinder scalability. Non-invasive alternatives like electroencephalography (EEG) are safer and more accessible but suffer from low signal-to-noise ratio (SNR) and spatial resolution, limiting their effectiveness in high-resolution decoding. Here, we investigate how advanced machine learning, combined with informative feature extraction, can overcome these limitations, enabling EEG-based decoding performance that approaches invasive methods, while supporting real-time inference on edge devices. We present the first real-time, low-latency, high-accuracy system for decoding imagined handwriting from non-invasive EEG signals on a portable edge device. EEG data were collected from 15 participants using a 32-channel headcap and preprocessed with bandpass filtering and artifact subspace reconstruction (ASR). We extracted 85 time domain, frequency domain, and graphical features, then applied Pearson correlation coefficient-based feature selection to reduce latency while preserving accuracy. We developed a hybrid architecture, EEdGeNet, which integrates a Temporal Convolutional Network (TCN) with a multilayer perceptron (MLP), trained on the extracted features and deployed on the NVIDIA Jetson TX2 for real-time inference. The system achieved [Formula: see text] accuracy with 914.18 ms per-character inference latency. By selecting only ten key features, the model incurred a minimal accuracy loss of [Formula: see text], while achieving a [Formula: see text] reduction in inference latency (202.62 ms) compared to the full 85-feature set. These findings show that non-invasive EEG, combined with efficient feature and model design, can enable accurate, real-time neural decoding on low-power edge devices, paving the way for practical, portable BCIs.},
}
@article {pmid41265571,
year = {2025},
author = {Luo, R and Meng, J and Wei, Y and Mai, X and Li, G},
title = {Outcome processing response coupled to feedback-related EEG dynamics during discrete and continuous performance monitoring.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110629},
doi = {10.1016/j.jneumeth.2025.110629},
pmid = {41265571},
issn = {1872-678X},
abstract = {BACKGROUND: Error-related potential (ErrP) reflects the inconsistency between internal expectation and external feedback outcome. Despite the exploration of numerous experimental paradigms, ErrP components exhibit distinct latency and amplitude across different paradigms. However, previous studies have not quantitatively correlated potential influencing factors with this ErrP variability. Additionally, these qualitatively analyzed factors offer limited predictions for ErrP in new paradigms.<br><br>NEW METHOD: We proposed that a neutral condition removing goal-directed outcome expectations reflects cross-paradigm variability in correct and erroneous outcome responses. This neutral condition was designed as a control condition for each paradigm. Three different paradigms were designed to provide discrete and continuous varied feedback outcomes. Correlations were assessed between neutral condition responses and correct and erroneous outcome responses in latency and amplitude. The predictive effectiveness of neutral condition responses for new paradigms was further evaluated through single-trial cross-paradigm classification.<br><br>RESULTS: Correct and erroneous outcome responses were observed to have significant latency and amplitude coupling with these neutral condition responses in the middle frontal and bilateral parietal regions. Results from source reconstruction, pupillometry data, and workload score confirm that the neutral condition serves as the baseline response for outcome processing responses. This baseline relationship explains the cross-paradigm ErrP variability.<br><br>The single-trial decoding results show that utilizing neutral condition responses can significantly increase the accuracy of cross-paradigm classification by up to 7% and 17% with covariance-based and amplitude-based approaches.<br><br>CONCLUSION: These findings provide a quantitative physiological explanation for cross-paradigm ErrP variability and promote transfer learning applications in ErrP-based BCIs.},
}
@article {pmid41264938,
year = {2025},
author = {Fang, S and Zhao, X and Wang, Z and Si, Y and Haifeng, L and Hu, H and Xu, T and Zhou, T},
title = {Enhancing SSVEP-BCI performance through multi-stimulus discriminant fusion analysis.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae220d},
pmid = {41264938},
issn = {1741-2552},
abstract = {To enhance frequency recognition in Steady-State Visual Evoked Potential (SSVEP)-based Brain-Computer Interfaces (BCIs), particularly under short data acquisition and complex environmental conditions. Approach. We propose Multi-Stimulus Discriminant Fusion Analysis (MSDFA), a novel method that integrates multi-stimulus strategies with discriminant modeling. MSDFA was evaluated on two public datasets (Benchmark and BETA) and compared with conventional approaches including eCCA, eTRCA, and their variants. Main results. MSDFA consistently outperformed existing methods across different data lengths and training block quantities. It achieved maximum information transfer rates of 247.17±10.15 bpm on the Benchmark dataset and 192.72±9.44 bpm on the BETA dataset, demonstrating superior robustness and efficiency. Significance. By combining complementary algorithmic strengths, MSDFA improves adaptability to individual variability and complex environments, advancing the practical utility and reliability of SSVEP-BCI systems. .},
}
@article {pmid41262557,
year = {2025},
author = {Zhang, X and Wang, S and Gao, Y and Wang, Y and Qiu, S and He, H},
title = {Enhancing visual brain-computer interface through V1-targeted RTMS by modulating visual attention.},
journal = {Imaging neuroscience (Cambridge, Mass.)},
volume = {3},
number = {},
pages = {},
pmid = {41262557},
issn = {2837-6056},
abstract = {Brain-computer interfaces (BCIs) enable users to control devices directly through brain activity. Despite recent advancements in machine-learning algorithms, the signal-to-noise ratio (SNR) of the brain's responses still limits decoding performance, highlighting the necessity for targeted neuromodulation techniques to overcome this limitation. To evaluate whether 5 Hz repetitive transcranial magnetic stimulation (rTMS) targeting the primary visual cortex (V1) can enhance SSVEP-based BCI performance by improving neural signal SNR and modulating visual network dynamics. Twenty-four healthy subjects underwent both real and sham rTMS in a randomized order. The rTMS was precisely implemented through magnetic resonance imaging (MRI)-guided navigation to stimulate V1 in participants. Electroencephalograms (EEGs) were recorded during SSVEP tasks and resting-state before, immediately after, and 20 min after rTMS. SSVEP tasks were conducted across four frequency bands: low frequency (LF: 8-12 Hz), middle frequency (MF: 18-22 Hz), high frequency (HF: 28-32 Hz), and super high frequency (SHF: 38-42 Hz). The discriminability of BCI commands in the MF (+7.53%) and HF (+11.4%) bands significantly improved (p < 0.001), driven by enhanced prominence of both fundamental and harmonic components (p < 0.01). Quantitative analysis indicated that the improved SNR was due to the suppression of the background activity (p < 0.05). This effect was linked to rTMS-induced enhancements in visual attention, evidenced by increased occurrence and contribution of microstate B during the SSVEP task (p < 0.01). This study highlights the potential of 5 Hz rTMS as an effective neuromodulatory tool for optimizing BCI performance, particularly through facilitating visual attention.},
}
@article {pmid41261122,
year = {2025},
author = {Gobert, F and Merida, I and Maby, E and Seguin, P and Jung, J and Morlet, D and André-Obadia, N and Dailler, F and Berthomier, C and Otman, A and Le Bars, D and Scheiber, C and Hammers, A and Bernard, E and Costes, N and Bouet, R and Mattout, J},
title = {Disorder of consciousness rather than complete Locked-In Syndrome for end stage Amyotrophic Lateral Sclerosis: a case series.},
journal = {Communications medicine},
volume = {5},
number = {1},
pages = {482},
pmid = {41261122},
issn = {2730-664X},
abstract = {BACKGROUND: The end-stage of amyotrophic lateral sclerosis (ALS) is commonly regarded as a complete Locked-In Syndrome (cLIS). Shifting the perspective from cLIS (assumed consciousness) to Cognitive Motor Dissociation (potentially demonstrable consciousness), we aimed to assess the preservation of covert awareness (internally preserved but externally inaccessible) using a multimodal battery.<br><br>METHODS: We evaluate two end-stage ALS patients using neurophysiological testing, passive and active auditory oddball paradigms, an auditory Brain-Computer Interface (BCI), functional activation-task imaging, long-term EEG, brain morphology, and resting-state metabolism to characterize underlying brain function.<br><br>RESULTS: Patient 1 initially follows simple commands but fails twice at BCI control. At follow-up, command following is no longer observed and his oddball cognitive responses disappear. Patient 2, at a single evaluation, is unable to follow commands or control the BCI. Both patients exhibit altered wakefulness, brain atrophy, and a global cortico-subcortical hypometabolism pattern consistent with a disorder of consciousness, regarded as an extreme manifestation of ALS-associated fronto-temporal dementia.<br><br>CONCLUSIONS: Although it is not possible to firmly prove the absence of awareness, each independent measure concurred with suggesting that a "degenerative disorder of consciousness" rather than a cLIS may constitute the final stage of ALS. This condition appears pathophysiologically distinct from typical tetraplegia and anarthria, in which behavioural communication and BCI use persist to enhance quality of life. Identifying the neuroimaging signatures of this condition represents a substantial milestone in understanding end-stage ALS. Large-scale longitudinal investigations are warranted to determine the prevalence of this profile among patients whose communication appears impossible.},
}
@article {pmid41260504,
year = {2025},
author = {Wang, J and Gan, X and Han, M and Dong, W and He, J and Fu, K and Bore, MC and Xu, T and Klugah-Brown, B and Ferraro, S and Becker, B},
title = {Effects of exogenous oxytocin on human brain function are regulated by oxytocin gene expression: a meta-analysis of 20 years of oxytocin neuroimaging and transcriptomic analyses.},
journal = {Neuroscience and biobehavioral reviews},
volume = {},
number = {},
pages = {106478},
doi = {10.1016/j.neubiorev.2025.106478},
pmid = {41260504},
issn = {1873-7528},
abstract = {Over the past two decades, numerous pharmaco-imaging studies have examined the role of oxytocin (OT) in human cognition and behavior, yet results remain highly heterogeneous and the link between large-scale functional effects and molecular architecture is unclear. To address this, we conducted a comprehensive analysis combining neuroimaging meta-analysis, meta-analytic connectivity modeling, and transcriptomics. Across 75 experiments (n=2,247), consistent, domain-general effects of OT emerged in the left thalamus, pallidum, caudate, and insula. Connectivity modeling showed these regions form an integrated thalamus-striatum-insula circuit directly modulated by OT. Transcriptomic analyses revealed that the expression of three OT pathway genes (CD38, OXT, and OXTR) is enriched in these subcortical regions and associated with the observed neural effects. OT's neural effects were also strongly linked with acetylcholinergic, dopaminergic, and opioidergic gene distributions, potentially reflecting functional interactions with these systems. Findings provide convergent evidence that OT exerts robust effects on human brain function via a biologically-plausible core circuit and can inform effective pharmacotherapeutic targets.},
}
@article {pmid41259181,
year = {2025},
author = {Qin, C and Yang, R and Zhu, L and Chen, Z and Huang, M and Alsaadi, FE and Wang, Z},
title = {EEG-Infinity: A Mathematical Modeling-Inspired Architecture for Addressing Cross-Device Challenges in Motor Imagery.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3635018},
pmid = {41259181},
issn = {1558-0210},
abstract = {The distribution of electroencephalogram (EEG) data generally varies across datasets due to the huge difference between the physical structure of brain-computer interface devices, known as cross-device variability. Such variability poses great challenges in EEG decoding and hinders the standardized utilization of EEG datasets. In this study, we explore a new issue concerning the cross-device variability problem, pointing to the gap in the existing studies facing cross-device variability. To tackle this challenge, our paper is the first to model the cross-device variability problem through a "sequentially comprehensive formula" and a "spatial comprehensive formula". Inspired by this modeling, a novel deep domain adaptation network named EEG-Infinity is proposed, incorporating replaceable EEG feature extraction backbones with a novel structure named "alignment head". To show the effectiveness of the proposed EEG-Infinity, systematic experiments are conducted across four different EEG-based motor imagery datasets under 48 cases. The experimental results highlight the superior performance of the proposed EEG-Infinity over commonly used approaches with an average classification accuracy improvement of 1.51% across 34 cases, laying a foundation for research in large-scale EEG models. The code can be assessed at https://github.com/Baizhige/cd-infinity.},
}
@article {pmid41257892,
year = {2025},
author = {Xiong, W and Ma, L and Li, H},
title = {Adaptive EEG preprocessing to mitigate electrode shift variability for robust motor imagery classification.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {40808},
pmid = {41257892},
issn = {2045-2322},
mesh = {*Electroencephalography/methods/instrumentation ; Humans ; Electrodes ; Brain-Computer Interfaces ; *Imagination/physiology ; Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; Algorithms ; },
abstract = {Electrode placement variability poses a critical challenge in EEG-based motor imagery tasks, often resulting in reduced classification robustness. We present the Adaptive Channel Mixing Layer (ACML), a plug-and-play preprocessing module that dynamically adjusts input signal weights through a learnable transformation matrix based on inter-channel correlations. By leveraging the inherent spatial structure of EEG caps, ACML effectively compensates for electrode misalignments and noise, enhancing resilience to signal distortion. Experimental validation on two motor imagery datasets with varying channel counts demonstrated consistent improvements in accuracy (up to 1.4%), kappa scores (up to 0.018), and robust performance across subjects, using five neural network architectures including a state-of-the-art model (ATCNet). Notably, ACML requires minimal computational overhead and no task-specific hyperparameter tuning, ensuring compatibility with diverse applications. This method offers a robust and efficient solution for advancing EEG-based motor imagery classification, with potential applications in real-time brain-computer interface systems and neurorehabilitation.},
}
@article {pmid41257158,
year = {2025},
author = {Ali, U and Khan, JA and Ahsan, MT and Altaf, B and Azreen, S and Alamu, OS and Rana, MS},
title = {Brain-Computer Interfaces in the Rehabilitation of Stroke and Spinal Cord Injury: A Systematic Review and Meta-Analysis of Clinical Efficacy.},
journal = {Cureus},
volume = {17},
number = {10},
pages = {e94833},
pmid = {41257158},
issn = {2168-8184},
abstract = {Brain-computer interfaces (BCIs) have emerged as innovative tools for neurorehabilitation, enabling patients with stroke and spinal cord injury (SCI) to engage in task-specific training through direct neural control of external devices. Despite growing evidence, the overall clinical efficacy of BCIs in functional recovery remains debated. This systematic review and meta-analysis evaluated the effectiveness of BCI-based rehabilitation on motor recovery in stroke and SCI, with a focus on upper and lower limb function. We systematically searched PubMed, EMBASE, Web of Science, and Cochrane CENTRAL for clinical trials published between January 2008 and October 2025, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Eligible studies included randomized controlled trials and controlled interventional trials employing BCI interventions for motor rehabilitation. Risk of bias was assessed with RoB-2 and ROBINS-I. Meta-analysis was performed using a random-effects model. Seventeen studies met the inclusion criteria, comprising both stroke (acute, subacute, and chronic phases) and SCI populations. The pooled analysis demonstrated a significant mean difference of 3.26 points on the Fugl-Meyer Assessment for Upper Extremity (FMA-UE) in favour of BCI interventions (95% CI: 2.73-3.78, p < 0.001). Heterogeneity was negligible (I[2] = 0%). Subgroup analyses suggested that combining BCI with functional electrical stimulation or robotics yielded larger gains. BCI-based rehabilitation significantly improves motor function in stroke and SCI populations, with effect sizes exceeding the minimal clinically important difference for FMA-UE. These findings highlight the translational potential of BCIs as adjunctive therapies in neurorehabilitation. Larger, multicenter trials with standardised protocols are warranted to establish long-term efficacy and guide clinical integration.},
}
@article {pmid41256527,
year = {2025},
author = {Shah, NP and Krasa, BA and Kunz, E and Hahn, N and Kamdar, F and Avansino, D and Hochberg, LR and Henderson, JM and Sussillo, D},
title = {Improved interpretability in LFADS models using a learned, context-dependent per-trial bias.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.10.03.680303},
pmid = {41256527},
issn = {2692-8205},
abstract = {The computation-through-dynamics perspective argues that biological neural circuits process information via the continuous evolution of their internal states. Inspired by this perspective, Latent Factor Activity using Dynamical systems (LFADS, [1]) identifies a generative model consistent with the neural activity recordings. LFADS models neural dynamics with a recurrent neural network (RNN) generator, which results in excellent fit to the data. However, it has been difficult to understand the dynamics of the LFADS generator. In this work, we show that this poor interpretability arises in part because the generator implements complex, multi-stable dynamics. We introduce a simple modification to LFADS that ameliorates issues with interpretability by providing an inferred per-trial bias (modeled as a constant input) to the RNN generator, enabling it to contextually adapt a simpler dynamical system to individual trials. In both simulated neural recordings from pendulum oscillations and real recordings during arm movements in nonhuman primates, we observed that the standard LFADS learned complex, multi-stable dynamics, whereas the modified LFADS learned easier-to-understand contextual dynamics. This enabled direct analysis of the generator, which reproduced at a single-trial level previous results shown only through more complex analyses at the trial average. Finally, we applied the per-trial inferred bias LFADS model to human intracortical brain computer interface recordings during attempted finger movements and speech. We show that modifying neural dynamics using linear operations of the per-trial bias addresses non-stationarity and identifies the extent of behavioral variability, problems known to plague BCI. We call our modification to LFADS as "contextual LFADS".},
}
@article {pmid41256173,
year = {2025},
author = {Candrea, DN and Angrick, M and Luo, S and Ganji, R and Coogan, C and Milsap, GW 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 = {Longitudinal study of gesture decoding in a clinical trial participant with ALS.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.09.26.25335804},
pmid = {41256173},
abstract = {Brain-computer interfaces (BCIs) have the potential to preserve or restore communication and device control in people with paralysis from a variety of causes. For people living with amyotrophic lateral sclerosis (ALS), however, the progressive loss of cortical motor neurons could theoretically pose a challenge to the stability of BCI performance. Here we tested the stability of gesture decoding with a chronic electrocorticographic (ECoG) BCI in a man living with ALS and participating in a clinical trial (ClinicalTrials.gov , NCT03567213). We evaluated offline decoding performance of attempted gestures over two periods: a 5-week period beginning roughly 2 years post-implant and a 6-week period ending roughly 5 months later. Decoder sensitivity was high in both periods (90 - 98%), while classification accuracy was 37 - 68% in the first period and worsened to 23 - 39% in the second. We investigated multiple frequency bands that were used as model features in both periods, and we observed reductions in high gamma band power (70 - 110 Hz) and between-class separation during the second period compared to the first. Over the 5-month period motor function did not appreciably decline. These results, albeit preliminary, suggest that declines in the neural population responses that drive ECoG BCI performance can occur without overt signs of disease progression in people living with ALS, and could serve as a biomarker for disease progression in the future.},
}
@article {pmid41255819,
year = {2025},
author = {Vooijs, M and Bassil, K and van den Brink, A and van Stuijvenberg, OC and Ramsey, NF and Jongsma, KR},
title = {Ethical, legal, and sociocultural considerations in neural device explantation: a systematic review.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1568800},
pmid = {41255819},
issn = {1662-4548},
abstract = {INTRODUCTION: Implantable neural devices, including brain-computer interfaces and spinal cord stimulators, hold significant therapeutic promise for conditions such as paralysis and chronic pain. However, the novelty of these technologies introduces unique ethical challenges. While much of the existing literature emphasizes development-related concerns such as device safety, the ethical issues surrounding explantation remain relatively underexplored.<br><br>METHODS: We conducted a systematic review to identify ethical, legal, and sociocultural considerations relevant to the explantation of neural devices. The review applied the IEEE BRAIN Neuroethics framework as a guiding structure for the categorization of the themes. A subsequent thematic analysis was performed to categorize and synthesize findings across studies.<br><br>RESULTS: Thematic analysis revealed that medical motives were the predominant factor in discussions of explantation, with 83% of studies citing medical complications as a central concern. Additional themes identified included changes in cognition and behavior, emotional well-being, lack of therapeutic benefit, identity, financial issues, autonomy, post-trial considerations, and neurorights.<br><br>DISCUSSION: Our findings underscore the multifaceted nature of neural device explantation, extending beyond purely medical considerations to include psychological, financial, legal, and sociocultural dimensions. These results highlight the necessity of interdisciplinary approaches to adequately address the broad spectrum of challenges associated with explantation.},
}
@article {pmid41255549,
year = {2025},
author = {Hyung, W and Kim, M and Kim, Y and Im, CH},
title = {DeepAttNet: deep neural network incorporating cross-attention mechanism for subject-independent mental stress detection in passive brain-computer interfaces using bilateral ear-EEG.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1685087},
pmid = {41255549},
issn = {1662-5161},
abstract = {INTRODUCTION: Electroencephalography (EEG)-based mental stress detection has the potential to be applied in diverse real-world scenarios, including workplace safety, mental health monitoring, and human-computer interaction. However, most previous passive brain-computer interface (BCI) studies have employed EEG recorded during the performance of specific tasks, making the classification results susceptible to task engagement effects rather than reflecting stress alone. To address this limitation, we introduce a rest-versus-rest paradigm that compares resting EEG recorded immediately after exposure to a stressor with that recorded after meditation, thereby isolating mental stress from the task-related confounds. EEG recording setups were designed under the assumption of bilateral ear-EEG, a compact and discreet form factor suitable for real-world applications. Furthermore, we developed a novel subject-independent deep learning classifier tailored to model interhemispheric neural dynamics for enhanced mental stress detection performance.<br><br>METHODS: Thirty-two adults participated in the experiment. To classify mental stress status in a subject-independent manner, we proposed DeepAttNet, a deep learning model based on cross-attention and pointwise temporal compression, specifically designed to effectively capture left and right hemispherical interactions. Classification performance was assessed using eight-fold subject-level cross-validation against conventional deep learning models, including EEGNet, ShallowConvNet, DeepConvNet, and TSception. Ablation studies evaluated the impact of the cross-attention and/or pointwise compression modules.<br><br>RESULTS: DeepAttNet achieved the highest average accuracy and macro-F1 values, with performance declining when either the cross-attention or pointwise compression module was removed in the ablation studies. Explainability analyses indicated lower cross-attention entropy with stronger directional ear-to-ear asymmetry under stress, and temporal occlusion identified mid-late windows supporting stress decisions. Moreover, six of seven canonical scalp-EEG markers were FDR-significant for post-stressor vs. post-relaxation rest.<br><br>CONCLUSION: The proposed rest-versus-rest paradigm and DeepAttNet enabled robust, subject-independent mental stress detection with a fairly high accuracy using only two-channel EEG recordings. This approach is expected to offer a practical solution for continuous stress monitoring, potentially advancing passive BCI applications outside laboratory settings.},
}
@article {pmid41253791,
year = {2025},
author = {Shi, J and Chen, D and Zhao, X and Zhao, Z and Li, S and Xu, Y and Ding, T and Zhu, Z and Zhang, P and Ye, Q and Tang, Y and Zhang, P and Tao, B and Tang, Z},
title = {HEFMI-ICH: a hybrid EEG-fNIRS motor imagery dataset for brain-computer interface in intracerebral hemorrhage.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {1816},
pmid = {41253791},
issn = {2052-4463},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography ; *Cerebral Hemorrhage/rehabilitation/physiopathology ; Spectroscopy, Near-Infrared ; },
abstract = {This study introduces the first hybrid brain-computer interface dataset specifically designed for research on intracerebral hemorrhage (ICH) rehabilitation. It offers a novel data source through the synchronized acquisition of electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals. The dataset innovatively incorporated neural recordings from 17 normal subjects and 20 patients with ICH under standardized left-right hand motor imagery (MI) paradigms, featuring systematically collected and preprocessed dual-modality neural data. Beyond raw neural signals, the resource provides feature-engineered data optimized for classification algorithms and multidimensional signal decoding. The public availability of this dataset can facilitate the validation and optimization of MI decoding algorithms and advance the development of precision rehabilitation systems based on multimodal neural feedback.},
}
@article {pmid41253750,
year = {2025},
author = {Sun, X and Dias, L and Peng, C and Zhang, Z and Ge, H and Wang, Z and Jin, J and Jia, M and Xu, T and Guo, W and Zheng, W and He, Y and Wu, Y and Cai, X and Agostinho, P and Qu, J and Cunha, RA and Zhou, X and Bai, R and Chen, JF},
title = {Author Correction: 40 Hz light flickering facilitates the glymphatic flow via adenosine signaling in mice.},
journal = {Cell discovery},
volume = {11},
number = {1},
pages = {92},
doi = {10.1038/s41421-025-00845-6},
pmid = {41253750},
issn = {2056-5968},
}
@article {pmid41253390,
year = {2025},
author = {Ji, X and Deng, S},
title = {Cognitive Change as an Early Warning for Late-Life Depression: Implications for Population Health Screening Strategies.},
journal = {Population health management},
volume = {},
number = {},
pages = {},
doi = {10.1177/19427891251395738},
pmid = {41253390},
issn = {1942-7905},
abstract = {Cognitive decline and late-life depression are intertwined public health challenges for aging populations globally. To inform effective prevention, the current study investigated the dynamic temporal associations between multidimensional cognitive functions and depressive symptoms. Using four waves of longitudinal data (2013-2020) from a large panel study of older adults, the current study employed an integrated framework combining optimized dynamic time warping, cross-lagged panel models, and network analysis to model complex, lagged relationships. Results provided strong empirical support for the "cognition-first" hypothesis, with declines in several cognitive domains-notably temporal orientation, calculation, and immediate recall-acting as significant upstream predictors of subsequent depressive symptoms. A modest but significant protective feedback effect from positive affect to cognitive maintenance was also identified, while negative affect showed no significant predictive role sample of older adults who were cognitively and emotionally healthy at baseline. These findings offer preliminary empirical support for a strategic shift in population health management from reactive treatment toward proactive prevention. Based on these results, the current study discusses a conceptual framework for integrating cognitive screening into primary care to identify at-risk older adults, an approach that warrants further investigation and validation. This proactive approach could enable timely, low-cost interventions aimed at promoting positive affect and cognitive resilience, offering a potentially cost-effective strategy to mitigate the long-term burden of mental illness and advance the goals of healthy aging.},
}
@article {pmid41253019,
year = {2025},
author = {Fan, YS and Ye, M and Xu, Y and Xu, Y and Guo, J and Yang, M and Huang, W and Chen, H},
title = {Spatio-temporal information transition abnormalities across brain functional networks in early-onset schizophrenia.},
journal = {Schizophrenia research},
volume = {287},
number = {},
pages = {37-45},
doi = {10.1016/j.schres.2025.11.007},
pmid = {41253019},
issn = {1573-2509},
abstract = {Schizophrenia is a complex neurodevelopmental disorder characterized by widespread functional dysconnectivities across the brain. While disturbed temporal dynamics have been reported in schizophrenia, the information flow involving both temporal and spatial dynamics remains unclear. To capture spatio-temporal transition of brain information and to investigate these processes from a neurodevelopmental perspective, we collected resting-state functional MRI (rs-fMRI) data from 86 early-onset schizophrenia (EOS) patients (onset before age 18) and 91 demographically matched typically developing (TD) controls. We employed a non-homogeneous Markov model (NHMM) on dynamic functional connectivities derived from fMRI data. By means of transition probabilities, we modeled the switching of information flow in brain functional networks over time. Stationary probability vectors were used to describe the information convergence distribution of each network, while optimal reachable steps were used to characterize inter-network transmission efficiency. Compared to controls, EOS patients showed significantly increased stationary transition probabilities in the ventral attention network (VAN) and the dorsal attention network (DAN) but decreased probabilities in the default mode network (DMN). In terms of the dynamic interaction characteristics between networks, patients showed increased optimal reachable steps relative to controls, particularly in the VAN-DMN pathway. By integrating NHMM with neuroimaging data, this study revealed VAN- and DMN-involved information transition abnormalities in the early stage of schizophrenia spatio-temporal dynamics, offering novel insights into the developmental pathophysiology of the disorder. Our approach thus provides a novel analytical framework for quantifying spatio-temporal brain dynamics in neurodevelopmental disorders.},
}
@article {pmid41252716,
year = {2025},
author = {Yi, L and Yang, Y and Zeng, BF and Liu, X and Edel, JB and Ivanov, AP and Tang, L},
title = {Single-molecule quantum tunnelling sensors.},
journal = {Chemical Society reviews},
volume = {},
number = {},
pages = {},
doi = {10.1039/d4cs00375f},
pmid = {41252716},
issn = {1460-4744},
abstract = {Single-molecule sensors are pivotal tools for elucidating chemical and biological phenomena. Among these, quantum tunnelling sensors occupy a unique position, due to the exceptional sensitivity of tunnelling currents to sub-ångström variations in molecular structure and electronic states. This capability enables simultaneous sub-nanometre spatial resolution and sub-millisecond temporal resolution, allowing direct observation of dynamic processes that remain concealed in ensemble measurements. This review outlines the fundamental principles of electron tunnelling through molecular junctions and highlights the development of key experimental architectures, including mechanically controllable break junctions and scanning tunnelling microscopy-based approaches. Applications in characterising molecular conformation, supramolecular binding, chemical reactivity, and biomolecular function are critically examined. Furthermore, we discuss recent methodological advances in data interpretation, particularly the integration of statistical learning and machine learning techniques to enhance signal classification and improve throughput. This review highlights the transformative potential of quantum-tunnelling-based single-molecule sensors to advance our understanding of molecular-scale mechanisms and to guide the rational design of functional molecular devices and diagnostic platforms.},
}
@article {pmid41250658,
year = {2025},
author = {Gonzalez-Astudillo, J and de Vico Fallani, F},
title = {Feature Interpretability in Motor Imagery Brain Computer Interfaces: A Meta-Analysis Across Connectivity, Spatial Filtering, and Riemannian Methods.},
journal = {Brain connectivity},
volume = {},
number = {},
pages = {},
doi = {10.1177/21580014251392230},
pmid = {41250658},
issn = {2158-0022},
abstract = {Introduction: Brain-computer interfaces (BCIs) translate brain activity into commands, enabling applications in communication, control, and neurorehabilitation. A major challenge in noninvasive BCIs is balancing classification performance with interpretability, as many approaches prioritize accuracy while overlooking the neural mechanisms underlying their predictions. Methods: In this study, we conduct a meta-analysis of feature interpretability across widely used methods in motor imagery (MI)-based BCIs, including power spectral density, common spatial patterns (CSP), Riemannian geometry, and functional connectivity. Specifically, we explore how network topology and spatial organization contribute to MI decoding by investigating brain network lateralization. Results: Through evaluations on multiple EEG-based BCI datasets, our results confirm the superior classification performance of CSP and Riemannian methods. However, network lateralization provides stronger neurophysiological plausibility, revealing robust lateralization patterns in sensorimotor and frontal regions contralateral to imagined movements. Discussion: These findings underscore the potential of connectivity-based features as a complementary tool for enhancing interpretability, supporting the development of more transparent and clinically relevant MI-based BCIs. Impact Statement This study addresses a critical gap in motor imagery-based brain-computer interfaces (BCIs) by systematically evaluating and comparing the interpretability of widely used methods, including power spectral density, common spatial pattern, Riemannian geometry, and functional connectivity. By analyzing these approaches across wide-ranging datasets, we offer valuable insights into the underlying neural mechanisms driving their performance. Our findings contribute to enhancing the transparency and biological relevance of BCI systems, ultimately advancing the development of more clinically meaningful and neurophysiologically interpretable BCIs.},
}
@article {pmid41250191,
year = {2025},
author = {Plontke, SK and Lenarz, T and Toner, J and Keintzel, T and Sprinzl, G and Baumgartner, WD and Koitschev, A and Schmutzhard, J and Götze, G and Rahne, T and Knoelke, N and Busch, S and Corkill, S and Raffelsberger, T and Niederwanger, L and Magele, A and Schörg, P and Honeder, C and Liepins, R and Berger, N and Koci, V and Wiek, R},
title = {The Bonebridge BCI 602 Safety and Performance 1 Year Post-Implantation in Adults and Children: A Multicentric Post-Market Study.},
journal = {Otology &amp; neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology},
volume = {},
number = {},
pages = {},
doi = {10.1097/MAO.0000000000004688},
pmid = {41250191},
issn = {1537-4505},
abstract = {OBJECTIVE: To confirm the long-term safety and performance of the Bonebridge BCI 602 in patients suffering from conductive or mixed hearing loss (CMHL) or single-sided deafness (SSD) over a 12-month period post-implantation.<br><br>STUDY DESIGN: Multicentric, multinational, ambidirectional, observational Post-Market Clinical Follow-Up (PMCF) study.<br><br>SETTING: Eight tertiary referral hospitals.<br><br>PARTICIPANTS: Fifty-two participants in 3 categories: adults CMHL (N=24), children CMHL (N=17), and SSD (N=11; 9 adults and 2 children).<br><br>INTERVENTION: Participants were implanted with the Bonebridge BCI 602 device.<br><br>MAIN OUTCOME MEASURES: Outcome measures focused on sound field thresholds (SF), word recognition scores (WRS), speech reception thresholds (SRT) in both quiet and noise, adverse events, and subjective satisfaction (SSQ and AQoL questionnaires) at initial activation and at 3-month and 12-month post-implantation.<br><br>RESULTS: Safety was established by stable bone conduction (BC) thresholds and a low adverse event rate with no unanticipated events. Safety was underlined by clinically relevant improvements in the health-related assessment of Quality of Life (AQoL) questionnaire of mean+0.1 (adults and children CMHL) and +0.07 (SSD). Hearing significantly improved in sound field thresholds with mean functional gains of 24.05&#xb1;8.68 dB (adults CMHL), 21.34&#xb1;25.43 dB (children CMHL), and 32.89&#xb1;25.87 dB (SSD). Mean word recognition scores improved by 65.83&#xb1;28.62 percent points (PP) for adult CMHL and 65.77&#xb1;27.53 PP for children CMHL and speech reception thresholds (SRT) in quiet by 15.4&#xb1;9.34 dB and 19.96&#xb1;14.66 dB, respectively. SRT in noise improved by -5.57&#xb1;4.23 dB (adults; S0&#xb0;N0&#xb0;), -5.12&#xb1;5.08 dB (children, S0&#xb0;N0&#xb0;), and -3.05&#xb1;3.06 dB (SSD, SSSDNNH). Subjective hearing ability tested with the Speech, Spatial, and Qualities (SSQ) of Hearing questionnaire improved and was clinically relevant for the adult (+2.23) and children (+1.51) CMHL groups.<br><br>CONCLUSIONS: The Bonebridge BCI 602 demonstrates significant enhancements in hearing and speech understanding 12 months postoperatively, showing high user satisfaction and safety.},
}
@article {pmid41248054,
year = {2025},
author = {Xie, X and Mou, H and Chen, W and Zhang, S and Xu, Y and Cheng, R and Wang, M},
title = {Noninvasive Temporal Interference Electrical Stimulation for Spinal Cord Rehabilitation.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {224},
pages = {},
doi = {10.3791/68574},
pmid = {41248054},
issn = {1940-087X},
mesh = {*Spinal Cord Injuries/rehabilitation ; Humans ; *Spinal Cord Stimulation/methods/instrumentation ; *Electric Stimulation Therapy/methods/instrumentation ; },
abstract = {Spinal cord injury (SCI) can lead to permanent loss of motor, sensory, and autonomic functions, presenting a significant clinical challenge for rehabilitation. In addition to conventional rehabilitation approaches, epidural spinal cord stimulation (eSCI) is often used to enhance recovery. However, the invasive nature of eSCI limits patient acceptance and widespread application. Compared to traditional spinal cord stimulation, temporal interference (TI) stimulation offers a noninvasive approach to stimulate deep spinal cord regions, making it a promising technique for SCI treatment. A critical factor in achieving effective TI stimulation for SCI rehabilitation is the accurate placement of two electrode pairs on the skin surface to generate a high electric field envelope within the targeted spinal cord area. We propose a unique protocol that utilizes electric field simulations and parameter optimization to determine the optimal electrode placement for specific SCI regions. Additionally, this protocol provides a systematic description of how to efficiently implement the optimized electrode placement strategy in clinical TI stimulation.},
}
@article {pmid41245956,
year = {2025},
author = {Lu, Y and Jin, Z and Jian, Y and Kong, D and Zhou, H and Xu, Y and Cao, R and Xia, Z and Yang, F and Wu, Q and Gao, Y and Cui, A and Yang, S and Zheng, N and Bang, J and Yang, G and Ko, SH and Yang, H and Xu, K},
title = {Metal-hydrogel chelation interfaces for ultrasoft and bidirectional bioelectronics.},
journal = {National science review},
volume = {12},
number = {11},
pages = {nwaf399},
doi = {10.1093/nsr/nwaf399},
pmid = {41245956},
issn = {2053-714X},
abstract = {Emerging demand in soft bioelectronic systems poses critical challenges in stiffness control and end-to-end connections due to the huge modulus difference in various components. Here, a bidirectional electrical interface of hydrogel and metal electrodes to bridge soft skin/tissue and data collection circuits is enabled by coordination interactions. The dual-mode chelation including internal chelation and surface chelation effectively configures the cross-linking structure of hydrogel, as well as enhances the binding interface of metal-hydrogel complex surfaces. Internally, strong chelation competes with esterification, yielding tissue-like softness of hydrogel with an ultra-low modulus of ∼339.9 Pa. Externally, the hydrogel passivates the combined metal surfaces, promoting the formation of interlocked structures between metal oxide nanoislands, achieving a high binding strength of ∼1.95 MPa without compromising electrical conductivity. The stable electrical interconnections via hybrid interfacial bonding enable high signal-to-noise ratio signal recordings from the skin, neural surfaces and brain, maintaining reliable performance, even under mechanical disturbances. This work provides an effective strategy for achieving mechanically and electrically robust hybrid bioelectronic interfaces, advancing their applications in capturing both in vitro and in vivo electrical signals.},
}
@article {pmid41245196,
year = {2025},
author = {Wang, KJ and Vinjamuri, R and Alimardani, M and Kumar Reddy, T and Mao, ZH},
title = {Editorial: NeuroDesign in human-robot interaction: the making of engaging HRI technology your brain can't resist.},
journal = {Frontiers in robotics and AI},
volume = {12},
number = {},
pages = {1699371},
doi = {10.3389/frobt.2025.1699371},
pmid = {41245196},
issn = {2296-9144},
}
@article {pmid41242443,
year = {2025},
author = {Mir, M and Badea, I and Wilson, LD},
title = {Hierarchical chitosan-lignocellulosic duplex system: An in vitro evaluation of controlled release, anti-pathogenic and hemostatic effects.},
journal = {International journal of biological macromolecules},
volume = {},
number = {},
pages = {149018},
doi = {10.1016/j.ijbiomac.2025.149018},
pmid = {41242443},
issn = {1879-0003},
abstract = {Critical design challenges that affect novel drug delivery systems concern chemical processing and tissue compatibility of source materials, which highlight the need for sustainable, biocompatible materials and versatile manufacturing methods. This investigation leverages the unique surface chemistry of biomass-derived lignocellulose substrates to form biocomposite frameworks with effective antipathogenic and hemostatic properties via noncovalent synthesis. Complementary electrostatic interactions support a highly porous biocomposite framework, according to spectral (IR, Raman and NMR) and microscopy results. Biocomposite complexes with antipathogenic agents (gentamicin and rifamycin salts) are revealed by kinetic release profiles, as noted by an initial burst and sustained release profile under physiological conditions. In vitro biocompatibility was demonstrated by MTT cell viability assays (ca. 90 % after 48 h). Anti-pathogenic effects are revealed by agar diffusion assays with E. coli (up to 16 mm inhibition zones). In vitro blood sorption, cell adhesion and blood clotting index (BCI) results of biocomposites reveal impressive blood absorption capacity (ca. 10-fold; w/w) with good cell adhesion and efficient hemostatic properties with BCI below 2 %. This study challenges the current limits of specialized biomedical applications of lignocellulose fiber-chitosan biocomposites via in-vitro results at bioactive interfaces for anti-infective targeted drug delivery and trauma management.},
}
@article {pmid41242344,
year = {2025},
author = {Huang, Y and Lin, Z and Huang, J and Chen, T and Liu, T},
title = {Correlating the Evans index and bicaudate index with ventricle volume at the three kinds of scanning baselines.},
journal = {Brain research bulletin},
volume = {},
number = {},
pages = {111641},
doi = {10.1016/j.brainresbull.2025.111641},
pmid = {41242344},
issn = {1873-2747},
abstract = {PURPOSE: Evans' Index (EI) and Bicaudate Index (BCI), as two-dimensional linear indexes, are commonly used to evaluate ventricle size. This study is investigated the differences in linear measures at the three kinds of scanning baselines and their correlations with ventricle volume.<br><br>METHODS: In 186 healthy volunteers,117 hydrocephalus patients with complete skull and 72 hydrocephalus patients without complete skull, the linear indexes, intracranial volume and ventricle volume were calculated by 3D Slicer. Wilcoxon rank test was used for comparisons of the linear indexes at the scanning baselines respectively. Spearman analysis was applied for the correlations between linear indexes and ventricle volume respectively.<br><br>RESULTS: There were statistical differences in the linear indexes of the three scanning baselines. Comparison of the linear indexes in people from three groups, the difference of linear indexes was minimum at Reid's base line (RBL), but max at supraorbitomeatal line (SML). Compared with the third and the fourth ventricle, the linear indexes had a stronger correlation with lateral ventricle volume or total ventricle volume. On the other hand, EI at RBL had a stronger correlation with ventricle volume, compared with other two kinds of scanning baselines.<br><br>CONCLUSION: For consistent and representative linear measurements, we recommend using the Evans Index at Reid's baseline (RBL). However, the correlations between linear indices and ventricular volume were only modest, underscoring the limitation of 2D indices for precise volumetric assessment.},
}
@article {pmid41241353,
year = {2025},
author = {Li, G and Said, FM and Liang, J and Li, Y and Jing, Z},
title = {Fabrication of dual physically cross-linked agarose-based double network composite hydrogels with antibacterial and hemostatic properties for infected wound healing.},
journal = {International journal of biological macromolecules},
volume = {},
number = {},
pages = {149011},
doi = {10.1016/j.ijbiomac.2025.149011},
pmid = {41241353},
issn = {1879-0003},
abstract = {An agarose-based double network composite hydrogel with good mechanical, antibacterial, and hemostatic properties was synthesized to accelerate the healing of infected wounds. The double network composite hydrogel was fabricated by hydrogen bonding between poly(ACG-co-NBAA) chains generated by free radical polymerization and helical conformation formed by the agarose-graft-gelatin chains in the presence of Zn-MOF. The synthesized hydrogels exhibited a three-dimensional network structure and excellent pH sensitivity. The disintegration of hydrogen bonds in the hydrogel network caused the increase of swelling ratio of the hydrogels as the pH rose. The mechanical and antibacterial properties of agarose-based composite hydrogels can be well adjusted by changing their composition. The special structure of the hydrogels and Zn-MOF embedding endowed them with good antibacterial properties against S. aureus and E. coli. The results of the hemostasis experiment found that the agarose-based composite hydrogels had a lower BCI value, and the mice treated with the hydrogel sample had lower blood loss and shorter hemostasis time, indicating that the synthesized hydrogels had good hemostatic performance. In addition, a full-layer skin wound infection model demonstrated that the agarose-based composite hydrogels can accelerate the healing of infected wounds, and the wound healing rate of mice treated with the hydrogel sample can reach 97.6 ± 0.8 % at 14 days. Therefore, a biocompatible agarose-based double network composite hydrogel with good mechanical, antibacterial, and hemostatic properties, is expected to be used as a medical dressing to promote the healing of infected wounds.},
}
@article {pmid41241070,
year = {2025},
author = {Kong, L and Wang, H and Sang, R and Saeed, S and Shen, Y and Lai, J and Hu, S},
title = {Down-regulated expressions of LOC151174, GSTT1, and IFI27L1 in the peripheral blood exhibit the biological and immunological features of major depressive disorder.},
journal = {Journal of affective disorders},
volume = {},
number = {},
pages = {120687},
doi = {10.1016/j.jad.2025.120687},
pmid = {41241070},
issn = {1573-2517},
abstract = {BACKGROUND: Mechanism of major depressive disorder (MDD), especially the associations between genetic and peripheral immune changes remain to be elucidated.<br><br>METHODS: Databases including Gene Expression Omnibus and GWAS Catalog were investigated and analyzed via differential analyses and summary data-based Mendelian randomization to identify feature genes. Functional annotations, gene-gene interaction network were performed, with immune functions and immune infiltration further analyzed.<br><br>RESULTS: Three RNA sequencing datasets and seven genome-wide association study datasets were considered eligible. Genes including LOC151174 (logFC&#x202f;=&#x202f;-0.704, Padjusted&#x202f;=&#x202f;0.024), GSTT1 (logFC&#x202f;=&#x202f;-0.713, Padjusted&#x202f;=&#x202f;0.028), and IFI27L1 were identified (logFC&#x202f;=&#x202f;-0.138, Padjusted&#x202f;=&#x202f;0.043; betaSMR&#x202f;=&#x202f;-0.018, PSMR&#x202f;=&#x202f;6.714e[-13], PHEIDI&#x202f;=&#x202f;0.058), and all showed a down-regulated trend in the background of MDD. Functioning pathways including cytokine receptor interaction, ABC transporters, and Ca[2+] signaling pathways were shared by more than one feature gene. As for immune function, scores of antigen presenting cell co-inhibition, natural killer cells, and T cell co-inhibition were significantly higher in the group with low-expression of GSTT1 (P&#x202f;<&#x202f;0.001), score of T cell co-inhibition was higher in the group with high-expression of IFI27L1 (P&#x202f;<&#x202f;0.01), and score of dendritic cells was higher in the group with high-expression of both LOC151174 (P&#x202f;<&#x202f;0.01) and IFI27L1 (P&#x202f;<&#x202f;0.05). Macrophages M0 showed the highest significance of immune infiltration (P&#x202f;<&#x202f;0.001). Moreover, expression of GSTT1 showed significant correlation with the activity of plasma cells (R&#x202f;=&#x202f;-0.2, P&#x202f;=&#x202f;0.041) and activated memory CD4(+) T cells (R&#x202f;=&#x202f;-0.2, P&#x202f;=&#x202f;0.045).<br><br>CONCLUSION: Our work indicates that peripheral expressions of LOC151174, GSTT1, and IFI27L1 might be correlated with MDD particularly through peripheral immune abnormalities.},
}
@article {pmid41240747,
year = {2025},
author = {Rosenblum, D and Karandinos, G and Unick, J and Cauchon, D and Ciccarone, D},
title = {Early evidence of the effects of xylazine-adulterated fentanyl in Ohio.},
journal = {The International journal on drug policy},
volume = {146},
number = {},
pages = {105066},
doi = {10.1016/j.drugpo.2025.105066},
pmid = {41240747},
issn = {1873-4758},
abstract = {BACKGROUND: Xylazine is becoming a prevalent fentanyl adulterant in the US. It has been associated with severe wounds and withdrawal symptoms. However, its impact on fatal overdose rates is poorly understood.<br><br>METHODS: Poisson and ordinary least squares regression analyses are used to estimate the relationship between xylazine prevalence and unintentional overdose death and death rates at the county-month level in Ohio from April through December 2023. Xylazine prevalence is calculated from the Ohio Bureau of Criminal Investigation's (BCI) Crime Lab Data, and mortality data is from the Ohio Department of Health.<br><br>RESULTS: Xylazine prevalence is positively correlated with overdose deaths and death rates in large population counties. Xylazine adulteration is associated with 319 more overdose deaths [95 percent CI: 147-491 deaths], 10 percent of all unintentional overdose deaths in Ohio, over the nine-month period. Our estimates predict that if all fentanyl had been adulterated with xylazine over these nine months, this would have led to an additional 519 deaths.<br><br>DISCUSSION: Although the data covers a limited time period, our estimates provide evidence that xylazine-adulterated fentanyl is likely to lead to additional overdose deaths as it continues to spread across the US, blunting the initial signs of a declining trend in overdose deaths. If the findings can be extrapolated to the rest of the country, it is likely that overdose deaths would have fallen more substantially in 2023 if xylazine had not already been so prevalent in large parts of the US.},
}
@article {pmid41239595,
year = {2025},
author = {Deng, X and Lai, K and Huang, W and Liao, F},
title = {A retrospective study on the clinical efficacy of pneumatic hand rehabilitation devices in managing post-stroke chirospasm following ischemic stroke.},
journal = {Medicine},
volume = {104},
number = {46},
pages = {e45389},
doi = {10.1097/MD.0000000000045389},
pmid = {41239595},
issn = {1536-5964},
support = {2022A01146//Research on Wearable Brain-Computer Interface for Robotic Rehabilitation of Hand Function after Stroke/ ; },
mesh = {Humans ; Retrospective Studies ; Male ; Female ; Middle Aged ; Aged ; *Ischemic Stroke/complications ; *Hand/physiopathology ; *Stroke Rehabilitation/methods/instrumentation ; *Muscle Spasticity/etiology/rehabilitation ; Activities of Daily Living ; Treatment Outcome ; Quality of Life ; *Intermittent Pneumatic Compression Devices ; },
abstract = {Chirospasm is a common sequela of ischemic stroke (IS), often resulting in substantial impairment of hand function and quality of life. Although conventional rehabilitation can partially improve motor recovery, it is often insufficient in effectively reducing spasticity and edema, thereby necessitating adjunctive interventions. This retrospective study aimed to evaluate the effectiveness of a pneumatic hand rehabilitation device in improving hand function and alleviating spasticity in IS patients with chirospasm. Clinical data from 76 patients with chirospasm following IS, treated at our institution between March 2022 and March 2024, were retrospectively analyzed. Patients were divided into 2 groups based on treatment modality: a control group receiving standard rehabilitation therapy and an intervention group receiving additional treatment with a pneumatic hand rehabilitation device. Key evaluation indicators included metacarpophalangeal joint circumference, finger swelling volume, hand function scores (STEF, Fugl-Meyer, MFT), spasticity grading (Ashworth and MAS), neurological deficit indices, pain scores (Visual Analogue Scale), and activities of daily living (ADL). Clinical efficacy was assessed at baseline and after 8 weeks of treatment. Both groups demonstrated improvements after treatment; however, the intervention group showed significantly greater reductions in joint circumference, finger swelling, and muscle tone, as well as higher improvements in hand function scores (P < .05). Notably, Visual Analogue Scale scores were lower and ADL scores were higher in the intervention group. Furthermore, the total effective rate in the intervention group (94.74%) was significantly higher than that in the control group (76.32%). This retrospective analysis suggests that pneumatic hand rehabilitation devices, when integrated with conventional therapy, are more effective in reducing spasticity, alleviating hand edema, improving hand motor function, and enhancing quality of life in post-IS patients with chirospasm. These findings support the broader clinical application of such devices in stroke rehabilitation programs.},
}
@article {pmid41238552,
year = {2025},
author = {Zou, Q and Zou, G and Wang, S and Wang, Y and Xu, J and Long, Y and Zhou, S and Wu, X and Yang, G and Qin, L and Su, ZH and Cui, Z and Zuo, XN and Tang, X and Rao, H and Gao, JH},
title = {Cortical hierarchy underlying homeostatic sleep pressure alleviation.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {10014},
pmid = {41238552},
issn = {2041-1723},
mesh = {Humans ; Adult ; *Homeostasis/physiology ; Magnetic Resonance Imaging ; Male ; *Sleep/physiology ; Female ; Electroencephalography ; Sleep Deprivation/physiopathology ; *Cerebral Cortex/physiology/diagnostic imaging ; Young Adult ; Wakefulness/physiology ; Oxygen/blood ; Brain Mapping ; Middle Aged ; },
abstract = {Sleep dissipates accumulated sleep pressure and restores brain function, yet how this recovery unfolds across the cortical hierarchy remains unclear. Here, we record simultaneous electroencephalogram (EEG) and blood oxygen level-dependent (BOLD) functional magnetic resonance imaging data from 130 healthy adults to map spatial patterns underlying sleep pressure alleviation. Compared to wakefulness, sleep elicits spatially heterogeneous changes in BOLD fluctuation along a sensory-association cortical gradient. The magnitude of these sleep-wake differences correlates with individual slow-wave activity and is most pronounced during the first hour of sleep. As slow waves dissipates, these hierarchical differences are progressively downscaled, implicating homeostatic regulation in sculpting cortical plasticity. In addition, the homeostatic regulation of BOLD fluctuation amplitude is spatially associated with the regional distribution of glycolysis. Finally, recovery sleep reinstates hierarchical BOLD dynamics after sleep loss in an independent sleep deprivation study. These findings consistently suggest a cortical hierarchy underlying the dynamic changes in sleep homeostasis.},
}
@article {pmid41237236,
year = {2025},
author = {Li, F and Wang, G and Genon, S and Eickhoff, SB and He, R and Yi, C and Dong, D and Yao, D and Jiang, L and Wu, W and Xu, P},
title = {Mapping neurophysiological and molecular profiles of heterogeneity and homogeneity in schizophrenia-bipolar disorder.},
journal = {Science advances},
volume = {11},
number = {46},
pages = {eadz0389},
pmid = {41237236},
issn = {2375-2548},
mesh = {Humans ; *Bipolar Disorder/physiopathology/metabolism/diagnosis ; *Schizophrenia/physiopathology/metabolism/diagnosis ; Adult ; Male ; Female ; Electroencephalography ; Machine Learning ; Middle Aged ; Psychotic Disorders/physiopathology ; Brain Mapping ; },
abstract = {The heterogeneity of psychotic disorders leads to instability in subjectively defined diagnoses. This study used a machine learning framework termed common orthogonal basis extraction (COBE) to decompose electroencephalography-based functional connectivity (FC) in patients with psychotic bipolar disorder (PBD), schizophrenia (SCZ), and schizoaffective disorder (SAD) into individualized and shared subspaces. The results demonstrated that individualized FCs captured disease heterogeneity and predicted symptom severity more accurately than raw FCs, while shared FCs revealed diagnosis-specific abnormalities and achieved an accuracy of 79.30% in differentiating PBD, SCZ, and SAD. Furthermore, molecular decoding implicated regionally selective serotonin systems and astrocytes in the neurobiological differences among disorders, suggesting disorder-specific pharmacological targets. Critically, these findings were replicated in an independent cohort, confirming the effectiveness of the COBE framework in mining neurophysiological and molecular profiles of schizophrenia-bipolar disorder. These findings advance mechanistic understanding of psychotic disorders and offer a promising avenue toward objective, clinically relevant tools for psychotic evaluation.},
}
@article {pmid41236698,
year = {2025},
author = {Cai, M and Liu, H and Shao, C and Li, T and Jin, J and Liang, Y and Wang, J and Cao, J and Yang, B and He, Q and Shao, X and Ying, M},
title = {Metabolomics and metabolites in cancer diagnosis and treatment.},
journal = {Molecular biomedicine},
volume = {6},
number = {1},
pages = {109},
pmid = {41236698},
issn = {2662-8651},
support = {U23A20534//National Natural Science Foundation of China/ ; LR23H310001//Science Fund for Distinguished Young Scholars of Zhejiang Province/ ; LR24H310001//Science Fund for Distinguished Young Scholars of Zhejiang Province/ ; 2024C03181//"Pioneer" and "Leading Goose" R&amp;D Program of Zhejiang Province/ ; 226-2024-00178//Fundamental Research Funds for the Central Universities/ ; },
mesh = {Humans ; *Metabolomics/methods ; *Neoplasms/diagnosis/metabolism/therapy ; Biomarkers, Tumor/metabolism ; *Metabolome ; Prognosis ; },
abstract = {Cancer is a leading cause of death worldwide. Metabolic reprogramming in cancers plays an important role in tumor initiation, malignant progression and therapeutic response. Based on this, significant progress has been made in the development of the metabolite-based early cancer detection and targeted interventions. Over the past decade, metabolomics has been widely applied to detect metabolic alterations in tumor cells as well as their microenvironment. However, an up-to-date systematic review to summarize the current metabolomic and metabolites in cancer, especially their connections to cancer diagnostics/prognostic biomarkers and therapeutic strategies, is lacking. Here, we first introduced the platforms and analytical processes of metabolomics, as well as their application in different biological matrix of tumor patients. Then, we summarized representative cancer studies in which specific metabolites was found to be act as diagnostic or prognostic/stratification biomarkers. Furthermore, we reviewed the current therapeutic strategies targeting cancer metabolism, particularly the drugs/compounds that are either market-approved or in clinical trials, and also analyzed the potential of metabolites in personalizing precision treatment. Finally, we discussed the key challenges in this field, including the technical limitations of metabolomics and the clinical limitations of therapeutic targeting cancer metabolism, and further explored the future directions such as multi-omics perspective and lifestyle interventions. Taken together, we provides a comprehensive overview from technological platforms of metabolomics to translational applications of metabolites, facilitating the discovery of novel biomarkers and targeting strategies for precision oncology.},
}
@article {pmid41235174,
year = {2025},
author = {Ge, J and Wang, J and Zheng, X and Li, M and Wang, F and Xu, G},
title = {A multi-domain graph convolutional network-based prediction model for personalized motor imagery action.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1637018},
pmid = {41235174},
issn = {1662-4548},
abstract = {Motor imagery (MI)-based brain-computer interfaces (BCIs) offer a novel method to decode action imagination. Our previous study demonstrated that actions play a key role in causing individual differences. Cognitive EEG signals showed a positive correlation with MI, reflecting these differences and providing a foundation for predicting suitable MI actions for each individual. This study aimed to propose a multi-domain graph convolutional network (M-GCN) for predicting personalized MI action using cognitive data. The M-GCN extracts time, frequency, and spatial domain features from cognitive tasks to construct multi-domain brain networks using different EEG quantization methods according to the characteristics of the three domains. Subsequently, the M-GCN utilizes spectral GCN to learn the topology relationship between EEG channels by analyzing functional connection strength. Finally, for each action, the M-GCN can accurately map cognitive data to the corresponding MI action and output a personalized action for each subject. A subject-independent decoding paradigm with leave-one-subject-out cross-validation is adopted to validate the model on ten subjects. Compared to baseline and single-domain models, the M-GCN achieves the highest prediction accuracy of 73.60% (p = 7.1 × 10[-3]), improving by 15.87% (p = 2.0 × 10[-4]) and by 7.2% (p = 4.0 × 10[-4]), respectively. This study proves that the M-GCN can precisely predict personalized MI actions, reflecting the efficiency of the multi-domain feature fusion based on cognitive tasks and GCN and offering a novel method for personalized BCI.},
}
@article {pmid41235025,
year = {2025},
author = {Chen, ZJ and Huang, XL and Xia, N and Gu, MH and Xu, J and Lu, M and Chen, H and Xiong, CH and Chen, Y},
title = {Next-Generation Neurotechnologies Inspired by Motor Primitive Model for Restoring Human Natural Movement.},
journal = {Research (Washington, D.C.)},
volume = {8},
number = {},
pages = {0942},
pmid = {41235025},
issn = {2639-5274},
abstract = {Advances in neuroengineering and artificial intelligence are transforming the landscape of motor rehabilitation, aiming to restore human movement as natural as possible. In recent decades, more advanced interventions are increasingly achievable via hybrid robotic systems, neuroprosthetics, and brain-computer interfaces. However, a fundamental gap of these neurotechnologies remains in modeling the complexity of neuromotor control, particularly how the central nervous system coordinates high-dimensional motor outputs in naturalistic behaviors. Rooted in theoretical neuroscience, the motor primitive (MP) model proposes an adaptable framework to deconstruct and reproduce motor tasks through low-dimensional modules. Interestingly, recent studies have indicated that the MP model may reform current-generation neurotechnologies by digitally shaping the course of human-machine interaction. In this narrative review, we will critically examine conventional target settings and identify their limitations in guiding biomimetic control in neurotechnologies. We then introduce the MP model with its machine learning and physiological scaffolds for better understanding and replicating human natural movement. Finally, we will present its potential in facilitating the next-generation neurotechnologies across kinematic, muscular, and neural domains. By modeling motor control in human and neuroengineering, we believe that the MP-inspired paradigms can initiate a new era of intelligent, patient-specific, and naturalistic motor restoration for various neurological and traumatic diseases.},
}
@article {pmid41233249,
year = {2025},
author = {Yu, Y and Wang, Z and Kroemer, NB and Zhang, L and Lu, L and Sun, J},
title = {Closed-loop brain-body interface: integrating brain-computer interfaces and peripheral nerve stimulation for adaptive neuromodulation.},
journal = {Science bulletin},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.scib.2025.10.037},
pmid = {41233249},
issn = {2095-9281},
}
@article {pmid41232683,
year = {2025},
author = {Cai, X and Sun, W and Zheng, X and Ding, N and Luo, M and Tu, Y and Meng, D and Liu, Y and Ding, S and Yuan, B and Long, X},
title = {Safety and efficacy of low intensity transcranial ultrasound stimulation for depression: A single-blind randomized controlled clinical study.},
journal = {Journal of affective disorders},
volume = {},
number = {},
pages = {120666},
doi = {10.1016/j.jad.2025.120666},
pmid = {41232683},
issn = {1573-2517},
abstract = {AIMS: This study aimed to confirm the safety and effectiveness of transcranial ultrasound stimulation (TUS) in treating depression by targeting a subregion of the left dorsolateral prefrontal cortex (dlPFC).<br><br>METHODS: A single-blind, randomized, sham-controlled clinical study was conducted involving 24 patients with depression in the TUS group and 12 in the sham group. Participants underwent psychiatric assessments and functional MRI scans. We employed an MR-compatible transducer, integrating dual navigation through optical guidance and MR acoustic radiation force imaging, to accurately target Brodmann area 46 (BA46) of the left dlPFC. The treatment group received active TUS, while the sham group received identical treatment without energy output, followed by actual TUS treatment.<br><br>RESULTS: Following treatment, the TUS group exhibited significant improvements in depression and anxiety scores, as well as sleep quality, with benefits lasting up to four weeks. The sham group showed minor improvements after sham stimulation, but these became significant after subsequent real TUS treatment. Functional connectivity analysis revealed changes in the TUS group, particularly in connectivity with regions implicated in emotion processing, including the subgenual anterior cingulate cortex, ventral posterior cingulate cortex, and precuneus, all of which correlated with symptom improvements. Adverse effects of TUS were minimal and well-tolerated.<br><br>CONCLUSION: This study underscores the potential of low-intensity TUS as a safe and effective treatment for depression, with the capacity to modulate neural activity in targeted brain areas. Future research should emphasize optimizing TUS parameters and exploring its effects on other brain regions linked to depression.},
}
@article {pmid41232376,
year = {2025},
author = {Zhang, H and Xie, J and Liu, K and Liu, Y and Dong, W},
title = {Dual-TTFNet: An end-to-end dual-branch temporal and time-frequency fusion network for auditory attention decoding in steady state motion auditory evoked potential.},
journal = {Computers in biology and medicine},
volume = {199},
number = {},
pages = {111284},
doi = {10.1016/j.compbiomed.2025.111284},
pmid = {41232376},
issn = {1879-0534},
abstract = {Auditory attention decoding based on steady-state motion auditory evoked potential (SSMAEP) offers a promising pathway for developing auditory brain-computer interface (BCI) driven by auditory selective attention. However, achieving high decoding performance with strong interpretability remains a major challenge. To address this issue, we proposed an end-to-end dual-branch neural network that fuses temporal and time-frequency information (Dual-TTFNet) to enhance SSMAEP decoding performance. The model consisted of a temporal convolutional branch and a time-frequency branch with learnable S-transform convolutional kernels for modeling of time-frequency patterns. To further strengthen inter-branch interactions, bidirectional cross-branch EEG channel attention mechanism and attention mechanism-based Transformer was introduced to achieve deep integration of temporal and time-frequency representations. Experiments on two and three-target SSMAEP-BCI datasets demonstrate that Dual-TTFNet consistently outperforms state-of-the-art methods under various tasks, time windows, and EEG channel configurations. It achieved accuracies of 95.08 ± 7.46 % (two-class) and 91.50 ± 4.90 % (three-class) at 5 s, with information transfer rate of 7.94 ± 3.08 bits/min and 11.06 ± 2.35 bits/min, respectively. Ablation studies and visualization analyses further validated the crucial role of the attention mechanisms and S-transform kernels in enhancing feature discriminability and neural interpretability. Dual-TTFNet achieves a synergistic optimization of SSMAEP-BCI decoding performance and interpretability, demonstrating excellent generalization ability and application potential.},
}
@article {pmid41231905,
year = {2025},
author = {Ergün, E and Aydemir, &#xd6; and Korkmaz, OE},
title = {A novel scrolling text reading paradigm for improving the performance of multiclass and hybrid brain computer interface systems.},
journal = {PloS one},
volume = {20},
number = {11},
pages = {e0334784},
pmid = {41231905},
issn = {1932-6203},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; Male ; Female ; Adult ; *Reading ; Spectroscopy, Near-Infrared ; Young Adult ; Algorithms ; *Brain/physiology ; },
abstract = {A Brain-Computer Interface (BCI) enables direct communication between the brain and external devices, such as computers or prosthetic limbs. This allows the brain to send commands while receiving sensory feedback from the device. Despite their potential, the performance limitations of existing BCI systems have motivated researchers to improve their efficiency and reliability. To address this challenge, the present study introduces a novel BCI paradigm centered on a cognitive task involving the reading of scrolling text in four different directions: right, left, up and down. The primary objective was to explore the electroencephalography (EEG) and near-infrared spectroscopy (NIRS) signals within this framework and assess the potential of hybrid BCI systems based on this innovative paradigm. The experimental protocol involved eight participants performing tasks across four classes of scrolling text. To optimize system accuracy and speed, EEG and NIRS data were segmented into discrete temporal windows. Features were extracted using the Hilbert Transform, while classification was performed via the k-nearest neighbor algorithm. The proposed approach achieved a classification accuracy of 96.28% [Formula: see text] 1.30% for multi-class tasks, demonstrating the effectiveness of hybrid modalities. This study not only introduces a novel paradigm for hybrid BCI systems, but also validates its performance, providing a promising direction for advancing the field.},
}
@article {pmid41231687,
year = {2025},
author = {Chang, TY and Wang, JB and Tsai, YH and Tsao, Y and Yang, CH},
title = {A 40-nm 3.9mW, 200words/min Neural Signal Processor in Speech Decoding for Brain-Machine Interface.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBCAS.2025.3625650},
pmid = {41231687},
issn = {1940-9990},
abstract = {Brain-machine interface (BMI) technology enables the human brain to communicate directly with machines. This work presents a neural signal processor for real-time BMI, supporting translation from user's speech attempt to sentences. By employing speech attempt detection, the energy consumption is reduced by 46% and the number of channels for speech attempt detection can be decreased from 128 to 16. The proposed weight encoding, which leverages both sparse encoding and mixed-precision arithmetic, reduces the off-chip memory size of the neural network by 80%. Computation reordering decreases the processing latency by 55%. For the partial sum caching technique, the number of neural network operations is reduced by 25%. The processing element (PE) array in the neural network engine exploits both input and weight sparsity to lower the processing latency by 95%. By using the proposed mixed-precision multiplier in the PE array, the area is reduced by 27% compared with the PE array with the full precision. In the beam search engine, the proposed approximate top-k selection architecture exhibits 16× fewer comparators. The neural signal processor achieves speech decoding with a phone error rate of 16.6% and a word error rate of 23.5%. Fabricated in 40-nm CMOS, the chip achieves the maximum communication rate of 200 words/min, which is 16.7-to-42.6× faster than the state-of-the-art designs. This work is able to decode up to 125,000 words, which is not achievable by prior works that can only decode up to 31 characters.},
}
@article {pmid41230997,
year = {2025},
author = {Tang, R and Sun, C and Chang, J and Ju, Z and Si, Y and Yang, Y and Shi, Y and Wu, J and Ye, Y and Bao, K and Deng, Q and Wu, Y and Jian, J and Chen, Z and Wang, Y and Sun, H and Wang, Y and Ji, B and Lin, H and Li, L},
title = {Ambient-Stable NIR Nanolasing: Monolithic Integration of PbS CQDs on a Silicon Photonic Platform.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {},
number = {},
pages = {e16460},
doi = {10.1002/adma.202516460},
pmid = {41230997},
issn = {1521-4095},
support = {2024SDXHDX0005//"Pioneer" and "Leading Goose" R&amp;D Program of Zhejiang Province/ ; 62175202//National Natural Science Foundation of China/ ; 62205274//National Natural Science Foundation of China/ ; 2023GD003/110500Y0022303//Key Project of Westlake Institute for Optoelectronics/ ; 210230006022302/002//Research Center for Industries of the Future (RCIF) at Westlake University/ ; },
abstract = {Nanolasers based on colloidal quantum dots (CQDs), while transformative in the visible spectrum, face critical roadblocks in the near-infrared (NIR) regime due to material instability under ambient conditions and ultrafast Auger recombination in large NIR CQDs. Here, these limitations are addressed through zinc-doped PbS CQDs that suppress nonradiative decay, integrated with compact high-Q silicon nanobeam cavities to leverage the Purcell effect for efficiently guiding spontaneous emission into laser modes, thereby significantly reducing the threshold power. This work demonstrates a monolithic CQD-integrated silicon photonic platform that achieves NIR lasing under pulsed optical pumping, featuring a record narrow linewidth of 0.29 nm (0.15 meV) at 1579.20 nm and an ultralow threshold of 127 µJ cm[-2]. Notably, under continuous-wave (CW) pumping, the device exhibits cavity-filtered spontaneous emission with a sub-nanometer linewidth across the 1350-1600 nm spectrum. This emission showcases <6% peak power decay over 15 h at 300 K, robust performance up to 360 K, and negligible degradation after 250 days of ambient storage. By monolithically integrating solution-processed CQDs with CMOS-compatible silicon photonics, this platform establishes a reliable, scalable, and low-cost route toward multiwavelength on-chip nanolaser arrays in the NIR regime, unlocking transformative potential for compact photonic technologies in imaging, sensing, and communications.},
}
@article {pmid41230692,
year = {2025},
author = {Shao, X and Xia, Z and Cai, M and Shao, C and Bing, S and Wang, T and Du, W and Liu, J and Shen, D and Cao, J and Yang, B and He, Q and Xu, X and Zhang, J and Ying, M},
title = {Chromosomal rearrangement-enhanced mRNA stability drives the oncogenic potential of fusion genes in pediatric leukemia.},
journal = {Haematologica},
volume = {},
number = {},
pages = {},
doi = {10.3324/haematol.2025.288256},
pmid = {41230692},
issn = {1592-8721},
abstract = {Acute lymphoblastic leukemia (ALL), the most common type of pediatric leukemia, is frequently driven by fusion genes generated by chromosomal rearrangements. Compared with wild-type genes, many oncogenic fusions show increased expression and sustained functional activity that drives tumorigenesis. However, the mechanisms by which chromosomal rearrangements lead to functional enhancement remain largely elusive. In addition, although large-scale sequencing has identified numerous fusion events, the functional significance of most remains unclear. Here, we demonstrate that enhanced mRNA stability represents an important tumorigenic mechanism for oncogenic fusions, including classical PAX5 fusions. Based on this mechanism, we characterize a novel oncogenic fusion, STK38-PXT1, which exhibits upregulated STK38 mRNA levels and drives the development of ALL. Mechanistically, the increased mRNA stability results primarily from enhanced m6A modification of oncogenic fusions, which is attributable to "gene truncation" (as in PAX5 fusions) and "partner collaboration" (as in STK38-PXT1). Furthermore, the m6A reader IGF2BP3 is crucial for maintaining the high mRNA stability of oncogenic fusions. We further propose venetoclax as an innovative and clinically available therapy for ALL driven by these oncogenic fusions characterized by high mRNA stability. Our study not only highlights mRNA stabilization as a crucial mechanism by which oncogenic fusions to drive tumorigenesis, but also presents a promising therapeutic strategy for patients with ALL.},
}
@article {pmid41224809,
year = {2025},
author = {Zhang, J and Zhang, ZY and Wang, YL and Zhou, B and Xia, CY and Su, M and Li, CG},
title = {Unveiling the upper-limb functional recovery mechanisms in stroke patients using brain-machine interfaces: a near-infrared functional imaging-based study.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {39704},
pmid = {41224809},
issn = {2045-2322},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; *Upper Extremity/physiopathology ; *Stroke Rehabilitation/methods ; Middle Aged ; Spectroscopy, Near-Infrared/methods ; Aged ; *Recovery of Function/physiology ; *Stroke/physiopathology/diagnostic imaging ; },
abstract = {Upper limb dysfunction is highly prevalent among patients in the chronic stage of stroke. Brain-computer interface (BCI) technology, which creates a direct link between the brain's electrical signals and external devices, stroke patients with motor disabilities are able to perform BCI tasks for clinical rehabilitation. However, traditional BCI applications are often limited in their capacity to monitor the brain function of patients. In this study, functional near-infrared spectroscopy (fNIRS) was employed to observe changes in brain cortex activation patterns before and after BCI use in ischemic stroke patients with upper limb dysfunction. Thirty-four ischemic stroke patients with upper limb dysfunction meeting the inclusion criteria were selected and randomly assigned to either a treatment group or a control group using a random number table, with 17 patients in each group. During the study, 4 participants dropped out, leaving 30 patients for the final statistical analysis, 15 in each group. Both groups received routine upper limb rehabilitation training. Additionally, the treatment group underwent daily BCI training for 30 min, 5 days a week, for 4 consecutive weeks. Upper limb function was evaluated using the Fugl-Meyer assessment for upper extremity (FM), and daily living activities were assessed with the modified barthel index (MBI). The six regions of interest (ROIs) in the cortex for fNIRS measurement were the ipsilesional and contralesional primary motor cortex (PMC), supplementary motor area (SMA), and somatosensory motor cortex (SMC). The three time points of measurement were baseline (prior to any treatment), 2 weeks of treatment, and 4 weeks of treatment. fNIRS was used to detect the oxygenated hemoglobin values (HbO) in six ROIs at each time point. After treatment, both groups exhibited improvements in FM and MBI scores. The treatment group demonstrated significantly greater functional gains than the control group at both 2 and 4 weeks, as reflected in FM (T1T0: 5.867 ± 3.482 vs. 3.200 ± 2.077, P < 0.01, d = 0.93; T2T0: 13.533 ± 5.705 vs. 7.133 ± 2.503, P < 0.05, d = 1.45) and MBI scores (T1T0: 13.400 ± 7.129 vs. 8.133 ± 4.357, P < 0.05, d = 0.89; T2T0: 27.867 ± 10.106 vs. 16.467 ± 7.010, P < 0.05, d = 1.31). fNIRS data revealed that after 4 weeks, the treatment group showed significantly increased oxygenated hemoglobin levels in PMC and SMA compared to baseline (PMC: P < 0.001, d = 0.62; SMA: P < 0.001, d = 0.89), along with a more pronounced PMC activation and higher brain network efficiency relative to the control group (PMC: 0.019 ± 0.017 vs. 0.007 ± 0.005, P < 0.01, d = 1.01; network efficiency: P < 0.05). Moreover, improvements in brain network efficiency were positively correlated with gains in both FM and MBI scores across the cohort. Our study suggests that BCI treatment combined with conventional medical and rehabilitation therapy can effectively enhance motor function and activities of daily living in stroke patients with upper-limb dysfunction. Additionally, it can promote cortical activation in the ipsilesional PMC and SMA regions and improve the network efficiency between brain regions.},
}
@article {pmid41223449,
year = {2025},
author = {Jia, Z and Wang, H and Shen, Y and Hu, F and An, J and Shu, K and Wu, D},
title = {Magnetoencephalography (MEG) based non-invasive Chinese speech decoding.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae1ea2},
pmid = {41223449},
issn = {1741-2552},
abstract = {OBJECTIVE: As an emerging paradigm of brain-computer interfaces (BCIs), speech BCI has the potential to directly reflect auditory perception and thoughts, offering a promising communication alternative for patients with aphasia. Chinese is one of the most widely spoken languages in the world, whereas there is very limited research on speech BCIs for Chinese language.<br><br>APPROACH: This paper reports a text-magnetoencephalography (MEG) dataset for non-invasive Chinese speech BCIs. It also proposes a multi-modality assisted speech decoding (MASD) algorithm to capture both text and acoustic information embedded in brain signals during speech activities.<br><br>MAIN RESULTS: Experiment results demonstrated the effectiveness of both our text-MEG dataset and our proposed MASD algorithm.<br><br>SIGNIFICANCE: To our knowledge, this is the first study on multi-modality assisted decoding for non-invasive Chinese speech BCIs.},
}
@article {pmid41223103,
year = {2025},
author = {Sun, J and Meng, J and Wang, H and He, F and Jung, TP and Xu, M and Yu, H and Ming, D},
title = {Joint-Shrinkage Pattern Matching for Small-Sample and Imbalanced ERP Decoding in Brain-Computer Interfaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3632096},
pmid = {41223103},
issn = {1558-2531},
abstract = {Event-related potential (ERP)-based brain-computer interface (BCI) systems are approaching sub-microvolt-level resolution, enabling detailed decoding of sophisticated cognitive processes. This progress has increased the demand for robust classifiers. Current algorithms encounter two fundamental challenges when decoding ERPs: data scarcity and class imbalance. To address these challenges, we propose a joint-shrinkage pattern matching (JSPM) algorithm consisting of two modules. First, a novel joint-shrinkage spatial filter is constructed by integrating shrinkage-based regularization with the ℓℓ22,pp norm. This regularization approach effectively bridges the gap between complex structured regularization and implementation simplicity, which introduces automated regularization to enhance module robustness under data-scarce conditions. The ℓℓ22,pp-norm provides a flexible feature distance measurement, enabling adaptation to data quality variability. Second, a weighted template matching module mitigates decision boundary shift caused by class imbalance. Using error-related potentials (ErrPs) as representative signals, we validated the algorithm through comprehensive comparisons. JSPM significantly outperformed 14 state-of-the-art classifiers on one self-collected and two public ErrP datasets. With only 40 imbalanced training samples, it achieved up to 14.84% higher average balanced accuracy (bAcc) than competing methods, maintaining a 4.88% average bAcc advantage over its nearest competitor. Notably, JSPM significantly enhanced inter-class discriminability for ErrP features with approximately 1 μV amplitude, achieving a maximum bAcc enhancement of 8.80%compared to deep learning methods. Overall, JSPM effectively addresses small-sample and imbalanced ERP decoding in BCI systems, facilitating the transition from laboratory research to real-world applications.},
}
@article {pmid41222907,
year = {2025},
author = {Wen, H and Xu, M and Cui, S},
title = {Global research trends in brain-computer interfaces for Alzheimer's disease: a bibliometric perspective.},
journal = {International journal of surgery (London, England)},
volume = {},
number = {},
pages = {},
doi = {10.1097/JS9.0000000000004000},
pmid = {41222907},
issn = {1743-9159},
}
@article {pmid41222817,
year = {2025},
author = {Şahin, E and Özdemir, D},
title = {ThinkSTra: a transformer-driven architecture for decoding imagined speech from EEG with spatial-temporal dynamics.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {},
number = {},
pages = {},
pmid = {41222817},
issn = {1741-0444},
support = {125E067//Scientific and Technological Research Council of T&#xfc;rkiye (TUBITAK)/ ; },
abstract = {Brain-Computer Interfaces (BCIs) enable direct communication between the brain and external devices without requiring physical movement, offering a transformative solution particularly for individuals with impaired or lost motor functions. By providing an alternative communication pathway, BCIs hold considerable promise for both clinical interventions and cognitive neuroscience research. In this study, we introduce ThinkSTra, a novel Transformer-based framework for classifying inner speech commands from electroencephalography (EEG) signals. Unlike conventional models, ThinkSTra jointly captures the temporal dynamics and spatial distributions of neural activity, thereby enabling a more comprehensive representation of the complex structure inherent in EEG signals. We systematically evaluated ThinkSTra on multiple datasets, including the sentence-level TSEEG dataset and the Kumar EEG datasets encompassing character, digit, and visual object classification. To rigorously examine its robustness and generalizability, we additionally performed region- and channel-wise contribution analyses, conducted pretraining and cross-validation experiments, and visualized the learned feature representations using t-SNE. ThinkSTra consistently surpassed existing state-of-the-art approaches, achieving accuracies of 100% on sentence-level, 98.10% on character recognition, 98.34% on digit classification, and 99.5% on visual object tasks. Overall, this study advances inner speech decoding by introducing a robust Transformer-based framework and uncovering how distinct cortical regions contribute to this process, offering both methodological and neuroscientific insights for future brain-computer interfaces.},
}
@article {pmid41221369,
year = {2025},
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 = {Brain-computer interface control with artificial intelligence copilots.},
journal = {Nature machine intelligence},
volume = {7},
number = {9},
pages = {1510-1523},
pmid = {41221369},
issn = {2522-5839},
support = {DP2 NS122037/NS/NINDS NIH HHS/United States ; R01 NS121097/NS/NINDS NIH HHS/United States ; },
abstract = {Motor brain-computer interfaces (BCIs) decode neural signals to help people with paralysis move and communicate. Even with important advances in the past two decades, BCIs face a key obstacle to clinical viability: BCI performance should strongly outweigh costs and risks. To significantly increase the BCI performance, we use shared autonomy, where artificial intelligence (AI) copilots collaborate with BCI users to achieve task goals. We demonstrate this AI-BCI in a non-invasive BCI system decoding electroencephalography signals. We first contribute a hybrid adaptive decoding approach using a convolutional neural network and ReFIT-like Kalman filter, enabling healthy users and a participant with paralysis to control computer cursors and robotic arms via decoded electroencephalography signals. We then design two AI copilots to aid BCI users in a cursor control task and a robotic arm pick-and-place task. We demonstrate AI-BCIs that enable a participant with paralysis to achieve 3.9-times-higher performance in target hit rate during cursor control and control a robotic arm to sequentially move random blocks to random locations, a task they could not do without an AI copilot. As AI copilots improve, BCIs designed with shared autonomy may achieve higher performance.},
}
@article {pmid41219281,
year = {2025},
author = {Bhaskara, S and Shabari Girishan, KV and Murugaiyan, S and Dwivedi, AA and Krishnakumaran, R and Pandya, HJ},
title = {An L-shaped flexible neural implant for chronic ECoG signal acquisition in M2 region of control and Parkinsonian rat models.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {39461},
pmid = {41219281},
issn = {2045-2322},
support = {/WT_/Wellcome Trust/United Kingdom ; FG/PTCH-22-2098//Pratiksha Trust BCD-Moonshot project/ ; IA/TSG/23/1/600493//DBT/Wellcome Trust India Alliance (India Alliance) Team Science Grant (TSG)/ ; },
mesh = {Animals ; Rats ; Disease Models, Animal ; *Electrocorticography/methods/instrumentation ; *Electrodes, Implanted ; *Parkinson Disease/physiopathology ; Male ; Rats, Sprague-Dawley ; *Motor Cortex/physiopathology ; },
abstract = {Neural implants help understand neurological disorders and are actively used to study deep and cortical brain surface regions. Dealing with cortical surface regions is less complicated in clinical therapy than deep brain regions. Researchers are interested in identifying cortical surface region/s for a particular neurological disorder. Rodent models are extensively used in preclinical studies. Usually, microwires, screws, and grid-type implants are used for such studies, but they are not designed for specific rodent brain regions. Since the grids are typically standard in size, in some cases, the craniotomy required to implant the grid will be significantly bigger than the region of interest, which may pose challenges for chronic studies due to infection. Additionally, the grids may block the nearby brain regions in multisite studies, posing difficulty for another device to be implanted. In this study, a novel L-shaped surface neural implant with five electrodes (diameter: 400 μm) spanning a 1 mm × 3 mm area is fabricated using biocompatible Polyimide material for cortical surface studies. The overall thickness of the neural implant is around 25 μm. The average impedance of the electrodes is 18.315 kΩ at 1 kHz. A bilateral craniotomy is performed to place the neural implants in the secondary motor area for subdural chronic electrocorticography (ECoG) signal acquisition in control and hemi parkinsonian rats. After the recovery period, the ECoG signals are acquired using the Open BCI Cyton Daisy Biosensing board for two weeks from the rats.},
}
@article {pmid41218505,
year = {2025},
author = {Wu, X and Ge, H and Zhao, W and Thummavichai, K and Bi, L and Chen, B},
title = {Multi-functional 3D printed hydrogel electrodes for brain-computer interfaces and wearable sensing.},
journal = {Journal of colloid and interface science},
volume = {704},
number = {Pt 2},
pages = {139418},
doi = {10.1016/j.jcis.2025.139418},
pmid = {41218505},
issn = {1095-7103},
abstract = {In this study, a 3D printing-based polyvinyl alcohol (PVA)/κ-carrageenan (κ-CA)/ carbon nanotubes (CNTs) hydrogel composite (referred to as PCC) was developed for the fabrication of flexible electrodes, targeting applications in brain-computer interfaces (BCIs) and wearable strain sensors. The hydrogel composite exhibited excellent mechanical properties, including a tensile strength of 633 kPa, an elastic modulus of 243 kPa, and a maximum tensile strain of 283 %. In BCI tests, the PCC hydrogel electrode achieved a scalp contact impedance of 76.08 kΩ across five channels, with signal quality comparable to wet electrodes (3.06 μV at 13 Hz stimulation) and significantly higher than dry electrodes (2.16 μV). The decoding accuracy for the PCC hydrogel electrode was 78.2 % with a 1.25 s window length, comparable to the wet electrode, and the information transfer rate (ITR) reached 71.3 bits/min. Furthermore, the hydrogel demonstrated excellent strain sensing performance, with a gauge factor (GF) of 2.7 in the 0-75 % strain range and fast self-recovery, making it a promising material for dynamic wearable sensing devices. This work highlights the successful integration of material optimization and structural design, offering a new approach for development of next-generation flexible bioelectronic devices.},
}
@article {pmid41218224,
year = {2025},
author = {Kenyeres, B and Helmeczi, A and Pytel, &#xc1;},
title = {Efficacy of Transurethral Resection of the Prostate in Male Patients With Impaired Detrusor Contractile Function and Urinary Retention.},
journal = {Lower urinary tract symptoms},
volume = {17},
number = {6},
pages = {e70040},
pmid = {41218224},
issn = {1757-5672},
mesh = {Humans ; Male ; *Urinary Retention/surgery/physiopathology/etiology ; *Transurethral Resection of Prostate/methods/adverse effects ; Retrospective Studies ; Aged ; Urodynamics ; *Urinary Bladder, Underactive/surgery/physiopathology/complications ; Treatment Outcome ; Middle Aged ; *Prostatic Hyperplasia/surgery/complications ; Aged, 80 and over ; },
abstract = {OBJECTIVES: Detrusor underactivity (DUA) increasingly affects aging male patients with voiding symptoms, while its management remains challenging, with less favorable surgical outcomes compared to bladder outlet obstruction. Our aim was to evaluate the efficacy of TURP in male patients with urinary retention and unfavorable urodynamic findings.<br><br>MATERIALS AND METHODS: This retrospective, single-center study included 67 male patients undergoing TURP between September 2021 and September 2024 after a failed trial of voiding. Patients were divided into three groups labeled as detrusor acontractility (DA, n&#x2009;=&#x2009;18, voided without detrusor contraction), DUA (n&#x2009;=&#x2009;19, voided with BCI&#x2009;<&#x2009;100 and BOOI&#x2009;<&#x2009;20), or non-voiders (n&#x2009;=&#x2009;30, failed to urinate and lacked measurable detrusor contractions on pressure-flow study). Surgical success was defined as successful voiding with post-void residual (PVR) <&#x2009;150&#x2009;mL at 3&#x2009;months. Baseline parameters (PSA, prostate volume, cystoscopy and urodynamic findings), rate of surgical success, Patient Global Impression of Improvement (PGI-I) score and adverse events (subsequent surgeries and urinary tract infection) were registered and analyzed.<br><br>RESULTS: Overall 37 (55.2%) patients became catheter-free within 3&#x2009;months. The mean follow-up duration was 25.4&#x2009;&#xb1;&#x2009;9.6&#x2009;months. Surgical success was achieved in DA, DUA, and non-voider groups in 6 (33%), 13 (68.4%), and 18 (60%) cases, respectively, and a PGI-I score greater than 4 was reported by 35 (52.2%) patients. Multivariate analysis showed higher prostate volume as an independent predictor for failure (OR: 1.7; 95% CI: 1.010-2.940; p&#x2009;=&#x2009;0.046). Two patients developed stress urinary incontinence, and three required additional surgical intervention due to urethral stricture. Urinary tract infections occurred more frequently in the treatment failure group: Nine patients (30%) were hospitalized, and 16 (53%) required more than two antibiotic prescriptions within a 6-month period. In contrast, among the success group, only two patients (5.4%) were hospitalized, and none required frequent antibiotic therapy.<br><br>CONCLUSION: TURP offers a reasonable chance for catheter discontinuation in case of unfavorable urodynamic parameters. With careful patient selection in mind, surgery remains a viable option even in this patient population.},
}
@article {pmid41217924,
year = {2025},
author = {Liu, D and Li, S and Wang, Z and Li, W and Wu, D},
title = {SDDA: Spatial Distillation based Distribution Alignment for Cross-Headset EEG Classification.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3631604},
pmid = {41217924},
issn = {1558-2531},
abstract = {OBJECTIVE: A non-invasive brain-computer interface (BCI) enables direct interaction between the user and external devices, typically via electroencephalogram (EEG) signals. This paper tackles the problem of decoding EEG signals across different headsets, which is challenging due to differences in the number and locations of the electrodes.<br><br>METHODS: We propose a spatial distillation based distribution alignment (SDDA) approach for heterogeneous cross-headset transfer in non-invasive BCIs. SDDA uses first spatial distillation to make use of the full set of electrodes, and then input/feature/output space distribution alignments to cope with the significant differences between the source and target domains.<br><br>RESULTS: Extensive experiments on six EEG datasets from two BCI paradigms demonstrated that SDDA achieved superior performance in both offline unsupervised domain adaptation and online supervised domain adaptation scenarios, consistently outperforming 10 classical and state-of-the-art transfer learning algorithms.<br><br>SIGNIFICANCE: Our approach enables effective transfer between heterogenous EEG headsets, improving and expediting BCI calibration.},
}
@article {pmid41217916,
year = {2025},
author = {Wu, Z and Chen, Z and He, W and Xie, Q and Pan, J},
title = {Cross-Subject P300-Based Audiovisual BCI System via Continual Learning: A Clinical Application for Disorders of Consciousness.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3631664},
pmid = {41217916},
issn = {1558-0210},
abstract = {This study proposes an advanced cross-subject P300-based audiovisual brain-computer interface (BCI) system to assess consciousness levels and predict clinical outcomes in patients with disorders of consciousness (DOC). The system employs an audiovisual stimulus paradigm, integrating face photos and corresponding name sounds, to enhance the elicitation of P300 signals. It further incorporates a hybrid prototype-based continual learning method (HPC) to improve diagnostic accuracy and robustness. The HPC constructs P300 prototypes for each historical task and selectively integrates both similar and dissimilar prototypes when a new task is introduced. Dissimilar prototypes are hybridized and masked, while similar prototypes are merged via an attention mechanism, effectively preventing catastrophic forgetting. Experimental results demonstrate the efficacy of this approach, with the HPC achieving 98.33% accuracy in a P300 spelling task among healthy subjects and 95.50% accuracy in healthy controls within a clinical setting. Significantly, eight out of ten DOC patients exhibited notable accuracy, underscoring the system's clinical potential. This BCI system thus offers a robust and adaptable solution for assessing consciousness levels and predicting outcomes in DOC patients, contributing to enhanced clinical diagnosis and prognosis.},
}
@article {pmid41217794,
year = {2025},
author = {Filippov, MS and Pogonchenkova, IV and Kostenko, EV and Rassulova, MA and Makarova, MR and Egorov, PD},
title = {[Ideomotor training combining the use with integrated application of electromyostimulation and a robotic brain-computer interface in post-stroke upper limb dysfunction: a randomized controlled trial].},
journal = {Voprosy kurortologii, fizioterapii, i lechebnoi fizicheskoi kultury},
volume = {102},
number = {5},
pages = {5-19},
doi = {10.17116/kurort20251020515},
pmid = {41217794},
issn = {0042-8787},
mesh = {Humans ; Middle Aged ; Male ; Female ; *Upper Extremity/physiopathology ; *Stroke Rehabilitation/methods ; *Brain-Computer Interfaces ; *Robotics ; *Stroke/physiopathology/complications/therapy ; *Electric Stimulation Therapy/methods ; Aged ; },
abstract = {UNLABELLED: One of the leading causes that disrupt human interaction with the environment is upper limb (UL) dysfunction, which develops in 48-77% of cases after a stroke. The combination of electromyostimulation (EMS) with neurocomputer interface (NCI) technology demonstrates the greatest clinical effectiveness among various types of sensorimotor BOS, the study of which seems promising.<br><br>OBJECTIVE: To study the effect of combined use the integrated of EMS and robotic NCI on the functioning of UL in post-stroke spastic paresis in the early recovery period of ischemic stroke (IS).<br><br>MATERIAL AND METHODS: A randomized controlled trial involved 120 patients in the early recovery period of IS with moderate to severe spastic paresis of UL, with an average age of 57.43&#xb1;3.68 years. By simple randomization, the patients were divided into 4 groups of 30 people each, depending on the medical rehabilitation program (MR). All patients received a basic MR program: therapeutic gymnastics for 30 minutes; magnetic field therapy on the neck and collar area for 20 minutes; therapeutic massage for 20 minutes. The patients of the control group (GC) received only the basic program; The main group (MG) - interval complex multi-purpose EMS of the agonist muscles and antagonist muscles of the forearm in combination with the use of NCI with exoskeletons of both hands; comparison group 1 (CG-1) - training using a robotic NCI; comparison group 2 (CG-2) - EMS. The duration of the MR course is 2 weeks, daily, 5 days a week, 10 treatments for each factor. The effectiveness of MR was evaluated at three control points (T): after completion of 5 procedures (T1) and 10 procedures (T2), 3 months after completion of MR (T3). Assessment tools: Medical Research Committee Scale (MRCs), Modified Ashworth Scale (mAs), The Fugl-Meyer Assessment for upper extremity (FMA-UE), The Action Research Arm Test (ARAT).<br><br>RESULTS: Patients with MG demonstrated significant (p<0.05) positive dynamics of recovery of UL function at the end of the MR course and after 3 months. The increase in muscle strength in MG and CG-1 averaged 0.77 and 0.59 points (p<0.05) in the distal muscle group, in CG-2 (0.24 points) and GC (0.21 points), p>0.05 compared with baseline values. Only patients with MG (+7.7 points) achieved a clinically significant difference (&#x394;) in FMA-UE-total at the end of MR, while patients with CG-1 achieved &#x394;=+4.9 points. In patients with GC and CG-2, the values of &#x394; according to FMA-UE-total were comparable (+2.3 and 2.6 points, respectively). According to the ARAT test, only MG patients also achieved a clinically significant difference (+6.2 points). Patients with CG-1 - &#x394;=+3.5 points. In patients with GC and CG-2, &#x394; values were comparable (+1.3 and 2.2 points, respectively).<br><br>CONCLUSION: Ideomotor training with EMS in MR of impaired VC function in patients with IS, combining stimulation of visual, vestibular, and proprioceptive analyzers with training of cognitive functions, promotes regression of sensorimotor disorders of UL and restoration of manipulative activity.},
}
@article {pmid41216611,
year = {2025},
author = {Li, J and Chen, H and Liao, W},
title = {Mapping the white-matter functional connectome: a personal perspective.},
journal = {Psychoradiology},
volume = {5},
number = {},
pages = {kkaf028},
pmid = {41216611},
issn = {2634-4416},
abstract = {In contemporary neuroscience, mapping the human brain's functional connectomes is essential to understanding its functional organization. Functional organizations in the brain gray matter have been the subject of previous research, but the functional information in white matter (WM), the other half of the brain, has been relatively underexplored. However, the dynamics of functional magnetic resonance imaging (fMRI) have been reliably identified in the brain WM. This review summarizes current knowledge about task-free (resting-state) fMRI neuroimaging analyses for the WM functional connectome. We present comparative findings of the WM functional connectome, including its mapping, physiological underpinnings, cognitive neuroscience relationships, and clinical applications. Furthermore, we explore the emerging consensus that WM functional networks have valid topological characteristics that can distinguish between individuals with brain diseases and healthy controls, predict general intelligence, and identify inter-subject variabilities. Lastly, we emphasize the need for further studies and the limitations, challenges, and future directions for the WM functional connectome. An overview of these developments could lead to new directions for cognitive neuroscience and clinical neuropsychiatry.},
}
@article {pmid41214430,
year = {2025},
author = {Cheng, X and Zhang, R and Chen, P and Song, Z and Cheng, F and Dikker, S and Pan, Y},
title = {Promoting Social Connectedness Through Interbrain Neurofeedback.},
journal = {Annals of the New York Academy of Sciences},
volume = {},
number = {},
pages = {},
doi = {10.1111/nyas.70135},
pmid = {41214430},
issn = {1749-6632},
abstract = {Humans are inherently driven to form meaningful relationships, yet attempts at social connection often fall short or fail. This study investigates whether social connectedness can be improved by modulating interbrain coupling-a neural correlate of successful social interactions-through neurofeedback. Using a multibrain computer interface that visualized, in real time, the degree to which dyad members' electroencephalography (EEG) signals synchronized, dyads were randomly assigned to receive either neurofeedback or sham feedback generated from random signals. Compared with the sham group, dyads receiving neurofeedback showed greater interbrain coupling, and increases in coupling were associated with stronger feelings of social connectedness. A chain-mediation analysis suggested that the experience of enhanced social connectedness was driven by a sense of joint control and shared intentionality. Together, these findings demonstrate the potential of interbrain neurofeedback to modulate interbrain coupling and support key components of social connectedness.},
}
@article {pmid41214065,
year = {2025},
author = {He, Y and Jan, YH and Yang, F and Ma, Y and Chen, XY and Pei, C},
title = {The fatigue status feature of bicycle movement based on deep learning and signal processing technology.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {39328},
pmid = {41214065},
issn = {2045-2322},
support = {2020J01653//The Upper-Level Project of the Natural Science Foundation of Fujian Province/ ; 2020J01653//The Upper-Level Project of the Natural Science Foundation of Fujian Province/ ; 2023J01323//The Upper-Level Project of the Natural Science Foundation of Fujian Province/ ; 2020J01653//The Upper-Level Project of the Natural Science Foundation of Fujian Province/ ; 22SCZZX009//Fujian Special Financial Project for Research/ ; XRCZX2022010//Start-up funds for scientific research of high-level talents, Fujian Medical University/ ; 2023CDPFAT-02//China Disabled Persons' Federation Research Project on Assistive Devices for Persons with Disabilities/ ; },
mesh = {Humans ; Adult ; Male ; *Bicycling/physiology ; Female ; Middle Aged ; *Deep Learning ; *Fatigue/physiopathology ; Young Adult ; *Signal Processing, Computer-Assisted ; Movement/physiology ; Adolescent ; Algorithms ; Aged ; },
abstract = {Cycling is a common and effective home-based rehabilitation exercise. Accurate and accessible assessment of the onset of fatigue is essential to achieving optimal exercise benefits and preventing overuse injuries. To obtain fatigue-related parameters in different age groups, we applied deep learning algorithms and signal processing technology to analyze cycling movement features for the people aged over 45. 20 healthy adults aged over 45 and 20 aged 18-30 were recruited. Participants were asked to ride a stationary exercise bike at their self-regulated pedaling speeds for 10 min and wear a COSMED K5 device to collect physiological signals. The Keypoint RCNN (KR) algorithm and three signal processing methods (Fourier transform, short-time Fourier transform, and multiscale entropy analysis were used to analyze the cycling movement data. Based on time-frequency analysis, subjects' movement status change points were identified when fatigue occurred. Four movement status parameters were calculated, including the peak frequency before/after the movement status change point and the complexity index average (CIA) before/after the movement status change point. Inter-group and intra-group movement features, movement status, and physiological data were compared to determine fatigue-related features. Results showed that the peak frequency (p = 0.005), the peak frequency before/after the change point (p = 0.008/0.019), the CIA after the change point (p = 0.014), the maximum heart rate, maximal oxygen consumption, metabolic equivalents, and energy efficiency exhibited significant inter-group differences. The KR algorithm demonstrated outstanding performance in keypoint detection, achieving an accuracy of 86.5%, significantly outperforming OpenPose. With an inference speed of 30 FPS, it fulfills the demands for real-time monitoring. In addition, CIA valuses before and after change pointsshowed significant differences in the the middle-aged and elderly people group. After the change point, the CIA canidentify movement status changes in inter-group and intra-group comparisons, suggesting it can be used as a indicator of fatigue status, especially for people aged over 45.},
}
@article {pmid41213447,
year = {2025},
author = {Calabrò, RS and Calderone, A and Simoncini, L and Naro, A and Haughton, LOS and Quartarone, A and Leochico, CFD},
title = {The potential of robotics: A systematic review of neuroplastic changes following advanced lower limb rehabilitation in neurological disorders.},
journal = {Neuroscience and biobehavioral reviews},
volume = {180},
number = {},
pages = {106459},
doi = {10.1016/j.neubiorev.2025.106459},
pmid = {41213447},
issn = {1873-7528},
abstract = {BACKGROUND: Neurological diseases are among the most common pathologies that strongly influence a person's ability to walk and move, affecting the lower extremities. They disrupt motor brain networks that enable precise movement, leading to deficits in gait, balance, and coordination; while conventional therapies remain essential, advances in robotic technologies show growing promise for rehabilitation.<br><br>AIM OF REVIEW: This systematic review aims to investigate the role of robotic rehabilitation in improving neuroplasticity and motor outcomes for individuals with neurological disorders, with a particular focus on studies incorporating neurophysiological or neuroimaging techniques to assess neuroplastic changes and their long-term impact on recovery.<br><br>A systematic review was carried out utilizing an online search of articles from 2014 to 2025 on the PubMed, Web of Science, Cochrane Library, Embase, EBSCOhost, and Scopus databases in accordance with PRISMA guidelines. Studies were chosen based on predetermined inclusion criteria, with an emphasis on robotic rehabilitation therapies targeted at improving neuroplasticity in lower limb rehabilitation for people with neurological conditions. This review has been registered on Prospero with the following number: CRD42025640347. The search identified 12,769 records; after screening and eligibility assessment, 25 studies met inclusion criteria. Studies demonstrate that robot-assisted gait training (RAGT) and exoskeleton-based therapies improve motor function, gait, balance, and neuroplasticity across stroke, spinal cord injury, cerebral palsy, and brain injury populations. Adjunctive approaches such as brain-computer interface (BCI) integration, virtual reality feedback, and neuromodulation further enhance outcomes, with increases in cortical activation and improvements in functional connectivity supported by convergent neurophysiological and neuroimaging data; changes in corticospinal excitability are also reported. Taken together, robotic interventions, often combined with neuromodulation or virtual reality (VR), appear to catalyze neuroplasticity in ways that align with clinically meaningful gains. These findings underscore their transformative potential for tailored, multimodal rehabilitation strategies in neurological recovery.},
}
@article {pmid41212978,
year = {2025},
author = {Miller, KJ and Abosch, A},
title = {A Moment of Reckoning for Implanted Brain-Computer Interface Studies.},
journal = {Neurosurgery},
volume = {97},
number = {2},
pages = {277-280},
doi = {10.1227/neu.0000000000003585},
pmid = {41212978},
issn = {1524-4040},
}
@article {pmid41212707,
year = {2025},
author = {Fitriah, N and Zakaria, H and Budikayanti, A and Suksmono, AB and Mengko, TLER},
title = {Decoding Speech Imagery: A Spectro-spatial Approach to Electroencephalography Band Power Analysis.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3631062},
pmid = {41212707},
issn = {2168-2208},
abstract = {Decoding speech imagery from brain signals potentially assists individuals with speech impairments. However, limited data and complex brain activity in recent studies have made accurate decoding challenging. We analyzed both the spectral (frequency) and spatial (location) aspects of brain activity to enhance decoding accuracy in small datasets. To our knowledge, previous studies have mainly used generated features without adequately considering spectro-spatial aspects. We trained machine learning with time-frequency representation (TFR) features using a public dataset from the Brain-Computer Interface (BCI) competition (BCI-DB) and our own recordings (PrimAudio-DB). The results showed prominent feature patterns of speech imagery in the frontal region and Gamma band, achieving accuracies of 98.6% in BCI-DB (exceeding benchmarks) and 81.7% in PrimAudio-DB. Moreover, our analysis revealed insights regarding speech differences (language and semantics). This study contributed to non-invasive speech imagery decoding and offered valuable insights for future speech rehabilitation and assistive technologies.},
}
@article {pmid41212618,
year = {2025},
author = {Liu, X and Cao, L and Du, Z and Cui, Y and Saleem, KS and Zhang, Y and Lu, Y and Zhang, B and Liu, Y and Hou, X and Cheng, L and Li, K and Fan, L and Yang, Z and Jiang, T},
title = {Proteomic insights into the macaque insular parcellation based on structural connectivity gradients.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {35},
number = {11},
pages = {},
doi = {10.1093/cercor/bhaf307},
pmid = {41212618},
issn = {1460-2199},
support = {2021ZD0200200//Science and Technology Innovation 2030 - Brain Science and Brain-Inspired Intelligence Project of China/ ; 62327805//National Natural Science Foundation of China/ ; 62336007//National Natural Science Foundation of China/ ; 82151307//National Natural Science Foundation of China/ ; },
mesh = {Animals ; *Proteomics/methods ; Male ; *Insular Cortex/metabolism/diagnostic imaging/anatomy &amp; histology ; Diffusion Magnetic Resonance Imaging/methods ; Neural Pathways/metabolism/diagnostic imaging ; Macaca mulatta ; Macaca ; },
abstract = {Gradients across microstructure, macro-connectivity, and gene expression scales have been identified in the primate brain, offering a continuous perspective to explore regional heterogeneity. The macaque insula, with its extensive connections with other cortical regions and involvement in diverse functions, exhibits gradient transitions at the microstructural level. However, the gradients of macroscopic structural connectivity (SC) and its relationship with gene expression in the macaque insula remain unclear. We hypothesized that SC gradients are closely associated with gene expression, driving insular parcellation. To test this, we analyzed high-resolution diffusion-weighted MR imaging alongside spatially aligned proteomic data. Our findings revealed a rostrocaudal organization of the dominant SC gradient in the macaque insula, leading to the identification of a four-subregion pattern within the insula based on the first two SC gradients. Proteomic profiles strongly correlated with the dominant SC gradient and the clustering of proteomic similarity aligned with the four-subregion pattern. Notably, the dominant SC gradient more effectively captured spatial protein expression variations than T1w/T2w and cortical thickness maps. Overall, this study demonstrated that the SC gradient analysis revealed a four-subregion pattern of parcellation aligned with the spatial distribution of proteomic profiles along the rostro-caudal axis.},
}
@article {pmid41211047,
year = {2025},
author = {Zhang, N and Hu, BW and Li, XM and Huang, H},
title = {Rethinking parvalbumin: From passive marker to active modulator of hippocampal circuits.},
journal = {IBRO neuroscience reports},
volume = {19},
number = {},
pages = {760-773},
pmid = {41211047},
issn = {2667-2421},
abstract = {Parvalbumin (PV)-expressing interneurons are critical regulators of neural circuit dynamics, and for decades, the PV protein has served as their definitive molecular marker. This review confronts a central, yet underappreciated, paradox: the incongruity of a kinetically slow Ca[2+] buffer (PV) being the defining feature of the brain's fastest-spiking neurons. We synthesize evidence from molecular biophysics, genetics, in vivo circuit analysis, and disease modeling to dissect the dual role of PV as both a cellular marker and an active functional regulator. We argue that PV's slow kinetics are not a coincidence but a crucial adaptation that shapes short-term synaptic plasticity, protects against metabolic stress during high-frequency firing, and allows the circuit to shift between states of plasticity and stability. This reframing resolves the paradox by demonstrating how a "slow" molecule is essential for "fast" neuronal function. Furthermore, we highlight that dysfunction of the PV system is a convergent hub of pathology in numerous neurological and psychiatric disorders, including schizophrenia, epilepsy, and Alzheimer's disease. By moving beyond its identity as a passive marker, we establish PV as an active modulator of neural computation and a potential therapeutic target for restoring network function in disease.},
}
@article {pmid41209581,
year = {2025},
author = {Zeng, YY and Saeed, S and Hu, SH},
title = {Non-Suicidal Self-Injury: Pain Addiction Mechanisms, Neurophysiological Signatures, and Therapeutic Advances.},
journal = {Journal of clinical medicine research},
volume = {17},
number = {10},
pages = {537-549},
pmid = {41209581},
issn = {1918-3003},
abstract = {The aim of this study was to review the neurobiological mechanisms, epidemiology, and therapeutic interventions for non-suicidal self-injury (NSSI), emphasizing the pain addiction model and electroencephalographic biomarkers as frameworks for precision intervention. A narrative review of the literature was conducted using PubMed, Web of Science, CNKI, and Wanfang Data up to October 2025. Search strategy employed the terms "non-suicidal self-injury," "pain addiction," "electroencephalography," "endogenous opioid system," and "HPA axis." Selection criteria prioritized original human studies, high-quality systematic reviews, and mechanistic investigations. Pain addiction and electroencephalography (EEG) were selected as focal variables based on their explanatory power: pain addiction elucidates NSSI perpetuation through endogenous opioid-mediated reward sensitization and dopaminergic reinforcement, while event-related potentials (ERPs) provide temporal precision in mapping cognitive-affective dysregulation underlying emotional impulsivity and regulatory deficits. Global adolescent NSSI prevalence averages 17.2%, with Chinese rates reaching 24.7% and trends toward earlier onset. Neurobiological substrates include fronto-limbic dysregulation, hypoactive hypothalamic-pituitary-adrenal (HPA) axis function with blunted cortisol reactivity, and endogenous opioid system alterations producing widespread hypoalgesia. EEG/ERP studies demonstrate increased N2 amplitude with decreased P3 amplitude and prolonged latency during negative stimuli processing, reflecting impaired conflict monitoring and attentional resource allocation. Dialectical behavior therapy shows established efficacy, while repetitive transcranial magnetic stimulation and opioid antagonists demonstrate therapeutic potential. NSSI emerges from neurobiological vulnerability within pain-reward-emotion circuits interacting with psychosocial factors. The pain addiction framework and EEG signatures provide translatable targets for biomarker development and personalized intervention. Future research requires multimodal neuroimaging, longitudinal designs, and genetic integration to establish predictive algorithms and precision therapeutics.},
}
@article {pmid41209401,
year = {2025},
author = {Lu, Y and Yang, W and Wu, S and Li, Y and Wei, J and Li, M and Li, Y and Huai, Y},
title = {Exploring neural activity changes during motor imagery-based brain-computer interface training with robotic hand for upper limb rehabilitation in ischemic stroke patients: a pilot study.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1626000},
pmid = {41209401},
issn = {1662-5161},
abstract = {OBJECTIVE: This pilot study aimed to evaluate the feasibility and tolerability of motor imagery (MI)-based brain-computer interface (BCI) training with robotic hand assistance for upper limb rehabilitation, and to explore preliminary neural markers in ischemic stroke patients.<br><br>METHODS: Three post-stroke participants performed MI tasks combined with exoskeleton-assisted movements to facilitate rehabilitation training. Electroencephalography (EEG) signals were recorded to assess the neural correlates of MI. Functional outcomes were evaluated using standard assessment tools.<br><br>RESULTS: Our results demonstrated significant improvements in motor function across all participants. Additionally, EEG analysis revealed event-related desynchronization (ERD) in the high-alpha band power at motor cortex locations, with individual differences in both the frequency and power of neural activity. However, no significant trends in neural activity were observed across the training sessions.<br><br>CONCLUSION: These findings suggest that MI-based BCI training, combined with robotic assistance, offer a promising approach for enhancing upper limb function in ischemic stroke patients.},
}
@article {pmid41209398,
year = {2025},
author = {Milyani, AH and Attar, ET},
title = {Deep learning for inner speech recognition: a pilot comparative study of EEGNet and a spectro-temporal Transformer on bimodal EEG-fMRI data.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1668935},
pmid = {41209398},
issn = {1662-5161},
abstract = {BACKGROUND: Inner speech-the covert articulation of words in one's mind-is a fundamental phenomenon in human cognition with growing interest across BCI. This pilot study evaluates and compares deep learning models for inner-speech classification using non-invasive EEG derived from a bimodal EEG-fMRI dataset (4 participants, 8 words). The study assesses a compact CNN (EEGNet) and a spectro-temporal Transformer using leave-one-subject-out validation, reporting accuracy. Macro-F1, precision, and recall.<br><br>OBJECTIVE: This study aims to evaluate and compare deep learning models for inner speech classification using non-invasive electroencephalography (EEG) data, derived from a bimodal EEG-fMRI dataset. The goal is to assess the performance and generalizability of two architectures: the compact convolutional EEGNet and a novel spectro-temporal Transformer.<br><br>METHODS: Data were obtained from four healthy participants who performed structured inner speech tasks involving eight target words. EEG signals were preprocessed and segmented into epochs for each imagined word. EEGNet and Transformer models were trained using a leave-one-subject-out (LOSO) cross-validation strategy. Performance metrics included accuracy, macro-averaged F1 score, precision, and recall. An ablation study examined the contribution of Transformer components, including wavelet decomposition and self-attention mechanisms.<br><br>RESULTS: The spectro-temporal Transformer achieved the highest classification accuracy (82.4%) and macro-F1 score (0.70), outperforming both the standard and improved EEGNet models. Discriminative power was also substantially improved by using wavelet-based time-frequency features and attention mechanisms. Results showed that confusion patterns of social word categories outperformed those of number concepts, corresponding to different mental processing strategies.<br><br>CONCLUSION: Deep learning models, in particular attention-based Transformers, demonstrate great promise in decoding internal speech from EEG. These findings lay the groundwork for non-invasive, real-time BCIs for communication rehabilitation in severely disabled patients. Future work will take into account vocabulary expansion, wider participant variety, and real-time validation in clinical settings.},
}
@article {pmid41207999,
year = {2025},
author = {Yang, J and Xia, F and Jin, H and Mahanand, C and Lin, H and Cao, Y and Bian, J and Wei, D and Nevo, E and Du, J and Duan, S and Guo, F and Zhao, Y and Chen, X},
title = {Variations of Corticotropin-Releasing Factor Receptor 1α Contribute to the Blunted HPA Axis Responses to Hypoxia in Plateau Mammals.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {41207999},
issn = {1995-8218},
abstract = {Corticotropin-releasing factor (CRF) and its receptor (CRFR1) are critical components of the hypothalamic-pituitary-adrenocortical (HPA) axis. Ochotona curzoniae (O. curzoniae), Myospalax baileyi (M. baileyi), and Microtus oeconomus (M. oeconomus) have diversely evolved adaptive strategies to the extreme environment at high altitude. Here, we found blunted HPA axis responsiveness in native Tibetan mammals. CRF was 100% conserved, three amino-acid variations were in M. oeconomus-urocortin (UCN), and unique amino-acid variations in ligand-receptor binding domains of O. curzoniae-, M. baileyi-, and M. oeconomus-CRFR1αs. The native mammals' binding affinity and cAMP production varied depending on different doses of ligand-CRF/UCN treatment. Variations in M. oeconomus-UCN and O. curzoniae-, M. baileyi-, M. oeconomus-CRFR1α were responsible for weaker CRF-CRFR1α binding and higher EC50. They had the same HPA response pattern as that of CRF-CRFR1α binding affinity, cAMP production, and cell permeability. AlphaFold3.0 predicted altered structural interactions for both CRF-CRFR1α and UCN-CRFR1α complexes corroborate our findings. This study reveals that the variations of UCN/CRFR1α contribute to the different responsiveness of the HPA axis to extreme environments.},
}
@article {pmid41207468,
year = {2025},
author = {Upadhyay, PK and Chandra, KA},
title = {Quantum enhanced EEG classifier towards brain-controlled wheelchair navigation.},
journal = {Neuroscience},
volume = {591},
number = {},
pages = {1-20},
doi = {10.1016/j.neuroscience.2025.10.047},
pmid = {41207468},
issn = {1873-7544},
abstract = {Brain-computer interfaces (BCIs) provide a pathway to assistive technologies such as brain-controlled wheelchairs, yet accurate motor imagery (MI) classification from electroencephalography (EEG) remains challenging due to noise and subject variability. In this work, we propose a hybrid Quantum Enhanced CNN-LSTM model EEG Classifier (HQeCL), incorporating a simulated quantum pooling layer for richer feature abstraction. The framework integrates power spectral density (PSD) from the frequency domain, common spatial patterns (CSP) from the spatial domain, and quantum entropy from the non-linear domain to capture complementary EEG characteristics. The model was evaluated using leave-one-subject-out (LOSO) cross-validation on the 8-channel motor imagery dataset, achieving 92.1%±5.9 accuracy, 93.1%±6.2 precision, 91.9%±1.3 recall, 92.5%±1.3 F1-score, and Cohen's κ=0.89±0.02. Compared to existing methods, HQeCL outperformed CSP-LDA (74.5%±1.4), ShallowConvNet (83.3%±1.6), and CNN-LSTM (88.8%±1.2), while remaining competitive with QuEEGNet (91.4%±1.3). Ablation analysis confirmed the contribution of quantum pooling, which provided a +0.7% gain over average pooling, and UMAP, which improved performance by +14.8% over PCA and +29.7% over t-SNE. Complexity analysis further demonstrated the efficiency of HQeCL with only 0.12M parameters, 270.2M FLOPs, and an inference latency of 77.6ms. While these results demonstrate near real-time feasibility in simulation, translation to hardware remains a challenge, positioning HQeCL as a quantum-inspired, Pareto-efficient EEG classifier advancing motor imagery decoding for brain-controlled wheelchair navigation.},
}
@article {pmid41207160,
year = {2025},
author = {Shirodkar, VR and Reddy Edla, D and Kumari, A and Afonso, MM},
title = {Multi-domain feature extraction and Sand Cat Swarm Optimized Broad Learning System for EEG-based Motor Imagery decoding in stroke patients.},
journal = {Computers in biology and medicine},
volume = {199},
number = {},
pages = {111285},
doi = {10.1016/j.compbiomed.2025.111285},
pmid = {41207160},
issn = {1879-0534},
abstract = {Brain-Computer Interfaces (BCIs) enable the translation of brain activity into executable commands, with Motor Imagery (MI)- based systems gaining prominence for their intuitive and non-invasive control. Electroencephalography is widely used due to its portability and time resolution, though its non-stationary and subject-specific nature poses major challenges for reliable classification. This research proposes a lightweight and efficient classification architecture that first selects discriminative filter bands based on Event-Related Desynchronization (ERD) scores. It then integrates Empirical Mode Decomposition (EMD), the Hilbert-Huang Transform (HHT), Riemannian Geometry (RG), and Common Spatial Pattern (CSP)-based feature extraction with a Broad Learning System (BLS) classifier. The BLS parameters are optimized using the Sand Cat Swarm Optimization (SCSO) algorithm to enhance convergence speed, avoid local minima, and improve generalization. EMD separates the EEG signal into a set of Intrinsic Mode Functions, while HHT extracts instantaneous amplitude and frequency features, effectively modeling the nonlinear and dynamic properties of EEG signals. Performance assessment was done on two datasets: the BCI IV 2a dataset and a clinical stroke EEG dataset. It achieved classification accuracies of 90.78% on BCI-IV 2a and 96.41% on the stroke dataset. The proposed approach also showed competitive generalization performance in All-subjects and Leave-One-Subject-Out (LOSO) validation settings. Analysis reveals that the proposed pipeline effectively extracts discriminative features and handles inter-subject variability, illustrating its applicability to real-world BCI systems.},
}
@article {pmid41206890,
year = {2025},
author = {Zapata-Catzin, GA and Vargas-Coronado, RF and Ceballos-Gongora, E and Arana-Argáez, VE and Rodríguez-Velázquez, E and Alatorre-Meda, M and Molina-Salinas, GM and Uc-Cachon, A and Gallardo, A and Copes, F and Mantovani, D and Cauich-Rodríguez, JV},
title = {Effect of Polyurethane Structure on the Physicochemical, Mechanical, and Biological Properties on their Copper Complexes Composites.},
journal = {Macromolecular bioscience},
volume = {},
number = {},
pages = {e00419},
doi = {10.1002/mabi.202500419},
pmid = {41206890},
issn = {1616-5195},
support = {AtencionaProblemasNacionales(248378)//Consejo Nacional de Humanidades, Ciencia y Tecnolog&#xed;a/ ; FronterasdelaCiencia(1360)//Consejo Nacional de Humanidades, Ciencia y Tecnolog&#xed;a/ ; 1360//Atenci&#xf3;n a Problemas Nacionales/ ; //Fronteras de la Ciencia/ ; },
abstract = {Polyurethanes and their composites are versatile materials widely used in numerous medical applications. However, limited information is available regarding their copper composites. Copper is a trace element in the human body that functions as an enzyme cofactor in both normal and pathological angiogenesis, as well as in muscle and brain formation. Considering this, copper complexes of D-penicillamine (DP), L-cysteine (LC), and dopamine (DOP) were incorporated into segmented polyurethanes (SPU) synthesized with either a semi-crystalline (poly-ε-caprolactone, PCL) or an amorphous (polytetramethylene ether glycol, PTMEG) soft segment. FTIR and Raman revealed new absorptions and peak shifts, confirming the presence of the complexes within the matrix of all composites. XPS further corroborated the presence of copper and sulfur. The crystallinity of the PCL-based polyurethanes was influenced by the addition of the filler, as observed through DSC and DRX. Furthermore, TGA analysis indicated the emergence of new decomposition temperatures following the incorporation of copper complexes. In general, no significant reduction in Young's modulus was observed, except for certain composites containing DPENCUII as filler, which exhibited a slight increase compared to pristine SPU´s. Finally, the composites demonstrated neither hemolytic nor procoagulating behavior (hemolysis < 5% and BCI > 20), although they exhibited some degree of impairment in cytocompatibility compared to their respective pristine SPUs. Collectively, these findings suggest that some composites possess promising properties for potential cardiovascular applications.},
}
@article {pmid41206869,
year = {2025},
author = {Tian, Y and Jiang, R and Guo, F},
title = {Protocol for in vivo two-photon calcium imaging of the Drosophila brain.},
journal = {STAR protocols},
volume = {6},
number = {4},
pages = {104194},
doi = {10.1016/j.xpro.2025.104194},
pmid = {41206869},
issn = {2666-1667},
abstract = {Two-photon calcium imaging facilitates the real-time observation of neuronal activity. Here, we present a protocol for conducting in vivo two-photon calcium imaging of the Drosophila melanogaster brain. We describe steps for fly preparation, recording chamber construction, and preparation of the buffer solution. We then detail procedures for fly brain surgery, execution of the recording, and data analysis. This protocol enables the monitoring and assessment of neuronal responses to external stimuli and the mapping of functional connectivity coupled with optogenetics. For complete details on the use and execution of this protocol, please refer to Jiang et al.[1].},
}
@article {pmid41205898,
year = {2025},
author = {Taranath, JR},
title = {On questions of predictability and control of an intelligent system using probabilistic state-transitions.},
journal = {Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuroscience.2025.10.062},
pmid = {41205898},
issn = {1873-7544},
abstract = {One of the central aims of neuroscience is to reliably predict the behavioral response of an organism using its neural activity. If possible, this implies we can causally manipulate the neural response and design brain-computer-interface systems to alter behavior, and vice-versa. Hence, predictions play an important role in both fundamental neuroscience and its applications. Can we predict the neural and behavioral states of an organism at any given time? Can we predict behavioral states using neural states, and vice-versa, and is there a memory-component required to reliably predict such states? Are the predictions computable within a given timescale to meaningfully stimulate and make the system reach the desired states? Through a series of mathematical treatments, such conjectures and questions are discussed. Answering them might be key for future developments in understanding intelligence and designing brain-computer-interfaces.},
}
@article {pmid41205562,
year = {2025},
author = {Sun, Z and Sun, Y and Zeng, Y},
title = {BACNet: A multi-attention network for cross-subject and cross-task EEG-based pilot operational intent recognition.},
journal = {Computer methods and programs in biomedicine},
volume = {274},
number = {},
pages = {109134},
doi = {10.1016/j.cmpb.2025.109134},
pmid = {41205562},
issn = {1872-7565},
abstract = {BACKGROUND AND OBJECTIVE: Recognizing pilot operational intent is crucial for enhancing flight safety and improving the efficiency of human-machine interaction. Electroencephalography (EEG), known for its high temporal resolution and non-invasive acquisition, has become a prominent modality for this task. However, current approaches often suffer from high model complexity and limited accuracy in EEG feature extraction. This study aims to address these limitations by proposing efficient and accurate neural network architecture for pilot intent recognition based on EEG signals.<br><br>METHODS: We introduce a novel framework, the Balanced Attention Convolutional Network (BACNet), designed to enhance EEG-based intent recognition through collaborative optimization in both channel and spatial dimensions. BACNet features: (1) a three-branch parallel convolutional structure that extracts multi-scale time-frequency features; and (2) dynamic feature modulation mechanisms to adaptively highlight salient channels and spatial locations. EEG data were collected from 15 participants across various simulated flight phases, forming a labeled dataset for model training and evaluation. Five-fold cross-validation was conducted to ensure the robustness of the performance assessment.<br><br>RESULTS: BACNet achieved an average classification accuracy of 96.07 % in a three-class EEG-based intent recognition task, outperforming five state-of-the-art baseline methods. The model also demonstrated a significant reduction in computational complexity. Ablation experiments validated the individual and combined contributions of the multi-scale attention modules, highlighting the effectiveness of the collaborative attention design.<br><br>CONCLUSION: With its lightweight architecture and high accuracy, BACNet not only provides a novel solution for pilot operational intent recognition but also demonstrates broad applicability in brain-computer interface (BCI) systems.},
}
@article {pmid41205408,
year = {2025},
author = {Bao, C and Ma, Y and Li, M and Li, Y and Zhang, C and Liu, X and Fan, R and Cui, W and Fan, X and Zheng, F and Duan, F and Liu, J},
title = {Assessment of glymphatic dysfunction in ulcerative colitis using DKI-ALPS: An innovative imaging biomarker.},
journal = {Journal of neuroradiology = Journal de neuroradiologie},
volume = {53},
number = {1},
pages = {101402},
doi = {10.1016/j.neurad.2025.101402},
pmid = {41205408},
issn = {0150-9861},
abstract = {PURPOSE: Ulcerative colitis (UC) is associated with higher anxiety, depression, and cognitive disorders linked to brain glymphatic dysfunction. In this study, we used along-the-perivascular-space (ALPS) index (based on DTI and DKI) to determine if UC relates to glymphatic dysfunction and explore how microbiota dysbiosis and inflammation affect brain glymphatic function.<br><br>MATERIALS AND METHODS: In this study, 63 patients with UC and 68 healthy controls underwent 3-Tesla MRI scans to evaluate DTI-ALPS and DKI-ALPS index. The protocol included diffusion-weighted imaging (DWI) and diffusion kurtosis imaging (DKI) sequences to calculate the ALPS index, which quantifies glymphatic system function. All participants completed cognitive (MMSE) and depression (SAS/SDS) assessments (SAS/SDS). Patients with UC also underwent assessment for inflammation and gut microbiota (based on metagenomic analysis). Data analysis was performed using correlation analysis and linear regression.<br><br>RESULTS: Patients with UC showed lower DTI-ALPS index (1.25) and DKI-ALPS index (1.40) compared to controls (1.40 vs. 1.69; P < 0.001). In multi-adjusted linear regression models, UC was associated with lower DTI-ALPS index and DKI-ALPS index (&#x3b2; =-0.142 vs.-0.284), with DKI-ALPS showing higher sensitivity. The results remained significant even after stratification by age and sex. The Mayo score correlated negatively with DTI and DKI-ALPS index. The ALPS index correlates with gut microbiota, particularly those involved in butyrate and short-chain fatty acid (SCFA) production. DTI-ALPS index was significantly correlated with ESR (&#x3b2; =-0.003), CRP (&#x3b2; =-0.035), SII (&#x3b2; =-0.062), INFLA (&#x3b2; =-0.010), and SIRI (&#x3b2; =-0.058). We also observed significant correlations between DKI ALPS index and ESR (&#x3b2; =-0.006), CRP (&#x3b2; =-0.051), SII (&#x3b2; =-0.130), INFLA (&#x3b2; =-0.017), SIRI (&#x3b2; =-0.095), IL-6 (&#x3b2; =-0.081) and NLR (&#x3b2; =-0.108).<br><br>CONCLUSIONS: UC is associated with brain glymphatic dysfunction, correlating with inflammation level. DKI-ALPS serves as a more sensitive method than DTI-ALPS, offering a new approach for managing ulcerative colitis through glymphatic dysfunction.},
}
@article {pmid41204711,
year = {2025},
author = {Scherer, J and Finke, A and Everding, V and Lindenbaum, L and Kayser, C and Kissler, J},
title = {NeuroCommTrainer: Toward an Adaptive and Wearable Multimodal Brain-Computer Interface.},
journal = {Brain connectivity},
volume = {},
number = {},
pages = {},
doi = {10.1177/21580014251393151},
pmid = {41204711},
issn = {2158-0022},
abstract = {Introduction: To date, brain-computer interfaces (BCIs) have not achieved reliable real-time communication through auditory or tactile modalities. Such interfaces would be crucial for brain-injured patients with severe motor impairments who are also blind or deaf. This study validates the functionality of the NeuroCommTrainer, a mobile and easy-to-use multimodal BCI with flex-printed electrode strips that does not require vision and adapts to users' attentiveness levels to initiate stimulation. Methods: In a study of 20 healthy participants, we evaluated auditory and vibrotactile oddball paradigms to train the system to differentiate rare and frequent event-related potentials (ERPs). In real-time online sessions, the system detected participants' mental focus to adaptively initiate stimulation through attentiveness monitoring. Results: The NeuroCommTrainer successfully captured auditory and tactile ERPs, achieving a classification accuracy of 75% for stimuli in the calibration session, which is not yet reflected in the online session with 34% of found targets (chance level = 16.7%). Discussion: The presented early-stage prototype of the NeuroCommTrainer requires several improvements before clinical application in brain-damaged patients, which include refined algorithms to reduce classification variance across participants, and enhanced attentiveness detection specifically tuned to brain activity of the targeted patient group. The present study makes a critical step in this direction and shows that a transition into a practicable communication system for brain-damaged patients may be achievable in the future.},
}
@article {pmid41204680,
year = {2025},
author = {Ehrlich, SK and Tougas, G and Bernstein, J and Buie, N and Rumbach, AF and Simonyan, K},
title = {Brain-Computer Interface Improves Symptoms of Isolated Focal Laryngeal Dystonia: A Single-Blind Study.},
journal = {Movement disorders : official journal of the Movement Disorder Society},
volume = {},
number = {},
pages = {},
doi = {10.1002/mds.70114},
pmid = {41204680},
issn = {1531-8257},
support = {R01DC019353/DC/NIDCD NIH HHS/United States ; },
abstract = {BACKGROUND AND OBJECTIVE: Laryngeal dystonia (LD) is a focal task-specific dystonia, affecting speaking but not whispering or emotional vocalizations. Therapeutic options for LD are limited. We developed and tested a non-invasive, closed-loop, neurofeedback, brain-computer interface (BCI) intervention for LD treatment.<br><br>METHODS: Ten patients with isolated focal LD participated in the study. The personalized BCI system included visual neurofeedback of individual real-time electroencephalographic (EEG) activity during symptomatic speaking compared to&#xa0;asymptomatic whispering, presented in the virtual reality (VR) environment of real-life scenarios. During five consecutive days of intervention, patients used the BCI to learn to modulate their abnormally increased brain activity during speaking and match it to near-normal activity of asymptomatic whispering. Changes in voice symptoms and EEG activity were quantified for the evaluation of BCI effects.<br><br>RESULTS: Compared to baseline, LD patients had a statistically significant reduction of their voice symptoms on Days 1-5 of BCI intervention. Thi&#xa0;was paralleled by improved controllability of the visual neurofeedback and a significant reduction of left frontal delta power, including superior and middle frontal gyri, on Day 1 and left central gamma power, including premotor, primary sensorimotor, and inferior parietal areas, on Days 3 and 5. The majority of patients (70%) reported sustained positive effects of the BCI intervention on their voice quality 1 week after the study participation.<br><br>CONCLUSION: The closed-loop BCI neurofeedback intervention specifically targeting disorder pathophysiology shows significant potential as a novel treatment option for patients with LD and likely other forms of task-specific focal dystonia. &#xa9; 2025 International Parkinson and Movement Disorder Society.},
}
@article {pmid41204389,
year = {2025},
author = {Zhang, H and Chen, WJ and Chao, YG and Su, N and Robba, C and Czosnyka, M and Smielewski, P and Czosnyka, Z and He, W and Hu, X and Yao, DZ and Hu, CG and Zhou, M and Wang, YJ and Ma, XC and Liu, XY and Ming, D},
title = {Neurogenic organ dysfunction syndrome after acute brain injury.},
journal = {Military Medical Research},
volume = {12},
number = {1},
pages = {77},
pmid = {41204389},
issn = {2054-9369},
support = {ZYGXQNJSKYCXNLZCXM-H15//Scientific Research Innovation Capability Support Project for Young Faculty/ ; 0401260011//National Science Fund for Excellent Overseas Scholars/ ; 82472098//Innovative Research Group Project of the National Natural Science Foundation of China/ ; 32300704//National Natural Science Foundation of China/ ; 24JCJQJC00250//National Outstanding Youth Science Fund Project of National Natural Science Foundation of China/ ; 24ZXZSSS00510//Major Science and Technology Special Projects and Engineering - Major Project of National Key Laboratories/ ; 2021YFF1200602//National Key Technologies Research and Development Program/ ; 2024-JKCS-16//the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences/ ; },
mesh = {Humans ; *Brain Injuries/complications/physiopathology ; *Multiple Organ Failure/etiology/physiopathology ; },
abstract = {Systemic complications are common after acute brain injury (ABI) and may trigger coagulation cascades, systemic inflammation, as well as dysfunction of the cardiovascular, respiratory, and gastrointestinal systems, etc. The pathogenesis of these systemic manifestations is multifactorial but not yet fully elucidated. This paper introduces the novel term neurogenic organ dysfunction syndrome (NODS) to characterize systemic instability arising from internal and external perturbations of the neuronal center following ABI. Elucidating the central neurogenic mechanisms of NODS is critical for early detection and prevention of complications, thereby reducing mortality and improving patient outcomes following ABI. In this paper, we explore the potential central neurogenic mechanisms of NODS from the perspective of complex brain network theory, focusing on the structural network of the central autonomic system (CAS) that maintains systemic stability, and the functional network governed by the central stress system (CSS). The CAS can be divided into the cortical autonomic network, which involves higher cortical regions, and the subcortical autonomic network, which is relatively conserved, with its main connections located in deep brain structures. The CSS is a large-scale complex network characterized by hierarchy, hubs, and modularity, which together enable the competitive optimization of functional segregation and integration. Under physiological conditions, modules (mediating functional segregation) and hubs (functional integration) within the CSS dynamically trade-off with each other to maintain the overall homeostasis. However, this balance is disrupted following pathological insults or injury, resulting in weakened functional integrity of the CSS following ABI, impaired module activity, and disturbed hub integration. This paper also demonstrates the distinct pathological manifestations arising from disturbances at different levels of the homeostatic system. Finally, this study proposes potential clinical interventions, including analgesia and sedation, neuromodulation, and receptor regulation, for early interventions and potential treatment of NODS, aiming to improve patient outcomes.},
}
@article {pmid41203630,
year = {2025},
author = {Thapa, BR and Boggess, J and Bae, J},
title = {A large electroencephalogram database of freewill reaching and grasping tasks for brain machine interfaces.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {1760},
pmid = {41203630},
issn = {2052-4463},
mesh = {Humans ; *Electroencephalography ; *Brain-Computer Interfaces ; Male ; Female ; Young Adult ; Adolescent ; *Hand Strength ; },
abstract = {Brain machine interfaces (BMIs) offer great potential to improve the quality of life for individuals with neurological disorders or severe motor impairments. Among various neural recording modalities, electroencephalogram (EEG) is particularly favorable for BMIs due to its noninvasive nature, portability, and high temporal resolution. Existing EEG datasets for BMIs are often limited to experimental settings that fail to address subjects' freewill in decision making. We present a large EEG dataset, containing a total of 6808 trials, recorded from 23 healthy young adults (eight females and 15 males with an age range from 18 to 24 years) while performing reaching and grasping tasks, where the target object is freely chosen at their desired pace according to their own will. This EEG dataset provides a realistic representation of reaching and grasping movement, making it useful for developing practical BMIs.},
}
@article {pmid41201930,
year = {2025},
author = {Yang, Y and Wang, Z and Jia, Z and Wang, B and Zhang, S and Wong, CM and Gao, X and Jung, TP and Wan, F},
title = {Dual-Branch Attention-based Frequency Domain Network for Cross-subject SSVEP-BCIs.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3630249},
pmid = {41201930},
issn = {2168-2208},
abstract = {Steady-state visual evoked potential-based brain-computer interfaces (SSVEP-BCIs) hold significant promise for enabling high-speed human-computer interaction in real-world scenarios. However, existing frequency-domain decoding methods treat frequency spectrum features (the real and imaginary spectrum features) as a single feature without considering their unique spatial and spectral characteristics, resulting in insufficient generalizable features and limited classification accuracy in cross-subject scenarios. To address this issue, we propose a Dual-Branch Attention-Based Frequency Domain Network (DB-AFDNet) to independently decode real and imaginary spectral components, aiming to acquire more discriminative and generalizable features for cross-subject applications. Specifically, we construct inter-branch attention similarity constraints to encourage the two branches to have similar attention properties, promoting to learn the consensus characteristics in the dual branches. Furthermore, we propose intra-branch orthogonality constraints to explore branch-specific discriminative features to learn generalizable features. Experimental studies on two public datasets, the Benchmark and Beta datasets, demonstrate that DB-AFDNet outperforms state-of-the-art methods in cross-subject classification, achieving a relative improvement of 1.36$\%$ and 1.45$\%$, respectively. The code is available at https://github.com/YYingDL/DBAFDNet.},
}
@article {pmid41199758,
year = {2025},
author = {Wang, Y and Yu, H and Zhao, X and Yin, X and Li, H and Wang, C},
title = {A dual-branch neural network and attention mechanism for decoding EEG-based motor imagery.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {177},
pmid = {41199758},
issn = {1871-4080},
abstract = {Motor imagery (MI) is a fundamental paradigm in brain-computer interfaces (BCIs), extensively employed to assist individuals with disabilities to operate external devices. Accurate decoding of MI signals is essential for effective interaction. However, robust decoding remains a challenge due to the inherent complexity and variability of MI-EEG signals. To address this issue, we propose an innovative Dual-Branch Multi-Attention Temporal Convolutional Network (DBMATCN) to improve the performance of MI-EEG signal classification. First, the dual-branch structure extracts rich spatial-temporal features. Then, the channel attention enhances local channel feature extraction and calibrate feature mapping. Next, by combining a sliding window technique and multi-head locality self-attention improves the feature representation of MI-EEG signals by emphasizing the most relevant features. Finally, the temporal convolution fusion network decoding module is used to extensively capture comprehensive temporal features from MI data and carry out the classification task. DBMATCN achieves average accuracies of 88.08%, 96.83%, and 89.71% in inter-session validation on the BCI-IV-2a, HGD, and BCI-IV-2b datasets, respectively. In cross-validation, the model reaches an accuracy of 85.14%, and in the subject-independent scenario, it attains 71.78%. DBMATCN outperforms all baseline models in these cases. These results suggest that our model is effective in decoding MI signals.},
}
@article {pmid41199757,
year = {2025},
author = {Zhu, L and Ding, Y and Hung, A and Tan, X and Zhang, J},
title = {SFT-HN: a novel spatial-frequency-temporal hybrid network for EEG-based emotion recognition.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {176},
pmid = {41199757},
issn = {1871-4080},
abstract = {Electroencephalograph (EEG) emotion recognition is a key task in the brain-computer interface(BCI) field. A mounting quantity of studies have shown that deep learning methods for emotion recognition exhibit superior performance compared to traditional techniques. However, it is still challenging to fuse the EEG's Spatial, Frequency and Temporal information, as well as how to make full use of discriminative local patterns among the features for different emotions. To address these issues, a novel hybrid model called Spatial-Frequency-Temporal Hybrid Network(SFT-HN) is proposed. This model includes three Spatial Frequency Residual Modules (SFRM) and an attention-based Bidirectional Long Short-Term Memory (ATBI-LSTM). The former module extracts spatial-frequency features, while the latter learns temporal contexts. SFT-HN is trained to seize the complementarity among the spatial-frequency-temporal information and adaptively explore discriminative local patterns. Specifically, 4D representations are created from raw EEG signals to preserve spatial, frequency, and temporal information. The SFRM module then adopts split-convert-merge techniques, residual and attention mechanisms to enhance its spatial-frequency feature extraction ability for each input 4D representation tensor time slice. Moreover, an attention-enhanced mechanism is incorporated into a bidirectional LSTM module to capture the crucial temporal dependencies among the extracted features, thereby enhancing the discriminative power of the EEG features. The proposed method attains average accuracies of 97.61% and 97.57% for arousal-based and valence-based classification on the DEAP dataset, respectively. On SEED dataset, the method achieves average accuracy of 97.44%. Furthermore, we validate the robust generalization of our proposed model on a novel dataset, FACED, achieving an average accuracy of 96.24%. The model code is available at: https://github.com/AllGGI/SFT-HN-model.},
}
@article {pmid41199756,
year = {2025},
author = {Wu, X and Long, D and Yang, J},
title = {Generative motor imagery dynamic networks: EEG-controlled grasping via individualized model training.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {174},
pmid = {41199756},
issn = {1871-4080},
abstract = {Improving the accuracy of non-invasive brain-computer interface (BCI) and promoting their daily use can be achieved by developing an individualized model training framework, where individual training means that the model is based on small-sample learning from individual data. In the process of data augmentation through synthetic data, the criteria for data generation needs to be further specified according to the requirements. Therefore, in this study, the proposed BCI model utilizes dynamic networks to describe electroencephalogram (EEG) activity during the motor imagery (MI) task, innovatively generates individualized dynamic networks from individual data, and ultimately achieves EEG-controlled grasping through model training. Specifically, this study involves the EEG signals of the right-hand grasping movements of eight subjects and proposes using morphological pattern spectrum (MPS) to encode EEG potentials during MI processes. The MI condition representation was achieved by combining the dynamic networks with MPS encoding, and more dynamic network EEG encoding samples were synthesized through generative adversarial network (GAN) or variational autoencoder (VAE). The AUCs based on the long short-term memory (LSTM) architecture for generating and classifying can be improved by 0.003-0.07. The optimal BCI model based on the Wasserstein GAN and Granger causality (GC) dynamic network encoded by MPS achieved a mean true/false positive rate (TPR/FPR) of 90.0%/0.0%, far better than the 52.9%/4.4% achieved without individualized modeling. Moreover, the BCI establishment of handling multi-task and complex command outputs further demonstrates the reliability of MPS encoding of the GC dynamic network in BCI modeling. The advantage of this "generative-individual" approach is that it not only reduces the sample size requirement while ensuring accuracy but also avoids building models that are applicable to all individuals, which leads to difficult convergence.},
}
@article {pmid41199478,
year = {2025},
author = {Bobby J, S and V Francis, S and Ramya V, S and C L, A},
title = {Preliminary Findings on a Deep Learning Model Using Electroencephalogram for Multi-Level Neuropathic Pain Detection in Post-Stroke Patients.},
journal = {The International journal of neuroscience},
volume = {},
number = {},
pages = {1-10},
doi = {10.1080/00207454.2025.2584081},
pmid = {41199478},
issn = {1563-5279},
abstract = {AIM: Neuropathic pain occurs commonly after stroke and represents a major source of disability for affected patients. This study aims to develop an accurate and computationally efficient framework for multi-level neuropathic pain detection using electroencephalography signals.<br><br>METHODS: A Quantum-Inspired Pyramid Depthwise Separable Residual Network is proposed, which integrates three innovations: a depthwise separable Residual Network to reduce computational complexity, a pyramid attention mechanism to capture multi-scale patterns, and a quantum-inspired transformation layer to model complex nonlinear dependencies among Electroencephalogram features.<br><br>RESULTS: Experiments conducted on benchmark electroencephalography datasets confirm that the proposed model gains a accuracy of 99.65%, with a recall of 98.00%.<br><br>CONCLUSION: The proposed model provides a reliable solution for objective neuropathic pain detection in post-stroke patients. The framework demonstrates potential for integration into intelligent clinical decision-support and brain-computer interface-based rehabilitation systems.},
}
@article {pmid41199005,
year = {2025},
author = {Zhang, H and Wang, X and Xi, K and Shen, Q and Xue, J and Zhu, Y and Zang, SK and Yu, T and Shen, DD and Guo, J and Chen, LN and Ji, SY and Qin, J and Dong, Y and Zhao, M and Yang, M and Wu, H and Yang, G and Zhang, Y},
title = {The molecular basis of μ-opioid receptor signaling plasticity.},
journal = {Cell research},
volume = {},
number = {},
pages = {},
pmid = {41199005},
issn = {1748-7838},
support = {2019YFA0508800//Chinese Ministry of Science and Technology | Department of S and T for Social Development (Department of S&amp;T for Social Development)/ ; 32430051//National Natural Science Foundation of China (National Science Foundation of China)/ ; 92353303//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32141004//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82271001//National Natural Science Foundation of China (National Science Foundation of China)/ ; 2024M752856//China Postdoctoral Science Foundation/ ; },
abstract = {Activation of the μ-opioid receptor (μOR) alleviates pain but also elicits adverse effects through diverse G proteins and β-arrestins. The structural details of μOR complexes with Gz and β-arrestins have not been determined, impeding a comprehensive understanding of μOR signaling plasticity. Here, we present the cryo-EM structures of the μOR-Gz and μOR-βarr1 complexes, revealing selective conformational preferences of μOR when engaged with specific downstream signaling transducers. Integrated receptor pharmacology, including high-resolution structural analysis, cell signaling assays, and molecular dynamics simulations, demonstrated that transmembrane helix 1 (TM1) acts as an allosteric regulator of μOR signaling bias through differential stabilization of the Gi-, Gz-, and βarr1-bound states. Mechanistically, outward TM1 displacement confers structural flexibility that promotes G protein recruitment, whereas inward TM1 retraction facilitates βarr1 recruitment by stabilizing the intracellular binding pocket through coordinated interactions with TM2, TM7, and helix8. Structural comparisons between the Gi-, Gz-, and βarr1-bound complexes identified a TM1-fusion pocket with significant implications for downstream signaling regulation. Overall, we demonstrate that the conformational and thermodynamic heterogeneity of TM1 allosterically drives the downstream signaling specificity and plasticity of μOR, thereby expanding the understanding of μOR signal transduction mechanisms and providing new avenues for the rational design of analgesics.},
}
@article {pmid41198855,
year = {2025},
author = {Heerspink, HJL and Collier, WH and Chaudhari, J and Miao, S and Tighiouart, H and Appel, GB and Caravaca-Fontán, F and Floege, J and Hannedouche, T and Imai, E and Jafar, TH and Lewis, JB and Li, PKT and Locatelli, F and Maes, BD and Neuen, BL and Perkovic, V and Perrone, RD and Remuzzi, G and Schena, FP and Wanner, C and Greene, T and Inker, LA},
title = {A meta-analysis of albuminuria as a surrogate endpoint for kidney failure.},
journal = {Nature medicine},
volume = {},
number = {},
pages = {},
pmid = {41198855},
issn = {1546-170X},
abstract = {Albuminuria is a central biomarker in chronic kidney disease (CKD), used for the detection and prognosis of the disease. In clinical trials assessing CKD progression, change in the level of albuminuria is a candidate surrogate endpoint for kidney failure. Evaluation of the validity of this surrogate endpoint across a diverse range of interventions and populations is required to support its further acceptance. Here, in an individual participant data analysis of 48 randomized controlled trials (studies) involving 85,681 participants, we assessed the association between treatment effects on 6-month urinary albumin:creatinine ratio (UACR) change and the established clinical endpoint of kidney failure or doubling of serum creatinine concentrations. Across all trials, each 30% reduction in the geometric mean of the UACR in the treatment group relative to the control group was associated with an average of 19% lower hazard for the clinical endpoint (95% Bayesian credible interval (BCI): 5-30%); median coefficient of determination (R[2]) = 0.66 (95% BCI: 0.06-0.98). There was no clear evidence that this association varied by CKD etiology. These results provide further support for use of albuminuria change as a surrogate endpoint in CKD clinical trials.},
}
@article {pmid41197164,
year = {2025},
author = {Cinquetti, E and Menegaz, G and Storti, SF},
title = {Toward in-silico data assessment for passive BCIs: Generating EEG rhythms with GANs.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae1c6f},
pmid = {41197164},
issn = {1741-2552},
abstract = {Passive brain-computer interface based on electroencephalography (EEG) has gained traction as reliable method for monitoring human vigilance in attention-demanding critical contexts. Unfortunately, the lack of extensive public datasets compromises artificial intelligence (AI) research. Proposing a solution to this issue, we augmented two EEG datasets using generative adversarial networks (GAN) and defined a quality-assessment pipeline to overcome the absence of a univocal method to test synthetic data. Approach. Using GAN, we augmented a publicly resting-state EEG dataset (SPIS) and a custom one simulating activity during repetitive tasks. After extracting relevant time-variant rhythms via the continuous wavelet transform, we quantitatively compared synthetic data with the real one using L2 distance and cross-correlation function. To evaluate the impact of data augmentation, we trained six forecasting models, three on the original and three on the augmented datasets, over the whole, half and a quarter of total available data, and compared improvements in MAE and SMAPE. To study the forecaster's embeddings, we computed a metric inspired by the Fréchet Inception Distance (FID) between latent values of real and synthetic data. Finally, to offer a baseline comparison, we extended the performance and embeddings analysis to data generated by a simple linear interpolation method. Main Results. The integration of GAN-produced synthetic data improved signal prediction, as evidenced by a 29.0%, 46.4%, 37.4% reduction in mean absolute error (MAE) for splits of the resting-state dataset, and an average MAE reduction of 15.4%, 21.2% for 100% and 50% splits, and a ∽-2.5\% increase for the 25% split). Conversely, training on interpolated data manifest worse performance and denotes extremely small FID distances w.r.t real signals, a sign of overspecialization. Significance. This study contributes a reproducible and complete framework for EEG signal generation and evaluation, addressing one of the main barriers to scalable AI application in BCI.},
}
@article {pmid41196745,
year = {2025},
author = {Zhang, S and Chen, W and Chang, S and Zhou, LF and Ding, X},
title = {How visual imagery representations are formed: Through suppression, not activation.},
journal = {Journal of experimental psychology. General},
volume = {},
number = {},
pages = {},
doi = {10.1037/xge0001863},
pmid = {41196745},
issn = {1939-2222},
support = {//National Natural Science Foundation of China/ ; //Natural Science Foundation of Guangdong Province/ ; //Guangzhou Science and Technology Plan Project-Leading Elite Program/ ; //Fundamental Research Funds for the Central Universities of China/ ; //Major Project Cultivation and Emerging Interdisciplinary Cultivation Plan/ ; //Shanghai Science and Technology Development Foundation/ ; },
abstract = {Voluntary imagery is described as "weak perception" and is thought to be represented through activating the neurons corresponding to imagined features, that is, activation hypothesis. However, direct evidence for this hypothesis is lacking. Inspired by Pace et al. (2023), we examine an alternative suppression hypothesis, which states imagery involves suppression of neurons favoring nearby nonimagined features. While the activation hypothesis predicts a bell-shaped tuning curve of the neural representation for the imagined feature, the suppression hypothesis predicts a W-shaped tuning curve. To test these two hypotheses, we combined an imagery task with a discrimination task following the logic that different imagery-induced tuning curves would differently bias the perceived difference in the discrimination task. We probed the bias pattern by systematically manipulating the physical orientation difference and the discrimination-imagery relation condition. A series of psychophysical experiments were conducted. Results showed that after an imagery prior, bias pattern in the discrimination task followed the prediction of suppression hypothesis (Experiment 1a). By contrast, when substituting the imagery prior with a strong/weak perceptual prior, bias pattern was consistent with the prediction of activation hypothesis (Experiments 2a and 2b). Confounding effects of visual attention and perceptual imagery cue were excluded (Experiments 1b and 1c). We further constructed mathematical models and again validated our findings. In conclusion, behavioral and modeling results coherently suggested that the suppression hypothesis was a better explanation for imagery than the activation hypothesis. Our study challenges the traditional activation theory and provides novel empirical evidence for the suppressive representation of voluntary visual imagery. (PsycInfo Database Record (c) 2025 APA, all rights reserved).},
}
@article {pmid41193512,
year = {2025},
author = {Kim, H and Won, K and Ahn, M and Jun, SC},
title = {A 40-Class SSVEP Speller Dataset: Beta Range Stimulation for Low-Fatigue BCI Applications.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {1751},
pmid = {41193512},
issn = {2052-4463},
support = {RS-2024-00361688//National Research Foundation of Korea (NRF)/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; *Fatigue ; Adult ; Photic Stimulation ; Male ; Female ; },
abstract = {The inherent non-stationarity of electroencephalography (EEG) signals necessitates large, consistent datasets for reliable brain-computer interface (BCI) research. In steady-state visual evoked potential (SSVEP) paradigms, prolonged exposure to visual stimuli can induce visual fatigue, leading to alterations in EEG patterns that degrade BCI performance. To mitigate fatigue-induced variability, this study employs visual stimulation in the beta frequency range (14-22 Hz), a range that appears less susceptible to the effects of fatigue. We present a comprehensive 40-class SSVEP speller dataset acquired from 40 participants, with EEG data recorded from 31 central-to-occipital channels. Each subject completed six sessions of the SSVEP speller task in addition to pre- and post-experiment resting-state recordings under both eyes-open and eyes-closed conditions. Subjective fatigue ratings combined with EEG band power analyses confirm that beta-range stimulation minimizes fatigue effects. Moreover, the high classification accuracy achieved by calibration-based algorithms indicates that the dataset is well-suited for training advanced SSVEP-based BCI systems.},
}
@article {pmid41193466,
year = {2025},
author = {Chiti, E and Micera, S and Palmerini, E},
title = {Making the case for sandboxes in implantable neurotechnologies.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {9783},
pmid = {41193466},
issn = {2041-1723},
abstract = {Regulatory sandboxes could be fruitfully used to boost Invasive Brain-Computer Interfaces, but they should be carefully designed. We highlight five elements are essential: they concern the entry criteria, the participated, adaptive and supervised design of decision-making process, and long-term risk management.},
}
@article {pmid41192731,
year = {2025},
author = {Dai, C and Lin, M and Xu, N and Fu, Y and Li, X and Shi, Y and Wu, M and Li, Y and Xie, J and Hu, S and Zhao, Q},
title = {The impact of CYP3A4 rs2242480 on oral lurasidone: A population pharmacokinetic model and exposure-efficacy analysis in Chinese bipolar depression patients.},
journal = {Journal of affective disorders},
volume = {394},
number = {Pt B},
pages = {120588},
doi = {10.1016/j.jad.2025.120588},
pmid = {41192731},
issn = {1573-2517},
abstract = {OBJECTIVE: This study aims to develop a population pharmacokinetic (PPK) model and perform an exposure-efficacy analysis for lurasidone in patients with bipolar depression, thus interpretating the inter-individual variability in its pharmacokinetics and optimizing dosing regimens.<br><br>METHODS: A PPK model and exposure-efficacy analysis were established in Chinese patients with bipolar depression. 241 lurasidone concentration measurments from 133 patients were included. Demographic information was collected and genotypes for CYP3A4 and HTR1A alleles were determined. Treatment efficacy was defined as the reduction in the Montgomery-Asberg Depression Rating Scale (MADRS) score at week 4.<br><br>RESULTS: A one-compartment model with first-order kinetics for lurasidone was fitted. The apparent clearance (CL/F) of lurasidone was significantly lower in CYP3A4 rs2242480 CC carriers (330&#xa0;L/h) than in TC (385&#xa0;L/h) and TT (441&#xa0;L/h) carriers, representing reductions of 14.3&#xa0;% and 25.2&#xa0;%, respectively. Additionally, CL/F was positively correlated with ideal body weight (IBW). Incorporating these covariates reduced the interindividual variability in CL/F from 40.5&#xa0;% to 37.1&#xa0;%. The exposure-efficacy analysis demonstrated a dose-denpedent increase in area under the curve (AUC), and MADRS score improved with an increasing AUC and reached a plateau at an AUC of approximately 167&#xa0;mg&#xb7;h&#xb7;L[-1], corresponding to an optimal daily dose range of 45-55&#xa0;mg.<br><br>CONCLUSION: The pharmacokinetics of lurasidone in patients with bipolar depression are significantly influenced by IBW and the rs2242480 genotype, enabling a practical framework for precision dosing.},
}
@article {pmid41192010,
year = {2025},
author = {Yang, M and Wang, Z and Zhou, Q and Zhang, Q and Li, Y and Wang, Z},
title = {The adjunctive efficacy of repetitive transcranial magnetic stimulation with non-pharmacological interventions in cognitive disorders: A meta-analysis of randomized sham-controlled trials.},
journal = {Asian journal of psychiatry},
volume = {114},
number = {},
pages = {104758},
doi = {10.1016/j.ajp.2025.104758},
pmid = {41192010},
issn = {1876-2026},
abstract = {OBJECTIVE: This meta-analysis aimed to systematically evaluate the specific, adjunctive efficacy of repetitive transcranial magnetic stimulation (rTMS) when combined with non-pharmacological interventions-namely, transcranial direct current stimulation (tDCS), Tai Chi, or cognitive training (CT)-in patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI). The goal is to isolate the net therapeutic contribution of rTMS beyond the effects of the base interventions alone.<br><br>METHODS: A comprehensive search of Chinese and English databases was conducted from their inception until April 26, 2025. Randomized controlled trials (RCTs) that compared "a non-pharmacological intervention plus active rTMS" versus "the same non-pharmacological intervention plus sham rTMS".This "add-on" study design was selected to precisely isolate the effect of rTMS. The risk of bias was assessed using the PEDro scale and Cochrane tools. Statistical analyses were performed using Review Manager 5.4 software.<br><br>RESULTS: 9 studies involving 391 participants were included. The pooled analysis revealed that the adjunctive use of rTMS was significantly superior to the sham control in improving global cognitive function at the immediate post-treatment assessment (SMD=0.38, 95&#x202f;%CI[0.20,0.56], P&#x202f;<&#x202f;.001,&#xa0;n&#x202f;=&#x202f;9). This benefit was consistent across the MMSE (SMD=0.38,&#xa0;n&#x202f;=&#x202f;6), MoCA (SMD=0.37,&#xa0;n&#x202f;=&#x202f;2), and ADAS-cog (SMD=0.39,&#xa0;n&#x202f;=&#x202f;3) scores. Subgroup analysis suggested that the rTMS-tDCS combination might offer a short-term advantage in improving MMSE scores (MD=4.67,&#xa0;P&#x202f;=&#x202f;.008). Furthermore, the adjunctive effect of rTMS was sustained, as particularly evidenced by the ADAS-cog at follow-up (SMD=0.74, P&#x202f;=&#x202f;.02). The pooled analysis indicated that rTMS combined with non-pharmacological therapy demonstrated a short-term, sustained (4-8weeks) improvement in global cognitive function (SMD=0.34, 95&#x202f;% CI[0.07, 0.60], P&#x202f;=&#x202f;.01). Subgroup analysis revealed that this sustained benefit reached statistical significance on the ADAS-cog scale (SMD = 0.41, 95&#x202f;%CI[0.01, 0.81], P&#x202f;=&#x202f;.04) but showed a non-significant positive trend on the MMSE (SMD=0.26,&#xa0;95&#x202f;%CI[-0.19, 0.72], P&#x202f;=&#x202f;.26). However, a key limitation was that most studies did not systematically report outcomes related to activities of daily living or behavioral function.<br><br>CONCLUSION: The evidence indicates that rTMS as an adjunct to non-pharmacological interventions provides a significant specific effect on global cognitive function in patients with AD and MCI shortly after treatment, which may be sustained in the short-term. However, long-term follow-up data are extremely limited, and the effect on activities of daily living remains to be validated. The combination of rTMS and tDCS shows promise,but conclusions are constrained by the small number of studies,limited sample sizes,and heterogeneity in intervention protocols. Future large-scale studies incorporating long-term, standardized follow-up and assessments of daily living abilities are warranted to confirm the specific clinical value of rTMS as an augmentative therapy.},
}
@article {pmid41191990,
year = {2025},
author = {Xing, Y and He, Y and Gong, Z and Zhou, J and Sun, Y and Zhong, Z},
title = {A study on the microstructure and micromechanical properties of Drosophila larval cuticle using scanning probe microscopy and viscoelastic modeling.},
journal = {Journal of biomechanics},
volume = {194},
number = {},
pages = {113051},
doi = {10.1016/j.jbiomech.2025.113051},
pmid = {41191990},
issn = {1873-2380},
abstract = {The Drosophila larval cuticle exhibits compliant yet resilient viscoelasticity, serving as a soft exoskeleton that enables effective locomotion while maintaining structural integrity. Investigating its microstructure and micromechanical properties not only advances our understanding of soft-bodied biomechanics but also guides the design of biomimetic materials and soft robotic systems. In this study, we employed scanning probe microscopy (SPM)-based stress relaxation tests to characterize viscoelastic properties across the denticle and smooth skin bands in three larval instars. Four viscoelastic models were evaluated, and the five-element Maxwell (MX5) model provided the best fit, enabling the extraction of mechanical parameters and plotting of relaxation modulus functions. Results showed that the larval instar stage had minimal influence on viscoelasticity, while the denticle and smooth skin bands exhibited distinct mechanical behaviors. Across all instars, the denticle bands showed higher moduli throughout the relaxation process, and notably, exhibited a greater degree and faster rate of relaxation compared to the smooth skin bands. These findings reveal region-specific viscoelastic adaptations that enable rapid stress dissipation while maintaining stiffness, supporting effective deformation during locomotion. This study provides essential quantitative foundations for bioinspired stretchable electronics, soft robotic materials, and broader understanding of soft exoskeleton mechanics.},
}
@article {pmid41191976,
year = {2025},
author = {Forrest, A and Kunigk, NG and Collinger, J and Gaunt, RA and Vande Geest, JP and Chen, X and Kozai, TDY},
title = {Finite element model predicts micromotion-induced strain profiles that correlate with the functional performance of Utah arrays in humans and non-human primates.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae1bda},
pmid = {41191976},
issn = {1741-2552},
abstract = {OBJECTIVE: Utah arrays are widely used in both humans and non-human primates (NHPs) for intracortical brain-computer interfaces (BCIs), primarily for detecting electrical signals from cortical tissue to decode motor commands. Recently, these arrays have also been applied to deliver electrical stimulation aimed at restoring sensory functions. A key challenge limiting their longevity is the micromotion between the array and cortical tissue, which may induce mechanical strain in surrounding tissue and contribute to performance decline. This strain, due to mechanical mismatch, can exacerbate glial scarring around the implant, reducing the efficacy of Utah arrays in recording neuronal activity and delivering electrical stimulation.<br><br>APPROACH: To investigate this, we employed a finite element model (FEM) to predict tissue strains resulting from micromotion.<br><br>MAIN RESULTS: Our findings indicated that strain profiles around edge and corner electrodes were greater than those around interior shanks, affecting both maximum and average strains within 50 &#xb5;m of the electrode tip. We then correlated these predicted tissue strains with in-vivo electrode performance metrics. We found negative correlations between 1 kHz impedance and tissue strains in human motor arrays and NHP area V4 arrays at 1-mo, 1-yr, and 2-yrs post-implantation. In human motor arrays, the peak-to-peak waveform voltage (PTPV) and signal-to-noise ratio (SNR) of spontaneous activity were also negatively correlated with strain. Conversely, we observed a positive correlation between the evoked SNR of multi-unit activity and strain in NHP area V4 arrays.<br><br>SIGNIFICANCE: This study establishes a spatial dependence of electrode performance in Utah arrays that correlates with tissue strain.},
}
@article {pmid41191971,
year = {2025},
author = {Bjanes, D and Bashford, L and Pejsa, K and Lee, B and Liu, CY and Andersen, RA},
title = {Charge density of multi-channel intra-cortical micro-stimulation modulates intensity and naturalness of evoked somatosensations.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae1bd8},
pmid = {41191971},
issn = {1741-2552},
abstract = {Human patients with somatosensory loss often experience severe motor deficits, causing profound challenges to independently accomplish typical tasks of daily life. Brain-machine Interfaces (BMIs) offer the potential to restore lost functionality through direct electrical stimulation of the somatosensory cortex via intra-cortical micro-stimulation (ICMS). By modulating temporal patterns of stimulation, our group has previously shown single-channel ICMS can evoke both naturalistic cutaneous and proprioceptive sensory feedback. However, accurate modulation of the sensory feedback's qualia (somatotopic location, intensity and description) will be critical for fluid, dexterous motor control. In nonhuman primate studies, multi-channel ICMS has shown promise in improving quantifiable metrics such as reaction time. In recent human work, multi-channel ICMS has improved discrimination performance; however, evoked qualia has not been well characterized. We hypothesized multi-channel ICMS could evoke unique qualia compared to single-channel. A human participant with tetraplegia and chronically implanted microelectrode arrays in primary somatosensory cortex, reported perceptual thresholds, sensation descriptions, intensity and somatotopic locations of single- and multi-channel ICMS patterns. We found multi-channel ICMS patterns evoked unique qualia compared to single-channel ICMS. To investigate the role of charge in producing these unique evoked sensory percepts, we delivered equal amounts of charge with differing spatial patterns across multiple electrodes. Multi-channel ICMS substantially reduced the minimum stimulation amplitude required to evoked somatosensations, lowering the charge per electrode detection threshold, while increasing the total charge injected. Delivered charge across multiple electrodes, positively modulated the sensation's perceived intensity; providing early evidence of spatial integration of ICMS in the target network. Multi-channel ICMS resulted in more frequent verbal reports of "natural" sensation descriptors (100% vs 85% for single-channel ICMS, p-val<0.05) and robustly evoked sensations with high repeatability in stable somatotopic locations. Multi-channel ICMS patterns demonstrated improvements in reliability, somatotopic coverage and "natural-ness" of the evoked sensations, marking significant advances towards state-of-the-art somatosensory brain-machine-interfaces (BMIs). By better understanding of the input/output relationship for somatosensory feedback BMIs, we can expect to improve movement accuracy and increase embodiment for human users. .},
}
@article {pmid41191851,
year = {2025},
author = {Williams, C and Anik, FI and Hasan, MM and Rodriguez-Cardenas, J and Chowdhury, A and Tian, S and He, S and Sakib, N},
title = {Advancing Brain-Computer Interface Closed-Loop Systems for Neurorehabilitation: Systematic Review of AI and Machine Learning Innovations in Biomedical Engineering.},
journal = {JMIR biomedical engineering},
volume = {10},
number = {},
pages = {e72218},
pmid = {41191851},
issn = {2561-3278},
abstract = {BACKGROUND: Brain-computer interface (BCI) closed-loop systems have emerged as a promising tool in health care and wellness monitoring, particularly in neurorehabilitation and cognitive assessment. With the increasing burden of neurological disorders, including Alzheimer disease and related dementias (AD/ADRD), there is a critical need for real-time, noninvasive monitoring technologies. BCIs enable direct communication between the brain and external devices, leveraging artificial intelligence (AI) and machine learning (ML) to interpret neural signals. However, challenges such as signal noise, data processing limitations, and privacy concerns hinder widespread implementation.<br><br>OBJECTIVE: The primary objective of this study is to investigate the role of ML and AI in enhancing BCI closed-loop systems for health care applications. Specifically, we aim to analyze the methods and parameters used in these systems, assess the effectiveness of different AI and ML techniques, identify key challenges in their development and implementation, and propose a framework for using BCIs in the longitudinal monitoring of AD/ADRD patients. By addressing these aspects, this study seeks to provide a comprehensive overview of the potential and limitations of AI-driven BCIs in neurological health care.<br><br>METHODS: A systematic literature review was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, focusing on studies published between 2019 and 2024. We sourced research articles from PubMed, IEEE, ACM, and Scopus using predefined keywords related to BCIs, AI, and AD/ADRD. A total of 220 papers were initially identified, with 18 meeting the final inclusion criteria. Data extraction followed a structured matrix approach, categorizing studies based on methods, ML algorithms, limitations, and proposed solutions. A comparative analysis was performed to synthesize key findings and trends in AI-enhanced BCI systems for neurorehabilitation and cognitive monitoring.<br><br>RESULTS: The review identified several ML techniques, including transfer learning (TL), support vector machines (SVMs), and convolutional neural networks (CNNs), that enhance BCI closed-loop performance. These methods improve signal classification, feature extraction, and real-time adaptability, enabling accurate monitoring of cognitive states. However, challenges such as long calibration sessions, computational costs, data security risks, and variability in neural signals were also highlighted. To address these issues, emerging solutions such as improved sensor technology, efficient calibration protocols, and advanced AI-driven decoding models are being explored. In addition, BCIs show potential for real-time alert systems that support caregivers in managing AD/ADRD patients.<br><br>CONCLUSIONS: BCI closed-loop systems, when integrated with AI and ML, offer significant advancements in neurological health care, particularly in AD/ADRD monitoring and neurorehabilitation. Despite their potential, challenges related to data accuracy, security, and scalability must be addressed for widespread clinical adoption. Future research should focus on refining AI models, improving real-time data processing, and enhancing user accessibility. With continued advancements, AI-powered BCIs can revolutionize personalized health care by providing continuous, adaptive monitoring and intervention for patients with neurological disorders.},
}
@article {pmid41191764,
year = {2025},
author = {Qian, Y and Liu, C and Yu, P and Ran, X and Li, S and Yang, Q and Liu, Y and Xia, L and Wang, Y and Qi, J and Zhou, E and Lu, J and Li, Y and Tao, TH and Zhou, Z and Wu, J},
title = {Real-time decoding of full-spectrum Chinese using brain-computer interface.},
journal = {Science advances},
volume = {11},
number = {45},
pages = {eadz9968},
pmid = {41191764},
issn = {2375-2548},
mesh = {*Brain-Computer Interfaces ; Humans ; Language ; Male ; *Speech/physiology ; Female ; Adult ; Electroencephalography ; China ; East Asian People ; },
abstract = {Speech brain-computer interfaces (BCIs) offer a promising means to provide functional communication capacity for patients with anarthria caused by neurological conditions such as amyotrophic lateral sclerosis (ALS) or brainstem stroke. Current speech decoding research has predominantly focused on English using phoneme-driven architectures, whereas real-time decoding of tonal monosyllabic languages such as Mandarin Chinese remains a major challenge. This study demonstrates a real-time Mandarin speech BCI that decodes monosyllabic units directly from neural signals. Using the 256-channel microelectrocorticographic BCI, we achieved robust decoding of a comprehensive set of 394 distinct syllables based purely on neural signals, yielding median syllable identification accuracy of 71.2% in a single-character reading task. Leveraging this high-performing syllable decoder, we further demonstrated real-time sentence decoding. Our findings demonstrate the efficacy of a tonally integrated, direct syllable neural decoding approach for Mandarin Chinese, paving the way for full-coverage systems in tonal monosyllabic languages.},
}
@article {pmid41187598,
year = {2025},
author = {Jui, JJ and Hettiarachchi, IT and Bhatti, A and Creighton, D},
title = {PLVNet: EEG-based trust classification using Phase Locking Value connectivity and deep neural networks.},
journal = {Computers in biology and medicine},
volume = {198},
number = {Pt B},
pages = {111269},
doi = {10.1016/j.compbiomed.2025.111269},
pmid = {41187598},
issn = {1879-0534},
mesh = {Humans ; *Electroencephalography/methods ; *Trust ; Male ; *Neural Networks, Computer ; Female ; Adult ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; *Brain/physiology ; Young Adult ; },
abstract = {Trust in automation is critical for effective human-automation interaction, yet traditional subjective measures are limited in capturing rapid and dynamic changes in user trust. This study introduces PLVNet, a novel deep neural network architecture designed to classify trust versus distrust states from EEG functional connectivity features derived using Phase Locking Value (PLV). PLV features were extracted across six canonical EEG frequency bands (Delta, Theta, Alpha, Beta, Low Gamma, High Gamma) from 30-channel EEG recordings. The PLVNet model was evaluated using three complementary approaches: aggregated analysis (5× 5 stratified cross-validation), participant-wise analysis, and leave-one-subject-out (LOSO) cross-validation. PLVNet significantly outperformed convolutional neural network (CNN), support vector machine (SVM) and k-nearest neighbours (KNN) classifiers across all evaluation schemes. Beta and Low Gamma bands provided the highest discriminative power, while functional connectivity analysis revealed that trust is associated with enhanced fronto-parietal and fronto-occipital synchronisation, reflecting global network integration, whereas distrust shows fragmented connectivity patterns. PLVNet's ability to capture non-linear inter-dependencies in connectivity patterns highlights its advantages over conventional methods. These findings demonstrate that PLV-based connectivity robustly reflects trust-related neural dynamics, underscoring the potential of PLVNet for real-time, objective monitoring of trust in human-automation systems, which paves the way for adaptive and neuro-aware interfaces.},
}
@article {pmid41187597,
year = {2025},
author = {Liu, J and Deng, X and Li, H and Kazemi, A and Grashei, C and Wilkens, G and You, X and Groll, T and Navab, N and Mogler, C and Schüffler, PJ},
title = {From pixels to pathology: Restoration diffusion for diagnostic-consistent virtual IHC.},
journal = {Computers in biology and medicine},
volume = {198},
number = {Pt B},
pages = {111264},
doi = {10.1016/j.compbiomed.2025.111264},
pmid = {41187597},
issn = {1879-0534},
mesh = {Humans ; *Immunohistochemistry/methods ; *Breast Neoplasms/metabolism/pathology/diagnosis/diagnostic imaging ; Female ; *Image Processing, Computer-Assisted/methods ; *Image Interpretation, Computer-Assisted/methods ; Biomarkers, Tumor/metabolism ; },
abstract = {Hematoxylin and eosin (H&amp;E) staining is the clinical standard for assessing tissue morphology, but it lacks molecular-level diagnostic information. In contrast, immunohistochemistry (IHC) provides crucial insights into biomarker expression, such as HER2 status for breast cancer grading, but remains costly and time-consuming, limiting its use in time-sensitive clinical workflows. To address this gap, virtual staining from H&amp;E to IHC has emerged as a promising alternative, yet faces two core challenges: (1) Lack of fair evaluation of synthetic images against misaligned IHC ground truths, and (2) preserving structural integrity and biological variability during translation. To this end, we present an end-to-end framework encompassing both generation and evaluation in this work. We introduce Star-Diff, a structure-aware staining restoration diffusion model that reformulates virtual staining as an image restoration task. By combining residual and noise-based generation pathways, Star-Diff maintains tissue structure while modeling realistic biomarker variability. To evaluate the diagnostic consistency of the generated IHC patches, we propose the Semantic Fidelity Score (SFS), a clinical-grading-task-driven metric that quantifies class-wise semantic degradation based on biomarker classification accuracy. Unlike pixel-level metrics such as SSIM and PSNR, SFS remains robust under spatial misalignment and classifier uncertainty. Experiments on the BCI dataset demonstrate that Star-Diff achieves state-of-the-art (SOTA) performance in both visual fidelity and diagnostic relevance. With rapid inference and strong clinical alignment, it presents a practical solution for applications such as intraoperative virtual IHC synthesis.},
}
@article {pmid41187327,
year = {2025},
author = {Bialostocki, LS and Adhia, DB and Mudiyanselage, DR and Smith, ML and Cakmak, YO and De Ridder, D and Mani, R and Mathew, J},
title = {Neurofeedback Training for Managing Neuropathic Pain-Like Features in Chronic Musculoskeletal Pain: Protocol for an Open-Label Pilot Feasibility Clinical Trial.},
journal = {JMIR research protocols},
volume = {14},
number = {},
pages = {e78806},
doi = {10.2196/78806},
pmid = {41187327},
issn = {1929-0748},
mesh = {Humans ; *Neurofeedback/methods ; Pilot Projects ; *Neuralgia/therapy/physiopathology ; Feasibility Studies ; *Musculoskeletal Pain/therapy/physiopathology ; Electroencephalography/methods ; *Chronic Pain/therapy ; Adult ; Male ; Female ; Middle Aged ; Pain Measurement ; *Pain Management/methods ; },
abstract = {BACKGROUND: Neuropathic pain (NP) is characterized as pain arising from lesions of the somatosensory nervous system. However, NP-like features have been found in several chronic secondary musculoskeletal (MSK) pain conditions in the absence of detectable lesion or damage to the somatosensory pathways. Emerging evidence has demonstrated associations between NP-like symptoms and altered neural activity within brain regions implicated in sensory perception and affective-emotional processing of pain with consistent findings of abnormal activity in the right insula (RIns) cortex and dorsal anterior cingulate cortex (dACC). Electroencephalography neurofeedback (EEG-NF) is a brain-computer interface biofeedback technique that allows individuals to self-regulate the real-time cortical brain activities of the regions of interest.<br><br>OBJECTIVE: The primary objective of this study is to investigate the feasibility and safety of a novel EEG-NF intervention designed to simultaneously downtrain activity in the RIns and dACC in individuals with a chronic secondary MSK pain condition exhibiting NP-like features. In addition, this study will conduct secondary exploratory analyses to investigate EEG-derived neuronal changes and their associations with clinical and experimental pain outcomes following the EEG-NF training.<br><br>METHODS: We will design a single-arm, open-label, pilot-feasibility trial. We will recruit adults aged 35-75 years with a score of &#x2265;19 using the PainDETECT questionnaire and an average pain score of &#x2265;4 on the 11-point Numeric Pain Rating Scale over the last 3 months, with a minimum pain duration of 3 months, to receive active EEG-NF training. Participants will receive auditory feedback as a reward for achieving a predetermined activity threshold of the RIns and dACC. Primary outcomes will evaluate feasibility, acceptability, and safety using both self-reported questionnaires and monitoring data. Collected data will be summarized descriptively, with mean (SD) reported where appropriate. Secondary outcomes will include EEG parameters, self-reported measures, heart rate variability, and quantitative sensory testing. An exploratory within-group pre-post statistical comparison will be conducted for all secondary outcome measures, and correlation analysis will be performed to explore relationships between EEG measures, self-reported outcomes, heart rate variability, and quantitative sensory testing measures.<br><br>RESULTS: This study has received approval from the Health and Disability Ethics Committee and is registered with the Australian New Zealand Clinical Trials Registry. Participant recruitment began in April 2025 and is ongoing. As of October 2025, data collection has been completed, with a total of 5 participants enrolled, all of whom have completed the study to date. We expect to complete the study in March 2026. This study will generate data on feasibility, safety, acceptability, and preliminary data to inform a fully powered effectiveness clinical trial.<br><br>CONCLUSIONS: The results and data generated will inform the design and sample size calculation for a fully powered randomized controlled trial aimed at evaluating the effectiveness of EEG-NF in targeting NP-like features in individuals with chronic MSK pain.<br><br>TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12625000706471; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=389568&amp;isReview=true.<br><br>DERR1-10.2196/78806.},
}
@article {pmid41183389,
year = {2025},
author = {Yang, T and Cai, S and Xu, D and Hu, N},
title = {End-to-End EEG Artifact Removal Method via Nested Generative Adversarial Network.},
journal = {Biomedical physics &amp; engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/ae1a8c},
pmid = {41183389},
issn = {2057-1976},
abstract = {As physiological artifacts commonly overlap with EEG signals in both time and frequency domains, developing an effective end-to-end EEG artifact removal method is essential for a brain-computer interface (BCI) system. Approach. An end-to-end artifact removal method based on nested generative adversarial network (GAN) is proposed, to recover the EEG signals from artifact-contaminated ones. The nested GAN consists of two components: an inner GAN operating in time-frequency domain and an outer GAN functioning in time domain. A light-weighted complex-valued restormer, designed in time-frequency domain, is employed as the generator to reconstruct the denoised EEG signal. Two metric discriminators in the inner GAN and two multi-resolution discriminators in the outer GAN are used, and gradient balance is used to address the partial learning issue during training. Main results. The performance of the nested GAN has been evaluated in the realistic EEG dataset and semi-synthetic dataset. Compared to the benchmark methods, the proposed one achieved best average performance evaluation metrics, including mean square error (MSE) = 0.098, Pearson correlation coefficient (PCC) = 0.892, relative root MSE (RRMSE) = 0.065, the percentage reduction of time domain artifacts () = 71.6%, and the percentage reduction of frequency domain artifacts () = 76.9%. The performance of artifact removal also showed robustness across a wide range of signal-to-noise ratio (SNR) levels. Significance. The superior performance of the proposed end-to-end artifact removal method is expected to contribute to the advancement of BCI system development. .},
}
@article {pmid41183383,
year = {2025},
author = {Sharafkhani, N and Zhang, H},
title = {Deployable electrode arrays for brain interfaces: structural reconfiguration strategies for long-term stability and high-fidelity recording - a review.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae1ab3},
pmid = {41183383},
issn = {1741-2552},
abstract = {Neural electrodes, as essential tools for recording and stimulating neural tissues, significantly impact therapeutic strategies for neurological disorders through deep brain stimulation, responsive neurostimulation, and brain-computer interfaces. Despite considerable advancements, the efficiency and longevity of neural electrodes are compromised by brain micromotion, induced by physiological activities such as cardiac pulsation and respiration. The mechanical mismatch between rigid electrodes and soft neural tissue generates persistent stresses at the electrode-tissue interface, triggering tissue damage, inflammatory responses, encapsulation, and ultimately electrode failure. Deployable neural electrodes, characterized by structural reconfiguration after implantation, have emerged to address these challenges. Deployment mechanisms, including unfolding, expanding, unrolling, or ejecting electrode arms from an initially compact configuration, reduce insertion trauma, maximize spatial coverage, and mitigate brain micromotion effects, thereby enhancing long-term stability and recording fidelity. Approach. This review provides the first comprehensive analysis of deployable intracortical and electrocorticography electrodes, emphasizing their design principles, deployment mechanisms, mechanical performance, advantages, and limitations. This review fills a critical gap in the existing neural electrode literature by transitioning the focus from traditional geometric and material considerations to advanced structural reconfiguration strategies. Significance. An understanding of the advantages and disadvantages of these deployment strategies provides essential insights and future directions for optimizing neural electrode technologies. .},
}
@article {pmid41182766,
year = {2025},
author = {O'Regan, RM and Ren, Y and Zhang, Y and Siuliukina, N and Schnabel, CA and Kammler, R and Viale, G and Dell'Orto, P and Munzone, E and Láng, I and Tondini, C and Gomez, HL and Chini, C and Nicoletti, SVL and Puglisi, F and Zaman, K and Goetz, MP and Stearns, V and Martino, S and Salim, M and Loibl, S and Geyer, CE and Bonnefoi, HR and Ciruelos, EM and Loi, S and Colleoni, M and Fleming, GF and Francis, PA and Walley, BA and Pagani, O and Treuner, K and Regan, MM},
title = {Assessment of Adjuvant Endocrine Therapy With Ovarian Function Suppression by Breast Cancer Index.},
journal = {JAMA network open},
volume = {8},
number = {11},
pages = {e2540931},
pmid = {41182766},
issn = {2574-3805},
mesh = {Humans ; Female ; *Breast Neoplasms/drug therapy ; *Tamoxifen/therapeutic use ; Chemotherapy, Adjuvant/methods ; Adult ; Middle Aged ; Androstadienes/therapeutic use ; Prospective Studies ; *Antineoplastic Agents, Hormonal/therapeutic use ; Prognosis ; Retrospective Studies ; *Ovary/drug effects ; Premenopause ; Biomarkers, Tumor ; Homeodomain Proteins ; Receptors, Interleukin-17 ; },
abstract = {IMPORTANCE: The Breast Cancer Index (BCI) previously identified premenopausal patients with tumors in which the ratio of expression of HOXB13 relative to IL17BR (hereafter, BCI [H/I]-low tumors) as likely to derive greatest benefit from ovarian function suppression (OFS)-containing adjuvant therapy in the Suppression of Ovarian Function Trial (SOFT) trial.<br><br>OBJECTIVES: To assess BCI as a predictive biomarker of benefit from exemestane plus OFS vs tamoxifen plus OFS and to validate BCI as a prognostic biomarker for premenopausal patients.<br><br>This prognostic study used a prospective-retrospective translational design within the Tamoxifen and Exemestane (TEXT) and SOFT trials (enrolled November 2003 to April 2011). Blinded BCI testing in all available tumor samples was completed in March 2024. Premenopausal women with hormone receptor-positive breast cancer randomized to tamoxifen plus OFS or exemestane plus OFS who had BCI assessed were included. Analysis occurred from March to August 2024.<br><br>EXPOSURE: 5 years of adjuvant tamoxifen plus OFS or exemestane plus OFS.<br><br>MAIN OUTCOMES AND MEASURES: The primary outcomes were breast cancer-free interval (BCFI) for predictive analyses and distant recurrence-free interval (DRFI) for prognostic analyses after a median follow-up of 13 years in the TEXT cohort. Secondary objectives examined the predictive performance of BCI (H/I) in the combined TEXT and SOFT cohort overall and in prespecified clinical subgroups.<br><br>RESULTS: Of 1782 patients in the TEXT study, 1034 (58.0%) had BCI (H/I)-low tumors; 915 (51.3%) of patients had N0 disease and 1077 (60.4%) were younger than 45 years. Patients with BCI (H/I)-low tumors had a 6.6% absolute benefit in 12-year BCFI (HR, 0.61; 95% CI, 0.44-0.85) for exemestane plus OFS vs tamoxifen plus OFS, while those with BCI (H/I)-high tumors had a 6.3% absolute benefit (HR, 0.78; 95% CI, 0.57-1.07; P for interaction&#x2009;=&#x2009;.29). Results were consistent in the combined TEXT plus SOFT cohort (2896 patients) and adjusting for clinicopathological variables. Clinical subgroup analyses consistently showed benefit of exemestane plus OFS vs tamoxifen plus OFS for BCI (H/I)-low tumors, and more variable relative treatment effects among BCI (H/I)-high tumors, including by age. Post hoc exploratory time-varying estimates suggested the treatment&#x2009;&#xd7;&#x2009;BCI associations may differ in years 0 to 5 vs greater than 5 years. BCI and BCI N+ as continuous indices were prognostic for distant recurrence in N0 (HR, 1.27; 95% CI, 1.11-1.44; P&#x2009;<&#x2009;.001) and N1 (HR, 1.58; 95% CI, 1.21-2.05; P&#x2009;<&#x2009;.001) cancers. The 12-year DRFI was 96.3%, 90.3%, and 84.9% for BCI low-, intermediate-, and high-risk N0 cancers, respectively.<br><br>CONCLUSIONS AND RELEVANCE: In this study of premenopausal women with hormone receptor-positive breast cancer, BCI (H/I) status did not clearly predict greater benefit of adjuvant exemestane plus OFS vs tamoxifen plus OFS for women with BCI (H/I)-low tumors than for those with BCI (H/I)-high tumors; BCI continuous indices were reconfirmed as prognostic for premenopausal women. These findings support prior results of SOFT, which compared tamoxifen-alone vs OFS with either exemestane or tamoxifen, indicating premenopausal patients with BCI (H/I)-low tumors may benefit from more intensive endocrine therapy.},
}
@article {pmid41182326,
year = {2025},
author = {Yuan, Z and Chen, F and Huang, X and Huang, K and Song, Z and Ding, Y and Gong, Z and Gu, G},
title = {Soft Tubular-Surface Rolling Robots.},
journal = {Soft robotics},
volume = {},
number = {},
pages = {},
doi = {10.1177/21695172251387190},
pmid = {41182326},
issn = {2169-5180},
abstract = {Soft creatures like Drosophila larvae can quickly ascend tubular surfaces via rolling, a capability not yet replicated by soft robots. Here, we present a single-piece soft robot capable of rolling along tubular structures by sequentially actuating its built-in axial muscles. We reveal that the sequential actuation generates distributed spinning torques along the robot's curved axis, enabling continuous non-coaxial rolling-distinct from current gravity-dependent rolling solutions. This non-coaxial rolling mechanism allows the robot to swiftly navigate tubular surfaces while conforming to their shapes and maintaining a stable grip. The robot's deformation and gripping force are actively adjusted to enhance its adaptability to various surfaces. We demonstrate that our robot can ascend pipes with varying geometries (e.g., varying-diameter, spiral-shaped, or non-cylindrical), traverse diverse terrains, pass through confined tunnels, and transition smoothly between planar rolling and pipe climbing. The robot's great adaptability and rapid movement underscore its potential for navigating scenarios with intricate surface geometries.},
}
@article {pmid41180699,
year = {2025},
author = {Ali, E and Kamran, S and Cheema, AAA},
title = {Brain-computer interfaces in post-stroke rehabilitation: a neurotechnological leap toward functional recovery.},
journal = {Annals of medicine and surgery (2012)},
volume = {87},
number = {11},
pages = {7784-7785},
pmid = {41180699},
issn = {2049-0801},
}
@article {pmid41180117,
year = {2025},
author = {Hall, R and Jackson, M and Maleki, M and Crogman, HT},
title = {Modeling cognition through adaptive neural synchronization: a multimodal framework using EEG, fMRI, and reinforcement learning.},
journal = {Frontiers in computational neuroscience},
volume = {19},
number = {},
pages = {1616472},
pmid = {41180117},
issn = {1662-5188},
abstract = {INTRODUCTION: Understanding the cognitive process of thinking as a neural phenomenon remains a central challenge in neuroscience and computational modeling. This study addresses this challenge by presenting a biologically grounded framework that simulates adaptive decision making across cognitive states.<br><br>METHODS: The model integrates neuronal synchronization, metabolic energy consumption, and reinforcement learning. Neural synchronization is simulated using Kuramoto oscillators, while energy dynamics are constrained by multimodal activity profiles. Reinforcement learning agents-Q-learning and Deep Q-Network (DQN)-modulate external inputs to maintain optimal synchrony with minimal energy cost. The model is validated using real EEG and fMRI data, comparing simulated and empirical outputs across spectral power, phase synchrony, and BOLD activity.<br><br>RESULTS: The DQN agent achieved rapid convergence, stabilizing cumulative rewards within 200 episodes and reducing mean synchronization error by over 40%, outperforming Q-learning in speed and generalization. The model successfully reproduced canonical brain states-focused attention, multitasking, and rest. Simulated EEG showed dominant alpha-band power (3.2&#x202f;&#xd7;&#x202f;10[-4]&#x202f;a.u.), while real EEG exhibited beta-dominance (3.2&#x202f;&#xd7;&#x202f;10[-4]&#x202f;a.u.), indicating accurate modeling of resting states and tunability for active tasks. Phase Locking Value (PLV) ranged from 0.9806 to 0.9926, with the focused condition yielding the lowest circular variance (0.0456) and a near significant phase shift compared to rest (t&#x202f;=&#x202f;-2.15, p&#x202f;=&#x202f;0.075). Cross-modal validation revealed moderate correlation between simulated and real BOLD signals (r&#x202f;=&#x202f;0.30, resting condition), with delayed inputs improving temporal alignment. General Linear Model (GLM) analysis of simulated BOLD data showed high region-specific prediction accuracy (R [2]&#x202f;=&#x202f;0.973-0.993, p&#x202f;<&#x202f;0.001), particularly in prefrontal, parietal, and anterior cingulate cortices. Voxel-wise correlation and ICA decomposition confirmed structured network dynamics.<br><br>DISCUSSION: These findings demonstrate that the framework captures both electrophysiological and spatial aspects of brain activity, respects neuroenergetic constraints, and adaptively regulates brain-like states through reinforcement learning. The model offers a scalable platform for simulating cognition and developing biologically inspired neuroadaptive systems.<br><br>CONCLUSION: This work provides a novel and testable approach to modeling thinking as a biologically constrained control problem and lays the groundwork for future applications in cognitive modeling and brain-computer interfaces.},
}
@article {pmid41179991,
year = {2025},
author = {Yuan, L and Wei, J and Liu, Y},
title = {Spiking neural networks for EEG signal analysis using wavelet transform.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1652274},
pmid = {41179991},
issn = {1662-4548},
abstract = {INTRODUCTION: Brain-computer interfaces (BCIs) leverage EEG signal processing to enable human-machine communication and have broad application potential. However, existing deep learning-based BCI methods face two critical limitations that hinder their practical deployment: reliance on manual EEG feature extraction, which constrains their ability to adaptively capture complex neural patterns, and high energy consumption characteristics that make them unsuitable for resource-constrained portable BCI devices requiring edge deployment.<br><br>METHODS: To address these limitations, this work combines wavelet transform for automatic feature extraction with spiking neural networks for energy-efficient computation. Specifically, we present a novel spiking transformer that integrates a spiking self-attention mechanism with discrete wavelet transform, termed SpikeWavformer. SpikeWavformer enables automatic EEG signal time-frequency decomposition, eliminates manual feature extraction, and provides energy-efficient classification decision-making, thereby enhancing the model's cross-scene generalization while meeting the constraints of portable BCI applications.<br><br>RESULTS: Experimental results demonstrate the effectiveness and efficiency of SpikeWavformer in emotion recognition and auditory attention decoding tasks.<br><br>DISCUSSION: These findings indicate that SpikeWavformer can address the key limitations of existing BCI methods and holds promise for practical deployment in portable, resource-constrained scenarios.},
}
@article {pmid41179694,
year = {2025},
author = {Fernández-Rodríguez, &#xc1; and Velasco-Álvarez, F and Vizcaíno-Martín, FJ and Ron-Angevin, R},
title = {Impact of stimulus presentation speed in a visual ERP-based BCI under RSVP.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {171},
pmid = {41179694},
issn = {1871-4080},
abstract = {Rapid serial visual presentation (RSVP) is one of the most effective gaze-independent paradigms for event-related potential (ERP)-based brain-computer interfaces (BCIs), particularly for individuals with limited muscle and eye movement control. The speed of visual stimulus presentation is a critical factor influencing system performance and warrants thorough investigation. This study evaluates the impact of different stimulus presentation speeds on the performance of an ERP-BCI used for pictogram selection under RSVP. Thirteen participants tested the ERP-BCI across three experimental conditions, each with a different stimulus onset asynchrony (SOA): 80 ms (C080), 160 ms (C160), and 320 ms (C320). In addition to performance metrics such as accuracy, information transfer rate (ITR), and pictograms per minute (PPM), a subjective evaluation of the user experience was conducted for each condition. The results indicate that C160 outperformed both C080 and C320 across all performance metrics, achieving an ITR of 26.49 bit/min (81.28% accuracy in 4.8 s). Subjective evaluations also revealed a preference for C160 and C320 over C080. Therefore, among the SOAs evaluated, 160 ms appears to be the most suitable for enhancing system usability. These findings underscore the crucial role of stimulus presentation speed in the usability of ERP-BCIs for pictogram selection under RSVP, emphasizing its importance in future gaze-independent ERP-BCI designs for communication purposes.},
}
@article {pmid41178032,
year = {2025},
author = {Li, Q and Choi, EPH and Gou, M and Tian, Y and Baptiste, D},
title = {Brain-Computer Interface: Bring Care Into a Future Phase? Challenges and Opportunities for Nursing in the Era of Emerging Technologies.},
journal = {Nursing open},
volume = {12},
number = {11},
pages = {e70345},
pmid = {41178032},
issn = {2054-1058},
}
@article {pmid41177816,
year = {2025},
author = {Fu, R and Liu, Y and Wang, Z and Liang, Z},
title = {Virtual Reality (VR) Paradigm-Agnostic Motor Imagery Decoding Using Lightweight Network With Adaptive Attention Mechanism.},
journal = {Journal of medical systems},
volume = {49},
number = {1},
pages = {152},
pmid = {41177816},
issn = {1573-689X},
support = {62073282//National Natural Science Foundation of China/ ; F2022203092//Natural Science Foundation of Hebei Province/ ; 202302B015//the S&amp;T Program of Qinhuangdao City/ ; KFKT2025B88//Project of State Key Laboratory for Novel Software Technology/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Virtual Reality ; Algorithms ; *Imagination/physiology ; Electroencephalography ; Movement/physiology ; Attention ; Male ; Adult ; },
abstract = {Motor imagery (MI) is widely used in brain-computer interfaces (BCIs) due to its simplicity and reproducibility, enabling individuals with motor impairments to perform non-muscular limb training for the rehabilitation of motor-related neurons. While MI-based BCIs have shown promise for neurorehabilitation, current 2D paradigms fail to engage critical sensorimotor networks. To address this limitation, we designed an immersive MI paradigm in a virtual reality (VR) environment, where participants imagined limb movements in response to continuous three-dimensional (3D) palm motion stimuli. Furthermore, we proposed a novel decoding algorithm that integrates depthwise separable convolution with multi-head self-attention mechanisms. The proposed method was evaluated against existing approaches, demonstrating superior classification accuracy while reducing the temporal and spatial complexity associated with attention mechanisms. To assess the generalizability and robustness of the algorithm across different scenarios, we conducted experiments on two publicly available datasets: BCI Competition IV-2a and the PhysioNet MI dataset. Results showed that our method achieved an average increase of nearly 8% in kappa score over EEGNet in decoding four-class MI tasks in 2D paradigms. Consistent performance across both VR and 2D paradigms confirmed the algorithm's effectiveness and applicability in multi-scenario MI decoding. This study introduces a novel immersive MI paradigm and decoding framework, offering a promising approach for enhancing user engagement in neurorehabilitation and advancing EEG-based intention recognition in VR environments.},
}
@article {pmid41177674,
year = {2025},
author = {Almufareh, MF and Kausar, S and Humayun, M and Tehsin, S and Farooq, A},
title = {Inner Speech Decoding: A Comprehensive Review.},
journal = {Wiley interdisciplinary reviews. Cognitive science},
volume = {16},
number = {6},
pages = {e70016},
doi = {10.1002/wcs.70016},
pmid = {41177674},
issn = {1939-5086},
support = {KSRG-2024-063//King Salman Center for Disability Research/ ; },
mesh = {Humans ; *Speech/physiology ; Electroencephalography ; Machine Learning ; Magnetic Resonance Imaging ; *Brain/physiology ; *Speech Perception/physiology ; Brain-Computer Interfaces ; },
abstract = {Inner speech decoding is the process of identifying silently generated speech from neural signals. In recent years, this candidate technology has gained momentum as a possible way to support communication in severely impaired populations. Specifically, this approach promises hope for people with a variety of physical or neurological disabilities who need alternative means of verbal expression. This review covers recording modalities that range from the noninvasive EEG to the high-density electrocorticography and discusses how linear discriminant analysis, deep convolutional networks, and hybrid fusion of EEG with fMRI are integrated into machine learning strategies to infer covert speech. This review synthesizes evidence to suggest that small vocabularies, under controlled conditions, can yield relatively reasonable accuracy while further refining the decoding outcome via context-based approaches. The impact of sensor quality, training data size, and domain adaptation is illustrated by focusing on public datasets of imagined or articulated speech. Throughout the article, the methodological standards emerging across laboratories will be discussed, emphasizing that effective inner speech recognition involves high-quality preprocessing, subject calibration, and informed modeling choices balanced against computational power for interpretability. In addition to technical advancements, this review also examines the ethical, societal, and regulatory challenges surrounding inner speech decoding, including brain data privacy, neural rights, informed consent, and user trust. Addressing these interdisciplinary issues is critical for the responsible development and real-world adoption of such technologies. This article is categorized under: Neuroscience > Computation Computer Science and Robotics > Machine Learning.},
}
@article {pmid41177306,
year = {2025},
author = {Zhong, X and Li, G and Xu, C and Luo, R and Meng, J and Schalk, G},
title = {Detection of eye movements and eye blinks using a portable two-channel EEG platform.},
journal = {Journal of neuroscience methods},
volume = {425},
number = {},
pages = {110616},
doi = {10.1016/j.jneumeth.2025.110616},
pmid = {41177306},
issn = {1872-678X},
abstract = {BACKGROUND: The ability to detect eye movements can facilitate human-computer interaction (HCI) and may complement brain-computer interfaces (BCIs). Recent studies have shown that multi-channel EEG systems can provide information about eye movements, but these systems can be bulky and/or require complex setup.<br><br>NEW METHOD: We introduce a portable, two-channel EEG platform that can be placed in seconds and detect eye blinks/movements and gaze trajectories. Forty adults performed cued blinks and horizontal/vertical gaze shifts; 21 EEG features were extracted, and machine learning models were evaluated with leave-one-subject-out validation.<br><br>RESULTS: Our system effectively identified eye blinks (avg. detection accuracy of 95%, 50% chance) and horizontal eye movements (avg. accuracy of 94%, 33% chance), and showed decreased performance detecting vertical eye movements (avg. accuracy of 60%, 33% chance). It was also able to predict horizontal and vertical eye movement trajectories (r = 0.79 and r = 0.14, respectively).<br><br>Classification accuracies for eye blinks and horizontal eye movements using our system with only two electrodes are comparable to those previously reported only for complex multi-channel EEG/EOG setups.<br><br>CONCLUSION: This study provides evidence, for the first time, that a wearable EEG device can give substantial information about eye blinks and eye movements. With further refinements, this approach may enable portable solutions for real-world HCI and BCI applications.},
}
@article {pmid41176895,
year = {2025},
author = {Cavallé Garrido, L and de Paula Vernetta, C and Guzmán Calvete, A and Álvarez Arocas, J and Gonçalves, C and Armengot Carceller, M},
title = {Bonebridge active transcutaneous bone conduction hearing implant: Results in the pediatric population.},
journal = {International journal of pediatric otorhinolaryngology},
volume = {199},
number = {},
pages = {112610},
doi = {10.1016/j.ijporl.2025.112610},
pmid = {41176895},
issn = {1872-8464},
abstract = {PURPOSE: This study provides prospective and retrospective data on safety and performance results with the Bonebridge BCI 602 (MED-EL) active transcutaneous bone conduction implant in children.<br><br>METHODS: Audiological data were collected at 3 intervals (preoperative, initial activation and 3 months postoperative). Quality of life was assessed with the Speech, Spatial, and Qualities of Hearing (SSQ12/P), KID KINDL and Audio Processor Satisfaction Questionnaire (APSQ) as well as a postoperative questionnaire specifically designed for this study.<br><br>RESULTS: 22 pediatric patients (20 conductive/mixed hearing loss (CHL/MHL) and 2 single-sided deafness (SSD)) aged 4-17 received a BCI 602. Three-month post-op pure-tone average (PTA4) functional gain (FG) was 31.9&#xa0;dB HL for the CHL/MHL group and 11.3&#xa0;dB HL in the SSD patients. CHL/MHL patients had a mean word recognition score (WRS) improvement of 80.6&#xa0;&#xb1;&#xa0;23.9&#xa0;% at initial activation and 83&#xa0;&#xb1;&#xa0;20.3&#xa0;% at 3 months post-op. Speech recognition in noise at +5&#xa0;dB SNR in the CHL/MHL group improved from 24.6&#xa0;&#xb1;&#xa0;28.3&#xa0;% unaided to 74.9&#xa0;&#xb1;&#xa0;26&#xa0;% aided at 3 months post-op. The mean post-op total scores were 5.5&#xa0;&#xb1;&#xa0;1.8 on the SSQ12/P and 8.87&#xa0;&#xb1;&#xa0;0.93 on the APSQ questionnaires. No major complications were noted on the postoperative questionnaire; minor complications were resolved by the end of the study. Stable bone and air conduction thresholds confirmed device safety.<br><br>CONCLUSION: The Bonebridge BCI 602 is safe and effective for use in the pediatric population.},
}
@article {pmid41174212,
year = {2025},
author = {Zhou, H and Iramina, K},
title = {Discovery of EEG effective connectivity during visual motor imagery with multi-scale symbolic transfer entropy.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {38200},
pmid = {41174212},
issn = {2045-2322},
mesh = {Humans ; *Electroencephalography/methods ; Male ; Female ; Adult ; *Imagination/physiology ; Brain-Computer Interfaces ; Entropy ; Young Adult ; Parietal Lobe/physiology ; *Brain/physiology ; Brain Mapping ; Occipital Lobe/physiology ; },
abstract = {Visual motor imagery (VMI) is an important component of motor imagery, with potential applications in brain-computer interfaces and motor rehabilitation due to its lower training cost compared to kinesthetic motor imagery (KMI). However, the neural mechanisms underlying VMI, particularly the effects of imagery hand and imagery perspective (first-person perspective, 1pp, vs. third-person perspective, 3pp) remain unclear. This study examines the effective connectivity of VMI EEG using multi-scale symbolic transfer entropy. Time-frequency analysis revealed prominent event-related synchronization (ERS) in the alpha and high-beta bands, while connectivity analysis emphasized strong information flow within the parieto-occipital network. Notably, hand effect dominant information flows were found between the motor and posterior parietal-occipital regions, while perspective suggested a more remarkable effect. 1pp imagery significantly enhanced top-down modulation of the occipital cortex, whereas 3pp imagery engaged the right posterior parietal region, suggesting stronger spatial localization processing. These findings provide novel insights into the distinct neural mechanisms of VMI and its potential applications in cognitive neuroscience and brain-machine engineering.},
}
@article {pmid41173359,
year = {2025},
author = {Chen, Z and Lu, Y and Xu, X},
title = {EEG-SGENet: A lightweight convolutional network integrating SGE for motor imagery brain-computer interfaces.},
journal = {Neuroscience},
volume = {589},
number = {},
pages = {300-307},
doi = {10.1016/j.neuroscience.2025.09.040},
pmid = {41173359},
issn = {1873-7544},
abstract = {In recent years, there has been a significant increase in research activity on electroencephalography (EEG)-based motor imagery brain-computer interfaces (MI-BCI) in the field of deep learning. However, despite achieving high accuracy, the size of models is increasing, requiring significant memory and computational resources. Therefore, finding a balance between accuracy and computational cost has always been a challenge in MI classification research. Convolutional Neural Networks (CNNs) generate feature representations of objects by collecting semantic sub-features. The activation of subfeatures is susceptible to noisy backgrounds. The Spatial Group-wise Enhance (SGE) module adjusts the importance of each sub-feature by generating an attention factor for the spatial location of each semantic group, thus enhancing useful features and suppressing noise. The design of the SGE module is lightweight, with few parameters and computations. Therefore, we introduce the SGE module to improve accuracy and minimize model parameters. In this paper, we propose EEG-SGENet, a novel end-to-end convolutional neural network model that considers both the lightweight model and accuracy. Experimental results on the BCI IV 2a dataset show that EEG-SGENet achieves an accuracy of 80.98% in the four categories of MI. The average classification accuracy for the two-category task of BCI IV 2b is 76.17%. Comparisons with other lightweight models in terms of classification accuracy and other aspects have shown that this model achieves a good balance between decoding performance and computational cost. Overall, experimental results demonstrate that the proposed model is expected to become a new method for decoding EEG signals.},
}
@article {pmid41171945,
year = {2025},
author = {Khanam, T and Siuly, S and Ahmad, K and Wang, H},
title = {A novel channel reduction concept to enhance the classification of motor imagery tasks in brain-computer interface systems.},
journal = {PloS one},
volume = {20},
number = {10},
pages = {e0335511},
pmid = {41171945},
issn = {1932-6203},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Algorithms ; Neural Networks, Computer ; Male ; Adult ; *Imagination/physiology ; Female ; Movement/physiology ; Brain/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {Electroencephalogram (EEG) signals play a critical role in advancing brain-computer interface (BCI) systems, particularly for detecting motor imagery (MI) movements. However, analysing large volume of EEG datasets faces some challenges due to redundant information, and performance degradation. Irrelevant channels introduce noise, which reduces accuracy and slows system performance. To address these issues, this study aims to develop a novel channel selection method to enhance EEG-based MI task performance in BCI applications. Our proposed hybrid approach combines statistical t-tests with a Bonferroni correction-based channel reduction technique, followed by the application of a Deep Learning Regularized Common Spatial Pattern with Neural Network (DLRCSPNN) framework. This framework employs DLRCSP for feature extraction and neural network (NN) algorithm for classification. Our developed method excluded channels with correlation coefficients below 0.5, retaining only significant, non-redundant channels and tested on three real-time EEG-based BCI datasets. This study produces the highest accuracy score in the case of every subjects above 90% for all the applied datasets. In the first dataset, our method achieved the highest accuracy, improving by 3.27% to 42.53% in terms of individual subject compared to seven existing machine learning algorithms. In the second and third dataset, it outperformed existing approaches, with accuracy gains of 5% to 45% and 1% to 17.47% respectively. Comparisons with a CSP and NN framework confirmed DLRCSPNN's algorithms superior performance. These results demonstrate the effectiveness of the approach, offering a new perspective on the identification of MI task performance in EEG based BCI technology. This proposed technique will enable rapid identification of motor-disabled individuals' intentions, supporting patient rehabilitation and improving daily living.},
}
@article {pmid41171651,
year = {2025},
author = {Liu, H and Wang, Z and Li, R and Zhao, X and Xu, T and Zhou, T and Hu, H},
title = {A Novel Binocular-Encoded SSVEP Framework for Efficient VR-Based Brain-Computer Interface.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3626332},
pmid = {41171651},
issn = {2168-2208},
abstract = {This paper presents a novel binocular-encoded SSVEP (beSSVEP) method, leveraging binocular vision in virtual reality (VR) to enhance brain-computer interface (BCI) applications. We introduce the Binocular Periodically Repeated Component Analysis (bPRCA) algorithm, designed to address the unique characteristics of binocular-encoded targets, which include combinations of monocular single-frequency SSVEP units or void units, with frequency units being reused multiple times in the encoded interface. To further optimize performance, we propose the Fusion Component Analysis (FusionCA) framework, which integrates bPRCA with Task-related Component Analysis (TRCA), effectively utilizing both steady-state periodic components and cross-trial aperiodic components. Experimental results demonstrate that ensemble-FusionCA achieves the highest information transfer rate (ITR) with an average accuracy of $71.39\%$ and an ITR of 138.50 bits/min at 0.4 seconds, among the comparison with ensemble-bPRCA and ensemble-TRCA. Compared to traditional SSVEP approaches, beSSVEP significantly enhances frequency utilization, making VR-BCI systems more efficient and practical. This study highlights the application of physiological mechanisms of binocular vision to improve BCI systems, offering a new perspective for developing fast and scalable brain-computer interactions in VR environments.},
}
@article {pmid41170547,
year = {2025},
author = {Ren, J and Mo, WY and Wang, L and Ni, GJ and Yang, JJ},
title = {[Research progress on the role of dopamine system in regulating hippocampal related brain functions].},
journal = {Sheng li xue bao : [Acta physiologica Sinica]},
volume = {77},
number = {5},
pages = {893-904},
doi = {10.13294/j.aps.2025.0055},
pmid = {41170547},
issn = {0371-0874},
mesh = {*Hippocampus/physiology ; *Dopamine/physiology ; Humans ; Animals ; Receptors, Dopamine D2/physiology ; Memory/physiology ; Signal Transduction/physiology ; Neurodegenerative Diseases/physiopathology ; },
abstract = {Dopamine, as a catecholamine neurotransmitter widely distributed in the central nervous system, is involved in physiological functions such as motivation, arousal, reinforcement, and movement through various dopamine signaling pathways. The hippocampus receives dopaminergic neuron projections from regions such as the ventral tegmental area, locus coeruleus, and substantia nigra. Through D1-like and D2-like receptors, dopamine exerts significant regulatory effects such as spatial navigation, episodic memory, fear, anxiety, and reward. This review mainly summarizes the research progress on the functions of dopamine in the hippocampus from aspects including the sources of dopamine, receptor distribution and function, and the association of hippocampal dopamine system dysregulation with neurodegenerative diseases. The aim is to provide insights into the involvement of the dopamine system in hippocampal functions and the diagnosis and treatment of related diseases.},
}
@article {pmid41170533,
year = {2025},
author = {Gherman, DE and Zander, TO},
title = {Towards neuroadaptive chatbots: a feasibility study.},
journal = {Frontiers in neuroergonomics},
volume = {6},
number = {},
pages = {1589734},
pmid = {41170533},
issn = {2673-6195},
abstract = {INTRODUCTION: Large-language models (LLMs) are transforming most industries today and are set to become a cornerstone of the human digital experience. While integrating explicit human feedback into the training and development of LLM-based chatbots has been integral to the progress we see nowadays, more work is needed to understand how to best align them with human values. Implicit human feedback enabled by passive brain-computer interfaces (pBCIs) could potentially help unlock the hidden nuance of users' cognitive and affective states during interaction with chatbots. This study proposes an investigation on the feasibility of using pBCIs to decode mental states in reaction to text stimuli, to lay the groundwork for neuroadaptive chatbots.<br><br>METHODS: Two paradigms were created to elicit moral judgment and error-processing with text stimuli. Electroencephalography (EEG) data was recorded with 64 gel electrodes while participants completed reading tasks. Mental state classifiers were obtained in an offline manner with a windowed-means approach and linear discriminant analysis (LDA) for full-component and brain-component data. The corresponding event-related potentials (ERPs) were visually inspected.<br><br>RESULTS: Moral salience was successfully decoded at a single-trial level, with an average calibration accuracy of 78% on the basis of a data window of 600 ms. Subsequent classifiers were not able to distinguish moral judgment congruence (i.e., moral agreement) and incongruence (i.e., moral disagreement). Error processing in reaction to factual inaccuracy was decoded with an average calibration accuracy of 66%. The identified ERPs for the investigated mental states partly aligned with other findings.<br><br>DISCUSSION: With this study, we demonstrate the feasibility of using pBCIs to distinguish mental states from readers' brain data at a single-trial level. More work is needed to transition from offline to online investigations and to understand if reliable pBCI classifiers can also be obtained in less controlled language tasks and more realistic chatbot interactions. Our work marks preliminary steps for understanding and making use of neural-based implicit human feedback for LLM alignment.},
}
@article {pmid41169537,
year = {2025},
author = {Li, X and Ji, X and Wang, Y and Chen, X},
title = {The influence of different visual eccentricity on SSVEPs elicited by ultra-low frequency visual stimulation in the lower peripheral visual field.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {170},
pmid = {41169537},
issn = {1871-4080},
abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been widely explored due to their high information transfer rate (ITR) and minimal training requirements. Traditional SSVEP-based BCIs typically use low- and medium-frequency visual stimuli from the central visual field to induce SSVEPs, but these can easily lead to visual fatigue. In order to improve system's comfort, some studies have attempted to use visual stimuli from the peripheral visual field to elicit SSVEPs. However, few studies have investigated the effects of different visual eccentricities on induced SSVEPs. In this study, we used ultra-low frequency (i.e., 2.00-3.32 Hz) visual stimulation in the lower peripheral visual field to induce SSVEPs. Furthermore, we further explored the effects of different visual eccentricities (i.e., 2.1°, 3.1°, and 4.1°) on induced SSVEPs. Experimental results obtained from twelve participants revealed that all three eccentricity conditions were capable of eliciting SSVEP responses. Moreover, SSVEP amplitude gradually decreased as eccentricity increased. These results provide new parametric references for optimizing the spatial layout of visual stimuli in peripheral SSVEP-based BCI systems.},
}
@article {pmid41167038,
year = {2025},
author = {Schippers, A and Berezutskaya, J and Vansteensel, MJ and Freudenburg, ZV and Crone, NE and Ramsey, NF},
title = {The effect of perceived auditory feedback on speech Brain-Computer Interface decoding performance.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {180},
number = {},
pages = {2111403},
doi = {10.1016/j.clinph.2025.2111403},
pmid = {41167038},
issn = {1872-8952},
abstract = {OBJECTIVE: Brain-Computer Interfaces (BCI) provide alternative means of communication for individuals with severe motor impairment. Implantable speech BCIs have shown great potential, particularly in individuals who could still produce some speech-related movements and/or sounds. As perception of auditory feedback is important for correct speech sound production in able-bodied people, it is conceivable that a complete absence of such feedback in individuals who lost all ability to produce audible speech affects BCI performance. The current study therefore set out to investigate to what extent perception of auditory feedback of self-produced speech contributes to speech decoding performance.<br><br>METHODS: In three able-bodied participants, patterns of 65-95&#xa0;Hz power over sensorimotor cortex were compared between normal speech and speech in which auditory feedback was masked by noise. In addition, decoding accuracy was compared between feedback situations.<br><br>RESULTS &amp; CONCLUSIONS: We found subtle differences in brain activity patterns associated with speech production between situations in which participants could versus could not perceive their produced speech. Importantly, absence of such auditory feedback led to lower speech decoding performance in all participants.<br><br>SIGNIFICANCE: These results underline the need to validate speech BCI efficacy with fully paralyzed individuals, as perceived feedback can influence the attainable speech decoding accuracy.},
}
@article {pmid41165717,
year = {2025},
author = {Lydiatt, WB},
title = {Mind, Machine, and Medicine-Challenges and Opportunities.},
journal = {JAMA otolaryngology-- head &amp; neck surgery},
volume = {},
number = {},
pages = {},
doi = {10.1001/jamaoto.2025.4108},
pmid = {41165717},
issn = {2168-619X},
}
@article {pmid41163348,
year = {2025},
author = {Song, K and Liu, Y and Xu, P},
title = {Acute Effects of Portable Dry-EEG Neurofeedback on Classical Chinese Learning: A Three-Arm Repeated-Measures Study.},
journal = {Brain and behavior},
volume = {15},
number = {11},
pages = {e70977},
pmid = {41163348},
issn = {2162-3279},
support = {2025ZSD017//Shanghai Municipal Education Science Research Project "Special Program for Philosophy and Social Sciences Research in Shanghai Higher Education Institutions"/ ; },
mesh = {Humans ; *Neurofeedback/methods ; Male ; Female ; Young Adult ; *Electroencephalography/methods ; Adult ; *Learning/physiology ; Attention/physiology ; Cognition/physiology ; Comprehension/physiology ; Language ; Adolescent ; East Asian People ; },
abstract = {OBJECTIVE: Dry-electrode electroencephalography (dry-EEG) systems offer promising opportunities for real-time neurofeedback in naturalistic educational settings, yet their effectiveness in supporting complex language learning remains underexplored. This study investigated the acute effects of portable dry-EEG neurofeedback on students' cognitive performance and attentional states during classical Chinese learning, using a repeated-measures design to compare neurofeedback, sham feedback, and device control conditions.<br><br>METHODS: A total of 20 undergraduate participants completed three sessions involving a customized semantic disambiguation task after passive reading. EEG signals were acquired using a dry-sensor OpenBCI system from four frontal sites (Fp1, Fp2, F3, F4). Real-time attention indices were computed based on the beta/(alpha+theta) ratio and fed back visually in the neurofeedback condition. Cognitive outcomes included comprehension test scores and semantic conflict resolution performance (RT, accuracy, cognitive load).<br><br>RESULTS: Compared to sham and control conditions, neurofeedback significantly improved comprehension accuracy (p < 0.001), reduced reaction times in the interference task (p&#xa0;<&#xa0;0.05), and lowered subjective cognitive load (p&#xa0;=&#xa0;0.002). EEG indices of attention were significantly elevated during neurofeedback (p&#xa0;<&#xa0;0.001) and positively correlated with behavioral gains (r&#xa0;=&#xa0;0.63, p&#xa0;<&#xa0;0.05).<br><br>CONCLUSIONS: Portable dry-electrode EEG systems can reliably support real-time neurofeedback to enhance attention and cognitive control in complex language learning contexts. This study provides empirical validation for deploying dry-EEG sensors in adaptive educational technologies and contributes to the broader integration of wearable brain-computer interfaces in cognitive augmentation applications.},
}
@article {pmid41161815,
year = {2025},
author = {Yang, Y and Liu, C and Liu, S and Ding, P and Bai, R and Chen, G and Li, S and Song, X and Cheng, Y and Xu, J},
title = {Role of combination immunotherapy in restoring brain synergistic functional connectivity in patients with systemic lupus erythematosus without overt neuropsychiatric manifestations.},
journal = {Lupus science &amp; medicine},
volume = {12},
number = {2},
pages = {},
pmid = {41161815},
issn = {2053-8790},
mesh = {Humans ; Female ; *Lupus Erythematosus, Systemic/drug therapy/physiopathology ; Adult ; Male ; Cyclophosphamide/therapeutic use/administration &amp; dosage ; Magnetic Resonance Imaging ; *Immunosuppressive Agents/therapeutic use/administration &amp; dosage ; Hydroxychloroquine/therapeutic use/administration &amp; dosage ; *Brain/physiopathology/diagnostic imaging/drug effects ; Glucocorticoids/therapeutic use/administration &amp; dosage ; Middle Aged ; Case-Control Studies ; Drug Therapy, Combination ; Young Adult ; *Immunotherapy/methods ; },
abstract = {OBJECTIVE: To determine whether subclinical brain dysfunction in SLE can be detected by disrupted interhemispheric connectivity and assess its modulation by immunosuppressive regimens.<br><br>METHODS: 234 subjects (140 patients with SLE and 94 healthy controls (HCs)) were included. Through stratified analysis, patients with SLE were divided into treatment-na&#xef;ve group (n=22), glucocorticoid monotherapy group (GC group, n=30) and GC combined with cyclophosphamide (CTX) and/or hydroxychloroquine (HCQ) treatment group (n=50) to assess the differences in voxel-mirrored homotopic connectivity (VMHC) between groups.<br><br>RESULTS: SLE group showed lower VMHC than the HC group in bilateral superior temporal gyrus, medial superior frontal gyrus, calcarine fissure and surrounding cortex and middle occipital cortices (Gaussian random field corrected: voxel p<0.005, cluster p<0.01). The VMHC in the bilateral superior temporal gyrus (rs=-0.250, p=0.024) and medial superior frontal gyrus (rs=-0.246, p=0.026) was negatively correlated with the depression score, while the VMHC in the medial superior frontal gyrus was negatively correlated with the anxiety score (rs=-0.239, p=0.031). Three SLE subgroups and HCs had different VMHC in the postcentral/precentral gyrus (F=8.942) and anterior cingulate/paracingulate gyrus (F=9.868). Post hoc analysis found that compared with the HC group, VMHC in the treatment-na&#xef;ve group was decreased in the bilateral posterior central gyrus (t=-2.953), while in the GC monotherapy group, it decreased in the posterior central gyrus (t=-2.999) and anterior cingulate/paracingulate gyrus (t=-2.999). Compared with GC combined with CTX and/or HCQ group, VMHC in GC monotherapy group was decreased in the postcentral gyrus (t=-2.999).<br><br>CONCLUSION: Even without overt neuropsychiatric symptoms, patients with SLE exhibit impaired interhemispheric functional synergy that is partially reversed by combination immunosuppression, suggesting an early targetable brain pathway.},
}
@article {pmid41160913,
year = {2025},
author = {Russo, JS and Colebatch, JG and Lin, CS and John, SE and Grayden, DB and Todd, NPM},
title = {Feasibility of decoding cerebellar movement-related potentials for brain-computer interface applications.},
journal = {Journal of neural engineering},
volume = {22},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ae18fa},
pmid = {41160913},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Cerebellum/physiology ; Adult ; Movement/physiology ; *Electroencephalography/methods ; Female ; Feasibility Studies ; Young Adult ; *Evoked Potentials, Motor/physiology ; Electromyography/methods ; },
abstract = {Objective.In brain-computer interface (BCI) applications, signals are conventionally acquired from the cerebrum, and only a subset of the complex interactions that occur in several areas of the brain are collected. One area that has not been investigated for BCI application is the cerebellum, despite its involvement in movement and executive function. The present study aimed to determine the features of movement-related cerebellar electrocerebellography (ECeG) that are most useful for decoding, and how performance compares with conventional electroencephalography (EEG) recordings from the cerebrum.Approach.ECeG and EEG data were collected from six healthy adults to identify useful movement-related features from both cerebrum and cerebellum. Electromyography was used to capture the movements from the muscles. Decoding was conducted in binary movement vs. rest and movement vs. movement systems using support vector machines. Decoding performance was compared between cerebral, cerebellar, a combination of both, and temporal groups. Re-referencing techniques were applied to compensate for possible common reference artefacts or volume conduction effects.Main results. Movement-related features were decoded from over the cerebellum and the cerebrum. Classification accuracies were similar in both the cerebrum and cerebellum, when classifying movement vs. rest (cerebrum: 0.78 ± 0.02, cerebellum: 0.70 ± 0.01) and movement vs. movement states (cerebrum: 0.76 ± 0.02, cerebellum: 0.71 ± 0.02). The delta band (1-3 Hz) was the most useful feature for decoding.Significance.This study demonstrated, for the first time, that ECeG is a feasible source of movement related signals for implementing a BCI. The present study also demonstrated that the ECeG closely resembled the EEG signals and represents an alternate approach for BCI where the signal from the cerebrum is unreliable either due to disease or injury.},
}
@article {pmid41160812,
year = {2025},
author = {Li, J and Lu, Y and Li, Z and Jin, L and Zhou, L and Ding, K and Liu, J and Hu, B and Liu, P and An, D and Liang, F and Hu, Y and Shao, Y and Ding, Y and Ma, L and Li, R and Mei, Y and Zhang, R and Song, E},
title = {An Active, Multimodal Neural Interface for Real-Time Monitoring of Cortical Electrical, Thermal, and Optical Dynamics.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e12114},
doi = {10.1002/advs.202512114},
pmid = {41160812},
issn = {2198-3844},
support = {2022ZD0209900//STI 2030-Major Project/ ; 62204057//National Natural Science Foundation of China/ ; 62304044//National Natural Science Foundation of China/ ; 82304124//National Natural Science Foundation of China/ ; U2230108//National Natural Science Foundation of China/ ; 22ZR1406400//Science and Technology Commission of Shanghai Municipality/ ; LGL-8998-09//Lingang Laboratory/ ; SKLICS-K202515//State Key Laboratory of Integrated Chips and Systems/ ; },
abstract = {Chronic neurophysiological monitoring devices facilitate the timely diagnosis and treatment of episodic or recurrent neurological disorders. Compared with passive electrodes, silicon-based active transistors provide intrinsic signal amplification and, when combined with capacitive-coupling measurement mechanisms, enable high-density, high-fidelity recordings. However, most existing systems remain limited to single-modality electrical sensing and fail to address the growing demands of contemporary neurodynamic research. Here, a chronically implantable, large-area cortical interface capable of real-time multimodal monitoring of electrical, thermal, and photodynamic signals is presented. Building upon a silicon-transistor array for neural electrical detection, the device integrates thin-film metal resistors for temperature sensing while preserving mechanical flexibility sufficient for stable, long-term tissue contact. By leveraging the photoelectric effect of silicon transistors and functional multiplexing of active elements, the interface also achieves precise photodynamic measurement. In vitro experiments confirm long-term stability and channel isolation. In vivo evaluation in Sprague-Dawley rats, together with biocompatibility assessments, demonstrates reliable performance under physiological conditions. The technology used in this multifunctional platform has universal applicability in neural interfaces, offering continuous multimodal neurodynamic data acquisition with potential utility in monitoring, diagnosing, and treating chronic neurological conditions such as epilepsy and brain tumors.},
}
@article {pmid41160441,
year = {2025},
author = {Jeong, SY and Lee, JW and Kim, TG},
title = {Comparative analysis across diverse plant species reveals superior antibiofilm efficacy and dose-dependency of root extracts compared to leaf extracts.},
journal = {FEMS microbiology letters},
volume = {372},
number = {},
pages = {},
doi = {10.1093/femsle/fnaf116},
pmid = {41160441},
issn = {1574-6968},
support = {RS-2023-00273372//Ministry of Education/ ; },
mesh = {*Biofilms/drug effects/growth &amp; development ; *Plant Roots/chemistry ; *Plant Extracts/pharmacology ; *Plant Leaves/chemistry ; *Anti-Bacterial Agents/pharmacology ; Microbial Sensitivity Tests ; Dose-Response Relationship, Drug ; },
abstract = {Although both root- and leaf-derived plant extracts hold potential as antibiofilm agents, research has predominantly focused on leaf tissues. In this study, we systematically compared the antibiofilm efficacy of 158 root and 248 leaf extracts from 360 plant species across five concentrations (0.1, 0.25, 0.5, 1.0, and 2.0 g/l). As concentration increased, the biofilm control incidence (BCI) of root extracts rose from 68.4% to 94.3%, while leaf extracts showed a smaller increase, from 52.2% to 71.7%. Similarly, the biofilm control efficacy (BCE) of root extracts increased from 27.6% to 54.2%, whereas leaf extracts ranged from -2.7% to 16.2%. Bootstrapping analysis (10 000 iterations) confirmed significantly higher antibiofilm activity of root extracts at concentrations ≥ 0.5 g/l (P < 0.05). Paired comparisons of species with both extract types further demonstrated the consistent superiority of root extracts across all concentrations (bootstrapped, P < 0.05), despite interspecific variation at higher doses. Linear regression revealed a significantly steeper dose-response slope for root extracts (29.2 ± 2.4) than for leaf extracts (8.1 ± 2.8) (bootstrapped, P < 0.05), indicating a stronger concentration-dependent effect of root extracts. These results suggest that plant roots typically harbor more potent and/or diverse antibiofilm compounds than leaves, underscoring their untapped potential for biofilm control applications.},
}
@article {pmid41160433,
year = {2025},
author = {Chen, Y and Liu, T and Jia, K and Theeuwes, J and Gong, M},
title = {Dual-format attentional template during preparation in human visual cortex.},
journal = {eLife},
volume = {13},
number = {},
pages = {},
pmid = {41160433},
issn = {2050-084X},
support = {Major Project 2021ZD0200409//National Science and Technology Innovation 2030/ ; 32371087//National Natural Science Foundation of China/ ; 32300855//National Natural Science Foundation of China/ ; 3200784//National Natural Science Foundation of China/ ; 226-2024-00118//Fundamental Research Funds for the Central University/ ; Non-profit Central Research Institute Fund 2023-PT310-01//Chinese Academy of Medical Sciences/ ; },
mesh = {Humans ; *Attention/physiology ; *Visual Cortex/physiology ; Magnetic Resonance Imaging ; Male ; Female ; Adult ; Young Adult ; *Visual Perception/physiology ; Brain Mapping ; Cues ; Photic Stimulation ; },
abstract = {Goal-directed attention relies on forming internal templates of key information relevant for guiding behavior, particularly when preparing for upcoming sensory inputs. However, evidence on how these attentional templates are represented during preparation remains controversial. Here, we combine functional magnetic resonance imaging with an orientation cueing task to isolate preparatory activity from stimulus-evoked responses. Using multivariate pattern analysis, we found decodable information about the to-be-attended orientation during preparation; yet preparatory activity patterns were different from those evoked when actual orientations were perceived. When perturbing the neural activity by means of a visual impulse ('pinging' technique), the preparatory activity patterns in visual cortex resembled those associated with perceiving these orientations. The observed differential patterns with and without the impulse perturbation suggest a predominantly non-sensory format and a latent, sensory-like format of representation during preparation. Furthermore, the emergence of the sensory-like template coincided with enhanced information connectivity between V1 and frontoparietal areas and was associated with improved behavioral performance. By engaging this dual-format mechanism during preparation, the brain is able to encode both abstract, non-sensory information and more detailed, sensory information, potentially providing advantages for adaptive attentional control. For example, consistent with recent theories of visual search, a predominantly non-sensory template can support the initial guidance and a latent sensory-like format can support prospective stimulus processing.},
}
@article {pmid41159356,
year = {2025},
author = {Atan, Y and Doğan, M and Karayel, F and Üzün, &#x130;},
title = {Fatal Isolated Right Ventricular Rupture Without External Chest Injury in a Young Driver: Forensic Autopsy Findings After a One-Sided Vehicle Collision.},
journal = {Archives of Iranian medicine},
volume = {28},
number = {9},
pages = {530-535},
pmid = {41159356},
issn = {1735-3947},
mesh = {Humans ; Male ; *Accidents, Traffic ; Young Adult ; *Heart Ventricles/injuries/pathology ; Fatal Outcome ; Autopsy ; *Heart Injuries/pathology/etiology ; *Wounds, Nonpenetrating/pathology ; Forensic Pathology ; },
abstract = {Traumatic deaths are common, with cardiac trauma affecting 7‒12% of patients with thoracic injuries. Blunt cardiac injury (BCI), although rare, is associated with a high mortality rate. This report presents a case of blunt cardiac rupture (BCR) observed at autopsy despite the absence of external chest trauma, suggesting the presence of severe internal injuries. A 19-year-old male was found dead in his vehicle which had collided with a wall. At the crime scene investigation, external examination revealed no substantial chest wall injuries in the individual despite significant damage to the vehicle. Autopsy revealed a 2-cm rupture of the right ventricle (heart), accompanied by 400 cc of partially coagulated blood in the pericardial cavity, consistent with cardiac tamponade. Pregabalin was detected in the toxicology analysis, but not in lethal concentrations. Traffic accidents are a major cause of BCI, typically resulting from compression of the heart between the thoracic structures during high-energy impacts. BCR is particularly fatal and often results in rapid death before arrival to the hospital. The absence of external trauma in the current case underscores the need for thorough internal examination in trauma-related deaths.},
}
@article {pmid41157441,
year = {2025},
author = {Moreno-Castelblanco, SR and Vélez-Guerrero, MA and Callejas-Cuervo, M},
title = {Lower-Limb Motor Imagery Recognition Prototype Based on EEG Acquisition, Filtering, and Machine Learning-Based Pattern Detection.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {20},
pages = {},
pmid = {41157441},
issn = {1424-8220},
mesh = {Humans ; *Electroencephalography/methods ; *Machine Learning ; Brain-Computer Interfaces ; *Lower Extremity/physiology ; Signal Processing, Computer-Assisted ; Male ; Algorithms ; Adult ; *Imagination/physiology ; Movement/physiology ; *Pattern Recognition, Automated/methods ; Female ; Young Adult ; },
abstract = {Advances in brain-computer interface (BCI) research have explored various strategies for acquiring and processing electroencephalographic (EEG) signals to detect motor imagery (MI) activities. However, the complexity of multichannel clinical systems and processing techniques can limit their accessibility outside specialized centers, where complex setups are not feasible. This paper presents a proof-of-concept prototype of a single-channel EEG acquisition and processing system designed to identify lower-limb motor imagery. The proposed proof-of-concept prototype enables the wireless acquisition of raw EEG values, signal processing using digital filters, and the detection of MI patterns using machine learning algorithms. Experimental validation in a controlled laboratory with participants performing resting, MI, and movement tasks showed that the best performance was obtained by combining Savitzky-Golay filtering with a Random Forest classifier, reaching 87.36% ± 4% accuracy and an F1-score of 87.18% ± 3.8% under five-fold cross-validation. These findings confirm that, despite limited spatial resolution, MI patterns can be detected using appropriate AI-based filtering and classification. The novelty of this work lies in demonstrating that a single-channel, portable EEG prototype can be effectively used for lower-limb MI recognition. The portability and noise resilience achieved with the prototype highlight its potential for research, clinical rehabilitation, and assistive device control in non-specialized environments.},
}
@article {pmid41157340,
year = {2025},
author = {Iadarola, G and Mengarelli, A and Iarlori, S and Monteriù, A and Spinsante, S},
title = {RGB-D Cameras and Brain-Computer Interfaces for Human Activity Recognition: An Overview.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {20},
pages = {},
pmid = {41157340},
issn = {1424-8220},
support = {CUP I33C2200133000//Vitality Project/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Human Activities ; Electroencephalography ; Brain/physiology ; },
abstract = {This paper provides a perspective on the use of RGB-D cameras and non-invasive brain-computer interfaces (BCIs) for human activity recognition (HAR). Then, it explores the potential of integrating both the technologies for active and assisted living. RGB-D cameras can offer monitoring of users in their living environments, preserving their privacy in human activity recognition through depth images and skeleton tracking. Concurrently, non-invasive BCIs can provide access to intent and control of users by decoding neural signals. The synergy between these technologies may allow holistic understanding of both physical context and cognitive state of users, to enhance personalized assistance inside smart homes. The successful deployment in integrating the two technologies needs addressing critical technical hurdles, including computational demands for real-time multi-modal data processing, and user acceptance challenges related to data privacy, security, and BCI illiteracy. Continued interdisciplinary research is essential to realize the full potential of RGB-D cameras and BCIs as AAL solutions, in order to improve the quality of life for independent or impaired people.},
}
@article {pmid41156422,
year = {2025},
author = {He, J and Xu, J and Wang, Y},
title = {Non-Linear Modeling and Precision Analysis Approach for Implantable Multi-Channel Neural Recording Systems.},
journal = {Micromachines},
volume = {16},
number = {10},
pages = {},
pmid = {41156422},
issn = {2072-666X},
support = {2021ZD0200401//STI 2030-Major Project/ ; 2025C01187,2024C03001//Pioneer R&amp;D Program of Zhejiang/ ; 62176232,62336007//National Natural Science Foundation of China/ ; SNZJU-SIAS-002//Starry Night Science Fund of Zhe- 406 jiang University Shanghai Institute for Advanced Study/ ; 2025ZFJH01,226-2024-00127//Fundamental Research Funds for the Central Universities/ ; },
abstract = {High-precision implantable multi-channel neural recording systems are considered as having a crucial role in the diagnosis and treatment of neurological disorders. However, it is a significant design challenge to achieve an optimal trade-off among linear parameters, signal fidelity, power consumption, and circuit area. To address this challenge, a Simulink-based modeling approach has been proposed to incorporate adjustable non-linear parameters across the front-end circuits and analog-to-digital converter (ADC) stages. The model evaluates non-linearity impacts on system performance through both quantitative spike detection accuracy analysis and a neural decoding paradigm based on Chinese handwriting reconstruction. Simulated results show that total harmonic distortion (THD) can be set to -34.32 dB for the low-noise amplifier (LNA), -33.73 dB for the programmable gain amplifier (PGA), and -57.95 dB for the ADC in order to achieve reliable detection accuracy with minimal design cost. Moreover, ADC non-linearity has a greater influence on system performance than that of the LNA and PGA. The proposed approach offers quantitative and systematic hardware design guidance to balance signal fidelity and resource efficiency for future low-power, high-accuracy neural recording systems.},
}
@article {pmid41155027,
year = {2025},
author = {Yao, Y and Wang, X and Hao, X and Sun, H and Dong, R and Li, Y},
title = {Trans-cVAE-GAN: Transformer-Based cVAE-GAN for High-Fidelity EEG Signal Generation.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {12},
number = {10},
pages = {},
pmid = {41155027},
issn = {2306-5354},
abstract = {Electroencephalography signal generation remains a challenging task due to its non-stationarity, multi-scale oscillations, and strong spatiotemporal coupling. Conventional generative models, including VAEs and GAN variants such as DCGAN, WGAN, and WGAN-GP, often yield blurred waveforms, unstable spectral distributions, or lack semantic controllability, limiting their effectiveness in emotion-related applications. To address these challenges, this research proposes a Transformer-based conditional variational autoencoder-generative adversarial network (Trans-cVAE-GAN) that combines Transformer-driven temporal modeling, label-conditioned latent inference, and adversarial learning. A multi-dimensional structural loss further constrains generation by preserving temporal correlation, frequency-domain consistency, and statistical distribution. Experiments on three SEED-family datasets-SEED, SEED-FRA, and SEED-GER-demonstrate high similarity to real EEG, with representative mean ± SD correlations of Pearson ≈ 0.84 ± 0.08/0.74 ± 0.12/0.84 ± 0.07 and Spearman ≈ 0.82 ± 0.07/0.72 ± 0.12/0.83 ± 0.08, together with low spectral divergence (KL ≈ 0.39 ± 0.15/0.41 ± 0.20/0.37 ± 0.18). Comparative analyses show consistent gains over classical GAN baselines, while ablations verify the indispensable roles of the Transformer encoder, label conditioning, and cVAE module. In downstream emotion recognition, augmentation with generated EEG raises accuracy from 86.9% to 91.8% on SEED (with analogous gains on SEED-FRA and SEED-GER), underscoring enhanced generalization and robustness. These results confirm that the proposed approach simultaneously ensures fidelity, stability, and controllability across cohorts, offering a scalable solution for affective computing and brain-computer interface applications.},
}
@article {pmid41154635,
year = {2025},
author = {Tabish, M and Malik, I and Akhtar, A and Afzal, M},
title = {A Review on Low-Dimensional Nanoarchitectonics for Neurochemical Sensing and Modulation in Responsive Neurological Outcomes.},
journal = {Biomolecules},
volume = {15},
number = {10},
pages = {},
pmid = {41154635},
issn = {2218-273X},
support = {KSRG-2024-41//King Salman Center for Disability Research/ ; },
mesh = {Humans ; Brain-Computer Interfaces ; Animals ; Artificial Intelligence ; *Biosensing Techniques/methods ; *Nanostructures/chemistry ; Nanotechnology/methods ; Brain ; },
abstract = {Low-Dimensional Nanohybrids (LDNHs) have emerged as potent multifunctional platforms for neurosensing and neuromodulation, providing elevated spatial-temporal precision, versatility, and biocompatibility. This review examines the intersection of LDNHs with artificial intelligence, brain-computer interfaces (BCIs), and closed-loop neurotechnologies, highlighting their transformative potential in personalized neuro-nano-medicine. Utilizing stimuli-responsive characteristics, optical, thermal, magnetic, and electrochemical LDNHs provide real-time feedback-controlled manipulation of brain circuits. Their pliable and adaptable structures surpass the constraints of inflexible bioelectronics, improving the neuronal interface and reducing tissue damage. We also examined their use in less invasive neurological diagnostics, targeted therapy, and adaptive intervention systems. This review delineates recent breakthroughs, integration methodologies, and fundamental mechanisms, while addressing significant challenges such as long-term biocompatibility, deep-tissue accessibility, and scalable manufacturing. A strategic plan is provided to direct future research toward clinical use. Ultimately, LDNHs signify a transformative advancement in intelligent, tailored, and closed-loop neurotechnologies, integrating materials science, neurology, and artificial intelligence to facilitate the next era of precision medicine.},
}
@article {pmid41154223,
year = {2025},
author = {Du, A and Huang, M and Wang, Z and Zhou, H and Duan, H and Hu, S and Zheng, Y},
title = {Using Low-Intensity Focused Ultrasound to Treat Depression and Anxiety Disorders: A Review of Current Evidence.},
journal = {Brain sciences},
volume = {15},
number = {10},
pages = {},
pmid = {41154223},
issn = {2076-3425},
support = {2023YFC2506200//National Key Research and Development Program of China/ ; 2023YFC2506203//National Key Research and Development Program of China/ ; 2022YFB3204300//National Key Research and Development Program of China/ ; 2022C01002//Zhejiang Provincial Key Research and Development Program of China/ ; },
abstract = {Background: Depression and anxiety disorders impact millions globally. In recent years, low-intensity focused ultrasound (LIFU), characterized by its high precision, deep penetration, and non-invasive nature, has garnered significant interest in neuroscience and clinical practice. To enhance understanding of its effects on mood, therapeutic availability in treatment of depression/anxiety disorders, and potential mechanisms, a systematic review of studies investigating the emotional impact of LIFU on depressive/anxious-like animal models, healthy volunteers, and patients with depression or anxiety disorders has been undertaken. Methods: Relevant papers published before 15 July 2025 were searched across four databases: Web of Science, PubMed, Science Direct, and Embase. A total of 28 papers which met the inclusion and exclusion criteria are included in this review. Results: Our findings indicate that LIFU reversed the depressive/anxious-like behaviors in the animal models and showed antidepressant/anti-anxiety effects among the state-of-art clinical studies. For example, immobility time in FST or TST is reduced in depressive animal models, and HRSD/BAI scales are improved in human studies. Key molecules such as BDNF/5-HT are found restored in animal models, and FC between key brain areas related to depression/anxiety is modulated after LIFU treatment. Notably, no brain tissue damage was observed in animal studies, and only mild adverse effects (such as dizziness and vomiting) were noted in a few human studies. Conclusions: The studies using LIFU to treat depression and anxiety remain in the preliminary stage. The mechanisms underlying LIFU's mood effects-such as activation or inhibition of specific brain regions or neural circuits, anti-inflammatory effects, alterations in functional connectivity, synaptic plasticity, neurotransmitter levels, and BDNF-remain incompletely understood and warrant further investigation. Nevertheless, the LIFU technique holds promise for regulating both cortical and subcortical brain areas implicated in depression/anxiety disorders as a precise neuromodulation tool.},
}
@article {pmid41154218,
year = {2025},
author = {Tan, L and Fang, H and Ding, P and Wang, F and Wei, Y and Fu, Y},
title = {P300 Spatiotemporal Prior-Based Transformer-CNN for Auxiliary Diagnosis of PTSD.},
journal = {Brain sciences},
volume = {15},
number = {10},
pages = {},
pmid = {41154218},
issn = {2076-3425},
support = {82172058, 62376112, 81771926, 61763022, 62366026, 62006246//The National Natural Science Foundation of China under Grant Nos/ ; },
abstract = {Objectives: To address the challenges of subjectivity, misdiagnosis and underdiagnosis in post-traumatic stress disorder (PTSD), this study proposes an objective auxiliary diagnostic method based on P300 signals. Existing studies largely rely on conventional P300 features, lacking the systematic integration of event-related potential (ERP) priors and facing limitations in spatiotemporal feature modeling. Methods: Using common spatiotemporal pattern (CSTP) analysis and quantitative evaluation, we revealed significant spatiotemporal differences in P300 signals between PTSD patients and healthy controls. ERP prior information was then extracted and integrated into a hybrid architecture combining transformer encoders and a convolutional neural network (CNN), enabling joint modeling of long-range temporal dependencies and local spatial patterns. Results: The proposed P300 spatiotemporal transformer-CNN (P300-STTCNet) achieved a classification accuracy of 93.37% in distinguishing PTSD from healthy controls, markedly outperforming traditional approaches. Conclusions: Significant spatiotemporal differences in P300 signals exist between PTSD and healthy control groups. The P300-STTCNet model effectively captures PTSD-related spatiotemporal features, demonstrating strong potential for electroencephalogram-based objective auxiliary diagnosis.},
}
@article {pmid41152182,
year = {2025},
author = {Cao, Y and Xue, Y and Yang, H and Wang, F and Li, T and Zhao, L and Fu, Y},
title = {[Ethical considerations for artificial intelligence-enhanced brain-computer interface].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {5},
pages = {1085-1091},
pmid = {41152182},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces/ethics ; *Artificial Intelligence/ethics ; Humans ; Deep Learning ; User-Computer Interface ; Electroencephalography ; },
abstract = {Artificial intelligence-enhanced brain-computer interfaces (BCI) are expected to significantly improve the performance of traditional BCIs in multiple aspects, including usability, user experience, and user satisfaction, particularly in terms of intelligence. However, such AI-integrated or AI-based BCI systems may introduce new ethical issues. This paper first evaluated the potential of AI technology, especially deep learning, in enhancing the performance of BCI systems, including improving decoding accuracy, information transfer rate, real-time performance, and adaptability. Building on this, it was considered that AI-enhanced BCI systems might introduce new or more severe ethical issues compared to traditional BCI systems. These include the possibility of making users' intentions and behaviors more predictable and manipulable, as well as the increased likelihood of technological abuse. The discussion also addressed measures to mitigate the ethical risks associated with these issues. It is hoped that this paper will promote a deeper understanding and reflection on the ethical risks and corresponding regulations of AI-enhanced BCIs.},
}
@article {pmid41152174,
year = {2025},
author = {Wang, P and Ji, X and Wang, J and Yu, X},
title = {[Brain computer interface nursing bed control system based on deep learning and dual visual feedback].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {5},
pages = {1021-1028},
pmid = {41152174},
issn = {1001-5515},
mesh = {Humans ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography ; *Feedback, Sensory ; Neural Networks, Computer ; Beds ; },
abstract = {In order to meet the need of autonomous control of patients with severe limb disorders, this paper designs a nursing bed control system based on motor imagery-brain computer interface (MI-BCI). In view of the low decoding performance of cross-subjects and the dynamic fluctuation of cognitive state in the existing MI-BCI technology, the neural network structure optimization and user interaction feedback enhancement are improved. Firstly, the optimized dual-branch graph convolution multi-scale neural network integrates dynamic graph convolution and multi-scale convolution. The average classification accuracy is higher than that of multi-scale attention temporal convolution network, Gram angle field combined with convolution long short term memory hybrid network, Transformer-based graph convolution network and other existing methods. Secondly, a dual visual feedback mechanism is constructed, in which electroencephalogram (EEG) topographic map feedback can improve the discrimination of spatial patterns, and attention state feedback can enhance the temporal stability of signals. Compared with the single EEG topographic map feedback and non-feedback system, the average classification accuracy of the proposed method is also greatly improved. Finally, in the four classification control task of nursing bed, the average control accuracy of the system is 90.84%, and the information transmission rate is 84.78 bits/min. In summary, this paper provides a reliable technical solution for improving the autonomous interaction ability of patients with severe limb disorders, which has important theoretical significance and application value.},
}
@article {pmid41152144,
year = {2025},
author = {Bek, J and Aziz, A and Brady, N},
title = {Transcranial Direct Current Stimulation to Augment Motor Imagery Training: A Systematic Review.},
journal = {The European journal of neuroscience},
volume = {62},
number = {8},
pages = {e70280},
pmid = {41152144},
issn = {1460-9568},
support = {101034345//H2020 Marie Sk&#x142;odowska-Curie Actions/ ; },
mesh = {Humans ; *Transcranial Direct Current Stimulation/methods ; *Motor Cortex/physiology ; *Imagination/physiology ; Brain-Computer Interfaces ; *Imagery, Psychotherapy/methods ; Neuronal Plasticity ; *Stroke Rehabilitation/methods ; },
abstract = {Motor imagery training (MIT) is a widely used technique for motor learning and recovery. To optimize training outcomes, researchers have explored the integration of MIT with complementary approaches. One such approach is transcranial direct current stimulation (tDCS), which also shows promise as a method to enhance motor performance and neuroplasticity. This systematic review aimed to synthesize the current evidence on the synergistic effects of MIT combined with tDCS, with a specific focus on behavioral outcomes. Heterogeneous methods across 16 studies with 432 participants in total, including both healthy and clinical populations, yielded mixed results. Nonetheless, the potential of anodal tDCS applied over the primary motor cortex to augment the beneficial effects of MIT for motor performance in healthy participants is suggested by the current literature. The benefits of combining tDCS with MIT in brain-computer interface (BCI) protocols with stroke patients were less clear, which may relate to population differences, timing of stimulation, or the similarity between outcome measures and trained tasks. Overall, small samples and heterogeneous methods limit interpretation of the findings of combined intervention studies, and further research should aim to measure both behavioral and neurophysiological outcomes in larger samples as well as examining longer-term synergistic effects.},
}
@article {pmid41151221,
year = {2025},
author = {Liu, S and Su, L and He, Q and Qiu, M and Liang, R},
title = {Comparative evaluation of ChatGPT and Gemini in brain-computer interfaces patient education: A multi-dimensional analysis of reliability, accuracy, comprehensibility, and readability.},
journal = {International journal of medical informatics},
volume = {206},
number = {},
pages = {106164},
doi = {10.1016/j.ijmedinf.2025.106164},
pmid = {41151221},
issn = {1872-8243},
abstract = {BACKGROUND: Brain-Computer Interfaces (BCI) are a type of life-altering neurotechnology, but their inherent complexity poses significant challenges to patient education. Large Language Models (LLMs), such as ChatGPT and Gemini, offer new possibilities to address this challenge. This study aims to conduct a multi-dimensional, rigorous comparative analysis of the performance of these two mainstream AI models in responding to common patient questions related to BCI.<br><br>METHODS: Through a structured process combining clinical expert consensus, literature review, and online patient community analysis, we identified 13 key patient questions covering the entire BCI treatment cycle. We then obtained responses to these questions from ChatGPT and Gemini on September 1, 2025. An evaluation panel, composed of clinical experts and non-medical professionals, conducted a blinded assessment of the response quality using standardized Likert scales across three dimensions: reliability, accuracy, and comprehensibility. Concurrently, we performed an objective, quantitative analysis of the response texts using the Flesch-Kincaid readability tests.<br><br>RESULTS: On core quality metrics such as reliability, accuracy, and comprehensibility, the performance of the two models was generally comparable, both demonstrating a high level of proficiency with only sporadic statistical differences on a few technical questions. However, a clear significant disparity emerged in the dimension of readability: for 12 of the 13 questions, the text generated by Gemini required a significantly lower reading grade level than that of ChatGPT (p&#xa0;<&#xa0;0.05) and had significantly higher reading ease scores. This difference stemmed from Gemini's tendency to use shorter sentences and simpler vocabulary.<br><br>CONCLUSION: AI chatbots possess immense potential in the field of BCI patient education. Although both ChatGPT and Gemini can provide high-quality information, Gemini demonstrates a clear advantage in the accessibility and approachability of information, making it a potentially more suitable tool for initial application across diverse patient populations. Nevertheless, the limitations of AI in handling highly specialized and dynamically changing knowledge underscore the indispensable role of human expert supervision and validation in any clinical application.},
}
@article {pmid41149457,
year = {2025},
author = {Lee, HH and Siu-Li, N and Pagano, I and Fukui, JA},
title = {Examining a Genomic Test in Predicting Extended Endocrine Benefit and Recurrence Risk in a Diverse Breast Cancer Population.},
journal = {Current oncology (Toronto, Ont.)},
volume = {32},
number = {10},
pages = {},
pmid = {41149457},
issn = {1718-7729},
support = {P30 CA071789/CA/NCI NIH HHS/United States ; P30CA071789-17S2/CA/NCI NIH HHS/United States ; },
mesh = {Humans ; *Breast Neoplasms/genetics/drug therapy/pathology ; Female ; Middle Aged ; *Neoplasm Recurrence, Local/genetics ; Aged ; Retrospective Studies ; Adult ; *Genomics/methods ; *Antineoplastic Agents, Hormonal/therapeutic use ; },
abstract = {(1) Background: Extended endocrine therapy (EET) beyond five years can reduce distant recurrence in early-stage hormone receptor-positive (HR+) breast cancer. The Breast Cancer Index (BCI) predicts recurrence risk and EET benefits, yet racial/ethnic differences in its results remain unexplored. This study evaluates such differences in a diverse early-stage HR+ breast cancer population. (2) Methods: We retrospectively analyzed demographics, tumor characteristics and BCI scores of 159 women in Hawaii with early-stage HR+ breast cancer, self-identifying as Caucasian, Filipino, Japanese, Native Hawaiian, Other Asian/Pacific Islander, or Other. Tumor characteristics included size, grade, histology, lymph node/receptor status, Oncotype DX score, and laterality. Logistic regression used demographics and tumor features as predictor variables, with BCI's benefit prediction and recurrence risk as outcome variables. (3) Results: Japanese and other Asian/Pacific Islander patients had significantly lower odds of high recurrence risk compared to Caucasian patients. Higher recurrence risk was associated with greater odds of predicted EET. Racial/ethnic differences in EET benefit prediction were not statistically significant. (4) Conclusions: No racial/ethnic differences in EET benefit prediction suggest BCI's applicability in racially and ethnically diverse populations. Findings among Japanese and other Asian/Pacific Islanders point to potential biological or socioeconomic variation. Limitations include sample size and underrepresentation of certain groups. Future studies should address these gaps and adjust for known risk factors to further clarify BCI's racial and ethnic implications.},
}
@article {pmid41149344,
year = {2025},
author = {Kucukselbes, H and Sayilgan, E},
title = {Real-Time EEG Decoding of Motor Imagery via Nonlinear Dimensionality Reduction (Manifold Learning) and Shallow Classifiers.},
journal = {Biosensors},
volume = {15},
number = {10},
pages = {},
pmid = {41149344},
issn = {2079-6374},
mesh = {Humans ; *Electroencephalography/methods ; Adult ; Male ; Signal Processing, Computer-Assisted ; Female ; Machine Learning ; Brain-Computer Interfaces ; Young Adult ; Dimensionality Reduction ; },
abstract = {This study introduces a real-time processing framework for decoding motor imagery EEG signals by integrating manifold learning techniques with shallow classifiers. EEG recordings were obtained from six healthy participants performing five distinct wrist and hand motor imagery tasks. To address the challenges of high dimensionality and inherent nonlinearity in EEG data, five nonlinear dimensionality reduction methods, t-SNE, ISOMAP, LLE, Spectral Embedding, and MDS, were comparatively evaluated. Each method was combined with three shallow classifiers (k-NN, Naive Bayes, and SVM) to investigate performance across binary, ternary, and five-class classification settings. Among all tested configurations, the t-SNE + k-NN pairing achieved the highest accuracies, reaching 99.7% (two-class), 99.3% (three-class), and 89.0% (five-class). ISOMAP and MDS also delivered competitive results, particularly in multi-class scenarios. The presented approach builds upon our previous work involving EEG datasets from individuals with spinal cord injury (SCI), where the same manifold techniques were examined extensively. Comparative findings between healthy and SCI groups reveal consistent advantages of t-SNE and ISOMAP in preserving class separability, despite higher overall accuracies in healthy subjects due to improved signal quality. The proposed pipeline demonstrates low-latency performance, completing signal processing and classification in approximately 150 ms per trial, thereby meeting real-time requirements for responsive BCI applications. These results highlight the potential of nonlinear dimensionality reduction to enhance real-time EEG decoding, offering a low-complexity yet high-accuracy solution applicable to both healthy users and neurologically impaired individuals in neurorehabilitation and assistive technology contexts.},
}
@article {pmid41146476,
year = {2025},
author = {Yue, X and Lu, L and Liu, H and Zang, Y},
title = {LRR-UNet: A Deep Unfolding Network With Low-Rank Recovery for EEG Signal Denoising.},
journal = {CNS neuroscience &amp; therapeutics},
volume = {31},
number = {10},
pages = {e70632},
pmid = {41146476},
issn = {1755-5949},
support = {2023YFF1204200//National Key Research and Development Program of China/ ; 62476197//National Natural Science Foundation of China/ ; },
mesh = {*Electroencephalography/methods ; Humans ; *Deep Learning ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; Artifacts ; Algorithms ; *Brain/physiology ; },
abstract = {BACKGROUND: Electroencephalogram (EEG) signals are crucial for brain-computer interface research but are highly susceptible to noise contamination, necessitating effective denoising. While deep learning has been widely applied, its "black-box" nature limits interpretability. In contrast, traditional model-based methods like Low-Rank Recovery (LRR) offer strong interpretability by decomposing signals into low-rank and sparse components.<br><br>OBJECTIVE: This paper aims to develop an interpretable deep-learning model for EEG denoising that combines the performance of deep learning with the interpretability of traditional LRR methods.<br><br>METHODS: We propose LRR-Unet, a deep unfolding network that transforms the traditional iterative LRR algorithm into a neural network architecture. Specifically, the time-consuming Singular Value Decomposition (SVD) and sparse optimization processes in LRR are replaced with learnable neural network modules.<br><br>RESULTS: Extensive experiments demonstrate that LRR-Unet outperforms other state-of-the-art models in removing ocular and electromyographic artifacts, achieving superior performance on both quantitative and qualitative metrics. Furthermore, in downstream classification tasks, EEG signals preprocessed with LRR-Unet yield better results across various evaluation indicators.<br><br>CONCLUSION: The proposed LRR-Unet provides an effective and interpretable solution for EEG denoising. Its superiority in denoising performance and practical utility in enhancing downstream application performance is validated through comprehensive experiments.},
}
@article {pmid41146424,
year = {2025},
author = {Yang, C and Wang, X and Ye, X and Shen, Y and Tong, J and Zhang, X and Zhou, Y},
title = {Spatiotemporal Immune Dynamics in Experimental Retinal Ganglion Cell Injury Models.},
journal = {Immunity, inflammation and disease},
volume = {13},
number = {10},
pages = {e70284},
pmid = {41146424},
issn = {2050-4527},
support = {//This study was supported by a National Natural Science Foundation of China (NSFC) Key Program grant 82430038, Key R&amp;D Program of Zhejiang Province 2025C02109, NSFC grants 82371455 and 82371084, a National Key Research and Development Program of China grant 2023YFC2506200, a China Postdoctoral Science Foundation grant 2024M752831, and the Open Project Program of Shaanxi Provincial Key Laboratory of Biological Psychiatry XJJSHTS-2504./ ; },
mesh = {*Retinal Ganglion Cells/immunology/pathology ; Animals ; *Optic Nerve Injuries/immunology/pathology ; Disease Models, Animal ; *Glaucoma/immunology/pathology ; Humans ; },
abstract = {BACKGROUND: The damage and regeneration of retinal ganglion cells (RGCs) have been extensively studied. Among them, immune cells in different parts of the visual pathway play an important role in injury, regeneration and repair, but a comprehensive analysis of their spatial and temporal distribution is lacking.<br><br>PURPOSE: This review emphasizes the unique characteristics of immune cells within the visual input pathway, focusing on their spatiotemporal dynamics in the retina, optic nerve head (ONH), and optic nerve during glaucoma and traumatic optic nerve injury.<br><br>METHODS: A comprehensive search was conducted across PubMed and Web of Science up to April 2025. Studies were included if they reported immune cells under glaucoma or optic nerve crush (ONC) animal models.<br><br>FINDINGS: Each region of the visual input pathway displays a distinct immune cell composition, including M&#xfc;ller cells, microglia, astrocytes, T cells, and oligodendrocytes, all of which work together to maintain homeostasis and respond to injury. Some immune cells are specific to certain regions, while others are shared across areas. Furthermore, even within a single glial cell type, there are different subtypes with unique developmental origins or marker profiles, reflecting a range of functions. In both glaucoma and traumatic optic nerve injury, retinal immune cells are rapidly activated, regardless of whether the initial impairment occurs in the soma or axon of RGCs, in the subacute or chronic course. The early stages of injury also see the presence of adaptive immune cells, such as T cells and neutrophils. Macrophages and microglia typically play complementary roles, while astrocytes show prolonged activation compared to microglia in the optic nerve, though this pattern does not hold in the retina following ONC.<br><br>CONCLUSIONS: Understanding the spatiotemporal dynamics of these immune responses in glaucoma and traumatic optic nerve injury is crucial for developing targeted therapies that can reduce RGC loss, mitigate neurotoxicity, and promote functional recovery, ultimately preventing vision impairment. Targeting specific immune cell subsets may provide new strategies for delaying RGC damage.},
}
@article {pmid41146245,
year = {2025},
author = {López-Larraz, E and Sarasola-Sanz, A and Birbaumer, N and Ramos-Murguialday, A},
title = {Uncovering attempted movements of the paralyzed upper limb after stroke through EEG and EMG.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {221},
pmid = {41146245},
issn = {1743-0003},
support = {2422-0-1//Fort&#xfc;ne program - University of T&#xfc;bingen/ ; E! 113928 SubliminalHomeRehab//Eureka-Eurostars/ ; MAIA 951910//European Union H2020-FETPROACT-EIC-2018-2020/ ; NanoNeuro//Basque IKUR initiative/ ; 01QE2023//Bundesministerium f&#xfc;r Bildung und Forschung/ ; },
mesh = {Humans ; *Electroencephalography/methods ; Male ; *Stroke/physiopathology/complications ; Female ; *Electromyography/methods ; Middle Aged ; *Upper Extremity/physiopathology ; Aged ; *Paralysis/physiopathology/etiology/rehabilitation ; Adult ; Movement/physiology ; *Stroke Rehabilitation ; },
abstract = {Detecting attempted movements of a paralyzed limb is a key step for neural interfaces for motor rehabilitation and restoration after a stroke. In this paper, we present a systematic evaluation of electroencephalographic (EEG) and electromyographic (EMG) activity to decode when stroke patients with severe upper-limb paralysis attempt to move their affected arm. EEG and EMG recordings of 35 chronic stroke patients were analyzed. We trained classifiers to discriminate between rest and movement attempt states relying on brain, muscle, or both types of signals combined. Our results reveal that: (i) EEG and residual EMG activity provide complementary information to detect attempted movements, obtaining significantly higher decoding accuracy when both sources of activity are combined; (ii) EMG-based, but not EEG-based, decoding accuracy correlates with the degree of impairment of the patient; and (iii) the percentage of patients that achieve decoding accuracy above the chance level strongly depends on the type of features considered, and can be as low as 50% of them if only ipsilesional EEG is used. These results offer new perspectives to develop improved neurotechnologies that establish a more accurate contingent link between the central and peripheral nervous system after a stroke, leveraging Hebbian learning and facilitating functional plasticity and recovery.},
}
@article {pmid41145802,
year = {2025},
author = {Hazrati, H and Daliri, MR},
title = {Decoding covert visual attention of electroencephalography signals using continuous wavelet transform and deep learning approach.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {37503},
pmid = {41145802},
issn = {2045-2322},
mesh = {Humans ; *Deep Learning ; *Electroencephalography/methods ; *Attention/physiology ; *Wavelet Analysis ; Male ; Adult ; Female ; Young Adult ; Brain-Computer Interfaces ; *Visual Perception/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {Covert visual attention decoding from EEG signals is a key challenge in cognitive neuroscience and brain-computer interface applications. Traditional approaches often rely on manual feature extraction and handcrafted pipelines, which limit scalability and generalization. In this study, we propose a deep learning-based framework that leverages time-frequency representations, specifically Continuous Wavelet Transform (CWT), to enable end-to-end classification of covert attention states without manual feature engineering. EEG data were recorded from ten healthy participants performing spatial and feature-based attention tasks. Among the tested models, ShallowConvNet achieved 100% accuracy in binary classification and over 90% in four-class conditions. EEGNet also performed competitively, exceeding 97% and 88% accuracy in two- and four-class scenarios, respectively. These findings demonstrate that integrating CWT with deep neural architectures significantly enhances decoding performance compared to conventional raw-signal approaches, offering a scalable and efficient solution for real-time attention monitoring.},
}
@article {pmid41145516,
year = {2025},
author = {Wei, Z and Lin, X and Zhang, L and Guo, L and Liu, J and Hu, L and Liu, Y and Kong, Y},
title = {CoSpine open access simultaneous cortico-spinal fMRI database of thermal pain and motor tasks.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {1696},
pmid = {41145516},
issn = {2052-4463},
support = {82072010//National Natural Science Foundation of China (National Science Foundation of China)/ ; IS23108//Natural Science Foundation of Beijing Municipality (Beijing Natural Science Foundation)/ ; },
mesh = {*Magnetic Resonance Imaging ; Humans ; *Pain/physiopathology/diagnostic imaging ; Brain/diagnostic imaging ; *Spinal Cord/diagnostic imaging ; },
abstract = {Simultaneous cortico-spinal functional magnetic resonance imaging (fMRI) enables non-invasive investigation of integrated central nervous system function, but acquisition challenges have restricted the availability of public datasets and slowed the development of advanced analytic methods. Here, we introduce the CoSpine database, the first open-access, BIDS-compliant cortico-spinal task-based fMRI resource (N = 61), acquired using a novel single-field-of-view (FOV) imaging protocol covering the whole brain (including cortical, subcortical, brainstem, and cerebellar regions) and cervical spinal cord. The dataset contains raw images, field maps, physiological recordings, and BIDS event files from thermal pain and voluntary motor tasks. An optimized acquisition and preprocessing framework is provided, validated by quality-control metrics such as temporal signal-to-noise ratio and alignment precision. Spanning a broad age range and standardized paradigms, CoSpine serves as a reference for neuroimaging methods development (e.g., hyperalignment) and for artificial intelligence (AI) model benchmarking. Potential applications include sensorimotor phenotyping, studies of age-related neurodegeneration, and exploratory work in neurorehabilitation, while also supporting early-stage development of brain-computer interface (BCI) systems involving spinal activity and personalized neuromodulation strategies.},
}
@article {pmid41145005,
year = {2025},
author = {Yao, Y and Wang, H and Chen, L and Peng, Y and Luo, J},
title = {Foundation models for EEG decoding: current progress and prospective research.},
journal = {Journal of neural engineering},
volume = {22},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ae17e9},
pmid = {41145005},
issn = {1741-2552},
mesh = {*Electroencephalography/methods/trends ; Humans ; *Brain/physiology ; Prospective Studies ; Deep Learning ; Brain-Computer Interfaces ; },
abstract = {Objective.Electroencephalography (EEG) records the spontaneous electrical activity in the brain. Despite the growing application of deep learning in EEG decoding, traditional methods still rely heavily on supervised learning, which is often limited by task specificity and dataset dependency, restricting model performance and generalization. Inspired by the success of large language models, EEG foundation models (EEG FMs) are attracting increasing attention as a unified paradigm for EEG decoding. In this study, we review a selection of representative studies on EEG FMs, aiming to extract trends and provide recommendations for future research.Approach.We provide a comprehensive analysis of recent advances in EEG FMs, with a focus on downstream tasks, benchmark datasets, model architectures, and pre-training techniques. We analyze and synthesize core FMs components, and systematically compare their performances and generalizabilities.Main results.Our review reveals that EEG FMs are pre-trained on large-scale datasets, typically involving several hundred subjects. The number of subjects can reach up to 14 987, with a maximum total duration of 27 062 h. Current EEG FMs most adopt mask-based reconstruction pre-training strategy and employ efficient transformer-based architectures. Our comparative analysis shows that EEG FMs demonstrate significant potential in advancing EEG decoding tasks, particularly in seizure detection. However, their performance in complex scenarios such as motor imagery decoding remains limited.Significance.This review summarizes the existing approaches and performance outcomes of EEG FM, offers valuable insights into their current limitations and delineates prospective avenues for future research.},
}
@article {pmid41144819,
year = {2025},
author = {Liu, H and Cao, X and Li, J and Zheng, L and Li, J and Li, Q and Xie, M and Li, H and Wang, X and Wu, Y and Zhang, X and Wang, Y and Gao, X and Sheng, T and Du, N and Xu, C and Zhou, K and Xu, J and Yan, C and Liu, L and Gao, L and Li, X and Zhang, M},
title = {Deciphering Neural Mechanisms Underlying Marmoset Dynamic Natural Behaviors Using a Miniaturized Wireless Large-Scale Coverage Neural Recorder.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e07110},
doi = {10.1002/advs.202507110},
pmid = {41144819},
issn = {2198-3844},
support = {2023YFB4705500//National Key Research and Development Program of China/ ; 62350710211//National Natural Science Foundation of China/ ; },
abstract = {Deciphering neural mechanisms underlying dynamic natural behaviors of freely moving species requires long-term recordings of large-scale brain activities. However, most conventional neural recorders are limited by their weights and measures, electrode coverage, and signal throughput, hindering the dissection of underlying neural mechanisms. This study reports real-time large-scale recordings and deciphering of brain activities from frontal and temporal cortices of freely moving marmoset across various natural behavioral repertoire using a miniaturized wireless neural recorder comprising a custom-designed 120-channel flexible µECoG array. Behavior-specific highly resolved spatiotemporal neural dynamics are observed, including alpha-band activations during drinking, anticipatory responses before vocalization, and transient high-gamma increase during vigilance to human intruders. Three phases of drinking behavior are identified using multi-area neural features captured by the recorder with an accuracy exceeding 87%. After over 16 months (March 13, 2024-August 1, 2025, remaining actively recording) of recordings, the neural signals acquired using the recorder maintain high fidelity and low attenuation during both the resting and drinking states, enabling potential long-term dissection of the neural mechanisms of natural behaviors in freely moving marmosets.},
}
@article {pmid41144414,
year = {2025},
author = {Lu, B and Chen, J and Wang, F and Wen, G and Fu, R and Hua, C},
title = {Causality-Driven Convolutional Manifold Attention Network for Electroencephalogram Signal Decoding.},
journal = {IEEE transactions on pattern analysis and machine intelligence},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TPAMI.2025.3625631},
pmid = {41144414},
issn = {1939-3539},
abstract = {Deep learning-based methods have achieved remarkable success in brain-computer interfaces (BCIs). However, its inherent assumption of independent and identically distributed (i.i.d.) data renders it vulnerable to out-of distribution (OOD) scenarios. To address this limitation, the present study proposed a causality-driven convolutional manifold attention network (CD-CMAN) that learned invariant representations from electroencephalogram (EEG) signals to enhance OOD generalization. The framework began with a spatiotemporal convolution module to extract rich temporal and spatial features. Guided by the defined structural causal model and leveraging the strengths of Riemannian geometry and deep learning, dual latent encoders with manifold attention units were crafted to explicitly separate spatiotemporal feature maps into semantic and variation latent factors. A reconstruction module with a dedicated loss was implemented to ensure these factors retaining informative, while the Hilbert-Schmidt independence criterion (HSIC) was introduced to enforce their statistical independence. Further, a variational information bottleneck and gradient reversal layer were incorporated to compress and disentangle the semantic and variation factors. Evaluations on two public datasets under both subject-dependent and subject independent settings demonstrated that CD-CMAN consistently outperforms comparative baselines. These findings suggest that the proposed model could provide a new solution for the practical application of BCI technology.},
}
@article {pmid41143908,
year = {2025},
author = {Xie, X and Hu, F and Yuan, S and Wen, D and Duan, D},
title = {MS-CANet: lightweight multi-scale channel attention network with depthwise residual blocks for EEG-based spatial cognition evaluation.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {},
number = {},
pages = {},
pmid = {41143908},
issn = {1741-0444},
support = {62276022//National Natural Science Foundation of China/ ; 2023YFF1203702//National Key Research and Development Program of China/ ; 62206014//National Key Research and Development Program of China/ ; FRFBD-25-052//Fundamental Research Funds for the Central Universities/ ; },
abstract = {Objective assessment of spatial cognitive ability is crucial for screening cognitive impairment and in neurorehabilitation. While deep learning methods for electroencephalogram (EEG) analysis show great promise, they increasingly rely on complex, parameter-heavy architectures. This complexity often leads to poor generalization due to overfitting on small datasets and hinders deployment on mobile healthcare devices. To overcome these limitations, we propose a novel lightweight multi-scale channel attention network with depthwise residual blocks. The model incorporates multi-scale convolutional layers to capture diverse temporal and spatial patterns in EEG signals. It then leverages channel attention mechanisms to dynamically prioritize informative channels, focusing on task-critical features. Furthermore, a novel depthwise separable residual block is introduced to significantly reduce computational complexity and maintain stable model performance. Evaluations on a spatial cognition EEG dataset demonstrate that our network achieves higher accuracy than baselines with only 8.453M parameters, making it an efficient and practical solution for mobile deployment. It also holds strong potential for extension to early screening and intervention in a wider range of cognitive disorders.},
}
@article {pmid41142108,
year = {2025},
author = {Peng, Q and Huang, J and Li, C and Jiang, M and Huang, C and Luo, J and Li, H and Yin, T and Cai, M and Fu, S and Ma, G and Liu, Z and Xu, T},
title = {Magnetically Actuated Soft Electrodes for Multisite Bioelectrical Monitoring of Ex Vivo Tissues.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {6},
number = {},
pages = {0434},
pmid = {41142108},
issn = {2692-7632},
abstract = {Multisite electrophysiological monitoring of ex vivo tissues and organ models is essential for basic research and drug toxicity evaluation. However, conventional microelectrode arrays with fixed positions and rigid structures are insufficient for dynamic, curved tissue surfaces. Here, we present a magnetically actuated soft electrode (MSE) with precise navigation, adaptive attachment, and high-fidelity signal acquisition. Operating in a "locate-adhere-record-detach" cycle, the MSE enabled continuous multisite detection on beating ex vivo tissues. In isolated rat heart experiments, the MSE demonstrated millimeter-level navigation accuracy, stable contact, and high signal-to-noise ratio (average 28 dB). By integrating magnetic locomotion with electrophysiological sensing, this work establishes a programmable, actively addressable platform for multisite electrophysiological monitoring of organ models, tissue slices, and engineered constructs, offering broad potential for cardiotoxicity screening and cardiovascular research.},
}
@article {pmid41141194,
year = {2025},
author = {Jayalaksshme Srinivasan, K and Periasamy, P and Gunasekaran, S},
title = {Motor Imagery and Motor Execution: A Narrative Review of Electroencephalographic (EEG) Signatures, Methodological Consistency, and Translational Applications.},
journal = {Cureus},
volume = {17},
number = {9},
pages = {e93011},
pmid = {41141194},
issn = {2168-8184},
abstract = {This narrative review evaluates when electroencephalography (EEG) signatures elicited by kinesthetic motor imagery (MI) genuinely approximate those of motor execution (ME), appraises methodological consistency across studies, and outlines pragmatic routes to translation in brain-computer interfaces (BCIs) and neurorehabilitation. A keyword-driven search of Web of Science, Scopus, PubMed, and conference repositories was used to extract empirical, English-language EEG studies reporting sensorimotor rhythm (mu 8-13 Hz; beta 13-30 Hz) event-related desynchronization/synchronization (ERD/ERS) metrics and/or decoding performance for MI and/or ME, with structured extraction of task/sample features, imagery protocol, EEG methods/signatures, MI-ME overlap, translational readouts, and limitations. Across convergent datasets, MI reliably evokes contralateral mu/beta ERD with timing and topography akin to ME, typically with smaller amplitudes and broader fields; realistic decoding benchmarks cluster around the mid-70% for MI versus low-80% for ME, with ≈70% a usability threshold and 15%-30% of naïve users below it. Convergence and performance improve with first-person kinesthetic instructions, higher imagery vividness, synchronised action observation, object-oriented tasks, EMG monitoring, and contingent neurofeedback; source-space modelling and synergy-aware features can lift MI accuracy into the ~82%-95% range in constrained settings, though offline gains often overestimate online control. In stroke cohorts, most patients exhibit clear ERD/ERS, and a meaningful subset exceeds operational thresholds; however, calibration-to-online drops (e.g., ~80% to ~70%) are common and partially recover with adaptive retraining. The principal barriers to translation are heterogeneous protocols (band definitions, referencing, validation), small and selective samples, sparse EMG to exclude covert movement, non-stationarity across sessions, and persistent non-responders. To move from plausibility to practice, future studies should standardise mu/beta windows and baselines, report closed-loop outcomes, personalise training with vividness assessment and synchronised action observation, anticipate drift with adaptive algorithms and periodic recalibration, and integrate MI with robotics, functional electrical stimulation, or virtual reality in multisite trials that track durable functional gains.},
}
@article {pmid41140580,
year = {2025},
author = {Boonstra, JT},
title = {Ethical imperatives in the commercialization of brain-computer interfaces.},
journal = {IBRO neuroscience reports},
volume = {19},
number = {},
pages = {718-724},
pmid = {41140580},
issn = {2667-2421},
abstract = {The rapid commercialization of brain-computer interfaces (BCIs) raises urgent ethical and scientific challenges for human research oversight. While BCIs hold transformative potential for treating neurological disorders, their premature translation into consumer markets risks outpacing neuroscientific understanding and ethical frameworks. This essay critically examines the mismatch between commercial claims and the technical limitations of current BCI systems, decoding accuracy and biocompatibility, unresolved ethical dilemmas posed by neural data commodification and procedural risks, and the inadequacy of existing governance to address vulnerabilities in consent, privacy, and long-term safety. Responsible innovation demands proactive measures and robust public engagement to align development with societal values. Without such safeguards, the rush to commercialize BCIs risks prioritizing market interests over patient welfare and eroding public trust in neurotechnology.},
}
@article {pmid41139722,
year = {2025},
author = {Li, J and Chen, T and Yan, X and Luo, L},
title = {The effect of device-based neuromodulation on the motor recovery of patients with spinal cord injury.},
journal = {Spinal cord},
volume = {},
number = {},
pages = {},
pmid = {41139722},
issn = {1476-5624},
abstract = {STUDY DESIGN: This paper systematically analyzes literature from PubMed, MEDLINE, Embase, and Cochrane databases over the past 10 years (up to May 25, 2025). It employs defined search terms, inclusion/exclusion criteria, and a documented search flow to evaluate mechanisms, efficacy, challenges, and future directions of neuromodulation technologies for spinal cord injury rehabilitation. The results synthesize findings from clinical trials, and representative papers.<br><br>OBJECTIVE: This review aims to evaluate the mechanisms and clinical applications of device-based neuromodulation technologies in spinal cord injury (SCI) rehabilitation, focusing on their efficacy, challenges, and future directions.<br><br>SETTING: The countries and regions worldwide participating in neuromodulation.<br><br>METHODS: We systematically analyzed advancements in neuromodulation over the past decade, including brain-spinal interfaces (BSI), brain-computer interfaces (BCI), cranial stimulation techniques (DBS, TMS, tDCS), spinal cord stimulation (SCS), robotic exoskeletons. The review integrates findings from clinical trials.<br><br>RESULTS: Neuromodulation technologies demonstrate significant potential in restoring motor and sensory function post-SCI. BSI and BCI improve mobility but face infection and cybersecurity risks. Cranial stimulation techniques enhance neuroplasticity, with DBS and TMS showing efficacy, while tDCS requires further validation. Epidural SCS enables motor recovery in complete paralysis but has high infection rates. Robotic exoskeletons benefit younger patients.<br><br>CONCLUSION: Neuromodulation technologies represent promising interventions for SCI, yet challenges remain in precision, safety, and efficacy. Future research should prioritize AI-driven parameter optimization, wearable device development, and multicenter randomized trials to establish these methods as core treatments, ultimately improving patient outcomes and quality of life.},
}
@article {pmid41138930,
year = {2025},
author = {Wang, J and Wang, X and Qiao, S and La, H and Yu, Y and An, X},
title = {Investigation of fatigue mechanisms and detection methods for anesthesiologists based on multimodal physiological signals.},
journal = {Brain research bulletin},
volume = {232},
number = {},
pages = {111597},
doi = {10.1016/j.brainresbull.2025.111597},
pmid = {41138930},
issn = {1873-2747},
mesh = {Humans ; Electroencephalography/methods ; Male ; *Fatigue/physiopathology/diagnosis ; Female ; Adult ; Electrocardiography/methods ; *Anesthesiologists/psychology ; Attention/physiology ; Memory, Short-Term/physiology ; Cognition/physiology ; Young Adult ; },
abstract = {Anesthesiologists are highly susceptible to fatigue due to the demanding intensity and critical responsibility of their work, which poses substantial risks to both clinician health and patient safety. To elucidate fatigue mechanisms, this study systematically assessed cognitive and physiological alterations before and after prolonged high-intensity work. Cognitive performance was evaluated with paradigms targeting attention (0-back), working memory (2-back), and visuospatial processing, complemented by multimodal physiological monitoring with electroencephalogram (EEG) and electrocardiogram (ECG) recordings. Prolonged work was associated with significant declines in n-back accuracy, reflecting impaired attention and working memory, while visuospatial performance showed marked increases in both error rate and reaction time, indicating deterioration of spatial cognition and executive control. Concurrently, physiological analyses revealed enhanced EEG alpha-band connectivity, shortened RR intervals, a reduced LF/HF ratio, and elevated multiscale entropy, collectively indicating autonomic imbalance and central-autonomic dysregulation under fatigue. Building on these mechanistic findings, we applied transfer learning algorithms to statistically significant multimodal physiological features, achieving 99.4 % cross-subject classification accuracy. This integration of mechanistic insights with computational modeling underscores the reliability of the proposed strategy and its translational potential for real-world clinical fatigue monitoring.},
}
@article {pmid41137585,
year = {2025},
author = {Zadeh Makouei, ST and Uyulan, C and Erguzel, TT and Tarhan, N},
title = {Advanced Facial Expression Recognition Using Model Averaging Ensembles of Convolutional Neural Networks and CAM Analysis.},
journal = {Clinical EEG and neuroscience},
volume = {},
number = {},
pages = {15500594251366792},
doi = {10.1177/15500594251366792},
pmid = {41137585},
issn = {2169-5202},
abstract = {Facial expressions play a vital role in non-verbal communication, conveying a wide range of emotions and messages. Although prior research achieved notable advances through architecture design or dataset-specific optimization, few studies have integrated multiple advanced techniques into a unified facial expression recognition (FER) pipeline. Addressing this gap, we propose a comprehensive approach that combines (i) multiple pre-trained CNNs, (ii) MTCNN-based face detection for improved facial region localization, and (iii) Grad-CAM-based interpretability. While MTCNN enhances the quality of face localization, it may slightly affect classification accuracy by focusing on cleaner yet more challenging samples. We evaluate four pre-trained models - DenseNet121, ResNet-50, ResNet18, and MobileNetV2 - on two datasets: Raf-DB and Cleaned-FER2013. The proposed pipeline demonstrates consistent improvements in interpretability and overall system robustness. The results emphasize the strength of integrating face detection, transfer learning, and interpretability techniques within a single framework can significantly enhance the transparency and reliability of FER systems. Combining FER with EEG-based systems significantly enhances the emotional intelligence of brain-computer interfaces, enabling more adaptive and personalized user experiences. With this approach the paper bridges the gap between affective computing and cognitive neuroscience, aligning closely EEG-centered interaction methodologies. Besides understanding the relationship between facial expressions of emotions and EEG signals will be an important study for literature.},
}
@article {pmid41136747,
year = {2025},
author = {Zuo, H and Zhang, W and Wang, L and Wu, Y and Zheng, Y and Hao, S and Chen, QY and Cao, P and Ouyang, M and Huang, S and Zhou, W and Xue, YX and Pan, Y and Wei, W and Zhuo, M and Yuan, T and Zha, R and Zhang, Z and Zhang, X},
title = {Transcranial direct current stimulation restores addictive behavior via prefrontal-striatal circuit.},
journal = {Molecular psychiatry},
volume = {},
number = {},
pages = {},
pmid = {41136747},
issn = {1476-5578},
support = {32171080//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32400919//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32200914//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Dependence on methamphetamine (METH) is a severe brain disorder characterized by high relapse rates and cognitive decline following detoxification. Recent research suggests that transcranial direct current stimulation (tDCS) may treat addiction, but the underlying neural mechanisms remain unknown. Here, we employed METH-conditioned place preference (CPP) paradigm integrated with fMRI, electrophysiology, chemogenetics, in vivo fiber photometry recordings and a novel rodent tDCS model to examine the neural circuit underlying tDCS modulation on METH-induced addictive behavior. We demonstrated that tDCS targeted at the medial prefrontal cortex (mPFC) prevents relapse. Specifically, tDCS enhanced the activity of neurons in both the infralimbic cortex (IL) and the nucleus accumbens shell (NAcSh) simultaneously. Furthermore, chemogenetic inhibition of the IL-NAcSh circuit eliminated the modulatory effects of tDCS, while activation of the IL-NAcSh circuit was sufficient to suppress the relapse. These findings reveal that the IL-NAcSh pathway functions as a descending regulatory circuit mediating the therapeutic outcomes of tDCS in the treatment of substance use disorder, offering new insights into circuit-based neuro-modulatory treatments for addiction.},
}
@article {pmid41135743,
year = {2025},
author = {Wang, Y and Chen, HJ and Cheng, Y and Xie, Y and Cheng, Y and Zhao, S and Jiang, Y and Bai, T and Huo, Y and Wang, K and Zhang, M and Huang, W and Feng, G and Han, Y and Shu, N},
title = {Multimodal integration of plasma biomarkers, MRI, and genetic risk to predict cerebral amyloid burden in Alzheimer's disease.},
journal = {NeuroImage},
volume = {322},
number = {},
pages = {121550},
doi = {10.1016/j.neuroimage.2025.121550},
pmid = {41135743},
issn = {1095-9572},
mesh = {Humans ; *Alzheimer Disease/genetics/pathology/diagnostic imaging/blood ; Male ; Female ; *Magnetic Resonance Imaging/methods ; Aged ; Biomarkers/blood ; *Amyloid beta-Peptides/blood/metabolism ; Machine Learning ; Aged, 80 and over ; Positron-Emission Tomography ; Genetic Predisposition to Disease ; Neuroimaging/methods ; Middle Aged ; Cognitive Dysfunction ; *Brain/pathology/diagnostic imaging ; Longitudinal Studies ; },
abstract = {Alzheimer's disease (AD), the most prevalent neurodegenerative disorder, is marked by the accumulation of amyloid-β (Aβ) plaques. Although cerebral Aβ positron emission tomography (Aβ-PET) remains the gold standard for assessing cerebral Aβ burden, its clinical utility is hindered by cost, radiation exposure, and limited availability. Plasma biomarkers have emerged as promising, non‑invasive indicators of Aβ pathology, yet they do not incorporate individual genetic risk or neuroanatomical context. To address this gap, we developed a multimodal machine‑learning framework that integrates plasma biomarkers, MRI‑derived brain structural features (regional volumes, cortical thickness, cortical area and structural connectivity), and genetic risk profiles to predict cerebral Aβ burden. This approach was evaluated in 150 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and 101 participants from a domestic Chinese Sino Longitudinal Study of Cognitive Decline (SILCODE). Incorporating multimodal features substantially improved predictive performance: the baseline model using plasma and clinical variables alone achieved an R[2] of 0.56, whereas integrating neuroimaging and genetic information increased accuracy (R[2] = 0.63 with apolipoprotein E genotypes and R[2] = 0.64 with polygenic risk scores). Furthermore, a multiclass classifier trained on the same multimodal features achieved robust discrimination of cognitive status, with area‑under‑the‑curve values of 0.87 for normal controls, 0.76 for mild cognitive impairment, and 0.95 for AD dementia. These findings highlight the value of combining plasma, imaging, and genetic data to non-invasively estimate cerebral Aβ burden, offering a potential alternative to PET imaging for early AD risk assessment.},
}
@article {pmid41135661,
year = {2025},
author = {Pan, Y and Yang, X and Wu, M and Hu, S},
title = {Latent profile analysis of childhood trauma in Chinese individuals with bipolar disorder: Differential associations with suicidality and clinical symptomatology.},
journal = {Journal of affective disorders},
volume = {394},
number = {Pt A},
pages = {120490},
doi = {10.1016/j.jad.2025.120490},
pmid = {41135661},
issn = {1573-2517},
abstract = {BACKGROUND: Childhood trauma is a well-established risk factor for poor clinical outcomes in bipolar disorder (BD), yet most studies have relied on cumulative trauma scores, potentially overlooking heterogeneity in trauma exposure and its differential impact on psychopathology.<br><br>METHODS: This study employed latent profile analysis (LPA) to identify distinct subtypes of childhood trauma based on the Childhood Trauma Questionnaire (CTQ) among 725 individuals with BD in a Chinese clinical sample. Differences across trauma profiles were examined in relation to demographic features, psychiatric symptoms (anxiety, depression, mania), and suicidal ideation (Beck Scale for Suicide Ideation, BSSI).<br><br>RESULTS: A four-class solution was identified, and the relationship with mental health outcomes was analyzed. Class 4 group, characterized by the most severe emotional abuse and physical neglect, along with the lowest emotional neglect, reported the highest levels of anxiety (HAMA), depression (HAMD), and suicidal ideation (BSSI). In contrast, manic symptoms (YMRS) were present across all groups but did not differ significantly between trauma profiles. Logistic regression indicated that emotional abuse was the strongest predictor of trauma class membership.<br><br>CONCLUSIONS: Distinct trauma profiles in BD are differentially associated with symptom severity and suicide risk. These findings highlight the clinical value of moving beyond cumulative trauma scores to identify trauma-specific subtypes. Early identification of high-risk trauma configurations may inform personalized assessment and intervention strategies for individuals with BD.},
}
@article {pmid41135523,
year = {2025},
author = {Wang, Z and Tang, Q and Li, K and Mou, J and Chen, Y and Kuang, W and Sun, L and Ma, Z and Wei, Y and Bao, R and Sun, X and Wang, S and Lu, W and Xu, GY and Tang, YQ and Duan, S and Ni, JD},
title = {An enteric-DRG pathway for interoception and visceral pain in mice.},
journal = {Neuron},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuron.2025.09.035},
pmid = {41135523},
issn = {1097-4199},
abstract = {Sensory afferents are major interoceptive pathways for organ-brain communication. Within the distal colon, dorsal root ganglia (DRGs) afferents regulate key gut physiology. Inflammation causes hypersensitivity of DRG pathways, leading to visceral pain. However, whether enteric neurons contribute to interoception and visceral pain remains unclear. Here, we surveyed the DRG innervation along the gastrointestinal tract in mice and found extensive associations between DRG terminals and enteric neurons. Optogenetic activation of different DRG terminals in the distal colon elicited variable degrees of behavioral responses, but only designated subpopulations induced aversion. Notably, optogenetic activation of colon cholinergic, but not nitrergic, enteric neurons signaled through the DRG-spinal pathway to evoke a non-aversive nociceptive-like reflex. Acetylcholine is part of the enteric-DRG signaling. Remarkably, inflammation shifted the nature of the enteric-DRG pathway from non-aversive to aversive. These findings expand the previous understanding of DRG-mediated visceral sensation, highlighting the contribution of enteric neuron-DRG communication to inflammation-induced visceral pain.},
}
@article {pmid41134961,
year = {2025},
author = {Jin, J and Qin, K and Allison, BZ and Li, S and Zhang, Y and Wang, X and Cichocki, A},
title = {A Transfer Learning SSVEP Decoding Algorithm Calibrated With Single-Trial Data.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNNLS.2025.3617508},
pmid = {41134961},
issn = {2162-2388},
abstract = {Training-based algorithms significantly outperform training-free methods in terms of recognition performance for steady-state visual-evoked potential (SSVEP)-based brain-computer Interfaces (BCIs). However, collecting training data requires calibration experiments that are effort-intensive and often costly. These calibration demands limit the practicality of BCI, as users (and even system operators) may experience fatigue or lose interest in continued use. Transfer learning (TL) offers an effective solution, but it typically relies on either a certain amount of target domain data or extensive source domain data. To address this limitation, we introduce the concept of cross-dataset TL in SSVEP for the first time to extract transfer knowledge from other datasets. During this process, we identified a data mismatch problem that severely compromises the generalizability of transfer knowledge. To overcome this challenge, we propose a TL-SSVEP decoding algorithm calibrated with single-trial data (TL-CSTD). Specifically, we use 2 s of 8 Hz single-trial calibration data from the target domain to obtain matched transfer templates from the source domain. These templates are then corrected to extract holistic and single-period transfer knowledge, which are subsequently employed to construct an efficient TL-SSVEP decoding model for the target subject. Experimental results on three large SSVEP datasets demonstrate that TL-CSTD effectively addresses the data mismatch problem and achieves excellent SSVEP recognition performance using only 2 s of single-trial calibration data, showing its significant application potential and practicality.},
}
@article {pmid41134945,
year = {2025},
author = {Kim, DS and Lee, SH and Yin, K and Lee, SW},
title = {Reconstructing Unseen Sentences From Speech-Related Biosignals for Open-Vocabulary Neural Communication.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {4338-4348},
doi = {10.1109/TNSRE.2025.3625219},
pmid = {41134945},
issn = {1558-0210},
mesh = {Humans ; Electroencephalography ; *Speech/physiology ; Male ; Female ; Adult ; Electromyography ; Young Adult ; Algorithms ; Brain-Computer Interfaces ; Phonetics ; Communication ; Brain/physiology ; Signal Processing, Computer-Assisted ; Speech Perception/physiology ; },
abstract = {Brain-to-speech (BTS) systems represent a groundbreaking approach to human communication by enabling the direct transformation of neural activity into linguistic expressions. While recent non-invasive BTS studies have largely focused on decoding predefined words or sentences, achieving open-vocabulary neural communication comparable to natural human interaction requires decoding unconstrained speech. Additionally, effectively integrating diverse signals derived from speech is crucial for developing personalized and adaptive neural communication and rehabilitation solutions for patients. This study investigates the potential of speech synthesis for previously unseen sentences across various speech modes by leveraging phoneme-level information extracted from high-density electroencephalography (EEG) signals, both independently and in conjunction with electromyography (EMG) signals. Furthermore, we examine the properties affecting phoneme decoding accuracy during sentence reconstruction and offer neurophysiological insights to further enhance EEG decoding for more effective neural communication solutions. Our findings underscore the feasibility of biosignal-based sentence-level speech synthesis for reconstructing unseen sentences, highlighting a significant step toward developing open-vocabulary neural communication systems adapted to diverse patient needs and conditions. Additionally, this study provides meaningful insights into the development of communication and rehabilitation solutions utilizing EEG-based decoding technologies.},
}
@article {pmid41134943,
year = {2025},
author = {Sun, J and Lin, PJ and Zhai, X and Wang, W and Jia, T and Li, Z and Pan, Y and Ji, L and Zhou, B and Li, C},
title = {Multimodal behavioral data predict stroke patient's response to BCI treatment through explainable AI.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3625222},
pmid = {41134943},
issn = {1558-0210},
abstract = {Brain-computer interface (BCI)-based neurorehabilitation holds promise in enhancing motor recovery after stroke. However, recent research has reported heterogeneous results, indicating both responders and non-responders to BCI therapy. Using explainable artificial intelligence (XAI) methods, this study aims to investigate the independent and combined importance of multimodal behavioral data to predict patients' response to BCI therapy. Forty-two subacute stroke patients with lower-limb motor impairment underwent behavioral assessments, and received two-week BCI rehabilitation training. Linear regression, elastic net and artificial neural network models were developed to predict response to BCI therapy. Two XAI techniques, the stepwise method and Shapley additive explanation, were used to interpret model outcomes. The multivariate model (R[2]=0.852, P<0.001) that combines an optimal subset of multimodal behavioral data outperformed the univariate model (R[2]=0.758, P<0.001) trained on a single variable. Elastic net and artificial neural network models both demonstrated high prediction performance, as indicated by classification accuracies of 0.810 and 0.762, and areas under the receiver operating characteristic curve of 0.782 and 0.771. Our results revealed that multimodal behavioral data, including demographic, clinical, and biomechanical characteristics, provided unique and complementary information for interpreting the response of subacute patients to BCI therapy. Particularly, baseline motor impairment, muscle spasticity and balance function were primary predictors. Our findings highlight the core role of XAI methods towards precision medicine, which can help clinicians to identify individual recovery potentials and plan optimal treatment strategies.},
}
@article {pmid41132843,
year = {2025},
author = {Jin, S and Lin, C and Li, P and Wang, X and Wang, Y and Zhang, C and Wang, X and Peng, Y and Li, H and Lu, Y and Wang, X},
title = {Cannabidiol alleviates methamphetamine addiction via targeting ATP5A1 and modulating the ATP-ADO-A1R signaling pathway.},
journal = {Acta pharmaceutica Sinica. B},
volume = {15},
number = {10},
pages = {5261-5276},
pmid = {41132843},
issn = {2211-3835},
abstract = {Cannabidiol (CBD), a non-psychoactive cannabinoid, shows great promise in treating methamphetamine (METH) addiction. Nonetheless, the molecular target and the mechanism through which CBD treats METH addiction remain unexplored. Herein, CBD was shown to counteract METH-induced locomotor sensitization and conditioned place preference. Additionally, CBD mitigated the adverse effects of METH, such as cristae loss, a decline in ATP content, and a reduction in membrane potential. Employing an activity-based protein profiling approach, a target fishing strategy was used to uncover CBD's direct target. ATP5A1, a subunit of ATP synthase, was identified and validated as a CBD target. Moreover, CBD demonstrated the ability to ameliorate METH-induced ubiquitination of ATP5A1 via the D376 residue, thereby reversing the METH-induced reduction of ATP5A1 and promoting the assembly of ATP synthase. Pharmacological inhibition of the ATP efflux channel pannexin 1, blockade of ATP hydrolysis by a CD39 inhibitor, and blocking the adenosine A1 receptor (A1R) all attenuated the therapeutic benefits of CBD in mitigating METH-induced behavioral sensitization and CPP. Moreover, the RNA interference of ATP5A1 in the ventral tegmental area resulted in the reversal of CBD's therapeutic efficacy against METH addiction. Collectively, these data show that ATP5A1 is a target for CBD to inhibit METH-induced addiction behaviors through the ADO-A1R signaling pathway.},
}
@article {pmid41132728,
year = {2025},
author = {Berlet, R and Azapagic, A and Jha, NK and Aksenov, D and Bookwalter, J and Ullah, A and Bobustuc, G and Lee, J and Sant, H and McDaid, J and Walker, M and Shea, J and Graff, D and Barlow, AK and Frigerio, R and Aliee, D and Bailes, C and Gale, BK and Bailes, JE},
title = {An implantable, intracerebral osmotic pump for convection-enhanced drug delivery in glioblastoma multiforme.},
journal = {Frontiers in oncology},
volume = {15},
number = {},
pages = {1676691},
pmid = {41132728},
issn = {2234-943X},
abstract = {BACKGROUND: Glioblastoma multiforme (GBM; WHO Grade 4) is an aggressive brain tumor that invariably recurs after surgical resection, chemoradiation, and adjuvant chemotherapy. Treatment is limited, in part, because the blood-brain barrier (BBB) restricts entry of chemotherapeutic agents to the brain. Introducing drugs directly into the brain circumvents the BBB, but diffusion of these typically large drug molecules within brain parenchyma is limited. Convection-enhanced delivery (CED), based on the principles of bulk flow, can achieve drug distribution over a wider area to target residual cancer cells and thus remains a promising technique for treating GBM and other neuro-oncologic pathologies. Here, we propose a new method that combines direct brain delivery and CED using a fully implantable, microfluidic pump placed at the time of initial resection surgery.<br><br>METHODS: In this initial proof-of-concept study, we evaluated the function of a 3D-printed pump in an in vitro system and in vivo in a rat C6 glioma model.<br><br>RESULTS: In vitro osmosis-driven distribution of a high molecular-weight marker dye extended up to 18 mm from the pump with minimal reflux, including under simulations of increased intracranial pressure. In vivo, MRI imaging demonstrated wide distribution of superparamagnetic iron oxide particles from a pump implanted after the resection of a C6 glioma. Histological staining indicated that pump implantation did not cause additional inflammatory changes compared to controls.<br><br>CONCLUSION: This preliminary study demonstrated the feasibility of using an implantable, osmosis-driven pump to bypass the BBB and provide targeted delivery for treatment of GBM.},
}
@article {pmid41129590,
year = {2025},
author = {Cao, D and Yu, Z and Wang, J and Wu, Y},
title = {SMMTM: Motor imagery EEG decoding algorithm using a hybrid multi-branch separable convolutional self-attention temporal convolutional network.},
journal = {PloS one},
volume = {20},
number = {10},
pages = {e0333805},
pmid = {41129590},
issn = {1932-6203},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Algorithms ; Neural Networks, Computer ; *Imagination/physiology ; },
abstract = {Motor imagery (MI) is a brain-computer interface (BCI) technology with the potential to change human life in the future. MI signals have been widely applied in various BCI applications, including neurorehabilitation, smart home control, and prosthetic control. However, the limited accuracy of MI signals decoding remains a significant barrier to the broader growth of the BCI applications. In this study, we propose the SMMTM model, which combines spatiotemporal convolution (SC), multi-branch separable convolution (MSC), multi-head self-attention (MSA), temporal convolution network (TCN), and multimodal feature fusion (MFF). Specifically, we use the SC module to capture both temporal and spatial features. We design a MSC to capture temporal features at multiple scales. In addition, MSA is designed to extract valuable global features with long-term dependence. The TCN is employed to capture higher-level temporal features. The MFF consists of feature fusion and decision fusion, using the features output from the SMMTM to improve robustness. The SMMTM was evaluated on the public benchmark BCI Comparison IV 2a and 2b datasets, the results showed that the within-subject classification accuracies for the datasets were 84.96% and 89.26% respectively, with kappa values of 0.797 and 0.756. The cross-subject classification accuracy for the 2a dataset was 69.21%, with a kappa value of 0.584. These results indicate that the SMMTM significantly enhances decoding performance, providing a strong foundation for advancing practical BCI implementations.},
}
@article {pmid41129446,
year = {2025},
author = {Dang, W and Ren, Z and Sun, J and Lv, D and Xiong, Z and Guo, W and Gao, Z and Yu, H},
title = {ML-TGNet: A Multi-Level Topology Guidance Network for Motor Imagery Decoding.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3624298},
pmid = {41129446},
issn = {2168-2208},
abstract = {Brain-computer interfaces (BCIs) based on motor imagery electroencephalogram (MI-EEG) signals have been extensively applied in various neural rehabilitation scenarios. However, existing methods primarily focus on designing complex architectures to extract spatio-temporal features from MI-EEG signals, often neglecting the brain dynamics information embedded within them. This oversight leads to the extraction of redundant information, ultimately reducing decoding performance. To address these challenges, we design a multi-level topology-guidance network (ML-TGNet) that leverages topological brain synchronization information to more effectively extract features related to MI tasks. ML-TGNet specifically comprises a multi-level topology guidance module, a feature pool module, and a multi-branch decoding module. To evaluate its performance, extensive experiments are conducted on three publicly available MI datasets: the BCI Competition IV-2a dataset, the High Gamma dataset, and the OpenBMI dataset. ML-TGNet achieves classification accuracies of 82.33%, 96.42%, and 85.26% on these three datasets, respectively, outperforming current state-of-the-art models. These findings confirm the efficacy of using brain synchronization information to guide MI decoding, thereby opening a novel approach for EEG-based brain state decoding by integrating brain dynamics into deep learning.},
}
@article {pmid41129442,
year = {2025},
author = {Wang, Z and Wang, H and Jia, T and He, X and Li, S and Wu, D},
title = {DBConformer: Dual-Branch Convolutional Transformer for EEG Decoding.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3622725},
pmid = {41129442},
issn = {2168-2208},
abstract = {Electroencephalography (EEG)-based brain computer interfaces (BCIs) transform spontaneous/evoked neural activity into control commands for external communication. While convolutional neural networks (CNNs) remain the mainstream backbone for EEG decoding, their inherently short receptive field makes it difficult to capture long-range temporal dependencies and global inter-channel relationships. Recent CNN-Transformer (Con former) hybrids partially address this issue, but most adopt a serial design, resulting in suboptimal integration of local and global features, and often overlook explicit channel-wise modeling. To address these limitations, we propose DBConformer, a dual-branch convolutionalTrans former network tailored for EEG decoding. It integrates a temporal Conformer to model long-range temporal dependencies and a spatial Conformer to extract inter-channel interactions, capturing both temporal dynamics and spatial patterns in EEG signals. A lightweight channel attention module further refines spatial representations by assigning data-driven importance to EEG channels. Extensive experiments under four evaluation settings on three paradigms, including motor imagery, seizure detection, and steady state visual evoked potential, demonstrated that DBCon former consistently outperformed 13 competitive baseline models, with over an eight-fold reduction in parameters than current high-capacity EEG Conformer architecture. Furthermore, the visualization results confirmed that the features extracted by DBConformer are physiologically in terpretable and aligned with prior knowledge. The superior performance and interpretability of DBConformer make it reliable for accurate, robust, and explainable EEG decoding. Code is publicized at https://github.com/wzwvv/ DBConformer.},
}
@article {pmid41129430,
year = {2025},
author = {Bai, Y and Zhang, S and Zhao, R and Han, X and Ni, G and Ming, D},
title = {Cross-Hemispheric Spatial-Temporal Attention Network for Decoding Silent Speech From EEG.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3624878},
pmid = {41129430},
issn = {1558-2531},
abstract = {OBJECTIVE: Speech, as the core of advanced human cognition, is fundamental to social interaction and daily life. Electroencephalogram (EEG)-based speech brain-computer interface (BCI) offers a novel communication pathway for patients with speech disorders, where deep learning has demonstrated significant advantages. Given the established dominance of the left hemisphere in speech processing, exploring methods to extract speech related neural features fully is crucial for enhancing decoding per formance.<br><br>APPROACH: In this study, EEG signals were recorded during a silent speech task involving the articulation of 10 distinct Chinese characters. Leveraging the principle of language function lateralization, we proposed a novel deep learning model, the cross hemispheric spatial-temporal attention network (CHSTAN), for EEG-based silent speech recognition. A multiscale temporal con volution block was employed to extract the temporal dynamics of EEG signals. A hemispheric spatial convolutional block was designed to independently process spatial information from the left and right hemispheres. Furthermore, the cross-attention mechanism was introduced to enhance inter-hemispheric feature inter action and specifically reinforce left-hemispheric feature representation for the final classification.<br><br>RESULTS: We compared CHSTAN with other existing methods using 5-fold cross-validation on the collected dataset. CHSTAN achieved an average classification accuracy of 49.88% and an average F1-score of 48.75% in decoding the 10 Chinese characters, significantly outperforming other methods.<br><br>CONCLUSION: The results indicate that the CHSTAN performs effectively in silent speech EEG classification tasks. Notably, the feature patterns learned through its innovative architecture correspond to neural speech processing mechanism.<br><br>SIGNIFICANCE: CHSTAN provides valuable insights and practical solutions for improving the performance of EEG-based speech decoding.},
}
@article {pmid41127543,
year = {2025},
author = {Sun, J and Li, H and Wang, J and Yang, W},
title = {Application of biomimetic approaches in the treatment of neurological disorders.},
journal = {Materials today. Bio},
volume = {35},
number = {},
pages = {102334},
pmid = {41127543},
issn = {2590-0064},
abstract = {Neurological disorders usually involve nerve cell damage or death, and traditional treatments have significant limitations in neural repair. Biomimetic approaches mimic the structure and function of biological systems, providing an important approach to neural repair and regeneration. This paper first summarizes the current challenges in treating neurological disorders. It then explores the applications of bioinspired strategies in drug delivery systems (DDS), neural repair, three-dimensional (3D) printed neural scaffolds, and brain-machine interfaces (BMIs) with neuromodulation. Additionally, it discusses the challenges, strategies, advantages, and prospects of bioinspired methods in neurological disease treatment. The aim is to provide a comprehensive perspective on the potential of biomimicry-based methods in this field.},
}
@article {pmid41127304,
year = {2025},
author = {He, B and Guo, Y and Yang, G},
title = {Integrated Piezoelectric Vibration and In Situ Force Sensing for Low-Trauma Tissue Penetration.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {6},
number = {},
pages = {0417},
pmid = {41127304},
issn = {2692-7632},
abstract = {Precision-controlled microscale manipulation tasks-including neural probe implantation, ophthalmic surgery, and cell membrane puncture-often involve minimally invasive membrane penetration techniques with real-time force feedback to minimize tissue trauma. This imposes rigorous design requirements on the corresponding miniaturized instruments with robotic assistance. This paper proposes an integrated piezoelectric module (IPEM) that combines high-frequency vibration-assisted penetration with real-time in situ force sensing. The IPEM features a compact piezoelectric actuator integrated with a central tungsten probe, generating axial micro-vibration (4,652 Hz) to enable smooth tissue penetration while simultaneously measuring contact and penetration forces via the piezoelectric effect. Extensive experiments were conducted to validate the effectiveness and efficacy of the proposed IPEM. Both static and dynamic force-sensing tests demonstrate the linearity, sensitivity (9.3 mV/mN), and accuracy (mean absolute error < 0.3 mN, mean absolute percentage error < 1%) of the embedded sensing unit. In gelatin phantom tests, the module reduced puncture and insertion forces upon activation of vibration. In vivo experiments in mouse brains further confirmed that the system could reduce penetration resistance (from an average of 11.67 mN without vibration to 7.8 mN with vibration, decreased by 33%) through the pia mater and accurately mimic the electrode implantation-detachment sequence, leaving a flexible electrode embedded with minimal trauma. This work establishes a new paradigm for smart surgical instruments by integrating a compact actuator-sensor design with real-time in situ force feedback capabilities, with immediate applications in brain-machine interfaces and microsurgical robotics.},
}
@article {pmid41121568,
year = {2025},
author = {Skarzynski, PH and Cywka, KB and Czaplicka, EA and Skarzynski, H},
title = {The Bonebridge Active Bone Conduction Hearing Implant: Safety, Effectiveness and Outcomes Based on 355 Patients.},
journal = {Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology &amp; Cervico-Facial Surgery},
volume = {},
number = {},
pages = {},
doi = {10.1111/coa.70050},
pmid = {41121568},
issn = {1749-4486},
abstract = {OBJECTIVES: This study evaluates the safety and efficacy of the Bonebridge BCI 601 and 602 bone conduction implants in our largest cohort to date of 355 patients. The patients had a wide age range and exhibited conductive, mixed, or single-sided deafness (SSD).<br><br>DESIGN: All patients underwent Bonebridge implantation. Pre- and post-implantation evaluations included pure-tone audiometry, speech recognition tests, and free-field audiometry. Word recognition was measured using the Polish Monosyllabic Word Test, while speech reception in noise was assessed using the Polish Sentence Matrix Test. Subjective benefit was assessed using the APHAB questionnaire. Follow-up tests were performed 3-6&#x2009;months after activation.<br><br>RESULTS: Revision surgery was required in 17 patients (4.8%) due to complications, including implant removal in 5 cases. Reimplantation was successful in 4 of these. The APHAB questionnaire showed improved hearing function and all hearing tests also showed significant improvement.<br><br>CONCLUSION: Active bone conduction implantation is an effective method for the rehabilitation of conductive hearing loss, mixed hearing loss, and unilateral deafness. This large cohort study confirms significant hearing improvement and subjective benefits. The low complication rate supports the reliability of the Bonebridge system.},
}
@article {pmid41121378,
year = {2025},
author = {Chen, HJ and Dong, X and Wang, Y and Wang, K and Feng, G and Bai, T and Zhang, M and Gan, K and Peng, JJ and Huang, W and Zhang, Z and Shu, N and Ma, G},
title = {Polygenic risk for Alzheimer's disease in healthy aging: age-related and APOE-driven effects on brain structures and cognition.},
journal = {Genome medicine},
volume = {17},
number = {1},
pages = {126},
pmid = {41121378},
issn = {1756-994X},
support = {2022ZD0213300//STI2030-Major Projects/ ; },
mesh = {Humans ; *Alzheimer Disease/genetics/pathology ; *Multifactorial Inheritance ; Male ; Aged ; Female ; *Cognition ; Middle Aged ; *Apolipoproteins E/genetics ; *Brain/pathology ; *Healthy Aging/genetics ; *Genetic Predisposition to Disease ; Aged, 80 and over ; Risk Factors ; Gray Matter/pathology ; White Matter/pathology ; Magnetic Resonance Imaging ; },
abstract = {BACKGROUND: Alzheimer's disease (AD) is characterized by progressive neurodegeneration and cognitive decline with age. The genetic architecture of AD involves multiple loci, including the apolipoprotein E gene (APOE). The polygenic risk scores for AD (AD-PRS) provide a comprehensive genome-wide assessment of AD risk, yet their age-related effects on brain structures and cognitive function in cognitively unimpaired individuals remain largely undefined.<br><br>METHODS: We analyzed cognitively unimpaired, genetically unrelated Caucasians from the UK Biobank (N&#x2009;=&#x2009;21,236, 64.5&#x2009;&#xb1;&#x2009;7.6 years). AD-PRS was derived using a Bayesian approach incorporating approximately 5 million genetic variants (UK Biobank's standard PRS). Brain structures were measured with regional gray matter (GM) volumes and tract-wise microstructural white matter (WM) integrity. Cognitive performance was evaluated with executive function, visuospatial function, reasoning, and memory. Sliding window analyses were performed to investigate age-related polygenic effects on brain structures, and mediation analyses tested whether structural changes mediated the gene-cognition relationship across different age groups. Analyses were replicated using two custom PRSs-one including APOE and the other excluding APOE regions-calculated with the clumping-and-thresholding approach.<br><br>RESULTS: High AD-PRS was associated with accelerated GM atrophy (particularly in the hippocampus, thalamus, and parahippocampus), increased cerebral ventricular volume, and reduced&#xa0;WM integrity (especially in the fornix, cingulum, and superior fronto-occipital fasciculus). These polygenic effects demonstrated significant age-related amplification (pBonf&#x2009;<&#x2009;0.05), with the strongest effects in individuals aged&#x2009;&#x2265;&#x2009;75. Elevated AD-PRS was linked to lower cognitive performance across aging, especially in executive function, reasoning, and memory, which were significantly mediated by structural brain changes in subcortical and posterior limbic regions and their WM connections, predominantly in late aging (p&#x2009;<&#x2009;0.05). Sensitivity analyses confirmed the robustness of these findings, emphasizing the dominant contribution of APOE, while also identifying age-specific effects from non-APOE variants.<br><br>CONCLUSIONS: High polygenic risk for AD may be associated with accelerated cognitive decline in healthy aging, mediated by structural changes within hippocampal-thalamic regions and their connecting WM tracts. We provide insights into the early pathogenesis of AD and support the potential for age-targeted screening and early intervention for individuals at high genetic risk.},
}
@article {pmid41120372,
year = {2025},
author = {He, S and Li, Z and Dang, J and Luo, Y and Zhang, G},
title = {CIRE: A Chinese EEG Dataset for decoding speech intention modulated by prosodic emotion.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {1664},
pmid = {41120372},
issn = {2052-4463},
support = {No. 62276185, No. 61876126//National Natural Science Foundation of China (National Science Foundation of China)/ ; No. 62276185, No. 61876126//National Natural Science Foundation of China (National Science Foundation of China)/ ; No. 62276185, No. 61876126//National Natural Science Foundation of China (National Science Foundation of China)/ ; No. 62276185, No. 61876126//National Natural Science Foundation of China (National Science Foundation of China)/ ; No. 62276185, No. 61876126//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Electroencephalography ; *Emotions ; *Speech ; Brain-Computer Interfaces ; China ; Machine Learning ; Speech Perception ; Intention ; Female ; Male ; Adult ; East Asian People ; },
abstract = {Neural decoding of speech intention could advance the development and application of brain-computer interface (BCI) technology. Currently, lack of dataset limited the research on decoding the true speech intention, especially the diverse intentions expressed by the same text when no context is given. This study provides an EEG dataset, CIRE, on spoken language interaction intention featuring aligned textual expressions with divergent intentional meanings due to the differences in prosodic emotion. The dataset comprises preprocessed high-density (128-channel) EEG recordings from 38 participants engaged in comprehension of attitude-conveying speech stimuli, accompanied by Wav2vec2-derived acoustic embeddings of the listening materials. To validate our dataset through cognitive neuroscience studies and binary intent classification, we applied signal processing pipelines, cognitive analysis frameworks, and machine learning (ML) approaches. Our baseline model achieved a cross-subject classification accuracy of 68.2%, with differences exhibiting interpretable neurophysiological correlates. The high-density and high temporal resolution EEG data offer broader application areas, both in cognitive neuroscience and speech BCI, and can also contribute to the brain-inspired algorithms.},
}
@article {pmid41117243,
year = {2025},
author = {Mou, T and Lai, J and Kong, L},
title = {Effects of Paliperidone on Serum D-dimer Levels: Clinical and Experimental Findings.},
journal = {Actas espanolas de psiquiatria},
volume = {53},
number = {5},
pages = {959-966},
pmid = {41117243},
issn = {1578-2735},
mesh = {*Paliperidone Palmitate/therapeutic use/pharmacology ; Humans ; *Antipsychotic Agents/therapeutic use/pharmacology ; *Fibrin Fibrinogen Degradation Products/analysis ; Animals ; Male ; Adult ; *Schizophrenia/drug therapy/blood ; Female ; Mice ; Mice, Inbred C57BL ; Middle Aged ; Young Adult ; },
abstract = {BACKGROUND: Dysregulation of coagulation function associated with antipsychotic treatment remains poorly understood. This study investigates the potential impact of paliperidone on serum D-dimer levels during the early stages of treatment.<br><br>METHODS: Nine patients diagnosed with first-episode schizophrenic spectrum disorder were assessed for serum D-dimer levels before and after a 2-week paliperidone regimen. Additionally, eight adult C57 mice in the experimental group (EG) received 3 mg/kg of paliperidone daily for 10 consecutive days, while eight mice in the control group (CG) were untreated. Venous blood was collected and analyzed for D-dimer at baseline, and on the 5th and 10th days in the EG, as well as on the 10th day for the CG.<br><br>RESULTS: No significant differences were observed in serum D-dimer levels before and after paliperidone treatment in the patient cohort. In animal experiments, compared to the CG on the 10th day, serum D-dimer levels in the EG on the 10th day showed no significant difference (p > 0.05), while the level in the EG on the 5th day was significantly lower (p < 0.05). Compared to its baseline, serum D-dimer levels within the EG on the 5th day was significantly decreased (p < 0.05).<br><br>CONCLUSION: Short-term paliperidone treatment had minimal effects on serum D-dimer levels in both human participants and mice, though transient changes were noted early in treatment. Nonetheless, the potential for drug-induced coagulation disruption should be considered in clinical practice.},
}
@article {pmid41115264,
year = {2025},
author = {Ye, Y and Zhang, Y and Li, J and Wu, P and Zhang, T and Liao, T and Neculai, D and Lou, J and Li, Z and Chen, W and Hu, W},
title = {Nanoscale Mechanical Force Primes NOD1-LRR for Efficient Pathogen Recognition.},
journal = {The journal of physical chemistry letters},
volume = {16},
number = {43},
pages = {11196-11205},
doi = {10.1021/acs.jpclett.5c02875},
pmid = {41115264},
issn = {1948-7185},
mesh = {*Nod1 Signaling Adaptor Protein/chemistry/metabolism ; Molecular Dynamics Simulation ; Diaminopimelic Acid/analogs &amp; derivatives/chemistry/metabolism ; Humans ; Protein Domains ; },
abstract = {Detecting pathogens requires molecular sensors that can rapidly and precisely respond to local threats. While cytosolic innate immune receptors such as NOD1 are known as biochemical detectors, their ability to interpret physical cues remains a critical unknown. Here, we combine piconewton-resolution single-molecule manipulation, molecular dynamics simulations, and structural modeling to demonstrate that NOD1 is not a passive detector but an active nanomechanical sensor. We show that the receptor's LRR domain, with its curved, horseshoe-like nanoarchitecture, functions as a mechanical force concentrator. Physiologically relevant piconewton-scale forces, such as those at the membrane-cytosol interface, are concentrated into a high-stress hotspot that primes the domain for a conformational transition. This force-induced priming acts as an allosteric nanoswitch, transducing mechanical energy into a biochemical output: a dramatic increase in binding strength and sensitivity for its bacterial ligand iE-DAP. This mechanochemical coupling positions NOD1 as a force-responsive sensor, enabling rapid and spatially restricted immune activation. Our work establishes a new paradigm for cytosolic pathogen recognition and suggests that force-sensing LRR domains represent a generalizable design principle in nanobiology, bridging a conceptual gap between mechanobiology and innate immunity.},
}
@article {pmid41112520,
year = {2025},
author = {Shi, J and Wang, J and Fei, W and Feleke, AG and Bi, L},
title = {Neuroanatomy-Informed Brain-Machine Hybrid Intelligence for Robust Acoustic Target Detection.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {6},
number = {},
pages = {0438},
pmid = {41112520},
issn = {2692-7632},
abstract = {Sound target detection (STD) plays a critical role in modern acoustic sensing systems. However, existing automated STD methods show poor robustness and limited generalization, especially under low signal-to-noise ratio (SNR) conditions or when processing previously unencountered sound categories. To overcome these limitations, we first propose a brain-computer interface (BCI)-based STD method that utilizes neural responses to auditory stimuli. Our approach features the Triple-Region Spatiotemporal Dynamics Attention Network (Tri-SDANet), an electroencephalogram (EEG) decoding model incorporating neuroanatomical priors derived from EEG source analysis to enhance decoding accuracy and provide interpretability in complex auditory scenes. Recognizing the inherent limitations of stand-alone BCI systems (notably their high false alarm rates), we further develop an adaptive confidence-based brain-machine fusion strategy that intelligently combines decisions from both the BCI and conventional acoustic detection models. This hybrid approach effectively merges the complementary strengths of neural perception and acoustic feature learning. We validate the proposed method through experiments with 16 participants. Experimental results demonstrate that the Tri-SDANet achieves state-of-the-art performance in neural decoding under complex acoustic conditions. Moreover, the hybrid system maintains reliable detection performance at low SNR levels while exhibiting remarkable generalization to unseen target classes. In addition, source-level EEG analysis reveals distinct brain activation patterns associated with target perception, offering neuroscientific validation for our model design. This work pioneers a neuro-acoustic fusion paradigm for robust STD, offering a generalizable solution for real-world applications through the integration of noninvasive neural signals with artificial intelligence.},
}
@article {pmid41110663,
year = {2025},
author = {Gordleeva, S and Grigorev, N and Pitsik, E and Kurkin, S and Kazantsev, V and Hramov, A},
title = {Detection and rehabilitation of age-related motor skills impairment: Neurophysiological biomarkers and perspectives.},
journal = {Ageing research reviews},
volume = {113},
number = {},
pages = {102923},
doi = {10.1016/j.arr.2025.102923},
pmid = {41110663},
issn = {1872-9649},
abstract = {Age-related decline in motor control, manifesting as impaired posture, gait, and slowed movement execution, significantly diminishes the quality of life in older adults. These functional deficits are associated with alterations in neurophysiological data, which are analyzed using advanced techniques including spectral analysis, complexity measures, and functional connectivity network analysis. These methodologies provide valuable insights into the neurobiological mechanisms underpinning age-related motor function impairments, linking physiological changes to non-invasively recorded electrophysiological and hemodynamic responses. Recent investigations have demonstrated an age-dependent impairment in access to working memory during motor tasks, evidenced by significant correlations between electroencephalographic biomarkers and neural response latencies. Furthermore, these functional biomarkers are associated with the degradation of motor learning abilities in older individuals. There is a broad consensus that non-invasive assessment of brain activity accurately reflects the processes underlying age-related motor decline, thereby opening avenues for targeted intervention strategies. A key area of investigation is the utilization of motor system function for the early detection of neurodegenerative diseases. Seemingly, simple motor tasks engage cortical regions responsible for attention, vision, and memory through a process known as sensorimotor integration. Sensorimotor training implemented via brain-computer interfaces with neurofeedback demonstrates potential for ameliorating both cognitive and motor deficits in both healthy older adults and those with age-related conditions. This review synthesizes current research on age-related changes revealed through neuroimaging data analysis, highlighting how biomarkers derived from brain electrical and hemodynamic activity reflect both normative and pathological aging processes. Finally, we emphasize the considerable potential of neurophysiological data analysis for advancing the field of aging research. Digital medicine platforms, including brain-computer interfaces and a range of wearable monitoring devices, hold significant promise for transforming the diagnosis of age-related diseases. These technologies empower continuous, objective monitoring of older adults, paving the way for personalized, precision-based medical interventions.},
}
@article {pmid41110656,
year = {2025},
author = {Huang, X and Xu, S},
title = {Mitigating choice overload: The interactive effects of set size and overall preference revealed by hierarchical drift diffusion modeling and electroencephalography.},
journal = {NeuroImage},
volume = {321},
number = {},
pages = {121542},
doi = {10.1016/j.neuroimage.2025.121542},
pmid = {41110656},
issn = {1095-9572},
mesh = {Humans ; Electroencephalography/methods ; Male ; Female ; Young Adult ; *Choice Behavior/physiology ; Adult ; *Evoked Potentials/physiology ; *Brain/physiology ; Attention/physiology ; },
abstract = {Excessive choice can overwhelm cognitive resources and trigger choice overload, yet its neurophysiological basis-particularly the moderating role of overall preference level-remains underexplored. This study employed a two-stage experimental paradigm manipulating choice set size (large vs. small) and overall preference level (high vs. low). We integrated event-related potentials (ERPs), multivariate pattern analysis (MVPA), and hierarchical drift diffusion modeling (HDDM) to investigate how these factors interactively shape decision processes. Behavioral and computational modeling results revealed that high-preference conditions enhanced participants' ability to identify satisfactory options, with this advantage persisting and significantly accelerating final selection speed, particularly for large choice sets. Conversely, low-preference conditions amplified choice set size effects, with large sets exacerbating choice overload. ERP analyses showed larger P2 amplitudes for small choice sets, indicating greater early attentional allocation. More negative N2 amplitudes consistently appeared for small sets across both overall preference levels, reflecting elevated conflict and cognitive control demands. Small-set/low-preference conditions elicited the largest P3 amplitudes, suggesting small sets triggered compensatory attentional allocation under low-preference conditions. MVPA identified stable and distinct neural representation patterns across all experimental conditions, confirming that overall preference level modulates neural encoding of choice overload. These findings demonstrate that subjective preference strength functions as a key regulatory factor in mitigating choice overload. Our multimodal approach advances theoretical accounts of value-based decision-making by revealing how internal preferences interact with external complexity to shape the temporal and computational architecture of cognitive control.},
}
@article {pmid41109958,
year = {2025},
author = {Suffian, M and Ieracitano, C and Morabito, FC and Mammone, N},
title = {An Explainable 3D-Deep Learning Model for EEG Decoding in Brain-Computer Interface Applications.},
journal = {International journal of neural systems},
volume = {},
number = {},
pages = {2550073},
doi = {10.1142/S012906572550073X},
pmid = {41109958},
issn = {1793-6462},
abstract = {Decoding electroencephalographic (EEG) signals is of key importance in the development of brain-computer interface (BCI) systems. However, high inter-subject variability in EEG signals requires user-specific calibration, which can be time-consuming and limit the application of deep learning approaches, due to general need of large amount of data to properly train these models. In this context, this paper proposes a multidimensional and explainable deep learning framework for fast and interpretable EEG decoding. In particular, EEG signals are projected into the spatial-spectral-temporal domain and processed using a custom three-dimensional (3D) Convolutional Neural Network, here referred to as EEGCubeNet. In this work, the method has been validated on EEGs recorded during motor BCI experiments. Namely, hand open (HO) and hand close (HC) movement planning was investigated by discriminating them from the absence of movement preparation (resting state, RE). The proposed method is based on a global- to subject-specific fine-tuning. The model is globally trained on a population of subjects and then fine-tuned on the final user, significantly reducing adaptation time. Experimental results demonstrate that EEGCubeNet achieves state-of-the-art performance (accuracy of [Formula: see text] and [Formula: see text] for HC versus RE and HO versus RE, binary classification tasks, respectively) with reduced framework complexity and with a reduced training time. In addition, to enhance transparency, a 3D occlusion sensitivity analysis-based explainability method (here named 3D xAI-OSA) that generates relevance maps revealing the most significant features to each prediction, was introduced. The data and source code are available at the following link: https://github.com/AI-Lab-UniRC/EEGCubeNet.},
}
@article {pmid41109909,
year = {2025},
author = {Yu, J and Chen, J and Zhang, Y and Lyu, H and Ma, T and Huang, H and Wang, Z and Xu, X and Hu, S and Xu, Y},
title = {Emoface: AI-assisted diagnostic model for differentiating major depressive disorder and bipolar disorder via facial biomarkers.},
journal = {Npj mental health research},
volume = {4},
number = {1},
pages = {52},
pmid = {41109909},
issn = {2731-4251},
support = {2025C01104, 2025C02108 and 2021C03107//Key R&amp;D Program of Zhejiang/ ; LZ23H180002 and LQ23F030001//Zhejiang Provincial Natural Science Foundation/ ; 62406280 and 72274170//National Natural Science Foundation of China/ ; 2022RC009//Cao Guangbiao High-tech Development Fund/ ; 20231203A13//Key Projects of Hangzhou Science and Technology Bureau/ ; 2023YFC2506200//National Key Research and Development Program of China/ ; JNL-2023001B//Research Project of Jinan Microecological Biomedicine Shandong Laboratory/ ; 2021R52016//Leading Talent of Scientific and Technological Innovation of Zhejiang Province/ ; },
abstract = {Affective disorders, including Major Depressive Disorder (MDD) and Bipolar Disorder (BD), exhibit significant mood abnormalities, making rapid diagnosis essential for social stability and healthcare efficiency. Traditional diagnostic solutions, including medical history collection and psychological assessments, often struggle to differentiate their similar clinical presentations, leading to time-consuming, laborious, and a high rate of misdiagnosis. Here, we propose Emoface, an AI-assisted diagnostic model that reads the emotional activities of faces in affective disorders. By analyzing videos from 353 participants exposed to various emotional stimuli, Emoface identified unique facial digital biomarkers distinguishing BD from MDD. Based on this, Emoface contributed to develop the first digital facial mapping for clinical and teaching use. In clinical practice with 347 patients, Emoface achieved precise diagnosis based on various facial visual signals, with accuracy rates of 95.29% for BD and 87.05% for MDD, offering a reliable face-based AI solution in a new era of affective disorders.},
}
@article {pmid41108907,
year = {2025},
author = {Akazawa, A and Fujita, T and Uraguchi, K and Kitayama, M and Ito, T and Osaki, Y and Shirai, K and Yoshida, H and Yamamoto, N and Doi, K and Iwasaki, S and Oishi, N},
title = {Establishing a comprehensive national auditory implant registry in Japan: Trends and demographics from the first two years (2023-2024).},
journal = {Auris, nasus, larynx},
volume = {52},
number = {6},
pages = {679-686},
doi = {10.1016/j.anl.2025.09.009},
pmid = {41108907},
issn = {1879-1476},
abstract = {OBJECTIVE: To describe the establishment and initial findings of Japan's first comprehensive nationwide registry covering cochlear implants (CIs), active middle ear implants (AMEIs), and bone conduction implants (BCIs), launched in 2023. The registry aims to improve national data collection, support evidence-based policymaking, and track trends in surgical practice and patient demographics.<br><br>METHODS: A web-based electronic data capture (EDC) system was implemented to replace the previous paper-based reporting system. Between January 2023 and December 2024, data were voluntarily submitted by participating facilities across Japan. Collected data included patient demographics, implant types, hearing thresholds, etiologies, and manufacturer information. Registry completeness was assessed by comparison with Japan's National Database of Health Insurance Claims (NDB).<br><br>RESULTS: A total of 1880 patients were registered, and 1809 patients with surgical information entered from 104 facilities were selected for analysis, comprising 1723 CI cases and 86 AMEI or BCI cases (11 VSB, 22 BB, 53 Baha). Among 605 pediatric CI recipients, early-age implantation was increasingly observed, with 58 patients (10 %) aged under 1 year and 183 (30 %) aged 1 year. Among adult CI recipients, 271 patients were aged 75 years or older, including 40 patients aged 85 years or older. Additionally, simultaneous bilateral CI surgery was performed in 265 patients, of whom 175 were children, reflecting the expanding indications. Patients with better ear thresholds <90 dB HL accounted for 33 % of adults and 29 % of children. Congenital hearing loss predominated in children, while acquired causes were more common in adults. Among cases with a known etiology, hereditary deafness was the most common (24.5 %), although 39.6 % of etiologies were unknown. CI data completeness reached 73 % compared with NDB, indicating strong nationwide participation and a high level of data reliability.<br><br>CONCLUSION: This is the first comprehensive report from the national registry in Japan that includes not only CIs but also AMEIs and BCIs. The registry demonstrated reliable data capture and highlighted important trends in patient demographics and surgical practices. Continued data collection will enhance clinical decision-making and support policy development, ultimately improving care for auditory implant recipients.},
}
@article {pmid41107816,
year = {2025},
author = {Chen, B and Gan, H and Yang, L and Yan, X and Lv, X and Zhang, X and Bu, J},
title = {A novel imagery-based retrieval-extinction training for intervention in nicotine addiction.},
journal = {BMC medicine},
volume = {23},
number = {1},
pages = {568},
pmid = {41107816},
issn = {1741-7015},
support = {32471140//National Natural Science Foundation of China/ ; 2021xkjT018//Scientific Research Improvement Project of Anhui Medical University/ ; 2022zhyx-C02//Research Fund of Anhui Institute of Translational Medicine/ ; YQZD2023018//Anhui Province Outstanding Young Teacher Cultivation Key Project/ ; JKS2023013//Research Funds of Center for Big Data and Population Health of IHM/ ; YESS20240007//Young Elite Scientists Sponsorship Program by CAST/ ; 2024AH030021//Excellent Youth of Natural Science Research Projects in Universities of Anhui Province/ ; },
mesh = {Humans ; Male ; *Tobacco Use Disorder/therapy/psychology ; Female ; Adult ; *Imagery, Psychotherapy/methods ; Craving ; *Extinction, Psychological ; Middle Aged ; Electroencephalography ; *Smoking Cessation/methods ; Young Adult ; *Mental Recall ; },
abstract = {BACKGROUND: Retrieval-extinction training based on the theory of memory reconsolidation has promising intervention effects for addiction. However, the conventional conditioned stimuli used in retrieval-extinction training have limitations in lack of contextual and selective activation of memories, which limits intervention efficacy and clinical translation. Therefore, we developed a novel imagery-based retrieval-extinction training (I-RE) and examined its effects on nicotine addiction.<br><br>METHODS: This study included 57 nicotine-dependent individuals randomly assigned to either the experimental (n&#x2009;=&#x2009;29) or control (n&#x2009;=&#x2009;28) group. Participants were exposed to a 5-min imagery script cue, followed by a 10-min rest period and 60-min extinction training session. Short- and long-term (1&#xa0;week, 1&#xa0;month, 3&#xa0;months, 6&#xa0;months, 12&#xa0;months) intervention effects were assessed via the smoking imagery vividness score, smoking craving, and daily cigarette consumption. Electroencephalogram (EEG) data were collected pre- and post-intervention.<br><br>RESULTS: Regarding short-term effects, smoking imagery vividness score [pre- vs. post-intervention: p&#x2009;<&#x2009;0.001; pre- vs. 1-day follow-up (FU): p&#x2009;=&#x2009;0.003] and craving significantly decreased (pre- vs. post-intervention: p&#x2009;<&#x2009;0.001; pre- vs. 1-day FU: p&#x2009;<&#x2009;0.001). Decreased imagery vividness score mediated decreased smoking craving induced by smoking-related I-RE. Moreover, the significant correlation observed between these variables at pre-intervention disappeared at post-intervention. For effects on EEG microstate, a significant decrease was observed in microstate C duration induced by the smoking-related imagery script cue reactivity task post-intervention (p&#x2009;<&#x2009;0.001). This mediated a decreased smoking craving induced by smoking-related I-RE. Degree of decrease in duration was positively correlated with addict imagery ability (p&#x2009;=&#x2009;0.035). Consistently, the microstate C occurrence rate significantly decreased during the memory reconsolidation phase (p&#x2009;<&#x2009;0.001). Regarding long-term effects, the smoking imagery vividness score (1-week FU: p&#x2009;=&#x2009;0.004; 1-month FU: p&#x2009;<&#x2009;0.001), smoking craving (1-week FU: p&#x2009;<&#x2009;0.001; 1-month FU: p&#x2009;<&#x2009;0.001), and daily cigarette consumption (1-week FU: p&#x2009;<&#x2009;0.001; 1-month FU: p&#x2009;<&#x2009;0.001) significantly decreased at 1-week and 1-month FU. Furthermore, decreased smoking craving mediated decreased Daily cigarette consumption in the experimental group. The significant correlation observed between the imagery vividness score and craving at pre-intervention disappeared at the 1-week and 1-month FU.<br><br>CONCLUSIONS: This novel I-RE demonstrated significant effects on nicotine addiction for 1&#xa0;month after a single intervention session, suggesting that it is a promising treatment tool.<br><br>TRIAL REGISTRATION: Chinese Clinical Trial Registry identifier: ChiCTR2200064469.},
}
@article {pmid41107249,
year = {2025},
author = {Zhu, X and Jiang, L and Shi, L and Li, F and Yang, Q and Zhang, M and Li, Y and Yu, Q and Chen, J and Gao, X and Wang, Z and Wang, Y and Xu, P and Lu, L and Deng, J},
title = {Modulation of brain oscillations by continuous theta burst stimulation in patients with insomnia.},
journal = {Translational psychiatry},
volume = {15},
number = {1},
pages = {416},
pmid = {41107249},
issn = {2158-3188},
support = {82271528//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82201646//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; Male ; Female ; *Sleep Initiation and Maintenance Disorders/therapy/physiopathology ; Adult ; *Theta Rhythm/physiology ; Middle Aged ; Electroencephalography ; *Transcranial Magnetic Stimulation/methods ; Polysomnography ; Cross-Over Studies ; Wakefulness/physiology ; },
abstract = {Continuous theta burst stimulation (cTBS) induces long-lasting depression of cortical excitability in motor cortex. In the present study, we explored the modulation of cTBS on resting state electroencephalogram (rsEEG) during wakefulness and subsequent sleep in patients with insomnia disorder. Forty-one patients with insomnia received three sessions active and sham cTBS in a counterbalanced crossover design. Each session comprised 600 pulses over right dorsolateral prefrontal cortex. Closed-eyes rsEEG were recorded at before and after each session. Effects of cTBS in subsequent sleep were measured by overnight polysomnography screening. Power spectral density (PSD) and phase locking value (PLV) were used to calculate changes in spectral power and phase synchronization after cTBS during wakefulness and subsequent sleep. Compared with sham cTBS intervention, PSD of delta and theta bands were increased across global brain regions with a cumulative effect after three active cTBS sessions. PLV of delta and theta bands were enhanced between stimulated frontal area and occipital areas. Efficiency of information communication within frontal-occipital networks was consistently improved through three active sessions. Increased theta power during wakefulness was positively related with that during the first sleep cycle. Active cTBS significantly enhanced the spectral power of delta and theta bands during wakefulness, with a cumulative effect observed over time. This modulation also extended to influence theta power during subsequent sleep onset period. Collectively, these findings provide a robust theoretical foundation for further investigating the therapeutic potential of long-term cTBS in the treatment of insomnia disorders.},
}
@article {pmid41106071,
year = {2025},
author = {Yasen, A and Sun, W and Gong, Y and Xu, G},
title = {Progress in the combined application of Brain-Computer Interface and non-invasive brain stimulation for post-stroke motor recovery.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {180},
number = {},
pages = {2111383},
doi = {10.1016/j.clinph.2025.2111383},
pmid = {41106071},
issn = {1872-8952},
abstract = {Stroke remains one of the leading causes of disability and death among adults globally. Both Brain-Computer Interface (BCI) and Non-invasive Brain Stimulation (NIBS) have shown significant potential in facilitating motor recovery in stroke patients. The combination of BCI and NIBS enhances brain functional reorganization and accelerates motor recovery post-stroke through a real-time feedback mechanism. By modulating neural plasticity, this combined approach can alter the trajectory of motor recovery, offering a novel therapeutic avenue for stroke rehabilitation. This review examines the application and recent advancements of BCI integrated with NIBS in motor function rehabilitation for stroke patients. Specifically, it outlines the advantages and challenges of this combined approach, including the use of TMS, tDCS, tACS, and other emerging neurostimulation technologies. While the integration of BCI and NIBS is still in the early stages of exploration, a unified, standardized protocol has yet to be established. Future research should focus on optimizing multimodal integration, investigating the underlying neuroplasticity mechanisms, and evaluating the long-term efficacy of BCI combined with NIBS.},
}
@article {pmid41105834,
year = {2025},
author = {Clemesha, J and Chung, M},
title = {A different bimodal: case series of patients with a cochlear implant and a contralateral bone conduction implant.},
journal = {Cochlear implants international},
volume = {},
number = {},
pages = {1-8},
doi = {10.1080/14670100.2025.2571990},
pmid = {41105834},
issn = {1754-7628},
abstract = {INTRODUCTION: An increasing number of long-term users of bone conduction implants (BCI) have been observed to no longer obtain sufficient benefit from their device due to deteriorations in hearing thresholds. At the multidisciplinary auditory implant centre at the University College London Hospitals NHS Trust, these patients are assessed and considered for cochlear implantation (CI). This case series describes the history and outcomes of patients who became bimodal implant users, utilising electrical and vibratory auditory stimulation with a BCI and CI. This unique patient group has seldom been described in the literature.<br><br>METHODS: Case series from a retrospective chart review of patients who utilise the combination of electrical and vibratory auditory stimulation with the use of a bone conduction implant and cochlear implant, up to November 2023.<br><br>RESULTS: Six bimodal patients were identified from the patient cohort. Their case history and outcome are described.<br><br>CONCLUSION: The synergy of electrical and vibratory auditory stimulation observed in this case series provided subjective functional benefits and measurable speech perception benefits for some patients, while others experienced minimal or no measurable benefit and ceased usage.},
}
@article {pmid41105410,
year = {2025},
author = {Saver, JL and Duncan, PW and Stein, J and Cramer, SC and Fox, EJ and Zorowitz, RD and Billinger, SA and Eickmeyer, SM and Kirshblum, SC and Androwis, GJ and Edwards, J and Savitz, SI and Koch, S and Shall, MB and Black-Schaffer, RM and Bonato, P and Cuccurullo, SJ and Barcikowski, J and Cao, N and Bornstein, NM and , },
title = {Electromagnetic Stimulation to Reduce Disability After Ischemic Stroke: The EMAGINE Randomized Clinical Trial.},
journal = {JAMA network open},
volume = {8},
number = {10},
pages = {e2537880},
pmid = {41105410},
issn = {2574-3805},
mesh = {Humans ; Male ; Female ; Middle Aged ; Double-Blind Method ; Aged ; *Stroke Rehabilitation/methods ; *Ischemic Stroke/therapy/rehabilitation ; *Magnetic Field Therapy/methods ; Treatment Outcome ; Upper Extremity/physiopathology ; Persons with Disabilities/rehabilitation ; },
abstract = {IMPORTANCE: Ischemic stroke remains a leading cause of disability worldwide. Preliminary studies have suggested that noninvasive, frequency-tuned, low-intensity electromagnetic network targeting field (ENTF) stimulation may have recovery benefit for patients with stroke.<br><br>OBJECTIVE: To evaluate the safety and effectiveness of ENTF therapy in reducing global disability among patients in the subacute ischemic stroke phase with moderate to severe disability and upper-extremity impairment.<br><br>This multicenter, double-blind, sham-controlled, randomized clinical trial was conducted at 15 US-based acute care and inpatient rehabilitation facilities from December 2021 to November 2023. Participants were enrolled 4 to 21 days after a stroke and had a baseline modified Rankin Scale (mRS) score of 3 or 4 (moderate or moderately severe global disability) and Fugl-Meyer Assessment for Upper Extremity score of 10 to 45 (higher scores indicating better arm function). Target sample size was 150 participants. Participants were randomly allocated to receive either active or sham ENTF stimulation. Modified intention-to-treat approach was used in primary efficacy and safety analyses.<br><br>INTERVENTION: Participants allocated to the active or sham ENTF stimulation were treated with a proprietary brain-computer interface-based stimulation device paired with an evidence-based, functional, repetitive, home-based physical and occupational exercise regimen for 45 one-hour sessions, 5 times per week within the first 90 days after a stroke.<br><br>MAIN OUTCOMES AND MEASURES: The primary end point was change in global disability, assessed with the mRS (score range: 0 [indicating normal or no symptoms] to 6 [indicating death]), from baseline to day 90. Secondary end points were change from baseline to day 90 in upper-limb impairment, arm motor function, gait speed, hand function, and physical and functional limitations as well as day-90 health-related quality of life, each of which was assessed with a specific metric.<br><br>RESULTS: The trial was stopped early after enrollment of 100 participants (50 in active group, 50 in sham group) when a promising zone threshold was not attained at planned interim analysis of the first 78 evaluable participants. Participants had a mean age of 59.0 (12.5) years and included 66 males (67.3%). The median (IQR) time from stroke to first ENTF treatment was 14 (12-19) days. Study groups were similar in age, sex, and baseline mRS scores, but imbalances were noted with participants in the active, compared with the sham, group having more right-hemisphere strokes (31 of 49 [63.3%] vs 22 of 49 [44.9%]), more severe upper-extremity impairment (Shoulder Abduction Finger Extension score <5; 31 of 49 [63.3%] vs 24 of 49 [49.0%]), and fewer small-vessel infarcts (14 of 49 [28.6%] vs 21 of 49 [42.9%]). For the primary outcome, the mean (SD) disability reduction on mRS at day 90 was not statistically significantly higher in the active group than in the sham group (-1.96 [0.12] vs -1.72 [0.12]), including mRS score of 0 to 1 attained in 12 participants (26.0%) vs 5 participants (10.0%) (odds ratio, 2.99; 95% CI, 0.96-9.30; P&#x2009;=&#x2009;.05). Point estimates for secondary outcomes favored the active group, although the differences were not statistically significant, in the prespecified analysis. No ENTF device-related serious adverse events were noted.<br><br>CONCLUSION AND RELEVANCE: This trial found that ENTF therapy is safe. Although the difference between groups was not statistically significant, ENTF therapy may reduce global disability in patients with severe baseline disability after ischemic stroke. These results warrant confirmation in a higher powered pivotal trial of ENTF therapy.<br><br>TRIAL REGISTRATION: ClinicalTrials.gov Identifier NCT05044507.},
}
@article {pmid41104953,
year = {2025},
author = {Kamaleddin, MA},
title = {Simultaneous encoding of sensory features: the role of multiplexing and noise in tactile perception and neural representation.},
journal = {Biological reviews of the Cambridge Philosophical Society},
volume = {},
number = {},
pages = {},
doi = {10.1111/brv.70093},
pmid = {41104953},
issn = {1469-185X},
abstract = {The nervous system's capacity to process complex stimuli has long intrigued neuroscientists, with multiplexing now recognized as a fundamental neural coding strategy. Multiplexing refers to the simultaneous encoding of multiple stimulus features via vi distinct components of neuronal responses, such as firing rates and precise temporal spike patterns. This paper reviews the neural coding mechanisms underlying multiplexing, with a particular emphasis on the somatosensory system and its ability to represent tactile stimuli. The encoding of various sensory attributes, including vibration, texture, motion, and shape, is examined, highlighting the complementary roles of rate and temporal codes in capturing these features. The discussion further addresses how intrinsic and extrinsic noise, often viewed as detrimental, can facilitate multiplexed coding by supporting the concurrent encoding of both stimulus frequency and intensity. The relevance of multiplexing is also considered in translational contexts, such as the development of brain-machine interfaces. By synthesizing recent advances and integrating insights from empirical and theoretical studies, this review establishes multiplexing as a foundational principle in sensory neuroscience and identifies key directions for future research in both basic science and neuroengineering applications.},
}
@article {pmid41104690,
year = {2025},
author = {Chehroudi, C and Chandrasekhar, V and Yu, H and De, S},
title = {Simple Prostatectomy is an Effective Option for BPH Patients With Hypocontractile Bladders.},
journal = {The Prostate},
volume = {},
number = {},
pages = {},
doi = {10.1002/pros.70079},
pmid = {41104690},
issn = {1097-0045},
support = {//The authors received no specific funding for this work./ ; },
abstract = {BACKGROUND: The impact of preoperative bladder function on outcomes of simple prostatectomy (SP) is unknown. The goal of this study was to determine if detrusor contractility affects postoperative catheter-free status in patients undergoing SP for benign prostatic hyperplasia (BPH).<br><br>METHODS: Patients who underwent SP (either open or minimally invasive) from 2017 to 2024 at our institution and had preoperative urodynamics were identified retrospectively. Bladder contractility index (BCI) was used to categorize patients as normocontractile (BCI&#x2009;&#x2265;&#x2009;100) or hypocontractile (BCI&#x2009;<&#x2009;100). Demographics, preoperative urodynamics, peri-operative characteristics, and postoperative variables were compared between the two groups with postoperative catheter status being the primary outcome.<br><br>RESULTS: Among 101 SP patients with preoperative urodynamics, 47 had hypocontractile bladders (median BCI 69 vs. 131). Both groups had similar median age, preoperative prostate specific antigen (PSA), and rates of diabetes. The majority of procedures in both the normocontracile and hypocontractile groups were robot-assisted (83% vs. 81%, respectively). Patients in the hypocontractile group were significantly more likely to be catheter dependent pre-operatively (77% vs. 57%, p&#x2009;=&#x2009;0.04). There was no difference in preoperative prostate size or use of BPH pharmacotherapy. Overall, 97% of hypocontractile and 100% of normocontractile patients were catheter-free following surgery. There were no differences in postoperative outcomes including pathology tissue weight and post-op PSA.<br><br>CONCLUSIONS: This is one of the first studies assessing outcomes of SP in patients with hypocontractile bladders. SP is an effective surgical option for patients with impaired detrusor function including those who are catheter dependent.},
}
@article {pmid41104354,
year = {2025},
author = {Näher, T and Bastian, L and Vorreuther, A and Fries, P and Goebel, R and Sorger, B},
title = {Riemannian geometry boosts functional near-infrared spectroscopy-based brain-state classification accuracy.},
journal = {Neurophotonics},
volume = {12},
number = {4},
pages = {045002},
pmid = {41104354},
issn = {2329-423X},
abstract = {BACKGROUND: Functional near-infrared spectroscopy (fNIRS) has recently gained momentum as a reliable and accurate tool for assessing brain states based on the vascular response to neural activity. This increase in popularity is due to its robustness to movement, non-invasive nature, portability, and user-friendly application. However, compared with other hemodynamic functional brain-imaging methods such as functional magnetic resonance imaging (fMRI), fNIRS is constrained by its limited spatial resolution and coverage with a particularly limited penetration depth. In addition, due to comparatively fewer methodological advancements, the performance of fNIRS-based brain-state classification still lags behind more prevalent methods such as fMRI.<br><br>METHODS: We introduce a classification approach grounded in Riemannian geometry for the classification of kernel matrices, leveraging the temporal and spatial relationships between channels and the inherent duality of fNIRS signals, specifically oxygenated and deoxygenated hemoglobin. For the Riemannian-geometry-based models, we compared different kernel matrix estimators and two classifiers: Riemannian Support Vector Classifier and Tangent Space Logistic Regression. These were benchmarked against four models employing traditional feature extraction methods. Our approach was tested on seven participants in two brain-state classification scenarios based on the same fNIRS dataset: an eight-choice classification, which includes seven established plus an individually selected imagery task, and a two-choice classification of all possible 28 two-task combinations.<br><br>RESULTS: This approach achieved a mean eight-choice classification accuracy of 65%, significantly surpassing the mean accuracy of 42% obtained with traditional methods. In addition, the best-performing model achieved an average accuracy of 96% for two-choice classification across all task combinations, compared with 78% with traditional models.<br><br>CONCLUSION: To our knowledge, we are the first to demonstrate that the proposed Riemannian-geometry-based classification approach is both powerful and viable for fNIRS data, substantially increasing the accuracy in binary and multi-class classification of brain activation patterns.},
}
@article {pmid41104262,
year = {2025},
author = {Bublitz, C and Chandler, JA and Molnár-Gábor, F and Navarro, MS and Kellmeyer, P and Soekadar, SR},
title = {A Moratorium on Implantable Non-Medical Neurotech Until Effects on the Mind are Properly Understood.},
journal = {Neuroethics},
volume = {18},
number = {3},
pages = {46},
pmid = {41104262},
issn = {1874-5490},
abstract = {The development of non-medical consumer neurotechnology is gaining momentum. As companies chart the course for future implanted and invasive brain-computer interfaces (BCIs) in non-medical populations, the time has come for concrete steps toward their regulation. We propose three measures: First, a mandatory Mental Impact Assessment that comprehensively screens for adverse mental effects of neurotechnologies under realistic use conditions needs to be developed and implemented. Second, until such an assessment is developed and further ethical concerns are effectively resolved, a moratorium on placing implantable non-medical devices on markets should be established. Third, implantable consumer neurotech for children should be banned. These measures are initial steps in a process seeking to define the necessary requirements for placing these devices on markets. They are grounded in a human rights-based approach to technology regulation that seeks to promote the interests protected by human rights while minimizing the risks posed to them. Neurotechnologies have the potential to profoundly alter cognitive, emotional, and other mental processes, with implications for the rights to mental health and integrity, and possibly for societal dynamics.},
}
@article {pmid41102402,
year = {2025},
author = {Feng, Y and Zhao, W and Li, Y and Yin, Q and Wang, X and Huang, X and Li, L and Shan, X and Hu, W and Ming, Y and Wang, P and Xiao, J and Chen, H and Duan, X},
title = {Diffusion trajectory of atypical morphological development in autism spectrum disorder.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {1476},
pmid = {41102402},
issn = {2399-3642},
support = {82121003//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82322035//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62333003//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62273076//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62036003//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Autism Spectrum Disorder/diagnostic imaging/pathology/physiopathology ; Child ; Adolescent ; Male ; Female ; *Gray Matter/diagnostic imaging/pathology/growth &amp; development ; *Brain/growth &amp; development/diagnostic imaging/pathology/physiopathology ; Magnetic Resonance Imaging ; },
abstract = {Brain development from childhood through adolescence is crucial for understanding autism spectrum disorder (ASD). Yet how functional networks regulate developmental changes in brain morphology remains unclear. Here, we analyzed gray matter volume (GMV) and functional connectivity (FC) in 301 individuals with ASD and 375 typically developing controls (TDCs), aged 8-18 years, from the Autism Brain Imaging Data Exchange (ABIDE). Using a sliding-window approach, participants were stratified by age, and GMV distribution deviations (DEV) were quantified with Kullback-Leibler divergence and expected value analysis. Network diffusion modeling (NDM) was applied to predict developmental alterations and evaluate how functional networks constrain atypical neurodevelopment. Results revealed a developmental shift in GMV divergence: during early adolescence, ASD participants showed positive GMV deviations relative to TDCs, which shifted to negative in late adolescence. The largest DEV were observed in the superior temporal sulcus, cingulate gyrus, insula, and superior parietal lobule. Furthermore, NDM demonstrated cross-stage predictability, as DEV values of atypical brain regions at preceding age stages significantly predicting subsequent ones, constrained by network architecture. These findings highlight a dynamic developmental shift from GMV overgrowth to delayed maturation during adolescence in ASD and revealing the role of intrinsic functional networks in constraining atypical anatomical development.},
}
@article {pmid41101308,
year = {2025},
author = {Zheng, D and Xin, Q and Jin, S and Zhou, A and Jia, X and Tan, Y and Hu, H},
title = {Neural mechanism of the sexually dimorphic winner effect in mice.},
journal = {Neuron},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuron.2025.09.029},
pmid = {41101308},
issn = {1097-4199},
abstract = {The "winner effect," where prior victories increase the likelihood of future wins, profoundly shapes social hierarchy dynamics and competitive motivation. Although human literature suggests a less pronounced winner effect in females, the neural mechanisms underlying these sex differences remain unclear. Here, we show that, compared with male mice, female mice take longer to form social hierarchies and exhibit a weaker winner effect. The dorsomedial prefrontal cortex (dmPFC), crucial for social dominance in males, plays a similar role in female mice. However, female mice exhibit reduced long-term potentiation (LTP) at the mediodorsal thalamus (MDT)-to-dmPFC synapses. In vitro recordings revealed that female mice have heightened excitability of dmPFC parvalbumin interneurons (PV-INs). Modulation of dmPFC PV-IN activity regulates LTP and the winner effect in a sexually dimorphic manner. This work identifies dmPFC PV-INs as a target for enhancing the winner effect, establishing a circuit-level framework for sex differences in competitive behaviors.},
}
@article {pmid41100980,
year = {2026},
author = {Ban, S and Chong, D and Kwon, J and Lee, S and Huang, Y and Yoo, S and Yeo, WH},
title = {Advances in flexible high-density microelectrode arrays for brain-computer interfaces.},
journal = {Biosensors &amp; bioelectronics},
volume = {292},
number = {},
pages = {118102},
doi = {10.1016/j.bios.2025.118102},
pmid = {41100980},
issn = {1873-4235},
mesh = {*Brain-Computer Interfaces/trends ; Humans ; Microelectrodes ; *Biosensing Techniques/instrumentation/methods ; Equipment Design ; Animals ; *Brain/physiology ; Electroencephalography/instrumentation ; },
abstract = {Recent advances in flexible high-density microelectrode arrays (FHD-MEA) have revolutionized brain-computer interfaces (BCIs) by providing high spatial resolution, mechanical compliance, and long-term biocompatibility. This technology enables stable neural recording and precise stimulation, addressing the shortcomings of conventional rigid BCI arrays. In this review, we outline the challenges of signal acquisition and stimulation of conventional low-density, rigid BCI systems. These include poor spatial resolution, micro-motor-induced instability, electrochemical degradation, wiring bottlenecks, off-target activation, and charge injection hazards. We then describe how these barriers are addressed through advanced materials, device designs, and system-level integration. We summarize representative applications of clinical therapy for sensory enhancement, human-machine interfaces, and neurological diseases, highlighting translational potential. Collectively, this review article presents recent progress and emerging trends in establishing FHD-MEAs as a crucial foundation for next-generation, clinically viable BCIs.},
}
@article {pmid41100231,
year = {2025},
author = {Li, R and Liu, J and Liu, J and Yang, S and Liu, W and Deng, K and Wang, W},
title = {A Novel Grasping Robot Control Method Using Motion Execution BCI Combining Knowledge Reasoning.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3622255},
pmid = {41100231},
issn = {2168-2208},
abstract = {Recently, with the growing number of disabled people, brain-controlled technology offers a novel way to help patients restore their daily abilities. However, the conventional brain-controlled system based on the motion related task lacks intelligence in real-world environments. To address above problem, this study proposed a share controlled system combining a precise hand movement (PHM)-based brain computer interface (BCI) system and knowledge-driven reasoning method. Six types of precise hand movements were selected to design novel motion execution paradigm for BCI system. A feature intermediate fusion convolutional neural network was employed to accurately decode electroencephalogram. Furthermore, a shared control grasping technology based on knowledge based reasoning combined PHM-based BCI system was designed for grasping robot, which enhancing the system's intelligence and versatility in selecting objects. To verify the improvement of proposed method, experiments were conducted with 15 ࣥhealthy subjects and 2 patients. The proposed method achieved an average accuracy of 82.80±6.08%, with the highest accuracy reaching 94.27%. All the experimental results demonstrate the effectiveness of the proposed shared control method.},
}
@article {pmid41096996,
year = {2025},
author = {Ga, YJ and Yeh, JY},
title = {siRNA Cocktail Targeting Multiple Enterovirus 71 Genes Prevents Escape Mutants and Inhibits Viral Replication.},
journal = {International journal of molecular sciences},
volume = {26},
number = {19},
pages = {},
pmid = {41096996},
issn = {1422-0067},
support = {2020//Incheon National University/ ; },
mesh = {*Enterovirus A, Human/genetics/physiology ; *Virus Replication/genetics ; *RNA, Small Interfering/genetics/pharmacology ; Humans ; *Mutation ; Enterovirus Infections/virology/genetics ; RNA Interference ; Animals ; Cell Line ; },
abstract = {RNA interference (RNAi) is a powerful mechanism of post-transcriptional gene regulation in which small interfering RNA (siRNA) is utilized to target and degrade specific RNA sequences. In this study, experiments were conducted to evaluate the efficacy of combination siRNA therapy against enterovirus 71 (EV71) and the potential of this therapy to delay or prevent the emergence of resistance in vitro. siRNAs targeting multiple genes of EV71 were designed, and the effects of a cocktail of siRNAs on viral replication were assessed compared to those of single-siRNA treatment. Cotransfection of multiple siRNAs targeting different protein-coding genes of the EV71 genome effectively suppressed escape mutants resistant to RNAi. Combination therapy with siRNAs targeting multiple viral genes successfully prevented viral escape mutations over five passages. By contrast, serial passaging with a single siRNA led to the rapid emergence of resistance, with mutations identified in the siRNA target sites. The combination of siRNAs specifically targeting different regions demonstrated an additive effect and was more effective than individual siRNAs at inhibiting EV71 replication. This study supports the effectiveness of combination therapy using siRNAs targeting multiple genes of EV71 to inhibit viral replication and prevent the emergence of resistant escape mutants. Overall, the findings identify RNAi targeting multiple viral genes as a potential strategy for therapeutic development against viral diseases and for preventing the emergence of escape mutants resistant to antiviral RNAi.},
}
@article {pmid41096009,
year = {2025},
author = {von Altdorf, LAWR and Bracewell, M and Cooke, A},
title = {Effectiveness of Electroencephalographic Neurofeedback for Parkinson's Disease: A Systematic Review and Meta-Analysis.},
journal = {Journal of clinical medicine},
volume = {14},
number = {19},
pages = {},
pmid = {41096009},
issn = {2077-0383},
abstract = {Background: Electroencephalographic (EEG) neurofeedback training is gaining traction as a non-pharmacological treatment option for Parkinson's disease (PD). This paper reports the first pre-registered, integrated systematic review and meta-analysis of studies examining the effects of EEG neurofeedback on cortical activity and motor function in people with PD. Method: We searched Cochrane Databases, PubMed, Embase, Scopus, Web of Science, PsycInfo, grey literature repositories, and trial registers for EEG neurofeedback studies in people with PD. We included randomized controlled trials, single-group experiments, and case studies. We assessed risk of bias using the Cochrane Risk of Bias 2 and Risk of Bias in Non-Randomized Studies tools, and we used the Grading of Recommendations, Assessment, Development and Evaluations tool to assess certainty in the evidence and resultant interpretations. Random-effects meta-analyses were performed. Results: A total of 11 studies (143 participants; Hoehn and Yahr I-IV) met the criteria for inclusion. A first meta-analysis revealed that EEG activity is modified in the prescribed way by neurofeedback interventions. The effect size is large (SMD = 1.30, 95% CI = 0.50-2.10, p = 0.001). Certainty in the estimate is high. Despite successful cortical modulation, a subsequent meta-analysis revealed inconclusive effects of EEG neurofeedback on motor symptomology. The effect size is small (SMD = 0.10, 95% CI = -1.03-1.23, p = 0.86). Certainty in the estimates is low. Narrative evidence revealed that interventions are well-received and may yield specific benefits not detected by general symptomology reports. Conclusion: EEG neurofeedback successfully modulates cortical activity in people with PD, but downstream impacts on motor function remain unclear. The neuromodulatory potential of EEG neurofeedback in people with PD is encouraging. Additional well-powered and high-quality research into the effects of EEG neurofeedback in PD is warranted.},
}
@article {pmid41095845,
year = {2025},
author = {Kollu, K and Yortanli, BC and Cicek, AN and Susam, E and Karakas, N and Kizilarslanoglu, MC},
title = {Investigation of the Prognostic Value of Novel Laboratory Indices in Patients with Sepsis in an Intensive Care Unit: A Retrospective Observational Study.},
journal = {Journal of clinical medicine},
volume = {14},
number = {19},
pages = {},
pmid = {41095845},
issn = {2077-0383},
abstract = {Background: This study aimed to evaluate the prognostic value of some novel laboratory indices in intensive care unit (ICU)-hospitalized sepsis patients. Methods: This retrospective, observational study included 400 patients with sepsis. The indices studied were the C-reactive protein/albumin ratio (CAR), hemoglobin, albumin lymphocyte, and platelet (HALP) score, lymphocyte/monocyte ratio (LMR), prognostic nutritional index (PNI), systemic immune inflammatory index (SII), vitamin B12xC-reactive protein index (BCI), systemic inflammatory response index (SIRI), and platelet/lymphocyte ratio (PLR). The predicting effects of these indices in ICU mortality, along with other clinical outcomes, were investigated. Results: The median age of the study population was 73 (18-95) years and 51.6% were males. The ICU mortality rate was 51.7%. Deceased patients with sepsis had an increased age and high APACHE II and SOFA scores compared to the survivors (p < 0.05 for all). In the multivariate logistic regression analysis, age (HR = 1.069, p = 0.038 in Model 1 vs. HR = 1.053, p = 0.001 in Model 2), SOFA score (HR = 2.145, p < 0.001 in Model 1 vs. HR = 1.740, p < 0.001 in Model 2), phosphorus levels (in Model 1, HR = 0.608, p = 0.037), and CAR (in Model 2, HR = 1.012, p = 0.023) were independent associated factors for ICU mortality. According to the ROC analyses, the SOFA (AUC = 0.879, p < 0.001) and APACHE II (AUC = 0.769, p < 0.001) scores showed high accuracy in predicting ICU mortality, while the PNI (AUC = 0.675, p < 0.001), CAR (AUC = 0.609, p < 0.001), and the BCI (AUC = 0.648, p < 0.001) showed limited accuracy. However, the HALP score did not reach a significant level in predicting ICU mortality (p = 0.067). Conclusions: Excluding the HALP score, the new laboratory indices mentioned above may be prognostic markers for predicting clinical outcomes in intensive care units for patients with sepsis. However, these indices need to be supported by larger patient populations.},
}
@article {pmid41095026,
year = {2025},
author = {Reyes, D and Sieghartsleitner, S and Loaiza, H and Guger, C},
title = {Motor Imagery Acquisition Paradigms: In the Search to Improve Classification Accuracy.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {19},
pages = {},
pmid = {41095026},
issn = {1424-8220},
support = {Bicentenario 1st Call//Colfuturo/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Imagination/physiology ; Algorithms ; Electroencephalography/methods ; Stroke/physiopathology ; Male ; },
abstract = {In recent years, advances in medicine have been evident thanks to technological growth and interdisciplinary research, which has allowed the integration of knowledge, for example, of engineering into medical fields. This integration has generated developments and new methods that can be applied in alternative situations, highlighting, for example, aspects related to post-stroke therapies, Multiple Sclerosis (MS), or Spinal Cord Injury (SCI) treatments. One of the methods that has stood out and is gaining more acceptance every day is Brain-Computer Interfaces (BCIs), through the acquisition and processing of brain electrical activity, researchers, doctors, and scientists manage to transform this activity into control signals. In turn, there are several methods for operating a BCI, this work will focus on motor imagery (MI)-based BCI and three types of acquisition paradigms (traditional arrow, picture, and video), seeking to improve the accuracy in the classification of motor imagination tasks for naive subjects, which correspond to a MI task for both the left and the right hand. A pipeline and methodology were implemented using the CAR+CSP algorithm to extract the features and simple standard and widely used models such as LDA and SVM for classification. The methodology was tested with post-stroke (PS) subject data with BCI experience, obtaining 96.25% accuracy for the best performance, and with the novel paradigm proposed for the naive subjects, 97.5% was obtained. Several statistical tests were carried out in order to find differences between paradigms within the collected data. In conclusion, it was found that the classification accuracy could be improved by using different strategies in the acquisition stage.},
}
@article {pmid41094934,
year = {2025},
author = {Zhang, Y and Yin, B and Yuan, X},
title = {TSFNet: Temporal-Spatial Fusion Network for Hybrid Brain-Computer Interface.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {19},
pages = {},
pmid = {41094934},
issn = {1424-8220},
support = {62171152//National Natural Science Foundation of China/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography/methods ; Spectroscopy, Near-Infrared/methods ; Algorithms ; Brain/physiology ; Signal Processing, Computer-Assisted ; Neural Networks, Computer ; },
abstract = {Unimodal brain-computer interfaces (BCIs) often suffer from inherent limitations due to the characteristic of using single modalities. While hybrid BCIs combining electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) offer complementary advantages, effectively integrating their spatiotemporal features remains a challenge due to inherent signal asynchrony. This study aims to develop a novel deep fusion network to achieve synergistic integration of EEG and fNIRS signals for improved classification performance across different tasks. We propose a novel Temporal-Spatial Fusion Network (TSFNet), which consists of two key sublayers: the EEG-fNIRS-guided Fusion (EFGF) layer and the Cross-Attention-based Feature Enhancement (CAFÉ) layer. The EFGF layer extracts temporal features from EEG and spatial features from fNIRS to generate a hybrid attention map, which is utilized to achieve more effective and complementary integration of spatiotemporal information. The CAFÉ layer enables bidirectional interaction between fNIRS and fusion features via a cross-attention mechanism, which enhances the fusion features and selectively filters informative fNIRS representations. Through the two sublayers, TSFNet achieves deep fusion of multimodal features. Finally, TSFNet is evaluated on motor imagery (MI), mental arithmetic (MA), and word generation (WG) classification tasks. Experimental results demonstrate that TSFNet achieves superior classification performance, with average accuracies of 70.18% for MI, 86.26% for MA, and 81.13% for WG, outperforming existing state-of-the-art multimodal algorithms. These findings suggest that TSFNet provides an effective solution for spatiotemporal feature fusion in hybrid BCIs, with potential applications in real-world BCI systems.},
}
@article {pmid41094901,
year = {2025},
author = {Anzalone, A and Acampora, E and Liu, C and Hajra, SG},
title = {Passive Brain-Computer Interface Using Textile-Based Electroencephalography.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {19},
pages = {},
pmid = {41094901},
issn = {1424-8220},
mesh = {*Electroencephalography/methods ; *Brain-Computer Interfaces ; Humans ; *Textiles ; Support Vector Machine ; Male ; Adult ; Electrodes ; Female ; Machine Learning ; *Brain/physiology ; Cognition/physiology ; },
abstract = {Background: Passive brain-computer interface (pBCI) systems use a combination of electroencephalography (EEG) and machine learning (ML) to evaluate a user's cognitive and physiological state, with increasing applications in both clinical and non-clinical scenarios. pBCI systems have been limited by their traditional reliance on sensor technologies that cannot easily be integrated into non-laboratory settings where pBCIs are most needed. Advances in textile-electrode-based EEG show promise in overcoming the operational limitations; however, no study has demonstrated their use in pBCIs. This study presents the first application of fully textile-based EEG for pBCIs in differentiating cognitive states. Methods: Cognitive state comparisons between eyes-open (EO) and eyes-closed (EC) conditions were conducted using publicly available data for both novel textile and traditional dry-electrode EEG. EO vs. EC differences across both EEG sensor technologies were assessed in delta, theta, alpha, and beta EEG power bands, followed by the application of a Support Vector Machine (SVM) classifier. The SVM was applied to each EEG system separately and in a combined setting, where the classifier was trained on dry EEG data and tested on textile EEG data. Results: The textile EEG system accurately captured the characteristic increase in alpha power from EO to EC (p < 0.01), but power values were lower than those of dry EEG across all frequency bands. Classification accuracies for the standalone dry and textile systems were 96% and 92%, respectively. The cross-sensor generalizability assessment resulted in a 91% classification accuracy. Conclusions: This study presents the first use of textile-based EEG for pBCI applications. Our results indicate that textile-based EEG can reliably capture changes in EEG power bands between EO and EC, and that a pBCI system utilizing non-traditional textile electrodes is both accurate and generalizable.},
}
@article {pmid41093880,
year = {2025},
author = {Aguilera-Rodríguez, E and Cuevas-Romero, A and Mendoza-Franco, S and Wornovitzky-Green, J and Rivera-Cerros, E and Villanueva-Cazares, D and Muñoz-Ubando, LA and Ibarra-Zárate, D and Alonso-Valerdi, LM},
title = {An EEG-based Imagined Speech Database for comparing Paradigm Designs.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {1644},
pmid = {41093880},
issn = {2052-4463},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography ; *Speech ; Female ; Male ; *Imagination ; Adult ; Video Games ; },
abstract = {Brain-computer interfaces (BCIs) attempt to establish a connection between the human mind and a computer system. While recent computational advances continue to improve these interfaces, human factors have been overlooked. Factors such as fatigue and attention play a key role in brain signal modulation. This arises the need for paradigms designed and implemented in terms of human factors. Therefore, it is proposed to improve the level of engagement to diminish fatigue and increase attention by a video game-based paradigm for an imagined speech BCI. For this purpose, a sample of 15 volunteers (females = 7) was recruited to study the quality of their imagined speech when it is evoked under an abstract scenario (traditional paradigm) and a video-game paradigm. This dataset helps to study the differences in imagined speech signals when using two different paradigms: (1) one that does not consider human factors, and (2) one that does. Additional applications may include designing imagined speech decoding models for BCI and studying the relationship between users' profile and their imagined speech signals.},
}
@article {pmid41092418,
year = {2025},
author = {Oliveira, I and Russo, M and Almeida, AI and Vourvopoulos, A and Mendes Pereira, C},
title = {Recommendations for Combining Brain-Computer Interface, Motor Imagery, and Virtual Reality in Upper Limb Stroke Rehabilitation: Qualitative Participatory Design Study.},
journal = {JMIR rehabilitation and assistive technologies},
volume = {12},
number = {},
pages = {e71789},
pmid = {41092418},
issn = {2369-2529},
abstract = {BACKGROUND: The high incidence and prevalence of upper limb impairment post stroke highlights the need for advancements in rehabilitation. Brain-computer interfaces (BCIs) represent a promising technology by directly training the central nervous system. The integration of motor imagery (MI) and motor observation through virtual reality (VR) using BCIs provides valuable opportunities for rehabilitation. However, the diversity in intervention designs demonstrates the lack of guiding recommendations integrating neurorehabilitation principles for BCIs.<br><br>OBJECTIVE: This study aims to develop recommendations for BCI interventions using task specificity and ecological validity through simulated VR tasks for upper limb stroke survivors by gathering tacit knowledge from neurorehabilitation experts, patients' experiences, and engineers' expertise to ensure a comprehensive approach.<br><br>METHODS: A multiperspective qualitative study was conducted through collaborative design workshops involving stroke survivors (n=17), neurorehabilitation experts (n=13), and biomedical engineers (n=3), totaling 33 participants. This innovative approach aimed to actively engage stakeholders in developing multifaceted solutions for complex health interventions.<br><br>RESULTS: Six themes emerged from the thematic analysis: (1) importance of patient-centered approach, (2) clinical evaluation and patient selection, (3) recommendations for task design, (4) guidelines for structuring BCI intervention, (5) key factors influencing motivation, and (6) technology features. From these themes, the following recommendations (R) are established: (R1) MI-based VR-BCI interventions must be conducted through a patient-centered approach, based on individualized preferences, needs, and goals of the user, by an interdisciplinary team; (R2) selection criteria must include upper limb impairment, cognitive and communication assessment, and clinical traits, such as MI capacity, neglect, and depression must be assessed since they might influence intervention outcomes; (R3) tasks to perform should preferably be based on daily living activities, including unilateral and bilateral tasks, and a variety of tasks must be available for selection to ensure meaningfulness for the user and suitability to clinical traits; (R4) intervention must be structured by different progressing levels starting with simple, gross movements and adding complexity through additional movement features, cognitive demand, or MI difficulty; (R5) optimal levels of motivation must be sustained through task variability, gamification elements, and task demand adequacy; and (R6) multisensorial potential of MI-based VR-BCI must be effectively harnessed through the adequate adjustment of visual, haptic, and proprioceptive feedback modalities to the patient.<br><br>CONCLUSIONS: Current results contribute to establishing clear guidelines on patient selection, task design, intervention structuring, motivation factors, and tailoring of sensory feedback. This framework presents a foundation for optimal implementation of VR-BCI-based interventions that associate MI and motor observation, optimizing cortical activity during the intervention, patients' engagement, and clinical outcomes. Future research should explore the application of these guidelines for validation and investigate BCIs' efficacy according to different combinations of patients' profiles, task characteristics, and technology features.},
}
@article {pmid41091050,
year = {2025},
author = {Levy, L and Feinsinger, A},
title = {Participant Engagement, Epistemic Injustice, and Early-Phase Implanted Neural Device Research.},
journal = {The Hastings Center report},
volume = {55},
number = {5},
pages = {18-28},
pmid = {41091050},
issn = {1552-146X},
support = {RF1 MH121373/MH/NIMH NIH HHS/United States ; //Dana Foundation/ ; RF1MH121373/NH/NIH HHS/United States ; },
mesh = {Humans ; *Social Justice ; Motivation ; *Biomedical Research/ethics ; Knowledge ; *Prostheses and Implants ; },
abstract = {In recent years, participant engagement initiatives in research on implanted neural devices have significantly increased. However, there remains little consensus on the motivations, goals, and best practices for engagement efforts. Drawing on the concept of participatory epistemic injustice, we argue that one core ethical motivation for engagement is epistemic in nature. Based on their subject positions, participants should be key knowledge contributors to implanted neurotech research. Therefore, we argue, participants experience participatory epistemic injustice when their insights do not result in changes to or otherwise influence research protocols, device development, and task design. We contend that engagement can resist this type of injustice only if it establishes robust methods not only to gather but also to actively incorporate participant knowledge into the research and development process.},
}
@article {pmid41090855,
year = {2025},
author = {Liu, Y and Wu, H and Wang, S and Yang, Q and Zhang, B},
title = {The Implantable Electrode Co-Deposited with Iron Oxide Nanoparticles and PEDOT:PSS.},
journal = {Nanomaterials (Basel, Switzerland)},
volume = {15},
number = {19},
pages = {},
pmid = {41090855},
issn = {2079-4991},
support = {5216202252162022//National Natural Science Foundation of China/ ; 2021JJA160015//Guangxi Natural Science Foundation/ ; },
abstract = {Iron oxide nanoparticles (IONs) exhibit biocompatibility, ease of drug loading, and potential for generating forces and heat in a magnetic field, enhancing Magnetic Resonance Imaging (MRI). This study proposes coating IONs on electrode surfaces to improve performance and neuron bonding. Methods included synthesizing IONs, grafting chondroitin sulfate (CS), and co-depositing with poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS). Results showed reduced impedance, increased charge storage, and improved signal quality in vivo.},
}
@article {pmid41089660,
year = {2025},
author = {Dai, Y and Chen, Z and Cao, TA and Zhou, H and Fang, M and Dai, Y and Jiang, L and Tong, J},
title = {A time-frequency feature fusion-based deep learning network for SSVEP frequency recognition.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1679451},
pmid = {41089660},
issn = {1662-4548},
abstract = {INTRODUCTION: Steady-state visual evoked potential (SSVEP) has emerged as a pivotal branch in brain-computer interfaces (BCIs) due to its high signal-to-noise ratio (SNR) and elevated information transfer rate (ITR). However, substantial inter-subject variability in electroencephalographic (EEG) signals poses a significant challenge to current SSVEP frequency recognition. In particular, it is difficult to achieve high cross-subject classification accuracy in calibration-free scenarios, and the classification performance heavily depends on extensive calibration data.<br><br>METHODS: To mitigate the reliance on large calibration datasets and enhance cross-subject generalization, we propose SSVEP time-frequency fusion network (SSVEP-TFFNet), an improved deep learning network fusing time-domain and frequency-domain features dynamically. The network comprises two parallel branches: a time-domain branch that ingests raw EEG signals and a frequency-domain branch that processes complex-spectrum features. The two branches extract the time-domain and frequency-domain features, respectively. Subsequently, these features are fused via a dynamic weighting mechanism and input to the classifier. This fusion strategy strengthens the feature expression ability and generalization across different subjects.<br><br>RESULTS: Cross-subject classification was conducted on publicly available 12-class and 40-class SSVEP datasets. We also compared SSVEP-TFFNet with traditional approaches and principal deep learning methods. Results demonstrate that SSVEP-TFFNet achieves an average classification accuracy of 89.72% on the 12-class dataset, surpassing the best baseline method by 1.83%. SSVEP-TFFNet achieves average classification accuracies of 72.11 and 82.50% (40-class datasets), outperforming the best controlled method by 7.40 and 6.89% separately.<br><br>DISCUSSION: The performance validates the efficacy of dynamic time-frequency feature fusion and our proposed method provides a new paradigm for calibration-free SSVEP-based BCI systems.},
}
@article {pmid41089381,
year = {2025},
author = {Liu, B and Hu, C and Bao, P},
title = {Precision TMS through the integration of neuroimaging and machine learning: optimizing stimulation targets for personalized treatment.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1682852},
pmid = {41089381},
issn = {1662-5161},
abstract = {Transcranial Magnetic Stimulation (TMS), a non-invasive neuromodulation technique based on electromagnetic induction, modulates cortical excitability by inducing currents with a magnetic field. TMS has demonstrated significant clinical potential in the treatment of various neuropsychiatric disorders, including depression, anxiety, and Parkinson's disease. However, conventional TMS targeting methods that rely on anatomical landmarks do not adequately account for individual differences in brain structure and functional networks, leading to considerable variability in treatment responses. In recent years, advances in neuroimaging techniques-such as functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI)-together with the application of machine learning (ML) and artificial intelligence (AI) algorithms in big data analysis, have provided novel approaches for precise TMS targeting and individualized treatment. This review summarizes the latest developments in the integration of multimodal neuroimaging and AI technologies for precision neuromodulation with TMS. It focuses on critical issues such as imaging resolution, AI model generalizability, real-time feedback modulation, as well as data privacy and ethical considerations. Future prospects including closed-loop TMS control systems, cross-modal data fusion, and AI-assisted brain-computer interfaces (BCIs) are also discussed. Overall, AI-driven personalized TMS strategies hold promise for markedly enhancing treatment precision and clinical efficacy, thereby offering new theoretical and practical guidance for individualized treatment in neuropsychiatric and neurodegenerative disorders.},
}
@article {pmid41088329,
year = {2025},
author = {Han, F and Chen, H},
title = {Does brain-computer interface-based mind reading threaten mental privacy? ethical reflections from interviews with Chinese experts.},
journal = {BMC medical ethics},
volume = {26},
number = {1},
pages = {134},
pmid = {41088329},
issn = {1472-6939},
support = {21ZDA017//National Social Science Fund of China/ ; 21ZDA017//National Social Science Fund of China/ ; },
mesh = {Humans ; *Brain-Computer Interfaces/ethics ; China ; *Privacy ; Male ; Female ; Adult ; Interviews as Topic ; Reading ; Neurosciences/ethics ; Qualitative Research ; East Asian People ; },
abstract = {BACKGROUND: The rapid development of brain-computer interface (BCI) technology has sparked profound debates about the right to privacy, particularly concerning its potential to enable mind reading. While scholars have proposed the establishment of neurorights to safeguard mental privacy, questions remain about whether BCIs can genuinely decode inner thoughts and what makes their ethical implications distinctive.<br><br>METHODS: This study conducted semi-structured interviews with 20 Chinese experts in the BCI and neuroscience fields to explore their perspectives on the concept, feasibility, and limitations of BCI-based mind reading (BMR). The transcriptions of the interviews were analyzed through reflexive thematic analysis to identify key themes and insights.<br><br>RESULTS: The findings reveal a range of expert perspectives on the interpretations and feasibility of BMR. Most participants believe that current BCI technology cannot decode inner thoughts, although they acknowledge the potential for future advancements. Key technical challenges, such as signal quality and reliance on background information, are highlighted.<br><br>CONCLUSION: We summarize the interpretations, feasibility, and limitations of BMR and introduce a distinction between "strong BMR" and "weak BMR" to clarify their technical and ethical implications. Based on our analysis, we argue that current BMR does not pose unique ethical challenges compared with other forms of mind reading, and therefore does not yet justify the establishment of a distinct right to mental privacy.},
}
@article {pmid41088296,
year = {2025},
author = {Tang, A and Chen, Y and Ding, J and Li, Z and Xu, C and Hu, S and Lai, J},
title = {Gut microbiota remodeling and sensory-emotional functional disruption in adolescents with bipolar depression.},
journal = {Journal of translational medicine},
volume = {23},
number = {1},
pages = {1083},
pmid = {41088296},
issn = {1479-5876},
support = {82201676//National Natural Science Foundation of China/ ; 82471542//National Natural Science Foundation of China/ ; No. JNL-2023001B//Research Project of Jinan Microecological Biomedicine Shandong Laboratory/ ; 2023YFC2506200//National Key Research and Development Program of China/ ; 2023ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; },
mesh = {Humans ; *Gastrointestinal Microbiome/drug effects/physiology ; Adolescent ; *Bipolar Disorder/microbiology/physiopathology/drug therapy/psychology ; Male ; Female ; *Emotions ; Quetiapine Fumarate/therapeutic use/pharmacology ; Magnetic Resonance Imaging ; Case-Control Studies ; Brain/physiopathology/diagnostic imaging ; Neuroimaging ; },
abstract = {BACKGROUND: Adolescence is the peak period of newly-onset bipolar disorder (BD). Accumulating studies have revealed disturbed gut microbiota can interfere with neurodevelopment in adolescents. In this study, we aimed to characterize the gut microbiota in adolescents with BD and its correlation with brain dysfunction.<br><br>METHODS: Thirty unmedicated BD adolescents within depressive episode were recruited and underwent four-week quetiapine treatment. Twenty-five age-, gender-, and BMI-matched healthy controls (HCs) were recruited. Fecal samples were collected from HCs and all BD adolescents before and after treatment and analyzed by metagenomic sequencing. Resting-state cranial functional magnetic images were collected from 21 BD adolescents before treatment. Random forest models were used to evaluate the discriminative power of gut microbiota and neuroimaging data for BD and the predictive power of treatment effect.<br><br>RESULTS: Although no significant difference was found in alpha-diversity, intra- and inter-group differences in beta-diversity were observed among HCs, pre- and post-treatment patients. Compared to HCs, unmedicated BD adolescents presented a differentiated gut microbial communities, which correlated to the short-chain fatty acids, choline, lipids, vitamins, polyamines, aromatic amino acids metabolic pathways. Four-week quetiapine treatment improved the abundance of specific genus, such as Odoribacter splanchnicus, Oribacterium sinus, Hafnia alvei, Fusobacterium periodonticum, Acidaminococcus interstini and Veillonella rogosae. Neuroimaging analysis revealed sensor-emotional brain regions were associated with BD severity. Finally, random forest models based on gut microbial biomarkers can well distinguish unmedicated BD from HCs (AUC&#x2009;=&#x2009;91.12%) and predict the treatment effect (AUC&#x2009;=&#x2009;91.84%). The random forest model integrating gut microbiota and neuroimaging data exhibited a better predictive efficacy than using microbiota data alone.<br><br>CONCLUSION: This study first characterized the gut microbiota architecture in adolescent BD. Combining gut microbiota and brain function biomarkers may benefit disease diagnosis and predict treatment outcome. Nonetheless, these findings should be carefully interpreted considering the limitations of a modest sample size and the absence of detailed mechanistic explorations. Trial registration NCT05480150. Registered 29 July 2022-Retrospectively registered, https://clinicaltrials.gov/study/NCT05480150 .},
}
@article {pmid41087533,
year = {2025},
author = {Ge, Y and Dong, Y and Sun, H and Liu, Y and Wang, C},
title = {An incremental adversarial training method enables timeliness and rapid new knowledge acquisition.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {35826},
pmid = {41087533},
issn = {2045-2322},
support = {JJKH20250945KJ//Science and Technology Development Project of the Department of Education of Jilin Province/ ; JJKH20250945KJ//Science and Technology Development Project of the Department of Education of Jilin Province/ ; JJKH20250945KJ//Science and Technology Development Project of the Department of Education of Jilin Province/ ; JJKH20250945KJ//Science and Technology Development Project of the Department of Education of Jilin Province/ ; JJKH20250945KJ//Science and Technology Development Project of the Department of Education of Jilin Province/ ; 2022IT096//New Generation Information Technology Innovation Project of China University Industry, University and Research Innovation Fund/ ; },
mesh = {*Neural Networks, Computer ; Humans ; Algorithms ; *Brain-Computer Interfaces ; *Deep Learning ; },
abstract = {Adversarial training is an effective defense method for deep models against adversarial attacks. However, current adversarial training methods require retraining the entire neural network, which consumes a significant amount of computational resources, thereby affecting the timeliness of deep models and further hindering the rapid learning process of new knowledge. In response to the above problems, this article proposes an incremental adversarial training method (IncAT) and applies it to the field of brain computer interfaces (BCI). Within this method, we first propose a deep model called Neural Hybrid Assembly Network (NHANet) and then train it. Then, based on the original samples and the trained deep model, calculate the Fisher information matrix to evaluate the importance of deep neural network parameters on the original samples. Finally, when calculating the loss of adversarial samples and real labels, an Elastic Weight Consolidation (EWC) loss is added to limit the variation of important weights and bias parameters in the Neural Hybrid Assembly Network (NHANet). The above incremental adversarial training method was applied to the publicly available epilepsy brain computer interface dataset at the University of Bonn. The experimental results showed that when facing three different attack algorithms, including fast gradient sign method (FGSM), projected gradient descent (PGD) and basic iterative method (BIM), the method proposed in this paper achieved robust accuracies of 95.33%, 94.67%, and 93.60%, respectively, without affecting the accuracy of clean samples, which is 5.06%, 4.67%, and 2.67% higher than traditional training methods respectively, thus fully verifying the generalization and effectiveness of the method.},
}
@article {pmid41087504,
year = {2025},
author = {Fang, T and Wang, R and Liu, W and Zhang, Y and Guo, Y and Hu, Y and Zhao, X and Chen, Y and Fan, Q and Ming, D},
title = {Edge participation coefficient unveiling the developmental dynamics of neonatal functional connectome.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {1463},
pmid = {41087504},
issn = {2399-3642},
mesh = {Humans ; *Connectome/methods ; Infant, Newborn ; *Brain/growth &amp; development/physiology/diagnostic imaging ; Magnetic Resonance Imaging ; Infant, Premature/growth &amp; development ; Male ; Female ; *Nerve Net/growth &amp; development/physiology ; Infant ; },
abstract = {Understanding how the brain's functional connections develop during infancy is crucial for uncovering the complexities of early neural maturation. Traditional node-based analyses have advanced our knowledge, but may overlook the transient dynamics of interregional connectivity. Leveraging the large neonatal functional MRI dataset from the Developing Human Connectome Project (n = 781, including 494 full-term and 287 preterm infants), we introduce an edge-centric metric to quantify cross-module functional integration. Here we show that preterm infants exhibit higher edge participation coefficients than full-term peers, suggesting delayed network specialization. We mapped developmental changes in edge participation coefficients and found that between-network connections-particularly those involving visual and higher-order systems-undergo the most pronounced changes and are associated with cognitive outcomes at 18 months. By analyzing gene expression in a developing brain, we identified genes involved in neurodevelopmental processes and cellular signalling that may underlie these patterns. Our findings illustrate how interregional diversity evolves in early life and provide insight into the molecular basis of early brain development.},
}
@article {pmid41083759,
year = {2025},
author = {Banaeian Far, S and Chalak Qazani, MR and Imani Rad, A},
title = {Cell-to-cell communication: from physical calling to remote emotional touching.},
journal = {Discover nano},
volume = {20},
number = {1},
pages = {178},
pmid = {41083759},
issn = {2731-9229},
abstract = {The emerging paradigm of cell-to-cell communication represents a transformative shift from device-mediated contact to bio-integrated, emotion-driven interactions. This article introduces a novel, multi-layered framework for enabling biologically integrated communication between cells, devices, and computational systems using the paradigm of Molecular Communication (MC). Moving beyond traditional digital interfaces, the proposed architecture, comprising in-body, on-chip, and external communication layers, models and processes intercellular signaling via molecular emissions, implantable biosensors, and nano-electronic processors. Theoretical foundations are extended to fractional-order diffusion systems and neuromorphic decoding, capturing complex behaviors in realistic biological environments. We further propose a cross-layer molecular digital twin model for context-aware interpretation and feedback. The framework's applications are grounded in the molecular underpinnings of emotion, where neurotransmitters like oxytocin and serotonin mediate prosocial behaviors and affective states through cell-to-cell signaling. For instance, remote emotional interfacing leverages MC to modulate oxytocin release, mimicking natural empathy circuits, while consensual telepathy draws from BCI-mediated neural pattern sharing, extending molecular-level decoding to cognitive-emotional relays. These are not mere metaphors but extensions of established neurochemical pathways, as evidenced by recent studies showing serotonin fluctuations amplify context-specific emotions. This work thus bridges cellular mechanisms to higher-order phenomena, ensuring scientific rigor in bio-digital systems .},
}
@article {pmid41082414,
year = {2025},
author = {Chen, Q and Ye, C and Xiao, R and Pan, J and Li, J},
title = {SemSTNet: Medical EEG Semantic Metric Learning with Class Prototypes Generated by Pretrained Language Model.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3620754},
pmid = {41082414},
issn = {1558-2531},
abstract = {Electroencephalography (EEG) feature learning is crucial for brain-machine interfaces and medical diagnostics. Existing deep learning models for classification often overlook the intrinsic semantic relationships between different EEG classes and rely on overly complex models with a large number of parameters. To address these challenges, we propose SemSTNet, a novel and lightweight framework for EEG analysis. Firstly, we designed an e ficient, lightweight convolutional architecture that decouples spatial and temporal feature extraction. Then we propose a framework which introduces a novel semantic metric learning paradigm that uses class prototypes generated by a pretrained language model to better capture inter-class relationships and enhance intra-class compactness. These prototypes are extracted and stored offline, requiring no additional inference from the language model during training or deployment. This design significantly reduces model complexity, resulting in a model with only 23K parameters-over 100 times fewer than common Transformer-based models. Exten sive experiments demonstrate that SemSTNet outperforms state of-the-art approaches on tasks such as epilepsy classification and sleep staging, highlighting its effectiveness and efficiency. Our work demonstrates that integrating semantic knowledge with a purpose-built lightweight architecture provides a highly effective and efficient solution.},
}
@article {pmid41082173,
year = {2025},
author = {Hodgkiss, DD and Balthazaar, SJT and Gee, CM and Boardley, ID and Janssen, TWJ and Krassioukov, AV and Nightingale, TE},
title = {Electroceuticals for Paralympic Athletes: A Fair Play and Classification Concern?.},
journal = {Sports medicine (Auckland, N.Z.)},
volume = {},
number = {},
pages = {},
pmid = {41082173},
issn = {1179-2035},
support = {NRB123//International Spinal Research Trust/ ; RG2698/21/23//Heart Research UK/ ; SBF009\1126/AMS_/Academy of Medical Sciences/United Kingdom ; },
abstract = {Electroceuticals such as brain computer interfaces and spinal cord stimulation (SCS) represent transformative strategies for neuromodulation. Research has demonstrated that SCS can ameliorate motor and autonomic cardiovascular dysfunctions, particularly in individuals with spinal cord injury (SCI). Notably, SCS has been shown to augment aerobic exercise performance. Owing to the nature of their injury, athletes with SCI are often predisposed to low resting blood pressure and impaired physiological responses to exercise. Therefore, some athletes intentionally induce autonomic dysreflexia ("boosting") to gain a competitive advantage - an act banned by the International Paralympic Committee (IPC). However, the emergence of electroceuticals facilitates an alternative performance enhancement strategy that could be considered unfair without equal access opportunities for all athletes. Currently, the World Anti-Doping Agency and the IPC have not acknowledged the potential impact of electroceuticals in parasport. Herein, we present an argument that the use of SCS meets the criteria for it to be placed on the World Anti-Doping Code Prohibited List (or at the very least be monitored) because collectively: SCS can enhance sport performance, represents a potential health risk to the athlete if misused, and may violate the spirit of sport. Acute and chronic use of SCS may also lead to classification changes, and increased opportunities for athletes to intentionally misrepresent, thereby raising concerns for the IPC. The growing access to electroceuticals (e.g. via clinical trial participation or private healthcare implantation) more than ever increases the likelihood of an athlete using SCS to gain an unfair advantage in parasport.},
}
@article {pmid41082005,
year = {2025},
author = {Kolarijani, NR and Salehi, M and Mirzaii, M and Farahani, MK and Zamani, S and Fazli, M and Alizadeh, M},
title = {Synthesis and characterization of silver nanoparticle-loaded carboxymethylcellulose hydrogels: in vitro and in vivo evaluation of wound healing and antibacterial properties.},
journal = {Cell and tissue banking},
volume = {26},
number = {4},
pages = {46},
pmid = {41082005},
issn = {1573-6814},
mesh = {Animals ; *Silver/pharmacology/chemistry ; *Wound Healing/drug effects ; *Hydrogels/pharmacology/chemistry/chemical synthesis ; *Carboxymethylcellulose Sodium/chemistry/pharmacology ; *Anti-Bacterial Agents/pharmacology/chemistry/chemical synthesis ; *Metal Nanoparticles/chemistry/ultrastructure ; Rats ; Pseudomonas aeruginosa/drug effects ; Staphylococcus aureus/drug effects ; Microbial Sensitivity Tests ; Male ; Hemolysis/drug effects ; },
abstract = {The current research was conducted to assess wound healing activity and antibacterial properties of carboxymethyl cellulose (CMC) hydrogels loaded with silver nanoparticles (AgNPs) against excisional wounds (15 × 15 mm[2]) infected with Pseudomonas aeruginosa and Staphylococcus aureus in a rat model.CMC/AgNPs hydrogels were synthesized using varying concentrations of AgNPs and subsequently lyophilized. A comprehensive range of in vitro tests were conducted, including nanoparticle characterization, scanning electron microscopy (SEM) morphology study, water uptake (WUE) study, blood uptake capacity study (BUC), weight loss study (WLA), pH, hemolysis percentage (HP), blood coagulation index (BCI), antibacterial activity (minimum inhibitory concentration [MIC] and minimum bactericidal concentration [MBC]), and cell viability through the MTT assay. In vivo wound healing studies were conducted using infected excisional wound models in rats. SEM confirmed a porous structure with a mean pore size ranging from 68 to 152 μm. The hydrogels exhibited dosage-dependent swelling and sustained physiological pH (7.4-7.6) for a period of time. The 125 μg/mL AgNPs formulation showed a BUC of 97.68% in 22 h. Hemocompatibility assay showed minimal hemolysis and acceptable coagulation indices for all concentrations of AgNPs. MIC and MBC against both strains of bacteria were found to be 250 μg/mL and 500 μg/mL, respectively. CMC/AgNPs hydrogel with the concentration of 250 μg/mL showed the optimal cell viability and the optimal in vivo wound healing result. The findings indicate that AgNPs-loaded CMC hydrogels possess favorable physicochemical, biocompatible, and antimicrobial properties, suggesting their potential as a wound dressing for managing infected wounds and supporting the wound healing process.},
}
@article {pmid41081225,
year = {2025},
author = {Cao, P and Guo, S and Zhang, G and Zan, X and Wang, J and Zhang, F and Muñoz, J and Lucke-Wold, B and Cheng, R},
title = {Brain-computer interface training for multimodal functional recovery in patients with brain injury: a case series.},
journal = {Quantitative imaging in medicine and surgery},
volume = {15},
number = {10},
pages = {9277-9293},
pmid = {41081225},
issn = {2223-4292},
abstract = {BACKGROUND: Patients with impaired brain function often face sequelae such as limb movement, cognitive, and language impairment, and there are limitations in the efficiency of traditional rehabilitation methods. This study examined whether motor imagery-based brain-computer interface (BCI) training could promote multimodal functional recovery-including limb movement, speech, and cognition-in patients with subacute brain injury. Unlike traditional BCI research focused on single functional domains, we combined multidimensional clinical assessments with multimodal neural analysis to examine cross-network plasticity.<br><br>METHODS: Five patients with subacute brain injury (four males and one female; mean age 54.4&#xb1;10.3 years) underwent 5 weeks of BCI training between 2021 and 2023. Pre- and post-intervention evaluations included the Fugl-Meyer Assessment Scale (FMA), Modified Ashworth Scale (MAS), Western Aphasia Battery (WAB), and Mini-Mental State Examination (MMSE). Neurophysiological metrics included classification accuracy (CA), power spectral density (PSD), and electroencephalography (EEG) topography. Functional connectivity analyses were conducted with functional magnetic resonance imaging (fMRI) and individualized connectomics based on the Human Connectome Project parcellation.<br><br>RESULTS: All five patients showed clinical improvement in motor, cognitive, or language functions. The average motor imagery CA increased by 14.2%. PSD flattening and event-related desynchronization (ERD) were observed in the central motor regions. EEG topographies showed enhanced activation converging toward the sensorimotor cortex. Patient-specific functional connectivity analyses revealed strengthened interactions among sensorimotor, language, and attention networks-most notably in one patient with marked clinical gains. Distinct patterns of connectivity reorganization were observed between patients with cortical and subcortical lesions. A critical 3-week time window for neural plasticity was identified.<br><br>CONCLUSIONS: Motor imagery-based BCI training may facilitate recovery across motor, language, and cognitive domains in patients with subacute brain injury. Functional gains were supported by neurophysiological and connectomics evidence of cross-network reorganization. These preliminary findings suggest that personalized BCI protocols could represent a promising avenue for multimodal neurorehabilitation.},
}
@article {pmid41079666,
year = {2025},
author = {Jia, Q and Xu, Z and Wang, Y and Duan, Y and Liu, Y and Shan, J and Ma, J and Li, Q and Luo, J and Luo, Y and Wang, Y and Duan, S and Yu, Y and Wang, M and Cai, X},
title = {Targeted-Modified MultiTransm Microelectrode Arrays Simultaneously Track Dopamine and Cellular Electrophysiology in Nucleus Accumbens during Sleep-Wake Transitions.},
journal = {Research (Washington, D.C.)},
volume = {8},
number = {},
pages = {0944},
pmid = {41079666},
issn = {2639-5274},
abstract = {Cellular-level electrophysiological and neurotransmitter signals serve as key biomarkers of sleep depth, offering insights into the dynamic sleep transitions and the neural mechanisms underlying sleep regulation. Microelectrode arrays (MEAs) provide an innovative solution for in situ, simultaneous detection of these signals with high spatial and temporal resolution. However, despite substantial progress in electrode material development, current multimodal MEA systems remain fundamentally constrained by partial integration. This study aims to address the performance limitations of multimodal MEAs by developing a MultiTransm MEA (MT MEA), integrating a 3-electrode system with site-specific surface modifications: platinum nanoparticle (PtNP)/poly(3,4-ethylene dioxythiophene):poly(styrene sulfonate) (PEDOT:PSS)-modified sites for electrophysiology, PtNP/PEDOT:PSS/Nafion-modified sites for dopamine sensing, and iridium oxide (IrOx)-based on-probe reference electrodes. The directional surface modification strategy was employed to enable compact integration, minimize inter-channel crosstalk, preserve high spatiotemporal resolution for both electrophysiological and electrochemical detection, and ensure long-term operational stability. By incorporating electroencephalography (EEG) and electromyography (EMG), MT MEAs enable real-time in vivo monitoring of sleep dynamics within the nucleus accumbens. Three distinct spike types were identified, whose coordinated activity shaped the sleep architecture. In addition, EEG and local field potential (LFP) signals exhibited distinct patterns during wakefulness, indicating region-specific neural processing. Notably, dopamine release was lowest during non-rapid eye movement (NREM) sleep and peaked during wakefulness, suggesting a neuromodulatory role in sleep-wake transitions. These results demonstrate that MT MEAs are powerful tools for probing neural and neurochemical activity across sleep states, offering new insights into the physiological regulation of sleep.},
}
@article {pmid41079401,
year = {2025},
author = {Esteves, D and Vagaja, K and Andrade, A and Vourvopoulos, A},
title = {When embodiment matters most: a confirmatory study on VR priming in motor imagery brain-computer interfaces training.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1681538},
pmid = {41079401},
issn = {1662-5161},
abstract = {BACKGROUND: Virtual Reality (VR) feedback is increasingly integrated into Brain-Computer Interface (BCI) applications, enhancing the Sense of Embodiment (SoE) toward virtual avatars and fostering more vivid motor imagery (MI). VR-based MI-BCIs hold promise for motor rehabilitation, but their effectiveness depends on neurofeedback quality. Although SoE may enhance MI training, its role as a priming strategy prior to VR-BCI has not been systematically examined, as prior work assessed embodiment only after interaction. This study investigates whether embodiment priming influences MI-BCI outcomes, focusing on event-related desynchronization (ERD) and BCI performance.<br><br>METHODS: Using a within-subject design, we combined data from a pilot study with an extended experiment, yielding 39 participants. Each completed an embodiment induction phase followed by MI training with EEG recordings. ERD and lateralization indices were analyzed across conditions to test the effect of prior embodiment.<br><br>RESULTS: Embodiment induction reliably increased SoE, yet no significant ERD differences were found between embodied and control conditions. However, lateralization indices showed greater variability in the embodied condition, suggesting individual differences in integrating embodied feedback.<br><br>CONCLUSION: Overall, findings indicate that real-time VR-based feedback during training, rather than prior embodiment, is the main driver of MI-BCI performance improvements. These results corroborate earlier findings that real-time rendering of embodied feedback during MI-BCI training constitutes the primary mechanism supporting performance gains, while highlighting the complex role of embodiment in VR-based MI-BCIs.},
}
@article {pmid41079152,
year = {2025},
author = {Bassil, K and Jongsma, K},
title = {To Explant or not to Explant Neural Implants: an Empirical Study into Deliberations of Dutch Research Ethics Committees.},
journal = {Neuroethics},
volume = {18},
number = {3},
pages = {45},
pmid = {41079152},
issn = {1874-5490},
abstract = {UNLABELLED: Neural implants such as brain-computer interfaces and spinal cord stimulation offer therapeutic prospects for people with neurological and psychiatric disorders. As neural devices are increasingly tested in clinical research, the decision to explant requires carefully weighing both known and unknown medical and psychological risks, necessitating a thorough evaluation of the benefits and risks of each available option. Research Ethics Committees (RECs) play an important role in assessing research protocols and determining the conditions under which neural implants should be explanted, yet little is understood about how RECs make these decisions. To better understand the role of RECs in explantation decisions of neural implants, we approached REC secretaries within the Netherlands via email, with a list of open-ended questions of which the explantation of neural devices, on informed consent and post-trial care and responsibilities, and psychological harm associated with such trials. The findings highlight the differential technology-specific safety assessments conducted for different types of neural devices. Variability was observed in plans regarding clinical follow-up, post-trial access, and explantation options. While RECs emphasized clear participant information on device maintenance and longevity, the timing of this disclosure varied. Additionally, the psychological impact of explantation was rarely addressed in REC assessments, indicating a gap in ethical oversight. These results shed light on some remaining gaps and suggest the need for improvement in achieving more consistent and comprehensive evaluations of neural device clinical trials, particularly regarding explantation and post-trial access.<br><br>SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12152-025-09619-z.},
}
@article {pmid41076093,
year = {2025},
author = {Althobaiti, M},
title = {Sensitivity analysis of the balloon model parameters in functional near-infrared spectroscopy simulation.},
journal = {Journal of neuroscience methods},
volume = {424},
number = {},
pages = {110599},
doi = {10.1016/j.jneumeth.2025.110599},
pmid = {41076093},
issn = {1872-678X},
mesh = {Spectroscopy, Near-Infrared/methods ; Humans ; *Computer Simulation ; Hemodynamics/physiology ; *Brain/physiology/blood supply ; *Models, Neurological ; Artifacts ; Signal Processing, Computer-Assisted ; Cerebrovascular Circulation/physiology ; },
abstract = {BACKGROUND: Accurate modeling of the hemodynamic response is critical for fNIRS data interpretation. While the Balloon model is a cornerstone for this, the quantitative impact of its key parameters on the fNIRS signal, particularly in the presence of realistic artifacts, remains under-characterized.<br><br>NEW METHOD: We developed an end-to-end fNIRS simulation pipeline. It incorporates a neural activity model, the Balloon model for hemodynamics, convolution for signal generation, and realistic motion, cardiac, and respiratory artifacts. We performed a sensitivity analysis by systematically varying Grubb's exponent (&#x3b1;) and transit time (&#x3c4;).<br><br>RESULTS: Both &#x3b1; and &#x3c4; significantly influence the simulated fNIRS response. &#x3b1; shows a non-linear relationship with peak amplitude, while &#x3c4; has a more linear effect on signal timing. Regression models quantifying these effects demonstrated a strong statistical fit (p&#x202f;<&#x202f;0.05, R&#xb2; > 0.9 for &#x3b1;).<br><br>Unlike prior fMRI-focused studies, this is the first quantitative sensitivity analysis specifically for fNIRS signals that incorporates a realistic noise model. Our framework characterizes the forward model's behavior, providing parameter-specific insights not previously available for fNIRS simulations.<br><br>CONCLUSIONS: The fNIRS hemodynamic response is highly sensitive to the Balloon model's &#x3b1; and &#x3c4; parameters. These findings highlight the importance of accounting for physiological variability in fNIRS analysis and provide a robust framework for generating synthetic data to test signal processing algorithms.},
}
@article {pmid41074421,
year = {2025},
author = {Hui, Z and Zhang, Y and Su, Y and Kang, J and Qi, W and Li, S and Zhang, J and Shi, K and Wang, M and Yang, Y and Zhang, G and Yang, L and Chen, G and Li, S and Hu, Y and Zhu, D},
title = {Abnormal Brain Connectivity Patterns in Children with Global Developmental Delay Accompanied by Cognitive Impairment: A Resting-State EEG Study.},
journal = {Journal of integrative neuroscience},
volume = {24},
number = {9},
pages = {44410},
doi = {10.31083/JIN44410},
pmid = {41074421},
issn = {0219-6352},
support = {NHCKLBDP202508//Open Research Program of the NHC Key Laboratory of Birth Defects Prevention/ ; SBGJ202402069//Key Project of Medical Science and Technology Tackling Plan of Henan Province 2024/ ; },
mesh = {Humans ; Male ; Female ; Child ; Electroencephalography ; *Cognitive Dysfunction/physiopathology/etiology ; *Developmental Disabilities/physiopathology/complications ; *Nerve Net/physiopathology ; *Brain Waves/physiology ; *Connectome ; Support Vector Machine ; Child, Preschool ; },
abstract = {BACKGROUND: Global developmental delay (GDD) is a common childhood neurodevelopmental disorder characterized by the core symptoms of cognitive impairment. However, the underlying neural mechanisms of the cognitive impairment remain unclear. This study aimed to both analyze differences in electroencephalography (EEG) connectivity patterns between children with GDD and typical development (TD) using brain functional connectivity and to explore the neural mechanisms linking these differences to cognitive impairment.<br><br>METHODS: The study enrolled 60 children with GDD and 60 TD children. GDD participants underwent clinical assessment via the Gesell Developmental Schedule (GDS). Resting-state EEG data were subjected to brain functional connectivity analysis and graph theory metric-based network analysis, with intergroup functional differences compared. Subsequently, correlation analysis characterized the relationships between GDD subject's brain network metrics and GDS-derived cognitive developmental quotient (DQ). Finally, three support vector machine (SVM) models were constructed for GDD classification and feature weight factors were calculated to screen potential EEG biomarkers.<br><br>RESULTS: The two groups exhibited complex differences in functional connectivity. Compared with the TD group, the GDD group showed a large number of increased functional connections in the &#x3b8;, &#x3b1;, and &#x3b3;-bands, along with a small number of decreased functional connections in the &#x3b1; and &#x3b3;-bands (all p < 0.025). Brain network analysis revealed lower global efficiency, local efficiency, clustering coefficient and small-world coefficient, as well as higher characteristic path length in GDD children across multiple bands (all p < 0.05). Correlation analysis indicated that global efficiency and small-world coefficient in &#x3b8; and &#x3b3;-bands were positively correlated with the DQ, while the characteristic path length in &#x3b1; and &#x3b3;-bands was negatively correlated with DQ in the GDD group (all p < 0.05). Machine learning models showed that a quantum particle swarm optimization SVM (QPSO-SVM) achieved the highest classification performance, with characteristic path length in the &#x3b3;-band being the highest weighted metric.<br><br>CONCLUSIONS: Children with GDD exhibit abnormal patterns of brain functional connectivity, characterized by global hypo-connectivity and local hyper-connectivity. Specific network metrics under these abnormal patterns are significantly correlated with cognitive impairment in GDD. This study also highlights the potential of the &#x3b3;-band characteristic path length as an EEG biomarker for diagnosing GDD.},
}
@article {pmid41073181,
year = {2025},
author = {Rudroff, T},
title = {Retraction notice to "Decoding thoughts, encoding ethics: A narrative review of the BCI-AI revolution" [Brain Res. 1850 (2025) 149423].},
journal = {Brain research},
volume = {1868},
number = {},
pages = {149969},
doi = {10.1016/j.brainres.2025.149969},
pmid = {41073181},
issn = {1872-6240},
}
@article {pmid41073040,
year = {2025},
author = {Wu, YJ and He, Q and Luo, FG and Li, T and Guo, WJ},
title = {Respiratory Dyskinesia With Refractory Tachypnea and Alkalosis Treated by Vesicular Monoamine Transporter 2 Inhibitor.},
journal = {Chest},
volume = {168},
number = {4},
pages = {e111-e113},
pmid = {41073040},
issn = {1931-3543},
mesh = {Humans ; Female ; Aged ; *Vesicular Monoamine Transport Proteins/antagonists &amp; inhibitors ; *Tachypnea/drug therapy/diagnosis/etiology ; *Alkalosis/drug therapy/diagnosis ; *Respiration Disorders/drug therapy/diagnosis ; Antipsychotic Agents/adverse effects ; Risperidone/adverse effects/therapeutic use ; Psychotic Disorders/drug therapy ; },
abstract = {We present the case of a 69-year-old woman with a 25-year history of psychosis, managed with risperidone, who developed refractory tachypnea and alkalosis over 2 weeks. Despite multidisciplinary evaluation, she was initially misdiagnosed with psychogenic hyperventilation. Ultimately, a diagnosis of respiratory dyskinesia (RD) was established, and substantial clinical improvement was achieved after initiation of a vesicular monoamine transporter 2 (VMAT2) inhibitor. The substantial effectiveness of this therapy was confirmed over a 7-month follow-up period, with monitoring of both clinical symptoms and arterial blood gas parameters. This case highlights the diagnostic challenges posed by RD and underscores the potential utility of VMAT2 inhibitor as a novel therapeutic option.},
}
@article {pmid41072470,
year = {2025},
author = {Hecker, D and Pillong, L and Reuss, K and Friedrich, KH and Alexandersson, J and Rekrut, M and Linxweiler, M and Bozzato, A and Schick, B and Metzler, P},
title = {[Novel analysis method to determine the neural activation function of the inner hair cell].},
journal = {Laryngo- rhino- otologie},
volume = {},
number = {},
pages = {},
doi = {10.1055/a-2681-5401},
pmid = {41072470},
issn = {1438-8685},
abstract = {Sensorineural hearing loss (SNH) is one of the most common forms of hearing loss. A special form of SNH is hidden hearing loss (HHL) with subjective normal hearing. Current research results indicate that these patients demonstrate a reduced wave I in the averaged signal of brainstem audiometry (ABR). Since the averaging technique is not susceptible to latency jitter and amplitude height variation, a single sweep analysis is required for a deeper insight in HHL.A total of 14 mice with significantly different calcium currents in the IHC at normal hearing thresholds were analysed. For the analysis in order to calculate four new parameters from the single sweeps in the time window of wave I. These parameters also served to describe a neural activation function (NAV).Looking at the wild type all new parameters differ significantly or highly significantly. With the transgenic mouse, there are only non-significant to significant differences. There is also a significant difference in the neural activity demonstrated in the resting EEG between the wild-type mouse and the mutant. There is a negative correlation between the wave amplitudes for the wild mouse - after a strong amplitude follows a weak amplitude and after weak amplitude follows a strong amplitude.Using new parameters based on single sweeps, surprising results are obtained. Obviously the function of the IHC correlates more strongly with the new parameters than it does with the average amplitude of wave I. The new parameters appear to be excellently suited for the diagnosis of hearing disorders even when hearing thresholds are still according to norm values.},
}
@article {pmid41072287,
year = {2025},
author = {Cao, X and Gong, P and Zhang, L and Zhang, D},
title = {EEG-CLIP: A transformer-based framework for EEG-guided image generation.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {194},
number = {},
pages = {108167},
doi = {10.1016/j.neunet.2025.108167},
pmid = {41072287},
issn = {1879-2782},
abstract = {Decoding visual perception from neural signals represents a fundamental step toward advanced brain-computer interfaces (BCIs), where functional magnetic resonance imaging (fMRI) has shown promising results despite practical constraints in deployment and costs. Electroencephalography (EEG), with its superior temporal resolution, portability, and cost-effectiveness, emerges as a promising alternative for real-time brain-computer interface (BCI) applications. While existing EEG-based approaches have advanced neural decoding capabilities, they remain constrained by inadequate architectural designs, limited reconstruction fidelity, and inconsistent evaluation protocols. To address these challenges, we present EEG-CLIP, a novel Transformer-based framework that systematically addresses each limitation: (1) We introduce a specialized EEG-ViT encoder that adeptly captures the spatial and temporal characteristics of EEG signals to augment model capacity, along with a Diffusion Prior Transformer architecture to approximate the image feature distribution. (2) We employ a dual-stage reconstruction pipeline that integrates class contrastive learning and pretrained diffusion models to enhance visual reconstruction quality. (3) We establish comprehensive evaluation protocols across multiple datasets. Our framework operates through two stages: first projecting EEG signals into CLIP image space via class contrastive learning and refining them into image priors, then reconstructing perceived images through a pretrained conditional diffusion model. Comprehensive empirical analysis, including temporal window sensitivity studies and regional brain activation visualization, demonstrates the framework's robustness. We demonstrate through ablations that EEG-CLIP's performance improvements over previous methods result from specialized architecture for EEG encoding and improved training techniques. Quantitative and qualitative evaluations on ThingsEEG and Brain2Image datasets establish EEG-CLIP's state-of-the-art performance in both classification and reconstruction tasks, advancing neural signal-based visual decoding capabilities.},
}
@article {pmid41072285,
year = {2025},
author = {Wu, J and Tang, B and Wang, Y and Li, C and Yang, Q},
title = {A multi-level teacher assistant-based knowledge distillation framework with dynamic feedback for motor imagery EEG decoding.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {194},
number = {},
pages = {108180},
doi = {10.1016/j.neunet.2025.108180},
pmid = {41072285},
issn = {1879-2782},
abstract = {Deep learning has shown promise in motor imagery-based electroencephalogram (MI-EEG) decoding, a critical task in non-invasive brain-computer interfaces (BCIs). In response to the computational complexity of deep learning models to be deployed in practical BCI applications, knowledge distillation (KD) has emerged as a solution for model compression. However, vanilla KD methods struggle to effectively extract and transfer the abundant multi-level knowledge from MI-EEG signals under high compression ratios. This study proposes a novel knowledge distillation framework termed Motor Imagery Knowledge Distillation (MIKD), which compresses deep learning models for MI classification tasks while maintaining high performance. The MIKD framework consists of two key modules: (1) a multi-level teacher assistant knowledge distillation (ML-TAKD) module designed to extract and transfer local representations and global dependencies of MI-EEG signals from the complex teacher network to the much smaller student network, and (2) a dynamic feedback module that allows the teacher assistant to adjust its teaching strategy based on the student's learning progress. Extensive experiments on three public EEG datasets demonstrate that the MIKD framework achieves state-of-the-art performance. The proposed framework improves the baseline student model's accuracy by 6.61 %, 1.91 %, and 3.29 % on the three datasets, while reducing the model size by nearly 90 %.},
}
@article {pmid41072048,
year = {2025},
author = {Li, C and Di, G and Xiong, Z and Sun, L and Li, Q and Li, H and Jiang, X and Wu, J},
title = {Three-dimensional microsurgical anatomy of the basal aspect of the cerebrum: a fiber dissection study.},
journal = {Journal of neurosurgery},
volume = {},
number = {},
pages = {1-13},
doi = {10.3171/2025.5.JNS242560},
pmid = {41072048},
issn = {1933-0693},
abstract = {OBJECTIVE: Due to the unique nature of the basal structures of the cerebrum, only a limited portion is exposed during surgery, leading to potential risk of damage to surrounding structures. The white matter fiber tracts in the basal cerebrum may be more critical than the cortex in determining the extent of resection. A thorough understanding of the 3D anatomy of these fiber tracts is essential for planning safe and precise surgical approaches and provides an anatomical foundation for studying brain function. This study aimed to examine the topographical anatomy of the fiber tracts and subcortical gray matter in the basal cerebrum, as well as their anatomical relationships with the cerebral cortex, ventricles, and associated nuclei.<br><br>METHODS: Using fiber dissection techniques and magnification ranging from &#xd7;6 to &#xd7;40, the authors studied 10 formalin-fixed human brains. The study focused on the fiber tracts and subcortical nuclei in the basal cerebrum, including the hippocampus, amygdala, and nucleus accumbens, and their relationships were documented through 3D photography.<br><br>RESULTS: The topographical relationships between the commissural, projection, and association fibers and the significant nuclei in the basal cerebrum were identified. Notable landmarks related to the fiber tracts include the cortical gyri and sulci, major basal nuclei, and lateral ventricles. The fiber tracts also exhibited consistent interrelationships.<br><br>CONCLUSIONS: The 3D microsurgical anatomy of the basal cerebrum provides valuable insights for planning precise and safe surgical approaches and offers anatomical evidence for further studies on brain function.},
}
@article {pmid41070190,
year = {2025},
author = {Li, Y and Zhu, L and Huang, A and Zhang, J and Yuan, P},
title = {Multimodal MBC-ATT: cross-modality attentional fusion of EEG-fNIRS for cognitive state decoding.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1660532},
pmid = {41070190},
issn = {1662-5161},
abstract = {With the rapid development of brain-computer interface (BCI) technology, the effective integration of multimodal biological signals to improve classification accuracy has become a research hotspot. However, existing methods often fail to fully exploit cross-modality correlations in complex cognitive tasks. To address this, this paper proposes a Multi-Branch Convolutional Neural Network with Attention (MBC-ATT) for BCI based cognitive tasks classification. MBC-ATT employs independent branch structures to process electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals separately, thereby leveraging the advantages of each modality. To further enhance the fusion of multimodal features, we introduce a cross-modal attention mechanism to discriminate features, strengthening the model's ability to focus on relevant signals and thereby improving classification accuracy. We conducted experiments on the n-back and WG datasets. The results demonstrate that the proposed model outperforms conventional approaches in classification performance, further validating the effectiveness of MBC-ATT in brain-computer interfaces. This study not only provides novel insights for multimodal BCI systems but also holds great potential for various applications.},
}
@article {pmid41066375,
year = {2025},
author = {, },
title = {Correction: Filter bank common spatial pattern and envelope-based features in multimodal EEG-fTCD brain-computer interfaces.},
journal = {PloS one},
volume = {20},
number = {10},
pages = {e0334075},
pmid = {41066375},
issn = {1932-6203},
abstract = {[This corrects the article DOI: 10.1371/journal.pone.0311075.].},
}
@article {pmid41065125,
year = {2025},
author = {Schnitzer, SA and DeFilippis, DM},
title = {Does increasing canopy liana density decrease the tropical forest carbon sink?.},
journal = {Ecology},
volume = {106},
number = {10},
pages = {e70196},
doi = {10.1002/ecy.70196},
pmid = {41065125},
issn = {1939-9170},
support = {DEB 06-13666//National Science Foundation/ ; DEB 20-01799//National Science Foundation/ ; IOS 15-58093//National Science Foundation/ ; },
mesh = {*Tropical Climate ; *Forests ; *Carbon Sequestration/physiology ; Panama ; *Plants/classification ; *Trees/physiology ; *Carbon/metabolism ; Time Factors ; },
abstract = {The ongoing decline in the American tropical forest carbon sink has serious ramifications for atmospheric carbon levels and global climate change. Increasing liana abundance may explain the decaying carbon sink because lianas reduce canopy tree growth and survival, which limits forest carbon storage. However, canopy lianas, not solely understory lianas, would have to be increasing for this hypothesis to be credible because canopy lianas compete especially intensely with canopy trees. We examined the change in canopy lianas over 10 years on Barro Colorado Island (BCI), Panama to test two main hypotheses. (1) Canopy lianas are increasing on BCI. (2) Increasing canopy lianas decrease aboveground canopy tree and forest carbon storage. We found that canopy liana density increased 8.3% over the 10-year period, and canopy lianas outnumbered canopy trees 3.59-1. There was a clear negative relationship between increasing canopy liana density and decreasing canopy tree carbon storage. Where liana density increased, tree carbon decreased, and where canopy lianas decreased, canopy tree carbon increased. Our findings indicate that lianas are the numerically dominant and diverse woody plant group in the BCI canopy, and this dominance is increasing, reducing forest-level carbon storage and possibly explaining the decaying American tropical forest carbon sink.},
}
@article {pmid41064793,
year = {2025},
author = {Gong, J and Zhao, Z and Niu, X and Ji, Y and Sun, H and Shen, Y and Chen, B and Wu, B},
title = {AI reshaping life sciences: intelligent transformation, application challenges, and future convergence in neuroscience, biology, and medicine.},
journal = {Frontiers in digital health},
volume = {7},
number = {},
pages = {1666415},
pmid = {41064793},
issn = {2673-253X},
abstract = {The rapid advancement of artificial intelligence (AI) is profoundly transforming research paradigms and clinical practices across neuroscience, biology, and medicine with unprecedented depth and breadth. Leveraging its robust data-processing capabilities, precise pattern recognition techniques, and efficient real-time decision support, AI has catalyzed a paradigm shift toward intelligent, precision-oriented approaches in scientific research and healthcare. This review comprehensively reviews core AI applications within these domains. Within neuroscience, AI advances encompass brain-computer interface (BCI) development/optimization, intelligent analysis of neuroimaging data (e.g., fMRI, EEG), and early prediction/precise diagnosis of neurological disorders. In biological research, AI applications include enhanced gene-editing efficiency (e.g., CRISPR) with off-target effect prediction, genomic big-data interpretation, drug discovery/design (e.g., virtual screening), high-accuracy protein structure prediction (exemplified by AlphaFold), biodiversity monitoring, and ecological conservation strategy optimization. For medical research, AI empowers auxiliary medical image diagnosis (e.g., CT, MRI), pathological analysis, personalized treatment planning, health risk prediction with lifespan health management, and robot-assisted minimally invasive surgery (e.g., da Vinci Surgical System). This review not only synthesizes AI's pivotal role in enhancing research efficiency and overcoming limitations of conventional methodologies, but also critically examines persistent challenges, including data access barriers, algorithmic non-transparency, ethical governance gaps, and talent shortages. Building upon this analysis, we propose a tripartite framework ("Technology-Ethics-Talent") to advance intelligent transformation in scientific and medical domains. Through coordinated implementation, AI will catalyze a transition toward efficient, accessible, and sustainable healthcare, ultimately establishing a life-cycle preservation paradigm encompassing curative gene editing, proactive health management, and ecologically intelligent governance.},
}
@article {pmid41064747,
year = {2025},
author = {Yue, J and Xiao, X and Wang, K and Yi, W and Jung, TP and Xu, M and Ming, D},
title = {Augmenting Electroencephalogram Transformer for Steady-State Visually Evoked Potential-Based Brain-Computer Interfaces.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {6},
number = {},
pages = {0379},
pmid = {41064747},
issn = {2692-7632},
abstract = {Objective: Advancing high-speed steady-state visually evoked potential (SSVEP)-based brain-computer interface (BCI) systems requires effective electroencephalogram (EEG) decoding through deep learning. However, challenges persist due to data sparsity and the unclear neural basis of most augmentation techniques. Furthermore, effective processing of dynamic EEG signals and accommodating augmented data require a more sophisticated model tailored to the unique characteristics of EEG signals. Approach: This study introduces background EEG mixing (BGMix), a novel data augmentation technique grounded in neural principles that enhances training samples by replacing background noise between different classes. Building on this, we propose the augment EEG Transformer (AETF), a Transformer-based model designed to capture the temporal, spatial, and frequential features of EEG signals, leveraging the advantages of Transformer architectures. Main results: Experimental evaluations of 2 publicly available SSVEP datasets show the efficacy of the BGMix strategy and the AETF model. The BGMix approach notably improved the average classification accuracy of 4 distinct deep learning models, with increases ranging from 11.06% to 21.39% and 4.81% to 25.17% in the respective datasets. Furthermore, the AETF model outperformed state-of-the-art baseline models, excelling with short training data lengths and achieving the highest information transfer rates (ITRs) of 205.82 ± 15.81 bits/min and 240.03 ± 14.91 bits/min on the 2 datasets. Significance: This study introduces a novel EEG augmentation method and a new approach to designing deep learning models informed by the neural processes of EEG. These innovations significantly improve the performance and practicality of high-speed SSVEP-based BCI systems.},
}
@article {pmid41062739,
year = {2025},
author = {Abinaya, G and Dinakaran, K},
title = {ACXNet hybrid deep learning model for cross task mental workload estimation using EEG neural manifolds.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {35178},
pmid = {41062739},
issn = {2045-2322},
mesh = {Humans ; *Electroencephalography/methods ; *Workload/psychology ; *Deep Learning ; Male ; *Cognition/physiology ; Task Performance and Analysis ; Adult ; Female ; Brain/physiology ; Neural Networks, Computer ; Young Adult ; Attention/physiology ; },
abstract = {Mental workload is an interdisciplinary construct that significantly influences human performance, particularly in tasks requiring sustained attention and cognitive processing. Effective mental workload assessment is critical for preventing cognitive overload, which can lead to errors and reduced efficiency in high-stakes environments. The approach leverages topographic neural manifolds (spatial electrode arrangements) and temporal neural manifolds (time-series patterns) to capture comprehensive brain activity representations.Traditional methods rely on subjective reports or task performance, but physiological signals like EEG provide a more objective and continuous means of monitoring cognitive states. Therefore, this paper proposes a hybrid novel approach ACXNet which integrates autoencoder, CNN and XGBoost to learn features of EEG from an individual cross task performance without prior subject-specific calibration or task specific pre-labeled .training data. Utilizing the STEW (Simultaneous Task EEG Workload) dataset, containing recordings from 48 participants experiencing different levels of cognitive demands. Unsupervised feature extraction was carried out using an autoencoder. Subsequently, a CNN was employed to capture the spatial-temporal dependencies in the data, and XGBoost was utilized for efficient mental workload classification. This research adopts a binary classification approach to differentiate between low and high mental workload during SIMKAP and No task. The ACXNet model proposed in this study outperforms the existing methods with an average accuracy of 92.10% for SIMKAP task and 89.94% for No task condition. These findings show that ACXNet significantly improves the robustness and precision of mental workload estimation, providing a scalable solution adaptable to real-world applications, opening new avenues for the development of intelligent systems in human-computer interaction, healthcare, and beyond.},
}
@article {pmid41061192,
year = {2025},
author = {Bushnell, BD and Jarvis, BT and Jarvis, RC and Piller, CP and Baudier, RS},
title = {Minimal Stiffness After Rotator Cuff Repair With Bioinductive Collagen Implants.},
journal = {Journal of the American Academy of Orthopaedic Surgeons. Global research &amp; reviews},
volume = {9},
number = {10},
pages = {},
pmid = {41061192},
issn = {2474-7661},
support = {N/A//Smith and Nephew/ ; },
mesh = {Humans ; Retrospective Studies ; *Rotator Cuff Injuries/surgery ; *Collagen ; Male ; Female ; Middle Aged ; Aged ; Range of Motion, Articular ; *Postoperative Complications/epidemiology/etiology ; *Prostheses and Implants ; Adult ; Rotator Cuff/surgery ; Reoperation/statistics &amp; numerical data ; },
abstract = {BACKGROUND: Bioinductive collagen implants (BCIs) have been growing in popularity for use in rotator cuff repair (RCR) over the past several years, but recent literature has raised concerns about the implants contributing to postoperative stiffness. The purpose of this study was to investigate the incidence of stiffness over a decade of experience with the BCI.<br><br>METHODS: A retrospective review was conducted of all cases of RCR using a BCI performed between September 2014 and December 2023. The primary outcome measure was postoperative range of motion, with significant stiffness defined by parameters in the existing literature. The secondary outcome measure was any revision procedure for stiffness.<br><br>RESULTS: After application of inclusion and exclusion criteria to 522 cases of RCR, there were 432 cases (390 individual patients) available for outcome analysis with an average follow-up of 34.9 months (range, 6 months to 9.25 years). There were only 12 cases (2.8%) of significant postoperative stiffness. All of them required additional operative intervention for stiffness, and all but two patients had at least one risk factor for stiffness. Stiffness rates were 4 of 291 (1.4%) for full-thickness tears and 8 of 141 (5.7%) for partial-thickness tears (P = 0.0149).<br><br>CONCLUSION: This study, the largest single cohort to date analyzing BCIs in RCR, found a low incidence of significant postoperative stiffness in cases associated with the use of the implant. Stiffness rates were markedly higher for repairs of partial-thickness tears. To further improve understanding of postoperative stiffness after RCR with BCI, better definitions and prospective comparative studies across larger groups are needed.<br><br>LEVEL OF EVIDENCE: Level IV, retrospective cohort with no comparison group.},
}
@article {pmid41061070,
year = {2025},
author = {Huang, K and Fu, P and Zhu, H and Feng, J and Zhang, L and Wang, B and Lu, Y and Zhang, D and Yao, M and Chen, L and Ying, Y and Chen, J and Li, X and Wu, Y and Xiong, W and Li, J and Wu, Y and Sun, J and Zhang, H and Lin, L},
title = {High-speed photoacoustic and ultrasonic computed tomography of the breast tumor for early diagnosis with enhanced accuracy.},
journal = {Science advances},
volume = {11},
number = {41},
pages = {eadz2046},
pmid = {41061070},
issn = {2375-2548},
mesh = {Humans ; *Breast Neoplasms/diagnostic imaging/diagnosis ; Female ; *Photoacoustic Techniques/methods ; *Tomography, X-Ray Computed/methods ; *Early Detection of Cancer/methods ; Middle Aged ; Adult ; Aged ; Ultrasonography, Mammary/methods ; },
abstract = {We have developed a high-speed dual-modal imaging system (HDMI), designed to concurrently reveal anatomical and hematogenous details of the human breast within seconds. Through innovative system design and technical advancements, HDMI integrates large-view photoacoustic and ultrasonic computed tomography with standardized scanning and batch data processing for computer-aided diagnosis. It achieves dual-modal imaging at a 10-hertz frame rate and completes a whole-breast scan in 12 seconds, providing penetration up to 5 centimeters in vivo. In a clinical study involving 170 patients with 186 breast tumors, we developed a diagnostic model leveraging combined photoacoustic and ultrasound features. In a triple-blinded comparison using pathological diagnosis as the ground truth, HDMI significantly improved diagnostic specificity from 22.5 to 75.0% compared to clinical ultrasonography. This technology shows strong potential for early breast tumor diagnosis, offering enhanced accuracy without the need for ionizing radiation, exogenous contrast agents, pain, invasiveness, operator dependence, or extended examination times.},
}
@article {pmid41060851,
year = {2025},
author = {Nguyen, MD and Do, T and Tran, XT and Nguyen, QT and Lin, CT},
title = {Edge AI-Brain-Computer Interfaces System: A Survey.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {4051-4066},
doi = {10.1109/TNSRE.2025.3618688},
pmid = {41060851},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography ; *Artificial Intelligence ; Signal Processing, Computer-Assisted ; Deep Learning ; Algorithms ; Surveys and Questionnaires ; Software ; Equipment Design ; },
abstract = {Edge artificial intelligence (Edge AI) has emerged as a transformative paradigm for enhancing the performance, portability, and autonomy of brain-computer interface (BCI) systems. By integrating advanced AI capabilities directly into electroencephalography (EEG)-based devices, Edge AI enables real-time signal processing, reduces dependence on external computational resources, and improves data privacy. However, deploying AI on resource-constrained hardware introduces challenges related to computational capacity, power consumption, and system latency. This survey provides a comprehensive examination of Edge AI-enabled BCI systems, covering the full pipeline from EEG hardware specifications and on-device data acquisition to signal preprocessing techniques and lightweight deep learning models optimized for embedded platforms. We review existing frameworks, specialized hardware accelerators, and energy-efficient AI approaches that facilitate real-time BCI processing at the edge. Furthermore, the paper reviews state-of-the-art solutions, examines key technical challenges, and outlines future research directions in hardware-software co-design and application development. This work aims to serve as a reference for researchers and practitioners seeking to design efficient, portable, and practical Edge AI-powered BCI systems.},
}
@article {pmid41060788,
year = {2025},
author = {Rosenthal, IA and Bashford, L and Bjanes, D and Pejsa, K and Lee, B and Liu, C and Andersen, RA},
title = {Visual context affects the perceived timing of tactile sensations elicited through intra-cortical microstimulation: a case study of two participants.},
journal = {Journal of neurophysiology},
volume = {},
number = {},
pages = {},
doi = {10.1152/jn.00518.2024},
pmid = {41060788},
issn = {1522-1598},
support = {N/A//T&amp;C Chen Brain-Interface Center/ ; N/A//James G. Boswell Foundation (Boswell Foundation)/ ; U01NS123127//HHS | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; T32 NS105595/NS/NINDS NIH HHS/United States ; },
abstract = {Intra-cortical microstimulation (ICMS) is a technique to provide tactile sensations for a somatosensory brain-machine interface (BMI). A viable BMI must function within the rich, multisensory environment of the real world, but how ICMS is integrated with other sensory modalities is poorly understood. To investigate how ICMS percepts are integrated with visual information, ICMS and visual stimuli were delivered at varying times relative to one another. Both visual context and ICMS current amplitude were found to bias the qualitative experience of ICMS. In two tetraplegic participants, ICMS and visual stimuli were more likely to be experienced as occurring simultaneously in a realistic visual condition compared to an abstract one, demonstrating an effect of visual context on the temporal binding window. The peak of the temporal binding window varied but was consistently offset from zero, suggesting that multisensory integration with ICMS can suffer from temporal misalignment. Recordings from primary somatosensory cortex (S1) during catch trials where visual stimuli were delivered without ICMS demonstrated that S1 represents visual information related to ICMS across visual contexts. This study was a part of a clinical trial (NCT01964261).},
}
@article {pmid41060749,
year = {2025},
author = {Ji, J and Luo, H and Su, J and Wang, S and Chen, X and Song, J},
title = {Multisensory electronic skin with decoupled pressure-temperature-sensing capabilities for similar object recognition.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {122},
number = {41},
pages = {e2519693122},
pmid = {41060749},
issn = {1091-6490},
support = {2022YFC2401901//MOST | National Key Research and Development Program of China (NKPs)/ ; 12225209//MOST | National Natural Science Foundation of China (NSFC)/ ; 12321002//MOST | National Natural Science Foundation of China (NSFC)/ ; U21A20502//MOST | National Natural Science Foundation of China (NSFC)/ ; Smart Grippers for Soft Robotics (SGSR) Programme under the National Research Foundation Prime Min//Prime Minister's Office Singapore (PMO)/ ; },
mesh = {Humans ; Pressure ; Touch/physiology ; Temperature ; Robotics ; Skin ; *Touch Perception/physiology ; *Wearable Electronic Devices ; *Thermosensing/physiology ; },
abstract = {Multisensory electronic skin (e-skin), which mimics the tactile capabilities of human skin, is pivotal in equipping robots with intelligent perceptual functions. Despite numerous advances in multifunctional perceptions, e-skin with combined mechano- and thermosensation capabilities for accurately recognizing objects with similar characteristics is still challenging. Here, we report a multisensory e-skin with a skin-like multilayer construction for smart perceptions, which features the patterned protrusion texture mimicking the skin texture to enhance the pressure-sensing sensitivity, the temperature-sensing component mimicking the thermoreceptors, the pressure-sensing component mimicking the mechanoreceptors, and the heater mimicking the body heat source. This multisensory e-skin exhibits excellent decoupled sensing performances of pressure and temperature, enabling the development of a haptic perception system for evaluating some discernible characteristics (e.g., shape and size) and experience-driven features (e.g., modulus and thermal conductivity) of objects through a simple grasp. Demonstrations of accurate recognition and automatic classification of various objects even with extremely similar surface features highlight the significant potential of this multisensory e-skin in applications such as intelligent soft robotics, prosthetics, and other related fields.},
}
@article {pmid41059626,
year = {2025},
author = {Meng, W and Hou, F and Chen, K and Ma, L and Liu, Q},
title = {Visually-Inspired Multimodal Iterative Attentional Network for High-Precision EEG-Eye-Movement Emotion Recognition.},
journal = {International journal of neural systems},
volume = {},
number = {},
pages = {2550072},
doi = {10.1142/S0129065725500728},
pmid = {41059626},
issn = {1793-6462},
abstract = {Advancements in artificial intelligence have propelled affective computing toward unprecedented accuracy and real-world impact. By leveraging the unique strengths of brain signals and ocular dynamics, we introduce a novel multimodal framework that integrates EEG and eye-movement (EM) features synergistically to achieve more reliable emotion recognition. First, our EEG Feature Encoder (EFE) uses a convolutional architecture inspired by the human visual cortex's eccentricity-receptive-field mapping, enabling the extraction of highly discriminative neural patterns. Second, our EM Feature Encoder (EMFE) employs a Kolmogorov-Arnold Network (KAN) to overcome the sparse sampling and dimensional mismatch inherent in EM data; through a tailored multilayer design and interpolation alignment, it generates rich, modality-compatible representations. Finally, the core Multimodal Iterative Attentional Feature Fusion (MIAFF) module unites these streams: alternating global and local attention via a Hierarchical Channel Attention Module (HCAM) to iteratively refine and integrate features. Comprehensive evaluations on SEED (3-class) and SEED-IV (4-class) benchmarks show that our method reaches leading-edge accuracy. However, our experiments are limited by small homogeneous datasets, untested cross-cultural robustness, and potential degradation in noisy or edge-deployment settings. Nevertheless, this work not only underscores the power of biomimetic encoding and iterative attention but also paves the way for next-generation brain-computer interface applications in affective health, adaptive gaming, and beyond.},
}
@article {pmid41059099,
year = {2025},
author = {Zhang, C and Liu, Y and Wu, X},
title = {TFANet: a temporal fusion attention neural network for motor imagery decoding.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1635588},
pmid = {41059099},
issn = {1662-4548},
abstract = {INTRODUCTION: In the field of brain-computer interfaces (BCI), motor imagery (MI) classification is a critically important task, with the primary objective of decoding an individual's MI intentions from electroencephalogram (EEG) signals. However, MI decoding faces significant challenges, primarily due to the inherent complex temporal dependencies of EEG signals.<br><br>METHODS: This paper proposes a temporal fusion attention network (TFANet), which aims to improve the decoding performance of MI tasks by accurately modeling the temporal dependencies in EEG signals. TFANet introduces a multi-scale temporal self-attention (MSTSA) mechanism that captures temporal variation in EEG signals across different time scales, enabling the model to capture both local and global features. Moreover, the model adaptively adjusts the channel weights through a channel attention module, allowing it to focus on key signals related to motor imagery. This further enhances the utilization of temporal features. Moreover, by integrating the temporal depthwise separable convolution fusion network (TDSCFN) module, TFANet reduces computational burden while enhancing the ability to capture temporal patterns.<br><br>RESULTS: The proposed method achieves a within-subject classification accuracy of 84.92% and 88.41% on the BCIC-IV-2a and BCIC-IV-2b datasets, respectively. Furthermore, using a transfer learning approach on the BCIC-IV-2a dataset, a cross-subject classification accuracy of 77.2% is attained.<br><br>CONCLUSION: These results demonstrate that TFANet is an effective approach for decoding MI tasks with complex temporal dependencies.},
}
@article {pmid41058890,
year = {2025},
author = {Benachour, A and Medvedev, V and Zinchenko, O},
title = {Mouse-tracking as a tool for investigating strategic behavior in Public Goods Game: an experimental pilot study.},
journal = {Frontiers in psychology},
volume = {16},
number = {},
pages = {1635677},
pmid = {41058890},
issn = {1664-1078},
abstract = {INTRODUCTION: Recent research has demonstrated the potential of utilizing mouse-tracking as a viable alternative method for examining attention-related attributes within the context of a multifaceted activity.<br><br>METHODS: In this study, a mouse-tracking technique was utilized to gather data from individuals who were involved in an online format of the Public Goods Game.<br><br>RESULTS: It was observed that participants exhibited distinct approaches to acquiring information while formulating decisions to propose high, moderate, or low offers. The mouse-tracking algorithm effectively distinguished between various types of offers made toward group funding, as evidenced by the measured distance of the cursor.<br><br>DISCUSSION: These findings suggest that mouse-tracking is a valuable tool for capturing decision-making processes and differentiating behavioral patterns in economic game contexts, offering insights into attention and choice mechanisms.},
}
@article {pmid41056741,
year = {2025},
author = {Yin, Y and Zhang, Y and Xu, S},
title = {The influence of money priming on conformity consumption: The distinct roles of self-sufficiency and self-control.},
journal = {Acta psychologica},
volume = {260},
number = {},
pages = {105682},
doi = {10.1016/j.actpsy.2025.105682},
pmid = {41056741},
issn = {1873-6297},
mesh = {Humans ; *Self-Control/psychology ; Male ; Female ; Young Adult ; Adult ; *Social Conformity ; *Consumer Behavior ; *Choice Behavior ; China ; Adolescent ; },
abstract = {Despite the pervasive role of money in society and the known psychological effects of money priming, research into its influence on consumer choices, especially regarding conformity behavior in consumption, remains limited. This study examines the impact of money priming on individual conformity behaviors within the context of Chinese consumption through three behavioral studies. Study 1 revealed that priming with money concepts reduces the tendency to conform. Study 2 investigated how feelings of monetary abundance and deprivation, elicited by money priming, affect conformity in consumption. The findings showed that a perceived sense of monetary abundance decreases conformity in consumption, whereas a sense of deprivation increases it. While product types did affect conformity consumption, they did not significantly interact with monetary primes. Study 3 explored the mediating roles of self-sufficiency and self-control, confirming that monetary abundance decreases conformity by enhancing self-sufficiency, and monetary deprivation increases conformity by diminishing self-control. These results suggest that money priming can trigger distinct feelings of abundance and deprivation, each having differential effects on conformity consumption. Understanding these effects can enable marketers to tailor strategies for personalized marketing or group purchasing initiatives, effectively addressing different market segments.},
}
@article {pmid41055454,
year = {2025},
author = {Xiang, Y and He, X and Cheng, T and Zhu, W and Pang, J and Cao, Y and Wu, M and Pei, R and Cao, Y},
title = {A Zwitterionic Conductive Hydrogel Interface for Enhanced Electrocorticography Signal Fidelity via High Conductivity, Antifouling, and Brain-Matched Mechanics.},
journal = {Biomacromolecules},
volume = {26},
number = {11},
pages = {7959-7973},
doi = {10.1021/acs.biomac.5c01412},
pmid = {41055454},
issn = {1526-4602},
mesh = {Animals ; Electric Conductivity ; *Hydrogels/chemistry ; *Electrocorticography/methods ; Rats ; *Brain/physiology ; Polymers/chemistry ; Biofouling/prevention &amp; control ; Rats, Sprague-Dawley ; Male ; Polystyrenes/chemistry ; Bridged Bicyclo Compounds, Heterocyclic/chemistry ; },
abstract = {Electrocorticography (ECoG) holds considerable promise for neural signal monitoring with high spatiotemporal resolution. However, conventional rigid ECoG electrodes are often hampered by poor mechanical compliance and insufficient resistance to biofouling, leading to high interfacial impedance and compromised signal quality. While integrating conductive hydrogels into ECoG interface offers a potential solution, concurrently achieving high conductivity, mechanical compatibility with brain tissue, biosafety, and robust antifouling remains a significant challenge. This study introduces SPP@NaCl, a novel zwitterionic conductive hydrogel synthesized by doping a poly(sulfobetaine methacrylate) (pSB) hydrogel matrix with poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) and employing NaCl as a Lewis acid to induce phase separation, thereby promoting an interconnected PEDOT network. The resultant SPP@NaCl hydrogel exhibits a compelling combination of properties: high electrical conductivity (∼9 S·m[-][1]), a low Young's modulus (1.74 kPa) that closely matches brain tissue, excellent conformability, and markedly reduced protein adsorption attributable to its zwitterionic structure. When integrated with commercial ECoG electrodes, the optimized SPP@NaCl-8 hydrogel dramatically lowers interfacial impedance. The resulting Au-SPP@NaCl electrodes enabled high-fidelity, real-time monitoring of cortical epileptiform discharges in a rat seizure model and demonstrated stable, long-term neural signal acquisition in anesthetized healthy rats. This work presents a new strategy for constructing ECoG interfaces that simultaneously deliver high conductivity, mechanical compliance, biosafety, and antifouling capabilities, highlighting the significant potential of these hydrogel-integrated ECoG electrodes for advanced brain-computer interface applications.},
}
@article {pmid41054887,
year = {2025},
author = {Ye, Y and Chen, S and Zhang, Y and Zhang, T and Liao, T and Ren, Z and Chen, W and Hu, W},
title = {Mechano-Locking Strategy for Broad-Spectrum SARS-CoV-2 Neutralization.},
journal = {Small (Weinheim an der Bergstrasse, Germany)},
volume = {},
number = {},
pages = {e05582},
doi = {10.1002/smll.202505582},
pmid = {41054887},
issn = {1613-6829},
support = {T2394511//National Science Foundation of China/ ; T2394510//National Science Foundation of China/ ; 92359303//National Science Foundation of China/ ; 92269101//National Science Foundation of China/ ; LY23A020002//Natural Science Foundation of Zhejiang Province/ ; },
abstract = {Viral entry into host cells is typically initiated by interactions between viral surface proteins and host cell receptors. Conventional neutralization strategies aim to disrupt these interactions but often lose effectiveness against rapidly mutating viral strains. This challenge extends beyond SARS-CoV-2 to other viruses such as HIV and influenza. To overcome this limitation, a novel mechano-locking strategy is proposed, using SARS-CoV-2 as a model system, in which bispecific antibodies (bsAbs) lock the spike protein in its prefusion conformation by preventing force-induced conformational changes. These bsAbs demonstrate broad-spectrum neutralization efficacy against multiple SARS-CoV-2 variants in pseudoviral assays. Single-molecule magnetic tweezers experiments further reveal that these bsAbs significantly raise the mechanical force threshold required for S1-S2 dissociation, thereby enhancing spike protein mechano-stability. This stabilization mechanism offers a mutation-resistant approach to neutralization and introduces a new design paradigm for antiviral therapeutics. These findings establish a mechanistically driven framework for developing biomechanically enhanced strategies potentially applicable to a wide range of mechanically activated enveloped viruses.},
}
@article {pmid41052978,
year = {2025},
author = {Liang, R and Fang, T and Wang, L and Ren, J and Meng, L and Zhao, M and Zheng, C and Fan, Q and Chen, Y and Yang, J and Ming, D},
title = {Multi-connectomics underpin emotional dysfunction in mouse exposed to simulated space composite environment.},
journal = {Translational psychiatry},
volume = {15},
number = {1},
pages = {359},
pmid = {41052978},
issn = {2158-3188},
mesh = {Animals ; Mice ; *Connectome ; Male ; Magnetic Resonance Imaging ; *Prefrontal Cortex/diagnostic imaging/physiopathology ; *Emotions/physiology ; Mice, Inbred C57BL ; Space Flight ; Behavior, Animal/physiology ; Gray Matter/diagnostic imaging/pathology/physiopathology ; *Space Simulation ; *Brain/diagnostic imaging/physiopathology ; Nerve Net/physiopathology/diagnostic imaging ; },
abstract = {Long-duration space exploration, including missions to the Moon and Mars, demands strategies to preserve astronauts' emotional well-being for optimal performance. This study combines behavioral phenotyping, multimodal MRI, in vivo calcium imaging, and brain-wide genomics to bridge macroscopic brain function with mesoscopic neural activity and microscopic genetic processes, providing a dynamic characterization of the mouse connectome under simulated spaceflight conditions. We observed a reduction in gray matter volume, particularly in the prefrontal cortex, with prolonged exposure. Simulated space composite environment (SSCE) disrupted multi-scale functional connectivity and altered the macro-organizational functional gradient, reversing the relationship between brain function and emotional behaviors. Neural activity in the medial prefrontal cortex demonstrated exposure-time-dependent changes across emotional tasks, while genetic analyses linked SSCE-induced alterations in functional profiles to synaptic function and ion channel activity. Our findings reveal how extreme environments impact emotional behaviors, brain networks, and neural activity, offering insights for interventions to maintain brain integrity during extended space missions.},
}
@article {pmid41052270,
year = {2025},
author = {Lu, Y and Xiong, T and Liu, Y and Zhou, H and Xie, B and Guo, G and Pan, C and Ma, W and Yu, P},
title = {Gate Capacitance-Dependent Neuromorphic Functions of Organic Electrochemical Transistors.},
journal = {The journal of physical chemistry letters},
volume = {16},
number = {41},
pages = {10678-10684},
doi = {10.1021/acs.jpclett.5c02510},
pmid = {41052270},
issn = {1948-7185},
abstract = {Neuromorphic functions of organic electrochemical transistors (OECTs) have attracted enormous research attention due to their promising application in the field of brain-mimicking computing and brain-computer interfaces. However, the essential role of gate electrodes in the neuromorphic functions of these synaptic transistors remains unclear. Herein, we systematically investigated the influence of gate electrodes on the neuromorphic functions of synaptic OECTs by rationally choosing four kinds of typical gate electrodes: bare glass carbon electrode (Bare-GCE), carbon nanotube-modified GCE (CNT-GCE), PEDOT:PSS modified GCE (PEDOT:PSS-GCE), and Ag/AgCl electrode. Evaluations of the neuromorphic functions indicated that gate capacitance controlled the performance of synaptic OECTs by tuning the electrical field distribution and doping kinetics in the ionic circuits. This systematic exploration of the gate electrode influences on the OECTs offers rational guidance for the structural design of synaptic OECTs.},
}
@article {pmid41052170,
year = {2025},
author = {Zhang, M and Zhao, S and Xie, L and Liu, T and Yao, D and Yin, E},
title = {Self-Supervised Contrastive Pre-Training for EEG-Based Recognition via Cross Device Representation Consistency.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3613730},
pmid = {41052170},
issn = {1558-2531},
abstract = {Electroencephalography (EEG) has emerged as a powerful tool for modeling human brain states. However, the widespread adoption of EEG-based recognition systems is hindered by low signal-to-noise ratios and the scarcity of labeled data. While existing studies often tackle these challenges in isolation, we propose a novel Cross-Device Representation Consistency (CDRC) pretraining paradigm that addresses both issues simultaneously. CDRC leverages self-supervised signals derived from representation distances and is trained through contrastive estimation. Specifically, our approach employs a transformer based dual-branch single-view embedding prediction task, combining with a contrastive feature alignment module to extract robust and discriminative representations. We first evaluate the CDRC model on a low signal-to-noise ratio emotion classification task involving wearable dry electrodes. Furthermore, we extend CDRC to a multimodal fusion setting to address a cross-device vigilance regression task involving heterogeneous physiological modalities. Extensive experiments on the PaDWEED and SEED-VIG datasets demonstrate that CDRC achieves performance comparable to fully supervised methods and reaches the stat-of-the-art results of existing self-supervised methods, setting a new benchmark in this field. Notably, its strong performance on subject-independent tasks highlights its effectiveness in mitigating subject variability. These results underscore the potential of CDRC to significantly enhance the practicality and scalability of EEG-based recognition systems, marking a meaningful step toward real-world brain-computer interfaces.},
}
@article {pmid41044400,
year = {2025},
author = {Serafini, ERDS and Guerrero-Mendez, CD and Blanco-Diaz, CF and da Silva Fiorin, F and de Albuquerque, TS and A Dantas, AFO and Delisle-Rodriguez, D and do Espírito-Santo, CC},
title = {Cortical modulation through robotic gait training with motor imagery brain-computer interface enhances bladder function in individuals with spinal cord injury.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {34633},
pmid = {41044400},
issn = {2045-2322},
mesh = {Humans ; *Spinal Cord Injuries/physiopathology/rehabilitation/complications ; Male ; *Brain-Computer Interfaces ; Adult ; *Gait/physiology ; Middle Aged ; *Robotics/methods ; *Urinary Bladder, Neurogenic/physiopathology/etiology/rehabilitation/therapy ; *Urinary Bladder/physiopathology ; Electroencephalography ; *Imagery, Psychotherapy/methods ; Neurofeedback ; },
abstract = {Neurogenic bladder (NB) dysfunction in individuals with complete spinal cord injury (SCI) is a condition that significantly affects quality of life. Despite the prevalence of interventions, there is a substantial gap in effective treatments for this dysfunction. This study proposes robotic-assisted gait training combined with motor imagery (MI)-based brain-computer interface (BCI) to induce improved cortical modulation, and consequently improve bladder function in patients with SCI. The study involved seven men with complete and chronic SCI in a protocol comprising 24 sessions of robotic-assisted walking with BCI and MI. This regimen was designed to teach both mu (µ, 8-12 Hz) and beta (β, 15-20 Hz) modulation through MI practices using multi-channel EEG neurofeedback (NFB), focusing on sensorimotor rhythm (SMR) activation. Clinical outcomes were measured using the neurogenic bladder symptom score (NBSS), which revealed substantial improvements in bladder control among participants. EEG analysis confirmed a significant correlation between modulation of µ and β rhythms with decreased NBSS scores. Our findings support that robotic-assisted gait training combined with MI-based BCI effectively modulates with more precision the cortical µ and β rhythms and improves NB dysfunction in SCI individuals.},
}
@article {pmid41044308,
year = {2025},
author = {Chen, Z and Cao, Y and Fu, Q and Hou, L},
title = {Hierarchical attention enhanced deep learning achieves high precision motor imagery classification in brain computer interfaces.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {34555},
pmid = {41044308},
issn = {2045-2322},
mesh = {*Brain-Computer Interfaces ; Humans ; *Deep Learning ; Electroencephalography/methods ; *Attention/physiology ; Male ; Adult ; *Brain/physiology ; Female ; *Imagination/physiology ; },
abstract = {Motor imagery-based Brain-Computer Interfaces (BCIs) hold transformative potential for individuals with severe motor impairments, yet their clinical deployment remains constrained by the inherent complexity of electroencephalographic (EEG) signal decoding. This study presents a systematic investigation of hierarchical deep learning architectures for motor imagery classification, introducing a novel attention-enhanced convolutional-recurrent framework that achieves state-of-the-art accuracy of 97.2477% on a custom four-class motor imagery dataset comprising 4,320 trials from 15 participants. By synergistically integrating spatial feature extraction through convolutional layers, temporal dynamics modeling via long short-term memory networks, and selective attention mechanisms for adaptive feature weighting, our approach significantly outperforms conventional methods while providing interpretable insights into the spatiotemporal signatures of motor imagery. Beyond demonstrating competitive performance, this work elucidates the critical role of attention mechanisms in capturing task-relevant neural patterns amidst the high-dimensional, non-stationary nature of EEG signals. Our findings demonstrate that biomimetic computational architectures that mirror the brain's own selective processing strategies can substantially enhance BCI reliability, offering immediate implications for neurorehabilitation technologies and broader applications in restorative neuroscience. Our code is available at https://github.com/Laboratory-EverythingAI/-EEG_Classification .},
}
@article {pmid41043460,
year = {2025},
author = {Rasheed, S and Bennett, J and Yoo, PE and Burkitt, AN and Grayden, DB},
title = {Decoding saccadic eye movements from brain signals using an endovascular neural interface.},
journal = {Journal of neural engineering},
volume = {22},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ae0f52},
pmid = {41043460},
issn = {1741-2552},
mesh = {Humans ; *Saccades/physiology ; *Brain-Computer Interfaces ; Male ; *Electroencephalography/methods ; Photic Stimulation/methods ; Middle Aged ; *Endovascular Procedures/methods ; Amyotrophic Lateral Sclerosis/physiopathology ; Female ; },
abstract = {Objective.An oculomotor brain-computer interface (BCI) records neural activity from brain regions involved in planning eye movements and translates this activity into control commands. While previous successful studies have relied on invasive implants in non-human primates or electrooculography artefacts in human electroencephalogram (EEG) data, this study aimed to demonstrate the feasibility of an oculomotor BCI using a minimally invasive endovascular Stentrode[TM]device implanted near the supplementary motor area of a patient with amyotrophic lateral sclerosis (ALS).Approach.One participant performed self-paced visually-guided and free-viewing saccade tasks in four directions (left, right, up, down) while endovascular EEG and eye gaze recordings were collected. Visually-guided saccades were cued with visual stimuli, whereas free-viewing saccades were self-directed without explicit cues. Brain signals were pre-processed to remove cardiac artefacts, downsampled, and classified using a Random Forest algorithm. For saccade onset classification (fixation vs saccade), features in time and frequency domains were extracted after xDAWN denoising, while for saccade direction classification, the downsampled time series were classified directly without explicit feature extraction.Main results.The neural responses of visually-guided saccades overlapped with cue-evoked potentials, while free-viewing saccades exhibited saccade-related potentials that began shortly before eye movement, peaked approximately 50 ms after saccade onset, and persisted for around 200 ms. In the frequency domain, these responses appeared as a low-frequency synchronisation below 15 Hz. Saccade onset classification was robust, achieving mean area under the receiver operating characteristic curve (AUC) scores of 0.88 within sessions and 0.86 across sessions. Saccade direction decoding yielded within-session AUC scores of 0.67 for four-class decoding and up to 0.75 for the best performing binary comparisons (left vs up and left vs down).Significance.This proof-of-concept study demonstrates the feasibility of an endovascular oculomotor BCI in a patient with ALS, establishing a foundation for future oculomotor BCI studies in human subjects.},
}
@article {pmid41042834,
year = {2025},
author = {Guo, M and Zhang, J and Liu, H and Bai, Y and Ni, G},
title = {Signal-to-Noise Ratio Effects Frontoparietal Network Lateralization: Electroencephalogram Evidence in Underwater Auditory Target Recognition.},
journal = {Annals of the New York Academy of Sciences},
volume = {},
number = {},
pages = {},
doi = {10.1111/nyas.70081},
pmid = {41042834},
issn = {1749-6632},
support = {2023YFF1203500//National Key Research and Development Program of China/ ; },
abstract = {Accurately recognizing auditory targets within background interference remains challenging at a low signal-to-noise ratio (SNR). Using an oddball paradigm, this electroencephalogram study investigated the impact of SNR (0, -10, and -20 dB) on psychophysiological processes underlying underwater auditory target recognition in twenty normal-hearing participants. Reduced SNR impaired the N1-P2 component and led to P300 variations, with delayed latencies (N1: p = 0.0355; P300: p = 0.0075) and reduced amplitudes (P2: p = 0.0075; P300: p = 0.0277), indicating increased attentional demands. Microstate analysis highlighted 300-400 ms frontoparietal activation for attention orientation and sensory information integration. Reduced accuracy correlates with alpha-band activity and phase variations over frontoparietal areas (event-related spectral perturbation [ERSP]: p = 0.0388; inter-trial coherence [ITC]: p = 0.0059), implying suppression of task-relevant processing. Gamma-band activity and phase at lower SNR levels suggest changes in the parietal network's function (ERSP: p = 0.0183; ITC: p = 0.0113), influencing reaction times due to increased integration difficulty. Right-lateralized alpha- and gamma-band network shifts support the functional advantages of the right hemisphere in noise, with enhanced local efficiency (frontal alpha: p = 0.0100; parietal-occipital gamma: p = 0.0116). These findings provide insights into the psychophysiological mechanisms underlying auditory target recognition in noise.},
}
@article {pmid41042451,
year = {2025},
author = {Huang, Y and Ke, Y and Li, J and Liu, S and Ming, D},
title = {Frontal Theta Modulation in Sequential Working Memory: the Impact of Spatial Regularity and Scenario.},
journal = {Brain topography},
volume = {38},
number = {6},
pages = {74},
pmid = {41042451},
issn = {1573-6792},
support = {No. 2021YFF1200603//the National Key Research and Development Program of China/ ; No. 62276184 and 61806141//the National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Memory, Short-Term/physiology ; *Theta Rhythm/physiology ; Male ; Female ; Young Adult ; Adult ; Electroencephalography ; *Frontal Lobe/physiology ; *Space Perception/physiology ; },
abstract = {Humans can quickly extract spatial regularities from sequences to reduce working memory (WM) load, yet the electrophysiological mechanisms remain unclear. Although previous studies have underscored the role of frontal-midline theta (FM-theta) in sequential WM processing, whether and how spatial regularity modulates FM-theta is unknown. To investigate this, we varied the spatial relation between successive items-more repetitions of the same displacement yielded fewer unique chunks and thus higher regularity-while sequence length stayed fixed. Participants were asked to encode, maintain and reproduce the temporal order of sequences utilizing their spatial structures. To enhance ecological validity, we further embedded the task in a complex scenario that included meaningful contexts, dispersed layouts, and variable stimulus sizes. Behavioral data revealed that sequences with higher regularity and the simple scenario yielded higher accuracy, confirming successful manipulations of regularity and scenario difficulty. The overall temporal dynamics of EEG data showed prominent theta enhancement and concurrent alpha/beta suppression during encoding and maintenance. Subsequent analyses across the 4-30 Hz and delay period demonstrated that theta power increased while alpha/beta power declined monotonically with sequence complexity. Notably, regularity-modulated alpha power differed in two scenarios. Moreover, the results found that only sequence regularity-not scenario difficulty-modulated fronto-posterior theta connectivity and slowed the FM-theta frequency. In sum, FM-theta, operating through long-range connectivity and frequency modulation, exclusively tracks spatial-regularity demands in sequential WM, while such neural mechanisms remain impervious to variations in scenario difficulty. These findings suggest that FM-theta may serve as a specific neural marker for spatial regularity processing, rather than a general index of task difficulty, thereby offering a concrete target for future neuromodulatory interventions.},
}
@article {pmid41042091,
year = {2025},
author = {Sato, K and Tanaka, R and Ota, K},
title = {BCI-Mediated Warfare, Psychological Distance, and the Duty to Care.},
journal = {AJOB neuroscience},
volume = {16},
number = {4},
pages = {344-346},
doi = {10.1080/21507740.2025.2557822},
pmid = {41042091},
issn = {2150-7759},
}
@article {pmid41040967,
year = {2025},
author = {Wood, C and Wang, H and Yang, WJ and Xi, Y},
title = {Facing the possibility of consciousness in human brain organoids.},
journal = {Patterns (New York, N.Y.)},
volume = {6},
number = {9},
pages = {101365},
pmid = {41040967},
issn = {2666-3899},
abstract = {Human brain organoids (HBOs) have emerged as transformative models for neurodevelopment and disease, yet ethical concerns persist regarding their potential to develop consciousness. Since 2020, a growing cohort of neuroscientists and philosophers has dismissed these concerns as unscientific, citing limited structural complexity, absence of bodily integration and environmental interaction, and a prevailing neuroscientific consensus against the feasibility of any, or any near-future, emergence of HBO consciousness, thus challenging any suggested revisions of ethical guidelines and safeguards. We argue that this dismissal is premature. Drawing on neuroscientific benchmarks, comparisons to the developing human brain, contemporary theories of consciousness, and principles of natural developmental progression, we question the basis for selectively excluding consciousness from among HBOs' expanding functional repertoire. We caution against enshrining such skepticism into dogma or using it to defer ethical engagement. Instead, we advocate for proactive, ongoing assessment of the moral implications of advancing HBO capabilities.},
}
@article {pmid41040697,
year = {2025},
author = {Chetty, N and Kacker, K and Feldman, AK and Yoo, PE and Bennett, J and Fry, A and Tal, I and Hardy, NF and Ebrahimi, S and Echavarria, C and Sawyer, A and Schone, HR and Harel, NY and Nogueira, RG and Majidi, S and Levy, EI and Kandel, A and Hill, KK and Opie, NL and Lacomis, D and Collinger, JL and Oxley, TJ and Putrino, DF and Weber, DJ},
title = {Signal properties and stability of a chronically implanted endovascular brain computer interface.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
pmid = {41040697},
support = {F32 MH139145/MH/NIMH NIH HHS/United States ; UH3 NS120191/NS/NINDS NIH HHS/United States ; },
abstract = {BACKGROUND: Implanted brain-computer interfaces (iBCIs) establish direct communication with the brain and hold the potential to enable people with severe disability to achieve control of digital devices, enabling communication and digital activities of daily living. The ability to access brain signals reliably and continuously over many years post-implantation is crucial for iBCIs to be effective and feasible. This study investigates the signal characteristics and long-term stability of neural activity recorded with a stent-electrode array over 1 year post-implant.<br><br>METHODS: We report on five participants with paralysis who were enrolled in an early feasibility clinical trial of an endovascular iBCI (Stentrode; ClinicalTrials.gov, NCT05035823). Each participant was implanted with a 16-channel stent-electrode array, deployed in the superior sagittal sinus to record bilaterally from the primary motor cortices. Neural activity was recorded during home-based sessions while the participants performed a set of standardized tasks. Metrics including motor signal strength during attempted movement, resting state signal features, and electrode impedances were quantified over time.<br><br>RESULTS: Motor-related modulation in neural activity was exhibited in the high-frequency bands (30-200 Hz) during attempted movements, with rest and attempted movement states showing sustained differentiation over time. Impedance and resting state band power for most channels did not change significantly over time.<br><br>CONCLUSIONS: These findings provide strong evidence that the endovascular BCIs may be suitable for long-term neural signal acquisition in the home environment, demonstrating the ability to record movement-related modulation over one year.},
}
@article {pmid41040692,
year = {2025},
author = {Schone, HR and Yoo, P and Fry, A and Chetty, N and Sawyer, A and Herbers, C and Liu, F and Moon, CH and Hill, K and Majidi, S and Harel, NY and Nogueira, RG and Levy, E and Putrino, DF and Lacomis, D and Oxley, TJ and Weber, DJ and Collinger, JL},
title = {Motor Cortex Coverage Predicts Signal Strength of a Stentrode Endovascular Brain-Computer Interface.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
pmid = {41040692},
abstract = {Brain-computer interfaces (BCIs) are an emerging assistive technology for individuals with motor impairments, enabling the command of digital devices using neural signals. The Stentrode BCI is an implant, positioned within the brain's neurovasculature, that can record movement-related electrocortical activity. Over 5 years, 10 participants (8 amyotrophic lateral sclerosis, 1 primary lateral sclerosis, 1 brainstem stroke) have been implanted with a Stentrode BCI and significant inter-participant variability has been observed in the recorded motor signal strength. This variability warrants a critical investigation to characterize potential predictors of signal strength to promote more successful BCI control in future participants. Therefore, we investigated the relationship between Stentrode BCI motor signal strength and a variety of user-specific factors: (1) clinical status, (2) pre-implant functional activity, (3) peri-implant neuroanatomy, (4) peri-implant neurovasculature, and (5) Stentrode device integrity. Data from 10 implanted participants, including clinical demographics, pre- and post-implant neuroimaging and longitudinal Stentrode BCI motor signal assessments were acquired over a year. Across all potential predictors, the strongest predictor of Stentrode motor signal strength was the degree to which the Stentrode BCI's deployment position overlapped with primary motor cortex (M1). These findings highlight the importance of targeting M1 during device deployment and, more generally, provides a scientific framework for investigating the role of user-specific factors on BCI device outcomes.},
}
@article {pmid41040179,
year = {2025},
author = {Rigotti-Thompson, M and Nason-Tomaszewski, SR and Bechefsky, P and Acosta, A and Hahn, N and Avansino, D and Richards, B and Nicolas, C and Ali, YH and Henderson, JM and Hochberg, LR and AuYong, N and Pandarinath, C},
title = {Preparatory encoding of diverse features of intended movement in the human motor cortex.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.09.24.678356},
pmid = {41040179},
issn = {2692-8205},
abstract = {Over the course of a voluntary movement, motor cortical activity exhibits a transition from preparation to execution, with markedly different activity across these phases. Preparatory activity in particular might be used to improve brain-computer interfaces (BCIs) that harness brain activity to control external assistive devices, for example by anticipating a user's intended movement trajectory for quick and fluid performance. However, to leverage preparatory activity for clinical BCIs, we must first understand which features of upcoming movements are encoded by preparatory activity in humans. In this work, we collected intracortical recordings from 3 research participants in the BrainGate2 clinical trial to investigate whether diverse features of movement, such as direction, curvature, and distance, are encoded by preparatory activity in the human motor cortex. We first show that preparatory activity is tuned to the direction of upcoming movements, and this tuning is largely preserved across movements with different effectors. Further investigation demonstrated this preparatory activity is also informative of initial and endpoint directions of curved movement trajectories, and encodes movement distance and speed independently. Finally, we present an online control paradigm that leverages preparatory activity to predict movements towards intended directions in advance, yielding rapid, self-paced control of a computer cursor by human participants. Altogether, these results demonstrate that preparatory activity in the human motor cortex encodes rich features of upcoming movement, highlighting its potential use for high performance brain-computer interface applications.},
}
@article {pmid41039114,
year = {2025},
author = {},
title = {High-resolution brain-computer interface with electrode scalability and minimally invasive surgery.},
journal = {Nature biomedical engineering},
volume = {},
number = {},
pages = {},
pmid = {41039114},
issn = {2157-846X},
}
@article {pmid41039113,
year = {2025},
author = {Hettick, M and Ho, E and Poole, AJ and Monge, M and Papageorgiou, D and Takahashi, K and LaMarca, M and Trietsch, D and Reed, K and Murphy, M and Rider, S and Gelman, KR and Byun, YW and Miller, JS and Hanson, T and Tolosa, V and Lee, SH and Bhatia, S and Konrad, PE and Mager, M and Mermel, CH and Rapoport, BI},
title = {Minimally invasive implantation of scalable high-density cortical microelectrode arrays for multimodal neural decoding and stimulation.},
journal = {Nature biomedical engineering},
volume = {},
number = {},
pages = {},
pmid = {41039113},
issn = {2157-846X},
abstract = {High-bandwidth brain-computer interfaces rely on invasive surgical procedures or brain-penetrating electrodes. Here we describe a cortical 1,024-channel thin-film microelectrode array and we demonstrate its minimally invasive surgical delivery that avoids craniotomy in porcine models and cadavers. We show recording and stimulation from the same electrodes to large portions of the cortical surface, and the reversibility of delivering the implants to multiple functional regions of the brain without damaging the cortical surface. We evaluate the performance of the interface for high-density neural recording and visualizing cortical surface activity at spatial and temporal resolutions and total spatial extents. We demonstrate accurate neural decoding of somatosensory, visual and volitional walking activity, and achieve focal neuromodulation through cortical stimulation at sub-millimetre scales. We report the feasibility of intraoperative use of the device in a five-patient pilot clinical study with anaesthetized and awake neurosurgical patients, characterizing the spatial scales at which sensorimotor activity and speech are represented at the cortical surface. The presented neural interface demonstrates the highly scalable nature of micro-electrocorticography and its utility for next-generation brain-computer interfaces.},
}
@article {pmid41039090,
year = {2025},
author = {Zhou, H and Wang, M and Qi, S and Chen, Q and Lai, J and Wu, Z and Liu, R and Wang, L and Zhou, H and Zhang, S and Hu, S},
title = {Transcranial temporal interference stimulation for treating bipolar disorder with depressive episodes: a feasibility Study.},
journal = {Molecular psychiatry},
volume = {30},
number = {12},
pages = {6099-6106},
pmid = {41039090},
issn = {1476-5578},
support = {52407261//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Bipolar Disorder/therapy/physiopathology ; Male ; Female ; Adult ; Middle Aged ; Feasibility Studies ; *Transcranial Direct Current Stimulation/methods/adverse effects ; Treatment Outcome ; Nucleus Accumbens ; Psychiatric Status Rating Scales ; Depression/therapy ; *Transcranial Magnetic Stimulation/methods ; Brain ; Executive Function/physiology ; },
abstract = {Bipolar depression (BD-D) is a significant clinical challenge associated with high disease burden. Transcranial temporal interference stimulation (tTIS), a novel and noninvasive approach for targeting deep brain structures, was investigated for its efficacy and safety in BD-D patients in this trial. Thirty-six patients were recruited for a single-arm, open-label trial, and 25 completed the 5-day intervention consisting of 10 tTIS sessions targeting the left nucleus accumbens. Each session lasted 20 min, with a maximum current intensity of 2 mA and an envelope stimulation frequency of 40 Hz. Significant symptom reductions were observed following treatment, with mean HAMD-17 scores decreasing from 23.36 to 16.16 (p < 0.0001), MADRS scores from 39.12 to 31.28 (p < 0.01), HAMA scores from 19.68 to 15.44 (p < 0.05), and QIDS scores from 13.52to 9.68 (p < 0.001). Eleven participants (44.0%) met improvement criteria and seven (28.0%) achieved response. Cognitive assessments indicated improvements in memory and executive function, and changes in reward-related brain activity correlated positively with symptom reduction. Adverse events were mild, mainly transient scalp discomfort. These findings provide preliminary evidence supporting the efficacy and safety of tTIS for alleviating depressive symptoms and cognitive impairments in BD-D.},
}
@article {pmid41038246,
year = {2025},
author = {Xie, H and Xu, H and Xu, K and Yu, C and Yang, W and Yang, C},
title = {Rat Robot Autonomous Border Detection Based on Wearable Sensors.},
journal = {Bioinspiration &amp; biomimetics},
volume = {},
number = {},
pages = {},
doi = {10.1088/1748-3190/ae0ee8},
pmid = {41038246},
issn = {1748-3190},
abstract = {Bio-robots, a novel type of robots created based on brain-machine interface, have shown great potential in search and rescue tasks. However, current research focuses on the bio-robot itself, such as locomotion, localization and navigation, but lacks interactions with the external environment. In this paper, we proposed a new system for rat robot to autonomously explore the border of unknown field out of sight, and then get the boundary map. We invented a wearable backpack, which is an embedded system with laser-ranging sensors, IMU and ultra-wide band (UWB) module, for the rat robot. Based on the wearable system, a classification method for motion states based on random forest (RF) and a navigation algorithm based on finite state machine (FSM) were developed for the autonomous exploration of border and tested in the locomotion experiment. Besides, with the localization and distance data from UWB and laser-ranging sensors, we mapped the distribution of the border, using Ramber-Douglas-Peucker (RDP) algorithm. The results show that the system could effectively navigate the rat robot to explore the field and accurately detect the border. The accuracy of classification reaches 97.86% and the error rate of border detection is 5.90%. This work provides a novel technology that has potential for practical applications such as prospect for minerals and search tasks. .},
}
@article {pmid41038061,
year = {2025},
author = {Wang, X and Li, X and Li, J and Fu, Y and Zhang, D and Peng, Y},
title = {RimeSleepNet: A hybrid deep learning network for s-EEG sleep stage classification.},
journal = {Sleep medicine},
volume = {136},
number = {},
pages = {106835},
doi = {10.1016/j.sleep.2025.106835},
pmid = {41038061},
issn = {1878-5506},
mesh = {Humans ; *Deep Learning ; *Electroencephalography/methods ; *Sleep Stages/physiology ; Neural Networks, Computer ; Algorithms ; },
abstract = {Sleep stage classification is essential for sleep research and clinical diagnostics. However, frequency aliasing in sleep electroencephalogram (s-EEG) signals remains a significant challenge, existing methods have yet to effectively address this issue. This study proposes a hybrid deep-learning model, RimeSleepNet, comprising four key components. First, the rime optimization algorithm adaptively tunes variational mode decomposition (VMD) to reduce frequency aliasing by generating intrinsic mode functions (IMFs). Second, a convolutional neural network (CNN) automatically extracts stage-specific features from IMFs. A multi-head self-attention (MHSA) mechanism then dynamically weights these features to prioritize stage-specific patterns, followed by long short-term memory (LSTM) networks that model temporal dynamics for robust classification of NREM, REM, and WAKE stages. Evaluated on the Chengdu People's Hospital and Sleep-EDF datasets, RimeSleepNet achieves the highest F1 scores of 0.94, 0.89, and 0.92 for NREM, REM, and WAKE stages, respectively, with an AUC of 0.92, outperforming baseline models like CNN and LSTM. Cross-dataset validation confirms its robust generalization (Cohen's κ = 0.90), and it reduces validation loss by 53 % compared to LSTM, providing an advanced tool for automated sleep stage analysis in sleep disorder diagnosis and personalized monitoring.},
}
@article {pmid41036535,
year = {2025},
author = {Arif, S and Rehman, MZU and Mushtaq, Z},
title = {Editorial: Advancements in smart diagnostics for understanding neurological behaviors and biosensing applications.},
journal = {Frontiers in computational neuroscience},
volume = {19},
number = {},
pages = {1693327},
doi = {10.3389/fncom.2025.1693327},
pmid = {41036535},
issn = {1662-5188},
}
@article {pmid41035957,
year = {2025},
author = {Wang, W and Liu, Y and Shi, P and Zhang, J and Wang, G and Li, Y and Liu, W and Ming, D},
title = {Altered tactile abnormalities in children with ASD during tactile processing and recognition revealed by dynamic EEG features.},
journal = {Frontiers in psychiatry},
volume = {16},
number = {},
pages = {1611438},
pmid = {41035957},
issn = {1664-0640},
abstract = {INTRODUCTION: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by sensory processing abnormalities, particularly in tactile perception, highlighting the need for objective screening methods beyond current subjective behavioral assessments.<br><br>METHODS: This study developed a portable electro-tactile stimulation system with EEG to evaluate tactile processing differences in children with ASD (n=36) versus typically developing controls (n=36).<br><br>RESULTS: Revealing significantly reduced ERP amplitudes at key processing stages: P200 at FP2 (F(1,70)=10.82, p=0.0454), N200 at F3 (F(1,70)=58.33, p<0.0001), and P300 at C4 (F(1,70)=45.62, p<0.0001). Topographic analysis identified pronounced group differences (>10&#xec;V) across frontal, central, and parietal regions (F8, FC5/6, CP1/2/5/6, Pz, Oz), with ASD children exhibiting prolonged but less efficient tactile discrimination and compensatory prefrontal activation (FP2 CV: p=0.043). The paradigm demonstrated strong reliability (CV ICC: ASD=0.779, TD=0.729) and achieved 85.2% classification accuracy (AUC=0.91) using ANN, with optimal performance from F8 P300 features (sensitivity=87.5%, specificity=83.7%).<br><br>DISCUSSION: These findings provide an objective, efficient (15-minute) screening method that advances understanding of tactile processing abnormalities in ASD and supports the development of physiological biomarkers for early identification, overcoming limitations of questionnaire-based approaches.},
}
@article {pmid41035905,
year = {2025},
author = {Xue, Y and Chen, Y and Wang, F and Zhao, L and Li, T and Gong, A and Nan, W and Fu, Y},
title = {Applications and interrelationships of brain function detection, brain-computer interfaces, and brain stimulation: a comprehensive review.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {161},
pmid = {41035905},
issn = {1871-4080},
abstract = {Brain-Computer Interface (BCI), Brain Function Detection (BFD), and Brain Stimulation (BS) are three pivotal technological domains in neuroscience and neuroengineering. Each plays a critical role in fundamental research, clinical applications, and human-computer interaction paradigms. Despite their distinct developmental pathways and application focuses, these technologies are frequently conflated or ambiguously referenced in both academic discourse and industrial practice, potentially leading to conceptual misinterpretations, suboptimal system designs, and clinical misapplications. Prior literature reviews have predominantly concentrated on BCI as a standalone subject, covering its historical evolution, specific neurophysiological signal modalities, or emergent technological trends. This manuscript's core contribution is critiquing the overuse of "passive BCI" (labeling feedback-absent monitoring as BCI). Through an application-oriented lens, it clarifies boundaries between BCI, BFD, and BS to resolve conceptual confusion. Further, the review interrogates the convergences and divergences among these modalities and critically evaluates the practical feasibility and challenges associated with their integrative deployment in clinical and experimental settings. Ultimately, this work aspires to provide a lucid, systematic, and conceptually coherent framework to support neuroscientific novices, interdisciplinary investigators, and clinical practitioners. By fostering precise comprehension and judicious utilization of BCI, BFD, and BS, it aims to propel their standardized advancement and enhance their translational impact across both research and clinical domains.},
}
@article {pmid41034549,
year = {2025},
author = {Di, S and Luo, N and Shi, W and Yang, Z and Sui, J and Jiang, R and Cui, Y and Du, Z and Zhang, J and Ma, Y and Wang, H and Chu, C and Zhong, Y and Li, W and Lu, Y and Yan, H and Liao, J and Zhang, D and Calhoun, V and Song, M and Jiang, T},
title = {Physical Activity and Depressive Mood Share the Structural Connectivity Between Motor and Reward Networks.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {41034549},
issn = {1995-8218},
abstract = {In various studies, exercise has been revealed to have a positive effect on alleviating depressive symptoms. However, the neural basis behind this phenomenon remains unknown, as well as its underlying biological mechanism. In this study, we used a large neuroimaging cohort [n = 1,027, major depressive disorder (MDD)/healthy controls (HCs) = 492/535] from the UK Biobank to identify structural connectivity (SC) patterns simultaneously linked with physical activity and depression, as well as the biological interpretation. An SC pattern linked with exercise was identified to be both significantly correlated with depressive mood and group discrimination between MDDs and HCs, primarily located between the motor-related regions and reward-related regions. This pattern was associated with multiple neurotransmitter receptors, such as serotonin and GABA receptors, and enriched in pathways like synaptic signaling and the astrocyte cell type. The SC pattern and genetic results were also replicated in another independent MDD dataset (n = 3,496) and present commonalities with bipolar disorder (n = 81). Overall, these findings not only initially identified a reproducible shared SC pattern between physical activity and depressive mood, but also elucidated the underlying biological mechanisms, which enhance our understanding of how exercise helps alleviate depression and may inform the development of novel neuromodulation targets.},
}
@article {pmid41034219,
year = {2025},
author = {Griggs, WS and Norman, SL and Tanter, M and Liu, C and Christopoulos, V and Shapiro, MG and Andersen, RA},
title = {Functional ultrasound neuroimaging reveals mesoscopic organization of saccades in the lateral intraparietal area.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {8752},
pmid = {41034219},
issn = {2041-1723},
support = {T32 GM008042/GM/NIGMS NIH HHS/United States ; R01NS123663//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; F30EY032799//U.S. Department of Health &amp; Human Services | NIH | National Eye Institute (NEI)/ ; T32GM008042//U.S. Department of Health &amp; Human Services | NIH | National Institute of General Medical Sciences (NIGMS)/ ; F30 EY032799/EY/NEI NIH HHS/United States ; R01 NS123663/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Saccades/physiology ; *Parietal Lobe/physiology/diagnostic imaging ; Male ; Macaca mulatta ; Ultrasonography/methods ; *Functional Neuroimaging/methods ; Brain Mapping/methods ; Magnetic Resonance Imaging ; },
abstract = {The lateral intraparietal cortex (LIP), contained within the posterior parietal cortex (PPC), is crucial for transforming spatial information into saccadic eye movements, yet its functional organization for movement direction remains unclear. Here, we used functional ultrasound imaging (fUSI), a technique with high sensitivity, large spatial coverage, and good spatial resolution, to map movement direction encoding across the PPC by recording local changes in cerebral blood volume within PPC as two male monkeys performed memory-guided saccades. Our analysis revealed a heterogeneous organization where small patches of neighboring LIP cortex encoded different directions. These subregions demonstrated consistent tuning across several months to years. A rough topography emerged where anterior LIP represented more contralateral downward movements and posterior LIP represented more contralateral upward movements. These results address two fundamental gaps in our understanding of LIP's functional organization: the neighborhood organization of patches and the stability of these populations across long periods of time. By tracking LIP populations over extended periods, we developed mesoscopic maps of direction specificity previously unattainable with fMRI or electrophysiology methods.},
}
@article {pmid41034198,
year = {2025},
author = {Singh, A and Thomas, T and Li, J and Hickok, G and Pitkow, X and Tandon, N},
title = {Transfer learning via distributed brain recordings enables reliable speech decoding.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {8749},
pmid = {41034198},
issn = {2041-1723},
support = {U01 NS128921/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Speech/physiology ; *Brain/physiology ; Electroencephalography/methods ; Male ; Female ; Adult ; Young Adult ; Middle Aged ; Learning ; },
abstract = {Speech brain-computer interfaces (BCIs) combine neural recordings with large language models to achieve real-time intelligible speech. However, these decoders rely on dense, intact cortical coverage and are challenging to scale across individuals with heterogeneous brain organization. To derive scalable transfer learning strategies for neural speech decoding, we used minimally invasive stereo-electroencephalography recordings in a large cohort performing a demanding speech motor task. A sequence-to-sequence model enabled decoding of variable-length phonemic sequences prior to and during articulation. This enabled development of a cross-subject transfer learning framework to isolate shared latent manifolds while enabling individual model initialization. The group-derived decoder significantly outperformed models trained on individual data alone, enabling decoding robustness despite variable coverage and activation. These results highlight a pathway toward generalizable neural prostheses for speech and language disorders by leveraging large-scale intracranial datasets with distributed spatial sampling and shared task demands.},
}
@article {pmid41033466,
year = {2025},
author = {Dong, Z and Xiang, Y and Wang, S},
title = {High - quality decoding of RGB images from the neuronal signals of the pigeon optic tectum.},
journal = {Journal of neuroscience methods},
volume = {424},
number = {},
pages = {110595},
doi = {10.1016/j.jneumeth.2025.110595},
pmid = {41033466},
issn = {1872-678X},
mesh = {Animals ; Columbidae ; *Superior Colliculi/physiology/cytology ; *Neurons/physiology ; *Image Processing, Computer-Assisted/methods ; Algorithms ; Photic Stimulation ; *Visual Perception/physiology ; Signal-To-Noise Ratio ; },
abstract = {BACKGROUND: Decoding neural activity to reverse-engineer sensory inputs advances understanding of neural encoding and boosts brain-computer interface and visual prosthesis tech. A major challenge is high-quality RGB image reconstruction from natural scenes, which this study tackles using pigeon optic tectum neurons.<br><br>NEW METHOD: We built a neural response dataset via microelectrode arrays capturing tectal neurons' ON-OFF responses to RGB images. A modular decoding algorithm, integrating a convolutional encoding network, linear decoder, and image enhancement network, enabled inverse RGB image reconstruction from neural signals.<br><br>RESULTS: Experimental results confirmed high-quality RGB image reconstruction by the proposed algorithm. For all test set reconstructions, average metrics were: correlation coefficient (R) of 0.853, structural similarity index (SSIM) of 0.618, peak signal-to-noise ratio (PSNR) of 19.94&#x202f;dB, and feature similarity index (FSIMc) of 0.801. These results confirm accurate recapitulation of both color and contour details of the original images.<br><br>In terms of key quantitative metrics, the proposed algorithm achieves a significant improvement over traditional linear reconstruction methods, with the correlation coefficient (R) increased by 12.65&#x202f;%, the structural similarity index (SSIM) increased by 38.92&#x202f;%, the peak signal-to-noise ratio (PSNR) increased by 12.65&#x202f;%, and the feature similarity index (FSIMc) increased by 9.28&#x202f;%.<br><br>CONCLUSIONS: This research provides a novel technical pathway for high-quality visual neural decoding, with robust experimental metrics validating its effectiveness. It also offers experimental evidence to support investigations into the information processing mechanisms of the avian visual pathway.},
}
@article {pmid41033328,
year = {2025},
author = {Deng, X and Fan, Z and Dong, W},
title = {MEFD dataset and GCSFormer model: cross-subject emotion recognition based on multimodal physiological signals.},
journal = {Biomedical physics &amp; engineering express},
volume = {11},
number = {6},
pages = {},
doi = {10.1088/2057-1976/ae0e28},
pmid = {41033328},
issn = {2057-1976},
mesh = {Humans ; *Emotions/physiology ; *Electroencephalography/methods ; Male ; Female ; Heart Rate/physiology ; Adult ; Electrooculography ; *Signal Processing, Computer-Assisted ; Young Adult ; Algorithms ; Brain-Computer Interfaces ; Galvanic Skin Response ; Spectroscopy, Near-Infrared ; },
abstract = {Cross-subject emotion recognition is an important research direction in the fields of affective computing and brain-computer interfaces, aiming to identify the emotional states of different individuals through physiological signals such as functional near-infrared spectroscopy (fNIRS) and electroencephalogram (EEG). Currently, most EEG-based emotion recognition datasets are unimodal or bimodal, which may overlook the emotional information reflected by other physiological signals of the subjects. In this paper, a multimodal dataset named Multimodal Emotion Four Category Dataset (MEFD) is constructed, which includes EEG, Heart Rate Variability (HRV), Electrooculogram (EOG), and Electrodermal Activity (EDA) data from 34 participants in four emotional states: sadness, happiness, fear, and calm. This will contribute to the development of multimodal emotion recognition research. To address the recognition difficulty caused by individual differences in cross-subject emotion recognition tasks, a classification model named Global Convolution Shifted Window Transformer (GCSFormer) composed of an EEG-Swin Convolution module and an improved Global Adaptive Transformer (GAT) module is proposed. By using a parallel network, the feature discrimination ability and generalization ability are enhanced. The model is applied to classify the EEG data in the self-built MEFD dataset, and the results are compared with those of mainstream methods. The experimental results show that the proposed EEG classification method achieves the best average accuracy of 85.36%, precision of 85.23%, recall of 86.35%, and F1 score of 84.52% in the cross-subject emotion recognition task. The excellent performance of GCSFormer in cross-subject emotion recognition task was verified.},
}
@article {pmid41032544,
year = {2025},
author = {Ju, J and Zhuang, Y and Yi, C},
title = {An EEG-EMG-Based Hybrid Brain-Computer Interface for Decoding Tones in Silent and Audible Speech.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {4206-4216},
doi = {10.1109/TNSRE.2025.3616276},
pmid = {41032544},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Male ; *Electromyography/methods ; Adult ; Female ; Young Adult ; Algorithms ; *Speech Perception/physiology ; Acoustic Stimulation ; *Speech/physiology ; Reproducibility of Results ; },
abstract = {Speech recognition can be widely applied to support people with language disabilities by enabling them to communicate through brain-computer interfaces (BCIs), thus improving their quality of life. Despite the essential role of tonal variations in conveying semantic meaning, there have been limited studies focusing on the neural signatures of tones and their decoding. This paper systematically investigates the neural signatures of the four tones of Mandarin. It explores the feasibility of tone decoding in both silent and audible speech using a multimodal BCI based on electroencephalography (EEG) and electromyography (EMG). The time-frequency analysis of EEG has revealed significant variations in neural activation patterns across various tones and speech modes. For example, in the silent speech condition, temporal-domain analysis shows significant tone-dependent activation in the frontal lobe (ANOVA p = 0.000, Tone1 vs Tone2: p = 0.000, Tone1 vs Tone4: p = 0.000, Tone2 vs Tone3: p = 0.000, Tone3 vs Tone4: p = 0.001) and in channel F8 (ANOVA p= 0.008, Tone1 vs Tone2: p= 0.014, Tone2 vs Tone3: p= 0.034). Spectral analysis shows significant differences between four tones in event-related spectral perturbation (ERSP) in the central region (p = 0.000) and channel C6 (p = 0.000). EMG analysis identifies a significant tone-related difference in activation of the left buccinator muscle (p = 0.023), and ERSP from the mentalis muscle also shows a marked difference across tones in both speech conditions (p = 0.00). Overall, tone-related neural differences were more pronounced in the audible speech condition than in the silent condition. For tone classification, RLDA and SVM classifiers achieved accuracies of 71.22% and 72.43%, respectively, using EEG temporal features in both speech modes. Additionally, the RLDA classifier with temporal features achieves binary tone classification accuracies of 90.92% (audible tones) and 91.00% (silent tones). The combination of EEG and EMG yields the highest speech modes decoding accuracy of 81.33%. These findings provide a potential strategy for speech restoration in tonal languages and further validate the feasibility of a speech brain-computer interface (BCI) as a clinically effective treatment for individuals with tonal language impairment.},
}
@article {pmid41031916,
year = {2025},
author = {Liu, M and Guo, X and Cao, L and Cui, H and Li, Z and Lin, Y and Yin, Z and Quan, W and Feng, C and Ma, T and Zhao, Z and Yang, L and Yao, L and Zhang, X and Wang, G},
title = {Revolutionizing brain-computer interfaces: Compact and high-speed wireless neural signal acquisition.},
journal = {The Review of scientific instruments},
volume = {96},
number = {10},
pages = {},
doi = {10.1063/5.0287033},
pmid = {41031916},
issn = {1089-7623},
mesh = {*Brain-Computer Interfaces ; *Wireless Technology/instrumentation ; Animals ; *Signal Processing, Computer-Assisted/instrumentation ; Mice ; Electroencephalography/instrumentation ; Signal-To-Noise Ratio ; *Brain/physiology ; Humans ; },
abstract = {A brain-computer interface (BCI) facilitates the connection between the human brain and external devices by decoding neurophysiological signals, thereby enabling seamless interaction between humans and machines. However, existing neural signal acquisition systems often suffer from limited channel counts, low sampling rates, and challenges in miniaturization and wireless bandwidth, which restrict their ability to support large-scale and real-time neural recordings. Given the rapid advancements in BCI technologies and the increasing demand for high-resolution neural data, there is an imperative need for BCI systems that are high-throughput, high-speed, and miniaturized. This paper presents a wireless neural signal acquisition system based on FPGA technology, supporting 1024 channels at 32 kSPS and employing a stacked architecture for compact, low-power wireless transmission. Following the creation of the functional prototype, laboratory electrical performance tests were conducted. The system exhibited a noise voltage of 8.56 μVrms, which is in close proximity to the 6 μVrms specified by the chip. In addition, the system accurately captured weak sine wave inputs in both time and frequency domains, confirming its ability to record weak bioelectrical signals. Subsequent animal experiments involving mice implanted with EEG electrodes demonstrated that the system could reliably acquire brain neural signals in real time. The maximum and minimum values of signal-to-noise ratios among the channels were measured at 28.66 and 30.56 dB, thereby providing additional validation for the system's signal quality and consistency.},
}
@article {pmid41031500,
year = {2025},
author = {Sisubalan, N and Vijay, N and Kesika, P and Newbegin, M and Shalini, R and Sivamaruthi, BS and Chaiysut, C},
title = {The Contribution of Wearable Devices and Artificial Intelligence to Promoting Healthy Aging.},
journal = {Current pharmaceutical biotechnology},
volume = {},
number = {},
pages = {},
doi = {10.2174/0113892010390500250911104231},
pmid = {41031500},
issn = {1873-4316},
abstract = {INTRODUCTION: Healthy aging involves consistently maximizing opportunities to maintain and enhance physical and mental well-being, fostering independence, and sustaining a high quality of life. This review examines recent technological innovations aimed at promoting the well-being of older adults. The scope encompasses wearable devices and telemedicine, showcasing their potential to enhance the health and overall well-being of older individuals. The review highlights the crucial role of assistive technologies, including mobility aids, hearing aids, and adaptive home devices, in addressing the specific challenges associated with aging.<br><br>METHODS: The relevant literature was collected and selected based on the objective of the study and reviewed.<br><br>RESULTS: Digital technologies, including brain-computer interfaces (BCIs), are explored as potential solutions to enhance communication between healthcare providers and aging patients, considering engagement levels and active interaction. Sophisticated BCIs, such as electroencephalograms, electrocorticography, and signal modeling for real-time identification, play a crucial role in event detection, with machine learning algorithms enhancing signal processing for accurate decoding. The exploration of smart wearable systems for health monitoring emerges as a dynamic and promising field in the context of aging.<br><br>DISCUSSION: Fitbit&#xae; showcases accurate step counting, making it suitable for monitoring physical activity in older adults engaged in slow walking. ActiGraph&#x2122; is evaluated for accuracy in monitoring physical activity in older adults, with results indicating reliable concurrence with Fitbit&#xae; devices. The study identifies several limitations, including sample size constraints, challenges in keeping pace with technological advancements, and the need for further investigation into the suitability of fitness trackers for individuals with significant mobility impairments.<br><br>CONCLUSION: The evolving landscape of wearable technologies, exemplified by Fitbit&#xae;, Acti- Graph&#x2122;, and other interventions, holds substantial promise for reshaping healthcare approaches for the aging population. Addressing the limitations will be crucial as research progresses to ensure the effective and ethical integration of wearables into geriatric care, maximizing their potential benefits.},
}
@article {pmid41028971,
year = {2025},
author = {Korkmaz, I and Tepe, C},
title = {EEG-based motor execution classification of upper and lower extremities using machine learning.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-17},
doi = {10.1080/10255842.2025.2566260},
pmid = {41028971},
issn = {1476-8259},
abstract = {This study classifies upper- and lower-extremity motor execution from electroencephalography (EEG). We compared two feature extractors, statistical features and Common Spatial Patterns (CSP), and four classifiers: K-Nearest Neighbors, Linear Discriminant Analysis (LDA), Multilayer Perceptron, and Support Vector Machine. Metrics were accuracy, F1, precision, and recall. CSP with LDA achieved the best, most consistent performance (72.5% accuracy); statistical features underperformed. We report real-time feasibility benchmarks, post-cue time-window analysis, and significance tests for classifiers. Findings support BCI and neuroprosthesis development, while noting subject variability and dataset specificity. Future work is real-time use, cross-dataset generalization, and hybrid deep learning.},
}
@article {pmid41028569,
year = {2025},
author = {Du, X and Liu, J and Wang, X},
title = {The transformational power of psychedelics: catalysts for creativity, consciousness, and mental health.},
journal = {Molecular psychiatry},
volume = {30},
number = {12},
pages = {6165-6171},
pmid = {41028569},
issn = {1476-5578},
support = {T2350008//National Natural Science Foundation of China (National Science Foundation of China)/ ; JCYJ20220804182935001//Shenzhen Science and Technology Innovation Commission/ ; },
mesh = {Humans ; *Hallucinogens/pharmacology/therapeutic use ; *Creativity ; *Consciousness/drug effects ; *Mental Health ; Lysergic Acid Diethylamide/pharmacology ; Psilocybin/pharmacology ; },
abstract = {Psychedelics, such as psilocybin, lysergic acid diethylamide (LSD), ketamine, and N,N-dimethyltryptamine (DMT), have captured the attention of scientists, artists, and seekers alike for their profound ability to alter consciousness and inspire creativity. The concept of "creation" encompasses multiple interpretations-ranging from generating novel ideas to fostering personal transformation. This perspective explores how psychedelics interact with the concept of creation, examining their role in enhancing artistic inspiration, facilitating spiritual experiences, and driving therapeutic breakthroughs in mental health treatment. By integrating findings from neurobiological research, clinical applications, and cultural analysis, we offer a holistic view of how psychedelics may catalyze innovative modes of thinking and awaken the mind's creative and transformative potential. As these substances gain prominence as tools for reshaping our understanding of consciousness and psychological healing, their broader integration into society requires careful consideration of legal complexities, ethical responsibilities, and cultural contexts to ensure their use is evidence-based, respectful, and responsibly guided.},
}
@article {pmid41025886,
year = {2025},
author = {Chaudhary, J and Gupta, E and Singh, PK and Yadav, RK and Chaudhary, M and Singh, S},
title = {Designing behavioural change intervention module for tobacco cessation counselling among pregnant tobacco users in India: a methodology paper.},
journal = {Health education research},
volume = {40},
number = {6},
pages = {},
doi = {10.1093/her/cyaf041},
pmid = {41025886},
issn = {1465-3648},
support = {2020-5325//Indian Council of Medical Research, New Delhi/ ; },
mesh = {Humans ; Female ; Pregnancy ; India ; *Counseling/methods ; Prenatal Care/methods ; Adult ; *Tobacco Use Cessation/methods ; *Smoking Cessation/methods ; Tobacco Use ; },
abstract = {Tobacco use has detrimental effects on women's reproductive health and is associated with poor pregnancy outcomes. Antenatal care (ANC) check-ups provide health professionals with a unique opportunity to screen and counsel pregnant tobacco users to quit. Currently, in India, pregnant women are not being screened for tobacco use during antenatal care visits and healthcare providers lack formal training to provide tobacco cessation advice. This article describes the designing and development of a tailored behaviour change intervention (BCI) module for tobacco cessation and its delivery to pregnant women attending antenatal clinics. The BCI module was designed to incorporate the components of the Capability, Opportunity and Motivation Model and the Behaviour Change Wheel guide. The development was done in three steps-understanding the behaviour, developing intervention model, and identifying implementation options along with monitoring and evaluation strategies. The module has three tools-counselling flipbook for healthcare provider, take home pamphlets, and information posters for patient waiting areas. A gender- and culture-specific BCI module was developed and implemented to screen and counsel 105 pregnant tobacco users during antenatal visits, leading to high self-reported tobacco quit rate (69%) which corroborated with urine cotinine levels at baseline and end line.},
}
@article {pmid41025122,
year = {2025},
author = {Mohan, A and Anand, RS},
title = {Innovative augmentation techniques and optimized ANN model for imagined speech decoding in EEG-based BCI.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {158},
pmid = {41025122},
issn = {1871-4080},
abstract = {Electroencephalogram (EEG) based Brain computer interface (BCI) emerges as a transformative technology with vast applications in neuroscience and rehabilitation. Imagined speech is the mental process of thinking and formulating words without vocalizing them through articulators. EEG signal is used to study imagined speech which can empower individuals with neurological impairments to communicate their thoughts effortlessly. The main challenge in decoding imagined speech is the nonstationary nature of EEG signals. Identifying robust features and scarcity of imagined speech datasets for properly training machine learning (ML) based algorithms is also a challenging task. The main objective of this study is to propose augmentation methods which mitigate data scarcity in EEG-based BCIs by introducing variations and strengthening model robustness through EEG data augmentation. The second objective is to propose a novel architecture capable of detecting variations in EEG signals for imagined speech datasets and show remarkable results. Seven diverse augmentation techniques are discussed, and the performance of the proposed model is analyzed in terms of accuracy, f1-score and kappa. The classification results are then compared with the case in which no data augmentation is used. The proposed model has shown remarkable accuracy of 91% for long words by using gaussian noise augmentation.},
}
@article {pmid41024222,
year = {2025},
author = {Zhang, Q and Li, W and Zhang, T and Xiong, R and Zhang, J and Jin, Z and Li, L},
title = {Representation of top-down versus bottom-up attention in the right dorsolateral prefrontal cortex and superior parietal lobule.},
journal = {Behavioral and brain functions : BBF},
volume = {21},
number = {1},
pages = {31},
pmid = {41024222},
issn = {1744-9081},
support = {BX202402//Sichuan Province Innovative Talent Funding Project for Postdoctoral Fellows/ ; 2025ZNSFSC0453//Sichuan Science and Technology Program/ ; 62176045//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Attention/physiology ; *Parietal Lobe/physiology/diagnostic imaging ; Male ; Female ; Magnetic Resonance Imaging/methods ; Adult ; Young Adult ; *Dorsolateral Prefrontal Cortex/physiology/diagnostic imaging ; Brain Mapping/methods ; Visual Perception/physiology ; Photic Stimulation/methods ; *Prefrontal Cortex/physiology ; Neural Pathways/physiology ; },
abstract = {BACKGROUND: Visual selective attention can be categorized into top-down (goal-driven) and bottom-up (stimulus-driven) attention, with the fronto-parietal network serving as the primary neural substrate. However, fewer studies have focused on the specific roles of the right dorsolateral prefrontal cortex (DLPFC) and superior parietal lobule (SPL) in top-down and bottom-up attention. This study aimed to investigate the activity and connectivity of the right DLPFC and SPL in top-down and bottom-up attention.<br><br>METHODS: Visual pop-out task mainly induces bottom-up attention, while the visual search task mainly induces top-down attention. Fifty-four participants completed the pop-out and search tasks during functional magnetic resonance imaging (fMRI) scanning. We used univariate analyses, multivariate pattern analyses (MVPA), and generalized psychophysiological interaction (gPPI) to assess activity and functional connectivity.<br><br>RESULTS: Univariate analyses revealed stronger activation in the right DLPFC and SPL during the search&#x2009;>&#x2009;pop-out condition. The activation of the DLPFC was driven by its deactivation in the pop-out task, whereas the SPL showed significant activation in both tasks. MVPA demonstrated that activation patterns in the right DLPFC and SPL could distinguish between the pop-out and search tasks above chance level (0.5), with the right SPL exhibiting higher classification accuracy. The gPPI analyses showed that higher functional connectivity between the two seeds (right DLPFC and SPL) and bilateral precentral gyrus, left SPL, and right insula.<br><br>CONCLUSIONS: These results indicate that the right DLPFC and SPL showed stronger activity and connectivity under top-down versus bottom-up attention, allowing for neural representation of visual selective attention. This study provides evidence for understanding the role of the fronto-parietal network in visual selective attention.},
}
@article {pmid41022774,
year = {2025},
author = {Li, L and Hartzler, A and Menendez-Lustri, DM and Zhang, J and Chen, A and Lam, DV and Traylor, B and Quill, E and Nethery, DE and Hoeferlin, GF and Pawlowski, CL and Bruckman, MA and Sen Gupta, A and Capadona, JR and Shoffstall, AJ},
title = {Dexamethasone-loaded platelet-inspired nanoparticles improve intracortical microelectrode recording performance.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {8579},
pmid = {41022774},
issn = {2041-1723},
support = {T32 EB004314/EB/NIBIB NIH HHS/United States ; GRANT12635707//U.S. Department of Veterans Affairs (Department of Veterans Affairs)/ ; HL121212//U.S. Department of Health &amp; Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)/ ; I01 RX003420/RX/RRD VA/United States ; T32EB004314//U.S. Department of Health &amp; Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB)/ ; R01 HL121212/HL/NHLBI NIH HHS/United States ; },
mesh = {Animals ; *Dexamethasone/administration &amp; dosage/analogs &amp; derivatives/pharmacology ; Microelectrodes/adverse effects ; Blood-Brain Barrier/drug effects/metabolism ; Rats ; *Nanoparticles/chemistry ; Male ; *Blood Platelets/chemistry ; Neurons/drug effects ; Rats, Sprague-Dawley ; Electrodes, Implanted ; Brain-Computer Interfaces ; Drug Delivery Systems ; Anti-Inflammatory Agents/administration &amp; dosage ; },
abstract = {Long-term robust intracortical microelectrode (IME) neural recording quality is negatively affected by the neuroinflammatory response following microelectrode insertion. This adversely impacts brain-machine interface (BMI) performance for patients with neurological disorders or amputations. Recent studies suggest that the leakage of blood-brain barrier (BBB) and microhemorrhage caused by IME insertions contribute to increased neuroinflammation and reduced neural recording performance. Here, we evaluated dexamethasone sodium phosphate-loaded platelet-inspired nanoparticles (DEXSPPIN) to simultaneously augment local hemostasis and serve as an implant-site targeted drug-delivery vehicle. Weekly systemic treatment or control therapy was provided to rats for 8 weeks following IME implantation, while evaluating extracellular single-unit recording performance. End-point immunohistochemistry was performed to further assess the local tissue response to the IMEs. Treatment with DEXSPPIN significantly increased the recording capabilities of IMEs compared to controls over the 8-week observation period. Immunohistochemical analyses of neuron density, activated microglia/macrophage density, astrocyte density, and BBB permeability suggested that the improved neural recording performance may be attributed to reduced neuron degeneration and neuroinflammation. Overall, we found that DEXSPPIN treatment promoted an anti-inflammatory environment that improved neuronal density and enhanced IME recording performance.},
}
@article {pmid41022701,
year = {2025},
author = {Li, J and Li, L and Gao, Z and Tian, Y},
title = {Molecular Dynamics and Neural Network Analysis Reveal Sequential Gating and Allosteric Communication in FMRFamide-Activated Sodium Channels.},
journal = {Journal of chemical information and modeling},
volume = {65},
number = {19},
pages = {10532-10548},
doi = {10.1021/acs.jcim.5c01255},
pmid = {41022701},
issn = {1549-960X},
mesh = {*Molecular Dynamics Simulation ; Allosteric Regulation/drug effects ; *Ion Channel Gating/drug effects ; *FMRFamide/pharmacology/metabolism ; *Neural Networks, Computer ; Ligands ; Protein Conformation ; },
abstract = {FMRFamide-activated sodium channels (FaNaCs) represent a unique class of neuropeptide-gated ion channels within the degenerin/epithelial sodium channel (DEG/ENaC) superfamily. While cryo-electron microscopy has revealed static binding architectures, the dynamic mechanisms underlying ligand recognition, allosteric signal transmission, and channel gating remain poorly understood. Here, we employed microsecond-scale molecular dynamics simulations coupled with neural relational inference analysis to elucidate the complete activation mechanism of FaNaC at atomic resolution. Our analysis revealed a sophisticated multistage activation process initiated by coordinated dynamics of FaNaC-specific insertions SI1 and SI2. Spontaneous FMRFamide-binding events suggested that SI1 functions as a dynamic gate that facilitates optimal ligand burial and stabilization, while SI2 appeared to serve as a conformational lid stabilizing the bound ligand through thermodynamically favorable induced-fit mechanisms. This ligand-induced conformational change, which involves the cooperative reorganization of the three peripheral loops (L1, L2, and L3) in the extracellular domain, propagates through the extracellular domain, particularly via a coordinated rigid-body motion of the β-ball/palm domain, leading to the reorganization of the central β-sheet in the extracellular vestibule and a subsequent conformational wave that compacts the intracellular vestibule. We further leveraged neural relational inference (NRI) to analyze residue-level allosteric networks, demonstrating that ligand binding enhances the network's connectivity and reorganizes allosteric communication pathways. These findings provide a high-resolution, dynamic view of FaNaC function, revealing a novel gating mechanism for the DEG/ENaC superfamily and laying the foundation for future studies into neuropeptide modulation.},
}
@article {pmid41022567,
year = {2025},
author = {Chen, X and Cao, L and Wieske, RE and Prada, J and Gramann, K and Haendel, BF},
title = {Walking Modulates Active Auditory Sensing.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {45},
number = {45},
pages = {},
pmid = {41022567},
issn = {1529-2401},
mesh = {Humans ; Female ; Male ; *Walking/physiology ; Adult ; Young Adult ; *Auditory Perception/physiology ; Acoustic Stimulation/methods ; Electroencephalography ; *Evoked Potentials, Auditory/physiology ; },
abstract = {Walking provides the motor foundation for navigation, while navigation ensures that walking is purposeful and adaptive to environmental contexts. Sensory processing of environmental information acts as the informational bridge that connects walking and adaptive navigation. In the current study, we assessed if walking and the walking direction influences neuronal dynamics underlying environmental information processing. To this end, we conducted two experiments with 12 male and 18 female participants while they walked along an 8-shaped path. Auditory entrainment stimuli were continuously presented, and mobile electroencephalogram was recorded. We found increased auditory entrainment (auditory steady-state response) and early auditory evoked responses during walking compared with standing or stepping in place. We also replicated the well-established reduction of occipital alpha power during walking. The increase of auditory entrainment and the decrease of alpha power were correlated across participants. In the second experiment, randomly presented transient burst sounds led to a perturbation of the auditory entrainment response. The perturbation response was stronger during walking compared with standing; however, only when the burst sounds were presented to one ear but not to both ears. Most importantly, we found that the auditory entrainment was systematically modulated dependent on the walking path. The entrainment responses changed as a function of the turning direction. In general, the current work shows that walking changes auditory processing in a walking path-dependent way which might serve to optimize navigation. The walking path-related modulation might further reflect a shift of attention, marking a form of higher-order active sensing.},
}
@article {pmid41022118,
year = {2025},
author = {Han, Y and Wang, S},
title = {E-Sort: empowering end-to-end neural network for multi-channel spike sorting with transfer learning and fast post-processing.},
journal = {Journal of neural engineering},
volume = {22},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ae0d33},
pmid = {41022118},
issn = {1741-2552},
mesh = {*Neural Networks, Computer ; *Action Potentials/physiology ; Humans ; *Neurons/physiology ; *Machine Learning ; Animals ; Algorithms ; Brain-Computer Interfaces ; },
abstract = {Objective.Spike sorting, which involves detecting and attributing spikes to their putative neurons from extracellular recordings, is a common process in electrophysiology and brain-computer interface systems. Recent advances in large-scale neural recording technologies are challenging the conventional algorithms because of the intensive computational workloads required and the accuracy degradation suffered from time-variant spike patterns and significant levels of noise. Neural networks (NNs) have demonstrated promising performance in processing these large-scale neural recordings. However, their applications are constrained by the labor-intensive data labeling and the lack of fully vectorized frameworks with end-to-end NNs.Approach.We propose E-Sort, an end-to-end NN-based spike sorter with transfer learning and parallelizable post-processing to address both obstacles.Main results.We examined our framework in both synthetic and real datasets. The results of the processing of the synthetic datasets show that our approach can reduce the number of annotated spikes required for training by 44% compared to training from scratch, achieving up to 25.7% higher accuracy. We evaluated E-Sort on various probe geometries, noise levels, and drift patterns, which demonstrates that our design can achieve an accuracy that is comparable with Kilosort4 while sorting 50 s of data in only 1.32 s. To test with real datasets, we first sorted the spikes using Kilosort4 and used the sorted spikes at the initial period to pre-train the NN; then we compared and measured the agreement between the results from the trained model and those from Kilosort4. On average the pre-training process improved the result agreement by 30% approximately.Significance.E-Sort offers a scalable, efficient, and accurate NN-based framework for large-scale spike sorting, significantly reducing manual labeling effort and processing time.},
}
@article {pmid41021940,
year = {2025},
author = {Bulfer, S and Gamez, J and Yan-Huang, A and Haghi, B and Pedroni, V and Andersen, RA and Emami, A},
title = {A 192-Channel 1D CNN-Based Neural Feature Extractor in 65nm CMOS for Brain-Machine Interfaces.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBCAS.2025.3615121},
pmid = {41021940},
issn = {1940-9990},
abstract = {We present a 192-channel 1D convolutional neural network (1D CNN) based neural feature extractor for Brain-Machine Interfaces (BMI) that achieves state-of-the-art decoding stability at 1.8 $μ$W and 12801 $μ$m[2] per channel in 65nm CMOS technology. Our device is a fully configurable, scalable, area and power efficient solution that supports models with 2-8 feature layers and a total kernel length of up to 256. This architecture reduces caching requirements by 5× over conventional computation schemes. Channels and layers are individually power-switchable to further optimize power efficiency for a given neural application. We introduce an on-chip model, FENet-66, that achieves the highest cross-validated decoding performance compared to all previously reported feature sets. We show that this model maintains superior stability over time using recorded data from tetraplegic human participants with spinal cord injury. Our features have 18% higher overall average cross-validated R2 decoding performance compared to Spiking Band Power (SBP), with 28% better performance during the 4th year. Our proposed architecture can also extract mean wavelet power features at low power and latency. We show that custom 1D-CNN kernels achieve 10% better performance compared to wavelet features while compressing the neural data stream by 38×. The models and hardware were validated in real time with a human subject in online closed-loop center-out cursor control experiments with micro-electrode arrays that were implanted for 6 years. Decoders using features generated with this work substantially improve the viability of longterm neural implants compared to other feature extraction methods currently present in low power BMI hardware.},
}
@article {pmid41021638,
year = {2025},
author = {Ferrea, E and Morel, P and Gail, A},
title = {Frontal and parietal planning signals encode adapted motor commands when learning to control a brain-computer interface.},
journal = {PLoS biology},
volume = {23},
number = {9},
pages = {e3003408},
pmid = {41021638},
issn = {1545-7885},
mesh = {Animals ; *Brain-Computer Interfaces ; Macaca mulatta ; *Parietal Lobe/physiology ; *Frontal Lobe/physiology ; Psychomotor Performance/physiology ; Male ; Feedback, Sensory/physiology ; *Learning/physiology ; Adaptation, Physiological ; Movement/physiology ; Neurons/physiology ; },
abstract = {Perturbing visual feedback is a powerful tool for studying visuomotor adaptation. However, unperturbed proprioceptive signals in common paradigms inherently co-varies with physical movements and causes incongruency with the visual input. This can create challenges when interpreting underlying neurophysiological mechanisms. We employed a brain-computer interface (BCI) in rhesus monkeys to investigate spatial encoding in frontal and parietal areas during a 3D visuomotor rotation task where only visual feedback was movement-contingent. We found that both brain regions better reflected the adapted motor commands than the perturbed visual feedback during movement preparation and execution. This adaptive response was observed in both local and remote neurons, even when they did not directly contribute to the BCI input signals. The transfer of adaptive changes in planning activity to corresponding movement corrections was stronger in the frontal than in the parietal cortex. Our results suggest an integrated large-scale visuomotor adaptation mechanism in a motor-reference frame spanning across frontoparietal cortices.},
}
@article {pmid41021378,
year = {2025},
author = {de Camargo, PS and Santos E Souza, GO and Arévalo, A and Lepski, G},
title = {Intraoperative Techniques for Language Mapping in Brain Surgery: A Comparison Between Direct Electrical Stimulation (DES) and Electrocorticography (ECoG).},
journal = {Brain and behavior},
volume = {15},
number = {10},
pages = {e70900},
pmid = {41021378},
issn = {2162-3279},
support = {2018/18900-1//Funda&#xe7;&#xe3;o de Amparo &#xe0; Pesquisa do Estado de S&#xe3;o Paulo/ ; 2023/17520-9//Funda&#xe7;&#xe3;o de Amparo &#xe0; Pesquisa do Estado de S&#xe3;o Paulo/ ; },
mesh = {Humans ; *Electrocorticography/methods ; *Language ; *Brain Mapping/methods ; *Intraoperative Neurophysiological Monitoring/methods ; *Electric Stimulation/methods ; *Brain/surgery ; Neurosurgical Procedures/methods ; },
abstract = {PURPOSE: The purpose of this overview is to compare Direct Electrical Stimulation (DES) and Electrocorticography (ECoG) techniques, assessing their respective strengths, limitations, and roles in ensuring successful language mapping during awake brain surgeries.<br><br>METHOD: This overview aims to compare two techniques used in intraoperative language mapping during awake brain surgery: Direct Electrical Stimulation (DES) and Electrocorticography (ECoG). By summarizing recent advances in both methods, we highlight their respective mechanisms, applications, and roles in improving surgical outcomes. DES is widely considered the gold standard for cortical brain mapping and is applicable in both awake and anesthetized surgeries for treating epilepsy and brain tumors. In contrast, ECoG involves monitoring the brain's electrical activity with or without direct stimulation, as it provides valuable insight into high gamma activity (70-150&#xa0;Hz), which is strongly associated with speech production.<br><br>FINDING: ECoG offers a high-resolution approach to language mapping by detecting high-gamma activity, reducing the risk of intraoperative seizures, and serving as a complementary or alternative tool to DES in specific clinical scenarios. While DES continues to be the most reliable technique for identifying functional brain areas, it does carry a higher risk of inducing seizures. Furthermore, recent advancements in ECoG-based speech decoding and brain-computer interfaces (BCIs) underscore the growing potential of ECoG in restoring communication in patients with severe language impairments, extending its applications beyond surgical mapping.<br><br>CONCLUSION: In conclusion, while DES remains the gold standard for intraoperative language mapping, ECoG is emerging as a promising complementary or alternative technique in some clinical cases. This overview highlights the evolving role of ECoG, particularly in the context of speech decoding and BCIs, offering new possibilities for improving surgical outcomes and postoperative quality of life in patients.},
}
@article {pmid41017975,
year = {2025},
author = {Adama, S and Bogdan, M},
title = {Assessing consciousness in patients with locked-in syndrome using their EEG.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1604173},
pmid = {41017975},
issn = {1662-4548},
abstract = {Research indicates that locked-in syndrome (LIS) patients retain both consciousness and cognitive functions, despite their inability to perform voluntary muscle movements or communicate. Brain-Computer Interfaces (BCIs) provide a means for these patients to communicate, which is crucial, as the ability to interact with their environment has been shown to significantly enhance their wellbeing and quality of life. This paper presents an innovative approach to analyzing electroencephalogram (EEG) data from four LIS patients to assess their consciousness levels, referred to as normalized consciousness levels (NCL) in this study. It consists of extracting different features based on frequency, complexity, and connectivity measures to maximize the probability of correctly determining the patients' actual states given the inexistence of ground truth. The consciousness levels derived from this approach aim to improve our understanding of the patients' condition, which is vital in order to build effective communication systems. Despite considerable inter-patient variability, the findings indicate that the approach is effective in detecting neural markers of consciousness and in differentiating between states across the majority of patients. By accurately assessing consciousness, this research aims to improve diagnosis in addition to determining the optimal time to initiate communication with these non-communicative patients. It is important to note that consciousness is a complex and difficult concept to define. In this study, the term "consciousness level" does not refer to a medical definition. Instead, it represents a scale of NCL values ranging from 0 to 1 representing the likelihood of the patient being fully conscious (1) or not (0).},
}
@article {pmid41017235,
year = {2025},
author = {Chen, D and Lu, Y and Zhang, S and Zhang, W and Yu, Z and Wang, S and Qu, Z and Cheng, M and Yao, Y and Wang, D and Yang, Z and Dong, L},
title = {An Ultra-Flexible Neural Electrode with Bioelectromechanical Compatibility and Brain Micromotion Detection.},
journal = {Advanced healthcare materials},
volume = {},
number = {},
pages = {e03101},
doi = {10.1002/adhm.202503101},
pmid = {41017235},
issn = {2192-2659},
support = {62127810//National Natural Science Foundation of China/ ; CityU11213720//Research Grants Council of the Hong Kong Special Administrative Region/ ; CityU11217221//Research Grants Council of the Hong Kong Special Administrative Region/ ; 9680347//City University of Hong Kong/ ; 9610608//City University of Hong Kong/ ; 9680103//City University of Hong Kong/ ; },
abstract = {Neural electrodes, as core components of brain-computer interfaces(BCIs), face critical challenges in achieving stable mechanical coupling with brain tissue to ensure high-quality signal acquisition. Current flexible electrodes, including semi-invasive meningeal-attached types and implantable cantilever designs, exhibit significant mechanical mismatches (elastic modulus 5-6 orders higher than brain tissue) due to material/structural limitations, leading to interfacial slippage. While thread-like implants (e.g., Neuralink's electrodes) improve compliance via elongated structures, quantitative characterization of mechano-bioelectric interactions remains unexplored. This study proposes a bioelectromechanical coupling strategy, emphasizing synchronized motion between the electrode and the brain tissue through exposed-end deformation. A 4-channel ultra-flexible electrode (40 mm in length, 164 µm in width, and 3 µm in thickness) is optimized using finite-element simulations and zero relative-motion criteria, achieving an equivalent stiffness of 0.023 N m[-1]-matching brain tissue micromotion stiffness. A nanorobotic manipulator installed inside a scanning electron microscope(SEM) with an atomic force microscope(AFM) cantilever enabled precision characterization under the simulated displacement of 25 µm, revealing interfacial forces of 575 nN and piezoresistive sensitivities of 6.4 pA mm[-1] (length) and 10.2 pA µm[-1] (displacement). The dual-functionality (signal acquisition and micromotion sensing) electrodes demonstrate breakthrough potential, establishing quantitative design standards for next-generation bioelectronic implants.},
}
@article {pmid41016568,
year = {2025},
author = {Li, J and Yang, W and Liu, X and Yang, K and Zhou, J and Yang, X},
title = {Research progress of lung organoids in infectious respiratory diseases.},
journal = {European journal of pharmacology},
volume = {1006},
number = {},
pages = {178201},
doi = {10.1016/j.ejphar.2025.178201},
pmid = {41016568},
issn = {1879-0712},
mesh = {*Organoids/virology/pathology/drug effects ; Humans ; *Lung/virology/pathology/cytology ; Animals ; COVID-19/virology/pathology ; SARS-CoV-2 ; },
abstract = {Infectious respiratory diseases are common epidemics that often exhibit phased outbreaks, increasing the healthcare burden. Past research models for these diseases were relatively simplistic, but the emergence of organoids has transformed this landscape. Organoids, three-dimensional in vitro tissue analogs that recapitulate specific spatial organ structures derived from stem cell culture, have advanced significantly over the decade since their inception. Compared to conventional animal models, organoids circumvent interspecies variations, enabling a more precise representation of human physiological and pathological traits. Relative to two-dimensional cell cultures, organoids exhibit enhanced complexity, incorporating diverse cell types and maintaining stable genomes, which facilitates a more faithful simulation of cellular interactions within the extracellular microenvironment. Consequently, as a three-dimensional in vitro model, lung organoids are pivotal for investigating lung organ development, infectious disease pathogenesis, and drug screening. Although SARS-CoV-2 is receding from the spotlight, advancing lung organoid development for addressing infectious respiratory diseases like influenza remains a priority. This review demonstrated the differentiation culture process of lung organoids and outlined advancements in utilizing organoids to elucidate pathogenic infection mechanisms, reveal virus-host interactions and screen therapeutic drugs over the past seven years. Additionally, we have summarized the advances in lung organoid model technologies and outlined their developmental directions.},
}
@article {pmid41016446,
year = {2026},
author = {Wang, L and An, X and Jiang, Z and Wang, J and Ming, D},
title = {The individual differences analysis of audiovisual bounce-inducing effects.},
journal = {Behavioural brain research},
volume = {496},
number = {},
pages = {115851},
doi = {10.1016/j.bbr.2025.115851},
pmid = {41016446},
issn = {1872-7549},
mesh = {Humans ; Male ; *Individuality ; Female ; Young Adult ; Electroencephalography ; Adult ; Acoustic Stimulation ; *Auditory Perception/physiology ; Evoked Potentials/physiology ; Photic Stimulation ; *Visual Perception/physiology ; *Illusions/physiology ; *Brain/physiology ; },
abstract = {The audiovisual bounce-inducing effect (ABE) is a phenomenon that the brain integrates spatial and temporal information from different sensory modalities of vision and hearing. At present, some researchers have conducted research on the individual differences of the ABE, but have not considered the factor of audiovisual stimulus intervals. This study investigated the neural mechanisms underlying the intra- and inter-individual differences in subjects' ABE at different audiovisual stimulus onset asynchronies (SOAs). This study adopted the experimental paradigm of Stream/Bounce illusion, in which visual and auditory stimuli were presented in 7 different SOAs. We recorded behavioral and EEG data during the experiment, compared and analyzed the amplitude differences of event-related potentials (ERPs), calculated statistical indicators, and studied the intra- and inter-individual differences of the ABE under different SOAs. The results show that in terms of the inter-individual differences in the ABE, the amplitude of N1 is more significant in the High ABE Group than the Low ABE Group at SOAs of "V100A" and "0". Individual ABE tendencies are also significantly correlated with N1 amplitude at the two SOAs. These results reveal the effect of stimuli interval on the processing of audiovisual stimuli, there is a complex interplay between the individual's sensory processing mechanisms and the specific temporal dynamics of audiovisual integration.},
}
@article {pmid41015681,
year = {2025},
author = {Parodi, F and Kording, KP and Platt, ML},
title = {Primate neuroethology: a new synthesis.},
journal = {Trends in cognitive sciences},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.tics.2025.09.002},
pmid = {41015681},
issn = {1879-307X},
abstract = {Neuroscience has probed only a sliver of the rich cognitive, emotional, and social behaviors that enable primates to thrive in the real world. Technological breakthroughs allow us to quantify these behaviors alongside wireless neural recordings. New studies reveal that neural activity is intricately bound to movement and is profoundly modulated by behavioral context, emotional states, and social dynamics. We frame our review of primate neuroethology around Niko Tinbergen's four foundational questions - function, mechanism, development, and evolution - to unify classic ethological insights with modern neuroscience tools. We demonstrate that investigating natural behavior promises deep insights into primate cognition, which are relevant for advanced brain-machine interfaces, improved therapies for neurological disorders, and deeper understanding of natural and artificial intelligence.},
}
@article {pmid41011900,
year = {2025},
author = {Tan, X and Tong, B and Zhang, K and Ni, C and Yang, D and Gao, Z and Huang, Y and Yao, N and Huang, L},
title = {Mechanical Behavior Analysis of Neural Electrode Arrays Implantation in Brain Tissue.},
journal = {Micromachines},
volume = {16},
number = {9},
pages = {},
pmid = {41011900},
issn = {2072-666X},
support = {2023BAA005//Major Program (JD) of Hubei Province(2023BAA005)./ ; },
abstract = {Understanding the mechanical behavior of implanted neural electrode arrays is crucial for BCI development, which is the foundation for ensuring surgical safety, implantation precision, and evaluating electrode efficacy and long-term stability. Therefore, a reliable FE models are effective in reducing animal experiments and are essential for a deeper understanding of the mechanics of the implantation process. This study established a novel finite element model to simulate neural electrode implantation into brain tissue, specifically characterizing the nonlinear mechanical responses of brain tissue. Synchronized electrode implantation experiments were conducted using ex vivo porcine brain tissue. The results demonstrate that the model accurately reproduces the dynamics of the electrode implantation process. Quantitative analysis reveals that the implantation force exhibits a positive correlation with insertion depth, the average implantation force per electrode within a multi-electrode array decreases with increasing electrode number, and elevation in electrode size, shank spacing, and insertion speed each contribute to a systematic increase in insertion force. This study provides a reliable simulation tool and in-depth mechanistic analysis for predicting the implantation forces of high-density neural electrode arrays and offer theoretical guidance for optimizing BCI implantation device design.},
}
@article {pmid41009567,
year = {2025},
author = {Haghighi, P and Smith, TJ and Tahmasebi, G and Vargas, S and Jiang, MS and Massaquoi, AC and Huff, J and Capadona, JR and Pancrazio, JJ},
title = {Piezo1 and Piezo2 Ion Channels in Neuronal and Astrocytic Responses to MEA Implants in the Rat Somatosensory Cortex.},
journal = {International journal of molecular sciences},
volume = {26},
number = {18},
pages = {},
pmid = {41009567},
issn = {1422-0067},
support = {R01 NS110823/NS/NINDS NIH HHS/United States ; 1R01NS110823-06/NH/NIH HHS/United States ; },
mesh = {Animals ; *Ion Channels/metabolism/genetics ; *Somatosensory Cortex/metabolism/cytology ; *Astrocytes/metabolism ; Rats ; Microelectrodes/adverse effects ; *Neurons/metabolism ; Male ; Electrodes, Implanted/adverse effects ; Rats, Sprague-Dawley ; },
abstract = {Intracortical microelectrode arrays (MEAs) are tools for recording and stimulating neural activity, with potential applications in prosthetic control and treatment of neurological disorders. However, when chronically implanted, the long-term functionality of MEAs is hindered by the foreign body response (FBR), characterized by gliosis, neuronal loss, and the formation of a glial scar encapsulating layer. This response begins immediately after implantation and is exacerbated by factors such as brain micromotion and the mechanical mismatch between stiff electrodes and soft brain tissue, leading to signal degradation. Despite progress in mitigating these issues, the underlying mechanisms of the brain's response to MEA implantation remain unclear, particularly regarding how cells sense and respond to the associated mechanical forces. Mechanosensitive ion channels, such as the Piezo family, are key mediators of cellular responses to mechanical stimuli. In this study, silicon-based NeuroNexus MEAs consisting of four shanks were implanted in the rat somatosensory cortex for sixteen weeks. Weekly neural recordings were conducted to assess signal quality over time, revealing a decline in active electrode yield and signal amplitude. Immunohistochemical analysis showed an increase in GFAP intensity and decreased neuronal density near the implant site. Furthermore, Piezo1-but not Piezo2-was strongly expressed in GFAP-positive astrocytes within 25 µm of the implant. Piezo2 expression appeared relatively uniform within each brain slice, both in and around the MEA implantation site across cortical layers. Our study builds on previous work by demonstrating a potential role of Piezo1 in the chronic FBR induced by MEA implantation over a 16-week period. Our findings highlight Piezo1 as the primary mechanosensitive channel driving chronic FBR, suggesting it may be a target for improving MEA design and long-term functionality.},
}
@article {pmid41008372,
year = {2025},
author = {Finnis, R and Mehmood, A and Holle, H and Iqbal, J},
title = {Exploring Imagined Movement for Brain-Computer Interface Control: An fNIRS and EEG Review.},
journal = {Brain sciences},
volume = {15},
number = {9},
pages = {},
pmid = {41008372},
issn = {2076-3425},
abstract = {Brain-Computer Interfaces (BCIs) offer a non-invasive pathway for restoring motor function, particularly for individuals with limb loss. This review explored the effectiveness of Electroencephalography (EEG) and function Near-Infrared Spectroscopy (fNIRS) in decoding Motor Imagery (MI) movements for both offline and online BCI systems. EEG has been the dominant non-invasive neuroimaging modality due to its high temporal resolution and accessibility; however, it is limited by high susceptibility to electrical noise and motion artifacts, particularly in real-world settings. fNIRS offers improved robustness to electrical and motion noise, making it increasingly viable in prosthetic control tasks; however, it has an inherent physiological delay. The review categorizes experimental approaches based on modality, paradigm, and study type, highlighting the methods used for signal acquisition, feature extraction, and classification. Results show that while offline studies achieve higher classification accuracy due to fewer time constraints and richer data processing, recent advancements in machine learning-particularly deep learning-have improved the feasibility of online MI decoding. Hybrid EEG-fNIRS systems further enhance performance by combining the temporal precision of EEG with the spatial specificity of fNIRS. Overall, the review finds that predicting online imagined movement is feasible, though still less reliable than motor execution, and continued improvements in neuroimaging integration and classification methods are essential for real-world BCI applications. Broader dissemination of recent advancements in MI-based BCI research is expected to stimulate further interdisciplinary collaboration among roboticists, neuroscientists, and clinicians, accelerating progress toward practical and transformative neuroprosthetic technologies.},
}
@article {pmid41008292,
year = {2025},
author = {Hasegawa, RP and Watanabe, S},
title = {Neurodetector: EEG-Based Cognitive Assessment Using Event-Related Potentials as a Virtual Switch.},
journal = {Brain sciences},
volume = {15},
number = {9},
pages = {},
pmid = {41008292},
issn = {2076-3425},
support = {A19-46//AMED/ ; JP24K 12215//JSPS KAKENHI/ ; },
abstract = {Background/Objectives: Motor decline in older adults can hinder cognitive assessments. To address this, we developed a brain-computer interface (BCI) using electroencephalography (EEG) and event-related potentials (ERPs) as a motor-independent EEG Switch. ERPs reflect attention-related neural activity and may serve as biomarkers for cognitive function. This study evaluated the feasibility of using ERP-based task success rates as indicators of cognitive abilities. The main goal of this article is the development and baseline evaluation of the Neurodetector system (incorporating the EEG Switch) as a motor-independent tool for cognitive assessment in healthy adults. Methods: We created a system called Neurodetector, which measures cognitive function through the ability to perform tasks using a virtual one-button EEG Switch. EEG data were collected from 40 healthy adults, mainly under 60 years of age, during three cognitive tasks of increasing difficulty. Results: The participants controlled the EEG Switch above chance level across all tasks. Success rates correlated with task difficulty and showed individual differences, suggesting that cognitive ability influences performance. In addition, we compared the pattern-matching method for ERP decoding with the conventional peak-based approaches. The pattern-matching method yielded a consistently higher accuracy and was more sensitive to task complexity and individual variability. Conclusions: These results support the potential of the EEG Switch as a reliable, non-motor-dependent cognitive assessment tool. The system is especially useful for populations with limited motor control, such as the elderly or individuals with physical disabilities. While Mild Cognitive Impairment (MCI) is an important future target for application, the present study involved only healthy adult participants. Future research should examine the sources of individual differences and validate EEG switches in clinical contexts, including clinical trials involving MCI and dementia patients. Our findings lay the groundwork for a novel and accessible approach for cognitive evaluation using neurophysiological data.},
}
@article {pmid41006944,
year = {2025},
author = {Huang, W and Li, H and Qin, F and Wu, D and Cheng, K and Chen, H},
title = {A Prompt-Guided Generative Language Model for Unifying Visual Neural Decoding Across Multiple Subjects and Tasks.},
journal = {International journal of neural systems},
volume = {},
number = {},
pages = {2550068},
doi = {10.1142/S0129065725500686},
pmid = {41006944},
issn = {1793-6462},
abstract = {Visual neural decoding not only aids in elucidating the neural mechanisms underlying the processing of visual information but also facilitates the advancement of brain-computer interface technologies. However, most current decoding studies focus on developing separate decoding models for individual subjects and specific tasks, an approach that escalates training costs and consumes a substantial amount of computational resources. This paper introduces a Prompt-Guided Generative Visual Language Decoding Model (PG-GVLDM), which uses prompt text that includes information about subjects and tasks to decode both primary categories and detailed textual descriptions from the visual response activities of multiple individuals. In addition to visual response activities, this study also incorporates a multi-head cross-attention module and feeds the model with whole-brain response activities to capture global semantic information in the brain. Experiments on the Natural Scenes Dataset (NSD) demonstrate that PG-GVLDM attains an average category decoding accuracy of 66.6% across four subjects, reflecting strong cross-subject generalization, and achieves text decoding scores of 0.342 (METEOR), 0.450 (Sentence-Transformer), 0.283 (ROUGE-1), and 0.262 (ROUGE-L), establishing state-of-the-art performance in text decoding. Furthermore, incorporating whole-brain response activities significantly enhances decoding performance by enabling the integration of distributed neural signals into coherent global semantic representations, underscoring its methodological importance for unified neural decoding. This research not only represents a breakthrough in visual neural decoding methodologies but also provides theoretical and technical support for the development of generalized brain-computer interfaces.},
}
@article {pmid41006379,
year = {2025},
author = {Altaheri, H and Karray, F and Karimi, AH},
title = {Temporal convolutional transformer for EEG based motor imagery decoding.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {32959},
pmid = {41006379},
issn = {2045-2322},
mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; Neural Networks, Computer ; *Imagination/physiology ; Signal Processing, Computer-Assisted ; Movement/physiology ; Algorithms ; },
abstract = {Brain-computer interfaces (BCIs) based on motor imagery (MI) offer a transformative pathway for rehabilitation, communication, and control by translating imagined movements into actionable commands. However, accurately decoding motor imagery from electroencephalography (EEG) signals remains a significant challenge in BCI research. In this paper, we propose TCFormer, a temporal convolutional Transformer designed to improve the performance of EEG-based motor imagery decoding. TCFormer integrates a multi-kernel convolutional neural network (MK-CNN) for spatial-temporal feature extraction with a Transformer encoder enhanced by grouped query attention to capture global contextual dependencies. A temporal convolutional network (TCN) head follows, utilizing dilated causal convolutions to enable the model to learn long-range temporal patterns and generate final class predictions. The architecture is evaluated on three benchmark motor imagery and motor execution EEG datasets: BCIC IV-2a, BCIC IV-2b, and HGD, achieving average accuracies of 84.79, 87.71, and 96.27%, respectively, outperforming current methods. These results demonstrate the effectiveness of the integrated design in addressing the inherent complexity of EEG signals. The code is publicly available at https://github.com/altaheri/TCFormer .},
}
@article {pmid41005779,
year = {2025},
author = {Kawakami, DMO and Karloh, M and Araujo, GHG and Colucci, MG and Pires Di Lorenzo, VA and Mendes, RG},
title = {Effects of an early behavioural change strategy following COPD exacerbation in hospital and outpatient settings in Brazil: protocol for a randomised clinical trial on cardiovascular risk, physical activity and functionality.},
journal = {BMJ open},
volume = {15},
number = {9},
pages = {e097954},
pmid = {41005779},
issn = {2044-6055},
mesh = {Humans ; *Pulmonary Disease, Chronic Obstructive/rehabilitation/psychology/physiopathology/complications ; Brazil ; Quality of Life ; *Exercise ; Randomized Controlled Trials as Topic ; Cost-Benefit Analysis ; *Cardiovascular Diseases/prevention &amp; control ; Disease Progression ; *Behavior Therapy/methods ; Outpatients ; },
abstract = {INTRODUCTION: Patients living with chronic obstructive pulmonary disease (COPD) experience periods of disease stability and exacerbations (ECOPD). COPD imposes a negative and impactful extrapulmonary impairment and commonly overlaps with multimorbidity, particularly cardiovascular disease. Pulmonary rehabilitation (PR) aims to improve physical activity (PA) and quality of life, while behavioural change interventions (BCIs) aim to promote lifestyle changes and autonomy. However, after ECOPD, a variety of barriers often delay patient referral to PR. This study aims to assess the effects of a BCI for patients after ECOPD, focusing on cardiovascular health, PA and functionality. Additionally, the study will assess 6-month sustainability of PA and conduct a cost-utility analysis comparing a non-intervention group in the Unified Health System.<br><br>METHODS AND ANALYSIS: This randomised clinical trial will assess patients with ECOPD over 12 weeks using a BCI based on self-determination theory to increase daily steps. First, the cardiovascular and functional profile will be evaluated. Afterwards, the patients will receive an accelerometer to monitor the PA level. After 7&#x2009;days, questionnaires will be applied on quality of life, symptoms and motivational levels for PA. Patients will be randomised into control group or intervention groups, both will receive educational booklets and IG will also receive an educational interview. PA will be tracked using activPAL accelerometer at weeks 1, 4 and 12, and follow-up at 6 months. Data analysis will include unpaired Student's t-test or Mann-Whitney test for group comparison, and a linear mixed model to assess intervention effects over time. Economic evaluation, using STATA (V.14), will involve correlation analysis, and p<0.05 significance will be considered.<br><br>ETHICS AND DISSEMINATION: This study has been approved by the Federal University of S&#xe3;o Carlos' Ethics Committee, Irmandade Santa Casa de Miseric&#xf3;rdia de S&#xe3;o Carlos and Base Hospital of S&#xe3;o Jos&#xe9; do Rio Preto. All procedures will be conducted in accordance with the Declaration of Helsinki, Good Clinical Practice guidelines and applicable regulatory requirements. All results will be presented in peer-reviewed medical journals and international conferences.<br><br>TRIAL REGISTRATION NUMBER: Brazilian Registry of Clinical Trials under the registration number RBR-6m9pwb7.},
}
@article {pmid41005749,
year = {2025},
author = {Zhang, H and Xie, J and Yu, H and Du, F and Jin, Z and Chen, Y},
title = {Enhancing transient motion-onset visual evoked potentials via stochastic resonance: Unimodal and cross-modal noise effects.},
journal = {Journal of neuroscience methods},
volume = {424},
number = {},
pages = {110589},
doi = {10.1016/j.jneumeth.2025.110589},
pmid = {41005749},
issn = {1872-678X},
mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Stochastic Processes ; Male ; Adult ; Female ; Photic Stimulation/methods ; Young Adult ; *Motion Perception/physiology ; *Brain/physiology ; Acoustic Stimulation ; Signal Processing, Computer-Assisted ; Noise ; },
abstract = {BACKGROUND: Motion-onset visual evoked potential (mVEP) are transient brain responses triggered by sudden motion stimuli and are widely used in brain-computer interface (BCI) systems. However, the inherently weak nature of mVEP signals poses a significant challenge to achieving reliable and accurate BCI performance. Enhancing the signal quality of mVEP responses is therefore critical for improving system robustness and usability.<br><br>NEW METHOD: This study introduces a novel approach based on stochastic resonance (SR) theory, where appropriate levels of noise can enhance the performance of nonlinear systems such as the brain. By applying auditory and visual noise of varying intensities alongside mVEP stimuli, both unimodal SR and cross-modal SR effects were investigated. The method examines the effects of these noise conditions on brain activation and classification performance in mVEP-BCI.<br><br>RESULTS: The results show that moderate levels of auditory or visual noise significantly enhance the P2 component amplitude of mVEP and improve classification accuracy in BCI tasks. In contrast, excessive noise leads to suppression of neural responses, forming an inverted U-shaped relationship between noise intensity and mVEP amplitude.<br><br>Conventional mVEP enhancement techniques typically rely on signal processing methods such as spatial filtering or feature extraction. In comparison, the proposed noise modulation strategy directly enhances neural responses, offering a biologically inspired and computationally simple alternative that complements existing approaches.<br><br>CONCLUSIONS: Both unimodal and cross-modal SR effectively enhance mVEP responses and BCI performance. This strategy provides new insights into SR mechanisms and supports the development of more robust mVEP-BCI systems.},
}
@article {pmid41005327,
year = {2025},
author = {Wang, N and Deng, X and Zhu, N and Wang, X and Wang, Y and Sun, B and Zheng, C},
title = {Bayesian decoding and its application in reading out spatial memory from neural ensembles.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae0c3c},
pmid = {41005327},
issn = {1741-2552},
abstract = {Spatial memory serves as a foundation to establish cognitive map, supporting navigation and decision-making processes across species. Essential brain regions such as the hippocampus and entorhinal cortex enable these functions through spatially tuned neurons, particularly place cells, which encode an animal's precise location. The continuous spatial trajectories are then able to be represented by temporally sequential firing of these cells at neural ensemble level. Bayesian frameworks are powerful tools for reconstructing such "mind travel". In this article, we focus on the principles and advances of Bayesian decoding methods for extracting spatial memory information from neural ensembles. First, we review non-recursive approaches and recursive point process filters, paying special attention to clusterless decoding strategies. We also discuss emerging approaches such as neural manifolds within Bayesian estimation. Next, we discuss the advanced application of Bayesian decoding in understanding the neuronal coding mechanisms of memory consolidation and planning, and in supporting computational model establishment and closed-loop manipulation. Finally, we discuss the limitations and challenges of recent approaches, highlighting the promising strategies that could raise the decoding efficiency and adapt the growing scale of neural data. We believe that the developing of Bayesian decoding approach would significantly benefit for techniques and applications of memory-related brain machine interface.},
}
@article {pmid41005325,
year = {2025},
author = {Botero, JP and Roberts, SM and Mackowiak, P and Witham, NS and Selzer, L and Srikanthan, B and Zoschke, K and Negi, S and Solzbacher, F},
title = {Neuralace: manufacture, parylene-C coating, and mechanical properties.},
journal = {Journal of neural engineering},
volume = {22},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ae0c39},
pmid = {41005325},
issn = {1741-2552},
mesh = {*Xylenes/chemistry ; *Polymers/chemistry ; *Coated Materials, Biocompatible/chemistry/chemical synthesis ; *Electrodes, Implanted ; *Brain-Computer Interfaces ; Humans ; Equipment Design ; Materials Testing ; },
abstract = {Objective.This study investigates the mechanical properties of the Neuralace, a novel ultra-thin, high-channel-count mesh-type subdural electrode array, to characterize its mechanical compatibility with neural tissue (i.e., the forces exerted onto the brain upon conformation) for chronic brain-computer interface (BCI) applications.Approach.A full-factorial design of experiments was used to assess the effects of geometrical variations, orientation, and polymeric encapsulation on the stiffness of silicon-based Neuralace structures. A custom low-force four-point bending setup was developed to measure flexural stiffness in a physiologically relevant displacement range.Main results.The stiffness values of Neuralace structures ranged from 2.99 N m[-1]to 7.21 N m[-1], depending on the cell-wall thickness (CWT) of the lace, orientation, and parylene-C (PPXC) encapsulation. Orientation and CWT had the largest impact on the stiffness of the structures, while the effects of PPXC encapsulation were statistically significant but more subtle. The stiffest Neuralace configuration is expected to exert forces approximately 10-100 times lower than commercially available subdural implants would when conforming to the brain's topology (considering a 60 mm radius of the gyrus).Significance.Subdural electrode arrays have traditionally been used for epilepsy monitoring and surgical planning. These arrays are now transitioning from short-term implantation in epilepsy monitoring to long-term use in BCIs, which requires consideration of the foreign body response to ensure long-term durability and functionality. Biocompatibility challenges, such as fibrotic encapsulation and reactive astrogliosis, highlight the need for conformal subdural implant designs that minimize mechanical stress on neural tissue. This study establishes a rigorous and reproducible framework for mechanical characterization of conformable neural implants and demonstrates the feasibility of tuning design parameters to reduce implant-induced mechanical stress on cortical tissue. The results support future development of chronic BCI-compatible subdural electrodes with improved biocompatibility through mechanical design.},
}
@article {pmid41005322,
year = {2025},
author = {Yue, J and Xiao, X and Zhang, H and Xu, M and Ming, D},
title = {BGTransform: a neurophysiologically informed EEG data augmentation framework.},
journal = {Journal of neural engineering},
volume = {22},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ae0c3a},
pmid = {41005322},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Evoked Potentials, Visual/physiology ; *Deep Learning ; *Brain/physiology ; Adult ; Databases, Factual ; Event-Related Potentials, P300/physiology ; },
abstract = {Objective. Deep learning has emerged as a powerful approach for decoding electroencephalography (EEG)-based brain-computer interface (BCI) signals. However, its effectiveness is often limited by the scarcity and variability of available training data. Existing data augmentation methods often introduce signal distortions or lack physiological validity. This study proposes a novel augmentation strategy designed to improve generalization while preserving the underlying neurophysiological structure of EEG signals.Approach. We propose Background EEG Transform (BGTransform), a principled data augmentation framework that leverages the neurophysiological dissociation between task-related activity and ongoing background EEG. In contrast to existing methods, BGTransform generates new trials by selectively perturbing the background EEG component while preserving the task-related signal, thus enabling controlled variability without compromising class-discriminative features. We applied BGTransform to three publicly available EEG-BCI datasets spanning steady-state visual evoked potential and P300 paradigms. The effectiveness of BGTransform is evaluated using several widely adopted neural decoding models under three training regimes: (1) without augmentation (baseline model), (2) with conventional augmentation methods, and (3) with BGTransform.Main results. Across all datasets and model architectures, BGTransform consistently outperformed both baseline models and conventional augmentation techniques. Compared to models trained without BGTransform, it achieved average classification accuracy improvements of 2.45%-15.52%, 4.36%-17.15% and 7.55%-10.47% across the three datasets, respectively. In addition, BGTransform demonstrated greater robustness across subjects and tasks, maintaining stable performance under varying recording conditions.Significance. BGTransform provides a principled and effective approach to augmenting EEG data, informed by neurophysiological insight. By preserving task-related components and introducing controlled variability, the method addresses the challenge of data sparsity in EEG-BCI training. These findings support the utility of BGTransform for improving the accuracy, robustness, and generalizability of deep learning models in neural engineering applications.},
}
@article {pmid41005320,
year = {2025},
author = {Berke Guney, O and Kucukahmetler, D and Ozkan, H},
title = {Source-free domain adaptation for SSVEP-based brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {22},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ae0c3d},
pmid = {41005320},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Evoked Potentials, Visual/physiology ; Male ; *Neural Networks, Computer ; Adult ; Female ; *Adaptation, Physiological/physiology ; Photic Stimulation/methods ; },
abstract = {Objective.Steady-state visually evoked potential-based Brain-computer interface (BCI) spellers assist individuals experiencing speech difficulties by enabling them to communicate at a fast rate. However, achieving a high information transfer rate (ITR) in most prominent methods requires an extensive calibration period before using the system, leading to discomfort for new users. We address this issue by proposing a novel method that adapts a powerful deep neural network (DNN) pre-trained on data from source domains (data from former users or participants of previous experiments), to the new user (target domain) using only unlabeled target data.Approach.Our method adapts the pre-trained DNN to the new user by minimizing our proposed custom loss function composed of self-adaptation and local-regularity terms. The self-adaptation term uses the pseudo-label strategy, while the novel local-regularity term exploits the data structure and forces the DNN to assign similar labels to adjacent instances.Main results.Our method achieves excellent ITRs of 201.15 bits min[-1]and 145.02 bits min[-1]on the benchmark and BETA datasets, respectively, and outperforms the state-of-the-art alternatives. Our code is available athttps://github.com/osmanberke/SFDA-SSVEP-BCI.Significance.The proposed method prioritizes user comfort by removing the burden of calibration while maintaining an excellent character identification accuracy and ITR. Because of these attributes, our approach could significantly accelerate the adoption of BCI systems into everyday life.},
}
@article {pmid41004906,
year = {2025},
author = {Cai, S and Lin, Z and Liu, X and Wei, W and Wang, S and Zhang, M and Schultz, T and Li, H},
title = {Spiking neural networks for EEG signal analysis: From theory to practice.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {194},
number = {},
pages = {108127},
doi = {10.1016/j.neunet.2025.108127},
pmid = {41004906},
issn = {1879-2782},
abstract = {The intricate and efficient information processing of the human brain, driven by spiking neural interactions, has led to the development of spiking neural networks (SNNs) as a cutting-edge neural network paradigm. Unlike traditional artificial neural networks (ANNs) that use continuous values, SNNs emulate the brain's spiking mechanisms, offering enhanced temporal information processing and computational efficiency. This review addresses the critical gap between theoretical advancements and practical applications of SNNs in EEG signal analysis. We provide a comprehensive examination of recent SNN methodologies and their application to EEG signals, highlighting their potential benefits over conventional deep learning approaches. The review encompasses foundational knowledge of SNNs, detailed implementation strategies for EEG analysis, and challenges inherent to SNN-based methods. Practical guidance is provided through step-by-step instructions and accessible code available on GitHub, aimed at facilitating researchers' adoption of these techniques. Additionally, we explore emerging trends and future research directions, emphasizing the potential of SNNs to advance brain-computer interfaces and neurofeedback systems. This paper serves as a valuable resource for bridging the gap between theoretical developments in SNNs and their practical implementation in EEG signal analysis.},
}
@article {pmid41004593,
year = {2025},
author = {Zhang, L and Wang, S and Xia, J and Li, B and Zhang, S and Luo, J and Zhang, F and Zheng, T and Pan, G and Hasan, T and Yu, Y and Ding, G and Jin, H and Yang, Z and Dong, S},
title = {Monolithic multimodal neural probes for sustained stimulation and long-term neural recording.},
journal = {Science advances},
volume = {11},
number = {39},
pages = {eadu1753},
pmid = {41004593},
issn = {2375-2548},
mesh = {*Electrodes, Implanted ; Animals ; *Neurons/physiology ; Optical Fibers ; Biocompatible Materials/chemistry ; Electric Stimulation ; },
abstract = {Long-term implantable neural probes with dual-mode optical stimulation and simultaneous electrical recording are crucial for modulating neural loop activity in vivo. Traditional probes using "add-on" strategies often suffer from mechanical rigidity, compromised electrical performance, and insufficient biocompatibility, limiting their clinical applicability. In this study, we present a method for the direct laser writing of electrode arrays onto the curved surface of optical fibers, integrating them within a biocompatible polymer coating to create monolithic neural probes. The monolithic probes demonstrate high mechanical bending endurance, stable impedance, and improved biocompatibility, resulting in a lower inflammatory response compared to conventional systems. Furthermore, our method facilitates the multilayer integration of multilayer electrodes onto optical fibers, enabling high-density electrical readout channels. This advancement represents substantial progress in neuroengineering, with promising implications for future neural monitoring and modulation applications.},
}
@article {pmid41003965,
year = {2025},
author = {Shao, X and Chang, C and Wang, H},
title = {Impact of fatigue levels on EEG-based personal recognition.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {},
number = {},
pages = {},
pmid = {41003965},
issn = {1741-0444},
support = {92270113//National Natural Science Foundation of China/ ; 62176054//National Natural Science Foundation of China/ ; },
abstract = {The uniqueness of the electroencephalogram (EEG), a distinct biometric marker inherent to each individual, yields significant advantages for user authentication and identification in brain-computer interface (BCI) systems. However, EEG features can easily change according to the user's state, which may affect the performance of biometric recognition systems based on EEG. Notably, in EEG data collection for such systems, fatigue levels can fluctuate over time-a factor that has yet to be thoroughly investigated concerning its impact on individual recognition performance. This study explores the implications of fatigue on EEG-based personal recognition systems. We derived six sub-datasets from two simulated driving datasets, each labeled with varying levels of fatigue. From each sub-dataset, we extracted six features for identity recognition within and across different fatigue levels. Single-session and cross-session studies revealed that the disparity of EEG fatigue levels between the training and testing sets increased, and system recognition accuracy experienced a decline. Specifically, recognition accuracy typically fell by over 30 % after 90 min of simulated driving. Furthermore, identity recognition results are better when the training set includes EEG in more fatigued states compared to the test set, rather than the other way around. Crucially, the method based on functional connectivity features shows the best recognition accuracy under different fatigue levels. This research emphasizes the potential benefits of considering fatigue variations in EEG-based personal recognition systems.},
}
@article {pmid41003117,
year = {2025},
author = {Bizzarri, FP and Campetella, M and Recupero, SM and Bellavia, F and D'Amico, L and Rossi, F and Gavi, F and Filomena, GB and Russo, P and Palermo, G and Foschi, N and Totaro, A and Ragonese, M and Sighinolfi, MC and Racioppi, M and Sacco, E and Rocco, B},
title = {Female Sexual Function After Radical Treatment for MIBC: A Systematic Review.},
journal = {Journal of personalized medicine},
volume = {15},
number = {9},
pages = {},
pmid = {41003117},
issn = {2075-4426},
abstract = {Background: Sexuality in women with muscle-invasive bladder cancer (MIBC) undergoing radical treatment represents a crucial aspect of their overall quality of life, which is increasingly recognized as a key component of patient-centered care and long-term well-being. This review aimed to analyze the available literature to provide a comprehensive overview of the effects of treatments on female sexual function. Methods: We included all qualitative and quantitative studies addressing sexual function in patients treated for MIBC. Excluded were narrative reviews, case reports, conference abstracts, systematic reviews, and meta-analyses. The included studies involved women undergoing either robot-assisted radical cystectomy (RARC) or open RC (ORC), often with nerve-sparing, vaginal-sparing, or pelvic organ-preserving techniques. Data on oncological and functional outcomes were collected. Results: A systematic review of 29 studies including 1755 women was conducted. RC was performed via robotic/laparoscopic approaches in 39% of cases and open techniques in 61%. Urinary diversions included orthotopic neobladders (48%), ileal conduits (42%), ureterocutaneostomies (3%), and Indiana pouches (7%). Radiotherapy, used in 6% of patients, was mainly applied in a curative, trimodal setting. Sexual function was evaluated using various pre- and/or postoperative questionnaires, most commonly the EORTC QLQ-C22, FACT-BL, Bladder Cancer Index (BCI), LENT SOMA, and Female Sexual Function Index (FSFI). Radiotherapy was associated with reduced sexual function, though outcomes were somewhat better than with surgery. Among surgical approaches, no differences in sexual outcomes were observed. Conclusions: Further qualitative research is essential to better understand the experience of FSD after treatment. Incorporating both patient and clinician perspectives will be key to developing tailored interventions. In addition, efforts should be made to standardize the questionnaires used to assess female sexual dysfunction, in order to improve comparability across studies and ensure consistent evaluation.},
}
@article {pmid41002025,
year = {2025},
author = {Tang, H and He, S and Tao, J and Wang, C and Wang, Z and Song, J},
title = {Mechanically Tunable Electromagnetic Metamaterials Based on Chains of Tension-rotation Coupling Units with Exceptional Reconfiguration Capability.},
journal = {Small methods},
volume = {},
number = {},
pages = {e01423},
doi = {10.1002/smtd.202501423},
pmid = {41002025},
issn = {2366-9608},
support = {12225209//National Natural Science Foundation of China/ ; 12321002//National Natural Science Foundation of China/ ; 12302223//National Natural Science Foundation of China/ ; GZC20232293//Postdoctoral Fellowship Program of CPSF/ ; 2022M710126//China Postdoctoral Science Foundation/ ; 2023M743011//China Postdoctoral Science Foundation/ ; BX20220268//China National Postdoctoral Program for Innovative Talents/ ; },
abstract = {Controlling the out-of-plane rotation of split-ring resonators (SRRs) represents an effective strategy to realize mechanically tunable electromagnetic (EM) materials. However, designing structures that can achieve substantial angular rotations via straightforward stretching operations while keeping the resonators intact remains a challenge. Here, a mechanically tunable EM metamaterial constructed from parallel chains of tension-rotation units that enable substantial out-of-plane rigid rotations exceeding 120° of the SRRs through simple stretch is reported. Theoretical, numerical, and experimental studies are conducted to reveal the deformation mechanism and quantify the relationship between tensile strain and rotation angles of SRRs. Comprehensive experimental and numerical studies show that the proposed metamaterial can extensively modulate the transmissions of both linearly and circularly polarized waves. Specifically, the transmission of TE wave exhibits a distinctive two-stage increasing-decreasing behavior, and the CD presents a unique zero-positive-zero-negative profile during stretching, which are not easily accessible by existing mechanically tunable EM metamaterials due to their limited deformation capabilities. Moreover, structural reconfiguration of chain arrangements enables tunable resonance frequencies while maintaining the frequency position of maximum CD, demonstrating robust preservation of the dominant chiral eigenmode. This study provides a valuable design strategy for developing mechanically tunable EM metamaterials with high tunability and multifunctionality.},
}
@article {pmid41000855,
year = {2025},
author = {Marin-Llobet, A and Lin, Z and Baek, J and Aljovic, A and Zhang, X and Lee, AJ and Wang, W and Lee, J and Shen, H and He, Y and Li, N and Liu, J},
title = {An AI Agent for cell-type specific brain computer interfaces.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {41000855},
issn = {2692-8205},
support = {DP1 DK130673/DK/NIDDK NIH HHS/United States ; R01 LM014465/LM/NLM NIH HHS/United States ; },
abstract = {Decoding how specific neuronal subtypes contribute to brain function requires linking extracellular electrophysiological features to underlying molecular identities, yet reliable in vivo electrophysiological signal classification remains a major challenge for neuroscience and clinical brain-computer interfaces (BCI). Here, we show that pretrained, general-purpose vision-language models (VLMs) can be repurposed as few-shot learners to classify neuronal cell types directly from electrophysiological features, without task-specific fine-tuning. Validated against optogenetically tagged datasets, this approach enables robust and generalizable subtype inference with minimal supervision. Building on this capability, we developed the BCI AI Agent (BCI-Agent), an autonomous AI framework that integrates vision-based cell-type inference, stable neuron tracking, and automated molecular atlas validation with real-time literature synthesis. BCI-Agent addresses three critical challenges for in vivo electrophysiology: (1) accurate, training-free cell-type classification; (2) automated cross-validation of predictions using molecular atlas references and peer-reviewed literature; and (3) embedding molecular identities within stable, low-dimensional neural manifolds for dynamic decoding. In rodent motor-learning tasks, BCI-Agent revealed stable, cell-type-specific neural trajectories across time that uncover previously inaccessible dimensions of neural computation. Additionally, when applied to human Neuropixels recordings-where direct ground-truth labeling is inherently unavailable-BCI-Agent inferred neuronal subtypes and validated them through integration with human single-cell atlases and literature. By enabling scalable, cell-type-specific inference of in vivo electrophysiology, BCI-Agent provides a new approach for dissecting the contributions of distinct neuronal populations to brain function and dysfunction.},
}
@article {pmid40999875,
year = {2025},
author = {Balendra, and Sharma, N and Sharma, S},
title = {Transformed wavelets for motor imagery EEG classification using hybrid CNN-modified vision transformer: an exploratory study of MI EEG.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-18},
doi = {10.1080/10255842.2025.2563351},
pmid = {40999875},
issn = {1476-8259},
abstract = {Wavelets capture signal characteristics across time and frequency, but traditional wavelets suffer from high time-bandwidth products (TBP), limiting feature discrimination in EEG classification. We propose transformed wavelets with improved TBP and frequency bandwidth, outperforming Morlet by 0.04 and 0.20, respectively. Using datasets BCI Competition IV 2a, 2b, and CLA, we evaluated both fundamental and transformed wavelets with a modified vision transformer (MViT). Enhanced scalograms generated through local mean and principal component analysis (PCA) consistently outperformed raw scalograms. A hybrid convolutional neural network (CNN)-MViT achieved 82.35% inter-subject and 89.02% intra-subject accuracy, with 3-4% average gains in motor imagery EEG decoding.},
}
@article {pmid40999234,
year = {2025},
author = {Cai, C and Gao, L and Zhu, Z and Chen, W and Zhang, F and Yu, C and Xu, K and Zhu, J and Wu, H},
title = {Change in brain molecular landscapes following electrical stimulation of the nucleus accumbens.},
journal = {Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology},
volume = {},
number = {},
pages = {},
pmid = {40999234},
issn = {1740-634X},
support = {82401781//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82171519//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Deep brain stimulation (DBS) targeting the nucleus accumbens (NAc) is a promising therapeutic intervention for treatment-resistant neuropsychiatric disorders such as depression, anxiety, and addiction. However, the molecular mechanisms underlying the clinical efficacy of NAc DBS remain largely unknown. One approach to address this question is by performing spatial gene expression analysis on cells located in different regions of the same circuit following NAc DBS. In this study, we utilized high-resolution spatial transcriptomics (Stereo-seq) to investigate gene expression changes induced by NAc DBS in the mouse brain. Mice were randomly allocated to receive continuous electrical stimulation (0.1 mA, 130 Hz) or sham treatment (electrode implanted, no electrical stimulation given) for one week, and subsequent Stereo-seq analysis identified differentially expressed genes (DEGs) across various brain regions. Functional enrichment analysis highlighted changes in synaptic and neuroplasticity processes as well as stress and inflammatory responses in the NAc circuit. Single-cell resolution mapping further identified key molecular players, including Nlgn1, Snca, Pde10a, and Syt1, particularly in glutamate receptor-expressing neurons in the NAc. These genes are critical for synaptic plasticity and neurotransmitter release, and have been implicated in various psychiatric disorders. These findings shed light on the molecular underpinnings of NAc DBS and provide insights into its therapeutic potential in modulating neural circuits associated with neuropsychiatric disorders.},
}
@article {pmid40998792,
year = {2025},
author = {Chen, G and Zhang, X and Hu, X and Liu, Y and Yang, Y and Wang, W},
title = {Chemical knowledge-informed framework for privacy-aware retrosynthesis learning.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {8389},
pmid = {40998792},
issn = {2041-1723},
abstract = {Chemical reaction data is a pivotal asset, driving advances in competitive fields such as pharmaceuticals, materials science, and industrial chemistry. Its proprietary nature renders it sensitive, as it often includes confidential insights and competitive advantages organizations strive to protect. However, in contrast to this need for confidentiality, the current standard training paradigm for machine learning-based retrosynthesis gathers reaction data from multiple sources into one single edge to train prediction models. This paradigm poses considerable privacy risks as it necessitates broad data availability across organizational boundaries and frequent data transmission between entities, potentially exposing proprietary information to unauthorized access or interception during storage and transfer. In the present study, we introduce the chemical knowledge-informed framework (CKIF), a privacy-preserving approach for learning retrosynthesis models. CKIF enables distributed training across multiple chemical organizations without compromising the confidentiality of proprietary reaction data. Instead of gathering raw reaction data, CKIF learns retrosynthesis models through iterative, chemical knowledge-informed aggregation of model parameters. In particular, the chemical properties of predicted reactants are leveraged to quantitatively assess the observable behaviors of individual models, which in turn determines the adaptive weights used for model aggregation. On a variety of reaction datasets, CKIF outperforms several strong baselines by a clear margin.},
}
@article {pmid40997885,
year = {2025},
author = {Rouse, TC and Lupkin, SM and McGinty, VB},
title = {Using economic value signals from primate prefrontal cortex in neuro-engineering applications.},
journal = {Journal of neural engineering},
volume = {22},
number = {5},
pages = {},
pmid = {40997885},
issn = {1741-2552},
support = {K01 DA036659/DA/NIDA NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces/economics ; *Prefrontal Cortex/physiology ; Macaca mulatta ; Male ; Choice Behavior/physiology ; Decision Making/physiology ; Reinforcement, Psychology ; Deep Learning ; },
abstract = {Objective.Brain-machine interface (BMI) research has shown the efficacy of using motor and sensory-related neural signals to assist physically impaired patients. Despite the comparable ability to extract more abstract cognitive signals from the brain, little effort has been devoted to leveraging these signals in neuro-engineering applications. In this study, we explore the use of neural signals related to economic value, a key cognitive construct, in a BMI context.Approach.Using multivariate time series data collected from the orbitofrontal cortex in non-human primates, we develop deep learning-based neural decoders to predict the monkeys' choices in a value-based decision-making task. We implement a reinforcement learning-based training approach to develop adaptive decoders that can be extended to handle multi-step decisions, which frequently arise in real-world settings.Main results.We develop neural decoders leveraging subjective value signals to predict the monkeys' choices with>70%accuracy on average, with above-chance accuracy even when choice options are objectively equal. We show that this same decoder architecture can be trained to execute choice-related actions and execute action sequences aligned with the user's goal. Finally, we explore a decoder architecture that uses a neural forecasting model equipped with task-related information, and show that it makes high accuracy predictions∼300 ms sooner than would otherwise be possible.Significance.These findings support the feasibility of user preference-informed neuro-engineering devices that leverage abstract cognitive signals to aid users in goal-directed behavior. They suggest that using abstract cognitive signals in real-world settings may be more accurate when combined with information from multiple sources, such as motor and sensory regions. This research also highlights the potential need for systems to measure their confidence in their actions when user input is minimal.},
}
@article {pmid40997041,
year = {2025},
author = {Lee, Y and Chen, R and Bhattacharyya, S},
title = {An Online Learning Framework for Neural Decoding in Embedded Neuromodulation Systems.},
journal = {Brain connectivity},
volume = {},
number = {},
pages = {0},
doi = {10.1177/21580014251374627},
pmid = {40997041},
issn = {2158-0022},
abstract = {Introduction: Advancements in brain-computer interfaces (BCIs) have improved real-time neural signal decoding, enabling adaptive closed-loop neuromodulation. These systems dynamically adjust stimulation parameters based on neural biomarkers, enhancing treatment precision and adaptability. However, existing neuromodulation frameworks often depend on high-power computational platforms, limiting their feasibility for portable, real-time applications. Methods: We propose RONDO (Recursive Online Neural DecOding), a resource-efficient neural decoding framework that employs dynamic updating schemes in online learning with recurrent neural networks (RNNs). RONDO supports simple RNNs, long short-term memory networks, and gated recurrent units, allowing flexible adaptation to different signal type, accuracy, and real-time constraints. Results: Experimental results show that RONDO's adaptive model updating improves neural decoding accuracy by 35% to 45% compared to offline learning. Additionally, RONDO operates within real-time constraints of neuroimaging devices without requiring cloud-based or high-performance computing. Its dynamic updating scheme ensures high accuracy with minimal updates, improving energy efficiency and robustness in resource-limited settings. Conclusions: RONDO presents a scalable, adaptive, and energy-efficient solution for real-time closed-loop neuromodulation, eliminating reliance on cloud computing. Its flexibility makes it a promising tool for clinical and research applications, advancing personalized neurostimulation and adaptive BCIs.},
}
@article {pmid40996498,
year = {2025},
author = {Zhang, M and Zhang, Y and Liu, W and Sun, S and Xu, G},
title = {Quantifying and evaluating motor imagery ability using EEG microstates in MI-BCI training.},
journal = {Experimental brain research},
volume = {243},
number = {10},
pages = {216},
pmid = {40996498},
issn = {1432-1106},
support = {2022YFC2402200//the Nationnal Key R&amp;D Program of China/ ; 2022YFC2402200//the Nationnal Key R&amp;D Program of China/ ; 2022YFC2402200//the Nationnal Key R&amp;D Program of China/ ; 2022YFC2402200//the Nationnal Key R&amp;D Program of China/ ; 2022YFC2402200//the Nationnal Key R&amp;D Program of China/ ; 52320105008//the National Natural Science Foundation of China/ ; 52320105008//the National Natural Science Foundation of China/ ; 52320105008//the National Natural Science Foundation of China/ ; 52320105008//the National Natural Science Foundation of China/ ; 52320105008//the National Natural Science Foundation of China/ ; },
}
@article {pmid40995804,
year = {2025},
author = {Kim, E and Chung, WG and Kim, E and Oh, M and Paek, J and Lee, T and Kim, D and An, SH and Kim, S and Park, JU},
title = {Multi-Channel Neural Interface for Neural Recording and Neuromodulation.},
journal = {Small methods},
volume = {},
number = {},
pages = {e01227},
doi = {10.1002/smtd.202501227},
pmid = {40995804},
issn = {2366-9608},
support = {//Ministry of Science &amp; ICT (MSIT)/ ; //Ministry of Trade, Industry and Energy/ ; 2023R1A2C2006257//National Research Foundation/ ; RS-2024-00464032//National Research Foundation/ ; RS-2025-16063568//National Research Foundation/ ; RS-2024-00460364//STEAM Research Programs/ ; RS-2024-00406240//ERC Program/ ; 2E33191//Korea Institute of Science and Technology/ ; 2E33190//Korea Institute of Science and Technology/ ; RS-2025-00514998//Sejong Science Fellowship/ ; IBS-R026-D1//Institute for Basic Science/ ; },
abstract = {Neural interfaces have emerged as pivotal platforms for advancing digital neurotherapies by enabling the real-time acquisition and monitoring of neural signals. Traditional single-channel systems are inherently limited in their capacity to capture the complex and large-scale interactions among diverse neuronal populations. In contrast, multi-channel systems provide the high spatiotemporal resolution necessary to decode the dynamic activity of neural circuits across multiple brain and spinal cord regions. This review provides a comprehensive overview of recent advances in multi-channel neural interface technologies, encompassing both penetrating and non-penetrating systems for high-resolution electrophysiological recording, as well as multifunctional platforms that integrate additional modalities such as drug delivery, optical stimulation, and chemical sensing. Recent progress in this field has been driven by advances in structural and material design, including the development of soft, flexible architectures and materials for both substrates and electrodes, which improve long-term stability and minimize tissue damage. In parallel, emerging data analysis techniques have enhanced the capacity to decode complex neural activity patterns from high-dimensional, multi-channel recordings. These technological advancements have broadened the potential applications of neural interfaces in brain-machine interfaces (BMIs), facilitating precise neuromodulation, real-time monitoring of neurological states, and integration with immersive systems such as virtual and augmented reality.},
}
@article {pmid40995145,
year = {2025},
author = {Zabolotniy, A and Chan, RW and Moiseeva, V and Fedele, T},
title = {Convolutional neural networks decode finger movements in motor sequence learning from MEG data.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1623380},
pmid = {40995145},
issn = {1662-4548},
abstract = {OBJECTIVE: Non-invasive Brain-Computer Interfaces provide accurate classification of hand movement lateralization. However, distinguishing activation patterns of individual fingers within the same hand remains challenging due to their overlapping representations in the motor cortex. Here, we validated a compact convolutional neural network for fast and reliable decoding of finger movements from non-invasive magnetoencephalographic (MEG) recordings.<br><br>APPROACH: We recorded healthy participants in MEG performing a serial reaction time task (SRTT), with buttons pressed by left and right index and middle fingers. We devised classifiers to identify left vs. right hand movements and among four finger movements using a recently proposed decoding approach, Linear Finite Impulse Response Convolutional Neural Network (LF-CNN). We also compared LF-CNN to existing deep learning architectures such as EEGNet, FBCSP-ShallowNet, and VGG19.<br><br>RESULTS: Sequence learning was reflected by a decrease in reaction times during SRTT performance. Movement laterality was decoded with an accuracy superior to 95% by all approaches, while for individual finger movement, decoding was in the 80-85% range. LF-CNN stood out for (1) its low computational time and (2) its interpretability in both spatial and spectral domains, allowing to examine neurophysiological patterns reflecting task-related motor cortex activity.<br><br>SIGNIFICANCE: We demonstrated the feasibility of finger movement decoding with a tailored Convolutional Neural Network. The performance of our approach was comparable to complex deep learning architectures, while providing faster and interpretable outcome. This algorithmic strategy holds high potential for the investigation of the mechanisms underlying non-invasive neurophysiological recordings in cognitive neuroscience.},
}
@article {pmid40995144,
year = {2025},
author = {Citarella, J and Siekierski, P and Ethridge, L and Westerkamp, G and Liu, Y and Blank, E and Voorhees, L and Batterink, L and Jones, SR and Smith, E and Reisinger, DL and Nelson, M and Binder, DK and Razak, KA and Miyakoshi, M and Wu, S and Gilbert, D and Horn, PS and De Stefano, LA and Erickson, CA and Pedapati, EV},
title = {FX ENTRAIN: scientific context, study design, and biomarker driven brain-computer interfaces in neurodevelopmental conditions.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1618804},
pmid = {40995144},
issn = {1662-4548},
abstract = {Fragile X Syndrome (FXS), caused by the loss of function of the Fmr1 gene, is characterized by varying degrees of intellectual disability, autistic features, and sensory hypersensitivity. Despite phenotypic rescue in animal deletion models, clinical trials in humans have been unsuccessful, likely due to the heterogeneous nature of FXS. To uncover the basis of individual- and subgroup-level variation driving treatment failures, we propose to test and modulate thalamocortical drive as a novel "bottom-up" neural probe to understand the mechanics of FXS-relevant circuits. Our study employs trial-level EEG analyses (neurodynamics) to detect fine-grained differences in brain activity using sensory and statistical learning paradigms in children with FXS, autism spectrum disorder (ASD), and typically developing controls. Parallel analysis in the FXS knockout mouse model will clarify its relevance to human FXS subgroups. In a randomized crossover study, we will evaluate the efficacy of closed-loop auditory entrainment, indexed on individual neurodynamic measures, aiming to normalize neural responses and enhance statistical learning performance. We anticipate this approach will yield opportunities to identify more effective early interventions that alter the trajectory of intellectual development in FXS.},
}
@article {pmid40993190,
year = {2025},
author = {Merk, T and Köhler, RM and Brotons, TM and Vossberg, SR and Peterson, V and Lyra, LF and Vanhoecke, J and Chikermane, M and Binns, TS and Li, N and Walton, A and Neudorfer, C and Bush, A and Sisterson, N and Busch, J and Lofredi, R and Habets, J and Huebl, J and Zhu, G and Yin, Z and Zhao, B and Merkl, A and Bajbouj, M and Krause, P and Faust, K and Schneider, GH and Horn, A and Zhang, J and Kühn, AA and Mark Richardson, R and Neumann, WJ},
title = {Invasive neurophysiology and whole brain connectomics for neural decoding in patients with brain implants.},
journal = {Nature biomedical engineering},
volume = {},
number = {},
pages = {},
pmid = {40993190},
issn = {2157-846X},
support = {R01NS110424//Bundesministerium fr Bildung und Forschung (Federal Ministry of Education and Research)/ ; R01NS110424//Bundesministerium fr Bildung und Forschung (Federal Ministry of Education and Research)/ ; 424778381//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; 424778381//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; R01NS110424//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; 424778381//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; 424778381//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; 424778381//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; 424778381//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; 424778381//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; 424778381//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; 424778381//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; R01NS110424//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; R01 13478451//U.S. Department of Health Human Services | National Institutes of Health (NIH)/ ; 1R01NS127892-01//U.S. Department of Health Human Services | National Institutes of Health (NIH)/ ; UM1NS132358//U.S. Department of Health Human Services | National Institutes of Health (NIH)/ ; 101077060//European Commission (EC)/ ; },
abstract = {Brain-computer interface research can inspire closed-loop neuromodulation therapies, promising spatiotemporal precision for the treatment of brain disorders. Decoding dynamic patient states from brain signals with machine learning is required to leverage this precision, but a standardized framework for invasive brain signal decoding from neural implants does not exist. Here we develop a platform that integrates brain signal decoding with magnetic resonance imaging connectomics and demonstrate its use across 123 h of invasively recorded brain data from 73 neurosurgical patients treated with brain implants for movement disorders, depression and epilepsy. We introduce connectomics-informed movement decoders that generalize across cohorts with Parkinson's disease and epilepsy from the United States, Europe and China. We reveal network targets for emotion decoding in left prefrontal and cingulate circuits in deep brain stimulation patients with major depression. Finally, we showcase opportunities to improve seizure detection in responsive neurostimulation for epilepsy. Our study highlights the clinical use of brain signal decoding for deep brain stimulation and provides methods that allow for rapid, high-accuracy decoding for precision medicine approaches that can dynamically adapt neurotherapies in response to the individual needs of patients.},
}
@article {pmid40990260,
year = {2025},
author = {Lebani, BR and da Silva, AB and Silva, LT and Girotti, ME and Pinto, ER and Skaff, M and Almeida, FG},
title = {Is It Necessary to Remove the Maximum Prostate Tissue in All Patients? the Percentage of Resected Prostate Tissue and the Influence on Surgery Outcomes: A One-Year Follow Up Study.},
journal = {Neurourology and urodynamics},
volume = {},
number = {},
pages = {},
doi = {10.1002/nau.70152},
pmid = {40990260},
issn = {1520-6777},
support = {//The study developed includes only patients treated inside the Brazilian public health system. There were not any additional costs involved./ ; },
abstract = {INTRODUCTION: To investigate whether the volume of the prostate tissue resected on TURP influences on short and medium term follow up.<br><br>METHODS: It was developed a prospective study between May 2020 and August 2022, embracing patients with severe LUTS due to BPO, including clinical and urodynamic parameters meeting obstruction criteria (BOOI&#x2009;>&#x2009;40), and good detrusor function (BCI&#x2009;>&#x2009;100). Patients were assessed at 1, 6 and 12 months follow up. The primary endpoint was to compare whether the amount of resected tissue after TURP influences uroflowmetry at 12 months follow up (Qmax, ml/sec). The secondary endpoint was to compare different percentages of resected tissue (RPT) and its relation to the outcomes.<br><br>RESULTS: Ninety-six patients with mean age of 70,4&#x2009;&#xb1;&#x2009;7.96 years. At baseline, prostate volume was 78.5&#x2009;&#xb1;&#x2009;51.8 cc&#xb3;, Qmax was 6.03&#x2009;&#xb1;&#x2009;3.09&#x2009;ml/sec and post void residual (PVR) was 113&#x2009;&#xb1;&#x2009;132&#x2009;ml, IPSS of 24.9&#x2009;&#xb1;&#x2009;6.75. All of them were urodinamically obstructed (BOOI 86.7&#x2009;&#xb1;&#x2009;35.6) and good detrusor function (BCI 130&#x2009;&#xb1;&#x2009;28.6). The general RPT was 45.5&#x2009;&#xb1;&#x2009;27.7%. The higher the RTP, the lower the PSA at 1 month follow up (p&#x2009;<&#x2009;0.001, R&#x2009;=&#x2009;0.521). Nevertheless, it was not found correlation between the RTP and Qmax, IPSS or PVR.<br><br>CONCLUSION: TURP improves clinical and urodynamic parameters at 1 year follow up, independent of the amount of resected prostate tissue, in patients with bladder outlet obstruction and good detrusor function, since the surgery is effective.},
}
@article {pmid40990135,
year = {2025},
author = {Jin, J and Wang, Z and Dai, L and Wang, A and Gao, L},
title = {An Exploratory Study of Loss Averse in Group Decision Contexts: Multiple Pieces of Evidence From ERPs and Machine Learning.},
journal = {Psychophysiology},
volume = {62},
number = {9},
pages = {e70155},
doi = {10.1111/psyp.70155},
pmid = {40990135},
issn = {1469-8986},
support = {72271166//National Natural Science Foundation of China/ ; 72501175//National Natural Science Foundation of China/ ; 22dz2261100//Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai)/ ; 41005067//Fundamental Research Funds for the Central Universities/ ; 2024DSYL051//Tutor Academic Leadership Program of shanghai international Studies University/ ; },
mesh = {Humans ; *Machine Learning ; Electroencephalography ; Male ; Female ; Young Adult ; *Decision Making/physiology ; Adult ; *Evoked Potentials/physiology ; *Group Processes ; Adolescent ; *Risk-Taking ; *Feedback, Psychological/physiology ; },
abstract = {Both laboratory and field evidence have shown differences in risk attitudes between individual and group decision contexts. Loss aversion, a crucial aspect of risk attitudes, whose behavioral performance and neural mechanism in group decision contexts remain unclear, differs from other risk attitudes such as risk aversion. Using behavioral and electroencephalography (EEG) experiments with non-student and student samples, we conducted an exploratory study to examine the behavioral performance and neural mechanisms of loss aversion in group decision contexts. Behaviorally, we found a reduction effect of loss aversion in group decision contexts compared to individual decision contexts. ERP results from the average and single-trial analyses jointly found that individuals are less sensitive to losses and gains in group (vs. individual) decision contexts, as evidenced by the vanishing Feedback-related Negativity (FRN) and P3b differences to losses and gains. We also found a significant negative correlation between the loss aversion coefficient and FRN amplitude induced by losses both in individual and group decision contexts, which indicated the relationship between loss aversion and neural signals that process loss outcomes. Furthermore, machine learning analyses revealed that EEG features exhibit a high accuracy rate of 81.25% in predicting the decision contexts. This finding underscores the intricate relationship between neural activity and loss aversion across varying decision contexts, highlighting the potential of neurophysiological activity to elucidate the underlying cognitive processes involved in loss aversion. This paper advances our understanding of loss aversion in group decision contexts by providing multiple pieces of evidence for behavioral performance, neural activities, and machine learning. Findings can help to optimize group decision-making processes and resource allocation, and to reduce inefficiencies caused by irrational behavior and resistance to beneficial changes.},
}
@article {pmid40989443,
year = {2025},
author = {Su, K and Tian, L},
title = {Systematic review: progress in EEG-based speech imagery brain-computer interface decoding and encoding research.},
journal = {PeerJ. Computer science},
volume = {11},
number = {},
pages = {e2938},
pmid = {40989443},
issn = {2376-5992},
abstract = {This article systematically reviews the latest developments in electroencephalogram (EEG)-based speech imagery brain-computer interface (SI-BCI). It explores the brain connectivity of SI-BCI and reveals its key role in neural encoding and decoding. It analyzes the research progress on vowel-vowel and vowel-consonant combinations, as well as Chinese characters, words, and long-words speech imagery paradigms. In the neural encoding section, the preprocessing and feature extraction techniques for EEG signals are discussed in detail. The neural decoding section offers an in-depth analysis of the applications and performance of machine learning and deep learning algorithms. Finally, the challenges faced by current research are summarized, and future directions are outlined. The review highlights that future research should focus on brain region mechanisms, paradigms innovation, and the optimization of decoding algorithms to promote the practical application of SI-BCI technology.},
}
@article {pmid40988031,
year = {2025},
author = {Rab, P and Shirinskiy, IJ and Kimmeyer, M and Macken, AA and Calamita, AG and Colombini, AG and Buijze, GA and Lafosse, T},
title = {Augmentation of full-thickness rotator cuff tears with a bioinductive collagen implant does not reduce retear rates - a propensity matched cohort study.},
journal = {BMC musculoskeletal disorders},
volume = {26},
number = {1},
pages = {855},
pmid = {40988031},
issn = {1471-2474},
mesh = {Humans ; Female ; Male ; Middle Aged ; *Rotator Cuff Injuries/surgery/diagnostic imaging ; Retrospective Studies ; Aged ; *Collagen/administration &amp; dosage ; Range of Motion, Articular ; Treatment Outcome ; Propensity Score ; Follow-Up Studies ; *Prostheses and Implants ; },
abstract = {PURPOSE: To compare the clinical and radiographic outcomes after full-thickness RC repair with and without performing augmentation with a bioinductive collagen implant (BCI).<br><br>MATERIALS AND METHODS: Consecutive patients who underwent primary repair of a full-thickness supraspinatus tear between 05/2021 and 11/2023 were retrospectively identified. Patients at elevated risk for retear were defined by biological, radiographic, and intraoperative risk factors. Those who underwent repair with or without concomitant augmentation using a BCI and who had both clinical and radiographic follow-up at 1 year postoperatively were matched in a 1:1 ratio according to age, sex, body mass index, tear size, smoking status, diabetes, and American Society of Anesthesiologists physical status classification. Range of motion (ROM) as well as patient-reported outcome measures (Auto-Constant-Score (CS), American Shoulder and Elbow Surgeons (ASES) Score, Subjective Shoulder Value (SSV), and Visual Analog Scale (VAS) for pain) were recorded. Magnetic resonance imaging performed at 1 year postoperatively was analyzed and the presence of retear was recorded.<br><br>RESULTS: In total, 149 patients with a radiographic and clinical follow-up at 1 year postoperatively were identified. Of these, 23 patients with BCI augmentation were matched to 23 patients without placement of BCI (48% female, 59.2&#x2009;&#xb1;&#x2009;8.4 years at surgery). A retear occurred in 5 patients (21.7%) in the BCI augmentation group and in 3 patients (13.0%) in the control group (p&#x2009;=&#x2009;0.72). No significant difference was reported regarding the CS (77 [71-83] vs. 76 [63-81], p&#x2009;=&#x2009;0.5), ASES Score (92 [82-98] vs. 90 [84-95], p&#x2009;=&#x2009;0.8), SSV (90 [80-100] vs. 90 [88-95], p&#x2009;=&#x2009;0.9), VAS for pain (p&#x2009;=&#x2009;0.74), or ROM between the groups.<br><br>CONCLUSION: In this retrospective matched cohort of patients at elevated risk for retear, augmentation of full-thickness RC repair with a BCI was not associated with a reduced retear rate. Moreover, no significant differences regarding clinical and functional outcome were found between the two groups.<br><br>LEVEL OF EVIDENCE: III - Retrospective case series with a matched control group.},
}
@article {pmid40987603,
year = {2025},
author = {Rana, D and Babushkina, N and Gini, M and Flores Cáceres, A and Li, H and Maybeck, V and Criscuolo, V and Mayer, D and Ienca, M and Musall, S and Rincon Montes, V and Offenhäusser, A and Santoro, F},
title = {Neural vs Neuromorphic Interfaces: Where Are We Standing?.},
journal = {Chemical reviews},
volume = {125},
number = {19},
pages = {9092-9139},
pmid = {40987603},
issn = {1520-6890},
mesh = {Humans ; *Brain-Computer Interfaces ; Animals ; *Neurons/physiology ; Brain/physiology ; },
abstract = {Neuromorphic interfaces represent a transformative frontier in neural engineering, enabling seamless communication between the nervous system and external devices through biologically inspired computing architectures. These systems offer promising avenues for diagnosing and treating neurological disorders by emulating the brain's computational strategies. Neural devices, including sensors and stimulators, monitor or modulate neural activity, playing a pivotal role in deciphering brain function and neuropathologies. Yet, clinical translation remains limited due to persistent challenges such as foreign body responses, low signal-to-noise ratios, and constraints in real-time data processing. Recent breakthroughs in neuromorphic hardware, neural recording, and stimulation technologies are addressing these challenges, paving the way for more adaptive and efficient brain-machine interfaces and neuroprosthetics. This review highlights the emerging class of neurohybrid interfaces, where neuromorphic systems might be integrated to enhance bidirectional neural communication. It emphasizes novel material strategies engineered for seamless neural interfacing and their incorporation into advanced neuromorphic chip architectures capable of real-time signal processing and closed-loop feedback. Furthermore, this review explores cutting-edge neuromorphic biointerfaces and evaluates the technological, biological, and ethical challenges involved in their clinical deployment. By bridging materials science, neuroscience, and neuromorphic engineering, these systems hold the potential to redefine the landscape of neurotechnology.},
}
@article {pmid40984876,
year = {2025},
author = {Mishra, R and Agrawal, RK and Kirar, JS},
title = {Msst-eegnet: multi-scale spatio-temporal feature extraction using inception and temporal pyramid pooling for motor imagery classification.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {150},
pmid = {40984876},
issn = {1871-4080},
abstract = {Motor imagery classification is an essential component of Brain-computer interface systems to interpret and recognize brain signals generated during the visualization of motor imagery tasks by a subject. The objective of this work is to develop a novel DL model to extract discriminative features for better generalization performance to recognize motor imagery tasks. This paper presents a novel Multi-scale spatio-temporal network (MSST-EEGNet) to extract discriminative temporal, spectral, and spatial features for motor imagery task classification. The proposed MSST-EEGNet model includes three modules namely the inception module with dilated convolution, the temporal pyramid pooling module, and the classification module. Multi-scale temporal features along with spatial features are extracted using the inception block with the dilated convolution module. A set of multi-level fine-grained and coarse-grained features are extracted using a temporal pyramid pooling module. Further, categorical cross-entropy in combination with center loss is used as a loss function. Experiments are carried out on three benchmark datasets including the BCI Competition IV-2a dataset, the BCI Competition IV-2b dataset, and the OpenBMI dataset. The evaluation results shows that the proposed MSST-EEGNet model outperforms eight existing DL models in terms of classification accuracy for subject-specific and cross-session settings. It also outperforms eight existing DL models and six existing transfer-learning models for cross-subject setting. For the subject-specific classification the proposed MSST-EEGNet model achieved an accuracy of 0.8426 ± 0.1061, 0.7779 ± 0.0938, and 0.7365 ± 0.1477 on the BCI Competition IV-2a dataset, the BCI Competition IV-2b dataset, and the OpenBMI dataset respectively. For the cross-session setting, the proposed MSST-EEGNet model achieved an accuracy of 0.7709 ± 0.1098, 0.7524 ± 0.1017, and 0.6860 ± 0.0990 on the BCI Competition IV-2a dataset, the BCI Competition IV-2b dataset, and the OpenBMI dataset respectively. For the cross-subject setting, the proposed MSST-EEGNet model achieved an accuracy of 0.7288 ± 0.0730, 0.8161 ± 0.963, and 0.7075 ± 0.0746 on the BCI Competition IV-2a dataset, the BCI Competition IV-2b dataset, and the OpenBMI dataset respectively. Furthermore, a non-parametric Friedman statistical test demonstrates statistically significant superior performance of the proposed MSST-EEGNet model over the existing models.},
}
@article {pmid40983603,
year = {2025},
author = {Kim, G and Jeong, H and Kim, K and Lee, S and Baeg, E and Yang, S and Kim, B and Yang, S},
title = {The Pre-clinical Safety of Graphene-based Electrodes Implanted on Rat Cerebral Cortex.},
journal = {Experimental neurobiology},
volume = {34},
number = {5},
pages = {214-223},
pmid = {40983603},
issn = {1226-2560},
abstract = {Graphene has emerged as a promising nanomaterial for brain-computer interface (BCI) applications due to its excellent electrical properties and biocompatibility. However, its long-term structural compatibility on the cerebral cortex requires further validation. This study assessed both functional compatibility and preservation of neural tissue architecture for graphene/parylene C composite electrodes implanted on the rat cortical surface, in accordance with ISO 10993-6 guideline weekly neurobehavioral assessments and comprehensive histopathological analyses were conducted for four weeks post-implantation. Our results revealed no significant differences in neurobehavioral outcomes between graphene-based and medical-grade silicone implants. Histopathological examination showed no noticeable inflammatory responses, changes in cellular morphology, myelination status, or neuronal degeneration. These findings indicate that graphene electrodes preserve tissue integrity comparable to medical‑grade silicone. Our study supports graphene's potential use in clinical neuroprosthetics and neuromodulation devices.},
}
@article {pmid40983076,
year = {2025},
author = {He, Z and Wang, Y},
title = {TFDISNet: Temporal-frequency domain-invariant and domain-specific feature learning network for enhanced auditory attention decoding from EEG signals.},
journal = {Biomedical physics &amp; engineering express},
volume = {11},
number = {5},
pages = {},
doi = {10.1088/2057-1976/ae09b2},
pmid = {40983076},
issn = {2057-1976},
mesh = {*Electroencephalography/methods ; Humans ; *Attention/physiology ; Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; Algorithms ; *Machine Learning ; *Neural Networks, Computer ; *Auditory Perception/physiology ; Brain/physiology ; Adult ; },
abstract = {Auditory Attention Decoding (AAD) from Electroencephalogram (EEG) signals presents a significant challenge in brain-computer interface (BCI) research due to the intricate nature of neural patterns. Existing approaches often fail to effectively integrate temporal and frequency domain information, resulting in constrained classification accuracy and robustness. To address these shortcomings, a novel framework, termed the Temporal-Frequency Domain-Invariant and Domain-Specific Feature Learning Network (TFDISNet), is proposed to enhance AAD performance. A dual-branch architecture is utilized to independently extract features from the temporal and frequency domains, which are subsequently fused through an advanced integration strategy. Within the fusion module, shared features, common across both domains, are aligned by minimizing a similarity loss, while domain-specific features, essential for the task, are preserved through the application of a dissimilarity loss. Additionally, a reconstruction loss is employed to ensure that the fused features accurately represent the original signal. These fused features are then subjected to classification, effectively capturing both shared and unique characteristics to improve the robustness and accuracy of AAD. Experimental results show TFDISNet outperforms state-of-the-art models, achieving 97.1% accuracy on the KUL dataset and 88.2% on the DTU dataset with a 2 s window, validated across group, subject-specific, and cross-subject analyses. Component studies confirm that integrating temporal and frequency features boosts performance, with the full TFDISNet surpassing its variants. Its dual-branch design and advanced loss functions establish a robust EEG-based AAD framework, setting a new field standard.},
}
@article {pmid40982479,
year = {2025},
author = {Zhong, Y and Song, M and Shi, W and Di, S and Yu, C and Jiang, T},
title = {Robust population orientation encoding by orientation-untuned neurons in macaque V1.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {35},
number = {9},
pages = {},
doi = {10.1093/cercor/bhaf264},
pmid = {40982479},
issn = {1460-2199},
support = {2024M753502//China Postdoctoral Science Foundation/ ; GZC20232999//China Postdoctoral Science Foundation/ ; 2024RC4028//Science and Technology Innovation Program of Hunan Province/ ; YJKYYQ20190040//Equipment Development Project of the Chinese Academy of Sciences/ ; 62403465//National Natural Science Foundation of China/ ; 82151307//National Natural Science Foundation of China/ ; 62327805//National Natural Science Foundation of China/ ; 2021ZD0200200//Science and Technology Innovation (STI) 2030-Major Projects/ ; },
mesh = {Animals ; *Neurons/physiology ; *Orientation/physiology ; Photic Stimulation ; Macaca mulatta ; *Primary Visual Cortex/physiology/cytology ; Visual Pathways/physiology ; Male ; *Visual Perception/physiology ; *Visual Cortex/physiology ; *Orientation, Spatial/physiology ; Neural Networks, Computer ; Models, Neurological ; },
abstract = {Orientation is one of the most fundamental stimulus features in visual perception. In the primary visual cortex (V1), while most neurons are orientation-selective, a small portion exhibits a lack of this selectivity. However, it remains unclear what roles the orientation-untuned V1 neurons play in population orientation discrimination. Here, we analyzed data from a 2-photon calcium imaging study that recorded the responses of thousands of V1 neurons to a grating stimulus at various orientations in awake macaques. Our population analysis reveals that orientation-untuned neurons can independently decode stimulus orientation with accuracy comparable to tuned neurons. Remarkably, we found that the more critical role of orientation-untuned neuronal populations in orientation encoding is to enhance coding robustness, specifically by reducing sensitivity to noise. Moreover, when using artificial neural networks to model the primate ventral visual pathway, we found that the V1-like layer also contains a proportion of orientation-untuned units. Removing these units leads to significant impairments in natural object recognition. Overall, these results indicate that orientation-untuned neurons encode orientation information and play a crucial role in primate visual perception. The study provides compelling evidence for a continuous distribution of visual features across neurons and challenges the notion of highly specialized units.},
}
@article {pmid40982226,
year = {2025},
author = {Li, J and Yi, Y and Gao, X and Ren, Y and Gan, L and Zou, T and Qin, X and Tan, A and Yang, X and Jiang, F and Liu, X and Gao, H and Wang, Y and Aumont, E and Xiao, J and Zhou, B and Liao, W and Chen, H and Zhang, W and Montembeault, M and Rosa-Neto, P and Li, R},
title = {High brain network dynamics mediate audiovisual integration deficits and cognitive impairment in Alzheimer's disease.},
journal = {Journal of Alzheimer's disease : JAD},
volume = {108},
number = {1},
pages = {397-410},
doi = {10.1177/13872877251376717},
pmid = {40982226},
issn = {1875-8908},
mesh = {Humans ; *Alzheimer Disease/diagnostic imaging/psychology/physiopathology/complications ; Male ; Female ; Magnetic Resonance Imaging ; Aged ; *Cognitive Dysfunction/physiopathology/diagnostic imaging/psychology/etiology ; *Brain/diagnostic imaging/physiopathology ; *Visual Perception/physiology ; *Auditory Perception/physiology ; *Nerve Net/diagnostic imaging/physiopathology ; Neuropsychological Tests ; Middle Aged ; Photic Stimulation ; Aged, 80 and over ; Acoustic Stimulation ; },
abstract = {BackgroundAudiovisual integration deficits are frequent in patients with Alzheimer's disease (AD). In addition, patients with AD have altered functional brain networks, such as those supporting auditory and visual processing. However, the mechanisms driving this association remain unclear.ObjectiveTo investigate whether dynamic functional network disruptions underlie audiovisual integration and cognitive deficits in AD.MethodsSeventy-nine participants (41 AD, 38 controls) completed audiovisual stimuli tasks. A multilayer modularity algorithm was utilized to assess the resting-state fMRI-based brain dynamics of the primary sensory and higher-order functional networks. Mediation analysis was conducted to test our hypothesis.ResultsAD patients showed delayed response time and reduced peak benefit of audiovisual integration. Dynamic switching rates of primary sensory and higher-order networks were significantly increased in AD, particularly in the dynamic integration between the default mode network (DMN) and visual network (VN). The peak benefit of audiovisual integration negatively correlated with DMN-VN dynamic integration and positively with Mini-Mental State Examination, Montreal Cognitive Assessment, and Auditory Verbal Learning Test delayed scores. Notably, excessive integration between the DMN and VN mediated the relationship between audiovisual integration deficits and cognitive impairment in patients with AD.ConclusionsThese findings suggest that audiovisual integration impairment may disturb the dynamic integration between the DMN and VN, contributing to cognitive impairment in AD. The neural mechanisms underlying audiovisual integration deficit and cognitive decline might help with early diagnosis and intervention for AD.},
}
@article {pmid40978101,
year = {2025},
author = {Liu, H and Liu, W and Du, Z and Wu, L and Chen, M and Gao, Z and Jiang, K and Li, L and Fan, Z and Shen, G},
title = {Encoding of blink information via wireless contact lens for eye-machine interaction.},
journal = {National science review},
volume = {12},
number = {10},
pages = {nwaf338},
pmid = {40978101},
issn = {2053-714X},
abstract = {Blinks controlled by ocular muscles and nerves can manifest as either involuntary physiological behaviors or volitional control actions, with the former serving spontaneous protective functions while the latter constitutes a biologically meaningful communicative signal. The encoding of blink information provides a novel eye-machine interaction (EMI) prototype within the realm of human-machine interaction, expanding human consciousness and capability boundaries. It facilitates motor and language rehabilitation, silent communication and even voluntary command execution. However, existing EMI devices face challenges related to wireless functionalities, ocular comfort and multi-route encoding/decoding orders. Here, we propose a wireless eye-wearable lens to encode conscious blink information via introduction of an RLC oscillating loop in the soft contact lens. The developed EMI contact lens incorporates a mechanosensitive capacitor, an inductive coil and the inherent loop resistance, generating characteristic resonance frequency for front-end capacitance signal transition or back-end control signal extraction. The EMI device delivers a sensitivity of 0.153 MHz/mmHg in the wide range of 0-70 mmHg for a normal intraocular pressure monitor and realizes conscious blink-based control command coding. A trial with participants having the EMI contact lens inserted demonstrates its wearability and biocompatibility. Finally, the five-route blink-based control command decoding mechanism is constructed via the EMI lens, linking blink counts to a drone's flight trajectory. The EMI contact lens offers an innovative prototype that transcends the capabilities of traditional brain-computer interfaces.},
}
@article {pmid40977080,
year = {2025},
author = {Li, W and Zou, H and Yang, B and Xiao, L and Liu, S and Chen, Z and Xie, L and Zhu, W and Zhao, X and Wang, L and Li, T and Wang, T},
title = {From Electrophysiological to Biochemically-Modulated Interfaces: Evolution of Brain-Machine Communication.},
journal = {Small methods},
volume = {},
number = {},
pages = {e01471},
doi = {10.1002/smtd.202501471},
pmid = {40977080},
issn = {2366-9608},
support = {62235008//National Natural Science Foundation of China/ ; 62322108//Excellent Young Scholars of NSFC/ ; 62571260//General Program of NSFC/ ; 62201286//Young Scholars of NSFC/ ; 62301283//Young Scholars of NSFC/ ; 22405131//Young Scholars of NSFC/ ; 2023ZB587//Jiangsu Funding Program for Excellent Postdoctoral Talent/ ; NY222099//Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications/ ; BK20243057//Basic Research Program of Jiangsu Province/ ; },
abstract = {Brain-machine interfaces (BMIs) establish bidirectional communication between biological neural systems and external devices by decoding neural signals and delivering feedback stimulation. Achieving seamless integration with biological systems has driven the paradigmatic evolution of BMI technology through three interconnected dimensions. This review summarizes the shift from electrophysiological to biochemically-modulated BMIs, emphasizing key evolutionary trends that mirror biological neural characteristics. First, signal modalities have expanded from single electrophysiological detection to integrated biochemical sensing, enabling comprehensive neural circuit analysis through dual electrical-chemical communication pathways that capture both rapid electrical transmission and slower biochemical processes. Second, electrode morphology has transformed from rigid silicon structures to flexible, adaptive materials that mechanically match neural tissue properties, reducing mechanical mismatch and improving long-term biocompatibility. Third, system architectures have evolved from passive monitoring to active closed-loop platforms that incorporate neuromorphic intelligence and real-time therapeutic feedback, enabling dynamic neuromodulation based on multimodal signal analysis. Despite significant progress, challenges remain in achieving high electrode longevity, developing scalable multimodal interfaces, as well as understanding fundamental neural communication mechanisms. Future directions point toward biochemically-modulated brain interfaces incorporating living, adaptive, and evolutionarily responsive components that seamlessly integrate with biological neural networks for precision neurological therapeutics.},
}
@article {pmid40976830,
year = {2025},
author = {Chen, W and Xie, C and Wang, Y and Jin, Y and Zhao, Y and Xu, Y and Zhang, C and Chen, A and Wang, X and Jia, Z},
title = {Efficacy analysis of 450 nm semiconductor blue laser enucleation of the prostate in treating benign prostatic hyperplasia with urinary retention.},
journal = {Lasers in medical science},
volume = {40},
number = {1},
pages = {377},
pmid = {40976830},
issn = {1435-604X},
mesh = {Humans ; Male ; *Prostatic Hyperplasia/surgery/complications ; *Urinary Retention/surgery/etiology ; Aged ; Retrospective Studies ; *Lasers, Semiconductor/therapeutic use ; Middle Aged ; Quality of Life ; Treatment Outcome ; Aged, 80 and over ; Urodynamics ; *Laser Therapy/methods ; *Prostatectomy/methods ; },
abstract = {To evaluate the clinical efficacy of 450 nm semiconductor blue laser enucleation of the prostate in patients with benign prostatic hyperplasia (BPH) complicated by acute urinary retention, and to assess its outcomes in patients with concomitant detrusor underactivity (DU).A retrospective analysis was conducted on clinical data from patients diagnosed with BPH and acute urinary retention who underwent 450 nm blue laser enucleation of the prostate in the Department of Urology at our hospital between February 2023 and May 2024. All patients had indwelling catheters due to acute urinary retention prior to surgery. Maximum urinary flow rate (Qmax), postvoid residual urine volume (PVR), International Prostate Symptom Score (IPSS), and quality of life (QoL) scores were compared before surgery and at 3 months postoperatively. Based on preoperative urodynamic testing, patients were divided into a DU group (bladder contractility index, BCI < 100) and a non-DU group (BCI ≥ 100). Surgical outcomes were compared between the two groups.A total of 62 patients were included in the study, with a mean age of 71.5 years. Of these, 32 (54.8%) were in the DU group and 28 (45.2%) in the non-DU group. At 3 months postoperatively, all patients showed significant improvements in Qmax, PVR, IPSS, and QoL scores compared with baseline (P < 0.001). In the DU group, 2 patients experienced recurrent urinary retention after catheter removal on postoperative day 3, but both recovered spontaneous urination after re-catheterization for 1 week. Intergroup comparisons showed that Qmax was lower and PVR was higher in the DU group than in the non-DU group at 3 months (P < 0.001), while no significant differences were observed in IPSS and QoL scores between the two groups (P > 0.05).The 450 nm semiconductor blue laser enucleation of the prostate is a safe and effective treatment for BPH complicated by acute urinary retention. Although patients with DU show less improvement in early postoperative voiding function compared to those without DU, the procedure effectively alleviates symptoms and may prevent further deterioration of detrusor function. These findings support its clinical application and wider adoption.},
}
@article {pmid40976794,
year = {2025},
author = {Zhang, WL and Zeng, YH and Lai, YS},
title = {Spatial-temporal risk of Opisthorchis felineus infection in Western Siberia and the Ural Region of Russian Federation: a joint Bayesian modelling study based on survey and surveillance data.},
journal = {Infectious diseases of poverty},
volume = {14},
number = {1},
pages = {95},
pmid = {40976794},
issn = {2049-9957},
support = {82073665//The National Natural Science Foundation of China/ ; 2025A1515011200//Natural Science Foundation of Guangdong Province/ ; },
mesh = {Animals ; *Opisthorchiasis/epidemiology/parasitology ; Bayes Theorem ; *Opisthorchis/physiology ; Siberia/epidemiology ; Humans ; Russia/epidemiology ; Spatio-Temporal Analysis ; Prevalence ; Incidence ; Risk Factors ; },
abstract = {BACKGROUND: Opisthorchiasis infected by Opisthorchis felineus has represented a significant but understudied public health issue for the population residing in Western Siberia and the Ural Region of the Russian Federation. This study aimed to produce high-resolution spatial-temporal disease risk maps for guiding prevention strategy in the above region.<br><br>METHODS: Data on prevalence and surveillance data reflecting reported annual incidence rate of O. felineus infection in the study region were collected through systematic review and the annual reports of the Ministry of Health of the Russian Federation. Environmental, socioeconomic and demographic data were downloaded from different open-access data sources. An advanced multivariate Bayesian geostatistical modeling approach was developed to estimate the O. felineus infection risk at high-resolution spatial-temporal by joint analysis of survey and surveillance data, incorporating potential influencing factors and spatial-temporal random effects. The annual spatial-temporal risk maps of O. felineus infection at a resolution of 5&#x2009;&#xd7;&#x2009;5&#xa0;km[2] were produced.<br><br>RESULTS: The final dataset included 76 locations of survey data and 303 locations of surveillance data on O. felineus infection. The infection risk was high (>&#x2009;25%) in most part of central and eastern regions, and relatively low (<&#x2009;25%) in most part of western region, while temporal variations were observed across the sub-regions in recent decades. Particularly, in the densely populated eastern region, there was an increased trend of infection risk from 30.46% (95% Bayesian credible intervals, BCI 10.78-53.45%) in 1980 to 53.39% (95% BCI 13.77-91.93%) in 2019 and gradually transformed into high-risk. In the study region (excluding the western region due to data sparsity), the population-adjusted estimated prevalence was 46.61% (95% BCI 15.09-76.50%) in 2019, corresponding to approximately 7.91 million (95% BCI 2.56-12.98 million) people infected.<br><br>CONCLUSIONS: The high-resolution risk maps of O. felineus in Western Siberia and the Ural Region of the Russian Federation have effectively captured the risk profiles, suggesting the infection risk remains high in recent years and providing substantial evidence for spatial-target control and preventive strategies.},
}
@article {pmid40975869,
year = {2025},
author = {Ottenhoff, MC and Verwoert, M and Goulis, S and Tousseyn, S and van Dijk, JP and Shanechi, MM and Sani, OG and Kubben, P and Herff, C},
title = {Decoding continuous goal-directed movement from human brain-wide intracranial recordings.},
journal = {Cell reports},
volume = {44},
number = {10},
pages = {116328},
doi = {10.1016/j.celrep.2025.116328},
pmid = {40975869},
issn = {2211-1247},
mesh = {Humans ; Male ; Movement/physiology ; Female ; Adult ; *Goals ; *Brain/physiology ; Motor Cortex/physiology ; Brain-Computer Interfaces ; Electroencephalography/methods ; Biomechanical Phenomena ; Middle Aged ; Young Adult ; Electrocorticography ; },
abstract = {Reaching out your hand is an effortless yet complex behavior that is indispensable in daily life. Neural correlates of reaching behavior have been observed and decoded beyond the motor cortex, but the degree and granularity of movement representation are not fully understood. Here, we decode 12 kinematics of goal-directed reaching behavior from 18 participants implanted with stereotactic-electroencephalography electrodes performing a 3D reaching task. The decoder is able to decode continuous movement kinematics using low-, mid-, and high-frequency information in all participants using preferential subspace identification. Neural correlates of movements are observed throughout the brain, including deeper structures. Switching to a goal-centric reference frame enables the decoder to decode hand position, indicating that low-frequency activity is involved in higher-order processing of movements. Our results strengthen the evidence that brain-wide motor-related dynamics can be decoded and may provide opportunities for brain-computer interfaces for individuals with a compromised motor cortex.},
}
@article {pmid40974874,
year = {2025},
author = {Landau, O and Nissim, N},
title = {Mining multi-electrode and multi-wave electroencephalogram based time-interval temporal patterns for improved classification capabilities and explainability.},
journal = {Artificial intelligence in medicine},
volume = {170},
number = {},
pages = {103269},
doi = {10.1016/j.artmed.2025.103269},
pmid = {40974874},
issn = {1873-2860},
mesh = {*Electroencephalography/methods ; Humans ; *Algorithms ; *Brain-Computer Interfaces ; *Data Mining/methods ; Electrodes ; Time Factors ; *Signal Processing, Computer-Assisted ; *Brain/physiology ; },
abstract = {Brain-computer interface (BCI) systems, and particularly electroencephalogram (EEG) based BCI systems, have become more widely used in recent years and are utilized in various applications and domains ranging from medicine and marketing to games and entertainment. While different algorithms have been used to analyze EEG data and enable its classification, existing algorithms have two main drawbacks; both their classification and explainability capabilities are limited. Lacking in explainability, they cannot indicate which electrodes and waves led to a classification decision or explain how areas and frequencies of the brain's activity correlate to a specific task. In this study, we propose a novel extension for the time-interval temporal patterns mining algorithms aimed at enhancing the data mining process by enabling a richer set of patterns to be learned from the EEG data, thereby contributing to improved classification and explainability capabilities. The extended algorithm is designed to capture and leverage the unique nature of EEG data by decomposing it into different brain waves and modeling the relations among them and between different electrodes. Our evaluation of the proposed extended algorithm on multiple learning tasks and three EEG datasets demonstrated the extended algorithm's ability to mine richer patterns that improve the classification performance by 4-11 % based on the Area-Under the receiver operating characteristic Curve (AUC) metric, compared to the original version of the algorithm. Moreover, the algorithm was shown to shed light on the areas and frequencies of the brain's activity that are correlated with specific tasks.},
}
@article {pmid40974354,
year = {2025},
author = {Tyagi, M and Shotwell, M and Power, AE and Singh, G and Kalra, DK},
title = {Cardiac Injury Causing Traumatic Ventricular Septal Rupture With Right Ventricular Pseudoaneurysm.},
journal = {JACC. Case reports},
volume = {30},
number = {35},
pages = {105475},
doi = {10.1016/j.jaccas.2025.105475},
pmid = {40974354},
issn = {2666-0849},
abstract = {BACKGROUND: Ventricular septal rupture (VSR) is a rare, potentially fatal consequence of blunt cardiac injury (BCI). Concomitant right ventricular (RV) pseudoaneurysm formation is even rarer, and the occurrence of both complications has not to our knowledge been previously reported.<br><br>CASE SUMMARY: A 63-year-old man presented with a VSR and a torn tricuspid chord, flail leaflet, and severe tricuspid regurgitation after BCI due to a motor vehicle accident. He declined surgery initially and presented a month later with severe heart failure symptoms. Imaging at that time demonstrated a persistent VSR and a new RV pseudoaneurysm. His condition was not deemed to be amenable to percutaneous closure, and he again declined open surgical repair.<br><br>DISCUSSION: VSR after BCI results from acute mechanical forces and/or delayed necrosis, with RV pseudoaneurysm developing as a delayed complication likely due to inflammatory necrosis. Multimodality imaging provides comprehensive anatomical assessment and tissue characterization and guides accurate diagnosis, prognostication, and therapeutic planning.<br><br>TAKE-HOME MESSAGE: This case emphasizes the importance of early recognition and the value of serial imaging in blunt cardiac trauma, with surgical repair recommended for significant defects and management tailored to the anatomy, timing of complications, and patient preferences.},
}
@article {pmid40973382,
year = {2025},
author = {Ortner, J and Van Ewijk, R and Velthuis, L and Labenz, C and Arslanow, A and Nguyen-Tat, M and Wörns, MA and Reichert, MC and Farin-Glattacker, E and Binder, H and Fichtner, UA and Graf, E and Stelzer, D and Galle, PR and Lammert, F},
title = {Evaluating a population-based screening programme for early detection of liver fibrosis and cirrhosis in primary care in Germany: a cost assessment study.},
journal = {BMJ open},
volume = {15},
number = {9},
pages = {e090442},
pmid = {40973382},
issn = {2044-6055},
mesh = {Humans ; *Liver Cirrhosis/diagnosis/economics ; Germany ; Male ; Female ; Middle Aged ; *Primary Health Care/economics ; Adult ; Early Diagnosis ; *Mass Screening/economics/methods ; Aged ; Cost-Benefit Analysis ; Health Care Costs ; Elasticity Imaging Techniques ; Aspartate Aminotransferases/blood ; },
abstract = {OBJECTIVES: Structured Early detection of Asymptomatic Liver fibrosis and cirrhosis (SEAL) is a population-based screening programme using non-invasive tests for the early detection of liver fibrosis. This study evaluates the cost implications if the SEAL programme were to be implemented in routine care in Germany.<br><br>DESIGN: This study models cost differences with and without the SEAL screening programme. We regress costs of care on patient characteristics (age, comorbidities, sex, liver diseases, liver cancer and liver fibrosis and cirrhosis (LCI) stage) using statutory health insurance (SHI) data from routine care patients with LCI (n=4177). Based on these results, we predict per-patient costs for the patients newly diagnosed with LCI by SEAL (n=45). Costs with and without screening are estimated using patient age and LCI stage distributions from either SEAL or routine care.<br><br>SETTING: SEAL was conducted in two German states. Initial screening was performed by patients' primary care physicians.<br><br>PARTICIPANTS: Individuals insured by SHI without a prior diagnosis of LCI, eligible for Check-up 35, a general health check-up programme primarily targeting adults aged 35 and older, conducted by primary care physicians.<br><br>INTERVENTIONS: Screening via aspartate aminotransferase to platelet ratio index in primary care, for further evaluation serological diagnostics and ultrasound examinations in secondary care and specific assessment for definite diagnosis including transient elastography and liver biopsy for selected cases in tertiary care.<br><br>Primary outcome measures: expected 5-year cost changes for SEAL patients diagnosed with fibrosis or cirrhosis compared to costs without a screening programme.<br><br>SECONDARY OUTCOME MEASURES: case mix of leading chronic liver disease and LCI stages among patients diagnosed with advanced fibrosis or cirrhosis in SEAL versus routine care without screening.<br><br>RESULTS: Screening leads to fewer decompensated cases at initial diagnosis (4.6% in SEAL vs 22.8% in routine care) and thus savings in the costs of care within the first years of diagnosis: total expected costs per case were &#x20ac;2175 lower (bias-corrected bootstrap CIs (BCI): &#x20ac;527 to 3734), and LCI-associated costs were reduced by &#x20ac;1218 (BCI: &#x20ac;296 to 2164). Comparing the savings to the additional costs of diagnosis (range: &#x20ac;1575-1726 per detected LCI case) reveals that average changes in costs with screening range from moderate savings to moderate extra costs.<br><br>CONCLUSIONS: SEAL liver screening identifies patients in less advanced stages of LCI. If only costs were considered that are directly attributable to LCI, savings within 5 years are unlikely to fully outweigh the costs of screening. However, since this approach might miss additional LCI-related costs, SEAL appears to be cost-neutral compared with routine care when considering total healthcare costs.<br><br>REGISTRATION NUMBER: The SEAL registration number is DRKS00013460. This study relates to its results.},
}
@article {pmid40972658,
year = {2025},
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 = {Journal of neural engineering},
volume = {22},
number = {5},
pages = {},
pmid = {40972658},
issn = {1741-2552},
support = {UH3 NS114439/NS/NINDS NIH HHS/United States ; },
mesh = {Female ; Humans ; Male ; Middle Aged ; *Amyotrophic Lateral Sclerosis/physiopathology/diagnosis/complications ; *Brain-Computer Interfaces ; Computer Systems ; *Electrocorticography/methods ; Pilot Projects ; *Speech/physiology ; },
abstract = {Objective. Brain-computer interfaces 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 a leave-one-day-out cross-validation on 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 timing error of around 530 ms 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 {pmid40972647,
year = {2025},
author = {de Melo, GC and Forner-Cordero, A and Castellano, G},
title = {The role of the reference electrode in EEG recordings: looking from an inverted perspective.},
journal = {Biomedical physics &amp; engineering express},
volume = {11},
number = {5},
pages = {},
doi = {10.1088/2057-1976/ae093f},
pmid = {40972647},
issn = {2057-1976},
mesh = {Humans ; *Electroencephalography/methods/instrumentation ; Electrodes ; Brain-Computer Interfaces ; Principal Component Analysis ; Male ; Adult ; Female ; Signal Processing, Computer-Assisted ; Algorithms ; Young Adult ; *Brain/physiology ; },
abstract = {The electroencephalographic signal variability caused by the active reference electrode is a major challenge for classification of motor tasks in Brain-Computer Interfaces. In this work a strategy to deal with the reference is proposed: use the information from all channels to extract more reliable information from the reference, the Inverted Perspective Reference Electrode (IPRE). In this novel approach the original set of signals is re-referenced to the electrode of interest, in contrast with all other available methods. At total, eight scenarios were analyzed independently: C3 and C4 as reference electrode, alpha and beta frequency bands, and motor imagery and motor execution tasks. Principal Component Analysis (PCA) was used to extract the information from the reference. This information was analyzed by means of the separability between motor tasks. Thirty-six subsets of electrodes were analyzed, including four typical choices of channels for comparison. A dataset with 109 subjects was used. Results showed that the quantity and location of electrodes are determinant to provide class-separable signals at the reference electrode. The IPRE showed greater separability compared to typical channel choices. Therefore, the strategy revealed better outcomes, encouraging further investigation with the inverted perspective to overcome the challenge of the active reference.},
}
@article {pmid40971842,
year = {2025},
author = {Yang, X and Fang, X and Gao, M and Zhang, E and Zhu, B and Rao, H},
title = {Reducing Financial Misreporting Behavior with Noninvasive Brain Stimulation: The Moderating Effect of Moral Judgment.},
journal = {Social cognitive and affective neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1093/scan/nsaf094},
pmid = {40971842},
issn = {1749-5024},
abstract = {Building upon the distinct functions of the right dorsolateral prefrontal cortex (rDLPFC) and the right temporoparietal junction (rTPJ), this study investigates how moral judgment moderates the influence of these brain regions on financial misreporting-an effect that remains largely unknown. Employing transcranial direct current stimulation (tDCS), this study temporarily altered activity in these areas to investigate their influence on financial misreporting during a profit reporting task. Study 1 recruited university students, while Study 2 focused on finance professionals. The results showed that tDCS stimulation of rDLPFC and rTPJ reduced financial misreporting. However, the effects differed based on individuals' moral judgment levels. Those with lower moral judgment significantly reduced in misreporting with increased rDLPFC activity, whereas individuals with higher moral judgment remained consistent regardless of rDLPFC stimulation. In contrast, increased rTPJ activity reduced misreporting for subjects with higher moral judgment levels, whereas individuals with lower moral judgment remained consistent regardless of rTPJ stimulation. Importantly, these patterns hold whether participants are students or financial professionals. These findings emphasize distinct roles for rDLPFC and rTPJ in financial misreporting, highlighting the impact of individual moral judgment. This study has practical implications for enhancing ethical behavior by intervening in decision-making to effectively curb misreporting among individuals with different levels of moral judgment.},
}
@article {pmid40970086,
year = {2025},
author = {Zhai, Y and Li, C and Cao, L and Zhang, S and Liu, X and Ren, J and Liu, Y},
title = {The m6A demethylase FTO suppresses glioma proliferation by regulating the EREG/PI3K/Akt signaling pathway.},
journal = {Frontiers in cell and developmental biology},
volume = {13},
number = {},
pages = {1667990},
pmid = {40970086},
issn = {2296-634X},
abstract = {BACKGROUND: Glioma, the most prevalent primary intracranial tumor, is characterized by aggressive proliferation and formidable treatment challenges. The N6-methyladenosine (m6A) demethylase, Fat mass and obesity-associated protein (FTO), is a critical regulator of gene expression, but its precise role in glioma remains controversial. This study aimed to elucidate the function and underlying molecular mechanisms of FTO in glioma progression.<br><br>METHODS: We integrated bioinformatic analysis of 1,027 glioma patients from public cohorts (TCGA and CGGA) with a comprehensive experimental approach. In vitro studies in U251 and U87MG glioma cells involved gain- and loss-of-function assays to assess proliferation, colony formation, and cell cycle progression. Mechanistic investigations included Western blotting, qRT-PCR, and mRNA stability assays. An in vivo subcutaneous xenograft model was used to validate the tumor-suppressive role of FTO.<br><br>RESULTS: Our analysis revealed that lower FTO expression is significantly associated with higher tumor grade and poorer overall survival in glioma patients. Functionally, FTO overexpression inhibited proliferation and induced G1 phase cell cycle arrest, whereas FTO knockdown enhanced these malignant phenotypes. Mechanistically, we identified Epiregulin (EREG) as a key downstream target of FTO. Loss of FTO increased global m6A levels and enhanced EREG mRNA stability, leading to its upregulation. This, in turn, activated the PI3K/Akt signaling pathway, evidenced by increased phosphorylation of PI3K and Akt and subsequent downregulation of p53 and p21. The in vivo model confirmed that FTO overexpression suppressed tumor growth, while its knockdown accelerated it.<br><br>CONCLUSION: Our findings establish FTO as a tumor suppressor in glioma. It inhibits proliferation by destabilizing EREG mRNA in an m6A-dependent manner, thereby inactivating the PI3K/Akt signaling cascade. These results highlight FTO as a potential prognostic biomarker and a promising therapeutic target for glioma.},
}
@article {pmid40969901,
year = {2025},
author = {Zhang, J and Du, X and Li, X and Lv, X and Wang, X},
title = {Hypoxia, Psychedelics, and Terminal Lucidity: A Perspective on Neuroplasticity and Neuropsychiatric Disorders.},
journal = {ACS pharmacology &amp; translational science},
volume = {8},
number = {9},
pages = {2848-2854},
pmid = {40969901},
issn = {2575-9108},
abstract = {Hypoxia and psychedelics, despite their distinct origins, both induce altered states of consciousness and promote neuroplasticity, suggesting a shared underlying mechanism relevant to neuropsychiatric treatment and neurological recovery. Terminal lucidity, the transient resurgence of cognitive function in late-stage dementia, highlights the brain's latent capacity for rapid reorganization, a phenomenon that may be driven by transient hypoxia. Similarly, acute intermittent hypoxia and pharmacological agents like HypoxyStat, which modulate oxygen availability, have emerged as potential strategies for enhancing neural adaptability. This perspective explores the hypothesis that controlled reductions in oxygen availability(?)whether through psychedelics, near-death experiences, meditation, holotropic breathwork, or hypoxia therapies(?)trigger calcium signaling pathways that promote synaptogenesis and the formation of new neural circuits. Rather than restoring damaged connections, this process may enable functional rerouting, thereby supporting cognitive resilience and behavioral compensation in conditions such as stroke, Alzheimer's disease, and psychiatric disorders. By integrating insights from psychedelic research, hypoxia-based therapies, and neuroplasticity studies, we propose a unifying framework that leverages altered oxygen homeostasis as a novel therapeutic strategy for neuropsychiatric and neurodegenerative diseases.},
}
@article {pmid40969111,
year = {2025},
author = {Kodama, T and Yoshikawa, M and Minamii, K and Nishimoto, K and Kadowaki, S and Inoue, Y and Ito, H and Shigeto, H and Okuyama, K and Maeda, K and Katayama, O and Murata, S and Morita, K},
title = {Investigating the Neural Mechanisms of Self-Controlled and Externally Controlled Movement with a Flexible Exoskeleton Using EEG Source Localization.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {11},
pages = {},
pmid = {40969111},
issn = {1424-8220},
support = {JP22H03445//Japan Society for the Promotion of Science/ ; },
mesh = {Humans ; *Electroencephalography/methods ; Male ; Female ; *Exoskeleton Device ; Adult ; Movement/physiology ; Young Adult ; Motor Cortex/physiology ; Fingers/physiology ; Hand Strength/physiology ; },
abstract = {BACKGROUND: Self-controlled motor imagery combined with assistive devices is promising for enhancing neurorehabilitation. This study developed a soft, Flexible Exoskeleton (flexEXO) for finger movements and investigated whether self-controlled motor tasks facilitate stronger cortical activation than externally controlled conditions.<br><br>METHODS: Twenty-one healthy participants performed grasping tasks under four conditions: Self-Controlled Motion (SCC), Other-Controlled Motion (OCC), Self-Controlled Imagery Only (SCIOC), and Other-Controlled Imagery Only (OCIOC). EEG data were recorded, focusing on event-related desynchronization (ERD) in the &#x3bc; and &#x3b2; bands during imagery and motion and event-related synchronization (ERS) in the &#x3b2; band during feedback. Source localization was performed using eLORETA.<br><br>RESULTS: Higher &#x3bc;ERD and &#x3b2;ERD were observed during self-controlled tasks, particularly in the primary motor cortex and supplementary motor area. Externally controlled tasks showed enhanced activation in the inferior parietal lobule and secondary somatosensory cortex. &#x3b2;ERS did not differ significantly across conditions. Source localization revealed that self-controlled tasks engaged motor planning and error-monitoring regions more robustly.<br><br>CONCLUSIONS: The flexEXO device and the comparison of brain activity under different conditions provide insights into the neural mechanisms of motor control and have implications for neurorehabilitation.},
}
@article {pmid40969011,
year = {2025},
author = {He, R and Zhu, Y and Ye, J and Yao, D and Xu, P and Li, F and Jiang, L and Liang, Y},
title = {Brain Connectivity Variability Influences Anxiety Through the Behavioral Inhibition System.},
journal = {International journal of neural systems},
volume = {},
number = {},
pages = {2550055},
doi = {10.1142/S0129065725500558},
pmid = {40969011},
issn = {1793-6462},
abstract = {The behavioral inhibition system (BIS), mediating responses to punishment cues and avoidance behaviors, is implicated in anxiety. However, the neural dynamics underpinning BIS, particularly regarding the temporal variability of brain network interactions, remain less explored. Using resting-state functional magnetic resonance imaging (rs-fMRI) of 181 healthy adults, this study investigated the association between BIS sensitivity and the temporal variability of functional connectivity within and between functional brain networks. This finding revealed a significant positive correlation between BIS scores and temporal variability, specifically in the connectivity involving subnetworks' sensory somatomotor hand network (SSHN)-ventral attention network (VAN), and sensory somatomotor mouth network (SSMN)-VAN. Notably, the high-BIS sensitivity group exhibited significantly greater temporal variability between VAN and SSMN/SSHN compared to the low-BIS sensitivity group. Furthermore, predicted BIS scores based on network variability showed a strong correlation with actual BIS scores (Pearson's [Formula: see text]). Moreover, significant mediation effects highlighted the bridging role of BIS scores between brain network variability and anxiety scale scores. This enhances the comprehension of the relationship between BIS, anxiety, and brain function, while also offering new insights into the pathogenesis of anxiety.},
}
@article {pmid40968953,
year = {2025},
author = {Li, K and El-Fiqi, H and Wang, M},
title = {Gate Control Mechanisms of Autoencoders for EEG Signal Reconstruction.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {11},
pages = {},
pmid = {40968953},
issn = {1424-8220},
mesh = {*Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; Algorithms ; Brain-Computer Interfaces ; Brain/physiology ; Autoencoder ; },
abstract = {Electroencephalography (EEG) is a non-invasive and portable way to capture neurophysiological activity, which provides the basis for brain-computer interface systems and more innovative applications, from entertainment to security. However, the acquisition of EEG signals often suffers from noise contamination and even signal interruption problems due to poor contact of the electrodes, body movement, or heavy noise. Such heavily contaminated and lost signal segments are usually removed manually, which can hinder practical system deployment and application performance, especially in scenarios where continuous signals are required. In our previous work, we proposed the weighted gate layer autoencoder (WGLAE) and demonstrated its effectiveness in learning dependencies in EEG time series and encoding relationships among EEG channels. The WGLAE adopts a gate layer to encourage the AE to approximate multiple relationships simultaneously by controlling the data flow of each input variable. However, it only applies a sequential control scheme without taking into account the physical meaning of EEG channel locations. In this study, we investigate the gating mechanism for WGLAE and validate the importance of having a proper gating scheme for learning relationships between EEG channels. To this end, several gate control mechanisms are designed that embed EEG channel locations and their corresponding underlying physical meanings. The influences introduced by the proposed gate control mechanisms are examined on an open dataset with different scales and associated with various stimuli. The experimental results suggest that the gating mechanisms have varying influences on reconstructing EEG signals.},
}
@article {pmid40968884,
year = {2025},
author = {Mihai Ungureanu, AS and Geman, O and Toderean, R and Miron, L and SharghiLavan, S},
title = {The Next Frontier in Brain Monitoring: A Comprehensive Look at In-Ear EEG Electrodes and Their Applications.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {11},
pages = {},
pmid = {40968884},
issn = {1424-8220},
mesh = {*Electroencephalography/methods/instrumentation ; Humans ; *Brain/physiology ; Electrodes ; Signal-To-Noise Ratio ; Monitoring, Physiologic/methods ; *Ear/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {Electroencephalography (EEG) remains an essential method for monitoring brain activity, but the limitations of conventional systems due to the complexity of installation and lack of portability have led to the introduction and development of in-ear EEG technology. In-ear EEG is an emerging method of recording electrical activity in the brain and is an innovative concept that offers multiple advantages both from the point of view of the device itself, which is easily portable, and from the user's point of view, who is more comfortable with it, even in long-term use. One of the fundamental components of this type of device is the electrodes used to capture the EEG signal. This innovative method allows bioelectrical signals to be captured through electrodes integrated into an earpiece, offering significant advantages in terms of comfort, portability, and accessibility. Recent studies have demonstrated that in-ear EEG can record signals qualitatively comparable to scalp EEG, with an optimized signal-to-noise ratio and improved electrode stability. Furthermore, this review provides a comparative synthesis of performance parameters such as signal-to-noise ratio (SNR), common-mode rejection ratio (CMRR), signal amplitude, and comfort, highlighting the strengths and limitations of in-ear EEG systems relative to conventional scalp EEG. This study also introduces a visual model outlining the stages of technological development for in-ear EEG, from initial research to clinical and commercial deployment. Particular attention is given to current innovations in electrode materials and design strategies aimed at balancing biocompatibility, signal fidelity, and anatomical adaptability. This article analyzes the evolution of EEG in the ear, briefly presents the comparative aspects of EEG-EEG in the ear from the perspective of the electrodes used, highlighting the advantages and challenges of using this new technology. It also discusses aspects related to the electrodes used in EEG in the ear: types of electrodes used in EEG in the ear, improvement of contact impedance, and adaptability to the anatomical variability of the ear canal. A comparative analysis of electrode performance in terms of signal quality, long-term stability, and compatibility with use in daily life was also performed. The integration of intra-auricular EEG in wearable devices opens new perspectives for clinical applications, including sleep monitoring, epilepsy diagnosis, and brain-computer interfaces. This study highlights the challenges and prospects in the development of in-ear EEG electrodes, with a focus on integration into wearable devices and the use of biocompatible materials to improve durability and enhance user comfort. Despite its considerable potential, the widespread deployment of in-ear EEG faces challenges such as anatomical variability of the ear canal, optimization of ergonomics, and reduction in motion artifacts. Future research aims to improve device design for long-term monitoring, integrate advanced signal processing algorithms, and explore applications in neurorehabilitation and early diagnosis of neurodegenerative diseases.},
}
@article {pmid40968836,
year = {2025},
author = {Zych, P and Filipek, K and Mrozek-Czajkowska, A and Kuwałek, P},
title = {Classification of Electroencephalography Motor Execution Signals Using a Hybrid Neural Network Based on Instantaneous Frequency and Amplitude Obtained via Empirical Wavelet Transform.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {11},
pages = {},
pmid = {40968836},
issn = {1424-8220},
mesh = {*Neural Networks, Computer ; *Electroencephalography/classification ; Humans ; *Brain-Computer Interfaces ; Support Vector Machine ; Datasets as Topic ; Dimensionality Reduction ; Deep Learning ; *Movement ; *Brain/physiology ; *Gestures ; Male ; Female ; Young Adult ; Adult ; },
abstract = {Brain-computer interfaces (BCIs) have garnered significant interest due to their potential to enable communication and control for individuals with limited or no ability to interact with technologies in a conventional way. By applying electrical signals generated by brain cells, BCIs eliminate the need for physical interaction with external devices. This study investigates the performance of traditional classifiers-specifically, linear discriminant analysis (LDA) and support vector machines (SVMs)-in comparison with a hybrid neural network model for EEG-based gesture classification. The dataset comprised EEG recordings of seven distinct gestures performed by 33 participants. Binary classification tasks were conducted using both raw windowed EEG signals and features extracted via bandpower and the empirical wavelet transform (EWT). The hybrid neural network architecture demonstrated higher classification accuracy compared to the standard classifiers. These findings suggest that combining featuring extraction with deep learning models offers a promising approach for improving EEG gesture recognition in BCI systems.},
}
@article {pmid40967467,
year = {2025},
author = {Cai, M and Xia, Z and Shao, C and Du, W and Cao, J and Yang, B and He, Q and Xu, X and Zhang, J and Shao, X and Ying, M},
title = {P2RY8::TSC22D3 is a novel fusion associated with chemoresistance in leukemia by activating PI3K-AKT pathway.},
journal = {Cancer letters},
volume = {633},
number = {},
pages = {218040},
doi = {10.1016/j.canlet.2025.218040},
pmid = {40967467},
issn = {1872-7980},
}
@article {pmid40967240,
year = {2025},
author = {Del Sesto, MJ and Negoita, S and Bruzzone Giraldez, M and LaJoie, Z and Akhter Sathi, K and Wong, JK and Widge, AS and Okun, MS and Khalifa, A},
title = {Multitarget neurostimulation of the deep brain: clinical opportunities, challenges, and emerging technologies.},
journal = {Journal of neural engineering},
volume = {22},
number = {5},
pages = {},
pmid = {40967240},
issn = {1741-2552},
support = {DP2 EB037188/EB/NIBIB NIH HHS/United States ; },
mesh = {Humans ; *Deep Brain Stimulation/methods/trends/instrumentation ; Animals ; *Brain/physiology ; *Brain-Computer Interfaces/trends ; Electrodes, Implanted/trends ; },
abstract = {Recent computational, pre-clinical, and clinical studies have demonstrated the potential for using neuromodulation through simultaneous targeting of multiple deep brain regions. This approach has already been used for therapeutic and systems neuroscience applications. However, the broad clinical adoption of invasive distributed deep brain interfaces remains in its early stages. This review explores the barriers to implementation by addressing three key questions: do the benefits of implanting multiple electrodes justify the associated risks for specific applications? What is the risk-benefit ratio, and what technological advancements will be necessary to encourage clinical adoption? We also examine next-generation technologies that could enable multi-target brain interfaces, including system-on-chip micro-stimulators as well as nanoparticles. We highlight the role of novel machine learning algorithms in the optimization of stimulation parameters and for the guidance of device placement. Emerging hardware accelerators equipped with on-chip AI have demonstrated functionality that can be used to decode and to classify distributed neuronal data. This advance in hardware accelerators has also contributed to the potential for enhanced closed-loop stimulation control of devices. Despite these advances, significant technological and translational barriers persist, limiting the widespread clinical application of multi-target brain interfaces. This review provides a critical analysis of recent prototypes and novel hardware for use in multi-target systems. We will discuss both clinical and research applications. We will focus on the utilization of multi-site technologies to meet the needs of neurological diseases. We conclude that there exists a critical need for further innovation and integration of multi-site technologies into clinical practice.},
}
@article {pmid40967149,
year = {2025},
author = {Park, S and Mun, S},
title = {AI-driven pupillary-computer interface via binary-coded flickering stimuli.},
journal = {Computers in biology and medicine},
volume = {197},
number = {Pt B},
pages = {111057},
doi = {10.1016/j.compbiomed.2025.111057},
pmid = {40967149},
issn = {1879-0534},
mesh = {Humans ; Male ; Female ; Adult ; *Pupil/physiology ; *Photic Stimulation ; *Reflex, Pupillary/physiology ; *Artificial Intelligence ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Pupillary-computer interface (PCI) refers to a novel interaction modality that leverages pupil size variations elicited by changes in visual stimulus brightness. The PCI based on the pupillary light reflex (PLR) induced by binary-coded visual stimuli was proposed. A novel PCI interface was devised to overcome the limitations of conventional electroencephalogram hardware, using artificial intelligence to model subtle pupil signal patterns induced by visual stimuli. The proposed PCI system exhibited high performance in terms of the number of commands, classification accuracy, and information transfer rate (ITR) using a simple binary coding scheme and convolutional neural network-based deep learning. Twelve healthy subjects (six men and six women, aged 28.6 ± 3.4 year) participated in three experimental conditions, each using 4-, 10-, and 20-class binary-coded visual stimuli. Each visual stimulus was constructed by dividing the 3-s period into ten phases of 0.3 s each, with a single brightness change (e.g., from dark to bright) occurring within this interval. The proposed system achieved a high classification accuracy (91.84 %, 93.84 %, and 98.61 %) and ITR (59.74, 62.04, and 69.36 bits/min) for 20-, 10-, and 4-class stimuli in the test dataset, considerably outperforming previous PLR-based interface studies. The findings indicate that the proposed PCI system provides a simple, cost-effective, and low-training-requirement interface solution that does not require user training and maintains long-term stability.},
}
@article {pmid40966615,
year = {2025},
author = {Qian, X and Ng, KK and Yeo, SN and Loke, YM and Cheung, YB and Feng, L and Chong, MS and Ng, TP and Krishnan, KRR and Guan, C and Lee, TS and Zhou, JH},
title = {Brain-computer-interface-based intervention increases brain functional segregation in cognitively normal older adults.},
journal = {Age and ageing},
volume = {54},
number = {9},
pages = {},
pmid = {40966615},
issn = {1468-2834},
mesh = {Humans ; *Brain-Computer Interfaces ; Aged ; Male ; Female ; *Brain/physiology/diagnostic imaging ; Magnetic Resonance Imaging ; *Cognition ; *Healthy Aging/psychology ; Brain Mapping/methods ; *Cognitive Aging/psychology ; Age Factors ; Middle Aged ; Aged, 80 and over ; },
abstract = {Brain-computer interface (BCI)-based cognitive training systems have shown promise in enhancing cognitive performance in cognitively normal older adults. However, the brain network changes underlying these behavioural improvements remain poorly understood. To address this gap, we investigated topological alterations in intrinsic brain functional networks following BCI-based training and their behavioural relevance in cognitively normal older adults using resting-state functional magnetic resonance imaging and graph theoretical analysis. Compared to a non-intervention waitlist (WL) group, the intervention (INT) group did not show significant behavioural improvements. However, they exhibited positive changes in brain network organisation. Specifically, the INT group demonstrated a reduced nodal participation coefficient, indicating enhanced strength of a node's connections within its community, primarily within control and subcortical networks, as well as increased system segregation after training. Additionally, the modular organisation of the brain functional network in the INT group became more segregated and more aligned with a young adult-based partition template (quantified using the adjusted Rand index) compared to the WL group. Importantly, decreased participation coefficients, particularly in subcortical regions, were associated with language improvement, while increases in the adjusted Rand index were linked to enhancements in everyday memory function. These findings suggest that BCI-based cognitive training may contribute to maintaining brain network organisation in cognitively normal ageing by enhancing functional network segregation, potentially supporting cognitive performance. This study provides insights into the neural mechanisms underlying the effectiveness of BCI-based cognitive training for cognitively normal ageing.},
}
@article {pmid40966144,
year = {2025},
author = {Forenzo, D and Zhang, Y and Wittenberg, GF and He, B},
title = {Continuous Reaching and Grasping With a BCI Controlled Robotic Arm in Healthy and Stroke-Affected Individuals.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {3888-3899},
pmid = {40966144},
issn = {1558-0210},
support = {R01 NS124564/NS/NINDS NIH HHS/United States ; RF1 NS124564/NS/NINDS NIH HHS/United States ; R01 NS096761/NS/NINDS NIH HHS/United States ; RF1 NS131069/NS/NINDS NIH HHS/United States ; R01 NS127849/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Robotics ; Male ; Electroencephalography ; Adult ; Female ; *Stroke Rehabilitation/methods/instrumentation ; *Hand Strength/physiology ; Stroke/physiopathology ; Middle Aged ; *Arm ; Movement ; Deep Learning ; Algorithms ; Imagination ; Signal Processing, Computer-Assisted ; Young Adult ; Healthy Volunteers ; Signal-To-Noise Ratio ; },
abstract = {Recent advancements in signal processing techniques have enabled non-invasive Brain-Computer Interfaces (BCIs) to control assistive devices, like robotic arms, directly with users' EEG signals. However, the applications of these systems are currently limited by the low signal-to-noise ratio and spatial resolution of EEG from which brain intention is decoded. In this study, we propose a motor-imagery (MI) paradigm, inspired by the mechanisms of a computer mouse, that adds an additional "click" signal to an established 2D movement BCI paradigm. The additional output signal increases the degrees of freedom of the BCI system and may enable more complex tasks. We evaluated this paradigm using deep learning (DL) based signal processing on both healthy subjects and stroke-survivors in online BCI tasks derived from two potential applications: clicking on virtual targets and moving physical objects with a robotic arm in a continuous reach-and-grasp task. The results show that subjects were able to control both movement and clicking simultaneously to grab, move, and place up to an average of 7 cups in a 5-minute run using the robotic arm. The proposed paradigm provides an additional degree of freedom to EEG BCIs, and improves upon existing systems by enabling continuous control of reach-and-grasp tasks instead of selecting from a discrete list of predetermined actions. The tasks studied in these experiments show BCIs may be used to control computer cursors or robotic arms for complex real-world or clinical applications in the near future, potentially improving the lives of both healthy individuals and motor-impaired patients.},
}
@article {pmid40966137,
year = {2025},
author = {Gong, Y and Shi, K and Niu, X and Yang, L and Yang, X and Zheng, C},
title = {Multi-source Discriminant Dynamic Domain Adaptation for Cross-subject Motor Imagery EEG Recognition.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3610446},
pmid = {40966137},
issn = {2168-2208},
abstract = {Electroencephalography (EEG) has emerged as a widely utilized signal in motor imagery (MI) brain-computer interfaces(BCI) due to its convenience and safety. Recently, deep learning methods have rapidly developed in the field of brain computer interfaces. However, traditional EEG classification methods often face challenges related to limited generalization capability across subjects. To address this issue, this paper proposes a multi-source discriminant dynamic domain adaptation model(MSD-DDA) aimed at fully leveraging domain adaptation to enhance the accuracy of motor imagery classification. The model adeptly handles global and local disparities in motor imagery classification by dynamically minimizing differences between global domain and local subdomain. Furthermore, to ensure discriminability and diversity in the target domain, we introduce batch kernel norm maximization of the difference, thereby enhancing the model's discriminability in the target domain while preserving prediction diversity. To tackle variations in similarity between different source domains and the target domain, we devise a weighted joint prediction mechanism. This mechanism automatically adjusts the contribution weight of each source domain based on its similarity to the target domain, facilitating more precise discriminant prediction and improved adaptability to scenarios with multiple source domains. To evaluate our approach, we conducted a large number of experiments on datasets 1 and 2a of the Fourth BCI Competition and on the openBMI dataset, with average classification accuracy of 92.43%, 79.24% and 71.96%, respectively.Finally, we compare the proposed method with several classical and recent algorithms, and prove that its performance is better than the existing methods.},
}
@article {pmid40966133,
year = {2025},
author = {Wang, H and Zhang, J and Yang, K and Xiong, J and Liu, X and Chen, T and Song, L},
title = {FourierMask: Explain EEG-based End-to-end Deep Learning Models in the Frequency Domain.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3610742},
pmid = {40966133},
issn = {2168-2208},
abstract = {The rise of EEG-based end-to-end deep learning models has underscored the need to elucidate how these models process time-series raw EEG signals to generate predictions. The frequency domain provides a more suitable perspective for this task due to two key advantages: the strong correlation with cognitive states and the inherent capacity to model long-range temporal dependencies. However, this perspective remains underexplored in existing research. To bridge this gap, we propose FourierMask, the first mask perturbation framework specifically designed for frequency-domain explanation of EEG-based end-to-end models. Our method introduces three key innovations. First, the Fourier-based domain transformation enables direct manipulation of spectral components. Second, A learnable mask mechanism jointly models the spectral-spatial couplings relationship for EEG explanation. Third, a perturbation generator constrained by a target alignment loss ensures natural perturbations by minimizing distribution shift via cluster-aware regularization. We validate our method through experiments on an EEG benchmark dataset across EEGNet, TSCeption, and DeepConvNet models. Our method reaches a 36.0% average accuracy drop gap (vs. 8.6% for LIME and 6.6% for easyPEASI) at the group-level. And, it reaches a 17.8% average accuracy drop gap (vs. 8.9% for LIME and 9.9% for easyPEASI) at the instance-level. Our model-agnostic framework provides a plug-and-play solution for enhancing transparency of EEG-based end-to-end deep learning models. It links model decisions to frequency biomarkers, with potential applications in neuromedicine and brain-computer interfaces.},
}
@article {pmid40963811,
year = {2025},
author = {McDonald, C and Mayor, JJV and Lennon, O},
title = {Neurophysiological insights into sit-to-stand post stroke.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1646498},
pmid = {40963811},
issn = {1662-4548},
abstract = {INTRODUCTION: Stroke often results in the loss of ability to stand-up independently or to perform the transfer with compensatory movement patterns. While neurological disorders are associated with sit-to-stand disability, the neurophysiological mechanisms underlying the movement and the impact of injury at brain level remain poorly understood.<br><br>METHODS: Stroke participants (n =&#x202f;10, 4 males) performed five sets of three sit-to-stand transitions from an armless, backless seat adjusted to their knee joint height with three-dimensional kinematic data capture. Electromyography (EMG) was recorded from the bilateral vastus lateralis, biceps femoris, tibialis anterior, and gastrocnemius muscles. Surface electroencephalography (EEG) activity was recorded using eight focused bipolar channels over the sensorimotor cortex. Data were analyzed and compared with a reference dataset from healthy adults (n =&#x202f;10).<br><br>RESULTS: Kinematic data confirms post-stroke participants take significantly longer to complete a sit-to-stand transfer compared to healthy controls but maintain the same kinematic movement phases and temporal muscle activation patterns. EMG data indicates stroke survivors stand up using largely the same temporal muscle activation patterns, however they exhibit delayed peak activity of the vastus lateralis and biceps femoris compared to healthy controls. EEG data reveal stroke survivors demonstrate variable event-related spectral perturbation patterns and reduced event-related synchronization/de-synchronization in the alpha and beta frequency bands and increased asymmetry between brain hemispheres when compared to healthy controls.<br><br>CONCLUSION: EMG data supports the wider literature that confirms stroke survivors stand up using the same temporal muscle activation patterns compared to healthy controls, however peak activity of the vastus lateralis and biceps femoris are delayed. EEG data add new knowledge to our understanding of the central control of sit-to-stand transfers in a stroke population, highlighting differences in cortical activity from healthy controls, notably in ERSP patterns during sit to stand phases and in brain hemisphere asymmetry. Findings have relevance as a potential biomarker for stroke functional recovery and indicate that BCI-based applications of sit to stand may need to be trained individually in stroke survivors as they demonstrate variable cortical activation patterns compared to healthy controls.},
}
@article {pmid40963494,
year = {2025},
author = {Kim, G and Hong, Y and Lee, H and Kim, M and Eun, J and Lee, J and Lee, S and Chou, N and Shin, H},
title = {Single-Step Patterning of Biocompatible Neural Electrodes Using Black-Pt Functionalized Laser-Induced Graphene for in Vivo Electrophysiology.},
journal = {Small methods},
volume = {},
number = {},
pages = {e01384},
doi = {10.1002/smtd.202501384},
pmid = {40963494},
issn = {2366-9608},
support = {//Neuro-Semi-AI Fusion Superhuman Project/ ; 2025-04812973//Technology Innovation Program/ ; //Ministry of Trade, Industry &amp; Energy (MOTIE, Korea)/ ; //National Research Foundation (NRF)/ ; //Bio&amp;Medical Technology Development Program/ ; //National Research Foundation of Korea (NRF)/ ; 2025-00557203//Korean government (MSIT)/ ; 2025-02243041//Korean government (MSIT)/ ; //Innovative Human Resource Development/ ; //Local Intellectualization program/ ; //Institute of Information &amp; Communications Technology Planning &amp; Evaluation (IITP)/ ; 2025-RS-2022-00156389//Korea government (MSIT)/ ; 25-BR-04-01//Korea Brain Research Institute/ ; 25-BR-02-02//Korea Brain Research Institute/ ; },
abstract = {Neural electrodes are essential tools for monitoring electrophysiological activity in the brain, driving advances in neuroscience and neurotechnology. However, conventional semiconductor-based fabrication techniques suffer from high costs, complex procedures, and limited adaptability for customized designs. Here, a single-step patterning, scalable method is presented for fabricating biocompatible neural electrodes using laser-induced graphene (LIG) patterned directly onto polyimide substrates. This process requires only a standard CO2 laser system, a spray-coated biocompatible lubricant, and black-Platinum (Pt) functionalization to form conductive traces, electrode sites, and connector pads-eliminating the need for cleanroom infrastructure or photolithography. Selective laser ablation enables precise electrode exposure, allowing rapid prototyping across various formats, including electroencephalography (EEG), electrocorticography (ECoG), and penetrating neural probes. The entire fabrication process is completed within 5 h, reducing production time and cost by over two orders of magnitude compared to conventional approaches. Demonstrating mechanical robustness, reliable signal acquisition, and biocompatibility, the fabricated electrodes exhibit high fidelity in recording EEG, ECoG, and spike signals in anesthetized mice. These findings underscore the method's strong potential for rapid prototyping of personalized brain-computer interfaces, neurological monitoring systems, and scalable preclinical research tools.},
}
@article {pmid40962980,
year = {2025},
author = {Xie, R and Han, F and Yu, Q and Li, D and Han, X and Xu, X and Yu, H and Huang, J and Zhou, X and Zhao, H and Deng, X and Tian, Q and Li, Q and Li, H and Zhao, Y and Ma, G and Li, G and Zheng, H and Zhu, M and Yan, W and Xu, T and Liu, Z},
title = {A movable long-term implantable soft microfibre for dynamic bioelectronics.},
journal = {Nature},
volume = {645},
number = {8081},
pages = {648-655},
pmid = {40962980},
issn = {1476-4687},
mesh = {Animals ; Humans ; Male ; Rats ; Biomechanical Phenomena ; *Electrodes, Implanted ; Electronics/instrumentation ; *Prostheses and Implants ; Rats, Sprague-Dawley ; Time Factors ; },
abstract = {Long-term implantable bioelectronics offer a powerful means to evaluate the function of the nervous system and serve as effective human-machine interfaces[1-3]. Here, inspired by earthworms, we introduce NeuroWorm-a soft, stretchable and movable fibre sensor designed for bioelectronic interface. Our approach involves rolling to transform 2D bioelectronic devices into 1D NeuroWorm, creating a multifunctional microfibre that houses longitudinally distributed electrode arrays for both bioelectrical and biomechanical monitoring. NeuroWorm effectively records high-quality spatio-temporal signals in situ while steerably advancing within the brain or on the muscle as needed. This allows for the dynamic targeting and shifting of desired monitoring sites. Implanted in muscle through a tiny incision, NeuroWorm provides stable bioelectrical monitoring in rats for more than 43 weeks. Even after 54 weeks of implantation in muscle, fibroblast encapsulation around the fibre remains negligible. Our NeuroWorm represents a platform that promotes a substantial advance in bioelectronics-from an immobile probe fixed in place to active, intelligent and living devices for long-term, minimally invasive and mobile evaluation of the nervous system.},
}
@article {pmid40962872,
year = {2025},
author = {Qin, L and Guan, P and Shao, J and Xiao, Y and Yu, Y and Su, J and Zhang, C and Li, Y and Liu, S and Li, P and Ouyang, D and He, W and Liu, F and Zhu, K and Liu, K and Yao, Z and Wu, J and Zhao, Y and Li, H and Hui, F and Lin, P and Lanza, M and Li, Y and Zhai, T},
title = {Molecular crystal memristors.},
journal = {Nature nanotechnology},
volume = {},
number = {},
pages = {},
pmid = {40962872},
issn = {1748-3395},
abstract = {Memristors have emerged as a promising hardware platform for in-memory computing, but many current devices suffer from channel material degradation during repeated resistive switching. This leads to high energy consumption and limited endurance. Here we introduce a molecular crystal memristor, of which the representative channel material, Sb2O3, possesses a molecular crystal structure where molecular cages are interconnected via van der Waals forces. This unique configuration allows ions to migrate through intermolecular spaces with relatively low energy input, preserving the integrity of the crystal structure even after extensive switching cycles. Our molecular crystal memristor thus exhibits low energy consumption of 26 zJ per operation, with prominent endurance surpassing 10[9] switching cycles. The device delivers both reconfigurable non-volatile and volatile resistive switching behaviours over a broad range of device scales, from micrometres down to nanometres. Furthermore, we establish the scalability of this technology by fabricating large crossbar arrays on an 8 inch wafer. This enables the successful implementation of reservoir computing on a single CMOS-integrated chip using these memristors, achieving 100% accuracy in dynamic vision recognition.},
}
@article {pmid40962534,
year = {2025},
author = {Wang, Y},
title = {[Promote the application and innovation of artificial intelligence in pediatric neurological diseases].},
journal = {Zhonghua er ke za zhi = Chinese journal of pediatrics},
volume = {63},
number = {10},
pages = {1045-1047},
doi = {10.3760/cma.j.cn112140-20250722-00671},
pmid = {40962534},
issn = {0578-1310},
mesh = {Humans ; *Artificial Intelligence ; Child ; *Nervous System Diseases/diagnosis/therapy ; Machine Learning ; *Pediatrics/methods ; Brain-Computer Interfaces ; Decision Support Systems, Clinical ; },
}
@article {pmid40961966,
year = {2025},
author = {Ruszala, B and Mazurek, KA and Schieber, MH},
title = {Disentangling indirect versus direct effects of somatosensory cortex microstimulation on neurons in primary motor and ventral premotor cortex.},
journal = {Journal of neural engineering},
volume = {22},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ae087e},
pmid = {40961966},
issn = {1741-2552},
mesh = {*Somatosensory Cortex/physiology ; *Motor Cortex/physiology/cytology ; Animals ; *Neurons/physiology ; Macaca mulatta ; Male ; Brain-Computer Interfaces ; Electric Stimulation/methods ; },
abstract = {Objective.Intracortical microstimulation in the primary somatosensory cortex (S1-ICMS) is being developed to provide on-line feedback for bidirectional brain-machine interfaces. Because S1-ICMS can alter the discharge of the motor cortex neurons used to decode motor intent, successful application of S1-ICMS feedback requires understanding the modulation it produces in motor cortex neuron activity.Approach.We investigated the effects of S1-ICMS on neurons in both the primary motor cortex (M1) and the ventral premotor cortex (PMv) during a task in which some trials were instructed with visual cues and other trials with S1-ICMS.Main results.We observed both indirect modulation during and/or after ICMS trains, as well as direct modulation time-locked to the individual S1-ICMS pulses within trains, with all possible combinations of the two types of modulation found among the majority of M1 and PMv neurons. Indirect effects were more prevalent and larger than direct effects. When S1-ICMS produced both indirect and direct modulation in the same neuron, the effects could both be excitatory, both inhibitory, or one excitatory and the other inhibitory. By simulating direct effects, we isolated the concurrent indirect effects, revealing that isolated direct effects failed to account for isolated indirect effects. Furthermore, indirect effects could be present 1 s or more after ICMS trains had terminated, when no direct effects could have occurred. Although the performance of movement decoders trained on visually-instructed trials was poor when applied to ICMS-instructed trials, decoders trained on ICMS-instructed trials performed well on ICMS-instructed trials, indicating that S1-ICMS altered the discharge of M1 and PMv neurons but did not degrade the decodable information available.Significance.When decoding movement intent from neural activity in M1 and/or PMv, accounting for indirect and direct modulation may improve the ability of bidirectional brain-machine interfaces to incorporate artificial somatosensory feedback delivered with S1-ICMS and restore functional movement.},
}
@article {pmid40961213,
year = {2025},
author = {Kryt, G and Dougall, R and Borisoff, J},
title = {BCIT's BEAST wheelchair takes on Cybathlon with power, precision, and pilot-led design.},
journal = {Science robotics},
volume = {10},
number = {106},
pages = {eaeb1340},
doi = {10.1126/scirobotics.aeb1340},
pmid = {40961213},
issn = {2470-9476},
mesh = {*Wheelchairs ; Humans ; Equipment Design ; *Brain-Computer Interfaces ; *Robotics/instrumentation ; },
abstract = {An extending, articulating powered wheelchair competed and won the wheelchair race at Cybathlon 2024.},
}
@article {pmid40960388,
year = {2025},
author = {Jhilal, S and Marchesotti, S and Thirion, B and Soudrie, B and Giraud, AL and Mandonnet, E},
title = {Implantable Neural Speech Decoders: Recent Advances, Future Challenges.},
journal = {Neurorehabilitation and neural repair},
volume = {},
number = {},
pages = {15459683251369468},
doi = {10.1177/15459683251369468},
pmid = {40960388},
issn = {1552-6844},
abstract = {The social life of locked-in syndrome (LIS) patients is significantly impacted by their difficulties to communicate. Consequently, researchers have started to explore how to decode intended speech from neural signals directly recorded from the cortex. The first studies in the late 2000s reported modest decoding accuracies. However, thanks to fast advances in machine learning, the most recent studies have reached decoding accuracies high enough to be optimistic about the clinical benefit of neural speech decoders in the near future. We first discuss the selection criteria for implanting a neural speech decoder in LIS patients, emphasizing the advantages and disadvantages associated with conditions such as brainstem stroke and amyotrophic lateral sclerosis. We examine the key design considerations for neural speech decoders, demonstrating how successful implantation requires careful optimization of multiple interrelated factors including language representation, cortical recording areas, neural features, training paradigms, and decoding algorithms. We then discuss current approaches and provide arguments for potential improvements in decoder design and implementation. Finally, we explore the crucial question of who should learn to use the neural speech decoder-the patient, the machine, or both. In conclusion, while neural speech decoders present promising avenues for improving communication for LIS patients, interdisciplinary efforts spanning neurorehabilitation, neuroscience, neuroengineering, and ethics are imperative to design future clinical trials.},
}
@article {pmid40959706,
year = {2025},
author = {Kothe, C and Shirazi, SY and Stenner, T and Medine, D and Boulay, C and Grivich, MI and Artoni, F and Mullen, T and Delorme, A and Makeig, S},
title = {The lab streaming layer for synchronized multimodal recording.},
journal = {Imaging neuroscience (Cambridge, Mass.)},
volume = {3},
number = {},
pages = {},
pmid = {40959706},
issn = {2837-6056},
support = {KL2 TR001999/TR/NCATS NIH HHS/United States ; R01 NS047293/NS/NINDS NIH HHS/United States ; },
abstract = {Accurately recording the interactions of humans or other organisms with their environment and other agents requires synchronized data access via multiple instruments, often running independently using different clocks. Active, hardware-mediated solutions are often infeasible or prohibitively costly to build and run across arbitrary collections of input systems. The Lab Streaming Layer (LSL) framework offers a software-based approach to synchronizing data streams based on per-sample time stamps and time synchronization across a common local area network (LAN). Built from the ground up for neurophysiological applications and designed for reliability, LSL offers zero-configuration functionality and accounts for network delays and jitters, making connection recovery, offset correction, and jitter compensation possible. These features can ensure continuous, millisecond-precise data recording, even in the face of interruptions. In this paper, we present an overview of LSL architecture, core features, and performance in common experimental contexts. We also highlight practical considerations and known pitfalls when using LSL, including the need to take into account input device throughput delays that LSL cannot itself measure or correct. The LSL ecosystem has grown to support over 150 data acquisition device classes and to establish interoperability between client software written in several programming languages, including C/C++, Python, MATLAB, Java, C#, JavaScript, Rust, and Julia. The resilience and versatility of LSL have made it a major data synchronization platform for multimodal human neurobehavioral recording, now supported by a wide range of software packages, including major stimulus presentation tools, real-time analysis environments, and brain-computer interface applications. Beyond basic science, research, and development, LSL has been used as a resilient and transparent back-end in deployment scenarios, including interactive art installations, stage performances, and commercial products. In neurobehavioral studies and other neuroscience applications, LSL facilitates the complex task of capturing organismal dynamics and environmental changes occurring within and across multiple data streams on a common timeline.},
}
@article {pmid40959704,
year = {2025},
author = {Wu, X and Hu, K and Fu, Z and Zhang, D},
title = {Improved evaluation of waveform reconstruction in speech decoding based on invasive brain-computer interfaces.},
journal = {Imaging neuroscience (Cambridge, Mass.)},
volume = {3},
number = {},
pages = {},
pmid = {40959704},
issn = {2837-6056},
abstract = {Brain-computer interfaces (BCIs) that reconstruct speech waveforms from neural signals are a promising communication technology. However, the field lacks a standardized evaluation metric, making it difficult to compare results across studies. Existing objective metrics, such as correlation coefficient (CC) and mel cepstral distortion (MCD), are often used inconsistently and have intrinsic limitations. This study addresses the critical need for a robust and validated method for evaluating reconstructed waveform quality. Literature about waveform reconstruction from intracranial signals is reviewed, and issues with evaluation methods are presented. We collated reconstructed audio from 10 published speech BCI studies and collected Mean Opinion Scores (MOS) from human raters to serve as a perceptual ground truth. We then systematically evaluated how well combinations of existing objective metrics (STOI and MCD) could predict these MOS scores. To ensure robustness and generalizability, we employed a rigorous leave-one-dataset-out cross-validation scheme and compared multiple models, including linear and non-linear regressors. This work, for the first time, identifies a lack of a standard evaluation method, which prohibits cross-study comparison. Using 10 public datasets, our analysis reveals that a non-linear model, specifically a Random Forest regressor, provides the most accurate and reliable prediction of subjective MOS ratings (R[2] = 0.892). We propose this cross-validated Random Forest model, which maps STOI and MCD to a predicted MOS score, as a standardized objective evaluation metric for the speech BCI field. Its demonstrated accuracy and robust validation outperform the available methods. Moreover, it can provide the community with a reliable tool to benchmark performance, facilitate meaningful cross-study comparisons for the first time, and accelerate progress in speech neuroprosthetics.},
}
@article {pmid40956723,
year = {2025},
author = {Darley, G and Bonnet, S},
title = {A Unified Framework for Matrix Backpropagation.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNNLS.2025.3607405},
pmid = {40956723},
issn = {2162-2388},
abstract = {Computing matrix gradient has become a key aspect in modern signal processing/machine learning, with the recent use of matrix neural networks requiring matrix backpropagation. In this field, two main methods exist to calculate the gradient of matrix functions for symmetric positive definite (SPD) matrices, namely, the Daleckiǐ-Kreǐn/Bhatia formula and the Ionescu method. However, there appear to be a few errors. This brief aims to demonstrate each of these formulas in a self-contained and unified framework, to prove theoretically their equivalence, and to clarify inaccurate results of the literature. A numerical comparison of both methods is also provided in terms of computational speed and numerical stability to show the superiority of the Daleckiǐ-Kreǐn/Bhatia approach. We also extend the matrix gradient to the general case of diagonalizable matrices. Convincing results with the two backpropagation methods are shown on the EEG-based BCI competition dataset with the implementation of an SPDNet, yielding around 80% accuracy for one subject. Daleckiǐ-Kreǐn/Bhatia formula achieves an 8% time gain during training and handles degenerate cases.},
}
@article {pmid40956372,
year = {2025},
author = {Wang, T and Yi, T and Chen, T and Khan, NU and Yuan, Y},
title = {Spinal Cord Injury 2.0: Bridging the Gap Between Neurobiology, Technology, and Hope in the Era of Precision Medicine.},
journal = {Stem cell reviews and reports},
volume = {21},
number = {8},
pages = {2597-2615},
pmid = {40956372},
issn = {2629-3277},
mesh = {Humans ; *Spinal Cord Injuries/therapy/pathology/physiopathology ; *Precision Medicine/methods ; Animals ; *Neurobiology ; Nerve Regeneration ; },
abstract = {Spinal cord injury (SCI) is a devastating neurological condition with profound motor, sensory, and autonomic consequences, affecting 10-83 individuals per million annually worldwide. This review explores the evolving SCI landscape, from acute ionic imbalance, excitotoxicity, and vascular disruption to chronic neuroinflammation and glial fibrosis, which collectively impede neural regeneration. Breakthroughs in regenerative bioengineering-such as stem cell-driven neurogenesis and CRISPR-Cas9-mediated axonal growth modulation-are converging with neurotechnological advances, including spinal neuromodulation, brain-computer interface integration, and AI-enhanced robotic locomotor systems, to redefine therapeutic frontiers. Precision medicine, guided by multi-omic biomarker stratification and patient-specific computational modeling, enables individualized intervention strategies. Despite unprecedented progress, translation to the clinic demands optimized preclinical models, harmonized trial methodologies, and ethical frameworks ensuring equitable access. Together, these innovations herald a shift from compensatory care toward structural repair and functional restoration in SCI.},
}
@article {pmid40956157,
year = {2025},
author = {Yu, B and Li, P and Xu, H and Wang, Y and Xu, K and Hao, Y},
title = {Novel and optimized mouse behavior enabled by fully autonomous HABITS: Home-cage assisted behavioral innovation and testing system.},
journal = {eLife},
volume = {14},
number = {},
pages = {},
pmid = {40956157},
issn = {2050-084X},
support = {2021ZD0200405//STI 2030-Major Projects/ ; 62336007//National Natural Science Foundation of China/ ; SN-ZJU-SIAS-002//Starry Night Science Fund of Zhejiang University Shanghai Institute for Advanced Study/ ; 2023ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024C03001//Pioneer R&amp;D Program of Zhejiang/ ; },
mesh = {Animals ; Mice ; *Behavior, Animal ; *Cognition ; Male ; *Habits ; Mice, Inbred C57BL ; Algorithms ; },
abstract = {Mice are among the most prevalent animal models used in neuroscience, benefiting from the extensive physiological, imaging, and genetic tools available to study their brain. However, the development of novel and optimized behavioral paradigms for mice has been laborious and inconsistent, impeding the investigation of complex cognitions. Here, we present a home-cage assisted mouse behavioral innovation and testing system (HABITS), enabling free-moving mice to learn challenging cognitive behaviors in their home-cage without any human involvement. Supported by the general programming framework, we have not only replicated established paradigms in current neuroscience research but also developed novel paradigms previously unexplored in mice, resulting in more than 300 mice demonstrated in various cognition functions. Most significantly, HABITS incorporates a machine-teaching algorithm, which comprehensively optimized the presentation of stimuli and modalities for trials, leading to more efficient training and higher-quality behavioral outcomes. To our knowledge, this is the first instance where mouse behavior has been systematically optimized by an algorithmic approach. Altogether, our results open a new avenue for mouse behavioral innovation and optimization, which directly facilitates investigation of neural circuits for novel cognitions with mice.},
}
@article {pmid40956015,
year = {2025},
author = {Xu, JJ and Chen, YL and Sun, WB and Li, HF and Wu, ZY and Chen, DF},
title = {Functional Characterization and Pathogenicity Classification of PRRT2 Splice Variants in PRRT2-Related Disorders.},
journal = {Annals of clinical and translational neurology},
volume = {},
number = {},
pages = {},
doi = {10.1002/acn3.70189},
pmid = {40956015},
issn = {2328-9503},
support = {188020-193810101/089//distinguished scholar of Zhejiang University/ ; 81330025//National Natural Science Foundation of China/ ; },
abstract = {OBJECTIVE: Paroxysmal kinesigenic dyskinesia (PKD) is the most common hereditary paroxysmal movement disorder. The PRRT2 gene is the first identified causative gene and accounts for the majority of PKD. In this study, we investigated the pathogenicity of PRRT2 variants in the splice regions.<br><br>METHODS: Patients with clinically suspected PKD and no detectable pathogenic variants in the PRRT2 gene were included. Targeted next-generation sequencing technology was used to screen the full-length sequence of PRRT2. In silico analyses were performed on splice region variants identified in our cohort and compiled from the Human Gene Mutation Database (HGMD). Subsequently, a minigene system carrying these variants was constructed and introduced into HEK293T cells for functional assays to assess the pathogenicity.<br><br>RESULTS: Fourteen PRRT2 variants were analyzed, including four identified in patients with clinically suspected PKD from our center and 10 retrieved from HGMD. These variants comprised 10 intronic variants, two synonymous variants, one deletion, and one missense variant. In silico predictions suggested that all variants, except for one deep intronic variant, had the potential to affect normal splicing. Functional assays showed that 11 PRRT2 variants, including missense and intronic variants, caused aberrant splicing events, such as exon skipping and intron retention. The two synonymous variants and one deep intronic variant exhibited no splicing abnormalities. Based on these results, five patients with PRRT2 variants previously classified as variants of uncertain significance can now be genetically diagnosed with PKD or other PRRT2-related disorders.<br><br>INTERPRETATION: Combining in silico analyses with functional assays is essential for determining the pathogenicity of splice variants. It can help confirm the diagnosis of patients with clinically suspected PKD and other PRRT2-related disorders.},
}
@article {pmid40955442,
year = {2025},
author = {Evenblij, D and Lührs, M and Rafeh, RW and Benitez Andonegui, A and Kurban, D and Valente, G and Sorger, B},
title = {Two Seconds to Speak: Increasing Communication Speed for fMRI-Based Brain-Computer Interfaces.},
journal = {Brain connectivity},
volume = {15},
number = {8},
pages = {283-299},
doi = {10.1177/21580014251376731},
pmid = {40955442},
issn = {2158-0022},
mesh = {Humans ; *Brain-Computer Interfaces ; *Magnetic Resonance Imaging/methods ; Male ; Female ; *Brain/physiology ; Adult ; Young Adult ; Brain Mapping/methods ; *Communication ; },
abstract = {Background: Brain-computer interfaces (BCIs) can provide alternative, motor-independent means of communication for people who have lost motor function. A promising variant is the functional magnetic resonance imaging (fMRI)-based BCI, which exploits information on hemodynamic brain activity evoked by performing different mental tasks. However, due to the sluggish nature of the hemodynamic response, a current challenge is to make these BCIs as efficient and fast as possible to allow useful clinical application. Furthermore, there is yet no consensus on optimal mental-task selection for multi-voxel pattern analysis-based decoding, nor whether certain tasks generalize well across users, or if individualized task selection would yield a higher decoding accuracy. Methods: To increase BCI efficiency, we tested whether distributed patterns of 3T-fMRI brain activation evoked by two-second mental tasks could be reliably discriminated in 2- to 7-class classification. In addition, we identified optimal mental-task combinations for high-accuracy classification across all classes. Finally, we examined whether individualized task selection-based on subjects' previous decoding performance (accuracy-based tasks) or their subjective preference (preference-based tasks)-was superior to the other in a yes/no communication paradigm. Results: The 2-class decoding resulted in a mean accuracy of 78% and 3- to 7-class accuracies were above chance level. Mental calculation and spatial navigation were most frequently associated with the highest decoding accuracy. Furthermore, subjects could encode yes/no answers using their accuracy-based and preference-based tasks with mean accuracies of 83% and 81%, respectively. This implies that this paradigm, using short encoding durations, is well-suited to the diversity of patients and could greatly increase BCI efficiency.},
}
@article {pmid40954927,
year = {2025},
author = {Mehmood, A and Xu, S and Siddiqi, SM and Zhang, L and Huang, G and Liang, Z and Zhou, Y},
title = {Exploration of Nonsuicidal Self-Injury as an Addiction-Like Behaviour in Depressed Adolescents in the light of the I-PACE Model.},
journal = {Clinical psychology &amp; psychotherapy},
volume = {32},
number = {5},
pages = {e70147},
doi = {10.1002/cpp.70147},
pmid = {40954927},
issn = {1099-0879},
support = {62276169//National Natural Science Foundation of China/ ; 62201356//National Natural Science Foundation of China/ ; 2024YG008//Medical-Engineering Interdisciplinary Research Foundation of Shenzhen University/ ; 2023SHIBS0003//Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions/ ; 2021ZD0200500//STI 2030-Major Projects/ ; BMI2400008//Open Research Fund of the State Key Laboratory of Brain-Machine Intelligence, Zhejiang University/ ; JCYJ20241202124222027//Shenzhen Science and Technology Program/ ; JCYJ20241202124209011//Shenzhen Science and Technology Program/ ; },
mesh = {Humans ; Adolescent ; *Self-Injurious Behavior/psychology ; Male ; Female ; Child ; Resilience, Psychological ; *Behavior, Addictive/psychology ; Self Concept ; Models, Psychological ; *Depressive Disorder/psychology ; Rumination, Cognitive ; Executive Function ; },
abstract = {Nonsuicidal self-injury (NSSI) is increasingly conceptualized as an addiction-like behaviour characterized by dysregulated emotional and cognitive processes. Guided by the I-PACE model, this study investigated how person-level vulnerabilities interact with affective, mental and executive functioning to maintain NSSI in clinically depressed adolescents (N = 167, aged 12-18, M = 15.37 ± 1.75 years). Results revealed strong addiction-like patterns. Childhood trauma, depression and rumination demonstrated significant associations with NSSI frequency (r = 0.59-0.61), while resilience and self-esteem served as protective factors (r = -0.53 to -0.55). A hierarchical regression model explained 69% of variance, with trauma (OR = 1.12), depressive severity (OR = 1.11), rumination (OR = 1.11) and resilience (OR = 0.90) emerging as key predictors. Mediation analyses demonstrated how these factors operate in the addictive chain. Childhood trauma and borderline traits lead to affective dysregulation, which drives cognitive deficits that ultimately undermine resilience and increase NSSI risk (β = -0.28 and -0.24). These findings support the use of an addiction framework to conceptualize NSSI, while highlighting resilience-focused interventions as critical for breaking these maladaptive cycles.},
}
@article {pmid40954277,
year = {2025},
author = {Zhou, D and Zhou, Y and Sun, Z and Ji, F and Zhang, D and Wang, Q and Ruan, Y and Wang, Y and Zhu, Y and Sun, X and Li, MJ and Yuan, C and Liu, K and Sun, L and Zhai, W and Fan, J and Zhu, K and Qiu, W and Yan, X and Ma, C and Shen, Y and Bao, A and Yue, W and Shi, Y and Chen, C and Yang, J and Duan, S and Zhang, J and , },
title = {The China Brain Multi-omics Atlas Project (CBMAP).},
journal = {Molecular psychiatry},
volume = {},
number = {},
pages = {},
pmid = {40954277},
issn = {1476-5578},
abstract = {The China Brain Multi-omics Atlas Project (CBMAP) aims to generate a comprehensive molecular reference map of over 1000 human brains (Phase I), spanning a broad age range and multiple regions in China, to address the underrepresentation of East Asian populations in brain research. By integrating genome, epigenome, transcriptome, proteome (including multiple post-translational modifications), and metabolome data, CBMAP is set to provide a rich and invaluable resource for investigating the molecular underpinnings of aging-related brain phenotypes and neuropsychiatric disorders. Leveraging high-throughput omics data and advanced technologies, such as spatial transcriptomics, proteomics, and single-nucleus 3D chromatin structure analysis, this atlas will serve as a crucial resource for the brain science community, illuminating disease mechanisms and enhancing the utility of data from genome-wide association studies (GWAS). CBMAP is also poised to accelerate drug discovery and precision medicine for brain disorders.},
}
@article {pmid40953646,
year = {2025},
author = {Cao, Y and Pan, Z and Shen, X and Xu, Z and Yang, X and Yang, B and Luo, P and Yan, H and He, Q},
title = {CAMK2G in subcellular Ca[2+] homeostasis: Molecular mechanisms and therapeutic targeting.},
journal = {Biochemical pharmacology},
volume = {242},
number = {Pt 2},
pages = {117323},
doi = {10.1016/j.bcp.2025.117323},
pmid = {40953646},
issn = {1873-2968},
mesh = {Humans ; *Homeostasis/physiology/drug effects ; *Calcium-Calmodulin-Dependent Protein Kinase Type 2/metabolism/antagonists &amp; inhibitors/genetics ; Animals ; *Calcium/metabolism ; *Calcium Signaling/physiology/drug effects ; Molecular Targeted Therapy/methods ; },
abstract = {The Ca[2+]/calmodulin-dependent protein kinase II (CAMK2) family, consisting of subtypes A, B, D, and G, plays a pivotal role in decoding Ca[2+] signals, an essential process in cellular communication and function. Among these, CAMK2G is notably widespread across various body tissues, with predominant expression in neurons and cardiomyocytes, where it significantly influences Ca[2+] signal transduction and the cellular response to stress. Ca[2+] serves as the most plentiful second messenger within the human body, orchestrating critical regulatory roles across numerous physiological and pathological contexts. It is instrumental in managing aspects of the tumor microenvironment, neurodegenerative conditions, cardiovascular diseases, and metabolic disorders. Maintaining Ca[2+] homeostasis is crucial for the proper functioning of different subcellular organelles, impacting overall cellular health and activity. Here, we describe the central connection between CAMK2G and subcellular Ca[2+] homeostasis, highlight the molecular functions of CAMK2G therein, and finally detail the cutting-edge therapeutic strategies targeting CAMK2G.},
}
@article {pmid40953427,
year = {2025},
author = {Li, Z and Yan, C and Lan, Z and Xiang, X and Zhou, H and Lai, J and Tang, D},
title = {Adaptive Modality Balanced Online Knowledge Distillation for Brain-Eye-Computer-Based Dim Object Detection.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNNLS.2025.3605710},
pmid = {40953427},
issn = {2162-2388},
abstract = {Advanced cognition can be measured from the human brain using brain-computer interfaces (BCIs). Integrating these interfaces with computer vision techniques, which possess efficient feature extraction capabilities, can achieve more robust and accurate detection of dim targets in aerial images. However, existing target detection methods primarily concentrate on homogeneous data, lacking efficient and versatile processing capabilities for heterogeneous multimodal data. In this article, we first build a brain-eye-computer-based object detection system for aerial images under few-shot conditions. This system detects suspicious targets using region proposal networks (RPNs), evokes the event-related potential (ERP) signal in electroencephalogram (EEG) through the eye-tracking-based slow serial visual presentation (ESSVP) paradigm, and constructs the EEG-image data pairs with eye movement data. Then, an adaptive modality balanced online knowledge distillation (AMBOKD) method is proposed to recognize dim objects with the EEG-image data. AMBOKD fuses EEG and image features using a multihead attention module, establishing a new modality with comprehensive features. To enhance the performance and robust capability of the fusion modality, simultaneous training and mutual learning between modalities are enabled by end-to-end online KD (OKD). During the learning process, an adaptive modality balancing module is proposed to ensure multimodal equilibrium by dynamically adjusting the weights of the importance and the training gradients across various modalities. The effectiveness and superiority of our method are demonstrated by comparing it with existing state-of-the-art methods. Additionally, experiments conducted on public datasets and real-world scenarios demonstrate the reliability and practicality of the proposed system and the designed method. The dataset and the source code can be found at: https://github.com/lizixing23/AMBOKD.},
}
@article {pmid40949716,
year = {2025},
author = {Galang, EV and Velásquez, MA and Elcin, D and O'Connell, S and Wieck, J and McNair, S and Colombo, PJ},
title = {Systematic review and meta-analysis of the relationships between real-time neurofeedback training parameters and acquisition of neural modulation.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1652607},
pmid = {40949716},
issn = {1662-5161},
abstract = {INTRODUCTION: Real-time neurofeedback is an emerging method for regional modulation of neural activity with physiological and behavioral effects that persist beyond the duration of feedback sessions. However, many individuals fail to achieve successful modulation, a challenge known as the "neurofeedback inefficacy problem." This study examined how methodological parameters of neurofeedback training influence the acquisition and retention of neural modulation in healthy adults.<br><br>METHODS: A systematic search identified eligible studies, resulting in 55 participant groups included in the meta-analysis. Standardized mean differences (Hedges' g) were calculated for changes in neural activity from first to last session and from pre- to post-training. Subgroup analyses and meta-regression were conducted to assess the impact of discrete and continuous moderators.<br><br>RESULTS: This meta-analysis identified four parameters associated with significant neural modulation in the desired direction: the neurofeedback imaging device used, complexity of the feedback stimulus, presence of a pre-training rehearsal trial, and EEG target oscillations.<br><br>DISCUSSION: This meta-analysis highlights key methodological factors that shape neurofeedback efficacy in non-clinical populations. Findings may serve to better understand how methodological variables used in neurofeedback influence the acquisition and retention of neural modulation.<br><br>https://www.crd.york.ac.uk/PROSPERO/view/CRD42022357160, identifier: CRD42022357160.},
}
@article {pmid40949676,
year = {2025},
author = {Cai, H and Hu, J and Zhao, C and Lin, J},
title = {Wearable devices in neurological disorders: a narrative review of status quo and perspectives.},
journal = {Annals of translational medicine},
volume = {13},
number = {4},
pages = {46},
pmid = {40949676},
issn = {2305-5839},
abstract = {BACKGROUND AND OBJECTIVE: Neurological disorders are a group of diseases involving motor, sensory, cognitive, and autonomic functions, among which stroke, Alzheimer's disease (AD), and Parkinson's disease (PD) are prevalent. Their management, especially in conditions with chronic courses or long-term sequelae, remains a substantial unmet need. With the growing comprehension of neuroscience, the development of digital technology, and the rising demand for quality of life, wearable devices offer a promising solution for disease management. The review aimed to evaluate the application and prospect of wearable devices in neurological disorders.<br><br>METHODS: We conducted the review by searching papers on the application of wearable devices and wearable technology in neurology and neurological disorders using multiple databases. We summarized the present development status of wearable devices, and outlined the potential value and future direction for further research.<br><br>KEY CONTENT AND FINDINGS: Existing wearable devices for neurological diseases can be applied to diagnosis and follow-up, as an electronic biomarker detector capturing subtle and objective changes in motor, sensory, and cognitive function. The devices can also be utilized for treatment and rehabilitation, mainly through exoskeletons and brain-computer interface. The application of wearable devices in neurology currently faces several critical limitations, including technical bottlenecks in the detection of fine motor and sensory functions, a lack of industry standards, and a limited sample size.<br><br>CONCLUSIONS: This review demonstrates the potential of wearable technology in people with neurological disorders, enabling disease management and clinical trials outside clinical settings in the future. Nevertheless, further research is required to develop lighter, more user-friendly devices with various functions. It is believed that with increasing demand and technical support, wearable devices would have a promising range of applications.},
}
@article {pmid40948973,
year = {2025},
author = {De Pasquale, P and De Bartolo, D and Russo, M and Berger, DJ and Maselli, A and Borzelli, D and Colamarino, E and Mattia, D and Nissler, C and Nowak, M and Falomo, E and Soto Morras, J and Schiller, MR and Castellini, C and Morone, G and d'Avella, A},
title = {User-centered development of a personalized adaptive mirror therapy for upper-limb post-stroke rehabilitation using virtual reality and myoelectric control.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {13},
number = {},
pages = {1655416},
pmid = {40948973},
issn = {2296-4185},
abstract = {INTRODUCTION: Cerebral stroke often results in significant motor deficits, including contralateral hemiparesis of the upper limb. Rehabilitation protocols with high-intensity and task-specific exercises can improve these deficits. Recent technological advancements in virtual reality (VR), myoelectric control, and exergames may be exploited to enhance rehabilitation effectiveness. However, novel rehabilitation approaches combining these novel methodologies have rarely been developed with the active involvement of both therapists and patients.<br><br>METHODS: An interdisciplinary team developed a novel system, Validation of the Virtual Therapy Arm (VVITA), for post-stroke upper-limb rehabilitation combining VR, myoelectric control, and exergames using a user-centered design (UCD) approach. The VVITA hardware includes a head-mounted VR display, motion tracking devices integrated in the VR system, and wireless armbands to record electromyographic (EMG) signals, providing an interactive virtual environment for immersive rehabilitation exercises implementing a virtual mirror therapy. Assistance and task difficulty are adjusted dynamically based on patient performance, promoting active participation and motor learning.<br><br>RESULTS: The development process involved iterative phases, involving focus groups with stroke patients, therapists, and researchers. A pilot study with four stroke survivors assessed the system's feasibility, demonstrating its potential for personalized and adaptive rehabilitation.<br><br>CONCLUSION: The VVITA system enhances mirror therapy by integrating VR and myoelectric control, providing a tailored approach to upper-limb post-stroke rehabilitation. The UCD approach ensured the system met patient and therapist needs, showing promise for improving motor recovery and rehabilitation outcomes.},
}
@article {pmid40947448,
year = {2025},
author = {Wang, Q and Dong, X and Jiang, D and Tian, S and Qiu, Y and Zhu, Y and Wu, J and Shang, S and Zhang, Y and Wang, P and Zhuang, L},
title = {Bioelectronic Interfaces and Sensors for Neural Organoids.},
journal = {Microsystems &amp; nanoengineering},
volume = {11},
number = {1},
pages = {172},
pmid = {40947448},
issn = {2055-7434},
support = {No. 82330064, 32250008, 62271443//National Natural Science Foundation of China (National Science Foundation of China)/ ; LQ24H090008//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
abstract = {Neural organoids are emerging as promising in vitro models, offering a unique platform to partially recapitulate the structural and functional complexity of the human nervous system. These three-dimensional (3D) constructs, which mimic key aspects of organ architecture, can be reliably derived from pluripotent stem cells (iPSCs) or embryonic stem cells (ESCs). Their ability to faithfully model neural development and disease pathogenesis has positioned them as indispensable tools in neuroscience research. However, to further unleash their potential, there is a pressing need for long-term and stable monitoring of their dynamic functions in a 3D context. This review provides a brief overview on diverse types of neural organoids and their induction protocols. We further highlight recent advancements in bioelectronic interfaces and sensors tailored for 3D culture. Finally, we discuss future directions aimed at advanced methodologies for real-time, multidimensional functional analysis, ultimately paving the way for breakthroughs in understanding neural development and pathology.},
}
@article {pmid40946865,
year = {2025},
author = {Zhao, R and Daly, I and Chen, Y and Wu, W and Liu, L and Wang, X and Cichocki, A and Jin, J},
title = {MSAttNet: Multi-scale attention convolutional neural network for motor imagery classification.},
journal = {Journal of neuroscience methods},
volume = {424},
number = {},
pages = {110578},
doi = {10.1016/j.jneumeth.2025.110578},
pmid = {40946865},
issn = {1872-678X},
mesh = {Humans ; *Neural Networks, Computer ; *Electroencephalography/methods ; *Imagination/physiology ; *Attention/physiology ; *Motor Activity/physiology ; Algorithms ; *Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Convolutional Neural Networks ; },
abstract = {BACKGROUND: Convolutional neural networks (CNNs) are widely employed in motor imagery (MI) classification. However, due to cumbersome data collection experiments, and limited, noisy, and non-stationary EEG signals, small MI datasets present considerable challenges to the design of these decoding algorithms.<br><br>NEW METHOD: To capture more feature information from inadequately sized data, we propose a new method, a multi-scale attention convolutional neural network (MSAttNet). Our method includes three main components-a multi-band segmentation module, an attention spatial convolution module, and a multi-scale temporal convolution module. First, the multi-band segmentation module adopts a filter bank with overlapping frequency bands to enhance features in the frequency domain. Then, the attention spatial convolution module is used to adaptively adjust different convolutional kernel parameters according to the input through the attention mechanism to capture the features of different datasets. The outputs of the attention spatial convolution module are grouped to perform multi-scale temporal convolution. Finally, the output of the multi-scale temporal convolution module uses the bilinear pooling layer to extract temporal features and perform noise elimination. The extracted features are then classified.<br><br>RESULTS: We use four datasets, including BCI Competition IV Dataset IIa, BCI Competition IV Dataset IIb, the OpenBMI dataset and the ECUST-MI dataset, to test our proposed method. MSAttNet achieves accuracies of 78.20%, 84.52%, 75.94% and 78.60% in cross-session experiments, respectively.<br><br>Compared with state-of-the-art algorithms, MSAttNet enhances the decoding performance of MI tasks.<br><br>CONCLUSION: MSAttNet effectively addresses the challenges of MI-EEG datasets, improving decoding performance by robust feature extraction.},
}
@article {pmid40945816,
year = {2025},
author = {Fan, YS and Yang, P and Zhu, Y and Jing, W and Xu, Y and Xu, Y and Guo, J and Lu, F and Yang, M and Huang, W and Chen, H},
title = {Neurodevelopmental deviations in schizophrenia: Evidences from multimodal connectome-based brain ages.},
journal = {Progress in neuro-psychopharmacology &amp; biological psychiatry},
volume = {142},
number = {},
pages = {111498},
doi = {10.1016/j.pnpbp.2025.111498},
pmid = {40945816},
issn = {1878-4216},
mesh = {Humans ; *Schizophrenia/diagnostic imaging/physiopathology/pathology ; *Connectome/methods ; Female ; Male ; *Brain/growth &amp; development/diagnostic imaging/physiopathology/pathology ; Adolescent ; Magnetic Resonance Imaging ; Young Adult ; Child ; Adult ; Machine Learning ; Multimodal Imaging ; },
abstract = {BACKGROUND: Pathologic schizophrenia processes originate early in brain development, leading to detectable brain alterations via structural and functional magnetic resonance imaging (MRI). Recent MRI studies have sought to characterize disease effects from a brain age perspective, but developmental deviations from the typical brain age trajectory in youths with schizophrenia remain unestablished. This study investigated brain development deviations in early-onset schizophrenia (EOS) patients by applying machine learning algorithms to structural and functional MRI data.<br><br>METHODS: Multimodal MRI data, including T1-weighted MRI (T1w-MRI), diffusion MRI, and resting-state functional MRI (rs-fMRI) data, were collected from 80 antipsychotic-naive first-episode EOS patients and 91 typically developing (TD) controls. The morphometric similarity connectome (MSC), structural connectome (SC), and functional connectome (FC) were separately constructed by using these three modalities. According to these connectivity features, eight brain age estimation models were first trained with the TD group, the best of which was then used to predict brain ages in patients. Individual brain age gaps were assessed as brain ages minus chronological ages.<br><br>RESULTS: Both the SC and MSC features performed well in brain age estimation, whereas the FC features did not. Compared with the TD controls, the EOS patients showed increased absolute brain age gaps when using the SC or MSC features, with opposite trends between childhood and adolescence. These increased brain age gaps for EOS patients were positively correlated with the severity of their clinical symptoms.<br><br>CONCLUSION: These findings from a multimodal brain age perspective suggest that advanced brain age gaps exist early in youths with schizophrenia.},
}
@article {pmid40945543,
year = {2025},
author = {Bao, X and Feng, X and Chen, D and Huang, H and Cai, Y and Huang, Q and Li, Y},
title = {Thalamocortical dysrhythmia-related sleep spindle desynchronization in patients with tinnitus.},
journal = {Neurobiology of disease},
volume = {216},
number = {},
pages = {107081},
doi = {10.1016/j.nbd.2025.107081},
pmid = {40945543},
issn = {1095-953X},
mesh = {Humans ; *Tinnitus/physiopathology ; Female ; Male ; Middle Aged ; Adult ; Electroencephalography ; *Thalamus/physiopathology ; *Sleep/physiology ; *Cerebral Cortex/physiopathology ; Sleep Stages/physiology ; *Cortical Synchronization/physiology ; Aged ; },
abstract = {Patients with tinnitus commonly suffer from sleep problems, and the underlying neural mechanisms remain unclear. Previous studies have focused primarily on the correlation between patients' sleep structure and tinnitus, lacking exploration into the links between sleep problems and the underlying pathological mechanisms of tinnitus, such as thalamocortical dysrhythmia (TCD). Here, we present the first study on neural oscillatory patterns in patients with tinnitus during sleep spindles, a more precise subdivision of sleep that overlaps in neuropathological pathways with TCD. Sleep electroencephalogram (EEG) were recorded from 51 tinnitus participants and 51 healthy participants. During sleep spindles, patients with tinnitus exhibited a significant increase in 18-45 Hz and a stronger cross-frequency coupling, resembling the EEG abnormalities caused by TCD during wakefulness. With respect to spindle characteristics, tinnitus is linked to an increase in spindle quantity but a decrease in spindle root-mean-square and functional connectivity, suggesting that normal function of tinnitus spindles is impaired. Our findings indicated that neural oscillation dynamics related to TCD during sleep spindles serve as neural biomarkers for sleep disturbances in tinnitus participants. We demonstrate that the impact of the TCD pathological mechanism in tinnitus is not confined to the waking state but extends into the sleep stage as well, which advances our comprehension of the neural mechanisms underlying sleep-related problems in tinnitus.},
}
@article {pmid40944703,
year = {2025},
author = {Elliss, H and Proctor, K and Robertson, M and Bagnall, J and Kasprzyk-Hordern, B},
title = {A new wide-scope, multi-biomarker wastewater-based epidemiology analytical method to monitor the health and well-being of inhabitants at a metropolitan scale.},
journal = {Analytical and bioanalytical chemistry},
volume = {417},
number = {26},
pages = {5983-6005},
pmid = {40944703},
issn = {1618-2650},
mesh = {Humans ; *Biomarkers/analysis ; *Wastewater/analysis/chemistry ; *Water Pollutants, Chemical/analysis ; *Wastewater-Based Epidemiological Monitoring ; Limit of Detection ; *Environmental Monitoring/methods ; Mass Spectrometry/methods ; },
abstract = {This manuscript establishes a new, comprehensive biomarker list and a multiresidue trace quantification method for community-wide health and well-being assessment at a metropolitan scale using wastewater-based epidemiology (WBE) and mass spectrometry pipelines. This method enables the quantification of 204 biochemical indicators (BCIs) across a range of biomarker classes within influent wastewater and includes illicit drug BCIs, pharmaceuticals as proxies for disease, health markers (hormones, oxidative stress, lipid peroxidation, etc.), Lifestyle chemicals, food BCIs, and hazardous chemicals in personal care products. This method facilitates the combined assessment of community exposure to chemicals and the effects of this exposure in the same framework. The method enables full quantification of 141 BCIs with method detection Limits varying from 0.01 ng/L for amlodipine to 23.8 ng/L for stachydrine. Total average method accuracies were 102.7% whereas precision was 10.4%. During an initial assessment of this method to test its suitability, 62% of all targets were detected and quantified during a week-long feasibility study of a large city with weekly average Daily BCI loads ranging from 40.0 ± 20.0 mg/day for salbutamol to 5836.5 ± 1697.1 g/day for creatinine. The inclusion of new endogenous markers such as advanced glycation end products, detected in wastewater for the first time, enables more accurate determination of community-level health and lifestyle habits. Alongside an unbiased and comprehensive health assessment through endogenous markers, health is further assessed via the use of pharmaceuticals, acting as a proxy for health and disease status whilst additionally providing insights into community lifestyle habits through the monitoring of licit/illicit drug use and food consumption. The analysis of all biomarker classes combined aims to provide insights to exposure and health effect outcomes at the community level.},
}
@article {pmid40942766,
year = {2025},
author = {Han, Q and Ye, H and Sun, Y and Song, Z and Zhao, J and Shi, L and Kuang, Z},
title = {TopoTempNet: A High-Accuracy and Interpretable Decoding Method for fNIRS-Based Motor Imagery.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {17},
pages = {},
pmid = {40942766},
issn = {1424-8220},
support = {YDZJ202201ZYTS684//Development program project of the Science and Technology Department of Jilin Province, China/ ; },
mesh = {Humans ; Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; *Spectroscopy, Near-Infrared/methods ; },
abstract = {Functional near-infrared spectroscopy (fNIRS) offers a safe and portable signal source for brain-computer interface (BCI) applications, particularly in motor imagery (MI) decoding. However, its low sampling rate and hemodynamic delay pose challenges for temporal modeling and dynamic brain network analysis. To address these limitations in temporal dynamics, static graph modeling, and feature fusion interpretability, we propose TopoTempNet, an innovative topology-enhanced temporal network for biomedical signal decoding. TopoTempNet integrates multi-level graph features with temporal modeling through three key innovations: (1) multi-level topological feature construction using local and global functional connectivity metrics (e.g., connection strength, density, global efficiency); (2) a graph-modulated attention mechanism combining Transformer and Bi-LSTM to dynamically model key connections; and (3) a multimodal fusion strategy uniting raw signals, graph structures, and temporal representations into a high-dimensional discriminative space. Evaluated on three public fNIRS datasets (MA, WG, UFFT), TopoTempNet achieves superior accuracy (up to 90.04% ± 3.53%) and Kappa scores compared to state-of-the-art models. The ROC curves and t-SNE visualizations confirm its excellent feature discrimination and structural clarity. Furthermore, the statistical analysis of graph features reveals the model's ability to capture task-specific functional connectivity patterns, enhancing the interpretability of decoding outcomes. TopoTempNet provides a novel pathway for building interpretable and high-performance BCI systems based on fNIRS.},
}
@article {pmid40942721,
year = {2025},
author = {Kauati-Saito, E and Pereira, ADS and Fontana, AP and de Sá, AMFLM and Soares, JGM and Tierra-Criollo, CJ},
title = {Classification of Different Motor Imagery Tasks with the Same Limb Using Electroencephalographic Signals.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {17},
pages = {},
pmid = {40942721},
issn = {1424-8220},
support = {CNPq grants 312592/ 2020-5 and 303066/2025-3//Brazilian institutions National Council for Scientific and Technological Development/ ; CAPES process No. 88887.853338/2023-00 and 23038.008788/2017-27//Coordination of Superior Level Staff Improvement/ ; FINEP process No. 01.24.0122.00//Financier for Studies and Projects/ ; FAPERJ process No. E-26/204.393/2024, 201.618/2025, E-211.635/2021, E-26/202.587/2019, and E-26/ 200.338/2023//the Carlos Chagas Filho Foundation for Research Support of the State of Rio de Janeiro/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Movement/physiology ; *Imagination/physiology ; Signal Processing, Computer-Assisted ; Male ; *Extremities/physiology ; Adult ; Algorithms ; },
abstract = {Stroke is a neurological condition that often results in long-term motor deficits. Given the high prevalence of motor impairments worldwide, there is a critical need to explore innovative neurorehabilitation strategies that aim to enhance the quality of life of patients. One promising approach involves brain-computer interface (BCI) systems controlled by electroencephalographic (EEG) signals elicited when a subject performs motor imagery (MI), which is the mental simulation of movement without actual execution. Such systems have shown potential for facilitating motor recovery by promoting neuroplastic mechanisms. Controlling BCI systems based on MI-EEG signals involves the following sequential stages: recording the raw signal, preprocessing, feature extraction and selection, and classification. Each of these stages can be executed using several techniques and numerous parameter combinations. In this study, we searched for the combination of feature extraction technique, time window, frequency range, and classifier that could provide the best classification accuracy for the BCI Competition 2008 IV 2a benchmark dataset (BCI-C), characterized by EEG-MI data of different limbs (four classes, of which three were used in this work), and the NeuroSCP EEG-MI dataset, a custom experimental protocol developed in our laboratory, consisting of EEG recordings of different movements with the same limb (three classes-right dominant arm). The mean classification accuracy for BCI-C was 76%. When the subjects were evaluated individually, the best-case classification accuracy was 94% and the worst case was 54%. For the NeuroSCP dataset, the average classification result was 53%. The individual subject's evaluation best-case was 71% and the worst case was 35%, which is close to the chance level (33%). These results indicate that techniques commonly applied to classify different limb MI based on EEG features cannot perform well when classifying different MI tasks with the same limb. Therefore, we propose other techniques, such as EEG functional connectivity, as a feature that could be tested in future works to classify different MI tasks of the same limb.},
}
@article {pmid40938318,
year = {2025},
author = {Dash, D and Iwane, F and Hayward, W and Salamanca-Giron, RF and Bönstrup, M and Buch, ER and Cohen, LG},
title = {Sequence action representations contextualize during early skill learning.},
journal = {eLife},
volume = {13},
number = {},
pages = {},
pmid = {40938318},
issn = {2050-084X},
support = {NINDS Intramural Research Program/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; *Learning/physiology ; *Motor Skills/physiology ; Male ; Magnetoencephalography ; Female ; Adult ; Fingers/physiology ; Young Adult ; Machine Learning ; *Brain/physiology ; },
abstract = {Activities of daily living rely on our ability to acquire new motor skills composed of precise action sequences. Here, we asked in humans if the millisecond-level neural representation of an action performed at different contextual sequence locations within a skill differentiates or remains stable during early motor learning. We first optimized machine learning decoders predictive of sequence-embedded finger movements from magnetoencephalographic (MEG) activity. Using this approach, we found that the neural representation of the same action performed in different contextual sequence locations progressively differentiated-primarily during rest intervals of early learning (offline)-correlating with skill gains. In contrast, representational differentiation during practice (online) did not reflect learning. The regions contributing to this representational differentiation evolved with learning, shifting from the contralateral pre- and post-central cortex during early learning (trials 1-11) to increased involvement of the superior and middle frontal cortex once skill performance plateaued (trials 12-36). Thus, the neural substrates supporting finger movements and their representational differentiation during early skill learning differ from those supporting stable performance during the subsequent skill plateau period. Representational contextualization extended to Day 2, exhibiting specificity for the practiced skill sequence. Altogether, our findings indicate that sequence action representations in the human brain contextually differentiate during early skill learning, an issue relevant to brain-computer interface applications in neurorehabilitation.},
}
@article {pmid40937924,
year = {2025},
author = {Qu, Y and Hao, M and Hao, H and Ke, S and Li, Y and Wang, C and Xiao, Y and Jiang, B and Zhou, K and Ding, B and Chu, PK and Yu, XF and Wang, J},
title = {2D Vanadium Carbide/Oxide Heterostructure-Based Artificial Sensory Neuron for Multi-Color Near-Infrared Object Recognition.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {},
number = {},
pages = {e12238},
doi = {10.1002/adma.202512238},
pmid = {40937924},
issn = {1521-4095},
support = {2023YFA0915600//National Key R&amp;D Program of China/ ; 2024A1515030176//Natural Science Foundation of Guangdong Province/ ; 2025B1515020088//Natural Science Foundation of Guangdong Province/ ; 2024B1212010010//Guangdong Provincial Key Laboratory of Multimodality Non-Invasive Brain-Computer Interfaces/ ; JCYJ20220818100806014//Shenzhen Science and Technology Program/ ; XDB0930000//Strategic Priority Research Program of the Chinese Academy of Sciences/ ; 52273311//National Natural Science Foundation of China/ ; T2293693//National Natural Science Foundation of China/ ; KCXFZ2024090309420300//Shenzhen Innovation and Entrepreneurship Program-Science and Technology Major Project/ ; GZC20241837//Postdoctoral Fellowship Program of China Postdoctoral Science Foundation/ ; DON-RMG 9229021//City University of Hong Kong Donation Research Grants/ ; 9220061//City University of Hong Kong Donation Research Grants/ ; 2025WK2013//Key Project of Research and Development Plan of Hunan Province/ ; },
abstract = {Near-infrared (NIR) photon detection and object recognition are crucial technologies for all-weather target identification in autonomous navigation, nighttime surveillance, and tactical reconnaissance. However, conventional NIR detection systems, which rely on photodetectors and von Neumann computing algorithms, are plagued by energy inefficiency and signal transmission bottlenecks. Herein, a vanadium carbide/oxide (V2C/V2O5-x) heterostructure is designed and synthesized by a topochemical conversion method. The V2C/V2O5-x heterostructure-based memristor exhibits stable threshold-type resistance switching (RS) behavior with low coefficient of variation in transition voltages (1.62% and 1.7%) over thousands of cycles, and maintains stable performance even after storage for 90 days. Benefiting from the NIR responsivity of V2C and the volatile RS enabled by vacancy-enriched V2O5-x, devices exhibit a linear variation in threshold voltage in response to NIR light power density and wavelength. Based on the multi-color NIR modulable RS characteristics and the YOLOv7 algorithm model, an artificial neural network (ANN) architecture achieves average recognition accuracies of 89.6% for cars and 85.9% for persons on the FLIR dataset. This work reveals a heterostructure with versatile functionalities for neuromorphic devices and establishes a memristor-based ANN platform for multi-color object detection and recognition in complex real-world scenarios.},
}
@article {pmid40936365,
year = {2025},
author = {Liu, Y and Xu, G and Li, C and Ma, Y and Ji, N and Feng, X},
title = {Stretchable Multilevel Mesh Brain Electrodes for Neuroplasticity in Glioma Patients Undergoing Surgery.},
journal = {Advanced healthcare materials},
volume = {},
number = {},
pages = {e03358},
doi = {10.1002/adhm.202503358},
pmid = {40936365},
issn = {2192-2659},
support = {2023YFB3609002//National Basic Research Program of China/ ; 2023YFB3609002//National Basic Research Program of China/ ; 2022YFC2403905//National Basic Research Program of China/ ; U20A6001//National Natural Science Foundation of China/ ; 11921002//National Natural Science Foundation of China/ ; 12002190//National Natural Science Foundation of China/ ; 2023YFB3609002//National Key R&amp;D Program of China/ ; 2023YFB3609002//National Key R&amp;D Program of China/ ; 2022YFC2403905//National Key R&amp;D Program of China/ ; 2022-2-2047//Capital Health Research and Development of Special Fund/ ; },
abstract = {Brain disease surgical treatment usually leads to neurological dysfunction. Electroencephalogram (EEG)-based neuroplasticity study may facilitate patient nerve function recovery from injury, allowing a return to normal activities. Due to the limitations of wound infections and hair barrier effects, a traditional brain-computer interface system is not applicable to patients after tumor resection. Here, stretchable multilevel mesh brain electrodes with reconfigurable interfaces are developed. The electrode has a multilevel mesh and malleable structure to avoid hair blockage between the electrode and scalp, realizing the conformal attachment of the stretchable multilevel mesh brain electrodes to a nondevelopable curved brain surface. Moreover, the thermally reversible hydrogel forms a good reconfigurable interface contact between the electrode and scalp, reducing postoperative infection and secondary injury risks to ensure the high-quality acquisition EEGs. In this study, a newly invented stretchable multilevel mesh brain electrodes is applied to test the preoperative and postoperative EEGs of recurrent glioblastoma patients for the first time. The obvious inhibitory effects of tumors on brain activity (a-wave signals) are discovered. More importantly, the EEG signals gradually enhance with postoperative recovery, which is mutually confirmed with the Karnofsky score results, showing the possibility of neural function remodeling neurological rehabilitation in adults.},
}
@article {pmid40934551,
year = {2025},
author = {Das, N and Chakraborty, M},
title = {EEGOpt: A performance efficient Bayesian optimization framework for automated EEG signal classification.},
journal = {Computers in biology and medicine},
volume = {197},
number = {Pt B},
pages = {111023},
doi = {10.1016/j.compbiomed.2025.111023},
pmid = {40934551},
issn = {1879-0534},
mesh = {*Electroencephalography/methods ; Humans ; Bayes Theorem ; *Signal Processing, Computer-Assisted ; Algorithms ; },
abstract = {BACKGROUND: Accurate classification of electroencephalography (EEG) signals depends on the optimal combination of signal processing, feature extraction, and classification methods. Since no single approach is suitable across different domains, identifying the best methods for each application remains a critical challenge.<br><br>OBJECTIVE: We propose EEGOpt, a Bayesian optimization framework designed to automate and optimize methodological choices in electroencephalography (EEG) signal processing and classification.<br><br>METHODS: EEGOpt employed Tree-Structured Parzen Estimator (TPE) to optimize signal denoising, feature extraction, and classifier selection. The search space included Empirical Mode Decomposition and Wavelet Packet Decomposition (WPD) for denoising; spatiotemporal, nonlinear, and spectral features; and classifiers with distinct decision boundaries. A modular caching mechanism was used to minimize redundant computations. EEGOpt was evaluated on three datasets and benchmarked against deep-learning models (EEGNet, ShallowConvNet, and DeepConvNet). TPE was compared with sampling methods, including Gaussian Process, Covariance Matrix Adaptation Evolution Strategy, Quasi-Monte Carlo, and random search.<br><br>RESULTS: EEGOpt achieved classification accuracies of up to 99.63&#xa0;%, outperforming EEGNet (96.20&#xa0;%), ShallowConvNet (90.83&#xa0;%), and DeepConvNet (90.29&#xa0;%). The caching mechanism reduced computation time by 74.69&#xa0;% compared to no caching, and by 95&#xa0;% compared to deep learning models. TPE was effective in navigating hierarchical search spaces to locate global optima. EEGOpt identified covariance and wavelet features, k-nearest neighbor classifier, and WPD denoising as optimal for music-based EEG classification.<br><br>CONCLUSION: EEGOpt is a scalable and interpretable framework that automatically identifies optimal signal processing and classification strategies adaptable to EEG datasets, making it a valuable tool for neuroscientific research, diagnostics, and brain-computer interface development.},
}
@article {pmid40933818,
year = {2025},
author = {Tang, MY and Zhang, YY and Lin, L and Wu, LL and Hu, MT and Tan, LH and Yu, CX and Wang, H and Yu, YQ and Ding, Y and Han, JX and Hu, H and Li, XM and Lian, H},
title = {Medial preoptic CCKAR mediates anxiety and aggression induced by chronic emotional stress in male mice.},
journal = {National science review},
volume = {12},
number = {10},
pages = {nwaf152},
pmid = {40933818},
issn = {2053-714X},
abstract = {Anxiety disorders frequently accompany aggression, with their co-occurrence predicting greater functional impairment and poor prognosis. Nevertheless, the underlying neural mechanisms remain elusive, primarily due to a lack of appropriate animal models. Here, we designed a chronic conspecific outsider stress (CCS) model in which male mice underwent perceived social threats and exhibited increased anxiety-like behaviors accompanied by aggression. CCS led to Fos activation and hyperexcitability of GABAergic neurons in the medial preoptic area (mPOA). Inhibition of mPOA GABAergic (mPOA[Gad2]) neurons rescued CCS-induced anxiety-like and aggressive behaviors, whereas activating these cells induced susceptibility to CCS. Moreover, CCS upregulated the mRNA and protein expression of the sexual-dimorphic gene, cholecystokinin A receptor (CCKAR)-encoding Cckar gene in the mPOA. Importantly, the knock-down and overexpression of CCKAR in the mPOA[Gad2] neurons had alleviating and promoting effects on anxiety-like and aggressive behaviors, aligning with decreased and increased excitability by the anxiolytic CCKAR antagonist MK-329 and the anxiogenic CCKAR agonist A71623 in mPOA[Gad2] neurons, respectively. Overall, our study characterizes a novel mouse model of anxiety disorders accompanied by aggression and the neuronal subpopulation and molecular mediator of the aberrant behaviors provide potential targets of intervention for anxiety disorders with aggression.},
}
@article {pmid40933619,
year = {2025},
author = {Hu, D and Li, H and Takahata, T and Tanigawa, H},
title = {Clustered architecture of ipsilateral and interhemispheric connections in macaque ventrolateral prefrontal cortex.},
journal = {Frontiers in neural circuits},
volume = {19},
number = {},
pages = {1635105},
pmid = {40933619},
issn = {1662-5110},
mesh = {Animals ; *Prefrontal Cortex/cytology/physiology ; *Neural Pathways/physiology/cytology ; Macaca mulatta ; Male ; *Neurons/cytology/physiology ; *Functional Laterality/physiology ; },
abstract = {The fine-scale organization of intrinsic and extrinsic connections in the primate ventrolateral prefrontal cortex (VLPFC), a region essential for higher cognitive functions, remains poorly understood. This contrasts with, for example, the well-documented stripe-like intrinsic circuits of the dorsolateral prefrontal cortex (DLPFC). To elucidate the circuit architecture supporting VLPFC function, we investigated the spatial organization of connections targeting the caudal VLPFC (primarily area 45A) in macaque monkeys using multiple retrograde tracers. Analyzing the distribution of labeled neurons in flattened tangential sections revealed that laterally projecting connections within the same hemisphere formed distinct clusters, not only in the VLPFC but also in the DLPFC. These clusters often spanned multiple cortical layers, suggesting a columnar-like organization. The width (minor axis) of these clusters was approximately 1.2 mm. Similarly, contralateral callosal projection neurons were also arranged in clusters. Additionally, inputs originating from the superior temporal sulcus were found to arise from discrete clusters of neurons. Our findings demonstrate that both long-range ipsilateral and interhemispheric connections of the caudal VLPFC share a common, fine-scale clustered architecture. This study provides an anatomical framework for understanding the structural basis of information processing and interhemispheric coordination within this critical association cortex, suggesting that this architecture is fundamental to VLPFC's role in complex cognitive functions.},
}
@article {pmid40932879,
year = {2025},
author = {Attar, ET},
title = {EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1616456},
pmid = {40932879},
issn = {1662-5161},
abstract = {INTRODUCTION: This scientific investigation explored how meditation influences neural sound stimulus responses by employing EEG techniques during both meditative states and auditory oddball tasks. The study evaluated event-related potentials alongside theta, alpha and beta spectral power while employing machine learning techniques to distinguish meditative states from cognitive tasks.<br><br>METHODS: The study utilized data from 13 participants aged 24-58, which researchers obtained through an openly accessible OpenNeuro dataset.<br><br>RESULT: Examination of eventrelated potentials (ERPs) demonstrated that P300 amplitude showed significant growth when responding to oddball stimuli, which indicates increased attention allocation (p < 0.05). Spectral power analysis demonstrated an increase in frontal alpha and beta power during meditation while central theta power decreased, which suggests reduced cognitive load and enhanced internal focus. Meditation experience showed a statistical relationship with frontal alpha power, where r = 0.45 and p < 0.03. A Random Forest classifier reached 86. The system achieved a 7% accuracy rate in differentiating cognitive from meditative states while identifying P300 amplitude and frontal alpha power, together with beta power as significant predictors.<br><br>CONCLUSION: The EEG-based neurofeedback systems demonstrate potential alongside real-time cognitive state detection for healthcare brain-computer interfaces and mental health applications. The study of meditation's effects on brain activity reveals its benefits for emotional regulation and concentration improvement. The research findings deliver strong evidence that meditation induces distinct neural modifications detectable through ERP and spectral analysis. The potential for meditation to enhance cortical efficiency alongside emotion self-regulation indicates its viability as a mental health support tool. The integration of EEG biomarkers with machine learning methods emerges as a potential pathway for real-time cognitive and emotional state monitoring which enables tailored interventions through neurofeedback systems and brain-computer interfaces to boost cognitive function and emotional health across clinical settings and everyday life.},
}
@article {pmid40931087,
year = {2025},
author = {Liu, W and Xiang, M and Ding, N},
title = {Active use of latent tree-structured sentence representation in humans and large language models.},
journal = {Nature human behaviour},
volume = {},
number = {},
pages = {},
pmid = {40931087},
issn = {2397-3374},
abstract = {Understanding how sentences are represented in the human brain, as well as in large language models (LLMs), poses a substantial challenge for cognitive science. Here we develop a one-shot learning task to investigate whether humans and LLMs encode tree-structured constituents within sentences. Participants (total N = 372, native Chinese or English speakers, and bilingual in Chinese and English) and LLMs (for example, ChatGPT) were asked to infer which words should be deleted from a sentence. Both groups tend to delete constituents, instead of non-constituent word strings, following rules specific to Chinese and English, respectively. The results cannot be explained by models that rely only on word properties and word positions. Crucially, based on word strings deleted by either humans or LLMs, the underlying constituency tree structure can be successfully reconstructed. Altogether, these results demonstrate that latent tree-structured sentence representations emerge in both humans and LLMs.},
}
@article {pmid40930287,
year = {2025},
author = {Shirodkar, VR and Edla, DR and Kumari, A and Dharavath, R},
title = {Deep feature extraction and swarm-optimized enhanced extreme learning machine for motor imagery recognition in stroke patients.},
journal = {Journal of neuroscience methods},
volume = {424},
number = {},
pages = {110565},
doi = {10.1016/j.jneumeth.2025.110565},
pmid = {40930287},
issn = {1872-678X},
mesh = {Humans ; *Stroke/physiopathology ; *Electroencephalography/methods ; *Imagination/physiology ; *Brain-Computer Interfaces ; Male ; Female ; *Machine Learning ; Middle Aged ; *Motor Activity/physiology ; Neural Networks, Computer ; Aged ; Adult ; Signal Processing, Computer-Assisted ; Stroke Rehabilitation ; Extreme Learning Machines ; },
abstract = {BACKGROUND: Interpretation of motor imagery (MI) in brain-computer interface (BCI) applications is largely driven by the use of electroencephalography (EEG) signals. However, precise classification in stroke patients remains challenging due to variability, non-stationarity, and abnormal EEG patterns.<br><br>NEW METHODS: To address these challenges, an integrated architecture is proposed, combining multi-domain feature extraction with evolutionary optimization for enhanced EEG-based MI classification. The approach begins with subject-specific frequency band selection based on event-related desynchronization (ERD), aimed at reducing non-stationarity and improving signal relevance. Spatial and temporal features are then extracted using a combination of the scale-invariant feature transform (SIFT) and a one-dimensional convolutional neural network (1D CNN), providing a comprehensive representation of EEG signal dynamics. These features are fused and classified using an enhanced extreme learning machine (EELM), with hidden layer weights optimized using differential evolution (DE), particle swarm optimization (PSO), and dynamic multi-swarm PSO (DMS-PSO).<br><br>RESULTS: Experimental validation on a dataset of 50 stroke patients demonstrated an average classification accuracy of 97% using DMS-PSO with 10-fold cross-validation. Additional evaluation on the BCI Competition IV 1a dataset yielded 95% and 91.56% on IV 2a, indicating strong generalization performance.<br><br>Unlike conventional BCI approaches, this method combines adaptive filtering, spatial-temporal hybrid feature learning, and metaheuristic optimization, resulting in a lightweight model with improved classification accuracy and robustness.<br><br>CONCLUSION: These findings demonstrate the effectiveness of evolutionary optimization in dealing with the constraints provided by high-dimensional, non-stationary EEG data, making it a promising strategy for real-time MI classification in BCI-based stroke applications.},
}
@article {pmid40929275,
year = {2025},
author = {Kim, TY and Son, Y and Yook, KY and Lee, DG and Kim, Y and Kim, SJ and Park, K and Lee, Y and Lee, TK and Chung, JJ and Yang, C and Park, S and Seo, J},
title = {Bioadaptive liquid-infused multifunctional fibers for long-term neural recording via BDNF stabilization and enhanced neural interaction.},
journal = {Science advances},
volume = {11},
number = {37},
pages = {eadz1228},
pmid = {40929275},
issn = {2375-2548},
mesh = {*Brain-Derived Neurotrophic Factor/chemistry/metabolism ; *Neurons/physiology/metabolism/drug effects ; Animals ; *Brain-Computer Interfaces ; Astrocytes/metabolism ; Coated Materials, Biocompatible/chemistry ; Rats ; },
abstract = {Brain-computer interfaces (BCIs) enable direct communication between the brain and computers. However, their long-term functionality remains limited due to signal degradation caused by acute insertion trauma, chronic foreign body reaction (FBR), and biofouling at the device-tissue interface. To address these challenges, we introduce a multifunctional surface modification strategy called targeting-specific interaction and blocking nonspecific adhesion (TAB) coating for flexible fiber, achieving a synergistic integration of mechanical compliance and biochemical stability. The coating combines brain-derived neurotrophic factor (BDNF) conjugation and a lubricant-infused surface. This dual-functional design enables selective interaction with neurons and astrocytes while preventing nonspecific adhesion. Notably, high-quality single-unit neural signals were stably recorded for more than 12 months after implantation, demonstrating exceptional long-term recording performance. Integrating mechanical compatibility, antifouling properties, and selective neural cell interaction, the TAB-coated multifunctional fiber represents a transformative approach for neural implants, bridging biological systems with computational systems.},
}
@article {pmid40928672,
year = {2025},
author = {Mou, F and Lv, Z and Jin, X and Pan, J and Yun, L and Chen, Z},
title = {Decoding binocular color differences via EEG signals: linking ERP dynamics to chromatic disparity in CIELAB space.},
journal = {Experimental brain research},
volume = {243},
number = {10},
pages = {209},
pmid = {40928672},
issn = {1432-1106},
support = {62165019//National Science Foundation of China/ ; 202305 AC160084//Yunnan Youth and Middle-aged Academic and Technical Leaders Reserve Talent Program/ ; },
mesh = {Humans ; *Electroencephalography/methods ; Male ; Adult ; Female ; *Color Perception/physiology ; Young Adult ; *Evoked Potentials, Visual/physiology ; Photic Stimulation ; *Vision, Binocular/physiology ; *Vision Disparity/physiology ; Neural Networks, Computer ; Event-Related Potentials, P300/physiology ; *Evoked Potentials/physiology ; Support Vector Machine ; },
abstract = {This study explores how differences in colors presented separately to each eye (binocular color differences) can be identified through EEG signals, a method of recording electrical activity from the brain. Four distinct levels of green-red color differences, defined in the CIELAB color space with constant luminance and chroma, are investigated in this study. Analysis of Event-Related Potentials (ERPs) revealed a significant decrease in the amplitude of the P300 component as binocular color differences increased, suggesting a measurable brain response to these differences. Four classification models-Support Vector Machines (SVM), EEGNet, Temporal Convolutional Neural Network (T-CNN), and a hybrid CNN-LSTM model were employed to decode EEG data. The highest accuracy reached was 81.93% for binary classification tasks (the largest color differences) and 54.47% for a more nuanced four-class categorization, significantly exceeding random chance. This research offers the first evidence that binocular color differences can be objectively decoded through EEG signals, providing insights into the neural mechanisms of visual perception and forming a basis for developing color-based brain-computer interfaces (BCIs).},
}
@article {pmid40926818,
year = {2025},
author = {Wu, S and Li, K and Long, J and Zhang, C and Li, R and Cheng, B and Cao, M and Deng, W},
title = {Risk Factors for Postictal Delirium in Geriatric Patients Undergoing Electroconvulsive Therapy: The Role of Lithium and Quetiapine.},
journal = {Alpha psychiatry},
volume = {26},
number = {4},
pages = {45431},
pmid = {40926818},
issn = {2757-8038},
abstract = {BACKGROUND: Postictal delirium (PID) is a significant and often underrecognized adverse effect associated with electroconvulsive therapy (ECT) in geriatric patients. Despite its clinical relevance, the specific risk factors contributing to the development of PID in this vulnerable population remain inadequately understood, which may affect treatment outcomes and patient safety.<br><br>METHODS: In this retrospective study, we analyzed data from 168 elderly patients who underwent ECT between 2009 and 2020 at a general hospital in China. Univariate analyses of sociodemographic and clinical characteristics were performed to identify variables for inclusion in a logistic regression model. Multiple binary logistic regression analysis was performed to determine the relationship between these variables and PID occurrence.<br><br>RESULTS: The incidence of PID was 20.8% (35/168) among the study cohort. Univariate analysis revealed statistically significant differences between PID and non-PID groups for lithium (&#x3c7; [2] = 6.67, p = 0.010), quetiapine (&#x3c7; [2] = 4.36, p = 0.037), number of ECT sessions (U = 3065.50, p = 0.003), and response rate (&#x3c7; [2] = 12.86, p < 0.001). Logistic regression analysis demonstrated that lithium (odds ratio (OR) = 5.128; p = 0.009) and quetiapine (OR = 2.562; p = 0.024) were significantly associated with PID.<br><br>CONCLUSION: Our findings indicate that lithium and quetiapine use significantly increase the risk of developing PID, underscoring the need for clinical vigilance. Careful consideration of these medications when planning ECT treatment is recommended to minimize the risk of postictal complications and optimize therapeutic outcomes.},
}
@article {pmid40925406,
year = {2025},
author = {Chen, J and Yi, Z and Chen, T and Tong, H and Zhou, L and Hong, Z and Tan, C and Qin, J and Cai, F and Wu, Y and Li, J and Huang, Y},
title = {An adjustable three-layer skull phantom with realistic ultrasound transmission properties.},
journal = {Physics in medicine and biology},
volume = {70},
number = {18},
pages = {},
doi = {10.1088/1361-6560/ae0556},
pmid = {40925406},
issn = {1361-6560},
mesh = {*Phantoms, Imaging ; *Skull/diagnostic imaging ; Humans ; *Ultrasonic Waves ; Ultrasonography/instrumentation ; },
abstract = {Transcranial ultrasound research has garnered significant attention due to its non-invasive nature, absence of ionizing radiation, and portability, making it advantageous for both imaging and therapy. A critical aspect of advancing transcranial research lies in understanding the ultrasound transmission performance of the human skull. However, inherent variations in skull shape, physical parameters, and age-related changes pose challenges for comparative studies. To address these challenges, we designed a three-layer structured skull (TSS) phantom that closely mimics the structural and ultrasound transmission properties of real skulls. The TSS substrate is composed of epoxy resin/Al2O3powders, with purple perilla seeds incorporated into the middle layer to replicate the porous structure found in real skulls. Both simulation and experimental results demonstrate that TSS phantom achieves acoustic transmission properties closely approximating those of human skull bone within the 1.25-1.75 MHz frequency range. Experimentally, the TSS phantom containing 27 wt% purple perilla seeds shows a sound pressure transmission coefficient ranging from 5.0% to 6.6%, closely matching the skull's transmission characteristics (4.2%-9.8%). This performance represents a significant improvement over conventional phantom materials, outperforming epoxy resin plate phantoms (42.6%-48.4%) and polyetheretherketone phantoms (64.5%-75.2%). Notably, the transmission performance of TSS can be adjusted by varying the mass fraction of purple perilla seeds, making it adaptable to diverse research needs. The TSS phantom holds significant potential as a valuable tool in transcranial research, offering a reliable and accessible alternative for comprehensive investigations into ultrasound applications in brain therapy.},
}
@article {pmid40925395,
year = {2025},
author = {Bisla, M and Anand, RS},
title = {Machine learning based classification of imagined speech electroencephalogram data from the amplitude and phase spectrum of frequency domain EEG signal.},
journal = {Biomedical physics &amp; engineering express},
volume = {11},
number = {5},
pages = {},
doi = {10.1088/2057-1976/ae04ee},
pmid = {40925395},
issn = {2057-1976},
mesh = {Humans ; *Electroencephalography/methods ; *Machine Learning ; *Speech/physiology ; *Imagination/physiology ; Male ; Female ; *Signal Processing, Computer-Assisted ; Adult ; Algorithms ; *Brain/physiology ; Young Adult ; },
abstract = {Imagined speech classification involves decoding brain signals to recognize verbalized thoughts or intentions without actual speech production. This technology has significant implications for individuals with speech impairments, offering a means to communicate through neural signals. The prime objective of this work is to propose an innovative machine learning (ML) based classification methodology that combines electroencephalogram (EEG) data augmentation using a sliding window technique with statistical feature extraction from the amplitude and phase spectrum of frequency domain EEG segments. This work uses an EEG dataset recorded from a 64-channel device during the imagination of long words, short words, and vowels with 15 human subjects. First, the raw EEG data is filtered between 1 Hz and 100 Hz, then segmented using a sliding window-based data augmentation technique with a window size of 100 and 50% overlap. The Fourier Transform is applied to each windowed segment to compute the amplitude and phase spectrum of the signal at each frequency point. The next step is to extract 50 statistical features from the amplitude and phase spectrum of frequency domain segments. Out of these, the 25 most statistically significant features are selected by applying the Kruskal-Walli's test. The extracted feature vectors are classified using six different machine learning based classifiers named support vector machine (SVM), K nearest neighbor (KNN), Random Forest (RF), XGBoost, LightGBM, and CatBoost. The CatBoost classifier outperforms other machine learning classifiers by achieving the highest accuracy of 91.72 ± 1.52% for long words classification, 91.68 ± 1.54% for long versus short word classification, 88.05 ± 3.07% for short word classification, and 88.89 ± 1.97% for vowel classification. The performance of the proposed model is assessed using five performance evaluation metrics: accuracy, F1-score, precision, recall, and Cohen's kappa. Compared to the existing literature, this study has achieved a 5%-7% improvement with the CatBoost classifier and extracted feature matrix.},
}
@article {pmid40922973,
year = {2025},
author = {Denis-Robichaud, J and Nicola, I and Chupin, H and Roy, JP and Buczinski, S and Fauteux, V and Picard-Hagen, N and Dubuc, J},
title = {Nonesterified fatty acids during the dry period and their association with peripartum disorders, culling, and pregnancy in dairy cows.},
journal = {JDS communications},
volume = {6},
number = {5},
pages = {688-693},
pmid = {40922973},
issn = {2666-9102},
abstract = {The objective of this ambidirectional observational cohort study was to explore how nonesterified fatty acids (NEFA) 22 to 35 d before calving were related to NEFA 1 to 14 d before calving and to determine a threshold that could be used to identify cows at risk of poor postpartum health. We enrolled 855 dairy cows from 46 herds, 362 prospectively and 493 retrospectively. The NEFA concentrations were measured during the far-off period (foNEFA; 3 to 5 wk before calving) and in the close-up period (cuNEFA; up to 2 wk before calving), and postpartum infectious and metabolic disorders, reproduction success, and culling were recorded. Using a split dataset, we (1) determined a threshold maximizing the sum of sensitivity and specificity to identify peripartum conditions by classifying elevated NEFA and (2) assessed the associations between elevated NEFA and altered health and reproduction. The associations were expressed as the odds ratio (OR) and the 95% Bayesian credible interval (BCI). The concentration of foNEFA varied from 60 to 700 µmol/L (median = 149), and a threshold of ≥160 µmol/L was identified. Cows with elevated foNEFA had greater odds to have elevated cuNEFA (OR = 183, 95% BCI = 52.1-458), hyperketonemia (OR = 2.0, 95% BCI = 1.0-3.6), displaced abomasum (OR = 12.3, 95% BCI = 1.6-45.8), metritis (OR = 9.4, 95% BCI = 1.3-36.0), and clinical mastitis (OR = 5.8, 95% BCI = 1.9-12.1) than cows below the threshold. Our results suggest that foNEFA, using a threshold of ≥160 µmol/L, could be used by veterinarians as a monitoring or investigating tool to assess cows' negative energy balance before calving, even earlier than 2 wk prepartum. This monitoring could be used to implement early corrective actions to prevent the effect of negative energy balance on reproduction and peripartum health.},
}
@article {pmid40922815,
year = {2025},
author = {Zhang, Y and Zhou, M and Liang, R and Chen, J and Shi, P and Zheng, Y and Luo, X and Wu, Y and Yu, X and Wu, Y and Liang, S and Deng, W and Bueber, MA and Phillips, MR and Li, T},
title = {Mental health literacy and the stigmatisation and discrimination of individuals affected by mental illnesses in China: a scoping review.},
journal = {The Lancet regional health. Western Pacific},
volume = {61},
number = {},
pages = {101642},
pmid = {40922815},
issn = {2666-6065},
abstract = {Low mental health literacy (MHL) could contribute to misconceptions about mental illnesses and reinforce various forms of stigma (public, personal, and associative), leading to discrimination, reduced help-seeking, and poorer mental health outcomes. To summarise the current state of the literature on MHL, stigma, and discrimination, this scoping review identified 387 studies published from 2000 to 2024 in five English and three Chinese databases: 60.7% focused on stigma, 31.8% on MHL, and only 7.5% on discrimination. Most studies (84.8%) were descriptive cross-sectional studies, 14.5% evaluated interventions, and 0.7% were non-intervention longitudinal studies. Methodological quality was generally low: reports about 88.4% of the cross-sectional studies, 75.6% of the randomised controlled trials, and 83.4% of the quasi-experimental studies lacked descriptions of key methodological or statistical details. After excluding researcher-developed tools only reported in a single study, 125 assessment tools remained, 26.4% of which were developed in China. Although 21 different mental health conditions were studied, 91.0% of the studies focused on a single condition. Study locations were geographically skewed (one-third of all studies were conducted in Guangdong, Beijing, and Shanghai), and study participants were not representative of the target cohort. The number of publications increased substantially after 2010. Most of the 56 intervention studies, which primarily used psychoeducational interventions, reported improved MHL and decreased stigma. Recommendations for future studies include: 1) Develop standardised instruments to improve comparability. 2) Ensure detailed statistical analyses and clearly defined sample characteristics. 3) Assess variations in MHL, stigmatisation, and discrimination across different mental health conditions. 4) Increase research in underserved regions and conduct nationwide longitudinal studies. 5) Include a broader range of participants in intervention studies and consider new intervention strategies (i.e., other than psychoeducation interventions). 6) Align research objectives with national mental health policies to enhance their relevance and impact.},
}
@article {pmid40921216,
year = {2026},
author = {Saeed, S and Wang, H and Kong, L and Geng, Y and Zhang, J and Pan, Y and Le, X and Zhang, X and Liu, TT and Hu, S},
title = {Machine learning-enhanced mapping of suicide risk in Bipolar Disorder: A multi-modal analysis.},
journal = {Journal of affective disorders},
volume = {392},
number = {},
pages = {120183},
doi = {10.1016/j.jad.2025.120183},
pmid = {40921216},
issn = {1573-2517},
mesh = {Humans ; *Bipolar Disorder/psychology/diagnosis ; Male ; Female ; Cross-Sectional Studies ; Adult ; *Machine Learning ; *Suicide/psychology/statistics &amp; numerical data ; Middle Aged ; Risk Assessment/methods ; Psychiatric Status Rating Scales ; Suicidal Ideation ; Risk Factors ; },
abstract = {BACKGROUND: Bipolar disorder (BD) is associated with a high risk of suicide, but the complex interplay of factors contributing to this risk remains poorly understood. This study aimed to comprehensively analyze demographic, clinical, and biological factors associated with suicide risk in BD patients and develop a novel suicide risk assessment model integrating these factors.<br><br>METHODS: We conducted a cross-sectional study of 152 patients with BD, classified into four suicide-risk groups: no risk (n&#xa0;=&#xa0;19), low risk (n&#xa0;=&#xa0;45), moderate risk (n&#xa0;=&#xa0;38), and high risk (n&#xa0;=&#xa0;50). Participants underwent assessments using the Mini-International Neuropsychiatric Interview (M.I.N&#xb7;I.), Hamilton Depression Rating Scale-24 items (HAMD-24), Young Mania Rating Scale (YMRS), Montgomery-&#xc5;sberg Depression Rating Scale (MADRS), and Beck Scale for Suicide Ideation (BSSI). We evaluated thyroid function, inflammatory markers, and lymphocyte subsets. Univariate and multivariate analyses were performed to identify factors associated with suicide risk.<br><br>FINDINGS: Depressive symptoms were significantly associated with increased odds of medium (odds ratio (OR)&#xa0;=&#xa0;1.452, 95&#xa0;% confidence interval (CI): 1.122-1.878, P&#xa0;=&#xa0;0.005) and high (OR&#xa0;=&#xa0;1.405, 95&#xa0;% CI: 1.091-1.810, P&#xa0;=&#xa0;0.009) suicide risk. Lower free thyroxine 4 (FT4) levels were associated with higher odds of low (OR&#xa0;=&#xa0;0.581, 95&#xa0;% CI: 0.404-0.835, P&#xa0;=&#xa0;0.003) and medium (OR&#xa0;=&#xa0;0.694, 95&#xa0;% CI: 0.486-0.992, P&#xa0;=&#xa0;0.045) risk. The no-risk group exhibited higher levels of thyroid hormones and autoantibodies. CD3+ T-cell percentages varied significantly across risk groups, with the lowest mean percentage in the no-risk group (57.59&#xa0;&#xb1;&#xa0;14.64&#xa0;%). Our machine learning models achieved 87.1&#xa0;% accuracy in predicting suicide risk. Patient Health Questionnaire-9 items, Hamilton Depression Rating Scale-24 items, and Montgomery-&#xc5;sberg Depression Rating Scale scores were identified as the strongest predictors of suicide risk by a Random-Forest model with 100 decision trees. In addition, FT4 and interferon-&#x3b3; emerged as notable contributors to the model's predictions.<br><br>CONCLUSION: Depressive symptoms and thyroid function are crucial factors in assessing suicide risk in BD. Thyroid autoimmunity and T cell-mediated immunity emerge as potential biomarkers for risk stratification and therapeutic targets, offering new avenues for personalized intervention strategies.},
}
@article {pmid40920866,
year = {2025},
author = {Nyawanda, BO and Sullivan, KM and Tinkitina, B and Beinamaryo, P and Nabatte, B and Kyarisiima, H and Mubangizi, A and Emerson, PM and Utzinger, J and Vounatsou, P},
title = {Geostatistical analysis to guide treatment decisions for soil-transmitted helminthiasis control in Uganda.},
journal = {PLoS neglected tropical diseases},
volume = {19},
number = {9},
pages = {e0013467},
pmid = {40920866},
issn = {1935-2735},
mesh = {Uganda/epidemiology ; Humans ; *Soil/parasitology ; *Helminthiasis/epidemiology/prevention &amp; control/drug therapy/transmission/parasitology ; Prevalence ; *Anthelmintics/therapeutic use/administration &amp; dosage ; Child ; Animals ; Bayes Theorem ; Male ; Female ; Child, Preschool ; Adolescent ; Ascaris lumbricoides ; Hookworm Infections/epidemiology ; Trichuris ; Trichuriasis/epidemiology ; },
abstract = {BACKGROUND: Soil-transmitted helminth (STH) infections remain a public health problem in Uganda despite biannual national deworming campaigns implemented since the early 2000s. Recent surveys have indicated a heterogeneous STH infection prevalence, suggesting that the current blanket deworming strategy may no longer be cost-effective. This study identified infection predictors, estimated the geographic distribution of STH infection prevalence by species, and calculated deworming needs for school-age children (SAC).<br><br>METHODOLOGY: Bayesian geostatistical models were applied to STH survey data (2021-2023) for each species (i.e., Ascaris lumbricoides, hookworm, and Trichuris trichiura). Climatic, environmental, and socioeconomic predictors were obtained from remote sensing sources, model-based databases, and demographic and health surveys. Prevalence was predicted on a 1&#x2009;&#xd7;&#x2009;1 km2 grid across Uganda, and district-level estimates were used to classify each district into treatment frequency categories and to determine its deworming tablet requirements.<br><br>PRINCIPAL FINDINGS: The national prevalence of A. lumbricoides, T. trichiura, and hookworm was estimated at 5.0% (95% Bayesian credible interval [BCI]: 0.8-11.8%), 3.5% (0.7-9.3%), and 7.2% (5.7-11.1%), respectively. The overall prevalence of any STH infection was 14.3% (9.6-21.8%). High intra-district variation in prevalence was observed. Of 146 implementation units (136 districts and 10 cities), 49 require twice-year treatment, 34 once-yearly treatment, 61 every other year treatment, and 2 had a prevalence <2%, indicating treatment suspension or event-based treatment. Approximately 17 million tablets will be needed for preventive chemotherapy aimed at SAC in 2025.<br><br>CONCLUSIONS/SIGNIFICANCE: The prevalence of STH infection has declined considerably across Uganda compared to the early 2000s. However, deworming needs remain heterogeneous across districts. Through geostatistical modeling, districts were classified according to the latest World Health Organization's (WHO) treatment guidelines. This approach optimizes treatment distribution and allows for prioritization of populations with the greatest needs. We estimated that tablet requirements are approximately 40% lower compared to the current twice-a-year deworming regimen, which contributes towards WHO's goal of halving the number of tablets required for preventive chemotherapy by 2030.},
}
@article {pmid40919632,
year = {2025},
author = {Zhang, S and Ling, C and Wu, J and Li, J and Wang, J and Yu, Y and Liu, X and Lv, J and Vai, MI and Chen, R},
title = {EEG-ERnet: Emotion Recognition based on Rhythmic EEG Convolutional Neural Network Model.},
journal = {Journal of integrative neuroscience},
volume = {24},
number = {8},
pages = {41547},
doi = {10.31083/JIN41547},
pmid = {40919632},
issn = {0219-6352},
support = {2024B03J1361//Guangzhou Science and Technology Plan Project/ ; 2023B03J1327//Guangzhou Science and Technology Plan Project/ ; 2024SZFZ007//Research Fund of Key Laboratory of Numerical Simulation of Sichuan Provincial Universities/ ; 2025ZNSFSC0780//Sichuan Science and Technology Program/ ; 23XXK0402//Foundation of the 2023 Higher Education Science Research Plan of the China Association of Higher Education/ ; CSXL-25102//Foundation of the Sichuan Research Center of Applied Psychology (Chengdu Medical College)/ ; NJ2024ZD014//Neijiang Philosophy and Social Science Planning Project/ ; 2023KQNCX036//Guangdong Province Ordinary Colleges and Universities Young Innovative Talents Project/ ; 22GPNUZDJS17//Scientific Research Capacity Improvement Project of the Doctoral Program Construction Unit of Guangdong Polytechnic Normal University/ ; 2023YJSY04002//Graduate Education Demonstration Base Project of Guangdong Polytechnic Normal University/ ; 2025-M10//Open Research Fund of State Key Laboratory of Digital Medical Engineering/ ; 2022SDKYA015//Research Fund of Guangdong Polytechnic Normal University/ ; },
mesh = {Humans ; *Emotions/physiology ; *Electroencephalography/methods ; *Neural Networks, Computer ; Adult ; Young Adult ; *Recognition, Psychology/physiology ; *Evoked Potentials/physiology ; Brain-Computer Interfaces ; Male ; *Brain Waves/physiology ; Female ; Convolutional Neural Networks ; },
abstract = {BACKGROUND: Emotion recognition from electroencephalography (EEG) can play a pivotal role in the advancement of brain-computer interfaces (BCIs). Recent developments in deep learning, particularly convolutional neural networks (CNNs) and hybrid models, have significantly enhanced interest in this field. However, standard convolutional layers often conflate characteristics across various brain rhythms, complicating the identification of distinctive features vital for emotion recognition. Furthermore, emotions are inherently dynamic, and neglecting their temporal variability can lead to redundant or noisy data, thus reducing recognition performance. Complicating matters further, individuals may exhibit varied emotional responses to identical stimuli due to differences in experience, culture, and background, emphasizing the necessity for subject-independent classification models.<br><br>METHODS: To address these challenges, we propose a novel network model based on depthwise parallel CNNs. Power spectral densities (PSDs) from various rhythms are extracted and projected as 2D images to comprehensively encode channel, rhythm, and temporal properties. These rhythmic image representations are then processed by a newly designed network, EEG-ERnet (Emotion Recognition Network), developed to process the rhythmic images for emotion recognition.<br><br>RESULTS: Experiments conducted on the dataset for emotion analysis using physiological signals (DEAP) using 10-fold cross-validation demonstrate that emotion-specific rhythms within 5-second time intervals can effectively support emotion classification. The model achieves average classification accuracies of 93.27 &#xb1; 3.05%, 92.16 &#xb1; 2.73%, 90.56 &#xb1; 4.44%, and 86.68 &#xb1; 5.66% for valence, arousal, dominance, and liking, respectively.<br><br>CONCLUSIONS: These findings provide valuable insights into the rhythmic characteristics of emotional EEG signals. Furthermore, the EEG-ERnet model offers a promising pathway for the development of efficient, subject-independent, and portable emotion-aware systems for real-world applications.},
}
@article {pmid40919177,
year = {2025},
author = {Sha, G and Liu, Y and Cao, Y and Zhang, Q and Zhang, Y and Chen, Y and Fan, Q and Cheng, Y},
title = {Structural and functional neural correlates of sensorimotor deficits in progression of hepatic encephalopathy.},
journal = {Magnetic resonance letters},
volume = {5},
number = {2},
pages = {200156},
pmid = {40919177},
issn = {2772-5162},
abstract = {Hepatic encephalopathy (HE) is a neurological condition that occurs as a complication of liver dysfunction that involves sensorimotor symptoms in addition to cognitive and behavioral changes, particularly in cases of severe liver disease or cirrhosis. Previous studies have reported spatially distributed structural and functional abnormalities related to HE, but the exact relationship between the structural and functional alterations with respect to disease progression remains unclear. In this study, we performed surface-based cortical thickness comparisons and functional connectivity (FC) analyses between three cross-sectional groups: healthy controls (HC, N = 51), patients with minimal hepatic encephalopathy (MHE, N = 50), patients with overt hepatic encephalopathy (OHE, N = 51). In addition to the distributed cortical thinning that is extensively thought to be associated with cognitive decline in HE, we found significant cortical thickening in the left parahippocampal gyrus cortex in the OHE group (p < 0.001, p = 0.009) as compared to the HC and MHE group respectively, which is further corroborated by the significant correlation between the cortical thickness and digit symbol test (DST) scores. Furthermore, the decreased FC between the right postcentral gyrus and several sensory regions (bilateral somatosensory and visual cortices) was found to be significant in MHE patients as compared to the HC group. Our results revealed cross-sectional structural and functional variations concerning disease progression across different subsystems (e.g., visual, motor and sensory), providing evidence that can potentially explain the mechanisms underlying the sensorimotor and cognitive deficits related to HE.},
}
@article {pmid40916208,
year = {2025},
author = {Ahmadi Seyedkhani, S and Iraji Zad, A and Mohammadpour, R and Taghipoor, M and Vafaiee, M},
title = {Novel Brain-Inspired Hierarchical Micro-Nanostructured Poly(3,4-ethylenedioxythiophene)/Polydopamine Neural Interface on Titanium Nitride Electrodes for Electrophysiological Signal Recording.},
journal = {ACS applied bio materials},
volume = {8},
number = {10},
pages = {9332-9345},
doi = {10.1021/acsabm.5c01451},
pmid = {40916208},
issn = {2576-6422},
mesh = {*Titanium/chemistry ; *Polymers/chemistry ; *Bridged Bicyclo Compounds, Heterocyclic/chemistry ; *Indoles/chemistry ; Materials Testing ; *Biocompatible Materials/chemistry ; Surface Properties ; Particle Size ; *Nanostructures/chemistry ; *Brain ; Animals ; Electrodes ; Brain-Computer Interfaces ; },
abstract = {The development of high-performance neural interfaces is critical for advancing brain-machine communication and treating neurological disorders. A major challenge in neural electrode design is achieving a seamless biological-electronic interface with optimized electrochemical properties, mechanical stability, and biocompatibility. In this study, we introduce a hierarchical micronanostructured poly(3,4-ethylenedioxythiophene)-polydopamine (PEDOT-PDA) coating on titanium nitride (TiN) microelectrodes engineered to enhance electrophysiological signal recording and neural integration. The PEDOT-PDA films were synthesized via potentiodynamic electropolymerization, achieving a 90% reduction in impedance (∼353 Ω at 1 kHz) compared to conventional gold (Au) electrodes (∼3795 Ω) and a 60% decrease relative to TiN substrates (∼890 Ω). The brain-inspired hierarchical micronanostructure mimics the extracellular matrix (ECM), improving cell adhesion and biointegration. Wettability analysis revealed a 63% enhancement in hydrophilicity, reducing the water contact angle from ∼70° for pure PEDOT to ∼25° for PEDOT-PDA. Biocompatibility assessments demonstrated excellent cell viability of ∼97% for PEDOT-PDA electrodes and superior cell attachment with extended filopodia formation, promoting long-term neural interface stability. The PEDOT-PDA interface outperforms conventional PEDOT and metal-based electrodes in electrochemical stability, biocompatibility, and signal recording efficiency, making it a promising candidate for next-generation brain-computer interfaces (BCIs).},
}
@article {pmid40915552,
year = {2025},
author = {Wang, X and Zou, T and Wang, H and Han, H and Chen, H and Calhoun, VD and Li, R},
title = {A dynamic spatiotemporal representation framework for deciphering personal brain function.},
journal = {NeuroImage},
volume = {319},
number = {},
pages = {121443},
doi = {10.1016/j.neuroimage.2025.121443},
pmid = {40915552},
issn = {1095-9572},
mesh = {Humans ; *Magnetic Resonance Imaging/methods ; Male ; Female ; *Brain/physiology/diagnostic imaging ; Adult ; Young Adult ; Middle Aged ; *Nerve Net/physiology/diagnostic imaging ; *Brain Mapping/methods ; Adolescent ; },
abstract = {Functional magnetic resonance imaging (fMRI) opens a window on observing spontaneous activities of the human brain in vivo. However, the high complexity of fMRI signals makes brain functional representations intractable. Here, we introduce a state decomposition method to reduce this complexity and decipher individual brain functions at multiple levels. Briefly, brain dynamics are captured by temporal first-order derivatives and spatially divided into 'state sets' at each time point based on the velocity and direction of change. This approach transforms the original signals into discrete series consisting of four fundamental states, which efficiently encode individual-specific information. Subsequently, we designed a suite of state-based metrics to quantify regional activities and network interactions. Compared with conventional representations such as resting-state fluctuation amplitude and Pearson's functional connectivity, the state-based representations serve as more discriminative 'brain fingerprints' for individuals and produce reproducible spatial patterns across heterogeneous cohorts (n = 1015). Regarding functional organization, our proposed profiles extend previous representations into nonlinear domains, revealing not only the canonical default-mode dominant pattern but also patterns dominated by the attention network and basal ganglia. Moreover, we demonstrate that personal phenotypes (such as age and gender) can be decoded from regional representations with high accuracy. The equivalence between state series outperforms other existing network representations in predicting individual fluid intelligence. Overall, this framework establishes a foundation for enriching the repertoire of brain functional representations and enhancing the power of brain-phenotype modeling.},
}
@article {pmid40914696,
year = {2025},
author = {Li, W and Shi, W and Wang, H and Li, J and Chu, C and Zhang, Y and Cui, Y and Cheng, L and Li, K and Lu, Y and Ma, L and Song, M and Yang, Z and Banaschewski, T and Bokde, ALW and Desrivières, S and Flor, H and Grigis, A and Garavan, H and Gowland, P and Walter, H and Brühl, R and Martinot, JL and Martinot, MP and Artiges, E and Nees, F and Orfanos, DP and Lemaitre, H and Poustka, L and Hohmann, S and Millenet, S and Fröhner, JH and Robinson, L and Smolka, MN and Winterer, J and Whelan, R and Fan, L and Jiang, T},
title = {Anatomical connectivity development constrains medial-lateral topography in the dorsal prefrontal cortex.},
journal = {Science bulletin},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.scib.2025.08.045},
pmid = {40914696},
issn = {2095-9281},
}
@article {pmid40914528,
year = {2026},
author = {Chen, S and Guo, X and Liu, X and Liu, S and Ming, D},
title = {Transdiagnostic homogeneity, and diagnostic-specific biomarkers among major depressive disorder, bipolar disorder and schizophrenia during 40 Hz auditory steady-state response: a normative modeling analysis.},
journal = {Journal of affective disorders},
volume = {392},
number = {},
pages = {120189},
doi = {10.1016/j.jad.2025.120189},
pmid = {40914528},
issn = {1573-2517},
mesh = {Humans ; *Bipolar Disorder/physiopathology/diagnosis ; *Depressive Disorder, Major/physiopathology/diagnosis ; *Schizophrenia/physiopathology/diagnosis ; Male ; Female ; Adult ; Middle Aged ; *Evoked Potentials, Auditory/physiology ; Biomarkers ; Electroencephalography ; Case-Control Studies ; *Gamma Rhythm/physiology ; Young Adult ; },
abstract = {BACKGROUND: Abnormal gamma-band auditory steady-state response (gamma-ASSR) power has been reported in major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ), but distinguishing between these disorders based solely on power remains challenging. Directed functional connectivity (DFC), which captures topological patterns of causal information flow, may provide more diagnostic-specific markers. However, conventional case-control framework often disregards the substantial individual heterogeneity, yielding unreliable biomarkers.<br><br>METHODS: An adapted framework integrating DFC heterogeneity with normative modeling was developed. 52 MDD, 33 BD, 39 SZ patients and 107 healthy controls (HC) participated in the 40&#xa0;Hz-ASSR task. The normative model was established using data from 71 HC to define the population baseline. Thereafter, deviation Z-scores and the proportion of extreme deviations in DFC were calculated.<br><br>RESULTS: The DFC deviations showed high individual heterogeneity at most DFCs, with fewer than 2.6&#xa0;% of individuals exhibiting extreme deviations at the same time point. However, a small proportion of DFC deviations with high overlap were embedded within common connectivity pathways in three disorders, particularly in the frontal and parietal regions. Furthermore, distinct diagnostic-specific patterns were identified: MDD mainly exhibited right temporal-frontal alterations, BD showed a parietal-driven temporo-occipital loop, and SZ presented a midline-centered pyramidal topology linking bilateral temporal-occipital regions. The Z-scores of DFC involved in these diagnostic-specific patterns achieved a maximum accuracy of 99.43&#xa0;% with the K-nearest neighbors (KNN) algorithm.<br><br>CONCLUSIONS: These findings offer novel insights into gamma-ASSR alterations and provide a robust framework for transdiagnostic and disorder-specific identification across MDD, BD, and SZ.},
}
@article {pmid40914440,
year = {2025},
author = {Balam, VP},
title = {Automated EEG signal processing: A comprehensive investigation into preprocessing techniques and sub-band extraction for enhanced brain-computer interface applications.},
journal = {Journal of neuroscience methods},
volume = {424},
number = {},
pages = {110561},
doi = {10.1016/j.jneumeth.2025.110561},
pmid = {40914440},
issn = {1872-678X},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; *Brain/physiology ; Fourier Analysis ; Algorithms ; *Brain Waves/physiology ; Machine Learning ; Wavelet Analysis ; },
abstract = {The Electroencephalogram (EEG) is a vital physiological signal for monitoring brain activity and understanding neurological capacities, disabilities, and cognitive processes. Analyzing and classifying EEG signals are key to assessing an individual's reactions to various stimuli. Manual EEG analysis is time-consuming and labor-intensive, necessitating automated tools for efficiency. Machine learning techniques often rely on preprocessing and segmentation methods to integrate automated classification into EEG signal processing, with EEG sub-band components (δ,θ,α,β and γ) playing a crucial role. This paper presents a comprehensive exploration of EEG preprocessing methods, with a specific focus on sub-band extraction techniques used in Brain-Computer Interface (BCI) applications. Various methods-including Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) filters, and wavelet transforms (DWT, WPT)-are evaluated through qualitative and quantitative parametric analysis, along with a review of their practical applicability. The study also includes an application-based evaluation using an open-access EEG dataset for drowsiness detection.},
}
@article {pmid40913810,
year = {2026},
author = {Zheng, L and Su, Y and Li, S and Li, X and Zhang, Y and Tseomashko, NE and Sadikovna, AS and Wang, X},
title = {Injectable multifunctional sponges with rough sieve structure and efficient shape-recoverability for small-sized penetrating wound.},
journal = {Journal of colloid and interface science},
volume = {702},
number = {Pt 1},
pages = {138896},
doi = {10.1016/j.jcis.2025.138896},
pmid = {40913810},
issn = {1095-7103},
mesh = {Animals ; Rats ; Surface Properties ; Porosity ; *Wounds, Penetrating/therapy ; Rats, Sprague-Dawley ; *Hemostatics/chemistry/pharmacology/administration &amp; dosage ; Male ; Particle Size ; *Bandages ; Surgical Sponges ; },
abstract = {The emergence of special scenarios involving small-sized penetrating wounds has imposed stricter performance requirements on shape-recovery hemostatic materials, particularly regarding their shape fixity and water-triggered shape recovery efficiency. Herein, an efficient shape-recovery sponge dressing with high shape fixity and high-speed liquid absorption, designated as CQT, was developed by integrating a sieve structure with the rough surface coating. The sieve structure, characterized by microporous structures on macroporous walls, enhanced the multi-level and connectivity of the overall pore network, which could improve compressive fixity via enhancing the energy dissipation required for rebound and enabled efficient shape recovery through augmented capillary action during fluid absorption. Concurrently, the enhanced pore connectivity promoted rapid blood absorption (<0.5 s), expanded interfacial contact between blood and hydrophilic pore walls, and improved interception of blood active components, while the rough coating on the pore walls provided more binding sites along with its charge effect to enhance the adhesion and aggregation of blood cells (BCI of 7.8 %). The excellent in vivo hemostatic performance of the sponge (blood loss of 0.31 g and hemostasis time of 63 s) was further validated using a rat liver defect model, suggesting its potential for application in small-sized penetrating wounds. Additionally, this coating has antimicrobial and antioxidant properties that help to prevent infection and reduce inflammation. Thus, the unique sponge dressings possess excellent initial shape adaptability and efficient expansion hemostatic ability, making it very suitable for emergency hemostasis and subsequent repair of small-sized penetrating wounds.},
}
@article {pmid40913768,
year = {2025},
author = {Verwoert, M and Ottenhoff, MC and Tousseyn, S and van Dijk, JP and Kubben, PL and Herff, C},
title = {Moving beyond the motor cortex: A brain-wide evaluation of target locations for intracranial speech neuroprostheses.},
journal = {Cell reports},
volume = {44},
number = {9},
pages = {116241},
doi = {10.1016/j.celrep.2025.116241},
pmid = {40913768},
issn = {2211-1247},
mesh = {Humans ; *Brain-Computer Interfaces ; *Motor Cortex/physiology ; *Speech/physiology ; Male ; Female ; Adult ; Electroencephalography ; Young Adult ; Electrocorticography ; Brain Mapping ; Middle Aged ; },
abstract = {Speech brain-computer interfaces (BCIs) offer a solution for those affected by speech impairments by decoding brain activity into speech. Current neuroprosthetics focus on the motor cortex, which might not be suitable for all patient populations. We investigate potential alternative targets for a speech BCI across a brain-wide distribution. Thirty participants are recorded with intracranial electroencephalography during speech production. We continuously predict speech from a brain-wide global to a single-channel local scale, across anatomical features. We find significant speech detection accuracy in both gray and white matter, no significant difference between gyri and sulci, and limited contribution from subcortical areas. Potential targets are located within the depths of and surrounding the lateral fissure bilaterally, such as the (sub)central sulcus, the transverse temporal gyrus, the supramarginal cortex, and parts of the insula. The results highlight the potential benefit of extending beyond the motor cortical surface and reaching the sulcal depth for speech neuroprostheses.},
}
@article {pmid40913530,
year = {2025},
author = {Zhou, E and Wang, X and Liang, J and Liu, Y and Yang, Q and Ran, X and Xia, L and Zou, X and Liu, C and Sun, L and Peng, L and Chen, L and Mao, Y and Wu, Z and Tao, TH and Zhou, Z},
title = {Chronically Stable, High-Resolution Micro-Electrocorticographic Brain-Computer Interfaces for Real-Time Motor Decoding.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e06663},
doi = {10.1002/advs.202506663},
pmid = {40913530},
issn = {2198-3844},
support = {Y2023070//Youth Innovation Promotion Association for Excellent Members/ ; 22QA1410900//Shanghai Rising-Star Program/ ; ZDBS-LY-JSC024//Key Research Program of Frontier Sciences, CAS/ ; JCYJ-SHFY-2022-01//Shanghai Pilot Program for Basic Research-Chinese Academy of Science/ ; 82272116//National Natural Science Foundation of China/ ; 2021SHZDZX//Science and Technology Commission of Shanghai Municipality/ ; 2018AAA0103100//National Major Science and Technology Projects of China/ ; },
abstract = {Brain-computer interfaces (BCIs) enable communication between individuals and computers or other assistive devices by decoding brain activity, thereby reconstructing speech and motor functions for patients with neurological disorders. This study presents a high-resolution micro-electrocorticography (µECoG) BCI based on a flexible, high-density µECoG electrode array, capable of chronically stable and real-time motor decoding. Leveraging micro-nano manufacturing technology, the µECoG BCI achieves a 64-fold increase in electrode density compared to conventional clinical electrode arrays, enhancing spatial resolution while featuring scalability. Over a 203-day in vivo experiment, high-resolution µECoG carrying fine spatial specificity information demonstrated the potential to improve decoding performance while reduce implanted devices size. These advancements provide a pathway to overcome the limitations of conventional ECoG BCIs. During awake surgery, the µECoG BCI enabled game control after 7 min of model training. Furthermore, during practice of 19.87 h, the participant achieved cursor control with a bit rate of 1.13 bits per second (BPS) under full volitional control, and the bit rate reached up to 4.15 BPS with enhanced user interface. These results show that the µECoG BCI achieves comparable performance to intracortical electroencephalographic (iEEG) BCIs without intracortical invasiveness, marking a breakthrough in the clinical feasibility of flexible BCIs.},
}
@article {pmid40913389,
year = {2025},
author = {Ji, Z and Li, L and Zheng, M and Ye, X and Yan, W and Wang, Z and Liu, Y and Wang, Y and Zhang, Y and Zhou, P and Yang, J and Wang, M and Lin, S and Haick, H and Wang, Y},
title = {Conductive Hydrogel-Enabled Electrode for Scalp Electroencephalography Monitoring.},
journal = {Small methods},
volume = {},
number = {},
pages = {e01242},
doi = {10.1002/smtd.202501242},
pmid = {40913389},
issn = {2366-9608},
support = {52303371//National Natural Science Foundation of China/ ; W2521021//National Natural Science Foundation of China/ ; STKJ2023075//Guangdong Science and Technology Department/ ; 2022A1515110209//Guangdong Science and Technology Department/ ; 2021B0301030005//Guangdong Science and Technology Department/ ; GCII-Seed-202406//GTIIT Changzhou Innovation Institute/ ; //Education Foundation of Guangdong Technion-Israel Institute of Technology/ ; //Key Discipline (KD) Fund/ ; //Start-Up fund from Guangdong Technion/ ; },
abstract = {Scalp electroencephalography (EEG) serves as a pivotal technology for the noninvasive monitoring of brain functional activity, diagnosing neurological disorders, and assessing cognitive states. However, inherent compatibility barriers between traditional rigid electrodes and the hairy scalp interface significantly compromise signal quality, long-term monitoring comfort, and user compliance. This review examines conductive hydrogel electrodes' pivotal role in advancing scalp EEG, particularly their unique capacity to overcome hair-interface barriers. The superiority of scalp EEG is first established over forehead/ear EEG for capturing diverse neural signals and defining core requirements for hair-compatible interfaces: scalp conformability, electrical conductivity, low contact impedance, and interfacial stability. Conductive hydrogel electrode applications are then detailed in alpha wave detection, sleep monitoring, event-related potential studies, and brain-computer interfaces. Finally, persisting challenges and future opportunities are discussed.},
}
@article {pmid40913111,
year = {2025},
author = {Duan, C and Ma, S and Chen, M and Wang, J and Jiang, Y and Ye, M and Tan, Y and Cheng, S and Yang, X and Hu, H and Yang, Y and Huang, HF},
title = {Estrogen receptor beta in lateral habenula mediates antidepressant effects of estrogen in postpartum-hormone-withdrawal-induced depression.},
journal = {Molecular psychiatry},
volume = {},
number = {},
pages = {},
pmid = {40913111},
issn = {1476-5578},
abstract = {Dramatic drop in reproductive hormone, especially estrogen level, from pregnancy to postpartum period is known to contribute to postpartum depression (PPD), but the underlying mechanism and the role of the estrogen receptors (ERs) in this process were unclear. Here, we used an estrogen-withdrawal-induced PPD model following hormone simulated pregnancy (HSP) in female Sprague-Dawley rats to induce depressive-like behaviors. After estrogen withdrawal, we observe an up-regulation of astrocyte-specific potassium channel (Kir4.1) in the brain's anti-reward center lateral habenula (LHb), along with enhanced bursting and excitability of LHb neurons. Among all 3 subtypes of ERs in the LHb, only ERβ shows an HSP-correlated expression temporal dynamics. Systemic administration of selective ERβ agonist, but not agonists of other subtypes of ERs, inhibits neuronal bursting activities and blocks up-regulation of Kir4.1 in the LHb, as well as decreases estrogen-withdrawal-induced depressive-like behavior. Importantly, intra-LHb injection of ERβ agonist is sufficient to rescue depressive-like behaviors induced by estrogen withdrawal. Conversely, local knock-down of ERβ in the LHb suppresses the antidepressant-like effect of estrogen. Our results reveal a critical role of LHb in the pathogenesis of hormone-sensitive PPD and ERβ as a critical mediator of estrogen's antidepressant effects on PPD.},
}
@article {pmid40912944,
year = {2025},
author = {Zhou, J and Li, W and Xu, S and Biswal, BB and Chen, H and Li, J and Liao, W},
title = {Multimodal, multifaceted, imaging-based human brain white matter atlas.},
journal = {Science bulletin},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.scib.2025.08.021},
pmid = {40912944},
issn = {2095-9281},
}
@article {pmid40911452,
year = {2025},
author = {Cao, L and Li, H and Dong, Y and Liu, T and Li, J},
title = {Few-Shot Class-Incremental Learning with Dynamic Prototype Refinement for Brain Activity Classification.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3605108},
pmid = {40911452},
issn = {2168-2208},
abstract = {The brain-computer interface (BCI) system facilitates efficient communication and control, with Electroencephalography (EEG) signals as a vital component. Traditional EEG signal classification, based on static deeplearning models, presents a challenge when new classes of the subject's brain activity emerge. The goal is to develop a model that can recognize new few-shot classes while preserving its ability to discriminate between existing ones. This scenario is referred to as Few-Shot Class-Incremental Learning (FSCIL). This work introduces IncrementEEG, a novel framework meticulously designed to tackle the distinct challenges of FSCIL in EEG-based brain activity classification, focusing specifically on emotion recognition and steady-state visual evoked potential (SSVEP). Our work analyzes the role of additive angular margin loss in improving the model's discrimination capabilities. The proposed method is designed to demonstrate robustness in open-world conditions and adaptability to new tasks. Furthermore, we introduce a prototype refinement module comprising a prototype augmentation block and an update block. The prototype augmentation block in the deep feature space preserves the decision boundary for prior tasks, and the prototype update block utilizes a shared embedding space to compute the relation matrix for bootstrapping prototype updates. Extensive experiments conducted across multiple datasets show the superior performance of the IncrementEEG framework compared to state-of-the-art methods. The proposed method advances FSCIL brain activity classification, offering promising potential for applications in Brain-Computer Interface systems.},
}
@article {pmid40911443,
year = {2025},
author = {Zhang, J and Zhu, L and Kong, W and Zhang, J and Cao, J and Cichocki, A},
title = {Reinforcement Learning Decoding Method of Multi-User EEG Shared Information Based on Mutual Information Mechanism.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {9},
pages = {6588-6598},
doi = {10.1109/JBHI.2025.3565019},
pmid = {40911443},
issn = {2168-2208},
mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; *Reinforcement, Psychology ; Brain/physiology ; Adult ; Male ; Young Adult ; Algorithms ; Female ; Deep Learning ; },
abstract = {The multi-user motor imagery brain-computer interface (BCI) is a new approach that uses information from multiple users to improve decision-making and social interaction. Although researchers have shown interest in this field, the current decoding methods are limited to basic approaches like linear averaging or feature integration. They ignored accurately assessing the coupling relationship features, which results in incomplete extraction of multi-source information. To overcome these limitations, we propose a new reinforcement learning electroencephalography (EEG) decoding method based on mutual information mechanisms. Our method enhances the extraction of multi-source common information and uses a dynamic feedback model for inter-brain mutual information reward and punishment mechanisms in the reinforcement learning channel selection module. We feed the single-brain and inter-brain signals after channel selection into deep neural networks, which automatically extract coupled features. Finally, based on the attention indices calculated from EEG signals at prefrontal electrode positions, the output is obtained by voting. Our experimental results show that the average accuracy of dual-brain recognition is improved by 16% compared to single-brain mode. Furthermore, ablation experiments demonstrate that the reinforcement learning module and attention voting module enhance accuracy by 14.5% and 15.7%, respectively.},
}
@article {pmid40911279,
year = {2025},
author = {Li, CP and Wang, YY and Zhou, CW and Ding, CY and Teng, P and Nie, R and Yang, SG},
title = {Cutting-edge technologies in neural regeneration.},
journal = {Cell regeneration (London, England)},
volume = {14},
number = {1},
pages = {38},
pmid = {40911279},
issn = {2045-9769},
support = {2024C03028//The Pioneer and Leading Goose R&amp;D Program of Zhejiang Province/ ; 2023R01005//The Leading Innovation and Entrepreneurship Team Program of Zhejiang Province/ ; },
abstract = {Neural regeneration stands at the forefront of neuroscience, aiming to repair and restore function to damaged neural tissues, particularly within the central nervous system (CNS), where regenerative capacity is inherently limited. However, recent breakthroughs in biotechnology, especially the revolutions in genetic engineering, materials science, multi-omics, and imaging, have promoted the development of neural regeneration. This review highlights the latest cutting-edge technologies driving progress in the field, including optogenetics, chemogenetics, three-dimensional (3D) culture models, gene editing, single-cell sequencing, and 3D imaging. Prospectively, the advancements in artificial intelligence (AI), high-throughput in vivo screening, and brain-computer interface (BCI) technologies promise to accelerate discoveries in neural regeneration further, paving the way for more precise, efficient, and personalized therapeutic strategies. The convergence of these multidisciplinary approaches holds immense potential for developing transformative treatments for neural injuries and neurological disorders, ultimately improving functional recovery.},
}
@article {pmid40909568,
year = {2025},
author = {Gerrity, CG and Treuting, RL and Peters, RA and Womelsdorf, T},
title = {Neuronal Decoding of Decisions in Multidimensional Feature Space Using a Gated Recurrent Variational Autoencoder.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {40909568},
issn = {2692-8205},
support = {R01 MH123687/MH/NIMH NIH HHS/United States ; },
abstract = {Recent advances in neuroscience enable recording neuronal signals across hundreds of channels while subjects perform complex tasks involving multiple stimulus dimensions. In this study, we developed a novel encode-decode-classify framework employing a gated recurrent variational autoencoder (VAE) to decode decision-making processes from over 300 simultaneously recorded neuronal channels in the prefrontal cortex and basal ganglia of monkeys performing a multidimensional feature-learning task. Using hierarchical stratified sampling and balanced accuracy, we trained and evaluated the model's ability to predict behavioral choices based on neuronal population dynamics. The results revealed distinct neural coding roles, with anterior cingulate cortex (ACC) channels encoding decision variables collectively and prefrontal cortex (PFC) channels contributing individually to decoding accuracy. This approach demonstrated decoding accuracy for decisions in multi-dimensional feature space that is comparable to single-label decoding accuracy for lower dimensional problems, highlighting the potential of machine learning frameworks to capture complex spatiotemporal neuronal interactions involved in multidimensional cognitive behaviors. The code has been released in https://github.com/cgerrity/Neural-Data-Reading.},
}
@article {pmid40907818,
year = {2025},
author = {Pan, H and Gao, H and Zhang, Y and Yu, X and Li, Z and Lei, X and Mi, W},
title = {Design and implementation of a writing-stroke motor imagery paradigm for multi-character EEG classification.},
journal = {Neuroscience},
volume = {585},
number = {},
pages = {441-450},
doi = {10.1016/j.neuroscience.2025.08.058},
pmid = {40907818},
issn = {1873-7544},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Stroke/physiopathology ; *Imagination/physiology ; Male ; Female ; Adult ; Neural Networks, Computer ; *Brain/physiopathology ; Young Adult ; Writing ; Movement/physiology ; },
abstract = {Motor imagery (MI) based brain-computer interfaces (BCI) decode neural activity to generate command outputs. However, the limited number of distinguishable commands in traditional MI-BCI systems restricts practical applications. To overcome this limitation, we propose a multi-character classification framework based on Electroencephalography (EEG) signals. A structurally simplified MI paradigm for stroke writing is designed, and maximize Euclidean distance trajectory optimization enhances neural separability among five stroke categories. The EEG data cover 11 motor imagery tasks, including five stroke-writing tasks and six related movement tasks such as hand, foot, tongue movements and eye blinks, collected from ten participants. Ensemble Empirical Mode Decomposition (EEMD) eliminates artifact-related Intrinsic Mode Functions (IMFs) and reconstructs the signals. Kernel Principal Component Analysis (KPCA) then conducts nonlinear dimensionality reduction to extract discriminative features. Finally, a recurrent neural network based on Gated Recurrent Units (GRU) performs classification, effectively modeling the temporal dynamics of EEG signals. Experimental results indicate that the optimized stroke paradigm achieves an average classification accuracy of 84.77%, outperforming the unoptimized version at 76.83%. Compared to existing MI-BCI methods, the proposed framework improves classification accuracy and expands the set of distinguishable commands, demonstrating enhanced practicality and effectiveness.},
}
@article {pmid40907530,
year = {2025},
author = {J Bryan, M and Schwock, F and Yazdan-Shahmorad, A and P N Rao, R},
title = {Temporal basis function models for closed-loop neural stimulation.},
journal = {Journal of neural engineering},
volume = {22},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ae036a},
pmid = {40907530},
issn = {1741-2552},
mesh = {Animals ; *Optogenetics/methods ; *Models, Neurological ; *Deep Brain Stimulation/methods ; Macaca mulatta ; Artificial Intelligence ; },
abstract = {Objective.Closed-loop neural stimulation provides novel therapies for neurological diseases such as Parkinson's disease (PD), but it is not yet clear whether artificial intelligence (AI) techniques can tailor closed-loop stimulation to individual patients or identify new therapies. Further advancements are required to address a number of difficulties with translating AI to this domain, including sample efficiency, training time, and minimizing loop latency such that stimulation may be shaped in response to changing brain activity.Approach.We propose temporal basis function models (TBFMs) to address these difficulties, and explore this approach in the context of excitatory optogenetic stimulation. We demonstrate the ability of TBF models to provide a single-trial, spatiotemporal forward prediction of the effect of optogenetic stimulation on local field potentials measured in two non-human primates. The simplicity of TBF models allow them to be sample efficient (<20 min of training data), rapid to train (<5 min), and low latency (<0.2 ms) on desktop CPUs.Main results.We demonstrate the model on 40 sessions of previously published excitatory optogenetic stimulation data. Surprisingly, on test sets it achieved a prediction accuracy 44% higher than a complex nonlinear dynamical systems model that requires hours to train, and 158% higher than a linear state-space model requiring 90 min to train. Additionally, in two simulations we show that it successfully allows a closed-loop stimulator to drive neural trajectories, and to achieve the user-preferred trade-offs between under- and over-stimulation, given the uncertainty in the model; it achieves an area under curve of ∼0.7 in both cases.Significance.By optimizing for sample efficiency, training time, and latency, our approach begins to bridge the gap between complex AI-based approaches to modeling dynamical systems and the vision of using such forward prediction models to develop novel, clinically useful closed-loop stimulation protocols.},
}
@article {pmid40906512,
year = {2025},
author = {Zhou, L and Zhang, B and Kang, R and Wang, Y and Qin, J and Xiao, Q and Hui, V},
title = {Efficacy of the Conventional Rehabilitation Robot and bio-Signal Feedback-Based Rehabilitation Robot on Upper-Limb Function in Patients with Stroke: A Systematic Review and Network Meta-Analysis.},
journal = {NeuroRehabilitation},
volume = {57},
number = {2},
pages = {169-180},
doi = {10.1177/10538135251366668},
pmid = {40906512},
issn = {1878-6448},
mesh = {Humans ; *Stroke Rehabilitation/methods/instrumentation ; *Robotics ; *Upper Extremity/physiopathology ; Network Meta-Analysis as Topic ; *Stroke/physiopathology ; *Brain-Computer Interfaces ; Electromyography ; Randomized Controlled Trials as Topic ; },
abstract = {BackgroundWith the development of modern biomedical engineering, bio-signal feedback-based robots, such as electromyography (EMG)-based and brain-computer interface (BCI)-based rehabilitation robot, have emerged beyond conventional designs. However, their comparative effectiveness for improving upper limb function in stroke patients remains unassessed.ObjectiveTo evaluate the comparative effectiveness and ranking of the conventional rehabilitation robot and bio-signal feedback-based rehabilitation robot in improving upper limb function in stroke patients.MethodsPubMed, EMBASE, Cochrane Library, CINAHL, PEDro, EI, IEEEXplore, ClinicalTrials.gov, ICTRP, and ISRCTN Registry were searched for randomized controlled trials (RCTs) from their inception to December 25, 2024. The risk of bias was assessed using the Cochrane Risk of Bias tool (RoB 2.0) and evidence certainty with the GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach. Network meta-analyses were performed using a random-effects model within a frequentist framework.Results59 RCTs with 3,387 participants were included. Based on the surface under the cumulative ranking curve (SUCRA), the BCI-based rehabilitation robot demonstrated the highest overall effects (SUCRA: 99.9%), short-term effects (SUCRA: 99.4%), and long-term effects (SUCRA: 85.1%), though its long-term effects were not significant (mean difference: 2.21; 95% confidence interval: -0.79, 5.21). The EMG-based rehabilitation robot outperformed the conventional rehabilitation robot in short-term interventions (SUCRA: 59.8% vs. 40.3%), but it did not have the same advantage in long-term interventions (SUCRA: 27.1% vs. 66.8%).ConclusionsThe BCI-based rehabilitation robot might be the best choice for improving upper limb function in stroke patients. Future studies should focus on the intervention time for the EMG-based rehabilitation robot.},
}
@article {pmid40904893,
year = {2025},
author = {Patel, N and Verma, J and Jain, S},
title = {Improving EEG classification of alcoholic and control subjects using DWT-CNN-BiGRU with various noise filtering techniques.},
journal = {Frontiers in neuroinformatics},
volume = {19},
number = {},
pages = {1618050},
pmid = {40904893},
issn = {1662-5196},
abstract = {Electroencephalogram (EEG) signal analysis plays a vital role in diagnosing and monitoring alcoholism, where accurate classification of individuals into alcoholic and control groups is essential. However, the inherent noise and complexity of EEG signals pose significant challenges. This study investigates the impact of three signal denoising techniques' Discrete Wavelet Transform(DWT), Discrete Fourier Transform(DFT), and Discrete Cosine Transform (DCT) Non EEG signal classification performance. The motivation behind this study is to identify the most effective preprocessing method for enhancing deep learning model performance in this domain. A novel DWT-CNN-BiGRU model is proposed, which leverages CNN layers for spatial feature extraction and BiGRU layers for capturing temporal dependencies. Experimental results show that the DWT-based approach, combined with standard scaling, achieves the highest accuracy of 94%, with a precision of 0.94, a recall of 0.95, and an F1-score of 0.94. Compared to the baseline DWT-CNN-BiLSTM model, the proposed method provides a modest yet meaningful improvement of approximately 17% in classification accuracy. These findings highlight the superiority of DWT as a preprocessing method and validate the proposed model's effectiveness for EEG-based classification, contributing to the development of more reliable medical diagnostic tools.},
}
@article {pmid40904422,
year = {2025},
author = {Wang, X and Jin, X and Kong, W and Babiloni, F},
title = {CAGCNet: generalized contrastive learning for person identification based on channel aggregated EEG features.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {141},
pmid = {40904422},
issn = {1871-4080},
abstract = {Person identification method based on electroencephalograms (EEG) signals, or so called brainprint recognition is a novel way to distinguish identities with advantages of high security. However, existing methods neglect the distribution difference between training and test data, and the large distance between projected features in the latent space makes the performance of the model degrade in the unseen domain data. In this paper, we propose channel aggregated based generalized contrastive learning framework, which combines multiple modules to overcome this challenge. To capture features from different granularities, we involve multi-scale convolution with channel attention block. In face of distribution of unseen domain, we introduce feature enhancement-based generalized contrast learning to improve the model generalization ability. In the generalized contrast learning module, taking the difficulty of reconstructing EEG signals into consideration, we augment the source domain data at the feature level to improve the generalization ability of the model on the unseen domain data. Extensive experiments on two multi-session datasets shows that our model outperformed other baseline methods, demonstrating its capability of better generalization performance to unseen domain.},
}
@article {pmid40903968,
year = {2025},
author = {Yu, H and Mu, Q and Liu, C and Wang, S and Sun, J},
title = {Technical system of electroencephalography-based brain-computer interface: Advances, applications, and challenges.},
journal = {Neural regeneration research},
volume = {},
number = {},
pages = {},
doi = {10.4103/NRR.NRR-D-25-00217},
pmid = {40903968},
issn = {1673-5374},
abstract = {Electroencephalography-based brain-computer interfaces have revolutionized the integration of neural signals with technological systems, offering transformative solutions across neuroscience, biomedical engineering, and clinical practice. This review systematically analyzes advancements in electroencephalography-based brain-computer interface architectures, emphasizing four pillars, namely signal acquisition, paradigm design, decoding algorithms, and diverse applications. The aim is to bridge the gap between technology and application and guide future research. In signal acquisition, noninvasive systems using wet, dry, and semi-dry electrodes are more comfortable and gentler on the skin compared to traditional methods. However, ensuring stable signal quality over long periods of time remains a challenge. Minimally invasive approaches, such as microneedle arrays and endovascular probes, achieve near-invasive signal fidelity without major surgery. Paradigm design explores task-specific neural encoders. Although motor imagery paradigms are widely used in rehabilitation, they require weeks of user training. Steady-state visually evoked potential and P300 speller paradigms enable rapid calibration, but cause visual and cognitive fatigue. Advanced systems currently combine electroencephalography with electromyography or eye-tracking to better handle real-world tasks. Decoding algorithms have advanced through Riemannian geometry for improved noise filtering, deep learning architectures for automated spatiotemporal feature extraction, and transfer learning frameworks to minimize cross-subject calibration. However, challenges remain in managing inconsistent electroencephalography, reducing processing demands, and ensuring compatibility across different electroencephalography devices. Clinical trials reveal a predominant focus on stroke rehabilitation, while emerging frontiers include astronaut neuromonitoring in space exploration. Challenges include improving signal accuracy, minimizing movement interference, addressing ethical data concerns, and ensuring real-world use. Future advancements focus on biocompatible nanomaterials, adaptive algorithms, and multimodal integration, positioning electroencephalography-based brain-computer interfaces as pivotal tools in next-generation neurotechnology.},
}
@article {pmid40902296,
year = {2025},
author = {Bao, T and Wu, Y and Zhang, H and Cao, J and Wang, J and Liu, J and Fang, J},
title = {Determining microbial extracellular alkaline phosphatase activity in seawater based on surface-enhanced Raman spectroscopy.},
journal = {Marine environmental research},
volume = {212},
number = {},
pages = {107470},
doi = {10.1016/j.marenvres.2025.107470},
pmid = {40902296},
issn = {1879-0291},
abstract = {Microbial extracellular alkaline phosphatase (ALP) plays a significant role in marine phosphorus cycle. Therefore, it is of paramount importance to accurately and rapidly measure ALP activity (APA) in seawater. However, the applications of the existing APA measurement methods are constrained by cumbersome pre-processing, lengthy measurement times, and the influence of colored substances or suspended particles in seawater samples, which limit our accurate understanding of the marine phosphorus cycle. In this study, we developed a sensitive and rapid technique for the quantitative determination of microbial alkaline phosphatase activity in seawater based on surface-enhanced Raman spectroscopy (SERS). This method uses 5-bromo-4-chloro-3-indolyl phosphate (BCIP) as the substrate, and dimethyl sulfoxide (DMSO) as an internal standard to establish a model for quantifying APA in seawater samples. Our results show that the Raman intensity ratio (I600/I700) between the enzymatic reaction product 5-bromo-4-chloro-3-indole (BCI oxide dimers) (I600) and the internal standard (I700) is an ideal quantitation parameter, and there is a strong linear relationship between I600/I700 (y) and APA (x): y = 0.301x + 1.105, R[2] = 0.981. This method is capable of determining APA over a dynamic range of five orders of magnitude (from 0.1 to 10[4] mU L[-1]) with a detection limit of 0.1 mU L[-1]. The reliability of the method is confirmed by comparing the kinetic parameters of the fluorogenic method. Further, this method was tested and successfully applied to quantify APA in coastal and open ocean seawater samples from the Western Pacific Ocean, demonstrating the potential of this method for rapid and reliable detection of APA in the marine environment.},
}
@article {pmid40902051,
year = {2025},
author = {Wang, G and Jiang, L and Song, X and Zhang, Y and Yao, D and Lu, J and Xu, P and Li, F and Liang, Y},
title = {Enhancing Neural Representations of Motor Imagery Through Action-Specific Brain Connectivity Patterns.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {3555-3564},
doi = {10.1109/TNSRE.2025.3605612},
pmid = {40902051},
issn = {1558-0210},
mesh = {Humans ; *Imagination/physiology ; Algorithms ; Male ; *Brain/physiology ; Female ; Adult ; Young Adult ; Functional Laterality/physiology ; Movement/physiology ; Magnetic Resonance Imaging ; Neural Pathways/physiology ; *Nerve Net/physiology ; Brain Mapping ; Psychomotor Performance/physiology ; Electroencephalography ; Brain-Computer Interfaces ; },
abstract = {Motor imagery (MI) is a cognitive process that allows individuals to mentally simulate movements without physical executio n. However, the exploration of functional connectivity (FC) and lateralization mechanisms under different MI actions remains insufficiently understood. In this work, the common orthogonal basis extraction (COBE) algorithm was employed to isolate action-specific components by removing shared background components from the raw FC of the MI process. We demonstrate that action-specific FC effectively captures the hemispheric statistical differences between left- and right-hand MI, outperforming traditional FC and temporal variability measures. And through a comprehensive analysis of network properties at three distinct levels, encompassing the whole-brain network properties, hemispherical properties, and individual nodal strength, complex lateralization patterns associated with diverse types of MI processes were successfully discerned. Furthermore, lateralization indices were further calculated to quantitatively reveal the degree of brain lateralization. Notably, the lateralization performance (LP) derived from action-specific FC exhibited a significant predictive capacity for MI performance, thereby suggesting its potential to evaluate individual MI capability. Collectively, these findings validate the action-specific FC patterns in characterizing neural mechanisms of MI processes and indicate that the LP could potentially be a useful tool to predict the MI performance of MI-based brain-computer inference (BCI), thereby contributing to the formulation of personalized therapeutic strategies for clinical rehabilitation from a new perspective.},
}
@article {pmid40899667,
year = {2025},
author = {Han, NT and Yan, T and Zhuang, R and Kokkinakis, AV and Cao, L},
title = {Sensory Attenuation of Auditory P2 Responses is Modulated by the Sense of Action Timing Control.},
journal = {Psychophysiology},
volume = {62},
number = {9},
pages = {e70134},
pmid = {40899667},
issn = {1469-8986},
support = {32271078//National Natural Science Foundation of China/ ; 2023M733124//China Postdoctoral Science Foundation/ ; YJ20220315//China Postdoctoral Science Foundation/ ; 226-2024-00207//Fundamental Research Funds for the Central Universities/ ; },
mesh = {Humans ; Male ; Female ; Electroencephalography ; *Evoked Potentials, Auditory/physiology ; Young Adult ; *Auditory Perception/physiology ; Adult ; Acoustic Stimulation ; *Psychomotor Performance/physiology ; Reaction Time/physiology ; },
abstract = {Sensory attenuation is a well-established phenomenon in which the neurophysiological response elicited by self-initiated stimuli is attenuated compared to identical externally generated stimuli. This phenomenon is mostly studied by comparing the N1 and P2 components of the auditory ERP. Sensory attenuation has also been linked to our sense of agency and control. In the present study, we investigated the role of action timing control in sensory attenuation. Previous studies that investigated the attenuation of the N1/P2 components instructed participants to generate self-initiated stimuli by having the participants perform a series of keypresses while EEG is recorded. ERP responses are then compared to a second condition where participants passively listen to identical sounds. Studies using this paradigm, known as the self-stimulation paradigm, have used a wide range of stimulus onset asynchronies (SOAs) for keypress timing. However, the choice of SOA is rarely explained, perhaps due to an assumption of trial independence. We found that as SOA increased, participants enacted more action timing control to maintain the specified SOA level. The degree of P2 suppression also increased as participants enacted more control. Contrary to most studies in the literature, we did not find N1 suppression but instead found N1 enhancement. The results suggest that P2 suppression may be related to action timing control while N1 enhancement may reflect factors other than motor predictions, in line with more recent interpretations of the N1 suppression effect.},
}
@article {pmid40899634,
year = {2025},
author = {McGill, K and Bhullar, N and Carrandi, A and Batterham, PJ and Wayland, S and Maple, M},
title = {A Randomized Controlled Trial of an SMS-Based Brief Contact Intervention for People Bereaved by Suicide.},
journal = {Suicide &amp; life-threatening behavior},
volume = {55},
number = {5},
pages = {e70043},
pmid = {40899634},
issn = {1943-278X},
support = {//Suicide Prevention Australia/ ; },
mesh = {Humans ; Female ; Male ; *Bereavement ; *Text Messaging ; Adult ; *Suicide/psychology ; Middle Aged ; Suicidal Ideation ; Help-Seeking Behavior ; Psychological Distress ; Resilience, Psychological ; Young Adult ; },
abstract = {INTRODUCTION: Brief contact interventions (BCI) refer to short messages delivered proactively to a specific target population. The aim of this study was to test the effectiveness of a mobile phone short-message service (SMS) BCI for people bereaved by suicide.<br><br>METHODS: Participants were randomly allocated. The BCI group received text messages over a 6-week period. The active control group received the intervention website. Pre- and post-intervention surveys assessed demographic, suicide exposure and five key outcomes (psychological distress, suicidal ideation, complicated grief, resilience, and professional help-seeking intentions). BCI participants were also invited to participate in an interview post-intervention.<br><br>RESULTS: Of 99 participants randomized, 52 BCI and 47 control completed pre-intervention surveys. Post-intervention response rates were low (BCI: n&#x2009;=&#x2009;15; 28.85%; active control: n&#x2009;=&#x2009;16; 34.04%), with no statistically significant changes in key outcome measures. Eight BCI participants completed follow-up interviews. Relevance, timing of support, benefit to bereavement, and recommendations for scaling were identified.<br><br>CONCLUSIONS: Recruitment and retention challenges meant the effectiveness of the BCI could not be statistically determined. Qualitative evidence supported BCI acceptability for people bereaved by suicide. Recommendations to improve the intervention include embedding the BCI within existing postvention services offered soon after a death occurs and tailoring of messages to individuals' needs.<br><br>TRIAL REGISTRATION: This trial was registered with the Australian New Zealand Clinical Trial Register (ACTRN12621001430820).},
}
@article {pmid40898814,
year = {2025},
author = {Zhao, Y and Lu, P and Wang, X and Yin, M},
title = {Bidirectional optimization of firing rate in a mouse neuronal brain-machine interface.},
journal = {Biology letters},
volume = {21},
number = {9},
pages = {20250176},
pmid = {40898814},
issn = {1744-957X},
support = {//High-level Talent Project of Natural Science Foundation of Hainan Province/ ; //Sanya Yazhou Bay Science and Technology City/ ; //'Rising Star of South China Sea' Project of Hainan Province/ ; //National Natural Science Foundation of China/ ; //STI 2030-Major Projects/ ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Mice ; Reward ; *Neurons/physiology ; Male ; *Motor Cortex/physiology ; *Neuronal Plasticity ; Mice, Inbred C57BL ; Feedback, Sensory ; },
abstract = {Neuroplasticity enables the brain to adapt neural activity, but whether this can be harnessed for abstract optimization tasks like seeking curve extrema remains unclear. Here, we used a brain-machine interface in mice, pairing auditory feedback of neuronal firing rate with water rewards, to investigate whether motor cortex neurons can optimize activity along a unimodal curve ([Formula: see text]). The curve maps firing rate ([Formula: see text]) to sound frequency increase speed ([Formula: see text]), where the curve extremum accelerates reward acquisition. Over conditioning sessions, mice learned to modulate firing rates towards this peak, reducing reward time from 18.64 ± 7.30 s to 11.59 ± 4.38 s and increasing high-response events from 66 to 104 occurrences. Putative neurons increasingly prioritized high-response intervals, with positive proportion increments in upper intervals versus negative trends in lower ones. These findings demonstrate that cortical neurons can dynamically optimize activity along non-monotonic reward landscapes, revealing neuroplasticity as a substrate for adaptive self-optimization. This expands our understanding of how the brain learns abstract rules via feedback, with implications for neuroprosthetic design that leverage neural adaptability.},
}
@article {pmid40898635,
year = {2025},
author = {Isakova, EV and Kotov, SV and Borisova, VA},
title = {[Effectiveness of "brain-computer" interfaces with biofeedback in the rehabilitation of cognitive impairment after a stroke].},
journal = {Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova},
volume = {125},
number = {8. Vyp. 2},
pages = {54-60},
doi = {10.17116/jnevro202512508254},
pmid = {40898635},
issn = {1997-7298},
mesh = {Humans ; Male ; *Brain-Computer Interfaces ; Female ; Middle Aged ; *Stroke/complications/psychology ; *Stroke Rehabilitation/methods ; *Biofeedback, Psychology ; Aged ; *Cognitive Dysfunction/rehabilitation/etiology ; Electroencephalography ; Adult ; Neuropsychological Tests ; },
abstract = {OBJECTIVE: Comparison of the effectiveness of two "brain-computer" interface (BCI) software complexes using biofeedback (BF) and standard therapy in restoring cognitive functions after a stroke.<br><br>MATERIAL AND METHODS: Eighty-nine stroke patients were examined. Neuropsychological testing was carried out using the Montreal Cognitive Assessment Scale (MoCA), the Tracking test, the Wechsler subtest 9 Kohs block design test, the Schulte tables, the Memorization of 10 Words test (according to A.R. Luria). Using the simple randomization method, three groups were formed: the main group (n=37), the comparison group (n=33) and the control group (n=19). In Group 1, sessions were conducted with BCI+BF based on the rhythm P300; in Group 2, with BCI+BF based on the mu-rhythm of electroencephalography (EEG), Group 3 received standard therapy.<br><br>RESULTS: An increase in the total MoCA score was reported in all three groups. The results in Groups 1 and 2 were comparable, exceeding those in Group 3 (p1-2=0.199, p1-3<0.001, p2-3=0.037). The effectiveness in Group 1 did not depend on the baseline MoCA score, exceeding the indicators in Group 3; in Group 2, the advantage over Group 3 was with a baseline MoCA of at least 22. According to the Schulte tables and the Tracking test, comparable statistically significant changes were obtained in Groups 1 and 2; no statistically significant change was reported in the control group. The Kohs block design test showed a more statistically significant change in the main group. The Memorization of 10 Words test by A.R. Luria also showed a more consistent improvement in mnestic disorders in the main group.<br><br>CONCLUSION: The effectiveness of BCI+BF exceeded standard therapy for post-stroke cognitive impairment. The advantage of IMC+BFB used in the main group over IMC+BFB in the comparison group was noted, which was due to a decrease in the effectiveness of the latter with a baseline MoCA score of less than 22 points, lower performance in the Memorizing 10 Words test and the Kohs block design test.},
}
@article {pmid40898590,
year = {2025},
author = {Paveliev, M and Melnikova, A and Egorchev, AA and Parpura, V and Aganov, AV},
title = {Neuroimplants and the Glial Scar: What Makes the Brain-Computer Link Work?.},
journal = {Journal of neurochemistry},
volume = {169},
number = {9},
pages = {e70203},
doi = {10.1111/jnc.70203},
pmid = {40898590},
issn = {1471-4159},
support = {24-75-00123//Russian Science Foundation/ ; },
mesh = {Humans ; Animals ; *Brain-Computer Interfaces/trends ; *Cicatrix/pathology/prevention &amp; control ; *Neuroglia/pathology ; *Brain/pathology ; Tissue Engineering/methods ; *Brain Injuries/therapy/pathology ; },
abstract = {Neuroimplants are likely major technological breakthroughs of the next decade with the potential for unprecedented social impact. In addition to attractive and miracle-looking possibilities, the major obstacle for the industry is complicated, unpredictable, and unfavorable side effects due to tissue damage, biocompatibility limitations, and foreign body response at the brain-implant interface. Luckily, one major barrier preventing the connection of the neuroimplant to brain cells-the glial scar-has been studied previously for its role in brain trauma. This review highlights pharmacological and tissue engineering avenues that could be readily transferred from the brain trauma area to fast-growing neuroimplant engineering. The opportunities for chondroitinase ABC treatment, stem cells, and hydrogels for the prevention of glial scarring are emphasized. Alternatively, the glial scar may also be viewed not as an obstacle but as a possible regeneration-permissive component of the optimally working brain-neuroimplant interface. Feasible steps in that direction are discussed, including applications for chondroitin sulfate-binding peptides. Finally, the crucial role of new microscopy and data processing techniques for peri-implant glial scar monitoring is highlighted. To that end, we stress the importance of artificial intelligence, including artificial neuronal networks, for the analysis of cell morphology at the brain-neuroimplant interface.},
}
@article {pmid40897729,
year = {2025},
author = {Sakakibara, Y and Kusutomi, T and Kondoh, S and Etani, T and Shimada, S and Imamura, Y and Naruse, Y and Fujii, S and Ibaraki, T},
title = {A Nostalgia Brain-Music Interface for enhancing nostalgia, well-being, and memory vividness in younger and older individuals.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {32337},
pmid = {40897729},
issn = {2045-2322},
mesh = {Humans ; *Music/psychology ; Male ; Female ; Adult ; Aged ; Electroencephalography ; Middle Aged ; *Brain/physiology ; Young Adult ; Mental Recall/physiology ; *Emotions/physiology ; *Neurofeedback/methods ; Auditory Perception/physiology ; *Memory/physiology ; Memory, Episodic ; },
abstract = {Music-evoked nostalgia has the potential to assist in recalling autobiographical memories and enhancing well-being. However, nostalgic music preferences vary from person to person, presenting challenges for applying nostalgia-based music interventions in clinical settings, such as a non-pharmacological approach. To address these individual differences, we developed the Nostalgia Brain-Music Interface (N-BMI), a neurofeedback system that recommends nostalgic songs tailored to each individual. This system is based on prediction models of nostalgic feelings, developed by integrating subjective nostalgia ratings, acoustic features and in-ear electroencephalographic (EEG) data during song listening. To test the effects of N-BMI on nostalgic feelings, state-level well-being, and memory recall, seventeen older and sixteen younger participants took part in the study. The N-BMI was personalized for each individual, and songs were recommended under two conditions: the "nostalgic condition", where songs were selected to enhance nostalgic feelings, and the "non-nostalgic condition", to reduce nostalgic feelings. We found nostalgic feelings, state-level well-being, and subjective memory vividness were significantly higher after listening to the recommended songs in the nostalgic condition compared to the non-nostalgic condition in both groups. This indicates that the N-BMI enhanced nostalgic feelings, state-level well-being, and memory recall across both groups. The N-BMI paves the way for innovative therapeutic interventions, including non-pharmacological approaches.},
}
@article {pmid40897258,
year = {2025},
author = {Morozova, M and Yakovlev, L and Syrov, N and Lebedev, M and Kaplan, A},
title = {Cortical responses to tactile imagery: a high-density EEG study of the μ-rhythm event-related desynchronization and somatosensory evoked potentials.},
journal = {NeuroImage},
volume = {319},
number = {},
pages = {121440},
doi = {10.1016/j.neuroimage.2025.121440},
pmid = {40897258},
issn = {1095-9572},
mesh = {Humans ; *Evoked Potentials, Somatosensory/physiology ; Male ; Female ; Adult ; Electroencephalography/methods ; *Touch Perception/physiology ; *Somatosensory Cortex/physiology ; Young Adult ; *Imagination/physiology ; *Cortical Synchronization/physiology ; Attention/physiology ; },
abstract = {Tactile imagery (TI) engages somatosensory cortices in both hemispheres, along with widespread brain regions associated with the imagery process itself. Actively simulating touch can influence the processing of actual tactile stimuli, as reflected by modulations in somatosensory evoked potentials (SEPs) components. This study uses high-density electroencephalography (EEG) and sLORETA-based source localization to analyse cortical sources of SEPs components susceptible to active skin sensations imagery. Twenty healthy participants performed TI and tactile attention (TA) tasks. TI enhanced early SEP components (P100), indicating engagement of primary somatosensory cortical networks. This was accompanied with robust μ-rhythm event-related desynchronization (ERD) localized to the postcentral gyrus. While TA also elicited μ-ERD, its cortical distribution was broader, suggesting involvement of more distributed and possibly non-specific attentional mechanisms. Notably, sensor-space analysis revealed an enhanced late frontal P200 peak during TI, potentially indicating increased frontal activation. However, source-space analysis confirmed the absence of frontal pole involvement in SEPs during TI, underscoring the importance of accurate source localization. Thus, TI was found to significantly activate primary somatosensory cortices, influencing early stages of real tactile stimulus processing. Its effects were more spatially focused compared to those induced by TA. These findings suggest that TI could be a prospective approach for sensorimotor rehabilitation. On the other hand, TA could provide an effortless method for modulating sensorimotor rhythms in BCI control.},
}
@article {pmid40896338,
year = {2025},
author = {Liu, M},
title = {Editorial: Neural dynamics for brain-inspired control and computing: advances and applications.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1666218},
doi = {10.3389/fnins.2025.1666218},
pmid = {40896338},
issn = {1662-4548},
}
@article {pmid40896268,
year = {2025},
author = {Lee, D and Byun, K and Lee, S},
title = {Enhancing cognitive function through blood flow restriction: An effective resistance exercise modality for middle-aged women.},
journal = {Journal of exercise science and fitness},
volume = {23},
number = {4},
pages = {379-388},
pmid = {40896268},
issn = {1728-869X},
abstract = {PURPOSE: Cognitive decline progresses more rapidly in women than in men, with a higher prevalence of neurodegenerative diseases observed in females. Exercise has been shown to enhance cognitive function through the upregulation of neurotrophic factors such as brain-derived neurotrophic factor (BDNF), vascular endothelial growth factor (VEGF) and insulin-like growth factor-1 (IGF-1). However, high-load resistance exercise may not be suitable for all populations, particularly middle-aged women. Low-load resistance exercise with blood flow restriction (LLBFR) has emerged as an effective alternative. This study investigated the acute effects of LLBFR on neurotrophic factors and cognitive function in middle-aged women.<br><br>METHODS: Fifteen healthy middle-aged women completed a randomized crossover trial involving four conditions: control (CON), low-load resistance exercise (LLRE), LLBFR, and moderate-load resistance exercise (MLRE). Cognitive function was assessed before and after each session using the color-word matching Stroop task (CWST). Blood samples were analyzed for serum levels of BDNF, VEGF, and IGF-1, and lactate concentrations were measured to evaluate metabolic responses.<br><br>RESULTS: Only the LLBFR condition showed significant improvements in CWST reaction time (p&#xa0;=&#xa0;0.002) with no changes in error rates, indicating enhanced cognitive performance. Serum BDNF and VEGF levels increased significantly following both LLBFR (p&#xa0;<&#xa0;0.001, p&#xa0;=&#xa0;0.014, respectively) and MLRE (p&#xa0;<&#xa0;0.001, p&#xa0;=&#xa0;0.004, respectively), whereas IGF-1 levels remained unchanged across conditions. Increases in lactate concentrations were positively correlated with changes in BDNF and VEGF (p&#xa0;<&#xa0;0.001 for both), but not with IGF-1.<br><br>CONCLUSION: A single session of LLBFR improved cognitive function and upregulated neurotrophic factors, particularly BDNF and VEGF, in middle-aged women. These findings suggest that LLBFR may be an effective intervention for promoting cognitive health in this population.},
}
@article {pmid40894778,
year = {2025},
author = {Tong, JQ and Binder, JR and Conant, LL and Mazurchuk, S and Anderson, AJ and Fernandino, L},
title = {A Common Representational Code for Event and Object Concepts in the Brain.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {40894778},
issn = {2692-8205},
support = {R01 DC016622/DC/NIDCD NIH HHS/United States ; R01 DC020932/DC/NIDCD NIH HHS/United States ; },
abstract = {Events and objects are two fundamental ways in which humans conceptualize their experience of the world. Despite the significance of this distinction for human cognition, it remains unclear whether the neural representations of object and event concepts are categorically distinct or, instead, can be explained in terms of a shared representational code. We investigated this question by analyzing fMRI data acquired from human participants (males and females) while they rated their familiarity with the meanings of individual words (all nouns) denoting object and event concepts. Multivoxel pattern analyses indicated that both categories of lexical concepts are represented in overlapping fashion throughout the association cortex, even in the areas that showed the strongest selectivity for one or the other type in univariate contrasts. Crucially, in these areas, a feature-based model trained on neural responses to individual event concepts successfully decoded object concepts from their corresponding activation patterns (and vice versa), showing that these two categories share a common representational code. This code was effectively modeled by a set of experiential feature ratings, which also accounted for the mean activation differences between these two categories. These results indicate that neuroanatomical dissociations between events and objects emerge from quantitative differences in the cortical distribution of more fundamental features of experience. Characterizing this representational code is an important step in the development of theory-driven brain-computer interface technologies capable of decoding conceptual content directly from brain activity.},
}
@article {pmid40894619,
year = {2025},
author = {Spalding, Z and Duraivel, S and Rahimpour, S and Wang, C and Barth, K and Schmitz, C and Lad, SP and Friedman, AH and Southwell, DG and Viventi, J and Cogan, GB},
title = {Shared latent representations of speech production for cross-patient speech decoding.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {40894619},
issn = {2692-8205},
support = {R01 DC019498/DC/NIDCD NIH HHS/United States ; R01 NS129703/NS/NINDS NIH HHS/United States ; UG3 NS120172/NS/NINDS NIH HHS/United States ; UL1 TR002553/TR/NCATS NIH HHS/United States ; },
abstract = {Speech brain-computer interfaces (BCIs) can restore communication in individuals with neuromotor disorders who are unable to speak. However, current speech BCIs limit patient usability and successful deployment by requiring large volumes of patient-specific data collected over long periods of time. A promising solution to facilitate usability and accelerate their successful deployment is to combine data from multiple patients. This has proven difficult, however, due to differences in user neuroanatomy, varied placement of electrode arrays, and sparse sampling of targeted anatomy. Here, by aligning patient-specific neural data to a shared latent space, we show that speech BCIs can be trained on data combined across patients. Using canonical correlation analysis and high-density micro-electrocorticography (μECoG), we uncovered shared neural latent dynamics with preserved micro-scale speech information. This approach enabled cross-patient decoding models to achieve improved accuracies relative to patient-specific models facilitated by the high resolution and broad coverage of μECoG. Our findings support future speech BCIs that are more accurate and rapidly deployable, ultimately improving the quality of life for people with impaired communication from neuromotor disorders.},
}
@article {pmid40893910,
year = {2025},
author = {Teng, J and Cho, S and Lee, SM},
title = {Tri-manual interaction in hybrid BCI-VR systems: integrating gaze, EEG control for enhanced 3D object manipulation.},
journal = {Frontiers in neurorobotics},
volume = {19},
number = {},
pages = {1628968},
pmid = {40893910},
issn = {1662-5218},
abstract = {Brain-computer interface (BCI) integration with virtual reality (VR) has progressed from single-limb control to multi-limb coordination, yet achieving intuitive tri-manual operation remains challenging. This study presents a consumer-grade hybrid BCI-VR framework enabling simultaneous control of two biological hands and a virtual third limb through integration of Tobii eye-tracking, NeuroSky single-channel EEG, and non-haptic controllers. The system employs e-Sense attention thresholds (>80% for 300 ms) to trigger virtual hand activation combined with gaze-driven targeting within 45° visual cones. A soft maximum weighted arbitration algorithm resolves spatiotemporal conflicts between manual and virtual inputs with 92.4% success rate. Experimental validation with eight participants across 160 trials demonstrated 87.5% virtual hand success rate and 41% spatial error reduction (σ = 0.23 mm vs. 0.39 mm) compared to traditional dual-hand control. The framework achieved 320 ms activation latency and 22% NASA-TLX workload reduction through adaptive cognitive load management. Time-frequency analysis revealed characteristic beta-band (15-20 Hz) energy modulations during successful virtual limb control, providing neurophysiological evidence for attention-mediated supernumerary limb embodiment. These findings demonstrate that sophisticated algorithmic approaches can compensate for consumer-grade hardware limitations, enabling laboratory-grade precision in accessible tri-manual VR applications for rehabilitation, training, and assistive technologies.},
}
@article {pmid40892657,
year = {2025},
author = {Song, Z and Zhang, X and Li, M and Tan, J and Wang, Y},
title = {Online Neural-to-Movement Mapping Transfer for Task Switching and Retention in Brain-Machine Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {3674-3684},
doi = {10.1109/TNSRE.2025.3605246},
pmid = {40892657},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Animals ; Rats ; Algorithms ; Movement/physiology ; Male ; Machine Learning ; Reinforcement, Psychology ; Psychomotor Performance/physiology ; Brain/physiology ; Online Systems ; Memory ; Electroencephalography ; *Retention, Psychology/physiology ; *Brain Mapping/methods ; },
abstract = {Current brain-machine interfaces (BMIs) often rely on decoders trained for single tasks, limiting their flexibility in real-world applications. We propose an online learning framework that enables the transfer of neural-to-movement (knowledge) across tasks, supporting both task switching and memory retention. In our framework, neural activity is projected into a dynamical jPCA space to effectively dissociate into variant and invariant components. The variant components of the neural patterns are then aligned by deriving Gradient-based Kullback-Leibler Divergence Minimization (GKLD) for efficient online adaptation. A kernel reinforcement learning (KRL) model then decodes aligned neural signals while reusing prior neural-to-movement mapping. Evaluated on rats switching between a one-lever pressing and a two-lever discrimination task, the framework shows rapid convergence, over four times faster than the baseline method, and improves decoding accuracy by around 35% during task switching. Furthermore, when switching back to the original task, the framework successfully retains knowledge from the old task. Our method demonstrates general applicability to multiple task switching scenarios and maintains stable decoding across three representative days over a 21-day period, highlighting its potential for long-term, real-world use.},
}
@article {pmid40890094,
year = {2025},
author = {Du, Z and Chu, C and Shi, W and Luo, N and Lu, Y and Wang, H and Zhao, B and Xiong, H and Yang, Z and Jiang, T},
title = {Connectome-constrained ligand-receptor interaction analysis for understanding brain network communication.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {8179},
pmid = {40890094},
issn = {2041-1723},
support = {62403465//National Natural Science Foundation of China (National Science Foundation of China)/ ; GZC20232999//China Postdoctoral Science Foundation/ ; 2024M753502//China Postdoctoral Science Foundation/ ; },
mesh = {*Connectome/methods ; *Brain/physiology/diagnostic imaging/metabolism ; Humans ; Algorithms ; Ligands ; *Nerve Net/physiology/diagnostic imaging ; Diffusion Magnetic Resonance Imaging ; },
abstract = {Both diffusion magnetic resonance imaging and transcriptomic technologies have provided unprecedented opportunities to dissect brain network communication, offering insights from structural connectivity and signaling molecules separately. However, incorporating these complementary modalities for characterizing the interregional communication remains challenging. By simplifying the communication processes into an optimal transport problem, which is defined as the ligand-receptor expression coupling constrained by structurally-derived communication cost, we develop a method called CLRIA (connectome-constrained ligand-receptor interaction analysis) to infer a low-rank representation of ligand-receptor interaction-mediated communication networks. To solve the proposed optimization problem, the block majorization minimization algorithm is adopted and proven to converge globally. We benchmark CLRIA on simulated and published data, validating its accuracy and computational efficiency. Subsequently, we demonstrate the CLRIA's utility in evaluating communication strategies and asymmetric communication using its solution. Furthermore, CLRIA-derived communication patterns successfully decode brain state transitions. Overall, our results highlight CLRIA as a valuable tool for understanding complex communication in the brain.},
}
@article {pmid40887906,
year = {2025},
author = {Zhao, Y and Sun, R and Wang, Z and Ma, S and Wang, R and Li, F and Geng, H},
title = {Engineered Hydrogels as Functional Components in Controllable Neuromodulation for Translational Therapeutics.},
journal = {ACS applied bio materials},
volume = {8},
number = {9},
pages = {7587-7615},
doi = {10.1021/acsabm.5c01269},
pmid = {40887906},
issn = {2576-6422},
mesh = {*Hydrogels/chemistry/pharmacology ; Humans ; *Biocompatible Materials/chemistry/pharmacology/chemical synthesis ; Animals ; Tissue Engineering ; Materials Testing ; },
abstract = {Controllable neuromodulation leveraging multimodal triggers synergized with hydrogels represents a transformative therapeutic strategy for pro-regenerative neural repair. Strategic incorporation of programmable neuromodulatory interventions and engineered hydrogels within localized neural niches is critical for clinical translation, characterized by lower invasiveness and greater therapeutic efficacy. This review elucidates the physiochemical features of hydrogels, systematically classifying hydrogel-based neuromodulation into five distinct modes (electrical, ionic, biomechanical, optical, and biochemical) and highlighting the intrinsic multidimensional structural and chemical engineering employed to enhance neuromodulatory performance. Key principles of hydrogel design and fabrication are provided from the perspective of tissue-implant interactions, such as mechanical compatibility, electrointegration, adhesion, and wireless activation. Hydrogels embedded with low-impedance organic and inorganic components, such as conductive polymers and noble metals, are investigated to provide high-level evidence to enable precise cellular stimulation for intrinsic nerve repair, neural prosthesis, and brain-machine interface. Crucially, this review highlights the synergistic integration of these principles into multimodal, closed-loop systems, which combine functions like electrophysiological sensing with on-demand biochemical release for intelligent, autonomous therapies. Finally, this review confronts the critical challenges for clinical translation and discusses future directions, including the potential of artificial intelligence-driven materials design to accelerate the development of next-generation neural interfaces.},
}
@article {pmid40887182,
year = {2025},
author = {Li, S and Fu, Y and Zhang, Y and Lu, G},
title = {[Research on fatigue recognition based on graph convolutional neural network and electroencephalogram signals].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {4},
pages = {686-692},
pmid = {40887182},
issn = {1001-5515},
mesh = {Humans ; *Electroencephalography/methods ; *Neural Networks, Computer ; *Fatigue/diagnosis/physiopathology ; *Automobile Driving ; Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Convolutional Neural Networks ; },
abstract = {Electroencephalogram (EEG) serves as an effective indicator of detecting fatigue driving. Utilizing the open accessible Shanghai Jiao Tong University Emotion Electroencephalography Dataset (SEED-VIG), driving states are divided into three categories including awake, tired and drowsy for investigation. Given the characteristics of mutual influence and interdependence among EEG channels, as well as the consistency of the graph convolutional neural network (GCNN) structure, we designed an adjacency matrix based on the Pearson correlation coefficients of EEG signals among channels and their positional relationships. Subsequently, we developed a GCNN for recognition. The experimental results show that the average classification accuracy of driving state categories for 20 subjects, from the SEED-VIG dataset under the smooth feature of differential entropy (DE) linear dynamic system is 91.66%. Moreover, the highest classification accuracy can reach 98.87%, and the average Kappa coefficient is 0.83. This work demonstrates the reliability of this method and provides a guideline for the research field of safe driving brain computer interface.},
}
@article {pmid40887181,
year = {2025},
author = {Xiao, N and Li, M},
title = {[Motor imagery classification based on dynamic multi-scale convolution and multi-head temporal attention].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {4},
pages = {678-685},
pmid = {40887181},
issn = {1001-5515},
mesh = {*Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; *Attention ; Signal Processing, Computer-Assisted ; *Imagination/physiology ; Algorithms ; },
abstract = {Convolutional neural networks (CNNs) are renowned for their excellent representation learning capabilities and have become a mainstream model for motor imagery based electroencephalogram (MI-EEG) signal classification. However, MI-EEG exhibits strong inter-individual variability, which may lead to a decline in classification performance. To address this issue, this paper proposes a classification model based on dynamic multi-scale CNN and multi-head temporal attention (DMSCMHTA). The model first applies multi-band filtering to the raw MI-EEG signals and inputs the results into the feature extraction module. Then, it uses a dynamic multi-scale CNN to capture temporal features while adjusting attention weights, followed by spatial convolution to extract spatiotemporal feature sequences. Next, the model further optimizes temporal correlations through time dimensionality reduction and a multi-head attention mechanism to generate more discriminative features. Finally, MI classification is completed under the supervision of cross-entropy loss and center loss. Experiments show that the proposed model achieves average accuracies of 80.32% and 90.81% on BCI Competition IV datasets 2a and 2b, respectively. The results indicate that DMSCMHTA can adaptively extract personalized spatiotemporal features and outperforms current mainstream methods.},
}
@article {pmid40887179,
year = {2025},
author = {Pang, Z and Wang, Y and Dong, Q and Cheng, Z and Li, Z and Zhang, R and Cui, H and Chen, X},
title = {[Research on hybrid brain-computer interface based on imperceptible visual and auditory stimulation responses].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {4},
pages = {660-667},
pmid = {40887179},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Acoustic Stimulation ; *Photic Stimulation ; Electroencephalography ; Evoked Potentials, Auditory/physiology ; Adult ; },
abstract = {In recent years, hybrid brain-computer interfaces (BCIs) have gained significant attention due to their demonstrated advantages in increasing the number of targets and enhancing robustness of the systems. However, Existing studies usually construct BCI systems using intense auditory stimulation and strong central visual stimulation, which lead to poor user experience and indicate a need for improving system comfort. Studies have proved that the use of peripheral visual stimulation and lower intensity of auditory stimulation can effectively boost the user's comfort. Therefore, this study used high-frequency peripheral visual stimulation and 40-dB weak auditory stimulation to elicit steady-state visual evoked potential (SSVEP) and auditory steady-state response (ASSR) signals, building a high-comfort hybrid BCI based on weak audio-visual evoked responses. This system coded 40 targets via 20 high-frequency visual stimulation frequencies and two auditory stimulation frequencies, improving the coding efficiency of BCI systems. Results showed that the hybrid system's averaged classification accuracy was (78.00 ± 12.18) %, and the information transfer rate (ITR) could reached 27.47 bits/min. This study offers new ideas for the design of hybrid BCI paradigm based on imperceptible stimulation.},
}
@article {pmid40887178,
year = {2025},
author = {Fu, Y and Lu, H},
title = {[Technical maturity and bubble risks of brain-computer interface (BCI): Considerations from research to industrial translation].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {4},
pages = {651-659},
pmid = {40887178},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; Humans ; Evoked Potentials, Visual ; Electroencephalography ; Event-Related Potentials, P300 ; },
abstract = {Brain-computer interface (BCI) technology faces structural risks due to a misalignment between its technological maturity and industrialization expectations. This study used the Technology Readiness Level (TRL) framework to assess the status of major BCI paradigms-such as steady-state visual evoked potential (SSVEP), motor imagery, and P300-and found that they predominantly remained at TRL4 to TRL6, with few stable applications reaching TRL9. The analysis identified four interrelated sources of bubble risk: overly broad definitions of BCI, excessive focus on decoding performance, asynchronous translational progress, and imprecise terminology usage. These distortions have contributed to the misallocation of research resources and public misunderstanding. To foster the sustainable development of BCI, this paper advocated the establishment of a standardized TRL evaluation system, clearer terminological boundaries, stronger support for fundamental research, enhanced ethical oversight, and the implementation of inclusive and diversified governance mechanisms.},
}
@article {pmid40886590,
year = {2025},
author = {Zhu, S and Cao, T and He, Q and Wang, N and Jia, Y and Chai, X and Yang, Y},
title = {Advanced neuroimaging techniques to decipher brain connectivity networks in patients with disorder of consciousness: a narrative review.},
journal = {NeuroImage. Clinical},
volume = {48},
number = {},
pages = {103864},
pmid = {40886590},
issn = {2213-1582},
abstract = {Advanced neuroimaging techniques have revolutionized our ability to decode brain networks in patients with disorders of consciousness (DoC), offering unprecedented insights into the structural and functional underpinnings of consciousness impairment. This review systematically examines and summarizes the clinical applications of modern neuroimaging methodologies-specifically functional MRI and diffusion MRI- for DoC patients from three key perspectives: (1) pathogenic mechanism and theory evolution, (2) accurate diagnosis and prognosis assessment, and (3) treatment strategy and efficacy evaluation. By integrating network neuroscience with clinical insights, we highlight the transformative role of neuroimaging in unraveling network-level damage, refining clinical assessments, and guiding therapeutic innovations. We further outline the potential applicational challenges associated with leveraging neuroimaging techniques to advance both scientific research on consciousness networks and clinical practice in DoC management, hoping to better address these complex conditions.},
}
@article {pmid40883792,
year = {2025},
author = {Wan, C and Zhang, Q and Qiu, Y and Zhang, W and Nie, Y and Zeng, S and Wang, J and Shen, X and Yu, C and Wu, X and Zhang, Y and Li, Y},
title = {Effects of dual-task mode brain-computer interface based on motor imagery and virtual reality on balance and attention in patients with stroke: a randomized controlled pilot trial.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {187},
pmid = {40883792},
issn = {1743-0003},
support = {No. JBGS202414//Jiangsu Provincial People's Hospital, Clinical Diagnosis and Treatment Technology Innovation 'Open bidding for selecting the best candidates' Project/ ; No. ST242102//Major sports research projects of Jiangsu Sports Bureau/ ; 2024TGYY51//Ministry of Industry and Information Technology and National Health Commission High-end Equipment Promotion and Application Project/ ; },
mesh = {Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; *Attention/physiology ; *Brain-Computer Interfaces ; Imagery, Psychotherapy/methods ; Imagination/physiology ; Pilot Projects ; *Postural Balance/physiology ; Single-Blind Method ; *Stroke/physiopathology/psychology ; *Stroke Rehabilitation/methods ; *Virtual Reality ; },
abstract = {BACKGROUND: Brain-computer interface (BCI) has been shown to be beneficial in improving lower limb motility in stroke, but their effectiveness on balance and attention is unclear. In addition, current BCIs are mostly in single-task mode. The BCI system used in this study was based on a dual-task model of motor imagery (MI) and virtual reality (VR). Previous studies have demonstrated that dual-task seems to be beneficial for balance and attention. The purpose of this study was to validate the effects of MI-VR-based dual-task BCI on balance and attention in participants with stroke.<br><br>METHODS: This pilot, single-blind, randomized controlled trial involved 38 stroke participants, randomized to the BCI (BCI pedaling training) or control group (conventional pedaling). Both groups trained 20&#xa0;min daily, 5&#xa0;days a week for 4&#xa0;weeks, alongside conventional rehabilitation. Thirty participants completed the program (mean age: 56.56&#xa0;years, mean disease duration: 4.48&#xa0;months). Assessments were made before and after 4&#xa0;weeks. The primary outcome was the Berg Balance Scale (BBS), and secondary outcomes included the Timed Up and Go Test (TUGT), Fugl-Meyer Lower Extremity Assessment (FMA-LE), Symbol Digit Modalities Test (SDMT), and average attention index.<br><br>RESULTS: 30 participants completed the study (14 in the BCI and 16 in the control group). The retention rates were 73.68% and 84.21% respectively. No adverse events were reported in this study and participants did not report any discomfort. The changes in BBS, TUGT and SDMT values in the BCI group were significantly better than those in the control group (P&#x2009;<&#x2009;0.05). Average attention index of the BCI group's participants grew with the number of training sessions, and there was a significant difference comparing pre- to post-treatment (p&#x2009;<&#x2009;0.05). The value of BBS change is linearly correlated with the value of SDMT change (F&#x2009;=&#x2009;8.778, y&#x2009;=&#x2009;0.59x&#x2009;+&#x2009;1.90, P&#x2009;<&#x2009;0.001).<br><br>CONCLUSIONS: This study initially showed positive effects of dual-task mode of BCI pedalling training on balance and attention in stroke participants. However, given the preliminary nature of this study and its limitations, the results need to be treated with caution. Trial registration Chinese Clinical Trial Registry Identifier: ChiCTR2300071522. Registered on 2023/05/17.},
}
@article {pmid40883351,
year = {2025},
author = {Metwalli, D and Kiroles, AE and Radwan, YA and Mohamed, EA and Barakat, M and Ahmed, A and Omar, AM and Selim, S},
title = {ArEEG: an Open-Access Arabic Inner Speech EEG Dataset.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {1513},
pmid = {40883351},
issn = {2052-4463},
mesh = {*Electroencephalography ; Humans ; *Brain-Computer Interfaces ; *Speech ; Language ; },
abstract = {Recent advancements in Brain-Computer Interface (BCI) technology are shifting towards inner speech over motor imagery due to its intuitive nature and broader command spectrum, enhancing interaction with electronic devices. However, the reliance on a large number of electrodes in available datasets complicates the development of cost-effective BCIs. Additionally, the lack of publicly available datasets hinder the development of this technology. To address this, we introduce a new Arabic Inner Speech dataset, featuring five distinct classes, exceeding the typical four-class datasets, and recorded using only eight electrodes, making it an economical solution. Our primary objective is to provide an open-access, multi-class Electroencephalographic (EEG) dataset in Arabic for inner speech, encompassing five commands. This dataset is designed to enhance our understanding of brain activity, facilitate the integration of BCI technologies in Arabic-speaking regions, and serve as a valuable resource for developing and testing real-world BCI applications. Through this contribution, we aim to bridge the gap between language-specific neural data and the field of neurotechnology, fostering innovation and inclusivity in BCI research.},
}
@article {pmid40885826,
year = {2025},
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, DT and Kamdar, F and Williams, ZM and Henderson, JM and Hochberg, LR},
title = {Gesture encoding in human left precentral gyrus neuronal ensembles.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {1315},
pmid = {40885826},
issn = {2399-3642},
support = {U01 NS123101/NS/NINDS NIH HHS/United States ; U01DC017844, R01DC014034//U.S. Department of Health &amp; Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; T32 MH115895/MH/NIMH NIH HHS/United States ; R01 DC014034/DC/NIDCD NIH HHS/United States ; UH2NS095548, U01NS123101//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; UH2 NS095548/NS/NINDS NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; 19CSLOI34780000//American Heart Association (American Heart Association, Inc.)/ ; T32MH115895//U.S. Department of Health &amp; Human Services | NIH | National Institute of Mental Health (NIMH)/ ; },
mesh = {Humans ; *Gestures ; *Motor Cortex/physiology ; Brain-Computer Interfaces ; Male ; Adult ; Female ; *Neurons/physiology ; Hand/physiology ; Middle Aged ; Spinal Cord Injuries/physiopathology ; },
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 {pmid40881516,
year = {2025},
author = {Swarnakar, R},
title = {Brain-Computer Interfaces in Rehabilitation: Implementation Models and Future Perspectives.},
journal = {Cureus},
volume = {17},
number = {7},
pages = {e88873},
pmid = {40881516},
issn = {2168-8184},
abstract = {Brain-computer interfaces (BCIs) represent an emerging advancement in rehabilitation, enabling direct communication between the brain and external devices to aid recovery in individuals with neurological impairments. BCIs can be classified into invasive, semi-invasive, non-invasive, or hybrid types. By interpreting neural signals and converting them into control commands, BCIs can bypass damaged pathways, offering therapeutic potential for conditions such as stroke, spinal cord injury, traumatic brain injury, and neurodegenerative diseases such as amyotrophic lateral sclerosis. BCIs' current applications, such as motor restoration via robotic exoskeletons and functional electrical stimulation, cognitive enhancement through neurofeedback and attention training, and communication tools for individuals with severe physical limitations, are largely being explored within research settings and are not yet part of routine clinical practice. Advances in EEG signal acquisition, machine learning, wearable and wireless systems, and integration with virtual reality are enhancing the clinical utility of BCIs by improving accuracy, adaptability, and usability. However, widespread clinical adoption faces challenges, including signal variability, training complexity, data privacy, and ethical and regulatory issues. Ethical challenges in BCI include issues related to the ownership and misuse of brain data, risks of neural interference, threats to autonomy and personal identity, as well as concerns around data privacy, user consent, emotional manipulation, and accountability in neural interventions. In this context, this editorial has also proposed one model (NEURO model checklist) for BCI implementation in rehabilitation. The future of BCIs in rehabilitation lies in developing personalized, closed-loop, and home-based systems, enabled by interdisciplinary collaboration among clinicians, engineers, neuroscientists, and policymakers. With continued research and ethical implementation, BCIs have the potential to transform neurorehabilitation and greatly enhance patient outcomes and quality of life.},
}
@article {pmid40858626,
year = {2025},
author = {Hu, Y and Liu, Y and Hou, Y and Pan, Y and Gao, X},
title = {Dataset of natural conversations about appearance using fNIRS.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {1486},
pmid = {40858626},
issn = {2052-4463},
mesh = {Humans ; Female ; Young Adult ; Spectroscopy, Near-Infrared ; *Brain/physiology ; *Body Image ; Adult ; },
abstract = {Self-objectification, marked by an overemphasis on how one's appearance is viewed by others, promotes increased body surveillance and dissatisfaction. Natural conversations centered around appearance, such as "fat talk"-where individuals, often women, engage in negative or self-deprecating remarks about their bodies or weight-are commonly used to induce a state of self-objectification. However, there is a notable lack of public datasets on brain signals during fat talk. In this dataset, we collected brain data from 31 female participants (aged 19.55 ± 0.89 years) using a 40-channel portable near-infrared device during fat talk and non-fat talk (topics such as travel and home decoration), primarily covering the frontal and parietal areas. Data analyses of subjective reports and fNIRS data revealed an increase in body surveillance and dissatisfaction, suggesting a significant activation of the self-objectification state. This dataset can be utilized to explore fNIRS data processing during natural interpersonal conversations and to gain insights into emotional and cognitive responses under conditions of self-dysregulation.},
}
@article {pmid40883960,
year = {2026},
author = {Wang, Z and Tan, S and Lu, K and Li, Q and Jiao, B and Li, W and Wu, X and Zhang, L and Zeng, L and Bai, R},
title = {The contributions of aquaporin-4 to water exchange across the blood-brain barrier measured by filter-exchange imaging.},
journal = {Magnetic resonance in medicine},
volume = {95},
number = {1},
pages = {531-544},
doi = {10.1002/mrm.70049},
pmid = {40883960},
issn = {1522-2594},
support = {2024SSYS0019//Key R&amp;D Program of Zhejiang Province/ ; 82172050//National Natural Science Foundation of China/ ; 82222032//National Natural Science Foundation of China/ ; 92359303//National Natural Science Foundation of China/ ; 2022ZD0206000//STI2030-Major Projects of China/ ; },
mesh = {Animals ; *Aquaporin 4/metabolism ; *Blood-Brain Barrier/diagnostic imaging/metabolism ; Rats ; *Water/metabolism ; Male ; Ouabain/pharmacology ; Reproducibility of Results ; Rats, Sprague-Dawley ; Sodium-Potassium-Exchanging ATPase/metabolism/antagonists &amp; inhibitors ; *Magnetic Resonance Imaging/methods ; Endothelial Cells/metabolism ; Thiadiazoles/pharmacology ; Brain/diagnostic imaging/metabolism ; Niacinamide/analogs &amp; derivatives ; },
abstract = {PURPOSE: Water exchange across the blood-brain barrier (WEXBBB) is a promising biomarker for assessing the blood-brain barrier (BBB) integrity. However, the physiological mechanisms governing WEXBBB remain unclear. This study was conducted to investigate the contribution of Na[+]/K[+]-ATPase (NKA) on the luminal side of endothelial cells and aquaporin-4 (AQP4) to WEXBBB.<br><br>METHODS: WEXBBB was measured using filter-exchange imaging for BBB assessment (FEXI-BBB) on rats, and data were fitted using an adapted two-compartment crusher-compensated exchange rate (CCXR) model. Test-retest reliability of the vascular water efflux rate constant (kbo) was assessed. Ouabain and 2-(nicotinamide)-1,3,4-thiadiazole (TGN-020) were administered to inhibit NKA on the luminal side of endothelial cells and AQP4, respectively, to investigate their roles in WEXBBB measured by FEXI-BBB.<br><br>RESULTS: Fixing intravascular diffusivity in the two-compartment CCXR model significantly improved estimation accuracy and precision of kbo and other parameters. The test-retest experiment demonstrated that this method had good reproducibility in measuring kbo (intraclass correlation coefficient&#x2009;=&#x2009;0.79). Administering TGN-020, which inhibits AQP4, significantly decreased kbo by 32% (kbo&#x2009;=&#x2009;3.07&#x2009;&#xb1;&#x2009;0.81&#x2009;s[-1] vs. 2.09&#x2009;&#xb1;&#x2009;1.10&#x2009;s[-1], p&#x2009;<&#x2009;0.05). However, the ouabain-treated group showed no significant change in kbo compared with that of the control group (2.51&#x2009;&#xb1;&#x2009;0.58&#x2009;s[-1] vs. 2.37&#x2009;&#xb1;&#x2009;1.02&#x2009;s[-1], p&#x2009;=&#x2009;0.73) in the NKA inhibition experiment.<br><br>CONCLUSIONS: WEXBBB decreased by 32% after administering TGN-020, but no downward trend was noted after administering ouabain. Our findings indicate that AQP4 expression/function, but not NKA activity on the luminal side of endothelial cells, plays a significant role in regulating WEXBBB.},
}
@article {pmid40872076,
year = {2025},
author = {Zhang, M and Qian, B and Gao, J and Zhao, S and Cui, Y and Luo, Z and Shi, K and Yin, E},
title = {Recent Advances in Portable Dry Electrode EEG: Architecture and Applications in Brain-Computer Interfaces.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {16},
pages = {},
pmid = {40872076},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/instrumentation/methods ; Humans ; Electrodes ; *Brain/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; },
abstract = {As brain-computer interface (BCI) technology continues to advance, research on human brain function has gradually transitioned from theoretical investigation to practical engineering applications. To support EEG signal acquisition in a variety of real-world scenarios, BCI electrode systems must demonstrate a balanced combination of electrical performance, wearing comfort, and portability. Dry electrodes have emerged as a promising alternative for EEG acquisition due to their ability to operate without conductive gel or complex skin preparation. This paper reviews the latest progress in dry electrode EEG systems, summarizing key achievements in hardware design with a focus on structural innovation and material development. It also examines application advances in several representative BCI domains, including emotion recognition, fatigue and drowsiness detection, motor imagery, and steady-state visual evoked potentials, while analyzing system-level performance. Finally, the paper critically assesses existing challenges and identifies critical future research priorities. Key recommendations include developing a standardized evaluation framework to bolster research reliability, enhancing generalization performance, and fostering coordinated hardware-algorithm optimization. These steps are crucial for advancing the practical implementation of these technologies across diverse scenarios. With this survey, we aim to offer a comprehensive reference and roadmap for researchers engaged in the development and implementation of next-generation dry electrode EEG-based BCI systems.},
}
@article {pmid40872049,
year = {2025},
author = {Khuntia, PK and Bhide, PS and Manivannan, PV},
title = {Preliminary Analysis and Proof-of-Concept Validation of a Neuronally Controlled Visual Assistive Device Integrating Computer Vision with EEG-Based Binary Control.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {16},
pages = {},
pmid = {40872049},
issn = {1424-8220},
support = {SB22230362MEPMRF000758//Prime Minister's Research Fellowship by The Government of India/ ; },
mesh = {*Electroencephalography/methods ; Humans ; Algorithms ; *Self-Help Devices ; Signal Processing, Computer-Assisted ; Robotics ; },
abstract = {Contemporary visual assistive devices often lack immersive user experience due to passive control systems. This study introduces a neuronally controlled visual assistive device (NCVAD) that aims to assist visually impaired users in performing reach tasks with active, intuitive control. The developed NCVAD integrates computer vision, electroencephalogram (EEG) signal processing, and robotic manipulation to facilitate object detection, selection, and assistive guidance. The monocular vision-based subsystem implements the YOLOv8n algorithm to detect objects of daily use. Then, audio prompting conveys the detected objects' information to the user, who selects their targeted object using a voluntary trigger decoded through real-time EEG classification. The target's physical coordinates are extracted using ArUco markers, and a gradient descent-based path optimization algorithm (POA) guides a 3-DoF robotic arm to reach the target. The classification algorithm achieves over 85% precision and recall in decoding EEG data, even with coexisting physiological artifacts. Similarly, the POA achieves approximately 650 ms of actuation time with a 0.001 learning rate and 0.1 cm[2] error threshold settings. In conclusion, the study also validates the preliminary analysis results on a working physical model and benchmarks the robotic arm's performance against human users, establishing the proof-of-concept for future assistive technologies integrating EEG and computer vision paradigms.},
}
@article {pmid40871906,
year = {2025},
author = {Isaev, M and Bobrov, P and Mokienko, O and Fedotova, I and Lyukmanov, R and Ikonnikova, E and Cherkasova, A and Suponeva, N and Piradov, M and Ustinova, K},
title = {Hemodynamic Response Asymmetry During Motor Imagery in Stroke Patients: A Novel NIRS-BCI Assessment Approach.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {16},
pages = {},
pmid = {40871906},
issn = {1424-8220},
support = {No: 1021062411635-8-3.1.4 and Registration No: 122041800162-9//Ministry of Science and Higher Education of the Russian Federation/ ; },
mesh = {Humans ; Spectroscopy, Near-Infrared/methods ; *Brain-Computer Interfaces ; Male ; Female ; Middle Aged ; *Stroke/physiopathology ; Stroke Rehabilitation/methods ; *Hemodynamics/physiology ; Aged ; Hemoglobins/metabolism ; Adult ; },
abstract = {Understanding patterns of interhemispheric asymmetry is crucial for monitoring neuroplastic changes during post-stroke motor rehabilitation. However, conventional laterality indices often pose computational challenges when applied to functional near-infrared spectroscopy (fNIRS) data due to the bidirectional hemodynamic responses. In this study, we analyze fNIRS recordings from 15 post-stroke patients undergoing motor imagery brain-computer interface training across multiple sessions. We compare traditional laterality coefficients with a novel task response asymmetry coefficient (TRAC), which quantifies differential hemispheric involvement during motor imagery tasks. Both indices are calculated for oxygenated and deoxygenated hemoglobin responses using general linear model coefficients, and their day-to-day dynamics are assessed with linear regression. The proposed TRAC demonstrates greater sensitivity than conventional measures, revealing significantly higher oxygenated hemoglobin TRAC values (0.18 ± 0.19 vs. -0.05 ± 0.20, p < 0.05) and lower deoxygenated hemoglobin TRAC values (-0.15 ± 0.27 vs. 0.04 ± 0.23, p < 0.05) in lesioned compared to intact hemispheres. Among patients who exhibit substantial motor recovery, distinct daily TRAC dynamics were observed, with statistically significant temporal trends. Overall, the novel TRAC metric offers enhanced discrimination of interhemispheric asymmetry patterns and captures temporal neuroplastic changes not detected by conventional indices, providing a more sensitive biomarker for tracking rehabilitation progress in post-stroke brain-computer interface applications.},
}
@article {pmid40871892,
year = {2025},
author = {Moreno-Castelblanco, SR and Vélez-Guerrero, MA and Callejas-Cuervo, M},
title = {Artificial Intelligence Approaches for EEG Signal Acquisition and Processing in Lower-Limb Motor Imagery: A Systematic Review.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {16},
pages = {},
pmid = {40871892},
issn = {1424-8220},
support = {SGI 3904//Universidad Pedag&#xf3;gica y Tecnol&#xf3;gica de Colombia/ ; },
mesh = {Humans ; Algorithms ; *Artificial Intelligence ; Brain-Computer Interfaces ; *Electroencephalography/methods ; *Imagination/physiology ; *Lower Extremity/physiology ; Movement/physiology ; *Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: Motor imagery (MI) is defined as the cognitive ability to simulate motor movements while suppressing muscular activity. The electroencephalographic (EEG) signals associated with lower limb MI have become essential in brain-computer interface (BCI) research aimed at assisting individuals with motor disabilities.<br><br>OBJECTIVE: This systematic review aims to evaluate methodologies for acquiring and processing EEG signals within brain-computer interface (BCI) applications to accurately identify lower limb MI.<br><br>METHODS: A systematic search in Scopus and IEEE Xplore identified 287 records on EEG-based lower-limb MI using artificial intelligence. Following PRISMA guidelines (non-registered), 35 studies met the inclusion criteria after screening and full-text review.<br><br>RESULTS: Among the selected studies, 85% applied machine or deep learning classifiers such as SVM, CNN, and LSTM, while 65% incorporated multimodal fusion strategies, and 50% implemented decomposition algorithms. These methods improved classification accuracy, signal interpretability, and real-time application potential. Nonetheless, methodological variability and a lack of standardization persist across studies, posing barriers to clinical implementation.<br><br>CONCLUSIONS: AI-based EEG analysis effectively decodes lower-limb motor imagery. Future efforts should focus on harmonizing methods, standardizing datasets, and developing portable systems to improve neurorehabilitation outcomes. This review provides a foundation for advancing MI-based BCIs.},
}
@article {pmid40871810,
year = {2025},
author = {Alahaideb, L and Al-Nafjan, A and Aljumah, H and Aldayel, M},
title = {Brain-Computer Interface for EEG-Based Authentication: Advancements and Practical Implications.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {16},
pages = {},
pmid = {40871810},
issn = {1424-8220},
support = {(13461-imamu-2023-IMIU-R-3-1-HW-).//The Research, Development, and Innovation Authority (RDIA) - Kingdom of Saudi Arabia/ ; },
mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; Support Vector Machine ; Algorithms ; Neural Networks, Computer ; Computer Security ; },
abstract = {Authentication is a critical component of digital security, and traditional methods often encounter significant vulnerabilities and limitations. This study addresses the emerging field of EEG-based authentication systems, highlighting their theoretical advancements and practical applicability. We conducted a systematic review of the existing literature, followed by an experimental evaluation to assess the feasibility, limitations, and scalability of these systems in real-world scenarios. Data were collected from nine subjects using various approaches. Our results indicate that the CNN model achieved the highest accuracy of 99%, while Random Forest (RF) and Gradient Boosting (GB) classifiers also demonstrated strong performance with 94% and 93%, respectively. In contrast, classifiers such as Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) displayed significantly lower effectiveness, underscoring their limitations in capturing the complexities of EEG data. The findings suggest that EEG-based authentication systems have significant potential to enhance security measures, offering a promising alternative to traditional methods and paving the way for more robust and user-friendly authentication solutions.},
}
@article {pmid40868398,
year = {2025},
author = {Kumar, R and Sporn, K and Kaur, H and Khanna, A and Paladugu, P and Zaman, N and Tavakkoli, A},
title = {Current Mechanobiological Pathways and Therapies Driving Spinal Health.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {12},
number = {8},
pages = {},
pmid = {40868398},
issn = {2306-5354},
abstract = {Spinal health depends on the dynamic interplay between mechanical forces, biochemical signaling, and cellular behavior. This review explores how key molecular pathways, including integrin, yeas-associated protein (YAP) and transcriptional coactivator with PDZ-binding motif (TAZ), Piezo, and Wingless/Integrated (Wnt) with β-catenin, actively shape the structural and functional integrity of spinal tissues. These signaling mechanisms respond to physical cues and interact with inflammatory mediators such as interleukin-1 beta (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor alpha (TNF-α), driving changes that lead to disc degeneration, vertebral fractures, spinal cord injury, and ligament failure. New research is emerging that shows scaffold designs that can directly harness these pathways. Further, new stem cell-based therapies have been shown to promote disc regeneration through targeted differentiation and paracrine signaling. Interestingly, many novel bone and ligament scaffolds are modulating anti-inflammatory signals to enhance tissue repair and integration, as well as prevent scaffold degradation. Neural scaffolds are also arising. These mimic spinal biomechanics and activate Piezo signaling to guide axonal growth and restore motor function. Scientists have begun combining these biological platforms with brain-computer interface technology to restore movement and sensory feedback in patients with severe spinal damage. Although this technology is not fully clinically ready, this field is advancing rapidly. As implantable technology can now mimic physiological processes, molecular signaling, biomechanical design, and neurotechnology opens new possibilities for restoring spinal function and improving the quality of life for individuals with spinal disorders.},
}
@article {pmid40868333,
year = {2025},
author = {Tonin, A and Semprini, M and Kiper, P and Mantini, D},
title = {Brain-Computer Interfaces for Stroke Motor Rehabilitation.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {12},
number = {8},
pages = {},
pmid = {40868333},
issn = {2306-5354},
abstract = {Brain-computer interface (BCI) technology holds promise for improving motor rehabilitation in stroke patients. This review explores the immediate and long-term effects of BCI training, shedding light on the potential benefits and challenges. Clinical studies have demonstrated that BCIs yield significant immediate improvements in motor functions following stroke. Patients can engage in BCI training safely, making it a viable option for rehabilitation. Evidence from single-group studies consistently supports the effectiveness of BCIs in enhancing patients' performance. Despite these promising findings, the evidence regarding long-term effects remains less robust. Further studies are needed to determine whether BCI-induced changes are permanent or only last for short durations. While evaluating the outcomes of BCI, one must consider that different BCI training protocols may influence functional recovery. The characteristics of some of the paradigms that we discuss are motor imagery-based BCIs, movement-attempt-based BCIs, and brain-rhythm-based BCIs. Finally, we examine studies suggesting that integrating BCIs with other devices, such as those used for functional electrical stimulation, has the potential to enhance recovery outcomes. We conclude that, while BCIs offer immediate benefits for stroke rehabilitation, addressing long-term effects and optimizing clinical implementation remain critical areas for further investigation.},
}
@article {pmid40867492,
year = {2025},
author = {Guan, S and Meng, F and Wu, C},
title = {Authoritative Filial Piety Rather than Reciprocal Filial Piety Mediated the Relationship Between Parental Support, Career Decision Self-Efficacy, and Discrepancies Between Individual-Set and Parent-Set Career Goals.},
journal = {Behavioral sciences (Basel, Switzerland)},
volume = {15},
number = {8},
pages = {},
pmid = {40867492},
issn = {2076-328X},
support = {2023DSYL022//The Supervisor Guidance Program of Shanghai International Studies University/ ; 22YJC880018//General Project of Humanities and Social Sciences of the Ministry of Education 'Research on the Internationalization Path and Strategy of Vocational Education in China from the Perspective of Regional and Country Analysis'/ ; 23ZD010//Fundamental Research Funds for the Central Universities/ ; 2020EYY004//Shanghai Philosophy and Social Science Planning Youth Project/ ; 20CG40//Shanghai Chenguang Talent Program/ ; 2020114052//The Innovative Research Team of Shanghai International Studies University/ ; 2022KFKT009//Open project of Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior/ ; B202205//Open project of Key Laboratory of Multilingual Education with AI/ ; },
abstract = {Although a wealth of research has examined the predictors influencing the discrepancies between individual-set and parent-set career goals (DBIPCG), investigations grounded in collectivist cultural perspectives remain relatively scarce. Within collectivist societies, filial piety holds profound cultural significance. Drawing on a dual filial piety framework encompassing reciprocal filial piety (RFP) and authoritative filial piety (AFP), this study aims to explore the interconnections among parental support, self-efficacy in career decision-making, dual filial piety orientations, and DBIPCG. The results indicated that parental support was negatively associated with DBIPCG. By contrast, self-efficacy in career decision-making did not predict DBIPCG directly. Instead, self-efficacy indirectly influenced DBIPCG, an effect mediated specifically by AFP rather than RFP, Furthermore, AFP was found to mediate the link between parental support and DBIPCG. These findings underscore the role of parental support in minimizing differences in career goal formation between generations and highlight the potentially adverse implications of AFP in exacerbating such discrepancies.},
}
@article {pmid40867216,
year = {2025},
author = {Zhao, Y and Cao, L and Ji, Y and Wang, B and Wu, W},
title = {Interpretable EEG Emotion Classification via CNN Model and Gradient-Weighted Class Activation Mapping.},
journal = {Brain sciences},
volume = {15},
number = {8},
pages = {},
pmid = {40867216},
issn = {2076-3425},
support = {4244100//Beijing Natural Science Foundation/ ; },
abstract = {Background/Objectives: Electroencephalography (EEG)-based emotion recognition plays an important role in affective computing and brain-computer interface applications. However, existing methods often face the challenge of achieving high classification accuracy while maintaining physiological interpretability. Methods: In this study, we propose a convolutional neural network (CNN) model with a simple architecture for EEG-based emotion classification. The model achieves classification accuracies of 95.21% for low/high arousal, 94.59% for low/high valence, and 93.01% for quaternary classification tasks on the DEAP dataset. To further improve model interpretability and support practical applications, Gradient-weighted Class Activation Mapping (Grad-CAM) is employed to identify the EEG electrode regions that contribute most to the classification results. Results: The visualization reveals that electrodes located in the right prefrontal cortex and left parietal lobe are the most influential, which is consistent with findings from emotional lateralization theory. Conclusions: This provides a physiological basis for optimizing electrode placement in wearable EEG-based emotion recognition systems. The proposed method combines high classification performance with interpretability and provides guidance for the design of efficient and portable affective computing systems.},
}
@article {pmid40867214,
year = {2025},
author = {Han, Q and Sun, Y and Ye, H and Song, Z and Zhao, J and Shi, L and Kuang, Z},
title = {GAH-TNet: A Graph Attention-Based Hierarchical Temporal Network for EEG Motor Imagery Decoding.},
journal = {Brain sciences},
volume = {15},
number = {8},
pages = {},
pmid = {40867214},
issn = {2076-3425},
support = {YDZJ202201ZYTS684//Jilin Province Science and Technology Department/ ; },
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) based on motor imagery (MI) offer promising solutions for motor rehabilitation and communication. However, electroencephalography (EEG) signals are often characterized by low signal-to-noise ratios, strong non-stationarity, and significant inter-subject variability, which pose significant challenges for accurate decoding. Existing methods often struggle to simultaneously model the spatial interactions between EEG channels, the local fine-grained features within signals, and global semantic patterns.<br><br>METHODS: To address this, we propose the graph attention-based hierarchical temporal network (GAH-TNet), which integrates spatial graph attention modeling with hierarchical temporal feature encoding. Specifically, we design the graph attention temporal encoding block (GATE). The graph attention mechanism is used to model spatial dependencies between EEG channels and encode short-term temporal dynamic features. Subsequently, a hierarchical attention-guided deep temporal feature encoding block (HADTE) is introduced, which extracts local fine-grained and global long-term dependency features through two-stage attention and temporal convolution. Finally, a fully connected classifier is used to obtain the classification results. The proposed model is evaluated on two publicly available MI-EEG datasets.<br><br>RESULTS: Our method outperforms multiple existing state-of-the-art methods in classification accuracy. On the BCI IV 2a dataset, the average classification accuracy reaches 86.84%, and on BCI IV 2b, it reaches 89.15%. Ablation experiments validate the complementary roles of GATE and HADTE in modeling. Additionally, the model exhibits good generalization ability across subjects.<br><br>CONCLUSIONS: This framework effectively captures the spatio-temporal dynamic characteristics and topological structure of MI-EEG signals. This hierarchical and interpretable framework provides a new approach for improving decoding performance in EEG motor imagery tasks.},
}
@article {pmid40867208,
year = {2025},
author = {Lian, X and Liu, C and Gao, C and Deng, Z and Guan, W and Gong, Y},
title = {A Multi-Branch Network for Integrating Spatial, Spectral, and Temporal Features in Motor Imagery EEG Classification.},
journal = {Brain sciences},
volume = {15},
number = {8},
pages = {},
pmid = {40867208},
issn = {2076-3425},
support = {62173007//National Natural Science Foundation of China/ ; },
abstract = {Background: Efficient decoding of motor imagery (MI) electroencephalogram (EEG) signals is essential for the precise control and practical deployment of brain-computer interface (BCI) systems. Owing to the complex nonlinear characteristics of EEG signals across spatial, spectral, and temporal dimensions, efficiently extracting multidimensional discriminative features remains a key challenge to improving MI-EEG decoding performance. Methods: To address the challenge of capturing complex spatial, spectral, and temporal features in MI-EEG signals, this study proposes a multi-branch deep neural network, which jointly models these dimensions to enhance classification performance. The network takes as inputs both a three-dimensional power spectral density tensor and two-dimensional time-domain EEG signals and incorporates four complementary feature extraction branches to capture spatial, spectral, spatial-spectral joint, and temporal dynamic features, thereby enabling unified multidimensional modeling. The model was comprehensively evaluated on two widely used public MI-EEG datasets: EEG Motor Movement/Imagery Database (EEGMMIDB) and BCI Competition IV Dataset 2a (BCIIV2A). To further assess interpretability, gradient-weighted class activation mapping (Grad-CAM) was employed to visualize the spatial and spectral features prioritized by the model. Results: On the EEGMMIDB dataset, it achieved an average classification accuracy of 86.34% and a kappa coefficient of 0.829 in the five-class task. On the BCIIV2A dataset, it reached an accuracy of 83.43% and a kappa coefficient of 0.779 in the four-class task. Conclusions: These results demonstrate that the network outperforms existing state-of-the-art methods in classification performance. Furthermore, Grad-CAM visualizations identified the key spatial channels and frequency bands attended to by the model, supporting its neurophysiological interpretability.},
}
@article {pmid40867186,
year = {2025},
author = {Vanutelli, ME and Banzi, A and Cicirello, M and Folgieri, R and Lucchiari, C},
title = {Predicting State Anxiety Level Change Using EEG Parameters: A Pilot Study in Two Museum Settings.},
journal = {Brain sciences},
volume = {15},
number = {8},
pages = {},
pmid = {40867186},
issn = {2076-3425},
abstract = {Background: Museums are increasingly being recognized not only as cultural institutions but also as potential resources for enhancing psychological well-being. Prior research has shown that museum visits can reduce stress and anxiety, yet there is a pressing need for evidence-based interventions supported by neurophysiological data. While neuroscientific studies suggest a combined role of emotional and cognitive mechanisms in aesthetic experiences, less is known about the neural predictors of individual responsiveness to such interventions. Methods: This study was conducted in two Milan-based museums and included an initial profiling phase (sociodemographic information, trait anxiety, perceived stress, museum experience), followed by pre- and post-visit assessments of state anxiety and mood. Electrocortical activity was recorded via a portable brain-computer interface (BCI), focusing on the theta/beta ratio (TBR) as a marker of cortical-subcortical integration. Results: Museum visits were associated with significant improvements in mood (M = 1.17; p < 0.001) and reductions in state anxiety (M = -6.36; p < 0.001) in both arts and science museums. The baseline TBR predicted the magnitude of state anxiety change, alongside individual differences in trait anxiety and perceived stress. Conclusions: These findings support the idea that aesthetic experiences in museums engage both emotional and cognitive systems, and that resting state neurophysiological markers can help forecast individual responsiveness to well-being interventions. Such insights not only contribute to existing knowledge about the cognitive and emotional processes during aesthetic fruition, but could also guide future applications of personalized interventions in museum settings, further integrating cultural participation with mental health promotion.},
}
@article {pmid40867148,
year = {2025},
author = {Serna, B and Salazar, R and Alonso-Silverio, GA and Baltazar, R and Ventura-Molina, E and Alarcón-Paredes, A},
title = {Fear Detection Using Electroencephalogram and Artificial Intelligence: A Systematic Review.},
journal = {Brain sciences},
volume = {15},
number = {8},
pages = {},
pmid = {40867148},
issn = {2076-3425},
abstract = {Background/Objectives: Fear detection through EEG signals has gained increasing attention due to its applications in affective computing, mental health monitoring, and intelligent safety systems. This systematic review aimed to identify the most effective methods, algorithms, and configurations reported in the literature for detecting fear from EEG signals using artificial intelligence (AI). Methods: Following the PRISMA 2020 methodology, a structured search was conducted using the string ("fear detection" AND "artificial intelligence" OR "machine learning" AND NOT "fnirs OR mri OR ct OR pet OR image"). After applying inclusion and exclusion criteria, 11 relevant studies were selected. Results: The review examined key methodological aspects such as algorithms (e.g., SVM, CNN, Decision Trees), EEG devices (Emotiv, Biosemi), experimental paradigms (videos, interactive games), dominant brainwave bands (beta, gamma, alpha), and electrode placement. Non-linear models, particularly when combined with immersive stimulation, achieved the highest classification accuracy (up to 92%). Beta and gamma frequencies were consistently associated with fear states, while frontotemporal electrode positioning and proprietary datasets further enhanced model performance. Conclusions: EEG-based fear detection using AI demonstrates high potential and rapid growth, offering significant interdisciplinary applications in healthcare, safety systems, and affective computing.},
}
@article {pmid40867138,
year = {2025},
author = {Chen, X and Bao, X and Jitian, K and Li, R and Zhu, L and Kong, W},
title = {Hybrid EEG Feature Learning Method for Cross-Session Human Mental Attention State Classification.},
journal = {Brain sciences},
volume = {15},
number = {8},
pages = {},
pmid = {40867138},
issn = {2076-3425},
support = {62301196//National Science Foundation of China/ ; 2025C04001//"Pioneer" and "Leading &#xb7;Goose" R&amp;D &#xb7;Program of Zhejiang/ ; LQ24F020035//Zhejiang Provincial Natural Science Foundation of China/ ; },
abstract = {BACKGROUND: Decoding mental attention states from electroencephalogram (EEG) signals is crucial for numerous applications such as cognitive monitoring, adaptive human-computer interaction, and brain-computer interfaces (BCIs). However, conventional EEG-based approaches often focus on channel-wise processing and are limited to intra-session or subject-specific scenarios, lacking robustness in cross-session or inter-subject conditions.<br><br>METHODS: In this study, we propose a hybrid feature learning framework for robust classification of mental attention states, including focused, unfocused, and drowsy conditions, across both sessions and individuals. Our method integrates preprocessing, feature extraction, feature selection, and classification in a unified pipeline. We extract channel-wise spectral features using short-time Fourier transform (STFT) and further incorporate both functional and structural connectivity features to capture inter-regional interactions in the brain. A two-stage feature selection strategy, combining correlation-based filtering and random forest ranking, is adopted to enhance feature relevance and reduce dimensionality. Support vector machine (SVM) is employed for final classification due to its efficiency and generalization capability.<br><br>RESULTS: Experimental results on two cross-session and inter-subject EEG datasets demonstrate that our approach achieves classification accuracy of 86.27% and 94.01%, respectively, significantly outperforming traditional methods.<br><br>CONCLUSIONS: These findings suggest that integrating connectivity-aware features with spectral analysis can enhance the generalizability of attention decoding models. The proposed framework provides a promising foundation for the development of practical EEG-based systems for continuous mental state monitoring and adaptive BCIs in real-world environments.},
}
@article {pmid40859358,
year = {2025},
author = {He, J and Yuan, Z and Quan, L and Xi, H and Guo, J and Zhu, D and Chen, M and Yang, B and Cui, Z and Zhu, S and Qiao, J},
title = {Multimodal assessment of a BCI system for stroke rehabilitation integrating motor imagery and motor attempts: a randomized controlled trial.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {185},
pmid = {40859358},
issn = {1743-0003},
support = {2022SF-379//Key R &amp; D Program of Shanxi Province/ ; 2022SF-379//Key R &amp; D Program of Shanxi Province/ ; QYJC05//Xi 'an Jiaotong University Medical Engineering Interdisciplinary Program/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Stroke Rehabilitation/methods ; Male ; Female ; Middle Aged ; Double-Blind Method ; Electroencephalography ; Aged ; Electromyography ; *Imagination/physiology ; Spectroscopy, Near-Infrared ; Recovery of Function/physiology ; Adult ; },
abstract = {BACKGROUND: Brain-computer interface (BCI) technology based on motor imagery (MI) or motor attempt (MA) has shown promise in enhancing motor function recovery in stroke patients. This study aimed to evaluate the effectiveness of BCI-based rehabilitation in improving motor function through multimodal assessment, and to explore the potential neuroplastic changes resulting from this intervention.<br><br>METHODS: We conducted a randomized double-blind controlled clinical trial with multimodal assessment to evaluate the efficacy of a BCI system for enhancing motor recovery. A total of 48 ischemic stroke patients completed the study (25 BCI, 23 control). The BCI group used an 8-electrode electroencephalogram (EEG) system, a virtual reality training module, and a rehabilitation training robot for real-time motor intention-based feedback. The control group used identical BCI devices but without displaying real-time data and feedback. Participants underwent 20-minute upper and lower limb training sessions for two weeks. Motor function (Fugl-Meyer Extremity scale), electromyography (EMG), and functional near-infrared spectroscopy (fNIRS) were assessed pre- and post-intervention.<br><br>RESULTS: The BCI group demonstrated significantly greater improvement in upper extremity motor function compared to the control group (&#x394;FMA-UE: 4.0 vs. 2.0, p&#x2009;=&#x2009;0.046). EEG results of the BCI group showed a significant decrease in both DAR (p&#x2009;=&#x2009;0.031) and DABR (p&#x2009;<&#x2009;0.001) compared to baseline. EMG analysis revealed that BCI treatment resulted in significant increases in deltoid and bicipital muscle activity during both shoulder and elbow flexion movements compared to baseline (p&#x2009;<&#x2009;0.01). fNIRS results indicated enhanced functional connectivity and activation in key motor-related brain regions, including the prefrontal cortex, supplementary motor area, and primary motor cortex in the BCI group.<br><br>CONCLUSION: BCI-based rehabilitation using an attention-motor dual-task paradigm significantly improved upper limb motor function and enhanced motor and cognitive network activity in stroke patients. Multimodal assessment supports the potential of BCI rehabilitation as an effective tool for leveraging neuroplasticity and promoting motor recovery.},
}
@article {pmid40881023,
year = {2025},
author = {Zhang, S and Lu, Z and Zhang, B and Zhang, Y and Liang, Z and Zhang, L and Li, L and Huang, G and Zhang, Z and Li, Z},
title = {Graph-based feature learning methods for subject-dependent and subject-independent motor imagery EEG decoding.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {139},
pmid = {40881023},
issn = {1871-4080},
abstract = {UNLABELLED: The significant intra-individual variability and inter-individual differences in scalp electroencephalogram (EEG) make it difficult to learn task-distinguishable features, posing a challenge for motor imagery brain-computer interfaces. Current feature learning methods often produce an incomplete feature space, struggling to accommodate these variations and differences. Additionally, the weak discriminative nature of this feature space results in diminished EEG classification performance. This paper introduces novel graph-based feature learning methods to improve motor imagery decoding performance in both subject-dependent and subject-independent contexts. Firstly, construct a complete time-frequency-spatial-graph (TFSG) feature space. The original EEG signals are segmented into multiple time-frequency units using filter banks and sliding time windows. Spatial and brain network-based graph features are then extracted from each time-frequency unit and fused to create the TFSG features. This fused feature space is larger and more inclusive, effectively accommodating both intra- and inter-individual EEG variations. Secondly, learn a discriminative TFSG feature space. Two advanced methods are proposed. The first method employs a nonconvex sparse optimization model with log function regularization, which reduces bias in model estimation, thereby enabling more accurate learning of EEG patterns. The second method incorporates Fisher's criterion regularization into a sparse optimization framework to improve feature separability. A unified algorithmic framework is developed to solve the two new models. Our methods are validated on two motor imagery EEG datasets, achieving the highest average classification accuracies of 82.93, 68.52, and 71.69% for subject-dependent, subject-independent, and subject-adaptive evaluation methods, respectively. Experimental results demonstrate that the developed TFSG features significantly enhance both subject-dependent and subject-independent decoding performance, while the proposed regularization models improve the discriminability of the feature space, leading to further advancements in motor imagery decoding performance.<br><br>SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-025-10291-5.},
}
@article {pmid40880337,
year = {2025},
author = {Kim, J and Kim, SP},
title = {A Plug-and-Play P300-Based BCI With Zero-Training Application.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {3443-3454},
doi = {10.1109/TNSRE.2025.3603979},
pmid = {40880337},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Humans ; *Event-Related Potentials, P300/physiology ; Male ; Electroencephalography ; Adult ; Female ; Young Adult ; Neural Networks, Computer ; Algorithms ; Attention ; Calibration ; Electrodes ; },
abstract = {The practical deployment of P300-based brain-computer interfaces (BCIs) has long been hindered by the need for user-specific calibration and multiple stimulus repetitions. In this study, we build and validate a plug-and-play, zero-training P300 BCI system that operates in a single-trial setting using a pre-trained xDAWN spatial filter and a deep convolutional neural network. Without any subject-specific adaptation, participants could control an IoT device via the BCI system in real time, with decoding accuracy reaching 85.2% comparable to the offline benchmark of 87.8%, demonstrating the feasibility of realizing a plug-and-play BCI. Offline analyses revealed that a small set of parietal and occipital electrodes contributed most to decoding performance, supporting the viability of low-density, high-accuracy BCI configurations. A data sufficiency simulation provided quantitative guidelines for pre-training dataset size, and an error trial analysis showed that both stimulus timing and preparatory attentional state influenced real-time decoding performance. Together, these results demonstrate the real-time validation of a fully pre-trained, zero-training P300 BCI operating on a single-trial basis, without stimulus repetition or user-specific calibration, and offer practical insights for developing scalable, robust, and user-friendly BCI systems.},
}
@article {pmid40880336,
year = {2025},
author = {Li, X and Wang, X and Chen, S and Zhu, W and Jin, R and Peng, W},
title = {Gamma-Band Binaural Beats Neuromodulation Enhances P300 Classification in an Auditory Brain-Computer Interface Paradigm.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {3455-3465},
doi = {10.1109/TNSRE.2025.3604016},
pmid = {40880336},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; *Event-Related Potentials, P300/physiology ; Adult ; Electroencephalography ; Young Adult ; Acoustic Stimulation/methods ; Cross-Over Studies ; *Gamma Rhythm/physiology ; Healthy Volunteers ; Algorithms ; Machine Learning ; Deep Learning ; },
abstract = {While established neuromodulation techniques like transcranial magnetic stimulation and transcranial direct current stimulation have shown potential for enhancing brain-computer interface (BCI) performance, their clinical adoption faces challenges including high implementation costs, technical complexity, and safety concerns. This study investigated binaural beats (BB), a non-invasive auditory neuromodulation method characterized by operational simplicity and minimal adverse effects, as a practical alternative for optimizing auditory P300-BCI. Employing a crossover experimental design, thirty healthy participants underwent gamma-band (40 Hz) and alpha-band (10 Hz) BB stimulation in separate sessions. Auditory oddball paradigm experiments were conducted before and after each BB intervention. Electroencephalogram (EEG) data were decoded using both a machine learning classifier and a deep learning model for P300 classification. Additionally, irregular-resampling auto-spectral analysis (IRASA) was applied to extract aperiodic components from EEG during BB stimulation to evaluate changes in brain state. The results demonstrated frequency-dependent modulation effects: gamma-BB significantly improved P300 classification accuracy while alpha-BB impaired performance. Neurophysiological analysis revealed that gamma-BB decreased the aperiodic exponent, indicating enhanced brain arousal level, whereas alpha-BB produced the opposite pattern. Importantly, the aperiodic parameter change showed a significant association with BCI performance improvement. These findings established gamma-BB as an effective, low-cost neuromodulation strategy for augmenting auditory P300-BCI through brain state modulation.},
}
@article {pmid40880333,
year = {2025},
author = {Xu, M and Zhang, B and Zhang, L and Wang, D and Chen, Y},
title = {A Decade of Rapid Serial Visual Presentation Paradigm in Brain-Computer Interface for Target Detection: Current Status and Trends.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3603945},
pmid = {40880333},
issn = {1558-2531},
abstract = {OBJECTIVE: Electroencephalography (EEG)-based Rapid Serial Visual Presentation (RSVP) has steadily gained attention since 2015 as a paradigm to enhance image target detection in brain-computer interfaces (BCIs) used with healthy individuals.<br><br>METHODS: We reviewed the literature using Scopus and Web of Science as primary databases, covering publications from 2015 to 2024. After literature screening and filtering, a total of 86 papers on RSVP-BCI studies were analyzed over this decadelong period. The research categorizes RSVP into three dimensions: public datasets, paradigm encoding, and decoding methods, while exploring eight mode combinations involving target types, subject groups, and different modalities.<br><br>RESULTS: Our literature search revealed a scarcity of studies addressing diverse target types across different subject groups or modality combinations, indicating a promising direction for future RSVP-BCI development. Future efforts should prioritize inclusivity across all age groups, the design of user-friendly stimulus interfaces, and the development of advanced algorithms, with the goal of creating a more widely accessible RSVP-BCI system.<br><br>CONCLUSION: We have provided a comprehensive review of advances over the past decade in RSVP-based target detection, including datasets, encoding design, and decoding methods and potential applications.<br><br>SIGNIFICANCE: The present work aims to articulate prospective trajectories for the continued advancement of the RSVP community.},
}
@article {pmid40878633,
year = {2025},
author = {Jiang, H and Ren, B and Zhang, Y and Zhou, Y and Wu, J and Yu, X and Yu, H and Ni, P and Xu, Y and Deng, W and Guo, W and Hu, X and Qi, X and Li, T},
title = {Alterations of plasma neural-derived extracellular vesicles microRNAs in patients with bipolar disorder.},
journal = {Psychological medicine},
volume = {55},
number = {},
pages = {e256},
doi = {10.1017/S0033291725000741},
pmid = {40878633},
issn = {1469-8978},
mesh = {Humans ; *Bipolar Disorder/genetics/blood/metabolism ; *Extracellular Vesicles/metabolism ; Female ; Male ; *MicroRNAs/metabolism/blood/genetics ; Adult ; Middle Aged ; Microglia/metabolism ; Case-Control Studies ; Prefrontal Cortex/metabolism ; },
abstract = {BACKGROUND: MicroRNAs (miRNAs) alterations in patients with bipolar disorder (BD) are pivotal to the disease's pathogenesis. Since obtaining brain tissue is challenging, most research has shifted to analyzing miRNAs in peripheral blood. One innovative solution is sequencing miRNAs in plasma extracellular vesicles (EVs), particularly those neural-derived EVs emanating from the brain.<br><br>METHODS: We isolated plasma neural-derived EVs from 85 patients with BD and 39 healthy controls (HC) using biotinylated antibodies targeting a neural tissue marker, followed by miRNA sequencing and expression analysis. Furthermore, we conducted bioinformatic analyses and functional experiments to delve deeper into the underlying pathological mechanisms of BD.<br><br>RESULTS: Out of the 2,656 neural-derived miRNAs in EVs identified, 14 were differentially expressed between BD patients and HC. Moreover, the target genes of miR-143-3p displayed distinct expression patterns in the prefrontal cortex of BD patients versus HC, as sourced from the PsychENCODE database. The functional experiments demonstrated that the abnormal expression of miR-143-3p promoted the proliferation and activation of microglia and upregulated the expression of proinflammatory factors, including IL-1&#x3b2;, IL-6, and NLRP3. Through weighted gene co-expression network analysis, a module linking to the clinical symptoms of BD patients was discerned. Enrichment analyses unveiled these miRNAs' role in modulating the axon guidance, the Ras signaling pathway, and ErbB signaling pathway.<br><br>CONCLUSIONS: Our findings provide the first evidence of dysregulated plasma miRNAs within neural-derived EVs in BD patients and suggest that neural-derived EVs might be involved in the pathophysiology of BD through related biological pathways, such as neurogenesis and neuroinflammation.},
}
@article {pmid40877476,
year = {2025},
author = {Wu, Y and Qian, B and Li, T and Qin, Y and Guan, Z and Chen, T and Jia, Y and Zhang, P and Zeng, D and Moroi, S and Raman, R and Thinggaard, BS and Pedersen, F and Ñehe, JAO and Kamalden, TA and Zhou, Y and Jin, Y and Li, H and Ran, AR and Yang, D and Meng, Z and Peng, Q and Zheng, YF and Wang, D and Ji, H and Zang, P and Yin, C and Shen, J and Chen, Y and Yu, W and Dai, R and Zhang, C and Zhao, X and Wang, X and Chen, Y and Wu, Q and Xie, H and Szeto, SKH and Chan, JYY and Chan, VTT and Xie, HT and Wei, R and Li, J and Ma, W and Zhu, L and Wang, H and Fu, H and Wang, W and Lin, S and Xu, Z and Guan, N and Zhang, X and Grzybowski, A and Gołębiowska-Bogaj, M and Gawęcki, M and Smedowski, A and Szaraniec, W and Wu, Y and Wen, Y and Chen, X and Yao, Y and , and Lim, LL and Cheung, CY and Tan, GSW and Grauslund, J and Ruamviboonsuk, P and Sivaprasad, S and Keane, PA and Wang, YX and Tham, YC and Cheng, CY and Wong, TY and Sheng, B},
title = {An eyecare foundation model for clinical assistance: a randomized controlled trial.},
journal = {Nature medicine},
volume = {31},
number = {10},
pages = {3404-3413},
pmid = {40877476},
issn = {1546-170X},
support = {82388101//National Natural Science Foundation of China (National Science Foundation of China)/ ; IS23096//Natural Science Foundation of Beijing Municipality (Beijing Natural Science Foundation)/ ; },
mesh = {Humans ; Male ; Female ; Middle Aged ; Double-Blind Method ; *Retinal Diseases/diagnosis ; Aged ; China ; Ophthalmologists ; Adult ; },
abstract = {In the context of an increasing need for clinical assessments of foundation models, we developed EyeFM, a multimodal vision-language eyecare copilot, and conducted a multifaceted evaluation, including retrospective validations, multicountry efficacy validation as a clinical copilot and a double-masked randomized controlled trial (RCT). EyeFM was pretrained on 14.5 million ocular images from five imaging modalities paired with clinical texts from global, multiethnic datasets. Efficacy validation invited 44 ophthalmologists across North America, Europe, Asia and Africa in primary and specialty care settings, highlighting its utility as a clinical copilot. The RCT-a parallel, single-center, double-masked study-assessed EyeFM as a clinical copilot in retinal disease screening among a high-risk population in China. A total of 668 participants (mean age 57.5 years, 79.5% male) were randomized to 16 ophthalmologists, equally allocated into intervention (with EyeFM copilot) and control (standard care) groups. The primary endpoint indicated that ophthalmologists with EyeFM copilot achieved higher correct diagnostic rate (92.2% versus 75.4%, P < 0.001) and referral rate (92.2% versus 80.5%, P < 0.001). Secondary outcome indicated improved standardization score of clinical reports (median 33 versus 37, P < 0.001). Participant satisfaction with the screening was similar between groups, whereas the intervention group demonstrated higher compliance with self-management (70.1% versus 49.1%, P < 0.001) and referral suggestions (33.7% versus 20.2%, P < 0.001) at follow-up. Post-deployment evaluations indicated strong user acceptance. Our study provided evidence that implementing EyeFM copilot can improve the performance of ophthalmologists and the outcome of patients. Chinese Clinical Trial Registry registration: ChiCTR2500095518 .},
}
@article {pmid40877466,
year = {2025},
author = {Fan, YS and Xu, Y and Hettwer, MD and Yang, P and Sheng, W and Wang, C and Yang, M and Kirschner, M and Valk, SL and Chen, H},
title = {Neurodevelopmentally rooted epicenters in schizophrenia: sensorimotor-association spatial axis of cortical thickness alterations.},
journal = {Molecular psychiatry},
volume = {},
number = {},
pages = {},
pmid = {40877466},
issn = {1476-5578},
abstract = {Pathological disturbances in schizophrenia have been suggested to propagate via the functional and structural connectome across the lifespan. However, how the connectome guides early cortical reorganization of developing schizophrenia remains unknown. Here, we used early-onset schizophrenia (EOS) as a neurodevelopmental disease model to investigate putative early pathologic origins propagating through the functional and structural connectome. We compared 95 patients with antipsychotic-naïve first-episode EOS and 99 typically developing controls (total n = 194; 120 females; 7-17 years of age). While patients showed widespread cortical thickness reductions, thickness increases were observed in primary cortical areas. Using normative connectomics models, we found that epicenters of thickness reductions were located in association regions linked to language, affective, and cognitive functions, while epicenters of thickness increases in EOS were located in sensorimotor regions subserving visual, somatosensory, and motor functions. Using post-mortem transcriptomic data of six donors, we observed that the epicenter map differentiated oligodendrocyte-related transcriptional changes at its sensory apex, whereas the association end was related to the expression of excitatory/inhibitory neurons. More generally, the epicenter map was associated with dysregulation of neurodevelopmental disorder genes and human accelerated region genes, suggesting potential common genetic determinants across diverse neurodevelopmental conditions. Taken together, our results highlight the developmentally rooted pathological origins of schizophrenia and its transcriptomic overlap with other neurodevelopmental disorders.},
}
@article {pmid40876460,
year = {2025},
author = {Marino, PJ and Bahureksa, L and Fisac, CF and Oby, ER and Smoulder, AL and Motiwala, A and Degenhart, AD and Grigsby, EM and Joiner, WM and Chase, SM and Yu, BM and Batista, AP},
title = {A posture subspace in the primary motor cortex.},
journal = {Neuron},
volume = {113},
number = {21},
pages = {3647-3660.e10},
doi = {10.1016/j.neuron.2025.07.030},
pmid = {40876460},
issn = {1097-4199},
mesh = {*Motor Cortex/physiology ; Animals ; *Posture/physiology ; Macaca mulatta ; Movement/physiology ; *Psychomotor Performance/physiology ; Male ; Neurons/physiology ; Action Potentials/physiology ; Brain Mapping ; },
abstract = {To generate movements, the brain must combine information about movement goal and body posture. The motor cortex (primary motor cortex [M1]) is a key node for the convergence of these information streams. How are posture and goal signals organized within M1's activity to permit the flexible generation of movement commands? To answer this question, we recorded M1 activity while monkeys performed a variety of tasks with the forearm in a range of postures. We found that posture- and goal-related components of neural population activity were separable and resided in nearly orthogonal subspaces. The posture subspace was stable across tasks. Within each task, neural trajectories for each goal had similar shapes across postures. Our results reveal a simpler organization of posture signals in M1 than previously recognized. The compartmentalization of posture and goal signals might allow the two to be flexibly combined in the service of our broad repertoire of actions.},
}
@article {pmid40876238,
year = {2025},
author = {Azati, Y and Wang, X and Ye, X and Zhang, K},
title = {Refining the classification of combined alignment sections on mountainous freeways and analyzing the spatio-temporal effects on crash frequency.},
journal = {Accident; analysis and prevention},
volume = {221},
number = {},
pages = {108222},
doi = {10.1016/j.aap.2025.108222},
pmid = {40876238},
issn = {1879-2057},
mesh = {*Accidents, Traffic/statistics &amp; numerical data ; Humans ; Spatio-Temporal Analysis ; Weather ; *Environment Design ; *Automobile Driving/statistics &amp; numerical data ; Models, Statistical ; Seasons ; },
abstract = {Combined alignment sections of mountainous freeways often feature complex geometric configurations-such as downhill sag/convex curves, slope-changing curves, and uphill curves-that significantly affect crash risk. Existing studies typically apply homogeneous segmentation and broad classifications (e.g., downhill, uphill, sag/convex), which fail to capture the specific effects of geometric combinations on crash frequency. In addition, traffic operations and weather conditions in mountainous areas exhibit strong seasonal variation, and using annual data may obscure important patterns, making quarterly analysis necessary. The interaction of complex geometry, dynamic traffic flow, and adverse winter weather results in nonlinear spatio-temporal effects that conventional models cannot effectively capture. To address this, the study integrates road geometry, traffic operation, and environmental data into a Zero-Inflated Negative Binomial (ZINB) model enhanced with Gaussian processes, systematically analyzing the nonlinear spatio-temporal effects on crash frequency. Results show that the proposed model outperforms spatial- or temporal-only models in prediction accuracy (RMSE = 0.566) and model fit (LOOIC = 5961.2), with the variance of spatio-temporal interaction effects estimated at 1.35 (95 % BCI: 1.12-1.58), indicating substantial nonlinear influence. Key findings include a 56 % increase in crash frequency on straight downhill sag curves, a 2 % reduction on straight uphill convex curves, an 80.3 % increase for every additional 1,000 vehicles in daily traffic flow, and a 28.8 % decrease in crash frequency for each 1 °C rise in temperature. The study presents a refined classification and modeling framework that significantly improves crash risk identification and prediction for mountainous freeways, offering strong support for traffic safety management.},
}
@article {pmid40876195,
year = {2025},
author = {Liu, Z and Hong, Q and Huang, L and Sha, L and Peng, A and Chen, L},
title = {Women with epilepsy during pregnancy: A systematic review of current guidelines.},
journal = {Epilepsy &amp; behavior : E&amp;B},
volume = {171},
number = {},
pages = {110658},
doi = {10.1016/j.yebeh.2025.110658},
pmid = {40876195},
issn = {1525-5069},
mesh = {Humans ; Pregnancy ; Female ; *Epilepsy/therapy/drug therapy ; *Pregnancy Complications/therapy/drug therapy ; Anticonvulsants/therapeutic use ; *Practice Guidelines as Topic ; },
abstract = {OBJECTIVE: To systematically evaluate the quality of existing guidelines for the management of pregnancy in women with epilepsy (WWE) and compare their key recommendations.<br><br>METHODS: A systematic review of available clinical practice guidelines and expert consensus statements was conducted. The quality of the literature was assessed using the Appraisal of Guidelines for Research &amp; Evaluation II (AGREE II) instrument. Core information was extracted using a predefined form and subjected to comparative analysis.<br><br>RESULTS: Only 14 guidelines on WWE pregnancy management have been published worldwide. Most guidelines performed well in scope definition, clarity of purpose, and presentation, but the evidence base was relatively weak. Recommendations were largely consistent across guidelines regarding preconception counseling, folic acid supplementation, vaginal delivery, breastfeeding, and avoidance of valproate. However, discrepancies were observed in the selection of certain antiseizure medications (ASMs), therapeutic drug monitoring, and the timing and dosage of folic acid supplementation. Current guidelines lack recommendations on newer ASMs and antinociceptive management during delivery.<br><br>CONCLUSION: The variability in recommendations among WWE pregnancy management guidelines reflects the insufficiency of the existing evidence base, highlighting the need for enhanced methodological rigor in guideline development and more comprehensive, evidence-based recommendations. Establishing large-scale prospective pregnancy registries is critical for improving WWE pregnancy management guidelines.},
}
@article {pmid40857498,
year = {2025},
author = {Zhao, B and Huggins, JE and Kang, J},
title = {Bayesian Inference on Brain-Computer Interfaces via GLASS.},
journal = {Journal of the American Statistical Association},
volume = {},
number = {},
pages = {},
pmid = {40857498},
issn = {0162-1459},
support = {R01 DA048993/DA/NIDA NIH HHS/United States ; R01 GM124061/GM/NIGMS NIH HHS/United States ; R01 MH105561/MH/NIMH NIH HHS/United States ; R21 HD054697/HD/NICHD NIH HHS/United States ; },
abstract = {Brain-computer interfaces (BCIs), particularly the P300 BCI, facilitate direct communication between the brain and computers. The fundamental statistical problem in P300 BCIs lies in classifying target and non-target stimuli based on electroencephalogram (EEG) signals. However, the low signal-to-noise ratio (SNR) and complex spatial/temporal correlations of EEG signals present challenges in modeling and computation, especially for individuals with severe physical disabilities-BCI's primary users. To address these challenges, we introduce a novel Gaussian Latent channel model with Sparse time-varying effects (GLASS) under a Bayesian framework. GLASS is built upon a constrained multinomial logistic regression particularly designed for the imbalanced target and non-target stimuli. The novel latent channel decomposition efficiently alleviates strong spatial correlations between EEG channels, while the soft-thresholded Gaussian process (STGP) prior ensures sparse and smooth time-varying effects. We demonstrate GLASS substantially improves BCI's performance in participants with amyotrophic lateral sclerosis (ALS) and identifies important EEG channels (PO8, Oz, PO7, and Pz) in parietal and occipital regions that align with existing literature. For broader accessibility, we develop an efficient gradient-based variational inference (GBVI) algorithm for posterior computation and provide a user-friendly Python module available at https://github.com/BangyaoZhao/GLASS.},
}
@article {pmid40875414,
year = {2025},
author = {Korik, A and Du Bois, N and Sanchez Bornot, J and McShane, N and Guger, C and Del Felice, A and Lennon, O and Coyle, D},
title = {Decoding the Variable Velocity of Lower-Limb Stepping Movements From EEG.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {3511-3523},
doi = {10.1109/TNSRE.2025.3603635},
pmid = {40875414},
issn = {1558-0210},
mesh = {Humans ; *Electroencephalography/methods ; Male ; Female ; Adult ; Brain-Computer Interfaces ; Young Adult ; *Lower Extremity/physiology ; Deep Learning ; Neural Networks, Computer ; Movement/physiology ; Linear Models ; Algorithms ; Cues ; Biomechanical Phenomena ; Healthy Volunteers ; Walking/physiology ; Sensorimotor Cortex/physiology ; },
abstract = {Accurate decoding of lower-limb movement from electroencephalography (EEG) is essential for developing brain-computer interface (BCI) controlled exoskeletons in neurorehabilitation. This study investigates 3D velocity decoding at three fibular anatomical markers during overground stepping in healthy participants (${N}
={9}
$), using two approaches: (1) linear regression (LR) and (2) a deep learning (DL) framework combining convolutional neural networks (CNNs) and long short-term memory (LSTM) units. Participants were divided into two groups: G1 (${n}
={5}
$) performed cued forward and self-paced backward steps; G2 (${n}
={4}
$) performed cued forward and backward steps. The DL model significantly outperformed LR, achieving highest decoding accuracy (DA) in the forward-backward direction at the fibular head (R $= 0.63\pm 0.06$ , M±SD). Topographical analysis identified dominant contributions from the sensorimotor cortex (coupled with frontal regions in G2) within the 8-40 Hz band. Functional connectivity (FC) analysis revealed significant differences: only G2 showed statistically significant FC (${p}
\lt {0.05}
$), likely reflecting increased cognitive and sensorimotor demands under dual-cue conditions. In G2, FC occurred across delta (0-4 Hz), theta (4-8 Hz), alpha/mu (8-12 Hz), and low-beta (12-18 Hz) bands, linking motor areas associated with lower- and upper-limb control to other cortical regions, including the middle temporal gyrus (MTG), superior frontal gyrus (SFG), posterior cingulate cortex (PCC), superior parietal lobule (SPL), and supramarginal gyrus (SMG). These findings demonstrate that EEG-based 3D decoding of lower-limb kinematics is feasible during realistic locomotor tasks and suggest that cortical synchronization patterns vary with movement context. Our CNN-LSTM framework may support adaptive, intent-driven exoskeleton development for personalized neurorehabilitation.},
}
@article {pmid40875138,
year = {2025},
author = {Zhou, Q and Song, J and Zhao, Y and Zhang, S and Du, Q and Ke, L},
title = {IF-MMCL: an individual focused network with multi-view and multi-modal contrastive learning for cross-subject emotion recognition.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {},
number = {},
pages = {},
pmid = {40875138},
issn = {1741-0444},
abstract = {Electroencephalography (EEG) usage in emotion recognition has garnered significant interest in brain-computer interface (BCI) research. Nevertheless, in order to develop an effective model for emotion identification, features need to be extracted from EEG data in terms of multi-view. In order to tackle the problems of multi-feature interaction and domain adaptation, we suggest an innovative network, IF-MMCL, which leverages multi-modal data in multi-view representation and integrates an individual focused network. In our approach, we build an individual focused network with multi-view that utilizes individual focused contrastive learning to improve model generalization. The network employs different structures for multi-view feature extraction and uses multi-feature relationship computation to identify the relationships between features from various views and modalities. Our model is validated using four public emotion datasets, each containing various emotion classification tasks. In leave-one-subject-out experiments, IF-MMCL performs better than the previous methods in model generalization with limited data.},
}
@article {pmid40874066,
year = {2025},
author = {Tarara, P and Przybył, I and Schöning, J and Gunia, A},
title = {Motor imagery-based brain-computer interfaces: an exploration of multiclass motor imagery-based control for Emotiv EPOC X.},
journal = {Frontiers in neuroinformatics},
volume = {19},
number = {},
pages = {1625279},
pmid = {40874066},
issn = {1662-5196},
abstract = {INTRODUCTION: Enhancing the command capacity of motor imagery (MI)-based brain-computer interfaces (BCIs) remains a significant challenge in neuroinformatics, especially for real-world assistive applications. This study explores a multiclass BCI system designed to classify multiple MI tasks using a low-cost EEG device.<br><br>METHODS: A BCI system was developed to classify six mental states: resting state, left and right hand movement imagery, tongue movement, and left and right lateral bending, using EEG data collected with the Emotiv EPOC X headset. Seven participants underwent a body awareness training protocol integrating mindfulness and physical exercises to improve MI performance. Machine learning techniques were applied to extract discriminative features from the EEG signals.<br><br>RESULTS: Post-training assessments indicated modest improvements in participants' MI proficiency. However, classification performance was limited due to inter- and intra-subject signal variability and the technical constraints of the consumer-grade EEG hardware.<br><br>DISCUSSION: These findings highlight the value of combining user training with MI-based BCIs and the need to optimize signal quality for reliable performance. The results support the feasibility of scalable, multiclass MI paradigms in low-cost, user-centered neurotechnology applications, while pointing to critical areas for future system enhancement.},
}
@article {pmid40873632,
year = {2025},
author = {Ye, Y and Tian, Y and Liu, H and Liu, J and Zhou, C and Xu, C and Zhou, T and Nie, Y and Wu, Y and Qin, L and Zhou, Z and Wei, X and Zhao, J and Wang, Z and Li, M and Tao, TH and Sun, L},
title = {High-Precision, Low-Threshold Neuromodulation With Ultraflexible Electrode Arrays for Brain-to-Brain Interfaces.},
journal = {Exploration (Beijing, China)},
volume = {5},
number = {4},
pages = {e70040},
pmid = {40873632},
issn = {2766-2098},
abstract = {Neuromodulation is crucial for advancing neuroscience and treating neurological disorders. However, traditional methods using rigid electrodes have been limited by large stimulating currents, low precision, and the risk of tissue damage. In this work, we developed a biocompatible ultraflexible electrode array that allows for both neural recording of spike firings and low-threshold, high-precision stimulation for neuromodulation. Specifically, mouse turning behavior can be effectively induced with approximately five microamperes of stimulating current, which is significantly lower than that required by conventional rigid electrodes. The array's densely packed microelectrodes enable highly selective stimulation, allowing precise targeting of specific brain areas critical for turning behavior. This low-current, targeted stimulation approach helps maintain the health of both neurons and electrodes, as evidenced by stable neural recordings after extended stimulations. Systematic validations have confirmed the durability and biocompatibility of the electrodes. Moreover, we extended the flexible electrode array to a brain-to-brain interface system that allows human brain signals to directly control mouse behavior. Using advanced decoding methods, a single individual can issue eight commands to simultaneously control the behaviors of two mice. This study underscores the effectiveness of the flexible electrode array in neuromodulation, opening new avenues for interspecies communication and potential neuromodulation applications.},
}
@article {pmid40863235,
year = {2025},
author = {Shaw, J and Pyreddy, S and Rosendahl, C and Lai, C and Ton, E and Carter, R},
title = {Current Neuroethical Perspectives on Deep Brain Stimulation and Neuromodulation for Neuropsychiatric Disorders: A Scoping Review of the Past 10 Years.},
journal = {Diseases (Basel, Switzerland)},
volume = {13},
number = {8},
pages = {},
pmid = {40863235},
issn = {2079-9721},
abstract = {BACKGROUND: The use of neuromodulation for the treatment of psychiatric disorders has become increasingly common, but this emerging treatment modality comes with ethical concerns. This scoping review aims to synthesize the neuroethical discourse from the past 10 years on the use of neurotechnologies for psychiatric conditions.<br><br>METHODS: A total of 4496 references were imported from PubMed, Embase, and Scopus. The inclusion criteria required a discussion of the neuroethics of neuromodulation and studies published between 2014 and 2024.<br><br>RESULTS: Of the 77 references, a majority discussed ethical concerns of patient autonomy and informed consent for neuromodulation, with neurotechnologies being increasingly seen as autonomy enablers. Concepts of changes in patient identity and personality, especially after deep brain stimulation, were also discussed extensively. The risks and benefits of neurotechnologies were also compared, with deep brain stimulation being seen as the riskiest but also possessing the highest efficacy. Concerns about equitable access and justice were raised regarding the rise of private transcranial magnetic stimulation clinics and the current experimental status of deep brain stimulation.<br><br>CONCLUSIONS: Neuroethics discourse, particularly for deep brain stimulation, has continued to focus on how post-intervention changes in personality and behavior influence patient identity. Multiple conceptual frameworks have been proposed, though each faces critiques for addressing only parts of this complex phenomenon, prompting calls for pluralistic models. Emerging technologies, especially those involving artificial intelligence through brain computer interfaces, add new dimensions to this debate by raising concerns about neuroprivacy and legal responsibility for actions, further blurring the lines for defining personal identity.},
}
@article {pmid40863131,
year = {2025},
author = {Gao, L and Han, L and Ma, X and Wang, H and Li, M and Cai, J},
title = {An Integrated Analysis of Transcriptomics and Metabolomics Elucidates the Role and Mechanism of TRPV4 in Blunt Cardiac Injury.},
journal = {Metabolites},
volume = {15},
number = {8},
pages = {},
pmid = {40863131},
issn = {2218-1989},
support = {YDZJ202101ZYTS086//jilin province science and technology development plan/ ; },
abstract = {BACKGROUND/OBJECTIVES: Blunt cardiac injury (BCI) is a severe medical condition that may arise as a result of various traumas, including motor vehicle accidents and falls. The main objective of this study was to explore the role and underlying mechanisms of the TRPV4 gene in BCI. Elucidating the function of TRPV4 in BCI may reveal potential novel therapeutic targets for the treatment of this condition.<br><br>METHODS: Rats in each group, including the SD control group (SDCON), the SD blunt-trauma group (SDBT), the TRPV4 gene-knockout control group (KOCON), and the TRPV4 gene-knockout blunt-trauma group (KOBT), were all freely dropped from a fixed height with a weight of 200 g and struck in the left chest with a certain energy, causing BCI. After the experiment, the levels of serum IL-6 and IL-1&#x3b2; were detected to evaluate the inflammatory response. The myocardial tissue structure was observed by HE staining. In addition, cardiac transcriptome analysis was conducted to identify differentially expressed genes, and metabolomics studies were carried out using UHPLC-Q-TOF/MS technology to analyze metabolites. The results of transcriptomics and metabolomics were verified by qRT-PCR and Western blot analysis.<br><br>RESULTS: Compared with the SDCON group, the levels of serum IL-6 and IL-1&#x3b2; in the SDBT group were significantly increased (p < 0.001), while the levels of serum IL-6 and IL-1&#x3b2; in the KOBT group were significantly decreased (p < 0.001), indicating that the deletion of the TRPV4 gene alleviated the inflammation induced by BCI. HE staining showed that myocardial tissue injury was severe in the SDBT group, while myocardial tissue structure abnormalities were mild in the KOBT group. Transcriptome analysis revealed that there were 1045 upregulated genes and 643 downregulated genes in the KOBT group. These genes were enriched in pathways related to inflammation, apoptosis, and tissue repair, such as p53, apoptosis, AMPK, PPAR, and other signaling pathways. Metabolomics studies have found that TRPV4 regulates nucleotide metabolism, amino-acid metabolism, biotin metabolism, arginine and proline metabolism, pentose phosphate pathway, fructose and mannose metabolism, etc., in myocardial tissue. The combined analysis of metabolic and transcriptional data reveals that tryptophan metabolism and the protein digestion and absorption pathway may be the key mechanisms. The qRT-PCR results corroborated the expression of key genes identified in the transcriptome sequencing, while Western blot analysis validated the protein expression levels of pivotal regulators within the p53 and AMPK signaling pathways.<br><br>CONCLUSIONS: Overall, the deletion of the TRPV4 gene effectively alleviates cardiac injury by reducing inflammation and tissue damage. These findings suggest that TRPV4 may become a new therapeutic target for BCI, providing new insights for future therapeutic strategies.},
}
@article {pmid40863003,
year = {2025},
author = {Lee, J and Han, SY and Kwon, YW},
title = {Technological Advances and Medical Applications of Implantable Electronic Devices: From the Heart, Brain, and Skin to Gastrointestinal Organs.},
journal = {Biosensors},
volume = {15},
number = {8},
pages = {},
pmid = {40863003},
issn = {2079-6374},
support = {202302230001//Pusan National University Hospital/ ; },
mesh = {Humans ; Brain ; *Prostheses and Implants ; Skin ; Gastrointestinal Tract ; Heart ; },
abstract = {Implantable electronic devices are driving innovation in modern medical technology and have significantly improved patients' quality of life. This review comprehensively analyzes the latest technological trends in implantable electronic devices used in major organs, including the heart, brain, and skin. Additionally, it explores the potential for application in the gastrointestinal system, particularly in the field of biliary stents, in which development has been limited. In the cardiac field, wireless pacemakers, subcutaneous implantable cardioverter-defibrillators, and cardiac resynchronization therapy devices have been commercialized, significantly improving survival rates and quality of life of patients with cardiovascular diseases. In the field of brain-neural interfaces, biocompatible flexible electrodes and closed-loop deep brain stimulation have improved treatments of neurological disorders, such as Parkinson's disease and epilepsy. Skin-implantable devices have revolutionized glucose management in patients with diabetes by integrating continuous glucose monitoring and automated insulin delivery systems. Future development of implantable electronic devices incorporating pressure or pH sensors into biliary stents in the gastrointestinal system may significantly improve the prognosis of patients with bile duct cancer. This review systematically organizes the technological advances and clinical outcomes in each field and provides a comprehensive understanding of implantable electronic devices by suggesting future research directions.},
}
@article {pmid40862926,
year = {2025},
author = {Dong, L and Xu, C and Xie, R and Wang, X and Yang, W and Li, Y},
title = {Enhanced SSVEP Bionic Spelling via xLSTM-Based Deep Learning with Spatial Attention and Filter Bank Techniques.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {10},
number = {8},
pages = {},
pmid = {40862926},
issn = {2313-7673},
support = {62306106//National Natural Science Foundation of China/ ; 2023AFB377//Natural Science Foundation of Hubei Province/ ; },
abstract = {Steady-State Visual Evoked Potentials (SSVEPs) have emerged as an efficient means of interaction in brain-computer interfaces (BCIs), achieving bioinspired efficient language output for individuals with aphasia. Addressing the underutilization of frequency information of SSVEPs and redundant computation by existing transformer-based deep learning methods, this paper analyzes signals from both the time and frequency domains, proposing a stacked encoder-decoder (SED) network architecture based on an xLSTM model and spatial attention mechanism, termed SED-xLSTM, which firstly applies xLSTM to the SSVEP speller field. This model takes the low-channel spectrogram as input and employs the filter bank technique to make full use of harmonic information. By leveraging a gating mechanism, SED-xLSTM effectively extracts and fuses high-dimensional spatial-channel semantic features from SSVEP signals. Experimental results on three public datasets demonstrate the superior performance of SED-xLSTM in terms of classification accuracy and information transfer rate, particularly outperforming existing methods under cross-validation across various temporal scales.},
}
@article {pmid40862891,
year = {2025},
author = {Rusev, G and Yordanov, S and Nedelcheva, S and Banderov, A and Lafaye de Micheaux, H and Sauter-Starace, F and Aksenova, T and Koprinkova-Hristova, P and Kasabov, N},
title = {NEuroMOrphic Neural-Response Decoding System for Adaptive and Personalized Neuro-Prosthetics' Control.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {10},
number = {8},
pages = {},
pmid = {40862891},
issn = {2313-7673},
support = {101070891//European Commission/ ; },
abstract = {In our previous work, we developed a neuromorphic decoder of intended movements of tetraplegic patients using ECoG recordings from the brain motor cortex, called Motor Control Decoder (MCD). Even though the training data are labeled based on the desired movement, there is no guarantee that the patient is satisfied by the action of the effectors. Hence, the need for the classification of brain signals as satisfactory/unsatisfactory is obvious. Based on previous work, we upgrade our neuromorphic MCD with a Neural Response Decoder (NRD) that is intended to predict whether ECoG data are satisfactory or not in order to improve MCD accuracy. The main aim is to design an actor-critic structure able to adapt via reinforcement learning the MCD (actor) based on NRD (critic) predictions. For this aim, NRD was trained using not only an ECoG signal but also the MCD prediction or prescribed intended movement of the patient. The achieved accuracy of the trained NRD is satisfactory and contributes to improved MCD performance. However, further work has to be carried out to fully utilize the NRD for MCD performance optimization in an on-line manner. Possibility to include feedback from the patient would allow for further improvement of MCD-NRD accuracy.},
}
@article {pmid40862879,
year = {2025},
author = {Zare Lahijan, L and Meshgini, S and Afrouzian, R and Danishvar, S},
title = {Improved Automatic Deep Model for Automatic Detection of Movement Intention from EEG Signals.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {10},
number = {8},
pages = {},
pmid = {40862879},
issn = {2313-7673},
abstract = {Automated movement intention is crucial for brain-computer interface (BCI) applications. The automatic identification of movement intention can assist patients with movement problems in regaining their mobility. This study introduces a novel approach for the automatic identification of movement intention through finger tapping. This work has compiled a database of EEG signals derived from left finger taps, right finger taps, and a resting condition. Following the requisite pre-processing, the captured signals are input into the proposed model, which is constructed based on graph theory and deep convolutional networks. In this study, we introduce a novel architecture based on six deep convolutional graph layers, specifically designed to effectively capture and extract essential features from EEG signals. The proposed model demonstrates a remarkable performance, achieving an accuracy of 98% in a binary classification task when distinguishing between left and right finger tapping. Furthermore, in a more complex three-class classification scenario, which includes left finger tapping, right finger tapping, and an additional class, the model attains an accuracy of 92%. These results highlight the effectiveness of the architecture in decoding motor-related brain activity from EEG data. Furthermore, relative to recent studies, the suggested model exhibits significant resilience in noisy situations, making it suitable for online BCI applications.},
}
@article {pmid40862861,
year = {2025},
author = {Ortega-Robles, E and Carino-Escobar, RI and Cantillo-Negrete, J and Arias-Carrión, O},
title = {Brain-Computer Interfaces in Parkinson's Disease Rehabilitation.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {10},
number = {8},
pages = {},
pmid = {40862861},
issn = {2313-7673},
abstract = {Parkinson's disease (PD) is a progressive neurological disorder with motor and non-motor symptoms that are inadequately addressed by current pharmacological and surgical therapies. Brain-computer interfaces (BCIs), particularly those based on electroencephalography (eBCIs), provide a promising, non-invasive approach to personalized neurorehabilitation. This narrative review explores the clinical potential of BCIs in PD, discussing signal acquisition, processing, and control paradigms. eBCIs are well-suited for PD due to their portability, safety, and real-time feedback capabilities. Emerging neurophysiological biomarkers-such as beta-band synchrony, phase-amplitude coupling, and altered alpha-band activity-may support adaptive therapies, including adaptive deep brain stimulation (aDBS), as well as motor and cognitive interventions. BCIs may also aid in diagnosis and personalized treatment by detecting these cortical and subcortical patterns associated with motor and cognitive dysfunction in PD. A structured search identified 11 studies involving 64 patients with PD who used BCIs for aDBS, neurofeedback, and cognitive rehabilitation, showing improvements in motor function, cognition, and engagement. Clinical translation requires attention to electrode design and user-centered interfaces. Ethical issues, including data privacy and equitable access, remain critical challenges. As wearable technologies and artificial intelligence evolve, BCIs could shift PD care from intermittent interventions to continuous, brain-responsive therapy, potentially improving patients' quality of life and autonomy. This review highlights BCIs as a transformative tool in PD management, although more robust clinical evidence is needed.},
}
@article {pmid40857524,
year = {2025},
author = {Golabchi, A and Wu, B and Du, ZJ and Cui, XT},
title = {Long-Term Neural Recording Performance of PEDOT/CNT/Dexamethasone Coated Electrode Array Implanted in Visual Cortex of Rats.},
journal = {Advanced nanobiomed research},
volume = {},
number = {},
pages = {},
pmid = {40857524},
issn = {2699-9307},
support = {R01 NS110564/NS/NINDS NIH HHS/United States ; R01 NS136622/NS/NINDS NIH HHS/United States ; },
abstract = {Implantable neural electrode arrays can be inserted in the brain to provide single-cell electrophysiology recording for neuroscience research and brain-machine interface applications. However, maintaining signal quality over time is complicated by inflammatory tissue responses and degradation of electrode materials. Organic electrode coatings offer a solution by enhancing recording and stimulation capabilities, including reduced impedance, increased charge injection capacity, and the ability to incorporate and release anti-inflammatory drugs. In this study, acid-functionalized multi-walled carbon nanotubes (CNTs) loaded with dexamethasone (Dex) were incorporated into poly (3,4-ethylendioxythiophene) (PEDOT) as electrode coatings. We investigated the electrochemical stability and recording performance of the PEDOT/CNT/Dex coating over an extended period of approximately 18 months. Cyclic voltammetric (CV) stimulation was used to trigger Dex release in half of the recording sites during the first 11 days of implantation to reduce the acute inflammation. The PEDOT/CNT/Dex coated floating microelectrode arrays demonstrated stable in vivo electrode impedance and successful detection of visually evoked neural activity from the rat visual cortex even at chronic time points. Additionally, the CV-stimulated sites exhibited higher single-unit recording yield, amplitudes, and signal-to-noise ratio compared to unstimulated sites. These results highlight the potential of anti-inflammatory treatments to improve the quality and longevity of chronic neural recordings.},
}
@article {pmid40864570,
year = {2025},
author = {Chen, R and Xie, C and Zhang, J and You, Q and Pan, J},
title = {A Progressive Multi-Domain Adaptation Network With Reinforced Self-Constructed Graphs for Cross-Subject EEG-Based Emotion and Consciousness Recognition.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {3498-3510},
doi = {10.1109/TNSRE.2025.3603190},
pmid = {40864570},
issn = {1558-0210},
mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; Algorithms ; *Consciousness/physiology ; Brain-Computer Interfaces ; Male ; Female ; Adult ; *Neural Networks, Computer ; Young Adult ; Databases, Factual ; },
abstract = {Electroencephalogram (EEG)-based emotion recognition is a vital component in brain-computer interface applications. However, it faces two significant challenges: 1) extracting domain-invariant features while effectively preserving emotion-related information, and 2) aligning the joint probability distributions of data across different individuals. To address these challenges, we propose a progressive multi-domain adaptation network with reinforced self-constructed graphs. Specifically, we introduce EEG-CutMix to construct unlabeled mixed-domain data, facilitating the transition between source and target domains. Additionally, a reinforced self-constructed graphs module is employed to extract domain-invariant features. Finally, a progressive multi-domain adaptation framework is constructed to smoothly align the data distributions across individuals. Experiments on cross-subject datasets demonstrate that our model achieves state-of-the-art performance on the SEED and SEED-IV datasets, with accuracies of 97.03% $\pm ~1.65$ % and 88.18% $\pm ~4.55$ %, respectively. Furthermore, tests on a self-recorded dataset, comprising ten healthy subjects and twelve patients with disorders of consciousness (DOC), show that our model achieves a mean accuracy of 86.65% $\pm ~2.28$ % in healthy subjects. Notably, it successfully applies to DOC patients, with four subjects achieving emotion recognition accuracy exceeding 70%. These results validate the effectiveness of our model in EEG emotion recognition and highlight its potential for assessing consciousness levels in DOC patients. The source code for the proposed model is available at GitHub-seizeall/mycode.},
}
@article {pmid40862615,
year = {2025},
author = {Gartner, MJ and Smith, ML and Dapat, C and Liaw, YW and Tran, T and Suryadinata, R and Chen, J and Sun, G and Shepherd, RA and Taiaroa, G and Roche, M and Lee, WS and Robinson, P and Polo, JM and Subbarao, K and Neil, JA},
title = {Contemporary seasonal human coronaviruses display differences in cellular tropism compared to laboratory-adapted reference strains.},
journal = {Journal of virology},
volume = {99},
number = {9},
pages = {e0068425},
pmid = {40862615},
issn = {1098-5514},
support = {CGCPT00021//Cumming Global Centre for Pandemic Therapeutics/ ; APP1177174//National Health and Medical Research Council/ ; },
mesh = {Humans ; *Viral Tropism ; *Coronavirus 229E, Human/genetics/physiology/isolation &amp; purification ; Epithelial Cells/virology ; *Coronavirus NL63, Human/genetics/physiology/isolation &amp; purification ; *Coronavirus OC43, Human/genetics/physiology/isolation &amp; purification ; Seasons ; Common Cold/virology ; Cell Line ; Spike Glycoprotein, Coronavirus/genetics ; *Coronavirus/physiology/genetics ; Coronavirus Infections/virology ; },
abstract = {Seasonal human coronaviruses (sHCoVs) cause 15%-30% of common colds. The reference strains used for research were isolated decades ago and have been passaged extensively, but contemporary sHCoVs have been challenging to study as they are notoriously difficult to grow in standard immortalized cell lines. Here, we addressed these issues by utilizing primary human nasal epithelial cells (HNECs) and immortalized human bronchial epithelial cells (BCi) differentiated at an air-liquid interface, as well as human embryonic stem cell-derived alveolar type II (AT2) cells to recover contemporary sHCoVs from human nasopharyngeal specimens. From 21 specimens, we recovered four HCoV-229e, three HCoV-NL63, and eight HCoV-OC43 viruses. All contemporary sHCoVs showed sequence differences from lab-adapted CoVs, particularly within the spike gene. Evidence of nucleotide changes in the receptor binding domains within HCoV-229e and detection of recombination for both HCoV-229e and HCoV-OC43 isolates was also observed. Importantly, we developed methods for the amplification of high-titer stocks of HCoV-NL63 and HCoV-229e that maintained sequence identity, and we established methods for the titration of contemporary sHCoV isolates. Comparison of lab-adapted and contemporary strains in immortalized cell lines and airway epithelial cells revealed differences in cell tropism, growth kinetics, and cytokine production between lab-adapted and contemporary sHCoV strains. These data confirm that contemporary sHCoVs differ from lab-adapted reference strains and, using the methods established here, should be used for the study of CoV biology and evaluation of medical countermeasures.IMPORTANCEZoonotic coronaviruses have caused significant public health emergencies. The occurrence of a similar spillover event in the future is likely, and efforts to further understand coronavirus biology should be a high priority. Several seasonal coronaviruses circulate within the human population. Efforts to study these viruses have been limited to reference strains isolated decades ago due to the difficulty in isolating clinical isolates. Here, we use human airway and alveolar epithelial cultures to recover contemporary isolates of human coronaviruses HCoV-NL63, HCoV-229e, and HCoV-OC43. We establish methods to make high-titer stocks and titrate HCoV-229e and HCoV-NL63 isolates. We show that contemporary isolates of HCoV-NL63 and HCoV-OC43 have a different tropism within the respiratory epithelium compared to lab-adapted strains. Although HCoV-229e clinical and lab-adapted strains similarly infect the respiratory epithelium, differences in host response and replication kinetics are observed. Using the methods developed here, future research should include contemporary isolates when studying coronavirus biology.},
}
@article {pmid40860492,
year = {2025},
author = {Zhang, L and Guan, X and Wang, D and Wang, J and Liu, X and Liu, S and Ming, D},
title = {Understanding face processing in autism spectrum disorder: insights from cognitive neuroscience.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {137},
pmid = {40860492},
issn = {1871-4080},
abstract = {Faces convey critical information for social communication, such as identity, expression, and eye gaze. Unfortunately, individuals with autism spectrum disorder (ASD) often experience difficulties in processing this information, and these deficits lead to their suffering from social interactions. Importantly, since face processing is a social skill developed during early childhood, its deficits may be an early symptom of ASD. In recent years, researchers have made great progress in identifying face processing impairments in individuals with ASD and exploring their biological underpinnings. In this paper, we reviewed the research progress on face processing impairments in individuals with ASD. Moreover, we mainly summarized the mechanisms proposed to underlie these impairments, including the changes in brain structure and function, atypical social cognition, and genetic variation. Finally, we discussed the factors leading to the inconsistent results of existing studies. Focused efforts to research the alterations and mechanisms of face processing might improve our knowledge of this complex, heterogeneous neurodevelopmental disorder. The ultimate purpose is to help clinical diagnosis and treatment, thereby improving the function of individuals with ASD.},
}
@article {pmid40858781,
year = {2025},
author = {Cui, Y and Sun, J and Zhang, B and Guo, T and Zhang, S and Li, Z and Chen, Y and Su, M and Wu, D and Wu, J and Wang, Q and Yuan, Y and Wang, J and Tian, Q and He, F and Wu, L and Li, X and Gong, Y and Qin, W},
title = {Efficacy and safety of transcutaneous auricular vagus nerve stimulation for patients with treatment-resistant schizophrenia with predominantly negative symptoms: a randomized clinical trial and efficacy sensitivity biomarkers.},
journal = {Molecular psychiatry},
volume = {30},
number = {11},
pages = {5437-5447},
pmid = {40858781},
issn = {1476-5578},
mesh = {Humans ; Male ; Female ; *Vagus Nerve Stimulation/methods/adverse effects ; Adult ; Double-Blind Method ; Treatment Outcome ; Biomarkers/metabolism ; Middle Aged ; *Schizophrenia/therapy ; *Schizophrenia, Treatment-Resistant/therapy/physiopathology ; *Transcutaneous Electric Nerve Stimulation/methods ; Psychiatric Status Rating Scales ; Antipsychotic Agents/therapeutic use ; Electroencephalography ; Tumor Necrosis Factor-alpha/metabolism ; },
abstract = {Negative symptoms in treatment-resistant schizophrenia (TRS) are notably persistent and minimally affected by antipsychotics, the transcutaneous auricular vagus nerve stimulation (taVNS) is a promising treatment approach. However, clinical trials are scarce, and further efficacy data are needed. We conducted a double-blind, sham-controlled, randomized clinical trial to determine the efficacy and safety of taVNS as an add-on treatment for patients with TRS with predominantly negative symptoms and to investigate potential biomarkers of efficacy. A total of 50 patients underwent a two-week intervention of active taVNS (n = 25) or sham taVNS (n = 25), followed by a two-week follow-up. Primary outcome was the change in the PANSS-factor score for negative symptoms (PANSS-FSNS) assessed after the intervention. In the intention-to-treat analysis, patients receiving active taVNS showed a significantly greater improvement in negative symptoms compared with those receiving the sham procedure (PANSS-FSNS difference, -1.36; effect size, -0.62; 95% CI, -1.20 to -0.04; p = 0.033), with effects sustained at follow-up and good tolerability. Inflammatory cytokines and EEG coherence showed that in the active group, the change in PANSS-FSNS scores after treatment was significantly correlated with changes in tumour necrosis factor (TNF)-α (r = 0.56, corrected p = 0.017) and beta-band coherence between the left frontal and parietal regions (r = -0.56, p = 0.004), but not in the sham group. This study suggests that taVNS may effectively and safely ameliorate negative symptoms in TRS, with TNF-α and beta-band coherence between the left frontal and parietal regions as potential sensitivity efficacy biomarkers. Chinese Clinical Trial Registry (http://www.chictr.org.cn .), ChiCTR2400085198.},
}
@article {pmid40858497,
year = {2025},
author = {Tong, JQ and Binder, JR and Conant, LL and Mazurchuk, S and Anderson, AJ and Fernandino, L},
title = {A Common Representational Code for Event and Object Concepts in the Brain.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {45},
number = {41},
pages = {},
pmid = {40858497},
issn = {1529-2401},
mesh = {Humans ; Female ; Male ; Magnetic Resonance Imaging ; Adult ; Young Adult ; *Brain/physiology ; *Brain Mapping ; *Concept Formation/physiology ; *Recognition, Psychology/physiology ; Photic Stimulation/methods ; Image Processing, Computer-Assisted ; },
abstract = {Events and objects are two fundamental ways in which humans conceptualize their experience of the world. Despite the significance of this distinction for human cognition, it remains unclear whether the neural representations of object and event concepts are categorically distinct or, instead, can be explained in terms of a shared representational code. We investigated this question by analyzing fMRI data acquired from human participants (males and females) while they rated their familiarity with the meanings of individual words (all nouns) denoting object and event concepts. Multivoxel pattern analyses indicated that both categories of lexical concepts are represented in overlapping fashion throughout the association cortex, even in the areas that showed the strongest selectivity for one or the other type in univariate contrasts. Crucially, in these areas, a feature-based model trained on neural responses to individual event concepts successfully decoded object concepts from their corresponding activation patterns (and vice versa), showing that these two categories share a common representational code. This code was effectively modeled by a set of experiential feature ratings, which also accounted for the mean activation differences between these two categories. These results indicate that neuroanatomical dissociations between events and objects emerge from quantitative differences in the cortical distribution of more fundamental features of experience. Characterizing this representational code is an important step in the development of theory-driven brain-computer interface technologies capable of decoding conceptual content directly from brain activity.},
}
@article {pmid40857922,
year = {2025},
author = {Hu, W and Zhang, D and Chen, W},
title = {ITSEF: Inception-based two-stage ensemble framework for P300 detection.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {193},
number = {},
pages = {108014},
doi = {10.1016/j.neunet.2025.108014},
pmid = {40857922},
issn = {1879-2782},
abstract = {To address the problems of low signal-to-noise ratio, significant individual differences between subjects, and class imbalance in P300-based brain-computer interface (BCI), this paper proposes a novel Inception-based two-stage ensemble framework (ITSEF) to improve detection accuracy. Firstly, an Inception-based convolutional neural network (ICNN) is designed to extract multi-scale features and conduct cross-channel learning. In addition, a two-stage ensemble framework (TSEF) combined with a pre-training and fine-tuning strategy is developed, aiming to enhance the classification performance of the minority class and improve the generalization ability of the model. The framework comprises a conventional learning branch and a re-balancing branch, each based on an ICNN pre-trained with a different loss function. The prediction results of both branches are dynamically weighted by a cumulative learning strategy, so that the model gradually shifts its learning focus from the majority class to the minority class, comprehensively improving the identification ability for both classes. Experimental results on two datasets, Dataset II of BCI Competition III and BCIAUT-P300, demonstrate that the proposed ITSEF achieves state-of-the-art performance in the P300 classification task, with average classification accuracies of 86.16 % and 92.13 %, respectively. Compared with the existing state-of-the-art methods, the ITSEF achieves improvements of 4.61 % and 1.01 % on the two datasets, respectively. Furthermore, it exhibits significant improvements compared to baseline models and widely used class re-balancing strategies. The proposed ITSEF method provides an innovative deep learning framework for P300 signal analysis and has application potential in the field of P300-BCI.},
}
@article {pmid40855086,
year = {2025},
author = {Xiao, Q and Fan, LH and Ma, Q and Ning, YM and Gu, Z and Chen, L and Li, L and You, JW and Niu, YF and Cui, TJ},
title = {Secure wireless communication of brain-computer interface and mind control of smart devices enabled by space-time-coding metasurface.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {7914},
pmid = {40855086},
issn = {2041-1723},
support = {62288101//National Natural Science Foundation of China (National Science Foundation of China)/ ; 92167202//National Natural Science Foundation of China (National Science Foundation of China)/ ; 72171044//National Natural Science Foundation of China (National Science Foundation of China)/ ; K201924//State Key Laboratory of Millimeter Waves (State Key Lab of Millimeter Waves)/ ; BK20230822//Natural Science Foundation of Jiangsu Province (Jiangsu Provincial Natural Science Foundation)/ ; 2021M700761//China Postdoctoral Science Foundation/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Wireless Technology/instrumentation ; *Brain/physiology ; Electroencephalography ; *Computer Security ; Photic Stimulation ; Algorithms ; Brainwashing ; },
abstract = {Brain-computer interface (BCI) provides an interconnected pathway between the human brain and external devices and paves a potential route for mind manipulations. However, most existing BCI technologies are based on simple signal transmission and are independent of other interface devices, with limited consideration for the reliability and security of the human brain's information interaction in complicated wireless environments. Here, we propose a deep fusion coding scheme that combines the BCI visual stimulation coding with metasurface space-time coding at the physical layer, enabling reliable and secure information transfers between the human brain and external devices. A brain space-time-coding metasurface platform is designed to implement a secure wireless communication system by using harmonic-encrypted beams. We design and fabricate a proof-of-principle prototype and experimentally show that the proposed wireless BCI scheme can establish a remote but safeguarded paradigm for human-machine interactions and intelligent metasurfaces, providing a potential direction in future secure wireless communications.},
}
@article {pmid40856919,
year = {2025},
author = {Arns, M and Sokhadze, E and Birbaumer, N},
title = {Neurofeedback and Brain-Machine Interfaces: Where are We Now?.},
journal = {Applied psychophysiology and biofeedback},
volume = {},
number = {},
pages = {},
pmid = {40856919},
issn = {1573-3270},
}
@article {pmid40856229,
year = {2025},
author = {Yuan, Y and Gao, Z and Xiao, W},
title = {The Role of Oxytocin in Parental Care.},
journal = {Endocrinology},
volume = {166},
number = {9},
pages = {},
doi = {10.1210/endocr/bqaf129},
pmid = {40856229},
issn = {1945-7170},
support = {2021R52021//This work was supported by the Leading Talents in Science and Technology of Zhejiang Province/ ; 2021ZD0202700//National Science and Technology Innovation 2030-Major Projects/ ; 2025ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; },
mesh = {*Oxytocin/physiology/metabolism ; Humans ; Animals ; *Maternal Behavior/physiology ; *Paternal Behavior/physiology ; Female ; },
abstract = {Parental behaviors are essential for offspring survival and shaped by hormonal changes and adaptations in the neural circuits. Oxytocin, a nonapeptide, has been shown to play an important role in promoting parental behaviors. Using cutting-edge tools, studies have recently uncovered how oxytocin mediates parental behaviors through modulation of different neural circuits. We highlight recent advances in identifying neural pathways contributing to the role of oxytocin in parental care, focusing on how infant-related cues activate the oxytocin system and how oxytocin enhances the salience of sensory cues to enable parental behaviors in this review. We also discuss future challenges to further elucidate mechanisms involved.},
}
@article {pmid40853583,
year = {2025},
author = {Wang, S and Yang, Y and Hao, S and Sun, Y and Wang, H},
title = {Glutamatergic Periaqueductal Gray Projections to the Locus Coeruleus Orchestrate Adaptive Arousal States in Threatening Contexts.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {40853583},
issn = {1995-8218},
abstract = {The locus coeruleus (LC), a norepinephrine nucleus governing arousal states through tonic activity, requires precise regulatory mechanisms to maintain its dynamic activation levels. However, the neural circuitry underlying LC activity maintenance remains unclear. Here, we identify a glutamatergic projection from the ventrolateral periaqueductal gray (vlPAG) to the LC in mice as a critical regulator of arousal dynamics. Fiber photometry recordings revealed stress-induced Ca[2+] dynamics in vlPAG[CaMKIIα]-LC axon terminals across diverse threat paradigms. Slice electrophysiology demonstrated that this pathway mediates LC-norepinephrine (LC-NE) neuronal activity via glutamatergic transmission. Low-frequency pathway activation (1 Hz) mainly induced anxiety-like behaviors, whereas high-frequency stimulation (10 Hz) evoked more panic-like hyperlocomotion, establishing a frequency-dependent continuum of arousal states. Conversely, pathway inhibition reduced pupil size, a reliable biomarker for arousal, concurrently suppressing threat avoidance behaviors and alleviating anxiety-related behaviors without altering environmental preference. These findings reveal that the vlPAG[CaMKIIα]-LC pathway maintains baseline arousal while dynamically scaling threat-induced hyperarousal.},
}
@article {pmid40853288,
year = {2025},
author = {Rahman, MM and Banik, N and Sunny, MSH and Zarif, MII and Bedolla-Martinez, D and Schultz, K and Ahamed, SI and Rahman, MH},
title = {Wheelchair-mounted robotic arms: a systematic review of technical design and activities of daily living outcomes.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {20},
number = {7},
pages = {2532-2556},
doi = {10.1080/17483107.2025.2547042},
pmid = {40853288},
issn = {1748-3115},
mesh = {Humans ; *Activities of Daily Living ; *Wheelchairs ; *Robotics/instrumentation ; Equipment Design ; *Persons with Disabilities/rehabilitation ; *Self-Help Devices ; Quality of Life ; },
abstract = {PURPOSE: This review examines wheelchair-mounted robotic arms (WMRAs) as an emerging assistive technology that enhances independence and quality of life for individuals with upper- and lower-limb disabilities. By enabling independent performance of activities of daily living (ADLs), WMRAs hold significant promise for disability and rehabilitation. The article aims to critically evaluate the state of the art in WMRA research and development, identifying persistent challenges and highlighting promising innovations.<br><br>MATERIALS AND METHODS: The review systematically analyzes literature on WMRAs published between 2001 and 2025. The analysis emphasizes design specifications, degrees of freedom, actuation methods, control strategies, and performance evaluations. A comparative synthesis is conducted to assess how existing systems support ADL execution, while also integrating technical considerations with user-centered outcomes.<br><br>RESULTS AND CONCLUSIONS: The findings indicate that current WMRA designs face significant limitations, including restricted workspace coverage, inadequate gripper dexterity, suboptimal kinematic configurations, limited payload capacity, high cost, and lack of modularity. Safety mechanisms remain underdeveloped, creating barriers to broader adoption. Nevertheless, advancements in AI-driven control systems, modular design strategies, and integration with complementary assistive technologies demonstrate promising progress. The review concludes that WMRAs have substantial potential to improve autonomy and daily functioning for individuals with disabilities. Addressing technical and practical shortcomings is essential to ensure successful real-world deployment. These insights contribute to disability and rehabilitation research, as they highlight pathways to enhance accessibility, safety, and cost-effectiveness in assistive technologies that support independent living.},
}
@article {pmid40852670,
year = {2025},
author = {Zubayr, MO and Obimakinde, AM and Popoola, OA},
title = {PARENTAL RESPONSE AND COPING STRATEGIES FOR ADOLESCENTS' BEHAVIOURAL PROBLEMS: A COMMUNITY-BASED CROSS-SECTIONAL STUDY.},
journal = {Annals of Ibadan postgraduate medicine},
volume = {23},
number = {1},
pages = {15-23},
pmid = {40852670},
issn = {1597-1627},
abstract = {BACKGROUND: Adolescent behavioural problems can be burdensome for parental figures. The lack of good parental responses and coping strategies may worsen adolescent mental health issues. Research in this domain can be informative for effective management of adolescents' behavioural problems in resourcelimited settings like Nigeria.<br><br>AIM: We assessed parental responses and coping strategies for adolescents with behavioural problems.<br><br>METHODS: A cross-sectional community-based survey with cluster sampling was conducted. Coping strategies were assessed using the Brief Cope Inventory (BCI), dichotomized into Emotional-Based Strategies (EBS) and Problem- Based Strategies (PBS) coping. The Strength and Difficulty Questionnaire (SDQ) assessed adolescent behavioural problems. Data were analyzed using descriptive and inferential statistics.<br><br>RESULTS: Four hundred and ten (410) parental figures of adolescents aged 14.8&#xb1;2.3 years were recruited. Parental response to adolescent problem behaviours included corporal punishment in 44% and few (5.8%) sought medical or spiritual help for the adolescent. The most deployed parental coping strategy was 'active' coping (69%) while 'instrumental support' was the least adopted coping strategy. The age, gender, educational level and income of parental figures, were associated with the choice of utilizing PBS coping.<br><br>CONCLUSION: Parental figures employed more corporal punishment and utilized active coping, and planning as coping strategies when dealing with adolescents' problem behaviours. Interventions to discourage corporal punishment and promote more effective parental coping are needed.},
}
@article {pmid40852573,
year = {2025},
author = {Chen, L and Yang, T and Liu, R and Xu, Q and Ge, Q and Wu, M and Yu, H},
title = {Sensory and neural responses to flavor compound 3-Methylbutanal in dry fermented sausages: Enhancing perceived overall aroma.},
journal = {Food chemistry: X},
volume = {29},
number = {},
pages = {102769},
pmid = {40852573},
issn = {2590-1575},
abstract = {This study investigated the impact of 3-methylbutanal (0, 60, 120, 180, 240, and 300 μg/kg) on aroma and neural responses in fermented sausages. Among 33 volatiles identified, 3-methylbutanal exhibited the highest odor activity value of 868, indicating its dominant contribution. Sensory analysis showed that samples with 180 μg/kg received the highest ratings for savory (7.0), caramelized (7.1), and nutty (4.4) notes, whereas the 300 μg/kg group showed the lowest overall aroma intensity. EEG analysis indicated global power and α-band activity peaked at 180 μg/kg, increasing by 65.8 % and 73.2 % over baseline, then declined at higher doses. Time-resolved topographies showed odor decoding began at 100 ms and peaked at 500 ms. Source localization identified increased activity in dorsolateral, orbitofrontal, and ventromedial prefrontal cortices at 180 μg/kg. These results demonstrate that moderate levels of 3-methylbutanal enhance aroma perception and evoke heightened neural activity in brain regions associated with olfactory processing and emotion.},
}
@article {pmid40852333,
year = {2025},
author = {An, J and Goyal, P and Luft, AR and Schönhammer, JG},
title = {Functional near-infrared spectroscopy short-channel regression improves cortical activation estimates of working memory load.},
journal = {Neurophotonics},
volume = {12},
number = {3},
pages = {035009},
pmid = {40852333},
issn = {2329-423X},
abstract = {SIGNIFICANCE: Functional near-infrared spectroscopy (fNIRS) is a noninvasive technique commonly used to examine cognitive functions such as working memory (WM). However, fNIRS signals are often interfered with by extracerebral activity, such as scalp hemodynamics. Short separation channels (SSCs) allow direct measurement of these signals. Short-channel regression (SCR) is widely used to reduce scalp interference, but its added value in WM paradigms remains underexplored.<br><br>AIM: We aimed to examine the effect of SCR on improving the validity of fNIRS measurements for WM load (WML).<br><br>APPROACH: We used the N -Back task to induce WML-dependent brain activation by varying the " n " level. Data from 20 participants were collected using fNIRS with SSC. Hemodynamic responses were analyzed with generalized linear models and linear mixed models to assess SCR's effect on the sensitivity of cortical activation measures.<br><br>RESULTS: SCR enhanced the statistical effects of N -Back levels on measured hemodynamic responses at both group and subject levels, improving the validity and sensitivity of fNIRS.<br><br>CONCLUSIONS: SCR improves fNIRS measurement sensitivity and validity, even in tasks with minimal motor requirements.},
}
@article {pmid40850941,
year = {2025},
author = {Zhang, X and Yang, Y},
title = {Gut: The gate and key to brain.},
journal = {Chinese medical journal},
volume = {138},
number = {18},
pages = {2207-2219},
pmid = {40850941},
issn = {2542-5641},
mesh = {Humans ; *Brain/physiology ; Animals ; Gastrointestinal Microbiome/physiology ; *Gastrointestinal Tract/physiology/microbiology ; },
abstract = {Brain science is the frontier of modern science, and new advances have been made in brain-like designs and brain-computer interfaces to simulate or develop brain functions. However, given that the brain is hermetically sealed within the skull, exploration and deciphering of the brain structure and functions are limited. Growing evidence suggests that the gut is not just a digestive organ. It not only provides essential nutrients and electrolytes for brain neurodevelopment and the maintenance of brain function, but it also transmits external environmental and intestinal wall signals from the intestinal lumen to the central nervous system through multiple pathways to regulate brain activity, function, and structure. A variety of gut-brain interaction pathways have been identified, including neural pathways, neuroimmune signaling, endocrine pathways, and biochemical messengers produced by gut microbes. Gut microbes interact with food and the gut to modulate gut-brain communication. The gut's important role and potential in neurodevelopment, maintenance of normal function, and disease development make it an increasingly important area of research in brain science and neuropsychiatric disorders. The gut's unique role in brain functions and its accessibility for research (compared to direct brain studies) establish it as a critical gate to understanding the mysteries of brain science. Crucially, intestinal nutrients and microbes provide two unique keys to unlock this gate-enabling neural regulation and novel treatments for neuropsychiatric diseases.},
}
@article {pmid40850344,
year = {2025},
author = {Zhou, S and Liu, Y and Turnbull, A and Tapparello, C and Adeli, E and Lin, FV},
title = {Personalized cognitive enhancement for older adults: An aging-friendly closed-loop human-machine interface framework.},
journal = {Ageing research reviews},
volume = {112},
number = {},
pages = {102877},
doi = {10.1016/j.arr.2025.102877},
pmid = {40850344},
issn = {1872-9649},
mesh = {Humans ; *Cognition/physiology ; Aged ; *Aging/psychology/physiology ; *Precision Medicine/methods ; *Brain-Computer Interfaces ; },
abstract = {Emerging digitally delivered non-pharmacological interventions (dNPIs) offer scalable, low-risk solutions for enhancing cognitive function in older adults, yet their effectiveness remains inconsistent due to a lack of personalization and precise mechanisms of action. Generic, population-based designs often fail to predict individual gains, underscoring the need for more tailored approaches. To address this, we propose a closed-loop human-machine interface (HMI) framework for personalizing dNPIs by optimizing the engagement of neurocognitive resources for cognitive enhancement. Our framework tackles three major challenges: (1) comprehensive and effective neurobehavioral representations for cognitive decoding, (2) tailoring interventions for domain-specific cognitive processes, and (3) ensuring aging-friendly design on usability, validity, and reliability for long-term adherence. We provide reviews and perspectives to guide the development of closed-loop HMIs by outlining the operational details of three key components-sensor, controller, and external actuator-that monitor, analyze, and modulate neurobehavioral activities through real-time adaptive interventions. Centering on neurobehavioral characteristics of older adults, we propose to advance closed-loop HMIs toward (1) deploying multimodal sensor network that captures activities from both central and peripheral nervous systems, (2) artificial intelligence (AI)-powered cognitive decoding and modulation that integrates multi-modal easy-to-acquire neurobehavioral signals and predicts the cross-modal harder-to-acquire signals, and (3) targeting neurobehavioral processes via internal and/or external regulation. We envision that the proposed closed-loop HMI framework could provide personalized dNPI with enhanced effectiveness and scalability for cognitive enhancement in older adults, promoting brain resilience and healthy longevity in the aging population.},
}
@article {pmid40850267,
year = {2025},
author = {Li, J and Zhang, W and Liao, Y and Qiu, Y and Zhu, Y and Zhang, X and Wang, C},
title = {Neural decoding reliability: Breakthroughs and potential of brain-computer interfaces technologies in the treatment of neurological diseases.},
journal = {Physics of life reviews},
volume = {55},
number = {},
pages = {1-40},
doi = {10.1016/j.plrev.2025.08.007},
pmid = {40850267},
issn = {1873-1457},
abstract = {Neurological disorders such as Parkinson's disease, stroke, and epilepsy frequently result in irreversible disability. Brain-computer interface (BCI) technologies offer the promise of recovering or replacing impaired sensory, motor, and cognitive functions by directly stimulating cortical activity or by converting self-generated cortical activity into commands for external assistive devices. In-depth studies of cerebral cortex connectivity, function and neural hierarchical coding mechanisms can provide novel solutions for BCI-based treatments. This review summarizes the fundamental principles and history of BCI technology and current research progress, including the utilization of known cortical functions and the potential impact of newly discovered cortical functions on the future development of BCI-based applications. The article then systematically reviews the application of BCI technology for the treatment of motor, cognitive, and psychiatric disorders, innovative uses of hydrogels and carbon nanomaterials in BCI systems, and the current limitations and future research directions of BCI systems with respect to the reliability of neural decoding. This article aims to provide clinicians and researchers with the latest progress and a comprehensive overview of BCI applications for diagnosing and treating neurological diseases from in-depth studies on cerebral cortex structure and function, and to propose potential future applications based on interdisciplinary approaches, especially in enhancing the reliability of neural decoding.},
}
@article {pmid40848671,
year = {2025},
author = {Weng, Y and He, B and Zhou, J and Luo, P and Xu, Z and Yan, H and Yang, B and He, Q and Lu, J and Yang, X},
title = {Potential saviour of pulmonary fibrosis: multi-pathway treatment of natural products.},
journal = {Phytomedicine : international journal of phytotherapy and phytopharmacology},
volume = {147},
number = {},
pages = {157174},
doi = {10.1016/j.phymed.2025.157174},
pmid = {40848671},
issn = {1618-095X},
mesh = {Humans ; *Pulmonary Fibrosis/drug therapy ; *Biological Products/pharmacology/therapeutic use ; Oxidative Stress/drug effects ; Signal Transduction/drug effects ; Animals ; Phytotherapy ; Epithelial-Mesenchymal Transition/drug effects ; Inflammation/drug therapy ; },
abstract = {BACKGROUND: Pulmonary fibrosis (PF), a terminal manifestation of diverse interstitial lung diseases, remains incompletely understood in its pathogenesis. Natural products possess multifaceted biological activities and relatively favorable safety profiles, showing great advantage in treating complex disease including PF, though bioavailability limitations require formulation optimization.<br><br>PURPOSE: This review systematically consolidates insights into the underlying mechanisms of natural products and prospects several promising targets for the treatment of PF.<br><br>METHODS: A comprehensive literature search was conducted in PubMed, Web of Science, and specialized pharmacology texts using key terms related to pulmonary fibrosis, natural products (e.g., alkaloids, terpenoids, flavonoids, saponins), inflammation, and oxidative stress. The information was reviewed to emphasize the potential mechanisms of natural products in the treatment of PF.<br><br>RESULTS: Natural products ameliorate PF through multi-pathway interventions, including suppression of inflammation, antagonism of oxidative stress, inhibition of epithelial-mesenchymal transition and endothelial-to-mesenchymal transition, targeting of fibroblast activation, modulation of metabolic homeostasis, promotion of autophagy and repression of senescence and apoptosis. These effects are mediated by modulating intricate pathways such as the TGF-&#x3b2;1/SMAD, PI3K/Akt/mTOR, NOX4-Nrf2, AMPK, NF-&#x3ba;B and STAT3 signaling pathways. In addition, the toxicology and side effects of natural products for the treatment of pulmonary fibrosis, and various clinical questions and limitations are discussed.<br><br>CONCLUSION: These unveiling mechanisms provide robust support for the exploration of novel applications of existing medications. This review aims to contribute novel insights towards the further studies of natural products for the prevention and treatment of PF.},
}
@article {pmid40848318,
year = {2025},
author = {Yu, SH and Park, HY and Lee, E and Kam, TE and Jeong, JH},
title = {DeepSMR: Decoding high-complex motor imagery via subject-dependent multi-feature refinement in deep convolutional networks.},
journal = {Computers in biology and medicine},
volume = {197},
number = {Pt A},
pages = {110920},
doi = {10.1016/j.compbiomed.2025.110920},
pmid = {40848318},
issn = {1879-0534},
mesh = {Humans ; *Electroencephalography ; *Brain-Computer Interfaces ; Male ; Female ; Adult ; Fingers/physiology ; *Signal Processing, Computer-Assisted ; *Imagination/physiology ; *Neural Networks, Computer ; *Brain/physiology ; },
abstract = {Electroencephalography (EEG) is a noninvasive neuroimaging technique that records electrical activity in the brain using electrodes placed on the scalp. It is widely used in neuroscience, clinical diagnosis, and brain-computer interface (BCI) applications to analyze brain signals in real time. This study proposes an advanced EEG-based BCI framework designed to decode and classify individual finger movements within a single hand during a finger-tapping task involving all five fingers. Our method employs a subject-dependent multi-feature refinement framework called DeepSMR, a novel deep convolutional network architecture optimized for feature extraction from EEG signals is introduced. This approach integrates spectral, temporal, and spatial analyses, leveraging event-related desynchronization/event-related synchronization (ERD/ERS), common spatial pattern (CSP), and power spectral density (PSD) techniques. Further, a subject-dependent multi-feature refinement framework. The DeepSMR achieved high classification accuracy for fine-motor tasks, achieving an average accuracy of 0.7471 (±0.0270) for the thumb and 0.7485 (±0.0314) for the index finger during motor execution tasks. DeepSMR outperformed EEGNet and DeepConvNet across all finger classes, showing an improvement of up to 15% in accuracy compared with the baseline models. Spectral feature analysis confirmed increased activity in the sensorimotor rhythm (SMR) frequency bands (8-13 Hz and 13-30 Hz), whereas temporal analysis revealed distinct patterns during the active and relaxed states. Spatial feature analysis highlighted class-specific features, further enhancing model performance. In the motor imagery session, DeepSMR maintained a superior performance, achieving the highest accuracy of 0.6984 (±0.0324) for the index finger. The results show that DeepSMR improves BCI performance by increasing the classification accuracy and computational efficiency, particularly for challenging finger-movement tasks. The framework could provide applications in neuroprosthetics, assistive robotics, and rehabilitation. In future work, the method could be expanded to include more motor tasks and integrate additional data types to further enhance the decoding accuracy for specific users and complex actions.},
}
@article {pmid40847250,
year = {2025},
author = {Tan, H and Jin, S and Lv, W and Guo, L and Jiang, P and Li, Y and Shi, M and Wang, D and Wang, Y and Bao, A},
title = {Hypothalamic Oxytocin Neuronal Activation Induces Bipolar-Like Mood Changes in Mice in a Sex- and Dosage-Dependent Manner.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {40847250},
issn = {1995-8218},
abstract = {Clinical studies have suggested that increased plasma oxytocin (OT) levels are a promising biomarker for bipolar disorder (BD), and our earlier post-mortem study found increased OT activity in the hypothalamic paraventricular nucleus (OT[PVN]) in BD. However, the potential contribution of the supraoptic nucleus (SON, OT[SON]), a major part of the central OT system, to BD remains unknown. We therefore systematically performed independent acute or chronic chemogenetic activation of OT[PVN], OT[SON], or OT[PVN+SON] experiments in OT-cre mice. We found that acute activation of OT[PVN+SON] neurons led to slight mania-like (anti-depression-like) behaviors both in male and female mice, while chronic activation of OT[PVN] or OT[PVN+SON] led to sex-dependent behavioural changes from depression/anxiety-like to mania-like, accompanied by stress-related molecular changes in a sex- dependent manner in the medial prefrontal cortex. Our findings imply that OT may be involved in bipolar-like mood changes in a sex- and dosage-dependent manner.},
}
@article {pmid40841966,
year = {2025},
author = {Yang, W and Lu, J and Luo, P and Xu, Z and Yan, H and Yang, B and He, Q and Zhou, J and Yang, X},
title = {An exhaustive examination of the research progress in identifying potential JAK inhibitors from natural products: a comprehensive overview.},
journal = {Chinese medicine},
volume = {20},
number = {1},
pages = {130},
pmid = {40841966},
issn = {1749-8546},
support = {No.2020YFE0204300//the National Key Research and Development Program/ ; No.82404960//Youth Fund of the National Natural Science Foundation of China/ ; No. 82274018//the National Natural Science Foundation of China/ ; },
abstract = {The JAK-STAT signaling pathway serves as a central regulator of diverse cellular processes encompassing proliferation, apoptosis, inflammation, and differentiation. Specifically, extracellular ligands such as interleukins, and colony-stimulating factors induce JAKs phosphorylation, subsequently triggering dimerization and nuclear translocation of STATs protein. In this way, the JAK-STAT pathway modulates target gene expression. Dysregulation of the JAK-STAT pathways has been implicated in the pathogenesis of multiple diseases, including inflammatory diseases, autoimmune diseases, malignant tumors. Therefore, JAK inhibitors have been considered promising therapeutic candidates with substantial clinical potential. While previous reviews have primarily focused on natural products targeting JAK-STAT signaling pathways for the specific disease application, this paper comprehensively collected 88 natural products demonstrating JAKs inhibitory activity across multiple pathological conditions. We mainly referenced nearly 20 years of literature from 2005 to 2025, comprising 294 different types of publications including review articles and research papers. Through systematic analysis of the compounds, we further classified these phytochemicals according to their structural characteristics (flavonoids, alkaloids, terpenoids) and molecular targets within the signaling cascades. This study provides novel insights into the pathophysiological relationships between diseases and JAK kinases, while offering valuable guidance for developing next-generation JAK inhibitors with improved therapeutic profiles.},
}
@article {pmid40844935,
year = {2025},
author = {Zhu, Y and Chen, J and Cheng, L and Zhu, F and Zhang, X and Liu, Q},
title = {A Sparse-Integrated Filtering Residual Spiking Neural Network for High-Accuracy Spike Sorting and Co-optimization on Memristor Platforms.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBCAS.2025.3601403},
pmid = {40844935},
issn = {1940-9990},
abstract = {Brain-computer interfaces rely on precise decoding of neural signals, where spike sorting is a critical step to extract individual neuronal activities from complex neural data. This works presents a spiking neural network (SNN) framework for efficient spike sorting, named SIFT-RSNN. In the SIFT-RSNN, raw neural signals are encoded into spike trains using a threshold-based temporal encoding strategy, then a sparse-integrated filtering module refines misfiring spikes, enhancing data sparsity for pattern learning. The RSNN module with a membrane shortcut structure ensures efficient feature transfer and improves generalization performance of the overall system. The SIFT-RSNN achieves an accuracy of 96.2% and 99.6% on the Difficult1 and Difficult2 subset of Leicester dataset, surpassing state-of-the-art methods. Also, we conducted it on a compute-in-memory platform with 8k memristor cells utilizing quantization-free mapping method and propose two algorithm-hardware co-optimization strategies to mitigate non-ideal hardware effects: weight outlier pre-constraint (WOP) and noise adaptation training (NAT). After optimization, our algorithm continues to outperform existing spike sorting methods, achieving accuracies of 94.2% and 99.7%, while also demonstrating improved robustness. The memristor platform only exhibits a 2% and 1.5% accuracy drop compared to software results on the two difficult subsets. Additionally, it achieves 3.52 μJ energy consumption and 0.5 ms latency per inference. This work offers promising solutions for brain-computer interfaces systems and neural prosthesis applications in the future.},
}
@article {pmid40843108,
year = {2025},
author = {Hao, Y and Cheng, S},
title = {Motor imagery EEG classification method using 3D CNN and LSTM for rehabilitation application.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {131},
pmid = {40843108},
issn = {1871-4080},
abstract = {Due to the limitations in the accuracy and robustness of current EEG classification methods, applying motor imagery for practical Brain-Computer Interface applications remains challenging. Therefore, an EEG classification method with high accuracy and strong robustness is of significant importance. This paper proposed a method called 3D CNN and LSTM for Motor Imagery (3D-CLMI), which combines 3D CNN and LSTM network with attention to classify MI-EEG signals. This method combined MI-EEG signals from different channels into 3D features and extracted spatial features through convolution operations with multiple 3D convolutional kernels of different scales. At the same time, in order to ensure the integrity of the extracted temporal features of the MI-EEG signal, 3D-CLMI adopted a parallel structure to obtain spatial and temporal features respectively, and then combined the obtained features for classification. Experimental results showed that this method achieved a classification accuracy of 92.7% and an F1-score of 0.91 on BCI Competition IV 2a, which were both higher than the state-of-the-art methods in the field of MI tasks. Additionally, 12 participants were invited to complete a four-class MI task, and experiments on the collected dataset showed that our method also maintained the highest classification accuracy and F1-score. Our proposed method achieved the best results on both datasets, and we then demonstrated the effectiveness of each part of the proposed method through ablation experiments. Additionally, we designed a rehabilitation application system in a VR environment based on the proposed method, and the experimental results validated that it could assist patients with impaired hand motor function.},
}
@article {pmid40843107,
year = {2025},
author = {Ding, P and Wang, F and Zhao, L and Gong, A and Fu, Y},
title = {HWI encoding/decoding of a non-invasive HWI-BCI paradigm based on temporal variation abundance scale.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {130},
pmid = {40843107},
issn = {1871-4080},
abstract = {The performance of non-invasive Handwriting Imagery (HWI) input in Brain-computer interface (BCI) systems is highly dependent on the paradigms employed, yet there is limited research on interpretable scales to measure how HWI-BCI paradigms and neural encoding designs affect performance. This study introduces the "Temporal Variation Abundance" metric and utilizes it to design two classes of handwriting imagery paradigms: Low Temporal Variation Abundance (LTVA) and High Temporal Variation Abundance (HTVA). A dynamic time warping algorithm based on random templates (rt-DTW) is proposed to align HWI velocity fluctuations using EEG. Comprehensive comparisons of these experimental paradigms are conducted in terms of feature space distance, offline and online classification accuracy, and cognitive load assessment using functional near-infrared spectroscopy. Results indicate that HTVA-HWI exhibits lower velocity stability but demonstrates higher spatial distance, offline classification accuracy, online testing classification accuracy, and lower cognitive load. This study provides deep insights into paradigm design for non-invasive HWI-BCI and scales of neural encoding, offering new theoretical support and methodological insights for future advancements in brain-computer interaction.},
}
@article {pmid40843078,
year = {2025},
author = {Deb, N and Khan, Z and Sulaiman, M and Abu Bakar, M},
title = {Editorial: Interdisciplinary synergies in neuroinformatics, cognitive computing, and computational neuroscience.},
journal = {Frontiers in computational neuroscience},
volume = {19},
number = {},
pages = {1657167},
pmid = {40843078},
issn = {1662-5188},
}
@article {pmid40842871,
year = {2025},
author = {Wang, Y and Wang, X},
title = {Entheogen: an evolutionary medicine for neuropsychiatric disorders.},
journal = {National science review},
volume = {12},
number = {8},
pages = {nwaf168},
pmid = {40842871},
issn = {2053-714X},
}
@article {pmid40839508,
year = {2025},
author = {Liyanage, KA and Yoo, PE and Grayden, DB and Opie, NL and Oxley, TJ},
title = {Artifact Removal in Electrocorticography Devices With Cardiac Contamination.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {3400-3408},
doi = {10.1109/TNSRE.2025.3601445},
pmid = {40839508},
issn = {1558-0210},
mesh = {*Artifacts ; Humans ; *Electrocorticography/methods/instrumentation ; Electrocardiography/methods ; Algorithms ; Reproducibility of Results ; *Electroencephalography/methods ; Sensitivity and Specificity ; },
abstract = {Electrocorticography (ECoG) devices with electronics housed near the chest are susceptible to artifacts of a differing nature to electroencephalography (EEG) and standard ECoG. Using data obtained via an endovascular neural interface, we compared different artifact removal techniques in an offline setting with the aim of improving the quality and usefulness of clinically acquired data. Three different methods of filtration were applied and assessed: Common Average Referencing (CAR), Independent Component Analysis (ICA) with automated ECG channel selection, and Template-Based Removal (TBR). The automated ECG channel selection method was compared to manual selection. Methods were compared using signal-to-artifact root-mean-squared (RMS) values. The automated ECG source channel selection had high concordance with manual selection. All filtration methods decreased post-artifact RMS amplitudes and improved signal-to-artifact ratios. ICA took the most time to compute but had the most improved signal-to-artifact ratio. In regions with no ECG artifact, TBR preserved the underlying electrocorticography data better than the other methods. ICA with an automated method of ECG channel selection is the preferred method out of the three tested to remove ECG artifact while preserving the underlying signal. We establish methods that can be used to improve neural data of electrocorticography devices susceptible to cardiac contamination to facilitate translation as brain-computer interfaces.},
}
@article {pmid40837818,
year = {2025},
author = {Si, JY and Lin, ZY and Gan, DG and Zhang, XY and Liu, YN and Hu, YX and Bao, YP and Wang, XQ and Sun, HQ and Yu, X and Lu, L},
title = {Informed consent competency assessment for brain-computer interface clinical research and application in psychiatric disorders: A systematic review.},
journal = {World journal of psychiatry},
volume = {15},
number = {8},
pages = {107593},
pmid = {40837818},
issn = {2220-3206},
abstract = {BACKGROUND: Brain-computer interface (BCI) technology is rapidly advancing in psychiatry. Informed consent competency (ICC) assessment among psychiatric patients is a pivotal concern in clinical research.<br><br>AIM: To analyze the assessment of ICC and form a framework with multi-dimensional elements involved in ICC of BCI clinical research among psychiatric disorders.<br><br>METHODS: A systematic review of studies regarding ICC assessments of BCI clinical research in patients with six kinds of psychiatric disorders was conducted. A systematic literature search was performed using PubMed, ScienceDirect, and Web of Science. Peer-reviewed articles and full-text studies were included in the analysis. There were no date restrictions, and all studies published up to February 27, 2025, were included.<br><br>RESULTS: A total of 103 studies were selected for this review. Fifty-eight studies included ICC factors, and forty-five were classified in ICC related ethical issues of BCI research in six kinds of psychiatric disorders. Executive function impairment is widely recognized as the most significant factor impacting ICC, and processing speed deficits are observed in schizophrenia, mood disorders, and Alzheimer's disease. Memory dysfunction, particularly episodic and working memory, contributes to compromised ICC. Five core ethical issues in BCI research should be addressed: BCI specificity, vulnerability, autonomy, dynamic ICC, comprehensiveness, and uncertainty.<br><br>CONCLUSION: A Five-Dimensional evaluative framework, including clinical, ethical, sociocultural, legal, and procedural dimensions, is constructed and proposed for future ICC research in BCI clinical research involving psychiatric disorders.},
}
@article {pmid40837785,
year = {2025},
author = {Wang, P and Dai, AL and Guo, XR and Jiang, HT},
title = {Portable electroencephalography in early detection of depression: Progress and future directions.},
journal = {World journal of psychiatry},
volume = {15},
number = {8},
pages = {107725},
pmid = {40837785},
issn = {2220-3206},
abstract = {Traditional diagnostic tools for depression, such as the Patient Health Questionnaire-9, are susceptible to subjective bias, increasing the risk of misdiagnosis and emphasizing the critical need for objective biomarkers. This minireview evaluates the emerging role of portable electroencephalography (EEG) as a cost-effective, accessible solution for early depression detection. By synthesizing findings from 45 studies (selected from 764 screened articles), we highlight EEG's capacity to identify aberrant neural oscillations associated with core depressive symptoms, including anhedonia, excessive guilt, and persistent low mood. Advances in portable systems demonstrate promising classification accuracy when integrated with machine learning algorithms, with long short-term memory models achieving > 90% accuracy in recent trials. However, persistent challenges, such as signal quality variability, motion artifacts, and limited clinical validation, hinder widespread adoption. Further innovation in sensor optimization, multimodal data integration, and real-world clinical trials is essential to translate portable EEG into a reliable diagnostic tool. This minireview underscores the transformative potential of neurotechnology in psychiatry while advocating for rigorous standardization to bridge the gap between research and clinical practice.},
}
@article {pmid40836680,
year = {2025},
author = {Riemann, D and Nissen, C and Geoffroy, PA and Feige, B and Ellis, J},
title = {Sleep and Dreams as Reflected by Science Fiction Literature and Films-Anything to Learn From?.},
journal = {Journal of sleep research},
volume = {34},
number = {5},
pages = {e70183},
pmid = {40836680},
issn = {1365-2869},
mesh = {Humans ; *Dreams/physiology/psychology ; *Sleep/physiology ; *Motion Pictures ; *Literature ; },
abstract = {Sleep and dreams are frequent themes in science fiction (Sci-Fi) literature and films, often used to explore questions about consciousness, reality, technology and the human experience. Sci-Fi authors and filmmakers utilise the enigmatic nature of sleep and dreams to blur the boundaries between reality and imagination, raising philosophical questions or extrapolating the effects of futuristic technologies on human life. In this article, we want to highlight some areas that have been recurring themes relating to sleep and dreams in Sci-Fi. These will include the concepts of so-called hypno-paedagogics, space hibernation, brain machine interfaces, electrostimulation, genetic engineering and the impact of substances (viruses, bacteria, drugs, toxins) on sleep and dreams. We will then confront Sci-Fi concepts with what is known from contemporary sleep science and judge what might be feasible, or not, in the future. A question we also want to address is how the relationship between sleep science and sleep Sci-Fi can be conceptualised: whether novel concepts have been instigated by Sci-Fi and taken up by sleep science or whether Sci-Fi merely reflects state of the art topics of sleep science, with just adding a touch of fiction.},
}
@article {pmid40836202,
year = {2025},
author = {Agarwal, P and Kumar, S and Singh, R},
title = {Motor imagery-based neural networks for assisting tetraplegic patients.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {},
number = {},
pages = {},
pmid = {40836202},
issn = {1741-0444},
abstract = {Nowadays, deep network-based classification algorithms are used in a myriad of applications for brain-computer interfaces (BCIs). These interfaces can enhance the daily lives of quadriplegic patients. Electroencephalography (EEG) based motor imagery (MI) is an integral part of BCI, and the performance of the available deep classifiers is still limited. This paper presents a novel convolutional neural network (CNN) architecture designed to enhance the multiclass classification accuracy of motor imagery (MI) signals acquired through EEG-based sensing. We have selected the electrodes over the sensorimotor cortex region of the brain in the 8-30 Hz EEG frequency band. Further, we have computed the classification accuracy and kappa scores in an end-to-end deep classification network. Our framework surpasses the contemporary literature algorithms in classifying BCI competition IV-2a, a four-class MI dataset of nine subjects (left hand, right hand, both feet, tongue). The proposed network architecture has achieved an average and maximum accuracy of 95.19% and 99.28%, respectively. We have outperformed state-of-the-art accuracies of the individual subjects S1, S2, S3, S4, S5, S6, S8, and the average accuracy of the dataset by 8.28%, 40.97%, 5.54%, 14.83%, 19.09%, 25.5%, 10.43%, and 12.82% respectively.},
}
@article {pmid40835615,
year = {2025},
author = {Zuo, C and Yin, Y and Wang, H and Zheng, Z and Ma, X and Yang, Y and Wang, J and Wang, S and Huang, ZG and Ye, C},
title = {Enhancing classification of a large lower-limb motor imagery EEG dataset for BCI in knee pain patients.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {1451},
pmid = {40835615},
issn = {2052-4463},
support = {NO.2024M764330//China Postdoctoral Science Foundation/ ; },
mesh = {Humans ; Algorithms ; *Brain-Computer Interfaces ; Deep Learning ; *Electroencephalography ; Imagery, Psychotherapy ; *Lower Extremity/physiopathology ; *Osteoarthritis, Knee/physiopathology/rehabilitation ; },
abstract = {Chronic knee osteoarthritis pain significantly impacts patients' quality of life and motor function. While motor imagery (MI)-based brain-computer interface (BCI) systems have shown promise in rehabilitation, their application to lower-limb conditions, particularly in pain patients, is underexplored. This study evaluates the feasibility of applying an MI-BCI model to a large dataset of knee pain patients, utilizing a novel deep learning algorithm for signal decoding. This EEG data was collected and analysed from 30 knee pain patients, revealing significant event-related (de)synchronization (ERD/ERS) during MI tasks. Traditional decoding algorithms achieved accuracies of 51.43%, 55.71%, and 76.21%, while the proposed OTFWRGD algorithm reached an average accuracy of 86.41%. This dataset highlights the potential of lower-limb MI in enhancing neural plasticity and offers valuable insights for future MI-BCI applications in lower-limb rehabilitation, especially for patients with knee pain.},
}
@article {pmid40835596,
year = {2025},
author = {Molokanova, E and Zhou, T and Vasupal, P and Cherkas, VP and Narute, P and Ferraz, MSA and Reiss, M and Almenar-Queralt, A and Chaldaiopoulou, G and de Souza, JS and Hemati, H and Downey, F and Olajide, OO and Thörn Perez, C and Puppo, F and Mesci, P and Pfaff, SL and Kireev, D and Muotri, AR and Savchenko, A},
title = {Non-genetic neuromodulation with graphene optoelectronic actuators for disease models, stem cell maturation, and biohybrid robotics.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {7499},
pmid = {40835596},
issn = {2041-1723},
support = {R01 MH127077/MH/NIMH NIH HHS/United States ; R43 MH124563/MH/NIMH NIH HHS/United States ; R01 MH123828/MH/NIMH NIH HHS/United States ; 1R01ES033636//U.S. Department of Health &amp; Human Services | NIH | National Institute of Environmental Health Sciences (NIEHS)/ ; 1R43AG076088//U.S. Department of Health &amp; Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)/ ; 1R01MH128365//U.S. Department of Health &amp; Human Services | NIH | National Institute of Mental Health (NIMH)/ ; 1R43NS122666//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; MH123828//U.S. Department of Health &amp; Human Services | NIH | National Institute of Mental Health (NIMH)/ ; R01 NS123642/NS/NINDS NIH HHS/United States ; S10 OD026929/OD/NIH HHS/United States ; R01 ES033636/ES/NIEHS NIH HHS/United States ; R56 MH128365/MH/NIMH NIH HHS/United States ; R01NS123642//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; R43 AG076088/AG/NIA NIH HHS/United States ; R43 NS122666/NS/NINDS NIH HHS/United States ; R01 NS105969/NS/NINDS NIH HHS/United States ; DISC2-13866//California Institute for Regenerative Medicine (CIRM)/ ; R44 DA050393/DA/NIDA NIH HHS/United States ; 5R44DA050393//U.S. Department of Health &amp; Human Services | NIH | National Institute on Drug Abuse (NIDA)/ ; 1R43MH124563//U.S. Department of Health &amp; Human Services | NIH | National Institute of Mental Health (NIMH)/ ; },
mesh = {*Graphite/chemistry ; *Robotics/methods/instrumentation ; Induced Pluripotent Stem Cells/cytology ; Humans ; Organoids/cytology ; Neurons/cytology ; Brain/cytology ; Alzheimer Disease/pathology/therapy ; Optogenetics/methods ; Cell Differentiation ; Animals ; },
abstract = {Light can serve as a tunable trigger for neurobioengineering technologies, enabling probing, control, and enhancement of brain function with unmatched spatiotemporal precision. Yet, these technologies often require genetic or structural alterations of neurons, disrupting their natural activity. Here, we introduce the Graphene-Mediated Optical Stimulation (GraMOS) platform, which leverages graphene's optoelectronic properties and its ability to efficiently convert light into electricity. Using GraMOS in longitudinal studies, we found that repeated optical stimulation enhances the maturation of hiPSC-derived neurons and brain organoids, underscoring GraMOS's potential for regenerative medicine and neurodevelopmental studies. To explore its potential for disease modeling, we applied short-term GraMOS to Alzheimer's stem cell models, uncovering disease-associated alterations in neuronal activity. Finally, we demonstrated a proof-of-concept for neuroengineering applications by directing robotic movements with GraMOS-triggered signals from graphene-interfaced brain organoids. By enabling precise, non-invasive neural control across timescales from milliseconds to months, GraMOS opens new avenues in neurodevelopment, disease treatment, and robotics.},
}
@article {pmid40835360,
year = {2025},
author = {Xiao, X and Li, H},
title = {Improving brain-computer interface performance with optimized frequency interaction and enhancement techniques: CFC-PSO-XGBoost (CPX).},
journal = {Medical engineering &amp; physics},
volume = {143},
number = {},
pages = {104392},
doi = {10.1016/j.medengphy.2025.104392},
pmid = {40835360},
issn = {1873-4030},
mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography ; Male ; Female ; Adult ; *Signal Processing, Computer-Assisted ; Young Adult ; Imagination ; Boosting Machine Learning Algorithms ; },
abstract = {PURPOSE: This work aims to increase the classification accuracy of motor imagery-based brain-computer interface (MI-BCI) by employing Cross-Frequency Coupling (CFC) and using spontaneous EEG as an input for the features to increase the system's robustness.<br><br>METHODS: Using a benchmark MI-BCI dataset, we examined 25 participants who completed two trials of a motor imagery task split into two classes. Our methodology involved preprocessing EEG data, using Phase-Amplitude Coupling (PAC) to extract CFC characteristics and Particle Swarm Optimization (PSO) to identify the optimal channels. The XGBoost method was utilized to classify the data, and 10-fold cross-validation was employed to verify the results. They are integrated into a single pipeline, named CFC-PSO-XGBoost (CPX).<br><br>RESULTS: With an average classification accuracy of 76.7 % &#xb1; 1.0 %, with only eight EEG channels, the suggested approach (CPX) outperformed cutting-edge techniques like CSP (60.2 % &#xb1; 12.4 %), FBCSP (63.5 % &#xb1; 13.5 %), FBCNet (68.8 % &#xb1; 14.6 %), and EEGNet. This significant improvement demonstrates the effectiveness of CFC features and PSO for channel selection in MI-BCI classification. Furthermore, the method was evaluated on the public BCI Competition IV-2a dataset, achieving an average multi-class classification accuracy of 78.3 % (95 % CI: 74.85-81.76 %), confirming the scalability and robustness of CPX on external benchmarks.<br><br>CONCLUSION: CPX leveraging spontaneous EEG signals and CFC features significantly improves classification accuracy. We anticipate this methodology will be a robust and practical solution in BCI applications, providing better brain-to-device communication with low-channel utilization and considerable performance metrics.},
}
@article {pmid40834866,
year = {2025},
author = {Gupta, D and Brangaccio, J and Mojtabavi, H and Wolpaw, J and Hill, NJ},
title = {Extracting robust single-trial somatosensory evoked potentials for non-invasive brain computer interfaces.},
journal = {Journal of neural engineering},
volume = {22},
number = {5},
pages = {},
pmid = {40834866},
issn = {1741-2552},
support = {P41 EB018783/EB/NIBIB NIH HHS/United States ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Somatosensory/physiology ; Male ; Adult ; Female ; *Electroencephalography/methods ; Young Adult ; Electric Stimulation/methods ; Middle Aged ; Signal-To-Noise Ratio ; Tibial Nerve/physiology ; },
abstract = {Objective.Reliable extraction of single-trial somatosensory evoked potentials (SEPs) is essential for developing brain-computer interface (BCI) applications to support rehabilitation after brain injury. For real-time feedback, these responses must be extracted prospectively on every trial, with minimal post-processing and artifact correction. However, noninvasive SEPs elicited by electrical stimulation at recommended parameter settings (0.1-0.2 msec pulse width, stimulation at or below motor threshold, 2-5 Hz frequency) are typically small and variable, often requiring averaging across multiple trials or extensive processing. Here, we describe and evaluate ways to optimize the stimulation setup to enhance the signal-to-noise ratio (SNR) of noninvasive single-trial SEPs, enabling more reliable extraction.Approach.SEPs were recorded with scalp electroencephalography in tibial nerve stimulation in thirteen healthy people, and two people with CNS injuries. Three stimulation frequencies (lower than recommended: 0.2 Hz, 1 Hz, 2 Hz) with a pulse width longer than recommended (1 msec), at a stimulation intensity based on H-reflex and M-wave at Soleus muscle were evaluated. Detectability of single-trial SEPs relative to background noise was tested offline and in a pseudo-online analysis, followed by a real-time demonstration.Mainresults.SEP N70 was observed predominantly at the central scalp regions. Online decoding performance was significantly higher with Laplacian filter. Generalization performance showed an expected degradation, at all frequencies, with an average decrease of 5.9% (multivariate) and 6.5% (univariate), with an AUC score ranging from 0.78-0.90. The difference across stimulation frequencies was not significant. In individuals with injuries, AUC of 0.86 (incomplete spinal cord injury) and 0.81 (stroke) was feasible. Real-time demonstration showed SEP detection with AUC of 0.89.Significance.This study describes and evaluates a system for extracting single-trial SEPs in real-time, suitable for a BCI-based operant conditioning. It enhances SNR of individual SEPs by alternate electrical stimulation parameters, dry headset, and optimized signal processing.},
}
@article {pmid40834823,
year = {2025},
author = {Zhang, Z and Meng, W and Sun, H and Pan, G},
title = {CausalCOMRL: Context-based offline meta-reinforcement learning with causal representation.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {193},
number = {},
pages = {107955},
doi = {10.1016/j.neunet.2025.107955},
pmid = {40834823},
issn = {1879-2782},
abstract = {Context-based offline meta-reinforcement learning (OMRL) methods have achieved appealing success by leveragingpre-collected offline datasets to develop task representations that guide policy learning. However, current context-based OMRL methods often introduce spurious correlations, where task components are incorrectly correlated due to confounders. These correlations can degrade policy performance when the confounders in the test taskdiffer from those in the training task. To address this problem, we propose CausalCOMRL, a context-based OMRL method that integrates causal representation learning. This approach uncovers causal relationships among the task components and incorporates the causal relationships into task representations, enhancing the generalizability of RL agents. We further improve the distinction of task representations from different tasks by using mutual information optimization and contrastive learning. Utilizing these causal task representations, we employSAC to optimize policies on meta-RL benchmarks. Experimental results show that CausalCOMRL achieves better performance than other methods on most benchmarks.},
}
@article {pmid40833932,
year = {2025},
author = {Foster, MW and Sanhueza, C and Bahr, E and Kuo, JL and Wu, Y and Komolafe, DO and Blanchette, V and Brinza, T and Morgan-Daniel, J and Oshodi, Y and Sodimu, KA and Omuku, N and Akisanya, E and Trinder, L and Willmoth, S and Simpson, N and White, N and Shaw, TA and Moyse Fenning, H and Runefelt, A and Kolnik, M and Pokorn, M and Fietje, N and Sajnani, N},
title = {The effects of viewing visual artwork on patients, staff, and visitors in healthcare settings: A scoping review.},
journal = {PloS one},
volume = {20},
number = {8},
pages = {e0328215},
pmid = {40833932},
issn = {1932-6203},
mesh = {Humans ; *Health Personnel/psychology ; *Art ; *Patients/psychology ; Delivery of Health Care ; },
abstract = {BACKGROUND: The integration of visual art in healthcare settings has been demonstrated to contribute to well-being. However, the impact of visual arts in healthcare has been primarily evaluated among patients. Viewing visual art could be a health resource to a greater number of people in healthcare settings, including patients, staff, and visitors.<br><br>METHODS: We conducted a scoping review to synthesize literature on the impact of viewing visual artwork among patients, staff, and visitors in healthcare settings related to the reported outcomes of well-being, wellness, and belonging. The review was informed by Arksey and O'Malley and Joanna Briggs Institute frameworks with masked pairs of reviewers. Included studies were in English, with no restrictions on geographical settings or publication dates. Nine academic databases and twelve gray literature sources were searched, in addition to a hand search and global call for submissions.<br><br>RESULTS: From an initial 25,222 records, 68 publications met inclusion criteria across 20 locations. 35 were peer-reviewed studies and 33 constituted gray literature. Included publications that reported sample sizes reflected a total of 6,006 participants with the majority being patients (3,133) followed by staff (1,343), visitors (32), and other/unspecified participants (996). Reported outcomes for patients indicated that visual arts in hospitals reduced heart rates, improved reported mental health outcomes, increased well-being, and provided a positive distraction. Reported outcomes for healthcare staff included an increased well-being, belonging, and capacity to prioritize patient needs. Reported outcomes for visitors consisted of an improved experience in healthcare environments and increased well-being.<br><br>CONCLUSIONS: Our synthesis of evidence indicates that integration of visual arts within healthcare settings has positive outcomes for its viewers. Our findings are useful to promote the generation of evidence that can reliably inform the design and experience of healthcare environments.},
}
@article {pmid40833894,
year = {2025},
author = {Liu, T and Wang, Z and Shakil, S and Tong, RK},
title = {Uncovering Low-Dimensional Manifolds of Neural Dynamics for Motor-Imagery Based Stroke Rehabilitation: An EEG-Based Brain-Computer Interface Study.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {3281-3292},
doi = {10.1109/TNSRE.2025.3600824},
pmid = {40833894},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Stroke Rehabilitation/methods ; Male ; Female ; *Imagination/physiology ; Middle Aged ; Algorithms ; Adult ; Aged ; Stroke/physiopathology ; Brain/physiopathology ; },
abstract = {Stroke rehabilitation aims to repair neural circuits and dynamics through the remapping of neuronal functions. However, there is currently a gap in understanding the alteration of neural population dynamics-the fundamental computational unit driving functions-under clinical settings. In this study, we introduced a novel method to identify stable low-dimensional structures of neural population dynamics in stroke patients during motor tasks. Using whole-brain EEG recordings from chronic stroke patients performing motor imagery (MI) tasks before and after brain-computer interface (BCI) training, as well as a public EEG dataset of acute stroke patients performing MI tasks, we projected EEG signals from sensor space to voxel space via source localization (eLORETA), simulating neural population activity in regions of interest. By applying dimensionality reduction, we successfully obtained low-dimensional neural manifolds to represent neural population dynamics. Our analysis revealed three key findings: (1) For right-handed patients, task-related low-dimensional dynamics in the related brain regions remain stable across subjects, with their features holding potential as biomarkers for stroke rehabilitation; (2) BCI training promotes global and sustained restoration of neural population dynamics; (3) EEG theta-band oscillations show strong correlation with these dynamics, highlighting their macroscopic nature. This study proposes a new, simple, and powerful tool for comprehension and validation of stroke rehabilitation mechanisms confirming the effectiveness of BCI training in restoring neural dynamics.},
}
@article {pmid40832972,
year = {2025},
author = {Wu, X and Tan, S and Zhang, Y and Yin, Y and Hsu, YC and Xue, R and Bai, R},
title = {Feasibility of relaxation-exchange magnetic resonance imaging (REXI) for measuring water exchange across the blood-CSF barrier in the human choroid plexus.},
journal = {Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism},
volume = {},
number = {},
pages = {271678X251369218},
doi = {10.1177/0271678X251369218},
pmid = {40832972},
issn = {1559-7016},
abstract = {The choroid plexus (CP) is important for cerebrospinal fluid (CSF) secretion and forms the blood-CSF barrier (BCSFB), which is essential for brain homeostasis. However, noninvasive methods for evaluating BCSFB function remain limited. Previously, we introduced a novel magnetic resonance imaging (MRI) technique, relaxation-exchange MRI (REXI), to quantify water exchange between CP and CSF in rats by leveraging the substantial difference in transverse relaxation times between CP tissue and CSF. Here, we adapted REXI to human applications by implementing segmented echo-planar imaging readout for enhanced acquisition speed, optimizing key parameters based on the Cramér-Rao lower bound, and refining the analysis methodology. We conducted simulations and phantom experiments for methodological validation. Subsequently, we performed a scan-rescan experiment in healthy volunteers (n = 6, mean-age ∼22 years), revealing relatively good repeatability in measurements of the apparent water exchange rate kBCSFB (intraclass correlation coefficient = 0.84). REXI detected a 34% decrease in kBCSFB among middle-aged healthy adults (n = 6, mean-age ∼55 years) compared with young healthy adults (n = 9, mean-age ∼23 years, p = 0.0048). These results demonstrate the feasibility of REXI in quantifying water exchange in human CP in vivo, providing a promising tool for future investigations of BCSFB function.},
}
@article {pmid40832808,
year = {2025},
author = {Ma, C and Li, W and Gao, C and Li, X and She, J and Zou, Z and Zhang, D and Jin, Y and Xu, C and Liu, B and Luo, Z},
title = {Multifunctional Hydrogel Materials for Advanced Neural Interfaces.},
journal = {Small methods},
volume = {9},
number = {9},
pages = {e01134},
doi = {10.1002/smtd.202501134},
pmid = {40832808},
issn = {2366-9608},
support = {32471387//National Natural Science Foundation of China/ ; 2024BCB002//Key Research and Development Program of Hubei Province/ ; },
mesh = {*Hydrogels/chemistry ; Humans ; *Brain-Computer Interfaces ; Biocompatible Materials/chemistry ; Animals ; Nanotechnology ; },
abstract = {Conventional rigid neural electrodes mismatch the soft, wet nature of neural tissue, hindering long-term stable interfaces. Multifunctional hydrogels, with their tissue-like compliance, ionic conductivity, and biocompatibility, offer a promising solution to bridge bioelectronic systems and neural tissues. This review systematically examines critical hydrogel properties-mechanical compliance, adhesion, biocompatibility, conductivity, and injectability-for neural interfacing. It summarizes recent advances in hydrogel-based technologies, including hydrogel coatings, conductive hydrogel electrodes, and integrated hydrogel electronics. Future challenges involve balancing biodegradation with long-term stability, developing advanced fabrication strategies, and ensuring chronic performance stability. Key future directions include optimizing hydrogel properties for chronic applications, creating smart-responsive hydrogels, integrating artificial intelligence, and advancing wireless systems. Leveraging materials science, bioengineering, and nanotechnology, hydrogel-based neural interfaces are poised to unlock unprecedented capabilities in brain-computer interfaces, neural prosthetics, neuromodulation, and regenerative therapies, heralding a paradigm shift in neurotechnology.},
}
@article {pmid40832231,
year = {2025},
author = {Jude, JJ and Haro, S and Levi-Aharoni, H and Hashimoto, H and Acosta, AJ and Card, NS and Wairagkar, M and Brandman, DM and Stavisky, SD and Williams, ZM and Cash, SS and Simeral, JD and Hochberg, LR and Rubin, DB},
title = {Decoding intended speech with an intracortical brain-computer interface in a person with longstanding anarthria and locked-in syndrome.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.08.12.668516},
pmid = {40832231},
issn = {2692-8205},
abstract = {Intracortical brain-computer interfaces (iBCIs) for decoding intended speech have provided individuals with ALS and severe dysarthria an intuitive method for high-throughput communication. These advances have been demonstrated in individuals who are still able to vocalize and move speech articulators. Here, we decoded intended speech from an individual with longstanding anarthria, locked-in syndrome, and ventilator dependence due to advanced symptoms of ALS. We found that phonemes, words, and higher-order language units could be decoded well above chance. While sentence decoding accuracy was below that of demonstrations in participants with dysarthria, we are able to attain an extensive characterization of the neural signals underlying speech in a person with locked-in syndrome and through our results identify several directions for future improvement. These include closed-loop speech imagery training and decoding linguistic (rather than phonemic) units from neural signals in middle precentral gyrus. Overall, these results demonstrate that speech decoding from motor cortex may be feasible in people with anarthria and ventilator dependence. For individuals with longstanding anarthria, a purely phoneme-based decoding approach may lack the accuracy necessary to support independent use as a primary means of communication; however, additional linguistic information embedded within neural signals may provide a route to augment the performance of speech decoders.},
}
@article {pmid40831738,
year = {2025},
author = {},
title = {Corrigendum to "A Fuzzy Shell for Developing an Interpretable BCI Based on the Spatiotemporal Dynamics of the Evoked Oscillations".},
journal = {Computational intelligence and neuroscience},
volume = {2025},
number = {},
pages = {9842516},
doi = {10.1155/cone/9842516},
pmid = {40831738},
issn = {1687-5273},
abstract = {[This corrects the article DOI: 10.1155/2021/6685672.].},
}
@article {pmid40831229,
year = {2025},
author = {Chen, D and Shi, J and Tao, B and Zhao, X and Zhao, Z and Li, S and Xu, Y and Ding, T and Zhang, P and Ye, Q and Chen, K and Wu, Z and Tang, Y and Jiang, W and Shu, K and Huang, L and You, Z and Zhang, P and Tang, Z},
title = {A Novel Transfer Learning-Based Hybrid EEG-fNIRS Brain-Computer Interface for Intracerebral Hemorrhage Rehabilitation.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e05426},
doi = {10.1002/advs.202505426},
pmid = {40831229},
issn = {2198-3844},
support = {2023BAA005//Major Program (JD) of Hubei Province/ ; YCJJ20251401//Fundamental Research Funds for the Central Universities/ ; 92148206//National Natural Science Foundation of China/ ; 2024020702030123//Key Research and Development Program of Wuhan/ ; 2024JCYJ044//Huazhong University of Science and Technology/ ; 2022ZHFY01//Research Fund of Tongji Hospital/ ; AI2024B03//Research Fund of Tongji Hospital/ ; },
abstract = {Motor imagery (MI)-based neurorehabilitation shows promise for intracerebral hemorrhage (ICH) recovery, yet conventional unimodal brain-computer interfaces (BCIs) face critical limitations in cross-subject generalization. This study presents a multimodal electroencephalography (EEG)-functional near-infrared spectroscopy (fNIRS) fusion framework incorporating a Wasserstein metric-driven source domain selection method that quantifies inter-subject neural distribution divergence. Through comparative neuroactivation analysis of 17 normal controls and 13 ICH patients during MI tasks, the transfer learning model achieved 74.87% mean classification accuracy on patient data when trained with optimally selected normal templates. Cross-validation on two public hybrid EEG-fNIRS datasets demonstrated generalizability, increasing baseline accuracy to 82.30% and 87.24%, respectively. The proposed system synergistically combines the millisecond temporal resolution of EEG with the hemodynamic spatial specificity of fNIRS, establishing the first clinically viable multimodal analytical protocol for ICH rehabilitation. This paradigm advances neurotechnology translation by paving the way for personalized rehabilitation regimens through robust cross-subject neural pattern transfer while addressing the critical barrier of neurophysiological heterogeneity in post-ICH populations.},
}
@article {pmid40830580,
year = {2025},
author = {Li, Y and Li, H and Wang, H and Wang, X},
title = {Exploring the therapeutic potential of psychedelics in treating substance use disorders.},
journal = {Molecular psychiatry},
volume = {30},
number = {12},
pages = {6134-6143},
pmid = {40830580},
issn = {1476-5578},
support = {T2350008//National Natural Science Foundation of China (National Science Foundation of China)/ ; 22207103//National Natural Science Foundation of China (National Science Foundation of China)/ ; T2341003//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Substance-Related Disorders/drug therapy ; *Hallucinogens/therapeutic use/pharmacology ; Psilocybin/therapeutic use/pharmacology ; Animals ; },
abstract = {Psychedelics, particularly psilocybin, have garnered significant attention as potential therapeutic tools for treating substance use disorders (SUDs), such as those related to alcohol, nicotine, heroin (an opioid), or cocaine. Traditional treatments often fall short, leading to high relapse rates and an urgent need for innovative approaches. This article explores the emerging role of psychedelics in SUDs therapy, highlighting their ability to disrupt maladaptive neural circuits, promote neuroplasticity, and facilitate profound psychological insights that address the root causes of SUDs. Clinical trials demonstrate promising results across various forms of SUDs, with psilocybin-assisted therapy showing significant reductions in substance use and improved mental health outcomes. Despite the potential, challenges such as legal barriers, safety concerns, and the need for more rigorous research remain. The future of psychedelics in SUDs treatment is cautiously optimistic, with the possibility of transforming the field of SUDs therapy and offering hope to millions of individuals struggling with SUDs.},
}
@article {pmid40830488,
year = {2025},
author = {Schippers, A and Vansteensel, MJ and Freudenburg, ZV and Luo, S and Crone, NE and Ramsey, NF},
title = {Don't put words in my mouth: speech perception can falsely activate a brain-computer interface.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {181},
pmid = {40830488},
issn = {1743-0003},
support = {101070939//HORIZON EUROPE European Innovation Council/ ; UH3 NS114439/NS/NINDS NIH HHS/United States ; UGT7685//Stichting voor de Technische Wetenschappen/ ; ERC-Advanced 'iConnect' project, grant ADV 320708/ERC_/European Research Council/International ; UH3NS114439/NS/NINDS NIH HHS/United States ; U01DC016686/DC/NIDCD NIH HHS/United States ; PPS-2021-02//Dutch Brain Foundation/ ; U01 DC016686/DC/NIDCD NIH HHS/United States ; SGW-406-18-GO-086//Nederlandse Organisatie voor Wetenschappelijk Onderzoek/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Speech Perception/physiology ; Male ; Adult ; Electrocorticography ; Female ; Speech/physiology ; *Sensorimotor Cortex/physiology ; Support Vector Machine ; },
abstract = {BACKGROUND: Recent studies have demonstrated that speech can be decoded from brain activity which in turn can be used for brain-computer interface (BCI)-based communication. It is however also known that the area often used as a signal source for speech decoding BCIs, the sensorimotor cortex (SMC), is also engaged when people perceive speech, thus making speech perception a potential source of false positive activation of the BCI. The current study investigated if and how speech perception may interfere with reliable speech BCI control.<br><br>METHODS: We recorded high-density electrocorticography (HD-ECoG) data from five subjects while they performed a speech perception and a speech production task. We first evaluated whether speech perception and production activated the SMC. Second, we trained a support-vector machine (SVM) on the speech production data (including rest). To test the occurrence of false positives, this decoder was then tested on speech perception data where every perception segment that was classified as a produced syllable rather than rest was considered a false positive. Finally, we investigated whether perceived speech could be distinguished from produced speech and rest.<br><br>RESULTS: Our results show that both the perception and production of speech activate the SMC. In addition, we found that decoders that are highly reliable at detecting self-produced syllables from brain signals may generate false positive BCI activations during the perception of speech and that it is possible to distinguish perceived speech from produced speech and rest, with high accuracy.<br><br>CONCLUSIONS: We conclude that speech perception can interfere with reliable BCI control, and that efforts to limit the occurrence of false positives during daily-life BCI use should be implemented in BCI design to increase the likelihood of successful adoptation by end users.},
}
@article {pmid40830054,
year = {2025},
author = {Brannigan, J and Kian, A and Eiber, C and Tarigoppula, VSA and Bogard, J and Siddiqui, AH and Rind, G and Berenstein, A and Majidi, S and Oxley, TJ},
title = {Characterizing superficial cerebral cortical venous anatomy for endovascular device implantation: a cross-sectional imaging study.},
journal = {Journal of neurointerventional surgery},
volume = {},
number = {},
pages = {},
doi = {10.1136/jnis-2025-023532},
pmid = {40830054},
issn = {1759-8486},
abstract = {BACKGROUND: Neurovascular electronic devices, including brain-computer interfaces (BCIs), offer a minimally invasive approach to diagnosing and treating neurological disorders. Implanting BCIs in superficial cortical veins, owing to their proximity to sensorimotor cortices, may improve motor function restoration. However, marked anatomical variability and the complex anteriorly directed connection with the superior sagittal sinus (SSS) complicate device navigation. This exploratory study aimed to characterize cortical venous anatomy to inform device design and procedural planning.<br><br>METHODS: Retrospective imaging data from 25 patients were analyzed using magnetic resonance venography (MRV) and computed tomography venography (CTV). Vessel segmentation and analysis quantified parameters such as vein presence, diameter, length, angulation, and tortuosity. In 12 patients, T1-weighted magnetic resonance imaging (MRI) was used to extract cortical gyri and sulci, assessing vessel-cortex relationships.<br><br>RESULTS: The superior anastomotic vein (vein of Trolard) was identified bilaterally in 84% of patients, with a mean entrance diameter of 4.4&#x2009;mm. Frequent transient constrictions (<2&#x2009;mm) were reported. The precentral vein was present bilaterally in 52% of cases. Most cortical veins exhibited take-off angles >90 degrees from the SSS, presenting challenges for endovascular navigation, with overall considerable anatomical variability observed.<br><br>CONCLUSION: The vein of Trolard shows promise as a target for endovascular BCIs given its consistent presence and favorable dimensions. Nonetheless, constrictions and steep angulation at the SSS confluence pose challenges for device deployment. A new framework is necessary for the classification of cortical venous anatomy, to guide patient selection and procedural planning, which will require further development and validation.},
}
@article {pmid40829351,
year = {2025},
author = {Yadav, H and Maini, S},
title = {Decoding brain signals: A comprehensive review of EEG-Based BCI paradigms, signal processing and applications.},
journal = {Computers in biology and medicine},
volume = {196},
number = {Pt C},
pages = {110937},
doi = {10.1016/j.compbiomed.2025.110937},
pmid = {40829351},
issn = {1879-0534},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; *Brain/physiology ; Algorithms ; Evoked Potentials, Visual/physiology ; Event-Related Potentials, P300/physiology ; },
abstract = {Brain-computer interface (BCI) based on electroencephalography (EEG) is a fast-developing field with a wide range of applications such as assistive technology, neurorehabilitation, entertainment, cognitive enhancement, etc. Since EEG is a non-invasive technique that captures brain activity in real time, it is ideally suited for developing interfaces that enable direct brain-to-device communication. The different paradigms utilised in EEG-based BCIs, such as Motor Imagery (MI), Steady-State Visual Evoked Potentials (SSVEP), P300 Event-related Potentials (ERP), and Hybrid paradigms that integrate several strategies for enhanced performance, are the main emphasis of this systematic review. This paper also explores the signal processing techniques, feature extraction strategies, and classification algorithms necessary for handling low-amplitude and noisy EEG recordings. The applications of BCI in different fields, as well as the challenges and possible solutions of EEG-based BCIs, are also covered in this article. Overall, the state-of-the-art in EEG-based BCIs is thoroughly reviewed in this comprehensive review article, which also identifies important areas for further study and technological advancement.},
}
@article {pmid40829175,
year = {2025},
author = {Liu, N and Wang, J and Wang, H and Gao, B and Lin, Z and Xu, TL and Duan, S and Xu, H},
title = {A noncanonical parasubthalamic nucleus-to-extended amygdala circuit converts chronic social stress into anxiety.},
journal = {The Journal of clinical investigation},
volume = {135},
number = {16},
pages = {},
pmid = {40829175},
issn = {1558-8238},
mesh = {Animals ; Mice ; *Stress, Psychological/physiopathology/metabolism/pathology ; *Anxiety/physiopathology/metabolism/pathology ; *Amygdala/physiopathology/metabolism/pathology ; Male ; *Septal Nuclei/physiopathology/metabolism ; Chronic Disease ; *Thalamus/physiopathology ; Shal Potassium Channels/metabolism/genetics ; Neurons/metabolism ; },
abstract = {Anxiety disorders pose a substantial threat to global mental health, with chronic stress identified as a major etiologic factor. Over the past few decades, extensive studies have revealed that chronic stress induces anxiety states through a distributed neuronal network of interconnected brain structures. However, the precise circuit mechanisms underlying the transition from chronic stress to anxiety remain incompletely understood. Employing the chronic social defeat stress (CSDS) paradigm in mice, we uncovered a critical role of the parasubthalamic nucleus (PSTh) in both the induction and expression of anxiety-like behavior. The anxiogenic effect was mediated by an excitatory trisynaptic circuitry involving the lateral parabrachial nucleus (LPB), PSTh, and bed nucleus of the stria terminalis (BNST). Furthermore, CSDS downregulated Kv4.3 channels in glutamatergic neurons of the PSTh. Reexpression of these channels dampened neuronal overexcitability and alleviated anxiety-like behavior in stressed animals. In parallel with the well-known anxiety network centered on the amygdala, here we identify a noncanonical LPB-PSTh-BNST pathway in the transformation of stress into anxiety. These findings suggest that the PSTh may serve as a potential therapeutic target for anxiety-related disorders.},
}
@article {pmid40828021,
year = {2025},
author = {Martinez-Addiego, F and Liu, Y and Moon, K and Shytle, E and Amaral, L and O'Brien, C and Sen, S and Riesenhuber, M and Culham, JC and Striem-Amit, E},
title = {Action-type mapping principles extend beyond evolutionarily conserved actions, even in people born without hands.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {122},
number = {34},
pages = {e2503188122},
pmid = {40828021},
issn = {1091-6490},
support = {22YF1454200//Shanghai Youth Science and Technology Innovation Plan/ ; },
mesh = {Humans ; Magnetic Resonance Imaging ; Male ; Female ; *Hand/physiology ; Adult ; *Brain Mapping/methods ; *Sensorimotor Cortex/physiology ; Biological Evolution ; *Motor Cortex/physiology ; Young Adult ; Psychomotor Performance/physiology ; },
abstract = {How are actions represented in the motor system? Although the sensorimotor system is broadly organized somatotopically, higher-level sensorimotor areas encode action-type information for reaching and grasping actions-regardless of the acting body part. Does the brain similarly support generalization across acting body parts for more evolutionarily recent actions, such as tool-use? We tested whether there is a body-part-independent action-type organization in sensorimotor areas by examining fMRI responses for tool-use actions that participants performed with their hands or feet. We additionally included individuals born without hands to test whether hand sensorimotor experience is necessary for the development of this action-type organization. Across analyses, we found a consistent dissociation in the motor system. The primary sensorimotor cortices encoded concrete, body-part specific information in both groups. In contrast, higher-level motor areas within the tool-use network represent abstract, action-type information independent of the body part for both groups. Together, our results suggest that the hierarchical organization of the motor system is not dependent on a long evolutionary history of an action. Further, this organization is not dependent on an individual's manual sensorimotor experience. Our results also show that the functional reorganization in congenital handlessness follows the hierarchical organization of the intact cortex, revealing the limitations of brain plasticity. Finally, the results support using a readout of a more abstract code for hierarchical brain-computer interfaces.},
}
@article {pmid40827135,
year = {2025},
author = {Qiao, MX and Yu, H and Fu, Z and Wei, W and Li, XJ and Deng, W and Guo, WJ and Li, T},
title = {Combination Therapy Against Mood and Anxiety Disorders: Association Between Efficacy and White Blood Cell Count.},
journal = {Neuropsychiatric disease and treatment},
volume = {21},
number = {},
pages = {1655-1668},
pmid = {40827135},
issn = {1176-6328},
abstract = {BACKGROUND: Numerous studies suggest that hyperactivation of the immuno-inflammatory system, as reflected in cytokine levels, is associated with more severe symptoms in mood and anxiety disorders and weaker response to treatment. Here we examined whether the efficacy of a combination of bright light therapy, repetitive transcranial magnetic stimulation and medication is associated with another immuno-inflammatory index, white blood cell count, before and/or after treatment, in a retrospective observational study.<br><br>METHODS: We retrospectively analyzed 467 inpatients with major depressive, bipolar, or generalized anxiety disorder who were treated with combination therapy for at least one week at Hangzhou Seventh People's Hospital between April 2022 and April 2024. Potential associations between remission incidences within four weeks after treatment and white blood cell count both before treatment and post-treatment were explored. We used mixed-effects linear modeling to examine the association between treatment characteristics and changes in white blood cell count and depressive symptoms.<br><br>RESULTS: Bipolar and major depressive disorders were associated with significantly higher white blood cell counts at baseline than generalized anxiety disorder as well as with significantly lower remission incidences. Bright light therapy's effects depended on baseline inflammation, more sessions led to greater reductions in the Hamilton Depression Rating Scale score with low baseline white blood cell count, and greater decreases in white blood cell count with high baseline count. In contrast, repetitive transcranial magnetic stimulation sessions showed no association with white blood cell count.<br><br>CONCLUSION: These results highlight the need to account for an individual's immuno-inflammatory state when personalizing treatment for mental health disorders.},
}
@article {pmid40825359,
year = {2025},
author = {Voskoboynikov, A and Aliverdiev, M and Nekrasova, Y and Semenkov, I and Skalnaya, A and Sinkin, M and Ossadtchi, A},
title = {Towards stimulation-free automatic electrocorticographic speech mapping in neurosurgery patients.},
journal = {Journal of neural engineering},
volume = {22},
number = {5},
pages = {},
doi = {10.1088/1741-2552/adfc9c},
pmid = {40825359},
issn = {1741-2552},
mesh = {Humans ; *Electrocorticography/methods ; *Speech/physiology ; Male ; *Brain Mapping/methods ; Female ; Adult ; *Neurosurgical Procedures/methods ; Middle Aged ; Machine Learning ; Electrodes, Implanted ; Young Adult ; },
abstract = {Objective.The precise mapping of speech-related functions is crucial for successful neurosurgical interventions in epilepsy and brain tumor cases. Traditional methods like electrocortical stimulation mapping (ESM) are effective but carry a significant risk of inducing seizures.Methods.To address this, we have prepared a comprehensive ESM + electrocorticographic mapping (ECM) dataset from 14 patients with chronically implanted stereo-EEG electrodes. Then we explored several compact machine learning (ML) approaches to convert the ECM signals to the ground truth derived from the risky ESM procedure. Both procedures involved the standard picture naming task. As features, we used gamma-band power within successive temporal windows in the data averaged with respect to picture and voice onsets. We focused on a range of classifiers, including XGBoost, linear support vector classification (SVC), regularized logistic regression, random forest,k-nearest neighbors, decision tree, multi-Layer perceptron, AdaBoost and Gaussian Naive Bayes classifiers and equipped them with confidence interval estimates, crucial in a real-life application. We validated the ML approaches using a leave-one-patient-out procedure and computed ROC and Precision-Recall curves for various feature combinations.Results.For linear SVC we achieved ROC-AUC and PR-AUC scores of 0.91 and 0.88, respectively, which effectively distinguishes speech-related from non-related iEEG channels. We have also observed that the use of information on the voice onset moment notably improved the classification accuracy.Significance.We have for the first time rigorously compared the ECM and ESM results and mimicked a real-life use of the ECM technology. We have also provided public access to the comprehensive ECM+ESM dataset to pave the road towards safer and more reliable eloquent cortex mapping procedures.},
}
@article {pmid40824102,
year = {2025},
author = {Fang, P and Li, GH and Rao, YB and Cheng, C and He, WL and Wang, J and Li, XY and Lu, YR},
title = {Serum Cytokines as Biomarkers for Comorbid Anxiety in Postpartum Depression: A Machine Learning Approach.},
journal = {Psychiatry and clinical psychopharmacology},
volume = {35},
number = {3},
pages = {245-252},
pmid = {40824102},
issn = {2475-0581},
abstract = {Background: This study aimed to investigate the serum levels of interleukin 2, interleukin 6 (IL-6), interleukin 10, and tumor necrosis factor-alpha in patients with postpartum depression (PPD) and to explore their potential as biomarkers for PPD and comorbid anxiety using machine learning techniques. Methods: Serum samples were collected from 53 patients diagnosed with PPD and 35 healthy controls. Cytokine levels were measured using a flow cytometer analyzer. Machine learning models, including Multinomial Logistic Regression, Decision Trees, Random Forest, and Support Vector Machines (SVMs), were developed to predict PPD and comorbid anxiety based on cytokine levels. Results: Patients with PPD exhibited significantly elevated serum levels of IL-6 compared to the control group. A positive correlation was found between psychological anxiety scores and IL-6 levels (r = 0.483, P < .001). Machine learning models, particularly the Random Forest and SVMs, demonstrated high accuracy in predicting PPD and comorbid anxiety, with IL-6 being identified as a key predictor. Conclusion: The activation of serum cytokines is evident in PPD patients, with IL-6 potentially serving as an auxiliary biomarker for the diagnosis of PPD and comorbid anxiety. The incorporation of machine learning techniques has enhanced the understanding of the complex relationships between cytokines and PPD, with IL-6 levels showing a correlation to the severity of clinical symptoms.},
}
@article {pmid40821552,
year = {2025},
author = {Priya, S and Mohan, S and Kuppusamy, R and Suyambulingam, I and Baby, B and Ramesh, R and Han, SS},
title = {Advances in Bio-Microelectromechanical System-Based Sensors for Next-Generation Healthcare Applications.},
journal = {ACS omega},
volume = {10},
number = {31},
pages = {34088-34105},
pmid = {40821552},
issn = {2470-1343},
abstract = {Microelectromechanical system (MEMS)-based sensors have become essential in various fields, including healthcare, automotive, and industrial applications. These sensors integrate mechanical structures and electronics on a single chip, allowing precise, compact, and efficient measurements of parameters like pressure, force, acceleration, and chemical reactions. In this context, this review article presents the essential role of MEMS sensors in healthcare applications. In healthcare, MEMS sensors are widely used for monitoring vital signs, detecting glucose levels, managing cardiovascular and intracranial pressure, and enhancing drug delivery systems. They are also key in tactile sensing during surgeries and in improving neuromuscular monitoring through electromyography (EMG). Despite their advantages, such as small size, low energy consumption, and high performance, MEMS sensors face challenges like sensitivity drift, durability concerns, and long-term calibration stability. This article addresses these limitations and highlights ongoing advancements aimed at improving sensor accuracy, energy efficiency, and adaptability to diverse environments. By examining current trends and innovations, this review provides insights into how MEMS technology is driving breakthroughs in biomedical research, early cancer diagnosis, and bioimaging treatment. We have discussed inertial sensors, MEMS-based glucose sensors, intraocular pressure (IOP) sensors, intracranial pressure sensors, cardiovascular pressure sensors, tactile sensors, and smart inhalers. In addition, we have explored recent advancements in MEMS technologies applied to healthcare, particularly in microfluidic MEMS chips and brain-machine interfaces, with a focus on developments from the last five years. Future research directions focus on enhancing the flexibility, reliability, and energy efficiency of MEMS sensors, positioning them as key components in the next generation of healthcare and medical devices.},
}
@article {pmid40819304,
year = {2026},
author = {Kontogianni, A and Yang, H and Chen, W},
title = {Brain insulin resistance and neuropsychiatric symptoms in Alzheimer's disease: A role for dopamine signaling.},
journal = {Neural regeneration research},
volume = {21},
number = {5},
pages = {1995-1996},
doi = {10.4103/NRR.NRR-D-25-00281},
pmid = {40819304},
issn = {1673-5374},
}
@article {pmid40819087,
year = {2025},
author = {Kinreich, S},
title = {Neural transmission in the wired brain, new insights into an encoding-decoding-based neuronal communication model.},
journal = {Translational psychiatry},
volume = {15},
number = {1},
pages = {288},
pmid = {40819087},
issn = {2158-3188},
support = {R01 AA029448/AA/NIAAA NIH HHS/United States ; AA029448//U.S. Department of Health &amp; Human Services | NIH | National Institute on Alcohol Abuse and Alcoholism (NIAAA)/ ; },
mesh = {Humans ; Male ; Female ; Adult ; Aged ; Middle Aged ; Electroencephalography ; Young Adult ; Child ; Adolescent ; *Brain/physiopathology/physiology ; Aged, 80 and over ; Child, Preschool ; Schizophrenia/physiopathology ; Attention Deficit Disorder with Hyperactivity/physiopathology ; *Synaptic Transmission/physiology ; Parkinson Disease/physiopathology ; *Models, Neurological ; Obsessive-Compulsive Disorder/physiopathology ; },
abstract = {Brain activity is known to be rife with oscillatory activity in different frequencies, which are suggested to be associated with intra-brain communication. However, the specific role of frequencies in neuronal information transfer is still an open question. To this end, we utilized EEG resting state recordings from 5 public datasets. Overall, data from 1668 participants, including people with MDD, ADHD, OCD, Parkinson's, Schizophrenia, and healthy controls aged 5-89, were part of the study. We conducted a running window of Spearman correlation between the two frontal hemispheres' Alpha envelopes. The results of this analysis revealed a unique pattern of correlation states alternating between fully synchronized and desynchronized several times per second, likely due to the interference pattern between two signals of slightly different frequencies, also named "Beating". Subsequent analysis showed this unique pattern in every pair of ipsilateral/contralateral, across frequencies, either in eyes closed or open, and across all ages, underscoring its inherent significance. Biomarker analysis revealed significantly lower synchronization and higher desynchronization for people older than 50 compared to younger ones and lower ADHD desynchronization compared to age-matched controls. Importantly, we propose a new brain communication model in which frequency modulation creates a binary message encoded and decoded by brain regions for information transfer. We suggest that the binary-like pattern allows the neural information to be coded according to certain physiological and biological rules known to both the sender and recipient. This digital-like scheme has the potential to be exploited in brain-computer interaction and applied technologies such as robotics.},
}
@article {pmid40819020,
year = {2025},
author = {Shao, X and Chung, RS and Cavaleri, JM and Del Campo-Vera, RM and Parra, M and Sundaram, S and Zhang, S and Surabhi, A and McGinn, RJ and Liu, CY and Kellis, SS and Lee, B},
title = {Directional hand movement can be classified from insular cortex SEEG signals using recurrent neural networks and high-gamma band features.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {29993},
pmid = {40819020},
issn = {2045-2322},
support = {K23 NS114190/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; Male ; Female ; Adult ; *Hand/physiology ; Movement/physiology ; *Gamma Rhythm/physiology ; *Electroencephalography/methods ; *Insular Cortex/physiology/physiopathology ; *Neural Networks, Computer ; Middle Aged ; Machine Learning ; Motor Cortex/physiology ; Young Adult ; Brain-Computer Interfaces ; Recurrent Neural Networks ; },
abstract = {Motor BCIs, with the help of Artificial Intelligence (AI) and machine learning, have shown promise in decoding neural signals for restoring motor function. Structures beyond motor cortex have provided additional sources for movement signals. New evidence points to the role of the insula in motor control, specifically directional hand-movements. In this study, we applied AI and machine learning techniques to decode directional hand-movements from high-gamma band (70-200 Hz) activity in the insular cortex. Seven participants with medication-resistant epilepsy underwent stereo electroencephalographic (SEEG) implantation of depth electrodes for seizure monitoring in the insula. SEEG data were sampled throughout a cued motor task involving three conditions: left-hand movement, right-hand movement, or no movement. Neural signal processing focused on high-gamma band activity. Demixed Principal Component Analysis (dPCA) was used for dimension reduction (d = 10) and feature extraction from the time-frequency analysis. For movement classification, we implemented a bidirectional Long Short-Term Memory (LSTM) architecture with a single layer, utilizing the capacity to process temporal sequences in forward and back directions for optimal decoding of movement direction. Our findings revealed robust directional-specific high-gamma modulation within the insular cortex during motor execution. Temporal decomposition through dPCA demonstrated distinct spatiotemporal patterns of high-gamma activity across movement conditions. Subsequently, LSTM networks successfully decoded these condition-specific neural signatures, achieving a classification accuracy of 72.6% ± 13.0% (mean ± SD), which significantly exceeded chance-level performance of 33.3% (p < 0.0001, n = 16 sessions). Furthermore, we identified a strong negative correlation between temporal distance of training-testing sessions and decoding performance (r = -0.868, p < 0.0001), indicating temporal difference of the neural representations. Our study highlights the potential role of deep brain structures, such as the insula, in conditional movement discrimination. We demonstrate that LSTM networks and high-gamma band analysis can advance the understanding of neural mechanisms underlying movement. These insights may pave the way for improvements in SEEG-based BCI.},
}
@article {pmid40818100,
year = {2025},
author = {Wei, W and Li, C and Li, W and Jiang, M and Zhang, X and Xing, L and Qian, Z and Jin, X},
title = {Study of a non-water-cooled microwave ablation needle based on a vacuum needle rod to achieve carbonization-free operation: design, simulation, and experimental research.},
journal = {Minimally invasive therapy &amp; allied technologies : MITAT : official journal of the Society for Minimally Invasive Therapy},
volume = {},
number = {},
pages = {1-12},
doi = {10.1080/13645706.2025.2543894},
pmid = {40818100},
issn = {1365-2931},
abstract = {BACKGROUND: At present, the microwave ablation needle used in clinic needs to add water circulation in the needle rod to reduce the rod temperature. However, the water circulation will take away a lot of heat during the ablation process, which requires increasing the ablation dose to achieve the expected thermal coagulation target volume. This undoubtedly increases the risk of carbonization.<br><br>METHODS: A design scheme of non-water-cooled microwave ablation needle based on double-layer vacuum structure was proposed. Two types of non-water-cooled microwave ablation needles with long and short needles were designed, and the ablation simulation was carried out by establishing the finite element simulation model.<br><br>RESULTS: Simulation and experimental results indicate that, at 20&#x2009;W power, the long-needle vacuum tube ablation needle can create a carbonization-free solidification zone with a length of 34&#x2009;mm after 180&#x2009;s of ablation, whereas the short-needle vacuum tube ablation needle requires 300&#x2009;s to form a similar zone with a length of 30&#x2009;mm. Additionally, the axial ratio of the solidification zone created by the long-needle vacuum tube ablation needle exceeds that of the short-needle one. Consequently, the long-needle vacuum tube ablation needle is more apt for creating a larger solidification zone with minimal carbonization, while also achieving a more spherical shape.By comparing the ablation effects of long needle vacuum tube ablation needle and ky-2450b1 under low power,It is verified that the vacuum tube non-water-cooled ablation needle can ensure the effective ablation volume and non carbonization ablation under low-power and short-time ablation, which provides an important technical scheme for clinical optimization of the therapeutic effect of microwave ablation.<br><br>CONCLUSIONS: The LPH-W-F-MWA is more adept at creating a larger coagulation zone with minimal carbonization, achieving a more spherical shape to a greater extent. This ensures both an effective ablation volume and char-free ablation, offering a crucial technical solution for optimizing the therapeutic effect of MWA in clinical settings.},
}
@article {pmid40817330,
year = {2025},
author = {Jiang, L and Genon, S and Ye, J and Zhu, Y and Wang, G and He, R and Valdes-Sosa, PA and Wan, F and Yao, D and Eickhoff, SB and Dong, D and Li, F and Xu, P},
title = {Gene transcription, neurotransmitter, and neurocognition signatures of brain structural-functional coupling variability.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {7623},
pmid = {40817330},
issn = {2041-1723},
support = {U54 MH091657/MH/NIMH NIH HHS/United States ; },
mesh = {Humans ; *Brain/physiology/anatomy &amp; histology/metabolism/diagnostic imaging ; Male ; Female ; Adult ; *Neurotransmitter Agents/metabolism ; *Cognition/physiology ; Young Adult ; *Transcription, Genetic ; Brain Mapping ; Transcriptome ; Magnetic Resonance Imaging ; Middle Aged ; Adolescent ; Emotions/physiology ; },
abstract = {The relationship between brain structure and function, known as structural-functional coupling (SFC), is highly dynamic. However, the temporal variability of this relationship, referring to the fluctuating extent to which functional profiles interact with anatomy over time, remains poorly elucidated. Here, we propose a framework to quantify SFC temporal variability and determine its neurocognitive map, genetic architecture, and neurochemical basis in 1206 healthy human participants. Results reveal regional heterogeneity in SFC variability and a composite emotion dimension co-varying with variability patterns involving the dorsal attention, somatomotor, and visual networks. The transcriptomic signatures of SFC variability are enriched in synapse- and cell cycle-related biological processes and implicated in emotion-related disorders. Moreover, regional densities of serotonin, glutamate, γ-aminobutyric acid, and opioid systems are predictive of SFC variability across the cortex. Collectively, SFC variability mapping provides a biologically plausible framework for understanding how SFC fluctuates over time to support macroscale neurocognitive specialization.},
}
@article {pmid40816597,
year = {2025},
author = {Xu, T and Yu, L and Zheng, Y and Huang, S},
title = {BrainVision: Cross-domain EEG decoding for visual content retrieval and reconstruction.},
journal = {Neuroscience},
volume = {584},
number = {},
pages = {190-205},
doi = {10.1016/j.neuroscience.2025.07.047},
pmid = {40816597},
issn = {1873-7544},
mesh = {Humans ; *Electroencephalography/methods ; *Brain/physiology ; *Visual Perception/physiology ; Male ; Female ; Adult ; Emotions/physiology ; },
abstract = {Understanding human visual intent through brain signals remains a fundamental challenge in neuroscience and artificial intelligence. Despite recent advances in brain decoding, existing approaches typically operate within isolated datasets and modalities, limiting their generalization capabilities. This paper introduces BrainVision, a novel framework that bridges visual recognition and emotional EEG datasets to enable comprehensive visual content generation through cross-domain learning. BrainVision addresses the critical challenge of leveraging complementary information across heterogeneous EEG sources by implementing a unified cross-domain alignment strategy. Our framework maps neural patterns from the THINGS-EEG visual recognition dataset and the DEAP emotional response dataset into a shared representation space, enabling three distinct visual output capabilities: (1) accurate content retrieval and classification, (2) detailed linguistic descriptions through adapter-enhanced large language models, and (3) high-fidelity image reconstruction via stable diffusion models. Experimental results demonstrate that BrainVision significantly outperforms single-domain approaches, achieving a 15.3% increase in retrieval accuracy and a 12.7% improvement in structural similarity for reconstructed images compared to state-of-the-art methods. Furthermore, our framework demonstrates robust zero-shot generalization, maintaining 82% of its performance when applied to novel stimuli not seen during training. The multi-modal outputs provide complementary interpretations of neural activity, offering a more comprehensive understanding of visual intent. Our findings establish that integrating diverse neural datasets substantially enhances the capabilities of brain decoding systems, providing a promising direction for developing more intuitive and versatile brain-computer interfaces. BrainVision represents an important step toward bridging the gap between neural activity and rich visual experiences across different cognitive domains.},
}
@article {pmid40816538,
year = {2025},
author = {Kong, K and Wang, J and Li, M and Zhang, T and Qi, E and Zhao, Q},
title = {Action sequence guidance with exposure trajectory technology improves performance of motor imagery-based brain-computer interface.},
journal = {Journal of neuroscience methods},
volume = {423},
number = {},
pages = {110553},
doi = {10.1016/j.jneumeth.2025.110553},
pmid = {40816538},
issn = {1872-678X},
mesh = {Humans ; *Brain-Computer Interfaces ; *Imagination/physiology ; Male ; Electroencephalography/methods ; Female ; Adult ; Young Adult ; *Brain/physiology ; *Psychomotor Performance/physiology ; Movement/physiology ; },
abstract = {BACKGROUND: The paradigms greatly influence the performance of motor imagery (MI)-based brain-computer interfaces (BCI) by guiding subjects to imagine. How to make the guidance clear and intuitive is important for MI-BCI to improve performance.<br><br>NEW METHODS: This study proposes a novel MI-BCI paradigm based on action sequence (AS) guidance, which visualizes and choreographs sequential actions to support motor imagery. In a drawing task, the action exposure trajectory technique presents a gray nib at the starting point of the next stroke while the current stroke is being drawn, highlighting the order and details of the movement. Ten subjects participated in offline and online experiments under both AS and traditional MI conditions. EEG activation regarding multiple frequencies and periods, and MI-BCI performance are evaluated.<br><br>RESULTS: The AS paradigm evokes more significant ERD/ERS features, and improves offline and online BCI accuracies and information transfer rates to 85.69&#x202f;%, 78.77&#x202f;%, and 15.60 bits/min, which are 8.37&#x202f;%, 7.95&#x202f;%, and 7.13 bits/min higher than the traditional paradigm. In addition, the subjects are demonstrated more comfortable subjective feelings.<br><br>The AS paradigm offers clearer and more intuitive guidance, enhances EEG feature activation, and significantly improves MI-BCI performance in both offline and online experiments.<br><br>CONCLUSIONS: Dynamic action sequences action with exposure trajectory technique could enhance the subject's brian activation by offering richer content and more intuitive guidance, providing a new way for prompting BCI performance.},
}
@article {pmid40816265,
year = {2025},
author = {Kunz, EM and Abramovich Krasa, B and Kamdar, F and Avansino, DT and Hahn, N and Yoon, S and Singh, A and Nason-Tomaszewski, SR and Card, NS and Jude, JJ and Jacques, BG and Bechefsky, PH and Iacobacci, C and Hochberg, LR and Rubin, DB and Williams, ZM and Brandman, DM and Stavisky, SD and AuYong, N and Pandarinath, C and Druckmann, S and Henderson, JM and Willett, FR},
title = {Inner speech in motor cortex and implications for speech neuroprostheses.},
journal = {Cell},
volume = {188},
number = {17},
pages = {4658-4673.e17},
pmid = {40816265},
issn = {1097-4172},
support = {U01 DC019430/DC/NIDCD NIH HHS/United States ; /HHMI/Howard Hughes Medical Institute/United States ; DP2 NS127291/NS/NINDS NIH HHS/United States ; F32 HD112173/HD/NICHD NIH HHS/United States ; DP2 DC021055/DC/NIDCD NIH HHS/United States ; K23 DC021297/DC/NIDCD NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; },
mesh = {Humans ; *Motor Cortex/physiology ; *Brain-Computer Interfaces ; *Speech/physiology ; Male ; Female ; Adult ; Neural Prostheses ; Young Adult ; },
abstract = {Speech brain-computer interfaces (BCIs) show promise in restoring communication to people with paralysis but have also prompted discussions regarding their potential to decode private inner speech. Separately, inner speech may be a way to bypass the current approach of requiring speech BCI users to physically attempt speech, which is fatiguing and can slow communication. Using multi-unit recordings from four participants, we found that inner speech is robustly represented in the motor cortex and that imagined sentences can be decoded in real time. The representation of inner speech was highly correlated with attempted speech, though we also identified a neural "motor-intent" dimension that differentiates the two. We investigated the possibility of decoding private inner speech and found that some aspects of free-form inner speech could be decoded during sequence recall and counting tasks. Finally, we demonstrate high-fidelity strategies that prevent speech BCIs from unintentionally decoding private inner speech.},
}
@article {pmid40816112,
year = {2025},
author = {Chen, J and Chen, X and Tang, Z and Lei, L and Zhan, Y and Liu, S and Zhou, H and Wan, J and Chen, Z and Wu, Y and Luo, Z},
title = {Influence of eHealth literacy on acceptance of healthcare services with risks in China: chain-mediating effect of general risk propensity and self-efficacy.},
journal = {Public health},
volume = {247},
number = {},
pages = {105891},
doi = {10.1016/j.puhe.2025.105891},
pmid = {40816112},
issn = {1476-5616},
mesh = {Humans ; Male ; Female ; China ; *Self Efficacy ; *Health Literacy/statistics &amp; numerical data ; Cross-Sectional Studies ; *Telemedicine/statistics &amp; numerical data ; Middle Aged ; Adult ; *Patient Acceptance of Health Care/statistics &amp; numerical data/psychology ; COVID-19/prevention &amp; control ; Aged ; COVID-19 Vaccines/administration &amp; dosage ; Young Adult ; },
abstract = {OBJECTIVES: To investigate factors associated with the acceptance of healthcare services with risks among Chinese public.<br><br>STUDY DESIGN: This national cross-sectional study used data from the 2023 Psychology and Behavior Investigation of Chinese Residents.<br><br>METHODS: Structural equation modelling was used to analyse the chain-mediated pathways of e-health literacy acting through general risk propensity and self-efficacy on the acceptability of five risky healthcare services (COVID-19 vaccine booster shots, mixed vaccination with COVID-19 vaccine, telemedicine, internet-based home care, and brain-computer interface technology). Subgroup analyses were performed by gender, region, and age.<br><br>RESULTS: Mean acceptance ratings for the five services ranged from 53.01 to 65.62. eHealth literacy was positively associated with self-efficacy, general risk propensity, and acceptance of five services (r&#xa0;=&#xa0;0.012-0.048, P&#xa0;<&#xa0;0.05). General risk propensity was positively associated with mixed vaccination with COVID-19 vaccine, telemedicine, and brain-computer interface technology (r&#xa0;=&#xa0;0.009 to 0.041, P&#xa0;<&#xa0;0.05). After adjusting for covariates, the correlation between general risk propensity and acceptance of the COVID-19 vaccine booster shots and telemedicine was non-significant. eHealth literacy had a significant positive effect on five services, self-efficacy, and general risk propensity (P&#xa0;<&#xa0;0.05). Subgroup analyses showed that self-efficacy and general risk propensity acted as mediators in the relationship between e-health literacy and acceptance of four health services in addition to the mixed neocoronary vaccine in both male and urban populations.<br><br>CONCLUSIONS: This finding shows general risk propensity and self-efficacy mediate the link between eHealth literacy and risky healthcare acceptance, deepening understanding and providing practical guidance for promoting innovative healthcare services in China.},
}
@article {pmid40815626,
year = {2025},
author = {Padrão, N and Gregoricchio, S and Eickhoff, N and Dong, J and Luzietti, L and Bossi, D and Severson, TM and Siefert, J and Calcinotto, A and Buluwela, L and Donaldson Collier, M and Ali, S and Young, L and Theurillat, JP and Varešlija, D and Zwart, W},
title = {TRIM24 as a therapeutic target in endocrine treatment-resistant breast cancer.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {122},
number = {33},
pages = {e2507571122},
pmid = {40815626},
issn = {1091-6490},
support = {813599//EC | ERC | HORIZON EUROPE European Research Council (ERC)/ ; 9171640//KWF Kankerbestrijding (DCS)/ ; 016.156.401//ZonMw (Netherlands Organisation for Health Research and Development)/ ; 2014MayPR234//Breast Cancer Now (BCN)/ ; C37/A18784//Cancer Research UK (CRUK)/ ; 20/FFP-P/8597//Research Ireland/ ; 23/SPP/11783//Research Ireland/ ; 2019AugSF1310//Breast Cancer Now (BCN)/ ; 18239A01//Breast Cancer Ireland (BCI)/ ; 19/FFP/6443//Research Ireland/ ; 2021JulyPCC1460//Breast Cancer Now (BCN)/ ; },
mesh = {Humans ; *Breast Neoplasms/drug therapy/metabolism/genetics/pathology ; Female ; Estrogen Receptor alpha/metabolism/genetics ; *Drug Resistance, Neoplasm/drug effects/genetics ; Cell Line, Tumor ; *Carrier Proteins/metabolism/genetics/antagonists &amp; inhibitors ; Cell Proliferation/drug effects ; Gene Expression Regulation, Neoplastic/drug effects ; MCF-7 Cells ; Antineoplastic Agents, Hormonal/pharmacology ; Histones/metabolism ; },
abstract = {While Estrogen receptor alpha (ERα)+ breast cancer treatment is considered effective, resistance to endocrine therapy is common. Since ERα is still the main driver in most therapy-resistant tumors, alternative therapeutic strategies are needed to disrupt ERα transcriptional activity. In this work, we position TRIM24 as a therapeutic target in endocrine resistance, given its role as a key component of the ERα transcriptional complex. TRIM24 interacts with ERα and other well-known ERα cofactors to facilitate ERα chromatin interactions and allows for maintenance of active histone marks including H3K23ac and H3K27ac. Consequently, genetic perturbation of TRIM24 abrogates ERα-driven transcriptional programs and reduces tumor cell proliferation capacity. Using a recently developed degrader targeting TRIM24, ERα-driven transcriptional output and growth were blocked, effectively treating not only endocrine-responsive cell lines but also drug-resistant derivatives thereof as well as cell line models bearing activating ESR1 point mutations. Finally, using human tumor-derived organoid models, we could show the efficacy of TRIM24 degrader in the endocrine-responsive and -resistant setting. Overall, our study positions TRIM24 as a central component for the integrity and activity of the ERα transcriptional complex, with degradation-mediated perturbation of TRIM24 as a promising therapeutic avenue in the treatment of primary and endocrine resistance breast cancer.},
}
@article {pmid40815349,
year = {2025},
author = {Li, Z and Li, M and Yang, Y},
title = {Motor imagery decoding network with multisubject dynamic transfer.},
journal = {Brain informatics},
volume = {12},
number = {1},
pages = {20},
pmid = {40815349},
issn = {2198-4018},
support = {Nos. 62173010//National Natural Science Foundation of China/ ; },
abstract = {Brain computer interface (BCI) provides a promising and intelligent rehabilitation method for motor function, and it is crucial to acquire the patient's movement intentions accurately through decoding motor imagery EEG (MI-EEG) . Because of the inter-individual heterogeneity, the decoding model should demonstrate dynamic adaptation abilities.Domain adaptation (DA) is effective to enhance model generalization by reducing the inherent distribution difference among subjects. However, the existing DA methods usually mix the multiple source domains into a new domain, the resulting multi-source domain conflict may cause negative transfer. In this paper, we propose a multi-source dynamic conditional domain adaptation network (MSDCDA). First, a multi-channel attention block is employed in the feature extractor to focus on the channels relevant to the corresponding MI task. Subsequently, the shallow spatial-temporal features are extracted using a spatial-temporal convolution block. And a dynamic residual block is applied in the feature extractor to dynamically adapt specific features of each subject to alleviate conflicts among multiple source domains since each domain is viewed as a distribution of electroencephalogram (EEG) signals. Furthermore, we employ the Margin Disparity Discrepancy (MDD) as the metric to achieve conditional distribution domain adaptation between the source and target domains through adversarial learning with an auxiliary classifier. MSDCDA achieved accuracies of 78.55 % and 85.08 % on Datasets IIa and IIb of BCI Competition IV, respectively. Our experimental results demonstrate that MSDCDA can effectively address multi-source domain conflicts and significantly enhance the decoding performance of target subjects. This study positively facilitates the application of BCI based on motor function rehabilitation.},
}
@article {pmid40813381,
year = {2025},
author = {Hendry, MF and Cruz-Garza, JG and Delgado-Jiménez, EA and Lima-Carmona, YE and Aguilar-Herrera, AJ and Ramírez-Moreno, MA and Ravindran, AS and Paek, AY and Smith, M and Kan, J and Fors, M and Alam, A and Liu, R and Noble, A and Contreras-Vidal, JL},
title = {Mobile Brain-Body Imaging and Visual Data of Theatrical Actors During Rehearsal and Performance.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {1421},
pmid = {40813381},
issn = {2052-4463},
support = {1757949//National Science Foundation (NSF)/ ; 2137255//National Science Foundation (NSF)/ ; 2412731//National Science Foundation (NSF)/ ; },
mesh = {Humans ; *Brain/physiology/diagnostic imaging ; Electroencephalography ; },
abstract = {This longitudinal Mobile Brain-Body Imaging dataset was acquired during six rehearsal sessions and three public performances of a scene from a play with highly emotional components. Three student actor dyads (N=6), one theatre director (N=1) and three audience members (N=3) participated in this study. The MoBI data recorded includes mobile electroencephalography, electrooculography, blood volume pulse, heart rate, body temperature, electrodermal activity, triaxial arm and head acceleration. The visual data includes five streams of video. This article describes the experimental setup, the multi-modal data streams acquired using a hyperscanning methodology, and an assessment of the data quality.},
}
@article {pmid40813218,
year = {2025},
author = {Xie, J and Xu, G and Yang, Z and Su, H and Zhang, S},
title = {Modeling multiscale time-frequency complex networks on Riemannian manifolds for motor imagery BCI classification with graph convolutional networks.},
journal = {ISA transactions},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.isatra.2025.07.058},
pmid = {40813218},
issn = {1879-2022},
abstract = {Motor imagery brain-computer interface (MI-BCI) classification faces challenges such as low decoding accuracy and difficulty in capturing the spatiotemporal dynamics of EEG signals. The use of Riemannian geometry classifiers for this task has become one of the most popular classification methods. However, current Riemannian geometry classifiers typically compute the covariance matrix over a period of time to capture spatial features, neglecting the multiscale characteristics of EEG signals in both time and frequency, which limits their classification performance. To address these issues, this study proposes a novel framework. Specifically, we introduce graph convolutional network (GCN) on Riemannian geometry (GR) to process multiscale networks, using virtual nodes to capture global topological features and integrating spatial features across time and frequency domains. This method significantly enhances the feature extraction capability of Riemannian geometry classifiers. The proposed method was validated on three public datasets, with average classification accuracies of 91.87 % ± 7.33 %, 87.96 % ± 7.6 %, and 82.50 % ± 7.74 %, respectively. Ablation experiments show that, compared to traditional single-scale methods, the average classification accuracy improved by 9.85 %, highlighting the effectiveness and versatility of the proposed method. This research provides a new perspective for multiscale EEG signal analysis and advances the development of motor imagery BCI classification technology.},
}
@article {pmid40811166,
year = {2025},
author = {Zhang, X and Zheng, W and Li, Z and Yang, Y and Liu, W and Cai, H and Zhu, J and Liu, J and Hu, B and Dong, Q},
title = {Constraint-Driven Causal Representation Learning for Vigilance Robust Estimation in Brain-Computer Interface.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNNLS.2025.3594434},
pmid = {40811166},
issn = {2162-2388},
abstract = {Vigilance estimation is a critical task within the field of brain-computer interfaces, extensively applied in monitoring and optimizing user states during human-machine interaction using electroencephalography (EEG). However, most existing vigilance prediction frameworks are prone to spurious correlations stemming from inherent biases in collected data. These biases involve relevant but vigilance-independent information, which may lack robustness when applied to different data distributions, i.e., out-of-distribution (OOD) scenarios. The core idea of this study is to learn constraints that capture causal information from the input based on the assumed underlying data generating process. Leveraging the disentanglement and invariance principles behind the assumptions, we propose a constraint-driven causal representation learning (CCRL) to identify and separate spurious latent variables from biased training data for generalized vigilance estimation. The CCRL training process consists of two phases: self-supervised pretraining and constraint-driven causal information disentanglement. In the first phase, based on the masked autoencoder (MAE) architecture, unlabeled training data are used for reconstructing pretext tasks to capture the comprehensive and intrinsic contextual information from EEG data, which provides a powerful input for downstream disentanglement learning. In the second phase, we propose a novel disentanglement strategy to learn spurious-free latent representations causally related to the vigilance state driven by adversarial and invariance constraints. Comprehensive validation experiments conducted on two well-known public datasets demonstrate the effectiveness and superiority of the proposed framework. In general, this work has promising implications for addressing OOD challenges in vigilance estimation.},
}
@article {pmid40810162,
year = {2025},
author = {Zhang, M and Zhai, H and Yang, L and Li, H and Wang, X},
title = {The Medial Prefrontal Cortex Modulates Psychedelic-like Effects of Psilocin.},
journal = {ACS pharmacology &amp; translational science},
volume = {8},
number = {8},
pages = {2767-2776},
pmid = {40810162},
issn = {2575-9108},
abstract = {Recent advancements in the study of psilocybin and its active metabolite psilocin have highlighted their unique psychedelic properties and potential therapeutic applications, particularly in the rapid and sustained treatment of depression. However, the potent acute psychedelic effects of psilocybin necessitate a deeper understanding of the neural mechanisms underlying its action. In this study, we investigated the psilocin-induced neural activity in male mice using c-Fos immunofluorescent labeling and identified brain regions associated with psychedelic-like activity. Among the medial prefrontal cortex (mPFC), orbitofrontal cortex (OFC), interstitial nucleus of the posterior limb of the anterior commissure (IPAC), and dorsomedial striatum (DMS), only the mPFC was specifically associated with the head twitch response (HTR), a hallmark of psychedelic-like behavior. A picomolar dose of psilocin in the mPFC was sufficient to induce significant HTR, suggesting that c-Fos-positive neurons in this region modulate psychedelic-like activity. To validate this hypothesis, optogenetic activation of these neurons significantly increased spontaneous HTR in TRAP2 mice, whereas acute inhibition suppressed drug-induced HTR. These findings establish the mPFC as a critical regulator of psilocin-induced psychedelic-like activity and provide valuable insights for enhancing the clinical safety and therapeutic application of psychedelics.},
}
@article {pmid40807942,
year = {2025},
author = {Chen, J and Yang, C and Wei, R and Hua, C and Mu, D and Sun, F},
title = {Steady-State Visual-Evoked-Potential-Driven Quadrotor Control Using a Deep Residual CNN for Short-Time Signal Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {15},
pages = {},
pmid = {40807942},
issn = {1424-8220},
support = {BX2021157//Post Doctoral Innovative Talent Support Program under Grants/ ; 62103221//National Natural Science Foundation of China under Grant/ ; },
abstract = {In this paper, we study the classification problem of short-time-window steady-state visual evoked potentials (SSVEPs) and propose a novel deep convolutional network named EEGResNet based on the idea of residual connection to further improve the classification performance. Since the frequency-domain features extracted from short-time-window signals are difficult to distinguish, the EEGResNet starts from the filter bank (FB)-based feature extraction module in the time domain. The FB designed in this paper is composed of four sixth-order Butterworth filters with different bandpass ranges, and the four bandwidths are 19-50 Hz, 14-38 Hz, 9-26 Hz, and 3-14 Hz, respectively. Then, the extracted four feature tensors with the same shape are directly aggregated together. Furthermore, the aggregated features are further learned by a six-layer convolutional neural network with residual connections. Finally, the network output is generated through an adaptive fully connected layer. To prove the effectiveness and superiority of our designed EEGResNet, necessary experiments and comparisons are conducted over two large public datasets. To further verify the application potential of the trained network, a virtual simulation of brain computer interface (BCI) based quadrotor control is presented through V-REP.},
}
@article {pmid40807891,
year = {2025},
author = {Huang, Y and Cao, L and Chen, Y and Wang, T},
title = {Optimization of Dynamic SSVEP Paradigms for Practical Application: Low-Fatigue Design with Coordinated Trajectory and Speed Modulation and Gaming Validation.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {15},
pages = {},
pmid = {40807891},
issn = {1424-8220},
mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Male ; Adult ; Electroencephalography/methods ; Female ; Young Adult ; *Fatigue/physiopathology ; Video Games ; },
abstract = {Steady-state visual evoked potential (SSVEP) paradigms are widely used in brain-computer interface (BCI) systems due to their reliability and fast response. However, traditional static stimuli may reduce user comfort and engagement during prolonged use. This study proposes a dynamic stimulation paradigm combining periodic motion trajectories with speed control. Using four frequencies (6, 8.57, 10, 12 Hz) and three waveform patterns (sinusoidal, square, sawtooth), speed was modulated at 1/5, 1/10, and 1/20 of each frequency's base rate. An offline experiment with 17 subjects showed that the low-speed sinusoidal and sawtooth trajectories matched the static accuracy (85.84% and 83.82%) while reducing cognitive workload by 22%. An online experiment with 12 subjects participating in a fruit-slicing game confirmed its practicality, achieving recognition accuracies above 82% and a System Usability Scale score of 75.96. These results indicate that coordinated trajectory and speed modulation preserves SSVEP signal quality and enhances user experience, offering a promising approach for fatigue-resistant, user-friendly BCI application.},
}
@article {pmid40807821,
year = {2025},
author = {Aziz, MZ and Yu, X and Guo, X and He, X and Huang, B and Fan, Z},
title = {BCINetV1: Integrating Temporal and Spectral Focus Through a Novel Convolutional Attention Architecture for MI EEG Decoding.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {15},
pages = {},
pmid = {40807821},
issn = {1424-8220},
support = {2025A1515011449//Natural Science Foundation of Guangdong Province/ ; 20240001053007//Aviation Science Foundation Project/ ; 62220106006//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Adult ; Male ; *Attention/physiology ; Female ; Algorithms ; },
abstract = {Motor imagery (MI) electroencephalograms (EEGs) are pivotal cortical potentials reflecting cortical activity during imagined motor actions, widely leveraged for brain-computer interface (BCI) system development. However, effectively decoding these MI EEG signals is often overshadowed by flawed methods in signal processing, deep learning methods that are clinically unexplained, and highly inconsistent performance across different datasets. We propose BCINetV1, a new framework for MI EEG decoding to address the aforementioned challenges. The BCINetV1 utilizes three innovative components: a temporal convolution-based attention block (T-CAB) and a spectral convolution-based attention block (S-CAB), both driven by a new convolutional self-attention (ConvSAT) mechanism to identify key non-stationary temporal and spectral patterns in the EEG signals. Lastly, a squeeze-and-excitation block (SEB) intelligently combines those identified tempo-spectral features for accurate, stable, and contextually aware MI EEG classification. Evaluated upon four diverse datasets containing 69 participants, BCINetV1 consistently achieved the highest average accuracies of 98.6% (Dataset 1), 96.6% (Dataset 2), 96.9% (Dataset 3), and 98.4% (Dataset 4). This research demonstrates that BCINetV1 is computationally efficient, extracts clinically vital markers, effectively handles the non-stationarity of EEG data, and shows a clear advantage over existing methods, marking a significant step forward for practical BCI applications.},
}
@article {pmid40807788,
year = {2025},
author = {Siribunyaphat, N and Tohkhwan, N and Punsawad, Y},
title = {Investigation of Personalized Visual Stimuli via Checkerboard Patterns Using Flickering Circles for SSVEP-Based BCI System.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {15},
pages = {},
pmid = {40807788},
issn = {1424-8220},
support = {WU67260//Research and Innovation Institute of Excellence, Walailak University/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Male ; Adult ; Electroencephalography/methods ; *Photic Stimulation/methods ; Female ; Algorithms ; Young Adult ; },
abstract = {In this study, we conducted two steady-state visual evoked potential (SSVEP) studies to develop a practical brain-computer interface (BCI) system for communication and control applications. The first study introduces a novel visual stimulus paradigm that combines checkerboard patterns with flickering circles configured in single-, double-, and triple-layer forms. We tested three flickering frequency conditions: a single fundamental frequency, a combination of the fundamental frequency and its harmonics, and a combination of two fundamental frequencies. The second study utilizes personalized visual stimuli to enhance SSVEP responses. SSVEP detection was performed using power spectral density (PSD) analysis by employing Welch's method and relative PSD to extract SSVEP features. Commands classification was carried out using a proposed decision rule-based algorithm. The results were compared with those of a conventional checkerboard pattern with flickering squares. The experimental findings indicate that single-layer flickering circle patterns exhibit comparable or improved performance when compared with the conventional stimuli, particularly when customized for individual users. Conversely, the multilayer patterns tended to increase visual fatigue. Furthermore, individualized stimuli achieved a classification accuracy of 90.2% in real-time SSVEP-based BCI systems for six-command generation tasks. The personalized visual stimuli can enhance user experience and system performance, thereby supporting the development of a practical SSVEP-based BCI system.},
}
@article {pmid40807738,
year = {2025},
author = {Leerskov, KS and Spaich, EG and Jochumsen, MR and Andreasen Struijk, LNS},
title = {Design and Demonstration of a Hybrid FES-BCI-Based Robotic Neurorehabilitation System for Lower Limbs.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {15},
pages = {},
pmid = {40807738},
issn = {1424-8220},
support = {A33234//Hartmann Fonden/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Neurological Rehabilitation/methods/instrumentation ; Male ; *Lower Extremity/physiology/physiopathology ; Adult ; *Robotics/methods/instrumentation ; Electric Stimulation/methods ; Female ; Movement/physiology ; Electroencephalography ; Young Adult ; },
abstract = {BACKGROUND: There are only a few available options for early rehabilitation of severely impaired individuals who must remain bedbound, as most exercise paradigms focus on out-of-bed exercises. To enable these individuals to exercise, we developed a novel hybrid rehabilitation system combining a brain-computer interface (BCI), functional electrical stimulation (FES), and a robotic device.<br><br>METHODS: The BCI assessed the presence of a movement-related cortical potential (MRCP) and triggered the administration of FES to produce movement of the lower limb. The exercise trajectory was supported by the robotic device. To demonstrate the system, an experiment was conducted in an out-of-lab setting by ten able-bodied participants. During exercise, the performance of the BCI was assessed, and the participants evaluated the system using the NASA Task Load Index, Intrinsic Motivation Inventory, and by answering a few subjective questions.<br><br>RESULTS: The BCI reached a true positive rate of 62.6 &#xb1; 9.2% and, on average, predicted the movement initiation 595 &#xb1; 129 ms prior to the MRCP peak negativity. All questionnaires showed favorable outcomes for the use of the system.<br><br>CONCLUSIONS: The developed system was usable by all participants, but its clinical feasibility is uncertain due to the total time required for setting up the system.},
}
@article {pmid40803174,
year = {2025},
author = {Kamali, S and Baroni, F and Varona, P},
title = {Mu and beta power effects of fast response trait double dissociate during precue and movement execution in the sensorimotor cortex.},
journal = {Computers in biology and medicine},
volume = {196},
number = {Pt C},
pages = {110874},
doi = {10.1016/j.compbiomed.2025.110874},
pmid = {40803174},
issn = {1879-0534},
mesh = {Humans ; Male ; Female ; Adult ; Electromyography ; *Sensorimotor Cortex/physiology ; Electroencephalography ; Movement/physiology ; Brain-Computer Interfaces ; Young Adult ; *Beta Rhythm/physiology ; Signal Processing, Computer-Assisted ; Motor Cortex/physiology ; Muscle, Skeletal/physiology ; },
abstract = {A better understanding of the neural and muscular mechanisms underlying motor responses is essential for advancing neurorehabilitation protocols, brain-computer interfaces (BCI), feature engineering for biosignal classification algorithms, and identifying biomarkers of disease and performance enhancement strategies. In this study, we examined the neuromuscular dynamics of healthy individuals during a sequential finger-pinching task, focusing on the relationships between cortical oscillations and muscle activity in simultaneous electroencephalography (EEG) and electromyography (EMG) recordings. We contrasted two pairs of subsets of the dataset based on the latency of EMG onset: an across-subjects trait-based comparison and a within-subjects state-based comparison. Trait-based analyses showed that fast responders had higher baseline beta power, indicating stronger motor inhibition and efficient resetting of motor networks, and greater mu desynchronization during movement, reflecting enhanced motor cortex activation. Visual association areas also displayed more pronounced changes in different phases of the task in subjects with lower latency. Fast responders exhibited lower baseline EMG activity and stronger EMG power during movement initiation, showing effective motor inhibition and rapid muscle activation. State-based analyses revealed no significant EEG differences between fast and slow trials, while EMG differences were only detected after movement onset. These results highlight that fast response trait is related to electrophysiological differences at specific frequency bands and task phases, offering insights for enhancing motor function in rehabilitation, biomarker identification and BCI applications.},
}
@article {pmid40801596,
year = {2025},
author = {Henderson, FC and Tuchman, K},
title = {Angiogenic Cell Precursors and Neural Cell Precursors in Service to the Brain-Computer Interface.},
journal = {Cells},
volume = {14},
number = {15},
pages = {},
pmid = {40801596},
issn = {2073-4409},
mesh = {*Brain-Computer Interfaces ; Humans ; *Neural Stem Cells/cytology ; Animals ; *Neovascularization, Physiologic ; },
abstract = {The application of artificial intelligence through the brain-computer interface (BCI) is proving to be one of the great advances in neuroscience today. The development of surface electrodes over the cortex and very fine electrodes that can be stereotactically implanted in the brain have moved the science forward to the extent that paralyzed people can play chess and blind people can read letters. However, the introduction of foreign bodies into deeper parts of the central nervous system results in foreign body reaction, scarring, apoptosis, and decreased signaling. Implanted electrodes activate microglia, causing the release of inflammatory factors, the recruitment of systemic inflammatory cells to the site of injury, and ultimately glial scarring and the encapsulation of the electrode. Recordings historically fail between 6 months and 1 year; the longest BCI in use has been 7 years. This article proposes a biomolecular strategy provided by angiogenic cell precursors (ACPs) and nerve cell precursors (NCPs), administered intrathecally. This combination of cells is anticipated to sustain and promote learning across the BCI. Together, through the downstream activation of neurotrophic factors, they may exert a salutary immunomodulatory suppression of inflammation, anti-apoptosis, homeostasis, angiogenesis, differentiation, synaptogenesis, neuritogenesis, and learning-associated plasticity.},
}
@article {pmid40800758,
year = {2025},
author = {Kostorz, K and Nguyen, T and Pan, Y and Melinscak, F and Steyrl, D and Hu, Y and Sorger, B and Hoehl, S and Scharnowski, F},
title = {Investigating short windows of interbrain synchrony: A step toward fNIRS-based hyperfeedback.},
journal = {Imaging neuroscience (Cambridge, Mass.)},
volume = {3},
number = {},
pages = {},
pmid = {40800758},
issn = {2837-6056},
abstract = {Social interaction is of fundamental importance to humans. Prior research has highlighted the link between interbrain synchrony and positive outcomes in human social interaction. Neurofeedback is an established method to train one's brain activity and might offer a possibility to increase interbrain synchrony, too. Consequently, it would be advantageous to determine the feasibility of creating a neurofeedback system for enhancing interbrain synchrony to benefit human interaction. One vital step toward developing a neurofeedback setup is to determine whether the target metric can be determined in relatively short time windows. In this study, we investigated whether the most widely employed metric for interbrain synchrony, wavelet transform coherence, can be assessed accurately in short time windows using functional near-infrared spectroscopy (fNIRS), which is recognized for its mobility and ecological suitability for interactive research. To this end, we have undertaken a comprehensive approach where we created artificial data of different noise levels of a dyadic interaction and re-processed two human-interaction datasets. For both artificial and in vivo data, we computed short windows of interbrain synchrony of varying size and assessed significance at each window size. Our findings indicate that relatively short windows of wavelet transform coherence of integration durations of about 1 minute are feasible. This would align well with the methodology of an intermittent neurofeedback procedure. Our investigation lays a foundational step toward an fNIRS-based system to measure interbrain synchrony in real time and provide participants with information about their interbrain synchrony. This advancement is crucial for the future development of a neurofeedback training system tailored to enhance interbrain synchrony to potentially benefit human interaction.},
}
@article {pmid40800536,
year = {2024},
author = {Papadopoulos, S and Darmet, L and Szul, MJ and Congedo, M and Bonaiuto, JJ and Mattout, J},
title = {Surfing beta burst waveforms to improve motor imagery-based BCI.},
journal = {Imaging neuroscience (Cambridge, Mass.)},
volume = {2},
number = {},
pages = {},
pmid = {40800536},
issn = {2837-6056},
abstract = {Our understanding of motor-related, macroscale brain processes has been significantly shaped by the description of the event-related desynchronization (ERD) and synchronization (ERS) phenomena in the mu and beta frequency bands prior to, during, and following movement. The demonstration of reproducible, spatially- and band-limited signal power changes has, consequently, attracted the interest of non-invasive brain-computer interface (BCI) research for a long time. BCIs often rely on motor imagery (MI) experimental paradigms that are expected to generate brain signal modulations analogous to movement-related ERD and ERS. However, a number of recent neuroscience studies has questioned the nature of these phenomena. Beta band activity has been shown to occur, on a single-trial level, in short, transient, and heterogeneous events termed bursts rather than sustained oscillations. In a previous study, we established that an analysis of hand MI binary classification tasks based on beta bursts can be superior to beta power in terms of classification score. In this article, we elaborate on this idea, proposing a signal processing algorithm that is comparable to- and compatible with state-of-the-art techniques. Our pipeline filters brain recordings by convolving them with kernels extracted from beta bursts and then applies spatial filtering before classification. This data-driven filtering allowed for a simple and efficient analysis of signals from multiple sensors, thus being suitable for online applications. By adopting a time-resolved decoding approach, we explored MI dynamics and showed the specificity of the new classification features. In accordance with previous results, beta bursts improved classification performance compared to beta band power, while often increasing information transfer rate compared to state-of-the-art approaches.},
}
@article {pmid40800510,
year = {2024},
author = {Muraoka, Y and Iwama, S and Ushiba, J},
title = {Neurofeedback-induced desynchronization of sensorimotor rhythm elicits pre-movement downregulation of intracortical inhibition that shortens simple reaction time in humans: A double-blind, sham-controlled randomized study.},
journal = {Imaging neuroscience (Cambridge, Mass.)},
volume = {2},
number = {},
pages = {},
pmid = {40800510},
issn = {2837-6056},
abstract = {Sensorimotor rhythm event-related desynchronization (SMR-ERD) is associated with the activities of cortical inhibitory circuits in the motor cortex. The self-regulation of SMR-ERD through neurofeedback training has demonstrated that successful SMR-ERD regulation improves motor performance. However, the training-induced changes in neural dynamics in the motor cortex underlying performance improvement remain unclear. Here, we hypothesized that SMR-neurofeedback based on motor imagery reduces cortical inhibitory activities during motor preparation, leading to shortened reaction time due to the repetitive recruitment of neural populations shared with motor imagery and movement preparation. To test this, we conducted a double-blind, sham-controlled study on 24 participants using neurofeedback training and pre- and post-training evaluation for simple reaction time tests and cortical inhibitory activity using short-interval intracortical inhibition (SICI). The results showed that veritable neurofeedback training effectively enhanced SMR-ERD in healthy male and female participants, accompanied by reduced simple reaction times and pre-movement SICI. Furthermore, SMR-ERD changes correlated with changes in pre-movement cortical disinhibition, and the disinhibition magnitude correlated with behavioral changes. These results suggest that SMR-neurofeedback modulates cortical inhibitory circuits during movement preparation, thereby enhancing motor performance.},
}
@article {pmid40798628,
year = {2025},
author = {Zhao, SJ and Yin, ZY and Yu, SB and Wang, W and Yu, HZ and Li, WH and Tao, C},
title = {Block-based compressive imaging with a swin transformer.},
journal = {Optics express},
volume = {33},
number = {5},
pages = {9587-9603},
doi = {10.1364/OE.546585},
pmid = {40798628},
issn = {1094-4087},
abstract = {Block-based compressive imaging (BCI) is based on the compressive sensing principle, which uses a spatial light modulator and a low-resolution detector to perform parallel high-speed sampling, followed by super-resolution algorithm to reconstruct target image. When compared with traditional compressive imaging, BCI reduces the computational effort but introduces block artifacts. This paper proposes a data-driven deep neural network based on the swin transformer called SwinBCI, which introduces the local attention and shifted window mechanisms to improve the target image reconstruction quality. By using the dataset to train the model to obtain priori knowledge and performing graphics processing unit-accelerated computation, the computation time is greatly reduced to realize real-time BCI. We achieve better reconstruction performances with cake cutting-Hadamard matrix sampling than with Bernoulli matrix sampling. Comparison with three other classical compressed sensing reconstruction methods on four common image datasets and images acquired experimentally using the actual BCI system show that SwinBCI achieves faster high-quality reconstruction at each sampling rate.},
}
@article {pmid40797316,
year = {2025},
author = {Tang, A and Jiang, H and Li, J and Chen, Y and Zhang, J and Wang, D and Hu, S and Lai, J},
title = {Gut microbiota links to cognitive impairment in bipolar disorder via modulating synaptic plasticity.},
journal = {BMC medicine},
volume = {23},
number = {1},
pages = {470},
pmid = {40797316},
issn = {1741-7015},
support = {82201676//National Natural Science Foundation of China/ ; 82471542//National Natural Science Foundation of China/ ; No. JNL-2023001B//Research Project of Jinan Microecological Biomedicine Shandong Laboratory/ ; 2023YFC2506200//National Key Research and Development Program of China/ ; 2021C03107//Zhejiang Provincial Key Research and Development Program/ ; 2023ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; No. 2021R52016//Leading Talent of Scientific and Technological Innovation - "Ten Thousand Talents Program" of Zhejiang Province/ ; 2022KTZ004//Chinese Medical Education Association/ ; },
mesh = {*Gastrointestinal Microbiome/physiology ; *Neuronal Plasticity/physiology ; Animals ; *Cognitive Dysfunction/microbiology/physiopathology/etiology ; *Bipolar Disorder/microbiology/complications/physiopathology/psychology ; Male ; Mice ; Humans ; Mice, Inbred C57BL ; Middle Aged ; Adult ; Fecal Microbiota Transplantation ; Female ; Disease Models, Animal ; Case-Control Studies ; },
abstract = {BACKGROUND: Cognitive impairment is an intractable clinical manifestation of bipolar disorder (BD), but its underlying mechanisms remain largely unexplored. Preliminary evidence suggests that gut microbiota can potentially influence cognitive function by modulating synaptic plasticity. Herein, we characterized the gut microbial structure in BD patients with and without cognitive impairment and explored its influence on neuroplasticity in mice.<br><br>METHODS: The gut structure of microbiota in BD without cognitive impairment (BD-nCI) patients, BD with cognitive impairment (BD-CI) patients, and healthy controls (HCs) were characterized, and the correlation between specific bacterial genera and clinical parameters was determined. ABX-treated C57 BL/J male mice were transplanted with fecal microbiota from BD-nCI, BD-CI patients or HCs and subjected to behavioral testing. The change of gut microbiota in recipient mice and its influence on the dendritic complexity and synaptic plasticity of prefrontal neurons were examined. Finally, microbiota supplementation from healthy individuals in the BD-CI mice was performed to further determine the role of gut microbiota.<br><br>RESULTS: 16S-ribosomal RNA gene sequencing reveals that gut microbial diversity and composition are significantly different among BD-nCI patients, BD-CI patients, and HCs. The Spearman correlation analysis suggested that glucose metabolism-related bacteria, such as Prevotella, Faecalibacterium, and Roseburia, were correlated with cognitive impairment test scores, and inflammation-related bacteria, such as Lachnoclostridium and Bacteroides, were correlated with depressive severity. Fecal microbiota transplantation resulted in depression-like behavior, impaired working memory and object recognition memory in BD-CI recipient mice. Compared with BD-nCI mice, BD-CI mice exhibited more severely impaired object recognition memory, along with greater reductions in dendritic complexity and synaptic plasticity. Supplementation of gut microbiota from healthy individuals partially reversed emotional and cognitive phenotypes and neuronal plasticity in BD-CI mice.<br><br>CONCLUSIONS: This study first characterized the gut microbiota in BD-CI patients and highlighted the potential role of gut microbiota in BD-related cognitive deficits by modulating neuronal plasticity in mice model.},
}
@article {pmid40797003,
year = {2025},
author = {Lo, BWY and Fukuda, H},
title = {Advances in Ischemic Stroke Treatment: Current and Future Therapies.},
journal = {Neurology and therapy},
volume = {14},
number = {5},
pages = {1783-1796},
pmid = {40797003},
issn = {2193-8253},
abstract = {This review summarizes current concepts in our understanding of stroke anatomy, pathophysiology of cerebral hypoperfusion, and collateral circulation. It also provides an evidence-based update in stroke trials and treatments assessed using PRISMA guidelines. Intravenous thrombolysis, endovascular thrombectomy for anterior circulation strokes, blood pressure control after endovascular thrombectomy, and medical management principles are discussed. Endovascular thrombectomy and medical therapy improves functional independence at 90 days in anterior circulation strokes even in late windows up to 24 h post symptom onset regardless of infarct core size. Intensive systolic blood pressure control acutely post thrombectomy is associated with harm and worse outcomes. This review also provides an evidence-based update on neurorehabilitation strategies with emerging interventions such as brain-computer interface and robotics having the potential to maximize neuroplasticity for potential improvement and recovery post stroke.},
}
@article {pmid40796752,
year = {2025},
author = {Li, D and Zalesky, A and Wang, Y and Wang, H and Ma, L and Cheng, L and Banaschewski, T and Barker, GJ and Bokde, ALW and Brühl, R and Desrivières, S and Flor, H and Garavan, H and Gowland, P and Grigis, A and Heinz, A and Lemaître, H and Martinot, JL and Martinot, MP and Artiges, E and Nees, F and Orfanos, DP and Poustka, L and Smolka, MN and Vaidya, N and Walter, H and Whelan, R and Schumann, G and Jia, T and Chu, C and Fan, L and , },
title = {Mapping the coupling between tract reachability and cortical geometry of the human brain.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {7489},
pmid = {40796752},
issn = {2041-1723},
support = {R01 DA049238/DA/NIDA NIH HHS/United States ; R56 AG058854/AG/NIA NIH HHS/United States ; U54 EB020403/EB/NIBIB NIH HHS/United States ; U54 MH091657/MH/NIMH NIH HHS/United States ; },
mesh = {Humans ; Male ; Female ; *White Matter/diagnostic imaging/anatomy &amp; histology/physiology ; Adult ; *Brain Mapping/methods ; Young Adult ; *Cerebral Cortex/diagnostic imaging/anatomy &amp; histology/physiology ; Adolescent ; Magnetic Resonance Imaging ; *Brain/physiology/diagnostic imaging/anatomy &amp; histology ; Diffusion Tensor Imaging/methods ; Neural Pathways/physiology/diagnostic imaging/anatomy &amp; histology ; Reproducibility of Results ; },
abstract = {The study of cortical geometry and connectivity is prevalent in human brain research. However, these two aspects of brain structure are usually examined separately, leaving the essential connections between the brain's folding patterns and white matter connectivity unexplored. In this study, we aim to elucidate the fundamental links between cortical geometry and white matter tract connectivity. We develop the concept of tract-geometry coupling (TGC) by optimizing the alignment between tract connectivity to the cortex and multiscale cortical geometry. We confirm in two independent datasets that cortical geometry reliably characterizes tract reachability, and that TGC demonstrates high test-retest reliability and individual-specificity. Interestingly, low-frequency TGC is more heritable and behaviorally informative. Finally, we find that TGC can reproduce task-evoked cortical activation patterns and exhibits non-uniform maturation during youth. Collectively, our study provides an approach to mapping cortical geometry-connectivity coupling, highlighting how these two aspects jointly shape the connected brain.},
}
@article {pmid40796392,
year = {2025},
author = {Kim, J and Hong, SK and Lee, A and Kumar, SN and Suchi, M and Park, JI},
title = {Activity-Dependent Effects of ERK1/2 on Hepatic Ischemia-Reperfusion Injury.},
journal = {Transplantation proceedings},
volume = {57},
number = {8},
pages = {1659-1667},
doi = {10.1016/j.transproceed.2025.07.005},
pmid = {40796392},
issn = {1873-2623},
mesh = {Animals ; *Reperfusion Injury/pathology/enzymology/prevention &amp; control ; Male ; *Liver/pathology/enzymology/drug effects/blood supply ; Pyridones/pharmacology ; Rats, Sprague-Dawley ; Pyrimidinones/pharmacology ; Rats ; Disease Models, Animal ; *MAP Kinase Signaling System/drug effects ; *Mitogen-Activated Protein Kinase 3/metabolism/antagonists &amp; inhibitors ; *Mitogen-Activated Protein Kinase 1/metabolism/antagonists &amp; inhibitors ; Protein Kinase Inhibitors/pharmacology ; Liver Transplantation/adverse effects ; },
abstract = {BACKGROUND: Liver transplantation remains the only cure for end-stage liver disease, but ischemia-reperfusion injury (IRI) limits graft availability. Although extracellular signal-regulated kinase (ERK1/2) signaling is involved in cellular responses to IRI, its precise role in hepatic IRI remains unclear. We investigated the role of ERK1/2 in hepatic IRI by modulating its activity using small-molecule chemical inhibitors.<br><br>METHODS: ERK1/2 activation was monitored at different phases of hepatic IRI using a rat model in which liver ischemia was induced with varying reperfusion times. ERK1/2 activity was modulated in this model by administering different doses of trametinib (MEK1/2 inhibitor) and BCI (DUSP1/6 inhibitor). Liver injury was evaluated through histological assessment, serum markers, and molecular analysis of cell death pathways.<br><br>RESULTS: ERK1/2 activity increased early in the reperfusion phase and gradually decreased over 6 hours thereafter. Inhibiting the ERK1/2 activity increase using trametinib (0.3 mg/kg) as well as inhibiting its decreases using BCI (7.5 mg/kg) worsened the liver injury. However, the injury was reduced upon titrating ERK1/2 activity to a moderately increased level by BCI and trametinib coadministration. The reduced liver injury was accompanied by decreased expression of ferroptosis markers.<br><br>CONCLUSIONS: Our data demonstrate that ERK1/2 activity is required for hepatic cells to tolerate IRI. Our results suggest that modulation of ERK1/2 activity using existing drugs may be a potential therapeutic strategy for mitigating hepatic IRI.},
}
@article {pmid40795874,
year = {2025},
author = {Li, J and Le, T and Fan, C and Chen, M and Shlizerman, E},
title = {Brain-to-text decoding with context-aware neural representations and large language models.},
journal = {Journal of neural engineering},
volume = {22},
number = {5},
pages = {},
doi = {10.1088/1741-2552/adfab1},
pmid = {40795874},
issn = {1741-2552},
mesh = {Humans ; *Brain/physiology ; *Language ; *Brain-Computer Interfaces ; Phonetics ; Large Language Models ; },
abstract = {Objective. Decoding attempted speech from neural activity offers a promising avenue for restoring communication abilities in individuals with speech impairments. Previous studies have focused on mapping neural activity to text using phonemes as the intermediate target. While successful, decoding neural activity directly to phonemes ignores the context dependent nature of the neural activity-to-phoneme mapping in the brain, leading to suboptimal decoding performance.Approach. In this work, we propose the use of diphone-an acoustic representation that captures the transitions between two phonemes-as the context-aware modeling target. We integrate diphones into existing phoneme decoding frameworks through a novel divide-and-conquer strategy in which we model the phoneme distribution by marginalizing over the diphone distribution. Our approach effectively leverages the enhanced context-aware representation of diphones while preserving the manageable class size of phonemes, a key factor in simplifying the subsequent phoneme-to-text conversion task.Main results. We demonstrate the effectiveness of our approach on the Brain-to-Text 2024 benchmark, where it achieves state-of-the-art phoneme error rate (PER) of 15.34% compared to 16.62% PER of monophone-based decoding. When coupled with finetuned large language models (LLMs), our method yields a Word error rate (WER) of 5.77%, significantly outperforming the 8.93% WER of the leading method in the benchmark.Significance. These results demonstrate the effectiveness of leveraging context-aware neural representations and LLMs for brain-to-text decoding, thereby expanding the capabilities of speech neuroprostheses and paving the way toward restoring communication in individuals with speech impairments.},
}
@article {pmid40795479,
year = {2025},
author = {Albahri, AS and Hamid, RA and Alqaysi, ME and Al-Qaysi, ZT and Albahri, OS and Alamoodi, AH and Homod, RZ and Deveci, M and Sharaf, IM},
title = {Trust and explainability in robotic hand control via adversarial multiple machine learning models with EEG sensor data fusion: A fuzzy decision-making solution.},
journal = {Computers in biology and medicine},
volume = {196},
number = {Pt C},
pages = {110922},
doi = {10.1016/j.compbiomed.2025.110922},
pmid = {40795479},
issn = {1879-0534},
mesh = {*Electroencephalography ; Humans ; *Machine Learning ; *Fuzzy Logic ; *Brain-Computer Interfaces ; *Robotics ; *Hand/physiology ; *Signal Processing, Computer-Assisted ; },
abstract = {In the field of brain‒computer interfaces (BCIs), developing a reliable machine learning (ML) model for real-time robotic hand control systems based on motor imagery (MI) brain signals requires substantial research. For this purpose, a set of ML models has been developed and tested to identify robust models via MI sensor data fusion under both nonadversarial and adversarial attack conditions. This paper addresses numerous essential areas, including the development of ML models for electroencephalography (EEG) MI signal datasets, with a focus on proper preprocessing and evaluation under both nonadversarial and adversarial attack conditions. Three phases make up the process. In the first phase, raw MI-EEG datasets from the Graz University BCI competition are identified and preprocessed. The preprocessing encompasses six key stages: EEG-MI signal filtering, segmentation, time‒frequency domain feature extraction, merging and labeling, normalization (resulting in Dataset I), and feature fusion (resulting in Dataset II). In the second phase, both datasets are used to develop nine different ML methods and are evaluated via nine performance metrics. These models are trained and tested against adversarial and nonadversarial scenarios. In the third phase, the fuzzy decision by opinion score method (FDOSM) and the multiperspective decision matrix (MPDM) are combined to benchmark the ML models via the fuzzy multicriteria decision-making (MCDM) approach. The random forest (RF) model achieved the best overall performance, with the lowest FDOSM scores: 0.18241 for Dataset I and 0.21636 for Dataset II. A lower FDOSM score means better results across all evaluation criteria. To further assess the developed methodology, the RF model was tested on Dataset III, comprising EEG data from four participants collected via the EMOTIV EPOC. The mean classification accuracy achieved by the RF model was 83 % with standard preprocessing, and it improved to 86 % with the application of feature fusion techniques. Additionally, this study employed the local interpretability model-agnostic explanation (LIME) method to provide an understanding of the RF model's behavior and enhance the interpretability of the results in the context of individual predictions.},
}
@article {pmid40794110,
year = {2025},
author = {Loss, J and von Sommoggy Und Erdödy, J and Rüter, J and Helten, J and Germelmann, CC and Tittlbach, S},
title = {[Using behavioral and cultural insights to promote physical activity among university students-the "Smart Moving" project].},
journal = {Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz},
volume = {68},
number = {9},
pages = {994-1005},
pmid = {40794110},
issn = {1437-1588},
mesh = {Humans ; *Students/statistics &amp; numerical data/psychology ; *Exercise/psychology ; Universities ; *Health Promotion/methods/organization &amp; administration ; Female ; Male ; Young Adult ; Germany ; Adult ; Adolescent ; Motivation ; Health Behavior ; Health Knowledge, Attitudes, Practice ; },
abstract = {BACKGROUND: Physical inactivity is widespread at universities. To promote physical activity among students, it is important to understand their needs. Behavioral and cultural insights (BCIs) help to identify barriers to physical activity and to develop appropriate interventions. The aim of "Smart Moving" was to use BCIs to implement measures to promote physical activity in two universities.<br><br>METHOD: "Smart Moving" was carried out at the universities of Bayreuth and Regensburg between 2018 and 2021. The project was implemented in four&#xa0;steps: (1)&#xa0;the target behavior was defined as students being physically active on campus; (2)&#xa0;knowledge about physical activity behavior was gained using a&#xa0;standardized survey of students, photo voice, and expert interviews; (3) a&#xa0;planning group at each university developed and implemented measures to promote physical activity; and (4)&#xa0;acceptance and short-term effects of selected measures were evaluated in short surveys.<br><br>RESULTS: University students spent an average of 34&#x202f;h per week sitting during their stay on campus. Factors influencing physical activity were assigned to the following categories: capability (cognitive/physical ability), opportunity (physical/social environment), and motivation. These included, for example, a&#xa0;lack of knowledge about access, poor accessibility of exercise opportunities, the prevailing norm that learning involves sitting, and shame when exercising in front of others. Various approaches to promote physical activity were developed: movement breaks in lectures, activating desk furniture with sitting/standing options, movement instructions in the outdoor area, and motivational interventions for exercise. The measures were well received by students.<br><br>DISCUSSION: The BCI data helped implement needs-based physical activity promotion at universities. Further studies are needed to investigate the long-term effects on physical activity behavior.},
}
@article {pmid40791388,
year = {2025},
author = {Ponasso, GN and Drumm, DA and Oppermann, H and Wang, A and Noetscher, GM and Maess, B and Knösche, TR and Makaroff, SN and Haueisen, J},
title = {High-Resolution EEG Source Reconstruction from PCA-Corrected BEM-FMM Reciprocal Basis Funcions: A Study with Visual Evoked Potentials from Intermittent Photic Stimulation.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {40791388},
issn = {2692-8205},
support = {R01 EB035484/EB/NIBIB NIH HHS/United States ; R01 MH130490/MH/NIMH NIH HHS/United States ; },
abstract = {Modern automated human head segmentations can generate high-resolution computational meshes involving many non-nested tissues. However, most source reconstruction software is limited to 3 -4 nested layers of low resolution and a small number of dipolar sources ~10,000. Recently, we introduced modeling techniques for source reconstruction of magnetoencephalographic (MEG) signals using the reciprocal approach and the boundary element fast multipole method (BEM-FMM). The technique of BEM-FMM can process both nested and non-nested models with as many as 4 million surface elements. In this paper, we present an analogue technique for source reconstruction of electroencephalographic (EEG) signals based on cortical global basis functions. The present work uses Helmholtz reciprocity to relate the reciprocally-generated lead-field matrices to their direct counterpart, while resolving the issue of possible biases toward the reference electrode. Our methodology is tested with experimental EEG data collected from a cohort of 12, young and healthy, volunteers subjected to intermittent photic stimulation (IPS). Our novel high-resolution source reconstruction models can have impact on mental health screening as well as brain-computer interfaces.},
}
@article {pmid40791170,
year = {2025},
author = {Han, S and Pasquini, D and Sorieul, M and Boratto, MH and Gatecliff, L and Dickson, A and Jang, S and Davy, S and Malliaras, GG and Chen, Y},
title = {Implantable Ion-Selective Organic Electrochemical Transistors Enable Continuous, Long-Term, and In Vivo Plant Monitoring.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {12},
number = {41},
pages = {e04283},
pmid = {40791170},
issn = {2198-3844},
support = {NE/T012293/1//Natural Environment Research Council/ ; CSG-FRI12101//Royal Society Te Ap&#x101;rangi/ ; C04X2202//Ministry of Business, Innovation and Employment/ ; C04X1703//Ministry of Business, Innovation and Employment/ ; RS-2024-00452677//National Research Foundation of Korea/ ; RS-2024-00399300//National Research Foundation of Korea/ ; },
mesh = {*Biosensing Techniques/methods/instrumentation ; *Transistors, Electronic ; *Potassium/analysis ; *Xylem/chemistry/metabolism ; *Pinus/chemistry ; *Electrochemical Techniques/methods/instrumentation ; Ions ; },
abstract = {The development of plant-specific biosensors holds the potential to uncover new insights into plant physiology and advance precision agriculture. Current sensing platforms mainly focus on broad plant phenotypes (e.g., elongation and hydration) and local environmental monitoring (e.g., temperature and moisture). Here, an ion-selective organic electrochemical transistor (IS-OECT) is introduced that enables real-time monitoring of variations in potassium ion concentration within the xylem of pine trees. This work demonstrates that the high sensitivity of the IS-OECT enables the detection of subtle variations in potassium ion concentrations in the xylem sap of living trees, and the high stability of the sensor allows for in vivo measurements over five weeks. Furthermore, the implantable sensors are fabricated using processes that are compatible with low-cost manufacturing (i.e., lithography-free). This sensing technology, therefore, has great potential to be a game-changer in precision forestry and could extend to precision agriculture and horticulture practices.},
}
@article {pmid40789435,
year = {2025},
author = {Tozzi, A and Jaušovec, K},
title = {Takens' theorem to assess EEG traces: Regional variations in brain dynamics.},
journal = {Neuroscience letters},
volume = {865},
number = {},
pages = {138352},
doi = {10.1016/j.neulet.2025.138352},
pmid = {40789435},
issn = {1872-7972},
mesh = {Humans ; *Electroencephalography/methods ; Male ; *Brain/physiology ; Adult ; Female ; Young Adult ; Occipital Lobe/physiology ; Brain Mapping/methods ; },
abstract = {Takens' theorem (TT) proves that the behaviour of a dynamical system can be effectively reconstructed within a multidimensional phase space. This offers a comprehensive framework for examining temporal dependencies, dimensional complexity and predictability of time series data. We applied TT to investigate the physiological regional differences in EEG brain dynamics of healthy subjects, focusing on three key channels: FP1 (frontal region), C3 (sensorimotor region), and O1 (occipital region). We provided a detailed reconstruction of phase spaces for each EEG channel using time-delay embedding. The reconstructed trajectories were quantified through measures of trajectory spread and average distance, offering insights into the temporal structure of brain activity that traditional linear methods struggle to capture. Variability and complexity were found to differ across the three regions, revealing notable regional variations. FP1 trajectories exhibited broader spreads, reflecting the dynamic complexity of frontal brain activity associated with higher cognitive functions. C3, involved in sensorimotor integration, displayed moderate variability, reflecting its functional role in coordinating sensory inputs and motor outputs. O1, responsible for visual processing, showed constrained and stable trajectories, consistent with repetitive and structured visual dynamics. These findings align with the functional specialization of different cortical areas, suggesting that the frontal, sensorimotor and occipital regions operate with autonomous temporal structures and nonlinear properties. This distinction may have significant implications for advancing our understanding of normal brain function and enhancing the development of brain-computer interfaces. In sum, we demonstrated the utility of TT in revealing regional variations in EEG traces, underscoring the value of nonlinear dynamics.},
}
@article {pmid40788303,
year = {2025},
author = {Shotbolt, M and Bryant, J and Liang, P and Khizroev, S},
title = {Mechanism and applications of magnetoelectric nanoparticles in cancer therapy.},
journal = {Nanomedicine (London, England)},
volume = {20},
number = {19},
pages = {2469-2481},
pmid = {40788303},
issn = {1748-6963},
mesh = {Humans ; *Neoplasms/drug therapy/therapy ; *Nanoparticles/chemistry/therapeutic use ; Animals ; Drug Delivery Systems/methods ; Nanomedicine/methods ; Magnetic Fields ; *Antineoplastic Agents/therapeutic use/administration &amp; dosage ; },
abstract = {Cancer remains a major clinical challenge, with current therapies often hampered by off-target effects, drug resistance, and incomplete tumor eradication. There is a pressing need for more precise and effective treatment strategies. This review explores the mechanisms and applications of magnetoelectric nanoparticles (MENPs) in cancer therapy. MENPs, typically composed of magnetostrictive and piezoelectric materials in a core-shell structure, generate electric fields in response to magnetic fields, enabling targeted and noninvasive therapeutic actions. The literature search included recent advances in MENP synthesis, optimization of material composition and morphology, and preclinical studies demonstrating their ability to enhance drug delivery, disrupt tumor cell membranes, and induce tumor regression without systemic toxicity. Relevant studies were identified by searching electronic databases, including PubMed, Web of Science, Scopus, and Google Scholar. The search employed a combination of keywords and phrases such as "magnetoelectric nanoparticles," "MENPs," "cancer therapy," "nanomedicine," "core-shell nanoparticles," "magnetostrictive," "piezoelectric," "drug delivery," "magnetic field," "nano-electroporation," and "reactive oxygen species.." MENPs represent a promising option for precision oncology, offering remote control over therapeutic effects and the potential to overcome limitations of conventional treatments. Ongoing research should focus on optimizing MENP design for selectivity and efficacy, as well as advancing their clinical translation for cancer therapy.},
}
@article {pmid40786597,
year = {2024},
author = {Chen, ZS},
title = {Emerging Brain-to-Content Technologies from Generative AI and Deep Representation Learning.},
journal = {IEEE signal processing magazine},
volume = {41},
number = {6},
pages = {94-104},
pmid = {40786597},
issn = {1053-5888},
support = {R01 NS121776/NS/NINDS NIH HHS/United States ; R01 MH118928/MH/NIMH NIH HHS/United States ; P50 MH132642/MH/NIMH NIH HHS/United States ; RF1 DA056394/DA/NIDA NIH HHS/United States ; R01 MH139352/MH/NIMH NIH HHS/United States ; },
abstract = {Rapid advances in generative artificial intelligence (AI) and deep representation learning have revolutionized numerous engineering applications in signal processing, computer vision, speech recognition and translation, and natural language processing due to amazingly powerful representation power (e.g., [1,2]). Generative AI-empowered tools, such as ChatGPT and Sora, have fundamentally changed the landscape of human-computer communications research. One emerging application along this line is to link the brain to the computer (i.e., brain-computer interface or BCI) and to develop paradigm-shift brain-to-content technologies. This BCI system upgrade (i.e., BCI 2.0) is empowered by generative AI and deep learning ("new engine") and large amounts of data ("gas"). In this article, we will revisit the old song sung in a new tune, highlight some state-of-the-art progresses, and briefly discuss the future outlook.},
}
@article {pmid40786542,
year = {2025},
author = {Zulfiqar, AA},
title = {Hypervitaminemia B12 in the Elderly: A Forgotten Marker of Serious Underlying Diseases.},
journal = {European journal of case reports in internal medicine},
volume = {12},
number = {8},
pages = {005553},
pmid = {40786542},
issn = {2284-2594},
abstract = {UNLABELLED: Hypervitaminemia B12, long neglected in clinical practice, is a biological anomaly whose pathological significance remains largely underestimated, particularly in the elderly. While medical attention has historically focused on vitamin B12 deficiency, several recent studies suggest that elevated levels of this vitamin may reveal serious underlying pathologies, such as solid neoplasia, haematological malignancies, chronic liver disease or renal failure. We report the case of a 91-year-old man hospitalized for asthenia, anorexia and altered general condition, in whom vitamin B12 assay revealed major hypervitaminemia (1318 pg/ml). The work-up revealed hepatic cirrhosis of alcoholic origin, complicated by hepatocellular carcinoma which was metastatic from the outset. This case illustrates the potential prognostic value of vitamin B12 dosage, particularly when coupled with C-reactive protein (BCI index), a high value (> 40,000) of which is associated with short-term mortality in patients with advanced cancer. Beyond hepatopathy, hypervitaminemia B12 is associated in the literature with increased haptocorrin release in myeloproliferative syndromes, excess transcobalamins in renal failure, or paradoxical elevation in certain inflammatory diseases. This biological marker, which is easy to obtain, could therefore become part of standardized geriatric assessment, particularly in oncogeriatrics, in order to guide diagnostic and prognostic strategy. The systematic inclusion of vitamin B12 assays in the general assessment of elderly patients, particularly in oncology settings, deserves to be reassessed.<br><br>LEARNING POINTS: Hypervitaminemia B12 is an often overlooked but potentially significant marker of serious underlying pathologies-including solid neoplasms, liver disease, renal failure, and hematologic malignancies-especially in elderly patients.The B12 &#xd7; C-reactive protein (CRP) index, easily obtainable from routine labs, may serve as a prognostic tool in oncology, with values over 40,000 being strongly associated with short-term mortality in advanced cancers.Routine screening for vitamin B12 levels in geriatric assessments should consider both deficiency and excess, with hypervitaminemia prompting systematic diagnostic evaluation to uncover latent or advanced disease.},
}
@article {pmid40783421,
year = {2025},
author = {Hashemi, SI and Cheron, G and Demolin, D and Cebolla, AM},
title = {EEG oscillations and related brain generators of phonation phases in long utterances.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {29150},
pmid = {40783421},
issn = {2045-2322},
support = {ANR-20-CE23-0008//Agence Nationale de la Recherche/ ; },
mesh = {Humans ; Male ; Female ; *Electroencephalography ; Adult ; *Phonation/physiology ; *Brain/physiology ; Young Adult ; Electromyography ; *Speech/physiology ; },
abstract = {While the role of brain rhythms in respiratory and speech motor control has been mainly explored during brief utterances, the specific involvement of brain rhythms in the transition of regulating subglottic pressure phases which are concomitant to specific muscle activation during prolonged phonation remains unexplored. This study investigates whether power spectral variations of the electroencephalogram brain rhythms are related specifically to prolonged phonation phases. High-density EEG and surface EMG were recorded in nineteen healthy participants while they repeatedly produced the syllable [pa] without taking a new breath, until reaching respiratory exhaustion. Aerodynamic, acoustic, and electrophysiological signals were analyzed to detect the brain areas involved in different phases of prolonged phonation. Each phase was defined by successive thoracic and abdominal muscle activity maintaining estimated subglottic pressure. The results showed significant changes in power spectrum, with desynchronization and synchronization in delta, theta, low-alpha, and high-alpha bands during transitions among the phases. Brain source analysis estimated that the first phase (P1), associated with vocal initiation and elastic rib cage recoil, involved frontal regions, suggesting a key role in voluntary phonation preparation. Subsequent phases (P2, P3, P4) showed multiband dynamics, engaging motor and premotor cortices, anterior cingulate, sensorimotor regions, thalamus, and cerebellum, indicating progressive adaptation and fine-tuning of respiratory and articulatory muscle control. Additionally, the involvement of temporal and insular regions in delta rhythm suggests a role in maintaining phonetic representation and preventing spontaneous verbal transformations. These findings provide new insights into the mechanisms and brain regions involved in prolonged phonation. These findings pave the way for applications in vocal brain-machine interfaces, clinical biofeedback for respiratory and vocal disorders, and the development of more ecologically valid paradigms in speech neuroscience.},
}
@article {pmid40783082,
year = {2025},
author = {Wu, K and Gao, L and Feng, Z and Kakkos, I and Li, C and Sun, Y},
title = {Multimodal brain network analysis reveals divergent dysconnectivity patterns during mental fatigue: A concurrent EEG-fMRI study.},
journal = {Brain research bulletin},
volume = {230},
number = {},
pages = {111505},
doi = {10.1016/j.brainresbull.2025.111505},
pmid = {40783082},
issn = {1873-2747},
mesh = {Humans ; Electroencephalography/methods ; *Mental Fatigue/physiopathology/diagnostic imaging ; Magnetic Resonance Imaging/methods ; Male ; Female ; Adult ; *Brain/physiopathology/diagnostic imaging ; Young Adult ; *Nerve Net/physiopathology/diagnostic imaging ; Multimodal Imaging/methods ; Brain Mapping/methods ; Attention/physiology ; Psychomotor Performance/physiology ; },
abstract = {For the apparent importance of mental fatigue in neuroergonomics, continuous efforts have been made to reveal the underlying neural mechanisms. Using concurrent EEG-fMRI network analysis, this work aims to reveal fatigue-related brain network reorganization. Specifically, multimodal neuroimaging data were acquired from 35 healthy participants during a 15-min sustained attention task (i.e., psychomotor vigilance task). A monotonically decreasing pattern of behavioral performance was revealed where the first and last 3-min windows were determined as the most vigilant and fatigued states. Multimodal brain network architectures within these two states were then quantitatively compared. We found that EEG and fMRI networks exhibited divergent yet interrelated reorganizations. Specifically, MF-related deficiency in parallel information transmission was revealed in multiple EEG frequency bands, yet only local efficiency was altered in fMRI networks. Moreover, a convergent decrease of nodal efficiency mainly resided in the default mode network was found in both EEG and fMRI networks, indicating a decline in cognitive control capacity during mental fatigue. Overall, by integrating multimodal EEG-fMRI network analyses, this work provides novel insights into the dynamic neural adaptations to mental fatigue, enhancing our understanding of the underlying neural mechanisms.},
}
@article {pmid40780413,
year = {2025},
author = {Qiu, SJ and Zhang, YL and Gong, WB and Ding, YH and Wu, JW and Wang, ZX and Yao, HW},
title = {BCI inhibits MKP3 by targeting the kinase-binding domain and disrupting ERK2 interaction.},
journal = {The Journal of biological chemistry},
volume = {301},
number = {9},
pages = {110570},
pmid = {40780413},
issn = {1083-351X},
mesh = {Humans ; *Mitogen-Activated Protein Kinase 1/metabolism/chemistry/antagonists &amp; inhibitors/genetics ; *Dual Specificity Phosphatase 3/antagonists &amp; inhibitors/metabolism/chemistry/genetics ; Protein Binding ; *Dual Specificity Phosphatase 6/metabolism/antagonists &amp; inhibitors/chemistry/genetics ; Protein Domains ; MAP Kinase Signaling System/drug effects ; },
abstract = {Mitogen-activated protein kinase phosphatase 3 (MKP3), also known as dual-specificity phosphatase 6, is a critical regulator of extracellular signal-regulated kinase (ERK) signaling, and its dysregulation is implicated in diseases, such as cancer. The small-molecule inhibitor BCI ((E)-2-benzylidene-3-(cyclohexylamino)-2,3-dihydro-1H-inden-1-one) has been reported to inhibit MKP3, thereby enhancing ERK signaling and promoting selective cytotoxicity in cancer cells. However, the molecular mechanism underlying BCI-mediated MKP3 inhibition remains unclear. In this research, we characterized the interaction between BCI and MKP3 using NMR titration, microscale thermophoresis, enzymatic assays, and AlphaFold 3 modeling. Our results demonstrate that BCI selectively binds to the kinase-binding domain (KBD) of MKP3, rather than its catalytic domain, thereby disrupting the MKP3-ERK2 interaction and impairing MKP3 activation. Enzymatic assays further reveal that BCI significantly reduces ERK2-mediated MKP3 activity without directly interfering with substrate binding at the active site. AlphaFold 3 structural modeling suggests that BCI binding induces local conformational changes, notably an outward shift of the α4-helix, which exposes a hydrophobic pocket essential for BCI accommodation. Moreover, BCI exhibits differential binding affinities across the MKP family, showing significant interactions with the KBDs of MKPX and MKP5 but markedly weaker or negligible binding to those of MKP1, MKP2, and MKP4. Together, these findings uncover a novel KBD-targeting mechanism of MKP3 inhibition by BCI and highlight the potential of selectively modulating mitogen-activated protein kinase phosphatases through allosteric disruption of kinase-phosphatase interactions. This strategy may offer a new avenue for the design and optimization of targeted phosphatase inhibitors.},
}
@article {pmid40774731,
year = {2025},
author = {Liang, R and Gao, J and Liu, X and Li, X and Chang, H and Yang, R and Yang, J and Ming, D},
title = {Regulatory measures for mitigating physical and mental health impacts in aerospace environment: A systematic review.},
journal = {Life sciences in space research},
volume = {46},
number = {},
pages = {106-114},
doi = {10.1016/j.lssr.2025.04.003},
pmid = {40774731},
issn = {2214-5532},
mesh = {Humans ; *Space Flight ; *Mental Health ; *Astronauts/psychology ; *Aerospace Medicine ; Weightlessness/adverse effects ; Exercise ; },
abstract = {Long-term spaceflight poses significant challenges to astronauts' physical and mental health, resulting in physiological issues such as osteoporosis, muscle atrophy, and cardiovascular dysfunction, as well as psychological problems like depression, anxiety, social withdrawal, and cognitive decline. As the duration of space missions continues to increase, the above challenges cannot be ignored. Therefore, identifying effective regulatory measures is essential. This article provides a concise review of the latest domestic and international research on strategies to mitigate physiological and psychological risks in aerospace environment. Including coping strategies for musculoskeletal, cardiovascular, and psychological problems, such as exercise, physical stimulation, psychotherapy, and medication, especially traditional Chinese medicine, which need to be further explored and applied. Its ultimate goal is to offer insights for ensuring the safe execution of space missions by astronauts and advancing the field of space medicine.},
}
@article {pmid40774087,
year = {2025},
author = {Constant, M and Mandal, A and Asanowicz, D and Panek, B and Kotlewska, I and Yamaguchi, M and Gillmeister, H and Kerzel, D and Luque, D and Molinero, S and Vázquez-Millán, A and Pesciarelli, F and Borelli, E and Ramzaoui, H and Beck, M and Somon, B and Desantis, A and Castellanos, MC and Martín-Arévalo, E and Manini, G and Capizzi, M and Gokce, A and Özer, D and Soyman, E and Yılmaz, E and Eayrs, JO and London, RE and Steendam, T and Frings, C and Pastötter, B and Szaszkó, B and Baess, P and Ayatollahi, S and León Montoya, GA and Wetzel, N and Widmann, A and Cao, L and Low, X and Costa, TL and Chelazzi, L and Monachesi, B and Kamp, SM and Knopf, L and Itier, RJ and Meixner, J and Jost, K and Botes, A and Braddock, C and Li, D and Nowacka, A and Quenault, M and Scanzi, D and Torrance, T and Corballis, PM and Laera, G and Kliegel, M and Welke, D and Mushtaq, F and Pavlov, YG and Liesefeld, HR},
title = {A multilab investigation into the N2pc as an indicator of attentional selectivity: Direct replication of Eimer (1996).},
journal = {Cortex; a journal devoted to the study of the nervous system and behavior},
volume = {190},
number = {},
pages = {304-341},
doi = {10.1016/j.cortex.2025.05.014},
pmid = {40774087},
issn = {1973-8102},
mesh = {*Attention/physiology ; Humans ; Electroencephalography/methods ; *Evoked Potentials/physiology ; Male ; Female ; Adult ; Photic Stimulation ; Young Adult ; *Brain/physiology ; Reaction Time/physiology ; },
abstract = {The N2pc is widely employed as an electrophysiological marker of an attention allocation. This interpretation was largely driven by the observation of an N2pc elicited by an isolated relevant target object, which was reported as Experiment 2 in Eimer (1996). All subsequent refined interpretations of the N2pc had to take this crucial finding into account. Despite its central role for neurocognitive attention research, there have been no direct replications and only few conceptual replications of this seminal work. Within the context of #EEGManyLabs, an international community-driven effort to replicate the most influential EEG studies ever published, the present study was selected due to its strong impact on the study of selective attention. We revisit the idea of the N2pc being an indicator of attentional selectivity by delivering a high powered direct replication of Eimer's work through analysis of 779 datasets acquired from 22 labs across 14 countries. Our results robustly replicate the N2pc to form stimuli, but a direct replication of the N2pc to color stimuli technically failed. We believe that this pattern not only sheds further light on the functional significance of the N2pc as an electrophysiological marker of attentional selectivity, but also highlights a methodological problem with selecting analysis windows a priori. By contrast, the consistency of observed ERP patterns across labs and analysis pipelines is stunning, and this consistency is preserved even in datasets that were rejected for (ocular) artifacts, attesting to the robustness of the ERP technique and the feasibility of large-scale multilab EEG (replication) studies.},
}
@article {pmid40773224,
year = {2025},
author = {Zhang, K and Chen, G and Choi, SH},
title = {Converging technologies: vagus nerve stimulation and brain-computer interfaces as catalysts for advancing post-stroke aphasia rehabilitation.},
journal = {International journal of surgery (London, England)},
volume = {},
number = {},
pages = {},
doi = {10.1097/JS9.0000000000003148},
pmid = {40773224},
issn = {1743-9159},
}
@article {pmid40772250,
year = {2025},
author = {Wilkins, RB and Coffin, T and Pham, M and Klein, E and Marathe, M},
title = {Mind the gap: bridging ethical considerations and regulatory oversight in implantable BCI human subjects research.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1633627},
pmid = {40772250},
issn = {1662-5161},
abstract = {The advent of Brain-Computer Interface (BCI) technology brings groundbreaking advancements in medical science but also raises important ethical considerations. This manuscript explores the ethical dimensions of implantable BCIs (iBCIs), focusing on the central role of Institutional Review Boards (IRBs) in the United States, in safeguarding participant rights and welfare. As federally mandated bodies, IRBs ensure that informed consent is obtained ethically, emphasizing participant autonomy, preventing undue coercion, while supporting clear and practical communication of risks and benefits. As part of this discussion, this paper touches on the ethical challenges surrounding the enrollment of participants with impaired consent capacity and the long-term implications of implanted brain devices. Additionally, this work underscores the critical importance of robust cybersecurity measures to prevent data breaches and unauthorized manipulation of brain activity. By examining risk assessments, data management practices, and the need for external cybersecurity expertise, this work offers a comprehensive framework for IRB review of iBCI research. This perspective aims to guide ethical iBCI research and protect human subjects in this rapidly evolving field.},
}
@article {pmid40772248,
year = {2025},
author = {Mohamed, MA and Giles, J and AlSaleh, M and Arvaneh, M},
title = {Associations between pre-cue parietal alpha oscillations and event related desynchronization in motor imagery-based brain-computer interface.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1625127},
pmid = {40772248},
issn = {1662-5161},
abstract = {INTRODUCTION: Motor Imagery based brain-computer interfaces (MI-BCIs) offer a promising avenue for controlling external devices via neural signals generated through imagined movements. Despite their potential, the performance of MI-BCIs remains highly variable across users and sessions, presenting a barrier to broader adoption.<br><br>METHODS: This study explores the influence of pre-cue parietal alpha power on the quality of the event-related desynchronization (ERD) responses, a critical indicator of MI processes. Analyzing data from 102 sessions involving 77 participants.<br><br>RESULTS: We identified a robust significant correlation between pre-cue parietal alpha power and ERD magnitude, indicating that elevated pre-cue parietal alpha power is associated with enhanced ERD responses. Additionally, we observed a significant positive relationship between pre-cue parietal alpha power and MI-BCI classification accuracy, highlighting the potential relevance of this neurophysiological metric for BCI performance.<br><br>DISCUSSION: Our findings suggest that pre-cue parietal alpha power can serve as a potential marker for optimizing MI-BCI systems. Integrating this marker into individualized training protocols can potentially enhance MI-BCI systems' consistency, and overall accuracy.},
}
@article {pmid40770162,
year = {2025},
author = {Oikonomidou, O and Beresford, MJ and Galve-Calvo, E and Woeckel, A and Parikh, RC and Hitchens, A and Chen, C and Doan, J and Li, B and Ansquer, VD and Frugier, G and Jimenez, MI and Davis, KL and Broughton, EI},
title = {Real-world clinical outcomes associated with first-line palbociclib and aromatase inhibitor therapy among patients with HR+/HER2- advanced breast cancer in Europe.},
journal = {Breast cancer research and treatment},
volume = {213},
number = {3},
pages = {299-312},
pmid = {40770162},
issn = {1573-7217},
mesh = {Humans ; Female ; *Breast Neoplasms/drug therapy/pathology/mortality/metabolism ; *Pyridines/administration &amp; dosage/therapeutic use ; Receptor, ErbB-2/metabolism ; Retrospective Studies ; Middle Aged ; *Aromatase Inhibitors/therapeutic use/administration &amp; dosage ; Receptors, Estrogen/metabolism ; Aged ; *Piperazines/administration &amp; dosage/therapeutic use ; *Antineoplastic Combined Chemotherapy Protocols/therapeutic use ; Europe/epidemiology ; Adult ; Receptors, Progesterone/metabolism ; Aged, 80 and over ; Treatment Outcome ; },
abstract = {PURPOSE: Cyclin-dependent kinase 4/6 inhibitors (CDK4/6is) combined with endocrine therapy is the recommended first-line (1L) treatment for hormone receptor-positive and human epidermal growth factor receptor 2-negative (HR+/HER2-) advanced breast cancer (ABC). Real-world evidence (RWE) describing 1L CDK4/6i regimens and associated clinical outcomes in Europe is limited. The study objective was to describe clinical characteristics, tumor response, and survival outcomes in patients with HR+/HER2- ABC.<br><br>METHODS: This retrospective, observational cohort study used data from 52 treatment centers in the UK, Spain, and Germany and included patients who initiated 1L palbociclib&#x2009;+&#x2009;aromatase inhibitor (AI) therapy for ABC between 2016 and 2020. Primary endpoints were real-world progression-free survival (rwPFS) and overall survival (OS).<br><br>RESULTS: Data were abstracted from 856 patients. During treatment, complete response, partial response, or stable disease was achieved for 86.1% of patients in Spain, 80.7% in the UK, and 79.0% in Germany, while complete or partial response was achieved for 43.8% of patients in Spain, 34.9% in the UK, and 16.9% in Germany. Median rwPFS during treatment was 28.1&#xa0;months for patients in Spain, 33.9&#xa0;months in the UK, and 48.1&#xa0;months in Germany. Median OS was 51.3&#xa0;months (95% CI 46.6-NE) in the UK, 65.2&#xa0;months (95% CI 65.2-NE) in Germany, and not reached in Spain.<br><br>CONCLUSION: This RWE supports the clinical effectiveness of 1L palbociclib&#x2009;+&#x2009;AI in routine clinical practice in European countries-consistent with the efficacy observed in clinical trials-and further supports the implementation of palbociclib-based regimens as frontline treatment of HR+/HER2- ABC.},
}
@article {pmid40769403,
year = {2025},
author = {Key, B and Brown, DJ},
title = {How pain fools everyone: An inference to the best explanation.},
journal = {Neuroscience and biobehavioral reviews},
volume = {177},
number = {},
pages = {106317},
doi = {10.1016/j.neubiorev.2025.106317},
pmid = {40769403},
issn = {1873-7528},
mesh = {Humans ; *Pain/physiopathology/psychology ; *Decision Making/physiology ; *Brain/physiopathology/physiology ; Animals ; },
abstract = {There is a commonly held assumption that feelings such as pain are causes of behaviour. We say we withdrew our hand from the hotplate because it hurt or that we flinched at the needle because it stung. The causal role of pain is widely implicated in theories of learning and decision-making. But what if this commonsense idea that feelings cause behaviour is just wrong? To date, there is no known mechanism for how subjectively experienced pain directly modulates neural activity and it is hard to see how there could be. There is no known mechanism by which pain could directly gate ion channels. On this basis, we contend that the real cause of behaviour is neural activity and that feelings of pain have no known causal role. This raises the question of whether pain has any function at all-i.e., whether it has causal powers or is merely epiphenomenal. Epiphenomenalism faces the intractable problem of explaining how such an attention-consuming feeling as pain could be epiphenomenal and yet still have survived evolutionary selection. In response, we infer from the available neuroscientific evidence that the best explanation is that pain has a novel, non-causal function and that decisions to act are instead caused by an internal decoding process involving threshold detection of accumulated evidence of pain rather than by pain per se. Because pain is necessarily implicated in the best explanation of subsequent decision-making, we do not conclude that pain is epiphenomenal or functionless even if it has no causal influence over decisions or subsequent actions. On this view, pain functions to mark neural pathways that are the causes of behaviour as salient, serving as a ground but not a cause of subsequent decision-making and action. This perspective has far-reaching implications for diverse fields including neuropsychiatry, biopsychosocial modelling, robotics, and brain-computer interfaces.},
}
@article {pmid40769034,
year = {2025},
author = {Guggisberg, AG and Siebner, HR and Lundell, H and Madsen, MAJ and Madsen, KH and Wiggermann, V and Mégevand, P and Proix, T and Dalal, SS and Grouiller, F and Vulliémoz, S and Ušćumlić, M and Marchesotti, S},
title = {Emergent technologies in clinical neurophysiology to study the central nervous system: IFCN handbook chapter.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {178},
number = {},
pages = {2110942},
doi = {10.1016/j.clinph.2025.2110942},
pmid = {40769034},
issn = {1872-8952},
mesh = {Humans ; *Electroencephalography/methods/trends ; Magnetic Resonance Imaging/methods/trends ; Magnetoencephalography/methods/trends ; *Brain/physiology/physiopathology/diagnostic imaging ; *Brain Mapping/methods/trends ; *Central Nervous System/physiology/physiopathology ; *Neurophysiology/methods/trends ; },
abstract = {This chapter reviews recent breakthroughs in neurophysiological brain mapping, focusing on EEG, MEG, and MRI technologies and their integration with stimulation techniques. High-density and portable EEG systems now allow more precise, user-friendly, and mobile recordings. Machine learning enhances biomarker detection and diagnostic power, particularly in epilepsy, cognitive disorders, and sleep pathology. MEG has become more versatile with the development of wearable optically pumped magnetometers (OPMs), enabling recordings during natural movement and broadening clinical access. Intracranial EEG (iEEG) remains central in epilepsy surgery and neuroscience research, with innovations in seizure forecasting and high-resolution speech decoding via microelectrode arrays and Neuropixels probes. Structural and functional MRI have advanced through ultra-high field imaging, quantitative tissue characterization, and connectomics, while functional MRS (fMRS) enables real-time tracking of neurochemical changes. Crucially, these mapping tools increasingly converge with brain stimulation-TMS, TES, focused ultrasound, and deep brain stimulation-to enable real-time, individualized modulation of brain networks. Simultaneous EEG-fMRI and artifical intelligence-driven brain-computer interfaces further enhance precision interventions. Together, these technologies are transforming clinical neurophysiology, offering new insights into brain function and advancing personalized neuromodulation therapies for neurological and psychiatric disorders.},
}
@article {pmid40768522,
year = {2025},
author = {Singh, G and Chharia, A and Upadhyay, R and Kumar, V and Longo, L},
title = {PyNoetic: A modular python framework for no-code development of EEG brain-computer interfaces.},
journal = {PloS one},
volume = {20},
number = {8},
pages = {e0327791},
pmid = {40768522},
issn = {1932-6203},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; *Software ; Algorithms ; *Brain/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) have emerged as a transformative technology with applications spanning robotics, virtual reality, medicine, and rehabilitation. However, existing BCI frameworks face several limitations, including a lack of stage-wise flexibility essential for experimental research, steep learning curves for researchers without programming expertise, elevated costs due to reliance on proprietary software, and a lack of all-inclusive features leading to the use of multiple external tools affecting research outcomes. To address these challenges, we present PyNoetic, a modular BCI framework designed to cater to the diverse needs of BCI research. PyNoetic is one of the very few frameworks in Python that encompasses the entire BCI design pipeline, from stimulus presentation and data acquisition to channel selection, filtering, feature extraction, artifact removal, and finally simulation and visualization. Notably, PyNoetic introduces an intuitive and end-to-end GUI coupled with a unique pick-and-place configurable flowchart for no-code BCI design, making it accessible to researchers with minimal programming experience. For advanced users, it facilitates the seamless integration of custom functionalities and novel algorithms with minimal coding, ensuring adaptability at each design stage. PyNoetic also includes a rich array of analytical tools such as machine learning models, brain-connectivity indices, systematic testing functionalities via simulation, and evaluation methods of novel paradigms. PyNoetic's strengths lie in its versatility for both offline and real-time BCI development, which streamlines the design process, allowing researchers to focus on more intricate aspects of BCI development and thus accelerate their research endeavors.},
}
@article {pmid40768143,
year = {2025},
author = {Gallien, Y and Broussouloux, S and Demesmaeker, A and Fouillet, A and Mertens, C and Chin, F and Cassourret, G and Caserio-Schonemann, C and du Roscoät, E and Le Strat, Y and , },
title = {Outcomes and Cost-Benefit of a National Suicide Reattempt Prevention Program.},
journal = {JAMA network open},
volume = {8},
number = {8},
pages = {e2525671},
pmid = {40768143},
issn = {2574-3805},
mesh = {Humans ; Female ; Male ; Cost-Benefit Analysis ; Adult ; Retrospective Studies ; Middle Aged ; France/epidemiology ; *Suicide Prevention ; *Suicide, Attempted/statistics &amp; numerical data ; },
abstract = {IMPORTANCE: Suicide attempts (SA) are a major public health concern and a preventable cause of premature death with a significant societal cost. Suicide reattempt (SR) rates are high in the postdischarge period for an SA. Brief contact interventions (BCIs) aim to prevent SR by recontacting patients after discharge through crisis cards, calls, letters, or messages. A nationwide BCI was deployed in 6 French regions between 2015 and 2017.<br><br>OBJECTIVE: To assess the outcomes and the cost benefit of the program in reducing SR risk within 12 months after discharge.<br><br>Retrospective multicenter cohort study using nationwide data from the French health insurance database and emergency department surveillance system. Patients exposed to the program between 2015 and 2017 were matched 1:1 with unexposed patients based on age, sex, history of SA, and diagnosis codes using propensity scores and followed up for 12 months. Survival and cost-benefit analyses were conducted in [month to month] 2022.<br><br>EXPOSURE: Participation in the program, including structured follow-up using crisis cards, telephone calls, and/or postcards for up to 6 months after discharge.<br><br>MAIN OUTCOMES AND MEASURES: The primary outcome was time to first SR or suicide-related death within 12 months. The secondary outcome was the number of SRs and cost savings.<br><br>RESULTS: Among 23&#x202f;146 individuals, 14&#x202f;504 (62.6%) were female, 12&#x202f;244 (52.9%) had no history of SA, and the mean (SD) age was 39 (17) years. Exposure to the program was associated with a lower risk of SR (adjusted hazard ratio [aHR], 0.62; 95% CI, 0.59-0.67). This association was consistent regardless of patients' history of SAs (aHR, 0.63; 95% CI, 0.57-0.71 for those without prior attempts; aHR, 0.61; 95% CI, 0.56-0.66 for those with prior attempts) and appeared greater among female participants (aHR, 0.59; 95% CI, 0.54-0.68) than male participants (aHR, 0.68; 95% CI, 0.61-0.76). The program yielded a return on investment of &#x20ac;2.06 (95% CI, &#x20ac;1.58-&#x20ac;2.50) per euro spent.<br><br>CONCLUSION AND RELEVANCE: In this cohort study, exposure to the program was associated with a reduced risk of SR and favorable economic outcomes.},
}
@article {pmid40763175,
year = {2025},
author = {Voola, M and Vignali, L and Mojallal, H and Bogdanov, C and Távora-Vieira, D},
title = {Using Cortical Auditory Evoked Potentials in Active Middle Ear and Bone Conduction Implant Users: An Objective Method to Optimize the Fitting.},
journal = {Otology &amp; neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology},
volume = {46},
number = {9},
pages = {1037-1044},
doi = {10.1097/MAO.0000000000004581},
pmid = {40763175},
issn = {1537-4505},
mesh = {Humans ; *Bone Conduction/physiology ; *Evoked Potentials, Auditory/physiology ; Female ; Male ; Adult ; Middle Aged ; *Ossicular Prosthesis ; Aged ; *Hearing Aids ; Acoustic Stimulation ; Speech Perception/physiology ; *Prosthesis Fitting/methods ; Young Adult ; },
abstract = {OBJECTIVE: The study aimed to investigate whether cortical auditory evoked potential (CAEP) measures could be used to optimize active middle ear implant (aMEI) and bone conduction implant (BCI) fitting, with the goal of improving hearing outcomes in adults.<br><br>DESIGN: CAEPs were measured in response to LING sounds /OO/, /AH/, and /SH/ presented in sound field. If CAEP responses were recorded for all sounds, no map adjustments were performed. If a CAEP response was absent for one or more sounds, map parameters were optimized until a CAEP response could be induced. Functional outcomes were measured as pre- vs postoptimization adaptive speech-in-noise results. Subjective feedback was also collected.<br><br>RESULTS: Of the 15 participants, one was excluded from the study, three did not need optimization, nine were successfully optimized using CAEP measurements, and two could not be optimized. Comparison of CAEP morphology showed significant differences pre- vs postoptimization for middle- and high-frequency sounds (i.e., /AH/ and /SH/). Speech-in-noise testing revealed significant improvements pre- vs postoptimization, and participants were generally satisfied with the overall procedure.<br><br>CONCLUSION: These findings demonstrated that middle- and high-frequency tokens could be successfully optimized using CAEPs, resulting in significant improvements in hearing performance. Our results support the use of CAEPs for the optimization of aMEI and BCI adult users' fitting.},
}
@article {pmid40761593,
year = {2025},
author = {Ding, S and Wang, K and Jiang, W and Xu, C and Bo, H and Ma, L and Li, H},
title = {DGAT: a dynamic graph attention neural network framework for EEG emotion recognition.},
journal = {Frontiers in psychiatry},
volume = {16},
number = {},
pages = {1633860},
pmid = {40761593},
issn = {1664-0640},
abstract = {INTRODUCTION: Emotion recognition based on electroencephalogram (EEG) signals has shown increasing application potential in fields such as brain-computer interfaces and affective computing. However, current graph neural network models rely on predefined fixed adjacency matrices during training, which imposes certain limitations on the model's adaptability and feature expressiveness.<br><br>METHODS: In this study, we propose a novel EEG emotion recognition framework known as the Dynamic Graph Attention Network (DGAT). This framework dynamically learns the relationships between different channels by utilizing dynamic adjacency matrices and a multi-head attention mechanism, allowing for the parallel computation of multiple attention heads. This approach reduces reliance on specific adjacency structures while enabling the model to learn information in different subspaces, significantly improving the accuracy of emotion recognition from EEG signals.<br><br>RESULTS: Experiments conducted on the EEG emotion datasets SEED and DEAP demonstrate that DGAT achieves higher emotion classification accuracy in both subject-dependent and subject-independent scenarios compared to other models. These results indicate that the proposed model effectively captures dynamic changes, thereby enhancing the accuracy and practicality of emotion recognition.<br><br>DISCUSSION: DGAT holds significant academic and practical value in the analysis of emotional EEG signals and applications related to other physiological signal data.},
}
@article {pmid40761318,
year = {2025},
author = {Xiao, H and Huang, C and Wu, Y and Wang, JJ and Wang, H},
title = {Establishing a social behavior paradigm for female mice.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1630491},
pmid = {40761318},
issn = {1662-4548},
abstract = {INTRODUCTION: Social behavior assessment in female mice has been historically challenged by inconsistent results from the classic three-chamber test, which reliably detects social preferences in males but fails to capture female specific social dynamics.<br><br>METHODS: We developed a modified three-chamber paradigm by replacing standard social stimuli with familiar cagemates (co-housed for 2 weeks, 1 week or 24 hours) to better assess sociability and novelty preference in female mice.<br><br>RESULTS: In the sociability phase, female mice showed a significant preference for interacting with cagemates compared to empty chambers. Crucially, during the social preference phase, test females demonstrated robust novelty seeking behavior, spending significantly more time exploring novel conspecifics compared to 2-week cagemates or 1-week cagemates. This preference trended similarly, though non significantly, with 24-hour cagemates. Notably, our paradigm enhanced social preference indices without altering total interaction time, confirming its specificity for detecting novelty driven exploration.<br><br>DISCUSSION: These findings overcome the limitations of traditional paradigms and establish a validated framework for studying female social behavior, with critical implications for modeling neurodevelopmental disorders like autism spectrum disorder (ASD) in female preclinical research.},
}
@article {pmid40761312,
year = {2025},
author = {Chen, Y and Xu, R and Lau, AT and He, X and Chen, W and Wang, X and Cichocki, A and Jin, J},
title = {Leveraging low-frequency components for enhanced high-frequency steady-state visual evoked potential based brain computer interface in fast calibration scenario.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {124},
pmid = {40761312},
issn = {1871-4080},
abstract = {High-frequency steady-state visual evoked potential-based brain-computer interface (SSVEP-BCI) systems offer improved user comfort but suffer from reduced performance compared to their low-frequency counterparts, limiting their practical application. To address this issue, we propose a transfer learning-based method that leverages low-frequency SSVEP data to enhance high-frequency SSVEP performance. A filtering mechanism is designed to extract informative components from low-frequency signals, and the least squares algorithm is employed to generate high-quality synthetic high-frequency data. Experiments conducted on two public datasets using TDCA, eTRCA, and advanced TRCA-based algorithms demonstrate significant performance improvements. Our approach requires only two calibration trials, achieving 9.03% and 14.49% accuracy increases for eTRCA and TDCA in Dataset 1, and 13.91% and 14.53% improvements in Dataset 2, all within 1.5 s. Moreover, our approach effectively addresses the issue of single calibration data for high-frequency SSVEP-BCI systems. These results support the feasibility of fast calibration and improved performance in real-world high-frequency BCI applications.},
}
@article {pmid40760397,
year = {2025},
author = {Mondal, S and Nag, A},
title = {A computational eye state classification model using EEG signal based on data mining techniques: comparative analysis.},
journal = {Physical and engineering sciences in medicine},
volume = {},
number = {},
pages = {},
doi = {10.1007/s13246-025-01619-w},
pmid = {40760397},
issn = {2662-4737},
abstract = {Artificial Intelligence has shown great promise in healthcare, particularly in non-invasive diagnostics using bio signals. This study focuses on classifying eye states (open or closed) using Electroencephalogram (EEG) signals captured via a 14-electrode neuroheadset, recorded through a Brain-Computer Interface (BCI). A publicly available dataset comprising 14,980 instances was used, where each sample represents EEG signals corresponding to eye activity. Fourteen classical machine learning (ML) models were evaluated using a tenfold cross-validation approach. The preprocessing pipeline involved removing outliers using the Z-score method, addressing class imbalance with SMOTETomek, and applying a bandpass filter to reduce signal noise. Significant EEG features were selected using a two-sample independent t-test (p < 0.05), ensuring only statistically relevant electrodes were retained. Additionally, the Common Spatial Pattern (CSP) method was used for feature extraction to enhance class separability by maximizing variance differences between eye states. Experimental results demonstrate that several classifiers achieved strong performance, with accuracy above 90%. The k-Nearest Neighbours classifier yielded the highest accuracy of 97.92% with CSP, and 97.75% without CSP. The application of CSP also enhanced the performance of Multi-Layer Perceptron and Support Vector Machine, reaching accuracies of 95.30% and 93.93%, respectively. The results affirm that integrating statistical validation, signal processing, and ML techniques can enable accurate and efficient EEG-based eye state classification, with practical implications for real-time BCI systems and offering a lightweight solution for real-time healthcare wearable applications healthcare applications.},
}
@article {pmid40759633,
year = {2025},
author = {Chen, C and Wu, J and Qin, C and Qiu, Y and Jiang, N and Wang, Q and Liu, M and Jiang, D and Yuan, Q and Wei, X and Zhuang, L and Wang, P},
title = {Planar-electroporated cell biosensor for investigating potential therapeutic effects of ectopic bitter receptors.},
journal = {Microsystems &amp; nanoengineering},
volume = {11},
number = {1},
pages = {147},
pmid = {40759633},
issn = {2055-7434},
support = {32201082//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62301481//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Bitter receptors were initially identified within the gustatory system. In recent years, bitter receptors have been found in various non-gustatory tissues, including the cardiovascular system, where they participate in diverse physiological processes. To investigate the electrophysiological and potential therapeutic implications of bitter receptors, we have developed a highly sensitive, multifunctional planar-electroporated cell biosensor (PECB) for high-throughput evaluation of the effects of bitter substances on cardiomyocytes. The PECB demonstrated the capability for high-throughput, stable, and reproducible detection of intracellular action potentials (IAPs). In comparison to conventional biosensors that utilize extracellular action potentials (EAPs) for data analysis, the IAPs recorded by the PECB provided high-resolution insights into action potentials, characterized by increased amplitudes and an enhanced signal-to-noise ratio (SNR). The PECB successfully monitored IAPs induced by the activation of bitter receptors by using three bitter substances: diphenidol, denatonium benzoate, and arbutin in cardiomyocytes. To further assess the drug development ability of our PECB, we established an in vitro long QT syndrome (LQTS) model to investigate the potential therapeutic effects of arbutin. The results indicated that arbutin altered the electrophysiological properties of cardiomyocytes and significantly shortened the repolarization time in the LQTS model. Moreover, it demonstrated its potential mechanistic pathway by activating bitter receptors to modulate cardiac ion channel activities. The developed PECB provides an effective platform for high-throughput screening of substrates of bitter receptors for the treatment of heart disease, presenting new opportunities for the development of antiarrhythmic therapies.},
}
@article {pmid40757371,
year = {2025},
author = {Landsmeer, LPL and Hua, E and Abunahla, H and Siddiqi, MA and Ishihara, R and De Zeeuw, CI and Hamdioui, S and Strydis, C},
title = {Efficient implementation of the Hodgkin-Huxley potassium channel via a single volatile memristor.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1569397},
pmid = {40757371},
issn = {1662-4548},
abstract = {INTRODUCTION: In 2012, potassium and sodium ion channels in Hodgkin-Huxley-based brain models were shown to exhibit memristive behavior. This positioned memristors as strong candidates for implementing biologically accurate artificial neurons. Memristor-based brain simulations offer advantages in energy efficiency, scalability, and compactness, benefiting fields such as soft robotics, embedded systems, and neuroprosthetics.<br><br>METHODS: Previous approaches used current-controlled Mott memristors, which poorly matched the voltage-controlled nature of ion channels. This study employs volatile, oxide-based memristors that leverage electric-field-driven oxygen-vacancy migration to emulate voltage-dependent channel behavior. We selected candidate WOx and NbOx memristors and modeled their dynamics to verify performance as Hodgkin-Huxley potassium channels.<br><br>RESULTS: The device exhibits sigmoidal gating and voltage-dependent time constants consistent with the theoretical model. By scaling the passive circuitry around the memristors, we show that they capture the essential mechanisms of potassium ion-channels, although spike height is reduced due to strong non-linear voltage-dependence. Still, by cascading multiple compartments, typical spike propagation is retained.<br><br>DISCUSSION: This is the first demonstration of a voltage-controlled memristor replicating the Hodgkin-Huxley potassium channel, validating its potential for more efficient brain simulation hardware.},
}
@article {pmid40754610,
year = {2025},
author = {Alghamdi, AM and Ashraf, MU and Bahaddad, AA and Almarhabi, KA and Al Shehri, WA and Daraz, A},
title = {Cross-subject EEG signals-based emotion recognition using contrastive learning.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {28295},
pmid = {40754610},
issn = {2045-2322},
support = {UJ-24-SUCH-1247//University of Jeddah/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; *Brain-Computer Interfaces ; *Brain/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; Adult ; Male ; *Machine Learning ; Female ; },
abstract = {Electroencephalography (EEG) signals based emotion brain computer interface (BCI) is a significant field in the domain of affective computing where EEG signals are the cause of reliable and objective applications. Despite these advancements, significant challenges persist, including individual differences in EEG signals across subjects during emotion recognition. To cope this challenge, current study introduces a cutting-edge cross subject contrastive learning (CSCL) scheme for EEG signals representation of brain region. The proposed scheme addresses the generalisation across subjects directly, which is a primary challenge in EEG signals-based emotions recognition. The proposed CSCL scheme captures the complex patterns effectively by employing emotions and stimulus contrastive losses within hyperbolic space. CSCL is designed primarily to learn representations that can effectively distinguish signals originating from different brain regions. Further, we evaluate the significance of our proposed CSCL scheme on five different datasets, including SEED, CEED, FACED and MPED, and obtain 97.70%, 96.26%, 65.98%, and 51.30% respectively. The experimental results show that our proposed CSCL scheme demonstrates strong effectiveness while addressing the challenges related to cross subject variability and label noise in the EEG-based emotion recognition system.},
}
@article {pmid40754454,
year = {2025},
author = {Ikegaya, Y},
title = {Semantics of Brain-Machine Hybrids.},
journal = {Biological &amp; pharmaceutical bulletin},
volume = {48},
number = {8},
pages = {1150-1164},
doi = {10.1248/bpb.b25-00285},
pmid = {40754454},
issn = {1347-5215},
mesh = {*Brain-Computer Interfaces ; Humans ; Semantics ; *Brain/physiology ; Animals ; Electroencephalography ; },
abstract = {Brain-machine interfaces, also known as brain-computer interfaces, represent a rapidly advancing field at the intersection of neuroscience and technology, enabling direct communication pathways between the brain and external devices. This review charts the historical evolution of brain-machine interfaces, from fundamental discoveries such as electroencephalography and volitional single-neuron control to sophisticated decoding of neural population activity for real-time control of robotics and sensory reconstruction. Clinical breakthroughs lead to unprecedented success in restoring motor function after paralysis through brain-spine interfaces, enabling high-speed communication through thought-to-text/speech systems, providing sensory feedback for prosthetics, and implementing closed-loop neuromodulation for the treatment of neurological disorders such as epilepsy and depression. Beyond therapeutic applications, brain-machine interfaces drive innovation in neurotech art (neuroart) and entertainment (neurogames), allowing neural activity to directly generate music, visual art, and interactive experiences. In addition, the potential for human augmentation is expanding, with technologies that enhance physical strength, sensory perception, and cognitive abilities. These converging advances challenge fundamental concepts of human identity and suggest that brain-machine interfaces may enable humanity to transcend inherent biological limitations, potentially ushering in an era of technologically guided evolution.},
}
@article {pmid40754053,
year = {2025},
author = {Zhao, X and Xu, R and Zhang, Y and Lau, AT and Xu, R and Wang, X and Cichocki, A and Jin, J},
title = {A novel paradigm based on radar-like scanning for directional recognition in event-related potentials based brain-computer interfaces.},
journal = {Journal of neuroscience methods},
volume = {423},
number = {},
pages = {110546},
doi = {10.1016/j.jneumeth.2025.110546},
pmid = {40754053},
issn = {1872-678X},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; Electroencephalography/methods ; Young Adult ; Adult ; *Evoked Potentials/physiology ; *Brain/physiology ; Photic Stimulation/methods ; *Radar ; *Recognition, Psychology/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: Event-related potentials (ERPs) based brain-computer interface (BCI) systems have shown significant potential for directional control applications. Existing paradigms are constrained by the limited scalability of directional commands that demand interface reconfiguration for varying target numbers.<br><br>NEW METHOD: We propose a novel radar-like scanning (RS) paradigm for 32-directional recognition tasks to address these limitations. This paradigm continuously scans through directions using a sector-shaped visual stimulus, naturally evoking ERP responses without discrete directional indicators. During the online experiments, an early-stopping strategy is employed to enhance efficiency. Additionally, this study analyzes subjects' directional recognition performance using EEGNet under three sector rotation periods. Thirteen subjects participated in the experiments.<br><br>RESULTS: The grand-averaged ERP amplitudes exhibited a stronger negative deflection in the parietal, occipital, and temporoparietal regions. The results demonstrated that, with a 2&#x202f;s rotation period and early-stopping strategy, the best subject achieved an accuracy of 87.50&#x202f;% with a mean absolute angle error of 1.64&#xb0;. When the directional error tolerance was set to 11.25&#xb0;, the subject-averaged accuracy reached 91.83&#x202f;% under the same conditions. Longer rotation periods led to better subject-averaged recognition performance. When the rotation period was short (1&#x202f;s), targets close to the scanning center were challenging to recognize.<br><br>Compared with others, the RS paradigm enables more fine-grained directional target recognition and is unaffected by the target numbers.<br><br>CONCLUSIONS: The proposed paradigm demonstrates significant potential for applications in ERP-BCI systems.},
}
@article {pmid40749591,
year = {2025},
author = {Elliss, H and Kevill, JL and Proctor, K and Farkas, K and Bailey, O and Shuttleworth, J and Jones, DL and Kasprzyk-Hordern, B},
title = {Flow-driven biomarker movement in gravitational sewers for wastewater-based epidemiology and public health monitoring.},
journal = {Water research},
volume = {287},
number = {Pt A},
pages = {124269},
doi = {10.1016/j.watres.2025.124269},
pmid = {40749591},
issn = {1879-2448},
mesh = {*Wastewater ; *Sewage ; Biomarkers/analysis ; *Environmental Monitoring ; Public Health ; *Wastewater-Based Epidemiological Monitoring ; Water Pollutants, Chemical ; Gravitation ; },
abstract = {The movement of biological (genetic viral, fungal or bacterial) and chemical indicators (BCIs) within sewer networks is critical to wastewater-based epidemiology (WBE) enabling accurate calculation of chemical and pathogen loads within a community. These quantified BCIs, which include genetic material from pathogens as well as pharmaceuticals, from a range of classes, serve as proxies for community-wide health and behaviour patterns. However, a critical knowledge gap exists in understanding how different BCIs move within complex sewer systems, which could lead to misinterpretation of community-level data. This study aims to address this gap by investigating the transport behaviour of 5 common BCIs (carbamazepine, metoprolol, naproxen, venlafaxine and PMMoV) in a real-world gravitational sewer network. In addition, we also spiked the wastewater with deuterated caffeine-d9, allowing discrimination from native caffeine present in the network and therefore, enabling an accurate assessment of recovery due to no public use. Our results revealed that all targets travelled with limited dispersion throughout the first stage of the gravitational sewer, 0.8 km after introduction into the network. It was observed that carbamazepine (logD = 2.8 at sewer pH), exhibited more dispersion throughout the remaining 2.3 km of the gravitational system, showing a broader, more asymmetric trace with increased tailing, which potentially indicates sorption to the solid phase, impacting its movement through the network. All other chemical targets had similar movement patterns, indicating a lower tendency to bind to the solid phase (logD < 1, at average sewer pH). Loads were calculated using dye-predicted flow rates and normalized to caffeine-d9. Carbamazepine loads were under-predicted by 74 %, attributed to losses to the solid phase throughout the sewer system. Conversely, metoprolol, naproxen, and venlafaxine loads were over-predicted (146 %, 32 %, and 129 %, respectively), likely due to additional public inputs. Our results demonstrate that more hydrophilic chemicals move throughout the sewer network with limited dispersion while hydrophobic compounds may experience significant losses. These findings have important implications for the accurate interpretation of WBE data, future BCI tracing studies and the selection of appropriate chemical markers for community health monitoring.},
}
@article {pmid40748806,
year = {2025},
author = {Yuan, X and Zhang, Y and Rolfe, P},
title = {IIMCNet: Intra- and Inter-modality Correlation Network for Hybrid EEG-fNIRS Brain-Computer Interface.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3594203},
pmid = {40748806},
issn = {2168-2208},
abstract = {Hybrid Brain-Computer Interface (BCI) enhances accuracy and reliability by leveraging the complementary information provided by multi-modality signal fusion. EEG-fNIRS, a fusion of electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS), have emerged as the suitable techniques for real-world BCI applications due to their portability and economic viability. Existing methods typically focus on the high-level feature representation with late-fusion or early-fusion strategies during the recognition tasks. However, they usually overlook the joint feature extraction of both intra-modality and inter-modality, which is crucial for optimizing BCI performance. In this study, we introduce an Intra- and Inter-modality Correlation Network (IIMCNet) to integrate both the inherent features derived from individual modalities: EEG, deoxygenated hemoglobin (HbR), and oxygenated hemoglobin (HbO), as well as the cross-modality features between EEG-HbR, EEG-HbO, and HbR-HbO data. The intra-modality correlation features are generated using a late fusion method (Intra-net), which combines the uni-modality features extracted by E-Net and f-Net. Concurrently, the inter-modality correlation features are extracted using an early fusion method (Inter-net). Inter-net is consist of three dilated convolution-based C-Nets that focus on neurovascular coupling across modalities. Finally, three intra-modality features, three inter-modality features, and the concatenate hybrid feature are fed into deep supervision module to enhance robustness and accuracy. Experiment results demonstrate the IIMCNet exhibits superior performance compared to methods that rely solely on either intra-modality or inter-modality correlation networks. Furthermore, IIMCNet outperforms other state-of-the-art methods in motor imagery and mental arithmetic tasks, respectively. (The code is available at: github.com/Y-xiaoyang/IIMCNet).},
}
@article {pmid40748802,
year = {2025},
author = {Wang, J and Wang, Z and Xu, T and Li, A and Si, Y and Zhou, T and Zhao, X and Hu, H},
title = {Enhancing the Reliability of Affective Brain-Computer Interfaces by Using Specifically Designed Confidence Estimator.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3594219},
pmid = {40748802},
issn = {2168-2208},
abstract = {Recent years, the diverse applications of electroencephalography (EEG) - based affective brain-computer interfaces (aBCIs) are being extensively explored. However, due to adverse factors like noise and physiological variability, the recognition capability of aBCIs can unforeseeably suffer abrupt declines. Since the timing of these aBCI failures is unknown, placing trust in aBCIs without scrutiny can lead to undesirable consequences. To alleviate this issue, we propose an algorithm for estimating the reliability of aBCI (primarily Graph Convolutional Network), synchronously delivering a probabilistic confidence score upon aBCI decision completion, thereby reflecting the aBCI's real-time recognition capabilities. Methodologically, we use the Maximum Softmax Probability (MSP) from EEG recognition networks as confidence scores and leverage the Scaling Operator to calibrate them. Then, the Projection Operator is employed to address confidence estimation biases caused by noise and subject variability. For the numerical concentration of MSP, we provide fresh insights into its causes and propose corresponding solutions. The derivation of the estimator from the Maximum Entropy Principle is also substantiated for robust theoretical underpinnings. Finally, we confirm theoretically that the estimator does not compromise BCI performance. In experiments conducted on public datasets SEED and SEED-IV, the proposed algorithm demonstrates superior performance in estimating aBCIs reliability compared to other benchmarks, and commendable adaptability to new subjects. This research has the potential to lead to more trustworthy aBCIs and advance their broader application in complex real-world scenarios.},
}
@article {pmid40746978,
year = {2025},
author = {Enemark, C},
title = {Loyal Wingmen, Artificial Intelligence, and Cognitive Enhancement: A Warning against Cyborg-Drone Warfare.},
journal = {Journal of military ethics},
volume = {24},
number = {1},
pages = {4-20},
pmid = {40746978},
issn = {1502-7589},
abstract = {Some states are planning to acquire armed drones that incorporate artificial intelligence (AI) and fly alongside inhabited aircraft. The use of drones according to this "Loyal Wingman" concept is an example of tactical human-machine teaming, and it could be militarily advantageous in future aerial warfare. Incorporating AI into the operation of a weapon system's critical functions (selecting and engaging targets) nevertheless carries an ethical risk: that a human will be unable to exercise adequate control over the use of force and unable to take responsibility for any injustice caused. To reduce this risk, one potential approach is to pursue "meaningful human control" over armed and AI-enabled drones by increasing their human supervisors' cognitive capacity. The use of brain-computer interfaces (BCIs) to achieve such an increase might be beneficial from the perspective of military ethics if it enabled faster human interventions to prevent unjust, AI-associated harms. However, as this article shows, that benefit would be outweighed by the ethical downsides of waging cyborg-drone warfare: the undermining of pilots' hors de combat noncombatant status and of human moral agency in the use of force.},
}
@article {pmid40746851,
year = {2025},
author = {Aziz, NA and Ng, K and Alifrangis, C and Tran, B and Conduit, C and Liow, E and Ackerman, C and Georgescu, R and Jamal, T and Relton, C and Mayer, E and Nicol, D and Cazzaniga, W and Huddart, R and Reid, A and Shamash, J and Rajan, P},
title = {Therapy de-escalation for testicular cancer (THERATEST): A multi-centre observational cohort feasibility study of de-escalation therapies for good prognosis stage II germ cell tumours.},
journal = {BJUI compass},
volume = {6},
number = {8},
pages = {e70057},
pmid = {40746851},
issn = {2688-4526},
abstract = {BACKGROUND: Standard of care (SOC) treatments for International Germ Cell Cancer Collaborative Group (IGCCCG) good prognosis stage II germ cell tumours (GCT) involve primary orchidectomy followed by combination chemotherapy for both seminoma and non-seminomatous germ cell tumours (NSGCT). Alternatively, external beam radiotherapy may be used for seminoma and retroperitoneal lymph node dissection (RPLND) for NSGCT. While these treatments achieve high cure rates, they are associated with significant toxicities. De-escalation strategies including three cycles of Carboplatin AUC10 or robotic RPLND with or without adjuvant chemotherapy have demonstrated potential to reduce treatment-related toxicity in stage II seminoma while preserving oncological efficacy. However, these approaches are not widely adopted due to limited prospective comparative trials.<br><br>STUDY DESIGN: The THERATEST trial is a prospective multicentre observational feasibility study evaluating participants receiving SOC treatments for good prognosis stage II seminoma and NSGCT or de-escalated treatments for stage II seminoma.<br><br>ENDPOINTS: The primary endpoints are to assess feasibility of recruitment and retention. Secondary endpoints include assessing health-related quality of life (HRQOL), sexual function and satisfaction, progression-free survival (PFS), overall survival (OS) and safety and treatment-related complications.<br><br>PATIENTS AND METHODS: Thirty participants with good prognosis stage II seminoma or NSGCTs will be recruited over 18&#x2009;months into two cohorts: de-escalation arm and SOC arm. The de-escalation cohort will receive either Carboplatin AUC10 or robotic RPLND with or without adjuvant therapy depending on institutional SOC. Participants who decline or are ineligible for de-escalation will receive SOC treatment: combination chemotherapy or radiotherapy for seminoma and combination chemotherapy for NSGCT. All participants will be followed for two years post-treatment or until withdrawal. Data collection includes recruitment and retention rates, disease status, surgical outcomes, adverse events and patient-reported outcomes using validated questionnaire: EORTC QLQ-TC26, EORTC QLQ-C30, Brief Male Sexual Function Inventory (BMSFI) and additional enquiries on anejaculation.<br><br>COORDINATING CENTRE: THERATEST Trial Coordinator, Centre for Experimental Cancer Medicine, Barts Cancer Institute, Queen Mary University of London, Old Anatomy Building, Charterhouse Square, London, EC1M 6BQ|T: 0207882 8497|E: bci-theratest@qmul.ac.uk.<br><br>TRIAL REGISTRATION NUMBER: ISRCTN61007118.},
}
@article {pmid40746199,
year = {2025},
author = {Madhavan, S},
title = {Harnessing Neuroplasticity: The Role of Priming in Enhancing Post Stroke Motor Function.},
journal = {Restorative neurology and neuroscience},
volume = {},
number = {},
pages = {9226028251358162},
doi = {10.1177/09226028251358162},
pmid = {40746199},
issn = {1878-3627},
abstract = {Stroke remains a leading cause of disability worldwide, highlighting the need for innovative neurorehabilitation strategies to enhance recovery. Recent advancements emphasize neuroplasticity-the brain's ability to reorganize and form new connections-through targeted interventions. Among these, cortical priming has emerged as a promising approach to enhance neuroplasticity and improve motor recovery post-stroke by modulating brain excitability for optimal motor learning. This review explores the role of cortical priming in stroke rehabilitation, highlighting its ability to enhance neural excitability and plasticity in motor-related brain regions. Various priming techniques, including non-invasive brain stimulation (rTMS, tDCS), deep brain stimulation (DBS), vagus nerve stimulation (VNS), brain-computer interfaces (BCIs), movement-based priming, aerobic exercise, and sensory stimulation, are examined. Despite promising findings, challenges remain in optimizing protocols and addressing individual variability. Future directions focus on biomarker-driven rehabilitation, personalized strategies, and large-scale trials to integrate cortical priming into clinical practice.},
}
@article {pmid40745321,
year = {2025},
author = {Chen, J and Liu, Q and Chen, G and Cai, G and Jiang, J and Yang, X and Tan, C and Zhang, C and Xu, G and Lan, Y},
title = {iTBS on RDLPFC improves performance of motor imagery: a brain-computer interface study combining EEG and fNIRS.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {172},
pmid = {40745321},
issn = {1743-0003},
support = {82072548//National Science Foundation of China/ ; 82472619//National Science Foundation of China/ ; 2022YFC2009700//Natural Key Research and Development Program of China/ ; 202206010197 and 202201020378//Guangzhou Municipal Science and Technology Program/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; Spectroscopy, Near-Infrared ; Electroencephalography/methods ; *Imagination/physiology ; Adult ; Young Adult ; *Transcranial Magnetic Stimulation/methods ; *Dorsolateral Prefrontal Cortex/physiology ; Neuronal Plasticity/physiology ; Psychomotor Performance/physiology ; },
abstract = {BACKGROUND: Some individuals using brain-computer interfaces (BCIs) exhibit ineffective control during motor imagery-based BCI (MI-BCI) training. MI-BCI performance correlates with the activation in the frontoparietal attention network, premotor-parietal network, and supplementary motor area (SMA). This study aimed to enhance motor imagery ability and MI-BCI performance by modulating the excitability of the right dorsolateral prefrontal cortex (RDLPFC) through intermittent theta-burst stimulation (iTBS), inducing neuroplastic changes.<br><br>METHODS: Fifty-two healthy right-handed participants were randomly assigned to either the iTBS or sham group. They undertook two MI-BCI training sessions, with electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) used to assess acute neuroplasticity changes. The intervention was administered between sessions. Corticospinal excitability and motor imagery vividness were assessed using single-pulse transcranial magnetic stimulation (spTMS) and the Kinesthetic and Visual Imagery Questionnaire-20 (KVIQ-20) before and following the trial.<br><br>RESULTS: The iTBS group significantly improved motor state percentage (MSP). Significant &#xb5; event-related desynchronization (&#xb5;-ERD) was observed at the F4 electrode in the iTBS group. Functional connectivity (FC) analyses revealed decreased connectivity among several electrodes during the post-intervention period. The hemodynamic response function (HRF) indicated significant activation in the right PMC and SMA, with reduced FC among motor areas. No significant differences in MEP, CSP, and KVIQ-20 scores were found between groups.<br><br>CONCLUSION: iTBS targeting the RDLPFC may improve MI-BCI training performance and address the "BCI inefficiency" problem. RDLPFC stimulation induced changes in FC of brain regions associated with motor imagery and increased the activation of motor areas, suggesting that the RDLPFC could be a promising target for enhancing motor imagery and optimizing BCI systems.},
}
@article {pmid40745252,
year = {2025},
author = {Ke, Y and Han, Y and Liu, P and Ming, D},
title = {Dataset of binocularly coded steady-state visual evoked potentials recorded with an augmented reality headset.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {1338},
pmid = {40745252},
issn = {2052-4463},
support = {62276184 and 81925020//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Evoked Potentials, Visual ; *Brain-Computer Interfaces ; Electroencephalography ; *Augmented Reality ; Vision, Binocular ; },
abstract = {Steady-state visually evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have shown significant promise for practical applications. The integration of SSVEP-BCIs with head-mounted augmented-reality (AR) displays is expected to foster wearable, portable systems; nevertheless, empirical resources for such configurations are scarce, especially for paradigms employing innovative stimulation paradigms. Here we present a curated SSVEP dataset recorded with a binocular AR headset that independently modulates the visual input to each eye and a lightweight electroencephalography recorder. Beyond the conventional binocular-congruent single-frequency stimulation adopted in AR-SSVEP studies, the dataset systematically explores binocular-incongruent dual-frequency encoding whereby the two lenses render flickers with distinct frequencies and/or phases. We report comparative analyses of SSVEP characteristics and BCI performance under congruent versus incongruent protocols, and delineate the influence of inter-ocular frequency and phase disparities. The results substantiate the feasibility of wearable AR-SSVEP-BCIs and highlight binocular-incongruent dual-frequency stimulation as a compelling strategy for improving target separability. The dataset should accelerate research on portable SSVEP-BCIs, novel encoding schemes, and the neural mechanisms of binocular vision.},
}
@article {pmid40744250,
year = {2025},
author = {Li, Q and Ping, A and Feng, Y and Xu, B and Zhang, B and Roe, AW and Gao, L and Li, X},
title = {Mesoscale functional connectivity of amygdala to the auditory and prefrontal cortex of macaque monkeys revealed by INS-fMRI.},
journal = {NeuroImage},
volume = {318},
number = {},
pages = {121406},
doi = {10.1016/j.neuroimage.2025.121406},
pmid = {40744250},
issn = {1095-9572},
mesh = {Animals ; *Prefrontal Cortex/physiology/diagnostic imaging ; *Amygdala/physiology ; *Auditory Cortex/physiology ; *Magnetic Resonance Imaging/methods ; Male ; Macaca mulatta ; Neural Pathways/physiology ; Brain Mapping/methods ; Acoustic Stimulation ; },
abstract = {Mammals rely heavily on their auditory system to perceive environmental threats, socially communicate, and care for the young. As an extension of the multiple sensory system including the auditory system, the amygdala evaluates the emotional salience of acoustic stimuli, and mediates its impact on sensory, cognitive, and physiological aspects of emotional processing via the lateral amygdala (LA), basal amygdala (BA), and central amygdala (CeA) nuclei of the amygdala in acoustic domain. However, the functional connections of LA, BA, and CeA with the auditory cortex (AC) and the prefrontal cortex (PFC) remain unclear, particularly at the mesoscale level. Here we employed a novel method called INS-fMRI (Infrared Neural Stimulation combined with high-resolution functional magnetic resonance imaging) in Macaque monkeys, this method permits stimulation of multiple sites within single animals in vivo, so that the relative organization of auditory networks can be studied. We found that: (1) Focal INS stimulation of the amygdala elicited robust and reliable responses in both the AC and the PFC; (2) Amygdala stimulation mainly activated ipsilateral AC and PFC; (3) The stimulation of the amygdala mainly activated the secondary AC, and the dorsolateral PFC; (4) The connection between the amygdala and the cortex is mainly mediated by neurons in LA and BA connection area. Our study further revealed the functional connectivity among the amygdala subnucleus, the auditory cortex and the prefrontal cortex, and will shed light on the research for processing biologically meaningful complex sounds.},
}
@article {pmid40744238,
year = {2025},
author = {Wen, Z and Yang, D and Yang, Y and Hu, J and Parviainen, A and Chen, X and Li, Q and VanDeusen, E and Ma, J and Tay, F},
title = {The path to biotechnological singularity: Current breakthroughs and outlook.},
journal = {Biotechnology advances},
volume = {84},
number = {},
pages = {108667},
doi = {10.1016/j.biotechadv.2025.108667},
pmid = {40744238},
issn = {1873-1899},
mesh = {Humans ; *Biotechnology/trends ; Gene Editing ; Artificial Intelligence ; Synthetic Biology ; Regenerative Medicine ; CRISPR-Cas Systems ; Brain-Computer Interfaces ; },
abstract = {Fueled by rapid advances in gene editing, synthetic biology, artificial intelligence, regenerative medicine, and brain-computer interfaces, biotechnology is approaching a transformative era often referred to as biotechnological singularity. CRISPR-based gene editing has revolutionized genetic engineering, enabling precise modifications for treating hereditary diseases and cancer. Synthetic biology facilitates sustainable biomaterial production and innovative therapeutic applications. Artificial intelligence accelerates drug discovery, enhances diagnostic accuracy, and personalizes treatment through deep learning models. Driven by stem cell research, regenerative medicine offers promising avenues for reversing aging and treating degenerative diseases. Brain-computer interfaces merge human cognition with technology, enabling direct neural control of prosthetics and expanding human-machine interactions. These breakthroughs, however, raise ethical, regulatory, and societal concerns, including equitable access, biosecurity risks, and the implications of human enhancement. The convergence of biological and computational technologies challenges traditional boundaries, necessitating comprehensive governance frameworks. By embracing responsible innovation, society can harness these advancements for transformative health interventions, environmental sustainability, and extended longevity. The realization of biotechnological singularity depends on interdisciplinary collaboration among scientists, policymakers, and the public to ensure that progress aligns with the well-being of humanity and ethical considerations.},
}
@article {pmid40743699,
year = {2025},
author = {Amiri, G and Shalchyan, V},
title = {Decoding muscle activity via CNN-LSTM from 3D spatiotemporal EEG.},
journal = {Computer methods and programs in biomedicine},
volume = {271},
number = {},
pages = {108983},
doi = {10.1016/j.cmpb.2025.108983},
pmid = {40743699},
issn = {1872-7565},
mesh = {Humans ; *Electroencephalography/methods ; *Neural Networks, Computer ; Brain-Computer Interfaces ; Male ; Adult ; Electromyography ; Female ; Deep Learning ; Signal Processing, Computer-Assisted ; Linear Models ; *Muscle, Skeletal/physiology ; Young Adult ; Algorithms ; },
abstract = {OBJECTIVE: Reconstructing muscle activity from electromyogram (EMG) data using non-invasive electroencephalogram (EEG) signals could lead to significant advancements in brain-computer interfaces (BCIs). However, extracting muscle-related signals from EEG poses considerable challenges due to the mixed nature of signals captured by EEG sensors from various cortical regions.<br><br>APPROACH: This study introduces a new method for estimating muscle activity from non-invasive EEG signals while participants performed the grasp and lift (GAL) task. Envelopes of the delta, theta, alpha, beta, and gamma frequency bands were chosen as EEG features for the decoding models, computed similarly to muscle activity (EMG envelopes). These were converted into three-dimensional spatiotemporal matrices based on EEG electrode locations. A deep learning model, combining convolutional neural networks (CNN) for spatial and long short-term memory (LSTM) network for temporal EEG information extraction, was applied. This model was compared with two linear and nonlinear decoding methods: multivariate linear regression (mLR) and multilayer perceptron (MLP).<br><br>MAIN RESULTS: The average &#xb1; standard deviation of the normalized root mean square error (nRMSE), coefficient of determination (R&#xb2;), and correlation coefficient (CC) between the estimated and actual muscle activity of two muscles in five participants were 0.21 &#xb1; 0.05, 0.54 &#xb1; 0.17, and 0.76 &#xb1; 0.10, respectively. The CNN-LSTM model outperformed both mLR and MLP approaches (p-value < 0.016), with higher frequencies proving more effective for decoding.<br><br>SIGNIFICANCE: The proposed model effectively captures nonlinear relationships between brain and muscle activities, indicating its potential to enhance the accuracy and reliability of non-invasive BCIs.},
}
@article {pmid40742862,
year = {2025},
author = {Heo, D and Kim, SP},
title = {Freeing P300-Based Brain-Computer Interfaces From Daily Recalibration by Extracting Daily Common ERPs.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {2977-2987},
doi = {10.1109/TNSRE.2025.3594341},
pmid = {40742862},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Humans ; *Event-Related Potentials, P300/physiology ; Male ; Algorithms ; Electroencephalography/methods ; Adult ; Female ; Young Adult ; Calibration ; Reproducibility of Results ; },
abstract = {When people use brain-computer interfaces (BCIs) based on event-related potentials (ERPs) over different days, they often need to repeatedly calibrate BCIs every day using ERPs acquired on the same day. This cumbersome recalibration procedure would make it difficult to use BCIs daily. We aim to address the daily recalibration issue by examining across-day variations of the BCI performance and proposing a method to avoid daily recalibration. To this end, we implemented a P300-based BCI system designed to control a home appliance over five days. We first examined how the BCI performance varied across days with or without daily recalibration. On each day, the BCIs were tested using recalibration-based and recalibration-free decoders (RB and RF), with an RB or an RF decoder being built on the training data on each day or those on the first day, respectively. Using the RF decoder resulted in lower BCI performance on subsequent days compared to the RB decoder. Then, we developed a method to extract daily common ERP patterns from observed ERP signals using the sparse dictionary learning algorithm. We applied this method to the RF decoder and retested the BCI performance over days. Using the proposed method improved the RF decoder performance on subsequent days; the performance was closer to the level of the RB decoder compared to the original RF decoder. The method may provide a novel approach to addressing the daily-recalibration issue for P300-based BCIs, which is essential to implementing BCIs into daily life.},
}
@article {pmid40741299,
year = {2025},
author = {Si, Y and Sun, Y and Wu, K and Gao, L and Wang, S and Xu, M and Qi, X},
title = {Effects of ASMR on mental fatigue recovery revealed by EEG power and brain network analysis.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1619424},
pmid = {40741299},
issn = {1662-5161},
abstract = {INTRODUCTION: Mental fatigue, resulting from prolonged cognitive tasks or sleep deprivation, significantly impacts safety and performance, particularly in high-risk environments. However, effective intervention methods are limited, highlighting the urgent need for new approaches to alleviate mental fatigue. This study explores the effectiveness of Autonomous Sensory Meridian Response (ASMR) as a novel intervention for alleviating mental fatigue.<br><br>METHODS: A within-subject design was employed in this work, where 28 healthy young subjects (M/F = 17/11, age = 21.82 &#xb1; 0.37 years) were requested to perform a continuous 30 min sustained attention task (named No-Break session) and a 30 min task with a 4-min mid-task ASMR break (named ASMR-Break session) at a counterbalanced order. The immediate effect and general effect of ASMR were quantitatively assessed on behavioral performance and EEG characteristics.<br><br>RESULTS: Behaviorally, only significant immediate effect was revealed as showing in reduced reaction time. Further interrogation of brain dynamics showed complex patterns of spatio-spectrum alterations and an interaction in small-world metric in theta band. Specifically, the ASMR intervention prevented an increase in small-worldness, and the correlation between changes in small-worldness and reaction times diminished after the intervention.<br><br>DISCUSSION: In sum, this preliminary investigation provides insight into ASMR's neural mechanisms and suggests it may help attenuate fatigue. Further research in larger, more diverse samples will be necessary to confirm its utility for mental fatigue management in real-world settings.},
}
@article {pmid40741298,
year = {2025},
author = {Zhu, T and Tang, H and Jiang, L and Li, Y and Li, S and Wu, Z},
title = {A study of motor imagery EEG classification based on feature fusion and attentional mechanisms.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1611229},
pmid = {40741298},
issn = {1662-5161},
abstract = {INTRODUCTION: Motor imagery EEG-based action recognition is an emerging field arising from the intersection of brain science and information science, which has promising applications in the fields of neurorehabilitation and human-computer collaboration. However, existing methods face challenges including the low signal-to-noise ratio of EEG signals, inter-subject variability, and model overfitting.<br><br>METHODS: We propose HA-FuseNet, an end-to-end motor imagery action classification network. This model integrates feature fusion and attention mechanisms to classify left hand, right hand, foot, and tongue movements. Its innovations include: (1) multi-scale dense connectivity, (2) hybrid attention mechanism, (3) global self-attention module, and (4) lightweight design for reduced computational overhead.<br><br>RESULTS: On BCI Competition IV Dataset 2A, HA-FuseNet achieved 77.89% average within-subject accuracy (8.42% higher than EEGNet) and 68.53% cross-subject accuracy.<br><br>CONCLUSION: The model demonstrates robustness to spatial resolution variations and individual differences, effectively mitigating key challenges in motor imagery EEG classification.},
}
@article {pmid40741296,
year = {2025},
author = {Degirmenci, M and Yuce, YK and Perc, M and Isler, Y},
title = {Classification of finger movements through optimal EEG channel and feature selection.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1633910},
pmid = {40741296},
issn = {1662-5161},
abstract = {INTRODUCTION: Electrencephalography (EEG)-based brain-computer interfaces (BCIs) have become popular as EEG is accepted as the simplest and non-invasive neuroimaging modality to record the brain's electrical activity. In the current BCI research context, apart from predicting extremity movements, recent BCI studies have been interested in accurately predicting finger movements of the same hand using different pattern recognition methods over EEG data collected based on motor imagery (MI), through which a mental image of the desired action is generated when a person ideally simulates or imagines carrying out a certain motor task. Although several pattern recognition methods have already been recommended in literature, majority of the studies focusing on classifying five finger movements, were based on study designs that neglected or excluded the idle state of brain (i.e., no mental task state) during which brain does not carry out any MI task. This study design may result in an increasing number of false positives and a significant decrease in the prediction rates and classification performance. Moreover, recent studies have focused on improving prediction performance using complex feature extraction and machine learning algorithms while ignoring comprehensive EEG channels and feature investigation in the prediction of finger movements from EEGs. Therefore, the objectives of this study are threefold: (i) to develop a more viable and practical system to predict the movements of five fingers and the no mental task (NoMT) state from EEG signals (ii) to analyze the effects of the statistical-significance based feature selection method over four different feature domains (nonlinear domain, time-domain, frequency-domain and time-frequency domain) and their combinations, and (iii) to test these feature sets with different and prominent classifiers.<br><br>METHODS: In this study, our major goal is not to explore the best machine algorithm performance, but to investigate the best EEG channels and features that can be used in the classification of finger movements. Hence, the comprehensive analysis of the effectiveness of EEG channels and features is performed utilizing a statistically significant feature distribution over 19 EEG channels for each feature set independently. A bulky dataset of electroencephalographic MI for EEG-based BCIs is used in this study. A total of 1102 EEG features supplied from different feature domains have been investigated. Subsequently, these features were tested with eight well-known classifiers, comprising Decision tree, Discriminant analysis, Naive Bayes, Support vector machine, k-nearest neighbor, Ensemble learning, Neural networks, and Kernel approximation.<br><br>RESULTS: For subject-dependent analysis, the maximum accuracy of 59.17% was obtained using the EEG features that were selected the most (including (i) energy and variance of five frequency bands in frequency-domain feature set, (ii) all feature types in time-domain, time-frequency domain, and nonlinear domain feature sets) and all EEG channels by the Support vector machine algorithm. For subject-independent analysis, the maximum accuracy of 39.30% was obtained using the mostly selected EEG features (which are (i) all feature types excluding the waveform length, average amplitude change value, absolute difference in standard deviation, and slope-change value feature types in time-domain feature set, (ii) the energy and variance values of all frequency bands except gamma frequency band in frequency-domain feature set, (iii) the entropy value of five frequency bands in time-frequency-domain feature set, and (iv) SD 2 and SD 1/SD 2 values where lag = 1 in nonlinear feature set) and EEG channels (which are (i) some definite EEG channels including 2nd, 3rd, 7th, 11th, 13th, 14th, and 15th channels in time-frequency-domain feature set and (ii) all EEG channels in time-domain, frequency-domain, and nonlinear feature sets) by the Support vector machine classifier.<br><br>DISCUSSION: Experimental results demonstrate that despite the high-class number, the proposed approach obtained a modest yet considerable advancement in finger movement prediction when the results are compared to the results of similar studies. Additionally, for almost all feature sets, the statistical significance-based feature reduction method improves the prediction performance in the most of classifiers, contributing elaborate EEG channel and feature analysis. Nonetheless, in this study, we used an EEG dataset recorded from only 13 healthy subjects; therefore, a dataset covering more subjects is necessary to reach a more general conclusion.},
}
@article {pmid40740060,
year = {2025},
author = {Kumar, A and Kumar, A},
title = {BiLSTM-Based Human Emotion Classification Using EEG Signal.},
journal = {Clinical EEG and neuroscience},
volume = {},
number = {},
pages = {15500594251364017},
doi = {10.1177/15500594251364017},
pmid = {40740060},
issn = {2169-5202},
abstract = {Emotion recognition using electroencephalography (EEG) signals has garnered significant attention due to its applications in affective computing, human-computer interaction, and healthcare. This study employs a Bidirectional Long Short-Term Memory (BiLSTM) network to classify emotions using EEG data from four well-established datasets: SEED, SEED-IV, SEED-V, and DEAP. By leveraging the temporal dependencies inherent in EEG signals, the BiLSTM model demonstrates robust learning of emotional states. The model achieved notable classification accuracies, with 92.30% for SEED, 99.98% for SEED-IV, 99.97% for SEED-V, and 88.33% for DEAP, showcasing its effectiveness across datasets with varying class distributions. The superior performance on SEED-IV and SEED-V underscores the BiLSTM's capability to capture bidirectional temporal information, which is crucial for emotion recognition tasks. Moreover, this work highlights the importance of utilizing diverse datasets to validate the generalizability of EEG-based emotion recognition models. The integration of both dimensional and discrete emotion models in the study demonstrates the framework's flexibility in addressing various emotion representation paradigms. Future directions include optimizing the framework for real-world applications, such as wearable EEG devices, and exploring transfer learning techniques to enhance cross-subject and cross-cultural adaptability. Overall, this study advances EEG-based emotion recognition methodologies, establishing a robust foundation for integrating affective computing into various domains and paving the way for real-time, reliable emotion recognition systems.},
}
@article {pmid40739107,
year = {2025},
author = {Jia, O and Tan, Q and Zhang, S and Jia, K and Gong, M},
title = {The precision of attention selection during reward learning influences the mechanisms of value-driven attention.},
journal = {NPJ science of learning},
volume = {10},
number = {1},
pages = {49},
pmid = {40739107},
issn = {2056-7936},
support = {2023-PT310-01//Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences/ ; 32371087//National Natural Science Foundation of China/ ; 32300855//National Natural Science Foundation of China/ ; 226-2024-00118//Fundamental Research Funds for the Central University/ ; 2021ZD0200409//National Science and Technology Innovation 2030-Major Project/ ; },
abstract = {Reward-predictive items capture attention even when task-irrelevant. While value-driven attention typically generalizes to stimuli sharing critical reward-associated features (e.g., red), recent evidence suggests an alternative generalization mechanism based on feature relationships (e.g., redder). Here, we investigated whether relational coding of reward-associated features operates across different learning contexts by manipulating search mode and target-distractor similarity. Results showed that singleton search training induced value-driven relational attention regardless of target-distractor similarity (Experiments 1a-1b). In contrast, feature search training produced value-driven relational attention only when targets and distractors were dissimilar, but not when they were similar (Experiments 2a-2c). These findings indicate that coarse selection training (singleton search or feature search among dissimilar items) promotes relational coding of reward-associated features, while fine selection (feature search among similar items) engages precise feature coding. The precision of target selection during reward learning thus critically determines value-driven attentional mechanisms.},
}
@article {pmid40737169,
year = {2025},
author = {Xiong, D and Hu, L and Jin, J and Ding, Y and Tan, C and Zhang, J and Tian, Y},
title = {Interpretable Cross-Modal Alignment Network for EEG Visual Decoding With Algorithm Unrolling.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {36},
number = {11},
pages = {19894-19908},
doi = {10.1109/TNNLS.2025.3592646},
pmid = {40737169},
issn = {2162-2388},
mesh = {*Electroencephalography/methods ; Humans ; *Algorithms ; *Neural Networks, Computer ; Signal-To-Noise Ratio ; *Visual Perception/physiology ; Photic Stimulation/methods ; Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Machine Learning ; },
abstract = {Accurate decoding in electroencephalography (EEG) technology, particularly for rapid visual stimuli, remains challenging due to the low signal-to-noise ratio (SNR). Additionally, existing neural networks struggle with issues related to generalization and interpretability. This article proposes a cross-modal aligned network, E2IVAE, which leverages shared information from multiple modalities for self-supervised alignment of EEG to images for extracting visual perceptual information and features a novel EEG encoder, ISTANet, based on algorithm unrolling. This network framework significantly enhances the accuracy and stability of EEG decoding for object recognition in novel classes while reducing the extensive neural data typically required for training neural decoders. The proposed ISTANet employs algorithm unrolling to transform the multilayer sparse coding algorithm into an end-to-end format, extracting features from noisy EEG signals while incorporating the interpretability of traditional machine learning. The experimental results demonstrate that our method achieves SOTA top-1 accuracy of 62.39% and top-5 accuracy of 88.98% on a comprehensive rapid serial visual presentation (RSVP) dataset for public comparison in a 200-class zero-shot neural decoding task. Additionally, ISTANet enables visualization and analysis of multiscale atom features and overall reconstruction features, exploring biological plausibility across temporal, spatial, and spectral dimensions. On another more challenging RSVP large-scale dataset, the proposed framework also achieves significantly above chance-level performance, proving its robustness and generalization. This research provides critical insights into neural decoding and brain-computer interfaces (BCIs) within the fields of cognitive science and artificial intelligence.},
}
@article {pmid40735361,
year = {2025},
author = {Zhang, Q and Zhang, C and Ji, H and Chen, J and Wang, X and Zhang, T and Liu, P and Wang, Z and Xu, Y},
title = {Ethical governance of clinical research on the brain-computer interface for mental disorders: a modified Delphi study.},
journal = {General psychiatry},
volume = {38},
number = {4},
pages = {e101755},
pmid = {40735361},
issn = {2517-729X},
abstract = {BACKGROUND: Clinical brain-computer interface (BCI) for mental disorders is an emerging interdisciplinary research field, posing new ethical concerns and challenges, yet lacking practical ethical governance guidelines for stakeholders and the entire community.<br><br>AIMS: This study aims to establish a multidisciplinary consensus of principles for ethical governance of clinical BCI research for mental disorders and offer practical ethical guidance to stakeholders involved.<br><br>METHODS: A systematic literature review, symposium and roundtable discussions, and a pre-Delphi (round 0) survey were conducted to form the questionnaire for the three-round modified Delphi study. Two rounds of surveys, followed by a third round of independent interviews of 25 experts from BCI-related research domains, were involved. We conducted quantitative analysis of responses and agreements among experts to reveal the consensus and differences regarding the ethical governance of mental BCI research from a multidisciplinary perspective.<br><br>RESULTS: The Delphi panel emphasised important concerns of ethical review practices and ethical principles within the BCI context, identified qualified and highly influential institutions and personnel in conducting and advancing clinical BCI research, and recognised prioritised aspects in the risk-benefit evaluation. Experts expressed diverse opinions on specific ethical concerns, including concerns about invasive technology, its impact on humanity and potential social consequences. Agreement was reached that the practices of ethical governance of clinical BCI for mental disorders should focus on patient voluntariness, autonomy, long-term effects and related assessments of BCI interventions, as well as privacy protection, transparent reporting and ensuring that the research is conducted in qualified institutions with strong data security.<br><br>CONCLUSIONS: Ethical governance of clinical research on BCI for mental disorders should include interdisciplinary experts to balance various needs and incorporate the expertise of different stakeholders to avoid serious ethical issues. It requires scientifically grounded approaches, continuous monitoring and interdisciplinary collaboration to ensure evidence-based policies, comprehensive risk assessments and transparency, thereby promoting responsible innovations and protecting patient rights and well-being.},
}
@article {pmid40735214,
year = {2025},
author = {Ran, J and Xu, J and Luo, D and Li, T and Xu, J},
title = {Problematic internet use and aggression in Chinese middle school students: mediation effect of reality social connectedness.},
journal = {Frontiers in public health},
volume = {13},
number = {},
pages = {1587400},
pmid = {40735214},
issn = {2296-2565},
mesh = {Humans ; *Aggression/psychology ; China/epidemiology ; Male ; Female ; Cross-Sectional Studies ; *Students/psychology/statistics &amp; numerical data ; Adolescent ; Surveys and Questionnaires ; *Internet Addiction Disorder/psychology/epidemiology ; *Internet Use/statistics &amp; numerical data ; East Asian People ; },
abstract = {INTRODUCTION: Problematic internet use (PIU) has become a prevalent concern worldwide and is associated with increased aggression. However, the underlying effect of PIU on aggression remains unclear. In this study, we aimed to investigate the potential influence of reality social connectedness (RSC) on the relationship between PIU and aggression.<br><br>METHODS: We used cross-sectional data from a large survey conducted among middle school students in four provinces of China between September 2022 and March 2023. PIU, RSC, and aggression were assessed using Young's 20-item Internet Addiction Test (IAT-20), the modified Social Connectedness Scale-Revised (SCS-R), and the Buss-Perry Aggression Questionnaire (BPAQ), respectively.<br><br>RESULTS: We found that students who experienced PIU had significantly higher scores on the BPAQ, which reflects the aggression levels, compared to students without PIU. Specifically, all four dimensions of aggression-verbal aggression, physical aggression, hostility, and anger-were elevated in the PIU group. Additionally, RSC was significantly reduced among individuals with PIU. Notably, RSC significantly mediated the relationship between PIU and aggression, accounting for 18.89% of the total effect. Among the four dimensions of aggression, the mediating effect of RSC was strongest for hostility, followed by anger and physical aggression, with the weakest observed for verbal aggression.<br><br>DISCUSSION: RSC significantly mediated the relationship between PIU and aggression, suggesting that reduced RSC partially explains how PIU exacerbates aggression. This result highlights the importance of fostering RSC as a strategy to reduce aggression related to PIU.},
}
@article {pmid40734822,
year = {2025},
author = {Liu, ZY and Zhang, L and Wang, ZD and Huang, ZQ and Li, MC and Lu, Y and Hu, JP and Chen, QL and Chen, XY},
title = {Magnetic resonance imaging for spinocerebellar ataxia: a bibliometric analysis based on web of science.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1512800},
pmid = {40734822},
issn = {1664-2295},
abstract = {The objective of this study was to review the history of magnetic resonance imaging (MRI) research on spinocerebellar ataxia (SCA) over the last 16 years. We conducted a comprehensive bibliometric analysis of relevant scientific literature that explores the use of MRI in studying SCA using CiteSpace. A total of 761 scientific manuscripts, published between January 2009 and March 2025 and available in the Web of Science (WoS) database, were included in this analysis. A total of 197 out of 761 articles were analyzed using CiteSpace to determine the number and centrality of publications, countries, institutions, journals, authors, cited references, and keywords related to MRI and SCA. Overall, the number of publications that use MRI to study SCA has gradually increased over the years. The United States, China, Italy, Germany, and Brazil are at the forefront in this research field; a total of 420 authors from 317 research institutions in these nations have published articles in neuroscience-related journals. Among the most cited publications are an article by Rezende et al. on brain structural damage in SCA3 patients and an review by Klockgether et al. on spinocerebellar ataxia. The keyword "spinocerebellar ataxia" has the highest frequency of occurrence. However, "feature" may become a research hotspot in the coming years based on the analysis of the keyword's citation burst. The findings of this bibliometric study provide a summary of the last 16 years of SCA research using MRI technology. More importantly, the present study identifies current trends and future research hotspots in the field, helping researchers to identify new and unexplored research areas.},
}
@article {pmid40731219,
year = {2025},
author = {Motiwala, A and Soldado-Magraner, J and Batista, AP and Smith, MA and Yu, BM},
title = {Brain-computer interfaces as a causal probe for scientific inquiry.},
journal = {Trends in cognitive sciences},
volume = {},
number = {},
pages = {},
pmid = {40731219},
issn = {1879-307X},
support = {R01 MH118929/MH/NIMH NIH HHS/United States ; R01 NS105318/NS/NINDS NIH HHS/United States ; R01 NS129584/NS/NINDS NIH HHS/United States ; },
abstract = {Establishing causal relationships between neural activity and brain function requires experimental perturbations of neural activity. Many existing perturbation methods modify activity by directly applying external signals to the brain. We review an alternative approach where brain-computer interfaces (BCIs) leverage volitional control of neural activity to manipulate and causally perturb it. We highlight the potential of BCIs to manipulate neural activity in ways that are flexible, accurate, and adhere to intrinsic biophysical and network-level constraints to investigate the consequences of configuring neural population activity in specified ways. We discuss the advantages and disadvantages of using BCIs as a perturbation tool compared with other perturbation methods and how BCIs can expand the scope of questions that can be addressed about brain function.},
}
@article {pmid40731189,
year = {2025},
author = {Zhao, X and Yu, J and Xu, B and Xu, Z and Lei, X and Han, S and Luo, S and Zhang, C and Peng, G and Li, J and Yu, J and Ling, Y and Fan, Z and Mo, W and Yang, Y and Zhang, J},
title = {Gut-derived bacterial vesicles carrying lipopolysaccharide promote microglia-mediated synaptic pruning.},
journal = {Alzheimer's &amp; dementia : the journal of the Alzheimer's Association},
volume = {21},
number = {8},
pages = {e70331},
pmid = {40731189},
issn = {1552-5279},
support = {82020108012//National Natural Science Foundation of China/ ; 82371250//National Natural Science Foundation of China/ ; 2024C03098//Key Research and Development Program of Zhejiang Province/ ; 2024SSYS0018//Key Research and Development Program of Zhejiang Province/ ; LZ23H090002//Natural Science Foundation of Zhejiang Province/ ; LY24H090006//Natural Science Foundation of Zhejiang Province/ ; //Innovative Institute of Basic Medical Science of Zhejiang University/ ; },
mesh = {*Lipopolysaccharides/metabolism ; *Microglia/metabolism ; Humans ; *Gastrointestinal Microbiome/physiology ; Animals ; Mice ; *Extracellular Vesicles/metabolism ; *Alzheimer Disease/metabolism ; *Neuronal Plasticity/physiology ; Blood-Brain Barrier/metabolism ; Male ; Female ; Brain/metabolism ; },
abstract = {INTRODUCTION: Growing evidence links gut microbiota (GM) to Alzheimer's disease (AD). Elevated lipopolysaccharide (LPS) levels, a Gram-negative bacteria component, are found in AD brains, but how LPS breaches the blood-brain barrier (BBB) remains unclear. Hypotheses suggest that bacteria-derived extracellular vesicles (bEVs) may transport LPS across the BBB.<br><br>METHODS: bEVs were extracted from human and mouse feces and blood, and LPS levels were measured. In vivo imaging and immunofluorescence confirmed the transport of blood LPS-carrying bEVs across the BBB. The role of these bEVs in microglia was investigated both in vivo and in vitro.<br><br>RESULTS: Elevated LPS-containing bEVs were detected in the plasma of AD patients compared to healthy individuals. These bEVs activated microglial Piezo1, consequently precipitating an excessive synaptic pruning process mediated by the C1q-C3 complement pathway.<br><br>DISCUSSION: These findings illuminate the complex interplay between the gut microbiota, bEVs, neuroinflammation, and synaptic plasticity - a key early event in AD - offering insights for potential therapeutic interventions.<br><br>HIGHLIGHTS: GM-derived bEVs can traverse the BBB. LPS was necessary for bEVs' penetration into the brain, and bEVs might be closely related to AD progression. bEVs mediated microglial activation and synaptic pruning via C1q-C3 complement pathway. Microglia Piezo1 was involved in bEV-induced excessive synaptic pruning.},
}
@article {pmid40730254,
year = {2025},
author = {Alouani, Z and Gannour, OE and Saleh, S and El-Ibrahimi, A and Daanouni, O and Cherradi, B and Bouattane, O},
title = {A novel contrastive Dual-Branch Network (CDB-Net) for robust EEG-Based Alzheimer's disease diagnosis.},
journal = {Brain research},
volume = {1865},
number = {},
pages = {149863},
doi = {10.1016/j.brainres.2025.149863},
pmid = {40730254},
issn = {1872-6240},
mesh = {*Alzheimer Disease/diagnosis/physiopathology ; Humans ; *Electroencephalography/methods ; Deep Learning ; *Neural Networks, Computer ; Male ; Brain/physiopathology ; Aged ; Female ; Signal Processing, Computer-Assisted ; },
abstract = {Alzheimer's Disease (AD) is neurodegenerative disorder that causes cognitive decline, memory loss, confusion, and changes in behavior. Early and accurate detection is important for timely intervention, current diagnostic methods can be slow, expensive, and have limited sensitivity. Electroencephalography (EEG) offers a simple and non-invasive way to measure brain activity, and it has shown promise in supporting AD diagnosis. However, EEG signals are often affected by noise-such as muscle movement, blinking, or electrical interference-which can make it harder for models to give reliable results. To address these challenges, we introduce CDB-Net (Contrastive Dual-Branch Network), a deep learning model built to improve the accuracy and robustness of EEG-based AD classification. The model uses two parallel branches: one processes clean EEG data, while the other processes a noisy version of the same data. By training these branches together using contrastive learning, the model learns to focus on features that stay consistent even when the signal is distorted by noise. A classification head is trained jointly using cross-entropy loss for downstream diagnosis. We tested our method on a public EEG dataset and found that CDB-Net achieved 97.92% accuracy on clean data and 83.41% accuracy even under adversarial attacks (FGSM), outperforming traditional machine learning classifiers and deep learning baselines models. These results highlight the effectiveness of contrastive dual-branch learning in enhancing model generalization and robustness, positioning CDB-Net as a promising tool for reliable EEG-based clinical decision support in the context of Alzheimer's Disease diagnosis.},
}
@article {pmid40728869,
year = {2025},
author = {Kawaguchi, N and Koyano, K and Morita, H and Pengiran Mohamad Fadly, DNRAC and Shinabe, Y and Noguchi, Y and Arioka, M and Nakao, Y and Ozaki, M and Nakamura, S and Kondo, S and Konishi, Y and Kuboi, T and Okada, H and Yasuda, S and Itoh, S and Murao, K and Kusaka, T},
title = {Quantitative effects of bilirubin photoisomers on the measurement of direct bilirubin by the enzymatic bilirubin oxidase method.},
journal = {Annals of clinical biochemistry},
volume = {},
number = {},
pages = {45632251367245},
doi = {10.1177/00045632251367245},
pmid = {40728869},
issn = {1758-1001},
abstract = {BackgroundBilirubin photoisomers, generated during phototherapy or incidental light exposure, may interfere with direct bilirubin (DB) measurement using the bilirubin oxidase method. This interference is particularly relevant in neonates, who physiologically exhibit elevated levels of unconjugated bilirubin.MethodsResidual serum samples from 30 neonates were irradiated under controlled conditions to selectively produce bilirubin configurational isomers (BCIs) and structural isomers (BSIs). DB and total bilirubin (TB) were measured pre- and post- irradiation using the bilirubin oxidase method. BCI and BSI concentrations were quantified using high-performance liquid chromatography (HPLC), and their contributions to DB values were evaluated using linear and multiple regression analyses.ResultsPost-irradiation, DB levels increased significantly in correlation with BCI and BSI concentrations. Approximately 11% of BCI and 32% of BSI were quantified as DB using the bilirubin oxidase method. These findings were consistent across both individual and multiple regression models.ConclusionsBilirubin photoisomers significantly influence DB values measured by the bilirubin oxidase method, potentially leading to overestimation of conjugated bilirubin. In neonatal care, accurate interpretation of DB values requires attention to sample handling and awareness of photoisomer interference, particularly under light-expose conditions.},
}
@article {pmid40727297,
year = {2025},
author = {Ebrahimibasabi, S and Golshahi, M and Shahraki, N and Tamjid Shabestari, D and Sajjadi, M and Hashemi, S and Borchert, A and Baker, I and Khalifehzadeh, L and Arami, H},
title = {Designing parylene coating for implantable brain-machine interfaces.},
journal = {RSC advances},
volume = {15},
number = {33},
pages = {26660-26672},
pmid = {40727297},
issn = {2046-2069},
abstract = {Parylene is widely recognized as an effective candidate for encapsulating implantable bioelectronics due to its outstanding chemical stability, conformity and biocompatibility. However, its weak adhesion to inorganic substrates remains a significant challenge. Here, we explored various pre- and post-deposition treatments to enhance adhesion and stability of parylene coating for implantable brain-machine interfaces (BMIs). We utilized 0%, 0.5%, 1%, and 1.5% (v/v) 3-(trimethoxysilyl)propyl-methacrylate as an adhesion promoter for substrate treatment prior to deposition. Deposited samples were subsequently subjected to post-heat treatments at various temperatures. Samples were exposed to an in vitro accelerated aging bath at 87 °C for 7 days to assess their post-implantation durability. Cytotoxicity and in vivo biocompatibility were also investigated to further evaluate biocompatibility and encapsulation efficiency of parylene coatings on commonly used rigid and flexible bioelectronic substrates. The emergence of carboxyl groups in FTIR and chlorine abstraction in EDS analyses, indicated that the as-deposited samples were degraded during aging. The chemical stability of these coatings was improved in heat-treated samples due to their higher crystallinity. Additionally, delamination and microcrack initiation/growth reduced due to post-heat treatments. We found the optimal heat treatment temperature to be 150 °C; any increase beyond this compromised coating quality by increasing delamination and defect formation. Increasing the concentration of adhesion promoter enhanced coating adhesion to the substrates in both as-deposited samples and the ones heat-treated at 150 °C. In contrast, the adhesion strength decreased when heat-treatment was performed at higher temperatures, even when the concentration of adhesion promoter was increased. Numerical analysis was used to assess the effect of parylene coating on the electrical performance of a typical implantable, wirelessly powered model device. The results demonstrated that the presence of the parylene layer not only preserved the wireless coupling between this device and the pickup probe, but also enhanced it. In addition to these favourable physiochemical improvements, parylene also promoted general in vivo brain compatibility and cell viability of the devices. This study revealed the synergistic effects of pre- and post-deposition treatments and systematically optimized adhesion and stability of parylene coatings for implantable BMIs for the first time.},
}
@article {pmid40722830,
year = {2025},
author = {Ga, YJ and Go, YY and Yeh, JY},
title = {Small Interfering RNAs Targeting VP4, VP3, 2B, or 3A Coding Regions of Enterovirus A71 Inhibit Viral Replication In Vitro.},
journal = {Biomedicines},
volume = {13},
number = {7},
pages = {},
pmid = {40722830},
issn = {2227-9059},
support = {2019//Incheon National University/ ; },
abstract = {Background: Enterovirus A71 (EV-A71) is considered as the primary causative agent of hand, foot, and mouth disease (HFMD) in young children, leading to severe neurological complications and contributing to substantial mortalities in recent HFMD outbreaks across Asia. Despite this, there is currently no effective antiviral treatment available for EV-A71. RNA interference (RNAi) is a powerful mechanism of post-transcriptional gene regulation that utilizes small interfering RNA (siRNA) to target and degrade specific RNA sequences. Objectives: The aim of this study was to design various siRNAs targeting EV-A71 genomic regions and evaluate the RNAi efficacy against a novel, previously genetically uncharacterized EV-A71 strain. Methods: A novel EV-A71 strain was first sequenced to design target-specific siRNAs. The viral titers, viral protein expression, cytopathic effects, and cell viability of EV-A71-infected HeLa cells were examined to evaluate the specific viral inhibition by the siRNAs. Results: A substantial reduction in viral titers and viral protein synthesis was observed in EV-A71-infected HeLa cells treated with specific siRNAs targeting the VP4, VP3, 2B, and 3A genes. siRNAs delayed cytopathic effects and increased cell viability of EV-A71-infected HeLa cells. Nonspecific interferon induction caused by siRNAs was not observed in this study. In contrast, replication of coxsackievirus B3, another important member of the Enterovirus genus, remained unaffected. Conclusions: Overall, the findings demonstrate that RNAi targeting genomic regions of EV-A71 VP4, VP3, 2B, or 3A could become a potential strategy for controlling EV-A71 infection, and this promising result can be integrated into future anti-EV-A71 therapy developments.},
}
@article {pmid40722467,
year = {2025},
author = {Zhan, H and Li, X and Song, X and Lv, Z and Li, P},
title = {MCTGNet: A Multi-Scale Convolution and Hybrid Attention Network for Robust Motor Imagery EEG Decoding.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {12},
number = {7},
pages = {},
pmid = {40722467},
issn = {2306-5354},
support = {No. 2108085MF207//Anhui Natural Science Foundation/ ; No. 2024AH050054//Natural Science Research Project of Anhui Educational Committee under Grant/ ; No. 2208085J05//Distinguished Youth Foundation of Anhui Scientific Committee/ ; No. 62476004//National Natural Science Foundation of China (NSFC)/ ; },
abstract = {Motor imagery (MI) EEG decoding is a key application in brain-computer interface (BCI) research. In cross-session scenarios, the generalization and robustness of decoding models are particularly challenging due to the complex nonlinear dynamics of MI-EEG signals in both temporal and frequency domains, as well as distributional shifts across different recording sessions. While multi-scale feature extraction is a promising approach for generalized and robust MI decoding, conventional classifiers (e.g., multilayer perceptrons) struggle to perform accurate classification when confronted with high-order, nonstationary feature distributions, which have become a major bottleneck for improving decoding performance. To address this issue, we propose an end-to-end decoding framework, MCTGNet, whose core idea is to formulate the classification process as a high-order function approximation task that jointly models both task labels and feature structures. By introducing a group rational Kolmogorov-Arnold Network (GR-KAN), the system enhances generalization and robustness under cross-session conditions. Experiments on the BCI Competition IV 2a and 2b datasets demonstrate that MCTGNet achieves average classification accuracies of 88.93% and 91.42%, respectively, outperforming state-of-the-art methods by 3.32% and 1.83%.},
}
@article {pmid40722306,
year = {2025},
author = {Deniz, SM and Ademoglu, A and Duru, AD and Demiralp, T},
title = {Application of Graph-Theoretic Methods Using ERP Components and Wavelet Coherence on Emotional and Cognitive EEG Data.},
journal = {Brain sciences},
volume = {15},
number = {7},
pages = {},
pmid = {40722306},
issn = {2076-3425},
abstract = {Background/Objectives: Emotion and cognition, two essential components of human mental processes, have traditionally been studied independently. The exploration of emotion and cognition is fundamental for gaining an understanding of human mental functioning. Despite the availability of various methods to measure and evaluate emotional states and cognitive processes, physiological measurements are considered to be one of the most reliable methods due to their objective approach. In particular, electroencephalography (EEG) provides unique insight into emotional and cognitive activity through the analysis of event-related potentials (ERPs). In this study, we discriminated pleasant/unpleasant emotional moods and low/high cognitive states using graph-theoretic features extracted from spatio-temporal components. Methods: Emotional data were collected at the Physiology Department of Istanbul Medical Faculty at Istanbul University, whereas cognitive data were obtained from the DepositOnce repository of Technische Universität Berlin. Wavelet coherence values for the N100, N200, and P300 single-trial ERP components in the delta, theta, alpha, and beta frequency bands were investigated individually. Then, graph-theoretic analyses were performed using wavelet coherence-based connectivity maps. Global and local graph metrics such as energy efficiency, strength, transitivity, characteristic path length, and clustering coefficient were used as features for classification using support vector machines (SVMs), k-nearest neighbor(K-NN), and linear discriminant analysis (LDA). Results: The results show that both pleasant/unpleasant emotional moods and low/high cognitive states can be discriminated, with average accuracies of up to 92% and 89%, respectively. Conclusions: Graph-theoretic metrics based on wavelet coherence of ERP components in the delta band with the SVM algorithm allow for the discrimination of emotional and cognitive states with high accuracy.},
}
@article {pmid40722299,
year = {2025},
author = {Zhang, Z and Lu, G},
title = {Multimodal Knowledge Distillation for Emotion Recognition.},
journal = {Brain sciences},
volume = {15},
number = {7},
pages = {},
pmid = {40722299},
issn = {2076-3425},
abstract = {Multimodal emotion recognition has emerged as a prominent field in affective computing, offering superior performance compared to single-modality methods. Among various physiological signals, EEG signals and EOG data are highly valued for their complementary strengths in emotion recognition. However, the practical application of EEG-based approaches is often hindered by high costs and operational complexity, making EOG a more feasible alternative in real-world scenarios. To address this limitation, this study introduces a novel framework for multimodal knowledge distillation, designed to improve the practicality of emotion decoding while maintaining high accuracy, with the framework including a multimodal fusion module to extract and integrate interactive and heterogeneous features, and a unimodal student model structurally aligned with the multimodal teacher model for better knowledge alignment. The framework combines EEG and EOG signals into a unified model and distills the fused multimodal features into a simplified EOG-only model. To facilitate efficient knowledge transfer, the approach incorporates a dynamic feedback mechanism that adjusts the guidance provided by the multimodal model to the unimodal model during the distillation process based on performance metrics. The proposed method was comprehensively evaluated on two datasets based on EEG and EOG signals. The accuracy of the valence and arousal of the proposed model in the DEAP dataset are 70.38% and 60.41%, respectively. The accuracy of valence and arousal in the BJTU-Emotion dataset are 61.31% and 60.31%, respectively. The proposed method achieves state-of-the-art classification performance compared to the baseline method, with statistically significant improvements confirmed by paired t-tests (p < 0.05), and the framework effectively transfers knowledge from multimodal models to unimodal EOG models, enhancing the practicality of emotion recognition while maintaining high accuracy, thus expanding the applicability of emotion recognition in real-world scenarios.},
}
@article {pmid40722278,
year = {2025},
author = {Yazıcı, M and Ulutaş, M and Okuyan, M},
title = {Effect of EEG Electrode Numbers on Source Estimation in Motor Imagery.},
journal = {Brain sciences},
volume = {15},
number = {7},
pages = {},
pmid = {40722278},
issn = {2076-3425},
abstract = {The electroencephalogram (EEG) is one of the most popular neurophysiological methods in neuroscience. Scalp EEG measurements are obtained using various numbers of channels for both clinical and research applications. This pilot study explores the effect of EEG channel count on motor imagery classification using source analysis in brain-computer interface (BCI) applications. Different channel configurations are employed to evaluate classification performance. This study focuses on mu band signals, which are sensitive to motor imagery-related EEG changes. Common spatial patterns are utilized as a spatiotemporal filter to extract signal components relevant to the right hand and right foot extremities. Classification accuracies are obtained using configurations with 19, 30, 61, and 118 electrodes to determine the optimal number of electrodes in motor imagery studies. Experiments are conducted on the BCI Competition III Dataset Iva. The 19-channel configuration yields lower classification accuracy when compared to the others. The results from 118 channels are better than those from 19 channels but not as good as those from 30 and 61 channels. The best results are achieved when 61 channels are utilized. The average accuracy values are 83.63% with 19 channels, increasing to 84.70% with 30 channels, 84.73% with 61 channels, and decreasing to 83.95% when 118 channels are used.},
}
@article {pmid40721281,
year = {2025},
author = {Ognard, J and Douri, D and El Hajj, G and Ghozy, S and Rohleder, M and Gentric, JC and Kadirvel, R and Kallmes, DF and Brinjikji, W},
title = {Future is Ven(o)us: A 5-year narrative update on the venous route for therapeutics in Neurointervention.},
journal = {AJNR. American journal of neuroradiology},
volume = {},
number = {},
pages = {},
doi = {10.3174/ajnr.A8942},
pmid = {40721281},
issn = {1936-959X},
abstract = {Over the past five years, transvenous (TV) techniques have rapidly expanded the neurointerventional landscape, offering new diagnostic and therapeutic strategies for a range of cerebrovascular conditions. This narrative review synthesizes contemporary evidence and technical advances across multiple venous applications, including TV embolization for arteriovenous malformations and dural fistulas, treatment of cerebrospinal fluid-venous fistulas, and venous sinus stenting for pulsatile tinnitus, intracranial hypertension, and skull-base leaks. Recent data underscore high efficacy rates and favorable safety profiles in carefully selected patients, often matching or surpassing traditional arterial approaches. Innovations such as fetal vein of Galen embolization, vein-targeted brain-computer interface implantation, and endovascular cerebrospinal fluid shunting exemplify the therapeutic versatility of venous access. However, procedural challenges, such as venous anatomy, access, and embolic control, require meticulous planning and advanced skillsets. Trials like TATAM and DIVE-IIN are and will shape evidence-based indications for TV therapy. With expanding indications and growing operator expertise, the venous route is evolving from a niche adjunct into a cornerstone of neurovascular care.ABBREVIATIONS: bAVM(s)＝ brain arteriovenous malformation(s); CVF(s)＝ cerebrospinal fluid-venous fistula(s); CVT＝ cerebral venous thrombosis; DAVF(s)＝ dural arteriovenous fistula(s); EVT＝ endovascular therapy; EVOH＝ ethylene-vinyl alcohol copolymer; IIH＝ idiopathic intracranial hypertension; JR-NET3＝ Japanese Registry of NeuroEndovascular Therapy; PT＝ pulsatile tinnitus; RPCT＝ retrograde pressure-cooker technique; SIH＝ spontaneous intracranial hypotension; sCSFL＝ spontaneous cerebrospinal fluid leak; SSWA＝ sigmoid sinus wallabnormality/abnormalities; TV＝ transvenous; TVE＝ transvenous embolization; VSS＝ venous sinus stenting.},
}
@article {pmid40720979,
year = {2025},
author = {Silva, AB and Liu, JR and Anderson, VR and Kurtz-Miott, CM and Hallinan, IP and Littlejohn, KT and Brosler, SC and Tu-Chan, A and Ganguly, K and Moses, DA and Chang, EF},
title = {Implications of shared motor and perceptual activations on the sensorimotor cortex for neuroprosthetic decoding.},
journal = {Journal of neural engineering},
volume = {22},
number = {4},
pages = {},
pmid = {40720979},
issn = {1741-2552},
support = {F30 DC021872/DC/NIDCD NIH HHS/United States ; U01 DC018671/DC/NIDCD NIH HHS/United States ; },
mesh = {Adult ; Female ; Humans ; Male ; Middle Aged ; *Brain-Computer Interfaces ; *Electrocorticography/methods ; Motor Cortex/physiology ; *Neural Prostheses ; *Sensorimotor Cortex/physiology ; *Speech Perception/physiology ; Clinical Trials as Topic ; },
abstract = {Objective.Neuroprostheses can restore communicative ability to people with paralysis by decoding intended speech motor movements from the sensorimotor cortex (SMC). However, overlapping neural populations in the SMC are also engaged in visual and auditory perceptual processing. The nature of these shared motor and perceptual activations and their potential to interfere with decoding are particularly relevant questions for speech neuroprostheses, as reading and listening are essential daily functions.Approach.In two participants with vocal-tract paralysis and anarthria (ClinicalTrials.gov; NCT03698149), we developed an online electrocorticography (ECoG) based speech-decoding system that maintained accuracy and specificity to intended speech, even during common daily tasks like reading and listening. Offline, we studied the spectrotemporal characteristics and spatial distribution of reading, listening, and attempted-speech responses across our participants' ECoG arrays.Main results.Across participants, the speech-decoding system had zero false-positive activations during 63.2 min of attempted speech and perceptual tasks, maintaining accuracy and specificity to volitional speech attempts. Offline, though we observed shared neural populations that responded to attempted speech, listening, and reading, we found they leveraged different neural representations with differentiable spectrotemporal responses. Shared populations localized to the middle precentral gyrus and may have a distinct role in speech-motor planning.Significance.Potential neuroprosthesis users strongly desire reliable systems that will retain specificity to volitional speech attempts during daily use. These results demonstrate a decoding framework for speech neuroprostheses that maintains this specificity and further our understanding of shared perceptual and motor activity on the SMC.},
}
@article {pmid40720264,
year = {2025},
author = {Cobilean, V and Mavikumbure, HS and Drake, D and Stuart, M and Manic, M},
title = {Investigating Membership Inference Attacks Against CNN Models for BCI Systems.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {11},
pages = {8164-8174},
doi = {10.1109/JBHI.2025.3593443},
pmid = {40720264},
issn = {2168-2208},
mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography ; *Neural Networks, Computer ; *Deep Learning ; *Computer Security ; Algorithms ; *Signal Processing, Computer-Assisted ; },
abstract = {As Deep Learning (DL) algorithms become more widely adopted in healthcare applications, there is a greater emphasis on understanding and addressing the potential privacy risks associated with these models. The purpose of this study is to investigate the privacy vulnerabilities of the Convolutional Neural Network (CNN) classifiers for Electroencephalogram (EEG) data in the Brain-Computer Interfaces (BCIs). Specifically, it focuses on the Membership Inference Attack (MIA), which seeks to determine if data from an individual were used in model training. The novelty of this work lies in its empirical analysis of MIA, by addressing two key challenges that are less common in other domains: 1) heterogeneous datasets and 2) spatio-temporal design choices. Motivated by these challenges, we investigate the susceptibility to MIA based on: 1) the specifics of the training data set (number of participants, demographics), and 2) specifics of the CNN (such as architecture, regularization). Our experiments revealed that an adversary with limited knowledge of the model and its training process can compromise the privacy of training participants, noting that the same attack is not effective against deep learning models trained on image and tabular datasets. Some of our findings are: 1) training on diverse participant datasets improves the privacy of most participants but increases risks of memorization and vulnerabilities for underrepresented groups; 2) regularization is less effective in defending against the MIA on EEG data CNN classifiers when compared to other types of input data; 3) the depth and width of the model architecture have no impact on the effectiveness of membership attack. We hope that the insights presented will help future researchers develop more privacy-aware deep learning-based BCI systems.},
}
@article {pmid40720262,
year = {2025},
author = {Wang, K and Liu, Y and Tian, F and Yi, W and Zhang, Y and Jung, TP and Xu, M and Ming, D},
title = {Adaptive Neurofeedback Training Using a Virtual Reality Game Enhances Motor Imagery Performance in Brain-Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {2956-2966},
doi = {10.1109/TNSRE.2025.3592988},
pmid = {40720262},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; *Neurofeedback/methods ; Male ; Female ; Electroencephalography ; Adult ; *Imagination/physiology ; Young Adult ; *Virtual Reality ; *Video Games ; Psychomotor Performance/physiology ; Healthy Volunteers ; Hand/physiology ; Sensorimotor Cortex/physiology ; Algorithms ; },
abstract = {Neurofeedback training (NFT) has been widely used in motor rehabilitation. However, NFT combined with motor imagery-based brain-computer interface (MI-BCI) faces challenges such as mental fatigue and non-personalized training strategies. Therefore, we proposed an adaptive NFT based on a VR game that simulates real-life motor tasks to improve training efficiency. We conducted a detailed comparative analysis of the efficiency of the VR-based NFT and traditional Graz-based NFT. Forty-eight healthy subjects were randomly assigned to five groups and underwent various NFT protocols. Among them, the subjects in the four experimental groups were required to perform the NFT three times over five days, including virtual or real scenarios, as well as unilateral or bilateral hands training. We evaluated training effects by analyzing EEG features and classification performance, while online recognition duration served as the primary measure for assessing the adaptive NFT strategy. EEG analysis showed that VR-based NFT significantly enhanced the Event-related desynchronization (ERD) activations in the sensorimotor cortices over five days. The VR-based NFT group achieved a classification accuracy of 81.85%, representing a 10.14% improvement from baseline, which exceeded the 6.43% increase observed in the Graz-based NFT group. Furthermore, implementing the adaptive NFT strategy reduced the mean task duration by over 30% compared to the fixed-time training protocol. The results demonstrated that the adaptive MI-BCI-based NFT in a VR game achieves superior training outcomes while reducing training duration. These findings suggest the promising potential for applying MI-BCI NFT with VR games in motor rehabilitation following a stroke.},
}
@article {pmid40719991,
year = {2025},
author = {Luo, X and Dong, J and Li, T},
title = {The Role of CCL11-CCR3 Induced Mitochondrial Dysfunction and Oxidative Stress in Cognitive Impairment in Early-onset Schizophrenia: Insights from Preclinical Studies.},
journal = {Inflammation},
volume = {},
number = {},
pages = {},
pmid = {40719991},
issn = {1573-2576},
support = {81920108018//National Nature Science Foundation of China Key Project/ ; },
abstract = {Abnormal cytokine expression has been implicated as a potential contributor to neurodegeneration. This study aimed to investigate the plasma cytokine profiles in patients with early-onset schizophrenia (SCZ) and to explore the molecular mechanisms underlying the role of the key cytokine CCL11 in contributing to cognitive impairment. Plasma concentrations of 44 cytokines were quantified in individuals with SCZ. The effects of CCL11 on mitochondrial function were examined in vitro using primary hippocampal neurons. An in vivo model was subsequently developed by administering CCL11 into the lateral ventricle. The impact of the CCL11-CCR3 signaling pathway on mitochondrial function, oxidative stress, and cognitive function within the hippocampus was assessed using a combination of behavioral testing, molecular biology experiments, transcriptomic analysis, and non-targeted metabolomics. In individuals with SCZ, CCL11 and IL-13 levels were notably higher than in controls. In vitro, CCL11 exposure caused mitochondrial dysfunction and increased reactive oxygen species in hippocampal neurons. In vivo, CCL11-treated mice showed cognitive deficits, mitochondrial fission, and neuroinflammation in the hippocampus. Comprehensive integration of transcriptomic and metabolomic data revealed that CCL11 significantly disrupted the Glucokinase/Glucose-6-phosphate metabolism pathway, coinciding with elevated metabolites indicative of oxidative damage. Finally, downregulation of the CCR3 receptor in the hippocampus mitigated CCL11-induced oxidative stress, mitochondrial dysfunction, and cognitive impairment. CCL11 causes cytotoxicity in neurons by increasing oxidative stress and mitochondrial dysfunction. In a mouse model, knockout of the CCR3 receptor alleviates CCL11-induced cognitive impairment, mitochondrial dysfunction, and oxidative stress.},
}
@article {pmid40719383,
year = {2025},
author = {Greenbaum, D},
title = {Enhancing the Warfighter: Ethical, Legal, and Strategic Implications of Brain-Machine Interface-Enabled Military Exoskeletons.},
journal = {AJOB neuroscience},
volume = {16},
number = {4},
pages = {222-247},
doi = {10.1080/21507740.2025.2530952},
pmid = {40719383},
issn = {2150-7759},
mesh = {Humans ; *Brain-Computer Interfaces/ethics ; *Military Personnel/legislation &amp; jurisprudence ; *Exoskeleton Device/ethics ; },
abstract = {The integration of brain-machine interfaces (BMIs) with military exoskeletons represents a significant development in human-machine interaction, raising complex ethical, legal, and strategic challenges. Unlike conventional human enhancement technologies, BMI-exoskeleton systems translate neural intent directly into mechanical movement, generating new concerns regarding agency, accountability, long-term health outcomes, and the governance of neuroadaptive changes. This paper offers a structured interdisciplinary analysis, developing taxonomies of current technologies, tracing the historical trajectory of military exoskeleton development, and critically assessing the emerging convergence between exoskeletal augmentation and neural interface systems. We argue that BMI-exoskeletons constitute a distinct category of augmentation that blurs traditional boundaries between operator and tool, requiring governance frameworks attentive to both operational effectiveness and the ethical implications for individual service members, military institutions, and broader society. Drawing on research in engineering, neuroscience, military studies, and bioethics, we outline a comprehensive ethical-legal framework designed to guide the entire lifecycle of human enhancement-from recruitment and informed consent processes through active service, operational deployment, and post-discharge reintegration. Particular attention is given to autonomy, cybersecurity vulnerabilities, distributive justice, gender equity, and the risks associated with de-enhancement and neuroplastic adaptation. Recognizing the preliminary and rapidly evolving nature of empirical evidence in this domain, we emphasize the need for anticipatory, adaptive policy approaches that safeguard the dignity, rights, and long-term welfare of enhanced warfighters while ensuring that technological innovation proceeds with responsible, ethically-informed oversight.},
}
@article {pmid40719065,
year = {2025},
author = {Xu, G and Wang, Z and Xu, K and Zhu, J and Zhang, J and Wang, Y and Hao, Y},
title = {Decoding Handwriting Trajectories from Intracortical Brain Signals for Brain-to-Text Communication.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {12},
number = {40},
pages = {e05492},
pmid = {40719065},
issn = {2198-3844},
mesh = {Humans ; *Brain-Computer Interfaces ; *Handwriting ; *Brain/physiology ; Male ; Adult ; Electroencephalography/methods ; Female ; },
abstract = {The potential to decode handwriting trajectories from brain signals has yet to be fully explored in clinical brain-computer interfaces (BCIs). Here, intracortical neural signals are recorded from a paralyzed individual during attempted handwriting of complex characters. An innovative decoding framework is introduced to address both shape and temporal distortions between neural activity and movement, effectively resolving the misalignment issue commonly encountered in clinical BCIs due to the lack of accurate movement labels. The results demonstrated the reconstruction of highly accurate and human-recognizable handwriting trajectories, significantly outperforming conventional methods. Furthermore, the new framework enabled effective multi-day data fusion, leading to additional improvements in trajectory quality. By employing a dynamic time warping approach to translate trajectories into text, a recognition rate up to 91.1% is achieved within a 1000-character database. Additionally, the framework is applied to reconstruct single-trial trajectories of English letters using a previously published dataset, achieving similarly high recognition rates. Collectively, these findings present a novel BCI decoding scheme capable of accurately reconstructing handwriting trajectories, demonstrating its applicability to both alphabetic and logographic brain-to-text translation. This approach has the potential to revolutionize communication for individuals with motor impairments by enabling accurate brain-to-text translation across diverse languages.},
}
@article {pmid40718756,
year = {2025},
author = {Kim, R and Liu, Y and Zhang, J and Xie, C and Luan, L},
title = {Towards Precise Synthetic Neural Codes: High-dimensional Stimulation with Flexible Electrodes.},
journal = {Npj flexible electronics},
volume = {9},
number = {1},
pages = {},
pmid = {40718756},
issn = {2397-4621},
support = {R01 EY036094/EY/NEI NIH HHS/United States ; R01 NS102917/NS/NINDS NIH HHS/United States ; U01 NS115588/NS/NINDS NIH HHS/United States ; U01 NS131086/NS/NINDS NIH HHS/United States ; },
abstract = {Neural representations arise from the spatiotemporally structured activity of neuron populations, inherently residing in high-dimensional spaces. Writing specific information into the central nervous system requires precisely manipulating neural states within this framework. However, current neuromodulation methods lack the precision to fully address this complexity, presenting a significant challenge for advancing effective bidirectional interfaces. In this perspective, we advocate for high-dimensional stimulation as a systematic approach capable of approximating the high dimensionality of natural neural code for brain-machine interface applications. We outline key technological requirements on resolution, coverage, and safety, review recent advances in critical application areas, and highlight the promise of flexible electrode technology in enabling a transformative leap towards precise synthetic neural codes.},
}
@article {pmid40718596,
year = {2025},
author = {Chang, L and Yang, B and Zhang, J and Li, T and Feng, J and Xu, W},
title = {DSTA-Net: dynamic spatio-temporal feature augmentation network for motor imagery classification.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {118},
pmid = {40718596},
issn = {1871-4080},
abstract = {Accurate decoding and strong feature interpretability of Motor Imagery (MI) are expected to drive MI applications in stroke rehabilitation. However, the inherent nonstationarity and high intra-class variability of MI-EEG pose significant challenges in extracting reliable spatio-temporal features. We proposed the Dynamic Spatio-Temporal Feature Augmentation Network (DSTA-Net), which combines DSTA and the Spatio-Temporal Convolution (STC) modules. In DSTA module, multi-scale temporal convolutional kernels tailored to the α and β frequency bands of MI neurophysiological characteristics, while raw EEG serve as a baseline feature layer to retain original information. Next, Grouped Spatial Convolutions extract multi-level spatial features, combined with weight constraints to prevent overfitting. Spatial convolution kernels map EEG channel information into a new spatial domain, enabling further feature extraction through dimensional transformation. And STC module further extracts features and conducts classification. We evaluated DSTA-Net on three public datasets and applied it to a self-collected stroke dataset. In tenfold cross-validation, DSTA-Net achieved average accuracy improvements of 6.29% (p < 0.01), 3.05% (p < 0.01), 5.26% (p < 0.01), and 2.25% over the ShallowConvNet on the BCI-IV-2a, OpenBMI, CASIA, and stroke dataset, respectively. In hold-out validation, DSTA-Net achieved average accuracy improvements of 3.99% (p < 0.01) and 4.2% (p < 0.01) over the ShallowConvNet on the OpenBMI and CASIA datasets, respectively. Finally, we applied DeepLIFT, Common Spatial Pattern, and t-SNE to analyze the contributions of individual EEG channels, extract spatial patterns, and visualize features. The superiority of DSTA-Net offers new insights for further research and application in MI. The code is available in https://github.com/CL-Cloud-BCI/DSTANet-code.},
}
@article {pmid40718569,
year = {2025},
author = {Lopez Blanco, C and Tyler, WJ},
title = {The vagus nerve: a cornerstone for mental health and performance optimization in recreation and elite sports.},
journal = {Frontiers in psychology},
volume = {16},
number = {},
pages = {1639866},
pmid = {40718569},
issn = {1664-1078},
abstract = {Decades of physiological and psychological research into human performance and wellness have established a critical role for vagus nerve signaling in peak physical and cognitive performance. We outline models and perspectives that have emerged through neuroscience and psychophysiology studies to elucidate how the vagus nerve governs human performance through its influence on central nervous system functions and autonomic nervous system activity. These functions include the monitoring and regulation of cardio-respiratory activity, emotional responses, inflammation and physical recovery, cognitive control, stress resilience, and team cohesion. We briefly review some useful interventions such as transcutaneous auricular vagus nerve stimulation, heart-rate variability biofeedback, and controlled breathing as accessible tools for enhancing vagal tone, improving executive functioning under pressure, and mitigating fatigue and burnout. We describe how these approaches and their biological underpinnings are rooted by psychological models like the Yerkes-Dodson law and Polyvagal theory to contextualize their effects on athletic performance. These perspectives suppor recent shifts in sports science toward integrating vagal-centered approaches as scalable, evidence-based strategies that can enhance human performance and wellness.},
}
@article {pmid40717726,
year = {2025},
author = {Otarbay, Z and Kyzyrkanov, A},
title = {SVM-enhanced attention mechanisms for motor imagery EEG classification in brain-computer interfaces.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1622847},
pmid = {40717726},
issn = {1662-4548},
abstract = {Brain-Computer Interfaces (BCIs) leverage brain signals to facilitate communication and control, particularly benefiting individuals with motor impairments. Motor imagery (MI)-based BCIs, utilizing non-invasive electroencephalography (EEG), face challenges due to high signal variability, noise, and class overlap. Deep learning architectures, such as CNNs and LSTMs, have improved EEG classification but still struggle to fully capture discriminative features for overlapping motor imagery classes. This study introduces a hybrid deep neural architecture that integrates Convolutional Neural Networks, Long Short-Term Memory networks, and a novel SVM-enhanced attention mechanism. The proposed method embeds the margin maximization objective of Support Vector Machines directly into the self-attention computation to improve interclass separability during feature learning. We evaluate our model on four benchmark datasets: Physionet, Weibo, BCI Competition IV 2a, and 2b, using a Leave-One-Subject-Out (LOSO) protocol to ensure robustness and generalizability. Results demonstrate consistent improvements in classification accuracy, F1-score, and sensitivity compared to conventional attention mechanisms and baseline CNN-LSTM models. Additionally, the model significantly reduces computational cost, supporting real-time BCI applications. Our findings highlight the potential of SVM-enhanced attention to improve EEG decoding performance by enforcing feature relevance and geometric class separability simultaneously.},
}
@article {pmid40715543,
year = {2025},
author = {Xu, H and Huang, Q and Song, P and Chen, Y and Li, Q and Zhai, Y and Du, X and Ye, H and Bao, X and Mehmood, I and Tanigawa, H and Niu, W and Tu, Z and Chen, P and Zhang, T and Zhang, L and Zhao, X and Zhang, L and Wen, W and Cao, L and Yu, X},
title = {EEG neural indicator of temporal integration in the human auditory brain with clinical implications.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {1109},
pmid = {40715543},
issn = {2399-3642},
support = {32171044//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32100827//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32271078//National Natural Science Foundation of China (National Science Foundation of China)/ ; LGF22H170006//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
mesh = {Humans ; *Electroencephalography/methods ; Male ; Female ; Adult ; *Auditory Perception/physiology ; Acoustic Stimulation ; Young Adult ; Middle Aged ; *Auditory Cortex/physiology ; *Brain/physiology ; Evoked Potentials, Auditory ; },
abstract = {Temporal integration, the process by which the auditory system combines sound information over a certain period to form a coherent auditory experience, is essential for auditory perception, yet its neural mechanisms remain underexplored. We use a "transitional click train" paradigm, which concatenates two click trains with slightly differing inter-click intervals (ICIs), to investigate temporal integration in the human brain. Using a 64-channel electroencephalogram (EEG), we recorded responses from healthy participants exposed to regular and irregular transitional click trains and conducted change detection tasks. Regular transitional click trains elicited significant change responses in the human brain, indicative of temporal integration, whereas irregular trains did not. These neural responses were modulated by length, contrast, and regularity of ICIs. Behavioral data mirrored EEG findings, showing enhanced detection for regular conditions compared to irregular conditions and pure tones. Furthermore, variations in change responses were associated with decision-making processes. Temporal continuity was critical, as introducing gaps between click trains diminished both behavioral and neural responses. In clinical assessments, 22 coma patients exhibited diminished or absent change responses, effectively distinguishing them from healthy individuals. Our findings identify distinct neural markers of temporal integration and highlight the potential of transitional click trains for clinical diagnostics.},
}
@article {pmid40715225,
year = {2025},
author = {Das, A and Singh, S and Kim, J and Ahanger, TA and Pise, AA},
title = {Enhanced EEG signal classification in brain computer interfaces using hybrid deep learning models.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {27161},
pmid = {40715225},
issn = {2045-2322},
support = {No.RS-2022-00155857//Institute of Information &amp; communications Technology Planning &amp; Evaluation (IITP) grant funded by the Korea government (MSIT) (No.RS-2022-00155857, Artificial Intelligence Convergence Innovation Human Resources Development (Chungnam National University). Also supported part by Woosong university research fund 2024./ ; No.RS-2022-00155857//Institute of Information &amp; communications Technology Planning &amp; Evaluation (IITP) grant funded by the Korea government (MSIT) (No.RS-2022-00155857, Artificial Intelligence Convergence Innovation Human Resources Development (Chungnam National University). Also supported part by Woosong university research fund 2024./ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Deep Learning ; *Brain/physiology ; Neural Networks, Computer ; Support Vector Machine ; Bayes Theorem ; Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interfaces (BCIs) establish a communication pathway between the human brain and external devices by decoding neural signals. This study focuses on enhancing the classification of Motor Imagery (MI) within BCI systems by leveraging advanced machine learning and deep learning techniques. The accurate classification of electroencephalogram (EEG) data is crucial for enhancing BCI performance. The BCI architecture processes electroencephalography signals through three critical stages: data pre-processing, feature extraction, and classification. The research evaluates the performance of five traditional machine learning classifiers- K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), Logistic Regression (LR), Random Forest (RF), and Naive Bayes (NB)-using the "PhysioNet EEG Motor Movement/Imagery Dataset". This dataset encompasses EEG data from various motor tasks, including both actual and imagined movements. Among the traditional classifiers, Random Forest achieved the highest accuracy of 91%, underscoring its efficacy in motor imagery classification within BCI systems. In addition to conventional approaches, the study also explores deep learning techniques, with Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks yielding accuracies of 88.18% and 16.13%, respectively. However, the proposed hybrid model, which synergistically combines CNN and LSTM, significantly surpasses both traditional machine learning and individual deep learning methods, achieving an exceptional accuracy of 96.06%. This substantial improvement highlights the potential of hybrid deep learning models to advance the state of the art in BCI systems, offering a more robust and precise approach to motor imagery classification.},
}
@article {pmid40714477,
year = {2025},
author = {Wang, J and Zhao, S and Luo, Z and Zhou, Y and Li, S and Pan, G},
title = {EEGMamba: An EEG foundation model with Mamba.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {192},
number = {},
pages = {107816},
doi = {10.1016/j.neunet.2025.107816},
pmid = {40714477},
issn = {1879-2782},
abstract = {Electroencephalography (EEG) captures brain activity and has been widely used in clinic and brain-computer interfaces (BCIs). Classic EEG decoding methods rely on supervised learning, limiting their performance and generalizability. Inspired by the revolutionary impact of large models in other fields, researchers are now investigating EEG foundation models. Recently, state space models (SSMs), such as Mamba, have demonstrated strong sequence modeling capabilities, which may be suitable to model the spatiotemporal dependencies of EEG signals. However, the application of Mamba for EEG representation learning remains largely unexplored. In this paper, we investigate the potential of Mamba for learning generic EEG representations and propose a novel EEG foundation model, EEGMamba. Specifically, we employ Mamba encoder as the backbone of EEGMamba to model the spatiotemporal dependencies among EEG patches. Meanwhile, we use patch-based masked EEG reconstruction to learn generic EEG representations. EEGMamba is pre-trained on a large and diverse EEG corpus (16,724 h) from five datasets. We evaluate EEGMamba on up to six downstream BCI tasks using six public datasets. EEGMamba achieves the state-of-the-art performance across all the tasks, demonstrating its strong capability and generalizability.},
}
@article {pmid40714230,
year = {2025},
author = {Gao, C and Wu, X and Ma, L and Li, D and Wang, Y and Guo, C and Li, W and Wang, H and Chu, C and Madsen, KH and Fan, L},
title = {Iterative prior-guided parcellation (iPGP) for capturing inter-subject and inter-nuclei variability in thalamic mapping.},
journal = {NeuroImage},
volume = {318},
number = {},
pages = {121399},
doi = {10.1016/j.neuroimage.2025.121399},
pmid = {40714230},
issn = {1095-9572},
mesh = {Humans ; Male ; Female ; Adult ; Adolescent ; Young Adult ; *Thalamus/diagnostic imaging/anatomy &amp; histology ; *Brain Mapping/methods ; Reproducibility of Results ; *Image Processing, Computer-Assisted/methods ; Middle Aged ; Magnetic Resonance Imaging/methods ; Child ; },
abstract = {The thalamus, a critical relay station in the brain, consists of multiple nuclei that play essential roles in various brain circuits. Identifying these nuclei is crucial for understanding how thalamic structures influence cognitive functions. However, genetic and environmental factors introduce substantial variability in thalamic parcellation patterns, posing both challenges and opportunities for individualized mapping of thalamic function. This study proposes an iterative prior-guided parcellation (iPGP) framework to construct individualized thalamic parcellations. The iPGP method utilizes the Morel histological atlas as prior guidance, incorporates spatially constrained local diffusion characteristics as features, and employs an iterative framework to optimize an individual-specific parcellation model. As a result, iPGP automatically adapts to individual thalamic contrast variations, producing personalized and anatomically consistent parcellations. Through test-retest assessments, iPGP demonstrated a high degree of intra-subject reproducibility. By evaluating inter-subject and inter-nuclei variability, iPGP exhibited strong adaptability across different age groups while capturing subject-specific and region-specific variability. Furthermore, thalamic parcellations generated by iPGP showed significant associations with adolescent age and adult behavioral-cognitive scores. Our findings suggest that iPGP effectively captures inter-subject and inter-nuclei variability in thalamic parcellation, highlighting its potential for advancing thalamic mapping in exploring brain function.},
}
@article {pmid40712594,
year = {2025},
author = {Huang, J and Yang, P and Xiong, B and Lv, Y and Wang, Q and Wan, B and Zhang, ZQ},
title = {Mixup-based data augmentation for enhancing few-shot SSVEP detection performance.},
journal = {Journal of neural engineering},
volume = {22},
number = {4},
pages = {},
doi = {10.1088/1741-2552/adf467},
pmid = {40712594},
issn = {1741-2552},
mesh = {*Evoked Potentials, Visual/physiology ; Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Male ; Photic Stimulation/methods ; Adult ; Female ; Young Adult ; Algorithms ; },
abstract = {Objective.Few-shot steady-state visual evoked potential (SSVEP) detection remains a major challenge in brain-computer interface (BCI) systems, as limited calibration data often leads to degraded performance. This study aims to enhance few-shot SSVEP detection through an effective data augmentation (DA) strategy.Approach.We propose a mixup-based DA method that generates synthetic trials by linearly interpolating between real SSVEP signals extracted using a sliding window strategy. The interpolation weight is optimized by maximizing the similarity between the mixed signal and both the template and reference signals. The augmented data is then used to train spatial filters for improved SSVEP detection.Main results.The proposed method was evaluated on two benchmark SSVEP datasets using task-related component analysis and incorporating neighboring stimuli data as spatial filters. Results demonstrate that the mixup-based augmentation significantly improves detection accuracy under few-shot conditions, outperforming existing augmentation and baseline methods.Significance.The mixup-based method offers an effective and practical solution for enhancing SSVEP decoding with limited data, reducing calibration time, and improving BCI systems' usability in real-world scenarios.},
}
@article {pmid40712572,
year = {2025},
author = {Wang, J and Liu, Y and Ma, Y and Feng, Y and Lin, L and Ping, A and Tian, F and Zhang, X and Berman, AJL and Bollmann, S and Polimeni, JR and Roe, AW},
title = {In vivo 7 Tesla MRI of non-human primate intracortical microvascular architecture.},
journal = {Neuron},
volume = {113},
number = {16},
pages = {2621-2635.e5},
doi = {10.1016/j.neuron.2025.05.028},
pmid = {40712572},
issn = {1097-4199},
mesh = {Animals ; *Magnetic Resonance Imaging/methods ; *Microvessels/diagnostic imaging/anatomy &amp; histology ; *Cerebral Cortex/blood supply/diagnostic imaging ; *Cerebrovascular Circulation/physiology ; Male ; Macaca mulatta ; Arterioles/diagnostic imaging ; },
abstract = {Intracortical arterioles are key locations for blood flow regulation and oxygen supply in the brain and are critical to brain health and disease. However, imaging such small (<100-μm-sized) vessels in humans is challenging. Here, using non-human primates as a model, we developed a capability for imaging microvasculature in vivo with a clinical 7 T MRI scanner. Using simulations, we identified parameters for imaging intracortical vessels with slow flow and combined this with high-resolution imaging (64 × 64 μm[2] in-plane). Across large swaths of occipital, parietal, and temporal cortex, arrays of intracortical arterioles and venules were observed in gyral crowns and deep within sulcal folds. Systematic arteriole-venule patterns revealed potential architecture of input-output flow relationships. Even single vessels could be followed across cortical laminae. As a first step toward imaging microvasculature in humans, this method introduces a new technology and animal model for understanding relationships between functional and vascular architectures.},
}
@article {pmid40712216,
year = {2025},
author = {Li, S and Xu, R and Wang, X and Cichocki, A and Jin, J},
title = {Dual branch neural network with dynamic learning mechanism for P300-based brain-computer interfaces.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {192},
number = {},
pages = {107876},
doi = {10.1016/j.neunet.2025.107876},
pmid = {40712216},
issn = {1879-2782},
abstract = {Brain-computer interface (BCI) system offers an alternative or supplementary means of interaction for individuals with disabilities. P300 speller is a commonly utilized BCI system due to its high stability, and reliability and without intensive user training. Nevertheless, the inherent class imbalance within P300 datasets predisposes the system to overfit, potentially impacting the classification performances. Existing class rebalancing methods mainly rely on resampling or adjusting the class weight with a fixed value, thus it is still tricky to ensure that the output is evenly balanced. To mitigate the above class imbalance issue, this study proposes a dual branch learning (DBL) method that concurrently considers feature representation and class imbalance. This approach involves the ingestion of two distinct sample types-uniformly sampled and reverse-sampled data-into the feature extraction and classification modules during the training phase. Furthermore, a dynamic learning mechanism is implemented to incrementally emphasize minority class samples (specifically the P300 component) as training progresses. The effectiveness of the proposed DBL method is proved using both publicly accessible and self-collected datasets in a subject-dependent scheme. The proposed DBL method can achieve an accuracy of 97.37 % and 88.72 % in the above datasets. Besides, it provides superior and more reliable results compared with several deep learning and rebalancing methods. These findings highlight the promising potential of the proposed DBL framework in P300-based BCI.},
}
@article {pmid40710265,
year = {2025},
author = {Ma, S and Situ, Z and Peng, X and Li, Z and Huang, Y},
title = {Multi-Class Classification Methods for EEG Signals of Lower-Limb Rehabilitation Movements.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {10},
number = {7},
pages = {},
pmid = {40710265},
issn = {2313-7673},
support = {2024ZD0715801//The National Science and Technology Major Project of China/ ; },
abstract = {Brain-Computer Interfaces (BCIs) enable direct communication between the brain and external devices by decoding motor intentions from EEG signals. However, the existing multi-class classification methods for motor imagery EEG (MI-EEG) signals are hindered by low signal quality and limited accuracy, restricting their practical application. This study focuses on rehabilitation training scenarios, aiming to capture the motor intentions of patients with partial or complete motor impairments (such as stroke survivors) and provide feedforward control commands for exoskeletons. This study developed an EEG acquisition protocol specifically for use with lower-limb rehabilitation motor imagery (MI). It systematically explored preprocessing techniques, feature extraction strategies, and multi-classification algorithms for multi-task MI-EEG signals. A novel 3D EEG convolutional neural network (3D EEG-CNN) that integrates time/frequency features is proposed. Evaluations on a self-collected dataset demonstrated that the proposed model achieved a peak classification accuracy of 66.32%, substantially outperforming conventional approaches and demonstrating notable progress in the multi-class classification of lower-limb motor imagery tasks.},
}
@article {pmid40709513,
year = {2025},
author = {Guo, W and Wang, H and Deng, W and Dong, Z and Liu, Y and Luo, S and Yu, J and Huang, X and Chen, Y and Ye, J and Song, J and Jiang, Y and Li, D and Wang, W and Sun, X and Kuang, W and Qiu, C and Cheng, N and Li, W and Zhang, W and Liu, Y and Tang, Z and Du, X and Greenshaw, AJ and Zhang, L and Li, T},
title = {Impact of early detection and management of emotional distress on length of stay in non-psychiatric inpatients: A retrospective hospital-based cohort study.},
journal = {Chinese medical journal},
volume = {},
number = {},
pages = {},
pmid = {40709513},
issn = {2542-5641},
abstract = {BACKGROUND: While emotional distress, encompassing anxiety and depression, has been associated with negative clinical outcomes, its impact across various clinical departments and general hospitals has been less explored. Previous studies with limited sample sizes have examined the effectiveness of specific treatments (e.g., antidepressants) rather than a systemic management strategy for outcome improvement in non-psychiatric inpatients. To enhance the understanding of the importance of addressing mental health care needs among non-psychiatric patients in general hospitals, this study retrospectively investigated the impacts of emotional distress and the effects of early detection and management of depression and anxiety on hospital length of stay (LOS) and rate of long LOS (LLOS, i.e., LOS >30 days) in a large sample of non-psychiatric inpatients.<br><br>METHODS: This retrospective cohort study included 487,871 inpatients from 20 non-psychiatric departments of a general hospital. They were divided, according to whether they underwent a novel strategy to manage emotional distress which deployed the Huaxi Emotional Distress Index (HEI) for brief screening with grading psychological services (BS-GPS), into BS-GPS (n = 178,883) and non-BS-GPS (n = 308,988) cohorts. The LOS and rate of LLOS between the BS-GPS and non-BS-GPS cohorts and between subcohorts with and without clinically significant anxiety and/or depression (CSAD, i.e., HEI score &#x2265;11 on admission to the hospital) in the BS-GPS cohort were compared using univariable analyses, multilevel analyses, and/or propensity score-matched analyses, respectively.<br><br>RESULTS: The detection rate of CSAD in the BS-GPS cohort varied from 2.64% (95% confidence interval [CI]: 2.49%-2.81%) to 20.50% (95% CI: 19.43%-21.62%) across the 20 departments, with a average rate of 5.36%. Significant differences were observed in both the LOS and LLOS rates between the subcohorts with CSAD (12.7 days, 535/9590) and without CSAD (9.5 days, 3800/169,293) and between the BS-GPS (9.6 days, 4335/178,883) and non-BS-GPS (10.8 days, 11,483/308,988) cohorts. These differences remained significant after controlling for confounders using propensity score-matched comparisons. A multilevel analysis indicated that BS-GPS was negatively associated with both LOS and LLOS after controlling for sociodemographics and the departments of patient discharge and remained negatively associated with LLOS after controlling additionally for the year of patient discharge.<br><br>CONCLUSION: Emotional distress significantly prolonged the LOS and increased the LLOS of non-psychiatric inpatients across most departments and general hospitals. These impacts were moderated by the implementation of BS-GPS. Thus, BS-GPS has the potential as an effective, resource-saving strategy for enhancing mental health care and optimizing medical resources in general hospitals.},
}
@article {pmid40708811,
year = {2025},
author = {Tsytsarev, V and Volnova, A and Rojas, L and Sanabria, P and Ignashchenkova, A and Ortiz-Rivera, J and Alves, J and Inyushin, M},
title = {Vectorial principles of sensorimotor decoding.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1612626},
pmid = {40708811},
issn = {1662-5161},
support = {SC3 GM143983/GM/NIGMS NIH HHS/United States ; },
abstract = {This review explores the vectorial principles underlying sensorimotor decoding across diverse biological systems. From the encoding of light wavelength in retinal cones to direction-specific motor cortex activity in primates, neural representations frequently rely on population vector coding-a scheme, in which neurons with directional or modality-specific preferences integrate their activity to encode stimuli or motor commands. Early studies on color vision and motor control introduced concepts of vector summation and neuronal tuning, evolving toward more precise models such as the von Mises distribution. Research in invertebrates, including leeches and snails, reveals that even simple nervous systems utilize population vector principles for reflexes and coordinated movements. Furthermore, analysis of joint limb motion suggests biomechanical optimization aligned with Fibonacci proportions, facilitating efficient neural and mechanical control. The review highlights that motor units and neurons often display multimodal or overlapping tuning fields, reinforcing the need for population-based decoding strategies. These findings suggest a unifying vectorial framework for sensory and motor coding, with implications for periprosthetic and brain-machine interface.},
}
@article {pmid40708808,
year = {2025},
author = {Chowdhury, AT and Hassanein, A and Al Shibli, AN and Khanafer, Y and AbuHaweeleh, MN and Pedersen, S and Chowdhury, MEH},
title = {Neural signals, machine learning, and the future of inner speech recognition.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1637174},
pmid = {40708808},
issn = {1662-5161},
abstract = {Inner speech recognition (ISR) is an emerging field with significant potential for applications in brain-computer interfaces (BCIs) and assistive technologies. This review focuses on the critical role of machine learning (ML) in decoding inner speech, exploring how various ML techniques improve the analysis and classification of neural signals. We analyze both traditional methods such as support vector machines (SVMs) and random forests, as well as advanced deep learning approaches like convolutional neural networks (CNNs), which are particularly effective at capturing the dynamic and non-linear patterns of inner speech-related brain activity. Also, the review covers the challenges of acquiring high-quality neural signals and discusses essential preprocessing methods for enhancing signal quality. Additionally, we outline and synthesize existing approaches for improving ISR through ML, that can lead to many potential implications in several domains, including assistive communication, brain-computer interfaces, and cognitive monitoring. The limitations of current technologies were also discussed, along with insights into future advancements and potential applications of machine learning in inner speech recognition (ISR). Building on prior literature, this work synthesizes and organizes existing ISR methodologies within a structured mathematical framework, reviews cognitive models of inner speech, and presents a detailed comparative analysis of existing ML approaches, thereby offering new insights into advancing the field.},
}
@article {pmid40707971,
year = {2025},
author = {Ji, X and Lu, X and Xu, Y and Zhang, W and Yang, H and Yin, C and Wang, H and Ren, C and Ji, Y and Li, Y and Huang, G and Shen, Y},
title = {Effects and neural mechanisms of a brain-computer interface-controlled soft robotic glove on upper limb function in patients with subacute stroke: a randomized controlled fNIRS study.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {171},
pmid = {40707971},
issn = {1743-0003},
support = {No.Q202414//Youth Project of the Wuxi Municipal Health Commission/ ; No.2022YFC2009700//National Key Research &amp; Development Program of China/ ; No.BE2023023-2//the Key Project of Jiangsu Province's Key Research and Development Program/ ; No.BE2023034//the Competitive Project of Jiangsu Province's Key Research and Development Program/ ; No.JBGS202414//Jiangsu Province Hospital clinical diagnosis and treatment of technological innovation "Open bidding for selecting the best candidates" project/ ; 2025-K10//Open Research Fund of State Key Laboratory of Digital Medical Engineering/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; Middle Aged ; *Stroke Rehabilitation/methods/instrumentation ; *Upper Extremity/physiopathology ; Spectroscopy, Near-Infrared ; *Robotics/instrumentation ; Aged ; *Stroke/physiopathology/complications ; *Paresis/rehabilitation/physiopathology/etiology ; Adult ; },
abstract = {BACKGROUND AND PURPOSE: The brain-computer interface-based soft robotic glove (BCI-SRG) holds promise for upper limb rehabilitation in subacute stroke patients, yet its efficacy and neural mechanisms are unclear. This study aimed to investigate the therapeutic effects and neural mechanisms of BCI-SRGs by functional near-infrared spectroscopy (fNIRS).<br><br>METHODS: Forty subacute stroke patients with left-sided hemiparesis were randomized into the BCI-SRG (n&#x2009;=&#x2009;20) and soft robotic glove (SRG) (n&#x2009;=&#x2009;20) groups. Both groups received 20 sessions of intervention over 4&#xa0;weeks in addition to conventional rehabilitation. The BCI-SRG group was trained using a soft robotic glove controlled by a brain&#x2012;computer interface (BCI), whereas the SRG group used the same soft robotic glove without BCI control. The clinical outcomes included the Action Research Arm Test (ARAT), the Fugl-Meyer Assessment Upper Limb (FMA-UL), and Modified Barthel Index (MBI) scores. In addition, fNIRS was used to explore potential clinical brain mechanisms. All assessments were performed before treatment and after 4&#xa0;weeks of treatment.<br><br>RESULTS: A total of 39 participants completed the intervention and clinical assessments (BCI-SRG: n&#x2009;=&#x2009;20; SRG: n&#x2009;=&#x2009;19). Compared with the SRG group, the BCI-SRG group showed greater improvements in the ARAT (Z&#x2009;=&#x2009;-&#x2009;2.139, P&#x2009;=&#x2009;0.032) and FMA-UL (Z&#x2009;=&#x2009;-&#x2009;2.588, P&#x2009;=&#x2009;0.010), with no notable difference in the MBI (Z&#x2009;=&#x2009;-&#x2009;1.843, P&#x2009;=&#x2009;0.065). fNIRS data were available for 35 participants (BCI-SRG: n&#x2009;=&#x2009;17; SRG: n&#x2009;=&#x2009;18). Within-group comparisons revealed significant postintervention increases in cortical activation in the bilateral sensorimotor cortex (SMC) and medial prefrontal cortex (MPFC) in the BCI-SRG group, whereas no significant changes were observed in the SRG group. Between-group comparisons further revealed significantly greater changes in HbO concentrations in the BCI-SRG group than in the SRG group across the same cortical regions. Moreover, changes in prefrontal activation (post-pre) were positively correlated with improvements in ARAT scores, with significant correlations observed in the left dorsal lateral prefrontal cortex (LDLPFC) (Ch9, r&#x2009;=&#x2009;0.592, P&#x2009;=&#x2009;0.012; Ch25, r&#x2009;=&#x2009;0.488, P&#x2009;=&#x2009;0.047) and right dorsal lateral prefrontal cortex (RDLPFC) (Ch19, r&#x2009;=&#x2009;0.671, P&#x2009;=&#x2009;0.003).<br><br>CONCLUSIONS: BCI-SRG training significantly enhances upper limb function and facilitates bilateral motor and sensory cortical reorganization. PFC activation is correlated with functional improvements, suggesting a potential mechanism underlying the benefits of rehabilitation in stroke patients.<br><br>TRIAL REGISTRATION: This trial was registered under the Chinese Clinical Trial Registry (ChiCTR2400082786) and was retrospectively registered on April 8, 2024.},
}
@article {pmid40707673,
year = {2025},
author = {Zhang, X and Li, M and Chen, Y and Liu, J and Zhang, J and Shao, C and Deng, B and Zhang, J and Wang, T and Cao, J and Xu, X and He, Q and Yang, B and Shao, X and Ying, M},
title = {Deubiquitinase USP6 stabilizes oncogenic RUNX1 fusion proteins to promote the leukemic potential and malignant progression.},
journal = {Leukemia},
volume = {39},
number = {10},
pages = {2355-2363},
pmid = {40707673},
issn = {1476-5551},
support = {No. 82273942//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Core Binding Factor Alpha 2 Subunit/genetics/metabolism ; Animals ; *Oncogene Proteins, Fusion/genetics/metabolism ; Mice ; *Ubiquitin Thiolesterase/metabolism/genetics ; *Leukemia/pathology/genetics/metabolism ; Disease Progression ; Cell Proliferation ; Cell Line, Tumor ; },
abstract = {RUNX1-rearranged leukemia is one of the most common subtypes of leukemia associated with genetic abnormalities. Although the majority of patients respond to chemotherapy, relapse and long-term adverse effects remain significant challenges. RUNX1 fusions, resulting from chromosomal rearrangements, are pivotal oncogenic drivers, with over 70 distinct variants identified. Therefore, elucidating their regulatory mechanisms may help to develop novel therapeutic strategies. Herein, we identify a universal deubiquitinase, USP6, that stabilizes RUNX1 fusion proteins with different partners. Importantly, USP6 is specifically upregulated in RUNX1-rearranged leukemia and strongly correlates with poor patient outcomes. Mechanistically, USP6 stabilizes RUNX1 fusions to facilitate the formation of phase separation, leading to robust transcriptional activation of the fusions. Depletion of USP6 dramatically inhibits proliferation and induces differentiation of RUNX1-rearranged leukemic cells. The marketed drug auranofin is identified as a potential USP6 inhibitor, which induces degradation of different RUNX1 fusions, further triggering myeloid differentiation and arresting xenograft tumor growth. Notably, auranofin exhibits selective therapeutic efficacy in patient-derived leukemia blasts from RUNX1-rearranged cases. Together, we not only uncover a new biological function of USP6 in regulating the transcriptional activity of RUNX1 fusions but also validate USP6 as a promising drug target and auranofin as a candidate therapy for RUNX1-rearranged leukemia.},
}
@article {pmid40706724,
year = {2025},
author = {Kim, YS and Kim, CU and Han, H and Kim, MY and Choi, SI and Im, CH},
title = {Performance enhancement of steady-state visual evoked field-based brain-computer interfaces using spatial distribution of synchronization index in MEG channel space.},
journal = {NeuroImage},
volume = {318},
number = {},
pages = {121391},
doi = {10.1016/j.neuroimage.2025.121391},
pmid = {40706724},
issn = {1095-9572},
mesh = {Humans ; *Magnetoencephalography/methods ; *Brain-Computer Interfaces ; Algorithms ; Adult ; Male ; *Evoked Potentials, Visual/physiology ; Female ; Young Adult ; Signal Processing, Computer-Assisted ; },
abstract = {The development of helmet-type magnetoencephalography (MEG) systems that do not require liquid helium (e.g., OPM-MEG) has sparked growing interest in steady-state visual evoked field (SSVEF)-based brain-computer interfaces (BCIs). Unlike electroencephalography (EEG), MEG records less distorted signals with a high spatial resolution, covering the entire head without requiring cumbersome electrode attachment. However, conventional algorithms, such as the filter bank-driven multivariate synchronization index (FBMSI), are prone to misclassification in ambiguous cases where the differences between synchronization indices (S indices) are minimal. Additionally, these algorithms fail to fully exploit high spatial resolution and whole-head coverage of MEG. To address these limitations, this study proposes a novel, calibration-free SSVEF classification algorithm termed Spatial Distribution Analysis (SDA). The SDA algorithm utilizes the center of gravity of the S index distribution in the MEG channel space to enhance classification accuracy. Experimental evaluations with 20 participants using a helmet-type SQUID MEG system demonstrated that the proposed SDA algorithm achieved significantly higher classification accuracy and information transfer rate (ITR) across all window sizes. Notably, the largest improvements of 5.76 % in accuracy and 4.87 bits/min in ITR were reported for a window size of 2.5 s. Furthermore, the generalizability of the SDA algorithm was validated on an OPM-MEG dataset, showing performance improvements across all window sizes. The SDA algorithm also mitigated misclassification due to adjacent stimuli and showed short time delay of 0.0907 s, enough to be used for real-time BCIs. These findings highlight the potential of SDA algorithm to enhance the overall performance of SSVEF-based BCI.},
}
@article {pmid40705590,
year = {2025},
author = {Ko, BK and Lee, SH and Lee, SW},
title = {Imagined Speech Detection Using Multi-Receptive CNN for Asynchronous BCI Communication and Neurorehabilitation.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {2904-2914},
doi = {10.1109/TNSRE.2025.3592312},
pmid = {40705590},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; *Imagination/physiology ; *Speech/physiology ; *Neurological Rehabilitation/methods ; *Neural Networks, Computer ; Male ; Adult ; Female ; Algorithms ; Young Adult ; Communication Devices for People with Disabilities ; Communication ; Signal Processing, Computer-Assisted ; },
abstract = {Imagined speech-based brain-computer interface (BCI) facilitates brain signal-driven intuitive communication which holds great promise as an effective speech rehabilitation tool, enabling real-time, hands-free interaction for individuals with speech and motor impairments. While speech-based assistant systems rely on wake-word detection (e.g., "Hey Siri"), BCI-based communication system must capture imagined onset from EEG signals to turn on the 'brain switch' to further convey user's imagined command. Nevertheless, the absence of reliable ground truth for the endogenous paradigm adds to the complexity to train the model to capture exact onset from continuous EEG. To address these issues, we introduce a multi-receptive field convolutional neural network, designed to capture speech and idle states based on behaviorally-aligned EEG features. We propose a voice-based ground truth alignment method with voting strategy that aims to synchronize imagined speech with overt speech onset and offset, providing a structured approach for capturing speech events in asynchronous BCI systems. Furthermore, spectral and phonological analyses revealed that beta and alpha bands, as well as syllable count, appear to influence speech state discriminability. Evaluations on imagined and overt speech tasks, including pseudo-online experiments, demonstrate the potential to enhance asynchronous BCI systems, supporting real-time communication for both healthy and impaired individuals.},
}
@article {pmid40703721,
year = {2025},
author = {Alkhoury, L and O'Sullivan, J and Scanavini, G and Dou, J and Arora, J and Hamill, L and Patchell, A and Radanovic, A and Watson, WD and Lalor, EC and Schiff, ND and Hill, NJ and Shah, SA},
title = {Leveraging meaning-induced neural dynamics to detect covert cognition via EEG during natural language listening-a case series.},
journal = {Frontiers in psychology},
volume = {16},
number = {},
pages = {1616963},
pmid = {40703721},
issn = {1664-1078},
abstract = {At least a quarter of adult patients with severe brain injury in a disorder of consciousness may have cognitive abilities that are hidden due to motor impairment. In this case series, we developed a tool that extracted acoustic and semantic processing biomarkers from electroencephalography recorded while participants listened to a story. We tested our method on two male adolescent survivors of severe brain injury and showed evidence of acoustic and semantic processing. Our method identifies cognitive processing while obviating demands on attention, memory, and executive function. This lays a foundation for graded assessments of cognition recovery across the spectrum of covert cognition.},
}
@article {pmid40703668,
year = {2025},
author = {Wang, F and Luo, Z and Lv, W and Zhu, X},
title = {DTCNet: finger flexion decoding with three-dimensional ECoG data.},
journal = {Frontiers in computational neuroscience},
volume = {19},
number = {},
pages = {1627819},
pmid = {40703668},
issn = {1662-5188},
abstract = {ECoG signals are widely used in Brain-Computer Interfaces (BCIs) due to their high spatial resolution and superior signal quality, particularly in the field of neural control. ECoG enables more accurate decoding of brain activity compared to traditional EEG. By obtaining cortical ECoG signals directly from the cerebral cortex, complex motor commands, such as finger movement trajectories, can be decoded more efficiently. However, existing studies still face significant challenges in accurately decoding finger movement trajectories. Specifically, current models tend to confuse the movement information of different fingers and fail to fully exploit the dependencies within time series when predicting long sequences, resulting in limited decoding performance. To address these challenges, this paper proposes a novel decoding method that transforms 2D ECoG data samples into 3D spatio-temporal spectrograms with time-stamped features via wavelet transform. The method further enables accurate decoding of finger bending by using a 1D convolutional network composed of Dilated-Transposed convolution, which together extract channel band features and temporal variations in tandem. The proposed method achieved the best performance among three subjects in BCI Competition IV. Compared with existing studies, our method made the correlation coefficient between the predicted multi-finger motion trajectory and the actual multi-finger motion trajectory exceed 80% for the first time, with the highest correlation coefficient reaching 82%. This approach provides new insights and solutions for high-precision decoding of brain-machine signals, particularly in precise command control tasks, and advances the application of BCI systems in real-world neuroprosthetic control.},
}
@article {pmid40703402,
year = {2025},
author = {Borra, D and Ma, M and Martinez-Martin, E and Xia, L},
title = {Editorial: Methods in brain-computer interfaces: 2023.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1647584},
pmid = {40703402},
issn = {1662-5161},
}
@article {pmid40703200,
year = {2025},
author = {Li, K and Zhang, J and Yu, B and Ward, MP and Liu, M and Liu, Y and Wang, Z and Chen, Z and Li, W and Wang, N and Zhao, Y and Yang, X and Yang, F and Wang, P and Zhang, Z},
title = {Meteorological, Socioeconomic, and Environmental Factors Influencing Human Brucellosis Occurrence in Yunnan, China, 2006-2021: A Bayesian Spatiotemporal Modeling Study.},
journal = {Transboundary and emerging diseases},
volume = {2025},
number = {},
pages = {8872434},
pmid = {40703200},
issn = {1865-1682},
mesh = {Humans ; China/epidemiology ; *Brucellosis/epidemiology ; Bayes Theorem ; Socioeconomic Factors ; Spatio-Temporal Analysis ; Risk Factors ; Meteorological Concepts ; Environment ; },
abstract = {Background: Brucellosis epidemics in Yunnan Province in southern China have increased and caused more impact in recent years. However, the epidemiological characteristics and driving factors for brucellosis have not been clearly described. The aim of this study was to analyze the spatiotemporal distribution and potential factors for human brucellosis (HB) in Yunnan Province, 2006-2021. Methods: HB data were obtained from the China National Notifiable Infectious Diseases Reporting Information System. Global spatial autocorrelation and spatial scanning statistics were used to analyze the spatial patterns of brucellosis. Zero-inflated negative binomial (ZINB) Bayesian spatiotemporal models were applied to the analysis of potential risk factors, including environmental, meteorological, and socioeconomic factors. Findings: Between 2006 and 2021, a total of 2794 brucellosis cases were reported. The central and western regions were the most severely affected. GDP showed a positive correlation with brucellosis risk when in the range 0-30.9 billion RMB, peaking with a relative risk (RR) of 13.64 (95% Bayesian credible interval [BCI]: 4.10, 49.10) at around 2.3 billion RMB. Conversely, a negative correlation was observed for GDP between 101 and 135 billion RMB, with the RR dropping to 0.14 (95% BCI: 0.01, 0.89) at 135 billion RMB. Brucellosis cases increased by 4.90% (95% BCI: 1.82%, 7.95%) per 1°C increase in temperature, while a 1° increase in slope reduced cases by 17.06% (95% BCI: 4.01%, 28.81%). Interpretation: Our findings suggest that socioeconomic factors play the greatest role in the occurrence of brucellosis in both northern and southern China; however, the effects of the environmental factors may be different between these areas. Differences in factors affecting each region need to be fully considered, and brucellosis prevention and control need to be adapted to these differences.},
}
@article {pmid40702984,
year = {2025},
author = {Yu, Y and Wang, RM and Dong, Y and Jia, XZ and Wu, ZY},
title = {Neuroimaging correlates of genetics in patients with Wilson's disease.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {35},
number = {7},
pages = {},
doi = {10.1093/cercor/bhaf186},
pmid = {40702984},
issn = {1460-2199},
support = {81125009//National Natural Science Foundation of China/ ; 81701126//National Natural Science Foundation of China/ ; 188020-193810101/089//Research Foundation for Distinguished Scholars of Zhejiang University/ ; },
mesh = {Humans ; *Hepatolenticular Degeneration/genetics/diagnostic imaging/pathology/physiopathology ; Male ; Female ; Adult ; *Brain/pathology/diagnostic imaging/physiopathology ; Young Adult ; *Mutation/genetics ; Magnetic Resonance Imaging ; Neuroimaging ; Copper-Transporting ATPases/genetics ; Adolescent ; Middle Aged ; Atrophy ; },
abstract = {Wilson's disease is an inherited disorder of copper metabolism. Despite significant advancements in neuroimaging studies, prior research into the pathological mechanism of Wilson's disease has ignored the crucial impact of mutation on the disease. This study examined brain imaging in relation to mutation in patients with Wilson's disease. A total of 57 Wilson's disease patients and 25 healthy controls were recruited in the current research. Patients were classified as having either the p.R778L or the p.P992L mutation (N = 43) or other mutations (N = 14). Utilizing the amplitude of low-frequency fluctuations, fractional amplitude of low-frequency fluctuations, and voxel-based morphology, the brain function and structure of Wilson's disease were explored. Compared to healthy controls, Wilson's disease patients with the p.R778L or p.P992L mutation showed greater atrophy in the bilateral putamen, caudate, globus pallidus, thalamus, amygdala, insula, and hippocampus. And these patients showed altered spontaneous neural activity in many more brain regions than healthy controls in three frequency bands. Significant correlation was found between altered brain volume and Unified Wilson's Disease Rating Scale neurological subscale scores. These findings reveal the functional and structural characteristics of Wilson's disease and emphasize the importance of exploring the neuroimaging correlation of genetic mutations in Wilson's disease.},
}
@article {pmid40702747,
year = {2025},
author = {Yang, A and Lv, X and Wang, H and Wang, X},
title = {Psychedelics, Spirituality, and Fundamentalism: A Brain Network Approach to Cognitive Flexibility and Rigidity.},
journal = {ACS chemical neuroscience},
volume = {16},
number = {15},
pages = {2750-2752},
doi = {10.1021/acschemneuro.5c00509},
pmid = {40702747},
issn = {1948-7193},
mesh = {Humans ; *Hallucinogens/pharmacology ; *Cognition/drug effects/physiology ; *Brain/drug effects/physiology ; *Spirituality ; Psilocybin/pharmacology ; Mysticism ; Cognitive Flexibility ; },
abstract = {This viewpoint reconceptualizes mysticism and fundamentalism as brain network disorders, with psychedelics like psilocybin, lysergic acid diethylamide, and N,N-dimethyltryptamine offering potential to modulate these states. By disrupting rigid neural patterns, psychedelics may foster cognitive flexibility, challenge inflexible belief systems, and offer therapeutic value for extremism and mental health disorders.},
}
@article {pmid40702190,
year = {2025},
author = {Kaifosh, P and Reardon, TR and , },
title = {A generic non-invasive neuromotor interface for human-computer interaction.},
journal = {Nature},
volume = {645},
number = {8081},
pages = {702-711},
pmid = {40702190},
issn = {1476-4687},
mesh = {Humans ; Gestures ; *Brain-Computer Interfaces ; *Electromyography/instrumentation/methods ; Male ; Female ; *User-Computer Interface ; Adult ; Young Adult ; Wrist/physiology ; },
abstract = {Since the advent of computing, humans have sought computer input technologies that are expressive, intuitive and universal. While diverse modalities have been developed, including keyboards, mice and touchscreens, they require interaction with a device that can be limiting, especially in on-the-go scenarios. Gesture-based systems use cameras or inertial sensors to avoid an intermediary device, but tend to perform well only for unobscured movements. By contrast, brain-computer or neuromotor interfaces that directly interface with the body's electrical signalling have been imagined to solve the interface problem[1], but high-bandwidth communication has been demonstrated only using invasive interfaces with bespoke decoders designed for single individuals[2-4]. Here, we describe the development of a generic non-invasive neuromotor interface that enables computer input decoded from surface electromyography (sEMG). We developed a highly sensitive, easily donned sEMG wristband and a scalable infrastructure for collecting training data from thousands of consenting participants. Together, these data enabled us to develop generic sEMG decoding models that generalize across people. Test users demonstrate a closed-loop median performance of gesture decoding of 0.66 target acquisitions per second in a continuous navigation task, 0.88 gesture detections per second in a discrete-gesture task and handwriting at 20.9 words per minute. We demonstrate that the decoding performance of handwriting models can be further improved by 16% by personalizing sEMG decoding models. To our knowledge, this is the first high-bandwidth neuromotor interface with performant out-of-the-box generalization across people.},
}
@article {pmid40701672,
year = {2025},
author = {Kundi, H and Popma, JJ and Granada, JF and Leon, MB and Kodesh, A and Ascione, G and George, I and Latib, A and Thompson, JB and Popma, A and Alu, MC and Cohen, DJ},
title = {Outcomes in Older Patients Undergoing Surgical Aortic Valve Replacement With Concomitant Procedures.},
journal = {Journal of the American College of Cardiology},
volume = {86},
number = {4},
pages = {280-283},
doi = {10.1016/j.jacc.2025.05.021},
pmid = {40701672},
issn = {1558-3597},
}
@article {pmid40700800,
year = {2025},
author = {Peng, J and Jia, S and Zhang, J and Wang, Y and Yu, Z and Liu, JK},
title = {Decoding natural visual scenes via learnable representations of neural spiking sequences.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {192},
number = {},
pages = {107863},
doi = {10.1016/j.neunet.2025.107863},
pmid = {40700800},
issn = {1879-2782},
abstract = {Visual input underpins cognitive function by providing the brain with essential environmental information. Neural decoding of visual scenes seeks to reconstruct pixel-level images from neural activity, a vital capability for vision restoration via brain-computer interfaces. However, extracting visual content from time-resolved spiking activity remains a significant challenge. Here, we introduce the Wavelet-Informed Spike Augmentation (WISA) model, which applies multilevel wavelet transforms to spike trains to learn compact representations that can be directly fed into deep reconstruction networks. When tested on recorded retinal spike data responding to natural video stimuli, WISA substantially improves reconstruction accuracy, especially in recovering fine-grained details. These results emphasize the value of temporal spike patterns for high-fidelity visual decoding and demonstrate WISA as a promising model for visual decoding.},
}
@article {pmid40700312,
year = {2025},
author = {Correia, P and Quintão, C and Quaresma, C and Vigário, R},
title = {A Framework for Corticomuscle Control Studies Using a Serious Gaming Approach.},
journal = {Methods and protocols},
volume = {8},
number = {4},
pages = {},
pmid = {40700312},
issn = {2409-9279},
support = {UI/BD/151321/2021//Funda&#xe7;&#xe3;o para a Ci&#xea;ncia e Tecnologia (FCT, Portugal)/ ; },
abstract = {Sophisticated voluntary movements are essential for everyday functioning, making the study of how the brain controls muscle activity a central challenge in neuroscience. Investigating corticomuscular control through non-invasive electrophysiological recordings is particularly complex due to the intricate nature of neuronal signals. To address this challenge, we present a novel experimental methodology designed to study corticomuscular control using electroencephalography (EEG) and electromyography (EMG). Our approach integrates a serious gaming biofeedback system with a specialized experimental protocol for simultaneous EEG-EMG data acquisition, optimized for corticomuscular studies. This work introduces, for the first time, a method for assessing brain-muscle functional connectivity during the execution of a demanding motor task. By identifying neuronal sources linked to muscular activity, this methodology has the potential to advance our understanding of motor control mechanisms. These insights could contribute to improving clinical practices and fostering the development of novel brain-computer interface technologies.},
}
@article {pmid40699544,
year = {2025},
author = {Pan, H and Chen, Z and Xu, N and Wang, B and Hu, Y and Zhou, H and Perry, A and Kong, XZ and Shen, M and Gao, Z},
title = {Dissecting Social Working Memory: Neural and Behavioral Evidence for Externally and Internally Oriented Components.},
journal = {Neuroscience bulletin},
volume = {41},
number = {11},
pages = {2049-2062},
pmid = {40699544},
issn = {1995-8218},
mesh = {Humans ; *Memory, Short-Term/physiology ; Male ; Female ; *Empathy/physiology ; Young Adult ; Magnetic Resonance Imaging ; Adult ; *Brain/physiology/diagnostic imaging ; Brain Mapping ; Facial Expression ; *Social Behavior ; Facial Recognition/physiology ; *Social Perception ; Personality/physiology ; },
abstract = {Social working memory (SWM)-the ability to maintain and manipulate social information in the brain-plays a crucial role in social interactions. However, research on SWM is still in its infancy and is often treated as a unitary construct. In the present study, we propose that SWM can be conceptualized as having two relatively independent components: "externally oriented SWM" (e-SWM) and "internally oriented SWM" (i-SWM). To test this external-internal hypothesis, participants were tasked with memorizing and ranking either facial expressions (e-SWM) or personality traits (i-SWM) associated with images of faces. We then examined the neural correlates of these two SWM components and their functional roles in empathy. The results showed distinct activations as the e-SWM task activated the postcentral and precentral gyri while the i-SWM task activated the precuneus/posterior cingulate cortex and superior frontal gyrus. Distinct multivariate activation patterns were also found within the dorsal medial prefrontal cortex in the two tasks. Moreover, partial least squares analyses combining brain activation and individual differences in empathy showed that e-SWM and i-SWM brain activities were mainly correlated with affective empathy and cognitive empathy, respectively. These findings implicate distinct brain processes as well as functional roles of the two types of SWM, providing support for the internal-external hypothesis of SWM.},
}
@article {pmid40697162,
year = {2025},
author = {Dohle, E and Swanson, E and Jovanovic, L and Yusuf, S and Thompson, L and Horsfall, HL and Muirhead, W and Bashford, L and Brannigan, J},
title = {Toward the Clinical Translation of Implantable Brain-Computer Interfaces for Motor Impairment: Research Trends and Outcome Measures.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {12},
number = {32},
pages = {e01912},
pmid = {40697162},
issn = {2198-3844},
support = {FC001153/WT_/Wellcome Trust/United Kingdom ; //Rosetrees Trust and Stoneygate Trust/ ; },
mesh = {*Brain-Computer Interfaces/trends ; Humans ; *Translational Research, Biomedical/trends ; Outcome Assessment, Health Care ; Electrocorticography ; },
abstract = {Implantable brain-computer interfaces (iBCIs) decode neural signals to control external effectors, offering potential to restore function in individuals with severe motor impairments, such as loss of limb function or speech. This systematic review examines the evolution of iBCI research and key bottlenecks to clinical translation, particularly the absence of standardized, clinically meaningful outcome measures. A comprehensive search of MEDLINE, Embase, and CINAHL identifies 112 studies, nearly half (49.1%) published since 2020. Eighty unique iBCI participants were identified, providing the most up-to-date estimate of global users. Research remains concentrated in the United States (83%), with growing contributions from Europe, China, and Australia. Electrocorticography (ECoG)-based devices increasingly emerge alongside micro-electrode arrays. iBCI devices are now being used to control a broader range of effectors, including robotic prosthetics and digital technologies. Although most (69.6%) studies reported outcome measures prospectively, these primarily related to decoding (69.6%) and task performance (62.5%), with only 17.9% assessing clinical outcomes. When cassessed, clinical outcomes were highly heterogeneous due to varied approaches across target populations. iBCIs show potential to restore functional independence at scale. However, challenges remain around cross-subject generalization, scalable implantation, and outcome standardization. Novel measures should be developed collaboratively with engineers, clinicians, and individuals with lived experience of motor impairment.},
}
@article {pmid40696184,
year = {2025},
author = {Yang, L and Guo, C and Zheng, Z and Dong, Y and Xie, Q and Lv, Z and Li, M and Lu, Y and Guo, X and Deng, R and Liu, Y and Feng, Y and Mu, R and Zhang, X and Ma, H and Chen, Z and Zhang, Z and Dong, Z and Yang, W and Zhang, X and Cui, Y},
title = {Author Correction: Stress dynamically modulates neuronal autophagy to gate depression onset.},
journal = {Nature},
volume = {644},
number = {8075},
pages = {E12},
doi = {10.1038/s41586-025-09404-1},
pmid = {40696184},
issn = {1476-4687},
}
@article {pmid40695313,
year = {2025},
author = {de Borman, A and Wittevrongel, B and Van Dyck, B and Van Rooy, K and Carrette, E and Meurs, A and Van Roost, D and Van Hulle, MM},
title = {Speech mode classification from electrocorticography: transfer between electrodes and participants.},
journal = {Journal of neural engineering},
volume = {22},
number = {4},
pages = {},
doi = {10.1088/1741-2552/adf2de},
pmid = {40695313},
issn = {1741-2552},
mesh = {Humans ; *Electrocorticography/methods/instrumentation/classification ; *Brain-Computer Interfaces ; *Speech/physiology ; Male ; Female ; Adult ; *Electrodes, Implanted ; Middle Aged ; Speech Perception/physiology ; Young Adult ; },
abstract = {Objective.Speech brain-computer interfaces (BCIs) aim to restore communication for individuals who have lost the ability to speak by interpreting their brain activity and decoding the intended speech. As an initial component of these decoders, speech detectors have been developed to distinguish between the intent to speak and silence. However, it is important that these detectors account for real-life scenarios in which users may engage language-related brain areas-such as during reading or listening-without any intention to speak.Approach.In this study, we analyze the interplay between different speech modes: speaking, listening, imagining speaking, reading and mouthing. We gathered a large dataset of 29 participants implanted with electrocorticography electrodes and developed a speech mode classifier. We also assessed how well classifiers trained on data from a specific participant transfer to other participants, both in the case of a single- and multi-electrode classifier.Main results.High accuracy was achieved using linear classifiers, for both single-electrode and multi-electrode configurations. Single-electrode classification reached 88.89% accuracy and multi-electrode classification 96.49% accuracy in distinguishing among three classes (speaking, listening, and silence). The best performing electrodes were located on the superior temporal gyrus and sensorimotor cortex. We found that single-electrode classifiers could be transferred across recording sites. For multi-electrode classifiers, we observed that transfer performance was higher for binary classifiers compared to multiclass classifiers, with the optimal source subject of the binary classifiers depending on the speech modes being classified.SignificanceAccurately detecting speech from brain signals is essential to prevent spurious outputs from a speech BCI and to advance its use beyond lab settings. To achieve this objective, the transfer between participants is particularly valuable as it can reduce training time, especially in cases where subject training is challenging.},
}
@article {pmid40694675,
year = {2025},
author = {Izac, M and N'Kaoua, B and Pillette, L and Jeunet-Kelway, C},
title = {[Improve athletes' performance with neurofeedback].},
journal = {Biologie aujourd'hui},
volume = {219},
number = {1-2},
pages = {51-58},
doi = {10.1051/jbio/2025001},
pmid = {40694675},
issn = {2105-0686},
mesh = {Humans ; *Neurofeedback/methods/physiology ; *Athletic Performance/physiology/psychology ; *Athletes/psychology ; Electroencephalography ; Cognition/physiology ; },
abstract = {In order to optimise their performance, athletes are looking for innovative, efficient and reliable training approaches. The development of electroencephalography and neurofeedback (NF) offers the opportunity to create innovative cognitive training procedures. Indeed, these technologies allow athletes to benefit from a feedback during mental training sessions and to objectively assess performance and progress. In addition, NF makes it possible to guide the athletes towards optimal cognitive strategies according to their objectives, and has a motivational dimension that pushes them to engage in the sessions. We first introduce the usefulness of NF to improve sports performance. Then, we review the current results concerning its efficiency. Finally, we provide an overview of the literature showing the heterogeneity of the studies published on the subject, focusing mainly on the aspects that could explain the variability of reported data.},
}
@article {pmid40694476,
year = {2025},
author = {Rangwani, R and Abbasi, A and Gulati, T},
title = {Effective cerebellar neuroprosthetic control after stroke.},
journal = {Cell reports},
volume = {44},
number = {8},
pages = {116030},
pmid = {40694476},
issn = {2211-1247},
support = {R00 NS097620/NS/NINDS NIH HHS/United States ; R01 NS128469/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Cerebellum/physiopathology ; *Stroke/physiopathology ; *Brain-Computer Interfaces ; Rats ; Motor Cortex/physiopathology ; Male ; Neurons/physiology ; Disease Models, Animal ; },
abstract = {Brain-machine interfaces (BMIs) offer a viable option for restoring function in patients with motor disabilities post-stroke. Most BMI systems rely on signals from the motor cortex (M1), which is often compromised after stroke. The cerebellum, a subcortical structure involved in motor control, remains an underexplored source for neuroprosthetic control. Using chronic electrophysiological recordings in a rat stroke model, we show that cerebellar neural activity can effectively drive BMI control, performing comparably to M1-driven control. We observed this even in animals with motor impairments post-stroke. Simultaneous M1-cerebellum recordings during cerebellar BMI control revealed that cerebellar "direct" neurons driving the interface were influenced by both local cerebellar and distant M1 neurons. While cerebellar influence remained stable, M1's interaction with cerebellar direct neurons shifted from longer to shorter timescales after stroke. These findings highlight that cerebellar direct neural control is possible in the stroke brain and reveal changes in M1-cerebellar network dynamics post-stroke.},
}
@article {pmid40694466,
year = {2025},
author = {Zhang, C and Li, G and Wu, X and Gao, X},
title = {A Novel Hybrid Brain-Computer Interface Integrating Motor Imagery and Multiple Visual Stimuli.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {2847-2857},
doi = {10.1109/TNSRE.2025.3591616},
pmid = {40694466},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Humans ; Male ; *Imagination/physiology ; Evoked Potentials, Visual/physiology ; Electroencephalography ; Adult ; Female ; *Photic Stimulation/methods ; Young Adult ; Algorithms ; Movement/physiology ; Attention/physiology ; Reproducibility of Results ; Psychomotor Performance/physiology ; Arm/physiology ; },
abstract = {Brain-Computer Interface (BCI) that integrate Motor Imagery (MI) with Steady-State Visual Evoked Potentials (SSVEP) or Overt Spatial Attention (OSA) have demonstrated superior performance compared to MI only BCI. Nonetheless, the exploration of BCI that combine MI with visual tasks remains limited, and the synchronization between MI and visual tasks is often weak. To address this gap, our study introduces a novel BCI paradigm that combines MI with two visual tasks: SSVEP and OSA. In this paradigm, dynamic images depicting left and right arm movements flash at distinct frequencies, serving as visual stimuli positioned on both sides of the screen. Four classification methods are used for testing. The MI+SSVEP+OSA paradigm achieves higher average accuracy than the MI, MI+SSVEP, and MI+OSA paradigms. This validates the effectiveness of our novel paradigm and confirms the feasibility of simultaneously integrating MI with two visual stimuli. Moreover, we observe that the integration of SSVEP offers significant improvements, especially for participants who exhibit limited performance in the MI only paradigm. Additionally, our results indicate comparable performance between the MI+SSVEP and MI+OSA paradigms. Overall, this study offers valuable insights that can guide future research in hybrid BCI development, paving the way for more efficient and user-friendly BCI.},
}
@article {pmid40694230,
year = {2025},
author = {Afkhaminia, F and Shamsollahi, MB and Bahraini, T},
title = {A distributed adaptive network framework for ERP-Based classification of multichannel EEG signals.},
journal = {Physical and engineering sciences in medicine},
volume = {48},
number = {3},
pages = {1207-1224},
pmid = {40694230},
issn = {2662-4737},
mesh = {*Electroencephalography ; Humans ; *Signal Processing, Computer-Assisted ; *Evoked Potentials ; Algorithms ; Brain-Computer Interfaces ; Machine Learning ; },
abstract = {Understanding brain function is one of the most challenging areas in brain signal processing. This study introduces a novel framework for electroencephalography (EEG) signal classification based on distributed adaptive networks using diffusion strategy. Our approach models the brain as a multitask network, where EEG electrodes are considered as nodes of this network. The network parameters are dynamically optimized based on the data from the nodes and inter-node cooperation. The proposed framework, which comprises network modeling and diffusion-based adaptation using the adapt then combine (ATC) algorithm, has been validated on different types of data. Experimental results indicate that the proposed framework outperforms common methods in classifying EEG data based on event-related potential (ERP) pattern identification, particularly in scenarios where machine learning-based models struggle with limited data. Furthermore, its ability to adapt to the non-stationary and dynamic nature of EEG signals and its efficient real-time implementation make this approach ideal for brain-computer interface (BCI), cognitive neuroscience, and clinical applications.},
}
@article {pmid40694026,
year = {2025},
author = {Guérin, V},
title = {Veteran and Brain-Computer Interfaces: The Duty to Care.},
journal = {AJOB neuroscience},
volume = {16},
number = {4},
pages = {300-308},
doi = {10.1080/21507740.2025.2530948},
pmid = {40694026},
issn = {2150-7759},
mesh = {*Brain-Computer Interfaces/psychology ; Humans ; *Veterans/psychology ; United States ; *Military Personnel/psychology ; },
abstract = {Anticipated by science fiction, the enhanced soldier crystallized in the United States at the dawn of the 21st century within the Pentagon's scientific agency, the Defense Advanced Research Projects Agency (DARPA). Fueled by the fear of being overtaken by the enemy, and then by its own technology, this agency's new vision produced a "bifurcation" within anthropotechnics: the modification of humans for war. The soldier is now at the heart of a process of radical innovation, with as yet unknown implications. Emblematic of this enhancement, the use of the brain-computer interfaces (BCIs) will not only expose the soldier to previously unknown psychocognitive and emotional effects, but also offer the enemy potential access to his/her inner self. By giving birth to a new kind of veteran, this hybridization will generate new responsibilities for military commanders and politicians, as well as a new type of care.},
}
@article {pmid40694018,
year = {2025},
author = {Fan, C and Ding, Y and Zhang, H},
title = {A commentary on "Brain-computer interfaces: the innovative to unlocking neurological conditions".},
journal = {International journal of surgery (London, England)},
volume = {},
number = {},
pages = {},
doi = {10.1097/JS9.0000000000003094},
pmid = {40694018},
issn = {1743-9159},
}
@article {pmid40691442,
year = {2025},
author = {Wang, X and Chen, S and Li, J and Gao, Y and Li, S and Li, M and Liu, X and Liu, S and Ming, D},
title = {Enhanced theta oscillations in the left temporoparietal region associated with refractory positive symptoms in schizophrenia.},
journal = {Schizophrenia (Heidelberg, Germany)},
volume = {11},
number = {1},
pages = {104},
pmid = {40691442},
issn = {2754-6993},
abstract = {Positive symptoms are a prominent feature of schizophrenia. Despite antipsychotic treatment, ~30% of patients develop refractory positive symptoms (RPSs). Current research fails to elucidate the potential neurophysiological mechanisms underlying RPSs, thereby hindering the development of additional treatments. This study, which included 37 patients with RPSs and 40 with non-refractory positive symptoms (NRPSs), aimed to explore their underlying neural mechanisms. Outcome measures were relative power spectrum density and interregional synchronization across frequency bands and theta-gamma phase-amplitude coupling (θ-γ PAC). The single-frequency analysis indicated that RPSs exhibited elevated theta power and reduced lateralization in the left temporal lobe and temporo-parietal junction, along with enhanced functional connectivity in the left frontocentral region. The cross-frequency analysis revealed that RPSs exhibited slightly higher θ-γ coupling at the left temporo-parietal junction compared to NRPSs. Correlation analysis revealed significant associations among theta power, the lateralization index, functional connectivity, and the severity of positive symptoms. The aberrant activation of the theta rhythm in the left temporo-parietal region may lead to increased functional asymmetry in the brain, impeding interregional and inter-frequency information transmission and thus significantly impairing the normal processing of auditory information. These findings offer potential insights into the neurophysiological basis of positive symptoms in schizophrenia and may inform future clinical interventions.},
}
@article {pmid40690341,
year = {2025},
author = {Wang, J and Yao, L and Wang, Y},
title = {Enhanced Online Continuous Brain-Control by Deep Learning-Based EEG Decoding.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {2834-2846},
doi = {10.1109/TNSRE.2025.3591254},
pmid = {40690341},
issn = {1558-0210},
mesh = {Humans ; *Deep Learning ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Male ; Adult ; Female ; Neural Networks, Computer ; Young Adult ; *Brain/physiology ; Algorithms ; Imagination/physiology ; Online Systems ; },
abstract = {OBJECTIVE: A growing amount of deep learning models for motor imagery (MI) decoding from electroencephalogram (EEG) have demonstrated their superiority over traditional machine learning approaches in offline dataset analysis. However, current online MI-based brain-computer interfaces (BCIs) still predominantly adopt machine learning decoders while falling short of high BCI performance. Yet, the generalization and advantages of deep learning-based EEG decoding in realistic BCI systems remain far unclear.<br><br>METHODS: We conduct a randomized and cross-session online MI-BCI study on 2D center-out tasks in 15 BCI-naive subjects. A newly proposed deep learning model named interactive frequency convolutional neural network (IFNet) is leveraged and rigorously compared with the prevailing benchmark namely filter-bank common spatial pattern (FBCSP) for online MI decoding.<br><br>RESULTS: Through extensive online analysis, the deep learning decoder consistently outperforms the classical counterpart across various performance metrics. In particular, IFNet significantly improves the average online task accuracy by 20% and 27% in two sessions compared with FBCSP, respectively. Moreover, a significant cross-session training effect is observed by the IFNet model (${P}
={0}
.{017}
$) while not for the controlled method (${P}
={0}
.{337}
$). Further offline evaluations also demonstrate the superior performance of IFNet over state-of-the-art deep learning models. Moreover, we present unique behavioral and neurophysiological insights underlying online brain-machine interaction.<br><br>CONCLUSION: We present one of the first studies about online MI-BCIs using deep learning, achieving substantially enhanced online performance for continuous BCI control.<br><br>SIGNIFICANCE: This study suggests the good utility of deep learning in MI-BCIs and has implications for clinical applications such as stroke rehabilitation.},
}
@article {pmid40688356,
year = {2025},
author = {Sonntag, J and Yu, L and Wang, X and Schack, T},
title = {Neurophysiological predictors of deep learning based unilateral upper limb motor imagery classification.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1617748},
pmid = {40688356},
issn = {1662-5161},
abstract = {INTRODUCTION: Motor imagery-based brain-computer interfaces (BCIs) are a technique for decoding and classifying the intention of motor execution, solely based on imagined (rather than executed) movements. Although deep learning techniques have increased the potential of BCIs, the complexity of decoding unilateral upper limb motor imagery remains challenging. To understand whether neurophysiological features, which are directly related to neural mechanisms of motor imagery, might influence classification accuracy, most studies have largely leveraged traditional machine learning frameworks, leaving deep learning-based techniques underexplored.<br><br>METHODS: In this work, three different deep learning models from the literature (EEGNet, FBCNet, NFEEG) and two common spatial pattern-based machine learning classifiers (SVM, LDA) were used to classify imagined right elbow flexion and extension from participants using electroencephalography data. From two recorded resting states (eyes-open, eyes-closed), absolute and relative alpha and beta power of the frontal, fronto-central and central electrodes were used to predict the accuracy of the different classifiers.<br><br>RESULTS: The prediction of classifier accuracies by neurophysiological features revealed negative correlations between the relative alpha band and classifier accuracies and positive correlations between the absolute and relative beta band and classifiers accuracies. Most ipsilateral EEG channels yielded significant correlations with classifier accuracies, especially for the machine learning classifier.<br><br>DISCUSSION: This pattern contrasts with previous findings from bilateral MI paradigms, where contralateral alpha and beta activity were more influential. These inverted correlations suggest task-specific neurophysiological mechanisms in unilateral MI, emphasizing the role of ipsilateral inhibition and attentional processes.},
}
@article {pmid40685778,
year = {2025},
author = {Niu, X and Jiang, L and Hu, J and Jia, Y and Zhao, S and Ma, Y and Qiu, Z and Lian, Y and Zhu, E and Ni, J},
title = {Femtosecond Laser-Engineered Multifunctional Bio-Metasurface for the Inhibition of Thrombosis and Bacterial Infections.},
journal = {ACS applied materials &amp; interfaces},
volume = {17},
number = {30},
pages = {43761-43776},
doi = {10.1021/acsami.5c05001},
pmid = {40685778},
issn = {1944-8252},
mesh = {*Thrombosis/prevention &amp; control/drug therapy ; Humans ; *Lasers ; Staphylococcus aureus/drug effects ; Surface Properties ; *Anti-Bacterial Agents/pharmacology/chemistry ; Escherichia coli/drug effects ; Platelet Adhesiveness/drug effects ; *Bacterial Infections ; Carbon/chemistry ; *Coated Materials, Biocompatible/chemistry/pharmacology ; },
abstract = {Surface engineering is an effective strategy for addressing thrombosis and bacterial infection associated with blood-contacting implants (BCIs). However, most functional surfaces rely on a single mechanism and surface engineering poses substantial processing challenges for chemically inert and difficult-to-process materials such as pyrolytic carbon. Herein, a multifunctional bio-metasurface (LDT surface) synergizing liquid-repellent (L), drag-reduction (D), and turbulence-attenuation (T) strategies is proposed. The LDT surface is achieved through the synergistic interplay of surface texture-mediated flow control and interfacial lubrication effects. The textured LDT surface with microgrooves exhibits a hemodynamic modulation capability, exhibiting an effective turbulence-attenuation effect. The slippery coating on the LDT surface exhibits liquid-repellent and drag-reduction effects, regulating bio (blood and bacteria)-material interfacial interactions. The complex, hierarchical micro-groove, micro-hole, and nano-ripples/gaps/protrusions structures on the surface are fabricated on pyrolytic carbon via temporally shaped femtosecond laser texturing, followed by functional coating. The LDT surface exhibits excellent stability under continuous turbulent flow, with no toxic byproducts generated during processing. The computational fluid dynamics simulation results confirm that the streamwise microgrooves on the wall significantly attenuate turbulence. Compared to the pristine sample surface, the experimental results reveal a 98.2% reduction in platelet adhesion on the LDT surface, with a platelet adhesion rate of only 0.22% and no detected activated platelets, while denatured fibrinogen adhesion decreases by 55.3%. Moreover, the antiadhesion capacities of the LDT surface against Staphylococcus aureus and Escherichia coli improve by 99.4% and 98.4%, respectively, relative to the pristine sample surface, without viable residual bacteria or biofilm formation. The study offers a promising strategy to mitigate BCI-associated thrombosis and bacterial infection on BCIs, particularly those made from difficult-to-machine materials.},
}
@article {pmid40683976,
year = {2025},
author = {Joshi, A and Matharu, PS and Malviya, L and Kumar, M and Jadhav, A},
title = {Advancing EEG based stress detection using spiking neural networks and convolutional spiking neural networks.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {26267},
pmid = {40683976},
issn = {2045-2322},
mesh = {*Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; Brain-Computer Interfaces ; Machine Learning ; Wavelet Analysis ; Signal Processing, Computer-Assisted ; Algorithms ; Deep Learning ; },
abstract = {Accurate and efficient analysis of Electroencephalogram (EEG) signals is crucial for applications like neurological diagnosis and Brain-Computer Interfaces (BCI). Traditional methods often fall short in capturing the intricate temporal dynamics inherent in EEG data. This paper explores the use of Convolutional Spiking Neural Networks (CSNNs) to enhance EEG signal classification. We apply Discrete Wavelet Transform (DWT) for feature extraction and evaluate CSNN performance on the Physionet EEG dataset, benchmarking it against traditional deep learning and machine learning methods. The findings indicate that CSNNs achieve high accuracy, reaching 98.75% in 10-fold cross-validation, and an impressive F1 score of 98.60%. Notably, this F1-score represents an improvement over previous benchmarks, highlighting the effectiveness of our approach. Along with offering advantages in temporal precision and energy efficiency, CSNNs emerge as a promising solution for next-generation EEG analysis systems.},
}
@article {pmid40683565,
year = {2025},
author = {Wu, Y and Lv, K and Zhao, Y and Yang, G and Hao, X and Zheng, B and Lv, C and An, Z and Zhou, H and Yuan, Q and Song, T},
title = {Prediction Model for Detrusor Underactivity via Noninvasive Clinical Parameters in Men With Benign Prostatic Hyperplasia.},
journal = {Urology},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.urology.2025.07.021},
pmid = {40683565},
issn = {1527-9995},
abstract = {OBJECTIVE: To develop a clinical prediction model for detrusor underactivity (DU) in patients with benign prostatic hyperplasia (BPH).<br><br>METHODS: A retrospective review was conducted on 546 individuals with BPH who had undergone urodynamic testing between January 2012 and May 2022. The bladder contractility index (BCI) was assessed using a pressure-flow study (PFS). Patients were categorized into DU (BCI <100, n = 196) and non-DU (BCI &#x2265;100, n = 350) groups. Univariate logistic regression was initially performed to identify potential DU-related factors, followed by multivariate analysis to determine independent risk factors.<br><br>RESULTS: A predictive model for DU in patients with BPH was developed using the coefficient of these independent risk factors. Among the 546 cases, 196 (35.9%) were diagnosed with DU. Older age, smaller prostate volume, lower urgency symptom score, lower incomplete emptying symptom score, higher straining symptom score, and lower maximum flow rate (Qmax) were identified as independent predictors of DU in patients with BPH. The model demonstrated an area under the curve of 0.78 (95% CI, 0.74-0.82), with internal validation yielding 0.75 (95% CI, 0.74-0.75).<br><br>CONCLUSION: We developed a predictive model that effectively estimates the DU probability in patients with BPH without requiring invasive pressure-flow study.},
}
@article {pmid40683191,
year = {2025},
author = {Li, Y and Sun, Y and Wan, F and Yuan, Z and Jung, TP and Wang, H},
title = {MetaNIRS: A general decoding framework for fNIRS based motor execution/imagery.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {192},
number = {},
pages = {107873},
doi = {10.1016/j.neunet.2025.107873},
pmid = {40683191},
issn = {1879-2782},
abstract = {Functional near-infrared spectroscopy (fNIRS) is a crucial brain activity monitoring tool with remarkable potential applications in brain-computer interfaces (BCI), particularly in rehabilitation therapy for disabilities. However, the performance of fNIRS-based BCI systems remains suboptimal, such as motor execution (ME) and motor imagery (MI) decoding. Inspired by the successful application of the PoolFormer framework in visual tasks, we first proposed a novel long-range dilation multilayer perceptron (LongDilMLP) to utilize the hemodynamic characteristics of fNIRS. Furthermore, the LongDilMLP was integrated with the PoolFormer framework, called as MetaNIRS in this study. The proposed framework MetaNIRS was employed for both ME and MI classification tasks, achieving rigorous validation of its effectiveness and practical applicability. To evaluate the performance of MetaNIRS, two publicly available ME datasets (A and C) and one self-collected MI dataset (B) were employed. The experimental results demonstrated that the average accuracy were 76.00 %, 57.45 %, and 84.14 %, with cross-subject accuracy of 77.24 %, 58.55 %, and 85.52 %, respectively. Moreover, sensitivity experiments of model parameters showed the robustness. Ablation experiments highlighted the significance of each MetaNIRS component and the efficacy of LongDilMLP over traditional MLP. Additionally, visualization techniques enhanced the interpretability of MetaNIRS, indicating the main contribution of the first half signals for classification. Using the first half of signals, the average accuracy only reduced 4.30 %, 1.69 %, and 1.11 %, respectively. These findings suggest that the superior performance of MetaNIRS, which provide an efficient general decoding framework for ME and MI.},
}
@article {pmid40683189,
year = {2025},
author = {Chen, H and Zeng, W and Chen, C and Cai, L and Wang, F and Shi, Y and Wang, L and Zhang, W and Li, Y and Yan, H and Siok, WT and Wang, N},
title = {EEG Emotion Copilot: Optimizing lightweight LLMs for emotional EEG interpretation with assisted medical record generation.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {192},
number = {},
pages = {107848},
doi = {10.1016/j.neunet.2025.107848},
pmid = {40683189},
issn = {1879-2782},
abstract = {In the fields of affective computing (AC) and brain-computer interface (BCI), the analysis of physiological and behavioral signals to discern individual emotional states has emerged as a critical research frontier. While deep learning-based approaches have made notable strides in EEG emotion recognition, particularly in feature extraction and pattern recognition, significant challenges persist in achieving end-to-end emotion computation, including rapid processing, individual adaptation, and seamless user interaction. This paper presents the EEG Emotion Copilot, a system optimizing a lightweight large language model (LLM) with 0.5B parameters operating in a local setting, which first recognizes emotional states directly from EEG signals, subsequently generates personalized diagnostic and treatment suggestions, and finally supports the automation of assisted electronic medical records. Specifically, we demonstrate the critical techniques in the novel data structure of prompt, model pruning and fine-tuning training, and deployment strategies aiming at improving performance and computational efficiency. Extensive experiments show that our optimized lightweight LLM-based copilot achieves an enhanced intuitive interface for participant interaction, superior accuracy of emotion recognition and assisted electronic medical records generation, in comparison to such models with similar scale parameters or large-scale parameters such as 1.5B, 1.8B, 3B and 7B. In summary, through these efforts, the proposed copilot is expected to advance the application of AC in the medical domain, offering innovative solution to mental health monitoring. The codes will be released at https://github.com/NZWANG/EEG_Emotion_Copilot.},
}
@article {pmid40681665,
year = {2025},
author = {Schrag, E and Comaduran Marquez, D and Kirton, A and Kinney-Lang, E},
title = {An investigation into the comfort and neural response of textured visual stimuli in pediatric SSVEP-based BCI.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {26168},
pmid = {40681665},
issn = {2045-2322},
mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Female ; Male ; Adolescent ; Child ; *Photic Stimulation/methods ; Electroencephalography/methods ; Child, Preschool ; Signal-To-Noise Ratio ; },
abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) are widely used due to their reliability and possible training-free setup. Common SSVEP stimuli are high contrast and solidly colored, potentially causing discomfort and visual fatigue, particularly when high stimulation frequencies are employed. To address this, textured stimuli, which may evoke visual responses in higher processing systems, have been proposed as an alternative to conventional flashing stimuli. We evaluate the effectiveness of textured stimuli for SSVEP-based BCIs by examining both user comfort and neural responses across different EEG channel subsets. Neurotypical participants aged 5-18 (n = 35, 57% female) were exposed to traditional and textured stimuli at three frequencies (9, 14, and 33 Hz) and asked to report perceived comfort. While textured stimuli were consistently rated as more comfortable, especially at lower frequencies, signal-to-noise ratio analysis indicated that they did not enhance neural responses compared to conventional stimuli. Classification accuracy was driven primarily by stimulation frequency rather than stimulus type and there was a sharp decline in accuracy at 33 Hz. These findings suggest that while textured stimuli improve user comfort, their utility in enhancing BCI performance remains unclear, warranting further investigation into stimulus design for SSVEP-based BCIs.},
}
@article {pmid40681115,
year = {2025},
author = {Deepika, D and Rekha, G},
title = {Multi-class mental Task Classification based Brain-Computer Interface using Improved Remora depthwise convolutional adaptive neuro-fuzzy inference network model.},
journal = {Journal of neuroscience methods},
volume = {423},
number = {},
pages = {110536},
doi = {10.1016/j.jneumeth.2025.110536},
pmid = {40681115},
issn = {1872-678X},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Fuzzy Logic ; *Deep Learning ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; *Brain/physiology ; },
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) offer a promising avenue for individuals with severe motor disabilities to interact with the world. By decoding brain signals, BCIs can enable users to control devices and communicate thoughts. However, challenges such as noise in EEG signals and limited data availability hinder the development of accurate and reliable BCI systems. Nonetheless, problems persist, including limited data availability, noisy EEG signals, real-time performance limitations, and reduced classification accuracy.<br><br>NEW METHOD: To overcome this, the present work proposes an efficient Multi-Class Mental Task Classification based BCI using deep learning techniques. Initially, the obtained EEG data is pre-processed with a Finite Linear Haar wavelet-based Filtering (FLHF) technique to remove disturbances in EEG data. Afterwards, optimal feature extraction utilizes a Hybrid dynamic centre binary pattern and multi-threshold-based ternary pattern (H-DCBP-MTTP) technique to extract characteristics from pre-processed EEG data. Finally, the Improved Remora depthwise convolutional adaptive neuro-fuzzy inference network (IRDCANFIN) model is used to classify the mental tasks. To improve classification results, the model's parameters are fine-tuned using an Improved Remora optimization approach (IROA).<br><br>RESULTS: The proposed approach's performance is examined using the BCI laboratory dataset and the EEG Psychiatric Disorders Dataset, which yield accuracy results of 99.3&#x202f;% and 99.56&#x202f;%, respectively. Furthermore, evaluation results show that the proposed approach outperforms existing models.<br><br>Compared to existing models, such as DQN with a 1D-CNN-LSTM, GSP-ML, Shallow 1D-CNN, KNN, and SVM, and the proposed approach yields effective results in terms of accuracy, robustness, and computational efficiency.<br><br>CONCLUSION: The proposed IRDCANFIN classifier is used to classify multiple classes of mental tasks like baseline, counting, multiplication, letter composing, and rotation.},
}
@article {pmid40681114,
year = {2025},
author = {Zhang, R and Li, Z and Pan, X and Cui, H and Chen, X},
title = {Hybrid BCI for upper limb rehabilitation: integrating MI with peripheral field SSVEP stimulation.},
journal = {Journal of neuroscience methods},
volume = {423},
number = {},
pages = {110537},
doi = {10.1016/j.jneumeth.2025.110537},
pmid = {40681114},
issn = {1872-678X},
mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Male ; Female ; Adult ; *Upper Extremity/physiopathology/physiology ; *Imagination/physiology ; Electroencephalography ; *Stroke Rehabilitation/methods ; Robotics ; Photic Stimulation ; Young Adult ; Middle Aged ; },
abstract = {BACKGROUND: Rehabilitation systems based on brain-computer interfaces (BCIs) hold significant potential for stroke patients. Existing systems, predominantly relying on motor imagery (MI), have room for improvement in both performance and user comfort. This study aims to enhance these aspects by developing a hybrid BCI system integrating MI with steady-state visual evoked potentials (SSVEPs) elicited by peripheral visual field stimulation.<br><br>NEW METHODS: The system is coupled with a soft robotic hand for feedback, forming a closed-loop framework. The design incorporates concentric rings with 7&#xb0; and 10&#xb0; eccentricities as peripheral stimuli, flashing at frequencies of 34&#x202f;Hz and 35&#x202f;Hz for left and right sides, respectively, to evoke SSVEPs. A central video (304&#x202f;&#xd7;304 pixels) of left-hand/right-hand grasping motions guides subjects in performing synchronized MI tasks simply by focusing on it, which could also complete the SSVEP task.<br><br>RESULTS: The offline results of 11 subjects showed that the classification result of MI was 70.65&#x202f;&#xb1;&#x202f;3.38&#x202f;%, and the SSVEP result was 96.04&#x202f;&#xb1;&#x202f;3.33&#x202f;%, and the fusion result reached 96.23&#x202f;&#xb1;&#x202f;3.21&#x202f;%, which confirmed the validity of the fusion method. The online experiment of 11 subjects achieved a result of 97.12&#x202f;&#xb1;&#x202f;2.09&#x202f;%, validating the feasibility of the system.<br><br>The proposed system improves the comfort level while ensuring the performance of the system as compared to the existing systems.<br><br>CONCLUSION: The feasibility of the proposed system was verified by offline and online experiments to advance the clinical applications.},
}
@article {pmid40680338,
year = {2025},
author = {Sun, R and Ma, D and Pan, G},
title = {Post-training quantization for efficient ANN-SNN conversion.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {191},
number = {},
pages = {107832},
doi = {10.1016/j.neunet.2025.107832},
pmid = {40680338},
issn = {1879-2782},
mesh = {*Neural Networks, Computer ; *Neurons/physiology ; Algorithms ; Humans ; *Action Potentials/physiology ; },
abstract = {Spiking Neural Networks (SNNs), inspired by the behavior of biological neurons, offer a promising direction for next-generation neural computing. Two primary methodologies have emerged for training deep SNNs: Direct Training, which optimizes SNNs using surrogate gradients, and ANN-to-SNN Conversion, which derives SNNs from Artificial Neural Networks (ANNs). In this work, we focus on the latter and investigate the conversion error that arises during the transformation. We provide a theoretical analysis showing that channel-wise thresholds are more effective than traditional layer-wise thresholds in mitigating this error. To achieve this efficiently, we leverage post-training quantization (PTQ), which enables calibration using only a small dataset without requiring retraining. Compared to conventional direct training and ANN-to-SNN conversion methods, our approach significantly reduces training time while improving accuracy on both static image and neuromorphic datasets.},
}
@article {pmid40679899,
year = {2025},
author = {Kim, YS and Han, H and Kim, CU and Choi, SI and Kim, MY and Im, CH},
title = {Performance Enhancement of Steady-State Visual Evoked Field-Based Brain-Computer Interfaces Incorporating MEG Source Imaging.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {2806-2813},
doi = {10.1109/TNSRE.2025.3590576},
pmid = {40679899},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Humans ; *Magnetoencephalography/methods ; Algorithms ; *Evoked Potentials, Visual/physiology ; Male ; Adult ; Female ; Young Adult ; Electroencephalography ; Reproducibility of Results ; Brain/physiology ; },
abstract = {Recent advancements in helmet-type magneto-encephalography (MEG) systems that operate without liquid helium, such as optically pumped magnetometer (OPM)-based MEG, have increased interest in MEG-based brain-computer interfaces (BCIs). Among various BCI paradigms, steady-state visual evoked field (SSVEF)-based BCIs have been actively studied owing to their high information transfer rate (ITR) and low demand for calibration sessions. Although MEG provides excellent spatial resolution and whole-head coverage, conventional algorithms such as the filter bank-driven multivariate synchronization index (FBMSI) do not fully exploit these advantages. To overcome this limitation, this study employed MEG source imaging to utilize information from whole-head MEG recordings fully and developed a novel weighting method called the averaged source location-based weighting (ASLW). ASLW leverages the averaged source locations of SSVEF signals to enhance BCI performance. Experimental results with 20 participants demonstrated that integrating ASLW with the FBMSI algorithm (ASLW-FBMSI) significantly improved both the classification accuracy and ITR across all tested window sizes. Notably, the largest performance gains included a 13.9% accuracy improvement at a 3-s window size and a 13.1 bits/min increase in ITR at a 2.5-s window size. Additionally, the ASLW-FBMSI algorithm exhibited a short processing delay of 0.107 s at a 4-s data length and was successfully validated in online BCI experiments with 20 participants. Although tested in SQUID-MEG in this study, our findings demonstrate the effectiveness of ASLW in significantly enhancing the overall performance of SSVEF-based BCIs.},
}
@article {pmid40678831,
year = {2025},
author = {Wang, J and Chen, H and Wang, X},
title = {Tirzepatide Induces Ferroptosis in Glioblastoma Cell Lines via the SOX2/SLC7A11 Axis: A Potential Therapeutic Strategy for Glioma Treatment.},
journal = {Journal of biochemical and molecular toxicology},
volume = {39},
number = {8},
pages = {e70392},
doi = {10.1002/jbt.70392},
pmid = {40678831},
issn = {1099-0461},
support = {//This study was supported by the Fifth Affiliated Hospital of Zhengzhou University./ ; },
mesh = {*Ferroptosis/drug effects ; Humans ; Cell Line, Tumor ; *Amino Acid Transport System y+/metabolism ; *SOXB1 Transcription Factors/metabolism ; *Glioblastoma/metabolism/drug therapy/pathology ; Cell Proliferation/drug effects ; Lipid Peroxidation/drug effects ; *Neoplasm Proteins/metabolism ; *Brain Neoplasms/metabolism/drug therapy/pathology ; Cell Movement/drug effects ; Tirzepatide ; },
abstract = {Tirzepatide, a dual agonist for glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1) receptors used in type 2 diabetes and obesity management, was investigated for its effects on glioma cells, focusing on its potential to induce ferroptosis. Tirzepatide treatment significantly inhibited glioma cell proliferation and migration, as demonstrated by the CCK-8 and Transwell migration assays. Tirzepatide also induced lipid peroxidation, evidenced by increased ROS levels, elevated MDA production, and reduced SOD activity, while the GSH/GSSG ratio was decreased, reflecting oxidative stress. Ferroptosis was further confirmed by increased Fe[2+] concentrations and alterations in iron metabolism-related genes (Ferritin and TFR1) and lipid metabolism-related genes (ACSL4 and GPX4). Tirzepatide also inhibited the SOX2/SLC7A11 axis, which plays a critical role in resisting ferroptosis. Fer-1, a ferroptosis inhibitor, or SOX2 overexpression, markedly reduced Tirzepatide's effects on proliferation, migration, lipid peroxidation, and ferroptosis, highlighting the critical role of the SOX2/SLC7A11 axis in mediating these effects. These findings indicate that Tirzepatide inhibits glioma cell growth by inducing ferroptosis, presenting a potential therapeutic approach for glioma.},
}
@article {pmid40678346,
year = {2025},
author = {Tsay, JJ and Velez, A and Collazo, D and Laniado, I and Bessich, J and Murthy, V and DeMaio, A and Rafeq, S and Kwok, B and Darawshy, F and Pillai, R and Wong, K and Li, Y and Schluger, R and Lukovnikova, A and Roldan, S and Blaisdell, M and Paz, F and Krolikowski, K and Gershner, K and Liu, Y and Gong, J and Borghi, S and Zhou, F and Tsirigos, A and Pass, H and Segal, LN and Sterman, DH},
title = {A Phase I Dose-Escalation Clinical Trial of Bronchoscopic Cryoimmunotherapy in Advanced-Stage NSCLC.},
journal = {JTO clinical and research reports},
volume = {6},
number = {8},
pages = {100849},
pmid = {40678346},
issn = {2666-3643},
abstract = {INTRODUCTION: Outcomes for NSCLC remain suboptimal. Recent data suggest that cryoablation can generate antitumor immune effects. In this first-in-human phase I clinical trial, we investigated the safety and feasibility of bronchoscopic cryoimmunotherapy (BCI) delivered during standard-of-care bronchoscopy and explored associated systemic immune responses.<br><br>METHODS: Subjects with known or suspected advanced-stage NSCLC were recruited. BCI was delivered in dose-escalated freeze-thaw cycles to determine maximum dose tolerance. Feasibility assessment was determined with a pre-set goal of achieving successful BCI in more than or equal to 80% of subjects. Safety was assessed by review of BCI-related complications, including grades 2 to 3 bleeding, pneumothorax requiring intervention, and National Cancer Institute Common Terminology Criteria for Adverse Events grade 3 to 5 adverse events. Pre- and post-BCI blood samples were collected to explore changes in the systemic immune profile.<br><br>RESULTS: Subjects with predominantly clinical TNM stage 3 or 4 adenocarcinoma or squamous cell carcinoma were enrolled. We reached the maximum dose of 30 seconds with 100% feasibility and no BCI-related adverse events. In peripheral blood analysis, we observed a significant decrease in derived neutrophil-to-lymphocyte ratio in the high-dose BCI group in comparison to the low-dose BCI cohort. We also observed increases in inflammatory cytokines-GM-CSF, IFN-&#x3b3;, IL-1&#x3b2;, IL-17A, and IL-2-and effector memory T cells post-BCI.<br><br>CONCLUSION: BCI is safe and feasible. In addition, we provide preliminary evidence that at higher dose levels there is a systemic immune response consistent with a cytotoxic profile. Further immune analyses will determine the potential of BCI as an adjunctive therapy in combination with immune checkpoint inhibition in NSCLC treatment.},
}
@article {pmid40677333,
year = {2025},
author = {Zheng, ZW and Xu, MH and Fan, LN and Wang, RM and Xu, WQ and Yang, GM and Guo, LY and Liu, C and Dong, Y and Wu, ZY},
title = {Renal Impairment in Wilson's Disease.},
journal = {Kidney international reports},
volume = {10},
number = {7},
pages = {2453-2456},
pmid = {40677333},
issn = {2468-0249},
}
@article {pmid40674496,
year = {2025},
author = {Nair, A},
title = {Unraveling the emergent chorus of the mind: Machine learning reveals how a hidden neural code orchestrates diverse emotion states.},
journal = {Science (New York, N.Y.)},
volume = {389},
number = {6757},
pages = {245},
doi = {10.1126/science.adx7811},
pmid = {40674496},
issn = {1095-9203},
mesh = {*Emotions/physiology ; *Machine Learning ; Humans ; *Brain/physiology ; *Neurons/physiology ; },
abstract = {Machine learning reveals how a hidden neural code orchestrates diverse emotion states.},
}
@article {pmid40672704,
year = {2025},
author = {Rekrut, M and Ihl, J and Jungbluth, T and Krüger, A},
title = {How low can you go: evaluating electrode reduction methods for EEG-based speech imagery BCIs.},
journal = {Frontiers in neuroergonomics},
volume = {6},
number = {},
pages = {1578586},
pmid = {40672704},
issn = {2673-6195},
abstract = {Speech imagery brain-computer interfaces (SI-BCIs) aim to decode imagined speech from brain activity and have been successfully established using non-invasive brain measures such as electroencephalography (EEG). However, current EEG-based SI-BCIs predominantly rely on high-resolution systems with 64 or more electrodes, making them cumbersome to set up and impractical for real-world use. In this study, we evaluated several electrode reduction algorithms in combination with various feature extraction and classification methods across three distinct EEG-based speech imagery datasets to identify the optimal number and position of electrodes for SI-BCIs. Our results showed that, across all datasets, the original 64 channels could be reduced by 50% without a significant performance loss in classification accuracy. Furthermore, the relevant areas were not limited to the left hemisphere, widely known to be responsible for speech production and comprehension, but were distributed across the cortex. However, we could not identify a consistent set of optimal electrode positions across datasets, indicating that electrode configurations are highly subject-specific and should be individually tailored. Nonetheless, our findings support the move away from extensive and costly high-resolution systems toward more compact, user-specific setups, facilitating the transition of SI-BCIs from laboratory settings to real-world applications.},
}
@article {pmid40672675,
year = {2025},
author = {Zhang, C and Wang, Y and Wang, X},
title = {Reimagining Neuropsychiatric and Neurological Disorders through the Lens of Brain Network Dynamics: Psychedelics as Catalysts for System-Level Plasticity.},
journal = {ACS pharmacology &amp; translational science},
volume = {8},
number = {7},
pages = {2308-2311},
pmid = {40672675},
issn = {2575-9108},
abstract = {Neuropsychiatric disorders reflect disruptions in brain network dynamics along an "order-complexity-chaos" continuum. Psychedelics may therapeutically increase neural entropy, disrupt maladaptive patterns, and promote network reorganization. This system-level framework emphasizes dynamic connectome remodeling over static molecular correction, offering a novel strategy for treating psychiatric and neurological conditions through controlled neural destabilization and reconnection.},
}
@article {pmid40672502,
year = {2025},
author = {Chaichanasittikarn, O and Diaz, L and Thomas, N and Candrea, D and Luo, S and Nathan, K and Tenore, FV and Fifer, MS and Crone, NE and Christie, B and Osborn, LE},
title = {High-gamma electrocorticography activity represents perceived vibration intensity in human somatosensory cortex.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
pmid = {40672502},
support = {UH3 NS114439/NS/NINDS NIH HHS/United States ; },
abstract = {Haptic feedback can play a useful role in rehabilitation and brain-computer interface applications by providing users with information about their system or performance. One challenge delivering tactile stimulation is not knowing how the haptic sensation is actually perceived, irrespective of the stimulation amplitude, during real-world use and beyond controlled psychophysical experiments. In a participant with chronically implanted electrocorticography arrays, we observed that perceived intensity of haptic vibration on the fingertips was represented in the high-gamma (HG) frequency band (70-170 Hz) in the somatosensory cortex. The five fingers of the participant's right hand were represented by distinct channels in the implanted array and modulated by the vibration amplitude at the fingertips. Although it reliably varied with the vibration amplitude, we found that HG activity had a stronger relationship with the actual perceived intensity of haptic stimulation (r s = 0.45, p < 10 [-6]). These results demonstrate that neural signals, specifically HG activity, in the somatosensory cortex can represent qualities of perceived haptic intensity regardless of the stimulation amplitude, which could enable a new way to passively quantify or ensure effective haptic feedback to a user.},
}
@article {pmid40672280,
year = {2025},
author = {Lei, T and Scheid, MR and Glaser, JI and Slutzky, MW},
title = {Active Dissociation of Intracortical Spiking and High Gamma Activity.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {40672280},
issn = {2692-8205},
support = {R00 NS119787/NS/NINDS NIH HHS/United States ; R01 NS094748/NS/NINDS NIH HHS/United States ; R01 NS112942/NS/NINDS NIH HHS/United States ; RF1 NS125026/NS/NINDS NIH HHS/United States ; K08 NS060223/NS/NINDS NIH HHS/United States ; T32 NS047987/NS/NINDS NIH HHS/United States ; R01 NS099210/NS/NINDS NIH HHS/United States ; T32 EB009406/EB/NIBIB NIH HHS/United States ; },
abstract = {Cortical high gamma activity (HGA) is used in many scientific investigations, yet its biophysical source is a matter of debate. Two leading hypotheses are that HGA predominantly represents summed postsynaptic potentials or-more commonly- predominantly represents summed local spikes. If the latter were true, the nearest neurons to an electrode should contribute most to HGA recorded on that electrode. We trained subjects to decouple spiking from HGA on a single electrode using a brain-machine interface. Their ability to decouple them indicated that HGA is not primarily generated by summed local spiking. Instead, HGA correlated with neuronal population co-firing of neurons that were widely distributed across millimeters. The neuronal spikes that contributed more to this co-firing also contributed more to, and preceded, spike-triggered HGA. These results suggest that HGA arises predominantly from summed postsynaptic potentials triggered by synchronous co-firing of widely distributed neurons.},
}
@article {pmid40668700,
year = {2025},
author = {Robinson, JT},
title = {Making Heads and Tails of the Coming Era of Neural Devices, Could Moore's Law Address the Declining Mental Health Trend.},
journal = {IEEE pulse},
volume = {16},
number = {3},
pages = {50-55},
doi = {10.1109/MPULS.2025.3572593},
pmid = {40668700},
issn = {2154-2317},
mesh = {Humans ; *Brain-Computer Interfaces/trends ; *Mental Health ; *Transcranial Magnetic Stimulation ; },
abstract = {Despite major advances in medicine and technology, mental health outcomes have declined globally over the past several decades. Fortunately we are in the early phases of exponential growth neurotech similar to Moore's Law. These emerging neural devices may provide a solution to the growing mental health crisis. Clinical data shows promising outcomes from technologies such as transcranial magnetic stimulation (TMS) leading to exponential improvement in performance improvements and cost reductions. As a result, neurotechnology could follow a similar path to personal computing going from a handful of niche markets to ubiquity over the next decade. Indeed, next generation therapeutic brain-computer interfaces (BCIs)-particularly minimally invasive implants-could become mass-market solutions for regulating mental states. The future may be one where neural devices help individuals thrive in an increasingly complex world, not by augmenting human intelligence but by enhancing emotional well-being and preserving the most precious aspects of our humanity.},
}
@article {pmid40668693,
year = {2025},
author = {Banks, J},
title = {Silicon Synapses: The Bold Frontier of Brain-Computer Integration.},
journal = {IEEE pulse},
volume = {16},
number = {3},
pages = {5-9},
doi = {10.1109/MPULS.2025.3572569},
pmid = {40668693},
issn = {2154-2317},
mesh = {*Brain-Computer Interfaces ; Humans ; *Silicon ; *Synapses/physiology ; Spinal Cord Injuries ; *Brain/physiology ; },
abstract = {The allure of Neuralink is attracting investors to funnel money into the development of brain-computer interface (BCI) technology, primarily aimed at treating spinal cord injury (SCI) patients. But what is the payoff? Jim Banks examines the inspired innovation in BCI that is reestablishing connections for patients with the world.},
}
@article {pmid40668691,
year = {2025},
author = {Goktas, P and Tun, NN},
title = {EEG-Based Brain-Computer Interfaces: Pioneering Frontier Research in the 21st Century.},
journal = {IEEE pulse},
volume = {16},
number = {3},
pages = {36-39},
doi = {10.1109/MPULS.2025.3572556},
pmid = {40668691},
issn = {2154-2317},
mesh = {*Brain-Computer Interfaces/trends ; Humans ; *Electroencephalography/methods/trends ; Artificial Intelligence ; *Signal Processing, Computer-Assisted ; Brain/physiology ; },
abstract = {Electroencephalography (EEG)-based brain-computer interface (BCI) systems are inevitably needed to set up non-invasive therapies in neurorehabilitation. Along with the artificial intelligence (AI) techniques trending, constructing EEG-based brain computer interfaces is still in demand with high classification accuracy for advancing the state-of-the-art BCIs. From the perspective of pioneering frontier research, this article highlights the 21st-century's EEG-based BCI systems, their challenges, and its future direction for neuroscientists and clinical applications.},
}
@article {pmid40668688,
year = {2025},
author = {Zaman, MH},
title = {The Potential of Brain-Computer Interface Technologies in Low- and Middle-Income Countries Global Health Perspective.},
journal = {IEEE pulse},
volume = {16},
number = {3},
pages = {40-42},
doi = {10.1109/MPULS.2025.3572574},
pmid = {40668688},
issn = {2154-2317},
mesh = {*Brain-Computer Interfaces/economics ; Humans ; *Developing Countries ; *Global Health ; },
abstract = {Historically, brain-computer interface (BCI) technologies have almost exclusively been available in high-income countries. What would it take for them to become more available and accessible in low- and middle-income countries, and in complex settings?},
}
@article {pmid40668686,
year = {2025},
author = {Grifantini, K},
title = {From Headsets to Healing: The Rise of Wearable Brain Tech and Its Impact on Mental Illness and Cognitive Health.},
journal = {IEEE pulse},
volume = {16},
number = {3},
pages = {25-29},
doi = {10.1109/MPULS.2025.3572580},
pmid = {40668686},
issn = {2154-2317},
mesh = {Humans ; *Brain-Computer Interfaces ; *Cognition/physiology ; *Mental Disorders/therapy ; Mental Health ; *Wearable Electronic Devices ; },
abstract = {The rapidly evolving field of noninvasive brain-machine interfaces (BMIs) is transforming wearable technology from science fiction into a powerful tool for health care, offering a surgery-free and drug-free alternative to traditional treatments. Such devices are currently being used to target conditions such as depression, anxiety, PTSD, insomnia and more through targeted neurostimulation techniques.},
}
@article {pmid40668685,
year = {2025},
author = {Bates, M},
title = {Why Consumer Neurofeedback Devices Are More Than Hype for Brain Health.},
journal = {IEEE pulse},
volume = {16},
number = {3},
pages = {21-24},
doi = {10.1109/MPULS.2025.3572577},
pmid = {40668685},
issn = {2154-2317},
mesh = {Humans ; *Neurofeedback/instrumentation ; *Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography ; },
abstract = {Neurofeedback uses a brain-computer interface to measure a person's brain activity and show it to them in real time. A number of companies offer neurofeedback devices directly to consumers, with promises of improving meditation and enhancing concentration. However, whether neurofeedback is actually effective remains controversial among researchers.},
}
@article {pmid40668684,
year = {2025},
author = {Anderson, C},
title = {Industry Corner Live With Synchron CEO Tom Oxley.},
journal = {IEEE pulse},
volume = {16},
number = {3},
pages = {43-49},
doi = {10.1109/MPULS.2025.3572578},
pmid = {40668684},
issn = {2154-2317},
mesh = {Humans ; Artificial Intelligence ; *Biomedical Engineering ; *Brain-Computer Interfaces ; },
abstract = {Pulse's Industry Corner Live featured a dynamic live Q&amp;A session between IEEE Pulse Editor-in-Chief Chad Andresen and Dr. Tom Oxley, CEO and co-founder of Synchron, a leader in minimally invasive brain-computer interface (BCI) technology. The discussion explored the intersection of neurotechnology, artificial intelligence, and the evolving landscape of entrepreneurship in the MedTech sector. Dr. Oxley shared insights into Synchron's pioneering work with endovascular BCIs, offering a less invasive alternative to traditional neurosurgical approaches, and how this technology is reshaping the possibilities for restoring communication in patients with paralysis. The conversation delved into the growing role of AI in decoding neural signals and driving clinical translation, while also addressing the regulatory, financial, and ethical challenges faced by entrepreneurs in the neurotechnology space. With candid reflections on his journey from clinician to startup founder, Oxley provided an inside look at what it takes to bring disruptive technologies from concept to clinic. This session offered a rare glimpse into the mindset of a neurotech innovator navigating the high-stakes interface of science, medicine, and industry.},
}
@article {pmid40668677,
year = {2025},
author = {Sorrell, E and Wilson, DE and Rule, ME and Yang, H and Forni, F and Harvey, CD and O'Leary, T},
title = {An optical brain-machine interface reveals a causal role of posterior parietal cortex in goal-directed navigation.},
journal = {Cell reports},
volume = {44},
number = {7},
pages = {115862},
doi = {10.1016/j.celrep.2025.115862},
pmid = {40668677},
issn = {2211-1247},
mesh = {Animals ; *Brain-Computer Interfaces ; *Parietal Lobe/physiology ; Mice ; *Goals ; *Spatial Navigation/physiology ; Male ; Mice, Inbred C57BL ; Virtual Reality ; },
abstract = {Cortical circuits contain diverse sensory, motor, and cognitive signals, and they form densely recurrent networks. This creates challenges for identifying causal relationships between neural populations and behavior. We develop a calcium-imaging-based brain-machine interface (BMI) to study the role of posterior parietal cortex (PPC) in controlling navigation in virtual reality. By training a decoder to estimate navigational heading and velocity from PPC activity during virtual navigation, we find that mice can immediately navigate toward goal locations when control is switched to the BMI. No learning or adaptation is observed during BMI, indicating that naturally occurring PPC activity patterns are sufficient to drive navigational trajectories in real time. During successful BMI trials, decoded trajectories decouple from the mouse's physical movements, suggesting that PPC activity relates to intended trajectories. Our work demonstrates a role for PPC in navigation and offers a BMI approach for investigating causal links between neural activity and behavior.},
}
@article {pmid40667422,
year = {2025},
author = {Schroeder, F and Fairclough, S and Dehais, F and Richins, M},
title = {The impact of cross-validation choices on pBCI classification metrics: lessons for transparent reporting.},
journal = {Frontiers in neuroergonomics},
volume = {6},
number = {},
pages = {1582724},
pmid = {40667422},
issn = {2673-6195},
abstract = {Neuroadaptive technologies are a type of passive Brain-computer interface (pBCI) that aim to incorporate implicit user-state information into human-machine interactions by monitoring neurophysiological signals. Evaluating machine learning and signal processing approaches represents a core aspect of research into neuroadaptive technologies. These evaluations are often conducted under controlled laboratory settings and offline, where exhaustive analyses are possible. However, the manner in which classifiers are evaluated offline has been shown to impact reported accuracy levels, possibly biasing conclusions. In the current study, we investigated one of these sources of bias, the choice of cross-validation scheme, which is often not reported in sufficient detail. Across three independent electroencephalography (EEG) n-back datasets and 74 participants, we show how metrics and conclusions based on the same data can diverge with different cross-validation choices. A comparison of cross-validation schemes in which train and test subset boundaries either respect the block-structure of the data collection or not, illustrated how the relative performance of classifiers varies significantly with the evaluation method used. By computing bootstrapped 95% confidence intervals of differences across datasets, we showed that classification accuracies of Riemannian minimum distance (RMDM) classifiers may differ by up to 12.7% while those of a Filter Bank Common Spatial Pattern (FBCSP) based linear discriminant analysis (LDA) may differ by up to 30.4%. These differences across cross-validation implementations may impact the conclusions presented in research papers, which can complicate efforts to foster reproducibility. Our results exemplify why detailed reporting on data splitting procedures should become common practice.},
}
@article {pmid40667167,
year = {2025},
author = {Sun, L and Qin, W and Liang, X and Wang, C and Men, W and Duan, Y and Fan, XR and Cai, Q and Qiu, S and Wang, M and Gong, Q and Tian, Y and Liang, P and Liu, Z and Zhang, X and Song, H and Ye, Z and Zhang, P and Dong, Q and Tao, S and Zhu, W and Zhang, J and Xie, F and Feng, J and Zhang, J and Liu, C and Qian, Q and Zhang, B and Meng, M and Hu, L and Gao, JH and Jiang, T and Zhu, X and Zhang, Y and Liu, L and Liu, H and Liao, W and Wang, D and Wang, H and Guo, T and Dai, Z and Lui, S and Xu, K and Li, L and Xie, P and Feng, C and Cui, G and Wu, J and Yin, X and Ding, G and Xian, J and Zhao, L and Lu, J and Liu, Z and Han, Y and Yuan, Z and Zhang, X and Si, T and Zhou, F and Bi, Y and Wu, D and Gao, F and Wang, F and Qin, S and Wang, G and Chen, F and Zhang, Z and Sui, J and Chen, H and Cai, J and Liu, S and Geng, Z and Zhang, C and Mao, N and Yin, H and Liu, B and Ma, H and Gao, B and Miao, Y and Kong, XZ and Zhou, Y and Liu, L and Hu, J and Wang, L and Zhang, Q and Shu, H and Wang, P and Lee, TMC and Cao, Q and Yang, L and Zhang, X and Luo, W and Liang, M and Yao, H and Li, M and Huang, H and Peng, Y and Han, Z and Zhou, C and Xu, H and Feng, M and Shen, W and Hu, Y and Chen, H and Wang, Y and Gong, G and Yan, Z and Xu, X and Liu, J and Chen, G and Wang, P and Yang, Y and Yao, D and Han, T and He, H and Chen, C and Zou, Q and Liu, H and Zhang, H and Chai, C and Lu, C and Tu, Y and Liu, Y and Lin, D and Zhao, W and Xu, X and Liu, X and Cui, Z and Wang, Z and Huang, R and Li, Z and Liu, Y and Li, X and Yang, X and Zhang, N and Chen, A and Zhang, B and Qin, P and Liu, C and Yao, Z and Wei, Y and Yuan, H and Wang, F and Zhang, Y and Zhang, Q and Hu, F and Xie, H and Wu, X and Wang, J and Fan, G and Wang, Z and Zhang, D and Zhong, H and Wang, Y and Bai, L and Li, Y and Wei, X and Wang, J and Zhang, Y and He, H and Li, S and Zhang, T and Jiang, F and Yang, J and Chen, F and Liu, F and Liu, H and Chen, N and Yang, J and Hou, B and Huang, CC and Zhu, J and Cai, H and Wei, D and Chen, Q and Wei, Y and Miao, P and Li, Y and Liu, Y and Yang, N and Gao, X and Liu, Y and Shen, Y and Huang, X and Ji, GJ and , and Zhang, L and Qiu, J and Yu, Y and Lin, CP and Feng, F and Li, K and Yu, C and He, Y},
title = {Population-specific brain charts reveal Chinese-Western differences in neurodevelopmental trajectories.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {40667167},
issn = {2692-8205},
support = {U24 DA041147/DA/NIDA NIH HHS/United States ; U01 DA051039/DA/NIDA NIH HHS/United States ; U01 DA041120/DA/NIDA NIH HHS/United States ; U01 DA051018/DA/NIDA NIH HHS/United States ; U01 AG024904/AG/NIA NIH HHS/United States ; U24 DA041123/DA/NIDA NIH HHS/United States ; U01 DA051037/DA/NIDA NIH HHS/United States ; U01 DA051016/DA/NIDA NIH HHS/United States ; U01 DA041106/DA/NIDA NIH HHS/United States ; U01 DA041148/DA/NIDA NIH HHS/United States ; U01 MH110274/MH/NIMH NIH HHS/United States ; P50 MH086385/MH/NIMH NIH HHS/United States ; U01 DA041174/DA/NIDA NIH HHS/United States ; U01 DA041093/DA/NIDA NIH HHS/United States ; U01 MH109589/MH/NIMH NIH HHS/United States ; U01 DA051038/DA/NIDA NIH HHS/United States ; R21 MH107045/MH/NIMH NIH HHS/United States ; U01 DA041134/DA/NIDA NIH HHS/United States ; U01 DA041022/DA/NIDA NIH HHS/United States ; U01 DA041156/DA/NIDA NIH HHS/United States ; U01 DA050987/DA/NIDA NIH HHS/United States ; U01 DA041025/DA/NIDA NIH HHS/United States ; U01 DA050989/DA/NIDA NIH HHS/United States ; U54 MH091657/MH/NIMH NIH HHS/United States ; U01 DA041089/DA/NIDA NIH HHS/United States ; U01 DA050988/DA/NIDA NIH HHS/United States ; R03 MH096321/MH/NIMH NIH HHS/United States ; U01 DA041117/DA/NIDA NIH HHS/United States ; U01 DA041028/DA/NIDA NIH HHS/United States ; U01 DA041048/DA/NIDA NIH HHS/United States ; K23 MH087770/MH/NIMH NIH HHS/United States ; /WT_/Wellcome Trust/United Kingdom ; },
abstract = {Human brain charts provide unprecedented opportunities for decoding neurodevelopmental milestones and establishing clinical benchmarks for precision brain medicine [1-7]. However, current lifespan brain charts are primarily derived from European and North American cohorts, with Asian populations severely underrepresented. Here, we present the first population-specific brain charts for China, developed through the Chinese Lifespan Brain Mapping Consortium (Phase I) using neuroimaging data from 43,037 participants (aged 0-100 years) across 384 sites nationwide. We establish the lifespan normative trajectories for 296 structural brain phenotypes, encompassing global, subcortical, and cortical measures. Cross-population comparisons with Western brain charts (based on data from 56,339 participants aged 0-100 years) reveal distinct neurodevelopmental patterns in the Chinese population, including prolonged cortical and subcortical maturation, accelerated cerebellar growth, and earlier development of sensorimotor regions relative to paralimbic regions. Crucially, these Chinese-specific charts outperform Western-derived models in predicting healthy brain phenotypes and detecting pathological deviations in Chinese clinical cohorts. These findings highlight the urgent need for diverse, population-representative brain charts to advance equitable precision neuroscience and improve clinical validity across populations.},
}
@article {pmid40666891,
year = {2025},
author = {Wang, Y and Cheng, L and Li, D and Lu, Y and Hopkins, WD and Sherwood, CC and Xu, T and Liu, C and Paxinos, G and Jiang, T and Chu, C and Fan, L},
title = {Evolutionary Convergence of the Arcuate Fasciculus in Marmosets and Humans.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {40666891},
issn = {2692-8205},
support = {R24 NS092988/NS/NINDS NIH HHS/United States ; P41 EB015897/EB/NIBIB NIH HHS/United States ; U54 MH091657/MH/NIMH NIH HHS/United States ; R01 AG067419/AG/NIA NIH HHS/United States ; R01 AG087945/AG/NIA NIH HHS/United States ; R01 HG011641/HG/NHGRI NIH HHS/United States ; },
abstract = {The marmoset is a highly vocal platyrrhine monkey that shares key anatomical and functional features with humans, offering insights into the evolution of brain connectivity. Although similarities in vocalization features with humans have been reported, it remains unclear whether marmosets possess an arcuate fasciculus (af) homolog. This study delineated white matter tracts in marmosets, establishing homologies with those observed in other primates, including macaques, chimpanzees, and humans. The presence of an af homolog in marmosets was confirmed by tracer and ultra-high-resolution diffusion magnetic resonance imaging datasets. We compared cortical connectivity patterns across these species and found the af in marmosets terminates in the ventral frontal cortex, with greater similarity to humans than macaques. Furthermore, we linked af connectivity with vocalization-related brain activation in both marmosets and humans. Collectively, our findings suggest that a dorsal pathway, which emerged early in marmoset evolution, has evolved convergently with humans, despite their distant phylogenetic kinship.},
}
@article {pmid40666829,
year = {2025},
author = {Bo, W and Che, R and Jia, F and Sun, K and Liu, Q and Guo, L and Zhang, X and Gong, Y},
title = {Study on the Effect of the Envelope of Terahertz Unipolar Stimulation on Cell Membrane Communication-Related Variables.},
journal = {Research (Washington, D.C.)},
volume = {8},
number = {},
pages = {0755},
pmid = {40666829},
issn = {2639-5274},
abstract = {The development of terahertz science and technology has shown new application prospects in artificial intelligence. Terahertz stimulation can lead to information communication of cells. Terahertz unipolar picosecond pulse train stimulation can activate cell membrane hydrophilic pores and protein ion channels. However, the effect of the envelope of the terahertz unipolar stimulation remains unknown. This paper studies the effect of the envelope on membrane communication-related variables and the accompanying energy consumption by a cell model with considerations of hydrophilic pores and Na[+], K[+]-ATPase. According to the results, terahertz unipolar picosecond pulse train stimulation can deliver the signal contained in its envelope into the variation rates of membrane potentials no matter whether the hydrophilic pores are activated or not and also into the variation rates of the ion flow via the pores after activation of the pores. In contrast, the ion flow via Na[+], K[+]-ATPase seems irrelevant to the signal in the envelope. Moreover, the ion flows show a modulation effect on the variation rates of membrane potentials. The accompanying power dissipations in the cases of different envelopes are similar, as low as around the level of 10[-11] W. The results lay the foundations for application in artificial intelligence, like brain-machine communications.},
}
@article {pmid40664224,
year = {2025},
author = {Wang, X and Meng, J and Zheng, Y and Wei, Y and Wang, F and Ding, H and Zhuo, Y},
title = {Characterizing the neural representations and decoding performance of foot rhythmic motor execution or imagery guided by action observation.},
journal = {Journal of neural engineering},
volume = {22},
number = {4},
pages = {},
doi = {10.1088/1741-2552/adf011},
pmid = {40664224},
issn = {1741-2552},
mesh = {Humans ; Male ; Female ; Magnetoencephalography/methods ; *Imagination/physiology ; Adult ; Young Adult ; *Foot/physiology ; Electroencephalography/methods ; *Psychomotor Performance/physiology ; Brain-Computer Interfaces ; Movement/physiology ; *Periodicity ; },
abstract = {Objective. The limited spatial resolution inherent in electroencephalography (EEG), a widely-adopted non-invasive neuroimaging technique, combined with the intrinsic complexity of performing unilateral lower-limb motor imagery (MI), restricts decoding accuracy. To address these challenges, we propose a paradigm based on action observation-guided rhythmic motor execution (AO-ME) and motor imagery (AO-MI), designed to simplify task demands and enhance decoding performance. Magnetoencephalography (MEG) serves as the data acquisition method, leveraging its superior spatiotemporal resolution.Approach. Spatiotemporal and spectral features were characterized at the sensor level, and source imaging techniques were employed to examine cortical activation patterns. Ensemble task-related component analysis (eTRCA) facilitated decoding of unilateral tasks. And multiple decoding algorithms were employed to validate the effectiveness of the proposed paradigm.Main results. Robust lateralized neural responses were observed, exhibiting low-frequency phase-locked components that distinctly reflected the task frequency and its second harmonic within sensorimotor, parietal, and occipital cortices. Moreover, significant contralateral suppression of the sensorimotor rhythm was observed. Decoding accuracies reached 95.22 ± 4.75% for AO-ME and 88.66 ± 8.52% for AO-MI across twenty participants based on the phase-locked features using eTRCA.Significance. Collectively, our findings demonstrate that the proposed paradigm provides an effective approach for eliciting robust, distinguishable neural responses, enabling high decoding performance of unilateral lower-limb movements. This work offers new insights into the underexplored domain of lower-limb MI and highlights the paradigm's potential for brain-computer interface applications.},
}
@article {pmid40664221,
year = {2025},
author = {Jochumsen, M and Petersen, BS and Vestergaard, LM and Falborg, NF and Wisler, L and Olesen, MV and Andersen, MS and Sørensen, NB and Jørgensen, ST},
title = {Detection of movement-related cortical potentials associated with upper and low limb movements in patients with multiple sclerosis for brain-computer interfacing.},
journal = {Journal of neural engineering},
volume = {22},
number = {4},
pages = {},
doi = {10.1088/1741-2552/adf010},
pmid = {40664221},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; *Multiple Sclerosis/physiopathology/diagnosis ; Male ; Female ; Middle Aged ; Movement/physiology ; Adult ; Electroencephalography/methods ; *Upper Extremity/physiology/physiopathology ; *Lower Extremity/physiology/physiopathology ; *Evoked Potentials, Motor/physiology ; },
abstract = {Objectives.Brain-computer interface (BCI) training has been shown to be effective for inducing neural plasticity and for improving motor function in stroke patients. BCI training could potentially have a positive effect on people with multiple sclerosis (MS) as well by pairing movement-related brain activity with congruent afferent feedback from e.g. functional electrical stimulation. In the current study, the aim was to detect movement-related cortical potentials (MRCPs) from single-trial EEG in people with MS across two separate days using different classifier calibration schemes to estimate the performance of a BCI that can be used for neurorehabilitation.Approach.Fifteen individuals with MS performed 100 wrist movements and 100 ankle movements while continuous EEG was recorded. Also, idle brain activity was recorded. This was repeated on a separate day. The data were filtered and divided into epochs containing data prior to the movement onset. Temporal, spectral and template matching features were extracted and classified with a random forest classifier using different calibration schemes to estimate the performance when training the classifier on data from the same day and same participant, different day but same participant, and across different participants.Main Results.Clear MRCPs were elicited across both recording days, and it was possible to discriminate between idle activity and movement-related brain activity with accuracies between ∼80%-90% when training and testing the classifier on data from the same day and participant. The performance decreased when using data from a separate day but same participant (∼70%-80%) or data from separate participants (∼70%) for training the classifier.Significance.The results showed that it is feasible for people with MS to use a BCI for inducing neural plasticity.},
}
@article {pmid40663645,
year = {2025},
author = {Wang, A and Lin, C and Mao, W and Jin, J},
title = {More generosity, less inequity aversion? Neural correlates of fairness perception under social distance and of its relation to generosity.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {35},
number = {7},
pages = {},
doi = {10.1093/cercor/bhaf152},
pmid = {40663645},
issn = {1460-2199},
support = {//Shanghai Philosophy and Social Sciences Planning Project/ ; //National Nature Science Foundation of China/ ; },
mesh = {Humans ; Male ; Female ; Young Adult ; Electroencephalography ; Adult ; *Psychological Distance ; *Social Behavior ; *Social Perception ; *Brain/physiology ; Interpersonal Relations ; Games, Experimental ; *Altruism ; Adolescent ; },
abstract = {Humans instinctively react negatively to inequity, while generosity counters this tendency. Previous studies show that both fairness perception and generosity involve balancing behaviors and motivations in social interactions. However, their relationship remains underexplored, limiting our understanding of the complex psychological processes underlying social behavior. Using a social discounting task, we assessed individual generosity, while an Ultimatum Game task with concurrent electroencephalogram recording allowed us to quantify inequity aversion and fairness perception by manipulating social distance and inequity levels. We found that both generosity and fairness perception decrease with increasing social distance, whereas inequity aversion increases. Modeling the decay of generosity across social distances, we found that decayed generosity was positively associated with inequity aversion in the friend condition and negatively correlated with the attenuation of fairness perception. These results suggest that the decay of generosity with social distance is linked to reduced sensitivity to inequity toward friends and heightened neural differences in fairness perception across social relationships. Our study provides electrophysiological evidence of individual variability in generosity and inequity aversion influenced by social distance, expanding inequity aversion theory.},
}
@article {pmid40662561,
year = {2025},
author = {Vikal, A and Maurya, R and Patel, BB and Patel, P and Kumar, M and Kurmi, BD},
title = {A Mini-Review on Unlocking Cognitive Enhancement: An Innovative Strategy for Optimal Brain Functions.},
journal = {Central nervous system agents in medicinal chemistry},
volume = {},
number = {},
pages = {},
doi = {10.2174/0118715249357704250702140026},
pmid = {40662561},
issn = {1875-6166},
abstract = {Cognitive enhancement, aimed at improving or preserving memory, attention, and executive functions, has gained significant interest from both the scientific community and the public. This review explores various strategies for enhancing cognitive function, including natural compounds, synthetic enhancers, and behavioural approaches. Natural compounds like curcumin, Ginkgo biloba, Panax ginseng, and Rhodiola rosea are examined for their cognitive benefits, with ongoing research on their mechanisms and potential nanoformulation-based drug delivery. Synthetic enhancers such as Modafinil, Piracetam, Methylphenidate, and Noopept show promise in improving cognitive functions. Additionally, substances influencing brain metabolism, like Creatine and Coenzyme Q10, are discussed. Behavioural interventions, including sleep optimization, meditation, and physical exercise, are evaluated for their cognitive-enhancing effects. Noninvasive brain stimulation techniques, such as TMS and tDCS, along with innovative methods like whole-body vibration and brain-machine interfaces, are also explored. The review emphasizes the complex interplay of these strategies and the need for continued research to fully exploit their potential. By highlighting natural compounds, synthetic drugs, and behavioural approaches, the review advocates for a multifaceted approach to cognitive enhancement and calls for more detailed and longitudinal studies to understand their long-term benefits and mechanisms.},
}
@article {pmid40661574,
year = {2025},
author = {Hou, X and Iacobacci, C and Card, NS and Wairagkar, M and Singer-Clark, T and Kunz, EM and Fan, C and Kamdar, F and Hahn, N and Hochberg, LR and Henderson, JM and Willett, FR and Brandman, DM and Stavisky, SD},
title = {Error encoding in human speech motor cortex.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {40661574},
issn = {2692-8205},
support = {DP2 DC021055/DC/NIDCD NIH HHS/United States ; },
abstract = {Humans monitor their actions, including detecting errors during speech production. This self-monitoring capability also enables speech neuroprosthesis users to recognize mistakes in decoded output upon receiving visual or auditory feedback. However, it remains unknown whether neural activity related to error detection is present in the speech motor cortex. In this study, we demonstrate the existence of neural error signals in speech motor cortex firing rates during intracortical brain-to-text speech neuroprosthesis use. This activity could be decoded to enable the neuroprosthesis to identify its own errors with up to 86% accuracy. Additionally, we observed distinct neural patterns associated with specific types of mistakes, such as phonemic or semantic differences between the person's intended and displayed words. These findings reveal how feedback errors are represented within the speech motor cortex, and suggest strategies for leveraging these additional cognitive signals to improve neuroprostheses.},
}
@article {pmid40660069,
year = {2025},
author = {Jeppsen, C and McMurray, B},
title = {Reduced Cochlear Implant Performance in Listeners with Single-Sided Deafness: Comparison with Bilateral Listeners.},
journal = {Journal of the Association for Research in Otolaryngology : JARO},
volume = {26},
number = {4},
pages = {477-489},
pmid = {40660069},
issn = {1438-7573},
support = {P50 DC000242/DC/NIDCD NIH HHS/United States ; R01 DC008089/DC/NIDCD NIH HHS/United States ; P50 DC00242//Foundation for the National Institutes of Health/ ; },
mesh = {Humans ; Female ; Male ; *Cochlear Implants ; Middle Aged ; *Speech Perception ; Adult ; Aged ; *Deafness ; *Hearing Loss, Unilateral ; },
abstract = {PURPOSE: The efficacy of the Cochlear Implant (CI) in listeners with single-sided deafness (SSD) was evaluated by comparing single-ear speech perception in SSD listeners and bilateral cochlear implant listeners (BCI).<br><br>METHODS: Consonant-nucleus-consonant (CNC) speech perception scores for the CI-only ear in SSD listeners (N&#x2009;=&#x2009;55; 36 female, 19 male) were compared to single-ear performance in age and device experience-matched BCI listeners (N&#x2009;=&#x2009;55; 29 female, 26 male). Separate analyses examined: (1) a matched ear from the BCI listeners (for sequentially implanted BCI listeners, the first-implanted ear in sequential BCI listeners, or, for simultaneously implanted BCI listeners, the ear on the same side as the CI in the matching SSD listener), and (2) the lower-performing ear across BCI listeners. Additional models included moderators such as age, time since activation, CI usage, and etiology. A final analysis compared first and second implants for sequential BCI listeners.<br><br>RESULTS: SSD listeners showed significantly lower CNC performance after controlling for age, time since activation, CI usage, and etiology. Sequential BCI listeners exhibited significantly lower CNC performance on their second ear, compared to their first ear.<br><br>CONCLUSION: Speech perception with CIs is reduced in SSD listeners compared to BCI users, likely due to blocking, where the normal-hearing ear diminishes reliance on the CI. Lower performance in the second implanted ear of sequential BCI listeners also suggests greater reliance on the more experienced ear. These findings highlight the need for additional training, resources, and support to optimize CI performance in SSD listeners, despite prior evidence of positive CNC outcomes.},
}
@article {pmid40659530,
year = {2025},
author = {Hu, Y and Ma, B and Jin, J},
title = {Neural Synchrony and Consumer Behavior: Predicting Friends' Behavior in Real-World Social Networks.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {45},
number = {32},
pages = {},
pmid = {40659530},
issn = {1529-2401},
mesh = {Humans ; Female ; Male ; *Friends/psychology ; Adult ; Young Adult ; *Social Networking ; *Brain/physiology/diagnostic imaging ; *Consumer Behavior ; Magnetic Resonance Imaging ; Longitudinal Studies ; *Social Behavior ; Adolescent ; Brain Mapping ; },
abstract = {The endogenous aspect of social influence, reflected in the spontaneous alignment of behaviors within close social relationships, plays a crucial role in understanding human social behavior. In two studies involving 222 human subjects (Study 1: n = 175, 106 females; Study 2: n = 47, 33 females), we used a longitudinal behavioral study and a naturalistic stimuli neuroimaging study to investigate the endogenous consumer behavior similarities and their neural basis in real-world social networks. The findings reveal that friends, compared with nonfriends, exhibit higher similarity in product evaluation, which undergoes dynamic changes as the structure of social networks changes. Both neuroimaging and meta-analytic decoding results indicate that friends exhibit heightened neural synchrony, which is linked to cognitive functions such as object perception, attention, memory, social judgment, and reward processing. Stacking machine learning-based predictive models demonstrate that the functional connectivity maps of brain activity can predict the purchase intention of their friends or their own rather than strangers. Based on the significant neural similarity which exists among individuals in close relationships within authentic social networks, the current study reveals the predictive capacity of neural activity in predicting the behavior of friends.},
}
@article {pmid40658672,
year = {2025},
author = {Eser, A and Erdoğan, SB},
title = {Decoding basic emotional states through integration of an fNIRS-based brain-computer interface with supervised learning algorithms.},
journal = {PloS one},
volume = {20},
number = {7},
pages = {e0325850},
pmid = {40658672},
issn = {1932-6203},
mesh = {Humans ; Spectroscopy, Near-Infrared/methods ; *Brain-Computer Interfaces ; *Emotions/physiology ; Male ; Female ; Adult ; Young Adult ; *Supervised Machine Learning ; Algorithms ; Prefrontal Cortex/physiology ; Support Vector Machine ; },
abstract = {Automated detection of emotional states through brain-computer interfaces (BCIs) offers significant potential for enhancing user experiences and personalizing services across domains such as mental health, adaptive learning and interactive entertainment. Within this advancing field, the aim of this study was to test the feasibility of a functional near-infrared spectroscopy (fNIRS)-based BCI system for accurate prediction and objective identification of three fundamental emotional states that involved positive, negative and neutral conditions. Consequently, the efficacy of fNIRS signals in predicting the valence of standardized stimuli from the International Affective Picture System (IAPS) was assessed. fNIRS data were collected from twenty healthy participants while images from the IAPS database were presented. The images varied in both valence (i.e., positive, neutral, negative) and arousal (i.e., high, low) level. Hemodynamic responses of prefrontal cortical (PFC) regions were recorded with a twenty-two channel system. Twenty fNIRS derived time domain features were extracted from HbO time traces of each channel corresponding to each stimulus period. Classification performances of three machine learning algorithms, namely the k-Nearest Neighbors (kNN), Ensemble (Subspace kNN) and Support Vector Machines (SVM), in two class and three class classification of positive, neutral and negative states were evaluated with ten runs of a tenfold cross-validation procedure through splitting the data into test, train and validation groups at each run. Three class classification performances of all algorithms were above 90% in terms of accuracy, sensitivity, specificity, F-1 score and precision metrics while two class accuracy performances of all algorithms were above 93% in terms of each performance metric. The high-performance classification results highlight the potential of fNIRS-based BCI systems for real-time, objective detection of basic emotional states for daily life and clinical applications. fNIRSbased BCIs may show promise for future developments in personalized user experiences and clinical applications due to their practicality and low computational complexity.},
}
@article {pmid40658035,
year = {2025},
author = {Niu, Y and Li, Z and Zeng, G and Zhang, Y and Yao, L and Wu, X},
title = {Electroencephalogram-Based Satisfaction Assessment Brain-Computer Interface in Emerging Video Service by Using Graph Representation Learning.},
journal = {Brain connectivity},
volume = {},
number = {},
pages = {},
doi = {10.1177/21580014251359107},
pmid = {40658035},
issn = {2158-0022},
abstract = {Background: Emerging video services (EVS) offer users various multimedia presentations, and satisfaction assessment is crucial for enhancing their user experience and competitiveness. However, existing research methods are unable to provide a quantitative satisfaction assessment. Electroencephalogram (EEG), as a popular signal source in brain-computer interface (BCI), with the advantage of being difficult to disguise and containing rich brain activity information, has gained increasing attention from researchers. This article aims to investigate the advantages of employing EEG for modeling satisfaction in EVS. Unlike the subjective metrics assessment in traditional video services, generating satisfaction in EVS involves a range of cognitive functions, including cognitive load, emotion, and audiovisual perception, which are difficult to characterize using a single feature. The representation of brain states for complex cognitive functions has been a major challenge for EEG modeling approaches. Methods: To address this challenge, we propose an EEG-based EVS satisfaction assessment BCI by raising a Point-to-Global graph representation learning strategy (P2G) that efficiently identifies satisfaction level through a parallel coding module and a graph-based brain region perception module. P2G captures satisfaction-sensitive graph representations in EEG samples based on coding and integrating point features and the global topography. Results: We validate the effectiveness of introducing a P2G learning strategy in EVS satisfaction modeling using a self-constructed dataset and a relevant public dataset, and our method outperforms existing methods. Additionally, we provide a detailed visual analysis to unveil neural markers associated with EVS satisfaction, thereby laying a scientific foundation for the optimization and development of video services.},
}
@article {pmid40656548,
year = {2025},
author = {Mueller, NN and Ocoko, MYM and Kim, Y and Li, K and Gisser, K and Glusauskas, G and Lugo, I and Dernelle, P and Hermoso, AC and Wang, J and Duncan, J and Druschel, LN and Graham, F and Capadona, JR and Hess-Dunning, A},
title = {Mechanically-adaptive, resveratrol-eluting neural probes for improved intracortical recording performance and stability.},
journal = {Npj flexible electronics},
volume = {9},
number = {1},
pages = {64},
pmid = {40656548},
issn = {2397-4621},
abstract = {Intracortical microelectrodes are used for recording activity from individual neurons, providing both a valuable neuroscience tool and an enabling medical technology for individuals with motor disabilities. Standard neural probes carrying the microelectrodes are rigid silicon-based structures that can penetrate the brain parenchyma to interface with the targeted neurons. Unfortunately, within weeks after implantation, neural recording quality from microelectrodes degrades, owing largely to a neuroinflammatory response. Key contributors to the neuroinflammatory response include mechanical mismatch at the device-tissue interface and oxidative stress. We developed a mechanically-adaptive, resveratrol-eluting (MARE) neural probe to mitigate both mechanical mismatch and oxidative stress and thereby promote improved neural recording quality and longevity. In this work, we demonstrate that compared to rigid silicon controls, highly-flexible MARE probes exhibit improved recording performance, more stable impedance, and a healing tissue response. With further optimization, MARE probes can serve as long-term, robust neural probes for brain-machine interface applications.},
}
@article {pmid40656455,
year = {2025},
author = {Komosar, M and Tamburro, G and Graichen, U and Comani, S and Haueisen, J},
title = {Combination of spatial and temporal de-noising and artifact reduction techniques in multi-channel dry EEG.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1576954},
pmid = {40656455},
issn = {1662-4548},
abstract = {INTRODUCTION: Dry electroencephalography (EEG) allows for recording cortical activity in ecological scenarios with a high channel count, but it is often more prone to artifacts as compared to gel-based EEG. Spatial harmonic analysis (SPHARA) and ICA-based methods (Fingerprint and ARCI) have been separately used in previous studies for dry EEG de-noising and physiological artifact reduction. Here, we investigate if the combination of these techniques further improves EEG signal quality. For this purpose, we also introduced an improved version of SPHARA.<br><br>METHODS: Dry 64-channel EEG was recorded from 11 healthy volunteers during a motor performance paradigm (left and right hand, feet, and tongue movements). EEG signals were denoised separately using Fingerprint + ARCI, SPHARA, a combination of these two methods, and a combination of these two methods including an improved SPHARA version. The improved version of SPHARA includes an additional zeroing of artifactual jumps in single channels before application of SPHARA. The EEG signal quality after application of each denoising method was calculated by means of standard deviation (SD), signal to noise ratio (SNR), and root mean square deviation (RMSD), and a generalized linear mixed effects (GLME) model was used to identify significant changes of these parameters and quantify the changes in the EEG signal quality.<br><br>RESULTS: The grand average values of SD improved from 9.76 (reference preprocessed EEG) to 8.28, 7.91, 6.72, and 6.15 &#x3bc;V for Fingerprint + ARCI, SPHARA, Fingerprint + ARCI + SPHARA, and Fingerprint + ARCI + improved SPHARA, respectively. Similarly, the RMSD values improved from 4.65 to 4.82, 6.32, and 6.90 &#x3bc;V, and the SNR values changed from 2.31 to 1.55, 4.08, and 5.56 dB.<br><br>DISCUSSION: Our results demonstrate the different performance aspects of Fingerprint + ARCI and SPHARA, artifact reduction and de-noising techniques that complement each other. We also demonstrated that a combination of these techniques yields superior performance in the reduction of artifacts and noise in dry EEG recordings, which can be extended to infant EEG and adult MEG applications.},
}
@article {pmid40655558,
year = {2025},
author = {Alonso-Vázquez, D and Mendoza-Montoya, O and Caraza, R and Martinez, HR and Antelis, JM},
title = {From pronounced to imagined: improving speech decoding with multi-condition EEG data.},
journal = {Frontiers in neuroinformatics},
volume = {19},
number = {},
pages = {1583428},
pmid = {40655558},
issn = {1662-5196},
abstract = {INTRODUCTION: Imagined speech decoding using EEG holds promising applications for individuals with motor neuron diseases, although its performance remains limited due to small dataset sizes and the absence of sensory feedback. Here, we investigated whether incorporating EEG data from overt (pronounced) speech could enhance imagined speech classification.<br><br>METHODS: Our approach systematically compares four classification scenarios by modifying the training dataset: intra-subject (using only imagined speech, combining overt and imagined speech, and using only overt speech) and multi-subject (combining overt speech data from different participants with the imagined speech of the target participant). We implemented all scenarios using the convolutional neural network EEGNet. To this end, twenty-four healthy participants pronounced and imagined five Spanish words.<br><br>RESULTS: In binary word-pair classifications, combining overt and imagined speech data in the intra-subject scenario led to accuracy improvements of 3%-5.17% in four out of 10 word pairs, compared to training with imagined speech only. Although the highest individual accuracy (95%) was achieved with imagined speech alone, the inclusion of overt speech data allowed more participants to surpass 70% accuracy, increasing from 10 (imagined only) to 15 participants. In the intra-subject multi-class scenario, combining overt and imagined speech did not yield statistically significant improvements over using imagined speech exclusively.<br><br>DISCUSSION: Finally, we observed that features such as word length, phonological complexity, and frequency of use contributed to higher discriminability between certain imagined word pairs. These findings suggest that incorporating overt speech data can improve imagined speech decoding in individualized models, offering a feasible strategy to support the early adoption of brain-computer interfaces before speech deterioration occurs in individuals with motor neuron diseases.},
}
@article {pmid40654838,
year = {2025},
author = {Lin, X and Zhang, X and Wang, Z and Chen, J and Lee, J and Lee, AJ and Yang, H and Remy, A and Shen, H and He, Y and Zhao, H and Zhang, X and Wang, W and Aljović, A and Vlassak, JJ and Lu, N and Liu, J},
title = {Plastic-elastomer heterostructure for robust flexible brain-computer interfaces.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.04.29.651325},
pmid = {40654838},
issn = {2692-8205},
abstract = {Electronics for neural signal recording must be robust across multiple and deep brain regions while preserving tissue-level flexibility to ensure stable tracking over months or years. However, existing electronics cannot simultaneously achieve robustness and tissue-level flexibility, limiting their potential for customizable and scalable neuroscience research and clinical applications. Here, we introduce FlexiSoft, an electronic platform based on a plastic-elastomer heterostructure that uniquely integrates mechanical robustness and tissue-level flexibility. Compared to conventional flexible electronics of similar thickness, the FlexiSoft platform demonstrates an order-of- magnitude improvement in both mechanical robustness (critical energy release rate) and flexibility (flexural rigidity). Leveraging these mechanical advantages, we developed FlexiSoft probe for robust implantation, demonstrated by its ability to withstand repeated insertion and removal, as well as to reach centimeter-scale depths comparable to those in the human brain. The platform enables long-term recording from the same neurons across the hippocampus (HPC) and primary motor cortex (M1) during a months-long motor learning task, thereby revealing long-term dynamic changes in neuronal firing patterns. Additionally, FlexiSoft's unique robustness and flexibility enable curved implantation routes, opening new directions of customizable implantation pathways. In summary, we present FlexiSoft as a novel, robust, and tissue-level flexible heterostructure electronics platform that advances flexible brain-computer interfaces (BCIs) with strong translational potential for neuroscience and clinical applications.},
}
@article {pmid40653584,
year = {2025},
author = {Monteiro, RV and Amarante, JEV and Bona, VS and Lins, RBE and Lopes, GC and Blackburn, M and Swanson, C and Latorre, JA and De Souza, GM},
title = {Microshear bond strength of conventional and bioactive restorative materials to irradiated and non-irradiated dentin: an in vitro study.},
journal = {Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer},
volume = {33},
number = {8},
pages = {688},
pmid = {40653584},
issn = {1433-7339},
mesh = {Humans ; *Dentin/radiation effects/ultrastructure ; *Composite Resins/chemistry ; *Dental Bonding/methods ; Materials Testing ; In Vitro Techniques ; Shear Strength ; Microscopy, Electron, Scanning ; Resin Cements/chemistry ; Dentin-Bonding Agents/chemistry ; Surface Properties ; Time Factors ; },
abstract = {PURPOSE: To evaluate the effect of conventional and bioactive restorative materials on bond strength to control (non-irradiated) and irradiated dentin.<br><br>METHODS: Human dentin fragments (240) were polished and divided into non-irradiated dentin (NI; n&#x2009;=&#x2009;120) and irradiated dentin (ID; n&#x2009;=&#x2009;120). ID specimens received 70&#xa0;Gy irradiation (2&#xa0;Gy/fraction, 5&#xa0;days/week for 7&#xa0;weeks). All dentin surfaces were bonded to restorative materials with Scotchbond universal adhesive in self-etching mode. Microshear bond strength cylinders were built on the bonded surface according to the restorative material (4 subgroups, n&#x2009;=&#x2009;30): conventional resin composite (CC-Filtek Z250) and three bioactive restorative composites (BCI-Activa BioActive-Restorative; BCII-Beautiful II; BCIII-Predicta Bulk). Specimens were stored in water at 37&#xa0;&#xb0;C for 24&#xa0;h or 30&#xa0;days and subjected to microshear bond strength test. The data was analyzed using two-way ANOVA and Tukey's post-hoc test (&#x237a;&#x2009;<&#x2009;0.05). The morphological surface of both NI and ID dentin (n&#x2009;=&#x2009;3) was analyzed using Scanning Electron Microscopy (SEM).<br><br>RESULTS: Two-way ANOVA revealed a significant effect of the Time/Radiation (p&#x2009;<&#x2009;0.001). Restorative material (p&#x2009;=&#x2009;0.191) and the interaction Time/Radiation*Restorative material (p&#x2009;=&#x2009;0.169) were not significant. Irradiation decreased the bond strength of CC specimens at both 24&#xa0;h (p&#x2009;<&#x2009;0.001) and 30&#xa0;days (p&#x2009;<&#x2009;0.001). None of the bioactive materials were significantly affected by irradiation and storage time. The SEM analysis revealed morphological changes in the ID specimens.<br><br>CONCLUSION: Ionizing radiation-induced morphological changes in the dentin surface. These changes negatively affected the conventional resin composite bond strengths to dentin. However, these morphological alterations did not affect the bond strength of the bioactive restorative materials.},
}
@article {pmid40649800,
year = {2025},
author = {Jaszczuk, P and Bratelj, D and Capone, C and Rudnick, M and Pötzel, T and Verma, RK and Fiechter, M},
title = {Advances in Neuromodulation and Digital Brain-Spinal Cord Interfaces for Spinal Cord Injury.},
journal = {International journal of molecular sciences},
volume = {26},
number = {13},
pages = {},
pmid = {40649800},
issn = {1422-0067},
mesh = {Humans ; *Spinal Cord Injuries/therapy/rehabilitation/physiopathology ; *Brain-Computer Interfaces ; *Spinal Cord Stimulation/methods ; Spinal Cord/physiopathology ; Animals ; },
abstract = {Spinal cord injury (SCI) results in a significant loss of motor, sensory, and autonomic function, imposing substantial biosocial and economic burdens. Traditional approaches, such as stem cell therapy and immune modulation, have faced translational challenges, whereas neuromodulation and digital brain-spinal cord interfaces combining brain-computer interface (BCI) technology and epidural spinal cord stimulation (ESCS) to create brain-spine interfaces (BSIs) offer promising alternatives by leveraging residual neural pathways to restore physiological function. This review examines recent advancements in neuromodulation, focusing on the future translation of clinical trial data to clinical practice. We address key considerations, including scalability, patient selection, surgical techniques, postoperative rehabilitation, and ethical implications. By integrating interdisciplinary collaboration, standardized protocols, and patient-centered design, neuromodulation has the potential to revolutionize SCI rehabilitation, reducing long-term disability and enhancing quality of life globally.},
}
@article {pmid40648603,
year = {2025},
author = {Bonanno, M and Saracino, B and Ciancarelli, I and Panza, G and Manuli, A and Morone, G and Calabrò, RS},
title = {Assistive Technologies for Individuals with a Disability from a Neurological Condition: A Narrative Review on the Multimodal Integration.},
journal = {Healthcare (Basel, Switzerland)},
volume = {13},
number = {13},
pages = {},
pmid = {40648603},
issn = {2227-9032},
abstract = {BACKGROUND/OBJECTIVES: Neurological disorders often result in a broad spectrum of disabilities that impact mobility, communication, cognition, and sensory processing, leading to significant limitations in independence and quality of life. Assistive technologies (ATs) offer tools to compensate for these impairments, support daily living, and improve quality of life. The World Health Organization encourages the adoption and diffusion of effective assistive technology (AT). This narrative review aims to explore the integration, benefits, and challenges of assistive technologies in individuals with neurological disabilities, focusing on their role across mobility, communication, cognitive, and sensory domains.<br><br>METHODS: A narrative approach was adopted by reviewing relevant studies published between 2014 and 2024. Literature was sourced from PubMed and Scopus using specific keyword combinations related to assistive technology and neurological disorders.<br><br>RESULTS: Findings highlight the potential of ATs, ranging from traditional aids to intelligent systems like brain-computer interfaces and AI-driven devices, to enhance autonomy, communication, and quality of life. However, significant barriers remain, including usability issues, training requirements, accessibility disparities, limited user involvement in design, and a low diffusion of a health technology assessment approach.<br><br>CONCLUSIONS: Future directions emphasize the need for multidimensional, user-centered solutions that integrate personalization through machine learning and artificial intelligence to ensure long-term adoption and efficacy. For instance, combining brain-computer interfaces (BCIs) with virtual reality (VR) using machine learning algorithms could help monitor cognitive load in real time. Similarly, ATs driven by artificial intelligence technology could be useful to dynamically respond to users' physiological and behavioral data to optimize support in daily tasks.},
}
@article {pmid40648390,
year = {2025},
author = {Jezierska, K and Turoń-Skrzypińska, A and Rotter, I and Syroka, A and Łukowiak, M and Rawojć, K and Rawojć, P and Rył, A},
title = {Latency and Amplitude of Cortical Activation in Interactive vs. Passive Tasks: An fNIRS Study Using the NefroBall System.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {13},
pages = {},
pmid = {40648390},
issn = {1424-8220},
mesh = {Adult ; Female ; Humans ; Male ; Young Adult ; Brain Mapping/methods ; Brain-Computer Interfaces ; Movement/physiology ; *Prefrontal Cortex/physiology ; *Spectroscopy, Near-Infrared/methods ; *Visual Cortex/physiology ; },
abstract = {Functional near-infrared spectroscopy (fNIRS) allows non-invasive assessment of cortical activity during naturalistic tasks. This study aimed to compare cortical activation dynamics-specifically the latency (tmax) and amplitude (ΔoxyHb) of oxygenated haemoglobin changes-in passive observation and an interactive task using the Nefroball system. A total of 117 healthy adults performed two tasks involving rhythmic hand movements: a passive protocol and an interactive game-controlled condition. fNIRS recorded signals from the visual, parietal, motor, and prefrontal cortices of the left hemisphere. The Mann-Whitney test revealed significantly shorter tmax in all areas during the interactive task, suggesting faster recruitment of cortical networks. ΔoxyHb amplitude was significantly higher only in the visual cortex during the interactive task, indicating increased visual processing demand. No significant ΔoxyHb differences were observed in the motor, prefrontal, or parietal cortices. Weak but significant positive correlations were found between tmax and ΔoxyHb in the motor and prefrontal regions, but only in the passive condition. These findings support the notion that interactive tasks elicit faster, though not necessarily stronger, cortical responses. The results have potential implications for designing rehabilitation protocols and brain-computer interfaces involving visual-motor integration.},
}
@article {pmid40648293,
year = {2025},
author = {Zhang, J and Zhao, D and Li, Y and Ming, G and Pei, W},
title = {Four-Dimensional Adjustable Electroencephalography Cap for Solid-Gel Electrode.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {13},
pages = {},
pmid = {40648293},
issn = {1424-8220},
support = {62401325//National Natural Science Foundation of China/ ; },
mesh = {*Electroencephalography/instrumentation/methods ; Humans ; Electrodes ; Adult ; Signal-To-Noise Ratio ; Brain-Computer Interfaces ; Male ; Head ; Equipment Design ; Female ; },
abstract = {Currently, the electroencephalogram (EEG) cap is limited to a finite number of sizes based on head circumference, lacking the mechanical flexibility to accommodate the full range of skull dimensions. This reliance on head circumference data alone often results in a poor fit between the EEG cap and the user's head shape. To address these limitations, we have developed a four-dimensional (4D) adjustable EEG cap. This cap features an adjustable mechanism that covers the entire cranial area in four dimensions, allowing it to fit the head shapes of nearly all adults. The system is compatible with 64 channels or lower electrode counts. We conducted a study with numerous volunteers to compare the performance characteristics of the 4D caps with the commercial (COML) caps in terms of contact pressure, preparation time, wearing impedance, and performance in brain-computer interface (BCI) applications. The 4D cap demonstrated the ability to adapt to various head shapes more quickly, reduce impedance during testing, and enhance measurement accuracy, signal-to-noise ratio (SNR), and comfort. These improvements suggest its potential for broader application in both laboratory settings and daily life.},
}
@article {pmid40648241,
year = {2025},
author = {Carìa, A},
title = {Towards Predictive Communication: The Fusion of Large Language Models and Brain-Computer Interface.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {13},
pages = {},
pmid = {40648241},
issn = {1424-8220},
support = {no number available//5xMille Unitn/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Language ; Artificial Intelligence ; *Communication ; Brain/physiology ; Deep Learning ; Electroencephalography ; Large Language Models ; },
abstract = {Integration of advanced artificial intelligence with neurotechnology offers transformative potential for assistive communication. This perspective article examines the emerging convergence between non-invasive brain-computer interface (BCI) spellers and large language models (LLMs), with a focus on predictive communication for individuals with motor or language impairments. First, I will review the evolution of language models-from early rule-based systems to contemporary deep learning architectures-and their role in enhancing predictive writing. Second, I will survey existing implementations of BCI spellers that incorporate language modeling and highlight recent pilot studies exploring the integration of LLMs into BCI. Third, I will examine how, despite advancements in typing speed, accuracy, and user adaptability, the fusion of LLMs and BCI spellers still faces key challenges such as real-time processing, robustness to noise, and the integration of neural decoding outputs with probabilistic language generation frameworks. Finally, I will discuss how fully integrating LLMs with BCI technology could substantially improve the speed and usability of BCI-mediated communication, offering a path toward more intuitive, adaptive, and effective neurotechnological solutions for both clinical and non-clinical users.},
}
@article {pmid40648159,
year = {2025},
author = {Ionita, S and Coman, DA},
title = {Narrowband Theta Investigations for Detecting Cognitive Mental Load.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {13},
pages = {},
pmid = {40648159},
issn = {1424-8220},
mesh = {Humans ; *Electroencephalography/methods ; *Cognition/physiology ; *Theta Rhythm/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; Male ; Adult ; Female ; },
abstract = {The way in which EEG signals reflect mental tasks that vary in duration and intensity is a key topic in the investigation of neural processes concerning neuroscience in general and BCI technologies in particular. More recent research has reinforced historical studies that highlighted theta band activity in relation to cognitive performance. In our study, we propose a comparative analysis of experiments with cognitive load imposed by arithmetic calculations performed mentally. The analysis of EEG signals captured with 64 electrodes is performed on low theta components extracted by narrowband filtering. As main signal discriminators, we introduced an original measure inspired by the integral of the curve of a function-specifically the signal function over the period corresponding to the filter band. Another measure of the signal considered as a discriminator is energy. In this research, it was used just for model comparison. A cognitive load detection algorithm based on these signal metrics was developed and tested on original experimental data. The results present EEG activity during mental tasks and show the behavioral pattern across 64 channels. The most precise and specific EEG channels for discriminating cognitive tasks induced by arithmetic tests are also identified.},
}
@article {pmid40646750,
year = {2025},
author = {Li, S and Tang, Z and Li, M and Yang, L and Shang, Z},
title = {Neural Correlates of Flight Acceleration in Pigeons: Gamma-Band Activity and Local Functional Network Dynamics in the AId Region.},
journal = {Animals : an open access journal from MDPI},
volume = {15},
number = {13},
pages = {},
pmid = {40646750},
issn = {2076-2615},
support = {62301496//the National Natural Science Foundation of China/ ; GZC20232447//the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation/ ; 252102311095//the Key Scientific and Technological Projects of Henan Province/ ; 252102210008//the Key Scientific and Technological Projects of Henan Province/ ; },
abstract = {Flight behavior in pigeons is governed by intricate neural mechanisms that regulate movement patterns and flight dynamics. Among various kinematic parameters, flight acceleration provides critical information for the brain to modulate movement intensity, speed, and direction. However, the neural representation mechanisms underlying flight acceleration remain insufficiently understood. To address this, we conducted outdoor free-flight experiments in homing pigeons, during which GPS data, flight posture, and eight-channel local field potentials (LFPs) were synchronously recorded. Our analysis revealed that gamma-band activity in the dorsal intermediate arcopallium (AId) region was more prominent during behaviorally demanding phases of flight. In parallel, local functional network analysis showed that the clustering coefficient of gamma-band activity in the AId followed a nonlinear, U-shaped relationship with flight acceleration-exhibiting the strongest and most widespread connectivity during deceleration, moderate connectivity during acceleration, and the weakest network coupling during steady flight. This pattern likely reflects the increased neural demands associated with flight phase transitions, where greater cognitive and sensorimotor integration is required. Furthermore, using LFP signals from five distinct frequency bands as input, machine learning models were developed to decode flight acceleration, further confirming the role of gamma-band dynamics in motor regulation during natural flight. This study provides the first evidence that gamma-band activity in the avian AId region encodes flight acceleration, offering new insights into the neural representation of motor states in natural flight and implications for bio-inspired flight control systems.},
}
@article {pmid40645213,
year = {2025},
author = {Meng, L and Zhao, H and Dong, M and Wang, Q and Shi, Y and Wang, D and Zhu, X and Xu, R and Ming, D},
title = {Cortical changes induced by increased cognitive task difficulty during dual task balancing correlate with postural instability in elders and patients with Parkinson's disease.},
journal = {Journal of neural engineering},
volume = {22},
number = {4},
pages = {},
doi = {10.1088/1741-2552/adeeca},
pmid = {40645213},
issn = {1741-2552},
mesh = {Humans ; *Parkinson Disease/physiopathology/psychology/diagnosis ; Male ; Female ; *Postural Balance/physiology ; Aged ; *Psychomotor Performance/physiology ; *Cerebral Cortex/physiopathology ; Electroencephalography/methods ; Middle Aged ; *Cognition/physiology ; Memory, Short-Term/physiology ; Adult ; Young Adult ; },
abstract = {Objective. The flexibility of cognitive resource allocation is deteriorated due to aging and neurological degenerative diseases, such as Parkinson's disease (PD). Dual task performance reflects a subject's ability to allocate cognitive resources, and the investigation of cortical activation changes during dual tasking can provide a deep insight into the reallocation of neural resources. However, the cortical changes induced by increased cognitive task difficulty during dual tasking with changes in behavioral outcomes have not been explored in PD and older adults (OAs).Approach.We designed a novel dual task paradigm comprising of balance maintenance and visual working memory (VWM) task to assess cognitive-motor interaction. Nineteen early-stage PD, 13 age-matched OA and 15 young adults completed 4 blocks of 25 trials each for two VWM difficulty levels (2 squares and 4 squares). Behavioral performance, postural stability, and 32-channel EEG were recorded. One-way ANOVA was used to examine group and task effects while Spearman's correlation analysis assessed associations between EEG changes and behavioral performance.Main results.Both PD and OA groups exhibited significantly longer reaction time, reduced postural stability, prolonged P300 latency and less alpha event related desynchronization (ERD) enhancement in response to the increased VWM task difficulty. Moreover, PD patients demonstrated significantly alpha ERD reduction at FC3, C3 and P4 in the 500-700 ms compared to the OAs. The ERD changes at the central and parietal regions were found to be significantly related to postural stability and clinical scores, respectively.Significance.The results provide novel evidence that cortical EEG responses during dual tasking may reflect deficits in attention resource reallocation and reduced cognitive flexibility in PD and OA groups. These observed cortical changes with increasing cognitive task difficulty are correlated with postural instability, highlighting their potential as neurophysiological biomarkers for dual-task dysfunction.},
}
@article {pmid40645212,
year = {2025},
author = {Li, Y and Zhao, Z and Liu, J and Peng, Y and Camilleri, K and Kong, W and Cichocki, A},
title = {EEG-based speech imagery decoding by dynamic hypergraph learning within projected and selected feature subspaces.},
journal = {Journal of neural engineering},
volume = {22},
number = {4},
pages = {},
doi = {10.1088/1741-2552/adeec8},
pmid = {40645212},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; *Imagination/physiology ; *Brain-Computer Interfaces ; *Machine Learning ; *Speech/physiology ; Male ; Adult ; Female ; Young Adult ; },
abstract = {Objective.Speech imagery is a nascent paradigm that is receiving widespread attention in current brain-computer interface (BCI) research. By collecting the electroencephalogram (EEG) data generated when imagining the pronunciation of a sentence or word in human mind, machine learning methods are used to decode the intention that the subject wants to express. Among existing decoding methods, graph is often used as an effective tool to model the data structure; however, in the field of BCI research, the correlations between EEG samples may not be fully characterized by simple pairwise relationships. Therefore, this paper attempts to employ a more effective data structure to model EEG data.Approach.In this paper, we introduce hypergraph to describe the high-order correlations between samples by viewing feature vectors extracted from each sample as vertices and then connecting them through hyperedges. We also dynamically update the weights of hyperedges, the weights of vertices and the structure of the hypergraph in two transformed subspaces, i.e. projected and feature-weighted subspaces. Accordingly, two dynamic hypergraph learning models, i.e. dynamic hypergraph semi-supervised learning within projected subspace (DHSLP) and dynamic hypergraph semi-supervised learning within selected feature subspace (DHSLF), are proposed for speech imagery decoding.Main results.To validate the proposed models, we performed a series of experiments on two EEG datasets. The obtained results demonstrated that both DHSLP and DHSLF have statistically significant improvements in decoding imagined speech intentions to existing studies. Specifically, DHSLP achieved accuracies of 78.40% and 66.64% on the two datasets, while DHSLF achieved accuracies of 71.07% and 63.94%.Significance.Our study indicates the effectiveness of the learned hypergraphs in characterizing the underlying semantic information of imagined contents; besides, interpretable results on quantitatively exploring the discriminative EEG channels in speech imagery decoding are obtained, which lay the foundation for further exploration of the physiological mechanisms during speech imagery.},
}
@article {pmid40644990,
year = {2025},
author = {Cai, G and Chen, Y and Yang, B and Yang, Y and Ma, T and Wang, Y},
title = {CGNet: A Complex-valued Graph Network for jointly learning amplitude-phase information in EEG-based brain-computer interfaces.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {191},
number = {},
pages = {107795},
doi = {10.1016/j.neunet.2025.107795},
pmid = {40644990},
issn = {1879-2782},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; Algorithms ; *Brain/physiology ; *Deep Learning ; },
abstract = {The synergy between amplitude and phase in electroencephalogram (EEG)-based brain-computer interfaces (BCIs) provides comprehensive and essential insights into neural oscillatory processes. However, constrained by real-valued computation paradigms, most deep learning methods have to process amplitude and phase independently, neglecting their crucial interaction mechanisms. To address this issue, we construct a Complex-valued Graph Network (CGNet) to capture comprehensive information from EEG signals, where both amplitude and phase information are encoded into the complex-valued representation. Specifically, we design a two-scale complex-valued convolutional network to learn local spatio-temporal information, develop a spatial attention module to enhance spatial information learning, and formulate a dynamic graph convolution to capture global temporal dependencies. Furthermore, we extend CGNet to Filter-Band CGNet (FBCGNet), enhancing the model's adaptability to broadband EEG data. Applied to motor imagery and execution BCI tasks, CGNet achieves state-of-the-art classification performance while maintaining computational efficiency comparable to other advanced algorithms. Notably, FBCGNet further improves CGNet's performance. Visualization results show that CGNet can identify the key spatio-temporal information consistent with paradigm principles. In addition, compared with using amplitude or phase alone, CGNet can capture more comprehensive task-related neural activities, thereby showing higher classification performance. CGNet is a promising tool for mining amplitude-phase information and offering more comprehensive neural decoding in EEG-based BCIs.},
}
@article {pmid40644885,
year = {2025},
author = {Al-Hadithy, SS and Abdalkafor, AS and Al-Khateeb, B},
title = {Emotion recognition in EEG Signals: Deep and machine learning approaches, challenges, and future directions.},
journal = {Computers in biology and medicine},
volume = {196},
number = {Pt A},
pages = {110713},
doi = {10.1016/j.compbiomed.2025.110713},
pmid = {40644885},
issn = {1879-0534},
mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; *Deep Learning ; *Signal Processing, Computer-Assisted ; *Machine Learning ; Neural Networks, Computer ; *Brain-Computer Interfaces ; },
abstract = {A crucial part of brain-computer interfaces is the use of electroencephalogram (EEG) signals for human emotion identification, which analyzes patterns of brain activity to determine the emotional state. This field of study is becoming increasingly important for developing advanced applications that enhance brain machine interaction and improve brain health assessment systems. However, EEG signal analysis faces significant challenges due to their subject-specific nature, high noise levels, and the scarcity of high-quality labeled data, which collectively limit model generalizability and complicate signal analysis. Traditional approaches have employed handcrafted features with Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Random Forests (RF) for EEG feature extraction and classification. Recent advances in deep learning, particularly Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), enable automatic feature learning from raw data to extract temporal, spatial, and spectral properties. The study employs a literature review approach and the analysis of the popular datasets (e.g., DEAP, SEED, AMIGOS). Despite technological advances, the fundamental challenges of noisy subject variability, and limited labeled data persist, requiring future research to focus on improving model robustness, scalability, and interpretability while addressing current limitations.},
}
@article {pmid40644284,
year = {2025},
author = {Petrich, LC and Neumann, S and Pilarski, PM and Fyshe, A},
title = {Neural Network Sparsity in Brain-Body-Machine Interfaces.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {1-8},
doi = {10.1109/ICORR66766.2025.11062950},
pmid = {40644284},
issn = {1945-7901},
mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography/methods ; *Neural Networks, Computer ; Algorithms ; *Signal Processing, Computer-Assisted ; },
abstract = {Brain-body-machine interfaces acquire, process, and translate brain signals for individuals with severe motor impairments to communicate and control the assistive technology that supports their daily life activities. Electroencephalography (EEG) is a standard approach for acquiring such brain signals due to its low cost and high temporal resolution. EEG signals can be thought of as a proxy for the user's intent. One established method for translating this intent into inferences and actions are neural networks. However, densely connected neural networks can be computationally expensive-a problem for real-time, deployed brain-body-machine interface systems. In this paper we investigate the use of sparsity in neural networks for EEG-based motor classification, with the goal of reducing the number of neuronal connections without sacrificing a system's performance. We compare two sparsity-inducing algorithms, weight pruning and sparse evolutionary training, with a dense neural network under three experimental conditions. Overall, our results show that sparse neural networks can achieve higher performance accuracy and generalization than their densely-connected counterparts for an EEG-based classification task. We found that sparse evolutionary training achieves the highest and most stable performance across all experiments. Introducing sparsity into the network is a viable option for efficient EEG-based control, with promising applications in a range of related rehabilitation and assistive technologies. This brings us closer to helping individuals with severe motor impairments reclaim independence through more computationally realizable methods of interacting with their technology and the world around them.},
}
@article {pmid40644274,
year = {2025},
author = {Patarini, F and Maronati, C and Manuello, J and Cuturi, LF and Monti, M and Savina, G and Ferrari, E and Iarrobino, I and Iani, C and Rubichi, S and Ciaramidaro, A and Astolfi, L and Cavallo, A and Toppi, J},
title = {Handling Kinematic Features in an Action Observation Task to Optimize a Brain Computer Interface-Based Rehabilitation Training.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {1078-1082},
doi = {10.1109/ICORR66766.2025.11062958},
pmid = {40644274},
issn = {1945-7901},
mesh = {Humans ; *Brain-Computer Interfaces ; Biomechanical Phenomena/physiology ; Electroencephalography ; *Stroke Rehabilitation ; Male ; Female ; Adult ; Middle Aged ; },
abstract = {Brain-Computer Interface (BCI) technology promotes neuroplasticity mechanisms which favor the functional motor recovery in stroke survivors. Patients' residual motor abilities determine the intention, which, once detected by the BCI is fed back via an effector. The majority of studies aimed at optimizing the feedback branch, but not enough attention has been posed to supporting patient in the movement intention that should be detected by the BCI system. The inclusion of a visual motor priming (observed action before a task) in a BCI could promote the retrieval of movements from the patient's own impaired motor repertoire. None of the motor priming proposed until so far have been tailored to the patients' residual motor ability, although it is well known that the human brain recognizes movements closer from a kinematic perspective to its own repertoire more easily. The aim of this study was to investigate how individual motor style in an action observation task would affect the observer's cortical excitability. EEG signals were recorded from 10 individuals during an action observation task where different levels of motor distance between the observer and the agent were modulated. EEG-based group spectral activations shown an involvement of bilateral parietal areas in beta band in case of more unpredictable kinematics. The results would open the way for the design of a kinematic-based visual motor priming to be embedded in a BCI system for post-stroke rehabilitation.},
}
@article {pmid40644240,
year = {2025},
author = {Gonzalez-Cely, AX and Soekadar, SR and Delisle-Rodriguez, D and Bastos-Filho, T},
title = {Lower-Limb Motor Imagery-Based Brain-Computer Interface to Control Treadmill Velocities.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {76-81},
doi = {10.1109/ICORR66766.2025.11063181},
pmid = {40644240},
issn = {1945-7901},
mesh = {Humans ; *Brain-Computer Interfaces ; *Lower Extremity/physiology ; Male ; Adult ; Electroencephalography ; *Exercise Test ; *Imagination/physiology ; Female ; Signal Processing, Computer-Assisted ; },
abstract = {Lower-limb rehabilitation traditionally relies on physical therapy, but motor imagery(MI)-based brain- computer interfaces (BCIs) promise to facilitate neuroplasticity and adaptation by closing the perception-action cycle. Here, we present a BCI system based on kinesthetic MI that enables treadmill velocity control, establishing a closed-loop feedback mechanism. The system was tested in a healthy participant translating mu (8-12 Hz) and high-beta (18-24 Hz) rhythm modulation into treadmill velocity control commands. Feature extraction techniques, including power spectral density (PSD) and Riemannian geometry (RG), were used for MI- and resting state classification. Additionally, Logistic Regression (LR), k-nearest neighbors, support vector machine, and Linear Discriminant Analysis (LDA) were employed and optimized for accuracy. The results showed increased mu and highbeta activation modulation at the vertex. The online RG+LDA classifier achieving an average accuracy of 72%, while the pseudo-online RG+LR reached 95%. The study's novelty lies in combining kinesthetic MI with treadmill control and employing RG for feature extraction, demonstrating its potential to enhance cortical modulation during rehabilitation. Future work will have to validate the system in poststroke patients for clinical applicability.},
}
@article {pmid40644193,
year = {2025},
author = {Mannan, MMN and Lloyd, DG and Pizzolato, C},
title = {Optimising Continuous Control of Real-Time Brain-Computer Interfaces Through Trial Length and Feedback Update Interval Selection.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {284-288},
doi = {10.1109/ICORR66766.2025.11063010},
pmid = {40644193},
issn = {1945-7901},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; Male ; Adult ; Female ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {Brain-computer interfaces (BCIs) offer promising potential to aid neurorehabilitation by transforming motor imagery (MI) signals into control commands, bypassing damaged neural pathways to support motor recovery. However, a key challenge in BCI research is achieving an effective balance between classification accuracy and real-time responsiveness, as both are critical for enhancing user embodiment and control for neurorehabilitation outcomes. This study investigates the impact of trial length and feedback update interval (FUI) on classification accuracy in an MI-based BCI system. Using EEG data from five subjects across 50 sessions, we evaluated classification performance across various trial length (1-5 seconds) and FUI (0.2-1 second) configurations. Results revealed that both trial length and FUI significantly influenced classification accuracy, with longer trial length (4-5 seconds) and FUI (0.4-1 seconds) yielding the highest accuracy. However, post-hoc analyses indicated a saturation effect, with no significant differences in the accuracy for these parameters. These findings underscore the importance of balancing signal stability with responsiveness for optimal BCI performance, providing insights into parameter settings that can enhance BCI usability in neurorehabilitation. Future work may explore adaptive approaches to dynamically adjust these parameters based on real-time requirements, potentially offering a more responsive and efficient BCI for clinical rehabilitation.},
}
@article {pmid40644184,
year = {2025},
author = {Koellner, J and Wimpff, M and Gizzi, L and Vujaklija, I and Yang, B},
title = {Exploring Cortical Responses to Blood Flow Restriction through Deep Learning.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {546-552},
doi = {10.1109/ICORR66766.2025.11063023},
pmid = {40644184},
issn = {1945-7901},
mesh = {Humans ; *Deep Learning ; Magnetoencephalography ; Male ; Adult ; Female ; Brain-Computer Interfaces ; *Cerebral Cortex/physiology ; Young Adult ; Signal Processing, Computer-Assisted ; Resistance Training ; },
abstract = {Blood flow restriction (BFR) training, which combines low-intensity resistance exercises with restricted blood flow, is effective in promoting muscle hypertrophy and strength. However, its impact on cortical activity remains largely unexplored, presenting an opportunity to investigate neural mechanisms using brain-computer interfaces (BCIs). Deep learning (DL)-based BCIs, with their large capacity for decoding complex brain signals, offer a promising avenue for such exploration. This study utilized magnetoencephalography (MEG) to analyze cortical responses in six subjects across three conditions-before, during, and after BFR. After preprocessing steps, such as data standardization and Euclidean-space alignment to optimize performance, the BaseNet architecture was utilized to classify the data. The models were tested using within-subject, cross-subject, and cross-time data splits. The results revealed classification accuracy well above 90% for individual subjects, indicating that cortical responses to BFR are detectable on a personal level. However, cross-subject models achieved only chance-level accuracy (33%), highlighting significant variability between individuals. Cross-time models showed better performance, with accuracy exceeding 50%. These findings suggest that while BFR elicits distinct cortical activity patterns, these responses are highly individualized, presenting challenges for generalization.},
}
@article {pmid40644160,
year = {2025},
author = {Toppi, J and Savina, G and Colamarino, E and De Seta, V and Patarini, F and Cincotti, F and Pichiorri, F and Mattia, D},
title = {Hybrid Brain Computer Interface-Based Rehabilitation Addressing Post-Stroke Maladaptive Movement Patterns.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {431-436},
doi = {10.1109/ICORR66766.2025.11062988},
pmid = {40644160},
issn = {1945-7901},
mesh = {Humans ; *Stroke Rehabilitation/methods ; *Brain-Computer Interfaces ; Male ; Middle Aged ; Female ; Aged ; Movement/physiology ; Adult ; Stroke/physiopathology ; Electromyography ; },
abstract = {Hybrid Brain-Computer Interfaces (hBCI) integrate brain and muscle signals to enhance motor rehabilitation of stroke survivors, by closing the loop between the lesioned brain and the paretic limb. To date, little attention has been devoted to their potential efficacy in managing the maladaptive movement patterns that afflict post-stroke motor outcome (unwanted abnormal co-contrations, spasticity). This study proposes a comparison of Cortico-Muscular Coherence (CMC) patterns assessed in stroke patients before and after a 1-month rehabilitation intervention based on a hBCI-controlled Functional Electrical Stimulation (FES) treatment, which included a module to monitor non-physiological movement patterns. Results demonstrated the efficacy of this type of assistive technology for post-stroke rehabilitation, addressing patient-tailored interventions able to reduce the maladaptive mechanisms.},
}
@article {pmid40644144,
year = {2025},
author = {Bastos-Filho, T and Gonzalez-Cely, AX and Mehrpour, S and Souza, F and Villa-Parra, AC and Cabral, F},
title = {Rehabilitation of Chronic Stroke Using Neurofeedback, Functional Electrical Stimulation and Cerebrospinal Direct Current Stimulation.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {1203-1208},
doi = {10.1109/ICORR66766.2025.11063073},
pmid = {40644144},
issn = {1945-7901},
mesh = {Humans ; *Stroke Rehabilitation/methods ; *Neurofeedback/methods ; Male ; Brain-Computer Interfaces ; *Electric Stimulation Therapy/methods ; Chronic Disease ; Electromyography ; Middle Aged ; Stroke/physiopathology ; },
abstract = {This work presents the application of a rehabilitation protocol using a novel Non-Invasive Brain Stimulation (NIBS) technique, called cerebrospinal Direct Current Stimulation (csDCS), together with the use of a Brain-Computer Interface (BCI) based on Motor Imagery (MI) with Neurofeedback (NFB), and applying Functional Electrical Stimulation (FES) plus the use of a pedal exerciser. This protocol uses the concept of Alternating Treatment Design (ATD), in which a chronic post-stroke subject is submitted to these techniques to recover his left hand and leg movements. The rehabilitation progress was verified through metrics, such as Fugl Meyer Assessment (FMA), Functional Independence Measure (FIM), Ashworth Scale, Muscle Strength Grading (MSG), and surface Electromyography (sEMG). Results from these metrics include a 41% gain in hand function recovery, a 5% gain in performance in motor and cognitive/social domains, and a 50% improvement in both wrist extensor muscle strength and finger extensor muscle strength. In addition, there was a 17% gain of Maximum Voluntary Contraction (MVC) for the tibialis anterior muscle of the patient's left leg. On the other hand, there was a worsening in some values of EMG, probably due to the participant having received application of botulinum toxin in his hand.},
}
@article {pmid40644135,
year = {2025},
author = {Sun, Q and Merino, EC and Yang, L and Faes, A and Van Hulle, MM},
title = {On the Impact of Proprioception in EEG Representations and Decoding During Human-Hand Exoskeleton Interaction.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {186-192},
doi = {10.1109/ICORR66766.2025.11063039},
pmid = {40644135},
issn = {1945-7901},
mesh = {Humans ; *Electroencephalography/methods ; *Proprioception/physiology ; Male ; *Exoskeleton Device ; Female ; Adult ; Brain-Computer Interfaces ; Young Adult ; *Hand/physiology ; Movement/physiology ; Fingers/physiology ; },
abstract = {Controlling a hand exoskeleton based on electroencephalogram (EEG)-based brain-computer interfacing (BCI) holds promise for human motor augmentation and neurore-habilitation. To achieve natural control, a critical step is to understand the impact of proprioception provided by the exoskeleton during interaction. In this study, we aim to approach the goal by quantifying EEG representations and BCI performance. We monitored 25 healthy subjects' full-scalp EEG while performing different finger movement tasks with a cable-driven hand exoskeleton. Each task involves three movement modalities, i.e., imagined (IM), passive (PM), and congruent imagined and passive (IPM) finger flexion. We found that alpha (8 - 13 Hz) and beta (13 - 30 Hz) band desynchronization in the sensorimotor area was significantly stronger for PM and IPM tasks compared to IM, with no significant difference between PM and IPM. Using machine learning models, we achieved a high accuracy in classifying exoskeleton-assisted movements from the rest condition (IPM vs. REST: 0.80 ± 0.07, PM vs. REST: 0.72 ± 0.10), with the IPM modality returning the highest accuracy. However, distinguishing between IPM and PM yielded only 0.61 ± 0.09, significantly lower than the condition of intention detection without the exoskeleton (IM vs. REST: 0.73 ± 0.08). Our findings suggest that sensorimotor EEG activity can track proprioceptive feedback induced by the hand exoskeleton. While this feedback is pronounced and distinguishable, detecting motor intention during exoskeleton movement remains highly challenging. This highlights the need for advanced decoders and control strategies for the future development of continuous BCI-actuated hand exoskeletons.},
}
@article {pmid40644105,
year = {2025},
author = {Shevchenko, O and Yeremeieva, S and Laschowski, B},
title = {Comparative Analysis of Neural Decoding Algorithms for Brain-Machine Interfaces.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {222-227},
doi = {10.1109/ICORR66766.2025.11063033},
pmid = {40644105},
issn = {1945-7901},
mesh = {*Brain-Computer Interfaces ; Humans ; *Algorithms ; Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; Brain/physiology ; Machine Learning ; Neural Networks, Computer ; },
abstract = {Accurate neural decoding of brain dynamics remains an open challenge in brain-machine interfaces. While various signal processing, feature extraction, and classification algorithms have been proposed, a systematic comparison of these is lacking. Accordingly, here we conducted one of the largest comparative studies to evaluate different combinations of state-of-the-art algorithms for motor neural decoding in order to find the optimal combination. We studied three signal processing methods (i.e., artifact subspace reconstruction, surface Laplacian filtering, and data normalization), four feature extractors (i.e., common spatial patterns, independent component analysis, short-time Fourier transform, and no feature extraction), and four machine learning classifiers (i.e., support vector machine, linear discriminant analysis, convolutional neural networks, and long short-term memory networks). Using a large-scale EEG dataset, we optimized each combination for individual subjects (i.e., resulting in 672 total experiments) and evaluated performance based on classification accuracy. We also compared the computational and memory storage requirements, which are important for real-time embedded computing. Our comparative analysis provides novel insights that can help inform the design of next-generation neural decoding algorithms for brain-machine interfaces.},
}
@article {pmid40644100,
year = {2025},
author = {Feng, Z and Kakkos, I and Matsopoulos, GK and Guan, C and Sun, Y},
title = {Explaining E/MEG Source Imaging and Beyond: An Updated Review.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3588350},
pmid = {40644100},
issn = {2168-2208},
abstract = {E/MEG source imaging (ESI) provides noninvasive measurements of brain activity with high spatial and temporal resolution. In particular, the wearability and portability of EEG make it an attractive area of research not only in the biomedical communities especially when considering the wide applications prospects including brain-computer interface (BCI), neuromarketing, neuroergonomics, etc. Although there are already some valuable and impressive reviews on ESI, these reviews introduce the ESI models in a relatively isolated way and lack the recent advances in ESI. In this work, we aim to: 1) provide a timely in-depth review of the widely-explored/state-of-the art ESI models including their underlying neurophysiological assumptions and mathematical derivations; 2) list the primary applications of ESI and highlight the crucial steps regarding its implementations; 3) discuss the challenges in ESI and suggest several future research prospects; 4) demonstrate practical usage and implementation details of various representative ESI models along with open-source dataset/codes (link). As a rapidly expanding field, the development of ESI is continuously growing and evolving to embrace new technologies. We believe the widespread applications of ESI is happening, and it will dramatically expand our understanding of brain dynamics.},
}
@article {pmid40644042,
year = {2025},
author = {Kim, M and Jo, S and Cho, H and Ye, S and Kim, Y and Park, HS},
title = {Development of Multimodal EEG-EMG Human Machine Interface for Hand-Wrist Rehabilitation: A Preliminary Study.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {1564-1569},
doi = {10.1109/ICORR66766.2025.11063079},
pmid = {40644042},
issn = {1945-7901},
mesh = {Humans ; *Electroencephalography/methods ; *Hand/physiology ; *Electromyography/methods/instrumentation ; *Wrist/physiology ; Male ; Adult ; Wearable Electronic Devices ; Stroke Rehabilitation ; *Robotics/instrumentation ; Female ; Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; },
abstract = {Patients with neurological disorders, such as stroke, often undergo upper limb motor impairments, severely limiting their ability to perform activities of daily living (ADL). Wearable robots have been developed to provide intensive and precise repetitive training for upper limb rehabilitation. Effective rehabilitation requires aligning robotic assistance with patient movement intention to promote brain plasticity. Additionally, robotic assistance must accommodate the complex, coordinated upper limb motions required for ADL tasks, including not only isolated hand movements but also integrated hand and wrist actions. This paper presents a multimodal human-machine interface (HMI) for integrated hand-wrist rehabilitation using both EEG and EMG signals. A three-degrees-of-freedom (3-DOF) soft wearable robot, combining a robotic hand glove and forearm skin brace, was designed to assist coordinated hand and wrist movements during reaching and grasping. EEG signals classified rest and grasp states using a Riemannian geometry approach, while EMG signals from three forearm muscles detected reaching onset to trigger the wrist adjustment. Preliminary tests with four healthy participants demonstrated 85% accuracy in EEG-based classification and sufficient EMG amplitude for motion onset detection. Future studies will expand participant testing to improve system robustness and evaluate its effectiveness for stroke rehabilitation.},
}
@article {pmid40640801,
year = {2025},
author = {Gao, W and Yan, Z and Zhou, H and Xie, Y and Wang, H and Yang, J and Yu, J and Ni, C and Liu, P and Xie, M and Huang, L and Ye, Z},
title = {Revolutionizing brain‒computer interfaces: overcoming biocompatibility challenges in implantable neural interfaces.},
journal = {Journal of nanobiotechnology},
volume = {23},
number = {1},
pages = {498},
pmid = {40640801},
issn = {1477-3155},
support = {National Innovation Platform Development Program (No. 2020021105012440), the National Natural Science Foundation of China (No. 82172524, 81974355)//Zhewei Ye/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Electrodes, Implanted ; Animals ; *Biocompatible Materials/chemistry ; Brain/physiology ; },
abstract = {Brain‒computer interfaces (BCIs) exhibit significant potential for various applications, including neurofeedback training, neurological injury management, and language, sensory and motor rehabilitation. Neural interfacing electrodes are positioned between external electronic devices and the nervous system to capture complex neuronal activity data and promote the repair of damaged neural tissues. Implantable neural electrodes can record and modulate neural activities with both high spatial and high temporal resolution, offering a wide window for neuroscience research. Despite significant advancements over the years, conventional neural electrode interfaces remain insufficient for fully achieving these objectives, particularly in the context of long-term implantation. The primary limitation stems from the poor biocompatibility and mechanical mismatch between the interfacing electrodes and neural tissues, which induce a local immune response and scar tissue formation, thus decreasing the performance and useful lifespan. Therefore, neural interfaces should ideally exhibit appropriate stiffness and minimal foreign body reactions to mitigate neuroinflammation and enhance recording quality. This review provides an exhaustive analysis of the current understanding of the critical failure modes that may impact the performance of implantable neural electrodes. Additionally, this study provides a comprehensive overview of the current research on coating materials and design strategies for implanted neural interfaces and discusses the primary challenges currently facing long-term implantation of neural electrodes. Finally, we present our perspective and propose possible future research directions to improve implantable neural interfaces for BCIs.},
}
@article {pmid40640486,
year = {2025},
author = {Pierrieau, E and Dussard, C and Plantey-Veux, A and Guerrini, C and Lau, B and Pillette, L and George, N and Jeunet-Kelway, C},
title = {Changes in cortical beta power predict motor control flexibility, not vigor.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {1041},
pmid = {40640486},
issn = {2399-3642},
support = {ANR-20-CE37-0012//Agence Nationale de la Recherche (French National Research Agency)/ ; },
mesh = {Humans ; Male ; Female ; *Beta Rhythm/physiology ; Adult ; Electroencephalography ; Young Adult ; *Motor Cortex/physiology ; Neurofeedback ; Brain-Computer Interfaces ; Movement/physiology ; Psychomotor Performance/physiology ; },
abstract = {The amplitude of beta-band activity (β power; 13-30 Hz) over motor cortical regions is used to assess and decode movement in clinical settings and brain-computer interfaces, as β power is often assumed to predict the strength of the brain's motor output, or "vigor". However, recent conflicting evidence challenges this assumption and underscores the need to clarify the relationship between β power and movement. In this study, sixty participants were trained to self-regulate β power using electroencephalography-based neurofeedback before performing different motor tasks. Results show that β power modulations can impact different motor variables, or the same variables in opposite directions, depending on task constraints. Importantly, downregulation of β power is associated with better task performance regardless of whether performance implied increasing or decreasing motor vigor. These findings demonstrate that β power should be interpreted as a measure of motor flexibility, which underlies adaptation to environmental constraints, rather than vigor.},
}
@article {pmid40638250,
year = {2025},
author = {Zhang, X and Ma, D and Wang, J and Su, N and Guo, J},
title = {Structures and Molecular Mechanisms of Insect Odorant and Gustatory Receptors.},
journal = {Physiology (Bethesda, Md.)},
volume = {40},
number = {6},
pages = {0},
doi = {10.1152/physiol.00011.2025},
pmid = {40638250},
issn = {1548-9221},
support = {2020YFA0908501//Ministry of Science and Technology of China/ ; 32371204//National Science Foundation of China/ ; LD25C050004//Zhejiang Provincial Natural Science Foundation/ ; //Foundamental Research Funds for the Central Universities/ ; //Ministry of Education Frontier Science Center for Brain Science &amp; Brain-Machine Integration/ ; //K.C. Wong Education Foundation/ ; },
mesh = {*Receptors, Odorant/chemistry/metabolism ; Animals ; *Insecta/metabolism/physiology ; *Receptors, Cell Surface/chemistry/metabolism ; Ligands ; *Insect Proteins/chemistry/metabolism ; },
abstract = {Insects rely on chemoreceptors in sensory neurons to detect and discriminate various chemicals in constantly changing environments. Among the chemoreceptors, odorant receptors (ORs) and gustatory receptors (GRs) play essential roles in sensing different odorant and tastant molecules, thereby regulating insects' physiology and behaviors such as feeding, mating, and alarming. ORs and GRs are evolutionarily related seven-transmembrane helical proteins that constitute a large family of tetrameric ion channels. In recent years, great progress has been made in the structures and molecular mechanisms of insect ORs and GRs. In this review, we summarize the available structures of insect ORs and GRs, analyze their diverse ligand recognition modes, and examine their conserved ligand activation mechanisms. These structural analyses will not only enhance our understanding of the molecular basis of insect ORs and GRs but also provide critical insights for the future discovery of repellents and attractants.},
}
@article {pmid40636103,
year = {2025},
author = {Qi, R and Lin, Y and Liu, S and Cao, X and Xie, M and Yu, C and Sun, H and Gao, L and Li, X},
title = {Vocal taking turns is premature at birth and improved by the postnatal phonetic environment in marmosets.},
journal = {National science review},
volume = {12},
number = {7},
pages = {nwaf162},
pmid = {40636103},
issn = {2053-714X},
abstract = {Precisely time-controlled vocal antiphony is crucial for the social communication of arboreal marmosets. However, it remains unclear when this antiphony is formed and how postnatal acoustic environments affect its development. In the present study, we systematically recorded the emitted calls of infant marmosets in an antiphonal calling scenario from postnatal day one (P1) to postnatal 10 weeks (W10). We found that infant marmosets emit most types of adult calls and engage in turn-taking as early as in P1. In addition, parent-reared infants emitted more antiphonal phee calls than hand-reared marmosets in W10. Call transitions in parent-reared W10 animals mainly occurred between phee calls or from phee calls to other call types. In contrast, P1 and hand-reared W10 marmosets displayed call transitions among various types of calls. These findings suggest that the antiphony in marmosets emerges on P1 but remains immature, and the antiphony skills can be improved by development environments, especially by the vocal feedback from parents.},
}
@article {pmid40633885,
year = {2025},
author = {Li, J and Chen, H and Liao, W},
title = {Biologically Annotated Heterogeneity of Depression Through Neuroimaging Normative Modeling.},
journal = {Biological psychiatry},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.biopsych.2025.07.002},
pmid = {40633885},
issn = {1873-2402},
abstract = {Depression is not a unitary disorder; it is heterogeneous in nature. Likewise, no 2 individuals with depression are entirely alike, and therefore their associated symptoms are highly personalized. Over the past decade, numerous approaches have been developed to identify neuroimaging-derived biomarkers to advance our understanding of the neurobiology of patients with depression at the group level. However, substantial clinical heterogeneity among individuals with depression hinders the development of biomarkers for personalized interventions. Recently, publicly available resources have enabled researchers to investigate precision neuromarkers for depression using integrative multineuroimaging approaches. In this review, we systematically revisit previous findings and discuss the advances in data-driven neuroimaging analyses for depression heterogeneity, including the disentangling of dimensional and overlapping strategies, individual-specific abnormal patterns based on normative modeling frameworks, and associations between multiscale organizations. We also discuss the limitations, challenges, and future directions for depression heterogeneity. A summary of these advances is crucial for enhancing the understanding of the neurobiology of depression and will facilitate more accurate diagnoses and personalized interventions.},
}
@article {pmid40632037,
year = {2025},
author = {Kumar, R and Soni, A and Ahmed, T and Beniwal, K},
title = {Experiences and Well-Being of Early-Career Trauma Nurses in India: A Mixed Methods Study.},
journal = {Journal of trauma nursing : the official journal of the Society of Trauma Nurses},
volume = {32},
number = {4},
pages = {189-200},
pmid = {40632037},
issn = {1078-7496},
mesh = {*Trauma Nursing ; *Nurses/psychology ; *Psychological Well-Being ; *Burnout, Professional/psychology ; Sleep Quality ; Anxiety ; *Occupational Stress/psychology ; Quality of Life ; Resilience, Psychological ; India ; Patient Acuity ; Sleep Initiation and Maintenance Disorders/etiology ; Humans ; Male ; Female ; Young Adult ; Adult ; Compassion Fatigue/etiology ; Qualitative Research ; Job Satisfaction ; *Nursing Staff, Hospital ; *Trauma Centers ; },
abstract = {BACKGROUND: Trauma nursing is fast-paced and high-pressure work that can affect nurses' physical and mental health. However, these effects remain unexplored among novice trauma nurses in a newly established trauma center in India.<br><br>OBJECTIVE: To examine relationships between professional quality of life, sleep disturbances, anxiety, and resilience and to explore the experiences of novice trauma nurses in a newly established trauma center in India.<br><br>METHODS: This sequential mixed-methods study was conducted between April and June 2024 in a newly established trauma center in India. A purposive sample of 80 nurses was surveyed using a demographic questionnaire, the Brief Resilience Scale, the Generalized Anxiety Disorder Scale, the Insomnia Severity Index, and the Professional Quality of Life Scale. Nine nurses were interviewed to explore their trauma experiences. The analysis included descriptive and inferential statistics, bootstrap-based mediation testing, and thematic content analysis.<br><br>RESULTS: A total of 80 nurses completed the survey (response rate: 67.8%) with a mean age of 27.7&#xa0;years (standard deviation [SD]&#xa0;=&#xa0;2.89) and average years of trauma experience of 2.08&#xa0;years (SD&#xa0;=&#xa0;1.93). Higher compassion satisfaction correlated with fewer sleep disturbances (r =&#xa0;-.23, p&#xa0;=&#xa0;.037). Burnout positively correlated with anxiety (r =&#xa0;.24, p =&#xa0;.033) and sleep disturbances (r =&#xa0;.34, p =&#xa0;.023), and negatively with nurses' resilience (r =&#xa0;-.12, p&#xa0;=&#xa0;.049). Professional quality of life significantly correlated with resilience (r =&#xa0;.18, p&#xa0;=&#xa0;.048). Resilience mediated the relationship between anxiety and both burnout (&#x3b2;&#xa0;=&#xa0;.24, bootstrap confidence interval [BCI]&#xa0;=&#xa0;0.04, 0.46, p =&#xa0;.041) and secondary traumatic stress (&#x3b2;&#xa0;=&#xa0;.24, BCI&#xa0;=&#xa0;0.03, 0.52, p&#xa0;=&#xa0;.029). Qualitative analysis revealed three major themes: personal and professional adaptation to trauma life, adverse physical and psychological issues, and challenges faced in trauma care.<br><br>CONCLUSION: Our findings highlight the adverse impact of trauma nursing on sleep, resilience, anxiety, and professional quality of life among novice nurses in a newly established Level I trauma center in India. Targeted interventions are required to enhance resilience and mitigate the impact of caring for trauma patients.},
}
@article {pmid40631920,
year = {2025},
author = {Wang, X and Jun, F and Lin, C and Wang, X},
title = {Psychedelics and the Gut Microbiome: Unraveling the Interplay and Therapeutic Implications.},
journal = {ACS chemical neuroscience},
volume = {16},
number = {15},
pages = {2747-2749},
doi = {10.1021/acschemneuro.5c00418},
pmid = {40631920},
issn = {1948-7193},
mesh = {*Gastrointestinal Microbiome/drug effects/physiology ; Humans ; *Hallucinogens/pharmacology/therapeutic use/metabolism ; Probiotics ; Animals ; Receptor, Serotonin, 5-HT2A/metabolism ; Neuronal Plasticity/drug effects/physiology ; },
abstract = {Classic psychedelics and the gut microbiome interact bidirectionally through mechanisms involving 5-HT2A receptor signaling, neuroplasticity, and microbial metabolism. This viewpoint highlights how psychedelics may reshape microbiota and how microbes influence psychedelic efficacy, proposing microbiome-informed strategies─such as probiotics or dietary interventions─to personalize and enhance psychedelic-based mental health therapies.},
}
@article {pmid40631106,
year = {2025},
author = {Ding, Y and Dunn, SLS and Sakon, JJ and Aghajan, ZM and Duan, C and Zhang, Y and Berger, JI and Rhone, AE and Nourski, KV and Kawasaki, H and Howard, MA and Roychowdhury, VP and Fried, I},
title = {Reading specific memories from human neurons before and after sleep.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {40631106},
issn = {2692-8205},
support = {R01 NS084017/NS/NINDS NIH HHS/United States ; U01 NS123128/NS/NINDS NIH HHS/United States ; },
abstract = {The ability to retrieve a single episode encountered just once is a hallmark of human intelligence and episodic memory[1]. Yet, decoding a specific memory from neuronal activity in the human brain remains a formidable challenge. Here, we develop a transformer neural network model[2, 3] trained on neuronal spikes from intracranial microelectrodes recorded during a single viewing of an audiovisual episode. Combining spikes throughout the brain via cross-channel attention[4], capable of discovering neural patterns spread across brain regions and timescales, individual participant models predict memory retrieval of specific concepts such as persons or places. Brain regions differentially contribute to memory decoding before and after sleep. Models trained using only medial temporal lobe (MTL) spikes significantly decode concepts before but not after sleep, while models trained using only frontal cortex (FC) spikes decode concepts after but not before sleep. These findings suggest a system-wide distribution of information across neural populations that transforms over wake/sleep cycles[5]. Such decoding of internally generated memories suggests a path towards brain-computer interfaces to treat episodic memory disorders through enhancement or muting of specific memories.},
}
@article {pmid40631097,
year = {2025},
author = {Lee, W and Scherschligt, X and Nishimoto, M and Rouse, AG},
title = {Neural trajectories improve motor precision.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {40631097},
issn = {2692-8205},
support = {R00 NS101127/NS/NINDS NIH HHS/United States ; },
abstract = {Populations of neurons in motor cortex signal voluntary movement. Most classic neural encoding models and current brain-computer interface decoders assume individual neurons sum together along a neural dimension to represent movement features such as velocity or force. However, large population neural analyses continue to identify trajectories of neural activity evolving with time that traverse multiple dimensions. Explanations for these neural trajectories typically focus on how cortical circuits processes learn, organize, and implement movements. However, descriptions of how these neural trajectories might improve performance, and specifically motor precision, are lacking. In this study, we proposed and tested a computational model that highlights the role of neural trajectories, through the selective co-activation and selective timing of firing rates across the neural populations, for improving motor precision. Our model uses experimental results from a center-out reaching task as inspiration to create several physiologically realistic models for the neural encoding of movement. Using a recurrent neural network to simulate how a downstream population of neurons might receive such information, like the spinal cord and motor units, we show that movements are more accurate when neural information specific to the phase and/or amplitude of movement are incorporated across time instead of an instantaneous, velocity-only tuning model. Our finding suggests that precise motor control arises from spatiotemporal recruitment of neural populations that create distinct neural trajectories. We anticipate our results will significantly impact not only how neural encoding of movement in motor cortex is described but also future understating for how brain networks communicate information for planning and executing movements. Our model also provides potential inspiration for how to incorporate selective activation across a neural population to improve future brain-computer interfaces.},
}
@article {pmid40630938,
year = {2025},
author = {Liu, L and Wang, F and Chen, X and Liu, L and Wang, Y and Bei, J and Lei, L and Zhao, Z and Tang, C},
title = {Designing Multifunctional Microneedles in Biomedical Engineering: Materials, Methods, and Applications.},
journal = {International journal of nanomedicine},
volume = {20},
number = {},
pages = {8693-8728},
pmid = {40630938},
issn = {1178-2013},
mesh = {*Needles ; Humans ; *Drug Delivery Systems/instrumentation/methods ; Tissue Engineering/methods ; *Biomedical Engineering/methods/instrumentation ; Animals ; Biocompatible Materials/chemistry ; Equipment Design ; *Microinjections/instrumentation ; Brain-Computer Interfaces ; },
abstract = {This review focuses on the emerging technology of multifunctional microneedles (MNs) within the biomedical engineering (BME) field, highlighting their potential in drug delivery, diagnostics, and therapeutics. Previous studies have explored MNs in various applications; however, their diverse functionalities across different material types and advanced application domains have been rarely comprehensively explored. This review bridges this gap by providing insights into the application of MNs in materials science, drug delivery, diagnostic monitoring, and tissue engineering. The unique properties and skin effects of various inorganic (eg, silicon, metals) and organic materials (eg, polysaccharides, polymers, proteins) used in MNs are examined. The analysis emphasizes the advantages of different MN materials, ie, their biocompatibility, degradation rates, and application specificity. In addition, the preparation processes and application scenarios of each MN type, such as minimally invasive drug delivery in transdermal applications and their benefits in tissue engineering for promoting repair, regeneration, and precise delivery of cells and growth factors in tissues like skin, cartilage, muscle, bone, and nerves, are discussed. Furthermore, this review explores the innovative use of MNs in brain-computer interfaces-an area not yet thoroughly examined. This novel application offers significant opportunities in neuroscience and clinical practice. Overall, this review provides valuable insights into the current research landscape and unexplored areas of MNs, contributing to future advancements in BME.},
}
@article {pmid40630584,
year = {2025},
author = {Hahn, NV and Stein, E and , and Donoghue, JP and Simeral, JD and Hochberg, LR and Willett, FR},
title = {Long-term performance of intracortical microelectrode arrays in 14 BrainGate clinical trial participants.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
pmid = {40630584},
support = {R01 DC009899/DC/NIDCD NIH HHS/United States ; U01 NS123101/NS/NINDS NIH HHS/United States ; R01 NS062092/NS/NINDS NIH HHS/United States ; N01 HD053403/HD/NICHD NIH HHS/United States ; R01 DC014034/DC/NIDCD NIH HHS/United States ; UH2 NS095548/NS/NINDS NIH HHS/United States ; U01 NS098968/NS/NINDS NIH HHS/United States ; RC1 HD063931/HD/NICHD NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; R01 HD077220/HD/NICHD NIH HHS/United States ; U01 DC019430/DC/NIDCD NIH HHS/United States ; },
abstract = {Brain-computer interfaces have enabled people with paralysis to control computer cursors, operate prosthetic limbs, and communicate through handwriting, speech, and typing. Most high-performance demonstrations have used silicon microelectrode "Utah" arrays to record brain activity at single neuron resolution. However, reports so far have typically been limited to one or two individuals, with no systematic assessment of the longevity, decoding accuracy, and day-to-day stability properties of chronically implanted Utah arrays. Here, we present a comprehensive evaluation of 20 years of neural data from the BrainGate and BrainGate2 pilot clinical trials. This dataset spans 2,319 recording sessions and 20 arrays from the first 14 participants in these trials. On average, arrays successfully recorded neural spiking waveforms on 35.6% of electrodes, with only a 7% decline over the study enrollment period (up to 7.6 years, with a mean of 2.8 years). We assessed movement intention decoding performance using a "decoding signal-to-noise ratio" (dSNR) metric, and found that 11 of 14 arrays provided meaningful movement decoding throughout study enrollment (dSNR > 1). Three arrays reached a peak dSNR greater than 4.5, approaching that achieved during able-bodied computer mouse control (6.29). We also found that dSNR increases logarithmically with the number of electrodes, providing a pathway for scaling performance. Longevity and reliability of Utah array recordings in this study were better than in prior nonhuman primate studies. However, achieving peak performance consistently will require addressing unknown sources of variability.},
}
@article {pmid40629288,
year = {2025},
author = {Paret, C and Jindrová, M and Kleindienst, N and Eck, J and Breman, H and Lührs, M and Barth, B and Ethofer, T and Fallgatter, AJ and Goebel, R and Hoell, A and Lockhofen, D and Reinhold, AS and Maier, S and Matthies, S and Mulert, C and Schönholz, C and van Elst, LT and Schmahl, C},
title = {A randomised controlled trial of amygdala fMRI-neurofeedback versus sham-feedback in borderline-personality disorder - systematic literature review and introduction to the BrainSTEADy trial.},
journal = {BMC psychiatry},
volume = {25},
number = {1},
pages = {687},
pmid = {40629288},
issn = {1471-244X},
support = {PA 3107/4-1//Deutsche Forschungsgemeinschaft/ ; SCHM 1526/26-1//Deutsche Forschungsgemeinschaft/ ; },
mesh = {Adult ; Female ; Humans ; Male ; *Amygdala/physiopathology/diagnostic imaging ; *Borderline Personality Disorder/therapy/physiopathology/diagnostic imaging ; Emotional Regulation ; *Magnetic Resonance Imaging/methods ; *Neurofeedback/methods ; Randomized Controlled Trials as Topic ; Multicenter Studies as Topic ; },
abstract = {BACKGROUND: Individuals with Borderline-Personality Disorder (BPD) experience intensive, unstable negative emotions. Hyperactivity of the amygdala is assumed to drive exaggerated emotional responses in BPD. Functional Magnetic Resonance Imaging (fMRI)-based neurofeedback is an endogenous neuromodulation method intended to address the imbalance of neural circuits and thus holds the potential as a treatment for BPD. Many original articles and meta-analyses show that fMRI-neurofeedback can improve psychiatric symptoms. In contrast, there is a lack of publications that aggregate and evaluate data of the safety of the treatment. Furthermore, evidence on the efficacy of fMRI-neurofeedback for the treatment of BPD is limited. Preliminary evidence suggests that downregulation of amygdala hyperactivation through fMRI-neurofeedback can ameliorate emotion dysregulation. To test this assumption, BrainSTEADy (Brain Signal Training to Enhance Affect Down-regulation), a multi-center clinical trial, is conducted. First, we present a systematic literature review evaluating the safety of fMRI-neurofeedback and assessing clinical performance in BPD. Second, we describe the study protocol of BrainSTEADy.<br><br>METHODS: Literature research: From 2,609 screened paper abstracts, 758 were identified as potentially relevant. Twenty studies reported adverse events or undesirable side effects. Two papers provided relevant data for the assessment of clinical performance in BPD. BrainSTEADy study protocol: During four sessions, patients will receive graphical fMRI-neurofeedback from their right amygdala or sham-feedback while viewing images with aversive content. The primary endpoint, 'negative affect intensity', will be assessed after the last neurofeedback session using Ecological Momentary Assessment (EMA). Secondary endpoints will be assessed after the last neurofeedback session, at 3-month and at 6-month follow-up. This trial is a multi-center, patient- and investigator-blind, randomized, parallel-group superiority study with a planned interim-analysis once half of the recruitment target is met (N&#x2009;=&#x2009;82).<br><br>DISCUSSION: As suggested by literature review, fMRI-neurofeedback is a safe treatment for patients, although future studies should systematically assess and report adverse events. Although fMRI-neurofeedback showed promising effects in BPD, current evidence is limited and calls for a randomized controlled trial such as BrainSTEADy, which aims to test whether amygdala-fMRI-neurofeedback specifically reduces emotion instability in BPD beyond nonspecific benefit. Endpoint measures encompassing EMA, clinical interviews, psychological questionnaires, quality of life, and neuroimaging will enable a comprehensive analysis of effects and mechanisms of neurofeedback treatment.<br><br>TRIAL REGISTRATION: The study protocol was first posted 2024/10/04 on ClinicalTrials.gov and received the ID NCT06626789.},
}
@article {pmid40629037,
year = {2025},
author = {Wood, H},
title = {Brain-computer interface restores naturalistic speech to a man with ALS.},
journal = {Nature reviews. Neurology},
volume = {21},
number = {8},
pages = {409},
pmid = {40629037},
issn = {1759-4766},
}
@article {pmid40628758,
year = {2025},
author = {Mathiyazhagan, S and Devasena, MSG},
title = {Motor imagery EEG signal classification using novel deep learning algorithm.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {24539},
pmid = {40628758},
issn = {2045-2322},
mesh = {*Electroencephalography/methods ; Humans ; *Deep Learning ; Algorithms ; *Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Wavelet Analysis ; *Imagination/physiology ; },
abstract = {Electroencephalography (EEG) signal classification plays a critical role in various biomedical and cognitive research applications, including neurological disorder detection and cognitive state monitoring. However, these technologies face challenges and exhibit reduced performances due to signal noise, inter-subject variability, and real-time processing demands. Thus, to overcome these limitations a novel model is presented in this research work for motor imagery (MI) EEG signal classification. To begin, the preprocessing stage of the proposed approach includes an innovative hybrid approach that combines empirical mode decomposition (EMD) for extracting intrinsic signal modes. In addition to that, continuous wavelet transform (CWT) is used for multi-resolution analysis. For spatial feature enhancement the proposed approach utilizes source power coherence (SPoC) integrated with common spatial patterns (CSP) for robust feature extraction. For final feature classification, an adaptive deep belief network (ADBN) is proposed. To attain enhanced performance the parameters of the classifier network are optimized using the Far and near optimization (FNO) algorithm. This combined approach provides superior classification accuracy and adaptability to diverse conditions in EEG signal analysis. The evaluations of the proposed approach were conducted using benchmark BCI competition IV Dataset 2a and Physionet dataset. On the BCI dataset, the proposed approach achieves 95.7% accuracy, 96.2% recall, 95.9% precision, and 97.5% specificity. In addition, it delivers 94.1% accuracy, 94.0% recall, 93.6% precision, and 95.0% specificity on the PhysioNet dataset. With better results, the proposed model attained superior performance compared to existing methods such as CNN, LSTM, and BiLSTM algorithms.},
}
@article {pmid40628277,
year = {2025},
author = {Afdideh, F and Shamsollahi, MB},
title = {Subject-specific feature extraction approach for a three-class motor imagery-based brain-computer interface enabling navigation in a virtual environment: open-access framework.},
journal = {Biomedical physics &amp; engineering express},
volume = {11},
number = {5},
pages = {},
doi = {10.1088/2057-1976/aded19},
pmid = {40628277},
issn = {2057-1976},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Male ; *Virtual Reality ; Adult ; *Imagination/physiology ; Female ; Young Adult ; *Brain/physiology ; Movement ; User-Computer Interface ; },
abstract = {Brain-Computer Interface (BCI) is a system that aids individuals with disabilities to establish a novel communication channel between the brain and computer. Among various electrophysiological sources that can drive a BCI system, Motor Imagery (MI) facilitates more natural communication for users with motor disabilities, whereas electroencephalogram (EEG) is considered the most practical brain imaging modality. However, subject training is a critical aspect of such a type of BCI. One possible solution to address this challenge is to leverage the Virtual Reality (VR) technology. This study proposes a VR in MI- and EEG-based BCI (MI-EEG-BCI-VR) framework wherein users navigate a Virtual Environment (VE) following cue-based training, and employing a subject-specific feature extraction approach. The assigned task involves performing the left hand, right hand, and feet movement imagination to navigate from the start station to the end station as quickly as possible. The generated brain signals are collected using three bipolar EEG channels only. The proposed open-access MATLAB-based MI-EEG-BCI-VR framework was validated with eight healthy participants. One participant demonstrated satisfactory performance in navigating the VE. Notably, it achieved the highest performance of 82.28 ± 5.11% for MI and 97.72 ± 4.55% for Motor Execution (ME) after just a single training session.},
}
@article {pmid40628276,
year = {2025},
author = {Fedosov, N and Medvedeva, D and Shevtsov, O and Ossadtchi, A},
title = {A reliable and reproducible real-time access to sensorimotor rhythm with a small number of optically pumped magnetometers.},
journal = {Journal of neural engineering},
volume = {22},
number = {4},
pages = {},
doi = {10.1088/1741-2552/aded35},
pmid = {40628276},
issn = {1741-2552},
mesh = {Humans ; Male ; Adult ; *Brain-Computer Interfaces ; Female ; Reproducibility of Results ; *Magnetometry/instrumentation/methods ; Young Adult ; Equipment Design ; Movement/physiology ; Imagination/physiology ; *Sensorimotor Cortex/physiology ; Computer Systems ; *Magnetoencephalography/instrumentation/methods ; },
abstract = {Objective.Recent advances in biomagnetic sensing have led to the development of compact, wearable devices capable of detecting weak magnetic fields generated by biological activity. Optically pumped magnetometers (OPMs) have shown significant promise in functional neuroimaging. Brain rhythms play a crucial role in diagnostics, cognitive research, and neurointerfaces. Here we demonstrate that a small number of OPMs can reliably capture sensorimotor rhythms (SMRs).Approach.We conducted movement execution and motor imagery (MI) experiments with nine participants in two distinct magnetically shielded rooms (MSRs), each equipped with different ambient field suppression systems. We used only 4 OPMs located above the sensorimotor region and standard common-spatial-patterns (CSPs) based processing to decode the real and imaginary movement intentions of our participants. We evaluated reproducibility of the CSP components' spectral profiles and assessed the decoding accuracy deterioration with reduction of OPM's count. We also assessed the influence of the magnetic field orientation on the decoding accuracy and implemented a real-time MI brain-computer interface (BCI) solution.Main results.Under optimal conditions, OPM sensors deliver informative signals suitable for practical MI BCI applications. Those subjects who participated in the experiments in both MSRs exhibit highly reproducible SMR spectral patterns across two different magnetically shielded environments. The magnetic field components with radial orientation yield higher decoding accuracy than their tangential counterparts. In some subjects we observed more than 80% of binary decoding accuracy using a single OPM sensor. Finally we demonstrate real-time performance of our system along with clearly pronounced and behaviorally relevant fluctuations of the SMR power.Significance.For the first time, we demonstrated reliable and reproducible tracking of SMR components using a small number of contactless OPM sensors during movement execution and MI. Our findings pave the way for more efficient post-stroke neurorehabilitation by enabling MI-based BCI solutions to accelerate functional recovery.},
}
@article {pmid40627787,
year = {2025},
author = {Li, Y and Zhang, J},
title = {Utilizing statistical analysis for motion imagination classification in brain-computer interface systems.},
journal = {PloS one},
volume = {20},
number = {7},
pages = {e0327121},
pmid = {40627787},
issn = {1932-6203},
mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography/methods ; *Imagination/physiology ; Algorithms ; *Motion ; *Brain/physiology ; Male ; },
abstract = {In this study, we introduce a novel Field-Agnostic Riemannian-Kernel Alignment (FARKA) method to advance the classification of motion imagination in Brain-Computer Interface (BCI) systems. BCI systems enable direct control of external devices through brain activity, bypassing peripheral nerves and muscles. Among various BCI technologies, electroencephalography (EEG) based on non-intrusive cortical potential signals stands out due to its high temporal resolution and non-invasive nature. EEG-based BCI technology encodes human brain intentions into cortical potentials, which are recorded and decoded into control commands. This technology is crucial for applications in motion rehabilitation, training optimization, and motion control. The proposed FARKA method combines Riemannian Alignment for sample alignment, Riemannian Tangent Space for spatial representation extraction, and Knowledge Kernel Adaptation to learn field-agnostic kernel matrices. Our approach addresses the limitations of current methods by enhancing classification performance and efficiency in inter-individual MI tasks. Experimental results on three public EEG datasets demonstrate the superior performance of FARKA compared to existing methods.},
}
@article {pmid40627473,
year = {2025},
author = {Zhao, Z and Cao, Y and Yu, H and Yu, H and Huang, J},
title = {CNNViT-MILF-a: A Novel Architecture Leveraging the Synergy of CNN and ViT for Motor Imagery Classification.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3587026},
pmid = {40627473},
issn = {2168-2208},
abstract = {Accurate motor imagery (MI) classification in EEG-based brain-computer interfaces (BCIs) is essential for applications in engineering, medicine, and artificial intelligence. Due to the limitations of single-model approaches, hybrid model architectures have emerged as a promising direction. In particular, convolutional neural networks (CNNs) and vision transformers (ViTs) demonstrate strong complementary capabilities, leading to enhanced performance. This study proposes a series of novel models, termed as CNNViT-MI, to explore the synergy of CNNs and ViTs for MI classification. Specifically, five fusion strategies were defined: parallel integration, sequential integration, hierarchical integration, early fusion, and late fusion. Based on these strategies, eight candidate models were developed. Experiments were conducted on four datasets: BCI competition IV dataset 2a, BCI competition IV dataset 2b, high gamma dataset, and a self-collected MI-GS dataset. The results demonstrate that CNNViT-MILF-a achieves the best performance among all candidates by leveraging ViT as the backbone for global feature extraction and incorporating CNN-based local representations through a late fusion strategy. Compared to the best-performing state-ofthe-art (SOTA) methods, mean accuracy was improved by 2.27%, 2.31%, 0.74%, and 2.50% on the respective datasets, confirming the model's effectiveness and broad applicability, other metrics showed similar improvements. In addition, significance analysis, ablation studies, and visualization analysis were conducted, and corresponding clinical integration and rehabilitation protocols were developed to support practical use in healthcare.},
}
@article {pmid40627471,
year = {2025},
author = {Chen, X and Fu, Z and Zhang, P and Chen, X and Huang, J},
title = {Intracortical Brain-Machine Interfaces with High-Performance Neural Decoding through Efficient Transfer Meta-learning.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3586870},
pmid = {40627471},
issn = {1558-2531},
abstract = {Implantable brain-machine interfaces (iBMIs) have emerged as a groundbreaking neural technology for restoring motor function and enabling direct neural communication pathways. Despite their therapeutic potential in neurological rehabilitation, the critical challenge of neural decoder calibration persists, particularly in the context of transfer learning. Traditional calibration approaches assume the availability of extensive neural recordings, which is often impractical in clinical settings due to patient fatigue and neural signal variability. Furthermore, the inherent constraints of implanted neural processors-including limited computational capacity and power consumption requirements-demand streamlined processing algorithms. To address these clinical and technical challenges, we developed DMM-WcycleGAN (Dimensionality Reduction Model-Agnostic Meta-Learning based Wasserstein Cycle Generative Adversarial Networks), a novel neural decoding framework that integrates meta-learning principles with optimal transfer learning strategies. This innovative approach enables efficient decoder calibration using minimal neural data while implementing dimensionality reduction techniques to optimize computational efficiency in implanted devices. In vivo experiments with non-human primates demonstrated DMM-WcycleGAN's superior performance in mitigating neural signal distribution shifts between historical and current recordings, achieving a 3% enhancement in neural decoding accuracy using only ten calibration trials while reducing the calibration duration by over 70%, thus significantly improving the clinical viability of iBMI systems.},
}
@article {pmid40626564,
year = {2025},
author = {Hu, Z and Luo, K and Liu, Y},
title = {Classification of motor imagery based on multi-scale feature extraction and fusion-residual temporal convolutional network.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-12},
doi = {10.1080/10255842.2025.2528892},
pmid = {40626564},
issn = {1476-8259},
abstract = {Brain-computer interface (BIC) decodes electroencephalogram (EEG) signals to realize the interaction between brain and external devices. However, traditional methods show limited performance in motor imagery electroencephalogram (MI-EEG) classification. In this paper, we introduce a multi-scale temporal convolutional network (MS-TCNet) that employs parallel multi-scale convolutions for spatiotemporal feature extraction, efficient channel attention (ECA) for channel weights optimization, and fusion-residual temporal convolution (FR-TCN) for high-level temporal feature capture. Experimental results show that MS-TCNet achieved remarkable decoding accuracies of 87.85% and 92.85% on the BCI IV-2a and BCI IV-2b datasets, respectively. The proposed MS-TCNet surpasses existing baseline models across various performance metrics, demonstrating its effectiveness in advancing MI-EEG decoding.},
}
@article {pmid40624803,
year = {2025},
author = {Li, S and Gao, S and Hu, Y and Xu, J and Sheng, W},
title = {Brain-Computer Interfaces in Spinal Cord Injury: A Promising Therapeutic Strategy.},
journal = {The European journal of neuroscience},
volume = {62},
number = {1},
pages = {e70183},
doi = {10.1111/ejn.70183},
pmid = {40624803},
issn = {1460-9568},
support = {2023TSYCLJ0031//Program of Technological Leading Talent of Tianshan Talent/ ; 2023YFY-QKMS-06//Youth Foundation of Research and Development/ ; 2021D01D18//Key Program of Natural Science Foundation of Xinjiang Uygur Autonomous Region/ ; 82360257//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Spinal Cord Injuries/rehabilitation/physiopathology/therapy ; *Neurological Rehabilitation/methods ; Animals ; },
abstract = {The current treatment regimen for spinal cord injury (SCI), a neurological disorder with a high incidence of disability, is based on early surgical decompression and administration of pharmacological agents. However, the efficacy of such an approach remains limited, and most patients have sensory and functional deficits below the level of injury, which seriously affects their quality of life. This necessitates further exploration into effective treatment modalities. In recent years, considerable advancements have been made in developing and utilizing brain-computer interfaces (BCI), which facilitate neurorehabilitation and enhance motor function by transforming brain signals into diverse forms of output commands. BCI-assisted systems provide alternative means of rehabilitative exercise or limb movement in patients with SCI, including electrical stimulation and exoskeleton robots. BCI shows great potential in the rehabilitation of patients with SCI. This review summarizes the current research status and limitations of BCI for SCI to provide novel insights into the concept of multimodal rehabilitation and treatment of SCI and facilitate BCI's future development.},
}
@article {pmid40624755,
year = {2025},
author = {Barios, JA and Vales, Y and Catalán, JM and Blanco-Ivorra, A and Martínez-Pascual, D and García-Aracil, N},
title = {Post-Movement Beta Rebound for Longitudinal Monitoring of Motor Rehabilitation in Stroke Patients Using an Exoskeleton-Assisted Paradigm.},
journal = {International journal of neural systems},
volume = {35},
number = {9},
pages = {2550044},
doi = {10.1142/S0129065725500443},
pmid = {40624755},
issn = {1793-6462},
mesh = {Humans ; *Stroke Rehabilitation/methods/instrumentation ; Male ; Middle Aged ; Female ; Aged ; *Stroke/physiopathology/diagnosis ; *Exoskeleton Device ; *Sensorimotor Cortex/physiopathology ; *Beta Rhythm/physiology ; Electroencephalography ; Adult ; Longitudinal Studies ; *Motor Activity/physiology ; Brain-Computer Interfaces ; Movement/physiology ; },
abstract = {Task-oriented rehabilitation is essential for hand function recovery in stroke patients, and recent advancements in BCI-controlled exoskeletons and neural biomarkers - such as post-movement beta rebound (PMBR) - offer new pathways to optimize these therapies. Movement-related EEG signals from the sensorimotor cortex, particularly PMBR (post-movement) and event-related desynchronization (ERD, during movement), exhibit high task specificity and correlate with stroke severity. This study evaluated PMBR in 34 chronic stroke patients across two cohorts, along with a control group of 16 healthy participants, during voluntary and exoskeleton-assisted movement tasks. Longitudinal tracking in the second cohort enabled the analysis of PMBR changes, with EEG recordings acquired at three timepoints over a 30-session rehabilitation program. Findings revealed significant PMBR alterations in both passive and active movement tasks: patients with severe impairment lacked a PMBR dipole in the ipsilesional hemisphere, while moderately impaired patients showed a diminished response. The marked differences in PMBR patterns between stroke patients and controls highlight the extent of sensorimotor cortex disruption due to stroke. ERD showed minimal task-specific variation, underscoring PMBR as a more reliable biomarker of motor function impairment. These findings support the use of PMBR, particularly the PMBR/ERD ratio, as a biomarker for EEG-guided monitoring of motor recovery over time during exoskeleton-assisted rehabilitation.},
}
@article {pmid40622874,
year = {2025},
author = {Annett, EG and Shook, JR and Giordano, J},
title = {Super Soldiers or Social Burden? Ethical Exploration of the Benefits and Costs of Military Bioenhancement.},
journal = {AJOB neuroscience},
volume = {16},
number = {4},
pages = {212-221},
doi = {10.1080/21507740.2025.2519457},
pmid = {40622874},
issn = {2150-7759},
mesh = {Humans ; *Military Personnel/psychology ; *Biomedical Enhancement/ethics/economics ; },
abstract = {Biotechnological enhancements for military personnel arouse scrutiny, beyond the ethics of experimental research and due care during operational service, to the eventual return to a civilian life. Reversal of enhancements-by withdrawal, extraction, deactivation, modification, destruction, etc.-will be just as experimental and consequential. Super soldiering may not smoothly transition to ordinary habilitation and lifestyle. Complete reversions of dramatic augmentations, such as prosthetics or brain-computer interfacing, could be more damaging to the person than the initial installation. Partial reversions would be just as perplexing, as discharged personnel retain workable technology to prevent disability while other careers next beckon for a suitably empowered individual. Either way, all such biotechnological enhancements must be treated as ethical and social experiments having both positive and negative potential outcomes. Life stages of technologically modified military personnel require special ethical consideration beyond the lifecycle of the technology itself. The post-enhancement veteran is a largely unexplored area, and we propose that these civilian "supra-soldiers" will become a cohort of increasing interest, requiring continued care and ethical support. To that end, we suggest a system of guidelines to ensure ethically sound support for those who serve, and have served, in national defense.},
}
@article {pmid40622660,
year = {2025},
author = {Kong, L and Zhu, B and Zhuang, Y and Lai, J and Hu, S},
title = {Viewing Psychiatric Disorders Through Viruses: Simple Architecture, Burgeoning Implications.},
journal = {Neuroscience bulletin},
volume = {41},
number = {9},
pages = {1669-1688},
pmid = {40622660},
issn = {1995-8218},
mesh = {Humans ; *Mental Disorders/virology ; Animals ; *Brain/virology ; *Gastrointestinal Microbiome/physiology ; *Viruses ; *Virus Diseases/complications ; },
abstract = {A growing interest in the comprehensive pathogenic mechanisms of psychiatric disorders from the perspective of the microbiome has been witnessed in recent decades; the intrinsic link between microbiota and brain function through the microbiota-gut-brain axis or other pathways has gradually been realized. However, little research has focused on viruses-entities characterized by smaller dimensions, simpler structures, greater diversity, and more intricate interactions with their surrounding milieu compared to bacteria. To date, alterations in several populations of bacteriophages and viruses have been documented in both mouse models and patients with psychiatric disorders, including schizophrenia, major depressive disorder, autism spectrum disorder, and Alzheimer's disease, accompanied by metabolic disruptions that may directly or indirectly impact brain function. In addition, eukaryotic virus infection-mediated brain dysfunction provides insights into the psychiatric pathology involving viruses. Efforts towards virus-based diagnostic and therapeutic approaches have primarily been documented. However, limitations due to the lack of large-scale cohort studies, reliability, clinical applicability, and the unclear role of viruses in microbiota interactions pose a challenge for future studies. Nevertheless, it is conceivable that investigations into viruses herald a new era in the field of precise psychiatry.},
}
@article {pmid40621214,
year = {2025},
author = {Kwon, J and Min, BK},
title = {Deep learning-based electroencephalic decoding of the phase-lagged transcranial alternating current stimulation.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1545726},
pmid = {40621214},
issn = {1662-5161},
abstract = {We investigated whether the phase-lag types of cross-frequency coupled alternating current stimulation (CFC-tACS), a non-invasive technique aimed at enhancing cognitive functions, could be decoded using task-based electroencephalographic (EEG) signals. EEG recordings were obtained from 21 healthy individuals engaged in a modified Sternberg task. CFC-tACS was administered online for 6 s during the middle of the retention period with either a 45° or 180° phase lag between the central executive network and the default mode network. To decode different phase-lag tACS conditions, we trained a modified EEGNet using task-based EEG signals before and after the online tACS application. When utilizing parietal EEG signals, the model achieved a decoding accuracy of 81.73%. Feature maps predominantly displayed EEG beta activity in the parietal region, suggesting that the model heavily weighted the beta band, indicative of top-down cognitive control influenced by tACS phase-lag type. Thus, EEG signals can decode online stimulation types, and task-related EEG spectral characteristics may indicate neuromodulatory activity during brain stimulation. This study could advance communicative strategies in brain-machine interfacing (BMI)-neuromodulation within a closed-loop system.},
}
@article {pmid40620352,
year = {2025},
author = {Ying, A and Lv, J and Huang, J and Wang, T and Si, P and Zhang, J and Zuo, G and Xu, J},
title = {A feature fusion network with spatial-temporal-enhanced strategy for the motor imagery of force intensity variation.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1591398},
pmid = {40620352},
issn = {1662-4548},
abstract = {INTRODUCTION: Motor imagery (MI)-based brain-computer interfaces (BCI) offers promising applications in rehabilitation. Traditional force-based MI-BCI paradigms generally require subjects to imagine constant force during static or dynamic state. It is challenging to meet the demands of dynamic interaction with force intensity variation in MI-BCI systems.<br><br>METHODS: To address this gap, we designed a novel MI paradigm inspired by daily life, where subjects imagined variations in force intensity during dynamic unilateral upper-limb movements. In a single trial, the subjects were required to complete one of three combinations of force intensity variations: large-to-small, large-to-medium, or medium-to-small. During the execution of this paradigm, electroencephalography (EEG) features exhibit dynamic coupling, with subtle variations in intensity, timing, frequency coverage, and spatial distribution, as the force intensity imagined by the subjects changed. To recognize these fine-grained features, we propose a feature fusion network with a spatial-temporal-enhanced strategy and an information reconstruction (FN-SSIR) algorithm. This model combines a multi-scale spatial-temporal convolution module with a spatial-temporal-enhanced strategy, a convolutional auto-encoder for information reconstruction, and a long short-term memory with self-attention, enabling the comprehensive extraction and fusion of EEG features across fine-grained time-frequency variations and dynamic spatial-temporal patterns.<br><br>RESULTS: The proposed FN-SSIR achieved a classification accuracy of 86.7% &#xb1; 6.6% on our force variation MI dataset, and 78.4% &#xb1; 13.0% on the BCI Competition IV 2a dataset.<br><br>DISCUSSION: These findings highlight the potential of this paradigm and algorithm for advancing MI-BCI systems in rehabilitation training based on dynamic force interactions.},
}
@article {pmid40619564,
year = {2025},
author = {Beressa, G and Feyissa, GT and Murimi, M and Muhammed, AH and Abdulkadir, A and Jema, AT and Alenko, A and Kebede, A and Lencha, B and Sahiledengle, B and Solomon, D and Atlaw, D and Gomora, D and Zenbaba, D and Dibaba, D and Nigussie, E and Nugusu, F and Desta, F and Ejigu, N and Wake, SK and Girma, S and Jidha, TD and Yazew, T and Tadesse, TM and Elala, T and Tekalegn, Y and Belachew, T},
title = {Nutritional status and associated factors among school age children in Southeast Ethiopia using a bayesian analysis approach.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {24141},
pmid = {40619564},
issn = {2045-2322},
mesh = {Humans ; Child ; Ethiopia/epidemiology ; *Nutritional Status ; Bayes Theorem ; Adolescent ; Male ; Female ; Cross-Sectional Studies ; *Growth Disorders/epidemiology ; Prevalence ; Body Mass Index ; *Thinness/epidemiology ; Malnutrition/epidemiology ; Schools ; },
abstract = {Undernutrition among school-age children is a major public health concern in sub-Saharan Africa. This study aimed to assess the nutritional status and associated factors among school-age children in the hard-to-reach pastoral communities in Southeast Ethiopia. We conducted a school-based cross-sectional study among 395 randomly selected schoolchildren aged 7-14 years in pastoral communities in Bale Zone. We employed a hybrid of multistage sampling and systematic random sampling to select the respondents. We used the Z scores of height for age (HAZ) and body mass index for age (BAZ) based on the World Health Organization (WHO) guidance to classify nutritional status of the school-age children. We conducted a Bayesian linear regression analysis estimation using Markov chain Monte Carlo (MCMC). We calculated the mean, along with a 95% Bayesian credible interval (BCI), to identify factors associated with nutritional status. The overall prevalence of stunting and thinness among school-age children 7-14 years was 26.6% (95% CI: 21.8, 31.4%) and 28.9% (95% CI: 24.3, 33.2%), respectively. The mean and SD of HAZ and BAZ scores were -0.82 (2.13) and -0.87 (1.73), respectively. A unit increment in the age of the child and a unit increment in dietary diversity score were associated with an increment in HAZ scores by 0.122 and 0.120 units, respectively. Travelling to school for more than 30 min and more (compared to travelling less than 30 min) and being a child of a literate father (compared to being a child of an illiterate father) were associated with a decrement in the mean HAZ scores by 0.81 and 0.675 units, respectively. Children who come from rich families had BAZ scores, which are about 0.50 units higher when compared to those children coming from poor families. The high burden of stunting and thinning among the hard-to-reach pastoral communities underscores the importance of strengthening nutrition intervention programs such as school feeding and multisectoral collaboration and economic empowerment to improve accessibility of diversified food among school-age children in the hard-to-reach pastoral communities. Younger school children, children from poor families and children who have less access to school and diverse diets should be prioritised during school based nutritional interventions.},
}
@article {pmid40616172,
year = {2025},
author = {Ji, X and Zhang, J and Chen, D and Qin, Q and Huang, F},
title = {Research on transcranial magnetic stimulation for stroke rehabilitation: a visual analysis based on CiteSpace.},
journal = {European journal of medical research},
volume = {30},
number = {1},
pages = {575},
pmid = {40616172},
issn = {2047-783X},
support = {No.CRSI2022CZ-17//China Rehabilitation Research Center under the Central Public Welfare Scientific Research Institute Basic Research Business Fund Project/ ; },
mesh = {Humans ; *Transcranial Magnetic Stimulation/methods ; *Stroke Rehabilitation/methods ; *Stroke/therapy ; Bibliometrics ; },
abstract = {OBJECTIVE: This study aimed to analyze recent research and emerging trends in transcranial magnetic stimulation (TMS) for stroke rehabilitation.<br><br>METHODS: We employed bibliometric methods to retrieve relevant Chinese and English literature on TMS for stroke rehabilitation from China National Knowledge Infrastructure (CNKI) and Web of Science Core Collection (WOSCC) respectively, including publications up to April 10, 2025. CiteSpace 6.4.R1 was utilized to generate knowledge maps, focusing on authors, institutions, countries, and keywords.<br><br>RESULTS: We identified 1301 publications since the inception of the database through April 10, 2025, including 797 articles in Chinese and 504 articles in English. The number of articles available in both languages increased over time. Fudan University and University of Manchester were the institutions with the most outputs. Co-occurrence and clustering keyword analyses revealed similarities between Chinese and English terms, with key research areas include the role of TMS in motor cortex areas, post-stroke cognitive impairment (PSCI), and dysphagia, and TMS has been integrated with other therapeutic approaches for stroke patients.<br><br>CONCLUSION: TMS, a noninvasive brain stimulation technique, has been applied to improve stroke patients' functional outcomes and daily living skills. Future investigations should integrate TMS with cutting-edge technologies including artificial intelligence and brain&#x2012;computer interfaces to uncover its full potential in restoring neural function in stroke survivors.},
}
@article {pmid40615688,
year = {2025},
author = {Li, Y and Wang, YJ and Su, C and Deng, F and Pan, Y},
title = {Bidirectional information flow in cooperative learning reflects emergent leadership.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {1000},
pmid = {40615688},
issn = {2399-3642},
support = {Nos. 62207025, 62337001//National Natural Science Foundation of China (National Science Foundation of China)/ ; No. LMS25C090002//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
mesh = {Humans ; *Leadership ; Male ; Female ; *Learning/physiology ; *Cooperative Behavior ; Adult ; Spectroscopy, Near-Infrared ; Young Adult ; *Brain/physiology ; },
abstract = {Advances in social neuroscience have shown that one of the fundamental characteristics of cooperative learning is synchronization between learners' brains. However, the directionality of this synchronization, and the role of emergent leadership (i.e., a group leader emerges naturally), in cooperative learning remain unclear. Here, we investigated the directionality and dynamics of information flow by leveraging functional near-infrared spectroscopy (fNIRS) hyperscanning and Granger causality analysis (GCA). Through a 6 min dyadic cooperative learning task, we observed that dyads' utterance score increased over time and remained stable at the end of interaction, suggesting successful cooperative learning. At the neural level, we found a stronger leader-to-follower Granger causality in the left middle temporal gyrus, alongside a more pronounced follower-to-leader causality in the left sensorimotor cortex. Moreover, we found that information transfer in both directions increased and peaked around the first half of time into the task, followed by a decline. These temporally similar yet spatially dissociable patterns of directional information flow suggest a hierarchical organization of bidirectional communication during cooperative learning with emergent leadership.},
}
@article {pmid40615618,
year = {2025},
author = {Hobbs, FDR and Dorward, J and Hayward, G and Yu, LM and Saville, BR and Butler, CC and , },
title = {The PRINCIPLE randomised controlled open label platform trial of hydroxychloroquine for treating COVID19 in community based patients at high risk.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {23850},
pmid = {40615618},
issn = {2045-2322},
support = {CV220-074//UK Research and Innovation/ ; CV220-074//UK Research and Innovation/ ; CV220-074//UK Research and Innovation/ ; CV220-074//UK Research and Innovation/ ; CV220-074//UK Research and Innovation/ ; CV220-074//UK Research and Innovation/ ; MC_PC_19079//National Institute for Health and Care Research/ ; MC_PC_19079//National Institute for Health and Care Research/ ; MC_PC_19079//National Institute for Health and Care Research/ ; MC_PC_19079//National Institute for Health and Care Research/ ; MC_PC_19079//National Institute for Health and Care Research/ ; MC_PC_19079//National Institute for Health and Care Research/ ; },
mesh = {Humans ; *Hydroxychloroquine/therapeutic use/adverse effects/administration &amp; dosage ; *COVID-19 Drug Treatment ; Female ; Male ; Aged ; Middle Aged ; United Kingdom/epidemiology ; SARS-CoV-2 ; COVID-19/virology ; *Antiviral Agents/therapeutic use ; Hospitalization/statistics &amp; numerical data ; Prospective Studies ; Aged, 80 and over ; Treatment Outcome ; },
abstract = {Early on in the COVID-19 pandemic, we aimed to assess the effectiveness of hydroxychloroquine on reducing the need for hospital admission in patients in the community at higher risk of complications from COVID-19 syndromic illness (testing was largely unavailable at the time, hence not microbiologically confirmed SARS-CoV-2 infection), as part of the national open-label, multi-arm, prospective, adaptive platform, randomised clinical trial in community care in the United Kingdom (UK). People aged 65 and over, or aged 50 and over with comorbidities, and who had been unwell for up to 14 days with suspected COVID-19 were randomised to usual care with the addition of hydroxychloroquine, 200 mg twice a day for seven days, or usual care without hydroxychloroquine (control). Participants were recruited based on symptoms and approximately 5% had confirmed SARS-COV2 infection. The primary outcome while hydroxychloroquine was in the trial was hospital admission or death related to suspected COVID-19 infection within 28 days from randomisation. First recruitment was on April 2, 2020, and the hydroxychloroquine arm was suspended by the UK Medicines Regulator on May 22, 2020. 207 were randomised to hydroxychloroquine and 206 to usual care, and 190 and 194 contributed to the primary analysis results presented, respectively. There was no swab result available within 28 days of randomisation for 39% in both groups: 107 (54%) in the hydroxychloroquine group and 111 (55%) in the usual care group tested negative for SARS-Cov-2, and 13 (7%) and 11 (5%) tested positive. 13 participants, (seven (3·7%) in the usual care plus hydroxychloroquine and six (3.1%) in the usual care group were hospitalized (odds ratio 1·04 [95% BCI 0·36 to 3.00], probability of superiority 0·47). There was one serious adverse event, in the usual care group. More people receiving hydroxychloroquine reported nausea. We found no evidence from this treatment arm of the PRINCIPLE trial, stopped early and therefore under-powered for reasons external to the trial, that hydroxychloroquine reduced hospital admission or death in people with suspected, but mostly unconfirmed COVID-19.},
}
@article {pmid40615558,
year = {2025},
author = {Xi, C and Lu, B and Guo, X and Qin, Z and Yan, C and Hu, S},
title = {Characteristics of brain network connectome and connectome-based efficacy predictive model in bipolar depression.},
journal = {Molecular psychiatry},
volume = {30},
number = {11},
pages = {5150-5160},
pmid = {40615558},
issn = {1476-5578},
mesh = {Humans ; *Bipolar Disorder/physiopathology/drug therapy ; Connectome/methods ; Male ; Female ; Magnetic Resonance Imaging/methods ; Brain/physiopathology ; Adult ; Nerve Net/physiopathology ; Quetiapine Fumarate/therapeutic use/pharmacology ; Middle Aged ; Neural Pathways/physiopathology ; Treatment Outcome ; Machine Learning ; },
abstract = {Aberrant functional connectivity (FC) between brain networks has been indicated closely associated with bipolar disorder (BD). However, the previous findings of specific brain network connectivity patterns have been inconsistent, and the clinical utility of FCs for predicting treatment outcomes in bipolar depression was underexplored. To identify robust neuro-biomarkers of bipolar depression, a connectome-based analysis was conducted on resting-state functional MRI (rs-fMRI) data of 580 bipolar depression patients and 116 healthy controls (HCs). A subsample of 148 patients underwent a 4-week quetiapine treatment with post-treatment clinical assessment. Adopting machine learning, a predictive model based on pre-treatment brain connectome was then constructed to predict treatment response and identify the efficacy-specific networks. Distinct brain network connectivity patterns were observed in bipolar depression compared to HCs. Elevated intra-network connectivity was identified within the default mode network (DMN), sensorimotor network (SMN), and subcortical network (SC); and as to the inter-network connectivity, increased FCs were between the DMN, SMN and frontoparietal (FPN), ventral attention network (VAN), and decreased FCs were between the SC and cortical networks, especially the DMN and FPN. And the global network topology analyses revealed decreased global efficiency and increased characteristic path length in BD compared to HC. Further, the support vector regression model successfully predicted the efficacy of quetiapine treatment, as indicated by a high correspondence between predicted and actual HAMD reduction ratio values (r(df=147)=0.4493, p = 2*10[-4]). The identified efficacy-specific networks primarily encompassed FCs between the SMN and SC, and between the FPN, DMN, and VAN. These identified networks further predicted treatment response with r = 0.3940 in the subsequent validation with an independent cohort (n = 43). These findings presented the characteristic aberrant patterns of brain network connectome in bipolar depression and demonstrated the predictive potential of pre-treatment network connectome for quetiapine response. Promisingly, the identified connectivity networks may serve as functional targets for future precise treatments for bipolar depression.},
}
@article {pmid40614757,
year = {2025},
author = {Mishler, J and Yun, R and Perlmutter, S and Rao, RPN and Fetz, E},
title = {Manipulation of neuronal activity by an artificial spiking neural network implemented on a closed-loop brain-computer interface in non-human primates.},
journal = {Journal of neural engineering},
volume = {22},
number = {4},
pages = {},
pmid = {40614757},
issn = {1741-2552},
support = {P51 OD010425/OD/NIH HHS/United States ; P51 RR000166/RR/NCRR NIH HHS/United States ; U42 OD011123/OD/NIH HHS/United States ; R37 NS012542/NS/NINDS NIH HHS/United States ; R01 NS012542/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; *Neurons/physiology ; *Action Potentials/physiology ; *Neural Networks, Computer ; Macaca mulatta ; Motor Cortex/physiology ; Male ; },
abstract = {Objective.Closed-loop brain-computer interfaces can be used to bridge, modulate, or repair damaged connections within the brain to restore functional deficits. Towards this goal, we demonstrate that small artificial spiking neural networks can be bidirectionally interfaced with single neurons (SNs) in the neocortex of non-human primates (NHPs) to create artificial connections between the SNs to manipulate their activity in predictable ways.Approach.Spikes from a small group of SNs were recorded from primary motor cortex of two awake NHPs during rest. The SNs were then interfaced with a small network of integrate-and-fire units (IFUs) that were programmed on a custom clBCI. Spikes from the SNs evoked excitatory and/or inhibitory postsynaptic potentials in the IFUs, which themselves spiked when their membrane potentials exceeded a predetermined threshold. Spikes from the IFUs triggered single pulses of intracortical microstimulation (ICMS) to modulate the activity of the cortical SNs.Main results.We show that the altered closed-loop dynamics within the cortex depends on several factors including the connectivity between the SNs and IFUs, as well as the precise timing of the ICMS. We additionally show that the closed-loop dynamics can reliably be modeled from open-loop measurements.Significance.Our results demonstrate a new type of hybrid biological-artificial neural system based on a clBCI that interfaces SNs in the brain with artificial IFUs to modulate biological activity in the brain. Our model of the closed-loop dynamics may be leveraged in the future to develop training algorithms that shape the closed-loop dynamics of networks in the brain to correct aberrant neural activity and rehabilitate damaged neural circuits.},
}
@article {pmid40614457,
year = {2025},
author = {Yan, H and Wang, Z and Li, J},
title = {MSC-transformer-based 3D-attention with knowledge distillation for multi-action classification of separate lower limbs.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {191},
number = {},
pages = {107806},
doi = {10.1016/j.neunet.2025.107806},
pmid = {40614457},
issn = {1879-2782},
mesh = {Humans ; *Lower Extremity/physiology ; Electroencephalography/methods ; *Attention/physiology ; *Deep Learning ; Neural Networks, Computer ; *Imagination/physiology ; Movement/physiology ; },
abstract = {Deep learning has been extensively applied to motor imagery (MI) classification using electroencephalogram (EEG). However, most existing deep learning models do not extract features from EEG using dimension-specific attention mechanisms based on the characteristics of each dimension (e.g., spatial dimension), while effectively integrate local and global features. Furthermore, implicit information generated by the models has been ignored, leading to underutilization of essential information of EEG. Although MI classification has been relatively thoroughly investigated, the exploration of classification including real movement (RM) and motor observation (MO) is very limited, especially for separate lower limbs. To address the above problems and limitations, we proposed a multi-scale separable convolutional Transformer-based filter-spatial-temporal attention model (MSC-T3AM) to classify multiple lower limb actions. In MSC-T3AM, spatial attention, filter and temporal attention modules are embedded to allocate appropriate attention to each dimension. Multi-scale separable convolutions (MSC) are separately applied after the projections of query, key, and value in self-attention module to improve computational efficiency and classification performance. Furthermore, knowledge distillation (KD) was utilized to help model learn suitable probability distribution. The comparison results demonstrated that MSC-T3AM with online KD achieved best performance in classification accuracy, exhibiting an elevation of 2 %-19 % compared to a few counterpart models. The visualization of features extracted by MSC-T3AM with online KD reiterated the superiority of the proposed model. The ablation results showed that filter and temporal attention modules contributed most for performance improvement (improved by 2.8 %), followed by spatial attention module (1.2 %) and MSC module (1 %). Our study also suggested that online KD was better than offline KD and the case without KD. The code of MSC-T3AM is available at: https://github.com/BICN001/MSC-T3AM.},
}
@article {pmid40611671,
year = {2025},
author = {Alemu, RZ and Blakeman, A and Fung, AL and Hazen, M and Negandhi, J and Papsin, BC and Cushing, SL and Gordon, KA},
title = {Children With Bilateral Cochlear Implants Show Emerging Spatial Hearing of Stationary and Moving Sound.},
journal = {Trends in hearing},
volume = {29},
number = {},
pages = {23312165251356333},
pmid = {40611671},
issn = {2331-2165},
mesh = {Humans ; *Sound Localization ; *Cochlear Implants ; Child ; Male ; Female ; *Cochlear Implantation/instrumentation ; Auditory Threshold ; Speech Perception ; Adolescent ; Cues ; Acoustic Stimulation ; *Persons with Hearing Disabilities/rehabilitation/psychology ; Case-Control Studies ; Eye Movements ; Noise/adverse effects ; Head Movements ; *Hearing Loss, Bilateral/physiopathology/rehabilitation/psychology ; },
abstract = {Spatial hearing in children with bilateral cochlear implants (BCIs) was assessed by: (a) comparing localization of stationary and moving sound, (b) investigating the relationship between sound localization and sensitivity to interaural level and timing differences (ILDs/ITDs), (c) evaluating effects of aural preference on sound localization, and (d) exploring head and eye (gaze) movements during sound localization. Children with BCIs (n = 42, MAge = 12.3 years) with limited duration of auditory deprivation and peers with typical hearing (controls; n = 37, MAge = 12.9 years) localized stationary and moving sound with unrestricted head and eye movements. Sensitivity to binaural cues was measured by a lateralization task to ILDs and ITDs. Spatial separation effects were measured by spondee-word recognition thresholds (SNR thresholds) when noise was presented in front (colocated/0°) or with 90° of left/right separation. BCI users had good speech reception thresholds (SRTs) in quiet but higher SRTs in noise than controls. Spatial separation of noise from speech revealed a greater advantage for the right ear across groups. BCI users showed increased errors localizing stationary sound and detecting moving sound direction compared to controls. Decreased ITD sensitivity occurred with poorer localization of stationary sound in BCI users. Gaze movements in BCI users were more random than controls for stationary and moving sounds. BCIs support symmetric hearing in children with limited duration of auditory deprivation and promote spatial hearing which is albeit impaired. Spatial hearing was thus considered to be "emerging." Remaining challenges may reflect disruptions in ITD sensitivity and ineffective gaze movements.},
}
@article {pmid40611622,
year = {2025},
author = {Dahò, M and Monzani, D},
title = {The multifaceted nature of inner speech: Phenomenology, neural correlates, and implications for aphasia and psychopathology.},
journal = {Cognitive neuropsychology},
volume = {42},
number = {1-2},
pages = {1-21},
doi = {10.1080/02643294.2025.2527983},
pmid = {40611622},
issn = {1464-0627},
mesh = {Humans ; *Aphasia/physiopathology/diagnostic imaging ; *Speech/physiology ; *Theory of Mind/physiology ; *Brain/physiopathology ; },
abstract = {This narrative review explores the phenomenon of inner speech - mental speech without visible articulation - and its implications for cognitive science and clinical practice. Despite its importance, the many neural mechanisms underlying inner speech remain unclear. We propose classifying inner speech into monologic, dialogal, elicited, and spontaneous forms, and discuss related phenomenological and neural correlates theories. A literature review on PubMed (1990-2024) identified 83 studies. Dialogal forms recruit Theory of Mind networks, compared to monologic forms. Task-elicited inner speech activates the left inferior frontal gyrus more strongly, while spontaneous inner speech engages Heschl's gyrus, suggesting auditory involvement. Evidence regarding aphasia suggests inner speech may be partially preserved even when overt speech is impaired, offering a potential route for rehabilitation. Future research should also address the emotional aspects of inner speech, its role in psychopathology, and its developmental trajectory. Such studies may improve interventions for disorders related to dysfunctional inner speech.Abbreviation: ACC: anterior cingulate cortex; ALE: activation likelihood estimation; AVH: auditory verbal hallucination; BMI: brain-machine interface; CD: corollary discharge; ConDialInt: consciousness-dialogue-intentionality; DES: descriptive experience sampling; DTI: diffusion tensor imaging; dPMC: dorsal premotor cortex; dmPFC: dorsomedial prefrontal cortex; IFG: inferior frontal gyrus; M1: primary motor cortex; MedFG: medial frontal gyrus; MFG: middle frontal gyrus; MTG: middle temporal gyrus; MRI: magnetic resonance imaging; preSMA: presupplementary motor area; PrG: precentral gyrus; SMA: supplementary motor area; SMG: supramarginal gyrus; SPC: superior parietal cortex; SPL: superior parietal lobule; STG: superior temporal gyrus; STS: superior temporal sulcus; TVA: temporal vocal areas; ToM: theory of mind; vmPFC: ventromedial prefrontal cortex.},
}
@article {pmid40611619,
year = {2025},
author = {Ponomarev, T and Vasilyev, A and Novikova, E and Pokidko, A and Zaitseva, N and Zaitsev, D and Kaplan, A},
title = {Brain mechanisms of (dis)agreement: ERP evidence from binary choice responses.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {35},
number = {7},
pages = {},
doi = {10.1093/cercor/bhaf167},
pmid = {40611619},
issn = {1460-2199},
support = {121032300070-1//Lomonosov Moscow State University/ ; },
mesh = {Humans ; Male ; Female ; *Evoked Potentials/physiology ; Electroencephalography ; *Brain/physiology ; Young Adult ; Adult ; *Choice Behavior/physiology ; Brain-Computer Interfaces ; *Decision Making/physiology ; },
abstract = {Agreement and disagreement are essential brain processes that enable effective communication and decision-making. However, a clear neurophysiological framework explaining their organization is still lacking. The present study aimed to identify EEG correlates of implicit agreement and disagreement, developing a novel experimental paradigm to model these internal responses. Participants were tasked with mentally responding to binary ("yes" or "no") questions and evaluating the accuracy of a computer system's attempts to "guess" their responses. Event-related potentials (ERP) revealed distinct patterns associated with agreement and disagreement in two key contexts: when participants read the final word of a question and when they observed the computer's "guess." Disagreement, compared to agreement, elicited larger ERP amplitudes, specifically an enhanced N400 component in the first context and increased feedback-related negativity in the second. Considering the associations of these ERP components with cognitive processes, this research offers robust evidence linking agreement and disagreement to the brain's effort in reconciling personal beliefs and expectations with new information. Furthermore, the experimental framework and findings provide a foundation for the development of brain-computer interfaces (BCIs) capable of detecting "yes" and "no" commands based on their intrinsic EEG predictors, offering promising applications in assistive technologies and neural communication systems.},
}
@article {pmid40611612,
year = {2025},
author = {Saeed, S and Wang, H and Jia, M and Liu, TT and Xu, L and Zhang, X and Hu, SH},
title = {The spectrum of overlapping anti-NMDAR encephalitis and demyelinating syndromes: a systematic review of presentation, diagnosis, management, and outcomes.},
journal = {Annals of medicine},
volume = {57},
number = {1},
pages = {2517813},
pmid = {40611612},
issn = {1365-2060},
mesh = {Humans ; *Anti-N-Methyl-D-Aspartate Receptor Encephalitis/diagnosis/therapy/complications/immunology ; *Demyelinating Diseases/diagnosis/therapy/immunology/complications ; Autoantibodies ; Treatment Outcome ; },
abstract = {BACKGROUND: Anti-NMDAR encephalitis frequently overlaps with demyelinating diseases (MOGAD, NMOSD, MS), creating complex syndromes with diverse presentations and challenging management.<br><br>METHODS: Systematic search of databases including MEDLINE, Google Scholar, Embase, Scopus, Cochrane Library, and Web of Science up to March 2024 for studies on co-existing anti-NMDAR encephalitis and demyelinating syndromes. Data extracted on clinical characteristics, diagnostics, treatments, and outcomes.<br><br>RESULTS: Twenty-five studies identified 256 patients (16.2%) with co-existing Anti-NMDAR encephalitis and demyelinating syndromes, primarily MOGAD (94.5%), with fewer cases involving NMOSD or MS. The Anti-NMDAR&#xa0;+&#xa0;MOGAD subgroup exhibited seizures (51-72.7%), psychiatric symptoms (45.5-71.4%), cognitive dysfunction (30.6%), and movement disorders (30.6%). All patients had CSF anti-NMDAR antibodies, with MOG (60%) or AQP4 (25%) antibodies. Use of standardized, cell-based assays and adherence to established criteria are essential to avoid false positives, particularly for MOG. MRI abnormalities were seen in 75% of patients. First-line immunotherapies were effective in 70% of cases; 80% of refractory cases responded to second-line therapies.<br><br>CONCLUSIONS: Anti-NMDAR encephalitis overlapping with demyelinating diseases is challenging. Tailored treatments based on detailed immune profiles are key to better outcomes.},
}
@article {pmid40611081,
year = {2025},
author = {Wei, Y and Xu, Y and Chen, W and Zheng, J and Chen, H and Chen, S},
title = {Can heart rate variability demonstrate the effects and the levels of mindfulness? A repeated-measures study on experienced and novice mindfulness practitioners.},
journal = {BMC complementary medicine and therapies},
volume = {25},
number = {1},
pages = {231},
pmid = {40611081},
issn = {2662-7671},
mesh = {Humans ; *Heart Rate/physiology ; *Mindfulness ; Male ; Female ; Adult ; Young Adult ; Middle Aged ; Meditation ; },
abstract = {BACKGROUND: Heart rate variability (HRV) is a potential biomarker that might demonstrate the effects of mindfulness, but it might be influenced by practice experiences. This study wanted to elucidate the possibility of using HRV metrics to reveal the effects of mindfulness and examine its variation between novice and experienced mindfulness practitioners.<br><br>METHODS: Forty-six participants (20 experienced practitioners, 26 novices) were enrolled to practice 14-day mindfulness training. HRV data were collected during three phases (20&#xa0;min baseline, T1; 20&#xa0;min mindfulness, T2; 20&#xa0;min post-mindfulness, T3) using Holter monitoring. The linear mixed model was conducted to explore the effects of group and time based on standardized data.<br><br>RESULTS: The experienced group had higher full-scale scores of FFMQ both in the pre-test (t&#x2009;=&#x2009;-3.34, df&#x2009;=&#x2009;44, p&#x2009;=&#x2009;0.002) and the post-test (t&#x2009;=&#x2009;-2.35, df&#x2009;=&#x2009;44, p&#x2009;=&#x2009;0.025). Both groups showed significant changes in HRV indices (e.g., RMSSD, SDNN, LnHF) from T1 to T2 or T3 (p&#x2009;<&#x2009;0.05). In the experienced group, significant fluctuations (p&#x2009;<&#x2009;0.05) were observed at T2, followed by recovery at T3, in SD1/SD2, Sample Entropy, normalized High Frequency (HFn), DFA_&#x3b1;1, and DFA_&#x3b1;2. In contrast, the novice participants only showed monotonic changes in SD1/SD2 and DFA_&#x3b1;1.<br><br>CONCLUSIONS: This study revealed significant HRV changes during mindfulness practice, with distinct patterns observed between novice and experienced practitioners.},
}
@article {pmid40609489,
year = {2026},
author = {Cui, H and Hu, D and Yang, T and Huang, C and Yang, Z and Dong, S},
title = {Humidity sensors based on surface-functionalized tunable photonic crystal grating.},
journal = {Talanta},
volume = {296},
number = {},
pages = {128521},
doi = {10.1016/j.talanta.2025.128521},
pmid = {40609489},
issn = {1873-3573},
abstract = {Photonic crystal (PC)-based humidity sensors detect changes in humidity using periodic structural color variations and have significant potential in the humidity detection field. However, current technologies typically rely on observing these structural color changes with the human eye. The human eye has limited color discrimination, thus resulting in insufficient detection accuracy. Meanwhile, viewing angles and ambient lighting can also disrupt observations. Here, we propose a humidity sensor based on surface-functionalized tunable PC grating. The tunable PC grating consists of a 600 nm polystyrene (PS) microsphere PC and a humidity-sensitive hydrogel. As ambient humidity increases, the hydrophilic amide groups (-CONH2) inside the hydrogel interact with the hydrogen bonds between water molecules and triggers hydrogel swelling, exerts interfacial stress on the PS microsphere lattice, thus expanding the lattice spacing of the PS microspheres and causing a red shift in the reflected wavelength. Integrating the surface-functionalized tunable PC grating into a Czerny-Turner (C-T) optical system enables us to directly translate humidity into precise spectral shifts, overcoming the limitations of human eye-based observations. Experimental results demonstrate a strong linear response over the range of 24-94 % relative humidity (RH), as well as excellent repeatability and long-term stability. We provide an innovative solution for high-precision optical humidity sensing.},
}
@article {pmid40609413,
year = {2025},
author = {Wang, Y and Gao, Y and He, R and Gao, Y and Xu, Z and Wang, C and Liu, F},
title = {Global ocean surface pCO2 retrieval and the influence of mesoscale eddies on its performance.},
journal = {The Science of the total environment},
volume = {993},
number = {},
pages = {179856},
doi = {10.1016/j.scitotenv.2025.179856},
pmid = {40609413},
issn = {1879-1026},
abstract = {CO2 exchange at air-sea interface is crucial for global carbon cycle. Uncertainties in CO2 flux quantification are constrained by ocean surface partial pressure of CO2 (pCO2) variations. While regional pCO2 retrieval algorithms exist, the impact of mesoscale eddies on accuracy remains understudies. We improve the global ocean surface pCO2 retrieval algorithm using XGBoost, incorporating sea surface temperature (SST), chlorophyll-a (Chl-a), sea surface salinity (SSS), mixed layer depth (MLD), and sea surface height (SSH), achieving high performance (R[2]= 0.95, RMSE = 10.52 μatm) at daily resolution. The SHAP method and the sequential feature removal method were used to assesses the individual impacts. The results reveal that SSH significantly enhances model accuracy, increasing R[2] by ∼10% and decreasing RMSE by ∼38%. Regional evaluations show better performance in the Atlantic, with overestimation (underestimation) at ocean gyre fronts (interiors). The models perform better in summer, while in winter, more overestimation is observed in the North Pacific. The future prediction in global field shows excellent spatiotemporal extrapolation performance. The results verify mesoscale dynamics significantly impact the retrieval accuracy in energetic regions. Relative error normalized quantities were calculated for cyclonic and anticyclonic eddies in eddy-active regions to analyze the influence of energetic mesoscale dynamic, suggesting that regional and seasonal variations in errors are linked to differences in eddy-induced nutrient flux and baroclinic instabilities.},
}
@article {pmid40609325,
year = {2025},
author = {Ren, X and Zhou, C and Jiang, Y and Zhao, J and Tina, X and Xu, N and Fu, M and Ni, P and Li, T and Zhang, X},
title = {Generation of an induced pluripotent stem cell line (HZSMHCi002-A) from a patient with neuronal intranuclear inclusion disease carrying GGC repeat expansion in the NOTCH2NLC gene.},
journal = {Stem cell research},
volume = {87},
number = {},
pages = {103761},
doi = {10.1016/j.scr.2025.103761},
pmid = {40609325},
issn = {1876-7753},
mesh = {Humans ; *Induced Pluripotent Stem Cells/metabolism/cytology/pathology ; Female ; *Intranuclear Inclusion Bodies/pathology/genetics/metabolism ; *Neurodegenerative Diseases/genetics/pathology/metabolism ; *Trinucleotide Repeat Expansion/genetics ; Cell Line ; Cell Differentiation ; Nerve Tissue Proteins ; Intercellular Signaling Peptides and Proteins ; },
abstract = {The NOTCH2NLC gene contains a GGC repeat expansion in its 5' untranslated region. This expansion is associated with neuronal intranuclear inclusion disease (NIID). NIID is a rare neurodegenerative disorder. Its clinical features include cognitive decline, paroxysmal symptoms, and autonomic dysfunction. We generated an induced pluripotent stem cell (iPSC) line from a female patient's PBMCs carrying a high GGC repeat expansion in NOTCH2NLC. The iPSC line displayed typical pluripotent morphology. It expressed key pluripotency markers and demonstrated differentiation potential in teratoma assays. This cell line serves as a useful model for studying disease mechanisms and developing therapeutic strategies.},
}
@article {pmid40609285,
year = {2025},
author = {Xu, JJ and Chen, YL and Yu, H and Chen, DF and Li, HF and Wu, ZY},
title = {Genetic and Clinical Features of SLC2A1-Related Paroxysmal Exercise-Induced Dyskinesia.},
journal = {Pediatric neurology},
volume = {170},
number = {},
pages = {31-37},
doi = {10.1016/j.pediatrneurol.2025.06.006},
pmid = {40609285},
issn = {1873-5150},
mesh = {Adolescent ; Adult ; Child ; Female ; Humans ; Male ; *Chorea/genetics/physiopathology ; *Exercise/physiology ; Exome Sequencing ; *Glucose Transporter Type 1/genetics ; Mutation, Missense ; Pedigree ; },
abstract = {BACKGROUND: Paroxysmal exercise-induced dyskinesia (PED) is a rare movement disorder characterized by choreoathetosis and dystonia triggered by sustained exercise, commonly affecting the lower extremities. PED is an autosomal dominant disorder genetically linked to mutations in the SLC2A1 gene. The transmembrane protein Glut1, encoded by the SLC2A1 gene, can transport glucose from blood to the brain. This study aimed to characterize the genetic and clinical features of SLC2A1-related PED.<br><br>METHODS: We reported two Chinese PED families presenting with involuntary movements after prolonged exercise. Whole-exome sequencing was performed on two probands, and cosegregation analysis was subsequently carried out in available family members. Additionally, we summarized and analyzed the genetic and clinical features of SLC2A1-related PED by retrieving information from the literature.<br><br>RESULTS: Genetic testing identified two missense mutations in SLC2A1 in these families, including a known disease-causing mutation, c.997C>T (p.R333W), and a novel mutation, c.823G>C (p.A275P). Upon review of the literature, mutations in certain regions of the Glut1 protein, particularly in transmembrane segments 3, 4, 5, 7, and 8, together with the intracellular domain, were more frequently seen in PED. Among the various types of epilepsy, absence seizures were the most common in patients with PED. Furthermore, familial PED had a later onset and a higher cerebrospinal fluid/blood glucose ratio. Patients with missense mutations exhibited a later onset than those with truncated mutations.<br><br>CONCLUSIONS: Our study identified a new disease-causing mutation and, through an extensive literature review, provided a detailed genetic and clinical description of PED associated with SLC2A1 mutations.},
}
@article {pmid40608885,
year = {2025},
author = {Yang, Z and Si, X and Jin, W and Huang, D and Zang, Y and Yin, S and Ming, D},
title = {SEEG Emotion Recognition Based on Transformer Network With Channel Selection and Explainability.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {11},
pages = {8153-8163},
doi = {10.1109/JBHI.2025.3585528},
pmid = {40608885},
issn = {2168-2208},
mesh = {Humans ; *Emotions/physiology/classification ; *Electroencephalography/methods ; Male ; Adult ; *Signal Processing, Computer-Assisted ; Female ; *Brain-Computer Interfaces ; Young Adult ; Brain/physiology ; Neural Networks, Computer ; Algorithms ; },
abstract = {Brain-computer interface (BCI) technology for emotion recognition holds significant potential for future applications in the treatment of refractory emotional disorders. Stereo-electroencephalography (SEEG), being less invasive, can precisely record neural activities originating from the cortex and the deep structures of the brain. Thus, it has broad application prospects in constructing emotion recognition BCI. In this study, SEEG data from nine subjects were collected to construct an emotion dataset, and a Spatial Transformer-based Hybrid Network (STHN) was proposed for SEEG emotion recognition. The triple-classification accuracy of STHN reached 83.56%, outperforming the baseline methods such as EEGNet, TSception, and the deep convolution neural network. Moreover, STHN can assign weights to each SEEG channel and select those channels that contribute more significantly to emotion recognition. It was found that when using the top 30% weighted SEEG channels as model inputs, the accuracy did not decrease significantly. Most of the channels with higher weights were located in brain regions strongly associated with emotions, such as the frontal lobe, the temporal lobe, and the hippocampus. This indicates that STHN is not merely a "black-box" model but possesses a degree of explainability. To the best of our knowledge, this is the first study to develop an SEEG emotion recognition algorithm, which is expected to play a crucial role in the monitoring and treatment of patients with refractory emotional disorders in the future.},
}
@article {pmid40608881,
year = {2025},
author = {Yu, X and Yu, X},
title = {Brain-Controlled Wheeled Mobile Robots: A Framework Combining Probabilistic Brain-Computer Interface and Model Predictive Control.},
journal = {IEEE transactions on cybernetics},
volume = {55},
number = {9},
pages = {4311-4321},
doi = {10.1109/TCYB.2025.3580726},
pmid = {40608881},
issn = {2168-2275},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Algorithms ; *Signal Processing, Computer-Assisted ; *Robotics/methods/instrumentation ; *Brain/physiology ; Computer Simulation ; },
abstract = {Brain-controlled systems have experienced significant advancements in overall performance, largely driven by continuous optimization and innovation in electroencephalography (EEG) acquisition experimental paradigms and decoding algorithms. However, their applications still face challenges, including limited control precision and low efficiency. In this article, we focus on a wheeled mobile robot (WMR) as the control object and propose a novel brain-controlled framework that combines a probabilistic brain-computer interface (BCI) and a model predictive controller (MPC). First, the probabilistic BCI is developed, featuring the sigmoid fitting-filter bank canonical correlation analysis (SF-FBCCA) algorithm, which serves as the core of the BCI system by decoding EEG signals and generating brain commands along with their associated probabilities. Second, an auxiliary MPC is integrated into the probabilistic BCI system to provide decision-making assistance while preserving the users' primary brain control authority. The weights of the cost function are adaptively determined based on the command probabilities. Finally, simulation-based evaluations were conducted using the WMR in a path-keeping scenario. The results demonstrate that the proposed framework significantly improves control accuracy and efficiency compared to direct brain control approaches, reducing the average lateral error by 58.02% and the average yaw angle error by 60.06%. Additionally, the MPC employing adaptive weights further improves overall performance. These findings offer theoretical insights and technical references for future research on BCI-based control frameworks.},
}
@article {pmid40606836,
year = {2025},
author = {Cantillo-Negrete, J and Rodríguez-García, ME and Carrillo-Mora, P and Arias-Carrión, O and Ortega-Robles, E and Galicia-Alvarado, MA and Valdés-Cristerna, R and Ramirez-Nava, AG and Hernandez-Arenas, C and Quinzaños-Fresnedo, J and Pacheco-Gallegos, MDR and Marín-Arriaga, N and Carino-Escobar, RI},
title = {The ReHand-BCI trial: a randomized controlled trial of a brain-computer interface for upper extremity stroke neurorehabilitation.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1579988},
pmid = {40606836},
issn = {1662-4548},
abstract = {BACKGROUND: Brain-computer interfaces (BCI) are a promising complementary therapy for stroke rehabilitation due to the close-loop feedback that can be provided with these systems, but more evidence is needed regarding their clinical and neuroplasticity effects.<br><br>METHODS: A randomized controlled trial was performed using the ReHand-BCI system that provides feedback with a robotic hand orthosis. The experimental group (EG) used the ReHand-BCI, while sham-BCI was given to the control group (CG). Both groups performed 30 therapy sessions, with primary outcomes being the Fugl-Meyer Assessment for the Upper Extremity (FMA-UE) and the Action Research Arm Test (ARAT). Secondary outcomes were hemispheric dominance, measured with electroencephalography and functional magnetic resonance imaging, white matter integrity via diffusion tensor imaging, and corticospinal tract integrity and excitability, measured with transcranial magnetic stimulation.<br><br>RESULTS: At post-treatment, patients in both groups had significantly different FMA-UE scores (EG: baseline&#x202f;=&#x202f;24.5[20, 36], post-treatment 28[23, 43], CG: baseline&#x202f;=&#x202f;26[16, 37.5], post-treatment&#x202f;=&#x202f;34[17.3, 46.5]), while only the EG had significantly different ARAT scores at post-treatment (EG: baseline&#x202f;=&#x202f;8.5[5, 26], post-treatment&#x202f;=&#x202f;20[7, 36], CG: baseline&#x202f;=&#x202f;3[1.8, 30.5], post-treatment&#x202f;=&#x202f;15[2.5, 40.8]). In addition, across the intervention, the EG showed trends of more pronounced ipsilesional cortical activity and higher ipsilesional corticospinal tract integrity, although these differences were not statistically different compared to the control group, likely due to the study's sample size.<br><br>CONCLUSION: To the authors' knowledge, this is the first clinical trial that has assessed such a wide range of physiological effects across a long BCI intervention, implying that a more pronounced ipsilesional hemispheric dominance is associated with upper extremity motor recovery. Therefore, the study brings light into the neuroplasticity effects of a closed-loop BCI-based neurorehabilitation intervention in stroke.<br><br>CLINICAL TRIAL REGISTRATION: https://clinicaltrials.gov/, identifier NCT04724824.},
}
@article {pmid40606655,
year = {2025},
author = {Jacob, JE and Chandrasekharan, S},
title = {Editorial: Advanced EEG analysis techniques for neurological disorders.},
journal = {Frontiers in neuroinformatics},
volume = {19},
number = {},
pages = {1637890},
doi = {10.3389/fninf.2025.1637890},
pmid = {40606655},
issn = {1662-5196},
}
@article {pmid40605914,
year = {2025},
author = {Yang, L and Zhu, W},
title = {Mifnet: a MamBa-based interactive frequency convolutional neural network for motor imagery decoding.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {106},
pmid = {40605914},
issn = {1871-4080},
abstract = {Motor imagery (MI) decoding remains a critical challenge in brain-computer interface (BCI) systems due to the low signal-to-noise ratio, non-stationarity, and complex spatiotemporal dynamics of electroencephalography (EEG) signals. Although deep learning architectures have advanced MI-EEG decoding, existing approaches-including convolutional neural networks (CNNs), Transformers, and recurrent neural networks (RNNs)-still face limitations in capturing global temporal dependencies, maintaining positional coherence, and ensuring computational efficiency. To address these challenges, we propose MIFNet, a MamBa-based Interactive Frequency Convolutional Neural Network that systematically integrates spectral, spatial, and temporal feature extraction. Specifically, MIFNet incorporates: non-overlapping frequency decomposition, which selectively extracts motor imagery-related mu (8-12 Hz) and beta (12-32 Hz) rhythms; a ConvEncoder module, which autonomously learns to fuse spectral-spatial features from both frequency bands; and a MamBa-based temporal module, leveraging selective state-space models (SSMs) to efficiently capture long-range dependencies with linear complexity. Extensive experiments on three public MI-EEG datasets (BCIC-IV-2A, OpenBMI, and High Gamma) demonstrate that MIFNet outperforms existing models, achieving an average classification accuracy improvement of 12.3%, 8.3%, 4.7%, and 5.5% over EEGNet, FBCNet, IFNet, and Conformer, respectively. Ablation studies further validate the necessity of each component, with the MamBa module contributing a 5.5% improvement in accuracy on the BCIC-IV-2A dataset. Moreover, MIFNet exhibits strong generalization performance in cross-validation settings, establishing a robust foundation for real-time BCI applications. Our findings highlight the potential of hybridizing CNNs with state-space models (SSMs) for improving EEG decoding performance, effectively bridging the gap between localized feature extraction and global temporal modeling.},
}
@article {pmid40603471,
year = {2025},
author = {Liao, W and Liu, H and Wang, W},
title = {Advancing BCI with a transformer-based model for motor imagery classification.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {23380},
pmid = {40603471},
issn = {2045-2322},
support = {2020AAA0105800//Ministry of Science and Technology of the People's Republic of China/ ; 2020AAA0105800//Ministry of Science and Technology of the People's Republic of China/ ; 2020AAA0105800//Ministry of Science and Technology of the People's Republic of China/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Imagination/physiology ; Neural Networks, Computer ; Algorithms ; Machine Learning ; Deep Learning ; },
abstract = {Brain-computer interfaces (BCIs) harness electroencephalographic signals for direct neural control of devices, offering significant benefits for individuals with motor impairments. Traditional machine learning methods for EEG-based motor imagery (MI) classification encounter challenges such as manual feature extraction and susceptibility to noise. This paper introduces EEGEncoder, a deep learning framework that employs modified transformers and Temporal Convolutional Networks (TCNs) to surmount these limitations. We propose a novel fusion architecture, named Dual-Stream Temporal-Spatial Block (DSTS), to capture temporal and spatial features, improving the accuracy of Motor Imagery classification task. Additionally, we use multiple parallel structures to enhance the model's performance. When tested on the BCI Competition IV-2a dataset, our proposed model achieved an average accuracy of 86.46% for subject dependent and average 74.48% for subject independent.},
}
@article {pmid40603333,
year = {2025},
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 = {Correction: A multiple session dataset of NIRS recordings from stroke patients controlling brain-computer interface.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {1132},
doi = {10.1038/s41597-025-05466-y},
pmid = {40603333},
issn = {2052-4463},
}
@article {pmid40602422,
year = {2025},
author = {Jin, J and Liang, W and Xu, R and Chen, W and Xu, R and Wang, X and Cichocki, A},
title = {A transformer-based network with second-order pooling for motor imagery EEG classification.},
journal = {Journal of neural engineering},
volume = {22},
number = {4},
pages = {},
doi = {10.1088/1741-2552/adeae8},
pmid = {40602422},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods/classification ; *Brain-Computer Interfaces ; *Imagination/physiology ; *Neural Networks, Computer ; Deep Learning ; Brain/physiology ; },
abstract = {Objective. Electroencephalography (EEG) signals can reflect motor intention signals in the brain. In recent years, motor imagery (MI) based brain-computer interfaces (BCIs) have attracted the attention of neuroinformatics researchers. Numerous deep learning models have been developed to decode EEG signals. Although deep learning models, particularly those based on convolutional neural networks, have shown promise in decoding EEG signals, most existing methods focus on attention mechanisms while neglecting high-order statistical dependencies that are critical for accurately capturing the complex structure of EEG data.Approach. To address this limitation, we propose a neural network integrating a transpose-attention mechanism and second-order pooling (SecTNet). The proposed model tackles two fundamental challenges in EEG decoding. It metrics the covariance structure of EEG signals using Riemannian geometry on symmetric positive definite (SPD) matrices, and it enhances the discriminability of these SPD features by introducing attention mechanisms that adaptively model inter-channel dependencies. Specifically, SecTNet is composed of three key components. First, a multi-scale spatial-temporal convolution module extracts detailed local features. Second, a transpose-attention mechanism captures dependency information from the internal interactions between channels. Lastly, a second-order pooling layer captures high-order statistical correlations in the EEG feature space.Main results. SecTNet is evaluated on two publicly available EEG datasets, namely BCI competition IV 2a dataset and OpenBMI dataset. It achieves an average accuracy of 86.88% on the BCI competition IV dataset 2a and 74.99% on the OpenBMI dataset. Moreover, results show that SecTNet maintains competitive performance even when trained on only 50% of the data, demonstrating strong generalization under limited data conditions.Significance. These results demonstrate the broad applicability and effectiveness of SecTNet in enhancing MI-BCI performance. SecTNet provides a robust and generalizable framework for EEG decoding, supporting the development of BCI applications across diverse real-world scenarios.},
}
@article {pmid40602419,
year = {2025},
author = {Li, L and Wei, B},
title = {A two-stage EEG zero-shot classification algorithm guided by class reconstruction.},
journal = {Journal of neural engineering},
volume = {22},
number = {4},
pages = {},
doi = {10.1088/1741-2552/adeaea},
pmid = {40602419},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods/classification ; *Algorithms ; Brain-Computer Interfaces ; *Brain/physiology ; Photic Stimulation/methods ; Adult ; Classification Algorithms ; },
abstract = {Objective. Researchers have long been dedicated to decoding human visual representations from neural signals. These studies are crucial in uncovering the mechanisms of visual processing in the human brain. Electroencephalogram (EEG) signals have garnered widespread attention recently due to their non-invasive nature and low cost. EEG classification is one of the most popular topics in brain-computer interface research. However, most traditional EEG classification algorithms are difficult to generalize to unseen classes that were not involved in the training phase. The main objective of this work is to improve the performance of these EEG classification algorithms for unseen classes.Approach. In this work, we propose a two-stage zero-shot EEG classification algorithm guided by class reconstruction. The method is specifically designed with a two-stage training strategy based on class reconstruction. This structure and training strategy enable the model to thoroughly learn the relations and distinctions among EEG embeddings of different classes. The contrastive language-image pre-training (CLIP) model has a well-aligned latent space and powerful cross-modality generalization ability.Main results. We conducted experiments on the ImageStimulus-EEG dataset to evaluate the performance of the proposed method. Meanwhile, it was compared with the state-of-the-art model and the baseline model. The experimental results demonstrate that our model achieves superior performance in among Top-1, Top-3, and Top-5 classification accuracy for a 50-way zero-shot classification task, reaching 17.77%, 38.76% and 54.75%, respectively.Significance. The proposed method bridges the modality gap between EEG, images, and text using CLIP features. It significantly improves the model's performance in unseen classes. The experimental results validate the effectiveness of it in EEG zero-shot classification.},
}
@article {pmid40602315,
year = {2025},
author = {Del Pup, F and Zanola, A and Tshimanga, LF and Bertoldo, A and Finos, L and Atzori, M},
title = {The role of data partitioning on the performance of EEG-based deep learning models in supervised cross-subject analysis: A preliminary study.},
journal = {Computers in biology and medicine},
volume = {196},
number = {Pt A},
pages = {110608},
doi = {10.1016/j.compbiomed.2025.110608},
pmid = {40602315},
issn = {1879-0534},
mesh = {Humans ; *Electroencephalography/methods ; *Deep Learning ; Alzheimer Disease/physiopathology ; Parkinson Disease/physiopathology ; *Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Reproducibility of Results ; },
abstract = {Deep learning is significantly advancing the analysis of electroencephalography (EEG) data by effectively discovering highly nonlinear patterns within the signals. Data partitioning and cross-validation are crucial for assessing model performance and ensuring study comparability, as they can produce varied results and data leakage due to specific signal properties (e.g., biometric). Such variability in model evaluation leads to incomparable studies and, increasingly, overestimated performance claims, which are detrimental to the field. Nevertheless, no comprehensive guidelines for proper data partitioning and cross-validation exist in the domain, nor is there a quantitative evaluation of the impact of different approaches on model accuracy, reliability, and generalizability. To assist researchers in identifying optimal experimental strategies, this paper thoroughly investigates the role of data partitioning and cross-validation in evaluating EEG deep learning models. Five cross-validation settings are compared across three supervised cross-subject classification tasks (brain-computer interfaces, Parkinson's, and Alzheimer's disease classification) and four established architectures of increasing complexity (ShallowConvNet, EEGNet, DeepConvNet, and Temporal-based ResNet). The comparison of over 100,000 trained models underscores, first, the importance of using subject-based cross-validation strategies for evaluating EEG deep learning architectures, except when within-subject analyses are acceptable (e.g., BCI). Second, it highlights the greater reliability of nested approaches (e.g., N-LNSO) compared to non-nested counterparts, which are prone to data leakage and favor larger models overfitting to validation data. In conclusion, this work provides EEG deep learning researchers with an analysis of data partitioning and cross-validation and offers guidelines to avoid data leakage, currently undermining the domain with potentially overestimated performance claims.},
}
@article {pmid40602314,
year = {2025},
author = {Huang, S and Wei, Q},
title = {A deep learning model combining convolutional neural networks and a selective kernel mechanism for SSVEP-Based BCIs.},
journal = {Computers in biology and medicine},
volume = {196},
number = {Pt A},
pages = {110691},
doi = {10.1016/j.compbiomed.2025.110691},
pmid = {40602314},
issn = {1879-0534},
mesh = {Humans ; *Brain-Computer Interfaces ; Convolutional Neural Networks ; *Deep Learning ; Electroencephalography ; *Evoked Potentials, Visual/physiology ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; },
abstract = {Existing deep learning methods for brain-computer interfaces (BCIs) based on steady-state visually evoked potential (SSVEP) face several challenges, such as overfitting when training data are insufficient, and the difficulty of effectively capturing global temporal features due to limited receptive fields. To address these challenges, we propose a novel deep learning model, FBCNN-TKS, which extracts harmonic components from SSVEP signals using a filter bank technique, followed by feature extraction through convolutional neural networks (CNNs) and a temporal kernel selection (TKS) module, and finally the weighted sum of cross-entropy loss and center loss is used as the objective function for model optimization. The key innovation of our approach lies in the introduction of the TKS module, which significantly enhances feature extraction capability by providing a broader receptive field. Additionally, dilated and grouped convolutions are used in TKS module to reduce the number of model parameters, minimizing the risk of overfitting and improving classification accuracy. Experimental results manifest that FBCNN-TKS outperforms state-of-the-art methods in terms of classification accuracy and information transfer rate (ITR). Specifically, FBCNN-TKS achieved the highest ITRs of 251.54 bpm and 203.47 bpm with the highest accuracies of 83.10 % and 72.98 % on public datasets Benchmark and BETA respectively at the data length of 0.4s, exhibiting superior performance. The FBCNN-TKS model bears big potential for the development of high-performance SSVEP-BCI character spelling systems.},
}
@article {pmid40601454,
year = {2025},
author = {Zhang, Y and Yu, Y and Li, H and Wu, A and Chen, X and Liu, J and Zeng, LL and Hu, D},
title = {DMAE-EEG: A Pretraining Framework for EEG Spatiotemporal Representation Learning.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {36},
number = {10},
pages = {17664-17678},
doi = {10.1109/TNNLS.2025.3581991},
pmid = {40601454},
issn = {2162-2388},
mesh = {*Electroencephalography/methods ; Humans ; Algorithms ; Brain/physiology ; *Machine Learning ; Signal Processing, Computer-Assisted ; Artifacts ; Neural Networks, Computer ; Signal-To-Noise Ratio ; Databases, Factual ; Spatio-Temporal Analysis ; },
abstract = {Electroencephalography (EEG) plays a crucial role in neuroscience research and clinical practice, but it remains limited by nonuniform data, noise, and difficulty in labeling. To address these challenges, we develop a pretraining framework named DMAE-EEG, a denoising masked autoencoder for mining generalizable spatiotemporal representation from massive unlabeled EEG. First, we propose a novel brain region topological heterogeneity (BRTH) division method to partition the nonuniform data into fixed patches based on neuroscientific priors. Second, we design a denoised pseudo-label generator (DPLG), which utilizes a denoising reconstruction pretext task to enable the learning of generalizable representations from massive unlabeled EEG, suppressing the influence of noise and artifacts. Furthermore, we utilize an asymmetric autoencoder with self-attention as the backbone in the proposed DMAE-EEG, which captures long-range spatiotemporal dependencies and interactions from unlabeled EEG data across 14 public datasets. The proposed DMAE-EEG is validated on both generative (signal quality enhancement) and discriminative tasks (motion intention recognition). In the quality enhancement, DMAE-EEG outperforms existing statistical methods with normalized mean squared error (nMSE) reduction of 27.78%-50.00% under corruption levels of 25%, 50%, and 75%, respectively. In motion intention recognition, DMAE-EEG achieves a relative improvement of 2.71%-6.14% in intrasession classification balanced accuracy across 2-6 class motor execution and imagery tasks, outperforming state-of-the-art methods. Overall, the results suggest that the pretraining framework DMAE-EEG can capture generalizable spatiotemporal representations from massive unlabeled EEG and enhance the knowledge transferability across sessions, subjects, and tasks in various downstream scenarios, advancing EEG-aided diagnosis and brain-computer communication and control, and other clinical practice.},
}
@article {pmid40601441,
year = {2025},
author = {Si, X and Han, Y and Li, S and Zhang, S and Ming, D},
title = {The Cortical Spatial Responses and Decoding of Emotion Imagery Toward a Novel fNIRS-Based Affective BCI.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {2577-2586},
doi = {10.1109/TNSRE.2025.3584765},
pmid = {40601441},
issn = {1558-0210},
mesh = {Humans ; Spectroscopy, Near-Infrared/methods ; *Brain-Computer Interfaces ; Male ; *Emotions/physiology ; Female ; Adult ; *Imagination/physiology ; Young Adult ; *Cerebral Cortex/physiology ; Brain Mapping/methods ; Algorithms ; Motor Cortex/physiology ; Hemodynamics ; },
abstract = {Functional near-infrared spectroscopy (fNIRS), with its non-invasive and high spatial resolution, holds promise in developing novel affective brain-computer interface (BCI). Similar to motor imagery BCI, emotion imagery BCI could recognize internal emotions and convey them to the external world. This holds clinical value for expressing emotions in patients with neurological impairments and serves as a proactive emotion regulation method. However, the fNIRS features of emotion imagery for affective BCI and the discriminability of different emotion categories remain unclear. Here, this study designed a novel emotion verbal imagery paradigm (imagining descriptions of happy or sad scenes). First, task-related hemodynamic responses were analyzed from 17 subjects. Then, statistical analyses were then conducted to reveal the significant cortical spatial response patterns. Additionally, decoding experiments and model interpretability are employed to assist in validating the feasibility of the emotion imagery BCI. Results showed: 1) Happy imagery recruited frontoparietal regions, such as the left dorsal secondary motor cortex, ventral secondary motor cortex, and inferior parietal lobe. 2) Sad imagery mainly recruited the right dorsolateral prefrontal cortex. 3) The left dorsal sensorimotor cortex exhibited selective responsiveness to happy imagery and sad imagery. 4) The classification results of the emotion imagery task exceeded the random level. 5) Emotional categories activation responses showed significant similarity with the hemodynamic responses of the imagination tasks. Taken together, by proposing the emotion imagery fNIRS paradigm, this work could shed light on the development of feature non-invasive BCI.},
}
@article {pmid40600191,
year = {2025},
author = {Ma Thi, C and Nguyen The, HA and Nguyen Minh, K and Vu Thanh, L and Nguyen Dinh, H and Huynh Thi, NY and Ha Thi, TH and Hoang Tien, TN and Au Dao, DT and Nguyen Hoang, KL and Huynh Kha, V and Le Hoang, TL},
title = {UET175: EEG dataset of motor imagery tasks in Vietnamese stroke patients.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1580931},
pmid = {40600191},
issn = {1662-4548},
}
@article {pmid40598468,
year = {2025},
author = {Chen, Y and Zhao, N and Zhang, J and Wu, X and Huang, J and Xu, X and Cai, F and Chen, S and Xu, L and Yan, W and Hong, Y and Wang, Y and Ling, H and Ji, J and Chen, G and Gu, H and Zhang, J and Wu, Q},
title = {Molecular signatures of invasive and non-invasive pituitary adenomas: a comprehensive analysis of DNA methylation and gene expression.},
journal = {BMC medicine},
volume = {23},
number = {1},
pages = {373},
pmid = {40598468},
issn = {1741-7015},
mesh = {Humans ; *DNA Methylation ; *Pituitary Neoplasms/genetics/pathology ; *Adenoma/genetics/pathology ; Male ; Female ; Middle Aged ; Adult ; *Gene Expression Regulation, Neoplastic ; Biomarkers, Tumor/genetics ; Neoplasm Invasiveness ; Gene Expression Profiling ; },
abstract = {BACKGROUND: Pituitary adenomas (PAs) are benign tumors in the pituitary gland. However, 30-40% of these tumors are invasive, complicating diagnosis and treatment. Invasive pituitary adenomas (IPAs) often respond poorly to conventional therapies, emphasizing the need for better diagnostic and therapeutic strategies. Understanding DNA methylation patterns in IPAs may reveal new biomarkers and therapeutic targets, leading to more effective management of this challenging disease.<br><br>METHODS: Reduced representation bisulfite sequencing (RRBS) and RNA sequencing (RNA-seq) were performed on 129 samples from the Second Affiliated Hospital of Zhejiang University, including 69 tissue samples from invasive and non-invasive pituitary adenomas (NPA) and 60 blood samples from IPA, NPA and healthy individuals. Differentially methylated regions (DMRs) and differentially expressed genes (DEGs) were identified in tissues. Pearson correlation analysis was used to identify associations between DNA methylation status and gene expression, as well as the effect of methylation on gene expression at different sites. Blood samples were analyzed to detect DMRs and DEGs, correlating with tissue-derived findings. Finally, ROC analysis and a random forest model were used to identify biomarkers for discriminating invasive from non-invasive phenotypes.<br><br>RESULTS: We identified 347 DMRs between IPA and NPA, of which 63% (219/347) were hypomethylated. Additionally, 543 mRNAs showed differential expression, with 350 upregulated and 193 downregulated. 17 genes demonstrated concurrent aberrant methylation and expression, primarily within introns, promoters, and CpG islands (CGIs). Notably, only protein tyrosine phosphatase receptor type T (PTPRT) exhibited a remarkably high correlation (r&#x2009;=&#x2009;0.81) between its DNA methylation levels and mRNA expression levels. This correlation was observed within the intronic region/opensea of the gene's CGIs. Plasma sample analysis revealed 852 DMRs between IPA and NPA, with 52% (447/852) being hypomethylated. Three tumor tissue-derived blood biomarkers (MIR4535, SLC8A1-AS1, and TTC34) accurately discriminated between IPA and NPA patients with a combined AUC of 0.980. These markers also differentiated NPA from healthy controls, though with different methylation patterns.<br><br>CONCLUSIONS: The relationship between DNA methylation and gene expression is complex. Plasma-based DNA methylation markers can effectively discriminate between IPA and NPA, as well as between NPA and healthy individuals (N group).},
}
@article {pmid40598460,
year = {2025},
author = {Lu, R and Pang, Z and Gao, T and He, Z and Hu, Y and Zhuang, J and Zhang, Q and Gao, Z},
title = {Multisensory BCI promotes motor recovery via high-order network-mediated interhemispheric integration in chronic stroke.},
journal = {BMC medicine},
volume = {23},
number = {1},
pages = {380},
pmid = {40598460},
issn = {1741-7015},
support = {82372570//the National Science Foundation of China/ ; 82372570//the National Science Foundation of China/ ; 82271422//the National Science Foundation of China/ ; 23Y11900900//Medical Innovation Research Project funded by Shanghai Science and Technology Commission/ ; 23Y11900900//Medical Innovation Research Project funded by Shanghai Science and Technology Commission/ ; 22ZR1479000//Shanghai Natural Science Foundation/ ; 20234Y0043//Shanghai Municipal Health Commission/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; *Stroke Rehabilitation/methods ; Middle Aged ; *Recovery of Function/physiology ; *Stroke/physiopathology ; Aged ; *Feedback, Sensory/physiology ; Chronic Disease ; Magnetic Resonance Imaging ; Adult ; Neuronal Plasticity ; },
abstract = {BACKGROUND: Chronic stroke patients often experience persistent motor impairments, and current rehabilitation therapies rarely achieve substantial functional recovery. Sensory feedback during movement plays a pivotal role in driving neuroplasticity. This study introduces a novel multi-modal sensory feedback brain-computer interface (Multi-FDBK-BCI) system that integrates proprioceptive, tactile, and visual stimuli into motor imagery-based training. We aimed to explore the potential therapeutic efficacy and elucidate its neural mechanisms underlying motor recovery.<br><br>METHODS: Thirty-nine chronic stroke patients were randomized to either the Multi-FDBK-BCI group (n = 20) or the conventional motor imagery therapy group (n = 19). Motor recovery was assessed using the Fugl-Meyer Assessment (primary outcome), Motor Status Scale, Action Research Arm Test, and surface electromyography. Functional MRI was used to examine brain activation patterns during upper limb tasks, while Granger causality analysis and machine learning evaluated inter-regional connectivity changes and their predictive value for recovery.<br><br>RESULTS: Multi-FDBK-BCI training led to significantly greater motor recovery compared to conventional therapy. Functional MRI revealed enhanced activation of high-order transmodal networks-including the default mode, dorsal/ventral attention, and frontoparietal networks-during paralyzed limb movement, with activation strength positively correlated with motor improvement. Granger causality analysis identified a distinct information flow pattern: signals from the lesioned motor cortex were relayed through transmodal networks to the intact motor cortex, promoting interhemispheric communication. These functional connectivity changes not only supported motor recovery but also served as robust predictors of therapeutic outcomes.<br><br>CONCLUSIONS: Our findings highlight the Multi-FDBK-BCI system as a promising strategy for chronic stroke rehabilitation, leveraging activity-dependent neuroplasticity within high-order transmodal networks. This multi-modal approach holds significant potential for patients with limited recovery options and sheds new light on the neural drivers of motor restoration, warranting further investigation in clinical neurorehabilitation.<br><br>TRIAL REGISTRATION: All data used in the present study were obtained from a research trial registered with the ClinicalTrials.gov database (ChiCTR-ONC-17010739, registered 26 February 2017, starting from 10 January 2017).},
}
@article {pmid40596215,
year = {2025},
author = {Rabbani, M and Sabith, NUS and Parida, A and Iqbal, I and Mamun, SM and Khan, RA and Ahmed, F and Ahamed, SI},
title = {EEG based real time classification of consecutive two eye blinks for brain computer interface applications.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {21007},
pmid = {40596215},
issn = {2045-2322},
mesh = {Humans ; *Blinking/physiology ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Male ; Adult ; Female ; Support Vector Machine ; Neural Networks, Computer ; Young Adult ; Machine Learning ; Signal Processing, Computer-Assisted ; Brain/physiology ; },
abstract = {Human eye blinks are considered a significant contaminant or artifact in electroencephalogram (EEG), which impacts EEG-based medical or scientific applications. However, eye blink detection can instead be transformed into a potential application of brain-computer interfaces (BCI). This study introduces a novel real-time EEG-based framework for classifying three blink states: no blink, single blink, and two consecutive blinks in one model. EEG data were collected from ten healthy participants using an 8-channel wearable headset under controlled blinking conditions. The data were preprocessed and analyzed using four feature extraction techniques: basic statistical, time-domain, amplitude-driven, and frequency-domain methods. The most significant features were selected to develop three machine learning models: XGBoost, support vector machine (SVM), and neural network (NN). We achieved the highest accuracy of 89.0% for classifying multiple-eye blink detection. To further enhance the model's capacity and suitability for real-life BCI applications, we trained and employed the You Only Look Once (YOLO) model, achieving a recall of 98.67%, a precision of 95.39%, and mAP50 of 99.5%, demonstrating its superior accuracy and robustness in classifying two consecutive eye blinks. In conclusion, this study will be the first groundwork and open a new dimension in EEG-based BCI research by classifying multiple-eye blink detection.},
}
@article {pmid40595635,
year = {2025},
author = {Liu, L and Gao, Z and Niu, X and Yu, H and Xin, X and Gu, Y and Ma, G and Gu, Y and Liu, Y and Fang, S and Marquardt, T and Wang, L},
title = {SEMA3B switches axon-axon to axon-glia interactions required for unmyelinated axon envelopment and integrity.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {5433},
pmid = {40595635},
issn = {2041-1723},
support = {32100758//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Animals ; *Axons/metabolism ; *Semaphorins/metabolism/genetics ; Mice ; Schwann Cells/metabolism ; Hyperalgesia/metabolism ; Male ; Mice, Inbred C57BL ; *Nerve Fibers, Unmyelinated/metabolism ; Peripheral Nerve Injuries/metabolism ; Endocytosis ; *Neuroglia/metabolism ; Cell Communication ; },
abstract = {During peripheral nerve (PN) development, unmyelinated axons (nmAs) tightly fasciculate before being separated and enveloped by non-myelinating Schwann cells (nmSCs), glial cells essential for maintaining nmA integrity. How such a switch from axon-axon to axon-glia interactions is achieved remains poorly understood. Here, we find that inactivating SC-derived SEMA3B or its axonal receptor components in mice leads to incomplete nmA separation and envelopment by nmSCs, eliciting hyperalgesia and allodynia. Conversely, increasing SEMA3B levels in SCs accelerates nmA separation and envelopment. SEMA3B transiently promotes nmA defasciculation accompanied by cell adhesion molecule (CAM) endocytosis, subsequently facilitating nmA-nmSC association. Restoring SEMA3B expression following PN injury promotes nmA-nmSC re-association and alleviates hyperalgesia and allodynia. We propose that SEMA3B-induced CAM turnover facilitates a switch from axon-axon to axon-glia interactions promoting nmA envelopment by nmSCs, which may be exploitable for alleviating PN injury-induced pain by accelerating the restoration of nmA integrity.},
}
@article {pmid40594904,
year = {2025},
author = {Sayem, M and Rafi, MA and Mishu, ID and Mahmud, Z},
title = {Comprehensive genomic analysis reveals virulence and antibiotic resistance genes in a multidrug-resistant Bacillus cereus isolated from hospital wastewater in Bangladesh.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {22915},
pmid = {40594904},
issn = {2045-2322},
mesh = {*Bacillus cereus/genetics/pathogenicity/isolation &amp; purification/drug effects ; *Wastewater/microbiology ; Bangladesh ; *Drug Resistance, Multiple, Bacterial/genetics ; Phylogeny ; Hospitals ; Virulence/genetics ; Genome, Bacterial ; Whole Genome Sequencing ; Genomics/methods ; Anti-Bacterial Agents/pharmacology ; Virulence Factors/genetics ; Humans ; },
abstract = {Hospital wastewater represents a significant reservoir for antimicrobial-resistant bacteria, including multidrug-resistant (MDR) Bacillus cereus, a pathogen of growing concern due to its potential impact on public health and environmental safety. This study characterizes the genomic features, antimicrobial resistance (AMR) mechanisms, and virulence potential of Bacillus cereus MBC, isolated from hospital wastewater in Dhaka, Bangladesh. Using whole-genome sequencing (WGS) and advanced bioinformatics, we analyzed the isolate's taxonomy, phylogenetics, functional annotation, and biosynthetic potential. The genome, spanning 5.6 Mb with a GC content of 34.84%, contained 5,881 protein-coding sequences, including 1,424 hypothetical proteins, and 28 genes associated with AMR. Phylogenetic analysis revealed a close genetic relationship with Bacillus cereus ATCC 14579, sharing virulence factors such as hemolysin BL (HBL), non-hemolytic enterotoxin (NHE), and cytotoxin K (CytK), all contributing to its pathogenicity. The ability to form biofilms further enhances the strain's persistence and resistance in hospital environments. AMR profiling identified genes conferring resistance to beta-lactams (e.g., BcI, BcII, BcIII), tetracyclines (tetB(P)), glycopeptides (vanY), and fosfomycin, highlighting the bacterium's capacity to resist a wide array of antibiotics. Functional annotation revealed metabolic pathways involved in iron acquisition and the biosynthesis of siderophores such as petrobactin and bacillibactin, reinforcing the bacterium's adaptability in nutrient-limited environments. Mobile genetic elements, including prophages, CRISPR-Cas systems, and transposable elements, suggest significant horizontal gene transfer (HGT), enhancing genetic plasticity and resistance spread. Pangenomic analysis, involving 125 B. cereus strains, revealed a high degree of genetic diversity and close relationships with strains from clinical, food, and agricultural environments, emphasizing the overlap between clinical and environmental reservoirs of resistance. The strain's isolation from hospital wastewater underscores the complex interplay between environmental contaminants and bacterial evolution, which fosters MDR traits. Our findings underscore the urgent need for enhanced genomic surveillance and wastewater management strategies to mitigate the spread of MDR B. cereus and AMR genes in hospital environments.},
}
@article {pmid40594760,
year = {2025},
author = {Kanna, RK and Shoran, P and Yadav, M and Ahmed, MN and Burje, S and Shukla, G and Sinha, A and Hussain, MR and Khalid, S},
title = {Improving EEG based brain computer interface emotion detection with EKO ALSTM model.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {20727},
pmid = {40594760},
issn = {2045-2322},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Emotions/physiology ; Algorithms ; *Brain/physiology ; Male ; Adult ; Signal Processing, Computer-Assisted ; Female ; },
abstract = {Decoding signals from the CNS brain activity is done by a computer-based communication device called a BCI. In contrast, the system is considered compelling communication equipment enabling command, communication, and action without using neuromuscular or muscle channels. Various techniques for automatic emotion identification based on body language, speech, or facial expressions are nowadays in use. However, the monitoring of exterior emotions, which are easily manipulated, limits the applicability of these procedures. EEG-based emotion detection research might yield significant benefits for enhancing BCI application performance and user experience. To overcome these issues, this study proposed a novel EKO-ALSTM for emotion detection in EEG-based brain-computer interfaces. The proposed study comprises EEG-based signals that record the electrical activity of the brain connected to various emotional states, which are gathered as real-time acquired EEG signals for emotion detection. The data was pre-processed using a bandpass filter to remove unwanted frequency noise for the obtained data. Then, feature extraction is performed using DWT from pre-processed data. Specifically, the proposed approach is implemented using Python software. The proposed system and existing algorithms are compared using a variety of evaluation criteria, including specificity, F1 score, accuracy, recall or sensitivity, and positive predictive values or precision. The results demonstrated that the proposed method achieved better performance in EEG-based BCI emotion detection with an accuracy of 97.93%, a positive predictive value of 96.24%, a sensitivity of 97.81%, and a specificity of 97.75%. This study emphasizes that innovative approaches have significantly increased the accuracy of emotion identification when applied to EEG-based emotion recognition systems. Additionally, the findings suggest that integrating advanced machine learning techniques can further enhance the effectiveness and reliability of these systems in real-world applications, paving the way for more responsive and intuitive BCI technologies.},
}
@article {pmid40594416,
year = {2025},
author = {Wechakarn, P and Klomchitcharoen, S and Jatupornpoonsub, T and Jirakittayakorn, N and Puttanawarut, C and Likitsuntonwong, W and Chaimongkolnukul, K and Wongsawat, Y},
title = {Modified stereotactic neurosurgery techniques for rodent surgery enhance survival and reduce surgery time in a severe traumatic brain injury model.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {22166},
pmid = {40594416},
issn = {2045-2322},
mesh = {Animals ; *Brain Injuries, Traumatic/surgery/mortality ; *Stereotaxic Techniques ; Disease Models, Animal ; Rats ; *Neurosurgical Procedures/methods/instrumentation ; Male ; Operative Time ; Rats, Sprague-Dawley ; Mice ; },
abstract = {Controlled cortical impact (CCI) is the most widely used mechanical model of traumatic brain injury (TBI) in rodent brains. This neurosurgical procedure generally involves the use of a stereotaxic system, which requires reaching a specific brain region with the most accurate position possible. In this study, a modified stereotaxic system for TBI induction was developed to evaluate preclinical research in rodents for conducting neural stimulation experiments by using an implanted electrode to assist in rehabilitation after severe TBI. The proposed model aims to reduce animal mortality during surgery and alleviate the negative side effects potentially caused by prolonged anesthesia drug usage. Isoflurane is applied as an anesthetic drug before stereotaxic surgery in rodents, which promotes hypothermia in the animal body. The result showed notable improvement in rodent survival after applying an active warming pad system to prevent hypothermia. Compared with the conventional stereotaxic system, the modified CCI device with a mounted 3D-printed header significantly improved performance in the surgical procedure, decreasing the total operation time by 21.7%, especially in the Bregma‒Lambda measurement. These findings indicate the tangible capability of our modified stereotaxic system, which allows surgeons to perform stereotaxic surgery faster and lowers the risk of intraoperative mortality.},
}
@article {pmid40594365,
year = {2025},
author = {Hadi-Saleh, Z and Mosleh, M and Al-Shahe, MA and Mosleh, M},
title = {Towards decoding motor imagery from EEG signal using optimized back propagation neural network with honey badger algorithm.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {21202},
pmid = {40594365},
issn = {2045-2322},
mesh = {*Electroencephalography/methods ; Humans ; *Algorithms ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; *Imagination/physiology ; Movement/physiology ; Brain/physiology ; },
abstract = {The importance of using Brain-Computer Interface (BCI) systems based on electro encephalography (EEG) signal to decode Motor Imagery(MI) is very impressive because of the possibility of analyzing and translating brain signals related to movement intentions. This technology has many applications in the fields of medicine, rehabilitation, mind-controlled computers and assistive technologies. Despite significant progress in EEG-based BCI systems, there are challenges such as signal noise, low decoding accuracy, instability and changeability of signals, etc. To address these limitations, this article presents a new approach to classify MI from EEG signals with the help of synergistic Hilbert-Huang Transform(HHT) as pre-processing, Permutation Conditional Mutual Information Common Space Pattern (PCMICSP) as features and optimized back propagation neural network(BPNN) based on Honey Badger Algorithm(HBA) as classifier. Using the ergodicity of the HBA, along with chaotic mechanisms and global convergence, this approach encodes and optimizes the weights and thresholds of a BPNN. Initially, a comprehensive optimal solution is obtained through the honey badger algorithm. Subsequently, this solution is further refined to reach a more precise optimal state by introducing chaotic disturbances. The proposed method efficiency was confirmed through experimental analysis on a set of data of benchmark that is generally accessible of EEGMMIDB (imagery database or motor movement of EEG). Our experimental analysis outcome showed that mechanism development is important. Now, two EEG signal levels were taken into consideration: the first being an epileptic and the other being non-epileptic. The presented technique generated a max accuracy of 89.82% in comparison with other methods.},
}
@article {pmid40590757,
year = {2025},
author = {Chang, T and Cho, SI and Chai, JY and Min, KD},
title = {Implications of predator species richness in terms of zoonotic spillover transmission of filovirus diseases in Africa.},
journal = {Transactions of the Royal Society of Tropical Medicine and Hygiene},
volume = {119},
number = {11},
pages = {1277-1287},
doi = {10.1093/trstmh/traf065},
pmid = {40590757},
issn = {1878-3503},
support = {NRF-2021R1C1C2012611//National Research Foundation of Korea/ ; },
mesh = {Animals ; Humans ; *Zoonoses/epidemiology/transmission/virology ; *Biodiversity ; Africa/epidemiology ; *Disease Outbreaks ; *Strigiformes/virology ; *Marburg Virus Disease/transmission/epidemiology ; *Hemorrhagic Fever, Ebola/transmission/epidemiology ; *Predatory Behavior ; Ebolavirus ; },
abstract = {BACKGROUND: A rich biodiversity of predators has been suggested to suppress the risk of zoonotic spillover by regulating prey abundance and behavior. We evaluated the association between predator species richness and spillover events of Ebolavirus and Marburgvirus in Africa.<br><br>METHODS: Historical records of filovirus outbreaks, along with ecological, geographical and socioeconomic factors, were considered in this environmental study. We used the maximum entropy approach (Maxent modeling) and stacked species distribution models to estimate predator species richness. Logistic regression analyses accounting for spatiotemporal autocorrelations were conducted to assess the association between predator species richness and spillover risk, adjusting for potential confounders.<br><br>RESULTS: Higher species richness of certain predators-the order Strigiformes and the family Colubridae-was associated with lower risks of Ebolavirus spillover, but not with Marburgvirus spillover. The third quartile (OR=0.02, 95% Bayesian credible interval [BCI]=0.00-0.84) and fourth quartile (OR=0.07, 95% BCI=0.00-0.42) of Strigiformes species richness, as well as the third quartile (OR=0.15, 95% BCI=0.01-0.73) and fourth quartile (OR=0.53, 95% BCI=0.03-0.85) of Colubridae species richness, were significantly associated with reduced odds of Ebolavirus index cases.<br><br>CONCLUSION: These findings support a possible role for predator species richness in suppressing zoonotic spillover.},
}
@article {pmid40590380,
year = {2025},
author = {Kushwaha, N and Mishra, N and Lalawat, RS and Padhy, PK and Gupta, VK},
title = {Automated posture adjustment system for immobilized patients using EEG signals.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-13},
doi = {10.1080/10255842.2025.2523322},
pmid = {40590380},
issn = {1476-8259},
abstract = {This paper presents a Brain Computing Interface (BCI) system utilizing Electroencephalography (EEG) for human posture Identification. The proposed approach follows a structured five-step process, ensuring accurate and efficient classification. The dataset collected using the MindRove EEG device captures brain activity during four motor imagery tasks: Leftward, Rightward, Upward, and Zeroth. Pre-processing involved filtering, followed by feature extraction using a Convolutional Recurrent Denoising Autoencoder (CRDAE) model. After that Classification is performed using artificial intelligence (AI) models, including Gated Recurrent Unit (GRU) with Attention, Temporal Transformer (TT), Bidirectional Long Short-Term Memory with attention mechanisms (Bi-LSTM with AM), and proposed Graph Transformer All Attention (GTAA). The GTAA model demonstrates superior performance, achieving the highest classification accuracy among the evaluated models. Additionally, the proposed system validated against the BCI Competition IV 2a datasets and ten-fold subject cross-validation, demonstrating its reliability and efficiency for real-time BCI applications. This study underscores the potential of integrating advanced AI techniques with EEG signal measurement and instrumentation for practical implementations.},
}
@article {pmid40590025,
year = {2025},
author = {Deuel, TA and Wenlock, J and McGovern, A and Rosenthal, J and Pampin, J},
title = {Musical auditory feedback BCI: clinical pilot study of the Encephalophone.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1592640},
pmid = {40590025},
issn = {1662-5161},
abstract = {INTRODUCTION: Therapeutic strategies for patients with severe acquired motor disability are relatively limited and show variable efficacy. Innovative technologies such as brain-computer interfaces (BCIs) have been developed recently that might benefit certain types of patients.<br><br>METHODS: Here, we tested a previously described auditory BCI, the Encephalophone, which may offer new options to improve quality of life and function. Eleven subjects with acquired moderate to severe motor disability, who had lost their ability to express themselves musically, were enrolled and 10 completed a clinical pilot study of the hands-free Encephalophone brain-computer interface (BCI). Subjects were briefly instructed on the use of the Encephalophone BCI, which uses EEG measured motor imagery to allow users to generate musical notes in real time without requiring movement. Subjects then underwent a pitch-matching task, a measure of accuracy, to attempt to match a given target pitch 3 times within 10&#x202f;s. They were allowed free play, where they could improvise music over a backing track. After 2-3 songs - approximately 10&#x202f;min - of freely improvised playing, subjects repeated the pitch-matching task. There were 3 sessions of testing and free play per subject, within 2&#x202f;weeks, with at least 1&#x202f;day separating sessions.<br><br>RESULTS: All subjects, on average, improved their pitch-matching accuracy by 15.6 percentage points and increased their number of hits by 58.7% over the 3 sessions, with all subjects scoring accuracy percentages significantly above random probability (19.05%). A subjective self-reporting survey of ratings of such factors as a feeling of expressing oneself, enjoyment, discomfort, and feeling of control showed a generally favorable response.<br><br>DISCUSSION: We suggest that this training approach using an auditory BCI may provide an innovative solution to challenges in recovery from motor disability.<br><br>CLINICAL TRIAL REGISTRATION: https://research.providence.org/clinical-research, Swedish Health Services #: STUDY2017000301.},
}
@article {pmid40589299,
year = {2025},
author = {George, I and Rao, DP and Jain, A and Ascione, G and Sharma, M and Meharwal, ZS and Sarkar, B and Kochar, N and Gan, MD and Shastri, N and Runt, J and Whisenant, B and Wilson, B and Kiser, A and Leon, MB and Pandey, K},
title = {1-Year Results From a Multicenter Trial of a Polymer Surgical Mitral Valve: Insights Into New Technology.},
journal = {Journal of the American College of Cardiology},
volume = {86},
number = {7},
pages = {515-526},
doi = {10.1016/j.jacc.2025.06.017},
pmid = {40589299},
issn = {1558-3597},
mesh = {Humans ; Female ; Male ; Middle Aged ; Adult ; Aged ; *Heart Valve Prosthesis ; *Mitral Valve/surgery/diagnostic imaging ; Prospective Studies ; *Polymers ; *Heart Valve Prosthesis Implantation/methods/instrumentation ; Prosthesis Design ; Young Adult ; India/epidemiology ; Treatment Outcome ; *Mitral Valve Insufficiency/surgery ; Follow-Up Studies ; },
abstract = {BACKGROUND: Polymer leaflet material may extend the durability of surgical mitral valve replacement (SMVR) to provide stable long-term hemodynamics. The India Mitral Surgical Trial sought to evaluate the safety and performance of a novel polymer leaflet material as part of a surgical mitral valve (MV) prosthesis.<br><br>OBJECTIVES: In this study, the authors sought to report 1-year outcomes in patients undergoing SMVR for MV disease using the Tria Mitral Valve (Foldax).<br><br>METHODS: Adult patients requiring MV replacement were enrolled in a prospective single-arm multicenter trial at 8 clinical sites in India from April to November 2023. An independent physician screening committee reviewed each patient for study eligibility before enrollment. Safety events were adjudicated per standard Valve Academic Research Consortium 3 criteria guidelines, and valve performance was assessed by means of echocardiographic and computed tomographic imaging at 30 days and 1 year. Patients were maintained on a vitamin K antagonist (target international normalized ratio: 2.5).<br><br>RESULTS: Sixty-seven patients, of whom 64% were female (48% of childbearing age), with a mean age of 42 years (range: 19-67 years), mean body mass index of 22.7 kg/m[2], and body surface area of 1.6 cm[2] were treated with SMVR with 100% technical success. Most patients (54%) were NYHA functional class III or IV at baseline. The mean Society of Thoracic Surgeons score was 1.4%. The etiology of MV disease was stenosis in 27%, regurgitation in 30%, and mixed in 43% of patients, primarily secondary to rheumatic heart disease. The 1-year rates for all-cause mortality, thromboembolic events, stroke, structural valve deterioration, and valve reintervention were 9.1%, 7.5%, 4.9%, 0%, and 0%, respectively. No death was valve related. One-year effective orifice area and mean inflow gradient were 1.4 &#xb1; 0.4 cm[2] and 4.6 &#xb1; 1.7 mm Hg, respectively. There were 2 thrombotic events and 3 ischemic strokes, all in patients with subtherapeutic international normalized ratio.<br><br>CONCLUSIONS: The polymer surgical MV demonstrated an acceptable safety profile and maintained stable hemodynamic performance through 1 year in patients undergoing MV replacement. Further study of this promising polymer leaflet technology is ongoing. (Clinical Investigation for the Foldax Tria Mitral Valve-India; NCT06191718).},
}
@article {pmid40588550,
year = {2025},
author = {Tian, Y and Li, H and Ye, W and Yuan, X and Guo, X and Guo, F},
title = {Temperature-dependent modulation of light-induced circadian responses in Drosophila melanogaster.},
journal = {The EMBO journal},
volume = {44},
number = {16},
pages = {4552-4576},
pmid = {40588550},
issn = {1460-2075},
support = {32171008//the National Natural Science Foundation of China/ ; 32471210//the National Natural Science Foundation of China/ ; 2023-PT310-01//the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences/ ; 2025ZFJH01-01//the Fundamental Research Funds for the Central Universities/ ; 226-2024-00133//the Fundamental Research Funds for the Central Universities/ ; },
mesh = {Animals ; *Drosophila melanogaster/physiology/radiation effects ; *Circadian Rhythm/physiology ; *Temperature ; *Light ; Neurons/physiology/metabolism ; Drosophila Proteins/metabolism/genetics ; *Circadian Clocks/physiology ; Neuropeptides/metabolism ; },
abstract = {Animals entrain their circadian rhythms to multiple external signals, such as light and temperature, which are integrated in master clock neurons to adjust circadian phases. However, the precise mechanisms underlying this process remain unclear. Here, we use in vivo two-photon calcium imaging while precisely controlling temperature to investigate how the Drosophila melanogaster circadian clock integrates light and temperature inputs in circadian neurons. We show that light responses modulate the circadian clock in central pacemaker neurons, with temperature acting as a fine-tuning mechanism to achieve optimal adaptation. Our results suggest that temperature-sensitive dorsal clock neurons DN1as regulate the light-induced firing of s-LNv circadian pacemaker neurons and release of the neuropeptide PDF through inhibitory glutamatergic signaling. Specifically, higher temperatures suppress s-LNv firing upon light exposure, while lower temperatures enhance this response. Behavioral analyses further indicate that lower temperatures accelerate phase adjustment, whereas higher temperatures decelerate them in response to new light-dark cycles. This novel mechanism of temperature-dependent modulation of circadian phase adjustment provides new insights into the adaptive strategies of animals for survival in fluctuating environments.},
}
@article {pmid40588517,
year = {2025},
author = {Ding, Y and Udompanyawit, C and Zhang, Y and He, B},
title = {EEG-based brain-computer interface enables real-time robotic hand control at individual finger level.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {5401},
pmid = {40588517},
issn = {2041-1723},
support = {R01 NS124564/NS/NINDS NIH HHS/United States ; R01 NS096761/NS/NINDS NIH HHS/United States ; RF1 NS131069/NS/NINDS NIH HHS/United States ; NS124564, NS131069, NS127849, NS096761//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; R01 NS127849/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Robotics/methods/instrumentation ; *Fingers/physiology ; Male ; Adult ; Female ; *Hand/physiology ; Young Adult ; Movement/physiology ; Brain/physiology ; Neural Networks, Computer ; Imagination/physiology ; },
abstract = {Brain-computer interfaces (BCIs) connect human thoughts to external devices, offering the potential to enhance life quality for individuals with motor impairments and general population. Noninvasive BCIs are accessible to a wide audience but currently face challenges, including unintuitive mappings and imprecise control. In this study, we present a real-time noninvasive robotic control system using movement execution (ME) and motor imagery (MI) of individual finger movements to drive robotic finger motions. The proposed system advances state-of-the-art electroencephalography (EEG)-BCI technology by decoding brain signals for intended finger movements into corresponding robotic motions. In a study involving 21 able-bodied experienced BCI users, we achieved real-time decoding accuracies of 80.56% for two-finger MI tasks and 60.61% for three-finger tasks. Brain signal decoding was facilitated using a deep neural network, with fine-tuning enhancing BCI performance. Our findings demonstrate the feasibility of naturalistic noninvasive robotic hand control at the individuated finger level.},
}
@article {pmid40588007,
year = {2025},
author = {Mahoney, TB and Grayden, DB and John, SE},
title = {Sub-scalp EEG for sensorimotor brain-computer interface.},
journal = {Journal of neural engineering},
volume = {22},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ade9f1},
pmid = {40588007},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Animals ; *Electroencephalography/methods ; Sheep ; *Evoked Potentials, Somatosensory/physiology ; *Sensorimotor Cortex/physiology ; },
abstract = {Objective. To establish sub-scalp electroencephalography (EEG) as a viable option for brain-computer interface (BCI) applications, particularly for chronic use, by demonstrating its effectiveness in recording and classifying sensorimotor neural activity.Approach. Two experiments were conducted in this study. The first aim was to demonstrate the high spatial resolution of sub-scalp EEG through analysis of somatosensory evoked potentials in sheep models. The second focused on the practical application of sub-scalp EEG, classifying motor execution using data collected during a sheep behavioural experiment.Main results. We successfully demonstrated the recording of sensorimotor rhythms using sub-scalp EEG in sheep models. Important spatial, temporal, and spectral features of these signals were identified, and we were able to classify motor execution with above-chance performance. These results are comparable to previous work that investigated signal quality and motor execution classification using ECoG and endovascular arrays in sheep models.Significance. These results suggest that sub-scalp EEG may provide signal quality that approaches that of more invasive neural recording methods such as ECoG and endovascular arrays, and support the use of sub-scalp EEG for chronic BCI applications.},
}
@article {pmid40587936,
year = {2025},
author = {Vadivelan D, S and Sethuramalingam, P},
title = {A hybrid approach for EEG motor imagery classification using adaptive margin disparity and knowledge transfer in convolutional neural networks.},
journal = {Computers in biology and medicine},
volume = {195},
number = {},
pages = {110675},
doi = {10.1016/j.compbiomed.2025.110675},
pmid = {40587936},
issn = {1879-0534},
mesh = {Humans ; *Electroencephalography/methods ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; *Imagination/physiology ; Convolutional Neural Networks ; },
abstract = {- Motor Imagery (MI) using Electroencephalography (EEG) is essential in Brain-Computer Interface (BCI) technology, enabling interaction with external devices by interpreting brain signals. Recent advancements in Convolutional Neural Networks (CNNs) have significantly improved EEG classification tasks; however, traditional CNN-based methods rely on fixed convolution modes and kernel sizes, limiting their ability to capture diverse temporal and spatial features from one-dimensional EEG-MI signals. This paper introduces the Adaptive Margin Disparity with Knowledge Transfer 2D Model (AMD-KT2D), a novel framework designed to enhance EEG-MI classification. The process begins by transforming EEG-MI signals into 2D time-frequency representations using the Optimized Short-Time Fourier Transform (OptSTFT), which optimizes windowing functions and time-frequency resolution to preserve dynamic temporal and spatial features. The AMD-KT2D framework integrates a guide-learner architecture where Improved ResNet50 (IResNet50), pre-trained on a large-scale dataset, extracts high-level spatial-temporal features, while a Customized 2D Convolutional Neural Network (C2DCNN) captures multi-scale features. To ensure feature alignment and knowledge transfer, the Adaptive Margin Disparity Discrepancy (AMDD) loss function minimizes domain disparity, facilitating multi-scale feature learning in C2DCNN. The optimized learner model then classifies EEG-MI images into left and right-hand movement motor imagery classes. Experimental results on the real-world EEG-MI dataset collected using the Emotiv Epoc Flex system demonstrated that AMD-KT2D achieved a classification accuracy of 96.75 % for subject-dependent and 92.17 % for subject-independent, showcasing its effectiveness in leveraging domain adaptation, knowledge transfer, and multi-scale feature learning for advanced EEG-based BCI applications.},
}
@article {pmid40587626,
year = {2025},
author = {Li, Z and Huang, Z and Li, J and Tang, Y and Li, J and Ding, X},
title = {Shear-Aligned Flexible Polarized Fluorescent Antennas for Wearable Visible Light Communications.},
journal = {ACS applied materials &amp; interfaces},
volume = {17},
number = {28},
pages = {40915-40927},
doi = {10.1021/acsami.5c06121},
pmid = {40587626},
issn = {1944-8252},
abstract = {Wearable visible light communication systems face fundamental limitations in dense multi-input multioutput configurations due to signal crosstalk between channels. Here, we demonstrate shear-aligned flexible polarized fluorescent antennas (FPFAs) fabricated through a scalable thermally assisted brush-coating induction (BCI) process. By systematically investigating the synergistic effects of ″coffee-ring″ phenomena and shear forces on halloysite nanotube alignment, we reveal the underlying physical mechanism enabling the formation of highly ordered structures with an orientation degree of 0.89. We encapsulate these structures in a sandwich configuration that maintains polarization performance while exhibiting mechanical stability, with parallel fracture strength 4.25 times higher than conventional designs. When integrated with quantum dot fluorescent conversion layers, these FPFAs achieve a 4.95-fold improvement in signal-to-noise ratio (SNR) compared to traditional receivers across wide viewing angles, even under extreme bending conditions. The resulting wearable communication system maintains 85.1% transmission accuracy at distances up to 9 m under ambient lighting, a 935% improvement over conventional approaches, with superior resilience to environmental disturbances including rain and fog. This work establishes an effective strategy for polarization multiplexing in wearable optical communications, with applications spanning healthcare monitoring, secure communications, and augmented reality interfaces in dynamic environments.},
}
@article {pmid40586414,
year = {2025},
author = {Tian, Y and Wallace, DM and Cederna, PS and Chestek, CA and Kemp, SWP},
title = {Toward Natural Limb Function: A New Era in Prosthetic Innovation.},
journal = {Annals of neurology},
volume = {98},
number = {5},
pages = {913-928},
pmid = {40586414},
issn = {1531-8249},
mesh = {Humans ; *Artificial Limbs/trends ; *Brain-Computer Interfaces/trends ; *Extremities/physiology ; Electroencephalography ; },
abstract = {The past decade has witnessed groundbreaking clinical implementation of neuroprosthetic limbs driven by signals from peripheral targets (eg, nerves and muscle) and the brain to restore limb function for individuals with limb loss or impairment. In this review, we highlight recent key clinical trials in peripheral neuroprosthetic interfaces directly with nerve, residual muscle, and reinnervated muscle. We then highlight the key advances in brain interfaces, including clinical trials using electroencephalography, electrocorticography, and intracortical electrodes to control neuroprosthetics. Finally, we explore the future of neuroprosthetic control where both peripheral and brain interfaces can be combined to improve neuroprosthetic performance. ANN NEUROL 2025;98:913-928.},
}
@article {pmid40586134,
year = {2025},
author = {Zhang, Q and Liu, B and Wang, Z and Zhou, J and Yang, X and Zhou, Q and Zhao, Y and Li, S and Zhou, J and Wang, C},
title = {Training-Free Regulation of Grasping by Intracortical Tactile Feedback Designed via S1-M1 Communication.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {12},
number = {36},
pages = {e03011},
pmid = {40586134},
issn = {2198-3844},
support = {2021ZD0201600//STI 2030-Major Projects, Ministry of Science and Technology of the People's Republic of China/ ; 2021ZD0201604//STI 2030-Major Projects, Ministry of Science and Technology of the People's Republic of China/ ; 82327810//National Major Scientific Instruments and Equipments Development Project of National Natural Science Foundation of China/ ; },
mesh = {Animals ; *Motor Cortex/physiology ; *Somatosensory Cortex/physiology ; *Hand Strength/physiology ; *Feedback, Sensory/physiology ; *Touch/physiology ; Male ; Brain-Computer Interfaces ; Macaca mulatta ; },
abstract = {Tactile feedback is essential for grip force control when operating a neuroprosthesis. Due to limited knowledge of cortical sensorimotor coordination, artificial feedback is mostly counterintuitive, requiring training to be associated with grasping behaviors. The current study investigates sensorimotor communication by recording neural activities from the primary sensory cortex (S1) and the primary motor cortex (M1) while macaques grasp targets of various textures and loads. Intracortical micro-stimulation is also delivered to S1 to validate the intervention of sensorimotor communication in grasping. The findings identify an S1→M1 functional pathway through which tactile information is transferred. The pathway is shared by both natural and artificial neural propagations. Moreover, it is demonstrated that sensory and motor decoding of neural activities in M1, as well as the actual grip force, are modulated by stimulation designed via S1→M1 communication, without prior training. The work provides a biomimetic strategy to design intuitive haptic feedback for brain-machine interfaces utilizing the S1→M1 pathway.},
}
@article {pmid40585760,
year = {2025},
author = {Zheng, Q and Wu, Y and Zhu, J and Feng, K and Bai, Y and Li, G and Ni, G},
title = {Applications and Challenges of Auditory Brain-Computer Interfaces in Objective Auditory Assessments for Pediatric Cochlear Implants.},
journal = {Exploration (Beijing, China)},
volume = {5},
number = {3},
pages = {20240078},
pmid = {40585760},
issn = {2766-2098},
abstract = {Cochlear implants (CI) are the premier intervention for individuals with severe to profound hearing impairment. Worldwide, an estimated 600,000 individuals have enhanced their hearing through cochlear implantation, with nearly half being children. The evaluations after implantation are crucial for appropriate clinical interventions and care. Current clinical practice lacks methods to assess the recovery of advanced auditory functions in cochlear-implanted children. Yet, recent advancements in electroencephalographic (EEG) techniques show promise in accurately evaluating auditory rehabilitation in this demographic. This review elucidates the evolution of brain-computer interface (BCI) technology for auditory assessment, focusing primarily on its application in pediatric cochlear implant recipients. Emphasis is placed on promising clinical biomarkers for auditory rehabilitation and the neural adaptability accompanying cortical adjustments after implantation. Additionally, we discuss emerging challenges and prospects in applying BCI technology to these children.},
}
@article {pmid40584823,
year = {2025},
author = {Jiang, M and Pan, X and Wang, X and Luo, Q},
title = {Will the embedded semantic radicals be activated when recognizing Chinese phonograms?.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1550536},
pmid = {40584823},
issn = {1662-5161},
abstract = {INTRODUCTION: A majority of Chinese characters are phonograms composed of phonetic and semantic radicals that serve different functions. While radical processing in character recognition has drawn significant interest, there is inconsistency regarding the semantic activation of embedded semantic radicals, and little is known about the duration of such sub-lexical semantic activation.<br><br>METHODS: Using a priming character decision task and a between-subjects design, this study examined whether semantic radicals embedded in SP phonograms (semantic radicals on the left and phonetic radicals on the right) can be automatically activated and how long such activation persists. We manipulated semantic relatedness between embedded radicals and target characters, prime frequency, and stimulus onset asynchronies (SOAs).<br><br>RESULTS: Facilitatory effects were observed on targets preceded by low-frequency primes at an SOA of 500 ms. No significant priming effects were found at SOAs of 100 ms or 1000 ms, regardless of prime frequency.<br><br>DISCUSSION: These findings suggest that sub-lexical semantic activation can occur and remain robust at 500 ms but may dissipate before 1000 ms. The study contributes valuable evidence for the automaticity and time course of embedded semantic radical processing in Chinese phonogram recognition, thereby enhancing our understanding of sub-lexical semantic processing in logographic writing systemse.},
}
@article {pmid40584523,
year = {2025},
author = {Shen, Y and Jiang, L and Lai, J and Hu, J and Liang, F and Zhang, X and Ma, F},
title = {A comprehensive review of rehabilitation approaches for traumatic brain injury: efficacy and outcomes.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1608645},
pmid = {40584523},
issn = {1664-2295},
abstract = {Traumatic Brain Injury (TBI), particularly in moderate-to-severe cases, remains a leading cause of long-term disability worldwide, affecting over 64 million individuals annually. Its complex and multifactorial nature demands an integrated, multidisciplinary rehabilitation approach to address the diverse physical, cognitive, behavioral, and psychosocial impairments that follow injury. We conducted a structured literature search using PubMed, Scopus, and Web of Science databases for suitable studies. This comprehensive review critically examines key rehabilitation strategies for TBI, including neuropsychological assessments, cognitive and neuroplasticity-based interventions, psychosocial support, and community reintegration through occupational therapy. The review emphasizes emerging technological innovations such as virtual reality, robotics, brain-computer interfaces, and tele-rehabilitation, which are expanding access to care and enhancing recovery outcomes. Furthermore, it also explores regenerative approaches, such as stem cell therapies and nanotechnology, highlighting their future potential in neurorehabilitation. Special attention is given to the importance of rigorous outcome evaluation, including standardized functional measures, neuropsychological testing, and advanced statistical methodologies to assess treatment efficacy and clinical significance. Patient-centered care is emphasized as a core element-rehabilitation plans are tailored to each individual's cognitive profile, functional needs, and life goals. Studies show this approach leads to better outcomes in executive functioning, emotional wellbeing, and community reintegration. It identifies gaps in current research, such as the lack of longitudinal studies, predictors of individualized treatment success, cost-benefit evaluations, and strategies to manage comorbidities like PTSD. Thus, combining conventional and technology-assisted rehabilitation-guided by patient-centered strategies-can enhance recovery in moderate-to-severe TBI. Future research should focus on long-term effectiveness, cost-efficiency, and scalable personalized care models.},
}
@article {pmid40584436,
year = {2025},
author = {Zamani, S and Sadeghi, J and Kamalabadi-Farahani, M and Aghayan, SN and Arabpour, Z and Djalilian, AR and Salehi, M},
title = {Comparison of cellular, mechanical, and optical properties of different polymers for corneal tissue engineering.},
journal = {Iranian journal of basic medical sciences},
volume = {28},
number = {8},
pages = {1082-1099},
pmid = {40584436},
issn = {2008-3866},
abstract = {OBJECTIVES: The invention of corneal tissue engineering is essential for vision due to the lack of effective treatments and donated corneas. Finding the right polymer is crucial for reducing inflammation, ensuring biocompatibility, and mimicking natural cornea properties.<br><br>MATERIALS AND METHODS: In this study, solvent casting and physical crosslinking (freeze-thaw cycles) were used to fabricate polymeric scaffolds of Polyvinyl alcohol, alginate, gelatin, carboxymethyl chitosan, carboxymethyl cellulose, polyacrylic acid, polyvinyl pyrrolidone, and their combinations. The mechanical evaluation of scaffolds for tension and suture ability was conducted. Biodegradability, swelling, water vapor, bacterial permeability, anti-inflammatory properties, blood compatibility, Blood Clotting Index (BCI), pH alterations, and cell compatibility with human Mesenchymal Stem cells (MSCs) were investigated with MTT. The hydrophilicity of the samples and the ability to adhere to surfaces were also compared with the contact angle and adhesive test, respectively. Finally, quantitative and qualitative analysis was used to check the transparency of the samples.<br><br>RESULTS: The mechanical strength of polyvinyl alcohol and polyvinyl pyrrolidone samples was highest, showing good suture ability. All samples had blood compatibility below 5% and cell compatibility above 75%. Polyvinyl alcohol was the most transparent at around 93%. Carboxymethyl chitosan effectively inhibited bacterial permeability, while its anti-inflammatory potential showed no significant difference.<br><br>CONCLUSION: This study aims to choose the best polymer composition for corneal tissue engineering. The selection depends on the study's goals, like mechanical strength or transparency. Comparing polymers across different dimensions provides better insight for polymer selection.},
}
@article {pmid40584269,
year = {2025},
author = {Ji, D and Yu, H and Xiao, X and Huang, Y and Zhou, X and Xu, M and Jung, TP and Ming, D},
title = {A user-friendly BCI encoding by high frequency single-frequency-SDMA SSaVEF using MEG.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {101},
pmid = {40584269},
issn = {1871-4080},
abstract = {Magnetoencephalography (MEG) delivers high spatial resolution and superior detection performance for high-frequency signals compared to Electroencephalography (EEG). Therefore, researchers can leverage MEG for high-frequency steady-state asymmetric visual evoked potential (SSaVEP). Current SSaVEP encoding typically uses low-frequency stimulation with relatively large stimulus areas, hindering the applicability of this encoding method in user-friendly brain-computer interface (BCI) systems. This study introduces an ultra critical flicker frequency (ultra-CFF) single-frequency-SDMA steady-state asymmetric visual evoked field (SSaVEF) encoding powered by MEG and presents an eight-command SSaVEF-BCI system. The BCI system features a 60 Hz SSVEF visual stimulus landmark and eight visual targets spaced 45° apart. Ten participants took part in the offline experiments, during which data from 41 channels in the occipital region were collected. This study analyzed the spatiotemporal characteristics, frequency-space characteristics, signal-to-noise ratio, and other features of the SSaVEF signals. We also evaluated the system's performance using the multi-DCPM algorithm. Using the multi-DCPM algorithm, the system achieved an impressive average classification accuracy of 81.65% with 4-s length data. With a data length of 1 s, the system achieved an average Information Transfer Rate (ITR) of 32.05 bits/min, with the highest individual ITR reached an astonishing 64.45 bits/min. This study represents the exploration of a high-frequency spatial encoding SSVEF-BCI system based on MEG. The results demonstrate MEG's feasibility and potential of applying MEG in such BCI systems, providing both theoretical and practical value for the further development and implementation of future BCI systems.},
}
@article {pmid40584164,
year = {2025},
author = {Sharma, MK and Chaudhary, S and Shenoy, S},
title = {Development and testing of range of motion driven motor unit recruitment device for knee rehabilitation: A randomized controlled trial.},
journal = {MethodsX},
volume = {14},
number = {},
pages = {103382},
pmid = {40584164},
issn = {2215-0161},
abstract = {Existing research on neuromuscular electrical stimulation (NMES) identifies two primary control approaches: therapist-operated systems and participant-controlled systems. Therapist-operated NMES devices typically employ switches and potentiometers for control, whereas participant-controlled systems offer diverse input methods, including switches, buttons, joysticks, electromyography electrodes, voice-activated commands, and sip-and-puff devices. A critical limitation of current NMES technology lies in its failure to mimic the body's natural muscle recruitment process during electrical stimulation, resulting in premature fatigue and diminished user engagement. A particularly significant drawback is the absence of joint range-of-motion dependency observed during voluntary movements and active involvement of participant. This limitation prevents precise control over spatial and temporal parameters, such as modulating motor unit recruitment relative to joint position, during neuromuscular rehabilitation. Furthermore, existing devices cannot accurately reproduce the co-contraction dynamics and reciprocal activation patterns seen in synergistic, agonist, and antagonist muscle groups during natural movement. Addressing these challenges requires developing innovative NMES technology capable of activating the neuromuscular system while replicating natural voluntary recruitment patterns. Such advancements would not only improve muscle strengthening outcomes but also enhance participant adherence through more effective cortical and peripheral neuromuscular engagement.•Development of neuromuscular electrical stimulation (NMES) device to replicate natural neuromuscular activation patterns through bio-inspired stimulation protocols.•Engineered to mitigate existing limitations of conventional NMES systems, optimizing therapeutic applications for neuromuscular re-education and functional recovery.•Integrates muscle synergy principles, enabling synchronized synergistic, agonist and antagonist activation for enhanced cortical and peripheral neuromuscular engagement and optimize functional rehabilitation outcomes.•Advances rehabilitation strategies by combining dual focus on muscular reconditioning and neural adaptation for holistic recovery.•Demonstrates potential to amplify strength gains while fostering neuroplasticity, supporting long-term functional recovery in neuromuscular rehabilitation.},
}
@article {pmid40581689,
year = {2025},
author = {Olza, A and Soto, D and Santana, R},
title = {Domain Adaptation-enhanced searchlight: enabling classification of brain states from visual perception to mental imagery.},
journal = {Brain informatics},
volume = {12},
number = {1},
pages = {17},
pmid = {40581689},
issn = {2198-4018},
support = {IT1504-22//IKUR strategy/ ; IT1504-22//IKUR strategy/ ; IT1504-22//IKUR strategy/ ; KK-2023/00090//Elkartek/ ; KK-2023/00090//Elkartek/ ; PID2019-105494GB-I00//Project grant/ ; PID2019-105494GB-I00//Project grant/ ; PID2019-105494GB-I00//Project grant/ ; PID2022-137442NB-I00//BERC by Spanish Ministry of Science and Innovation/ ; PID2022-137442NB-I00//BERC by Spanish Ministry of Science and Innovation/ ; CEX2020-001010-S//Severo Ochoa programme/ ; CEX2020-001010-S//Severo Ochoa programme/ ; },
abstract = {In cognitive neuroscience and brain-computer interface research, accurately predicting imagined stimuli is crucial. This study investigates the effectiveness of Domain Adaptation (DA) in enhancing imagery prediction using primarily visual data from fMRI scans of 18 subjects. Initially, we train a baseline model on visual stimuli to predict imagined stimuli, utilizing data from 14 brain regions. We then develop several models to improve imagery prediction, comparing different DA methods. Our results demonstrate that DA significantly enhances imagery prediction in binary classification on our dataset, as well as in multiclass classification on a publicly available dataset. We then conduct a DA-enhanced searchlight analysis, followed by permutation-based statistical tests to identify brain regions where imagery decoding is consistently above chance across subjects. Our DA-enhanced searchlight predicts imagery contents in a highly distributed set of brain regions, including the visual cortex and the frontoparietal cortex, thereby outperforming standard cross-domain classification methods. The complete code and data for this paper have been made openly available for the use of the scientific community.},
}
@article {pmid40581220,
year = {2025},
author = {Alsamri, J and Alamgeer, M and Alamri, MZ and Ghaleb, M and Asklany, SA and Almansour, H and Alsafari, S and Alghamdi, EA},
title = {Longitudinal EEG-based assessment of neuroplasticity and adaptive responses to transcranial focused ultrasound stimulation.},
journal = {Journal of neuroscience methods},
volume = {422},
number = {},
pages = {110521},
doi = {10.1016/j.jneumeth.2025.110521},
pmid = {40581220},
issn = {1872-678X},
mesh = {Humans ; *Electroencephalography/methods ; *Neuronal Plasticity/physiology ; Male ; Adult ; Female ; Longitudinal Studies ; *Brain/physiology ; Neural Networks, Computer ; Young Adult ; *Adaptation, Physiological/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: An emerging non-invasive neuromodulation technique named Transcranial-focused ultrasound stimulation (tFUS) offered several advantages than the conventional methods in terms of high spatial precision and penetration depth. In neurological disorders, this emerging method have gained a lot of attention, because of has the potential for therapeutic modulation of brain activity. Then, lack of standardized, Real-Time (RT) assessment protocols will result in unclear comprehension regarding the way the repeated tFUS applications may impacts the neuroplasticity and adaptive brain responses in a long-term. Here, the short-term and long-term neuroplastic modifications were effectively identified by the the longitudinal integration of EEG biomarkers with tFUS stimulation sessions. An adaptive modulation strategies customized for individual neural responses are also facilitated by this hypothesis.<br><br>NEW METHODS: To integrate the tFUS with high-resolution electroencephalogram (EEG) monitoring in many sessions, Integrated Longitudinal Evaluation Protocol (ILEP) model was suggested in this study. To extract amplitude, latency, spectral dynamics, and connectivity features from evoked potentials, pre-, during-, and post-stimulation EEG signals were identified by the protocol. Then, for monitoring neuroadaptive trajectories over time, the intrgration of the statistical modeling and neural network (NN)-based pattern recognition was employed, and it will assist in analysing those features. For the purpose of differentiating the short-term oscillatory effects from long-term neuroplastic shifts, the following ways will helps in processing the EEG signals: time-frequency decomposition, event-related potential (ERP) analysis, and machine learning (ML) classifiers. Here, the subject-specific response patterns and temporal evolution of brain dynamics were effectively detected by the application of the Deep learning (DL) models.<br><br>RESULTS ANALYSIS: After the tFUS, both the short-term and long-term modifications in brain activity were effectively detected by the application of ILEP, and it was demonstrated by the outcomes of the simulation and empirical data. Here, the location-specific, session-dependent EEG modifications are consistent with the adaptive neuroplastic processes, and it was revealed by the outcomes of the simulation. Then, accurate neuroadaptive signals were separated from noise and temporary conditions, and it was facilitated by the potential of the model.<br><br>A dynamic, session-over-session monitoring of brain responses was facilitated by the ILEP model. But static images was offered by those conventional methods. With an integration of closed-loop feedback and advanced neural modelling, the suggested model executes better than the conventional methods. This suggested model also facilitates in offering a customized neuromodulation therapies.<br><br>CONCLUSION: For monitoring the neuroplastic modifications induced by tFUS,this suggested ILEP model becomes an effective, sacalable. So, this suggested model facilitates an adaptive assessment model for that tracking, and it was demonstrated in this study. The future, RT, closed-loop neuromodulation systems in therapeutic and cognitive enhancement contexts may get benefits from the integration of EEG feedback mechanisms in the suggested model.},
}
@article {pmid40579488,
year = {2025},
author = {Ibáñez, J and Zicher, B and Burdet, E and Baker, SN and Mehring, C and Farina, D},
title = {Peripheral neural interfaces for reading high-frequency brain signals.},
journal = {Nature biomedical engineering},
volume = {9},
number = {9},
pages = {1391-1402},
pmid = {40579488},
issn = {2157-846X},
support = {EP/T020970/1//RCUK | Engineering and Physical Sciences Research Council (EPSRC)/ ; V00896X//RCUK | Biotechnology and Biological Sciences Research Council (BBSRC)/ ; BB/V00896X/1//RCUK | Biotechnology and Biological Sciences Research Council (BBSRC)/ ; 899626//EC | EU Framework Programme for Research and Innovation H2020 | H2020 Euratom (H2020 Euratom Research and Training Programme 2014-2018)/ ; 810346//EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council)/ ; 101077693//EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council)/ ; },
mesh = {Humans ; *Motor Neurons/physiology ; *Brain/physiology ; *Brain-Computer Interfaces ; Animals ; Deep Learning ; Muscle, Skeletal/physiology ; },
abstract = {Accurate and robust recording and decoding from the central nervous system (CNS) is essential for advances in human-machine interfacing. Technologies for direct measurements of CNS activity are limited by their resolution, sensitivity to interference and invasiveness. Motor neurons (MNs) represent the motor output layer of the CNS, receiving and sampling signals from different regions in the nervous system and generating the neural commands that control muscles. Muscle recordings and deep learning decode the spiking activity of spinal MNs in real time and with high accuracy. The input signals to MNs can be estimated from MN outputs. Here we argue that peripheral neural interfaces using muscle sensors represent a promising, non-invasive approach to estimate some of the neural activity from the CNS that reaches the MNs but does not directly modulate force production. We discuss the evidence supporting this concept and the advances needed to consolidate and test MN-based CNS interfaces in controlled and real-world settings.},
}
@article {pmid40579374,
year = {2025},
author = {Yang, C and Zhang, L and Liu, J and Li, K and Li, S and Yang, Z and Bishop, JR and Deng, W and Yao, L and Lui, S and Gong, Q},
title = {More Severe Brain Network Hierarchy Disorganization in Treatment-Naive Deficit Compared to Non-deficit Schizophrenia and Underlying Neurotransmitter Associations.},
journal = {Schizophrenia bulletin},
volume = {},
number = {},
pages = {},
doi = {10.1093/schbul/sbae231},
pmid = {40579374},
issn = {1745-1701},
support = {82102007//National Natural Science Foundation of China/ ; 82120108014//National Natural Science Foundation of China/ ; 82071908//National Natural Science Foundation of China/ ; 82202110//National Natural Science Foundation of China/ ; 2022YFC2009901//National Key Research and Development Program of China/ ; 2022YFC2009900//National Key Research and Development Program of China/ ; 2021JDTD0002//Sichuan Science and Technology Program/ ; 2022-YF09-00062-SN//Chengdu Science and Technology Office, major technology application demonstration project/ ; 2022-GH03-00017-HZ//Chengdu Science and Technology Office, major technology application demonstration project/ ; ZYGD23003//West China Hospital, Sichuan University/ ; ZYAI24010//West China Hospital, Sichuan University/ ; ZYGX2022YGRH008//Fundamental Research Funds for the Central Universities/ ; GZB20240493//Postdoctoral Fellowship Program of CPSF/ ; T2019069//Humboldt Foundation Friedrich Wilhelm Bessel Research Award and Chang Jiang Scholars/ ; },
abstract = {BACKGROUND AND HYPOTHESIS: Deficit schizophrenia (DS) represents a distinct entity characterized by primary and enduring negative symptoms, yet the neurobiological differences between DS and non-DS (NDS) remain undetermined. Using a gradient-based approach, we hypothesize that DS and NDS will exhibit convergent and divergent brain functional hierarchy patterns, each with a specific underlying neurotransmitter architecture.<br><br>STUDY DESIGN: Resting-state functional magnetic resonance imaging images were acquired from 44 treatment-naive DS, 55 treatment-naive NDS, and 60 matched healthy controls (HCs). Gradient metrics were calculated using the BrainSpace toolbox. The spatial correlation between gradient abnormalities in DS or NDS and density maps of 10 neurotransmitters derived by the JuSpace toolbox was analyzed to link the neuroimaging to underlying neurotransmitter information.<br><br>STUDY RESULTS: Both DS and NDS exhibited compressed gradient patterns compared to HC, suggesting reduced network differentiation, with more severe disorganization in DS. The ventral attention network was associated with depression symptoms in DS, whereas the visual network was related to total, general, and paranoid symptom scores in NDS. Moreover, spatial correlation of neurotransmitter analysis revealed that the gradient alterations of DS were primarily related to the serotonergic system while those of NDS were predominantly associated with the dopamine system.<br><br>CONCLUSIONS: The study suggests that independent from the potential effects of antipsychotic medication, DS and NDS are characterized by different neuropathology in brain hierarchy patterns, potentially linked to neurochemical metabolic distinction. Our findings support the hypothesis that DS is a distinct subtype versus NDS from neurodevelopmental perspective.},
}
@article {pmid40578761,
year = {2025},
author = {Brands, R and Fuchs, L and Seyffer, JM and Bajcinca, N and Bartsch, J and Peuker, UA and Schmidt, V and Thommes, M},
title = {Penetration depth and effective sample size characterization of UV/Vis radiation into pharmaceutical tablets.},
journal = {Journal of pharmaceutical sciences},
volume = {},
number = {},
pages = {103889},
doi = {10.1016/j.xphs.2025.103889},
pmid = {40578761},
issn = {1520-6017},
abstract = {The pharmaceutical industry is moving from off-line to real-time release testing (RTRT) to enhance quality while reducing costs. UV/Vis spectroscopy has emerged as a promising tool for RTRT given its simplicity, sensitivity and cost-effectiveness. Nevertheless, the effective sample size must be characterized in relation to the penetration depth to justify its representativeness and suitability for RTRT. In this study, bilayer tablets were produced using a hydraulic tablet press. The lower layer contained titanium dioxide and microcrystalline cellulose (MCC), while the upper layer consisted of MCC, lactose or a combination with theophylline. The thickness of the upper layer was stepwise increased. Spectra from 224 to 820 nm were recorded with an orthogonally aligned UV/Vis probe. Thereby, the experimental penetration depth reached up to 0.4 mm, while the Kubelka-Munk model yielded a theoretical maximum penetration depth of 1.38 mm. Based on these values, the effective sample sizes were determined. Considering a parabolic penetration profile, the maximum volume was 2.01 mm[3]. The results indicated a wavelength and particle size dependency. Micro-CT analysis confirmed the even distribution of the API in the tablets proving the sufficiency of the UV/Vis sample size. Consequently, UV/Vis spectroscopy is a reliable alternative for RTRT in tableting.},
}
@article {pmid40578508,
year = {2025},
author = {Metin, S and Altan, H and Tercan, E and Dedeoglu, BG and Gurdal, H},
title = {DUSP1 protein's impact on breast cancer: Anticancer response and sensitivity to cisplatin.},
journal = {Biochimica et biophysica acta. Gene regulatory mechanisms},
volume = {1868},
number = {3},
pages = {195103},
doi = {10.1016/j.bbagrm.2025.195103},
pmid = {40578508},
issn = {1876-4320},
mesh = {*Cisplatin/pharmacology ; Humans ; *Dual Specificity Phosphatase 1/genetics/metabolism/antagonists &amp; inhibitors ; Female ; Cell Line, Tumor ; Cell Proliferation/drug effects ; *Triple Negative Breast Neoplasms/drug therapy/genetics/pathology/metabolism ; *Antineoplastic Agents/pharmacology ; Cell Movement/drug effects ; Animals ; Gene Expression Regulation, Neoplastic/drug effects ; MAP Kinase Signaling System/drug effects ; Drug Resistance, Neoplasm/genetics ; Phosphorylation/drug effects ; p38 Mitogen-Activated Protein Kinases/metabolism ; Mice ; },
abstract = {Dual-Specificity Phosphatase 1 (DUSP1) modulates the activity of members of the Mitogen-Activated Protein Kinase (MAPK) family, including p38, JNK, and ERK1/2, which affects various cellular functions in cancer. Moreover, DUSP1 is known to influence the outcomes of cancer chemotherapy. This study aimed to reduce DUSP1 protein expression using CRISPR/Cas9 and siRNA and assess its effects on cell proliferation, migration, and tumor growth potential in triple-negative breast cancer (TNBC) cells. We examined the expression levels of p38, JNK, and ERK1/2, along with their phosphorylated forms, and investigated DUSP1's influence to cisplatin sensitivity. Our findings revealed that the downregulation of DUSP1 expression inhibited the proliferation, migration, and tumor growth potential of TNBC cells. Additionally, BCI, an inhibitor of DUSP1/6, demonstrated anti-proliferative effects on these cells. Decreasing the expression of DUSP1 increased the phosphorylation ratio of p38 and JNK, but not ERK1/2. Moreover, the anticancer response induced by cisplatin was enhanced by reducing DUSP1 expression or by treating the cells with BCI. Notably, cisplatin treatment increased p38 phosphorylation, which was significantly augmented by reduced DUSP1 expression. We also demonstrated that the DUSP1 inhibition-induced anticancer response in these cells predominantly relied on p38 activity. These findings contribute to a better understanding of the role of DUSP1 in breast cancer and offer insights into potential therapeutic strategies targeting DUSP1 to enhance the efficacy of cisplatin treatment. Our study highlights that decreased DUSP1 protein expression and activity mediates an anticancer response and increases the sensitivity of MDA-MB231 cells to cisplatin by regulating p38.},
}
@article {pmid40578406,
year = {2025},
author = {Cao, Y and Chen, Z and Yin, Y and Kang, X and Zhang, Y and Xu, Z and Yang, X and Yang, B and He, Q and Yan, H and Luo, P},
title = {Autophagy-dependent hepatocyte apoptosis mediates gilteritinib-induced hepatotoxicity.},
journal = {Toxicology letters},
volume = {410},
number = {},
pages = {189-196},
doi = {10.1016/j.toxlet.2025.06.018},
pmid = {40578406},
issn = {1879-3169},
mesh = {Animals ; *Autophagy/drug effects ; *Apoptosis/drug effects ; *Hepatocytes/drug effects/pathology/metabolism ; Humans ; *Chemical and Drug Induced Liver Injury/pathology/etiology/metabolism/genetics ; Mice, Inbred C57BL ; *Pyrazines/toxicity ; Autophagy-Related Protein 7/genetics/metabolism ; *Aniline Compounds/toxicity ; Mice ; Male ; Mice, Knockout ; *Protein Kinase Inhibitors/toxicity ; },
abstract = {Gilteritinib, a dual FLT3/AXL inhibitor, is clinically effective for relapsed/refractory FLT3-mutated acute myeloid leukemia (AML) but is limited by severe hepatotoxicity. This study investigates the molecular mechanisms underlying gilteritinib-induced liver injury, focusing on the interplay between autophagy and apoptosis. In vitro and in vivo models, including human hepatocyte HL-7702 cells and C57BL/6 J mice, were employed. Gilteritinib treatment significantly upregulated autophagy markers (LC3-II) and induced autophagosome formation, as confirmed by western blot, TEM, and mCherry-GFP-LC3 reporter assays. Concurrently, apoptosis markers (cleaved-PARP, cleaved-Caspase3, Annexin V/PI staining) increased dose- and time-dependently. Pharmacological inhibition of autophagy with autophagy inhibitor 3-methyladenine (3-MA, 5 mM) or gene silence of Atg7 attenuated apoptosis, mitochondrial membrane potential loss, and ROS overproduction, while autophagy induction by Torin1 (100 nM) exacerbated hepatocyte death. In vivo, gilteritinib-treated mice exhibited elevated serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), and lactate dehydrogenase (LDH) levels, alongside histopathological damage, all of which were mitigated in Atg7-deficient mice. These findings demonstrate that gilteritinib triggers excessive autophagy, which drives hepatocyte apoptosis and liver injury. Targeting autophagy pathways, represents a potential therapeutic strategy to alleviate gilteritinib-induced hepatotoxicity, enabling safer clinical use of this vital AML therapy. This study elucidates a critical autophagy-apoptosis axis in drug-induced liver injury and provides actionable insights for managing adverse effects of targeted cancer therapies.},
}
@article {pmid40578388,
year = {2025},
author = {Lin, Z and Jiang, X and Dai, C and Jia, F},
title = {Towards real time efficient and robust ECoG decoding for mobile brain-computer interface.},
journal = {Journal of neural engineering},
volume = {22},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ade917},
pmid = {40578388},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electrocorticography/methods ; Algorithms ; Male ; Adult ; Female ; Computer Systems ; *Brain/physiology ; },
abstract = {Objective. Decoding locomotion-related brain activities from electrocorticographic (ECoG) signals is essential in brain-computer interfaces (BCIs). Most previous ECoG decoders are computationally demanding and sensitive to noises/outliers. Mobile and robust BCIs are particularly important for physically disabled patients to restore motor ability in outdoor scenarios, where the processing pipeline should be implemented efficiently using constrained computation resources. In this work, we aim to explore the optimal pipeline for mobile BCI decoding.Approach. We comprehensively evaluated the trade-off between the decoding precision, computational efficiency and robustness of diverse decoding algorithms on a combined ECoG dataset of 12 subjects conducting individual finger movement, including partial-least-square and their N-way variants, Bayesian ridge regression, least absolute shrinkage and selection operator, support vector regression, neural networks (NNs) with diverse architectures, and random forest (RF). We further explored the feature optimization technique for selected models by using their inherent model explainability. We also compared the decoding performance of updatable algorithms when the data is split into multiple batches and used sequentially.Main results. The RF model, not valued by previous studies, can achieve the best trade-off between precision and efficiency, achieving an average Pearson's correlation coefficient (r) of 0.466 with only 0.5 K floating-point operations per second (FLOPs) per inference and a model size of 900KiB. We also demonstrate the inherent superior robustness of RF model on corrupted ECoG electrodes, with a>2×decoding precision on noisy signals compared with all state-of-the-art deep NNs. The optimized RF processing pipeline was deployed on a STM32-based embedded platform with only a 15.2 ms computation delay.Significance. In this study, we systematically explored the performance and efficiency of ECoG decoding algorithms in finger movement analysis. The proposed decoding pipeline is implemented on a compact embedded platform to achieve low-latency, power-efficient real-time decoding. This research accelerates the translation of mobile BCI into real-life practices.},
}
@article {pmid40578216,
year = {2025},
author = {Zhang, H and Wang, H and An, J and Zheng, S and Wu, D},
title = {A lightweight spiking neural network for EEG-based motor imagery classification.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {191},
number = {},
pages = {107741},
doi = {10.1016/j.neunet.2025.107741},
pmid = {40578216},
issn = {1879-2782},
mesh = {Humans ; *Neural Networks, Computer ; *Electroencephalography/methods ; *Imagination/physiology ; *Brain-Computer Interfaces ; *Brain/physiology ; *Action Potentials/physiology ; },
abstract = {Spiking neural networks (SNNs) aim to simulate the human brain neural network, using sparse spike event streams for effective and energy-efficient spatio-temporal signal processing. This paper proposes a lightweight SNN model for electroencephalogram (EEG) based motor imagery (MI) classification, a classical brain-computer interface paradigm. The model has three desirable characteristics: (1) it has a brain-inspired architecture; (2) it is energy efficient; and, (3) it is dataset agnostic. Within-subject and cross-subject experiments on three public datasets demonstrated the superiority of our SNN model over four classical convolutional neural network based models in EEG based MI classification.},
}
@article {pmid40576544,
year = {2025},
author = {Wu, Y and Bao, K and Liang, J and Li, Z and Shi, Y and Tang, R and Xu, K and Wei, M and Chen, Z and Jian, J and Luo, Y and Tang, Y and Deng, Q and Dai, H and Sun, C and Zhang, W and Lin, H and Zhang, K and Li, L},
title = {Photonic Interfaces: an Innovative Wearable Sensing Solution for Continuous Monitoring of Human Motion and Physiological Signals.},
journal = {Small methods},
volume = {},
number = {},
pages = {e2500727},
doi = {10.1002/smtd.202500727},
pmid = {40576544},
issn = {2366-9608},
support = {10300000H062401/001//Science and Technology Support Plan for Youth Innovation of Colleges and Universities of Shandong Province of China/ ; 2024SDXHDX0005//"Pioneer" and "Leading Goose" Key Research and Development Program of Zhejiang Province/ ; 12104375//National Natural Science Foundation of China/ ; 62175202//National Natural Science Foundation of China/ ; 2024C03150//Key Research and Development Program of Zhejiang Province/ ; 2023GD003/110500Y0022303//Key Project of Westlake Institute for Optoelectronics/ ; },
abstract = {Flexible integrated photonic sensors are gaining prominence in intelligent wearable sensing due to their compact size, exceptional sensitivity, rapid response, robust immunity to electromagnetic interference, and the capability to enable parallel sensing through optical multiplexing. However, integrating these sensors for practical applications, such as monitoring human motions and physiological activities together, remains a significant challenge. Herein, it is presented an innovative fully packaged integrated photonic wearable sensor, which features a delicately designed flexible necklace-shaped microring resonator (MRR), along with a pair of grating couplers (GCs) coupled to a fiber array (FA). The necklace-shaped MRR is engineered to minimize waveguide sidewall-induced scattering loss, with a measured intrinsic quality factor (Qint) of 1.68 × 10[5], ensuring highly sensitive and precise signal monitoring. GCs and FA enhance the seamless wearability of devices while maintaining superior sensitivity to monitor various human motions and physiological signs. These are further classified signals using machine learning algorithms, achieving an accuracy rate of 97%. This integrated photonic wearable sensor shows promise for human-machine interfaces, touch-responsive wearable monitors, and artificial skin, especially in environments susceptible to electromagnetic interference, such as intensive care units (ICUs) and spacecraft. This work significantly advances the field of smart wearable technology.},
}
@article {pmid40575493,
year = {2025},
author = {Mehta, D},
title = {Brain-Computer Interface tool use and the Contemplation Conundrum: a blueprint of mental action, agency, and control.},
journal = {Oxford open neuroscience},
volume = {4},
number = {},
pages = {kvaf002},
pmid = {40575493},
issn = {2753-149X},
abstract = {This paper approaches the role of intentional action in brain-computer interface (BCI) tool use to allow for an ethical discourse regarding the development and usage of neurotechnology. The exploration of mental actions and user control in BCI tool use brings us closer to understanding the philosophical underpinnings of intentions and agency for BCI-mediated actions. The author presents that under some theories of intentional action, certain BCI-mediated overt movements qualify as both voluntary and unintentional. This plausibly magnifies the ethical considerations surrounding BCI tool use. This problem is referred by the author as the contemplation conundrum. Thus, the paper proposes research scope for the neural correlates of intention formation and the neural correlates of imagination aimed at clarifying implementational control and safeguarding privacy of thought in BCI tool use.},
}
@article {pmid40574626,
year = {2025},
author = {Kaszás, A and Meszéna, D and Fiáth, R and Slézia, A and Ulbert, I and Katona, G},
title = {Capturing the Electrical Activity of all Cortical Neurons: Are Solutions Within Reach?.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {12},
number = {32},
pages = {e06225},
pmid = {40574626},
issn = {2198-3844},
support = {TKP2021-EGA-42//Thematic Programme of Excellence/ ; NAP2022-I-2/2022//Hungarian Brain Research Program/ ; RRF-2.3.1-21-2022-00015//Pharmaceutical Research and Development Laboratory Project/ ; HUN-REN-HAZAHIVO-2023//Hungarian Research Network/ ; KSZF-174/2023//Hungarian Research Network/ ; 2019-2.1.7-ERA-NET-2021-00023//ERA-NET/ ; //Bolyai J&#xe1;nos Scholarship of the Hungarian Academy of Sciences/ ; 150574//National Research, Development and Innovation Office/ ; PD143582//National Research, Development and Innovation Office/ ; },
mesh = {*Neurons/physiology ; Humans ; *Cerebral Cortex/physiology ; Animals ; *Electrodes, Implanted ; },
abstract = {Recent advancements in high-density implantable intracortical electrode technology have significantly improved neural interfaces for both research and clinical applications. However, a significant challenge persists: scaling up these devices to achieve recording of nearly all single-unit activity across large brain volumes. This critical review explores recent progress in neural electrode design, focusing on the challenges of achieving scalable solutions for this ambitious goal. The physical and technical constraints of both rigid and flexible probes are addressed, highlighting the limitations imposed by shank stiffness, mechanical tissue damage, and foreign body response. It is identified that the physics of inserting the electrodes into the brain tissue poses a fundamental constraint, which inherently restricts achievable electrode density. Biohybrid strategies, integrating biological and synthetic components, have shown promise, but they have yet to overcome the major challenges necessary to achieve a scalable functional interface. It is concluded that, given the current limitations of available techniques, there is a pressing need to explore fundamentally novel approaches to realize the vision of recording the electrical activity of every cortical neuron within the brain.},
}
@article {pmid40573719,
year = {2025},
author = {Safarov, F and Kutlimuratov, A and Khojamuratova, U and Abdusalomov, A and Cho, YI},
title = {Enhanced AlexNet with Gabor and Local Binary Pattern Features for Improved Facial Emotion Recognition.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {12},
pages = {},
pmid = {40573719},
issn = {1424-8220},
support = {20022362//Korean Agency for Technology and Standard under Ministry of Trade, Industry and Energy in 2024/ ; 2410003714//Establishment of standardization basis for BCI and AI Interoperability/ ; },
mesh = {Humans ; *Emotions/physiology ; *Facial Recognition/physiology ; *Facial Expression ; Algorithms ; Deep Learning ; *Pattern Recognition, Automated/methods ; Face/physiology ; *Automated Facial Recognition/methods ; Neural Networks, Computer ; Convolutional Neural Networks ; },
abstract = {Facial emotion recognition (FER) is vital for improving human-machine interactions, serving as the foundation for AI systems that integrate cognitive and emotional intelligence. This helps bridge the gap between mechanical processes and human emotions, enhancing machine engagement with humans. Considering the constraints of low hardware specifications often encountered in real-world applications, this study leverages recent advances in deep learning to propose an enhanced model for FER. The model effectively utilizes texture information from faces through Gabor and Local Binary Pattern (LBP) feature extraction techniques. By integrating these features into a specially modified AlexNet architecture, our approach not only classifies facial emotions more accurately but also demonstrates significant improvements in performance and adaptability under various operational conditions. To validate the effectiveness of our proposed model, we conducted evaluations using the FER2013 and RAF-DB benchmark datasets, where it achieved impressive accuracies of 98.10% and 93.34% for the two datasets, with standard deviations of 1.63% and 3.62%, respectively. On the FER-2013 dataset, the model attained a precision of 98.2%, a recall of 97.9%, and an F1-score of 98.0%. Meanwhile, for the other dataset, it achieved a precision of 93.54%, a recall of 93.12%, and an F1-score of 93.34%. These results underscore the model's robustness and its capability to deliver high-precision emotion recognition, making it an ideal solution for deployment in environments where hardware limitations are a critical concern.},
}
@article {pmid40573479,
year = {2025},
author = {Sasatake, Y and Matsushita, K},
title = {P300 ERP System Utilizing Wireless Visual Stimulus Presentation Devices.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {12},
pages = {},
pmid = {40573479},
issn = {1424-8220},
support = {JPMJSP2125//JST SPRING/ ; none//THERS/ ; },
mesh = {Humans ; *Event-Related Potentials, P300/physiology ; *Wireless Technology/instrumentation ; Brain-Computer Interfaces ; Male ; Adult ; *Photic Stimulation/methods ; Electroencephalography/methods ; Female ; Young Adult ; },
abstract = {The P300 event-related potential, evoked by attending to specific sensory stimuli, is utilized in non-invasive brain-computer interface (BCI) systems and is considered the only interface through which individuals with complete paralysis can operate devices based on their intention. Conventionally, visual stimuli used to elicit P300 have been presented using displays; however, placing a display directly in front of the user obstructs the field of view and prevents the user from perceiving their surrounding environment. Moreover, every time the user changes posture, the display must be repositioned accordingly, increasing the burden on caregivers. To address these issues, we propose a novel system that employs wirelessly controllable LED visual stimulus presentation devices distributed throughout the surrounding environment, rather than relying on traditional displays. The primary challenge in the proposed system is the communication delay associated with wireless control, which introduces errors in the timing of stimulus presentation-an essential factor for accurate P300 analysis. Therefore, it is necessary to evaluate how such delays affect P300 detection accuracy. The second challenge lies in the variability of visual stimulus strength due to differences in viewing distance caused by the spatial distribution of stimulus devices. This also requires the validation of its impact on P300 detection. In Experiment 1, we evaluated system performance in terms of wireless communication delay and confirmed an average delay of 352.1 ± 30.9 ms. In Experiment 2, we conducted P300 elicitation experiments using the wireless visual stimulus presentation device under conditions that allowed the precise measurement of stimulus presentation timing. We compared P300 waveforms across three conditions: (1) using the exact measured stimulus timing, (2) using the stimulus timing with a fixed compensation of 350 ms for the wireless delay, and (3) using the stimulus timing with both the 350 ms fixed delay compensation and an additional pseudo-random error value generated based on a normal distribution. The results demonstrated the effectiveness of the proposed delay compensation method in preserving P300 waveform integrity. In Experiment 3, a system performance verification test was conducted on 21 participants using a wireless visual presentation device. As a result, statistically significant differences (p < 0.01) in amplitude between target and non-target stimuli, along with medium or greater effect sizes (Cohen's d: 0.49-0.61), were observed under all conditions with an averaging count of 10 or more. Notably, the P300 detection accuracy reached 85% with 40 averaging trials and 100% with 100 trials. These findings demonstrate that the system can function as a P300 speller and be utilized as an interface equivalent to conventional display-based methods.},
}
@article {pmid40571414,
year = {2025},
author = {Yang, L and Li, M and Yang, L and Wang, Z and Shang, Z},
title = {Hippocampal LFP Responses during Pigeon Homing Flight in Outdoors.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {45},
number = {30},
pages = {},
pmid = {40571414},
issn = {1529-2401},
mesh = {Animals ; *Columbidae/physiology ; *Hippocampus/physiology ; Male ; *Homing Behavior/physiology ; *Flight, Animal/physiology ; Female ; *Spatial Navigation/physiology ; Theta Rhythm/physiology ; },
abstract = {The hippocampal formation (HF) plays a key role in avian spatial navigation. Previous studies suggest that the HF may serve different functions at various stages in pigeons' long-distance outdoor homing flight. However, it remains unclear whether the HF exhibits specific neural responses during these stages. In this study, we employed a wearable bimodal data recording system to simultaneously capture flight trajectories and hippocampal local field potential (LFP) signals of pigeons (either sex) during outdoor homing navigation. Our results revealed significant differences in hippocampal neural responses across the initial decision-making (DM) and en route navigation (ER) stages. Specifically, elevated LFP power in theta (4-12 Hz) and beta (12-30 Hz) bands was detected during the DM stage compared with the ER stage, while the high-gamma (60-120 Hz) band exhibited the opposite pattern. In addition, we examined typical theta-beta phase-amplitude coupling during the ER stage. Additionally, stage-specific hippocampal responses remained consistent across release sites. Notably, the difference in hippocampal responses across stages diminished along with the accumulation of homing experience. These results offer new insights into the role of the avian HF in homing flight navigation and suggest parallels between avian and mammalian hippocampal mechanisms in spatial learning.},
}
@article {pmid40567082,
year = {2025},
author = {Lachkar, S and Ibrahimi, A and Boualaoui, I and Sayegh, HE and Nouini, Y},
title = {Botulinum toxin A in idiopathic overactive bladder: a narrative review of 5410 cases.},
journal = {The Canadian journal of urology},
volume = {32},
number = {3},
pages = {145-165},
pmid = {40567082},
issn = {1488-5581},
mesh = {Humans ; *Urinary Bladder, Overactive/drug therapy ; *Botulinum Toxins, Type A/therapeutic use/adverse effects ; *Neuromuscular Agents/therapeutic use/adverse effects ; Treatment Outcome ; },
abstract = {INTRODUCTION: When conservative treatments fail, botulinum toxin A (BoNT-A) is an option for refractory idiopathic overactive bladder (OAB). This review evaluates the efficacy, safety, and predictive factors for BoNT-A in this situation.<br><br>MATERIALS AND METHODS: A literature search up to January 2025 was performed using PubMed, Google Scholar, and Embase to assess efficacy, safety, and predictors of adverse events (AE) related to BoNT-A. The risk of bias was assessed using the Risk of Bias 2 (RoB 2) tool for randomized studies and the Critical Appraisal Skills Programme (CASP) checklist for cohort studies. The quality of the review was evaluated based on the Oxford criteria, following the Strengthening the Assessment of Narrative Review Articles (SANRA) guidelines, and by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for systematic reviews.<br><br>RESULTS: 31 studies were included, involving 5410 patients. BoNT-A improves OAB symptoms even after reinjections. Higher doses do not enhance efficacy but increase AE. AE includes high post-void residual (PVR), clean intermittent self-catheterization (CISC), and Urinary Tract Infection (UTI). Predictors of CISC include age, male gender, hysterectomy, &#x2265;3 vaginal deliveries, mixed incontinence, prior mid-urethral sling (MUS), high PVR, low Pressure at Pdet at First Micturition (PIP1) in women, low Bladder Compliance Index (BCI) in men, and high Bladder Outlet Obstruction Index (BOOI). Diabetes and heart failure increase PVR. UTIs are more frequent in women and men with benign prostatic hyperplasia, with CISC increasing the risk fivefold. Severe complications are rare. Predictors of poor response include male gender, high BOOI, low urinary flow, and diabetes.<br><br>DISCUSSION: BoNT-A is effective for OAB, especially for incontinence. AE is dose-dependent and limits treatment adherence. Their link with poor response remains unclear.<br><br>CONCLUSION: BoNT-A effectively treats refractory idiopathic OAB, improving symptoms and quality of life with repeated injections.},
}
@article {pmid40566931,
year = {2025},
author = {Lin, K and Chen, J and Pan, J and Wang, R and Wu, S and Wen, W and Li, Y and Wang, L and Yuan, F},
title = {Electro-Acupuncture to Treat Disorder of Consciousness (AcuDoc): Study Protocol for a Randomized Sham-Controlled Trial.},
journal = {Brain and behavior},
volume = {15},
number = {6},
pages = {e70637},
pmid = {40566931},
issn = {2162-3279},
support = {//Health Commission of Guangzhou City/ ; //NATCM's Project of High-level Construction of Key TCM Disciplines/ ; //Guangzhou Municipal Science and Technology Bureau/ ; //2023A04J0473/ ; },
mesh = {Adult ; Female ; Humans ; Male ; Middle Aged ; Young Adult ; *Brain Injuries, Traumatic/complications/therapy/physiopathology ; *Consciousness Disorders/therapy/etiology/physiopathology ; *Electroacupuncture/methods ; Electroencephalography ; Randomized Controlled Trials as Topic ; },
abstract = {BACKGROUND: Treatment of disorders of consciousness (DOC) remains a clinical challenge. Electroacupuncture (EA) was shown to have the potential to promote the recovery of consciousness. This trial aims to explore the therapeutic effects and mechanisms of EA in patients with DOC due to traumatic brain injury (TBI) through a multimodal approach.<br><br>METHODS: A total of 50 adult patients with DOC due to TBI and 25 healthy subjects will be enrolled in the study. Patients enrolled in the study will be assigned to the EA group or the sham-EA group through stratified randomization. All patients receive behavioral assessments (CRS-R and brain-computer interface), neurophysiological evaluations (EEG, somatosensory evoked potentials, brainstem auditory evoked potentials), and neuroimaging evaluations (rs-fMRI, amide proton transfer, intravoxel incoherent motion, neurite orientation dispersion and density imaging) before and after the 14-day EA or sham-EA treatment. Each healthy subject will receive a set of neurophysiological and neuroimaging examinations but no treatments. The practitioner administering EA and sham-EA is the only one aware of the grouping results. In the sham-EA group, sham-acupoints, sham-acupuncture, and sham-wire are utilized. The primary outcome measurement is the change in CRS-R score after 14 days of treatment compared with the baseline CRS-R score.<br><br>DISCUSSION: The AcuDoc trial will be the first randomized sham-controlled study to investigate the clinical benefits of EA in patients with DOC. This trial will elucidate the role of EA in the treatment of DOC due to TBI and provide evidence of its therapeutic mechanisms.},
}
@article {pmid40566772,
year = {2025},
author = {Zhang, T and Chen, J and Lu, Y and Xu, D and Yan, S and Ouyang, Z},
title = {[The analysis of invention patents in the field of artificial intelligent medical devices].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {3},
pages = {504-511},
pmid = {40566772},
issn = {1001-5515},
mesh = {*Artificial Intelligence ; *Patents as Topic ; Humans ; *Inventions ; China ; Brain-Computer Interfaces ; Telemedicine ; *Equipment and Supplies ; Robotics ; Algorithms ; },
abstract = {The emergence of new-generation artificial intelligence technology has brought numerous innovations to the healthcare field, including telemedicine and intelligent care. However, the artificial intelligent medical device sector still faces significant challenges, such as data privacy protection and algorithm reliability. This study, based on invention patent analysis, revealed the technological innovation trends in the field of artificial intelligent medical devices from aspects such as patent application time trends, hot topics, regional distribution, and innovation players. The results showed that global invention patent applications had remained active, with technological innovations primarily focused on medical image processing, physiological signal processing, surgical robots, brain-computer interfaces, and intelligent physiological parameter monitoring technologies. The United States and China led the world in the number of invention patent applications. Major international medical device giants, such as Philips, Siemens, General Electric, and Medtronic, were at the forefront of global technological innovation, with significant advantages in patent application volumes and international market presence. Chinese universities and research institutes, such as Zhejiang University, Tianjin University, and the Shenzhen Institute of Advanced Technology, had demonstrated notable technological innovation, with a relatively high number of patent applications. However, their overseas market expansion remained limited. This study provides a comprehensive overview of the technological innovation trends in the artificial intelligent medical device field and offers valuable information support for industry development from an informatics perspective.},
}
@article {pmid40566769,
year = {2025},
author = {Wu, H and Chen, S and Jia, J},
title = {[Research progress on brain mechanism of brain-computer interface technology in the upper limb motor function rehabilitation in stroke patients].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {3},
pages = {480-487},
pmid = {40566769},
issn = {1001-5515},
mesh = {Humans ; *Brain-Computer Interfaces ; *Stroke Rehabilitation ; *Upper Extremity/physiopathology ; *Brain/physiopathology ; Electroencephalography ; Stroke/physiopathology ; },
abstract = {Stroke causes abnormality of brain physiological function and limb motor function. Brain-computer interface (BCI) connects the patient's active consciousness to an external device, so as to enhance limb motor function. Previous studies have preliminarily confirmed the efficacy of BCI rehabilitation training in improving upper limb motor function after stroke, but the brain mechanism behind it is still unclear. This paper aims to review on the brain mechanism of upper limb motor dysfunction in stroke patients and the improvement of brain function in those receiving BCI training, aiming to further explore the brain mechanism of BCI in promoting the rehabilitation of upper limb motor function after stroke. The results of this study show that in the fields of imaging and electrophysiology, abnormal activity and connectivity have been found in stroke patients. And BCI training for stroke patients can improve their upper limb motor function by increasing the activity and connectivity of one hemisphere of the brain and restoring the balance between the bilateral hemispheres of the brain. This article summarizes the brain mechanism of BCI in promoting the rehabilitation of upper limb motor function in stroke in both imaging and electrophysiology, and provides a reference for the clinical application and scientific research of BCI in stroke rehabilitation in the future.},
}
@article {pmid40566768,
year = {2025},
author = {Liu, X and Yang, B and Gan, A and Zhang, J},
title = {[Study on speech imagery electroencephalography decoding of Chinese words based on the CAM-Net model].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {3},
pages = {473-479},
pmid = {40566768},
issn = {1001-5515},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; *Speech/physiology ; Algorithms ; Male ; Adult ; Imagination ; },
abstract = {Speech imagery is an emerging brain-computer interface (BCI) paradigm with potential to provide effective communication for individuals with speech impairments. This study designed a Chinese speech imagery paradigm using three clinically relevant words-"Help me", "Sit up" and "Turn over"-and collected electroencephalography (EEG) data from 15 healthy subjects. Based on the data, a Channel Attention Multi-Scale Convolutional Neural Network (CAM-Net) decoding algorithm was proposed, which combined multi-scale temporal convolutions with asymmetric spatial convolutions to extract multidimensional EEG features, and incorporated a channel attention mechanism along with a bidirectional long short-term memory network to perform channel weighting and capture temporal dependencies. Experimental results showed that CAM-Net achieved a classification accuracy of 48.54% in the three-class task, outperforming baseline models such as EEGNet and Deep ConvNet, and reached a highest accuracy of 64.17% in the binary classification between "Sit up" and "Turn over". This work provides a promising approach for future Chinese speech imagery BCI research and applications.},
}
@article {pmid40566767,
year = {2025},
author = {Li, X and Cao, X and Wang, J and Zhu, W and Huang, Y and Wan, F and Hu, Y},
title = {[Performance evaluation of a wearable steady-state visual evoked potential based brain-computer interface in real-life scenario].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {3},
pages = {464-472},
pmid = {40566767},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography ; *Wearable Electronic Devices ; Algorithms ; Signal Processing, Computer-Assisted ; Adult ; Male ; },
abstract = {Brain-computer interface (BCI) has high application value in the field of healthcare. However, in practical clinical applications, convenience and system performance should be considered in the use of BCI. Wearable BCIs are generally with high convenience, but their performance in real-life scenario needs to be evaluated. This study proposed a wearable steady-state visual evoked potential (SSVEP)-based BCI system equipped with a small-sized electroencephalogram (EEG) collector and a high-performance training-free decoding algorithm. Ten healthy subjects participated in the test of BCI system under simplified experimental preparation. The results showed that the average classification accuracy of this BCI was 94.10% for 40 targets, and there was no significant difference compared to the dataset collected under the laboratory condition. The system achieved a maximum information transfer rate (ITR) of 115.25 bit/min with 8-channel signal and 98.49 bit/min with 4-channel signal, indicating that the 4-channel solution can be used as an option for the few-channel BCI. Overall, this wearable SSVEP-BCI can achieve good performance in real-life scenario, which helps to promote BCI technology in clinical practice.},
}
@article {pmid40566766,
year = {2025},
author = {Zhu, Y and Ji, Z and Li, S and Wang, H and Fu, Y and Wang, H},
title = {[A portable steady-state visual evoked potential brain-computer interface system for smart healthcare].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {3},
pages = {455-463},
pmid = {40566766},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography ; Signal Processing, Computer-Assisted ; Software ; Adult ; Male ; },
abstract = {This paper realized a portable brain-computer interface (BCI) system tailored for smart healthcare. Through the decoding of steady-state visual evoked potential (SSVEP), this system can rapidly and accurately identify the intentions of subjects, thereby meeting the practical demands of daily medical scenarios. Firstly, an SSVEP stimulation interface and an electroencephalogram (EEG) signal acquisition software were designed, which enable the system to execute multi-target and multi-task operations while also incorporating data visualization functionality. Secondly, the EEG signals recorded from the occipital region were decomposed into eight sub-frequency bands using filter bank canonical correlation analysis (FBCCA). Subsequently, the similarity between each sub-band signal and the reference signals was computed to achieve efficient SSVEP decoding. Finally, 15 subjects were recruited to participate in the online evaluation of the system. The experimental results indicated that in real-world scenarios, the system achieved an average accuracy of 85.19% in identifying the intentions of the subjects, and an information transfer rate (ITR) of 37.52 bit/min. This system was awarded third prize in the Visual BCI Innovation Application Development competition at the 2024 World Robot Contest, validating its effectiveness. In conclusion, this study has developed a portable, multifunctional SSVEP online decoding system, providing an effective approach for human-computer interaction in smart healthcare.},
}
@article {pmid40566765,
year = {2025},
author = {Chai, X and Wang, N and Song, J and Yang, Y},
title = {[Detection of motor intention in patients with consciousness disorder based on electroencephalogram and functional near infrared spectroscopy combined with motor brain-computer interface paradigm].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {3},
pages = {447-454},
pmid = {40566765},
issn = {1001-5515},
mesh = {Humans ; *Brain-Computer Interfaces ; Spectroscopy, Near-Infrared/methods ; *Electroencephalography/methods ; *Consciousness Disorders/physiopathology/diagnosis ; Male ; Movement ; Adult ; Female ; Intention ; Persistent Vegetative State/physiopathology/diagnosis ; },
abstract = {Clinical grading diagnosis of disorder of consciousness (DOC) patients relies on behavioral assessment, which has certain limitations. Combining multi-modal technologies and brain-computer interface (BCI) paradigms can assist in identifying patients with minimally conscious state (MCS) and vegetative state (VS). This study collected electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals under motor BCI paradigms from 14 DOC patients, who were divided into two groups based on clinical scores: 7 in the MCS group and 7 in the VS group. We calculated event-related desynchronization (ERD) and motor decoding accuracy to analyze the effectiveness of motor BCI paradigms in detecting consciousness states. The results showed that the classification accuracies for left-hand and right-hand movement tasks using EEG were 93.28% and 76.19% for the MCS and VS groups, respectively; the classification precisions using fNIRS were 53.72% and 49.11% for these groups. When combining EEG and fNIRS features, the classification accuracies for left-hand and right-hand movement tasks in the MCS and VS groups were 95.56% and 87.38%, respectively. Although there was no statistically significant difference in motor decoding accuracy between the two groups, significant differences in ERD were observed between different consciousness states during left-hand movement tasks (P < 0.001). This study demonstrates that motor BCI paradigms can assist in assessing the level of consciousness, with EEG being more sensitive for evaluating residual motor intention intensity. Moreover, the ERD feature of motor intention intensity is more sensitive than BCI classification accuracy.},
}
@article {pmid40566764,
year = {2025},
author = {Pan, J and Zhang, Z and Zhang, Y and Wang, F and Xiao, J},
title = {[Brain-computer interface technology and its applications for patients with disorders of consciousness].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {3},
pages = {438-446},
pmid = {40566764},
issn = {1001-5515},
mesh = {Humans ; *Brain-Computer Interfaces ; *Consciousness Disorders/diagnosis/rehabilitation/physiopathology ; Electroencephalography ; Brain/physiopathology ; Consciousness ; },
abstract = {With the continuous advancement of neuroimaging technologies, clinical research has discovered the phenomenon of cognitive-motor dissociation in patients with disorders of consciousness (DoC). This groundbreaking finding has provided new impetus for the development and application of brain-computer interface (BCI) in clinic. Currently, BCI has been widely applied in DoC patients as an important tool for assessing and assisting behaviorally unresponsive individuals. This paper reviews the current applications of BCI in DoC patients, focusing four main aspects including consciousness detection, auxiliary diagnosis, prognosis assessment, and rehabilitation treatment. It also provides an in-depth analysis of representative key techniques and experimental outcomes in each aspect, which include BCI paradigm designs, brain signal decoding method, and feedback mechanisms. Furthermore, the paper offers recommendations for BCI design tailored to DoC patients and discusses future directions for research and clinical practice in this field.},
}
@article {pmid40566763,
year = {2025},
author = {Pan, H and Ding, P and Wang, F and Li, T and Zhao, L and Nan, W and Gong, A and Fu, Y},
title = {[Evaluation methods for the rehabilitation efficacy of bidirectional closed-loop motor imagery brain-computer interface active rehabilitation training systems].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {3},
pages = {431-437},
pmid = {40566763},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; Humans ; *Imagination/physiology ; *Imagery, Psychotherapy/methods ; },
abstract = {The bidirectional closed-loop motor imagery brain-computer interface (MI-BCI) is an emerging method for active rehabilitation training of motor dysfunction, extensively tested in both laboratory and clinical settings. However, no standardized method for evaluating its rehabilitation efficacy has been established, and relevant literature remains limited. To facilitate the clinical translation of bidirectional closed-loop MI-BCI, this article first introduced its fundamental principles, reviewed the rehabilitation training cycle and methods for evaluating rehabilitation efficacy, and summarized approaches for evaluating system usability, user satisfaction and usage. Finally, the challenges associated with evaluating the rehabilitation efficacy of bidirectional closed-loop MI-BCI were discussed, aiming to promote its broader adoption and standardization in clinical practice.},
}
@article {pmid40566538,
year = {2025},
author = {Pilipović, K and Janković, T and Rajič Bumber, J and Belančić, A and Mršić-Pelčić, J},
title = {Traumatic Brain Injury: Novel Experimental Approaches and Treatment Possibilities.},
journal = {Life (Basel, Switzerland)},
volume = {15},
number = {6},
pages = {},
pmid = {40566538},
issn = {2075-1729},
support = {UIP-2017-05-9517//Croatian Science Foundation/ ; uniri-iskusni-biomed-23-56//University of Rijeka/ ; uniri-mladi-biomed-23-38//University of Rijeka/ ; uniri-iskusni-biomed-23-82//University of Rijeka/ ; },
abstract = {Traumatic brain injury (TBI) remains a critical global health issue with limited effective treatments. Traditional care of TBI patients focuses on stabilization and symptom management without regenerating damaged brain tissue. In this review, we analyze the current state of treatment of TBI, with focus on novel therapeutic approaches aimed at reducing secondary brain injury and promoting recovery. There are few innovative strategies that break away from the traditional, biological target-focused treatment approaches. Precision medicine includes personalized treatments based on biomarkers, genetics, advanced imaging, and artificial intelligence tools for prognosis and monitoring. Stem cell therapies are used to repair tissue, regulate immune responses, and support neural regeneration, with ongoing development in gene-enhanced approaches. Nanomedicine uses nanomaterials for targeted drug delivery, neuroprotection, and diagnostics by crossing the blood-brain barrier. Brain-machine interfaces enable brain-device communication to restore lost motor or neurological functions, while virtual rehabilitation and neuromodulation use virtual and augmented reality as well as brain stimulation techniques to improve rehabilitation outcomes. While these approaches show great potential, most are still in development and require more clinical testing to confirm safety and effectiveness. The future of TBI therapy looks promising, with innovative strategies likely to transform care.},
}
@article {pmid40564460,
year = {2025},
author = {Liu, Z and Fan, K and Gu, Q and Ruan, Y},
title = {Channel-Dependent Multilayer EEG Time-Frequency Representations Combined with Transfer Learning-Based Deep CNN Framework for Few-Channel MI EEG Classification.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {12},
number = {6},
pages = {},
pmid = {40564460},
issn = {2306-5354},
abstract = {The study of electroencephalogram (EEG) signals is crucial for understanding brain function and has extensive applications in clinical diagnosis, neuroscience, and brain-computer interface technology. This paper addresses the challenge of recognizing motor imagery EEG signals with few channels, which is essential for portable and real-time applications. A novel framework is proposed that applies a continuous wavelet transform to convert time-domain EEG signals into two-dimensional time-frequency representations. These images are then concatenated into channel-dependent multilayer EEG time-frequency representations (CDML-EEG-TFR), incorporating multidimensional information of time, frequency, and channels, allowing for a more comprehensive and enriched brain representation under the constraint of few channels. By adopting a deep convolutional neural network with EfficientNet as the backbone and utilizing pre-trained weights from natural image datasets for transfer learning, the framework can simultaneously learn temporal, spatial, and channel features embedded in the CDML-EEG-TFR. Moreover, the transfer learning strategy effectively addresses the issue of data sparsity in the context of a few channels. Our approach enhances the classification accuracy of motor imagery EEG signals in few-channel scenarios. Experimental results on the BCI Competition IV 2b dataset show a significant improvement in classification accuracy, reaching 80.21%. This study highlights the potential of CDML-EEG-TFR and the EfficientNet-based transfer learning strategy in few-channel EEG signal classification, laying a foundation for practical applications and further research in medical and sports fields.},
}
@article {pmid40564444,
year = {2025},
author = {Garcia-Palencia, O and Fernandez, J and Shim, V and Kasabov, NK and Wang, A and The Alzheimer's Disease Neuroimaging Initiative, },
title = {Spiking Neural Networks for Multimodal Neuroimaging: A Comprehensive Review of Current Trends and the NeuCube Brain-Inspired Architecture.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {12},
number = {6},
pages = {},
pmid = {40564444},
issn = {2306-5354},
support = {Project 22-UOA-120, 23-UOA-055-CSG//Health Research Council of New Zealand and Royal Society Catalyst/ ; 23-UOA-055-CSG//University of Auckland/ ; },
abstract = {Artificial intelligence (AI) is revolutionising neuroimaging by enabling automated analysis, predictive analytics, and the discovery of biomarkers for neurological disorders. However, traditional artificial neural networks (ANNs) face challenges in processing spatiotemporal neuroimaging data due to their limited temporal memory and high computational demands. Spiking neural networks (SNNs), inspired by the brain's biological processes, offer a promising alternative. SNNs use discrete spikes for event-driven communication, making them energy-efficient and well suited for the real-time processing of dynamic brain data. Among SNN architectures, NeuCube stands out as a powerful framework for analysing spatiotemporal neuroimaging data. It employs a 3D brain-like structure to model neural activity, enabling personalised modelling, disease classification, and biomarker discovery. This paper explores the advantages of SNNs and NeuCube for multimodal neuroimaging analysis, including their ability to handle complex spatiotemporal patterns, adapt to evolving data, and provide interpretable insights. We discuss applications in disease diagnosis, brain-computer interfaces, and predictive modelling, as well as challenges such as training complexity, data encoding, and hardware limitations. Finally, we highlight future directions, including hybrid ANN-SNN models, neuromorphic hardware, and personalised medicine. Our contributions in this work are as follows: (i) we give a comprehensive review of an SNN applied to neuroimaging analysis; (ii) we present current software and hardware platforms, which have been studied in neuroscience; (iii) we provide a detailed comparison of performance and timing of SNN software simulators with a curated ADNI and other datasets; (iv) we provide a roadmap to select a hardware/software platform based on specific cases; and (v) finally, we highlight a project where NeuCube has been successfully used in neuroscience. The paper concludes with discussions of challenges and future perspectives.},
}
@article {pmid40564430,
year = {2025},
author = {Darvishi, H and Mohammadi, A and Maghami, MH and Sadeghi, M and Sawan, M},
title = {EEG-Driven Arm Movement Decoding: Combining Connectivity and Amplitude Features for Enhanced Brain-Computer Interface Performance.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {12},
number = {6},
pages = {},
pmid = {40564430},
issn = {2306-5354},
abstract = {Brain-computer interfaces (BCIs) translate electroencephalography (EEG) signals into control commands, offering potential solutions for motor-impaired individuals. While traditional BCI studies often focus solely on amplitude variations or inter-channel connectivity, movement-related brain activity is inherently dynamic, involving interactions across regions and frequency bands. We propose that combining amplitude-based (filter bank common spatial patterns, FBCSP) and phase-based connectivity features (phase-locking value, PLV) improves decoding accuracy. EEG signals from ten healthy subjects were recorded during arm movements, with electromyography (EMG) as ground truth. After preprocessing (resampling, normalization, bandpass filtering), FBCSP and multi-lag PLV features were fused, and the ReliefF algorithm selected the most informative subset. A feedforward neural network achieved average metrics of: Pearson correlation 0.829 ± 0.077, R-squared value 0.675 ± 0.126, and root mean square error (RMSE) 0.579 ± 0.098 in predicting EMG amplitudes indicative of arm movement angles. Analysis highlighted contributions from both FBCSP and PLV, particularly in the 4-8 Hz and 24-28 Hz bands. This fusion approach, augmented by data-driven feature selection, significantly enhances movement decoding accuracy, advancing robust neuroprosthetic control systems.},
}
@article {pmid40563754,
year = {2025},
author = {Gkintoni, E and Vassilopoulos, SP and Nikolaou, G and Vantarakis, A},
title = {Neurotechnological Approaches to Cognitive Rehabilitation in Mild Cognitive Impairment: A Systematic Review of Neuromodulation, EEG, Virtual Reality, and Emerging AI Applications.},
journal = {Brain sciences},
volume = {15},
number = {6},
pages = {},
pmid = {40563754},
issn = {2076-3425},
abstract = {Background/Objectives: Mild Cognitive Impairment (MCI) represents a clinical syndrome characterized by cognitive decline greater than expected for an individual's age and education level but not severe enough to significantly interfere with daily activities, with variable trajectories that may remain stable, progress to dementia, or occasionally revert to normal cognition. This systematic review examines neurotechnological approaches to cognitive rehabilitation in MCI populations, including neuromodulation, electroencephalography (EEG), virtual reality (VR), cognitive training, physical exercise, and artificial intelligence (AI) applications. Methods: A systematic review following PRISMA guidelines was conducted on 34 empirical studies published between 2014 and 2024. Studies were identified through comprehensive database searches and included if they employed neurotechnological interventions targeting cognitive outcomes in individuals with MCI. Results: Evidence indicates promising outcomes across multiple intervention types. Neuromodulation techniques showed beneficial effects on memory and executive function. EEG analyses identified characteristic neurophysiological markers of MCI with potential for early detection and monitoring. Virtual reality enhanced assessment sensitivity and rehabilitation engagement through ecologically valid environments. Cognitive training demonstrated the most excellent efficacy with multi-domain, adaptive approaches. Physical exercise interventions yielded improvements through multiple neurobiological pathways. Emerging AI applications showed potential for personalized assessment and intervention through predictive modeling and adaptive algorithms. Conclusions: Neurotechnological approaches offer promising avenues for MCI rehabilitation, with the most substantial evidence for integrated interventions targeting multiple mechanisms. Neurophysiological monitoring provides valuable biomarkers for diagnosis and treatment response. Future research should focus on more extensive clinical trials, standardized protocols, and accessible implementation models to translate these technological advances into clinical practice.},
}
@article {pmid40563743,
year = {2025},
author = {Mróz, K and Jonak, K},
title = {Preliminary Electroencephalography-Based Assessment of Anxiety Using Machine Learning: A Pilot Study.},
journal = {Brain sciences},
volume = {15},
number = {6},
pages = {},
pmid = {40563743},
issn = {2076-3425},
support = {FD-20/II-3/999//Lublin University of Technology/ ; },
abstract = {Background: Recent advancements in machine learning (ML) have significantly influenced the analysis of brain signals, particularly electroencephalography (EEG), enhancing the detection of complex neural patterns. ML enables large-scale data processing, offering novel opportunities for diagnosing and treating mental disorders. However, challenges such as data variability, noise, and model interpretability remain significant. This study reviews the current limitations of EEG-based anxiety detection and explores the potential of advanced AI models, including transformers and VAE-D2GAN, to improve diagnostic accuracy and real-time monitoring. Methods: The paper presents the application of ML algorithms, with a focus on convolutional neural networks (CNN) and recurrent neural networks (RNN), in identifying biomarkers of anxiety disorders and predicting therapy responses. Additionally, it discusses the role of brain-computer interfaces (BCIs) in assisting individuals with disabilities by enabling device control through brain activity. Results: Experimental EEG research on BCI applications was conducted, focusing on motor imagery-based brain activity. Findings indicate that successive training sessions improve signal classification accuracy, emphasizing the need for personalized and adaptive EEG analysis methods. Challenges in BCI usability and technological constraints in EEG processing are also addressed. Conclusions: By integrating ML with EEG analysis, this study highlights the potential for future healthcare applications, including neurorehabilitation, anxiety disorder therapy, and predictive clinical models. Future research should focus on optimizing ML algorithms, enhancing personalization, and addressing ethical concerns related to patient privacy.},
}
@article {pmid40563723,
year = {2025},
author = {Fodor, MA and Cantürk, A and Heisenberg, G and Volosyak, I},
title = {Streamlining cVEP Paradigms: Effects of a Minimized Electrode Montage on Brain-Computer Interface Performance.},
journal = {Brain sciences},
volume = {15},
number = {6},
pages = {},
pmid = {40563723},
issn = {2076-3425},
support = {101118964//This project has received funding from the European Union's research and innovation programme under the Marie Sk&#x142;odowska-Curie grant agreement No 101118964./ ; },
abstract = {(1) Background: Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices using electroencephalography (EEG) signals, offering potential applications in assistive technology and neurorehabilitation. Code-modulated visual evoked potential (cVEP)-based BCIs employ code-pattern-based stimulation to evoke neural responses, which can then be classified to infer user intent. While increasing the number of EEG electrodes across the visual cortex enhances classification accuracy, it simultaneously reduces user comfort and increases setup complexity, duration, and hardware costs. (2) Methods: This online BCI study, involving thirty-eight able-bodied participants, investigated how reducing the electrode count from 16 to 6 affected performance. Three experimental conditions were tested: a baseline 16-electrode configuration, a reduced 6-electrode setup without retraining, and a reduced 6-electrode setup with retraining. (3) Results: Our results indicate that, on average, performance declines with fewer electrodes; nonetheless, retraining restored near-baseline mean Information Transfer Rate (ITR) and accuracy for those participants for whom the system remained functional. The results reveal that for a substantial number of participants, the classification pipeline fails after electrode removal, highlighting individual differences in the cVEP response characteristics or inherent limitations of the classification approach. (4) Conclusions: Ultimately, this suggests that minimal cVEP-BCI electrode setups capable of reliably functioning across all users might only be feasible through other, more flexible classification methods that can account for individual differences. These findings aim to serve as a guideline for what is currently achievable with this common cVEP paradigm and to highlight where future research should focus in order to move closer to a practical and user-friendly system.},
}
@article {pmid40562060,
year = {2025},
author = {Xu, F and Liu, Y and Li, Y and Zhang, C and Han, Z and He, T and Xiao, X and Feng, C and Leng, J and Xu, M},
title = {Research on coding and decoding algorithm of binocular brain-controlled unmanned vehicle.},
journal = {Journal of neural engineering},
volume = {22},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ade829},
pmid = {40562060},
issn = {1741-2552},
mesh = {Humans ; *Algorithms ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Male ; Adult ; Electroencephalography/methods ; Photic Stimulation/methods ; Female ; *Vision, Binocular/physiology ; Young Adult ; *Automobile Driving ; },
abstract = {Objective. With the rapid development of brain-computer interface (BCI) technology, steady-state visual evoked potential (SSVEP) has emerged as an effective method for high-efficiency information transmission. However, traditional single-frequency stimulation methods face limitations in command set scalability and visual comfort.Approach. To address these issues, we propose a novel binocular SSVEP stimulation paradigm for brain-controlled unmanned vehicles. (UV) This system uses a checkerboard and phase encoding for stimulus presentation, encoding a single target with two frequencies to expand the command set. The frequencies are set between 30-35 Hz to enhance visual comfort. By leveraging polarized light technology, each eye receives distinct frequencies, suppressing intermodulation components and reducing the stimulated area for each eye. We also introduce an improved filter bank dual-frequency task-discriminant component analysis (FBD-TDCA) algorithm.Main results. Experimental results show that, in a 15-command simulation, only six frequencies successfully encoded all commands, achieving comparable performance to traditional single-frequency paradigms. Furthermore, the FBD-TDCA algorithm outperformed existing methods such as filter bank task-related component analysis and filter bank canonical correlation analysis, achieving a classification accuracy of 89.27% ± 3.67 and an information translate rate of 163.87 ± 14.32 bits min[-1], with statistical significance confirmed through pairedt-tests. The system's practical application was further demonstrated in an online 12-command UV control task. Participants achieved an average classification accuracy of 90.34% ± 8.75%, with most maintaining low path deviation rates during navigation tasks.Significance. The proposed binocular SSVEP stimulation paradigm and FBD-TDCA algorithm address the limitations of traditional methods, offering enhanced command set scalability, improved visual comfort, and superior performance, paving the way for more efficient and user-friendly BCI applications in real-world scenarios.},
}
@article {pmid40561510,
year = {2025},
author = {Almanna, MA and Elkaim, LM and Alvi, MA and Levett, JJ and Li, B and Mamdani, M and Al-Omran, M and Alotaibi, NM},
title = {Public Perception of the Brain-Computer Interface Based on a Decade of Data on X: Mixed Methods Study.},
journal = {JMIR formative research},
volume = {9},
number = {},
pages = {e60859},
pmid = {40561510},
issn = {2561-326X},
mesh = {Humans ; *Brain-Computer Interfaces/psychology ; *Social Media/statistics &amp; numerical data ; Natural Language Processing ; *Public Opinion ; Male ; Emotions ; Female ; Adult ; },
abstract = {BACKGROUND: Given the recent evolution and achievements in brain-computer interface (BCI) technologies, understanding public perception and sentiments toward such novel technologies is important for guiding their communication strategies in marketing and education.<br><br>OBJECTIVE: This study aims to explore the public perception of BCI technology by examining posts on X (formerly known as Twitter) using natural language processing (NLP) methods.<br><br>METHODS: A mixed methods study was conducted on BCI-related posts from January 2010 to December 2021. The dataset included 65,340 posts from 38,962 unique users. This dataset was subject to a detailed NLP analysis including VADER, TextBlob, and NRCLex libraries, focusing on quantifying the sentiment (positive, neutral, and negative), the degree of subjectivity, and the range of emotions expressed in the posts. The temporal dynamics of sentiments were examined using the Mann-Kendall trend test to identify significant trends or shifts in public interest over time, based on monthly incidence. We used the Sentiment.ai tool to infer users' demographics by matching predefined attributes in users' profile biographies to certain demographic groups. We used the BERTopic tool for semantic understanding of discussions related to BCI.<br><br>RESULTS: The analysis showed a significant rise in BCI discussions in 2017, coinciding with Elon Musk's announcement of Neuralink. Sentiment analysis revealed that 59.38% (38,804/65,340) of posts were neutral, 32.75% (21,404/65,340) were positive, and 7.85% (5132/65,340) were negative. The average polarity score demonstrated a generally positive trend over the course of the study (Mann-Kendall Statistic=0.266; &#x3c4;=0.266; P<.001). Most posts were objective (50,847/65,340, 77.81%), with a smaller proportion being subjective (14,393/65,340, 22.02%). Biographic analysis showed that the "broadcasting" group contributed the most to BCI discussions (17,803/58,030, 30.67%), while the "scientific" group, contributing 27.58% (n=16,005), had the highest overall engagement metrics. The emotional analysis identified anticipation (score = 10,802/52,618, 20.52%), trust (score=9244/52,618, 17.56%), and fear (score=7344/52,618, 13.95%) as the most prominent emotions in BCI discussions. Key topics included Neuralink and Elon Musk, practical applications of BCIs, and the potential for gamification.<br><br>CONCLUSIONS: This NLP-assisted study provides a decade-long analysis of public perception of BCI technology based on data from X. Overall, sentiments were neutral yet cautiously apprehensive, with anticipation, trust, and fear as the dominant emotions. The presence of fear underscores the need to address ethical concerns, particularly around data privacy, safety, and transparency. Transparent communication and ethical considerations are essential for building public trust and reducing apprehension. Influential figures and positive clinical outcomes, such as advancements in neuroprosthetics, could enhance favorable perceptions. The gamification of BCI, particularly in gaming and entertainment, also offers potential for wider public engagement and adoption. However, public perceptions on X may differ from other platforms, affecting the broader interpretation of results. Despite these limitations, the findings provide valuable insights for guiding future BCI developments, policy making, and communication strategies.},
}
@article {pmid40561478,
year = {2025},
author = {Chen, CS and Chang, SH and Liu, CW and Pan, TM},
title = {Exploring the Potential of Electroencephalography Signal-Based Image Generation Using Diffusion Models: Integrative Framework Combining Mixed Methods and Multimodal Analysis.},
journal = {JMIR medical informatics},
volume = {13},
number = {},
pages = {e72027},
pmid = {40561478},
issn = {2291-9694},
mesh = {*Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; *Brain/physiology/diagnostic imaging ; *Image Processing, Computer-Assisted/methods ; Adult ; },
abstract = {BACKGROUND: Electroencephalography (EEG) has been widely used to measure brain activity, but its potential to generate accurate images from neural signals remains a challenge. Most EEG-decoding research has focused on tasks such as motor imagery, emotion recognition, and brain wave classification, which involve EEG signal analysis and classification. Some studies have explored the correlation between EEG and images, primarily focusing on EEG-image pair classification or transformation. However, EEG-based image generation remains underexplored.<br><br>OBJECTIVE: The primary goal of this study was to extend EEG-based classification to image generation, addressing the limitations of previous methods and unlocking the full potential of EEG for image synthesis. To achieve more meaningful EEG-to-image generation, we developed a novel framework, Neural-Cognitive Multimodal EEG-Informed Image (NECOMIMI), which was specifically designed to generate images directly from EEG signals.<br><br>METHODS: We developed a 2-stage NECOMIMI method, which integrated the novel Neural Encoding Representation Vectorizer (NERV) EEG encoder that we designed with a diffusion-based generative model. The Category-Based Assessment Table (CAT) score was introduced to evaluate the semantic quality of EEG-generated images. In addition, the ThingsEEG dataset was used to validate and benchmark the CAT score, providing a standardized measure for assessing EEG-to-image generation performance.<br><br>RESULTS: The NERV EEG encoder achieved state-of-the-art performance in several zero-shot classification tasks, with an average accuracy of 94.8% (SD 1.7%) in the 2-way task and 86.8% (SD 3.4%) in the 4-way task, outperforming models such as Natural Image Contrast EEG, Multimodal Similarity-Keeping Contrastive Learning, and Adaptive Thinking Mapper ShallowNet. This highlighted its superiority as a feature extraction tool for EEG signals. In a 1-stage image generation framework, EEG embeddings often resulted in abstract or generalized images such as landscapes instead of specific objects. Our proposed 2-stage NECOMIMI architecture effectively extracted semantic information from noisy EEG signals, showing its ability to capture and represent underlying concepts derived from brain wave activity. We further conducted a perturbation study to test whether the model overly depended on visual cortex EEG signals for scene-based image generation. The perturbation of visual cortex EEG channels led to a notable increase in Fr&#xe9;chet inception distance scores, suggesting that our model relied heavily on posterior brain signals to generate semantically coherent images.<br><br>CONCLUSIONS: NECOMIMI demonstrated the potential of EEG-to-image generation, revealing the challenges of translating noisy EEG data into accurate visual representations. The novel NERV EEG encoder for multimodal contrastive learning reached state-of-the-art performance both on n-way zero-shot and EEG-informed image generation. The introduction of the CAT score provided a new evaluation metric, paving the way for future research to refine generative models. In addition, this study highlighted the significant clinical potential of EEG-to-image generation, particularly in enhancing brain-machine interface systems and improving quality of life for individuals with motor impairments.},
}
@article {pmid40554057,
year = {2025},
author = {Park, S and Ha, J and Kim, L},
title = {Improving single-trial detection of error-related potentials by considering the effect of heartbeat-evoked potentials in a motor imagery-based brain-computer interface.},
journal = {Computers in biology and medicine},
volume = {195},
number = {},
pages = {110563},
doi = {10.1016/j.compbiomed.2025.110563},
pmid = {40554057},
issn = {1879-0534},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; *Heart Rate/physiology ; Adult ; Electroencephalography ; Young Adult ; *Evoked Potentials/physiology ; *Imagination/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVE: This study aimed to determine the effect of heartbeat-evoked potentials (HEPs) on changes in the error-related potential (ErrP) epoch and classification performance in single trials, specifically distinguishing between correct and error conditions in a three-class motor imagery-based brain-computer interface.<br><br>METHODS: Eleven individuals participated in this study, with 10 participants assigned to the offline group and 10 to the real-time group. The experiment consisted of 360 motor imagery trials, involving both correct and erroneous feedback. The ErrP trial was categorized into three conditions based on whether the heartbeat was within the ErrP epoch time window or not: (1) including heartbeat trials (ErrPIHB), (2) excluding heartbeat trials (ErrPEHB), and (3) total trials (ErrPT).<br><br>RESULTS: A small negativity was observed at approximately 200&#xa0;ms, followed by a subsequent positivity at approximately 300&#xa0;ms. The prominent amplitudes at approximately 200 and 300&#xa0;ms in the ErrPEHB condition notably differed from those in the ErrPT and ErrPIHB conditions, showing the highest classification accuracy. In the offline experiment dataset of 10 participants, the ErrPEHB condition demonstrated the highest classification accuracy (0.89). This was higher by 0.07 and 0.11 compared to the ErrPT (0.82) and ErrPIHB (0.78) conditions, respectively. For real-time analysis, the novel ErrP paradigm proposed in this study achieved a classification accuracy of 0.89 for 10 participants, a 0.05 increase compared with that of the conventional ErrP paradigm.<br><br>CONCLUSION AND SIGNIFICANCE: These findings can contribute to obtaining pure or clear ErrP epochs associated with the target response and significantly improve classification performance.},
}
@article {pmid40553977,
year = {2025},
author = {Jiang, H and Qi, H and Tang, A and Hu, S and Lai, J},
title = {Start the engine of neuroregeneration: A mechanistic and strategic overview of direct astrocyte-to-neuron reprogramming.},
journal = {Ageing research reviews},
volume = {110},
number = {},
pages = {102808},
doi = {10.1016/j.arr.2025.102808},
pmid = {40553977},
issn = {1872-9649},
mesh = {Humans ; Animals ; *Astrocytes/physiology/metabolism ; *Neurons/physiology/metabolism ; *Cellular Reprogramming/physiology ; *Nerve Regeneration/physiology ; Cell Transdifferentiation/physiology ; *Neurogenesis/physiology ; *Aging/physiology ; Neurodegenerative Diseases/therapy/pathology ; },
abstract = {The decline of adult neurogenesis and neuronal function during aging underlies the onset and progression of neurodegenerative diseases such as Alzheimer's disease. Conventional therapies, including neurotransmitter modulators and antibodies targeting pathogenic proteins, offer only symptomatic improvement. As the most abundant glial cells in the brain, astrocytes outnumber neurons nearly fivefold. However, their proliferative and transdifferentiation potential renders them ideal candidates for in situ neuronal replacement. Direct astrocyte-to-neuron reprogramming offers a promising regenerative approach to restore damaged neural circuits. Herein, we propose a "car start-up" model to conceptualize this process, emphasizing the need to inhibit non-neuronal fate pathways (release the handbrake), suppress transcriptional repressors (release the footbrake), and activate neuron-specific gene expression (step on the gas). Additionally, overcoming metabolic barriers in the cytoplasm is essential for successful lineage conversion. Viral or non-viral vectors deliver reprogramming factors, while small molecules serve as metabolic and epigenetic fuel to boost efficiency. In summary, we review the current evidence supporting direct astrocyte-to-neuron reprogramming as a viable regenerative strategy in the aging brain. We also highlight the conceptual "car start-up" model as a useful framework to dissect the molecular logic of lineage conversion and emphasize its promising therapeutic potential for combating neurodegenerative diseases.},
}
@article {pmid40553738,
year = {2025},
author = {Zhang, HG and Wang, JF and Jialin, A and Zhao, XY and Wang, C and Deng, W},
title = {Relationship between multimorbidity burden and depressive symptoms in older Chinese adults: A prospective 10-year cohort study.},
journal = {Journal of affective disorders},
volume = {389},
number = {},
pages = {119714},
doi = {10.1016/j.jad.2025.119714},
pmid = {40553738},
issn = {1573-2517},
mesh = {Aged ; Aged, 80 and over ; Female ; Humans ; Male ; Middle Aged ; China/epidemiology ; Chronic Disease/epidemiology/psychology ; *Depression/epidemiology ; Longitudinal Studies ; *Multimorbidity ; Proportional Hazards Models ; Prospective Studies ; Risk Factors ; },
abstract = {BACKGROUND: Recent research indicates that multimorbidity clusters due to common mechanisms and risk factors, leading to different effects on the development of depressive symptoms (DS) in older populations. This study innovatively examined the association of both the number and specific patterns of multimorbidity with DS.<br><br>METHODS: A total of 1988 participants aged 60&#xa0;years and older were selected from the China Health and Retirement Longitudinal Study (CHARLS) and monitored for DS between June 2011 and September 2020. Twelve chronic conditions were assessed through self-reports. DS was evaluated using the 10-item Center for Epidemiological Studies Depression Scale (CESD-10). Latent class analysis (LCA) was used to identify multimorbidity patterns, and Cox proportional hazards regression models examined the associations of specific diseases, multimorbidity count and multimorbidity patterns with DS.<br><br>RESULTS: During the 9.17-year follow-up period, 986 cases of DS were identified. Hypertension (adjusted hazard ratio [HR]&#xa0;=&#xa0;1.21, 95&#xa0;% confidence interval [CI]&#xa0;=&#xa0;1.05-1.39), stroke (HR&#xa0;=&#xa0;1.77, 95%CI&#xa0;=&#xa0;1.20-2.63), stomach or other digestive disease (HR&#xa0;=&#xa0;1.28, 95%CI&#xa0;=&#xa0;1.11-1.48), arthritis or rheumatism (HR&#xa0;=&#xa0;1.41, 95%CI&#xa0;=&#xa0;1.24-1.60), chronic lung diseases (HR&#xa0;=&#xa0;1.25, 95%CI&#xa0;=&#xa0;1.03-1.52) and kidney disease (HR&#xa0;=&#xa0;1.38, 95%CI&#xa0;=&#xa0;1.07-1.78) were significantly associated with increased DS risk. Each additional chronic condition increased the DS hazard by 13&#xa0;% (adjusted HR&#xa0;=&#xa0;1.13, 95&#xa0;% CI&#xa0;=&#xa0;1.08-1.18). Four multimorbidity patterns were identified by LCA, with the digestion/arthritis pattern (HR&#xa0;=&#xa0;1.47, 95&#xa0;% CI&#xa0;=&#xa0;1.22-1.77) and respiratory pattern (HR&#xa0;=&#xa0;1.47, 95&#xa0;% CI&#xa0;=&#xa0;1.07-2.04) showing higher DS risk compared to the relatively healthy group.<br><br>CONCLUSION: The number and patterns of multimorbidity are significantly associated with heightened DS risk in older populations. Older adults in complex health conditions, particularly those with digestion/arthritis and respiratory multimorbidity patterns, should receive closer mental health monitoring.},
}
@article {pmid40551292,
year = {2025},
author = {Chu, J and Yao, J and Li, Z and Li, J and Zhang, Y and Liu, C and He, H and Li, B and Wei, H},
title = {Brain tissue electrical conductivity as a promising biomarker for dementia assessment using MRI.},
journal = {Alzheimer's &amp; dementia : the journal of the Alzheimer's Association},
volume = {21},
number = {6},
pages = {e70270},
pmid = {40551292},
issn = {1552-5279},
support = {2024YFC2421100//National Key Research and Development Program of China/ ; //National Natural Science Foundation of China/ ; //62471296, 82271441, 62071299, 82372036, 82001342/ ; 23TS1400200//Shanghai Science and Technology Development Funds/ ; STAR 20220103 YG2023LC02//SJTU Trans-med Awards Research/ ; },
mesh = {Humans ; *Magnetic Resonance Imaging/methods ; Biomarkers ; *Brain/diagnostic imaging/metabolism/physiopathology ; *Dementia/diagnostic imaging/diagnosis/metabolism ; Male ; Female ; Amyloid beta-Peptides/metabolism ; *Electric Conductivity ; tau Proteins/metabolism ; Aged ; Cognitive Dysfunction ; Positron-Emission Tomography ; },
abstract = {INTRODUCTION: Dementia, particularly Alzheimer's disease, involves cognitive decline linked to amyloid beta (Aβ) and tau protein aggregation. Magnetic resonance imaging (MRI)-based brain tissue conductivity, which increases in dementia, may serve as a non-invasive biomarker for protein aggregation. We investigate the relationship between MRI-based brain electrical conductivity, protein aggregation, cognition, and gene expression.<br><br>METHODS: Brain conductivity maps were reconstructed and correlated with PET protein signals, cognitive performance, and plasma protein levels. The diagnostic potential of conductivity for dementia was assessed, and transcriptomic analysis using the Allen Human Brain Atlas elucidated the underlying biological processes.<br><br>RESULTS: Increased brain conductivity was associated with A&#x3b2; and tau aggregation in specific brain regions, cognitive decline, and plasma protein levels. Conductivity also improved dementia discrimination performance, and higher gene expression related to ion transport, cellular development, and signaling pathways was observed.<br><br>DISCUSSION: Brain electrical conductivity shows promise as a biomarker for dementia, correlating with protein aggregation and relevant cellular processes.<br><br>HIGHLIGHTS: Brain tissue conductivity correlates with A&#x3b2; and tau aggregation in dementia. Brain tissue conductivity correlates with cognitive scores and GMV. CSF conductivity correlates with plasma protein levels. Combining conductivity with GMV improves dementia diagnosis accuracy. Gene expression in ion processes, cell development, and signaling links to conductivity.},
}
@article {pmid40550006,
year = {2025},
author = {Liu, Y and Fan, P and Pan, Y and Ping, J},
title = {Flexible Microinterventional Sensors for Advanced Biosignal Monitoring.},
journal = {Chemical reviews},
volume = {125},
number = {17},
pages = {8246-8318},
doi = {10.1021/acs.chemrev.5c00115},
pmid = {40550006},
issn = {1520-6890},
mesh = {*Biosensing Techniques/instrumentation/methods ; Humans ; Animals ; Electrodes ; },
abstract = {Flexible microinterventional sensors represent a transformative technology that enables the minimal intervention required to access and monitor complex biosignals (e.g., bioelectrical, biophysical, and biochemical signals) originating from deep tissues, thereby providing accurate data for diagnostics, robotics, prosthetics, brain-computer interfaces, and therapeutic systems. However, fully unlocking their potential hinges on establishing a nondisruptive, intimate, and nonrestrictive interface with the tissue surface, facilitating efficient integration between the microinterventional sensor and the target tissue. In this comprehensive review, we highlight the critical tissue characteristics in both physiologically and pathologically relevant contexts that are pivotal for the design of microinterventional sensors. We also summarize recent advancements in flexible substrate materials and conductive materials, which are tailored to facilitate effective information interaction between bioelectronic components and biological tissues. Furthermore, we classify various electrode architectures─spanning 1D, 2D, and 3D─designed to accommodate the mechanics of soft tissues and enable nonrestrictive interfaces in diverse sensing scenarios. Additionally, we outline critical challenges for next-generation microinterventional sensors and propose integrating advanced materials, innovative fabrication, and embedded intelligence to drive breakthroughs in biosignal sensing. Ultimately, we aim to both provide foundational understanding and highlight emerging strategies in biosignal capture, leveraging recent advancements in these critical components.},
}
@article {pmid40549688,
year = {2025},
author = {Meijs, S and Andreis, FR and Kjærgaard, B and Janjua, TAM and Jensen, W},
title = {Chronic Cranial Window Technique for Repeated Cortical Recordings During Anesthesia in Pigs.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {220},
pages = {},
doi = {10.3791/67931},
pmid = {40549688},
issn = {1940-087X},
mesh = {Animals ; Swine ; *Electrocorticography/methods/instrumentation ; *Anesthesia/methods ; *Somatosensory Cortex/physiology ; Dura Mater/surgery ; Electrodes, Implanted ; },
abstract = {Cortical recordings are essential for extracting neuronal signals to inform various applications, including brain-computer interfaces and disease diagnostics. Each application places specific requirements on the recording technique, and invasive solutions are often selected for long-term recordings. However, invasive recording methods are challenged by device failure and adverse tissue responses, which compromise long-term signal quality. To improve the reliability and quality of chronic cortical recordings while minimizing risks related to device failure and tissue reactions, we developed a cranial window technique. In this protocol, we report methods to implant and access a cranial window in juvenile landrace pigs, which facilitates temporary electrocorticography (ECoG) array placement on the dura mater. We further describe how cortical signals can be recorded using the cranial window technique. Cranial window access can be repeated several times, but a minimum of 2 weeks between implant and access surgeries is advised to facilitate recovery and tissue healing. The cranial window approach successfully minimized common electrode failure modes and tissue responses, resulting in stable and reliable cortical recordings over time. We recorded event-related potentials (ERPs) from the primary somatosensory cortex as an example. The method provided highly reliable recordings, which also allowed the assessment of the effect of an intervention (high-frequency stimulation) on the ERPs. The absence of significant device failures and the reduced number of electrodes used (two electrodes, 43 recording sessions, 16 animals) suggest an improved research economy. While minor surgical access is required for electrode placement, the method offers advantages such as reduced infection risk and improved animal welfare. This study presents a scalable, reliable, and reproducible method for chronic cortical recordings, with potential applications in various fields of neuroscience, including pain research and neurological disease diagnosis. Future adaptations may extend its use to other species and recording modalities, such as intracortical recordings and imaging techniques.},
}
@article {pmid40549518,
year = {2025},
author = {Yu, F and Rao, Z and Chen, N and Liu, L and Jiang, M},
title = {ArmBCIsys: Robot Arm BCI System With Time-Frequency Network for Multiobject Grasping.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {36},
number = {10},
pages = {18327-18341},
doi = {10.1109/TNNLS.2025.3579332},
pmid = {40549518},
issn = {2162-2388},
mesh = {Humans ; *Brain-Computer Interfaces ; *Hand Strength/physiology ; *Robotics/methods/instrumentation ; Electroencephalography/methods ; Algorithms ; *Neural Networks, Computer ; Signal-To-Noise Ratio ; *Arm/physiology ; Adult ; },
abstract = {Brain-computer interface (BCI) offers a direct communication and control channel between the human brain and external devices, presenting new pathways for individuals with physical disabilities to operate robotic arms for complex tasks. However, achieving multiobject grasping tasks under low signal-to-noise ratio (SNR) consumer-grade EEG signals is a significant challenge due to the lack of robust decoding algorithms and precise visual tracking methods. This article proposes, ArmBCIsys, an integrated robotic arm system that combines a novel dual-branch frequency-enhanced network (DBFENet) to robustly decode EEG signals under noisy conditions with the high-precision vision-guided grasping module. The proposed DBFENet designs the scaling temporal convolution block (STCB) to extract multiscale spatiotemporal features from the time domain, while the designed DropScale projected Transformer (DSPT) utilizes discrete cosine transform (DCT) to capture key frequency-domain features, significantly improving decoding robustness. We fine-tune the masked-attention mask Transformer (Mask2Former) model on the Jacquard dataset and incorporate the multiframe centroid-intersection over union (IoU) tracking algorithm to build visual grasp segmenter (VisGraspSeg), enabling reliable segmentation and dynamic tracking for diverse daily objects. Experimental validations on both self-built code-modulated visual evoked potential (c-VEP) dataset (1344 samples) and two public c-VEP datasets demonstrate that DBFENet achieves the state-of-the-art recognition performance, and the system integrates the DBFENet and proposed vision-guided module and ensures stable multiobject selecting and automatic object grasping in dynamic environments, extending promising applications in healthcare robotics, assistive technology, and industrial automation. The self-built dataset has been made publicly accessible at https://github.com/wtu1020/ ArmBCIsys-Self-built-cVEP-Dataset.},
}
@article {pmid40548156,
year = {2025},
author = {Tzimourta, KD},
title = {Human-Centered Design and Development in Digital Health: Approaches, Challenges, and Emerging Trends.},
journal = {Cureus},
volume = {17},
number = {6},
pages = {e85897},
pmid = {40548156},
issn = {2168-8184},
abstract = {Human-centered design (HCD) has emerged as a critical approach for developing digital health technologies that are usable, acceptable, and effective within complex healthcare environments. Rooted in systems theory, ergonomics, and information systems research, HCD prioritizes the needs, capabilities, and limitations of diverse user groups - including patients, clinicians, and caregivers - throughout the design and implementation process. This review synthesizes current applications of HCD in four key domains: brain-computer interfaces (BCIs), augmented and virtual reality (AR/VR), artificial intelligence (AI)-based clinical decision support systems AI-CDSS, and mobile health (mHealth) technologies. It explores design frameworks, usability strategies, and models of human-technology collaboration that contribute to successful adoption and sustained use. Ethical and legal considerations - such as data privacy, informed consent, and algorithmic fairness - are also addressed, particularly in contexts involving biometric and neurophysiological data. While HCD practices have shown substantial potential to improve digital health outcomes, their implementation remains uneven across technologies and settings. Emerging trends, including adaptive personalization, explainable AI, and participatory co-design, are identified as promising directions for the development of more inclusive, trustworthy, and sustainable digital health innovations.},
}
@article {pmid40546334,
year = {2025},
author = {Cruz, MV and Jamal, S and Sethuraman, SC},
title = {A Comprehensive Survey of Brain-Computer Interface Technology in Health care: Research Perspectives.},
journal = {Journal of medical signals and sensors},
volume = {15},
number = {},
pages = {16},
pmid = {40546334},
issn = {2228-7477},
abstract = {The brain-computer interface (BCI) technology has emerged as a groundbreaking innovation with profound implications across diverse domains, particularly in health care. By establishing a direct communication pathway between the human brain and external devices, BCI systems offer unprecedented opportunities for diagnosis, treatment, and rehabilitation, thereby reshaping the landscape of medical practice. However, despite its immense potential, the widespread adoption of BCI technology in clinical settings faces several challenges. These include the need for robust signal acquisition and processing techniques and optimizing user training and adaptation. Overcoming these challenges is crucial to unleashing the complete potential of BCI technology in health care and realizing its promise of personalized, patient-centric care. This review work underscores the transformative potential of BCI technology in revolutionizing medical practice. This paper offers a comprehensive analysis of medical-oriented BCI applications by exploring the various uses of BCI technology and its potential to transform patient care.},
}
@article {pmid40545006,
year = {2025},
author = {Feng, J and Jia, W and Li, Z},
title = {Electroencephalography: A valuable tool for assessing motor impairment and recovery post-stroke.},
journal = {Journal of neuroscience methods},
volume = {422},
number = {},
pages = {110518},
doi = {10.1016/j.jneumeth.2025.110518},
pmid = {40545006},
issn = {1872-678X},
mesh = {Humans ; *Electroencephalography/methods ; *Stroke/physiopathology/diagnosis/complications ; *Recovery of Function/physiology ; *Stroke Rehabilitation/methods ; Brain-Computer Interfaces ; *Brain/physiopathology ; },
abstract = {Stroke is a leading cause of adult disability, and restoring motor function post-stroke is critical to improving the well-being and quality of life of affected individuals. Accurate and timely assessment of motor function is essential for developing effective rehabilitation strategies and predicting recovery outcomes. Electroencephalography (EEG) offers a non-invasive, real-time monitoring of brain activity, offering personalized insights into motor impairment and recovery. Its simplicity and bedside applicability make EEG a valuable tool and a potential biomarker for brain function. In recent years, the integration of EEG with the brain-computer interface technology and neuromodulation techniques has revolutionized personalized rehabilitation therapy, offering new hope for patients with motor dysfunction following stroke. This review synthesizes evidence on EEG-derived biomarkers and their integration with brain-computer interface and neuromodulation techniques, proposing a framework for personalized rehabilitation strategies in stroke recovery.},
}
@article {pmid40544658,
year = {2025},
author = {Mathon, B and Navarro, V and Pons, T and Lecas, S and Roussel, D and Charpier, S and Carpentier, A},
title = {Ultrasound-induced blood-brain barrier opening and selenium-nanoparticle injection lower seizure activity: A mouse model of temporal lobe epilepsy.},
journal = {Ultrasonics},
volume = {155},
number = {},
pages = {107734},
doi = {10.1016/j.ultras.2025.107734},
pmid = {40544658},
issn = {1874-9968},
mesh = {Animals ; *Blood-Brain Barrier ; *Selenium/administration &amp; dosage ; Mice ; *Epilepsy, Temporal Lobe/therapy/chemically induced/drug therapy ; Disease Models, Animal ; *Nanoparticles/administration &amp; dosage ; Male ; Kainic Acid ; Microbubbles ; *Ultrasonic Waves ; Mice, Inbred C57BL ; },
abstract = {BACKGROUND: Given the limitations of current treatment options for drug-resistant mesial temporal lobe epilepsy (MTLE), the development of novel, nonablative and minimally invasive surgical techniques is essential.<br><br>OBJECTIVE AND METHODS: In this study, low-intensity pulsed ultrasound (LIPU)- and microbubble-induced (henceforth LIPU) blood-brain barrier (BBB) opening combined with selenium-nanoparticle (SeNP) intravenous injection in a mouse model of mesial temporal lobe optimized the latter's bioavailability in the brain epileptic tissue of the kainic acid (KA) mouse model of MTLE. We aimed to assess the safety and antiepileptic potential of LIPU-enhanced SeNP delivery against KA-induced seizures using long-term intracranial electroencephalogram video recordings and evaluating neuroinflammation, astrogliosis, neuronal apoptosis and neurogenesis in the hippocampal tissues of mice.<br><br>RESULTS: First, we established that SeNP intravenous injection combined with LIPU-induced BBB disruption was the most effective method to achieve high and sustained selenium levels in the brain. The safety of this treatment was demonstrated after three treatment sessions, 1-week apart, with no adverse effects observed. Our results further showed a significantly lower frequency of epileptic seizures (-90&#xa0;%, P = 0.001) in KA mice treated with LIPU&#xa0;+&#xa0;SeNPs compared to sham-treated controls. Short- and long-term histological changes were seen after that combined regimen, including less aberrant neurogenesis in the hippocampus hilum, less neuronal death throughout the hippocampus and less hippocampal microglial activation, which might collectively contribute to the observed antiseizure effect.<br><br>CONCLUSION: SeNP injection combined with LIPU-induced BBB disruption demonstrated potential as a promising approach to reduce seizure activity in MTLE; however, statistical comparison did not conclusively establish superiority over SeNPs alone. Further investigations are necessary to consider translational studies in humans.},
}
@article {pmid40542951,
year = {2025},
author = {Chen, J and Sun, G and Zhang, Y and Chen, W and Zheng, X and Zhang, S and Hao, Y},
title = {Interactively Integrating Reach and Grasp Information in Macaque Premotor Cortex.},
journal = {Neuroscience bulletin},
volume = {41},
number = {11},
pages = {1991-2009},
pmid = {40542951},
issn = {1995-8218},
mesh = {Animals ; *Motor Cortex/physiology ; *Hand Strength/physiology ; Macaca mulatta ; *Psychomotor Performance/physiology ; Neurons/physiology ; Male ; Cues ; Movement/physiology ; Gestures ; },
abstract = {Reach-to-grasp movements require integrating information on both object location and grip type, but how these elements are planned and to what extent they interact remains unclear. We designed a new experimental paradigm in which monkeys sequentially received reach and grasp cues with delays, requiring them to retain and integrate both cues to grasp the goal object with appropriate hand gestures. Neural activity in the dorsal premotor cortex (PMd) revealed that reach and grasp were similarly represented yet not independent. Upon receiving the second cue, the PMd continued encoding the first, but over half of the neurons displayed incongruent modulations: enhanced, attenuated, or even reversed. Population-level analysis showed significant changes in encoding structure, forming distinct neural patterns. Leveraging canonical correlation analysis, we identified a shared subspace preserving the initial cue's encoding, contributed by both congruent and incongruent neurons. Together, these findings reveal a novel perspective on the interactive planning of reach and grasp within the PMd, providing insights into potential applications for brain-machine interfaces.},
}
@article {pmid40541755,
year = {2025},
author = {Yang, A and Tian, J and Wang, W and Zhou, L and Zhou, K},
title = {Shared and distinct neural signatures of feature and spatial attention.},
journal = {NeuroImage},
volume = {317},
number = {},
pages = {121296},
doi = {10.1016/j.neuroimage.2025.121296},
pmid = {40541755},
issn = {1095-9572},
mesh = {Humans ; *Attention/physiology ; Male ; Female ; Adult ; Young Adult ; Machine Learning ; Magnetic Resonance Imaging/methods ; *Space Perception/physiology ; Brain Mapping/methods ; *Brain/physiology ; *Nerve Net/physiology/diagnostic imaging ; *Visual Perception/physiology ; },
abstract = {The debate on whether feature attention (FA) and spatial attention (SA) share a common neural mechanism remains unresolved. Previous neuroimaging studies have identified fronto-parietal-temporal attention-related regions that exhibited consistent activation during various visual attention tasks. However, these studies have been limited by small sample sizes and methodological constraints inherent in univariate analysis. Here, we utilized a between-subject whole-brain machine learning approach with a large sample size (N=235) to investigate the neural signatures of FA (FAS) and SA (SAS). Both FAS and SAS showed cross-task predictive capabilities, though inter-task prediction was weaker than intra-task prediction, suggesting both shared and distinct mechanisms. Specifically, the frontoparietal network exhibited the highest predictive performance for FA, while the visual network excelled in predicting SA, highlighting their respective prominence in the two attention processes. Moreover, both signatures demonstrated distributed representations across large-scale brain networks, as each cluster within the signatures was sufficient for predicting FA and SA, but none of them were deemed necessary for either FA or SA. Our study challenges traditional network-centric models of attention, emphasizing distributed brain functioning in attention, and provides comprehensive evidence for shared and distinct neural mechanisms underlying FA and SA.},
}
@article {pmid40541523,
year = {2025},
author = {Cao, S and Yin, Y and Li, W and Liu, Z and Chen, Z},
title = {Time-varying formation control for heterogeneous multi-agent systems in the presence of actuator faults and deception attacks.},
journal = {ISA transactions},
volume = {165},
number = {},
pages = {54-63},
doi = {10.1016/j.isatra.2025.06.004},
pmid = {40541523},
issn = {1879-2022},
abstract = {This paper explores the control of time-varying formations in a class of heterogeneous multi-agent systems. The key innovation lies in the simultaneous consideration of hybrid actuator faults and deception attacks. To achieve the control objective, a novel distributed double-layer control scheme, comprising a network layer and a physical layer, is proposed. In the network layer, a distributed observer with secure output feedback control is developed to mitigate severe deception attacks, ensuring that the mean square observer error remains within an acceptable range. In the physical layer, fault compensators are designed to address both additive and multiplicative faults. As a result, the followers achieve time-varying formation control, and closed-loop stability analysis is conducted using the Lyapunov method. Finally, to verify the validity of the theoretical findings, numerical simulations are subsequently conducted.},
}
@article {pmid40538971,
year = {2025},
author = {Miao, Y and Li, K and Zhao, W and Zhang, Y},
title = {EA-EEG: a novel model for efficient motor imagery EEG classification with whitening and multi-scale feature integration.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {94},
pmid = {40538971},
issn = {1871-4080},
abstract = {Electroencephalography (EEG) is a non-invasive technique widely used in neuroscience and brain-computer interfaces (BCI) due to its high temporal resolution. In motor imagery EEG (MI-EEG) tasks, EEG signals reflect movement-related brain activity, making them ideal for BCI control. However, the non-stationary nature of MI-EEG signals poses significant challenges for classification, as frequency characteristics vary across tasks and individuals. Traditional preprocessing methods, such as bandpass filtering and standardization, may struggle to adapt to these variations, potentially limiting classification performance. To address this issue, this study introduces EA-EEG, an improved MI-EEG classification model that incorporates whitening as a preprocessing step to reduce channel correlation and enhance the model feature extraction ability. EA-EEG further leverages a multi-scale pooling strategy, combining convolutional networks and root mean square pooling to extract key spatial and temporal features, and applies prototype-based classification to improve MI-EEG classification performance. Experiments on the BCI4-2A and BCI4-2B datasets demonstrate that EA-EEG achieves state-of-the-art performance, with 85.33% accuracy (Kappa = 0.804) on BCI4-2A and 88.05% accuracy (Kappa = 0.761) on BCI4-2B, surpassing existing approaches. These results confirm EA-EEG's effectiveness in handling non-stationary MI-EEG signals, demonstrating its potential for robust BCI applications, including rehabilitation, prosthetic control, and cognitive monitoring.},
}
@article {pmid40538970,
year = {2025},
author = {Lin, C and Lu, H and Pan, C and Ma, S and Zhang, Z and Tian, R},
title = {MBRSTCformer: a knowledge embedded local-global spatiotemporal transformer for emotion recognition.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {95},
pmid = {40538970},
issn = {1871-4080},
abstract = {Emotion recognition is an essential prerequisite for realizing generalized BCI, which possesses an extensive range of applications in real life. EEG-based emotion recognition has become mainstream due to its real-time mapping of brain emotional activities, so a robust EEG-based emotion recognition model is of great interest. However, most existing deep learning emotion recognition methods treat the EEG signal as a whole feature extraction, which will destroy its local stimulation differences and fail to extract local features of the brain region well. Inspired by the cognitive mechanisms of the brain, we propose the multi-brain regions spatiotemporal collaboration transformer (MBRSTCfromer) framework for EEG-based emotion recognition. First, inspired by the prior knowledge, we propose the Multi-Brain Regions Collaboration Network. The EEG data are processed separately after being divided by brain regions, and stimulation scores are presented to quantify the stimulation produced by different brain regions and feedback on the stimulation degree to the MBRSTCfromer. Second, we propose a Cascade Pyramid Spatial Fusion Temporal Convolution Network for multi-brain regions EEG features fusion. Finally, we conduct comprehensive experiments on two mainstream emotion recognition datasets to validate the effectiveness of our proposed MBRSTCfromer framework. We achieved 98.63 % , 98.15 % , and 98.58 % accuracy on the three dimensions (arousal, valence, and dominance) on the DEAP dataset; and 97.66 % , 97.07 % , and 97.97 % on the DREAMER dataset.},
}
@article {pmid40536865,
year = {2025},
author = {Li, Y and Su, D and Yang, X and Wang, X and Zhao, H and Zhang, J},
title = {From Frequency to Temporal: Three Simple Steps Achieve Lightweight High-Performance Motor Imagery Decoding.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3579528},
pmid = {40536865},
issn = {1558-2531},
abstract = {OBJECTIVE: To address the challenges of high data noise and substantial model computational complexity in Electroencephalography (EEG)-based motor imagery decoding, this study aims to develop a decoding method with both high accuracy and low computational cost.<br><br>METHODS: First, frequency domain analysis was performed to reveal the frequency modeling patterns of deep learning models. Utilizing prior knowledge from brain science regarding the key frequency bands for motor imagery, we adjusted the convolution kernels and pooling sizes of EEGNet to focus on effective frequency bands. Subsequently, a residual network was introduced to preserve high-frequency detailed features. Finally, temporal convolution modules were used to deeply capture temporal dependencies, significantly enhancing feature discriminability.<br><br>RESULTS: Experiments were conducted on the BCI Competition IV 2a and 2b datasets. Our method achieved average classification accuracies of 86.23% and 86.75% respectively, surpassing advanced models like EEG-Conformer and EEG-TransNet. Meanwhile, the Multiply-accumulate operations (MACs) were 27.16M, a reduction of over 50% compared to the comparison models, and the Forward/Backward Pass Size was 14.33MB.<br><br>CONCLUSION: By integrating prior knowledge from brain science with deep learning techniques-specifically frequency domain analysis, residual networks, and temporal convolutions-it is possible to effectively improve the accuracy of EEG motor imagery decoding while substantially reducing model computational complexity.<br><br>SIGNIFICANCE: This paper employs the simplest and most fundamental techniques in its design, highlighting the critical role of brain science knowledge in model development. The proposed method demonstrates broad application potential.},
}
@article {pmid40536747,
year = {2025},
author = {Fei, SW and Chen, JL and Hu, YB},
title = {A novel time-frequency feature extraction method of EEG signals utilizing fractional synchrosqueezing wavelet transform.},
journal = {Physical and engineering sciences in medicine},
volume = {48},
number = {3},
pages = {1237-1247},
pmid = {40536747},
issn = {2662-4737},
mesh = {*Electroencephalography/methods ; *Wavelet Analysis ; Humans ; Algorithms ; Time Factors ; *Signal Processing, Computer-Assisted ; },
abstract = {In order to improve the accuracy of Electroencephalogram (EEG) classification, Fractional Synchrosqueezing Wavelet Transform (FSSWT) is proposed to effectively overcome the contradiction between energy concentration and frequency separation in traditional time-frequency analysis methods. Firstly, the principle of FSSWT is introduced, and the time-frequency transformation equation for FSSWT applied to multi-frequency signals is established. The examples of synthetic signal and EEG signal show that the proposed method can suppress the mode aliasing of MI-EEG significantly while maintaining high resolution characteristics, and the energy concentration and related intermediate indexes perform well. The experimental results show that the proposed FSSWT-EEGDNN-ResNet model achieves an average classification accuracy of 95.17% under the condition of the MI-EEG signals processed by FSSWT of eight subjects, demonstrating the effectiveness of FSSWT in EEG signal feature extraction and classification.},
}
@article {pmid40536356,
year = {2025},
author = {Rizzo, M and Dawson, JD},
title = {AI in Neurology: Everything, Everywhere, All at Once Part 1: Principles and Practice.},
journal = {Annals of neurology},
volume = {98},
number = {2},
pages = {211-230},
pmid = {40536356},
issn = {1531-8249},
support = {R01AG017177/AG/NIA NIH HHS/United States ; U54 GM115458/GM/NIGMS NIH HHS/United States ; U54GM115458/GM/NIGMS NIH HHS/United States ; R01 AG017177/AG/NIA NIH HHS/United States ; //University of Nebraska Foundation/ ; },
mesh = {Humans ; *Neurology/methods/trends ; *Artificial Intelligence/trends ; Machine Learning ; Brain-Computer Interfaces ; *Nervous System Diseases/therapy/diagnosis ; },
abstract = {Artificial intelligence (AI) is rapidly transforming healthcare, yet it often remains opaque to clinicians, scientists, and patients alike. This review, part 1 of a 3-part series, provides neurologists and neuroscientists with a foundational understanding of AI's key concepts, terminology, and applications. We begin by tracing AI's origins in mathematics, human logic, and brain-inspired neural networks to establish a context for its development. The review highlights AI's growing role in neurological diagnostics and treatment, emphasizing machine learning applications, such as computer vision, brain-machine interfaces, and precision care. By mapping the evolution of AI tools and linking them to neuroscience and human reasoning, we illustrate how AI is reshaping neurological practice and research. We end the review with an overview of model selection in AI and a case scenario illustrating how AI may drive precision neurological care. Part 1 sets the stage for part 2, which will focus on practical applications of AI in real-world scenarios where humans and AI collaborate as joint cognitive systems. Part 3 will examine AI's integration with extensive healthcare and neurology networks, innovative clinical trials, and massive datasets, expanding our vision of AI's global impact on neurology, healthcare systems, and society. ANN NEUROL 2025;98:211-230.},
}
@article {pmid40535306,
year = {2025},
author = {Xavier Fidêncio, A and Grün, F and Klaes, C and Iossifidis, I},
title = {Hybrid brain-computer interface using error-related potential and reinforcement learning.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1569411},
pmid = {40535306},
issn = {1662-5161},
abstract = {Brain-computer interfaces (BCIs) offer alternative communication methods for individuals with motor disabilities, aiming to improve their quality of life through external device control. However, non-invasive BCIs using electroencephalography (EEG) often suffer from performance limitations due to non-stationarities arising from changes in mental state or device characteristics. Addressing these challenges motivates the development of adaptive systems capable of real-time adjustment. This study investigates a novel approach for creating an adaptive, error-related potential (ErrP)-based BCI using reinforcement learning (RL) to dynamically adapt to EEG signal variations. The framework was validated through experiments on a publicly available motor imagery dataset and a novel fast-paced protocol designed to enhance user engagement. Results showed that RL agents effectively learned control policies from user interactions, maintaining robust performance across datasets. However, findings from the game-based protocol revealed that fast-paced motor imagery tasks were ineffective for most participants, highlighting critical challenges in real-time BCI task design. Overall, the results demonstrate the potential of RL for enhancing BCI adaptability while identifying practical constraints in task complexity and user responsiveness.},
}
@article {pmid40535187,
year = {2025},
author = {Bao, Y and Zhou, H and Geng, F and Hu, Y},
title = {The relation between game disorder and interruption during game is mediated by game craving.},
journal = {Frontiers in psychology},
volume = {16},
number = {},
pages = {1579016},
pmid = {40535187},
issn = {1664-1078},
abstract = {The burgeoning user base and potential negative effects of excessive involvement in gaming, particularly Internet Gaming Disorder (IGD), demand significant attention. While existing research has explored the susceptibility of individuals with IGD to game-related stimuli, the question of why it is challenging for these individuals to disengage from gaming remains under-explored. Drawing parallels with the concept of interruption, we hypothesize that negative emotions triggered during gaming interruptions would drive individuals' craving for the game and compelling them to continue playing, reinforcing the IGD cycle. In this study, 42 male 'League of Legends' players, aged 19 to 29, experienced controlled interruptions every 3 min during gaming and non-gaming control tasks. Our findings demonstrate that interruptions during gaming elicited significantly higher levels of anger and anxiety compared to the control tasks. Further, we found a positive correlation between the severity of IGD symptoms and the intensity of anger and anxiety induced by gaming interruptions. Additionally, our analysis suggests that craving partially mediates the relationship between anger arousal during gaming interruptions and IGD severity. These findings provide new insights into how emotional responses to gaming interruptions contribute to IGD, offering a novel perspective for future research and potential treatment approaches.},
}
@article {pmid40534746,
year = {2025},
author = {Zheng, K and Guo, L and Liang, W and Liu, P},
title = {Comparison of the effects of transcranial direct current stimulation combined with different rehabilitation interventions on motor function in people suffering from stroke-related symptoms: a systematic review and network meta-analysis.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1586685},
pmid = {40534746},
issn = {1664-2295},
abstract = {BACKGROUND: This study employs network meta-analysis to assess the efficacy of transcranial direct current stimulation (tDCS) combined with different rehabilitation approaches in enhancing motor function in people suffering from stroke-related symptoms (PSSS). The objective is to determine the most effective tDCS-based rehabilitation approach and offer valuable evidence to guide clinical decision-making.<br><br>METHODS: This study included randomized controlled trials (RCTs) published before September 23, 2024. We conducted a systematic search across eight databases: PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure (CNKI), China Biology Medicine (SinoMed), Wanfang, and VIP. Network meta-analysis (NMA) was conducted utilizing R Studio and Stata 15.0 for data analysis.<br><br>RESULTS: A total of 74 RCTs were included in this study, encompassing 4,335 PSSS and 11 intervention strategies. The NMA revealed that brain-computer interface therapy (BCIT) in combination with tDCS [surface under the cumulative ranking curve (SUCRA)&#x202f;=&#x202f;88.34%] was the most effective tDCS-based intervention for improving the Fugl-Meyer Assessment for Upper Extremity score in PSSS. Mirror therapy (MT) in combination with tDCS (SUCRA&#x202f;=&#x202f;85.96%) was identified as the optimal intervention for enhancing the Action Research Arm Test score in PSSS. MT&#x202f;+&#x202f;tDCS (SUCRA&#x202f;=&#x202f;84.29%) was the best approach for improving the Fugl-Meyer Assessment for Lower Extremity score. Additionally, acupuncture and moxibustion (AM) in combination with tDCS (SUCRA&#x202f;=&#x202f;77.16%) was the most effective intervention for increasing the Berg Balance Scale score in PSSS. The two-dimensional clustering analysis showed that MT&#x202f;+&#x202f;tDCS (SUCRA&#x202f;=&#x202f;75.83%/85.96%) was the optimal tDCS-based rehabilitation strategy for treating upper limb motor dysfunction in PSSS, while AM+tDCS (SUCRA&#x202f;=&#x202f;76.94%/77.16%) was the best tDCS-based rehabilitation strategy for improving lower limb motor dysfunction in PSSS.<br><br>CONCLUSION: BCIT+tDCS was identified as the optimal tDCS-based rehabilitation strategy for improving upper limb motor ability in PSSS, MT&#x202f;+&#x202f;tDCS was the most effective intervention for enhancing arm mobility, MT&#x202f;+&#x202f;tDCS was the best protocol for improving lower limb motor ability, while AM+tDCS was the best strategy for improving balance ability. Furthermore, MT&#x202f;+&#x202f;tDCS was the optimal tDCS-based rehabilitation approach for treating upper limb motor dysfunction, whereas AM+tDCS was the most effective strategy for addressing lower limb motor dysfunction in PSSS. Future studies may focus on investigating the therapeutic effects of MT combined with tDCS on Berg Balance Scale score in PSSS, as well as the effects of AM combined with tDCS on Action Research Arm Test score, in order to further explore the therapeutic potential of these two intervention strategies.<br><br>https://www.crd.york.ac.uk/PROSPERO/view/CRD42024621998, Identifier PROSPERO CRD42024621998.},
}
@article {pmid40534671,
year = {2025},
author = {Zhai, H and Wang, H and Li, H and Wang, X},
title = {The Intersection of Psychedelics and Sleep: Exploring the Impacts on Sleep Architecture, Dream States, and Therapeutic Implications.},
journal = {ACS pharmacology &amp; translational science},
volume = {8},
number = {6},
pages = {1832-1836},
pmid = {40534671},
issn = {2575-9108},
abstract = {The interplay between psychedelics, such as psilocybin, lysergic acid diethylamide (LSD) and dimethyltryptamine (DMT), and sleep is an emerging area, but their impact on sleep remains relatively underexplored. This viewpoint provides a perspective on how psychedelics may alter sleep phases, dreaming, and their potential therapeutic applications for sleep disorders.},
}
@article {pmid40533772,
year = {2025},
author = {Lv, S and Ran, X and Xia, M and Zhang, Y and Pang, T and Zhou, X and Zhao, Z and Yu, Y and Gao, Z},
title = {Classification of left and right-hand motor imagery in acute stroke patients using EEG microstate.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {137},
pmid = {40533772},
issn = {1743-0003},
support = {221100310500//the Major Science and Technology Projects of Henan Province/ ; 82302298//the National Natural Science Foundation of China/ ; 82201709//the National Natural Science Foundation of China/ ; 24IRTSTHN042//Innovative Research Team (in Science and Technology) in University of Henan Province/ ; XTkf01//the Open Project Program of Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder/ ; 242102521012//International Science and Technology Cooperation Project of Henan Province/ ; 242102310055//the Science and Technology Research Project of Henan Province/ ; },
mesh = {Humans ; Male ; Female ; *Electroencephalography/methods ; Middle Aged ; *Brain-Computer Interfaces ; *Imagination/physiology ; *Stroke/physiopathology ; Aged ; *Hand/physiopathology ; *Stroke Rehabilitation/methods ; *Functional Laterality/physiology ; Support Vector Machine ; Adult ; },
abstract = {BACKGROUND: Stroke is one of the leading causes of adult disability, often resulting in motor dysfunction and brain network reorganization. Brain-computer interface (BCI) systems offer a novel approach to post-stroke motor rehabilitation, with motor imagery (MI) serving as a key paradigm that requires decoding left and right-hand MI differences to optimize system performance. However, the neural dynamics underlying these differences, especially from the perspective of Electroencephalography(EEG) microstate, remain poorly understood in acute stroke patients.<br><br>METHODS: This study enrolled 14 acute stroke patients and recorded their EEG data during left and right-hand MI tasks. Four EEG microstate (A, B, C, and D) were analyzed to extract temporal feature parameters, including Duration, Occurrence Coverage, and transition probabilities(TP). Significant features were used to construct classification models using Linear Discriminant Analysis(LDA), Support Vector Machines(SVM), and K-Nearest Neighbors(KNN) algorithms.<br><br>RESULTS: Microstate analysis revealed significant differences in temporal features of microstate A and C during left and right-hand MI tasks. During left-hand MI, microstate A exhibited longer Duration(Pfdr=0.032), higher Occurrence(Pfdr=0.018), and greater Coverage(Pfdr=0.004) compared to the right-hand, whereas microstate C showed the opposite pattern(Pfdr=0.044, Pfdr=0.004, Pfdr=0.004). Additionally, the TP from microstate B&#x2192;A, D&#x2192;A and D&#x2192;C also demonstrated significant differences(Pfdr=0.04, Pfdr<0.001, Pfdr=0.006). Among classification models, the KNN algorithm achieved the highest accuracy of 75.00%, outperforming LDA and SVM. Fisher analysis indicated that the Occurrence of microstate C was the most discriminative feature for distinguishing between left and right-hand MI tasks in acute stroke patients.<br><br>CONCLUSION: Differences in EEG microstate features during left and right-hand MI tasks in acute stroke patients may reflect lateralized mechanisms of brain network reorganization. Microstate features hold significant potential for both post-stroke brain function assessment and the optimization of BCI systems. These features could enhance adaptive BCI strategies in acute stroke rehabilitation.},
}
@article {pmid40532880,
year = {2025},
author = {Luo, X and Dong, J and Li, T},
title = {Comparative cytokine signatures and cognitive deficits in early-onset schizophrenia and adolescent major depression: Toward refined diagnostic classification frameworks.},
journal = {Journal of affective disorders},
volume = {389},
number = {},
pages = {119667},
doi = {10.1016/j.jad.2025.119667},
pmid = {40532880},
issn = {1573-2517},
mesh = {Humans ; *Depressive Disorder, Major/blood/diagnosis/classification/psychology/complications ; Male ; Adolescent ; Female ; *Schizophrenia/blood/diagnosis/classification/complications ; *Cytokines/blood ; *Cognitive Dysfunction/blood/diagnosis ; Neuropsychological Tests ; Machine Learning ; Biomarkers/blood ; },
abstract = {BACKGROUND: This study analyzed plasma cytokine patterns in individuals with schizophrenia (SCZ), major depressive disorder (MDD), and healthy controls, explored the link between cytokine levels and cognitive function, and created machine learning models to evaluate the diagnostic potential of cytokine and cognitive assessments.<br><br>METHODS: This study involved 64 early-onset SCZ patients, 53 adolescents with MDD, and 33 healthy controls. The plasma concentrations of 44 cytokines were measured using the LUMINEX multiplex assay. Cognitive function was tested with the Cambridge Neuropsychological Test Automated Battery. Random Forest and Extreme Gradient Boosting models were used for classification, with their effectiveness evaluated via ROC curve analysis.<br><br>RESULTS: SCZ patients exhibited significantly elevated levels of CCL11, IL-2 and IL-13, while MDD patients displayed increased CXCL2 and G-CSF levels but decreased CCL20 and CCL11 levels. SCZ patients showed significant cognitive impairments compared to healthy controls. Elevated CCL11 were associated with poorer memory accuracy, and higher G-CSF were linked to worse executive function. The XGBoost model was more sensitive in classifying MDD than the Random Forest model, but both struggled to differentiate SCZ patients from healthy controls due to low specificity.<br><br>CONCLUSION: Early-onset SCZ and adolescent MDD patients showed unique peripheral cytokine profiles, with SCZ patients experiencing significant cognitive deficits. The cytokine CCL11 was found to have a significant association with cognitive dysfunction. Cytokine levels and cognitive assessments may serve as potential biomarkers for the diagnosis of MDD.},
}
@article {pmid40530005,
year = {2025},
author = {Shao, W and Meng, W and Zuo, J and Li, X and Ming, D},
title = {Opportunities and Challenges of Brain-on-a-Chip Interfaces.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {6},
number = {},
pages = {0287},
pmid = {40530005},
issn = {2692-7632},
abstract = {The convergence of life sciences and information technology is driving a new wave of scientific and technological innovation, with brain-on-a-chip interfaces (BoCIs) emerging as a prominent area of focus in the brain-computer interface field. BoCIs aim to create an interactive bridge between lab-grown brains and the external environment, utilizing advanced encoding and decoding technologies alongside electrodes. Unlike classical brain-computer interfaces that rely on human or animal brains, BoCIs employ lab-grown brains, offering greater experimental controllability and scalability. Central to this innovation is the advancement of stem cell and microelectrode array technologies, which facilitate the development of neuro-electrode hybrid structures to ensure effective signal transmission in lab-grown brains. Furthermore, the evolution of BoCI systems depends on a range of stimulation strategies and novel decoding algorithms, including artificial-intelligence-driven methods, which has expanded BoCI applications to pattern recognition and robotic control. Biological neural networks inherently grant BoCI systems neuro-inspired computational properties-such as ultralow energy consumption and dynamic plasticity-that surpass those of conventional artificial intelligence. Functionally, BoCIs offer a novel framework for hybrid intelligence, merging the cognitive capabilities of biological systems (e.g., learning and memory) with the computational efficiency of machines. However, critical challenges span 4 domains: optimizing neural maturation and functional regionalization, engineering high-fidelity bioelectronic interfaces for robust signal transduction, enhancing adaptive neuroplasticity mechanisms in lab-grown brains, and achieving biophysically coherent integration with artificial intelligence architectures. Addressing these limitations could offer insights into emergent intelligence while enabling next-generation biocomputing solutions.},
}
@article {pmid40529543,
year = {2025},
author = {Faber, J and Tsytsarev, V and Pais-Vieira, M and Aksenova, T},
title = {Editorial: Sensorimotor decoding: characterization and modeling for rehabilitation and assistive technologies, volume II.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1619232},
pmid = {40529543},
issn = {1662-5161},
}
@article {pmid40527877,
year = {2025},
author = {Moreira, JPC and Carvalho, VR and Mendes, EMAM and Fallah, A and Sejnowski, TJ and Lainscsek, C and Comstock, L},
title = {An open-access EEG dataset for speech decoding: Exploring the role of articulation and coarticulation.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {1017},
pmid = {40527877},
issn = {2052-4463},
mesh = {Humans ; *Electroencephalography ; *Speech ; *Brain-Computer Interfaces ; Phonetics ; Transcranial Magnetic Stimulation ; Adult ; Male ; Female ; },
abstract = {Electroencephalography (EEG) holds promise for brain-computer interface (BCI) devices as a non-invasive measure of neural activity. With increased attention to EEG-based BCI systems, publicly available datasets incorporating the complex stimuli found in naturalistic speech are necessary to establish a common standard of performance within the BCI community. Effective solutions must overcome noise in the EEG signal and remain reliable across sessions and stimuli that reflect types of real-world linguistic complexity without overfitting to a dataset or task. We present two validated datasets (N=8 and N=16) for classification at the phoneme and word level and by the articulatory properties of phonemes. EEG signals were recorded from 64 channels while subjects listened to and repeated six consonants and five vowels. Individual phonemes were combined in different phonetic environments to produce coarticulated variation in 40 consonant-vowel pairs, 20 real words, and 20 pseudowords. Phoneme pairs and words were presented during a control condition and during transcranial magnetic stimulation (TMS) to assess whether stimulation would augment the EEG signal associated with specific articulatory processes.},
}
@article {pmid40527666,
year = {2025},
author = {Toner, AA and Eberlin, L and Pichaimuthu, R and Tompkins, T and Szekeres, M},
title = {The use of robotics and artificial intelligence in upper extremity rehabilitation following traumatic injury: A scoping review.},
journal = {Journal of hand therapy : official journal of the American Society of Hand Therapists},
volume = {38},
number = {2},
pages = {254-265},
doi = {10.1016/j.jht.2025.04.009},
pmid = {40527666},
issn = {1545-004X},
mesh = {Humans ; *Artificial Intelligence ; *Robotics ; *Upper Extremity/injuries ; *Arm Injuries/rehabilitation ; },
abstract = {BACKGROUND: With the recent advances in technology and its increased use in society, healthcare practices work to identify areas where technology can be implemented to enhance patient care. Rehabilitation has begun to incorporate the use of robotics and artificial intelligence to facilitate positive outcomes and assist in achieving patient goals following injury. While traumatic upper extremity injuries can result in increased levels of pain and disability for an individual, it is not clear how robotics and artificial intelligence have been used in hand rehabilitation to address these issues.<br><br>PURPOSE: The objective of this study is to understand the extent of the use of robotics and artificial intelligence for traumatic upper extremity injuries.<br><br>STUDY DESIGN: Scoping review.<br><br>METHODS: The search strategy was conducted in Embase, CINAHL, MEDLINE, and PsycINFO and identified 7105 studies published between 2014 and 2024. Following title and abstract screening and removal of duplicates, 122 full-text articles were screened. A total of 13 papers were included that used artificial intelligence, robotics, or other technology in rehabilitation programs for individuals with traumatic upper extremity injuries.<br><br>RESULTS: Of the 13 included studies: 11 used robotics such as the KINARM Exoskeleton, the Hybrid Assistive Limb, and the WRISTBOT, and two used artificial intelligence including chatbots and brain-computer interface. Multiple outcomes were reported with the most common including range of motion, strength, pain, function, and joint sense.<br><br>CONCLUSIONS: Currently, there is a wide variety of different forms of robotics with very little reported use of artificial intelligence for traumatic upper extremity injuries. There exists opportunities for future research to further investigate how these technologies can influence clinical outcomes for patients with traumatic upper extremity injuries.},
}
@article {pmid40527337,
year = {2025},
author = {Temmar, H and Willsey, MS and Costello, JT and Mender, MJ and Cubillos, LH and DeMatteo, JC and Lam, JL and Wallace, DM and Kelberman, MM and Patil, PG and Chestek, CA},
title = {Investigating the benefits of artificial neural networks over linear approaches to BMI decoding.},
journal = {Journal of neural engineering},
volume = {22},
number = {3},
pages = {},
pmid = {40527337},
issn = {1741-2552},
support = {T32 NS007222/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Male ; Macaca mulatta ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; *Motor Cortex/physiology ; Linear Models ; Fingers/physiology ; Movement/physiology ; },
abstract = {Objective.Brain-machine interfaces (BMI) aim to restore function to persons living with spinal cord injuries by 'decoding' neural signals into behavior. Recently, nonlinear BMI decoders have outperformed previous state-of-the-art linear decoders, but few studies have investigated what specific improvements these nonlinear approaches provide. In this study, we compare how nonlinear and linear approaches predict individuated finger movements in open and closed-loop settings.Approach.Two adult male rhesus macaques were implanted with Utah arrays in the motor cortex and performed a 2D dexterous finger movement task for a juice reward. Multiple linear and nonlinear 'decoders' were used to map from recorded spiking band power into movement kinematics. Performance of these decoders was compared and analyzed to determine how nonlinear decoders perform in both open and closed-loop scenarios.Main Results.We show that nonlinear decoders enable control which more closely resembles true hand movements, producing distributions of velocities 80.7% closer to true hand control than linear decoders. Addressing concerns that neural networks may come to inconsistent solutions, we find that regularization techniques improve the consistency of temporally-convolved feedforward neural network convergence by up to 188.9%, along with improving average performance and training speed. Finally, we show that TCNs and long short-term memory can effectively leverage training data from multiple task variations to improve generalization.Significance.The results of this study support artificial neural networks of all kinds as the future of BMI decoding and show potential for generalizing over less constrained tasks.},
}
@article {pmid40527331,
year = {2025},
author = {Xin, H and Li, H and Qi, H},
title = {A novel paradigm for two-degree-of-freedom BCI control based on ERP in-duced by overt and covert visual attention.},
journal = {Journal of neural engineering},
volume = {22},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ade56a},
pmid = {40527331},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; *Attention/physiology ; Male ; *Electroencephalography/methods ; Female ; Young Adult ; Adult ; *Evoked Potentials/physiology ; *Photic Stimulation/methods ; *Visual Perception/physiology ; *Evoked Potentials, Visual/physiology ; Psychomotor Performance/physiology ; },
abstract = {Objective.This study developed a novel brain-computer interface (BCI) paradigm based on event-related potentials (ERPs) to achieve simultaneous two-degree-of-freedom control through overt and covert visual selective attention.Approach.In this paradigm, three stimuli were arranged equidistantly around the cursor. Participants selected two stimuli as attention targets based on the relative position of the cursor and the intended movement destination, focusing overtly on one while covertly attending to the other. EEG data collected during offline experiments were used to train classifiers for overt and covert targets (CT), and the outputs of these classifiers were employed in online experiments to construct movement vectors for controlling the cursor in a 2D space.Main results.EEG analysis demonstrated that overt and CT elicited distinct ERP signals, with classification accuracies of 96.2% and 92.4%, respectively. The accuracy of simultaneously identifying both targets reached 91.0%. In online experiments, the success rate of moving the cursor to the target region was 92.6%, and 88.2% of cursor movements were in the desired direction. These results confirm the feasibility of achieving 2D control through ERP based selective attention and validate the effectiveness of the proposed paradigm.Significance.This study introduces a novel EEG-based approach for multi-degree-of-freedom control, expanding the capabilities of traditional ERP based BCIs, which have primarily been limited to single-degree-of-freedom applications.},
}
@article {pmid40526548,
year = {2025},
author = {Li, C and Cao, Z and Pan, Y and Zhu, P and Li, P and Li, F and Chen, H and Lu, BL and Wan, F and Yao, D and Xu, P},
title = {EEG-Based Emotion Monitoring and Regulation System by Learning the Discriminative Brain Network Manifold.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {36},
number = {10},
pages = {17751-17765},
doi = {10.1109/TNNLS.2025.3576182},
pmid = {40526548},
issn = {2162-2388},
mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; *Brain/physiology ; *Neural Networks, Computer ; Male ; *Machine Learning ; Adult ; Young Adult ; Algorithms ; Female ; *Emotional Regulation/physiology ; Brain-Computer Interfaces ; },
abstract = {Emotion recognition based on electroencephalogram (EEG) is fundamentally associated with human-like intelligence system. However, due to the noise-sensitive characteristics of EEGs and the individual variability of emotions, it is very challenging to extract inherent emotion dependent patterns from emotional EEG signals. In this work, we propose a L1-norm space defined discriminative brain network manifold learning model (L1-SGL), in which the EEG noise outliers can be effectively separated and the pseudolabeled samples caused by subjective feelings can be automatically corrected. Off-line experimental results consistently indicate that the L1-SGL can effectively suppress the influence of noise and achieve an incomparable superiority performance over other existing methods in EEG emotion recognition. Besides, benefiting from the time efficiency of the L1-SGL, an online emotion monitoring and regulation system is further implemented in this work. On-line emotion decoding experimental results (86.30%) of 25 participants prove that the L1-SGL can effectively satisfy the real-time requirements of on-line emotional monitoring applications, and the significant negative emotion regulation experimental results ($p \lt 0.001$) further confirm the feasibility and effectiveness of L1-SGL model in real-time emotion regulation and interactive applications. Overall, the L1-SGL provides a promising solution for the real-time online affective brain-computer interfaces (aBCIs) and the intelligent clinical closed-loop treatments.},
}
@article {pmid40526539,
year = {2025},
author = {Jia, T and Long, H and Ji, L and Guan, X},
title = {EEG-based Spatial-Channel Interaction Attention Neural Networks for Detecting Empathy in Motor Collaboration.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3580617},
pmid = {40526539},
issn = {2168-2208},
abstract = {Embodied intelligence and humanoid robots aim to mimic interpersonal interactions to achieve affective human-robot interaction (HRI). A major challenge in advancing HRI lies in effectively emulating interpersonal affective interactions and evaluating the resulting artificial empathy. To address these challenges, we propose SpatialChannel Interaction Attention Neural Networks (SCIANN)-a novel EEG-based architecture that combines topological brain activation and connectivity patterns to decode empathy in motor collaboration. A private EEG dataset from a collaborative brain-computer interface motor control experiment and a public EEG dataset from a dyadic perceptual crossing experiment were used for evaluating SCIANN's performance with comparisons with five baseline models. Results showed that SCIANN outperformed the state-of-the-art baseline models. In the private dataset, SCIANN reached an accuracy of 100% both in inter-subject and cross-subject tests for detecting whether empathy is induced or not. For classifying 4-class empathy, it achieved an accuracy of 98.3% in the inter-subject test, and 48.1% in the cross-subject test. In the public dataset, SCIANN reached a classification accuracy of 92.2% in inter-subject and 91.7% in cross-subject tests for detecting whether empathy is induced or not. Feature visualization results revealed that contributing EEG channel importance features and channel interaction features aligned with established neurophysiological findings. These results collectively demonstrate SCIANN's potential as a robust, generalizable framework for artificial empathy assessment in HRI applications.},
}
@article {pmid40526534,
year = {2025},
author = {Luo, T and Zhang, J and Qiu, Y and Zhang, L and Hu, Y and Yu, Z and Liang, Z},
title = {M3D: Manifold-Based Domain Adaptation With Dynamic Distribution for Non-Deep Transfer Learning in Cross-Subject and Cross-Session EEG-Based Emotion Recognition.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {11},
pages = {8126-8139},
doi = {10.1109/JBHI.2025.3580612},
pmid = {40526534},
issn = {2168-2208},
mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology/classification ; *Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Adult ; Male ; Female ; Deep Learning ; Young Adult ; Algorithms ; Machine Learning ; },
abstract = {Emotion decoding using Electroencephalography (EEG)-based affective brain-computer interfaces (aBCIs) is crucial for affective computing but is hindered by EEG's non-stationarity, individual variability, and the high cost of large-scale labeled data. Deep learning-based approaches, while effective, require substantial computational resources and large datasets, limiting their practicality. To address these challenges, we propose Manifold-based Domain Adaptation with Dynamic Distribution (M3D), a lightweight non-deep transfer learning framework. M3D includes four main modules: manifold feature transformation, dynamic distribution alignment, classifier learning, and ensemble learning. The data undergoes a transformation onto an optimal Grassmann manifold space, enabling dynamic alignment of the source and target domains. This process prioritizes both marginal and conditional distributions according to their significance, ensuring enhanced adaptation efficiency across various types of data. In the classifier learning, the principle of structural risk minimization is integrated to develop robust classification models. This is complemented by dynamic distribution alignment, which refines the classifier iteratively. Additionally, the ensemble learning module aggregates the classifiers obtained at different stages of the optimization process, which leverages the diversity of the classifiers to enhance the overall prediction accuracy. The proposed M3D framework is evaluated on three benchmark EEG emotion recognition datasets using two validation protocols (cross-subject single-session and cross-subject cross-session), as well as on a clinical EEG dataset of Major Depressive Disorder (MDD). Experimental results demonstrate that M3D outperforms traditional non-deep learning methods, achieving an average improvement of 6.67%, while achieving deep learning-comparable performance with significantly lower data and computational requirements. These findings highlight the potential of M3D to enhance the practicality and applicability of aBCIs in real-world scenarios.},
}
@article {pmid40524963,
year = {2025},
author = {Zhao, W and Lu, H and Zhang, B and Zheng, X and Wang, W and Zhou, H},
title = {TCANet: a temporal convolutional attention network for motor imagery EEG decoding.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {91},
pmid = {40524963},
issn = {1871-4080},
abstract = {Decoding motor imagery electroencephalogram (MI-EEG) signals is fundamental to the development of brain-computer interface (BCI) systems. However, robust decoding remains a challenge due to the inherent complexity and variability of MI-EEG signals. This study proposes the Temporal Convolutional Attention Network (TCANet), a novel end-to-end model that hierarchically captures spatiotemporal dependencies by progressively integrating local, fused, and global features. Specifically, TCANet employs a multi-scale convolutional module to extract local spatiotemporal representations across multiple temporal resolutions. A temporal convolutional module then fuses and compresses these multi-scale features while modeling both short- and long-term dependencies. Subsequently, a stacked multi-head self-attention mechanism refines the global representations, followed by a fully connected layer that performs MI-EEG classification. The proposed model was systematically evaluated on the BCI IV-2a and IV-2b datasets under both subject-dependent and subject-independent settings. In subject-dependent classification, TCANet achieved accuracies of 83.06% and 88.52% on BCI IV-2a and IV-2b respectively, with corresponding Kappa values of 0.7742 and 0.7703, outperforming multiple representative baselines. In the more challenging subject-independent setting, TCANet achieved competitive performance on IV-2a and demonstrated potential for improvement on IV-2b. The code is available at https://github.com/snailpt/TCANet.},
}
@article {pmid40523119,
year = {2025},
author = {Kamitani, Y and Tanaka, M and Shirakawa, K},
title = {Visual Image Reconstruction from Brain Activity via Latent Representation.},
journal = {Annual review of vision science},
volume = {11},
number = {1},
pages = {611-634},
doi = {10.1146/annurev-vision-110423-023616},
pmid = {40523119},
issn = {2374-4650},
mesh = {Humans ; *Brain/physiology ; *Visual Perception/physiology ; Neural Networks, Computer ; *Image Processing, Computer-Assisted/methods ; Brain-Computer Interfaces ; },
abstract = {Visual image reconstruction, the decoding of perceptual content from brain activity into images, has advanced significantly with the integration of deep neural networks (DNNs) and generative models. This review traces the field's evolution from early classification approaches to sophisticated reconstructions that capture detailed, subjective visual experiences, emphasizing the roles of hierarchical latent representations, compositional strategies, and modular architectures. Despite notable progress, challenges remain, such as achieving true zero-shot generalization for unseen images and accurately modeling the complex, subjective aspects of perception. We discuss the need for diverse datasets, refined evaluation metrics aligned with human perceptual judgments, and compositional representations that strengthen model robustness and generalizability. Ethical issues, including privacy, consent, and potential misuse, are underscored as critical considerations for responsible development. Visual image reconstruction offers promising insights into neural coding and enables new psychological measurements of visual experiences, with applications spanning clinical diagnostics and brain-machine interfaces.},
}
@article {pmid40522886,
year = {2025},
author = {Nam, J and Shin, H and You, C and Baeg, E and Kim, JG and Yang, S and Han, MR},
title = {Cortical Stimulation-Based Transcriptome Shifts on Parkinson's Disease Animal Model.},
journal = {ASN neuro},
volume = {17},
number = {1},
pages = {2513881},
pmid = {40522886},
issn = {1759-0914},
mesh = {Animals ; *Transcriptome/physiology ; Male ; Disease Models, Animal ; *Motor Cortex/metabolism ; *Parkinson Disease/genetics/therapy/metabolism ; Mice ; Mice, Inbred C57BL ; },
abstract = {Parkinson's disease is the second most prevalent neurodegenerative disorder and is characterized by the degeneration of dopaminergic neurons. Significant improvements in gait balance, particularly in step length and velocity, were observed with less invasive wireless cortical stimulation. Transcriptome sequencing was performed to demonstrate the cellular mechanism, specifically targeting the primary motor cortex, where stimulation was applied. Our findings indicated that 38 differentially expressed genes (DEGs), initially downregulated following Parkinson's disease induction, were subsequently restored to normal levels after cortical stimulation. These 38 DEGs are potential targets for the treatment of motor disorders in Parkinson's disease. These genes are implicated in crucial processes, such as astrocyte-mediated blood vessel development and microglia-mediated phagocytosis of damaged motor neurons, suggesting their significant roles in improving behavioral disorders. Moreover, these biomarkers not only facilitate the rapid and accurate diagnosis of Parkinson's disease but also assist in precision medicine approaches.},
}
@article {pmid40522806,
year = {2025},
author = {Wang, P and Qi, Y and Pan, G},
title = {Partial Domain Adaptation for Stable Neural Decoding in Disentangled Latent Subspaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3577222},
pmid = {40522806},
issn = {1558-2531},
abstract = {OBJECTIVE: Brain-Computer Interfaces (BCI) have demonstrated significant potential in neural rehabilitation. However, the variability of non-stationary neural signals often leads to instabilities of behavioral decoding, posing critical obstacles to chronic applications. Domain adaptation technique offers a promising solution by obtaining the invariant neural representation against non-stationary signals through distribution alignment. Here, we demonstrate domain adaptation that directly applied to neural data may lead to unstable performance, mostly due to the common presence of task-irrelevant components within neural signals. To address this, we aim to identify task-relevant components to achieve more stable neural alignment.<br><br>METHODS: In this work, we propose a novel partial domain adaptation (PDA) framework that performs neural alignment within the task-relevant latent subspace. With pre-aligned short-time windows as input, the proposed latent space is constructed based on a causal dynamical system, enabling more flexible neural decoding. Within this latent space, task-relevant dynamical features are disentangled from task-irrelevant components through VAE-based representation learning and adversarial alignment. The aligned task-relevant features are then employed for neural decoding across domains.<br><br>RESULTS: Using Lyapunov theory, we analytically validated the improved stability of late our neural representations through alignment. Experiments with various neural datasets verified that PDA significantly enhanced the cross-session decoding performance.<br><br>CONCLUSION: PDA successfully achieved stable neural representations across different experimental days, enabling reliable long-term decoding.<br><br>SIGNIFICANCE: Our approach provides a novel aspect for addressing the challenge of chronic reliability in real-world BCI deployments.},
}
@article {pmid40522801,
year = {2025},
author = {Yao, Y and De Swaef, W and Geirnaert, S and Bertrand, A},
title = {EEG-Based Decoding of Selective Visual Attention in Superimposed Videos.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {10},
pages = {7248-7261},
doi = {10.1109/JBHI.2025.3580261},
pmid = {40522801},
issn = {2168-2208},
mesh = {Humans ; *Electroencephalography/methods ; *Attention/physiology ; Adult ; Male ; Female ; Young Adult ; *Signal Processing, Computer-Assisted ; Eye Movements/physiology ; Photic Stimulation ; Video Recording ; *Visual Perception/physiology ; },
abstract = {Selective attention enables humans to efficiently process visual stimuli by enhancing important elements and filtering out irrelevant information. Locating visual attention is fundamental in neuroscience with potential applications in brain-computer interfaces. Conventional paradigms often use synthetic stimuli or static images, but visual stimuli in real life contain smooth and highly irregular dynamics. We show that these irregular dynamics can be decoded from electroencephalography (EEG) signals for selective visual attention decoding. To this end, we propose a free-viewing paradigm in which participants attend to one of two superimposed videos, each showing a center-aligned person performing a stage act. Superimposing ensures that the relative differences in the neural responses are not driven by differences in object locations. A stimulus-informed decoder is trained to extract EEG components correlated with the motion patterns of the attended object, and can detect the attended object in unseen data with significantly above-chance accuracy. This shows that the EEG responses to naturalistic motion are modulated by selective attention. Eye movements are also found to be correlated to the motion patterns in the attended video, despite the spatial overlap with the distractor. We further show that these eye movements do not dominantly drive the EEG-based decoding and that complementary information exists in EEG and gaze data. Moreover, our results indicate that EEG may also capture neural responses to unattended objects. To our knowledge, this study is the first to explore EEG-based selective visual attention decoding on natural videos, opening new possibilities for experiment design.},
}
@article {pmid40522765,
year = {2026},
author = {Kinfe, T and Brenner, S and Etminan, N},
title = {Brain-computer interfaces re-shape functional neurosurgery.},
journal = {Neural regeneration research},
volume = {21},
number = {3},
pages = {1122-1123},
pmid = {40522765},
issn = {1673-5374},
}
@article {pmid40522539,
year = {2025},
author = {Ullah, A and Bookwalter, J and Sant, H and Azapagic, A and Shea, J and Berlet, R and Jha, N and Bailes, J and Gale, BK},
title = {An Osmosis-driven 3D-printed brain implant for drug delivery.},
journal = {Biomedical microdevices},
volume = {27},
number = {3},
pages = {29},
pmid = {40522539},
issn = {1572-8781},
mesh = {*Printing, Three-Dimensional ; *Osmosis ; *Drug Delivery Systems/instrumentation ; *Brain/surgery/metabolism ; *Brain Neoplasms/drug therapy ; Humans ; *Prostheses and Implants ; Glioblastoma/drug therapy ; },
abstract = {Glioblastoma is a highly malignant brain tumor with limited survival rates due to challenges in complete surgical excision, high recurrence (> 90%), and the inefficacy of systemic drug delivery. Significant efforts have been made to develop drug-loaded brain implants, catheters, and wafers aimed at enhancing survival rates by suppressing tumor recurrence. However, these devices often fail due to clogging, reflux, and the inability to be fully implanted intracranially. Furthermore, a lack of tissue penetration, diffusion distance, and duration of therapy have limited effectiveness of these implants. To address existing challenges, this study reports an osmosis-driven, 3D-printed brain implant with the potential for precise device customization to meet therapeutic needs, while negating systemic toxicity. It is capable of being loaded with two distinct therapeutic agents and implanted directly into the tumor resection cavity during surgery. The device features dual reservoirs, osmotic membranes, and precision-engineered needles for anchoring the device in the resection cavity and perfusing. Further, the device was characterized in vitro using 0.2% agarose gel as a brain tissue analog, with food dye as a drug analog and sodium chloride serving as an osmogen. A design of experiment approach was implemented to investigate various parameters, including membrane pore size, osmogen concentration, needle length, and their effects on release rates. The results demonstrated that the optimized implant achieves flow rates of 2.5 ± 0.1 µl/Hr and diffusion distance of up to 15.5 ± 0.4 mm, using 25 nm pore osmotic membranes with 25.3% osmogen concentration, aligning with model predictions.},
}
@article {pmid40520823,
year = {2025},
author = {Ivan Brown, A and MacDuffie, KE and Goering, S and Klein, E},
title = {The "wheels that keep me goin'": invisible forms of support for brain pioneers.},
journal = {Neuroethics},
volume = {18},
number = {1},
pages = {},
pmid = {40520823},
issn = {1874-5490},
support = {R01 MH130457/MH/NIMH NIH HHS/United States ; },
abstract = {Research participants in long-term, first-in-human trials of implantable neural devices (i.e., brain pioneers) are critical to the success of the emerging field of neurotechnology. How these participants fare in studies can make or break a research program. Yet, their ability to enroll, participate, and seamlessly exit studies relies on both the support of family/caregivers and care from researchers that is often hidden from view. The present study offers an initial exploration of the different kinds of support that play a role in neural device trials from the perspectives of brain pioneers and their support partners (spouses, paid caregivers, parents, etc.). Using a mixed methods approach (semi-structured, open-ended interviews and a survey) with interpretive grounded theory, we present narratives from a study of six pioneers -- four in brain-computer interface (BCI) trials, and two in deep brain stimulation (DBS) trials -- and five support partners, about their experiences of being supported and supporting participants in implantable neural device studies. Our findings indicate the substantial amount of work involved on the part of pioneers - and some support partners - to make these studies successful. A central finding of the study is that non-logistical forms of support - social, emotional, and epistemic support - play a role, alongside more widely acknowledged forms of support, such as transportation and physical and clinical care. We argue that developing a better understanding of the kinds of support that enable neurotechnology studies to go well can help bridge the gap between abstract ethical principles of caring for subjects and on-the-ground practice.},
}
@article {pmid40520820,
year = {2025},
author = {Aubinet, C and Gillet, A and Regnier, A},
title = {Disorders of Consciousness, Language and Communication Following Severe Brain Injury.},
journal = {Psychologica Belgica},
volume = {65},
number = {1},
pages = {169-188},
pmid = {40520820},
issn = {2054-670X},
abstract = {Patients with severe brain injuries and disorders of consciousness (DoC) represent a complex clinical population in terms of diagnosis, prognosis, and management, including critical ethical considerations. Behavioral assessment scales remain the primary tools for evaluating the level of consciousness of these patients following a coma; however, they heavily depend on language and communication abilities. This reliance can lead to underestimating residual consciousness in cases where language impairments go undetected. Accordingly, the latest international guidelines on DoC diagnosis have highlighted aphasia as a significant confounding factor that must be addressed. On the other hand, accurately assessing residual language abilities is essential for better characterizing the patient's cognitive profile. This, in turn, enables neuropsychologists and speech-language therapists to tailor and plan effective rehabilitation programs. This review examines the current literature on language function and communication skills in patients with DoC, detailing the latest tools for assessing and managing language and consciousness in individuals with severe brain injuries. We explore the critical role of language function in evaluating residual consciousness, particularly in DoC behavioral diagnoses and in identifying covert consciousness through neuroimaging passive or active paradigms. Furthermore, we discuss how therapies aimed at recovering consciousness-such as pharmacological treatments, electromagnetic therapies, sensory or cognitive stimulation, and communication aids like brain-computer interfaces-may also impact or rely on language function and communication abilities. Further research is needed to refine methodologies and better understand the interplay between language processing, communication and levels of consciousness.},
}
@article {pmid40519866,
year = {2025},
author = {Wang, K and Ren, S and Jia, Y and Yan, X and Wang, L and Fan, Y},
title = {Neuromorphic chips for biomedical engineering.},
journal = {Mechanobiology in medicine},
volume = {3},
number = {3},
pages = {100133},
pmid = {40519866},
issn = {2949-9070},
abstract = {The modern medical field faces two critical challenges: the dramatic increase in data complexity and the explosive growth in data size. Especially in current research, medical diagnostic, and data processing devices relying on traditional computer architecture are increasingly showing limitations when faced with dynamic temporal and spatial processing requirements, as well as high-dimensional data processing tasks. Neuromorphic devices provide a new way for biomedical data processing due to their low energy consumption and high dynamic information processing capabilities. This paper aims to reveal the advantages of neuromorphic devices in biomedical applications. First, this review emphasizes the urgent need of biomedical engineering for diversify clinical diagnostic techniques. Secondly, the feasibility of the application in biomedical engineering is demonstrated by reviewing the historical development of neuromorphic devices from basic modeling to multimodal signal processing. In addition, this paper demonstrates the great potential of neuromorphic chips for application in the fields of biosensing technology, medical image processing and generation, rehabilitation medical engineering, and brain-computer interfaces. Finally, this review provides the pathways for constructing standardized experimental protocols using biocompatible technologies, personalized treatment strategies, and systematic clinical validation. In summary, neuromorphic devices will drive technological innovation in the biomedical field and make significant contributions to life health.},
}
@article {pmid40519178,
year = {2025},
author = {Kumar, R and Waisberg, E and Ong, J and Lee, AG},
title = {Response to letter to the editor on 'the potential power of Neuralink - how brain-machine interfaces can revolutionize medicine'.},
journal = {Expert review of medical devices},
volume = {22},
number = {8},
pages = {781-782},
doi = {10.1080/17434440.2025.2521399},
pmid = {40519178},
issn = {1745-2422},
}
@article {pmid40519177,
year = {2025},
author = {Cordero, DA},
title = {Letter to the editor on 'the potential power of neuralink - how brain-machine interfaces can revolutionize medicine'.},
journal = {Expert review of medical devices},
volume = {22},
number = {8},
pages = {779-780},
doi = {10.1080/17434440.2025.2521393},
pmid = {40519177},
issn = {1745-2422},
}
@article {pmid40514107,
year = {2025},
author = {Zuo, M and Chen, X and Sui, L},
title = {A novel STA-EEGNet combined with channel selection for classification of EEG evoked in 2D and 3D virtual reality.},
journal = {Medical engineering &amp; physics},
volume = {141},
number = {},
pages = {104363},
doi = {10.1016/j.medengphy.2025.104363},
pmid = {40514107},
issn = {1873-4030},
mesh = {*Virtual Reality ; *Electroencephalography ; Humans ; Male ; *Signal Processing, Computer-Assisted ; Adult ; Brain-Computer Interfaces ; Female ; Young Adult ; Attention ; *Neural Networks, Computer ; },
abstract = {Virtual reality (VR), particularly through 3D presentations, significantly boosts user engagement and task efficiency in fields such as gaming, education, and healthcare, offering more immersive and interactive experiences than traditional 2D formats. This study investigates EEG classification in response to 2D and 3D VR stimuli to deepen our understanding of the neural mechanisms driving VR interactions, with implications for brain-computer interfaces (BCIs). We introduce STA-EEGNet, an innovative model that enhances EEGNet by incorporating spatial-temporal attention (STA), improving EEG signal classification from VR environments. A one-way analysis of variance (ANOVA) was utilized to optimize channel selection, enhancing model accuracy. Comparative experiments showed that STA-EEGNet surpassed traditional EEGNet, achieving a peak accuracy of 99.78 % with channel selection. These findings highlight the benefits of spatial-temporal attention and optimal channel selection in classifying VR-evoked EEG data. This study underscores the importance of integrating spatial-temporal attention with compact convolutional neural networks like EEGNet, not only improving EEG signal classification but also advancing neural decoding and optimizing BCI applications.},
}
@article {pmid40513959,
year = {2025},
author = {Yang, Q and Guo, W and Wang, L and Zhang, Y and Tian, Y and Ming, D and Xiao, X and Yang, J},
title = {Effects of Fstl1 on neuroinflammation and microglia activation in lipopolysaccharide-induced acute depression-like mice.},
journal = {Behavioural brain research},
volume = {493},
number = {},
pages = {115696},
doi = {10.1016/j.bbr.2025.115696},
pmid = {40513959},
issn = {1872-7549},
mesh = {Animals ; *Follistatin-Related Proteins/metabolism/genetics ; *Microglia/metabolism/drug effects ; Lipopolysaccharides/pharmacology ; Mice ; Male ; *Depression/metabolism/chemically induced ; Female ; Disease Models, Animal ; Hippocampus/metabolism ; *Neuroinflammatory Diseases/metabolism ; Cytokines/metabolism ; Mice, Inbred C57BL ; Behavior, Animal/physiology ; Neurons/metabolism ; Inflammation/metabolism ; },
abstract = {Depression is the most prevalent psychiatric illness, and its pathogenesis is associated with neuroinflammation. Follistatinlike protein 1 (FSTL1), a novel inflammatory protein, participates in the pathogenesis of diseases related to neuroinflammation. Therefore, we aimed to investigate the effect of FSTL1 in the pathogenesis of depression mediated using neuroinflammation-mediated models. Our results showed that lipopolysaccharide (LPS) administration could induce despair-like behavior and increase proinflammatory cytokine levels in both male and female mice. Then, a significant positive correlation between hippocampal Fstl1 mRNA expression, microglial activation and despair-like behaviors was observed in male mice. Moreover, knockdown FSTL1 significantly reduced microglial activation and the expression of proinflammatory cytokines, while overexpression of Fstl1 in hippocampus could exacerbate the activation of microglial under the LPS-induced condition in male mice. Mechanically, knockdown Fstl1 inhibited LPS-induced activation of BV2 microglia and reduced the production of proinflammatory cytokines, thereby protecting the survival of HT22 neurons. In conclusion, our results implied that Fstl1 may modulate despair-like behaviors through regulation of microglial activation and neuronal viability, which would lay the experimental and theoretical foundation for the neuroinflammatory mechanisms underlying depression.},
}
@article {pmid40513226,
year = {2025},
author = {Vasilyev, AN and Svirin, EP and Dubynin, IA and Butorina, AV and Nuzhdin, YO and Ossadtchi, AE and Stroganova, TA and Shishkin, SL},
title = {Intentionally versus spontaneously prolonged Gaze: A MEG study of active gaze-based interaction.},
journal = {Cortex; a journal devoted to the study of the nervous system and behavior},
volume = {189},
number = {},
pages = {76-96},
doi = {10.1016/j.cortex.2025.05.010},
pmid = {40513226},
issn = {1973-8102},
mesh = {Humans ; Magnetoencephalography/methods ; Male ; *Fixation, Ocular/physiology ; Female ; Adult ; Young Adult ; *Attention/physiology ; *Eye Movements/physiology ; Saccades/physiology ; *Brain/physiology ; *Intention ; Frontal Lobe/physiology ; },
abstract = {Eye fixations are increasingly employed to control computers through gaze-sensitive interfaces, yet the brain mechanisms supporting this non-visual use of gaze remain poorly understood. In this study, we employed 306-channel magnetoencephalography (MEG) to find out what is specific to brain activity when gaze is used voluntarily for control. MEG was recorded while participants played a video game controlled by their eye movements. Each move required object selection by fixating it for at least 500 msec. Gaze dwells were classified as intentional if followed by a confirmation gaze on a designated location and as spontaneous otherwise. We identified both induced oscillatory and sustained phase-locked MEG activity differentiating intentional and spontaneous gaze dwells. Induced power analysis revealed prominent alpha-beta band synchronization (8-30 Hz) localized in the frontal cortex, with location broadly consistent with the frontal eye fields. This synchronization began 500-750 msec before intentional fixation onset and peaked shortly after it, suggesting proactive inhibition of saccadic activity. Sustained evoked responses further distinguished the two conditions, showing gradually rising cortical activation with a maximum at 200 msec post-onset in the inferior temporal cortex during intentional fixations, likely indicative of focused attentional engagement on spatial targets. These findings illuminate the neural dynamics underlying intentional gaze control, shedding light on the roles of proactive inhibitory mechanisms and attentional processes in voluntary behavior. By leveraging a naturalistic gaze-based interaction paradigm, this study offers a novel framework for investigating voluntary control under free behavior conditions and holds potential applications for enhancing hybrid eye-brain-computer interfaces.},
}
@article {pmid40512634,
year = {2025},
author = {Yan, T and Ming, Z and Huang, Y and Liu, Z and Chen, Q and Zhang, D and Liu, M and Suo, D and Zhang, J and Liu, S},
title = {Enhanced Brain-Controlled Mobile Robot Based on SE-VEP Paradigm With Single Stimulus.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {2498-2507},
doi = {10.1109/TNSRE.2025.3579373},
pmid = {40512634},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; *Robotics/methods/instrumentation ; Electroencephalography/methods ; Male ; Adult ; Support Vector Machine ; Algorithms ; Female ; Photic Stimulation/methods ; Young Adult ; Reproducibility of Results ; Brain/physiology ; },
abstract = {Brain-computer interface (BCI) systems based on steady-state visually evoked potentials (SSVEPs) have been widely adopted because of their efficiency and accuracy. However, the traditional SSVEP method has limitations, including visual fatigue and interference between different stimuli. To address these issues, a new BCI paradigm, namely, a spatial encoding-visually evoked potential (SE-VEP) model, is proposed in this work. This paradigm involves deploying four target points to implement gaze restrictions around a stimulus block and optimizing the locations of these target points through offline data acquisition. This design facilitates electroencephalogram (EEG) encoding for four instructions while using only one stimulus block. Data with varying eccentricities are classified using the Riemann kernel-based support vector machine (R-SVM) approach, which achieves a classification accuracy of up to 86.11%. As the eccentricity increases, the classification accuracy initially increases but subsequently decreases. By evaluating the information transfer rate (ITR), the optimal time window length for online BCIs is determined to be 1.2 s. Additionally, an online brain-controlled robotic virtual system is developed to validate the feasibility of the proposed paradigm for online brain-computer interface applications. The results confirm the effectiveness of the proposed paradigm in implementing an online BCI control system. An evaluation conducted with scales and the information transfer rate for a single stimulus (ITRSS) indicates that compared with the traditional BCI system, the proposed paradigm yields greater reductions in user fatigue (2.8 ± 0.5 vs. 4.1 ± 0.6) and stimulus block utilization (24.6 ± 2.3 vs. 8.2 ± 1.1 bits/min).},
}
@article {pmid40511861,
year = {2025},
author = {Ramirez, P},
title = {Alternative ways to access AAC technologies.},
journal = {Augmentative and alternative communication (Baltimore, Md. : 1985)},
volume = {41},
number = {3},
pages = {297-299},
doi = {10.1080/07434618.2025.2513902},
pmid = {40511861},
issn = {1477-3848},
mesh = {Humans ; *Communication Devices for People with Disabilities ; *Brain-Computer Interfaces ; Robotics ; *Communication Disorders/rehabilitation ; },
abstract = {More than 21 years ago, I had a car accident that led to a brain stem stroke, leaving me paralyzed and unable to speak. I was desperate to communicate. One day, my sister wrote down the alphabet and pointed to each letter accordingly. I nodded, yes or no, and she wrote my message down. Later, I used a laser light with a letter board and then a laptop with a head pointer. More recently, I started using a gyroscopic air mouse. During outings, I use the laser and the letter board. They are easy to carry and use. Plus, I can communicate in English and Spanish which is very important because my family does not speak English. I am currently enrolled in a clinical trial at the University of California, San Francisco to investigate brain computer interface to control a robotic arm and communicate. They placed an implant in the surface of my brain; the implant connects to a computer system that collects brain signals and translates neural activity from my sensorimotor cortex into intended speech and motor actions. This type of research is needed to enhance communication and improve lives.},
}
@article {pmid40510263,
year = {2025},
author = {Amande, TJ and Kaszyk, V and Brown, F},
title = {Identification of OqxB Efflux Pump and Tigecycline Resistance Gene Cluster tmexC3D2-toprJ3 in Multidrug-Resistant Pseudomonas Stutzeri Isolate G3.},
journal = {Infection and drug resistance},
volume = {18},
number = {},
pages = {2889-2899},
pmid = {40510263},
issn = {1178-6973},
abstract = {PURPOSE: To identify antibiotic resistance genes (ARGs) and understand the molecular basis of multidrug resistance in P. stutzeri isolate G3.<br><br>METHODS: Whole-genome sequencing of isolate G3 was conducted at 30X coverage using Illumina NovaSeq 6000. Reads were trimmed using Trimmomatic and assessed using a combination of scripts that incorporated Samtools, BedTools, and bwa-mem. De novo assembly was performed using SPAdes, and assembly metrics were evaluated using QUAST. The assembled genome was uploaded to a Type Strain Genome Server (TYGS) for taxonomic identification. Genome annotation was performed using the KBase and Proksee software using PROKKA. ARGs were identified using the Comprehensive Antibiotic Resistance Database (CARD).<br><br>RESULTS: P. stutzeri isolate G3 demonstrated resistance to most antibiotics tested, including meropenem (10&#xa0;&#xb5;g), ciprofloxacin (5&#xa0;&#xb5;g), gentamicin (10&#xa0;&#xb5;g), and tetracycline (30&#xa0;&#xb5;g). The ARGs identified were PmpM, AdeF, rsmA, vgb(A), BcI, cipA, OCH-2, and tet(45). A tigecycline-resistant gene cluster, tmexC3D2-toprJ3, was found in NODE_84, while the oqxB gene, encoding a resistance-nodulation-division (RND) efflux pump, was in NODE_309. Phylogenetic analysis showed OqxB clustered with Pseudomonas species, distinct from Klebsiella and Enterobacter. Comparative analysis of oqxB revealed P. stutzeri isolate G3 shared 78-100% identity with Pseudomonas aeruginosa strain 1334/14 in key components of the multidrug efflux system, including the transcriptional regulator MexT, periplasmic adaptor subunit MexE, and permease subunit MexF.<br><br>CONCLUSION: Our findings offer new insights into the reservoir of ARGs in the draft genome of Pseudomonas stutzeri isolate G3, including the tmexC3D2-toprJ3 and oqxB genes, highlighting its genomic plasticity and public health significance. This adaptability enables P. stutzeri to thrive in clinical environments, despite its natural habitat association. This study advances our understanding of the molecular mechanisms driving resistance in P. stutzeri and offers valuable insights to inform strategies for combating the spread of antimicrobial resistance in clinical and environmental settings.},
}
@article {pmid40510210,
year = {2025},
author = {Gazerani, P},
title = {A Hybrid Digital-4E Strategy for comorbid migraine and depression: a medical hypothesis on an AI-driven, neuroadaptive, and exposome-aware approach.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1587296},
pmid = {40510210},
issn = {1664-2295},
abstract = {OBJECTIVE: The co-occurrence of migraines and depression presents a critical clinical challenge, affecting up to 50% of individuals with either condition. This comorbidity leads to greater disability, higher healthcare costs, and poorer treatment outcomes than either disorder alone. Despite a bidirectional pathophysiological relationship, current models remain static and fragmented, treating each condition separately. This paper proposes a Hybrid Digital-4E Strategy, deployed on an AI-driven neuroadaptive digital health platform, integrating closed-loop therapy, digital phenotyping, and exposome tracking to enable real-time, personalized care.<br><br>METHODS: Grounded in the 4E cognition framework (Embodied, Embedded, Enactive, and Extended cognition), this strategy reconceptualizes migraine-depression as an interactive system rather than two separate conditions. The platform integrates real-time biomarker tracking, neuromorphic AI, and precision environmental analytics to dynamically optimize treatment. Adaptive chronotherapy, brain-computer interfaces (BCIs), and virtual reality (VR)-based neuroplasticity training further enhance intervention precision.<br><br>CONCLUSION: A closed-loop, AI-driven neuroadaptive system could improve outcomes by enabling early detection, real-time intervention, and precision care tailored to individual neurophysiological and environmental profiles. Addressing AI bias, data privacy, and clinical validation is crucial for implementation. If validated, this Hybrid Digital-4E Strategy could redefine migraine-depression management, paving the way for precision neuropsychiatry.},
}
@article {pmid40506548,
year = {2025},
author = {Wairagkar, M and Card, NS and Singer-Clark, T and Hou, X and Iacobacci, C and Miller, LM and Hochberg, LR and Brandman, DM and Stavisky, SD},
title = {An instantaneous voice-synthesis neuroprosthesis.},
journal = {Nature},
volume = {644},
number = {8075},
pages = {145-152},
pmid = {40506548},
issn = {1476-4687},
support = {DP2 DC021055/DC/NIDCD NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Humans ; Male ; *Voice/physiology ; *Speech/physiology ; Amyotrophic Lateral Sclerosis/physiopathology/rehabilitation/complications ; Dysarthria/rehabilitation/physiopathology ; *Neural Prostheses ; Electrodes, Implanted ; Microelectrodes ; Communication Devices for People with Disabilities ; },
abstract = {Brain-computer interfaces (BCIs) have the potential to restore communication for people who have lost the ability to speak owing to a 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 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 and sing short melodies. These results demonstrate the feasibility of enabling people with paralysis to speak intelligibly and expressively through a BCI.},
}
@article {pmid40506484,
year = {2025},
author = {Liu, J and Liu, H and Zhu, J and Han, X and Bai, Y and Ni, G and Ming, D},
title = {A Dataset of Pinna-Related Transfer Functions Using High-Resolution Pinna Models.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {992},
pmid = {40506484},
issn = {2052-4463},
mesh = {Humans ; *Sound Localization ; *Ear Auricle/physiology ; Acoustics ; },
abstract = {The pinna-related transfer function (PRTF) is critical for localizing and perceiving sound in three-dimensional space. PRTF largely depends on individual spectral cues and the unique physiology of the pinna, necessitating high-resolution data for accurate acoustic modeling. The accuracy of personalized acoustic models could be significantly improved using high-precision physiological data and incorporating advanced simulation methods such as the boundary element method (BEM). We describe a comprehensive dataset of 150 bilateral PRTFs from 75 participants to support developing, improving, and validating personalized PRTF modeling methods. The dataset includes simulated results from binaural laser-scanned models that are accurately validated through empirical measurements. This comprehensive dataset will contribute to acoustic and spatial audio research and support the ongoing advancements in personalized PRTF modeling techniques.},
}
@article {pmid40505916,
year = {2025},
author = {Bikiaris, RE and Matschek, NI and Koumentakou, I and Niti, A and Kyzas, GZ},
title = {Synergistic effects of arginine and tannic acid on chitosan matrices: An approach for hemostatic sponge development.},
journal = {International journal of biological macromolecules},
volume = {318},
number = {Pt 3},
pages = {145105},
doi = {10.1016/j.ijbiomac.2025.145105},
pmid = {40505916},
issn = {1879-0003},
mesh = {*Chitosan/chemistry/pharmacology ; *Arginine/chemistry/pharmacology ; *Tannins/chemistry/pharmacology ; *Hemostatics/pharmacology/chemistry ; Animals ; Humans ; Hydrogels/chemistry ; Bandages ; Alginates/chemistry ; Biocompatible Materials/chemistry/pharmacology ; Porosity ; Blood Coagulation/drug effects ; Antioxidants/pharmacology/chemistry ; Wound Healing/drug effects ; Polyphenols ; },
abstract = {This study presents the development of a novel multifunctional hydrogel biocomposite sponge designed to address the complexities of wound healing, including rapid hemostasis, infection prevention, and tissue regeneration. Recognizing the limitations of conventional wound dressings that lack multifunctionality, this study introduces a 3D chitosan/tannic acid (CS/TA) hydrogel. After testing three chitosan/tannic acid (CS/TA) ratios, CS/TA-1 (1:0.16), CS/TA-2 (1:0.25), and CS/TA-3 (1:0.34), the most effective formulation, CS/TA-2, was enhanced with sodium alginate (SA) and arginine (Arg) for optimal performance. Arginine, with its guanidinium functional group, served as a green crosslinker through physical interactions, enhancing the sponge's mechanical strength while also improving its hemostatic performance and biocompatibility, promoting cellular interactions. Its inclusion significantly amplified antioxidant activity (>90 %), mitigating oxidative stress and contributing to enhanced therapeutic outcomes. Ionic crosslinking and freeze-drying created a porous, absorbent sponge with high water retention and compression resilience. SEM confirmed the sponge's interconnected porosity, enabling cell infiltration and nutrient exchange. Blood Clotting Index (BCI) assessments demonstrated the hemostatic effectiveness of CS/TA/SA/Arg-3, with 25 % BCI at 5 min and 20 % at 15 min, along with excellent hemocompatibility, achieving a 2.08 % hemolysis rate. These results suggest the hydrogel sponge's potential for effective wound management in emergencies and clinical applications.},
}
@article {pmid40505654,
year = {2025},
author = {Becker, B},
title = {Will our social brain inherently shape and be shaped by interactions with AI?.},
journal = {Neuron},
volume = {113},
number = {13},
pages = {2037-2041},
doi = {10.1016/j.neuron.2025.04.034},
pmid = {40505654},
issn = {1097-4199},
mesh = {Animals ; Humans ; *Artificial Intelligence ; *Brain/physiology ; *Brain-Computer Interfaces ; *Interpersonal Relations ; *Social Behavior ; *Social Interaction ; },
abstract = {Social-specific brain circuits enable rapid understanding and affiliation in interpersonal interactions. These evolutionarily and experience-shaped mechanisms will influence-and be influenced by-interactions with conversational AI agents (chatbots, avatars). This NeuroView explores fundamental circuits, computations, and societal implications.},
}
@article {pmid40503091,
year = {2025},
author = {Li, Q and Pan, Y},
title = {Mobile eye-tracking and neuroimaging technologies reveal teaching and learning on the move: bibliometric mapping and content analysis.},
journal = {Psychoradiology},
volume = {5},
number = {},
pages = {kkaf013},
pmid = {40503091},
issn = {2634-4416},
abstract = {Mobile psychophysiological technologies, such as portable eye tracking, electroencephalography, and functional near-infrared spectroscopy, are advancing ecologically valid findings in cognitive and educational neuroscience research. Staying informed on the field's current status and main themes requires continuous updates. Here, we conducted a bibliometric and text-based content analysis on 135 articles from Web of Science, specifically parsing publication trends, identifying prolific journals, authors, institutions, and countries, along with influential articles, and visualizing the characteristics of cooperation among authors, institutions, and countries. Using a keyword co-occurrence analysis, five clusters of research trends were identified: (i) cognitive and emotional processes, intelligent education, and motor learning; (ii) professional vision and collaborative learning; (iii) face-to-face social learning and real classroom learning; (iv) cognitive load and spatial learning; and (v) virtual reality-based learning, child learning, and technology-assisted special education. These trends illustrate a consistent growth in the use of portable technologies in education over the past 20 years and an emerging shift towards "naturalistic" approaches, with keywords such as "face-to-face" and "real-world" gaining prominence. These observations underscore the need to further generalize the current research to real-world classroom settings and call for interdisciplinary collaboration between researchers and educators. Also, combining multimodal technologies and conducting longitudinal studies will be essential for a comprehensive understanding of teaching and learning processes.},
}
@article {pmid40502712,
year = {2025},
author = {Chen, H and Zhang, M and Ye, T and Wolpert, MA and Ding, N},
title = {Low-frequency cortical activity reflects context-dependent parsing of word sequences.},
journal = {iScience},
volume = {28},
number = {6},
pages = {112650},
pmid = {40502712},
issn = {2589-0042},
abstract = {During speech listening, it has been hypothesized that the brain builds representations of linguistic structures like sentences, which are tracked by neural activity entrained to the rhythm of these structures. Alternatively, others proposed that these sentence-tracking neural activities may reflect the predictability or syntactic properties of individual words. Here, to disentangle the neural responses to sentences and words, we design word sequences that are parsed into different sentences in different contexts. By analyzing neural activity recorded by magnetoencephalography, we find that low-frequency neural activity strongly depends on context-the difference between MEG responses to the same word sequence in two contexts yields a low-frequency signal, which precisely tracks sentences. The predictability and syntactic properties of words can partly explain the neural response in each context but not the difference between contexts. In summary, low-frequency neural activity encodes sentences and can reliably reflect how same-word sequences are parsed in different contexts.},
}
@article {pmid40502202,
year = {2025},
author = {Srinivasan, A and Wairagkar, M and Iacobacci, C and Hou, X and Card, NS and Jacques, BG and Pritchard, AL and Bechefsky, PH and Hochberg, LR and AuYong, N and Pandarinath, C and Brandman, DM and Stavisky, SD},
title = {Encoding of speech modes and loudness in ventral precentral gyrus.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {40502202},
issn = {2692-8205},
support = {DP2 DC021055/DC/NIDCD NIH HHS/United States ; },
abstract = {The ability to vary the mode and loudness of speech is an important part of the expressive range of human vocal communication. However, the encoding of these behaviors in the ventral precentral gyrus (vPCG) has not been studied at the resolution of neuronal firing rates. We investigated this in two participants who had intracortical microelectrode arrays implanted in their vPCG as part of a speech neuroprosthesis clinical trial. Neuronal firing rates modulated strongly in vPCG as a function of attempted mimed, whispered, normal or loud speech. At the neural ensemble level, mode/loudness and phonemic content were encoded in distinct neural subspaces. Attempted mode/loudness could be decoded from vPCG with an accuracy of 94% and 89% for two participants respectively, and corresponding neural preparatory activity could be detected hundreds of milliseconds before speech onset. We then developed a closed-loop loudness decoder that achieved 94% online accuracy in modulating a brain-to-text speech neuroprosthesis output based on attempted loudness. These findings demonstrate the feasibility of decoding mode and loudness from vPCG, paving the way for speech neuroprostheses capable of synthesizing more expressive speech.},
}
@article {pmid40501187,
year = {2025},
author = {Wang, YJ and Jie, Z and Liu, Y and Pan, Y},
title = {Dyad averaged BMI-dependent interbrain synchrony during continuous mutual prediction in social coordination.},
journal = {Social neuroscience},
volume = {20},
number = {3},
pages = {195-204},
doi = {10.1080/17470919.2025.2517068},
pmid = {40501187},
issn = {1747-0927},
mesh = {Humans ; *Body Mass Index ; Male ; Female ; Spectroscopy, Near-Infrared ; *Brain/physiology/diagnostic imaging ; Young Adult ; Adult ; *Social Interaction ; *Interpersonal Relations ; Obesity/psychology/physiopathology ; },
abstract = {Obesity is linked to notable psychological risks, particularly in social interactions where individuals with high body mass index (BMI) often encounter stigmatization and difficulties in forming and maintaining social connections. Although awareness of these issues is growing, there is a lack of research on real-time, dynamic interactions involving dyads with various BMI levels. To address this gap, our study employed a joint finger-tapping task, where participant dyads engaged in coordinated activity while their brain activity was monitored using functional near-infrared spectroscopy (fNIRS). Our findings showed that both Bidirectional and Unidirectional Interaction conditions exhibited higher levels of behavioral and interbrain synchrony compared to the No Interaction condition. Notably, only in the Bidirectional Interaction condition, higher dyadic BMI was significantly correlated with poorer behavioral coordination and reduced interbrain synchrony. This finding suggests that the ability to maintain social coordination, particularly in scenarios requiring continuous mutual prediction and adjustment, is modulated by dyads' BMI.},
}
@article {pmid40499369,
year = {2025},
author = {Banovoth, RS and K V, K},
title = {Roman domination-based spiking neural network for optimized EEG signal classification of four class motor imagery.},
journal = {Computers in biology and medicine},
volume = {194},
number = {},
pages = {110397},
doi = {10.1016/j.compbiomed.2025.110397},
pmid = {40499369},
issn = {1879-0534},
mesh = {*Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; *Brain-Computer Interfaces ; *Brain/physiology ; *Imagination/physiology ; *Models, Neurological ; },
abstract = {The Spiking Neural Network (SNN) is a third-generation neural network recognized for its energy efficiency and ability to process spatiotemporal information, closely imitating the behavioral mechanisms of biological neurons in the brain. SNN exhibit rich neurodynamic features in the spatiotemporal domain, making them well-suited for processing brain signals, mainly those captured using the widely used non-invasive Electroencephalography (EEG) technique. However, the structural limitations of SNN hinder their feature extraction capabilities for motor imagery signal classification, which leads to under performance of the task. To address the aforementioned challenge, the proposed study introduces a novel model that incorporates Roman Domination within a Spiking Neural Network (RDSNN), where Roman domination identifies the most highly correlated channels or nodes. These channels generate an appropriate threshold for spike generation in the signals, which are then classified using the SNN. The model's performance was evaluated on three typically representative motor imagery datasets: PhysioNet, BCI Competition IV-2a, and BCI Competition IV-2b. RDSNN achieved 73.65% accuracy on PhysioNet, 81.75% on BCI IV-2a, and 84.56% on BCI IV-2b. The results demonstrate not only superior accuracy compared to State-Of-the-Art (SOTA) methods but also a 35% reduction in computation time, attributed to the application of Roman domination.},
}
@article {pmid40499342,
year = {2025},
author = {Silveira, I and Varandas, R and Gamboa, H},
title = {Cognitive Lab: A dataset of biosignals and HCI features for cognitive process investigation.},
journal = {Computer methods and programs in biomedicine},
volume = {269},
number = {},
pages = {108863},
doi = {10.1016/j.cmpb.2025.108863},
pmid = {40499342},
issn = {1872-7565},
mesh = {Humans ; *Cognition ; Attention ; Male ; Emotions ; Adult ; Female ; Electroencephalography ; Learning ; User-Computer Interface ; Workload ; Young Adult ; Fatigue ; Machine Learning ; },
abstract = {BACKGROUND AND OBJECTIVE: Attention, cognitive workload/fatigue, and emotional states significantly influence learning outcomes, cognitive performance, and human-machine interactions. However, existing assessment methodologies fail to fully capture the multimodal nature of these cognitive processes, limiting their application in adaptive learning environments. This study presents the Cognitive Lab, a comprehensive multimodal dataset designed to investigate these cognitive processes across real-time learning scenarios. Specifically, it aims to capture and enable the classification of (1) attention and cognitive workload states using standard cognitive tasks, (2) cognitive fatigue arising from prolonged digital activities, and (3) emotional and learning states during interactive lessons.<br><br>METHODS: The Cognitive Lab dataset consists of three distinct subsets, each developed through specific experimental scenarios targeting different aspects of learning. Dataset 1 comprises recordings from eight participants performing N-Back and mental subtraction tasks, aimed at assessing attention and cognitive workload. Dataset 2 includes data from 10 participants engaged in a digital lesson, complemented by Corsi block-tapping and concentration tasks, to evaluate cognitive fatigue. Lastly, Dataset 3 captures data from 18 participants during an interactive Jupyter Notebook lesson, focusing on emotional states and learning processes. Each scenario combined biosignals (accelerometry, ECG, EDA, EEG, fNIRS, respiration) with Human-Computer Interaction (HCI) features (mouse-tracking, keyboard activity, screenshots). Machine learning models were applied to classify cognitive states, with cross-validation ensuring robust results.<br><br>RESULTS: The dataset enabled accurate classification of learning states, achieving up to 87% accuracy in differentiating learning states using mouse-tracking data. Furthermore, it successfully differentiated attention, cognitive workload, and cognitive fatigue states using biosignal and HCI data, with fNIRS, EEG, and ECG emerging as key contributors to classification performance. Variability across participants highlighted the potential for subject-specific calibration to enhance model accuracy.<br><br>CONCLUSIONS: The Cognitive Lab dataset represents a resource for investigating cognitive phenomena in real-world learning scenarios. Its integration of biosignals and HCI features enables the classification of cognitive states and supports advancements in adaptive learning systems, cognitive neuroscience, and brain-computer interface technologies.},
}
@article {pmid40498623,
year = {2025},
author = {Lian, Q and Wang, Y and Qi, Y},
title = {Dynamic Instance-Level Graph Learning Network of Intracranial Electroencephalography Signals for Epileptic Seizure Prediction.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {11},
pages = {8348-8360},
doi = {10.1109/JBHI.2025.3578627},
pmid = {40498623},
issn = {2168-2208},
mesh = {Humans ; *Signal Processing, Computer-Assisted ; *Electrocorticography/methods ; *Seizures/diagnosis/physiopathology ; *Neural Networks, Computer ; Deep Learning ; *Epilepsy/diagnosis/physiopathology ; Brain-Computer Interfaces ; *Electroencephalography/methods ; Adult ; },
abstract = {Brain-computer interface (BCI) technology is emerging as a valuable tool for diagnosing and treating epilepsy, with deep learning-based feature extraction methods demonstrating remarkable progress in BCI-aided systems. However, accurately identifying causal relationships in temporal dynamics of epileptic intracranial electroencephalography (iEEG) signals remains a challenge. This paper proposes a Dynamic Instance-level Graph Learning Network (DIGLN) for seizure prediction using iEEG signals. The DIGLN comprises two core components: a grouped temporal neural network that extracts node features and a graph structure learning method to capture the causality from intra-channel to inter-channel. Furthermore, we propose a graphical interactive writeback technique to enable DIGLN to capture the causality from inter-channel to intra-channel. Consequently, our DIGLN enables patient-specific dynamic instance-level graph learning, facilitating the modelling of evolving signals and functional connectivities through end-to-end data-driven learning. Experimental results on the Freiburg iEEG dataset demonstrate the superior performance of DIGLN, surpassing other deep learning-based seizure prediction methods. Visualization results further confirm DIGLN's capability to learn interpretable and diverse connections.},
}
@article {pmid40496741,
year = {2025},
author = {Tiwari, N and Anwar, S and Bhattacharjee, V},
title = {EEG dataset for natural image recognition through visual stimuli.},
journal = {Data in brief},
volume = {60},
number = {},
pages = {111639},
pmid = {40496741},
issn = {2352-3409},
abstract = {Electroencephalography (EEG) is a technique for measuring the brain's electrical activity in the form of action potentials with electrodes placed on the scalp. Because of its non-invasive nature and ease of use, the approach is becoming increasingly popular for investigations. EEG reveals a wide spectrum of human brain potentials, such as event-related, sensory, and visually evoked potentials (VEPs), which aids in the development of intricate applications. Developing Apps or Brain-Computer Interface (BCI) devices demands data on these potentials. The present dataset comprises EEG recordings generated by thirty-two individuals in reaction to visual stimuli (VEPs). The rationale behind gathering this data is its ability to support EEG-based image classification and reconstruction while also advancing visual decoding. The primary purpose is to examine the cognitive processes behind both familiar and unfamiliar observations. A standardized experimental setup comprising many experimental phases was employed to capture the essence of the investigation and gather the dataset.},
}
@article {pmid40496017,
year = {2025},
author = {Reid, LV and Spalluto, CM and Wilkinson, TMA and Staples, KJ},
title = {Influenza-induced microRNA-155 expression is altered in extracellular vesicles derived from the COPD epithelium.},
journal = {Frontiers in cellular and infection microbiology},
volume = {15},
number = {},
pages = {1560700},
pmid = {40496017},
issn = {2235-2988},
mesh = {*MicroRNAs/genetics/metabolism ; Humans ; *Pulmonary Disease, Chronic Obstructive ; *Extracellular Vesicles/metabolism ; *Epithelial Cells/virology/metabolism ; *Influenza, Human/virology ; Cells, Cultured ; Gene Expression Profiling ; Cell Line ; },
abstract = {BACKGROUND: Influenza virus particularly affects those with chronic lung conditions such as Chronic Obstructive Pulmonary Disease (COPD). Airway epithelial cells are the first line of defense and primary target of influenza infection and release extracellular vesicles (EVs). EVs can transfer of biological molecules such as microRNAs (miRNAs) that can modulate the immune response to viruses through control of the innate and adaptive immune systems. The aim of this work was to profile the EV miRNAs released from bronchial epithelial cells in response to influenza infection and discover if EV miRNA expression was altered in COPD.<br><br>METHODS: Influenza infection of air-liquid interface (ALI) differentiated BCi-NS1.1 epithelial cells were characterized by analyzing the expression of antiviral genes, cell barrier permeability and cell death. EVs were isolated by filtration and size exclusion chromatography from the apical surface wash of ALI cultured bronchial epithelial cells. The EV miRNA cargo was sequenced and reads mapped to miRBase. The BCi sequencing results were further investigated by RT-qPCR and by using healthy and COPD primary epithelial cells.<br><br>RESULTS: Infection of ALI cultured BCi cells with IAV at 3.6 x 10[6] IU/ml for 24 h led to significant upregulation of anti-viral genes without high levels of cell death. EV release from ALI-cultured BCi cells was confirmed using electron microscopy and detection of known tetraspanin EV markers using western blot and the ExoView R100 platform. Differential expression analyses identified 5 miRNA that had a fold change of >0.6: miR-155-5p, miR-122-5p, miR-378a-3p, miR-7-5p and miR-146a-5p (FDR<0.05). Differences between EV, non-EV and cellular levels of these miRNA were detected. Primary epithelial cell release of EV and their miRNA cargo was similar to that observed for BCi. Intriguingly, miR-155 expression was decreased in EVs derived from COPD patients compared to EVs from control samples.<br><br>CONCLUSION: Epithelial EV miRNA release may be a key mechanism in modulating the response to IAV in the lungs. Furthermore, changes in EV miRNA expression may play a dysfunctional role in influenza-induced exacerbations of COPD. However, further work to fully characterize the function of EV miRNA in response to IAV in both health and COPD is required.},
}
@article {pmid40495523,
year = {2025},
author = {Gou, H and Bu, J and Cheng, Y and Liu, C and Gan, H and Liu, M and Zhao, Q and Chen, X and Ren, J and Hong, W and Wang, R and Cao, Y and Yu, C and Chen, X and Zhang, X},
title = {Improved Response Inhibition Through Cognition-Guided EEG Neurofeedback in Men With Methamphetamine Use Disorder.},
journal = {The American journal of psychiatry},
volume = {182},
number = {9},
pages = {861-877},
doi = {10.1176/appi.ajp.20240475},
pmid = {40495523},
issn = {1535-7228},
mesh = {Humans ; Male ; *Neurofeedback/methods ; *Amphetamine-Related Disorders/psychology/physiopathology/therapy ; Adult ; *Methamphetamine/adverse effects ; Electroencephalography ; *Inhibition, Psychological ; *Cognition/physiology ; Cues ; Young Adult ; Middle Aged ; },
abstract = {OBJECTIVE: Impaired response inhibition is the core cognitive deficit in methamphetamine use disorder (MUD), and methamphetamine cue reactivity is a major factor that reduces inhibition efficiency. The authors sought to use cognition-guided neurofeedback to deactivate methamphetamine cue-related brain reactivity patterns in men with MUD to improve their response inhibition.<br><br>METHODS: A cognition-guided, closed-loop EEG-based neurofeedback protocol was employed. Methamphetamine cue-related brain activity patterns were identified offline using multivariate pattern analysis of EEG data from all channels during a methamphetamine cue reactivity task. In the real-time feedback phase, participants were trained to deactivate their methamphetamine cue-related patterns, which were presented as feedback. The study included two samples, totaling 99 men with MUD. In sample 1, 66 men received 10 neurofeedback sessions based either on their own brain activity patterns (real neurofeedback group 1, N=33) or on randomly matched participants' patterns (yoke neurofeedback group, N=33). Sample 2, which was used to validate findings in sample 1, included a real feedback group (real neurofeedback group 2; N=17) and a standard rehabilitation group (N=16) that received only standard rehabilitation without additional intervention. Response inhibition was assessed using a go/no-go task based on methamphetamine-related cues before and after the intervention.<br><br>RESULTS: Compared to the yoke feedback group, real neurofeedback group 1 successfully deactivated methamphetamine cue-related brain reactivity patterns, resulting in significantly enhanced response inhibition (d-prime, Cohen's f=0.31). Neurofeedback performance in real neurofeedback group 1 was significantly correlated with improved response inhibition. Additionally, response inhibition improvements could be predicted by initial neurofeedback performance and baseline characteristics. Sample 2 replicated these findings, showing that response inhibition in real neurofeedback group 2 was improved and predictable. Notably, these intervention effects in real neurofeedback group 2 were better than those in the standard rehabilitation group.<br><br>CONCLUSIONS: These findings underscore the efficacy of cognition-guided neurofeedback for treating MUD, thereby suggesting its potential applicability in other addiction interventions.},
}
@article {pmid40495436,
year = {2025},
author = {Rozovsky, R and Wolfe, M and Abdul-Waalee, H and Chobany, M and Malgireddy, G and Hart, JA and Lepore, B and Vahedifard, F and Phillips, ML and Birmaher, B and Skeba, A and Diler, RS and Bertocci, MA},
title = {Gray Matter Differences in Adolescent Psychiatric Inpatients: A Machine Learning Study of Bipolar Disorder and Other Psychopathologies.},
journal = {Brain and behavior},
volume = {15},
number = {6},
pages = {e70589},
pmid = {40495436},
issn = {2162-3279},
support = {R01-MH-121451/MH/NIMH NIH HHS/United States ; },
mesh = {Humans ; Adolescent ; *Bipolar Disorder/diagnostic imaging/pathology ; Male ; Female ; *Gray Matter/diagnostic imaging/pathology ; Inpatients ; Magnetic Resonance Imaging/methods ; Machine Learning ; Support Vector Machine ; *Brain/pathology/diagnostic imaging ; *Mental Disorders/diagnostic imaging ; },
abstract = {BACKGROUND: Bipolar disorder (BD) is among the psychiatric disorders most prone to misdiagnosis, with both false positives and false negatives resulting in treatment delay. We employed a whole-brain machine learning approach focusing on gray matter volumes (GMVs) to contribute to defining objective biomarkers of BD and discriminating it from other forms of psychopathology, including subthreshold manic presentations without a BD Type I/II diagnosis.<br><br>METHODS: Five support vector machine (SVM) models were used to detect differences in GMVs between inpatient adolescents aged 13-17 with BD-I/II (n = 34), other specified BD (OSB) (n = 106), other non-bipolar psychopathology (OP) (n = 52), and healthy controls (HC) (n = 27). We examined the most discriminative GMVs and tested their associations with clinical symptoms.<br><br>RESULTS: Whole-brain classifiers in the model BD-I/II versus OSB achieved total accuracy of 79%, (AUC = 0.70, p = 0.002); BD versus OP 66%, (AUC = 0.61, p = 0.014); BD versus HC 66%, (AUC = 0.67, p = 0.011); OSB versus HC 77%, (AUC = 0.61, p = 0.01); OP versus HC 68%, (AUC = 0.70, p = 0.001). The most discriminative GMVs that contributed to the classification were in areas associated with movement, sensory processing, and cognitive control. Correlations between these GMVs and self-reported mania, negative affect, or anxiety were observed in all inpatient groups.<br><br>CONCLUSIONS: These findings indicate that pattern recognition models focusing on GMVs in regions associated with movement, sensory processing, and cognitive control can effectively distinguish well-characterized BD-I/II from other forms of psychopathology, including other specified BD, in a pediatric population. These results may contribute to enhancing diagnostic accuracy and guiding earlier, more targeted interventions.},
}
@article {pmid40494420,
year = {2025},
author = {Spinelli, R and Sanchís, I and Siano, A},
title = {Fighting Alzheimer's naturally: Peptides as multitarget drug leads.},
journal = {Bioorganic &amp; medicinal chemistry letters},
volume = {127},
number = {},
pages = {130305},
doi = {10.1016/j.bmcl.2025.130305},
pmid = {40494420},
issn = {1464-3405},
mesh = {*Alzheimer Disease/drug therapy/metabolism ; Humans ; *Peptides/chemistry/pharmacology/therapeutic use ; Animals ; *Cholinesterase Inhibitors/chemistry/pharmacology/therapeutic use ; Monoamine Oxidase/metabolism ; Acetylcholinesterase/metabolism ; *Biological Products/chemistry/pharmacology ; },
abstract = {In this review, we provide a comprehensive analysis of the role of natural peptides-particularly those derived from amphibian skin secretions-as multitarget-directed ligands (MTDLs) in the context of Alzheimer's disease (AD). Given the multifactorial nature of AD, where cholinergic dysfunction intersects with amyloid-β aggregation, tau hyperphosphorylation, oxidative stress, metal ion imbalance, and monoamine oxidase dysregulation, therapeutic strategies capable of modulating several pathological pathways simultaneously are urgently needed. We begin by revisiting the cholinergic hypothesis and its molecular and structural underpinnings, emphasizing the relevance of key binding sites such as the catalytic active site (CAS) and the peripheral anionic site (PAS) of cholinesterases. The central axis of this review lies in the exploration of naturally occurring peptides that have demonstrated dual or multiple activities against AD-related targets. We highlight our group's pioneering work on amphibian-derived peptides such as Hp-1971, Hp-1935, and BcI-1003, which exhibit non-competitive inhibition of AChE and BChE, MAO-B modulation, and antioxidant properties. Furthermore, we describe additional peptide-rich extracts and bioactive sequences from various amphibians and other animal or plant sources, expanding the landscape of natural molecules with neuroprotective potential. We also delve into peptide modification strategies-such as amino acid substitution, cyclization, D-amino acid incorporation, and terminal/side-chain functionalization-that have been employed to enhance peptide stability, blood-brain barrier permeability, and target affinity. These strategies not only improve the pharmacokinetic profiles of native peptides but also open the door for the rational design of next-generation peptide therapeutics. Overall, this review underscores the vast potential of natural peptides as scaffolds for the development of multifunctional agents capable of intervening in the complex cascade of Alzheimer's pathology.},
}
@article {pmid40494387,
year = {2025},
author = {Wu, EG and Rudzite, AM and Bohlen, MO and Li, PH and Kling, A and Cooler, S and Rhoades, C and Brackbill, N and Gogliettino, AR and Shah, NP and Madugula, SS and Sher, A and Litke, AM and Field, GD and Chichilnisky, EJ},
title = {Decomposition of retinal ganglion cell electrical images for cell type and functional inference.},
journal = {Journal of neural engineering},
volume = {22},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ade344},
pmid = {40494387},
issn = {1741-2552},
mesh = {*Retinal Ganglion Cells/physiology/classification/cytology ; Animals ; Macaca mulatta ; *Action Potentials/physiology ; Algorithms ; },
abstract = {Objective.Identifying neuronal cell types and their biophysical properties based on their extracellular electrical features is a major challenge for experimental neuroscience and for the development of high-resolution brain-machine interfaces. One example is identification of retinal ganglion cell (RGC) types and their visual response properties, which is fundamental for developing future electronic implants that can restore vision.Approach.The electrical image (EI) of a RGC, or the mean spatio-temporal voltage footprint of its recorded spikes on a high-density electrode array, contains substantial information about its anatomical, morphological, and functional properties. However, the analysis of these properties is complex because of the high-dimensional nature of the EI. We present a novel optimization-based algorithm to decompose EI into a low-dimensional, biophysically-based representation: the temporally-shifted superposition of three learned basis waveforms corresponding to spike waveforms produced in the somatic, dendritic and axonal cellular compartments.Main results.The decomposition was evaluated using large-scale multi-electrode recordings from the macaque retina. The decomposition accurately localized the somatic and dendritic compartments of the cell. The imputed dendritic fields of RGCs correctly predicted the location and shape of their visual receptive fields. The inferred waveform amplitudes and shapes accurately identified the four major primate RGC types (ON and OFF midget and parasol cells) substantially more accurately than previous approaches.Significance.These findings contribute to more accurate inference of RGC types and their original light responses based purely on their electrical features, with potential implications for vision restoration technology.},
}
@article {pmid40494367,
year = {2025},
author = {Thielen, J},
title = {Addressing BCI inefficiency in c-VEP-based BCIs: A comprehensive study of neurophysiological predictors, binary stimulus sequences, and user comfort.},
journal = {Biomedical physics &amp; engineering express},
volume = {11},
number = {4},
pages = {},
doi = {10.1088/2057-1976/ade316},
pmid = {40494367},
issn = {2057-1976},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Evoked Potentials, Visual/physiology ; Female ; Electroencephalography/methods ; Adult ; Young Adult ; Photic Stimulation ; Heart Rate ; Attention ; *Brain/physiology ; },
abstract = {Objective.This study investigated the presence of brain-computer interface (BCI) inefficiency in BCIs using the code-modulated visual evoked potential (c-VEP). It further explored neurophysiological predictors of performance variability and evaluated a wide range of binary stimulus sequences in terms of classification accuracy and user comfort, aiming to identify strategies to mitigate c-VEP BCI inefficiency.Approach.In a comprehensive empirical analysis, ten different binary stimulus sequences were offline evaluated. These sequences included five code families (m-sequence, de Bruijn sequence, Golay sequence, Gold code, and a Gold code set), each in original and modulated form. To identify predictors of performance variability, resting-state alpha activity, heart rate and heart rate variability, sustained attention, and flash-VEP characteristics were studied.Main Results.Results confirmed substantial inter-individual variability in c-VEP BCI efficiency. While all participants reached a near-perfect classification accuracy, their obtained speed varied substantially. Four flash-VEP features were found to significantly correlate with the observed performance varibility: the N2 latency, the P2 latency and amplitude, and the N3 amplitude. Among the tested stimulus conditions, the m-sequence emerged as the best-performing universal stimulus. However, tailoring stimulus selection to individuals led to significant improvements in performance. Cross-decoding was successful between modulated stimulus conditions, but showed challenges when generalizing across other stimulus conditions. Lastly, while overall comfort ratings were comparable across conditions, stimulus modulation was associated with a significant decrease in user comfort.Significance.This study challenges the assumption of universal efficiency in c-VEP BCIs. The findings highlight the importance of accounting for individual neurophysiological differences and underscore the need for personalized stimulus protocols and decoding strategies to enhance both performance and user comfort.},
}
@article {pmid40493465,
year = {2025},
author = {Rabiee, A and Ghafoori, S and Cetera, A and Norouzi, M and Besio, W and Abiri, R},
title = {A Comparative Study of Conventional and Tripolar EEG for High-Performance Reach-to-Grasp BCI Systems.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3578235},
pmid = {40493465},
issn = {1558-2531},
abstract = {This study aims to enhance brain-computer interface (BCI) applications for individuals with motor impairments by comparing the effectiveness of noninvasive tripolar concentric ring electrode electroencephalography (tEEG) with conventional electroencephalography (EEG) technology. The goal is to determine which EEG technology is more effective in measuring and decoding different grasp-related neural signals. The approach involves experimenting on ten healthy participants who performed two distinct reach-and-grasp movements: power grasp and precision grasp, with a no-movement condition serving as the baseline. Our research compares EEG and tEEG in decoding grasping movements, focusing on signal-to-noise ratio (SNR), spatial resolution, and wavelet time-frequency analysis. Additionally, our study involved extracting and analyzing statistical features from the wavelet coefficients, and both binary and multiclass classification methods were employed. Four machine learning algorithms-Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Linear Discriminant Analysis (LDA)-were used to evaluate the decoding accuracies. Our results indicated that tEEG demonstrated higher quality performance compared to conventional EEG in various aspects. This included a higher signal-to-noise ratio and improved spatial resolution. Furthermore, wavelet timefrequency analyses validated these findings, with tEEG exhibiting increased power spectra, thus providing a more detailed and informative representation of neural dynamics. The use of tEEG led to significant improvements in decoding accuracy for differentiating grasp movement types. Specifically, tEEG achieved around 90.00% accuracy in binary and 75.97% for multiclass classification. These results exceed those from conventional EEG, which recorded a maximum of 77.85% and 61.27% in similar tasks, respectively.},
}
@article {pmid40493396,
year = {2025},
author = {Xiang, S and Chen, P and Shi, X and Cai, H and Shen, Z and Liu, L and Xu, A and Zhang, J and Zhang, X and Bing, S and Wang, J and Shao, X and Cao, J and Yang, B and He, Q and Ying, M},
title = {Disruption of the KLHL37-N-Myc complex restores N-Myc degradation and arrests neuroblastoma growth in mouse models.},
journal = {The Journal of clinical investigation},
volume = {135},
number = {14},
pages = {},
pmid = {40493396},
issn = {1558-8238},
mesh = {*Neuroblastoma/metabolism/pathology/genetics/drug therapy ; Animals ; *N-Myc Proto-Oncogene Protein/metabolism/genetics ; Humans ; Mice ; *Proteolysis ; Cell Line, Tumor ; Xenograft Model Antitumor Assays ; Proto-Oncogene Proteins c-myc ; },
abstract = {The N-Myc gene MYCN amplification accounts for the most common genetic aberration in neuroblastoma and strongly predicts the aggressive progression and poor clinical prognosis. However, clinically effective therapies that directly target N-Myc activity are limited. N-Myc is a transcription factor, and its stability is tightly controlled by ubiquitination-dependent proteasomal degradation. Here, we discovered that Kelch-like protein 37 (KLHL37) played a crucial role in enhancing the protein stability of N-Myc in neuroblastoma. KLHL37 directly interacted with N-Myc to disrupt N-Myc-FBXW7 interaction, thereby stabilizing N-Myc and enabling tumor progression. Suppressing KLHL37 effectively induced the degradation of N-Myc and had a profound inhibitory effect on the growth of MYCN-amplified neuroblastoma. Notably, we identified RTA-408 as an inhibitor of KLHL37 to disrupt the KLHL37-N-Myc complex, promoting the degradation of N-Myc and suppressing neuroblastoma in vivo and in vitro. Moreover, we elucidated the therapeutic potential of RTA-408 for neuroblastoma using patient-derived neuroblastoma cell and patient-derived xenograft tumor models. RTA408's antitumor effects may not occur exclusively via KLHL37, and specific KLHL37 inhibitors are expected to be developed in the future. These findings not only uncover the biological function of KLHL37 in regulating N-Myc stability, but also indicate that KLHL37 inhibition is a promising therapeutic regimen for neuroblastoma, especially in patients with MYCN-amplified tumors.},
}
@article {pmid40493186,
year = {2025},
author = {Alawieh, H and Liu, D and Madera, J and Kumar, S and Racz, FS and Fey, AM and Del R Millán, J},
title = {Electrical spinal cord stimulation promotes focal sensorimotor activation that accelerates brain-computer interface skill learning.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {122},
number = {24},
pages = {e2418920122},
pmid = {40493186},
issn = {1091-6490},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Adult ; *Learning/physiology ; *Spinal Cord Injuries/physiopathology/rehabilitation ; Female ; *Spinal Cord Stimulation/methods ; *Motor Cortex/physiology ; Young Adult ; *Motor Skills/physiology ; },
abstract = {Injuries affecting the central nervous system may disrupt neural pathways to muscles causing motor deficits. Yet the brain exhibits sensorimotor rhythms (SMRs) during movement intents, and brain-computer interfaces (BCIs) can decode SMRs to control assistive devices and promote functional recovery. However, noninvasive BCIs suffer from the instability of SMRs, requiring longitudinal training for users to learn proper SMR modulation. Here, we accelerate this skill learning process by applying cervical transcutaneous electrical spinal stimulation (TESS) to inhibit the motor cortex prior to longitudinal upper-limb BCI training. Results support a mechanistic role for cortical inhibition in significantly increasing focality and strength of SMRs leading to accelerated BCI control in healthy subjects and an individual with spinal cord injury. Improvements were observed following only two TESS sessions and were maintained for at least one week in users who could not otherwise achieve control. Our findings provide promising possibilities for advancing BCI-based motor rehabilitation.},
}
@article {pmid40490658,
year = {2025},
author = {Norizadeh Cherloo, M and Kashefi Amiri, H and Mijani, AM and Zhan, L and Daliri, MR},
title = {A comprehensive study of template-based frequency detection methods in SSVEP-based brain-computer interfaces.},
journal = {Behavior research methods},
volume = {57},
number = {7},
pages = {196},
pmid = {40490658},
issn = {1554-3528},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Signal-To-Noise Ratio ; *Evoked Potentials, Visual/physiology ; *Signal Processing, Computer-Assisted ; Algorithms ; },
abstract = {Recently, SSVEP-based brain-computer interfaces (BCIs) have received increasing attention from researchers due to their high signal-to-noise ratios (SNR), high information transfer rates (ITR), and low user training. Therefore, various methods have been proposed to recognize the frequency of SSVEPs. This paper reviewed the state-of-the-art frequency detection methods in SSVEP-based BCIs. Nineteen multi-channel SSVEP detection methods, organized into four categories based on different analytical approaches, were studied. All methods are template-based approaches and classified into four groups according to the basic models they employ: canonical correlation analysis (CCA), multivariate synchronization index (MSI), task-related component analysis (TRCA), and correlated component analysis (CORRCA). Each group consists of methods that use one of these basic models as the core model for their approach. This paper provides a description, a clear flowchart, and MATLAB code for each method and helps researchers use or develop the existing SSVEP detection methods. Although all methods were evaluated in separate studies, a comprehensive comparison of methods is still missing. In this study, several experiments were conducted to assess the performance of SSVEP detection methods. The benchmark 40-class SSVEP dataset from 35 subjects was used to evaluate methods. All methods were applied to the dataset and were evaluated in terms of classification accuracy, information transfer rate (ITR), and computational time. The experiment results revealed that four factors efficiently design an accurate, robust SSVEP detection method. (1) employing filter bank analysis to incorporate fundamental and harmonics frequency components, (2) utilizing calibration data to construct optimized reference signals, (3) integrating spatial filters of all stimuli to construct classification features, and (4) calculating spatial filters using training trials. Furthermore, results showed that filter bank ensemble task-related components (FBETRCA) achieved the highest performance.},
}
@article {pmid40490007,
year = {2025},
author = {Chen, Y and Peng, Y and Tang, J and Camilleri, T and Camilleri, K and Kong, W and Cichocki, A},
title = {EEG-based affective brain-computer interfaces: recent advancements and future challenges.},
journal = {Journal of neural engineering},
volume = {22},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ade290},
pmid = {40490007},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces/trends/psychology ; *Electroencephalography/trends/methods ; *Emotions/physiology ; *Brain/physiology ; Forecasting ; *Affect/physiology ; },
abstract = {Objective. As one of the most popular brain-computer interface (BCI) paradigms, affective BCI (aBCI) decodes the human emotional states from brain signals and imposes necessary feedback to achieve neural regulation when negative emotional states (i.e. depression, anxiety) are detected, which are considered as the two basic functions of aBCI systems. Electroencephalogram (EEG) is the scalp reflection of neural activities and has been regarded as the gold standard of emotional effects. Recently, rapid progresses have been made for emotion recognition and regulation with the purpose of constructing a high-performance closed-loop EEG-based aBCI system. Therefore, it is necessary to make a timely review for aBCI research by summarizing the current progresses as well as challenges and opportunities, to draw the attention from both academia and industry. Toward this goal, a systematic literature review was performed to summarize not only the recent progresses in emotion recognition and regulation from the perspective of closed-loop aBCI, but also the main challenges and future research focuses to narrow the gap between the current research and real applications of aBCI systems.Approach. A systematic literature review on EEG-based emotion recognition and regulation was performed on Web of Science and related databases, resulting in more than 100 identified studies. These studies were analyzed according to the experimental paradigm, emotion recognition methods in terms of different scenarios, and the applications of emotion recognition in diagnosis and regulation of affective disorders.Main results. Based on the literature review, advancements for EEG-based aBCI research were extensively summarized from six aspects including the 'emotion elicitation paradigms and data sets', 'inner exploration of EEG information', 'outer extension of fusing EEG with other data modalities', 'cross-scene emotion recognition', 'emotion recognition by considering real scenarios', and 'diagnosis and regulation of affective disorders'. In addition, future opportunities were concluded by focusing on the main challenges in hindering the aBCI system to move from laboratory to real applications. Moreover, the neural mechanisms and theoretical basis behind EEG emotion recognition and regulation are also introduced to provide support for the advancements and challenges in aBCI.Significance. This review summarizes the current practices and performance outcomes in emotion recognition and regulation. Future directions in response to the existing challenges are provided with the expectation of guiding the aBCI research to focus on the necessary key technologies of aBCI systems in practical deployment.},
}
@article {pmid40490003,
year = {2025},
author = {Tates, A and Matran-Fernandez, A and Halder, S and Daly, I},
title = {Speech imagery brain-computer interfaces: a systematic literature review.},
journal = {Journal of neural engineering},
volume = {22},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ade28e},
pmid = {40490003},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces/trends ; Humans ; *Imagination/physiology ; *Speech/physiology ; *Brain/physiology ; Electroencephalography/methods ; },
abstract = {Objective:Speech Imagery (SI) refers to the mental experience of hearing speech and may be the core of verbal thinking for people who undergo internal monologues. It belongs to the set of possible mental imagery states that produce kinesthetic experiences whose sensations are similar to their non-imagery counterparts. SI underpins language processes and may have similar building blocks to overt speech without the final articulatory outcome. The kinesthetic experience of SI has been proposed to be a projection of the expected articulatory outcome in a top-down processing manner. As SI seems to be a core human cognitive task it has been proposed as a paradigm for Brain-Computer Interfaces (BCI). One important aspect of BCI designs is usability, and SI may present an intuitive paradigm, which has brought the attention of researchers to attempt to decode SI from brain signals. In this paper we review the important aspects of SI-BCI decoding pipelines.Approach. We conducted this review according to the Preferred Reporting Items for Systematic reviews and Meta-Analysis guidelines. Specifically, we filtered peer-reviewed reports via a search of Google Scholar and PubMed. We selected a total of 104 reports that attempted to decode SI from neural activity.Main results. Our review reveals a growing interest in SI decoding in the last 20 years, and shows how different neuroimaging modalities have been employed to record SI in distinct ways to instruct participants to perform this task. We discuss the signal processing methods used along with feature extraction techniques and found a high preference for Deep Learning models. We have summarized and compared the decoding attempts by quantifying the efficacy of decoding by measuring Information Transfer Rates. Notably, fewer than 6% of studies reported real-time decoding, with the vast majority focused on offline analyses. This suggests existing challenges of this paradigm, as the variety of approaches and outcomes prevents a clear identification of the field's current state-of-the-art. We offer a discussion of future research directions.SignificanceSI is an attractive BCI paradigm. This review outlines the increasing interest in SI, the methodological trends, the efficacy of different approaches, and the current progress toward real-time decoding systems.},
}
@article {pmid40489280,
year = {2025},
author = {Wang, Z and Zhang, Y and Zhang, Z and Xie, SQ and Lanzon, A and Heath, WP and Li, Z},
title = {Instance-Based Transfer Learning with Similarity-Aware Subject Selection for Cross-Subject SSVEP-Based BCIs.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3577813},
pmid = {40489280},
issn = {2168-2208},
abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) can achieve high recognition accuracy with sufficient training data. Transfer learning presents a promising solution to alleviate data requirements for the target subject by leveraging data from source subjects; however, effectively addressing individual variability among both target and source subjects remains a challenge. This paper proposes a novel transfer learning framework, termed instance-based task-related component analysis (iTRCA), which leverages knowledge from source subjects while considering their individual contributions. iTRCA extracts two types of features: (1) the subject-general feature, capturing shared information between source and target subjects in a common latent space, and (2) the subject-specific feature, preserving the unique characteristics of the target subject. To mitigate negative transfer, we further design an enhanced framework, subject selection-based iTRCA (SS-iTRCA), which integrates a similarity-based subject selection strategy to identify appropriate source subjects for transfer based on their task-related components (TRCs). Comparative evaluations on the Benchmark, BETA, and a self-collected dataset demonstrate the effectiveness of the proposed iTRCA and SS-iTRCA frameworks. This study provides a potential solution for developing high-performance SSVEP-based BCIs with reduced target subject data.},
}
@article {pmid40485770,
year = {2025},
author = {Tyler, WJ and Adavikottu, A and Blanco, CL and Mysore, A and Blais, C and Santello, M and Unnikrishnan, A},
title = {Neurotechnology for enhancing human operation of robotic and semi-autonomous systems.},
journal = {Frontiers in robotics and AI},
volume = {12},
number = {},
pages = {1491494},
pmid = {40485770},
issn = {2296-9144},
abstract = {Human operators of remote and semi-autonomous systems must have a high level of executive function to safely and efficiently conduct operations. These operators face unique cognitive challenges when monitoring and controlling robotic machines, such as vehicles, drones, and construction equipment. The development of safe and experienced human operators of remote machines requires structured training and credentialing programs. This review critically evaluates the potential for incorporating neurotechnology into remote systems operator training and work to enhance human-machine interactions, performance, and safety. Recent evidence demonstrating that different noninvasive neuromodulation and neurofeedback methods can improve critical executive functions such as attention, learning, memory, and cognitive control is reviewed. We further describe how these approaches can be used to improve training outcomes, as well as teleoperator vigilance and decision-making. We also describe how neuromodulation can help remote operators during complex or high-risk tasks by mitigating impulsive decision-making and cognitive errors. While our review advocates for incorporating neurotechnology into remote operator training programs, continued research is required to evaluate the how these approaches will impact industrial safety and workforce readiness.},
}
@article {pmid40484831,
year = {2025},
author = {Zhao, JZ},
title = {[A historical review and future outlook of neurosurgery in China].},
journal = {Zhonghua yi xue za zhi},
volume = {105},
number = {21},
pages = {1679-1685},
doi = {10.3760/cma.j.cn112137-20250325-00727},
pmid = {40484831},
issn = {0376-2491},
mesh = {*Neurosurgery/trends/history ; China ; Humans ; History, 20th Century ; History, 21st Century ; Societies, Medical ; Artificial Intelligence ; },
abstract = {Since its inception in the early 20th century at Peking Union Medical College Hospital, neurosurgery in China has gone through a century-long process from its initial establishment, development to modernization, forming a complete system, covering vascular diseases, tumors, epilepsy, and other diseases. This article reviews the key pioneers and historical milestones in Chinese neurosurgery, highlights the founding and advancement of the Society of Neurosurgery of Chinese Medical Association, and shows major achievements in standardization, training, and international cooperation, etc. At present, with the application of technologies such as artificial intelligence and brain-computer interfaces, network-based neurosurgery has emerged and developed rapidly, marking the transition to Neurosurgery 4.0. In the future, Chinese neurosurgery is poised to further promote interdisciplinary integration and clinical translation in support of the high-quality development of brain science.},
}
@article {pmid40483841,
year = {2025},
author = {Zakrzewski, S and Stasiak, B and Wojciechowski, A},
title = {Supervised factor selection in tensor decomposition of EEG signal.},
journal = {Computer methods and programs in biomedicine},
volume = {269},
number = {},
pages = {108866},
doi = {10.1016/j.cmpb.2025.108866},
pmid = {40483841},
issn = {1872-7565},
mesh = {*Electroencephalography/methods/statistics &amp; numerical data ; Humans ; Algorithms ; *Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Factor Analysis, Statistical ; },
abstract = {BACKGROUND AND OBJECTIVE: Tensor decomposition methods are important tools for multidimensional data analysis, which have also proved useful for EEG signal processing. However, their direct application for tasks involving supervised training, such as EEG data classification in systems based on brain-computer interfaces, is limited by the inherently unsupervised nature of the optimization algorithms used for tensor factorization.<br><br>METHODS: In this work, we propose a solution for a motor imagery classification task based on parallel factor analysis (PARAFAC) of EEG data. The individual factors obtained through PARAFAC decomposition are subjected to statistical analysis, enabling us to select signal components relevant to the classification problem. To choose the rank of the decomposition, we propose a special score function based on cosine similarity of all possible pairs of decompositions.<br><br>RESULTS: The proposed method was shown to significantly increase the classification accuracy in the case of the best-performing subjects, when provided with an EEG signal satisfying certain conditions, such as sufficient trial length. Besides, representation of the statistically significant components in the form of a heatmap, defined over the space-frequency plane, proved suitable for direct interpretation in the context of event-related synchronization/desynchronization of cortical activity.<br><br>CONCLUSION: The proposed approach, joining universal tensor decomposition methods with statistical evaluation of the obtained components, has the potential to yield high accuracy and explainability of the results while significantly reducing the input space dimensionality.},
}
@article {pmid40483616,
year = {2025},
author = {Han, J and Zhan, G and Wang, L and Liang, D and Zhang, H and Zhang, L and Kang, X},
title = {Decoding EEG-based cognitive load using fusion of temporal and functional connectivity features.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-16},
doi = {10.1080/10255842.2025.2514132},
pmid = {40483616},
issn = {1476-8259},
abstract = {Evaluating cognitive load using electroencephalogram (EEG) signals is a crucial research area in the field of Brain-Computer Interfaces (BCI). However, achieving high accuracy and generalization in feature extraction and classification for cognitive load assessment remains a challenge, primarily due to the low signal-to-noise ratio of EEG signals and the inter-individual variability in EEG data. In this study, we propose a novel deep learning architecture that integrates temporal information features and functional connectivity features to enhance EEG signal analysis. Functional connectivity features capture inter-channel information, while temporal features are extracted from continuous signal segments using a Long Short-Term Memory (LSTM) network enhanced with an attention mechanism. The fusion strategy combines these two information streams to leverage their complementary strengths, resulting in improved classification performance. We evaluated our architecture on two publicly available datasets, and the results demonstrate its robustness in cognitive load recognition. Achieving performance comparable to the best existing methods on two public datasets. Ablation studies further substantiate the contributions of each module, demonstrating the importance of combining temporal and functional connectivity features for optimal results. These findings underscore the robustness and versatility of the proposed approach, paving the way for more effective EEG-based BCI applications.},
}
@article {pmid40482972,
year = {2025},
author = {M V, H and K, K and B, SB},
title = {An EEG-based imagined speech recognition using CSP-TP feature fusion for enhanced BCI communication.},
journal = {Behavioural brain research},
volume = {493},
number = {},
pages = {115652},
doi = {10.1016/j.bbr.2025.115652},
pmid = {40482972},
issn = {1872-7549},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Imagination/physiology ; Male ; Female ; Adult ; *Speech/physiology ; Young Adult ; *Brain/physiology ; Support Vector Machine ; Signal Processing, Computer-Assisted ; Machine Learning ; },
abstract = {BACKGROUND: Imagined speech has emerged as a promising paradigm for intuitive control of brain-computer interface (BCI)-based communication systems, providing a means of communication for individuals with severe brain disabilities. In this work, a non-invasive electroencephalogram (EEG)-based automated imagined speech recognition model was proposed to assist communication to convey the individual's intentions or commands. The proposed approach uses Common Spatial Patterns (CSP) and Temporal Patterns (TP) for feature extraction, followed by feature fusion to capture both spatial and temporal dynamics in EEG signals. This fusion of the CSP and TP domains enhances the discriminative power of the extracted features, leading to improved classification accuracy.<br><br>NEW METHOD: An EEG data set was collected from 15 subjects while performing an imagined speech task with a set of ten words that are more suitable for paralyzed patients. The EEG signals were preprocessed and a set of statistical characteristics was extracted from the fused CSP and TP domains. Spectral analysis of the signals was performed with respect to ten imagined words to identify the underlying patterns in EEG. Machine learning models, including Linear Discriminant Analysis (LDA), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR), were employed for pairwise and multiclass classification.<br><br>RESULTS: The proposed model achieved average classification accuracies of 83.83% &#xb1; 5.94 and 64.58% &#xb1; 10.43 and maximum accuracies of 97.78% and 79.22% for pairwise and multiclass classification, respectively. These results demonstrate the effectiveness of the CSP-TP feature fusion approach, outperforming existing state-of-the-art methods in imagined speech recognition.<br><br>CONCLUSION: The findings suggest that EEG-based automatic imagined speech recognition (AISR) using CSP and TP techniques has significant potential for use in BCI-based assistive technologies, offering a more natural and intuitive means of communication for individuals with severe communication limitations.},
}
@article {pmid40481499,
year = {2025},
author = {Zhang, N and Huang, Z and Xia, Y and Tao, S and Wu, T and Sun, S and Zhu, Y and Jiang, G and Lu, X and Gao, Y and Guo, F and Cao, R and Chen, S and Zhang, L and Zou, X and Chen, M and Zhang, G},
title = {Remote ischemia precondition protects against renal IRI through apoptosis associated vesicles carrying MIF protein via modulating DUSP6/JNK signaling axis.},
journal = {Journal of nanobiotechnology},
volume = {23},
number = {1},
pages = {422},
pmid = {40481499},
issn = {1477-3155},
support = {tsgn202103116//Tai-Shan Scholar Program from Shandong Province/ ; 81900618//the National Natural Science Foundation of China/ ; 2023GX026//the Program of Scientific and Technological Development of Weifang/ ; GSP-LCYJFH11//Zhongda Hospital Affiliated to Southeast University, Jiangsu Province High-Level Hospital Construction Funds/ ; 2023YXZDXK02//Jiangsu Provincial Key Discipline and Laboratory Construction Funds of Urology/ ; CZXM-ZK-47//National clinical key discipline construction funds/ ; 202305033//Nanjing Key Science and Technology Special Project (Life and Health) - Medical-Engineering Collaborative Project/ ; 82100732//Natural Science Foundation of China/ ; },
abstract = {BACKGROUND: Remote ischemic preconditioning (rIPC) has been reported to protect against kidney ischemia-reperfusion injury (IRI) through the delivery of extracellular vesicles (EVs). Among these, apoptosis-induced compensatory proliferation signaling-related vesicles (ACPSVs) can transmit proliferation signals to surrounding cells. However, the underlying mechanisms remain unclear. This study aimed to investigate the role of ACPSVs in renal IRI following rIPC and to elucidate the associated mechanisms.<br><br>RESULTS: We demonstrated that rIPC plasma or ACPSVs alleviated renal damage and inflammation, with the protective effects abolished upon the removal of ACPSVs from the plasma. EVs isolated via differential centrifugation exhibited defining characteristics of ACPSVs. Co-culture experiments revealed that ACPSVs reduced apoptosis and enhanced the viability of HK-2 cells under hypoxia/reoxygenation (H/R) conditions. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses highlighted the critical role of macrophage migration inhibitory factor (MIF) protein in ACPSVs. Using CRISPR/Cas9 technology, we generated MIF-knockout HeLa cells to induce the production of MIF-deficient ACPSVs. The protective effects of ACPSVs were significantly attenuated when MIF was knocked out. Transcriptome sequencing and chromatin immunoprecipitation (ChIP) assays revealed that MIF suppresses dual-specificity phosphatase 6 (DUSP6) expression by promoting H3K9 trimethylation (H3K9me3) in the DUSP6 promoter region, thereby activating the JNK signaling pathway. In rescue experiments, treatment with the DUSP6 inhibitor BCI effectively restored the protective function of MIF-deficient ACPSVs.<br><br>CONCLUSION: This study underscores the protective role of ACPSVs derived from rIPC-treated rats and serum-starved cells against renal IRI through the MIF/DUSP6/JNK signaling axis, offering a potential clinical therapeutic strategy for acute kidney injury induced by IRI.<br><br>GRAPHICAL ABSTRACT: [Image: see text]<br><br>SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12951-025-03505-9.},
}
@article {pmid40481295,
year = {2025},
author = {Zheng, J and Yu, J and Xu, M and Guan, C and Fu, Y and Shen, M and Chen, H},
title = {Expectation violation enhances short-term source memory.},
journal = {Psychonomic bulletin &amp; review},
volume = {},
number = {},
pages = {},
pmid = {40481295},
issn = {1531-5320},
abstract = {Recent studies of short-term source amnesia demonstrated that source information is rapidly forgotten in memory, reflecting a highly selective mode of memory encoding. In this study, we explored the flexibility of memory selection by investigating whether short-term source amnesia is affected by expectation violations. In seven experiments, we first replicated the short-term source amnesia phenomenon and then induced various forms of expectation violations. The results consistently showed that the short-term source amnesia was significantly reduced or attenuated when expectation violation occurred, indicating a strengthening effect of expectation violation on short-term source memory. This effect occurred quite quickly, nearly at the same time as the occurrence of unexpected events. Moreover, the source memory was improved even when the unexpected events were completely irrelevant to the task set or target stimuli. These findings suggest that short-term memory tends to encode and maintain more detailed source information when encountering expectation violations, which might be an adaptive mechanism for handling unexpected environmental changes.},
}
@article {pmid40481078,
year = {2025},
author = {Peng, L and Wang, L and Wu, S and Gu, M and Deng, S and Liu, J and Cheng, CK and Sui, X},
title = {Biomechanics characterization of an implantable ultrathin intracortical electrode through finite element method.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {19938},
pmid = {40481078},
issn = {2045-2322},
support = {No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 62176158//the National Natural Science Foundation of China/ ; No. 62176158//the National Natural Science Foundation of China/ ; No. 62176158//the National Natural Science Foundation of China/ ; No. 62176158//the National Natural Science Foundation of China/ ; No. 62176158//the National Natural Science Foundation of China/ ; WH410362603/001//the STAR Project of Shanghai Jiao Tong University/ ; WH410362603/001//the STAR Project of Shanghai Jiao Tong University/ ; WH410362603/001//the STAR Project of Shanghai Jiao Tong University/ ; WH410362603/001//the STAR Project of Shanghai Jiao Tong University/ ; WH410362603/001//the STAR Project of Shanghai Jiao Tong University/ ; WH410362603/001//the STAR Project of Shanghai Jiao Tong University/ ; },
mesh = {Finite Element Analysis ; *Electrodes, Implanted ; Biomechanical Phenomena ; Microelectrodes ; Brain/physiology ; Humans ; Stress, Mechanical ; Brain-Computer Interfaces ; },
abstract = {Neural electrodes are widely used in brain-computer interfaces and neuroprosthesis for the treatment of various neurological disorders. However, as components that come into direct contact with neural tissue, implanted neural electrodes could cause mechanical damage during surgical insertions or while inside the brain. Thus, accurately and timely assessing this damage was vital for chronic implantation, which posed a significant challenge. This study aimed to evaluate the biomechanical effects and clinical application risks of a polyimide-based ultrathin flexible intracortical microelectrode through the finite element method (FEM). It analyzed the electrode-brain biomechanical effects during the electrode's insertion process and under steady-state acceleration with the electrode inside the brain. Furthermore, the study examined the impact of factors including implantation depth (ranging from 5 to 5000 μm), cortical thickness (0.5 mm, 2.5 mm, and 4.5 mm), step displacement (from 1 to 5 μm) during insertion, and acceleration direction (vertical and horizontal) on the electrode's biomechanical effects. The primary findings showed that the 98th percentile Von Mises Strain (ε98) and Von Mises Stress (σ98) in the region of interest (ROI) decreased dual-exponentially with increasing implantation depth and increased linearly with larger step displacements. Compared to the Von Mises strain threshold of 14.65%, as proposed by Sahoo et al., indicating a 50% risk of diffuse axonal injury (DAI), it was recommended to limit the initial step displacement during insertion to 1 μm, increasing to 5 μm at deeper locations (over 500 μm) to balance safety and efficiency. Additionally, it was found that cortical thickness had a negligible impact during insertion and while experiencing steady-state acceleration in vivo, with the three fitted curves almost coinciding when cortical thicknesses were 0.5 mm, 2.5 mm, and 4.5 mm. The flexible electrode exhibited excellent mechanical performance under steady-state acceleration in vivo, with ε98 being less than 0.3% and σ98 being less than 50 Pa, although it was more sensitive to horizontal acceleration. Thus, it could be concluded that during long-duration accelerations from transportation modes such as elevators and high-speed trains, the electrode's mechanical effects on brain tissue could be neglected, demonstrating long-term mechanical stability. This research was significant for guiding surgical insertion and clinical applications of flexible electrodes.},
}
@article {pmid40481044,
year = {2025},
author = {Yi, W and Chen, J and Wang, D and Hu, X and Xu, M and Li, F and Wu, S and Qian, J},
title = {A multi-modal dataset of electroencephalography and functional near-infrared spectroscopy recordings for motor imagery of multi-types of joints from unilateral upper limb.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {953},
pmid = {40481044},
issn = {2052-4463},
support = {12275295//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62006014//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62006014//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12275295//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Electroencephalography ; Spectroscopy, Near-Infrared ; Brain-Computer Interfaces ; *Upper Extremity/physiology ; *Imagination ; *Joints/physiology ; Movement ; },
abstract = {As one of the important brain-computer interface (BCI) paradigms, motor imagery (MI) enables the control of external devices via identification of motor intention by decoding the features of Electroencephalography (EEG). Movement imagination of multi-types of joints from the same limb allows the development of more accurate and intuitive BCI systems. In this work, we reported an open dataset including EEG and functional near-infrared spectroscopy (fNIRS) recordings from 18 subjects performing eight MI tasks from four types of joints including hand open/close, wrist flexion/extension, wrist abduction/adduction, elbow pronation/supination, elbow flexion/extension, shoulder pronation/supination, shoulder abduction/adduction, and shoulder flexion/extension, resulting in a total of 5760 trials. The validity of multi-modal data was verified both from the EEG/fNIRS activation patterns and the classification performance. It is expected that this dataset will facilitate the development and innovation of decoding algorithms for MI of multi-types of joints based on multi-modal EEG-fNIRS data.},
}
@article {pmid40480870,
year = {2025},
author = {Gunda, NK and Khalaf, MI and Bhatnagar, S and Quraishi, A and Gudala, L and Venkata, AKP and Alghayadh, FY and Alsubai, S and Bhatnagar, V},
title = {Retraction notice to "Lightweight attention mechanisms for EEG emotion recognition for brain computer interface".},
journal = {Journal of neuroscience methods},
volume = {422},
number = {},
pages = {110502},
doi = {10.1016/j.jneumeth.2025.110502},
pmid = {40480870},
issn = {1872-678X},
}
@article {pmid40480308,
year = {2025},
author = {Zhang, T and Jia, Y and Wang, N and Chai, X and He, Q and Cao, T and Mu, Q and Lan, Q and Zhao, J and Yang, Y},
title = {Recent advances in potential mechanisms of epidural spinal cord stimulation for movement disorders.},
journal = {Experimental neurology},
volume = {392},
number = {},
pages = {115330},
doi = {10.1016/j.expneurol.2025.115330},
pmid = {40480308},
issn = {1090-2430},
mesh = {Humans ; *Spinal Cord Stimulation/methods/trends ; *Movement Disorders/therapy/physiopathology ; Animals ; *Spinal Cord/physiology ; Neuronal Plasticity/physiology ; Epidural Space/physiology ; },
abstract = {BACKGROUND: Epidural spinal cord stimulation (eSCS) has emerged as a promising neuromodulation technique for treating movement disorders. The underlying mechanisms of eSCS are still being explored, making it a compelling area for further research.<br><br>OBJECTIVE: This review aims to provide a comprehensive analysis of the mechanisms of eSCS, its stimulation parameters, and its clinical applications in movement disorders. It seeks to synthesize the current understanding of how eSCS interacts with the central nervous system to enhance motor function and promotes neural plasticity for sustained recovery.<br><br>METHODS: A literature search was performed in databases such as Web of Science, Scopus, and PubMed to identify studies on eSCS for movement disorders.<br><br>RESULTS: The therapeutic effects of eSCS are achieved through both immediate facilitative actions and long-term neural reorganization. By activating sensory neurons in the dorsal root, facilitating proprioceptive input and modulating spinal interneurons, eSCS enhances motor neuron excitability. Additionally, eSCS influences corticospinal interactions, increasing cortical excitability and promoting corticospinal circuit remodeling. Neuroplasticity plays a critical role in the long-term efficacy of eSCS, with evidence suggesting that stimulation can enhance axonal sprouting, synaptic formation, and neurotrophic factor expression while reducing neuroinflammation. Its regulation of the sympathetic nervous system further enhances recovery by improving blood flow, muscle tone, and other physiological parameters.<br><br>CONCLUSIONS: Epidural spinal cord stimulation shows promise in enhancing motor function and promoting neuroplasticity, but further research is needed to optimize treatment protocols and establish long-term efficacy.},
}
@article {pmid40480249,
year = {2025},
author = {Pritchard, M and Campelo, F and Goldingay, H},
title = {An investigation of multimodal EMG-EEG fusion strategies for upper-limb gesture classification.},
journal = {Journal of neural engineering},
volume = {22},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ade1f9},
pmid = {40480249},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods/classification ; *Electromyography/methods/classification ; *Gestures ; *Upper Extremity/physiology ; Male ; Adult ; Female ; Algorithms ; Young Adult ; Machine Learning ; Brain-Computer Interfaces ; },
abstract = {Objective. Upper-limb gesture identification is an important problem in the advancement of robotic prostheses. Prevailing research into classifying electromyographic (EMG) muscular data or electroencephalographic (EEG) brain data for this purpose is often limited in methodological rigour, the extent to which generalisation is demonstrated, and the granularity of gestures classified. This work evaluates three architectures for multimodal fusion of EMG &amp; EEG data in gesture classification, including a novel Hierarchical strategy, in both subject-specific and subject-independent settings.Approach. We propose an unbiased methodology for designing classifiers centred on Automated Machine Learning through Combined Algorithm Selection &amp; Hyperparameter Optimisation (CASH); the first application of this technique to the biosignal domain. Using CASH, we introduce an end-to-end pipeline for data handling, algorithm development, modelling, and fair comparison, addressing established weaknesses among biosignal literature.Main results. EMG-EEG fusion is shown to provide significantly higher subject-independent accuracy in same-hand multi-gesture classification than an equivalent EMG classifier. Our CASH-based design methodology produces a more accurate subject-specific classifier design than recommended by literature. Our novel Hierarchical ensemble of classical models outperforms a domain-standard CNN architecture. We achieve a subject-independent EEG multiclass accuracy competitive with many subject-specific approaches used for similar, or more easily separable, problems.Significance. To our knowledge, this is the first work to establish a systematic framework for automatic, unbiased designing and testing of fusion architectures in the context of multimodal biosignal classification. We demonstrate a robust end-to-end modelling pipeline for biosignal classification problems which if adopted in future research can help address the risk of bias common in multimodal BCI studies , enabling more reliable and rigorous comparison of proposed classifiers than is usual in the domain. We apply the approach to a more complex task than typical of EMG-EEG fusion research, surpassing literature-recommended designs and verifying the efficacy of a novel Hierarchical fusion architecture.},
}
@article {pmid40479831,
year = {2025},
author = {Li, C and Di, G and Li, Q and Sun, L and Wang, W and Wang, Y and Jiang, X and Wu, J},
title = {Microsurgical anatomy of the fiber tracts and vascular structures lateral to the internal capsule.},
journal = {Journal of neurosurgery},
volume = {143},
number = {4},
pages = {1068-1076},
doi = {10.3171/2025.2.JNS243025},
pmid = {40479831},
issn = {1933-0693},
mesh = {Humans ; *Microsurgery/methods ; *Internal Capsule/anatomy &amp; histology/surgery/blood supply ; *White Matter/anatomy &amp; histology/surgery/blood supply ; *Cerebral Cortex/anatomy &amp; histology/blood supply/surgery ; Cadaver ; },
abstract = {OBJECTIVE: The cerebral structures lateral to the internal capsule are frequently involved in studies of nervous system functions and diseases. This study aimed to investigate the fiber tracts and vascular structures of the brain lateral to the internal capsule using cranial specimens and specimen perfusion techniques.<br><br>METHODS: Ten cranial specimens were perfused via arteries and veins using specimen perfusion techniques and then processed using the fiber dissection method. The authors studied the fiber tracts and vascular structures from the cerebral cortex to the internal capsule, moving from lateral to medial.<br><br>RESULTS: The topographical relationships between the fiber tracts, nuclei, and vascular structures were identified. This was achieved by examining structures from the gray matter cortex of the brain's lateral surface, including U fibers, long association fiber tracts, and the insular lobe, extending to the level of the internal capsule.<br><br>CONCLUSIONS: Understanding the anatomical structures of white matter fiber tracts and vascular structures from the brain's lateral surface to the level of the internal capsule aids in planning safe, effective, and minimally invasive surgical procedures. It also contributes to advancements in neuroscience research.},
}
@article {pmid40478867,
year = {2025},
author = {Jiang, M and Luo, Q and Wang, X and Tan, Y},
title = {The "Dogs' Catching Mice" conjecture in Chinese phonogram processing.},
journal = {PloS one},
volume = {20},
number = {6},
pages = {e0324848},
pmid = {40478867},
issn = {1932-6203},
mesh = {Adult ; Animals ; Female ; Humans ; Male ; Young Adult ; China ; *Language ; *Phonetics ; Semantics ; },
abstract = {In Chinese phonogram processing studies, it is not strange that phonetic radicals contribute phonologically to phonograms' phonological recognition. The present study, however, based on previous findings of phonetic radicals' proneness to semantic activation, as well as free-standing phonetic radicals' possession of triadic interconnections of orthography, phonology, and semantics at the lexical level, proposed that phonetic radicals may contribute semantically to the host phonograms' phonological recognition. We label this speculation as the "Dogs' Catching Mice" Conjecture. To examine this conjecture, three experiments were conducted. Experiment 1 was designed to confirm whether phonetic radicals, when embedded in phonograms, can contribute semantically to their host phonograms' phonological recognition. Experiment 2 was intended to show that the embedded phonetic radicals employed in Experiment 1 were truly semantically activated. Experiment 3, on top of the first two experiments, was devoted to demonstrating that the semantically activated phonetic radicals, when used as independent characters, can truly contribute semantically to their phonological recognition. Results from the three experiments combine to confirm the conjecture. The implication drawn is that phonetic radicals may have forged two paths in contributing to the host phonograms' phonological recognition: one is the regular "Cats' Catching Mice" Path, the other is the novel "Dogs' Catching Mice" Path.},
}
@article {pmid40478707,
year = {2025},
author = {Li, H and Zhang, H and Chen, Y},
title = {Dual-TSST: A Dual-Branch Temporal-Spectral-Spatial Transformer Model for EEG Decoding.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {9},
pages = {6524-6537},
doi = {10.1109/JBHI.2025.3577611},
pmid = {40478707},
issn = {2168-2208},
mesh = {*Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; *Neural Networks, Computer ; Brain-Computer Interfaces ; Algorithms ; },
abstract = {The decoding of electroencephalography (EEG) signals allows access to user intentions conveniently, which plays an important role in the fields of human-machine interaction. To effectively extract sufficient characteristics of the multichannel EEG, a novel decoding architecture network with a dual-branch temporal-spectral-spatial transformer (Dual-TSST) is proposed in this study. Specifically, by utilizing convolutional neural networks (CNNs) on different branches, the proposed processing network first extracts the temporal-spatial features of the original EEG and the temporal-spectral-spatial features of time-frequency domain data converted by wavelet transformation, respectively. These perceived features are then integrated by a feature fusion block, serving as the input of the transformer to capture the global long-range dependencies entailed in the non-stationary EEG, and being classified via the global average pooling and multi-layer perceptron blocks. To evaluate the efficacy of the proposed approach, the competitive experiments are conducted on three publicly available datasets of BCI IV 2a, BCI IV 2b, and SEED, with the head-to-head comparison of more than ten other state-of-the-art methods. As a result, our proposed Dual-TSST performs superiorly in various tasks, which achieves the promising EEG classification performance of average accuracy of 82.79% in BCI IV 2a, 89.38% in BCI IV 2b, and 96.65% in SEED, respectively. Extensive ablation experiments conducted between the Dual-TSST and comparative baseline model also reveal the enhanced decoding performance with each module of our proposed method. This study provides a new approach to high-performance EEG decoding and has great potential for future CNN-Transformer based applications.},
}
@article {pmid40476694,
year = {2025},
author = {Nazareth, G},
title = {Speaking from the heart: a story about innovation, resilience, and infinite possibilities with AAC.},
journal = {Augmentative and alternative communication (Baltimore, Md. : 1985)},
volume = {41},
number = {3},
pages = {248-249},
doi = {10.1080/07434618.2025.2508491},
pmid = {40476694},
issn = {1477-3848},
mesh = {Humans ; Artificial Intelligence ; Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; *Communication Disorders/rehabilitation ; *Resilience, Psychological ; },
abstract = {Communication is the cornerstone of human connection, impacting everything from our personal relationships to our professional success. This concept became heartbreakingly real for me when I was diagnosed with motor neuron disease at the age of 25. The rapid decline of my speech left me feeling all alone and isolated. After experimenting with AAC options, I yearned for a system that was lightweight, portable and stylish. This sparked my entrepreneurial spirit, leading me to assemble components catering to my diverse interests and professional pursuits. Over the years, I have built multiple AAC systems using different hardware platforms. Currently, I am focused on integrating emotional expression and faster communication speeds into AAC technology. Artificial intelligence, multi-modal inputs and non-invasive brain-computer interfaces hold immense potential for people who use AAC. Building my communication tools has revealed profound truths about living life to the fullest, accepting complete responsibility for our lives and embracing the good, the bad and the ugly. Through innovation and resilience, I have discovered infinite possibilities and I continue to use AAC to work miracles in my own life.},
}
@article {pmid40475558,
year = {2025},
author = {Sicorello, M and Gianaros, PJ and Wright, AGC and Bogdan, P and Kraynak, TE and Manuck, SB and Schmahl, C and Wager, TD},
title = {The functional neurobiology of negative affective traits across regions, networks, signatures, and a machine learning multiverse.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {40475558},
issn = {2692-8205},
support = {P01 HL040962/HL/NHLBI NIH HHS/United States ; },
abstract = {Understanding the neural basis of negative affective traits like neuroticism remains a critical challenge across psychology, neuroscience, and psychiatry. Here, we investigate which level of brain organization-regions, networks, or validated whole-brain machine-learning signatures-best explains negative affective traits in a community sample of 458 adults performing the two most widely used affective fMRI tasks, viewing emotional faces and scenes. Neuroticism could not be predicted from brain activity, with Bayesian evidence against all theory-guided neural measures. However, preregistered whole-brain models successfully decoded vulnerability to stress, a lower-level facet of neuroticism, with results replicating in a hold-out sample. The neural stress vulnerability pattern demonstrated good psychometric properties and indicated that negative affective traits are best represented by distributed whole-brain patterns related to domain-general stimulation rather than localized activity. Together with results from a comprehensive multiverse analysis across 14 traits and 1,176 models-available for exploration in an online app-the findings speak against simplistic neurobiological theories of negative affective traits, highlight a striking gap between predicting individual differences (r<.35) and within-person emotional states (r=.88), and underscore the importance of aligning psychological constructs with neural measures at the appropriate level of granularity.},
}
@article {pmid40472937,
year = {2025},
author = {Qian, MB and Huang, JL and Wang, L and Zhou, CH and Zhu, TJ and Zhu, HH and He, YT and Zhou, XN and Lai, YS and Li, SZ},
title = {Clonorchiasis in China: Geospatial modeling of the population infected and at risk, based on national surveillance.},
journal = {The Journal of infection},
volume = {91},
number = {1},
pages = {106528},
doi = {10.1016/j.jinf.2025.106528},
pmid = {40472937},
issn = {1532-2742},
mesh = {Humans ; China/epidemiology ; *Clonorchiasis/epidemiology ; Male ; Female ; Middle Aged ; Prevalence ; Adult ; Adolescent ; Child ; Aged ; Young Adult ; Bayes Theorem ; Child, Preschool ; Clonorchis sinensis ; Infant ; Animals ; Risk Factors ; Aged, 80 and over ; Infant, Newborn ; Epidemiological Monitoring ; Spatial Analysis ; },
abstract = {OBJECTIVES: Clonorchiasis is highly endemic in China. The unavailability of fine-scale distribution of population with infection and at risk hinders the control.<br><br>METHODS: This study established Bayesian geostatistical models to estimate age- and gender-specific prevalence of Clonorchis sinensis infection at high spatial resolution (5 &#xd7; 5&#xa0;km[2]), based on the surveillance data in China between 2016 and 2021, together with socioeconomic, environmental and behavioral determinants. The population at risk and under infection, as well as chemotherapy need were then estimated.<br><br>RESULTS: In 2020, population-weighted prevalence of 0.67% (95% Bayesian credible interval (BCI): 0.58%-0.77%) was estimated for C. sinensis infection in China, corresponding to 9.46 million (95% BCI: 8.22 million-10.88 million) persons under infection. High prevalence was demonstrated in southern areas, including Guangxi (8.92%, 95% BCI: 7.10%-10.81%) and Guangdong (2.99%, 95% BCI: 2.43%-3.74%). A conservative estimation of 99.13 million (95% BCI: 88.61 million-114.40 million) people were at risk of infection, of which 51.69 million (95% BCI: 45.48 million-57.84 million) need chemotherapy.<br><br>CONCLUSIONS: Clonorchiasis is an important public health problem in China, especially in southern areas, due to the huge population at risk and large number of people under infection. Implementation of chemotherapy is urged to control the morbidity.},
}
@article {pmid40472336,
year = {2025},
author = {Ranieri, A and Pichiorri, F and Colamarino, E and Cincotti, F and Mattia, D and Toppi, J},
title = {SPectral graph theory And Random walK (SPARK) toolbox for static and dynamic characterization of (di)graphs: A tutorial.},
journal = {PloS one},
volume = {20},
number = {6},
pages = {e0319031},
pmid = {40472336},
issn = {1932-6203},
mesh = {Humans ; Algorithms ; Electroencephalography ; Stroke/physiopathology ; *Software ; Brain/physiopathology/physiology ; },
abstract = {Spectral graph theory and its applications constitute an important forward step in modern network theory. Its increasing consensus over the last decades fostered the development of innovative tools, allowing network theory to model a variety of different scenarios while answering questions of increasing complexity. Nevertheless, a comprehensive understanding of spectral graph theory's principles requires a solid technical background which, in many cases, prevents its diffusion through the scientific community. To overcome such an issue, we developed and released an open-source MATLAB toolbox - SPectral graph theory And Random walK (SPARK) toolbox - that combines spectral graph theory and random walk concepts to provide a both static and dynamic characterization of digraphs. Once described the theoretical principles grounding the toolbox, we presented SPARK structure and the list of available indices and measures. SPARK was then tested in a variety of scenarios including: two-toy examples on synthetic networks, an example using public datasets in which SPARK was used as an unsupervised binary classifier and a real data scenario relying on functional brain networks extracted from the EEG data recorded from two stroke patients in resting state condition. Results from both synthetic and real data showed that indices extracted using SPARK toolbox allow to correctly characterize the topology of a bi-compartmental network. Furthermore, they could also be used to find the "optimal" vertex set partition (i.e., the one that minimizes the number of between-cluster links) for the underlying network and compare it to a given a priori partition. Finally, the application to real EEG-based networks provides a practical case study where the SPARK toolbox was used to describe networks' alterations in stroke patients and put them in relation to their motor impairment.},
}
@article {pmid40471721,
year = {2025},
author = {Li, J and Fu, B and Li, F and Gu, W and Ji, Y and Li, Y and Liu, T and Shi, G},
title = {Applying SSVEP BCI on Dynamic Background.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {2225-2237},
doi = {10.1109/TNSRE.2025.3576984},
pmid = {40471721},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; Electroencephalography/methods ; Male ; Adult ; Algorithms ; Female ; Neural Networks, Computer ; Young Adult ; Photic Stimulation/methods ; Color ; },
abstract = {Brain-computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) have attracted much attention due to their high efficiency and accuracy. The SSVEP paradigm and decoding methods have been extensively studied and achieved remarkable results. This study proposed two modulation methods for the SSVEP paradigm, namely color inversion modulation and brightness compression modulation. Color inversion modulation adjusts the stimulus to adapt to the changing background, while brightness compression modulation ensures high contrast by reducing the background brightness. Furthermore, we proposed Multi-scale Temporal-Spatial Global average pooling Neural Network (MTSGNN), an end-to-end network for decoding SSVEP signals evoked by the post-modulation paradigm. MTSGNN is built with efficient convolutional structures and uses global average pooling to achieve classification, which effectively reduces the risk of model overfitting on small EEG datasets and improves classification performance. We conduct experiments to evaluate the performance of the proposed modulation and decoding methods. Compared with color inversion modulation and no modulation, the brightness compression modulation method achieved the best performance. In addition, MTSGNN outperforms the best competitive decoding method by 11.98%, 3.9% and 5.15% under color inversion modulation, brightness compression modulation and no modulation, respectively. The experimental results demonstrate the effectiveness of the proposed modulation methods and the robustness of the proposed decoding method. This study significantly improves the performance of SSVEP in dynamic backgrounds and effectively expands the practical application scenarios of BCI.},
}
@article {pmid40471491,
year = {2025},
author = {Liu, X and Jia, Z and Xun, M and Wan, X and Lu, H and Zhou, Y},
title = {MSFHNet: a hybrid deep learning network for multi-scale spatiotemporal feature extraction of spatial cognitive EEG signals in BCI-VR systems.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {},
number = {},
pages = {},
pmid = {40471491},
issn = {1741-0444},
support = {62276022//National Natural Science Foundation of China/ ; 62206014//National Natural Science Foundation of China/ ; },
abstract = {The integration of brain-computer interface (BCI) and virtual reality (VR) systems offers transformative potential for spatial cognition training and assessment. By leveraging artificial intelligence (AI) to analyze electroencephalogram (EEG) data, brain activity patterns during spatial tasks can be decoded with high precision. In this context, a hybrid neural network named MSFHNet is proposed, optimized for extracting spatiotemporal features from spatial cognitive EEG signals. The model employs a hierarchical architecture where its temporal module uses multi-scale dilated convolutions to capture dynamic EEG variations, while its spatial module integrates channel-spatial attention mechanisms to model inter-channel dependencies and spatial distributions. Cross-stacked modules further refine discriminative features through deep-level fusion. Evaluations demonstrate the superiority of MSFHNet in the beta2 frequency band, achieving 98.58% classification accuracy and outperforming existing models. This innovation enhances EEG signal representation, advancing AI-powered BCI-VR systems for robust spatial cognitive training.},
}
@article {pmid40470749,
year = {2025},
author = {Li, W and Gao, C and Li, Z and Diao, Y and Li, J and Zhou, J and Zhou, J and Peng, Y and Chen, G and Wu, X and Wu, K},
title = {BrainFusion: a Low-Code, Reproducible, and Deployable Software Framework for Multimodal Brain‒Computer Interface and Brain‒Body Interaction Research.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {12},
number = {32},
pages = {e17408},
pmid = {40470749},
issn = {2198-3844},
support = {2023YFC2414500//National Key Research and Development Program of China/ ; 2023YFC2414504//National Key Research and Development Program of China/ ; 81971585//Natural Science Foundation of China/ ; 72174082//Natural Science Foundation of China/ ; 82271953//Natural Science Foundation of China/ ; 82301688//Natural Science Foundation of China/ ; 2021B1515020064//Guangdong Basic and Applied Basic Research Foundation Outstanding Youth Project/ ; 2023B0303020001//Key Research and Development Program of Guangdong/ ; 2023B0303010003//Key Research and Development Program of Guangdong/ ; 2022A1515140142//Basic and Applied Basic Research Foundation of Guangdong Province/ ; 2024A1515013058//Natural Science Foundation of Guangdong Province/ ; 202206060005//Science and Technology Program of Guangzhou/ ; 202206080005//Science and Technology Program of Guangzhou/ ; 202206010077//Science and Technology Program of Guangzhou/ ; 202206010034//Science and Technology Program of Guangzhou/ ; 202201010093//Science and Technology Program of Guangzhou/ ; 2023A03J0856//Science and Technology Program of Guangzhou/ ; 2023A03J0839//Science and Technology Program of Guangzhou/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography/methods ; *Software ; Spectroscopy, Near-Infrared/methods ; Electrocardiography/methods ; *Brain/physiology ; Reproducibility of Results ; Electromyography/methods ; },
abstract = {This study presents BrainFusion, a unified software framework designed to improve reproducibility and support translational applications in multimodal brain-computer interface (BCI) and brain-body interaction research. While electroencephalography (EEG) -based BCIs have advanced considerably, integrating multimodal physiological signals remains hindered by analytical complexity, limited standardization, and challenges in real-world deployment. BrainFusion addresses these gaps through standardized data structures, automated preprocessing pipelines, cross-modal feature engineering, and integrated machine learning modules. Its application generator further enables streamlined deployment of workflows as standalone executables. Demonstrated in two case studies, BrainFusion achieves 95.5% accuracy in within-subject EEG-functional near-infrared spectroscopy (fNIRS) motor imagery classification using ensemble modeling and 80.2% accuracy in EEG-electrocardiography (ECG) sleep staging using deep learning, with the latter successfully deployed as an executable tool. Supporting EEG, fNIRS, electromyography (EMG) , and ECG, BrainFusion provides a low-code, visually guided environment, facilitating accessibility and bridging the gap between multimodal research and application in real world.},
}
@article {pmid40469097,
year = {2025},
author = {Wang, Z and Wang, Y},
title = {Multi-branch GAT-GRU-transformer for explainable EEG-based finger motor imagery classification.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1599960},
pmid = {40469097},
issn = {1662-5161},
abstract = {Electroencephalography (EEG) provides a non-invasive and real-time approach to decoding motor imagery (MI) tasks, such as finger movements, offering significant potential for brain-computer interface (BCI) applications. However, due to the complex, noisy, and non-stationary nature of EEG signals, traditional classification methods-such as Common Spatial Pattern (CSP) and Power Spectral Density (PSD)-struggle to extract meaningful, multidimensional features. While deep learning models like CNNs and RNNs have shown promise, they often focus on single-dimensional aspects and lack interpretability, limiting their neuroscientific relevance. This study proposes a novel multi-branch deep learning framework, termed Multi-Branch GAT-GRU-Transformer, to enhance EEG-based MI classification. The model consists of parallel branches to extract spatial, temporal, and frequency features: a Graph Attention Network (GAT) models spatial relationships among EEG channels, a hybrid Gated Recurrent Unit (GRU) and Transformer module captures temporal dependencies, and one-dimensional CNNs extract frequency-specific information. Feature fusion is employed to integrate these heterogeneous representations. To improve interpretability, the model incorporates SHAP (SHapley Additive exPlanations) and Phase Locking Value (PLV) analyses. Notably, PLV is also used to construct the GAT adjacency matrix, embedding biologically-informed spatial priors into the learning process. The proposed model was evaluated on the Kaya dataset, achieving a five-class MI classification accuracy of 55.76%. Ablation studies confirmed the effectiveness of each architectural component. Furthermore, SHAP and PLV analyses identified C3 and C4 as critical EEG channels and highlighted the Beta frequency band as highly relevant, aligning with known motor-related neural activity. The Multi-Branch GAT-GRU-Transformer effectively addresses key challenges in EEG-based MI classification by integrating domain-relevant spatial, temporal, and frequency features, while enhancing model interpretability through biologically grounded mechanisms. This work not only improves classification performance but also provides a transparent framework for neuroscientific investigation, with broad implications for BCI development and cognitive neuroscience research.},
}
@article {pmid40469096,
year = {2025},
author = {Li, P and Yu, D and Cheng, L and Wang, K},
title = {Influence of attentional state on EEG-based motor imagery of lower limb.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1545492},
pmid = {40469096},
issn = {1662-5161},
abstract = {INTRODUCTION: Motor imagery (MI) has emerged as a promising technique for enhancing motor skill acquisition and facilitating neural adaptation training. Attention plays a key role in regulating the neural mechanisms underlying MI. This study aims to investigate how attentional states modulate EEG-based lower-limb motor imagery performance by influencing event-related desynchronization (ERD) and the alpha modulation index (AMI) and to develop a real-time attention monitoring method based on the theta/beta ratio (TBR).<br><br>METHODS: Fourteen healthy right-handed subjects (aged 21-23) performed right-leg MI tasks, while their attentional states were modulated via a key-press paradigm. EEG signals were recorded using a 32-channel system and preprocessed with independent component analysis (ICA) to remove artifacts. Attentional states were quantified using both the traditional offline AMI and the real-time TBR index, with time-frequency analysis applied to assess ERD and its relationship with attention.<br><br>RESULTS: The results indicated a significant increase in ERD during high attentional states compared to low attentional states, with AMI values showing a strong positive correlation with ERD (r&#x202f;=&#x202f;0.9641, p&#x202f;<&#x202f;0.01). Cluster-based permutation testing confirmed that this &#x3b1;-band ERD difference was significant (corrected p&#x202f;<&#x202f;0.05). Moreover, the TBR index proved to be an effective real-time metric, decreasing significantly under focused attention. Offline paired t-tests showed a significant TBR reduction [t (13)&#x202f;=&#x202f;5.12, p&#x202f;=&#x202f;2.4&#x202f;&#xd7;&#x202f;10[-5]], and online analyses further validated second-by-second discrimination (Bonferroni-corrected p&#x202f;<&#x202f;0.01). These findings confirm the feasibility and efficacy of TBR for real-time attention monitoring and suggest that enhanced attentional focus during lower-limb MI can improve signal quality and overall performance.<br><br>CONCLUSION: This study reveals that attentional states significantly influence the neural efficiency of lower-limb motor imagery by modulating ERD/AMI and demonstrates that the TBR can serve as a real-time indicator of attention, providing a novel tool for optimizing attentional processes in motor skill training.},
}
@article {pmid40468342,
year = {2025},
author = {Wang, M and Zhou, H and Zhang, X and Chen, Q and Tong, Q and Han, Q and Zhao, X and Wang, D and Lai, J and He, H and Zhang, S and Hu, S},
title = {Alleviating cognitive impairments in bipolar disorder with a novel DTI-guided multimodal neurostimulation protocol: a double-blind randomized controlled trial.},
journal = {BMC medicine},
volume = {23},
number = {1},
pages = {334},
pmid = {40468342},
issn = {1741-7015},
support = {52407261, 82201675//National Natural Science Foundation of China/ ; 52407261, 82201675//National Natural Science Foundation of China/ ; 2023YFC2506200//National Key Research and Development Program of China/ ; No. JNL-2023001B//Research Project of Jinan Microecological Biomedicine Shandong Laboratory/ ; 2021R52016//Leading Talent of Scientific and Technological Innovation-"Ten Thousand Talents Program" of Zhejiang Province/ ; 2020R01001//Innovation team for precision diagnosis and treatment of major brain diseases/ ; 2022KTZ004//Chinese Medical Education Association/ ; 226-2022-00193, 226-2022-00002, 2023ZFJH01-01, 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; },
mesh = {Humans ; Double-Blind Method ; Female ; Male ; *Bipolar Disorder/therapy/complications/psychology ; *Diffusion Tensor Imaging/methods ; Adult ; *Transcranial Magnetic Stimulation/methods ; *Cognitive Dysfunction/therapy/etiology/diagnostic imaging ; Middle Aged ; *Transcranial Direct Current Stimulation/methods ; Treatment Outcome ; },
abstract = {BACKGROUND: Traditional neuromodulation strategies show promise in enhancing cognitive abilities in bipolar disorder (BD) but remain suboptimal. This study introduces a novel multimodal neurostimulation (MNS) protocol to improve therapeutic outcomes.<br><br>METHODS: The novel MNS protocol used individualized diffusion tensor imaging (DTI) data to identify fiber tracts between the dorsolateral prefrontal cortex and dorsal&#xa0;anterior cingulate cortex. The highest structural connectivity point is selected as the individualized stimulation site, which is then targeted using a combination of optimized transcranial alternating current stimulation (tACS) and robot-assisted navigated repetitive transcranial magnetic stimulation (rTMS). A double-blind randomized controlled trial was conducted to investigate the clinical efficacy of this innovative neuromodulation approach on cognitive abilities in stable-phase BD patients. One hundred BD patients were randomly assigned to four groups: group A (active tACS-active rTMS (MNS protocol)), group B (sham tACS-active rTMS), group C (active tACS-sham rTMS), and group D (sham tACS-sham rTMS). Participants underwent 15 sessions over 3&#xa0;weeks. Cognitive assessments (THINC integrated tool) were conducted at baseline (week 0) and post-treatment (week 3).<br><br>RESULTS: Sixty-six participants completed all 15 sessions. Group A (MNS protocol) showed superior improvements in Spotter CRT, TMT, and DSST scores compared to other groups at week 3. Only group A exhibited significant activation in the left frontal region post-MNS intervention. The novel MNS protocol was well tolerated, with no significant side effects observed.<br><br>CONCLUSIONS: The study indicates that DTI-guided multimodal neurostimulation mode significantly improves cognitive impairments and is safe for stable-phase BD patients.<br><br>TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT05964777.},
}
@article {pmid40467567,
year = {2025},
author = {Pang, J and Xu, J and Chen, L and Teng, H and Su, C and Zhang, Z and Gao, L and Zhang, R and Liu, G and Chen, Y and He, J and Pang, Y and Li, H},
title = {Family history, inflammation, and cerebellum in major depression: a combined VBM and dynamic functional connectivity study.},
journal = {Translational psychiatry},
volume = {15},
number = {1},
pages = {188},
pmid = {40467567},
issn = {2158-3188},
support = {222102310205//Science and Technology Department of Henan Province (Henan Provincial Department of Science and Technology)/ ; 62103377//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Depressive Disorder, Major/physiopathology/diagnostic imaging/pathology/genetics/blood ; Female ; Male ; Adult ; *Cerebellum/diagnostic imaging/physiopathology/pathology ; Magnetic Resonance Imaging ; *Inflammation/blood ; Interleukin-6/blood ; Gray Matter/diagnostic imaging/pathology ; C-Reactive Protein/metabolism/analysis ; Middle Aged ; Prefrontal Cortex/diagnostic imaging/physiopathology ; Case-Control Studies ; Young Adult ; },
abstract = {A family history (FH) of depression significantly influences the progress of major depressive disorder (MDD). However, the underlying neural mechanism of FH remains unclear. This study examined the association between brain structural and connectivity alterations, inflammation, and FH in MDD. A total of 134 MDD patients with (FH group, n = 43) and without (NFH group, n = 91) first-degree FH and 96 demographic-matched healthy controls (HCs) were recruited. Voxel-based morphometry (VBM) and sliding-window dynamic functional connectivity (dFC) analyses were performed, and inflammatory biomarkers (C-reactive protein (CRP) and interleukin-6 (IL-6)) were detected. Compared with HCs, FH and NFH groups showed decreased gray matter volume (GMV) in the left cerebellum posterior lobe and increased dFC between this region and the left inferior parietal lobule. The FH group showed increased dFC between the cerebellum region and medial prefrontal cortex (mPFC) compared to NFH and HCs. The combination of these brain measurements further differentiated between FH and NFH. Moreover, the GMV of the cerebellum was positively correlated with CRP in the NFH group, while the dFC between the cerebellum and mPFC was positively correlated with IL-6 in the FH group. The present findings indicate that cerebellar structure and dynamic function vary according to FH of MDD and are related to inflammatory factors, potentially offering novel insights into the underlying pathogenic mechanisms of MDD.},
}
@article {pmid40465456,
year = {2025},
author = {Li, C and Hasegawa, I and Tanigawa, H},
title = {Protocol for assisting frequency band definition and decoding neural dynamics using hierarchical clustering and multivariate pattern analysis.},
journal = {STAR protocols},
volume = {6},
number = {2},
pages = {103870},
pmid = {40465456},
issn = {2666-1667},
mesh = {Animals ; *Electrocorticography/methods ; Multivariate Analysis ; Cluster Analysis ; *Signal Processing, Computer-Assisted ; Macaca ; Brain/physiology ; },
abstract = {Traditional fixed frequency band divisions often limit neural data analysis accuracy. Here, we present a protocol for assisting frequency band definition for multichannel neural data using macaque electrocorticography (ECoG) data. We describe steps for performing time-frequency analysis on preprocessed signals and applying hierarchical clustering to frequency power profiles to identify data-informed groupings. We then detail procedures for defining frequency bands guided by these clusters and using multivariate pattern analysis (MVPA) on the derived bands for functional validation via time-series decoding. For complete details on the use and execution of this protocol, please refer to Tanigawa et al.[1].},
}
@article {pmid40463690,
year = {2025},
author = {Rabiee, A and Ghafoori, S and Cetera, A and Shahriari, Y and Abiri, R},
title = {Wavelet Analysis of Noninvasive EEG Signals Discriminates Complex and Natural Grasp Types.},
journal = {ArXiv},
volume = {},
number = {},
pages = {},
pmid = {40463690},
issn = {2331-8422},
support = {P20 GM103430/GM/NIGMS NIH HHS/United States ; },
abstract = {This research aims to decode hand grasps from Electroencephalograms (EEGs) for dexterous neuroprosthetic development and Brain-Computer Interface (BCI) applications, especially for patients with motor disorders. Particularly, it focuses on distinguishing two complex natural power and precision grasps in addition to a neutral condition as a no-movement condition using a new EEG-based BCI platform and wavelet signal processing. Wavelet analysis involved generating time-frequency and topographic maps from wavelet power coefficients. Then, by using machine learning techniques with novel wavelet features, we achieved high average accuracies: 85.16% for multiclass, 95.37% for No-Movement vs Power, 95.40% for No-Movement vs Precision, and 88.07% for Power vs Precision, demonstrating the effectiveness of these features in EEG-based grasp differentiation. In contrast to previous studies, a critical part of our study was permutation feature importance analysis, which highlighted key features for grasp classification. It revealed that the most crucial brain activities during grasping occur in the motor cortex, within the alpha and beta frequency bands. These insights demonstrate the potential of wavelet features in real-time neuroprosthetic technology and BCI applications.},
}
@article {pmid40462746,
year = {2025},
author = {Song, J and Chai, X and Zhang, X and Lv, Z and Wan, F and Yang, Y and Shan, X and Liu, J},
title = {HEGNet: EEG and EMG fusion decoding method in motor imagery and actual movement.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-14},
doi = {10.1080/10255842.2025.2512877},
pmid = {40462746},
issn = {1476-8259},
abstract = {The widespread adoption od brain-computer interface (BCI) has been hindered by the limited classification accuracy of electroencephalography (EEG) signals alone. This study proposes a novel BCI model, HEGNet, that addresses this challenge by fusing EEG and electromyography (EMG) signals. HEGNet incorporates an EMG feature extraction component to mitigate the inherent instability and low signal-to-noise ratio limitations of relying solely on EEG data. Additionally, HEGNet employs a feature fusion module to dynamically adjust the focus on EEG and EMG features, thereby enhancing its overall robustness. These findings suggest that EMG information can serve as a valuable supplement to EEG data.},
}
@article {pmid40461535,
year = {2025},
author = {He, X and Chen, J and Zhong, Y and Cen, P and Shen, L and Huang, F and Wang, J and Jin, C and Zhou, R and Zhang, X and Wang, A and Fan, J and Wu, S and Tu, M and Qin, X and Luo, X and Zhou, Y and Peng, J and Zhou, Y and Civelek, AC and Tian, M and Zhang, H},
title = {Forebrain neural progenitors effectively integrate into host brain circuits and improve neural function after ischemic stroke.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {5132},
pmid = {40461535},
issn = {2041-1723},
support = {82030049, 32027802//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82102095//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82302262//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82302267//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82394433//National Natural Science Foundation of China (National Science Foundation of China)/ ; LY23H180005//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
mesh = {Animals ; *Neural Stem Cells/transplantation/metabolism/cytology ; Rats ; Humans ; Forkhead Transcription Factors/metabolism/genetics ; *Prosencephalon/cytology ; Nerve Tissue Proteins/metabolism/genetics ; Neurons/metabolism/cytology ; *Ischemic Stroke/therapy/physiopathology/diagnostic imaging ; Induced Pluripotent Stem Cells/cytology/transplantation/metabolism ; Cell Differentiation ; Male ; Stem Cell Transplantation/methods ; Recovery of Function ; Rats, Sprague-Dawley ; Neurogenesis ; Disease Models, Animal ; *Stroke/therapy ; Positron-Emission Tomography ; Synapses ; },
abstract = {Human cortical neural progenitor cell transplantation holds significant potential in cortical stroke treatment by replacing lost cortical neurons and repairing damaged brain circuits. However, commonly utilized human cortical neural progenitors are limited in yield a substantial proportion of diverse cortical neurons and require an extended period to achieve functional maturation and synaptic integration, thereby potentially diminishing the optimal therapeutic benefits of cell transplantation for cortical stroke. Here, we generated forkhead box G1 (FOXG1)-positive forebrain progenitors from human inducible pluripotent stem cells, which can differentiate into diverse and balanced cortical neurons including upper- and deep-layer excitatory and inhibitory neurons, achieving early functional maturation simultaneously in vitro. Furthermore, these FOXG1 forebrain progenitor cells demonstrate robust cortical neuronal differentiation, rapid functional maturation and efficient synaptic integration after transplantation into the sensory cortex of stroke-injured adult rats. Notably, we have successfully utilized the non-invasive [18]F-SynVesT-1 PET imaging technique to assess alterations in synapse count before and after transplantation therapy of FOXG1 progenitors in vivo. Moreover, the transplanted FOXG1 progenitors improve sensory and motor function recovery following stroke. These findings provide systematic and compelling evidence for the suitability of these FOXG1 progenitors for neuronal replacement in ischemic cortical stroke.},
}
@article {pmid40460359,
year = {2025},
author = {Chen, Z and Zhang, Y and Ding, J and Li, Z and Tian, Y and Zeng, M and Wu, X and Su, B and Jiang, J and Wu, C and Wei, D and Sun, J and Lim, CT and Fan, H},
title = {Hydrogel-Based Multifunctional Deep Brain Probe for Neural Sensing, Manipulation, and Therapy.},
journal = {ACS nano},
volume = {19},
number = {23},
pages = {21600-21613},
doi = {10.1021/acsnano.5c03865},
pmid = {40460359},
issn = {1936-086X},
mesh = {Animals ; *Hydrogels/chemistry/pharmacology ; Rats ; Rats, Sprague-Dawley ; *Neurons/drug effects ; Optogenetics ; *Brain ; Photochemotherapy ; Hippocampus ; Male ; },
abstract = {Implantable deep brain probes (DBPs) constitute a vital component of brain-machine interfaces, facilitating direct interaction between neural tissues and the external environment. Most multifunctional DBPs used for neural system sensing and modulation are currently fabricated through thermal tapering of polymeric materials. However, this approach faces a fundamental challenge in selecting materials that simultaneously accommodate the thermal stretching process and yet match the modulus of brain tissues. Here, we introduce a kind of multifunctional hydrogel-based fiber (HybF) designed for neural sensing, on-demand deep brain manipulation, and photodynamic therapy, and was achieved by integrating ion chelation/dechelation effects with templating methods throughout the entire wet-spinning process. With a low bending stiffness of approximately 0.3 N/m and a high conductivity of about 97 S/m at 1 kHz, HybF facilitates a high-quality signal recording (SNR ∼10) while minimizing immune rejection. It also effectively mediates deep brain optogenetic stimulation, successfully manipulating the behavior of hippocampal neurons in hSyn-ChrimsonR-tdTomato SD rats. Importantly, by leveraging HybF, this study explores the use of a spatiotemporally controllable photodynamic strategy in antiepilepsy, in which the high-amplitude abnormal electrical discharges were instantaneously eliminated without affecting normal cognitive/memory abilities. The above innovative approach established a distinct paradigm for deep brain manipulation and degenerative disease treatment, providing interesting insights into brain circuits and bioelectronic devices.},
}
@article {pmid40459463,
year = {2025},
author = {Savitz, BL and Dean, YE and Popa, NK and Cornely, RM and Byers, V and Gutama, BW and Abbott, EN and Torres-Guzman, R and Alter, N and Stehr, JD and Hill, JB and Elmaraghi, S},
title = {Targeted Muscle Reinnervation and Regenerative Peripheral Nerve Interface for Myoelectric Prosthesis Control: The State of Evidence.},
journal = {Annals of plastic surgery},
volume = {94},
number = {6S Suppl 4},
pages = {S572-S576},
doi = {10.1097/SAP.0000000000004273},
pmid = {40459463},
issn = {1536-3708},
mesh = {Humans ; *Artificial Limbs ; Electromyography ; *Nerve Regeneration/physiology ; *Muscle, Skeletal/innervation ; *Peripheral Nerves/physiology/surgery ; *Amputation Stumps/innervation ; Phantom Limb/prevention &amp; control ; *Amputation, Surgical/rehabilitation ; },
abstract = {Prosthetic rehabilitation after amputation poses significant challenges, often due to functional limitations, residual limb pain (RLP), and phantom limb pain (PLP). These issues not only affect physical health but also mental well-being and quality of life. In this review, we describe targeted muscle reinnervation (TMR) and regenerative peripheral nerve interface (RPNI) and explore their clinical role in the evolution of myoelectric prosthetic control as well as postamputation pain and neuroma management. Early myoelectric prostheses, which detected electrical potentials from muscles to control prosthetic limbs, faced limitations such as inconsistent signal acquisition and complex control modes. Novel microsurgical techniques at the turn of the century such as TMR and RPNI significantly advanced myoelectric prosthetic control. TMR involves reinnervating denervated muscles with residual nerves to create electromyography (EMG) potentials and prevent painful neuromas. Similarly, RPNI relies on small muscle grafts to amplify EMG signals and distinguish from stochastic noise for refined prosthetic control. Techniques like TMR and RPNI not only improved prosthetic function, but also significantly reduced postamputation pain, making them critical in improving amputees' quality of life. Modern myoelectric prostheses evolved with advancements in microprocessor and sensor technologies, enhancing their functionality and user experience. Today, researchers have developed more intuitive and reliable prosthetic control by utilizing pattern recognition software and machine learning algorithms that may supersede reliance on surgically amplifying EMG signals. Future developments in brain-computer interfaces and machine learning hold promise for even greater advancements in prosthetic technology, emphasizing the importance of continued innovation in this field.},
}
@article {pmid40459258,
year = {2025},
author = {Seibert, B and Caceres, CJ and Gay, LC and Shetty, N and Faccin, FC and Carnaccini, S and Walters, MS and Marr, LC and Lowen, AC and Rajao, DS and Perez, DR},
title = {Air-liquid interface model for influenza aerosol exposure in vitro.},
journal = {Journal of virology},
volume = {99},
number = {7},
pages = {e0061925},
pmid = {40459258},
issn = {1098-5514},
support = {75N93021C00014/AI/NIAID NIH HHS/United States ; 75N93021C00017/AI/NIAID NIH HHS/United States ; },
mesh = {Animals ; Humans ; Dogs ; Aerosols ; Madin Darby Canine Kidney Cells ; *Influenza, Human/virology/transmission ; Influenza A Virus, H3N2 Subtype/pathogenicity ; Influenza A Virus, H1N1 Subtype/pathogenicity/physiology ; Swine ; Epithelial Cells/virology ; *Influenza A virus/pathogenicity ; Influenza A Virus, H9N2 Subtype/pathogenicity/physiology ; Cell Line ; *Air Microbiology ; },
abstract = {UNLABELLED: Airborne transmission is an essential mode of infection and spread of influenza viruses among humans. However, most studies use liquid inoculum for virus infection. To better replicate natural airborne infections in vitro, we generated a calm-aerosol settling chamber system designed to examine the aerosol infectivity of influenza viruses in different cell types. Aerosol inoculation was characterized for multiple influenza A virus (FLUAV) subtypes, including pandemic 2009 H1N1, seasonal swine H3N2, and avian H9N2, using this exposure system. While each FLUAV strain displayed high infectivity within MDCK cells via liquid inoculation, differences in infectivity were observed during airborne inoculation. This was further observed in recently developed immortalized differentiated human airway epithelial cells (BCi-NS1.1) cultured in an air-liquid interface. The airborne infectious dose 50 for each virus was based on the exposure dose per well. Our findings indicate that this system has the potential to enhance our understanding of the factors influencing influenza transmission via the airborne route. This could be invaluable for conducting risk assessments, potentially reducing the reliance on extensive and costly in vivo animal studies.<br><br>IMPORTANCE: This study presents a significant advancement in influenza research by developing a novel in vitro system to assess aerosol infectivity, a crucial aspect of influenza transmission. The system's ability to differentiate between mammalian-adapted and avian-adapted influenza viruses based on their aerosol infectivity offers a valuable tool for pre-screening the pandemic potential of different strains. This could potentially streamline the risk assessment process and inform public health preparedness strategies. Moreover, the system's capacity to examine aerosol infectivity in human airway epithelial cells provides a more relevant model for studying virus-host interactions in natural airborne infections. Overall, this study provides an accessible platform for investigating aerosol infectivity, which could significantly contribute to our understanding of influenza transmission and pandemic preparedness.},
}
@article {pmid40459142,
year = {2025},
author = {Gao, J and Jiang, D and Wang, H and Wang, X},
title = {Opioid Enantiomers: Exploring the Complex Interplay of Stereochemistry, Pharmacodynamics, and Therapeutic Potential.},
journal = {Journal of medicinal chemistry},
volume = {68},
number = {11},
pages = {10540-10555},
doi = {10.1021/acs.jmedchem.5c00136},
pmid = {40459142},
issn = {1520-4804},
mesh = {Stereoisomerism ; Humans ; *Analgesics, Opioid/chemistry/pharmacology/therapeutic use ; Animals ; Structure-Activity Relationship ; Neuralgia/drug therapy ; Morphine/chemistry/pharmacology/therapeutic use ; Receptors, Opioid/metabolism ; },
abstract = {Opioids have been essential in pain management, particularly when other analgesics prove insufficient, but their use is complicated by risks of addiction, tolerance, and a range of adverse effects. These challenges are further exacerbated by the presence of opioid enantiomers that interact in a variety of ways with biological systems. This Perspective provides a comprehensive exploration of opioid enantiomers, focusing on their synthesis, pharmacodynamics, and potential therapeutic applications beyond traditional pain management. It highlights the complexity of synthesizing morphine enantiomers and additional challenges in producing the less-studied (+)-morphine. The Perspective also examines structure-activity relationship studies on (+)-opioid compounds, revealing their potential in modulating neuroinflammatory responses through non-opioid pathways and suggesting new therapeutic avenues for conditions like neuropathic pain and drug addiction. Furthermore, it discusses the differential safety profiles of opioid enantiomers, emphasizing the need for future research to advance precision medicine in opioid therapy, ultimately aiming to develop safer and more effective pain management strategies.},
}
@article {pmid40458259,
year = {2025},
author = {Zhang, W and Wang, T and Qin, C and Xu, B and Hu, H and Wang, T and Shen, Y},
title = {Vibration stimulation enhances robustness in teleoperation robot system with EEG and eye-tracking hybrid control.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {13},
number = {},
pages = {1591316},
pmid = {40458259},
issn = {2296-4185},
abstract = {INTRODUCTION: The application of non-invasive brain-computer interfaces (BCIs) in robotic control is limited by insufficient signal quality and decoding capabilities. Enhancing the robustness of BCIs without increasing the cognitive load remains a major challenge in brain-control technology.<br><br>METHODS: This study presents a teleoperation robotic system based on hybrid control of electroencephalography (EEG) and eye movement signals, and utilizes vibration stimulation to assist motor imagery (MI) training and enhance control signals. A control experiment involving eight subjects was conducted to validate the enhancement effect of this tactile stimulation technique.<br><br>RESULTS: Experimental results showed that during the MI training phase, the addition of vibration stimulation improved the brain region activation response speed in the tactile group, enhanced the activation of the contralateral motor areas during imagery of non-dominant hand movements, and demonstrated better separability (p = 0.017). In the robotic motion control phase, eye movement-guided vibration stimulation effectively improved the accuracy of online decoding of MI and enhanced the robustness of the control system and success rate of the grasping task.<br><br>DISCUSSION: The vibration stimulation technique proposed in this study can effectively improve the training efficiency and online decoding rate of MI, helping users enhance their control efficiency while focusing on control tasks. This tactile enhancement technology has potential applications in robot-assisted elderly care, rehabilitation training, and other robotic control scenarios.},
}
@article {pmid40457516,
year = {2025},
author = {Zhang, Y and Deng, X and Wang, S and Zhou, W and Wu, Z and Tang, X and Lee, HJ and Zhang, D},
title = {High-Specificity Spatiotemporal Cholesterol Detection by Quadrature Phase-Shifted Polarization Stimulated Raman Imaging.},
journal = {Angewandte Chemie (International ed. in English)},
volume = {64},
number = {32},
pages = {e202505038},
pmid = {40457516},
issn = {1521-3773},
support = {2024YFA1408900//National Key Research and Development Program of China/ ; 82372011//National Natural Science Foundation of China/ ; 12074339//National Natural Science Foundation of China/ ; 2025ZFJH01-01//Fundamental Research Funds for the Central Universities of China/ ; },
mesh = {*Cholesterol/analysis ; *Spectrum Analysis, Raman/methods ; Caenorhabditis elegans/chemistry/metabolism ; Animals ; },
abstract = {Visualizing cholesterol dynamics in living systems in situ remains a fundamental challenge in biomedical imaging. Although fluorescence microscopy requires bulky tags that perturb small molecule behavior, stimulated Raman scattering (SRS) microscopy enables label-free detection of CH-rich molecules. However, conventional SRS probes only polarized Raman components, limiting molecular specificity by seemingly overlapped peaks. Here, we extend SRS microscopy to achieve rapid, comprehensive detection of Raman tensor through quadrature phase-shifted polarization SRS (QP[2]-SRS) microscopy. This technique exploits the underlying molecular signatures by detecting both polarized and depolarized components of third-order nonlinear susceptibility χ[(3)] that originates from molecular structural features. We adopt a specialized optical delay line that rapidly alternates between parallel- and perpendicular-polarization states. QP[2]-SRS enables unprecedented distinction of similar molecular species in complex mixtures, demonstrating approximately 10× enhancement in chemical specificity and 5× improvement in analytical accuracy. This enhanced sensitivity enables real-time monitoring of lipid dynamics in living C. elegans and reveals component heterogeneity and morphological changes of LD in NAFLD livers. QP[2]-SRS creates new opportunities for investigating cholesterol-dependent biological processes in their native environment, with broad potential for chemical imaging with enhanced molecular specificity.},
}
@article {pmid40457127,
year = {2025},
author = {Xiao, Z and She, Q and Fang, F and Meng, M and Zhang, Y},
title = {Auxiliary classifier adversarial networks with maximum subdomain discrepancy for EEG-based emotion recognition.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {},
number = {},
pages = {},
pmid = {40457127},
issn = {1741-0444},
support = {62371172//National Natural Science Foundation of China/ ; 62271181//National Natural Science Foundation of China/ ; ZY2024025//Wenzhou Institute of Biomaterials and Engineering/ ; },
abstract = {Domain adaptation (DA) is considered to be effective solutions for unsupervised emotion recognition cross-session and cross-subject tasks based on electroencephalogram (EEG). However, the cross-domain shifts caused by individual differences and sessions differences seriously limit the generalization ability of existing models. Moreover, existing models often overlook the discrepancies among task-specific subdomains. In this study, we propose the auxiliary classifier adversarial networks (ACAN) to tackle these two key issues by aligning global domains and subdomains and maximizing subdomain discrepancies to enhance model effectiveness. Specifically, to address cross-domain discrepancies, we deploy a domain alignment module in the feature space to reduce inter-domain and inter-subdomain discrepancies. Meanwhile, to maximum subdomain discrepancies, the auxiliary adversarial classifier is introduced to generate distinguishable subdomain features by promoting adversarial learning between feature extractor and auxiliary classifier. System experiment results on three benchmark databases (SEED, SEED-IV, and DEAP) validate the model's effectiveness and superiority in cross-session and cross-subject experiments. The method proposed in this study outperforms other state-of-the-art DA, that effectively address domain shifts in multiple emotion recognition tasks, and promote the development of brain-computer interfaces.},
}
@article {pmid40456926,
year = {2025},
author = {Slutzky, MW and Vansteensel, MJ and Herff, C and Gaunt, RA},
title = {A brain-computer interface working definition.},
journal = {Nature biomedical engineering},
volume = {9},
number = {6},
pages = {792},
pmid = {40456926},
issn = {2157-846X},
}
@article {pmid40456256,
year = {2025},
author = {Tangermann, M and Chevallier, S and Dold, M and Guetschel, P and Kobler, R and Papadopoulo, T and Thielen, J},
title = {Learning from small datasets-review of workshop 6 of the 10th International BCI Meeting 2023.},
journal = {Journal of neural engineering},
volume = {22},
number = {3},
pages = {},
doi = {10.1088/1741-2552/addf80},
pmid = {40456256},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces/trends ; *Congresses as Topic/trends ; *Datasets as Topic ; Deep Learning ; *Machine Learning/trends ; },
abstract = {In a brain-computer interface (BCI), a primary objective is to reduce calibration time by recording as few as possible novel data points to (re-)train decoder models.Objective.Minimizing the calibration can be crucial for enhancing the usability of a BCI application with patients, increasing the acceptance by healthy users, facilitating a fast adaptation during non-stationary recordings, or transferring between sessions.Approach.At the 10th International BCI Meeting in 2023, our workshop addressed the latest proposed techniques to train classification or regression machine learning models with small datasets.Main results.We explored methodologies from both traditional machine learning and deep learning. In addition to talks and discussions, we discussed Python toolboxes for various presented methods and for the benchmarking of classification models.Significance.This review provides a comprehensive overview of the workshop's content and discusses the insights that were obtained.},
}
@article {pmid40456243,
year = {2025},
author = {Galiotta, V and Caracci, V and Toppi, J and Pichiorri, F and Colamarino, E and Cincotti, F and Mattia, D and Riccio, A},
title = {P300-based brain-computer interface for communication in assistive technology centres: influence of users' profile on BCI access.},
journal = {Journal of neural engineering},
volume = {22},
number = {3},
pages = {},
doi = {10.1088/1741-2552/addf7f},
pmid = {40456243},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces/psychology ; Male ; Female ; *Event-Related Potentials, P300/physiology ; Adult ; Middle Aged ; Electroencephalography/methods ; *Self-Help Devices ; Young Adult ; *Communication Devices for People with Disabilities ; },
abstract = {Objective. Assistive technology (AT) refers to any product that enables people to live independently and with dignity and to participate in activities of daily life. A brain-computer interface (BCI) is an AT that provides an alternative output, based on neurophysiological signals, to control an external device. The aim of the study is to screen patients accessing an AT-centre to investigate their eligibility for BCI access and the factors influencing the BCI control.Approach. Thirty-five users and 11 healthy subjects were included in the study. Participants were required to operate a P300-speller BCI. We evaluated the influence of clinical diagnosis, socio-demographic factors, level of dependence and disability of users, neuropsychological profile on BCI performance.Main results. The 7.1% of the users controlled the system with a mean accuracy of 93.6 ± 8.0%, while 8 users had an online accuracy below 70%. We found that the neuropsychological profile significantly affected online accuracy and ITR.Significance. The percentage of users who had an accuracy considered functional for communication is an encouraging data in terms of BCI effectiveness. The results regarding accuracy and factors influencing (and not influencing) it, are a contribution to the introduction of BCIs in the AT-centres, considering the BCI for communication both as an AT and as an additional input to provide multimodal access to AT.},
}
@article {pmid40456242,
year = {2025},
author = {Schmid, P and Sweeney-Reed, CM and Dürschmid, S and Reichert, C},
title = {Stimulus predictability has little impact on decoding of covert visual spatial attention.},
journal = {Journal of neural engineering},
volume = {22},
number = {3},
pages = {},
doi = {10.1088/1741-2552/addf81},
pmid = {40456242},
issn = {1741-2552},
mesh = {Humans ; *Attention/physiology ; Male ; Female ; *Brain-Computer Interfaces ; Adult ; Young Adult ; *Photic Stimulation/methods ; Electroencephalography/methods ; *Space Perception/physiology ; Eye Movements/physiology ; *Visual Perception/physiology ; },
abstract = {Objective. Brain-computer interfaces (BCI) that are aimed at supporting completely locked-in patients require independence from eye movements. Since visual spatial attention (VSA) shifts precede eye movements, they can be used for non-invasive, gaze-independent BCI control. In VSA tasks, stimuli locations and presentation onsets are commonly unpredictable. In this study we investigated the impact of predictability of potential target stimuli on the decoding accuracy of a BCI.Approach. We presented visual stimuli simultaneously to the left and right visual fields while participants shifted attention to a target stimulus. Using canonical correlation analysis, we decoded the direction of attention under different combinations of temporal and spatial predictability and compared the performance.Main results. We found no variation in decoding accuracies with spatial predictability. In addition, jittered timing did not alter the decoding accuracy compared to a constant stimulus onset asynchrony (SOA). Finally, reducing the SOA enabled faster BCI communication without accuracy loss. Using time-resolved decoding and interpretable models, we show that a later positive difference wave (between 300 ms and 350 ms post-stimulus onset) at occipital sites, rather than the N2pc, primarily contributes to decoding the target receiving attention.Significance. Our results demonstrate that stimulus predictability has no beneficial impact on decoding accuracy, but the paradigm proved robust to alterations in various stimulus parameters, making VSA a promising cognitive process for use in non-invasive, gaze-independent BCI-based communication.},
}
@article {pmid40456241,
year = {2025},
author = {Pang, Z and Zhang, R and Li, M and Li, Z and Cui, H and Chen, X},
title = {SSVEP-based BCI using ultra-low-frequency and high-frequency peripheral flickers.},
journal = {Journal of neural engineering},
volume = {22},
number = {3},
pages = {},
doi = {10.1088/1741-2552/addf82},
pmid = {40456241},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; Male ; Adult ; Female ; *Photic Stimulation/methods ; Young Adult ; *Electroencephalography/methods ; *Flicker Fusion/physiology ; },
abstract = {Objective. existing steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems predominantly employ a flicker frequency range of 8-20 Hz, which often induces visual fatigue in users, thereby compromising system performance. Considering that, this study introduces an innovative paradigm to enhance the user experience of SSVEP-based BCIs while maintaining the performance.Approach. the system encodes 12 targets by integrating ultra-low-frequency (2.00-3.32 Hz) and high-frequency (34.00-35.32 Hz) flickers with peripheral stimulation, and task-related component analysis is employed for SSVEP signal identification.Main results. the feasibility of the ultra-low-frequency peripheral stimulation paradigm was validated through online experiments, achieving an average accuracy of 89.03 ± 9.95% and an information transfer rate (ITR) of 66.74 ± 15.44 bits min[-1]. For the high-frequency peripheral stimulation paradigm, only the stimulation frequency changed, the paradigm, the signal processing algorithm and the step of frequency and phase were unchanged. The online experiments demonstrated an average accuracy of 93.55 ± 3.02% and an ITR of 51.88 ± 3.74 bits min[-1].Significance. the performance of the proposed system has reached a relatively high level among the current user-friendly SSVEP-based BCI systems. This study successfully innovates the paradigm for SSVEP-based BCIs, offering new insights into the development of user-friendly systems that balance high performance and user comfort.},
}
@article {pmid40456131,
year = {2025},
author = {Jing, S and Dai, Z and Liu, X and Yang, X and Cheng, J and Chen, T and Feng, Z and Liu, X and Dong, F and Xin, Y and Han, Z and Hu, H and Su, X and Wang, C},
title = {Correction: Effectiveness of Neurofeedback-Assisted and Conventional 6-Week Web-Based Mindfulness Interventions on Mental Health of Chinese Nursing Students: Randomized Controlled Trial.},
journal = {Journal of medical Internet research},
volume = {27},
number = {},
pages = {e78147},
doi = {10.2196/78147},
pmid = {40456131},
issn = {1438-8871},
abstract = {[This corrects the article DOI: 10.2196/71741.].},
}
@article {pmid40456094,
year = {2025},
author = {Arpaia, P and Esposito, A and Galdieri, F and Natalizio, A},
title = {Acquisition Delay of Wireless EEG Instruments in Time-Sensitive Applications.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {2151-2159},
doi = {10.1109/TNSRE.2025.3575695},
pmid = {40456094},
issn = {1558-0210},
mesh = {Humans ; *Electroencephalography/instrumentation/methods ; *Brain-Computer Interfaces ; *Wireless Technology/instrumentation ; Reproducibility of Results ; Event-Related Potentials, P300/physiology ; Time Factors ; Male ; Adult ; Equipment Design ; Algorithms ; Female ; Sensitivity and Specificity ; Young Adult ; },
abstract = {The aim of this study is to characterize the acquisition delay in wireless EEG instruments and evaluate its impact on the detection of time-locked neural phenomena, such as P300 and movement-related cortical potentials (MRCP). Accurate timing is critical for both research and clinical applications, especially for real-time brain-computer interfaces (BCI). A measurement setup was thus developed to assess acquisition delays and their uncertainty. Delays were measured at both the start and stop of a reference signal generation to investigate the consistency and reliability of the devices. BCI experiments were also performed to evaluate the impact of the measured delay on the detection of the time-locked phenomena. Statistical tests confirmed significant differences in delays across devices and configurations (e.g., from few tens to a hundred ms). These delays directly impacted P300 and MRCP detection, raising concerns about potential misclassification. Nonetheless, the correction of the measured acquisition delay proved beneficial, especially with regard to the P300 latency measured through low-cost instrumentation.},
}
@article {pmid40456080,
year = {2025},
author = {Jin, L and Song, Y and Zhao, H and Cao, J and Cheung, VCK and Liao, WH},
title = {Frequency-Aware Spatial-Temporal Attention Explainable Network for EEG Decoding.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {10},
pages = {7175-7185},
doi = {10.1109/JBHI.2025.3576088},
pmid = {40456080},
issn = {2168-2208},
mesh = {Humans ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Brain/physiology ; *Neural Networks, Computer ; Emotions/physiology ; Adult ; },
abstract = {Representation learning in spatial and temporal domains has shown significant potential in EEG decoding, advancing the field of brain-computer interfaces (BCIs). However, the critical role of frequency information, closely tied to the brain's neurological mechanism, has been largely neglected. In this paper, we propose FSTNet, which integrates frequency-spatial-temporal domains synergistically. The network allows broadband EEG signals as input and adaptively learns informative frequency signatures. A frequency-aware module emphasizes the importance of frequency information by selectively assigning weights to latent representations in the frequency space. Subsequently, self-attention captures spatial and temporal dependencies, extracting discriminative neural signatures for EEG decoding. We conducted extensive experiments on EEG datasets for motor imagery and emotion recognition, achieving superior results on SEED, PhysioNet, and OpenBMI datasets in both individual and cross-subject scenarios. Additionally, visualization reveals that the network captures informative frequency ranges and spatial patterns associated with specific tasks, aligning with known physiological mechanisms. This enhances the transparency of the network's learning process. In conclusion, our method exhibits the potential for decoding EEG and advancing the understanding of neurological processes in the brain.},
}
@article {pmid40455568,
year = {2025},
author = {Chen, Y and Fan, Z and Shi, N and Cheng, B and Huang, C and Liu, X and Gao, X and Liu, R},
title = {MXene-Based Microneedle Electrode for Brain-Computer Interface in Diverse Scenarios.},
journal = {ACS applied materials &amp; interfaces},
volume = {17},
number = {23},
pages = {33451-33464},
doi = {10.1021/acsami.5c03798},
pmid = {40455568},
issn = {1944-8252},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/instrumentation ; *Needles ; Humans ; Electrodes ; Evoked Potentials, Visual/physiology ; *Brain/physiology ; Male ; Nitrites ; Transition Elements ; },
abstract = {In this study, we introduce a brain-computer interface (BCI) framework incorporating MXene microneedle EEG electrodes, tailored for versatile deployment. The dry electrodes, configured as 1 mm[2] microneedles, underwent meticulous processing to establish a cohesive integration with the MXene conductive material. The microneedle architecture facilitates epidermal penetration, yielding low contact impedance, enabling the recording of spontaneous EEG and induced brain activity, and ensuring high precision in steady-state visual evoked potential (SSVEP) speller. Simultaneously, the microneedle electrode demonstrates commendable biological compatibility and superior nuclear magnetic resonance compatibility. It exhibits minimal artifact generation and manifests no heating-related adaptations in nuclear magnetic environments. The inherent microneedle electrode structure endows it with robust anti-interference capabilities. In vibrational environments, the SSVEP text input accuracy of the microneedle electrode remains comparable to that of gel electrodes, maintaining consistent impedance and delivering high-fidelity EEG acquisition during real-motion scenarios. The microneedle electrode devised in this study serves as a reliable signal acquisition tool, thereby advancing the development of BCI systems tailored for practical usage scenarios.},
}
@article {pmid40454682,
year = {2025},
author = {Pitt, KM and Mikuls, A and Ousley, CL and Boster, JB and Mahmoudi, M and McCarthy, J and Burnison, J},
title = {Considering whether brain-computer interfaces have prospective potential to support children who have the physical abilities for touch-based AAC access: a forum manuscript.},
journal = {Augmentative and alternative communication (Baltimore, Md. : 1985)},
volume = {},
number = {},
pages = {1-9},
pmid = {40454682},
issn = {1477-3848},
support = {R21 DC021496/DC/NIDCD NIH HHS/United States ; },
abstract = {Augmentative and alternative communication (AAC) may help address communication challenges for both those with developmental disabilities (DD) and intellectual and developmental disabilities (IDD). This forum manuscript explores the possibility of various future applications of brain-computer interface technology for AAC control (BCI-AAC) by children who have the physical abilities to utilize touch-based AAC access. Due to the early status of BCI-AAC research, the forum focuses on those with DD, though considerations for those with IDD are also discussed. Departing from the prevalent focus on severe speech and physical impairments (SSPI), this work shifts the spotlight toward children who may employ touch selection for AAC access, exploring the challenges and prospective possibilities within this population. Applying the International Classification of Functioning, Disability, and Health (ICF) framework, we explore potential BCI-AAC considerations across Activities and Participation, Functions and Structures, Environmental Factors, and Personal Factors. Proposing prospective BCI-AAC strategies, such as leveraging brain activity for functional intent recognition and emotion detection, this paper is designed to fuel discussion on tailoring AAC interventions to the diverse profiles of children with DD and IDD. Acknowledging the significant hurdles faced by BCI-AAC technology, we support the inclusive consideration of individuals in BCI-AAC development. While not seeking to lay a definitive roadmap, this forum aims to serve as a catalyst for future interdisciplinary dialogues, including those who use AAC and their support network, laying the groundwork for considering diverse BCI-AAC applications in children.},
}
@article {pmid40450930,
year = {2025},
author = {Fan, C and Song, Y and Mao, X},
title = {A classification method of motor imagery based on brain functional networks by fusing PLV and ECSP.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {190},
number = {},
pages = {107684},
doi = {10.1016/j.neunet.2025.107684},
pmid = {40450930},
issn = {1879-2782},
mesh = {Humans ; *Brain/physiology ; *Imagination/physiology ; *Neural Networks, Computer ; Brain-Computer Interfaces ; Deep Learning ; Hand Strength/physiology ; Hand/physiology ; },
abstract = {In order to enhance the decoding ability of brain states and evaluate the functional connection changes of relevant nodes in brain regions during motor imagery (MI), this paper proposes a brain functional network construction method which fuses edge features and node features. And we use deep learning methods to realize MI classification of left and right hand grasping tasks. Firstly, we use phase locking value (PLV) to extract edge features and input a weighted PLV to enhanced common space pattern (ECSP) to extract node features. Then, we fuse edge features and node features to construct a novel brain functional network. Finally, we construct an attention and multi-scale feature convolutional neural network (AMSF-CNN) to validate our method. The performance indicators of the brain functional network on the SHU_Dataset in the corresponding brain region will increase and be higher than those in the contralateral brain region when imagining one hand grasping. The average accuracy of our method reaches 79.65 %, which has a 25.85 % improvement compared to the accuracy provided by SHU_Dataset. By comparing with other methods on SHU_Dataset and BCI IV 2a Dataset, the average accuracies achieved by our method outperform other references. Therefore, our method provides theoretical support for exploring the working mechanism of the human brain during MI.},
}
@article {pmid40450863,
year = {2025},
author = {Niu, X and Zhang, J and Peng, Y and Kong, Y and Li, Y and Han, Y and Shi, L and Zheng, G},
title = {Extraction and analysis of abnormal EEG features in children with amblyopia.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {175},
number = {},
pages = {2110765},
doi = {10.1016/j.clinph.2025.2110765},
pmid = {40450863},
issn = {1872-8952},
mesh = {Humans ; *Amblyopia/physiopathology/diagnosis ; *Electroencephalography/methods ; Male ; Female ; Child ; *Evoked Potentials, Visual/physiology ; Child, Preschool ; *Brain/physiopathology ; },
abstract = {OBJECTIVE: Early and accurate diagnosis of amblyopia is crucial for the healthy development of children. Existing clinical diagnostic methods rely on patient cooperation, which can easily lead to misdiagnosis. The commonly used features derived from visual evoked potentials (VEP) only provided limited information for characterizing the whole brain, highlighting the need for integrating additional data sources, such as brain network metrics, to achieve a more comprehensive understanding.<br><br>METHODS: We extracted 488 features from 64-channel EEG data recorded from thirty amblyopic children. The features mainly derived from a weighted functional brain network based on coherence across different frequency bands. Feature selection and linear classification techniques were employed to assess their effectiveness in distinguishing amblyopia from normal children.<br><br>RESULTS: Abnormal EEG features were distributed not only in the occipital lobe but also in non-visual regions, with a higher prevalence in the alpha and beta bands. Their decoding performance surpassed traditional VEP features, and their combination achieved the highest accuracy (89.00%). Moreover, features beyond the occipital lobe exhibited limited decoding performance when considered individually, yet they still have an obvious contribution.<br><br>CONCLUSIONS: The study identified novel abnormal EEG features associated with amblyopia and demonstrated the potential of multi-channel EEG recordings to assist in the diagnosis of amblyopia.<br><br>SIGNIFICANCE: The study suggests amblyopia may impair more abilities beyond visual cognition and further provides objective biomarkers for diagnosing amblyopia, which is essential for effective treatment.},
}
@article {pmid40450806,
year = {2025},
author = {Wosnick, N and Dörfer, T and Turner, M and Nicholls, C and Richardson, M and Génier, I and Hauser-Davis, RA},
title = {Assessing the potential physiological impacts of urban development around lemon shark (Negaprion brevirostris) nurseries: effects on neonate and juvenile health.},
journal = {Marine pollution bulletin},
volume = {218},
number = {},
pages = {118233},
doi = {10.1016/j.marpolbul.2025.118233},
pmid = {40450806},
issn = {1879-3363},
mesh = {Animals ; *Sharks/physiology ; *Urbanization ; *Environmental Monitoring ; },
abstract = {Urbanization driven by population growth, development and tourism increasingly threatens even remote areas, potentially impacting biodiversity. This is particularly concerning given the ecological and economic importance of biodiversity, especially for island nations, where ecotourism plays a crucial role in the economy. This study examines urban-driven degradation effects on the nurseries of lemon sharks, a predator with strong site fidelity to its birthing and nursery areas. Six sites in South Eleuthera, The Bahamas, were assessed, analyzing proxies indicative of body condition (triglycerides/cholesterol ratio, body condition index) and energetic stress markers (glucose, β-hydroxybutyrate, triglycerides, total cholesterol) in neonates and juveniles compared across nurseries relative to degradation scores. While TAG/CHOL and BCI were not significantly different between nurseries, energetic markers were overall higher in more degraded nurseries. Moreover, total urban score was a significant predictor for glucose, β-hydroxybutyrate, and triglyceride ciruclating concentrations. These findings, coupled with prior studies carried out in Bimini, suggest that urban development around lemon shark nurseries in The Bahamas may negatively impact shark health. Cooperative monitoring, community initiatives for mangrove preservation, and stronger urbanization laws are required to mitigate these impacts. As urbanization and environmental degradation are universal threats to mangroves worldwide, this approach can be adapted to study urbanization impacts on other species in regions such as Southeast Asia, the Caribbean, the Pacific Islands, and the coasts of Africa and South America, which face similar urban encroachment, habitat degradation, and biodiversity loss challenges.},
}
@article {pmid40450046,
year = {2025},
author = {Maltezou-Papastylianou, C and Scherer, R and Paulmann, S},
title = {Human voices communicating trustworthy intent: A demographically diverse speech audio dataset.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {921},
pmid = {40450046},
issn = {2052-4463},
mesh = {Humans ; Adult ; Middle Aged ; Female ; Male ; *Voice ; Young Adult ; Adolescent ; *Speech ; *Trust ; },
abstract = {The multi-disciplinary field of voice perception and trustworthiness lacks accessible and diverse speech audio datasets representing diverse speaker demographics, including age, ethnicity, and sex. Existing datasets primarily feature white, younger adult speakers, limiting generalisability. This paper introduces a novel open-access speech audio dataset with 1,152 utterances from 96 untrained speakers, across white, black and south Asian backgrounds, divided into younger (N = 60, ages 18-45) and older (N = 36, ages 60+) adults. Each speaker recorded both, their natural speech patterns (i.e. "neutral" or no intent), and their attempt to convey their trustworthy intent as they perceive it during speech production. Our dataset is described and evaluated through classification methods between neutral and trustworthy speech. Specifically, extracted acoustic and voice quality features were analysed using linear and non-linear classification models, achieving accuracies of around 70%. This dataset aims to close a crucial gap in the existing literature and provide additional research opportunities that can contribute to the generalisability and applicability of future research results in this field.},
}
@article {pmid40448829,
year = {2025},
author = {Marques, LM and Strauss, A and Castellani, A and Barbosa, S and Simis, M and Fregni, F and Battistella, L},
title = {Dynamics of sensorimotor-related brain oscillations: EEG insights from healthy individuals in varied upper limb movement conditions.},
journal = {Experimental brain research},
volume = {243},
number = {7},
pages = {160},
pmid = {40448829},
issn = {1432-1106},
support = {#21/05897-5//Funda&#xe7;&#xe3;o de Amparo &#xe0; Pesquisa do Estado de S&#xe3;o Paulo/ ; #21/12790-2//Funda&#xe7;&#xe3;o de Amparo &#xe0; Pesquisa do Estado de S&#xe3;o Paulo/ ; #20/08512-4//Funda&#xe7;&#xe3;o de Amparo &#xe0; Pesquisa do Estado de S&#xe3;o Paulo/ ; #17/12943-8//Funda&#xe7;&#xe3;o de Amparo &#xe0; Pesquisa do Estado de S&#xe3;o Paulo/ ; #17/12943-8//Funda&#xe7;&#xe3;o de Amparo &#xe0; Pesquisa do Estado de S&#xe3;o Paulo/ ; },
mesh = {Humans ; Male ; Female ; Adult ; Young Adult ; Cross-Sectional Studies ; Electroencephalography ; *Sensorimotor Cortex/physiology ; *Upper Extremity/physiology ; *Brain Waves/physiology ; Movement/physiology ; *Motor Activity/physiology ; Brain-Computer Interfaces ; *Psychomotor Performance/physiology ; *Cortical Synchronization/physiology ; Imagination/physiology ; Middle Aged ; },
abstract = {Event-related desynchronization (ERD) and event-related synchronization (ERS) are critical neurophysiological phenomena associated with motor execution and inhibitory processes. Their utility spans neurophysiological biomarker research and Brain-Computer Interface (BCI) development. However, standardized frameworks for analyzing ERD and ERS oscillations across motor tasks and frequency ranges remain scarce. This study conducted a cross-sectional analysis of 76 healthy participants from the DEFINE cohort to explore ERD and ERS variations across four motor-related tasks (Motor Execution, Motor Imagery, Active Observation, and Passive Observation) and six frequency bands (Delta, Theta, Low Alpha, High Alpha, Low Beta, and High Beta) using C3 electrode activity. Repeated measures ANOVA revealed task-sensitive ERD and ERS power modulations, with oscillatory responses spanning the 1-30 Hz spectrum. Beta activity exhibited pronounced differences between tasks, highlighting its relevance in motor control, while other bands showed distinct task-dependent variations. These findings underscore the variability in ERD/ERS patterns across different tasks and frequency bands, reinforcing the importance of further research into standardized analytical frameworks. By refining ERD/ERS analyses, our study contributes to developing reference frameworks that can enhance clinical and Brain-Computer Interface (BCI) applications.},
}
@article {pmid40448287,
year = {2025},
author = {Cao, L and Zheng, Q and Wu, Y and Liu, H and Guo, M and Bai, Y and Ni, G},
title = {A dual-modality study on the neural features of cochlear implant simulated tone and consonant perception.},
journal = {Annals of the New York Academy of Sciences},
volume = {1549},
number = {1},
pages = {260-273},
doi = {10.1111/nyas.15380},
pmid = {40448287},
issn = {1749-6632},
support = {2023YFF1203500//National Key Research and Development Program of China/ ; 824B2056//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Cochlear Implants ; Female ; Male ; *Speech Perception/physiology ; Adult ; Spectroscopy, Near-Infrared/methods ; Electroencephalography ; Evoked Potentials, Auditory/physiology ; Phonetics ; Young Adult ; Acoustic Stimulation ; },
abstract = {Accurately perceiving lexical tones and consonants is critical for understanding speech in tonal languages. Cochlear implant (CI) users exhibit reduced phonetic perception due to spectral loss in CI encoding, yet the underlying neural mechanisms remain unclear. This study combined electroencephalography and functional near-infrared spectroscopy (fNIRS) to investigate the neural processing mechanisms of CI-simulated channelized speech in 26 normal-hearing adults during the processing of tones (T1-T4) and consonants ("ba," "da," "ga," "za"). Results showed that the N1 amplitude in auditory evoked potentials was significantly lower for channelized speech than a natural human voice (NH), particularly for T2 and T4 tones, indicating a weaker perception of channelized speech. Functional connectivity analysis revealed that an NH exhibited significantly higher synchrony in the δ and θ frequency bands than channelized speech, which was more pronounced in the right temporal lobe. This finding was also observed with "za" consonants. fNIRS results showed stronger right temporal lobe activation for channelized speech, suggesting that the brain requires greater auditory effort to process channelized speech. Combining both modalities revealed neural compensatory mechanisms underlying channelized speech-manifesting as "low-efficiency perception with high cognitive load." This study provides potential biomarkers for CI rehabilitation assessment and a foundation for future research.},
}
@article {pmid40446349,
year = {2025},
author = {Wang, S and Chen, G and Xie, J and Yang, R and Wang, X and Shan, Q and Liu, W and Zhao, D and Wang, F and Li, K and Zhang, Q and Guo, Y},
title = {Development and validation of a predictive model for poor initial outcomes after Gamma Knife radiosurgery for trigeminal neuralgia: a prognostic correlative analysis.},
journal = {Journal of neurosurgery},
volume = {143},
number = {4},
pages = {987-998},
doi = {10.3171/2025.2.JNS242655},
pmid = {40446349},
issn = {1933-0693},
mesh = {Humans ; *Trigeminal Neuralgia/surgery/diagnosis ; *Radiosurgery/methods ; Male ; Female ; Middle Aged ; Aged ; Treatment Outcome ; Prognosis ; Adult ; Pain Measurement ; Aged, 80 and over ; Retrospective Studies ; Recurrence ; Carbamazepine/therapeutic use ; },
abstract = {OBJECTIVE: The present study aimed to develop a reliable predictive model for identifying preoperative predictors of poor initial outcomes in patients with primary trigeminal neuralgia (PTN) treated with Gamma Knife radiosurgery (GKRS) and further elucidate the clinical significance of these predictors in initial outcomes and long-term pain recurrence.<br><br>METHODS: A total of 217 PTN patients were divided into a training set (n = 167) and a validation set (n = 50). The initial outcomes of GKRS treatment were assessed based on the Barrow Neurological Institute pain intensity scale. A predictive model was developed through multivariate regression and validated with repeated sampling. The differences in predictors of long-term pain recurrence were assessed using Kaplan-Meier analysis. The association between predictors was tested using chi-square tests, and subgroup analyses were performed to compare initial outcomes and long-term pain recurrence between two clinically significant correlates.<br><br>RESULTS: The training and validation sets showed areas under the curve of 0.85 and 0.88, respectively. Calibration curves and decision curve analysis indicated significant clinical benefits in both sets. Independent risk factors for poor initial outcomes included hyperglycemia, absence of neurovascular contact, carbamazepine insensitivity, and atypical pain (trigeminal neuralgia type 2 [TN2]). Carbamazepine insensitivity was moderately associated with TN2 and predicted long-term pain recurrence. Patients with both phenotypes had significantly worse initial outcomes compared with other subgroups (adjusted p = 0.0125).<br><br>CONCLUSIONS: Patients with both TN2 and carbamazepine insensitivity have the poorest initial treatment outcomes and face an increased risk of recurrence. Furthermore, this predictive model is highly accurate and useful, offering a comprehensive method of identifying PTN patients likely to experience poor initial outcomes based on clinical characteristics and imaging perspectives. The authors believe that the nomogram presented in this model enables clinicians to calculate multiple variables and predict the probability of adverse events.},
}
@article {pmid40446280,
year = {2025},
author = {Liang, X and Ding, Y and Yuan, Z and Han, Y and Zhou, Y and Jiang, J and Xie, Z and Fei, P and Sun, Y and Jia, P and Gu, G and Zhong, Z and Chen, F and Si, G and Gong, Z},
title = {Mechanics of Soft-Body Rolling Motion without External Torque.},
journal = {Physical review letters},
volume = {134},
number = {19},
pages = {198401},
doi = {10.1103/PhysRevLett.134.198401},
pmid = {40446280},
issn = {1079-7114},
mesh = {Animals ; Robotics ; Larva/physiology ; *Models, Biological ; Biomechanical Phenomena ; *Drosophila/physiology ; Muscle Contraction/physiology ; Torque ; },
abstract = {The Drosophila larva, a soft-body animal, can bend its body and roll efficiently to escape danger. However, contrary to common belief, this rolling motion is not driven by the imbalance of gravity and ground reaction forces. Through functional imaging and ablation experiments, we demonstrate that the sequential actuation of axial muscles within an appropriate range of angles is critical for generating rolling. We model the interplay between muscle contraction, hydrostatic skeleton deformation, and body-environment interactions, and systematically explain how sequential muscle actuation generates the rolling motion. Additionally, we construct a pneumatic soft robot to mimic the larval rolling strategy, successfully validating our model. This mechanics model of soft-body rolling motion not only advances the study of related neural circuits, but also holds potential for applications in soft robotics.},
}
@article {pmid40443843,
year = {2025},
author = {Yang, H and Li, T and Zhao, L and Wei, Y and Chen, X and Pan, J and Fu, Y},
title = {Guiding principles and considerations for designing a well-structured curriculum for the brain-computer interface major based on the multidisciplinary nature of brain-computer interface.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1554266},
pmid = {40443843},
issn = {1662-5161},
abstract = {Brain-computer interface (BCI) is a novel human-computer interaction technology, and its rapid development has led to a growing demand for skilled BCI professionals, culminating in the emergence of the BCI major. Despite its significance, there is limited literature addressing the curriculum design for this emerging major. This paper seeks to bridge this gap by proposing and discussing a curricular framework for the BCI major, based on the inherently multidisciplinary nature of BCI research and development. The paper begins by elucidating the primary factors behind the emergence of the BCI major, the increasing demand for both medical and non-medical applications of BCI, and the corresponding need for specialized talent. It then delves into the multidisciplinary nature of BCI research and offers principles for curriculum design to address this nature. Based on these principles, the paper provides detailed suggestions for structuring a BCI curriculum. Finally, it discusses the challenges confronting the development of the BCI major, including the lack of consensus and international collaboration in the construction of the BCI major, as well as the inadequacy or lack of teaching materials. Future work needs to improve the curriculum design of the BCI major from a competency-oriented perspective. It is expected that this paper will provide a reference for the curriculum design and construction of the BCI major.},
}
@article {pmid40442937,
year = {2025},
author = {Wang, F and Wang, L and Zhu, X and Lu, Y and Du, X},
title = {Neuron-Inspired Ferroelectric Bioelectronics for Adaptive Biointerfacing.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {37},
number = {35},
pages = {e2416698},
doi = {10.1002/adma.202416698},
pmid = {40442937},
issn = {1521-4095},
support = {B2302045//Shenzhen Medical Research Fund/ ; 52022102//National Natural Science Foundation of China/ ; 52261160380//National Natural Science Foundation of China/ ; 32471042//National Natural Science Foundation of China/ ; 32300845//National Natural Science Foundation of China/ ; 2017YFA0701303//National Key R&amp;D Program of China/ ; Y2023100//Youth Innovation Promotion Association of CAS/ ; RCJC20221008092729033//Fundamental Research Program of Shenzhen/ ; JCYJ20220818101800001//Fundamental Research Program of Shenzhen/ ; 2024A1515010645//Basic and Applied Basic Research Foundation of Guangdong Province/ ; },
mesh = {*Neurons ; *Biocompatible Materials ; Titanium ; Barium Compounds ; Indoles ; Polymers ; *Nanoparticles ; Vinyl Compounds ; Animals ; Mice ; Peripheral Nervous System/physiology ; Central Nervous System/physiology ; *Brain-Computer Interfaces ; *Vagus Nerve Stimulation ; Humans ; Mice, Inbred BALB C ; Male ; },
abstract = {Implantable bioelectronics, which are essential to neuroscience studies, neurological disorder treatment, and brain-machine interfaces, have become indispensable communication bridges between biological systems and the external world through sensing, monitoring, or manipulating bioelectrical signals. However, conventional implantable bioelectronic devices face key challenges in adaptive interfacing with neural tissues due to their lack of neuron-preferred properties and neuron-similar behaviors. Here, innovative neuron-inspired ferroelectric bioelectronics (FerroE) are reported that consists of biocompatible polydopamine-modified barium titanate nanoparticles, ferroelectric poly(vinylidene fluoride-co-trifluoroethylene) copolymer, and cellular-scale micropyramid array structures, imparting adaptive interfacing with neural systems. These FerroE not only achieve neuron-preferred flexible and topographical properties, but also offer neuron-similar behaviors including highly efficient and stable light-induced polarization change, superior capability of producing electric signals, and seamless integration and adaptive communication with neurons. Moreover, the FerroE allows for adaptive interfacing with both peripheral and central neural networks of mice, enabling regulation of their heart rate and motion behavior in a wireless, non-genetic, and non-contact manner. Notably, the FerroE demonstrates unprecedented structural and functional stability and negligible immune response even after 3 months of implantation in vivo. Such bioinspired FerroE are opening new opportunities for next-generation brain-machine interfaces, tissue engineering materials, and biomedical devices.},
}
@article {pmid40442546,
year = {2025},
author = {Wang, L and Li, T and Li, X and Liu, F and Feng, C},
title = {Mapping trait justice sensitivity in the Brain: Whole-brain resting-state functional connectivity as a predictor of other-oriented not self-oriented justice sensitivity.},
journal = {Cognitive, affective &amp; behavioral neuroscience},
volume = {25},
number = {6},
pages = {1834-1849},
pmid = {40442546},
issn = {1531-135X},
support = {2024B0303390003//Research Center for Brain Cognition and Human Development, Guangdong, China/ ; 32020103008//National Natural Science Foundation of China/ ; 32271126//National Natural Science Foundation of China/ ; 81922036//National Natural Science Foundation of China/ ; },
mesh = {Humans ; Male ; Magnetic Resonance Imaging ; Female ; Machine Learning ; Young Adult ; Adult ; *Brain/physiology/diagnostic imaging ; *Social Justice ; Brain Mapping/methods ; Neural Pathways/physiology ; *Connectome/methods ; },
abstract = {Justice sensitivity (JS) reflects personal concern and commitment to the principle of justice, showing considerable heterogeneity among the general population. Despite a growing interest in the behavioral characteristics of JS over the past decades, the neurobiological substrates underlying trait JS are not well comprehended. We addressed this issue by employing a machine learning approach to decode the trait JS, encompassing its various orientations, from whole-brain resting-state functional connectivity. We demonstrated that the machine-learning model could decode the individual trait of other-oriented JS but not self-oriented JS from resting-state functional connectivity across multiple neural systems, including functional connectivity between and within parietal lobe and motor cortex as well as their connectivity with other brain systems. Key nodes that contributed to the prediction model included the parietal, motor, temporal, and subcortical regions that have been linked to other-oriented JS. Additionally, the machine learning model can distinctly distinguish between the distinct roles associated with other-oriented JS, including observer, perpetrator, and beneficiary, with key brain regions in the predictive networks exhibiting both similarities and disparities. These findings remained robust using different validation procedures. Collectively, these results support the separation between other-oriented JS and self-oriented JS, while also highlighting the distinct intrinsic neural correlates among the three roles of other-oriented JS: observer, perpetrator, and beneficiary.},
}
@article {pmid40442206,
year = {2025},
author = {Akama, T and Zhang, Z and Li, P and Hongo, K and Minamikawa, S and Polouliakh, N},
title = {Predicting artificial neural network representations to learn recognition model for music identification from brain recordings.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {18869},
pmid = {40442206},
issn = {2045-2322},
mesh = {*Music ; Humans ; *Neural Networks, Computer ; Electroencephalography ; *Brain/physiology ; Male ; *Auditory Perception/physiology ; Female ; Adult ; Acoustic Stimulation ; Young Adult ; Brain-Computer Interfaces ; },
abstract = {Recent studies have demonstrated that the representations of artificial neural networks (ANNs) can exhibit notable similarities to cortical representations when subjected to identical auditory sensory inputs. In these studies, the ability to predict cortical representations is probed by regressing from ANN representations to cortical representations. Building upon this concept, our approach reverses the direction of prediction: we utilize ANN representations as a supervisory signal to train recognition models using noisy brain recordings obtained through non-invasive measurements. Specifically, we focus on constructing a recognition model for music identification, where electroencephalography (EEG) brain recordings collected during music listening serve as input. By training an EEG recognition model to predict ANN representations-representations associated with music identification-we observed a significant improvement in classification accuracy. This study introduces a novel approach to developing recognition models for brain recordings in response to external auditory stimuli. It holds promise for advancing brain-computer interfaces (BCI), neural decoding techniques, and our understanding of music cognition. Furthermore, it provides new insights into the relationship between auditory brain activity and ANN representations.},
}
@article {pmid40442062,
year = {2025},
author = {Shah, NP and Avansino, D and Kamdar, F and Nicolas, C and Kapitonava, A and Vargas-Irwin, C and Hochberg, LR and Pandarinath, C and Shenoy, KV and Willett, FR and Henderson, JM},
title = {Pseudo-linear summation explains neural geometry of multi-finger movements in human premotor cortex.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {5008},
pmid = {40442062},
issn = {2041-1723},
support = {R01 DC009899/DC/NIDCD NIH HHS/United States ; DP2 NS127291/NS/NINDS NIH HHS/United States ; UH2 NS095548/NS/NINDS NIH HHS/United States ; U01 NS098968/NS/NINDS NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; Milton Safenowtiz Postdoctoral Scholarship//Amyotrophic Lateral Sclerosis Association (ALS Association)/ ; R01 DC014034/DC/NIDCD NIH HHS/United States ; },
mesh = {Humans ; *Fingers/physiology ; *Motor Cortex/physiology/physiopathology ; Male ; Movement/physiology ; Adult ; Quadriplegia/physiopathology ; },
abstract = {How does the motor cortex combine simple movements (such as single finger flexion/extension) into complex movements (such as hand gestures, or playing the piano)? To address this question, motor cortical activity was recorded using intracortical multi-electrode arrays in two male people with tetraplegia as they attempted single, pairwise and higher-order finger movements. Neural activity for simultaneous movements was largely aligned with linear summation of corresponding single finger movement activities, with two violations. First, the neural activity exhibited normalization, preventing a large magnitude with an increasing number of moving fingers. Second, the neural tuning direction of weakly represented fingers changed significantly as a result of the movement of more strongly represented fingers. These deviations from linearity resulted in non-linear methods outperforming linear methods for neural decoding. Simultaneous finger movements are thus represented by the combination of individual finger movements by pseudo-linear summation.},
}
@article {pmid40441574,
year = {2025},
author = {Rahman, MH and Mondal, MIH},
title = {Investigation of neem-oil-loaded PVA/chitosan biocomposite film for hydrophobic dressing, rapid hemostasis and wound healing applications.},
journal = {International journal of biological macromolecules},
volume = {316},
number = {Pt 1},
pages = {144712},
doi = {10.1016/j.ijbiomac.2025.144712},
pmid = {40441574},
issn = {1879-0003},
mesh = {*Chitosan/chemistry ; *Wound Healing/drug effects ; *Bandages ; Hydrophobic and Hydrophilic Interactions ; Animals ; *Hemostasis/drug effects ; *Polyvinyl Alcohol/chemistry ; Anti-Bacterial Agents/pharmacology/chemistry ; *Biocompatible Materials/chemistry/pharmacology ; Hemostatics/pharmacology/chemistry ; Male ; Humans ; Blood Coagulation/drug effects ; },
abstract = {The present work aims to develop a hydrophobic dressing with a blood-repellent surface that achieves fast clotting without blood loss, having antibacterial properties, clot self-detachment, and superior wound healing activity. For these reasons, a novel approach was applied by producing a hydrophobic film made of PVA, chitosan, and neem seed oil (NSO). The film had the necessary hydrophobicity, mechanical strength, stability and was able to transmit water vapor to be suitable for the wound skin surface and demonstrated faster blood clotting (BCI = 91.44 % in 5 min and 85.22 % in 10 min). The proportion of red blood cells (2.78 %) and platelets (17.33 %) attached to the film proved its excellent hemostatic activity. It was anti-adhesive, created spontaneous clot detachment, and exhibited antibacterial properties at the wound site, as evidenced by in vivo testing. Moreover, in vivo testing and histopathological findings showed enhanced wound healing activity, greater re-epithelialization, and decreased granulation tissue. Additionally, the film's eco-friendliness was evaluated using a soil burial degradation test, and the results show that it deteriorated into the soil but did so slowly because of its hydrophobic property. Thus, PVA/CS/NSO composite film may be a green biomedical material for hemostasis and wound healing.},
}
@article {pmid40440260,
year = {2025},
author = {Tong, B and Li, G and Bu, X and Wang, Y and Yu, X},
title = {A deep learning-based algorithm for the detection of personal protective equipment.},
journal = {PloS one},
volume = {20},
number = {5},
pages = {e0322115},
pmid = {40440260},
issn = {1932-6203},
mesh = {*Personal Protective Equipment ; *Deep Learning ; Humans ; *Algorithms ; Neural Networks, Computer ; Construction Industry ; },
abstract = {Personal protective equipment (PPE) is critical for ensuring the safety of construction workers. However, site surveillance images from construction sites often feature multi-size and multi-scale targets, leading to low detection accuracy for PPE in existing models. To address this issue, this paper proposes an improved model based on YOLOv8n.By enriching feature diversity and enhancing the model's adaptability to geometric transformations, the detection accuracy is improved.A Multi-Scale Group Convolution Module (MSGP) was designed to extract multi-level features using different convolution kernels. A Multi-Scale Feature Diffusion Pyramid Network (MFDPN) was developed, which aggregates multi-scale features through the Multiscale Feature Focus (MFF) module and diffuses them across scales, providing each scale with detailed contextual information. A customized Task Alignment Module was introduced to integrate interactive features, optimizing both classification and localization tasks. The DCNV2(Deformable Convolutional Networks v2) module was incorporated to handle geometric scale transformations by generating spatial offsets and feature masks from interactive features, thereby improving localization accuracy and dynamically selecting weights to enhance classification precision.The improved model incorporates rich multi-level and multi-scale features, allowing it to better adapt to tasks involving geometric transformations and aligning with the image data distribution in construction scenarios. Additionally, structured pruning techniques were applied to the model at varying levels, further reducing computational and parameter loads. Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. The proposed MFD-YOLO(1.5) model achieves significant progress in detecting personal protective equipment on construction sites, with a substantial reduction in parameter count, making it suitable for deployment on resource-constrained edge devices.},
}
@article {pmid40438784,
year = {2025},
author = {Du, Y and Yang, X and Wang, M and Lv, Q and Zhou, H and Sang, G},
title = {Longitudinal changes in children with autism spectrum disorder receiving applied behavior analysis or early start denver model interventions over six months.},
journal = {Frontiers in pediatrics},
volume = {13},
number = {},
pages = {1546001},
pmid = {40438784},
issn = {2296-2360},
abstract = {BACKGROUND: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder characterized by social communication difficulties, restricted interests, repetitive behaviors, and sensory abnormalities. The rising prevalence of ASD presents a significant public health concern, with no pharmacological treatments available for its core symptoms. Therefore, early and effective behavioral interventions are crucial to improving developmental outcomes for children with ASD. Current interventions primarily focus on educational rehabilitation methods, including Applied behavior Analysis (ABA) and the Early Start Denver Model (ESDM).<br><br>OBJECTIVE: This study aims to examine the developmental changes in children with ASD following six months of ABA therapy or ESDM intervention.<br><br>METHODS: From December 2021 to December 2023, 30 children receiving ABA therapy at the Zhejiang Rehabilitation Medical Center (40&#x2005;min/session, 4 sessions/day, 5 days/week), while another 30 children undergoing ESDM training at Hangzhou Children's Hospital (2&#x2005;h of one-on-one sessions and 0.5&#x2005;h of group sessions/day, 5 days/week). Both groups participated in their respective interventions for six months. Pre- and post-treatment assessments were conducted using the Psycho-educational Profile-Third Edition (PEP-3).<br><br>RESULTS: Both groups showed significant improvements in PEP-3 scores post-treatment, including cognitive verbal/pre-verbal, expressive language, receptive language, social reciprocity, small muscles, imitation, emotional expression, and verbal and nonverbal behavioral characteristics.<br><br>CONCLUSION: Both ABA and ESDM interventions were associated with comprehensive improvements in children with ASD over a six-month period.},
}
@article {pmid40438090,
year = {2025},
author = {Ding, W and Liu, A and Chen, X and Xie, C and Wang, K and Chen, X},
title = {Reducing calibration efforts of SSVEP-BCIs by shallow fine-tuning-based transfer learning.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {81},
pmid = {40438090},
issn = {1871-4080},
abstract = {The utilization of transfer learning (TL), particularly through pre-training and fine-tuning, in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) has substantially reduced the calibration efforts. However, commonly employed fine-tuning approaches, including end-to-end fine-tuning and last-layer fine-tuning, require data from target subjects that encompass all categories (stimuli), resulting in a time-consuming data collection process, especially in systems with numerous categories. To address this challenge, this study introduces a straightforward yet effective ShallOw Fine-Tuning (SOFT) method to substantially reduce the number of calibration categories needed for model fine-tuning, thereby further mitigating the calibration efforts for target subjects. Specifically, SOFT involves freezing the parameters of the deeper layers while updating those of the shallow layers during fine-tuning. Freezing the parameters of deeper layers preserves the model's ability to recognize semantic and high-level features across all categories, as established during pre-training. Moreover, data from different categories exhibit similar individual-specific low-level features in SSVEP-BCIs. Consequently, updating the parameters of shallow layers-responsible for processing low-level features-with data solely from partial categories enables the fine-tuned model to efficiently capture the individual-related features shared by all categories. The effectiveness of SOFT is validated using two public datasets. Comparative analysis with commonly used end-to-end and last-layer fine-tuning methods reveals that SOFT achieves higher classification accuracy while requiring fewer calibration categories. The proposed SOFT method further decreases the calibration efforts for target subjects by reducing the calibration category requirements, thereby improving the feasibility of SSVEP-BCIs for real-world applications.},
}
@article {pmid40437332,
year = {2025},
author = {Cherukuri, SB and Ramakrishnan, S},
title = {Eye-blink artifact removal in single-channel electroencephalogram using K-means and Savitzky Golay-singular Spectrum Analysis hybrid technique.},
journal = {Physical and engineering sciences in medicine},
volume = {48},
number = {3},
pages = {1127-1136},
pmid = {40437332},
issn = {2662-4737},
mesh = {*Electroencephalography/methods ; *Blinking/physiology ; *Artifacts ; Humans ; *Signal Processing, Computer-Assisted ; Algorithms ; *Spectrum Analysis ; Signal-To-Noise Ratio ; },
abstract = {Electroencephalogram (EEG) acquisition systems are used to record the neural condition of humans for diagnosing various neural problems. The eye-blink or Electrooculogram (EOG) artifact caused by eye-lid movements, influences the EEG signal measurements and interferes with the diagnosis. The complete removal of eye-blink artifact while preserving the EEG content is a challenging task that needs highly efficient denoising methods, particularly from Single-Channel EEG which is widely used for Out-Of-Hospital (OOH) neurological patients and for Brain-Computer-Interface (BCI) applications. When compared to multi-channel EEG systems, Single-channel EEG system suffers certain difficulties such as lack of spatial information, redundancy, etc. This paper proposes an innovative hybrid method combining K-Means clustering and Savitzky Golay-Singular Spectrum Analysis (SG-SSA) methods for effective eye-blink artifact removal from single channel EEG. The eye-blink artifact is extracted and then subtracted from the noisy EEG signal, so that the EEG content available in the eye-blink periods are preserved. Through extensive experiments with synthetic as well as real time EEG, we show that our proposed method outperforms the other contemporary methods from literature. Our proposed hybrid approach achieves a significant reduction in Mean Absolute Error (MAE) and Relative Root Mean Square Error (RRMSE) than the Fourier-Bessel Series Expansion based Empirical Wavelet Transform (FBSE-EWT), SSA combined with independent component analysis (SSA-ICA) and Ensemble Empirical Mode Decomposition combined with ICA (EEMD-ICA), proposed in recent literature.},
}
@article {pmid40436265,
year = {2025},
author = {Chopra, M and Kumar, H},
title = {Navigating the complexities of spinal cord injury: an overview of pathology, treatment strategies and clinical trials.},
journal = {Drug discovery today},
volume = {30},
number = {6},
pages = {104387},
doi = {10.1016/j.drudis.2025.104387},
pmid = {40436265},
issn = {1878-5832},
mesh = {*Spinal Cord Injuries/therapy/pathology/physiopathology/drug therapy ; Humans ; Animals ; Clinical Trials as Topic ; Quality of Life ; },
abstract = {Spinal cord injury (SCI) is a debilitating neurological condition characterized by sensory and motor deficits. It significantly affects patient quality of life and poses a substantial socioeconomic burden. The complex and multifaceted pathophysiology of SCI complicates its effective treatment. Following the primary mechanical insult, a secondary injury cascade disrupts the microenvironment at the injury site, exacerbating the tissue damage. Despite extensive research, no fully effective treatment is currently available. This review explores current pharmacological and non-pharmacological treatment strategies at the preclinical and clinical stages, providing insights into promising interventions. The findings highlight potential avenues for future research aimed at improving SCI management.},
}
@article {pmid40434889,
year = {2025},
author = {Ruszala, BM and Schieber, MH},
title = {Injecting information in the cortical reach-to-grasp network is effective in ventral but not dorsal nodes.},
journal = {Cell reports},
volume = {44},
number = {5},
pages = {115664},
pmid = {40434889},
issn = {2211-1247},
support = {F31 NS129099/NS/NINDS NIH HHS/United States ; R01 NS107271/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Motor Cortex/physiology ; *Somatosensory Cortex/physiology ; Brain-Computer Interfaces ; *Hand Strength/physiology ; Macaca mulatta ; *Parietal Lobe/physiology ; Male ; Movement/physiology ; Electric Stimulation ; },
abstract = {Although control of movement involves many cortical association areas, bidirectional brain-machine interfaces (BMIs) typically decode movement intent from the motor cortex and deliver feedback information to the primary somatosensory cortex (S1). Compared to the S1, the parietal and premotor areas encode more complex information about object properties, hand pre-shaping, and reach trajectories. BMIs therefore might deliver richer information to those cortical association areas than to primary areas. Here, we investigated whether instructions for a center-out task could be delivered via intracortical microstimulation (ICMS) in the anterior intraparietal area (AIP), dorsal posterior parietal cortex (dPPC), or dorsal premotor cortex (PMd) as well as the ventral premotor cortex (PMv) and S1. Two monkeys successfully learned to use AIP, PMv, or S1-ICMS instructions, but neither learned to use dPPC- or PMd-ICMS instructions. The AIP, PMv, and S1 may thus be effective cortical territory for delivering information to the brain, whereas the dPPC or PMd may be comparatively ineffective.},
}
@article {pmid40434816,
year = {2025},
author = {Liu, L},
title = {Did you see it?.},
journal = {eLife},
volume = {14},
number = {},
pages = {},
pmid = {40434816},
issn = {2050-084X},
mesh = {Humans ; *Brain/physiology ; *Consciousness/physiology ; },
abstract = {Cautious reporting choices can artificially enhance how well analyses of brain activity reflect conscious and unconscious experiences, making distinguishing between the two more challenging.},
}
@article {pmid40434551,
year = {2025},
author = {Sokhadze, E},
title = {Neurofeedback and Brain-Computer Interface-Based Methods for Post-stroke Rehabilitation.},
journal = {Applied psychophysiology and biofeedback},
volume = {},
number = {},
pages = {},
pmid = {40434551},
issn = {1573-3270},
abstract = {Stroke has been identified as a major public health concern and one of the leading causes contributing to long-term neurological disability. People suffering from stroke often present with upper limb paralysis impacting their quality of life and ability to work. Motor impairments in the upper limb represent the most prevalent symptoms in stroke sufferers. There is a need to develop novel intervention strategies that can be used as stand-alone techniques or combined with current gold standard post-stroke rehabilitation procedures. There was reported evidence about the utility of rehabilitation protocols with motor imagery (MI) used either alone or in combination with physical therapy resulting in enhancement of post-stroke functional recovery of paralyzed limbs. Brain-Computer Interface (BCI) and EEG neurofeedback (NFB) training can be considered as novel technologies to be used in conjunction with MI and motor attempt (MA) to enable direct translation of EEG induced by imagery or attempted movement to arrange training that has potential to enhance functional motor recovery of upper limbs after stroke. There are reported several controlled trials and multiple cases series that have shown that stroke patients are able to learn modulation of their EEG sensorimotor rhythm in BCI mode to control external devices, including exoskeletons, prosthetics, and such interventions were shown promise in facilitation of recovery in stroke sufferers. A review of the literature suggests there has been significant progress in the development of new methods for post-stroke rehabilitation procedures. There are reviewed findings supportive of NFB and BCI methods as evidence-based treatment for post-stroke motor function recovery.},
}
@article {pmid40433677,
year = {2025},
author = {He, J and Zhou, G and Sun, B and Yan, L and Lang, X and Yang, Y and Hao, H},
title = {Graphene quantum dots induced performance enhancement in memristors.},
journal = {Nanoscale},
volume = {17},
number = {23},
pages = {14082-14102},
doi = {10.1039/d5nr00597c},
pmid = {40433677},
issn = {2040-3372},
abstract = {With the rapid development of information technology, the demand for miniaturization, integration, and intelligence of electronic devices is growing rapidly. As a key device in the non-von Neumann architecture, memristors can perform computations while storing data, enhancing computational efficiency and reducing power consumption. Memristors have become pivotal in driving the advancement of artificial intelligence (AI) and Internet of Things technologies. Combining the electronic properties of graphene with the size effects of quantum dots, graphene quantum dot (GQD)-based memristors exhibit potential applications in constructing brain-inspired neuromorphic computing systems and achieving AI hardware acceleration, making them a focal point of research interest. This review provides an overview of the preparation, mechanism, and application of GQD-based memristors. Initially, the structure, properties, and synthesis methods of GQDs are introduced in detail. Subsequently, the memristive mechanisms of GQD-based memristors are presented from three perspectives: the metal conductive filament mechanism, the electron trapping and detrapping mechanism, and the oxygen vacancy conductive filament mechanism. Furthermore, the different application scenarios of GQD-based memristors in both digital and analog types are summarized, encompassing information storage, brain-like artificial synapses, visual perception systems, and brain-machine interfaces. Finally, the challenges and future development prospects of GQD-based memristors are discussed.},
}
@article {pmid40431969,
year = {2025},
author = {You, Z and Guo, Y and Zhang, X and Zhao, Y},
title = {Virtual Electroencephalogram Acquisition: A Review on Electroencephalogram Generative Methods.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {10},
pages = {},
pmid = {40431969},
issn = {1424-8220},
mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; Algorithms ; Machine Learning ; Signal Processing, Computer-Assisted ; Neural Networks, Computer ; Brain/physiology ; },
abstract = {Driven by the remarkable capabilities of machine learning, brain-computer interfaces (BCIs) are carving out an ever-expanding range of applications across a multitude of diverse fields. Notably, electroencephalogram (EEG) signals have risen to prominence as the most prevalently utilized signals within BCIs, owing to their non-invasive essence, exceptional portability, cost-effectiveness, and high temporal resolution. However, despite the significant strides made, the paucity of EEG data has emerged as the main bottleneck, preventing generalization of decoding algorithms. Taking inspiration from the resounding success of generative models in computer vision and natural language processing arenas, the generation of synthetic EEG data from limited recorded samples has recently garnered burgeoning attention. This paper undertakes a comprehensive and thorough review of the techniques and methodologies underpinning the generative models of the general EEG, namely the variational autoencoder (VAE), the generative adversarial network (GAN), and the diffusion model. Special emphasis is placed on their practical utility in augmenting EEG data. The structural designs and performance metrics of the different generative approaches in various application domains have been meticulously dissected and discussed. A comparative analysis of the strengths and weaknesses of each existing model has been carried out, and prospective avenues for future enhancement and refinement have been put forward.},
}
@article {pmid40431893,
year = {2025},
author = {Sasatake, Y and Matsushita, K},
title = {EEG Baseline Analysis for Effective Extraction of P300 Event-Related Potentials for Welfare Interfaces.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {10},
pages = {},
pmid = {40431893},
issn = {1424-8220},
support = {JPMJSP2125//JST SPRING/ ; Not Applicable//THERS Make New Standards Program for the Next Generation Researchers/ ; },
mesh = {Humans ; *Event-Related Potentials, P300/physiology ; *Electroencephalography/methods ; Male ; Adult ; Female ; *Brain-Computer Interfaces ; Young Adult ; Photic Stimulation ; Signal Processing, Computer-Assisted ; },
abstract = {Enabling individuals with complete paralysis to operate devices voluntarily requires an effective interface; EEG-based P300 event-related potential (ERP) interfaces are considered a promising approach. P300 is an EEG peak generated in response to specific sensory stimuli recognized by an individual. Accurate detection of this peak necessitates a stable pre-stimulus baseline EEG signal, which serves as the reference for baseline correction. Previous studies have commonly employed either a single-time-point amplitude (e.g., at 100 ms before stimulus onset) or a time-range-averaged amplitude over a specified pre-stimulus period (e.g., 0-200 ms) as a baseline correction method, assuming these provide the most stable EEG reference. However, in assistive P300 interfaces, continuous visual stimuli at 400 ms intervals are typically used to efficiently evoke P300 peaks. Since stimuli are presented before the EEG stabilizes, it remains unclear whether conventional neuroscience baseline correction methods are suitable for such applications. To address this, the present study conducted a P300 induction experiment based on continuous 400 ms interval visual stimuli. Using EEG data recorded from 0 to 1000 ms before each visual stimulus (sampled at 1 ms intervals), we applied three baseline correction methods-single-time-point amplitude, time-range-averaged amplitude, and multi-time-point amplitude-to determine the most effective EEG reference and evaluate the impact on P300 detection performance. The results showed that baseline correction using an amplitude at a single point in time is unstable when the basic EEG rhythm and low-frequency noise remain, while time-range-averaged baseline correction using the 0-200 ms pre-stimulus period led to relatively effective P300 detection. However, it was also found that using only one value averaged over the amplitude from 0 to 200 ms did not result in an accurate EEG reference potential, resulting in an error. Finally, this study confirmed that the multi-time-point baseline correction method, through which the amplitude state from 0 to 200 ms before the visual stimulus is comprehensively evaluated, may be the most effective method for P300 determination.},
}
@article {pmid40431780,
year = {2025},
author = {Polo-Hortigüela, C and Ortiz, M and Soriano-Segura, P and Iáñez, E and Azorín, JM},
title = {Time-Frequency Analysis of Motor Imagery During Plantar and Dorsal Flexion Movements Using a Low-Cost Ankle Exoskeleton.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {10},
pages = {},
pmid = {40431780},
issn = {1424-8220},
support = {PID2021-124111OB-C31//MICIU /AEI/10.13039/501100011033 and by ERDF, EU/ ; PRE2022-103336//MICIU/AEI/10.13039 501100011033/ ; //Valencian Graduate School and Research Network of Artificial Intelligence (ValgrAI), Generalitat Valenciana and European Union/ ; //Project "Neurokit" funded by Centro Internacional para la Investigaci&#xf3;n del Envejecimiento de la Fundaci&#xf3;n de la Comunitat Valenciana (ICAR)/ ; 101118964//European Union's research and innovation programme under the Marie Sk&#x142;odowska-Curie/ ; },
mesh = {Humans ; Electroencephalography/methods ; Brain-Computer Interfaces ; Movement/physiology ; Male ; *Ankle/physiology ; *Exoskeleton Device ; Adult ; Biomechanical Phenomena ; Foot/physiology ; Female ; Wearable Electronic Devices ; Fourier Analysis ; },
abstract = {Sensor technology plays a fundamental role in neuro-motor rehabilitation by enabling precise movement analysis and control. This study explores the integration of brain-machine interfaces (BMIs) and wearable sensors to enhance motor recovery in individuals with neuro-motor impairments. Specifically, different time-frequency transforms are evaluated to analyze the correlation between electroencephalographic (EEG) activity and ankle position, measured by using inertial measurement units (IMUs). A low-cost ankle exoskeleton was designed to conduct the experimental trials. Six subjects performed plantar and dorsal flexion movements while the EEG and IMU signals were recorded. The correlation between brain activity and foot kinematics was analyzed using the Short-Time Fourier Transform (STFT), Stockwell (ST), Hilbert-Huang (HHT), and Chirplet (CT) methods. The 8-20 Hz frequency band exhibited the highest correlation values. For motor imagery classification, the STFT achieved the highest accuracy (92.9%) using an EEGNet-based classifier and a state-machine approach. This study presents a dual approach: the analysis of EEG-movement correlation in different cognitive states, and the systematic comparison of four time-frequency transforms for both correlation and classification performance. The results support the potential of combining EEG and IMU data for BMI applications and highlight the importance of cognitive state in motion analysis for accessible neurorehabilitation technologies.},
}
@article {pmid40428890,
year = {2025},
author = {Endzelytė, E and Petruševičienė, D and Kubilius, R and Mingaila, S and Rapolienė, J and Rimdeikienė, I},
title = {Integrating Brain-Computer Interface Systems into Occupational Therapy for Enhanced Independence of Stroke Patients: An Observational Study.},
journal = {Medicina (Kaunas, Lithuania)},
volume = {61},
number = {5},
pages = {},
pmid = {40428890},
issn = {1648-9144},
mesh = {Humans ; Male ; Female ; *Brain-Computer Interfaces/standards/trends ; *Occupational Therapy/methods/standards ; *Stroke Rehabilitation/methods/standards ; Middle Aged ; Aged ; Activities of Daily Living/psychology ; Upper Extremity/physiopathology ; Adult ; Stroke/complications ; },
abstract = {Background and Objectives: Brain-computer interface (BCI) technology is revolutionizing stroke rehabilitation by offering innovative neuroengineering solutions to address neurological deficits. By bypassing peripheral nerves and muscles, BCIs enable individuals with severe motor impairments to communicate their intentions directly through control signals derived from brain activity, opening new pathways for recovery and improving the quality of life. The aim of this study was to explore the beneficial effects of BCI system-based interventions on upper limb motor function and performance of activities of daily living (ADL) in stroke patients. We hypothesized that integrating BCI into occupational therapy would result in measurable improvements in hand strength, dexterity, independence in daily activities, and cognitive function compared to baseline. Materials and Methods: An observational study was conducted on 56 patients with subacute stroke. All patients received standard medical care and rehabilitation for 54 days, as part of the comprehensive treatment protocol. Patients underwent BCI training 2-3 times a week instead of some occupational therapy sessions, with each patient completing 15 sessions of BCI-based recoveriX treatment during rehabilitation. The occupational therapy program included bilateral exercises, grip-strengthening activities, fine motor/coordination tasks, tactile discrimination exercises, proprioceptive training, and mirror therapy to enhance motor recovery through visual feedback. Participants received ADL-related training aimed at improving their functional independence in everyday activities. Routine occupational therapy was provided five times a week for 50 min per session. Upper extremity function was evaluated using the Box and Block Test (BBT), Nine-Hole Peg Test (9HPT), and dynamometry to assess gross manual dexterity, fine motor skills, and grip strength. Independence in daily living was assessed using the Functional Independence Measure (FIM). Results: Statistically significant improvements were observed across all the outcome measures (p < 0.001). The strength of the stroke-affected hand improved from 5.0 kg to 6.7 kg, and that of the unaffected hand improved from 29.7 kg to 40.0 kg. Functional independence increased notably, with the FIM scores rising from 43.0 to 83.5. Cognitive function also improved, with MMSE scores increasing from 22.0 to 26.0. The effect sizes ranged from moderate to large, indicating clinically meaningful benefits. Conclusions: This study suggests that BCI-based occupational therapy interventions effectively improve upper extremity motor function and daily functions and have a positive impact on the cognition of patients with subacute stroke.},
}
@article {pmid40988938,
year = {2024},
author = {Oganesian, LL and Shanechi, MM},
title = {Brain-computer interfaces for neuropsychiatric disorders.},
journal = {Nature reviews bioengineering},
volume = {2},
number = {8},
pages = {653-670},
pmid = {40988938},
issn = {2731-6092},
support = {R01 MH123770/MH/NIMH NIH HHS/United States ; R61 MH135407/MH/NIMH NIH HHS/United States ; },
abstract = {Neuropsychiatric disorders such as major depression are a leading cause of disability worldwide with standard treatments, including psychotherapy or medication, failing many patients. Deep brain stimulation holds great potential as an alternative therapy for treatment-resistant cases; however, improving the efficacy of stimulation therapy for neuropsychiatric disorders is hindered by the complexity as well as inter- and/or intra-individual variability in symptom manifestations, neural representations and response to therapy. These challenges motivate the development of brain-computer interfaces (BCIs) that can decode a patient's symptom-state from brain activity as feedback to personalize the stimulation therapy in closed loop. Here, we review progress on developing BCIs for neuropsychiatric care, focusing on neural biomarkers for decoding symptom-states, stimulation site selection and closed-loop stimulation strategies. Moreover, we highlight promising data-driven machine learning and system design approaches and provide a roadmap for realizing these BCIs. Finally, we review current limitations, discuss extensions to other treatment modalities, and outline the required scientific and technological advances. These advances can enable next-generation BCIs that provide an alternative therapy for treatment-resistant neuropsychiatric disorders.},
}
@article {pmid40800282,
year = {2024},
author = {Ladouce, S and Dehais, F},
title = {Frequency tagging of spatial attention using periliminal flickers.},
journal = {Imaging neuroscience (Cambridge, Mass.)},
volume = {2},
number = {},
pages = {},
pmid = {40800282},
issn = {2837-6056},
abstract = {Steady-State Visually Evoked Potentials (SSVEPs) manifest as a sustained rhythmic activity that can be observed in surface electroencephalography (EEG) in response to periodic visual stimuli, commonly referred to as flickers. SSVEPs are widely used in fundamental cognitive neuroscience paradigms and Brain-Computer Interfaces (BCI) due to their robust and rapid onset. However, they have drawbacks related to the intrusive saliency of flickering visual stimuli, which may induce eye strain, cognitive fatigue, and biases in visual exploration. Previous findings highlighted the potential of altering features of flicker stimuli to improve user experience. In this study, we propose to reduce the amplitude modulation depth of flickering stimuli down to the individuals' perceptual visibility threshold (periliminal) and below (subliminal). The stimulus amplitude modulation depth represents the contrast difference between the two alternating states of a flicker. A simple visual attention task where participants responded to the presentation of spatially cued target stimuli (left and right) was used to assess the validity of such periliminal and subliminal frequency-tagging probes to capture spatial attention. The left and right sides of the screen, where target stimuli were presented, were covered by large flickers (13 and 15 Hz, respectively). The amplitude modulation depth of these flickers was manipulated across three conditions: control, periliminal, and subliminal. The latter two levels of flickers amplitude modulation depth were defined through a perceptual visibility threshold protocol on a single-subject basis. Subjective feedback indicated that the use of periliminal and subliminal flickers substantially improved user experience. The present study demonstrates that periliminal and subliminal flickers evoked SSVEP responses that can be used to derive spatial attention in frequency-tagging paradigms. The single-trial classification of attended space (left versus right) based on SSVEP response reached an average accuracy of 81.1% for the periliminal and 58% for the subliminal conditions. These findings reveal the promises held by the application of inconspicuous flickers to both cognitive neuroscience research and BCI development.},
}
@article {pmid40800520,
year = {2024},
author = {Banville, H and Jaoude, MA and Wood, SUN and Aimone, C and Holst, SC and Gramfort, A and Engemann, DA},
title = {Do try this at home: Age prediction from sleep and meditation with large-scale low-cost mobile EEG.},
journal = {Imaging neuroscience (Cambridge, Mass.)},
volume = {2},
number = {},
pages = {},
pmid = {40800520},
issn = {2837-6056},
abstract = {Electroencephalography (EEG) is an established method for quantifying large-scale neuronal dynamics which enables diverse real-world biomedical applications, including brain-computer interfaces, epilepsy monitoring, and sleep staging. Advances in sensor technology have freed EEG from traditional laboratory settings, making low-cost ambulatory or at-home assessments of brain function possible. While ecologically valid brain assessments are becoming more practical, the impact of their reduced spatial resolution and susceptibility to noise remain to be investigated. This study set out to explore the potential of at-home EEG assessments for biomarker discovery using the brain age framework and four-channel consumer EEG data. We analyzed recordings from more than 5200 human subjects (18-81 years) during meditation and sleep, to predict age at the time of recording. With cross-validated R 2 scores between 0.3 - 0.5 , prediction performance was within the range of results obtained by recent benchmarks focused on laboratory-grade EEG. While age prediction was successful from both meditation and sleep recordings, the latter led to higher performance. Analysis by sleep stage uncovered that N2-N3 stages contained most of the signal. When combined, EEG features extracted from all sleep stages gave the best performance, suggesting that the entire night of sleep contains valuable age-related information. Furthermore, model comparisons suggested that information was spread out across electrodes and frequencies, supporting the use of multivariate modeling approaches. Thanks to our unique dataset of longitudinal repeat sessions spanning 153 to 529 days from eight subjects, we finally evaluated the variability of EEG-based age predictions, showing that they reflect both trait- and state-like information. Overall, our results demonstrate that state-of-the-art machine-learning approaches based on age prediction can be readily applied to real-world EEG recordings obtained during at-home sleep and meditation practice.},
}
@article {pmid40800349,
year = {2024},
author = {Zubarev, I and Nurminen, M and Parkkonen, L},
title = {Robust discrimination of multiple naturalistic same-hand movements from MEG signals with convolutional neural networks.},
journal = {Imaging neuroscience (Cambridge, Mass.)},
volume = {2},
number = {},
pages = {},
pmid = {40800349},
issn = {2837-6056},
abstract = {Discriminating patterns of brain activity corresponding to multiple hand movements are a challenging problem at the limit of the spatial resolution of magnetoencephalography (MEG). Here, we use the combination of MEG, a novel experimental paradigm, and a recently developed convolutional-neural-network-based classifier to demonstrate that four goal-directed real and imaginary movements-all performed by the same hand-can be detected from the MEG signal with high accuracy: > 70 % for real movements and > 60 % for imaginary movements. Additional experiments were used to control for possible confounds and to establish the empirical chance level. Investigation of the patterns informing the classification indicated the primary contribution of signals in the alpha (8-12 Hz) and beta (13-30 Hz) frequency range in the contralateral motor areas for the real movements, and more posterior parieto-occipital sources for the imagined movements. The obtained high accuracy can be exploited in practical applications, for example, in brain-computer interface-based motor rehabilitation.},
}
@article {pmid40918004,
year = {2023},
author = {Zong, F and Liu, H and Bai, R and Galvosas, P},
title = {Data inversion of multi-dimensional magnetic resonance in porous media.},
journal = {Magnetic resonance letters},
volume = {3},
number = {2},
pages = {127-139},
pmid = {40918004},
issn = {2772-5162},
abstract = {Since its inception in the 1970s, multi-dimensional magnetic resonance (MR) has emerged as a powerful tool for non-invasive investigations of structures and molecular interactions. MR spectroscopy beyond one dimension allows the study of the correlation, exchange processes, and separation of overlapping spectral information. The multi-dimensional concept has been re-implemented over the last two decades to explore molecular motion and spin dynamics in porous media. Apart from Fourier transform, methods have been developed for processing the multi-dimensional time-domain data, identifying the fluid components, and estimating pore surface permeability via joint relaxation and diffusion spectra. Through the resolution of spectroscopic signals with spatial encoding gradients, multi-dimensional MR imaging has been widely used to investigate the microscopic environment of living tissues and distinguish diseases. Signals in each voxel are usually expressed as multi-exponential decay, representing microstructures or environments along multiple pore scales. The separation of contributions from different environments is a common ill-posed problem, which can be resolved numerically. Moreover, the inversion methods and experimental parameters determine the resolution of multi-dimensional spectra. This paper reviews the algorithms that have been proposed to process multi-dimensional MR datasets in different scenarios. Detailed information at the microscopic level, such as tissue components, fluid types and food structures in multi-disciplinary sciences, could be revealed through multi-dimensional MR.},
}
@article {pmid40620639,
year = {2023},
author = {Zhang, Y and He, T and Boussard, J and Windolf, C and Winter, O and Trautmann, E and Roth, N and Barrell, H and Churchland, M and Steinmetz, NA and , and Varol, E and Hurwitz, C and Paninski, L},
title = {Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes.},
journal = {Advances in neural information processing systems},
volume = {36},
number = {},
pages = {77604-77631},
pmid = {40620639},
issn = {1049-5258},
support = {/WT_/Wellcome Trust/United Kingdom ; K99 MH128772/MH/NIMH NIH HHS/United States ; U19 NS123716/NS/NINDS NIH HHS/United States ; },
abstract = {Neural decoding and its applications to brain computer interfaces (BCI) are essential for understanding the association between neural activity and behavior. A prerequisite for many decoding approaches is spike sorting, the assignment of action potentials (spikes) to individual neurons. Current spike sorting algorithms, however, can be inaccurate and do not properly model uncertainty of spike assignments, therefore discarding information that could potentially improve decoding performance. Recent advances in high-density probes (e.g., Neuropixels) and computational methods now allow for extracting a rich set of spike features from unsorted data; these features can in turn be used to directly decode behavioral correlates. To this end, we propose a spike sorting-free decoding method that directly models the distribution of extracted spike features using a mixture of Gaussians (MoG) encoding the uncertainty of spike assignments, without aiming to solve the spike clustering problem explicitly. We allow the mixing proportion of the MoG to change over time in response to the behavior and develop variational inference methods to fit the resulting model and to perform decoding. We benchmark our method with an extensive suite of recordings from different animals and probe geometries, demonstrating that our proposed decoder can consistently outperform current methods based on thresholding (i.e. multi-unit activity) and spike sorting. Open source code is available at https://github.com/yzhang511/density_decoding.},
}
@article {pmid40477046,
year = {2020},
author = {Amoo-Adare, EA},
title = {The Art of (Un)Thinking: When Hyper Productivity Says 'Enough!', Is a Feast.},
journal = {Postdigital science and education},
volume = {2},
number = {3},
pages = {606-613},
pmid = {40477046},
issn = {2524-4868},
}
@article {pmid40428702,
year = {2025},
author = {Ma, X and Miao, T and Xie, F and Zhang, J and Zheng, L and Liu, X and Hai, H},
title = {Development of Wearable Wireless Multichannel f-NIRS System to Evaluate Activities.},
journal = {Micromachines},
volume = {16},
number = {5},
pages = {},
pmid = {40428702},
issn = {2072-666X},
abstract = {Functional near-infrared spectroscopy is a noninvasive neuroimaging technique that uses optical signals to monitor subtle changes in hemoglobin concentrations within the superficial tissue of the human body. This technology has widespread applications in long-term brain-computer interface monitoring within both traditional medical domains and, increasingly, domestic settings. The popularity of this approach lies in the fact that new single-channel brain oxygen sensors can be used in a variety of scenarios. Given the diverse sensor structure requirements across applications and numerous approaches to data acquisition, the accurate extraction of comprehensive brain activity information requires a multichannel near-infrared system. This study proposes a novel distributed multichannel near-infrared system that integrates two near-infrared light emissions at differing wavelengths (660 nm, 850 nm) with a photoelectric receiver. This substantially improves the accuracy of regional signal sampling. Through a basic long-time mental arithmetic paradigm, we demonstrate that the accompanying algorithm supports offline analysis and is sufficiently versatile for diverse scenarios relevant to the system's functionality.},
}
@article {pmid40428683,
year = {2025},
author = {Hong, S},
title = {Wireless Optogenetic Microsystems Accelerate Artificial Intelligence-Neuroscience Coevolution Through Embedded Closed-Loop System.},
journal = {Micromachines},
volume = {16},
number = {5},
pages = {},
pmid = {40428683},
issn = {2072-666X},
support = {N/A//Hongik University/ ; },
abstract = {Brain-inspired models in artificial intelligence (AI) originated from foundational insights in neuroscience. In recent years, this relationship has been moving toward a mutually reinforcing feedback loop. Currently, AI is significantly contributing to advancing our understanding of neuroscience. In particular, when combined with wireless optogenetics, AI enables experiments without physical constraints. Furthermore, AI-driven real-time analysis facilitates closed-loop control, allowing experimental setups across a diverse range of scenarios. And a deeper understanding of these neural networks may, in turn, contribute to future advances in AI. This work demonstrates the synergy between AI and miniaturized neural technology, particularly through wireless optogenetic systems designed for closed-loop neural control. We highlight how AI is now revolutionizing neuroscience experiments from decoding complex neural signals and quantifying behavior, to enabling closed-loop interventions and high-throughput phenotyping in freely moving subjects. Notably, AI-integrated wireless implants can monitor and modulate biological processes with unprecedented precision. We then recount how neuroscience insights derived from AI-integrated neuroscience experiments can potentially inspire the next generation of machine intelligence. Insights gained from these technologies loop back to inspire more efficient and robust AI systems. We discuss future directions in this positive feedback loop between AI and neuroscience, arguing that the coevolution of the two fields, grounded in technologies like wireless optogenetics and guided by reciprocal insight, will accelerate progress in both, while raising new challenges and opportunities for interdisciplinary collaboration.},
}
@article {pmid40428114,
year = {2025},
author = {Zheng, Y and Wu, S and Chen, J and Yao, Q and Zheng, S},
title = {Cross-Subject Motor Imagery Electroencephalogram Decoding with Domain Generalization.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {12},
number = {5},
pages = {},
pmid = {40428114},
issn = {2306-5354},
abstract = {Decoding motor imagery (MI) electroencephalogram (EEG) signals in the brain-computer interface (BCI) can assist patients in accelerating motor function recovery. To realize the implementation of plug-and-play functionality for MI-BCI applications, cross-subject models are employed to alleviate time-consuming calibration and avoid additional model training for target subjects by utilizing EEG data from source subjects. However, the diversity in data distribution among subjects limits the model's robustness. In this study, we investigate a cross-subject MI-EEG decoding model with domain generalization based on a deep learning neural network that extracts domain-invariant features from source subjects. Firstly, a knowledge distillation framework is adopted to obtain the internally invariant representations based on spectral features fusion. Then, the correlation alignment approach aligns mutually invariant representations between each pair of sub-source domains. In addition, we use distance regularization on two kinds of invariant features to enhance generalizable information. To assess the effectiveness of our approach, experiments are conducted on the BCI Competition IV 2a and the Korean University dataset. The results demonstrate that the proposed model achieves 8.93% and 4.4% accuracy improvements on two datasets, respectively, compared with current state-of-the-art models, confirming that the proposed approach can effectively extract invariant features from source subjects and generalize to the unseen target distribution, hence paving the way for effective implementation of the plug-and-play functionality in MI-BCI applications.},
}
@article {pmid40426690,
year = {2025},
author = {Taha, BN and Baykara, M and Alakuş, TB},
title = {Neurophysiological Approaches to Lie Detection: A Systematic Review.},
journal = {Brain sciences},
volume = {15},
number = {5},
pages = {},
pmid = {40426690},
issn = {2076-3425},
abstract = {Background and Objectives: Lie detection is crucial in domains such as security, law enforcement, and clinical assessments. Traditional methods suffer from reliability issues and susceptibility to countermeasures. In recent years, electroencephalography (EEG) and particularly the Event-Related Potential (ERP) P300 component have gained prominence for identifying concealed information. This systematic review aims to evaluate recent studies (2017-2024) on EEG-based lie detection using ERP P300 responses, especially in relation to recognized and unrecognized face stimuli. The goal is to summarize commonly used EEG signal processing techniques, feature extraction methods, and classification algorithms, identifying those that yield the highest accuracy in lie detection tasks. Methods: This review followed PRISMA guidelines for systematic reviews. A comprehensive literature search was conducted using IEEE Xplore, Web of Science, Scopus, and Google Scholar, restricted to English-language articles from 2017 to 2024. Studies were included if they focused on EEG-based lie detection, utilized experimental protocols like Concealed Information Test (CIT), Guilty Knowledge Test (GKT), or Deceit Identification Test (DIT), and evaluated classification accuracy using ERP P300 components. Results: CIT with ERP P300 was the most frequently employed protocol. The most used preprocessing method was Bandpass Filtering (BPF), and the Discrete Wavelet Transform (DWT) emerged as the preferred feature extraction technique due to its suitability for non-stationary EEG signals. Among classification algorithms, Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Convolutional Neural Networks (CNN) were frequently utilized. These findings demonstrate the effectiveness of hybrid and deep learning-based models in enhancing classification performance. Conclusions: EEG-based lie detection, particularly using the ERP P300 response to face recognition tasks, shows promising accuracy and robustness compared to traditional polygraph methods. Combining advanced signal processing methods with machine learning and deep learning classifiers significantly improves performance. This review identifies the most effective methodologies and suggests that future research should focus on real-time applications, cross-individual generalization, and reducing system complexity to facilitate broader adoption.},
}
@article {pmid40426631,
year = {2025},
author = {Mao, Q and Zhu, H and Yan, W and Zhao, Y and Hei, X and Luo, J},
title = {MCL-SWT: Mirror Contrastive Learning with Sliding Window Transformer for Subject-Independent EEG Recognition.},
journal = {Brain sciences},
volume = {15},
number = {5},
pages = {},
pmid = {40426631},
issn = {2076-3425},
support = {23JK0556//the Scientific Research Program Founded by Shaanxi Provincial Education Department of China/ ; 61906152, 62376213 and U21A20524//the National Natural Science Foundation of China/ ; },
abstract = {Background: In brain-computer interfaces (BCIs), transformer-based models have found extensive application in motor imagery (MI)-based EEG signal recognition. However, for subject-independent EEG recognition, these models face challenges: low sensitivity to spatial dynamics of neural activity and difficulty balancing high temporal resolution features with manageable computational complexity. The overarching objective is to address these critical issues. Methods: We introduce Mirror Contrastive Learning with Sliding Window Transformer (MCL-SWT). Inspired by left/right hand motor imagery inducing event-related desynchronization (ERD) in the contralateral sensorimotor cortex, we develop a mirror contrastive loss function. It segregates feature spaces of EEG signals from contralateral ERD locations while curtailing variability in signals sharing similar ERD locations. The Sliding Window Transformer computes self-attention scores over high temporal resolution features, enabling efficient capture of global temporal dependencies. Results: Evaluated on benchmark datasets for subject-independent MI EEG recognition, MCL-SWT achieves classification accuracies of 66.48% and 75.62%, outperforming State-of-the-Art models by 2.82% and 2.17%, respectively. Ablation studies validate the efficacy of both the mirror contrastive loss and sliding window mechanism. Conclusions: These findings underscore MCL-SWT's potential as a robust, interpretable framework for subject-independent EEG recognition. By addressing existing challenges, MCL-SWT could significantly advance BCI technology development.},
}
@article {pmid40426214,
year = {2025},
author = {Li, K and Liang, H and Qiu, J and Zhang, X and Cai, B and Wang, D and Zhang, D and Lin, B and Han, H and Yang, G and Zhu, Z},
title = {Reveal the mechanism of brain function with fluorescence microscopy at single-cell resolution: from neural decoding to encoding.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {118},
pmid = {40426214},
issn = {1743-0003},
support = {2024XHSZ-Y08//Zhejiang Health Information Association Research Program/ ; 82401786//National Natural Science Foundation of China/ ; 82201637//National Natural Science Foundation of China/ ; 2024KY246//Zhejiang Provincial Medical and Health Technology Project/ ; BMI2400025//Open Research Fund of the State Key Laboratory of Brain-Machine Intelligence, Zhejiang University/ ; 2024C03150//Key R&amp;D Program of Zhejiang Province/ ; J-202402//Qiushi Youth Program from Scientific Research Cultivation Foundation/ ; },
mesh = {*Brain/physiology/cytology ; Humans ; Microscopy, Fluorescence/methods ; Animals ; *Single-Cell Analysis/methods ; *Neurons/physiology ; Optogenetics ; *Brain Mapping/methods ; },
abstract = {As a key pathway for understanding behavior, cognition, and emotion, neural decoding and encoding provide effective tools to bridge the gap between neural mechanisms and imaging recordings, especially at single-cell resolution. While neural decoding aims to establish an interpretable theory of how complex biological behaviors are represented in neural activities, neural encoding focuses on manipulating behaviors through the stimulation of specific neurons. We thoroughly analyze the application of fluorescence imaging techniques, particularly two-photon fluorescence imaging, in decoding neural activities, showcasing the theoretical analysis and technological advancements from imaging recording to behavioral manipulation. For decoding models, we compared linear and nonlinear methods, including independent component analysis, random forests, and support vector machines, highlighting their capabilities to reveal the intricate mapping between neural activity and behavior. By employing synthetic stimuli via optogenetics, fundamental principles of neural encoding are further explored. We elucidate various encoding types based on different stimulus paradigms-quantity encoding, spatial encoding, temporal encoding, and frequency encoding-enhancing our understanding of how the brain represents and processes information. We believe that fluorescence imaging-based neural decoding and encoding techniques have deepened our understanding of the brain, and hold great potential in paving the way for future neuroscience research and clinical applications.},
}
@article {pmid40425805,
year = {2025},
author = {Liu, CW and Wang, YM and Chen, SY and Lu, LY and Liang, TY and Fang, KC and Chen, P and Lee, IC and Liu, WC and Kumar, A and Kuo, SH and Lee, JC and Lo, CC and Wu, SC and Pan, MK},
title = {The cerebellum shapes motions by encoding motor frequencies with precision and cross-individual uniformity.},
journal = {Nature biomedical engineering},
volume = {},
number = {},
pages = {},
pmid = {40425805},
issn = {2157-846X},
support = {NTUMC 110C101-011//NTU | College of Medicine, National Taiwan University (College of Medicine, National Taiwan University)/ ; NSC-145-11//National Taiwan University Hospital (NTUH)/ ; 113-UN0013//National Taiwan University Hospital (NTUH)/ ; 108-039//National Taiwan University Hospital (NTUH)/ ; 112-UN0024//National Taiwan University Hospital (NTUH)/ ; 113-E0001//National Taiwan University Hospital (NTUH)/ ; AS-TM-112-01-02//Academia Sinica/ ; NHRI-EX113-11303NI//National Health Research Institutes (NHRI)/ ; 109-2326-B-002-013-MY4//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 107-2321-B-002-020//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 108-2321-B-002-011//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 108-2321-002-059-MY2//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 110-2321-B-002-012//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 111-2628-B-002-036//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 112-2628-B-002-011//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 113-2628-B-002-002//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; R01NS118179//Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)/ ; R01NS104423//Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)/ ; R01NS124854//Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)/ ; },
abstract = {Understanding brain behaviour encoding or designing neuroprosthetics requires identifying precise, consistent neural algorithms across individuals. However, cerebral microstructures and activities are individually variable, posing challenges for identifying precise codes. Here, despite cerebral variability, we report that the cerebellum shapes motor kinematics by encoding dynamic motor frequencies with remarkable numerical precision and cross-individual uniformity. Using in vivo electrophysiology and optogenetics in mice, we confirm that deep cerebellar neurons encode frequencies using populational tuning of neuronal firing probabilities, creating cerebellar oscillations and motions with matched frequencies. The mechanism is consistently presented in self-generated rhythmic and non-rhythmic motions triggered by a vibrational platform or skilled tongue movements of licking in all tested mice with cross-individual uniformity. The precision and uniformity allowed us to engineer complex motor kinematics with designed frequencies. We further validate the frequency-coding function of the human cerebellum using cerebellar electroencephalography recordings and alternating current stimulation during voluntary tapping tasks. Our findings reveal a cerebellar algorithm for motor kinematics with precision and uniformity, the mathematical foundation for a brain-computer interface for motor control.},
}
@article {pmid40425792,
year = {2025},
author = {Chen, ZP and Zhao, X and Wang, S and Cai, R and Liu, Q and Ye, H and Wang, MJ and Peng, SY and Xue, WX and Zhang, YX and Li, W and Tang, H and Huang, T and Zhang, Q and Li, L and Gao, L and Zhou, H and Hang, C and Zhu, JN and Li, X and Liu, X and Cong, Q and Yan, C},
title = {GABA-dependent microglial elimination of inhibitory synapses underlies neuronal hyperexcitability in epilepsy.},
journal = {Nature neuroscience},
volume = {28},
number = {7},
pages = {1404-1417},
pmid = {40425792},
issn = {1546-1726},
support = {82373856//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31900824//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32371074//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32071097//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82471481//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32200778//National Natural Science Foundation of China (National Science Foundation of China)/ ; 020813005031//Natural Science Foundation of Jiangsu Province (Jiangsu Provincial Natural Science Foundation)/ ; 2019M651779//Postdoctoral Research Foundation of China (China Postdoctoral Research Foundation)/ ; },
mesh = {Animals ; *Microglia/physiology/metabolism ; Female ; Male ; *Synapses/physiology ; Mice ; *gamma-Aminobutyric Acid/metabolism ; *Neural Inhibition/physiology ; Mice, Inbred C57BL ; *Neurons/physiology ; *Epilepsy/physiopathology/pathology ; Synaptic Transmission/physiology ; Phagocytosis/physiology ; Mice, Transgenic ; },
abstract = {Neuronal hyperexcitability is a common pathophysiological feature of many neurological diseases. Neuron-glia interactions underlie this process but the detailed mechanisms remain unclear. Here, we reveal a critical role of microglia-mediated selective elimination of inhibitory synapses in driving neuronal hyperexcitability. In epileptic mice of both sexes, hyperactive inhibitory neurons directly activate surveilling microglia via GABAergic signaling. In response, these activated microglia preferentially phagocytose inhibitory synapses, disrupting the balance between excitatory and inhibitory synaptic transmission and amplifying network excitability. This feedback mechanism depends on both GABA-GABAB receptor-mediated microglial activation and complement C3-C3aR-mediated microglial engulfment of inhibitory synapses, as pharmacological or genetic blockage of both pathways effectively prevents inhibitory synapse loss and ameliorates seizure symptoms in mice. Additionally, putative cell-cell interaction analyses of brain tissues from males and females with temporal lobe epilepsy reveal that inhibitory neurons induce microglial phagocytic states and inhibitory synapse loss. Our findings demonstrate that inhibitory neurons can directly instruct microglial states to control inhibitory synaptic transmission through a feedback mechanism, leading to the development of neuronal hyperexcitability in temporal lobe epilepsy.},
}
@article {pmid40425030,
year = {2025},
author = {Dehgan, A and Abdelhedi, H and Hadid, V and Rish, I and Jerbi, K},
title = {Artificial neural networks for magnetoencephalography: a review of an emerging field.},
journal = {Journal of neural engineering},
volume = {22},
number = {3},
pages = {},
doi = {10.1088/1741-2552/addd4a},
pmid = {40425030},
issn = {1741-2552},
mesh = {*Magnetoencephalography/methods/trends ; Humans ; *Neural Networks, Computer ; *Brain/physiology ; Machine Learning/trends ; },
abstract = {Objective. Magnetoencephalography (MEG) is a cutting-edge neuroimaging technique that measures the intricate brain dynamics underlying cognitive processes with an unparalleled combination of high temporal and spatial precision. While MEG data analytics have traditionally relied on advanced signal processing and mathematical and statistical tools, the recent surge in artificial intelligence has led to the growing use of machine learning (ML) methods for MEG data classification. An emerging trend in this field is the use of artificial neural networks (ANNs) to address various MEG-related tasks. This review aims to provide a comprehensive overview of the state of the art in this area.Approach. This topical review included studies that applied ANNs to MEG data. Studies were sourced from PubMed, Google Scholar, arXiv, and bioRxiv using targeted search queries. The included studies were categorized into three groups: 'Classification', 'Modeling', and 'Other'. Key findings and trends were summarized to provide a comprehensive assessment of the field.Main results. We identified 119 relevant studies, with 70 focused on 'Classification', 16 on 'Modeling', and 33 in the 'Other' category. 'Classification' studies addressed tasks such as brain decoding, clinical diagnostics, and brain-computer interfaces implementations, often achieving high predictive accuracy. 'Modeling' studies explored the alignment between ANN activations and brain processes, offering insights into the neural representations captured by these networks. The 'Other' category demonstrated innovative uses of ANNs for artifact correction, preprocessing, and neural source localization.Significance. By establishing a detailed portrait of the current state of the field, this review highlights the strengths and current limitations of ANNs in MEG research. It also provides practical recommendations for future work, offering a helpful reference for seasoned researchers and newcomers interested in using ANNs to explore the complex dynamics of the human brain with MEG.},
}
@article {pmid40425024,
year = {2025},
author = {Wolpaw, JR},
title = {Making brain-computer interfaces as reliable as muscles.},
journal = {Journal of neural engineering},
volume = {22},
number = {4},
pages = {},
doi = {10.1088/1741-2552/addd47},
pmid = {40425024},
issn = {1741-2552},
support = {I01 CX001812/CX/CSRD VA/United States ; I01 BX002550/BX/BLRD VA/United States ; R01 NS069551/NS/NINDS NIH HHS/United States ; R01 HD036020/HD/NICHD NIH HHS/United States ; P41 EB018783/EB/NIBIB NIH HHS/United States ; P01 HD032571/HD/NICHD NIH HHS/United States ; R01 NS061823/NS/NINDS NIH HHS/United States ; R01 NS022189/NS/NINDS NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Muscle, Skeletal/physiology ; *Brain/physiology ; Reproducibility of Results ; },
abstract = {Objective.While brain-computer interfaces (BCIs) can restore basic communication to people lacking muscle control, they cannot yet restore actions that require the extremely high reliability of natural (i.e. muscle-based) actions. Most BCI research focuses on neural engineering; it seeks to improve the measurement and analysis of brain signals. But neural engineering alone cannot make BCIs reliable.Approach.A BCI does not simply decode brain activity; it enables its user to acquire a skill that is produced not by nerves and muscles but rather by the BCI. Thus, BCI research should focus also on neuroscience; it should seek to develop BCI skills that emulate natural skills.Main results.A natural skill is produced by a network of neurons and synapses that may extend from cortex to spinal cord. This network has been given the nameheksor, from the ancient Greek wordhexis. A heksor changes through life; it modifies itself as needed to maintain the key features of its skill, the attributes that make the skill satisfactory. Heksors overlap; they share neurons and synapses. Through their concurrent changes, heksors keep neuronal and synaptic properties in anegotiated equilibriumthat enables each to produce its skill satisfactorily. A BCI-based skill is produced by asynthetic heksor, a network of neurons, synapses, and software that produces a BCI-based skill and should change as needed to maintain the skill's key features.Significance.A synthetic heksor shares neurons and synapses with natural heksors. Like natural heksors, it can benefit from multimodal sensory feedback, using signals from multiple brain areas, and maintaining the skill's key features rather than all its details. A synthetic heksor also needs successful co-adaptation between its central nervous system and software components and successful integration into the negotiated equilibrium that heksors establish and maintain. With due attention to both neural engineering and neuroscience, BCIs could become as reliable as muscles.},
}
@article {pmid40425023,
year = {2025},
author = {Wu, D},
title = {Revisiting Euclidean alignment for transfer learning in EEG-based brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {22},
number = {3},
pages = {},
doi = {10.1088/1741-2552/addd49},
pmid = {40425023},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; *Machine Learning ; Algorithms ; *Transfer, Psychology/physiology ; },
abstract = {Due to large intra-subject and inter-subject variabilities of electroencephalogram (EEG) signals, EEG-based brain-computer interfaces (BCIs) usually need subject-specific calibration to tailor the decoding algorithm for each new subject, which is time-consuming and user-unfriendly, hindering their real-world applications. Transfer learning (TL) has been extensively used to expedite the calibration, by making use of EEG data from other subjects/sessions. An important consideration in TL for EEG-based BCIs is to reduce the data distribution discrepancies among different subjects/sessions, to avoid negative transfer. Euclidean alignment (EA) was proposed in 2020 to address this challenge. Numerous experiments from 13 different BCI paradigms demonstrated its effectiveness and efficiency. This paper revisits EA, explaining its procedure and correct usage, introducing its applications and extensions, and pointing out potential new research directions. It should be very helpful to BCI researchers, especially those who are working on EEG signal decoding.},
}
@article {pmid40424668,
year = {2025},
author = {Sawyer, A and Brannigan, J and Spielman, L and , and Putrino, D and Fry, A},
title = {Development of a novel clinical outcome assessment: digital instrumental activities of daily living.},
journal = {EBioMedicine},
volume = {116},
number = {},
pages = {105732},
pmid = {40424668},
issn = {2352-3964},
mesh = {Humans ; *Activities of Daily Living ; *Outcome Assessment, Health Care/methods ; Delphi Technique ; Surveys and Questionnaires ; Focus Groups ; Male ; Female ; },
abstract = {BACKGROUND: Digital technology is integral to activities of daily living, particularly instrumental activities of daily living (IADLs). However, tools that accommodate digital performance of IADLs are lacking. The aim of this study was to develop a novel Digital IADL Scale.<br><br>METHODS: The multi-stage methodology included: (i) deductive item generation via a systematic review and assignment to domains using a Delphi process, (ii) inductive item generation via a survey of individuals with lived experience (IWLE) of severe paralysis, (iii) item refinement via item rating surveys of content experts and IWLE, and (iv) focus group discussions with key opinion leaders.<br><br>FINDINGS: The systematic review identified 1250 IADL items from validated IADL measures, of which 353 met criteria. Deduplication reduced the deductive item set to 77, of which 42 remained following the Delphi process. IWLE generated 152 items, of which 132 met criteria. Deduplication reduced the inductive item set to 41. The combined item pool was reduced to 69 following the item rating surveys. Following focus group feedback, a list of nine domains, containing 37 items, and suggested response scale options are presented.<br><br>INTERPRETATION: We describe the initial development of a scale to assess functional independence within IADLs that may be completed digitally, which will be submitted to further validation.<br><br>FUNDING: Support for this project was provided in kind by the Abilities Research Center. No formal funding was received.},
}
@article {pmid40423756,
year = {2025},
author = {Moeller, A and Andres Porras, JM},
title = {Human enhancement, past and present.},
journal = {Monash bioethics review},
volume = {},
number = {},
pages = {},
pmid = {40423756},
issn = {1836-6716},
abstract = {One important role the medical humanities might and should play relates to public education. In this instance, we mean helping persons to think about their own aims or purposes as potential receivers of enhancement interventions, and similarly helping to inform the developers of said interventions. This article argues that, in the light of real and speculative applications of emerging biotechnologies and artificial intelligence aimed at human enhancement-including germline genetic engineering, the linking of the human brain with an artificial general intelligence by way of a brain-computer interface, and various interventions directed toward life extension-historians would do well to consider the following three practices as they participate in the medical humanities and the shared task of public education: (1) Taking under scrutiny a broad swath of topics and timeframes as it relates to past efforts aimed at human enhancement; (2) Focusing on past engagement with enhancement efforts and their perceived relation to the pursuit of living well; and (3) Entering into debates on enhancement as equal participants. In support of these assertions, this article takes efforts directed towards the prolongation of life in medieval Europe as an illustrative example. It also highlights continuities and discontinuities between past and present justifications for human enhancement, and addresses how similarities and differences can shape and challenge contemporary bioethical arguments.},
}
@article {pmid40423554,
year = {2025},
author = {Brackman, KN and Taychert, MT and Serrell, EC and Gralnek, D and Manakas, C and Knoedler, M and Antar, A and Allen, GO and Grimes, MD},
title = {Clinical Outcomes of Holmium Laser Enucleation of the Prostate in Patients With Diminished Bladder Contractility.},
journal = {Urology practice},
volume = {12},
number = {5},
pages = {524-532},
pmid = {40423554},
issn = {2352-0787},
support = {K12 DK100022/DK/NIDDK NIH HHS/United States ; },
mesh = {Humans ; Male ; *Lasers, Solid-State/therapeutic use ; Retrospective Studies ; *Prostatic Hyperplasia/surgery/complications ; Aged ; *Urinary Bladder Neck Obstruction/surgery/etiology/physiopathology ; Treatment Outcome ; *Prostatectomy/methods ; *Urinary Bladder/physiopathology ; Middle Aged ; Urodynamics ; Aged, 80 and over ; },
abstract = {INTRODUCTION: Bladder outlet obstruction (BOO) due to benign prostatic hyperplasia (BPH) is common in aging men and can be treated with holmium laser enucleation of the prostate (HoLEP). However, diminished bladder contractility (DC) is also highly prevalent (9%-48%) and can be clinically indistinguishable from BOO without urodynamics (UDS). While HoLEP effectively treats BPH/BOO, clinical outcomes data for patients with DC are limited and mixed. We aim to compare the prevalence and risk factors of catheter dependence among patients with and without DC after HoLEP.<br><br>METHODS: A retrospective cohort study was conducted on 179 patients with preoperative UDS who underwent HoLEP between June 2018 and December 2023. Diminished contractility was defined as Bladder Contractility Index (BCI) < 100. Statistical analyses included univariate and multivariate logistic regression.<br><br>RESULTS: Among 179 patients, 103 (57.5%) had DC (BCI < 100). After HoLEP, all patients with normal contractility (NC) were voiding while 7.8% of patients with DC were catheter dependent (P = .01) at a mean follow-up of 28 months. Preoperative BCI was associated with post-HoLEP catheter dependence (OR = 0.97, 95% CI 0.95-1.00, P = .046). Postoperative International Prostate Symptom Scores were significantly higher in DC compared with NC groups despite similar preoperative scores.<br><br>CONCLUSIONS: HoLEP rendered 95.5% (171/179) of patients catheter free. However, patients with DC were more likely to require catheterization postoperatively and reported worse urinary symptoms compared with patients with NC. Our results support obtaining UDS when there is clinical concern for DC because this may guide shared decision-making before pursuing HoLEP.},
}
@article {pmid40422053,
year = {2025},
author = {Avital, N and Shulkin, N and Malka, D},
title = {Automatic Calculation of Average Power in Electroencephalography Signals for Enhanced Detection of Brain Activity and Behavioral Patterns.},
journal = {Biosensors},
volume = {15},
number = {5},
pages = {},
pmid = {40422053},
issn = {2079-6374},
mesh = {Humans ; *Electroencephalography/methods ; *Brain/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; Male ; Female ; Adult ; Young Adult ; },
abstract = {Precise analysis of electroencephalogram (EEG) signals is critical for advancing the understanding of neurological conditions and mapping brain activity. However, accurately visualizing brain regions and behavioral patterns from neural signals remains a significant challenge. The present study proposes a novel methodology for the automated calculation of the average power of EEG signals, with a particular focus on the beta frequency band which is known for its pronounced activity during cognitive tasks such as 2D content engagement. An optimization algorithm is employed to determine the most appropriate digital filter type and order for EEG signal processing, thereby enhancing both signal clarity and interpretability. To validate the proposed methodology, an experiment was conducted with 22 students, during which EEG data were recorded while participants engaged in cognitive tasks. The collected data were processed using MATLAB (version R2023a) and the EEGLAB toolbox (version 2022.1) to evaluate various filters, including finite impulse response (FIR) and infinite impulse response (IIR) Butterworth and IIR Chebyshev filters with a 0.5% passband ripple. Results indicate that the IIR Chebyshev filter, configured with a 0.5% passband ripple and a fourth-order design, outperformed the alternatives by effectively reducing average power while preserving signal fidelity. This optimized filtering approach significantly improves the accuracy of neural signal visualizations, thereby facilitating the creation of detailed brain activity maps. By refining the analysis of EEG signals, the proposed method enhances the detection of specific neural behaviors and deepens the understanding of functional brain regions. Moreover, it bolsters the reliability of real-time brain activity monitoring, potentially advancing neurological diagnostics and insights into cognitive processes. These findings suggest that the technique holds considerable promise for future applications in brain-computer interfaces and advanced neurological assessments, offering a valuable tool for both clinical practice and research exploration.},
}
@article {pmid40421845,
year = {2025},
author = {Pizzolante, S and Covelli, E and Filippi, C and Barbara, M},
title = {Percutaneous Bone Implant Surgery: A MIPS Modified Technique.},
journal = {The Laryngoscope},
volume = {135},
number = {9},
pages = {3378-3381},
pmid = {40421845},
issn = {1531-4995},
mesh = {Humans ; *Hearing Aids ; *Minimally Invasive Surgical Procedures/methods ; *Prosthesis Implantation/methods ; },
abstract = {Since their introduction, passive percutaneous hearing aids have undergone substantial evolution, including changes in implant production, improvements in the sound processor, and simplification of surgical implantation techniques. The latest innovation comes from the minimally invasive technique proposed for the PONTO system (MIPS), which does not involve the creation of a mucoperiosteal flap in order to leave the surrounding soft tissue and vascular microcirculation intact. This study proposes a modified surgical technique compared to the one proposed for the PONTO system in order to overcome some steps of the traditional surgical technique for the placement of the Baha Connect prosthesis. Our technique does not involve any incision but the exposure of the periosteum using a skin punch and subsequent drilling without the use of any protective cannula. The described procedure allows one to overcome some steps of the traditional surgical technique and, consequently, also some post-operative complications. Moreover, a minimally invasive procedure can help reduce surgical time and the invasiveness of the application.},
}
@article {pmid40420994,
year = {2025},
author = {Esteves, D and Valente, M and Bendor, SE and Andrade, A and Vourvopoulos, A},
title = {Identifying EEG biomarkers of sense of embodiment in virtual reality: insights from spatio-spectral features.},
journal = {Frontiers in neuroergonomics},
volume = {6},
number = {},
pages = {1572851},
pmid = {40420994},
issn = {2673-6195},
abstract = {The Sense of Embodiment (SoE) refers to the subjective experience of perceiving a non-biological body part as one's own. Virtual Reality (VR) provides a powerful platform to manipulate SoE, making it a crucial factor in immersive human-computer interaction. This becomes particularly relevant in Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs), especially motor imagery (MI)-BCIs, which harness brain activity to enable users to control virtual avatars in a self-paced manner. In such systems, a strong SoE can significantly enhance user engagement, control accuracy, and the overall effectiveness of the interface. However, SoE assessment remains largely subjective, relying on questionnaires, as no definitive EEG biomarkers have been established. Additionally, methodological inconsistencies across studies introduce biases that hinder biomarker identification. This study aimed to identify EEG-based SoE biomarkers by analyzing frequency band changes in a combined dataset of 41 participants under standardized experimental conditions. Participants underwent virtual SoE induction and disruption using multisensory triggers, with a validated questionnaire confirming the illusion. Results revealed a significant increase in Beta and Gamma power over the occipital lobe, suggesting these as potential EEG biomarkers for SoE. The findings underscore the occipital lobe's role in multisensory integration and sensorimotor synchronization, supporting the theoretical framework of SoE. However, no single frequency band or brain region fully explains SoE. Instead, it emerges as a complex, dynamic process evolving across time, frequency, and spatial domains, necessitating a comprehensive approach that considers interactions across multiple neural networks.},
}
@article {pmid40420178,
year = {2025},
author = {Jiang, M and Luo, Q and Wang, X and Qu, D},
title = {Semantic radicals' semantic attachment to their composed phonograms.},
journal = {BMC psychology},
volume = {13},
number = {1},
pages = {559},
pmid = {40420178},
issn = {2050-7283},
support = {20BYY095//National Social Science Fund of China/ ; 2019YBYY131//Chongqing Social Science Planning Fund/ ; 22SKGH236//Humanities and Social Sciences Research Project Fund of Chongqing Municipal Education Commission/ ; },
mesh = {Humans ; *Semantics ; Female ; Male ; Young Adult ; Reaction Time ; Adult ; Decision Making ; *Reading ; },
abstract = {In Chinese character processing studies, it is widely accepted that semantic radicals, whether character or non-character ones, can undergo semantic activation. However, there is a notable absence of studies dedicated to understanding the nature and operation of the semantic radicals' semantic information. To address this gap, the present study employed a masked semantic priming paradigm combined with a part-of-speech decision task and a lexical decision task across three experiments. Experiment 1 was designed to examine the semantic autonomy and the semantic attachment of semantic radicals in transparent phonograms. Experiment 2 sought to further investigate the degree of semantic autonomy of semantic radicals in opaque phonograms. Experiment 3 was crafted to further probe into the presence of semantic attachment of semantic radicals in pseudo-characters. Results showed significant priming effects in both transparent and opaque phonogram conditions, with faster reaction times and higher accuracy for semantically related prime-target pairs. However, no such priming effect was observed in the pseudo-character condition, indicating that semantic radicals are not activated in non-lexical contexts. These findings suggest that semantic radicals were semantically activated when embedded in both transparent and opaque phonograms, but not when planted in pseudo-characters. The plausible account put forward is that semantic radicals stand on pars with their composed phonograms in possessing their own semantic information, but the former is semantically strongly attached to the latter, such that it cannot live without the latter's semantic company.},
}
@article {pmid40419791,
year = {2025},
author = {Chen, Y and Ding, K and Zheng, S and Gao, S and Xu, X and Wu, H and Zhou, F and Wang, Y and Xu, J and Wang, C and Ling, C and Xu, J and Wang, L and Wu, Q and Giamas, G and Chen, G and Zhang, J and Yi, C and Ji, J},
title = {Post-translational modifications in DNA damage repair: mechanisms underlying temozolomide resistance in glioblastoma.},
journal = {Oncogene},
volume = {44},
number = {23},
pages = {1781-1792},
pmid = {40419791},
issn = {1476-5594},
support = {82203035//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82403931//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Glioblastoma/drug therapy/genetics/pathology ; *Temozolomide/therapeutic use/pharmacology ; *Drug Resistance, Neoplasm/genetics ; *DNA Repair/drug effects ; *Protein Processing, Post-Translational/drug effects ; *DNA Damage ; *Antineoplastic Agents, Alkylating/therapeutic use/pharmacology ; *Brain Neoplasms/drug therapy/genetics/pathology ; Animals ; },
abstract = {Temozolomide (TMZ) resistance is one of the critical factors contributing to the poor prognosis of glioblastoma (GBM). As a first-line chemotherapeutic agent for GBM, TMZ exerts its cytotoxic effects through DNA alkylation. However, its therapeutic efficacy is significantly compromised by enhanced DNA damage repair (DDR) mechanisms in GBM cells. Although several DDR-targeting drugs have been developed, their clinical outcomes remain suboptimal. Post-translational modifications (PTMs) in GBM cells play a pivotal role in maintaining the genomic stability of DDR mechanisms, including methylguanine-DNA methyltransferase-mediated repair, DNA mismatch repair dysfunction, base excision repair, and double-strand break repair. This review focuses on elucidating the regulatory roles of PTMs in the intrinsic mechanisms underlying TMZ resistance in GBM. Furthermore, we explore the feasibility of enhancing TMZ-induced cytotoxicity by targeting PTM-related enzymatic to disrupt key steps in PTM-mediated DDR pathways. By integrating current preclinical insights and clinical challenges, this work highlights the potential of modulating PTM-driven networks as a novel therapeutic strategy to overcome TMZ resistance and improve treatment outcomes for GBM patients.},
}
@article {pmid40419502,
year = {2025},
author = {Rajabi, N and Zanettin, I and Ribeiro, AH and Vasco, M and Björkman, M and Lundström, JN and Kragic, D},
title = {Exploring the feasibility of olfactory brain-computer interfaces.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {18404},
pmid = {40419502},
issn = {2045-2322},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Odorants/analysis ; Male ; Adult ; Female ; *Smell/physiology ; Feasibility Studies ; Neural Networks, Computer ; Young Adult ; *Olfactory Perception/physiology ; *Brain/physiology ; },
abstract = {In this study, we explore the feasibility of single-trial predictions of odor registration in the brain using olfactory bio-signals. We focus on two main aspects: input data modality and the processing model. For the first time, we assess the predictability of odor registration from novel electrobulbogram (EBG) recordings, both in sensor and source space, and compare these with commonly used electroencephalogram (EEG) signals. Despite having fewer data channels, EBG shows comparable performance to EEG. We also examine whether breathing patterns contain relevant information for this task. By comparing a logistic regression classifier, which requires hand-crafted features, with an end-to-end convolutional deep neural network, we find that end-to-end approaches can be as effective as classic methods. However, due to the high dimensionality of the data, the current dataset is insufficient for either classifier to robustly differentiate odor and non-odor trials. Finally, we identify key challenges in olfactory BCIs and suggest future directions for improving odor detection systems.},
}
@article {pmid40419488,
year = {2025},
author = {Wang, D and Xue, H and Xia, L and Li, Z and Zhao, Y and Fan, X and Sun, K and Wang, H and Hamalainen, T and Zhang, C and Cong, F and Li, Y and Song, F and Lin, J},
title = {A tough semi-dry hydrogel electrode with anti-bacterial properties for long-term repeatable non-invasive EEG acquisition.},
journal = {Microsystems &amp; nanoengineering},
volume = {11},
number = {1},
pages = {105},
pmid = {40419488},
issn = {2055-7434},
support = {2022 ZD0210700//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; },
abstract = {Non-invasive brain-computer interfaces (NI-BCIs) have garnered significant attention due to their safety and wide range of applications. However, developing non-invasive electroencephalogram (EEG) electrodes that are highly sensitive, comfortable to wear, and reusable has been challenging due to the limitations of conventional electrodes. Here, we introduce a simple method for fabricating semi-dry hydrogel EEG electrodes with antibacterial properties, enabling long-term, repeatable acquisition of EEG. By utilizing N-acryloyl glycinamide and hydroxypropyltrimethyl ammonium chloride chitosan, we have prepared electrodes that not only possess good mechanical properties (compression modulus 65 kPa) and anti-fatigue properties but also exhibit superior antibacterial properties. These electrodes effectively inhibit the growth of both Gram-negative (E. coli) and Gram-positive (S. epidermidis) bacteria. Furthermore, the hydrogel maintains stable water retention properties, resulting in an average contact impedance of <400 Ω measured over 12 h, and an ionic conductivity of 0.39 mS cm[-1]. Cytotoxicity and skin irritation tests have confirmed the high biocompatibility of the hydrogel electrodes. In an N170 event-related potential (ERP) test on human volunteers, we successfully captured the expected ERP signal waveform and a high signal-to-noise ratio (20.02 dB), comparable to that of conventional wet electrodes. Moreover, contact impedance on the scalps remained below 100 kΩ for 12 h, while wet electrodes became unable to detect signals after 7-8 h due to dehydration. In summary, our hydrogel electrodes are capable of detecting ERPs over extended periods in an easy-to-use manner with antibacterial properties. This reduces the risk of bacterial infection associated with prolonged reuse and expands the potential of NI-BCIs in daily life.},
}
@article {pmid40419083,
year = {2025},
author = {Chen, L and Zhang, L and Wang, Z and Li, Q and Gu, B and Ming, D},
title = {Task-related reconfiguration patterns of frontoparietal network during motor imagery.},
journal = {Neuroscience},
volume = {579},
number = {},
pages = {302-311},
doi = {10.1016/j.neuroscience.2025.05.035},
pmid = {40419083},
issn = {1873-7544},
mesh = {Humans ; Male ; Female ; *Imagination/physiology ; *Parietal Lobe/physiology ; Adult ; Young Adult ; *Frontal Lobe/physiology ; Electroencephalography ; *Brain Waves/physiology ; *Nerve Net/physiology ; *Motor Activity/physiology ; },
abstract = {Motor imagery (MI) is closely associated with the frontoparietal network that includes prefrontal and posterior parietal regions. Studying task-related network reconfiguration after brain shifts from the resting state to the MI task is an important way to understand the brain's response process. However, how the brain modulates functional connectivity of the frontoparietal network when it shifts to MI has not been thoroughly studied. In this study, we attempted to characterize the frontoparietal network reconfiguration patterns as the brain transitioned to motor imagery tasks. We performed the analysis using EEG signals from 52 healthy subjects during left- and right-hand MI tasks. The results indicated distinct reconfiguration patterns in the frontoparietal network across four typical brain wave rhythms (theta (4 ∼ 7 Hz), alpha (8 ∼ 13 Hz), beta (14 ∼ 30 Hz), and gamma (31 ∼ 45 Hz)). Meanwhile, there was a significant positive correlation between the frontoparietal network reconfiguration and the event-related desynchronization of alpha and beta rhythms in the sensorimotor cortex. We further found that subjects with better MI-BCI performance exhibited greater reconfiguration of the frontoparietal network in alpha and beta rhythms. These findings implied that MI was accompanied by a shift in information interaction between brain regions, which might contribute to understanding the neural mechanisms of MI.},
}
@article {pmid40418615,
year = {2025},
author = {Song, Y and Wang, Y and He, H and Gao, X},
title = {Recognizing Natural Images From EEG With Language-Guided Contrastive Learning.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {36},
number = {9},
pages = {15896-15910},
doi = {10.1109/TNNLS.2025.3562743},
pmid = {40418615},
issn = {2162-2388},
mesh = {Humans ; *Electroencephalography/methods ; *Language ; Semantics ; *Machine Learning ; Neural Networks, Computer ; Algorithms ; *Image Processing, Computer-Assisted/methods ; *Pattern Recognition, Automated/methods ; Signal-To-Noise Ratio ; },
abstract = {Electroencephalography (EEG), known for its convenient noninvasive acquisition but moderate signal-to-noise ratio, has recently gained much attention due to the potential to decode image information. However, previous works have not delivered sufficient evidence of this task, primarily limited by performance and biological plausibility. In this work, we first introduce a self-supervised framework to demonstrate the feasibility of recognizing images from EEG signals. Contrastive learning is leveraged to align the representations of EEG responses with image stimuli. Then, language descriptions of the stimuli generated by large language models (LLMs) help guide learning core semantic information. With the framework, we attain significantly above-chance results on the THINGS-EEG2 dataset, achieving a top-1 accuracy of 19.7% and a top-5 accuracy of 51.5% in challenging 200-way zero-shot tasks. Furthermore, we conduct thorough experiments to resolve the human visual responses with EEG from temporal, spatial, spectral, and semantic perspectives. These results provide evidence of feasibility and plausibility regarding EEG-based image recognition, substantiated by comparative studies with the THINGS-Magnetoencephalography (MEG) dataset. The findings offer valuable insights for neural decoding and real-world applications of brain-computer interfaces (BCIs), such as health care and robot control. The code is available at https://github.com/eeyhsong/NICE-LLM.},
}
@article {pmid40416647,
year = {2025},
author = {Teng, Y and Song, L and Shi, J and Lv, Q and Hou, S and Ramakrishna, S},
title = {Advancing electrospinning towards the future of biomaterials in biomedical engineering.},
journal = {Regenerative biomaterials},
volume = {12},
number = {},
pages = {rbaf034},
pmid = {40416647},
issn = {2056-3418},
abstract = {Biomaterial is a material designed to take a form that can direct, through interactions with living systems, the course of any therapeutic or diagnostic procedure. Growing demand for improved and affordable healthcare treatments and unmet clinical needs seek further advancement of biomaterials. Over the past 25 years, the electrospinning method has been innovated to enhance biomaterials at nanometer and micrometer length scales for diverse healthcare applications. Recent developments include intelligent (smart) biomaterials and sustainable biomaterials. Intelligent materials can sense, adapt to and respond to external stimuli, autonomously adjusting to enhance functionality and performance. Sustainable biomaterials possess several key characteristics, including renewability, a low carbon footprint, circularity, durability, biocompatibility, biodegradability and others. Herein, advances in electrospun biomaterials, encompassing process innovations, working principles and the effects of process variables, are presented succinctly. The potential of electrospun intelligent biomaterials and sustainable biomaterials in specific biomedical applications, including tissue engineering, regenerative medicine, drug delivery systems, brain-computer interfaces, biosensors, personal protective equipment and wearable devices, is explored. More effective healthcare demands further advancements in electrospun biomaterials. In the future, the distinctive characteristics of intelligent biomaterials and sustainable biomaterials, integrated with various emerging technologies (such as AI and data transmission), will enable physicians to conduct remote diagnosis and treatment. This advancement significantly enhances telemedicine capabilities for more accurate disease prediction and management.},
}
@article {pmid40416500,
year = {2025},
author = {Mokienko, OA},
title = {The Potential of Near-Infrared Spectroscopy as a Therapeutic Tool Following a Stroke (Review).},
journal = {Sovremennye tekhnologii v meditsine},
volume = {17},
number = {2},
pages = {73-83},
pmid = {40416500},
issn = {2309-995X},
mesh = {Humans ; Spectroscopy, Near-Infrared/methods ; *Stroke Rehabilitation/methods ; *Stroke/physiopathology/therapy/diagnosis/diagnostic imaging ; },
abstract = {The advancement of novel technologies for the rehabilitation of post-stroke patients represents a significant challenge for a range of interdisciplinary fields. Near-infrared spectroscopy (NIRS) is an optical neuroimaging technique based on recording local hemodynamic changes at the cerebral cortex level. The technology is typically employed in post-stroke patients for diagnostic purposes, including the assessment of neuroplastic processes accompanying therapy, the study of hemispheric asymmetry, and the examination of functional brain networks. However, functional NIRS can also be used for therapeutic purposes, including the provision of biofeedback during rehabilitation tasks, as well as the navigation method during transcranial stimulation. The effectiveness of therapeutic NIRS application in stroke patients remains insufficiently studied, despite existing scientific evidence confirming its promising potential as a treatment method. The review examines the published literature on the therapeutic applications of NIRS after stroke, evaluating its potential role in the rehabilitation process. The paper describes NIRS features, advantages, and disadvantages, determining its position among other neuroimaging technologies; analyzes the findings of neurophysiological studies, which justified the clinical trials of NIRS technology; and evaluates the results of the studies on the therapeutic use of NIRS in post-stroke patients. Two potential applications of NIRS for therapeutic purposes following a stroke were suggested: the first was to provide real-time feedback during movement training (motor or ideomotor ones, including that in brain-computer interface circuits), and the second was to facilitate navigation during transcranial stimulation. Based on a comprehensive literature review, there were proposed and justified further research lines and development in this field.},
}
@article {pmid40414967,
year = {2025},
author = {Qian, L and Jia, C and Wang, J and Shi, L and Wang, Z and Wang, S},
title = {The dynamics of stimulus selection in the nucleus isthmi pars magnocellularis of avian midbrain network.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {18260},
pmid = {40414967},
issn = {2045-2322},
support = {2024M752934//China Postdoctoral Science Foundation/ ; },
mesh = {Animals ; *Mesencephalon/physiology ; Neurons/physiology ; Photic Stimulation ; *Nerve Net/physiology ; Birds/physiology ; },
abstract = {The nucleus isthmi pars magnocellularis (Imc) serves as a critical node in the avian midbrain network for encoding stimulus salience and selection. While reciprocal inhibitory projections among Imc neurons (inhibitory loop) are known to govern stimulus selection, existing studies have predominantly focused on stimulus selection under stimuli of constant relative intensity. However, animals typically encounter complex and changeable visual scenes. Thus, how Imc neurons represent stimulus selection under varying relative stimulus intensities remains unclear. Here, we examined the dynamics of stimulus selection by in vivo recording of Imc neurons' responses to spatiotemporally successive visual stimuli divided into two segments: the previous stimulus and the post stimulus. Our data demonstrate that Imc neurons can encode sensory memory of the previous stimulus, which modulates competition and salience representation in the post stimulus. This history-dependent modulation is also manifested in persistent neural activity after stimulus cessation. We identified, through neural tracing, focal inactivation, and computational modeling experiments, projections from the nucleus isthmi pars parvocellularis (Ipc) to "shepherd's crook" (Shc) neurons, which could be either direct or indirect. These projections enhance Imc neurons' responses and persistent neural activity after stimulus cessation. This connectivity supports a Shc-Ipc-Shc excitatory loop in the midbrain network. The coexistence of excitatory and inhibitory loops provides a neural substrate for continuous attractor network models, a proposed framework for neural information representation. This study also offers a potential explanation for how animals maintain short-term attention to targets in complex and changeable environments.},
}
@article {pmid40414233,
year = {2025},
author = {Paton, NI and Cousins, C and Sari, IP and Burhan, E and Ng, NK and Dalay, VB and Suresh, C and Kusmiati, T and Chew, KL and Balanag, VM and Lu, Q and Ruslami, R and Djaharuddin, I and Sugiri, JJR and Veto, RS and Sekaggya-Wiltshire, C and Avihingsanon, A and Saini, JK and Papineni, P and Nunn, AJ and Crook, AM and , },
title = {Efficacy and safety of 8-week regimens for the treatment of rifampicin-susceptible pulmonary tuberculosis (TRUNCATE-TB): a prespecified exploratory analysis of a multi-arm, multi-stage, open-label, randomised controlled trial.},
journal = {The Lancet. Infectious diseases},
volume = {25},
number = {10},
pages = {1084-1096},
pmid = {40414233},
issn = {1474-4457},
support = {/WT_/Wellcome Trust/United Kingdom ; },
mesh = {Humans ; *Rifampin/therapeutic use/administration &amp; dosage/adverse effects ; Adult ; Male ; Female ; Middle Aged ; *Tuberculosis, Pulmonary/drug therapy/microbiology ; *Antitubercular Agents/administration &amp; dosage/therapeutic use/adverse effects ; Treatment Outcome ; Young Adult ; Aged ; Adolescent ; Pyrazinamide/therapeutic use/administration &amp; dosage ; Drug Therapy, Combination ; Isoniazid/therapeutic use/administration &amp; dosage ; Ethambutol/therapeutic use/administration &amp; dosage ; Drug Administration Schedule ; Linezolid/administration &amp; dosage/therapeutic use ; Diarylquinolines/administration &amp; dosage/therapeutic use ; Mycobacterium tuberculosis/drug effects ; Clofazimine/administration &amp; dosage/therapeutic use ; },
abstract = {BACKGROUND: WHO recommends a 2-month optimal duration for new drug regimens for rifampicin-susceptible tuberculosis. We aimed to investigate the efficacy and safety of the 8-week regimens that were assessed as part of the TRUNCATE management strategy of the TRUNCATE-TB trial.<br><br>METHODS: TRUNCATE-TB was a multi-arm, multi-stage, open-label, randomised controlled trial in which participants aged 18-65 years with rifampicin-susceptible pulmonary tuberculosis were randomly assigned via a web-based system, using permuted blocks, to 24-week standard treatment (rifampicin, isoniazid, pyrazinamide, and ethambutol) or the TRUNCATE management strategy comprising initial 8-week treatment, then post-treatment monitoring and re-treatment where needed. The four 8-week regimens comprised five drugs, modified from standard treatment: high-dose rifampicin and linezolid, or high-dose rifampicin and clofazimine, or bedaquiline and linezolid, all given with isoniazid, pyrazinamide, and ethambutol; and rifapentine, linezolid, and levofloxacin, given with isoniazid and pyrazinamide. Here, we report the efficacy (proportion with unfavourable outcome; and difference from standard treatment, assessed via Bayesian methods) and safety of the 8-week regimens, assessed in the intention-to-treat population. This prespecified exploratory analysis is distinct from the previously reported 96-week outcome of the strategy in which the regimens were deployed. This trial is registered with ClinicalTrials.gov (NCT03474198).<br><br>FINDINGS: Between March 21, 2018, and March 26, 2020, 675 participants (674 in the intention-to-treat population) were enrolled and randomly assigned to the standard treatment group or one of the four 8-week regimen groups. Two 8-week regimens progressed to full enrolment. An unfavourable outcome (mainly relapse) occurred in seven (4%) of 181 participants on standard treatment; 46 (25%) of 184 on the high-dose rifampicin and linezolid-containing regimen (adjusted difference 21&#xb7;0%, 95% Bayesian credible interval [BCI] 14&#xb7;3-28&#xb7;1); and 26 (14%) of 189 on the bedaquiline and linezolid-containing regimen (adjusted difference 9&#xb7;3% [4&#xb7;3-14&#xb7;9]). Grade 3-4 adverse events occurred in 24 (14%) of 181 participants on standard treatment, 20 (11%) of 184 on the rifampicin-linezolid regimen, and 22 (12%) of 189 on the bedaquiline-linezolid regimen.<br><br>INTERPRETATION: Efficacy was worse with 8-week regimens, although the difference from standard treatment varied between regimens. Even the best 8-week regimen (bedaquiline-linezolid) should only be used as part of a management strategy involving post-treatment monitoring and re-treatment if necessary.<br><br>FUNDING: Singapore National Medical Research Council; UK Department of Health and Social Care; UK Foreign, Commonwealth, and Development Office; UK Medical Research Council; Wellcome Trust; and UK Research and Innovation Medical Research Council.},
}
@article {pmid40411529,
year = {2025},
author = {Sun, Y and Guan, M and Chen, X and Feng, F and He, R and Huang, L and Tong, X and Zhou, H and Liu, X and Ming, D},
title = {Deep learning-based classification and segmentation of interictal epileptiform discharges using multichannel electroencephalography.},
journal = {Epilepsia},
volume = {66},
number = {9},
pages = {3398-3410},
doi = {10.1111/epi.18463},
pmid = {40411529},
issn = {1528-1167},
support = {020/0903065111//Tianjin University Innovation Fund/ ; 2021YFF1200602//National Key Technologies Research and Development Program/ ; c02022088//National Defense Science and Technology Innovation Fund of Chinese Academy of Sciences/ ; 0401260011//National Science Fund for Excellent Overseas Scholars/ ; },
mesh = {Humans ; *Deep Learning ; *Electroencephalography/methods/classification ; *Epilepsy/physiopathology/diagnosis/classification ; Male ; Adult ; },
abstract = {OBJECTIVE: This study was undertaken to develop a deep learning framework that can classify and segment interictal epileptiform discharges (IEDs) in multichannel electroencephalographic (EEG) recordings with high accuracy, preserving both spatial information and interchannel interactions.<br><br>METHODS: We proposed a novel deep learning framework, U-IEDNet, for detecting IEDs in multichannel EEG. The U-IEDNet framework employs convolutional layers and bidirectional gated recurrent units as a temporal encoder to extract temporal features from single-channel EEG, followed by the use of transformer networks as a spatial encoder to fuse multichannel features and extract interchannel interaction information. Transposed convolutional layers form a temporal decoder, creating a U-shaped architecture with the encoder. This upsamples features to estimate the probability of each EEG sampling point falling within the IED range, enabling segmentation of IEDs from background activity. Two datasets, a public database with 370 patient recordings and our own annotated database with 43 patient recordings, were used for model establishment and validation.<br><br>RESULTS: The results showed prominent advantage compared with other methods. U-IEDNet achieved a recall of .916, precision of .911, F1-score of .912, and false positive rate (FPR) of .030 on the public database. The classification performance in our own annotated database achieved a recall of .905, a precision of .902, an F1-score of .903, and an FPR of .072. The segmentation performance had a recall of .903, a precision of .916, and an F1-score of .909. Additionally, this study analyzes attention weights in the transformer network based on brain network theory to elucidate the spatial feature fusion process, enhancing the interpretability of the IED detection model.<br><br>SIGNIFICANCE: In this paper, we aim to present an artificial intelligence-based toolbox for IED detection, which may facilitate epilepsy diagnosis at the bedside in the future. U-IEDNet demonstrates great potential to improve the accuracy and efficiency of IED detection in multichannel EEG recordings.},
}
@article {pmid40409524,
year = {2025},
author = {Li, Y and Pan, Y and Zhao, D},
title = {Understanding the Neurobiology and Computational Mechanisms of Social Conformity: Implications for Psychiatric Disorders.},
journal = {Biological psychiatry},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.biopsych.2025.05.011},
pmid = {40409524},
issn = {1873-2402},
abstract = {Social conformity and psychiatric disorders share overlapping brain regions and neural pathways, arousing our interest in uncovering their potentially shared underlying neural and computational mechanisms. Critically, the dynamics of group behavior may either mitigate or exacerbate mental health conditions, highlighting the need to bridge social neuroscience and psychiatry. Our work examines how aberrant neurobiological circuits and computations influence social conformity. We propose a hierarchical computational framework, based on dynamic systems and active inference, to facilitate the interpretation of the multilayered interplay among processes that drive social conformity. We underscore the significant implications of this hierarchical computational framework for guiding future research on psychiatry, particularly with respect to the clinical translation of interventions such as targeted pharmacotherapy and neurostimulation techniques. Interdisciplinary efforts hold the potential to propel the fields of social and clinical neuroscience forward, fostering the emergence of more efficacious and individualized therapeutic approaches tailored to psychiatric disorders characterized by aberrant social behaviors.},
}
@article {pmid40408764,
year = {2025},
author = {Jing, S and Dai, Z and Liu, X and Yang, X and Cheng, J and Chen, T and Feng, Z and Liu, X and Dong, F and Xin, Y and Han, Z and Hu, H and Su, X and Wang, C},
title = {Effectiveness of Neurofeedback-Assisted and Conventional 6-Week Web-Based Mindfulness Interventions on Mental Health of Chinese Nursing Students: Randomized Controlled Trial.},
journal = {Journal of medical Internet research},
volume = {27},
number = {},
pages = {e71741},
pmid = {40408764},
issn = {1438-8871},
mesh = {Humans ; *Students, Nursing/psychology ; *Mindfulness/methods ; Female ; Male ; *Mental Health ; China ; *Neurofeedback/methods ; Adult ; Young Adult ; Anxiety/therapy ; *Internet ; Depression/therapy ; *Internet-Based Intervention ; East Asian People ; },
abstract = {BACKGROUND: Nursing students experience disproportionately high rates of mental health challenges, underscoring the urgent need for innovative, scalable interventions. Web-based mindfulness programs, and more recently, neurofeedback-enhanced approaches, present potentially promising avenues for addressing this critical issue.<br><br>OBJECTIVE: This study aimed to explore the effectiveness of the neurofeedback-assisted online mindfulness intervention (NAOM) and the conventional online mindfulness intervention (COM) in reducing mental health symptoms among Chinese nursing students.<br><br>METHODS: A 3-armed randomized controlled trial was conducted among 147 nursing students in Beijing, China, using a 6-week web-based mindfulness program. Participants received NAOM, COM, or general mental health education across 6 weeks. Electroencephalogram and validated tools such as the Patient Health Questionnaire and the Generalized Anxiety Disorder Questionnaire were used to primarily assess symptoms of depression and anxiety at baseline, immediately after the intervention, and at 1 and 3 months after the intervention. Generalized estimating equations were used to evaluate the effects of intervention and time.<br><br>RESULTS: A total of 155 participants enrolled in the study, and 147 finished all assessments. Significant reductions in the symptoms of depression, anxiety, and fatigue were observed in the NAOM (mean difference [MD]=-3.330, Cohen d=0.926, P<.001; MD=-3.468, Cohen d=1.091, P<.001; MD=-2.620, Cohen d=0.743, P<.001, respectively) and the COM (MD=-1.875, Cohen d=0.490, P=.03; MD=-1.750, Cohen d=0.486, P=.02; MD=-2.229, Cohen d=0.629, P=.01, respectively) groups compared with the control group at postintervention assessment. Moreover, the NAOM group showed significantly better effects than the COM group in alleviating depressive symptoms (MD=-1.455; Cohen d=0.492; P=.04) and anxiety symptoms (MD=-1.718; Cohen d=0.670; P=.04) and improving the level of mindfulness (MD=-3.765; Cohen d=1.245; P<.001) at the postintervention assessment. However, no significant difference except for the anxiety symptoms was observed across the 3 groups at the 1- and 3-month follow-ups.<br><br>CONCLUSIONS: This 6-week web-based mindfulness intervention, both conventional and neurofeedback-assisted, effectively alleviated mental health problems in the short term among nursing students. The addition of neurofeedback demonstrated greater short-term benefits; however, but these effects were not sustained over the long term. Future research should focus on long-term interventions using a more robust methodological approach.<br><br>TRIAL REGISTRATION: Chinese Clinical Trial Registry (ChiCTR) ChiCTR2400080314; https://www.chictr.org.cn/bin/project/edit?pid=211845.},
}
@article {pmid40408491,
year = {2025},
author = {Zhou, H and Wu, J and Li, J and Pan, Z and Lu, J and Shen, M and Wang, T and Hu, Y and Gao, Z},
title = {Event cache: An independent component in working memory.},
journal = {Science advances},
volume = {11},
number = {21},
pages = {eadt3063},
pmid = {40408491},
issn = {2375-2548},
mesh = {*Memory, Short-Term/physiology ; Humans ; Magnetic Resonance Imaging ; Male ; Female ; Adult ; Young Adult ; Brain Mapping ; *Cerebellum/physiology ; Brain/physiology ; },
abstract = {Working memory (WM) has been a major focus of cognitive science and neuroscience for the past 50 years. While most WM research has centered on the mechanisms of objects, there has been a lack of investigation into the cognitive and neural mechanisms of events, which are the building blocks of our experience. Using confirmatory factor analysis, psychophysical experiments, and resting-state and task functional magnetic resonance imaging methods, our study demonstrated that events have an independent storage space within WM, named as event cache, with distinct neural correlates compared to object storage in WM. We found the cerebellar network to be the most essential network for event cache, with the left cerebellum Crus I being particularly involved in encoding and maintaining events. Our findings shed critical light on the neuropsychological mechanism of WM by revealing event cache as an independent component of WM and encourage the reconsideration of theoretical models for WM.},
}
@article {pmid40408214,
year = {2025},
author = {Chen, W and Li, Y and Zheng, N and Shi, W},
title = {DenoiseMamba: An Innovative Approach for EEG Artifact Removal Leveraging Mamba and CNN.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {9},
pages = {6551-6564},
doi = {10.1109/JBHI.2025.3573042},
pmid = {40408214},
issn = {2168-2208},
mesh = {*Electroencephalography/methods ; Humans ; *Artifacts ; *Signal Processing, Computer-Assisted ; *Neural Networks, Computer ; *Deep Learning ; Brain/physiology ; Algorithms ; },
abstract = {Electroencephalography (EEG) is a widely used tool for monitoring brain activity, but it is often disturbed by various artifacts, such as electrooculography (EOG), electromyography (EMG), and electrocardiography (ECG), which degrade signal quality and affect subsequent analysis. Effective EEG denoising is critical for enhancing the performance of EEG-based applications, including disease diagnosis and brain-computer interfaces (BCIs). While recent deep learning (DL) approaches have shown promise in this area, they often struggle to efficiently model the temporal dependencies inherent in EEG signals, as well as to capture local contextual information simultaneously. In this work, we introduce DenoiseMamba, a novel deep learning-based EEG denoising model. The model incorporates the ConvSSD module, which integrates convolutional neural networks (CNNs) with structured state-space duality (SSD) mechanisms. This allows DenoiseMamba to capture both local and global spatiotemporal features, resulting in more effective artifact suppression. Extensive experiments on three semi-simulated datasets demonstrate that DenoiseMamba outperforms existing methods in EEG reconstruction accuracy, effectively eliminating myoelectric, electrooculographic, and electrocardiographic artifacts while preserving critical EEG signal details.},
}
@article {pmid40408213,
year = {2025},
author = {Lan, Z and Li, Z and Yan, C and Xiang, X and Tang, D and Wu, M and Chen, Z},
title = {MTSNet: Convolution-Based Transformer Network With Multi-Scale Temporal-Spectral Feature Fusion for SSVEP Signal Decoding.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {11},
pages = {8034-8047},
doi = {10.1109/JBHI.2025.3573410},
pmid = {40408213},
issn = {2168-2208},
mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Signal Processing, Computer-Assisted ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Adult ; *Neural Networks, Computer ; Algorithms ; Male ; Female ; Young Adult ; },
abstract = {Improving the decoding performance of steady-state visual evoked (SSVEP) signals is crucial for the practical application of SSVEP-based brain-computer interface (BCI) systems. Although numerous methods have achieved impressive results in decoding SSVEP signals, most of them focus only on the temporal or spectral domain information or concatenate them directly, which may ignore the complementary relationship between different features. To address this issue, we propose a dual-branch convolution-based Transformer network with multi-scale temporal-spectral feature fusion, termed MTSNet, to improve the decoding performance of SSVEP signals. Specifically, the temporal branch extracts temporal features from the SSVEP signals using the multi-level convolution- based Transformer (Convformer) that can adapt to the dynamic fluctuations of SSVEP signals. In parallel, the spectral branch takes the complex spectrum converted from temporal signals by the zero-padding fast Fourier transform as input and uses the Convformer to extract spectral features. These extracted temporal and spectral features are then integrated by the multi-scale feature fusion module to obtain comprehensive features with different scale information, thereby enhancing the interactions between the features and improving the effectiveness and robustness. Extensive experimental results on two widely used public SSVEP datasets, Benchmark and BETA, show that the proposed MTSNet significantly outperforms the state-of-the-art calibration-free methods in terms of accuracy and ITR. The superior performance demonstrates the effectiveness of our method in decoding SSVEP signals, which may facilitate the practical application of SSVEP-based BCI systems.},
}
@article {pmid40408200,
year = {2025},
author = {Ingolfsson, TM and Kartsch, V and Benini, L and Cossettini, A},
title = {A Wearable Ultra-Low-Power System for EEG-Based Speech-Imagery Interfaces.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {19},
number = {4},
pages = {743-755},
doi = {10.1109/TBCAS.2025.3573027},
pmid = {40408200},
issn = {1940-9990},
mesh = {Humans ; *Electroencephalography/instrumentation ; *Brain-Computer Interfaces ; *Wearable Electronic Devices ; *Speech/physiology ; Signal Processing, Computer-Assisted ; Machine Learning ; Neural Networks, Computer ; Adult ; Male ; },
abstract = {Speech imagery-the process of mentally simulating speech without vocalization-is a promising approach for brain-computer interfaces (BCIs), enabling assistive communication for individuals with speech impairments or to enhance privacy. However, existing EEG-based speech imagery systems remain impractical for use outside specialized laboratories due to their reliance on high-channel-count and resource-intensive machine learning models running on external computing platforms. In this work, we present the first end-to-end demonstration of EEG-based speech imagery decoding on a low-channel, ultra-low-power wearable device. Building on our previous work on vowel imagery, we introduce an extended framework leveraging the BioGAP platform and VowelNet, a lightweight neural network optimized for embedded speech imagery classification. In particular, we demonstrate state-of-the-art accuracy in the classification of an expanded vocabulary comprising vowels, commands, and rest states (13 classes) with a subject-specific training approach, achieving up to 50.0% for one subject (42.8% average) in multi-class classification. We deploy our model on an embedded biosignal acquisition and processing platform (BioGAP), based on the GAP9 processor, for real-time inference with minimal power consumption (25.93 mW). Our system achieves continuous execution for more than 21 hours on a small LiPo battery while maintaining classification latencies of 40.9 ms. Finally, we also explore the benefits of applying Continual Learning techniques to progressively improve the system's performance throughout its operational lifetime, and we demonstrate that electrodes located on the temporal area contribute the most to the overall accuracy. This work marks a significant step toward practical, real-time, and unobtrusive speech imagery BCIs, unlocking new opportunities for covert communication and assistive technologies.},
}
@article {pmid40407663,
year = {2025},
author = {Astefanei, O and Martu, C and Cozma, S and Radulescu, L},
title = {Cochlear and Bone Conduction Implants in Asymmetric Hearing Loss and Single-Sided Deafness: Effects on Localization, Speech in Noise, and Quality of Life.},
journal = {Audiology research},
volume = {15},
number = {3},
pages = {},
pmid = {40407663},
issn = {2039-4330},
abstract = {BACKGROUND: Single-sided deafness (SSD) and asymmetric hearing loss (AHL) impair spatial hearing and speech perception, often reducing quality of life. Cochlear implants (CIs) and bone conduction implants (BCIs) are rehabilitation options used in SSD and AHL to improve auditory perception and support functional integration in daily life.<br><br>OBJECTIVE: We aimed to evaluate hearing outcomes after auditory implantation in SSD and AHL patients, focusing on localization accuracy, speech-in-noise understanding, tinnitus relief, and perceived benefit.<br><br>METHODS: In this longitudinal observational study, 37 patients (adults and children) received a CI or a BCI according to clinical indications. Outcomes included localization and spatial speech-in-noise assessment, tinnitus ratings, and SSQ12 scores. Statistical analyses used parametric and non-parametric tests (p < 0.05).<br><br>RESULTS: In adult CI users, localization error significantly decreased from 81.9&#xb0; &#xb1; 15.8&#xb0; to 43.7&#xb0; &#xb1; 13.5&#xb0; (p < 0.001). In children, regardless of the implant type (CI or BCI), localization error improved from 74.3&#xb0; to 44.8&#xb0;, indicating a consistent spatial benefit. In adult BCI users, localization error decreased from 74.6&#xb0; to 69.2&#xb0;, but the improvement did not reach statistical significance. Tinnitus severity, measured on a 10-point VAS scale, decreased significantly in CI users (mean reduction: 2.8 &#xb1; 2.0, p < 0.001), while changes in BCI users were small and of limited clinical relevance. SSQ12B/C scores improved in all adult groups, with the largest gains observed in spatial hearing for CI users (2.1 &#xb1; 1.2) and in speech understanding for BCI users (1.6 &#xb1; 0.9); children reported high benefits across all domains. Head shadow yielded the most consistent benefit across all groups (up to 4.9 dB in adult CI users, 3.8 dB in adult BCI users, and 4.6 dB in children). Although binaural effects were smaller in BCI users, positive gains were observed, especially in pediatric cases. Correlation analysis showed that daily device use positively predicted SSQ12 improvement (r = 0.57) and tinnitus relief (r = 0.42), while longer deafness duration was associated with poorer localization outcomes (r = -0.48).<br><br>CONCLUSIONS: CIs and BCIs provide measurable benefits in SSD and AHL rehabilitation. Outcomes vary with age, device, and deafness duration, underscoring the need for early intervention and consistent auditory input.},
}
@article {pmid40406801,
year = {2025},
author = {Moumdjian, RA},
title = {Bioethics of neurotechnologies: a field in effervescence.},
journal = {Neurological research},
volume = {47},
number = {8},
pages = {756-759},
doi = {10.1080/01616412.2025.2499896},
pmid = {40406801},
issn = {1743-1328},
mesh = {Humans ; *Bioethics ; *Brain-Computer Interfaces/ethics ; *Neurosciences/ethics ; },
abstract = {Brain-Computer Interface (BCI) comprises a device that detects brain signals conveying specific intentions and translates them into executable outputs by a machine. It enables neurologically impaired patients to regain some control over their environment, thereby aiding in their rehabilitation. Some authors argue that 'the use of BCI is the greatest ethical challenge that neuroscience faces today. Ethical issues highlighted in the literature include safety, justice, privacy, security, and the balance of risks and benefits.},
}
@article {pmid40403087,
year = {2025},
author = {Essam, AA and Ibrahim, A and Seif Al-Nasr, A and El-Saqa, M and Mohamed, S and Anwar, A and Eldeib, A and Akcakaya, M and Khalaf, A},
title = {Filter bank common spatial pattern and envelope-based features in multimodal EEG-fTCD brain-computer interfaces.},
journal = {PloS one},
volume = {20},
number = {5},
pages = {e0311075},
pmid = {40403087},
issn = {1932-6203},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Ultrasonography, Doppler, Transcranial/methods ; Male ; Adult ; Female ; Young Adult ; Bayes Theorem ; *Brain/physiology ; Algorithms ; },
abstract = {Brain-computer interfaces (BCIs) provide alternative means of communication and control for individuals with severe motor or speech impairments. Multimodal BCIs have been introduced recently to enhance the performance of BCIs utilizing single modality. In this paper, we aim to advance the state of the art in multimodal BCIs combining Electroencephalography (EEG) and functional transcranial Doppler ultrasound (fTCD) by introducing advanced analysis approaches that enhance system performance. Our EEG-fTCD BCIs employ two distinct paradigms to infer user intent: motor imagery (MI) and flickering mental rotation (MR)/word generation (WG) paradigms. In the MI paradigm, we introduce the use of Filter Bank Common Spatial Pattern (FBCSP) for the first time in an EEG-fTCD BCI, while in the flickering MR/WG paradigm, we extend FBCSP application to non-motor imagery tasks. Additionally, we extract previously unexplored time-series features from the envelope of fTCD signals, leveraging richer information from cerebral blood flow dynamics. Furthermore, we employ a Bayesian fusion framework that allows EEG and fTCD to contribute unequally to decision-making. The multimodal EEG-fTCD system achieved high classification accuracies across tasks in both paradigms. In the MI paradigm, accuracies of 94.53%, 94.9%, and 96.29% were achieved for left arm MI vs. baseline, right arm MI vs. baseline, and right arm MI vs. left arm MI, respectively - outperforming EEG-only accuracy by 3.87%, 3.80%, and 5.81%, respectively. In the MR/WG paradigm, the system achieved 95.27%, 85.93%, and 96.97% for MR vs. baseline, WG vs. baseline, and MR vs. WG, respectively, showing accuracy improvements of 2.28%, 4.95%, and 1.56%, respectively compared to EEG-only results. Overall, the proposed analysis approach improved classification accuracy for 5 out of 6 binary classification problems within the MI and MR/WG paradigms, with gains ranging from 0.64% to 9% compared to our previous EEG-fTCD studies. Additionally, our results demonstrate that EEG-fTCD BCIs with the proposed analysis techniques outperform multimodal EEG-fNIRS BCIs in both accuracy and speed, improving classification performance by 2.7% to 24.7% and reducing trial durations by 2-38 seconds. These findings highlight the potential of the proposed approach to advance assistive technologies and improve patient quality of life.},
}
@article {pmid40402697,
year = {2025},
author = {Chaisaen, R and Autthasan, P and Ditthapron, A and Wilaiprasitporn, T},
title = {AlphaGrad: Normalized Gradient Descent for Adaptive Multi-Loss Functions in EEG-Based Motor Imagery Classification.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {10},
pages = {7116-7128},
doi = {10.1109/JBHI.2025.3572197},
pmid = {40402697},
issn = {2168-2208},
mesh = {Humans ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; *Imagination/physiology ; Algorithms ; Neural Networks, Computer ; Adult ; Male ; },
abstract = {In this study, we propose AlphaGrad, a novel adaptive loss blending strategy for optimizing multi-task learning (MTL) models in motor imagery (MI)-based electroencephalography (EEG) classification. AlphaGrad is the first method to automatically adjust multi-loss functions with differing metric scales, including mean square error, cross-entropy, and deep metric learning, within the context of MI-EEG. We evaluate AlphaGrad using two state-of-the-art MTL-based neural networks, MIN2Net and FBMSNet, across four benchmark datasets. Experimental results show that AlphaGrad consistently outperforms existing strategies such as AdaMT, GradApprox, and fixed-weight baselines in classification accuracy and training stability. Compared to baseline static weighting, AlphaGrad achieves over 10% accuracy improvement on subject-independent MI tasks when evaluated on the largest benchmark dataset. Furthermore, AlphaGrad demonstrates robust adaptability across various EEG paradigms-including steady-state visually evoked potential (SSVEP) and event-related potential (ERP), making it broadly applicable to brain-computer interface (BCI) systems. We also provide gradient trajectory visualizations highlighting AlphaGrad's ability to maintain training stability and avoid local minima. These findings underscore AlphaGrad's promise as a general-purpose solution for adaptive multi-loss optimization in biomedical time-series learning.},
}
@article {pmid40401160,
year = {2025},
author = {Zhao, L},
title = {Advances in functional magnetic resonance imaging-based brain function mapping: a deep learning perspective.},
journal = {Psychoradiology},
volume = {5},
number = {},
pages = {kkaf007},
pmid = {40401160},
issn = {2634-4416},
abstract = {Functional magnetic resonance imaging (fMRI) provides a powerful tool for studying brain function by capturing neural activity in a non-invasive manner. Mapping brain function from fMRI data enables researchers to investigate the spatial and temporal dynamics of neural processes, providing insights into how the brain responds to various tasks and stimuli. In this review, we explore the evolution of deep learning-based methods for brain function mapping using fMRI. We begin by discussing various network architectures such as convolutional neural networks, recurrent neural networks, and transformers. We further examine supervised, unsupervised, and self-supervised learning paradigms for fMRI-based brain function mapping, highlighting the strengths and limitations of each approach. Additionally, we discuss emerging trends such as fMRI embedding, brain foundation models, and brain-inspired artificial intelligence, emphasizing their potential to revolutionize brain function mapping. Finally, we delve into the real-world applications and prospective impact of these advancements, particularly in the diagnosis of neural disorders, neuroscientific research, and brain-computer interfaces for decoding brain activity. This review aims to provide a comprehensive overview of current techniques and future directions in the field of deep learning and fMRI-based brain function mapping.},
}
@article {pmid40401149,
year = {2025},
author = {Leong, F and Micera, S and Shokur, S},
title = {Optimization frameworks for bespoke sensory encoding in neuroprosthetics.},
journal = {APL bioengineering},
volume = {9},
number = {2},
pages = {020901},
pmid = {40401149},
issn = {2473-2877},
abstract = {Restoring natural sensation via neuroprosthetics relies on the possibility of encoding complex and nuanced information. For example, an ideal brain-machine interface with sensory feedback would provide the user with sensation about movement, pressure, curvature, texture, etc. Despite advances in neural interfaces that allow for complex stimulation patterns (e.g., multisite stimulation or the possibility of targeting a precise neural ensemble), a key question remains: How can we best exploit the potential of these technologies? The increasing number of electrodes coupled with more parameters being explored leads to an exponential increase in the number of possible combinations, making a brute-force approach, such as systematic search, impractical. This Perspective outlines three different optimization frameworks-namely, the explicit, physiological, and self-optimized methods-allowing one to potentially converge faster toward effective parameters. Although our focus will be on the somatosensory system, these frameworks are flexible and applicable to various sensory systems (e.g., vision) and stimulator types.},
}
@article {pmid40399603,
year = {2025},
author = {Wang, Y and Fukuma, R and Seymour, B and Yang, H and Kishima, H and Yanagisawa, T},
title = {Neurofeedback modulation of insula activity via MEG-based brain-machine interface: a double-blind randomized controlled crossover trial.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {770},
pmid = {40399603},
issn = {2399-3642},
support = {JPMJER1801//MEXT | Japan Science and Technology Agency (JST)/ ; JP19dm0307008//Japan Agency for Medical Research and Development (AMED)/ ; 214251/Z/18/Z//Wellcome Trust (Wellcome)/ ; JP20H05705//MEXT | Japan Society for the Promotion of Science (JSPS)/ ; JP24wm0625517//Japan Agency for Medical Research and Development (AMED)/ ; 22H04998//MEXT | Japan Society for the Promotion of Science (JSPS)/ ; EP/W03509X/1//DH | National Institute for Health Research (NIHR)/ ; 19dm0207070h//Japan Agency for Medical Research and Development (AMED)/ ; 203316//DH | National Institute for Health Research (NIHR)/ ; JPMJMS2012//MEXT | Japan Science and Technology Agency (JST)/ ; /WT_/Wellcome Trust/United Kingdom ; },
mesh = {Humans ; *Neurofeedback/methods ; *Magnetoencephalography/methods ; Double-Blind Method ; Male ; Cross-Over Studies ; *Brain-Computer Interfaces ; Female ; Adult ; Young Adult ; *Insular Cortex/physiology ; Pain Threshold/physiology ; *Cerebral Cortex/physiology ; },
abstract = {Insula activity has often been linked to pain perception, making it a potential target for therapeutic neuromodulation strategies such as neurofeedback. However, it is not known whether insula activity is under cognitive control and, if so, whether this activity is consequently causally related to pain. Here, we conducted a double-blind randomized controlled crossover trial to test the modulation of insula activity and pain thresholds using neurofeedback training. Nineteen healthy subjects underwent neurofeedback training for upmodulation and downmodulation of right insula activity using our magnetoencephalography (MEG)-based brain-machine interface. We observed significant differences in insula activity between the upmodulation and downmodulation training sessions. Furthermore, resting-state insula activity significantly decreased following downmodulation training compared to following upmodulation training. Compared with upmodulation training, downmodulation training was also associated with increased pain thresholds, albeit with no significant interaction effect. These findings show that humans can cognitively modulate insula activity as a potential route to develop therapeutic MEG neurofeedback systems for clinical testing. However, the present findings do not provide direct evidence of a causal link between modulation of insula activity and changes in pain thresholds.},
}
@article {pmid40398443,
year = {2025},
author = {Jehn, C and Kossmann, A and Katerina Vavatzanidis, N and Hahne, A and Reichenbach, T},
title = {CNNs improve decoding of selective attention to speech in cochlear implant users.},
journal = {Journal of neural engineering},
volume = {22},
number = {3},
pages = {},
doi = {10.1088/1741-2552/addb7b},
pmid = {40398443},
issn = {1741-2552},
mesh = {Humans ; *Cochlear Implants ; *Attention/physiology ; *Speech Perception/physiology ; Female ; Male ; Electroencephalography/methods ; Middle Aged ; Adult ; *Neural Networks, Computer ; Aged ; Acoustic Stimulation/methods ; Support Vector Machine ; },
abstract = {Objective. Understanding speech in the presence of background noise such as other speech streams is a difficult problem for people with hearing impairment, and in particular for users of cochlear implants (CIs). To improve their listening experience, auditory attention decoding (AAD) aims to decode the target speaker of a listener from electroencephalography (EEG), and then use this information to steer an auditory prosthesis towards this speech signal. In normal-hearing individuals, deep neural networks (DNNs) have been shown to improve AAD compared to simpler linear models. We aim to demonstrate that DNNs can improve attention decoding in CI users too, which would make them the state-of-the-art candidate for a neuro-steered CI.Approach. To this end, we first collected an EEG dataset on selective auditory attention from 25 bilateral CI users, and then implemented both a linear model as well as a convolutional neural network (CNN) for attention decoding. Moreover, we introduced a novel, objective CI-artifact removal strategy and evaluated its impact on decoding accuracy, alongside learnable speaker classification using a support vector machine (SVM).Main results. The CNN outperformed the linear model across all decision window sizes from 1 to 60 s. Removing CI artifacts modestly improved the CNN's decoding accuracy. With SVM classification, the CNN decoder reached a peak mean decoding accuracy of 74% at the population level for a 60 s decision window.Significance. These results demonstrate the superior potential of CNN-based decoding for neuro-steered CIs, which could improve speech perception of its users in cocktail party situations significantly.},
}
@article {pmid40398442,
year = {2025},
author = {Peterson, V and Spagnolo, V and Galván, CM and Nieto, N and Spies, RD and Milone, DH},
title = {Towards subject-centered co-adaptive brain-computer interfaces based on backward optimal transport.},
journal = {Journal of neural engineering},
volume = {22},
number = {4},
pages = {},
doi = {10.1088/1741-2552/addb7a},
pmid = {40398442},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Male ; Adult ; Female ; *Imagination/physiology ; Young Adult ; *Adaptation, Physiological/physiology ; Algorithms ; },
abstract = {Objective. Controlling a motor imagery brain-computer interface (MI-BCI) can be challenging, requiring several sessions of practice. Electroencephalography (EEG)-based BCIs are particularly affected by cross-session variability. In this scenario, it is crucial to implement co-adaptive systems, where the machine adapts the decoding algorithm while the user learns how to control the BCI. To support the user learning process, it is essential to measure and provide real-time feedback on self-modulation skills. This study aims to develop a method for online assessment of MI modulation capability to build co-adaptive BCIs that improve both user performance and system accuracy.Approach. Backward optimal transport for domain adaptation allows across-session MI-BCI usage without classifier retraining. Using the cued label to guide the adaptation, a supportive backward adaptation (SBA) method is defined. The required model effort to perform a trial adaptation is proposed as an online metric of MI modulation skills. We conducted experiments on both real and simulated data to demonstrate that this metric effectively informs about the the discriminability and stability of the EEG patterns related to the MI task. The proposed metric is validated by means of Riemannian distinctiveness metrics.Main Results. Our findings show that the associated effort when applying SBA provides a meaningful way of evaluating EEG patterns discriminability, being significantly correlated with Riemannian distinctiveness metrics.Significance. This study introduces a novel framework for co-adaptive BCI learning that performs data adaptation while assessing the MI-BCI skills of the user. The proposed SBA approach can enhance BCI performance by facilitating session-to-session adaptation and empowering users with valuable feedback based on their current MI modulation strategy. This framework represents a significant advancement in developing user-centered, co-adaptive MI-BCIs that effectively support and enhance user capabilities.},
}
@article {pmid40398440,
year = {2025},
author = {van der Eerden, JHM and Liu, PC and Villalobos, J and Yanagisawa, T and Grayden, DB and John, SE},
title = {Decoding cortical responses from visual input using an endovascular brain-computer interface.},
journal = {Journal of neural engineering},
volume = {22},
number = {3},
pages = {},
doi = {10.1088/1741-2552/addb7c},
pmid = {40398440},
issn = {1741-2552},
mesh = {Animals ; *Brain-Computer Interfaces ; *Visual Cortex/physiology ; *Evoked Potentials, Visual/physiology ; Sheep ; *Electrocorticography/methods/instrumentation ; *Photic Stimulation/methods ; Electrodes, Implanted ; *Endovascular Procedures/methods/instrumentation ; },
abstract = {Objective.Implantable neural interfaces enable recording of high-quality brain signals that can improve our understanding of brain function. This work examined the feasibility of using a minimally invasive endovascular neural interface (ENI) to record interpretable cortical activity from the visual cortex.Approach. A sheep model (n= 5) was used to record and decode visually evoked potentials from the cortex both with an ENI and a subdural electrode grid. Sets of distinct experimental visual stimuli were presented to attempt decoding from the recorded cortical potentials, using perceptual categories of colour, contrast, movement direction orientation, spatial frequency and temporal frequency. Decoding performances are presented as accuracy scores from K-fold cross-validation of a stratified random forest classification model. The study compared the signal quality and decoding performance between the ENI and electrocorticography (ECoG) electrodes.Main results. Recordings from the ENI array resulted in lower decoding performances than the ECoG array, but the classification scores were significantly above chance in the stimuli categories of colour, contrast, direction and temporal frequency. This study is the first report of visually evoked neural activity using a minimally-invasive ENI.Significance. Overall, the results show that implantable macro-electrodes yield sufficient neural signal definition to discern primary visual percepts, using both endo-vascular and intracranial surgical placements.},
}
@article {pmid40398391,
year = {2025},
author = {Huang, W and Shu, N},
title = {AI-powered integration of multimodal imaging in precision medicine for neuropsychiatric disorders.},
journal = {Cell reports. Medicine},
volume = {6},
number = {5},
pages = {102132},
pmid = {40398391},
issn = {2666-3791},
mesh = {Humans ; *Precision Medicine/methods ; *Multimodal Imaging/methods ; *Mental Disorders/diagnostic imaging/therapy ; *Artificial Intelligence ; *Neuroimaging/methods ; },
abstract = {Neuropsychiatric disorders have complex pathological mechanism, pronounced clinical heterogeneity, and a prolonged preclinical phase, which presents a challenge for early diagnosis and development of precise intervention strategies. With the development of large-scale multimodal neuroimaging datasets and advancement of artificial intelligence (AI) algorithms, the integration of multimodal imaging with AI techniques has emerged as a pivotal avenue for early detection and tailoring individualized treatment for neuropsychiatric disorders. To support these advances, in this review, we outline multimodal neuroimaging techniques, AI methods, and strategies for multimodal data fusion. We highlight applications of multimodal AI based on neuroimaging data in precision medicine for neuropsychiatric disorders, discussing challenges in clinical adoption, their emerging solutions, and future directions.},
}
@article {pmid40398228,
year = {2025},
author = {Xiao, S and Huang, X and He, X and Chen, Z and Li, X and Wei, X and Liu, Q and Dong, H and Zeng, X and Bai, W},
title = {Interactions between curcumin and fish-/bovine-derived (type I and II) collagens: Preparation of nanoparticle and their application in Pickering emulsions.},
journal = {Food chemistry},
volume = {487},
number = {},
pages = {144781},
doi = {10.1016/j.foodchem.2025.144781},
pmid = {40398228},
issn = {1873-7072},
mesh = {*Curcumin/chemistry ; Animals ; *Nanoparticles/chemistry ; Cattle ; Emulsions/chemistry ; Hydrophobic and Hydrophilic Interactions ; *Collagen Type I/chemistry ; Fishes ; *Collagen Type II/chemistry ; Molecular Dynamics Simulation ; *Fish Proteins/chemistry ; Hydrogen Bonding ; Protein Binding ; },
abstract = {This study aims to elucidate the interaction mechanisms between curcumin (Cur) and four collagen subtypes (fish type I [FCI], bovine type I [BCI], fish type II [FCII], bovine type II [BCII]), with parallel characterization of the structural and functional attributes of their derived nanoparticles. Type I Collagen/Cur nanoparticles exhibited superior solution stability compared to type II. Cur binding significantly enhanced the surface hydrophobicity, absolute ζ potential, and surface tension of collagen, while reduced dynamic interfacial tension. The binding type of Cur to collagen was static, and binding process was enthalpy-driven exothermic reaction. Molecular dynamics simulations revealed that hydrophobic interactions, hydrogen bonds, and electrostatic forces dominated the binding process. The binding affinity followed the order: FCI/Cur > BCI/Cur > FCII/Cur > BCII/Cur. The binding sites of Cur to type I collagen and type II collagen were around Ser129-Glu135 and Asn179-Ser183 residues. Collagen/Cur nanoparticle stabilized emulsions and improved oxidative stability and storage modulus.},
}
@article {pmid40395924,
year = {2025},
author = {Russell, M and Hincks, S and Wang, L and Babar, A and Chen, Z and White, Z and Jacob, RJK},
title = {Visualization and workload with implicit fNIRS-based BCI: toward a real-time memory prosthesis with fNIRS.},
journal = {Frontiers in neuroergonomics},
volume = {6},
number = {},
pages = {1550629},
pmid = {40395924},
issn = {2673-6195},
abstract = {Functional Near-Infrared Spectroscopy (fNIRS) has proven in recent time to be a reliable workload-detection tool, usable in real-time implicit Brain-Computer Interfaces. But what can be done in terms of application of neural measurements of the prefrontal cortex beyond mental workload? We trained and tested a first prototype example of a memory prosthesis leveraging a real-time implicit fNIRS-based BCI interface intended to present information appropriate to a user's current brain state from moment to moment. Our prototype implementation used data from two tasks designed to interface with different brain networks: a creative visualization task intended to engage the Default Mode Network (DMN), and a complex knowledge-worker task to engage the Dorsolateral Prefrontal Cortex (DLPFC). Performance of 71% from leave-one-out cross-validation across participants indicates that such tasks are differentiable, which is promising for the development of future applied fNIRS-based BCI systems. Further, analyses within lateral and medial left prefrontal areas indicates promising approaches for future classification.},
}
@article {pmid40395688,
year = {2025},
author = {Tibermacine, IE and Russo, S and Citeroni, F and Mancini, G and Rabehi, A and Alharbi, AH and El-Kenawy, EM and Napoli, C},
title = {Adversarial denoising of EEG signals: a comparative analysis of standard GAN and WGAN-GP approaches.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1583342},
pmid = {40395688},
issn = {1662-5161},
abstract = {INTRODUCTION: Electroencephalography (EEG) signals frequently contain substantial noise and interference, which can obscure clinically and scientifically relevant features. Traditional denoising approaches, such as linear filtering or wavelet thresholding, often struggle with nonlinear or time-varying artifacts. In response, the present study explores a Generative Adversarial Network (GAN) framework to enhance EEG signal quality, focusing on two variants: a conventional GAN model and a Wasserstein GAN with Gradient Penalty (WGAN-GP).<br><br>METHODS: Data were obtained from two distinct EEG datasets: a "healthy" set of 64-channel recordings collected during various motor/imagery tasks, and an "unhealthy" set of 18-channel recordings from individuals with orthopedic impairments. Both datasets underwent comprehensive preprocessing, including band-pass filtering (8-30 Hz), channel standardization, and artifact trimming. The training stage involved adversarial learning, in which a generator sought to reconstruct clean EEG signals while a discriminator (or critic in the case of WGAN-GP) attempted to distinguish between real and generated signals. The model evaluation was conducted using quantitative metrics such as signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), correlation coefficient, mutual information, and dynamic time warping (DTW) distance.<br><br>RESULTS: Experimental findings indicate that adversarial learning substantially improves EEG signal fidelity across multiple quantitative metrics. Specifically, WGAN-GP achieved an SNR of up to 14.47 dB (compared to 12.37 dB for the standard GAN) and exhibited greater training stability, as evidenced by consistently lower relative root mean squared error (RRMSE) values. In contrast, the conventional GAN model excelled in preserving finer signal details, reflected in a PSNR of 19.28 dB and a correlation coefficient exceeding 0.90 in several recordings. Both adversarial frameworks outperformed classical wavelet-based thresholding and linear filtering methods, demonstrating superior adaptability to nonlinear distortions and dynamic interference patterns in EEG time-series data.<br><br>DISCUSSION: By systematically comparing standard GAN and WGAN-GP architectures, this study highlights a practical trade-off between aggressive noise suppression and high-fidelity signal reconstruction. The demonstrated improvements in signal quality underscore the promise of adversarially trained models for applications ranging from basic neuroscience research to real-time brain-computer interfaces (BCIs) in clinical or consumer-grade settings. The results further suggest that GAN-based frameworks can be easily scaled to next-generation wireless networks and complex electrophysiological datasets, offering robust and dynamic solutions to long-standing challenges in EEG denoising.},
}
@article {pmid40395354,
year = {2025},
author = {Zargarian, SS and Rinoldi, C and Ziai, Y and Zakrzewska, A and Fiorelli, R and Gazińska, M and Marinelli, M and Majkowska, M and Hottowy, P and Mindur, B and Czajkowski, R and Kublik, E and Nakielski, P and Lanzi, M and Kaczmarek, L and Pierini, F},
title = {Chronic Probing of Deep Brain Neuronal Activity Using Nanofibrous Smart Conducting Hydrogel-Based Brain-Machine Interface Probes.},
journal = {Small science},
volume = {5},
number = {5},
pages = {2400463},
pmid = {40395354},
issn = {2688-4046},
abstract = {The mechanical mismatch between microelectrode of brain-machine interfaces (BMIs) and soft brain tissue during electrophysiological investigations leads to inflammation, glial scarring, and compromising performance. Herein, a nanostructured, stimuli-responsive, conductive, and semi-interpenetrating polymer network hydrogel-based coated BMIs probe is introduced. The system interface is composed of a cross-linkable poly(N-isopropylacrylamide)-based copolymer and regioregular poly[3-(6-methoxyhexyl)thiophene] fabricated via electrospinning and integrated into a neural probe. The coating's nanofibrous architecture offers a rapid swelling response and faster shape recovery compared to bulk hydrogels. Moreover, the smart coating becomes more conductive at physiological temperatures, which improves signal transmission efficiency and enhances its stability during chronic use. Indeed, detecting acute neuronal deep brain signals in a mouse model demonstrates that the developed probe can record high-quality signals and action potentials, favorably modulating impedance and capacitance. Evaluation of in vivo neuronal activity and biocompatibility in chronic configuration shows the successful recording of deep brain signals and a lack of substantial inflammatory response in the long-term. The development of conducting fibrous hydrogel bio-interface demonstrates its potential to overcome the limitations of current neural probes, highlighting its promising properties as a candidate for long-term, high-quality detection of neuronal activities for deep brain applications such as BMIs.},
}
@article {pmid40395337,
year = {2025},
author = {Yao, J and Zhou, Z and Tong, Q and Li, L and Wei, J and Lu, J and Hu, S and Bao, A and He, H},
title = {Magnetic resonance imaging of postmortem human brain specimens: methodological considerations and prospects in psychoradiology.},
journal = {Psychoradiology},
volume = {5},
number = {},
pages = {kkaf012},
pmid = {40395337},
issn = {2634-4416},
abstract = {Ex vivo magnetic resonance imaging (MRI) has revolutionized psychoradiological research by enabling detailed structural and pathological assessments of the brain in conditions ranging from psychiatric disorders to neurodegenerative diseases. By providing high-resolution images of postmortem brain tissue, ex vivo MRI overcomes several limitations inherent in in vivo imaging, offering unparalleled insights into the underlying pathophysiology of mental disorders. This review critically summarizes the state-of-the-art ex vivo MRI methodologies for neuroanatomical mapping and pathological characterization in psychoradiology, while also establishing standardized specimen processing protocols. Furthermore, we explore the prospects of application in ex vivo MRI in schizophrenia, major depressive disorder and bipolar disorder, highlighting its role in understanding neuroanatomical alterations, disease progression, and the validation of in vivo neuroimaging biomarkers.},
}
@article {pmid40395088,
year = {2025},
author = {Zhang, S and Gu, J and Yang, Y and Li, J and Ni, L},
title = {Evolution Trend of Brain Science Research: An Integrated Bibliometric and Mapping Approach.},
journal = {Brain and behavior},
volume = {15},
number = {5},
pages = {e70451},
pmid = {40395088},
issn = {2162-3279},
support = {2020Z388//Jiangsu Postdoctoral Research Foundation/ ; //Top Talent Support Program for young and middle-aged people of the Wuxi Health Committee/ ; M202033//Wuxi Health Commission Scientific Research Project/ ; 24CC00903//Beijing Academy of Science and Technology Think Tank Research Project/ ; ZYYB05//Wuxi Administration of Traditional Chinese Medicine/ ; },
mesh = {*Bibliometrics ; Humans ; *Biomedical Research/trends ; *Neurosciences/trends ; *Brain/physiology ; United States ; China ; },
abstract = {BACKGROUND: Brain science research is considered the crown jewel of 21st-century scientific research; the United States, the United Kingdom, and Japan have elevated brain science research to a national strategic level. This study employs bibliometric analysis and knowledge graph visualization to map global trends, research hotspots, and collaborative networks in brain science, providing insights into the field's evolving landscape and future directions.<br><br>METHODS: We analyzed 13,590 articles (1990-2023) from the Web of Science Core Collection using CiteSpace and VOSviewer. Metrics included publication volume, co-authorship networks, citation patterns, keyword co-occurrence, and burst detection. Analytical tools such as VOSviewer, CiteSpace, and online bibliometric platforms were employed to facilitate this investigation.<br><br>RESULTS: The United States, China, and Germany dominated research output, with China's publications rising from sixth to second globally post-2016, driven by national initiatives like the China Brain Project. However, China exhibited limited international collaboration compared to the United States and European Union. Key journals included Human Brain Mapping and Journal of Neural Engineering, while emergent themes centered on "task analysis," "deep learning," and "brain-computer interfaces" (BCIs). Research clusters revealed three focal areas: (1) Brain Exploration (e.g., fMRI, diffusion tensor imaging), (2) Brain Protection (e.g., stroke rehabilitation, amyotrophic lateral sclerosis therapies), and (3) Brain Creation (e.g., neuromorphic computing, BCIs integrated with AR/VR). Despite China's high output, its influence lagged in highly cited scholars, reflecting a "quantity-over-quality" challenge.<br><br>CONCLUSION: Brain science research is in a golden period of development. This bibliometric analysis offers the first comprehensive review, encapsulating research trends and progress in brain science. It reveals current research frontiers and crucial directions, offering a strategic roadmap for researchers and policymakers to navigate countries when planning research layouts.},
}
@article {pmid40395013,
year = {2025},
author = {Pyo, YW and Kim, H and Park, HG},
title = {Graphene-Integrated Ultrathin Neural Probe for Multiregional Cortical Recordings.},
journal = {ACS nano},
volume = {19},
number = {21},
pages = {19951-19961},
doi = {10.1021/acsnano.5c03145},
pmid = {40395013},
issn = {1936-086X},
mesh = {*Graphite/chemistry ; Animals ; Mice ; *Somatosensory Cortex/physiology ; *Neurons/physiology ; Electric Stimulation ; Mice, Inbred C57BL ; },
abstract = {Electrophysiological measurement techniques are essential for understanding the functions of the central and peripheral nervous systems. Specifically, noninvasive neural probes, such as surface electrode arrays, provide stable electrophysiological recordings without eliciting an immunological response. However, the ability to capture complex interactions across multiple brain regions is limited by their localized recording site. Here, we present the "large-area NeuroWeb (LNW)", an ultrathin, minimally invasive neural probe designed for extensive cortical recording and stimulation. LNW consists of four recording areas, each containing 16-channel platinum electrodes interconnected by graphene networks. In vivo experiments of the mouse brain exhibit stable, high-quality single-unit spike recordings for up to 7 days post-surgery. Simultaneous high-resolution neural activity recordings are performed across left/right somatosensory cortex and cerebellum, simplifying the experimental procedure by eliminating the necessity for multiple synchronized probes, thus reducing tissue displacement and inflammation. Furthermore, whisker and electrical stimulations demonstrate that the LNW has precise and bidirectional connections with neurons for reliable, region-specific signal acquisition and activation. These findings highlight the capability of LNW to facilitate comprehensive and accurate mapping of neuronal dynamics, thereby advancing brain-machine interfaces and neural prostheses.},
}
@article {pmid40393988,
year = {2025},
author = {Garro, F and Fenoglio, E and Ceroni, I and Forsiuk, I and Canepa, M and Mozzon, M and Bruschi, A and Zippo, F and Laffranchi, M and De Michieli, L and Buccelli, S and Chiappalone, M and Semprini, M},
title = {An EEG-EMG dataset from a standardized reaching task for biomarker research in upper limb assessment.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {831},
pmid = {40393988},
issn = {2052-4463},
mesh = {Humans ; *Electroencephalography ; *Upper Extremity/physiology ; *Electromyography ; Biomarkers ; Adult ; Movement ; },
abstract = {This work describes a dataset containing high-density EEG (hd-EEG) and surface electromiography (sEMG) to capture neuromechanical responses during a reaching task with and without the assistance of an upper-limb exoskeleton. It was designed to explore electrophysiological biomarkers for assessing assistive technologies. Data were collected from 40 healthy participants performing 10 repetitions of three standardized reaching tasks. A custom-designed touch panel was built to standardize and simulate natural upper-limb movements relevant to daily activities. The dataset is formatted according to the Brain Imaging Data Structure (BIDS) standard, in alignment with FAIR principles. To provide an overview of data quality, we present subject-level analyses of event-related spectral perturbation (ERSP), inter-trial coherence (ITC), and event-related synchronization/desynchronization (ERS/ERD) for EEG, along with time- and frequency- domain decomposition for EMG. Beyond providing a methodology for evaluating assistive technologies, this dataset can be used for biosignal processing research, particularly for artifact removal and denoising techniques. It is also valuable for machine learning-based feature extraction, classification, and studying neuromechanical modulations during goal-oriented movements. Additionally, it can support research on human-robot interaction in non-clinical settings, hybrid brain-computer interfaces (BCIs) for robotic control and biomechanical modeling of upper-limb movements.},
}
@article {pmid40393212,
year = {2025},
author = {Lu, Q and Yi, M and Jiang, J},
title = {Bioelectronic nose for ultratrace odor detection via brain-computer interface with olfactory bulb electrode arrays.},
journal = {Biosensors &amp; bioelectronics},
volume = {285},
number = {},
pages = {117585},
doi = {10.1016/j.bios.2025.117585},
pmid = {40393212},
issn = {1873-4235},
mesh = {Animals ; *Olfactory Bulb/physiology ; *Electronic Nose ; Rats ; *Odorants/analysis ; *Biosensing Techniques/instrumentation ; *Brain-Computer Interfaces ; Male ; Smell ; Support Vector Machine ; Trinitrotoluene/isolation &amp; purification ; Equipment Design ; Rats, Sprague-Dawley ; Electrodes ; },
abstract = {Rapid and accurate detection of hazardous volatile compounds is crucial for public health and environmental safety. While conventional methods, including electronic noses, typically exhibit detection thresholds in the parts-per-million (ppm) range, many harmful substances pose risks at parts-per-billion (ppb) concentrations or lower. To address this challenge, we leverage the exceptional sensitivity of the mammalian olfactory system, specifically that of Rattus norvegicus (lab rat), which has evolved to detect and discriminate a vast array of odors at extremely low concentrations. In this study, we developed a novel bio-hybrid system that integrates behavioral training with in vivo electrophysiological recordings from the olfactory bulb (OB). Rats were operantly conditioned to recognize target odors, namely TNT (2,4,6-trinitrotoluene), TNP (2,4,6-trinitrophenol), and chlorine gas (Cl2), at ppb levels. Concurrent with behavioral testing, we recorded neural activity from both the dorsal and ventral OB using a customdesigned, multi-channel electrode array optimized for the rat OB's cytoarchitecture. Electrophysiological data were decoded using a Support Vector Machine algorithm, achieving a mean accuracy of over 90 % in classifying odor identity at ppb concentrations based on OB activity patterns. These results demonstrate the feasibility of utilizing a brain-computer interface with the olfactory system to achieve ultratrace detection of hazardous substances. This bio-hybrid approach offers significantly enhanced sensitivity compared to existing electronic nose technologies, paving the way for highly effective environmental and biomedical sensing applications.},
}
@article {pmid40390719,
year = {2025},
author = {Leung, ES and Mofatteh, M},
title = {Investigating the Feasibility and Safety of Osseointegration With Neural Interfaces for Advanced Prosthetic Control.},
journal = {Cureus},
volume = {17},
number = {4},
pages = {e82567},
pmid = {40390719},
issn = {2168-8184},
abstract = {Osseointegrated neural interfaces (ONI), particularly in conjunction with peripheral nerve interfaces (PNIs), have emerged as a promising advancement for intuitive neuroprosthetics. PNIs can decode neural signals and allow precise prosthetic movement control and bidirectional communication for haptic feedback, while osseointegration can address limitations of traditional socket-based prosthetics, such as poor stability, limited dexterity, and lack of sensory feedback. This review explores advancements in ONIs, including screw-fit and press-fit systems and their integration with PNIs for bidirectional communication. ONIs integrated with PNIs (OIPNIs) have shown improvements in signal fidelity, motor control, and sensory feedback compared to popular surface electromyography (sEMG) systems. Additionally, emerging technologies such as hybrid electrode designs (e.g., cuff and sieve electrode (CASE)) and regenerative peripheral nerve interfaces (RPNIs) show potential for enhancing selectivity and reducing complications such as micromotion and scarring. Despite these advances, challenges remain, including infection risk, electrode degradation, and variability in long-term signal stability. Osseointegration combined with advanced neural interfaces represents a transformative approach to prosthetic control, offering more natural and intuitive movement with sensory feedback. Further research is needed to address long-term biocompatibility, reduce surgical invasiveness, and explore emerging technologies such as machine learning for personalized ONI designs. The findings of this review underscore the potential of ONIs to enhance embodiment and quality of life for amputees and highlight current pitfalls and possible areas of improvement and future research.},
}
@article {pmid40389429,
year = {2025},
author = {Karpowicz, BM and Ali, YH and Wimalasena, LN and Sedler, AR and Keshtkaran, MR and Bodkin, K and Ma, X and Rubin, DB and Williams, ZM and Cash, SS and Hochberg, LR and Miller, LE and Pandarinath, C},
title = {Stabilizing brain-computer interfaces through alignment of latent dynamics.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {4662},
pmid = {40389429},
issn = {2041-1723},
support = {K12 HD073945/HD/NICHD NIH HHS/United States ; R01 NS074044/NS/NINDS NIH HHS/United States ; DP2 NS127291/NS/NINDS NIH HHS/United States ; R01 NS053603/NS/NINDS NIH HHS/United States ; T32 EB025816/EB/NIBIB NIH HHS/United States ; RF1 DA055667/DA/NIDA NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Animals ; *Motor Cortex/physiology ; Macaca mulatta ; Neural Networks, Computer ; Male ; Movement/physiology ; Humans ; },
abstract = {Intracortical brain-computer interfaces (iBCIs) restore motor function to people with paralysis by translating brain activity into control signals for external devices. In current iBCIs, instabilities at the neural interface result in a degradation of decoding performance, which necessitates frequent supervised recalibration using new labeled data. One potential solution is to use the latent manifold structure that underlies neural population activity to facilitate a stable mapping between brain activity and behavior. Recent efforts using unsupervised approaches have improved iBCI stability using this principle; however, existing methods treat each time step as an independent sample and do not account for latent dynamics. Dynamics have been used to enable high-performance prediction of movement intention, and may also help improve stabilization. Here, we present a platform for Nonlinear Manifold Alignment with Dynamics (NoMAD), which stabilizes decoding using recurrent neural network models of dynamics. NoMAD uses unsupervised distribution alignment to update the mapping of nonstationary neural data to a consistent set of neural dynamics, thereby providing stable input to the decoder. In applications to data from monkey motor cortex collected during motor tasks, NoMAD enables accurate behavioral decoding with unparalleled stability over weeks- to months-long timescales without any supervised recalibration.},
}
@article {pmid40387950,
year = {2025},
author = {Wu, P and Zhu, J and He, Q and Wang, Z and Shi, L},
title = {Visual numerical cognition in pigeons: conformity to the Weber-Fechner law.},
journal = {Animal cognition},
volume = {28},
number = {1},
pages = {39},
pmid = {40387950},
issn = {1435-9456},
mesh = {Animals ; *Columbidae/physiology ; *Cognition ; *Visual Perception ; Male ; },
abstract = {As representatives of a basal bird lineage, pigeons have exhibited remarkable visual numerical cognition, comparable even to that of monkeys. Nevertheless, whether visual numerical cognition in pigeons conforms to the Weber-Fechner law remains unknown. To address this, we designed a fully automated apparatus tailored for pigeons and used it to train them to perform a delayed match-to-numerosity task. The results showed that on a linear scale, pigeons represented smaller numerosities with higher precision and larger numerosities with lower precision, exhibiting a numerical magnitude effect. When the linear scale was compressed into a logarithmic scale, this magnitude effect was offset, resulting in similar representational characteristics across different numerosities. This finding suggests that the mental number line of pigeons is logarithmic rather than linear, consistent with the Weber-Fechner law. While biological brains seek precision in representing numerical information, they must also take computational load into account. This representational strategy may be the optimal outcome of the trade-off between computational precision and computational load that biological brains have achieved through long-term evolution.},
}
@article {pmid40382989,
year = {2025},
author = {Wang, T and Dai, Q and Xiong, W},
title = {Escarcitys: A framework for enhancing medical image classification performance in scarcity of trainable samples scenarios.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {189},
number = {},
pages = {107573},
doi = {10.1016/j.neunet.2025.107573},
pmid = {40382989},
issn = {1879-2782},
mesh = {Humans ; *Deep Learning ; Neural Networks, Computer ; *Diagnostic Imaging/classification/methods ; *Image Processing, Computer-Assisted/methods ; },
abstract = {In the field of healthcare, the acquisition and annotation of medical images present significant challenges, resulting in a scarcity of trainable samples. This data limitation hinders the performance of deep learning models, creating bottlenecks in clinical applications. To address this issue, we construct a framework (EScarcityS) aimed at enhancing the success rate of disease diagnosis in scarcity of trainable medical image scenarios. Firstly, considering that Transformer-based deep learning networks rely on a large amount of trainable data, this study takes into account the unique characteristics of pathological regions. By extracting the feature representations of all particles in medical images at different granularities, a multi-granularity Transformer network (MGVit) is designed. This network leverages additional prior knowledge to assist the Transformer network during training, thereby reducing the data requirement to some extent. Next, the importance maps of particles at different granularities, generated by MGVit, are fused to construct disease probability maps corresponding to the images. Based on these maps, a disease probability map-guided diffusion generation model is designed to generate more realistic and interpretable synthetic data. Subsequently, authentic and synthetical data are mixed and used to retrain MGVit, aiming to enhance the accuracy of medical image classification in scarcity of trainable medical image scenarios. Finally, we conducted detailed experiments on four real medical image datasets to validate the effectiveness of EScarcityS and its specific modules.},
}
@article {pmid40382679,
year = {2025},
author = {Covelli, E and Filippi, C and Lazzerini, F and Tromboni, E and Tarentini, S and Pizzolante, S and Forli, F and Berrettini, S and Bruschini, L},
title = {Traditional and adaptive speech audiometry in single-sided deaf (SSD) subjects rehabilitated by bone conductive implants (BCI), quality of life and long-term utilization.},
journal = {Acta oto-laryngologica},
volume = {145},
number = {7},
pages = {633-639},
doi = {10.1080/00016489.2025.2504032},
pmid = {40382679},
issn = {1651-2251},
mesh = {Humans ; *Quality of Life ; Female ; Male ; Retrospective Studies ; Middle Aged ; Adult ; *Bone Conduction ; *Hearing Loss, Unilateral/rehabilitation ; *Audiometry, Speech/methods ; Aged ; Young Adult ; },
abstract = {BACKGROUND: Single-sided deafness (SSD) encompasses the presence of a profoundly deaf ear with a normal, contralateral one. Patients with SSD may have difficulty with speech intelligibility in noise and localizing sounds.<br><br>AIMS/OBJECTIVES: This retrospective study aims to evaluate the long-term effectiveness of bone conduction implant (BCI) in a group of patients with SSD.<br><br>MATERIAL AND METHODS: Audiologic benefit was assessed through conventional speech audiometry and adaptive Matrix test. Impact on quality of life was evaluated with the Glasgow Benefit Inventory (GBI) questionnaire. BCI usage data were also obtained from each subject.<br><br>RESULTS: Thirty-two patients were included. No statistically significant improvements were found at standard audiometric tests using BCI, but at Matrix test the mean SRT is reached at S/N -1.16&#x2009;dB without BCI and -2.07 with BCI with a statistically significant difference (p&#x2009;=&#x2009;0.026). The mean GBI score was 25.12, ranging from -8.3 to 47.2. Ten subjects (31%) discontinued the BCI use overtime.<br><br>CONCLUSIONS AND SIGNIFICANCE: Benefit assessment of BCI in SSD recipients can be difficult. Adaptive audiometric test could be useful. Quality of life measures seem to suggest potential 'beyond-auditory' benefits. SSD recipients can be inconsistent users of BCI.},
}
@article {pmid40382338,
year = {2025},
author = {Zhou, S and Zhu, Y and Du, A and Niu, S and Du, Y and Yang, Y and Chen, W and Du, S and Sun, L and Liu, Y and Wu, H and Lou, H and Li, XM and Duan, S and Yang, H},
title = {A midbrain circuit mechanism for noise-induced negative valence coding.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {4610},
pmid = {40382338},
issn = {2041-1723},
support = {LR24C090001//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
mesh = {Animals ; *Ventral Tegmental Area/physiology/cytology ; *Noise ; Mice ; *Inferior Colliculi/physiology/cytology ; GABAergic Neurons/physiology/metabolism ; Male ; Mice, Inbred C57BL ; Acoustic Stimulation ; *Emotions/physiology ; Geniculate Bodies/physiology ; *Mesencephalon/physiology ; Auditory Pathways/physiology ; Optogenetics ; Auditory Perception/physiology ; Female ; Dopamine/metabolism ; Neurons/physiology ; },
abstract = {Unpleasant sounds elicit a range of negative emotional reactions, yet the underlying neural mechanisms remain largely unknown. Here we show that glutamatergic neurons in the central inferior colliculus (CIC[glu]) relay noise information to GABAergic neurons in the ventral tegmental area (VTA[GABA]) via the cuneiform nucleus (CnF), encoding negative emotions in mice. In contrast, the CIC[glu]→medial geniculate (MG) canonical auditory pathway processes salient stimuli. By combining viral tracing, calcium imaging, and optrode recording, we demonstrate that the CnF acts downstream of CIC[glu] to convey negative valence to the mesolimbic dopamine system by activating VTA[GABA] neurons. Optogenetic or chemogenetic inhibition of any connection within the CIC[glu]→CnF[glu] → VTA[GABA] circuit, or direct excitation of the mesolimbic dopamine (DA) system is sufficient to alleviate noise-induced negative emotion perception. Our findings highlight the significance of the CIC[glu]→CnF[glu] → VTA[GABA] circuit in coping with acoustic stressors.},
}
@article {pmid40381460,
year = {2025},
author = {Qi, G and Zhao, S and Yu, J and Li, P and Guan, W},
title = {Recognizing autonomous driving disengagement scenarios using the transferable knowledge from human driver's EEG cognitive data.},
journal = {Accident; analysis and prevention},
volume = {219},
number = {},
pages = {108102},
doi = {10.1016/j.aap.2025.108102},
pmid = {40381460},
issn = {1879-2057},
mesh = {Humans ; *Electroencephalography ; *Automobile Driving/psychology ; Male ; *Cognition/physiology ; Adult ; Female ; Computer Simulation ; },
abstract = {Without human participation in driving operations, the adoption of autonomous driving (AD) technology greatly enhances driving safety by reducing human errors. Even though AD can handle common scenarios properly, some exceptions still call for the human takeover with AD failing to engage due to the incomprehensible or intensely conflict situations that rarely occur. To help AD understand and recognize the disengagement scenarios effectively, this paper incorporates the human electroencephalogram (EEG) cognitive data into modeling and proposes a transfer learning framework to let AD absorb the integrative knowledge from the manual driving (MD). Several disengagement scenarios are designed using a driving simulator and EEG data are collected from both "drivers" in MD and "supervisors" in AD. A conditional maximum mean discrepancy (CMMD) function is introduced to identify the common brain activity characteristics, allowing the recognition model to be transferred from the cognitively demanding domain of MD to the less demanding domain of AD. The results indicate that the proposed model can achieve an 80 % recognition rate for typical disengagement scenarios, such as static obstacles, intersection conflict and vehicle cut-in, using only 30 % of AD training labels. The transferable common feature space from EEG data improves the recognition accuracy by 21.2 % compared with the model only using AD domain data. By accurately recognizing the type of disengagement scenarios, the AD system can activate appropriate safety mechanisms or provide more explicit takeover prompts, which could effectively reduce the risk of accidents due to delayed or incorrect takeovers.},
}
@article {pmid40380329,
year = {2025},
author = {Chen, W and Chen, H and Jiang, W and Chen, C and Xu, M and Ruan, H and Chen, H and Yu, Z and Chen, S},
title = {Heart rate variability and heart rate asymmetry in adolescents with major depressive disorder during nocturnal sleep period.},
journal = {BMC psychiatry},
volume = {25},
number = {1},
pages = {497},
pmid = {40380329},
issn = {1471-244X},
support = {A20240472//Hangzhou Municipal General Medical and Health Plan/ ; },
mesh = {Humans ; *Depressive Disorder, Major/physiopathology ; *Heart Rate/physiology ; Adolescent ; Male ; Female ; Electrocardiography ; *Sleep/physiology ; Case-Control Studies ; },
abstract = {BACKGROUND: Although reduced heart rate variability (HRV) has been observed in adolescents with major depressive disorder (MDD), substantial between-study heterogeneity and conflicting outcomes exist. Moreover, few studies have investigated heart rate asymmetry (HRA) features despite the high sensitivity of nonlinear indices to heart rate fluctuations. This study aimed to investigate the variations in HRV measures, especially the nonlinear features of HRA, among adolescents with MDD during the nocturnal sleep period.<br><br>METHODS: Adolescents with MDD and healthy controls completed the clinical assessment of depressive symptom severity and sleep quality followed by a three-night sleep electrocardiogram (ECG) monitoring. Traditional time-domain and frequency-domain HRV measures, nonlinear HRA measures, and the prevalence of different HRA forms and HRA compensation were calculated.<br><br>RESULTS: A total of 61 participants with 154 nocturnal ECG time series were available for analysis. Vagally-mediated HRV measures, such as RMSSD, PNN50, and HF, as well as C1d were statistically lower in clinically depressed adolescents compared with healthy controls, whereas C2d was significantly higher. A substantial decrease in the prevalence of short-term HRA, long-term HRA, and the corresponding compensation effect were also observed. In contrast to the medium to large effect sizes observed in traditional HRV indices, nonlinear HRA features showed extremely large effect sizes in discriminating MDD (C1d: Cohen's d= -&#x2009;1.38; C2d: Cohen's d&#x2009;=&#x2009;1.11), and exhibited a statistical correlation with the severity of depression (C1d: rho = -&#x2009;0.269; C2d: rho&#x2009;=&#x2009;0.243). Moreover, there were no significant differences in the distributions of nocturnal HRA measures collected over various nights.<br><br>CONCLUSION: Adolescents with MDD suffered a significant decrease in vagal tone compared to healthy controls, and the features focusing on the directionality of heart rate variations may provide further information on cardiac autonomic activity associated with depression.},
}
@article {pmid40379686,
year = {2025},
author = {Bom, MS and Brak, AMA and Raemaekers, M and Ramsey, NF and Vansteensel, MJ and Branco, MP},
title = {Large-scale fMRI dataset for the design of motor-based Brain-Computer Interfaces.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {804},
pmid = {40379686},
issn = {2052-4463},
mesh = {Humans ; *Brain-Computer Interfaces ; *Magnetic Resonance Imaging ; Child ; Adolescent ; Aged ; Adult ; Aged, 80 and over ; Middle Aged ; Young Adult ; Male ; Female ; },
abstract = {Functional Magnetic Resonance Imaging (fMRI) data is commonly used to map sensorimotor cortical organization and to localise electrode target sites for implanted Brain-Computer Interfaces (BCIs). Functional data recorded during motor and somatosensory tasks from both adults and children specifically designed to map and localise BCI target areas throughout the lifespan is rare. Here, we describe a large-scale dataset collected from 155 human participants while they performed motor and somatosensory tasks involving the fingers, hands, arms, feet, legs, and mouth region. The dataset includes data from both adults and children (age range: 6-89 years) performing a set of standardized tasks. This dataset is particularly relevant to study developmental patterns in motor representation on the cortical surface and for the design of paediatric motor-based implanted BCIs.},
}
@article {pmid40378852,
year = {2025},
author = {Ding, W and Liu, A and Cheng, L and Chen, X},
title = {Data augmentation using masked principal component representation for deep learning-based SSVEP-BCIs.},
journal = {Journal of neural engineering},
volume = {22},
number = {3},
pages = {},
doi = {10.1088/1741-2552/add9d1},
pmid = {40378852},
issn = {1741-2552},
mesh = {*Deep Learning ; Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Evoked Potentials, Visual/physiology ; *Principal Component Analysis/methods ; Photic Stimulation/methods ; Male ; Adult ; },
abstract = {Objective.Data augmentation has been demonstrated to improve the classification accuracy of deep learning models in steady-state visual evoked potential-based brain-computer interfaces (BCIs), particularly when dealing with limited electroencephalography (EEG) data. However, current data augmentation methods often rely on signal-level manipulations, which may lead to significant distortion of EEG signals. To overcome this limitation, this study proposes a component-level data augmentation method called masked principal component representation (MPCR).Approach.MPCR utilizes a principal component-based reconstruction approach, integrating a random masking strategy applied to principal component representations. Specifically, certain principal components are randomly selected and set to zero, thereby introducing random perturbations in the reconstructed samples. Furthermore, reconstructing samples via linear combinations of the remaining components effectively preserves the primary inherent structure of EEG signals. By expanding the input space covered by training samples, MPCR helps the trained model learn more robust features. To validate the efficacy of MPCR, experiments are performed on two widely utilized public datasets.Main results.Experimental results indicate that MPCR substantially enhances classification accuracy across diverse deep learning models. Additionally, in comparison to various state-of-the-art data augmentation approaches, MPCR demonstrates both greater performance and high compatibility.Significance.This study proposes a simple yet effective component-level data augmentation method, contributing valuable insights for advancing data augmentation methods in EEG-based BCIs.},
}
@article {pmid40377015,
year = {2025},
author = {Yang, T and Zhang, D and Huang, H and Liu, F and Wu, J and Ma, X and Liu, S and Huang, M and Zhou, YD and Shen, Y},
title = {Astrocytic mGluR5-dependent calcium hyperactivity promotes amyloid-β pathology and cognitive impairment.},
journal = {Brain : a journal of neurology},
volume = {},
number = {},
pages = {},
doi = {10.1093/brain/awaf186},
pmid = {40377015},
issn = {1460-2156},
abstract = {Astrocytic dysfunction is a crucial factor for the pathogenesis of Alzheimer's disease. Metabotropic glutamate receptor 5 (mGluR5) is ubiquitously expressed in the brain and is a key molecule that regulates synaptic transmission and plasticity. It has been shown that mGluR5 is elevated in astrocytes in Alzheimer's disease. However, it remains elusive how astrocytic mGluR5 contributes to the pathogenesis of Alzheimer's disease. Here, we first quantified a high expression level of astrocytic mGluR5 in the hippocampus of Alzheimer's disease brains and demonstrated that the expression of astrocytic mGluR5 was positively correlated with Alzheimer's disease progression in both humans and mice. Upregulating astrocytic mGluR5 in the CA1 area at an early stage accelerated, whereas downregulating these receptors rescued, Aβ pathology and cognitive impairment in Alzheimer's disease mice. Moreover, the activation of mGluR5 led to calcium hyperactivity in astrocytes, causing Aβ pathology progression due to dysregulated Aβ uptake and degradation in astrocytes. Importantly, attenuating astrocytic calcium hyperactivity in the hippocampal CA1 area in the prodromal phase ameliorated Aβ pathology and cognitive defects in Alzheimer's disease mice. Our findings thus reveal a fundamental contribution of astrocytic mGluR5 in presymptomatic Alzheimer's disease that may serve as a potential diagnostic and therapeutic target for early Alzheimer's disease pathogenesis.},
}
@article {pmid40374051,
year = {2025},
author = {Wang, M and Wang, Y and Yang, Y},
title = {Dynamic and low-dimensional modeling of brain functional connectivity on Riemannian manifolds.},
journal = {NeuroImage},
volume = {314},
number = {},
pages = {121243},
doi = {10.1016/j.neuroimage.2025.121243},
pmid = {40374051},
issn = {1095-9572},
mesh = {Humans ; *Brain/physiology ; *Models, Neurological ; Algorithms ; *Connectome/methods ; Magnetic Resonance Imaging/methods ; },
abstract = {Modeling brain functional connectivity (FC) is key in investigating brain functions and dysfunctions. FC is typically quantified by symmetric positive definite (SPD) matrices that are located on a Riemannian manifold rather than the regular Euclidean space, whose modeling faces three challenges. First, FC can be time-varying and the temporal dynamics of FC matrix time-series need to be modeled within the constraint of the SPD Riemannian manifold geometry, which remains elusive. Second, the FC matrix time-series exhibits considerable stochasticity, whose probability distribution is difficult to model on the Riemannian manifold. Third, FC matrices are high-dimensional and dimensionality reduction methods for SPD matrix time-series are still lacking. Here, we develop a Riemannian state-space modeling framework to simultaneously address the challenges. First, we construct a new Riemannian state-space model (RSSM) to define a hidden SPD matrix state to achieve dynamic, stochastic, and low-dimensional modeling of FC matrix time-series on the SPD Riemannian manifold. Second, we develop a new Riemannian Particle Filter (RPF) algorithm to estimate the hidden low-dimensional SPD matrix state and predict the FC matrix time-series. Third, we develop a new Riemannian Expectation Maximization (REM) algorithm to fit the RSSM parameters. We evaluate the proposed RSSM, RPF, and REM using simulation and real-world EEG datasets, demonstrating that the RSSM enables accurate prediction of the EEG FC time-series and classification of emotional states, outperforming traditional Euclidean methods. Our results have implications for modeling brain FC on the SPD Riemannian manifold to study various brain functions and dysfunctions.},
}
@article {pmid40373768,
year = {2025},
author = {Luo, T and Liu, C and Cheng, T and Zhao, GQ and Huang, Y and Luan, JY and Guo, J and Liu, X and Wang, YF and Dong, Y and Xiao, Y and He, E and Sun, RZ and Chen, X and Chen, J and Ma, J and Megason, S and Ji, J and Xu, PF},
title = {Establishing dorsal-ventral patterning in human neural tube organoids with synthetic organizers.},
journal = {Cell stem cell},
volume = {32},
number = {7},
pages = {1071-1086.e8},
doi = {10.1016/j.stem.2025.04.011},
pmid = {40373768},
issn = {1875-9777},
mesh = {*Organoids/cytology/metabolism ; Humans ; *Neural Tube/cytology/embryology/metabolism ; Animals ; *Body Patterning ; Zebrafish ; Pluripotent Stem Cells/cytology/metabolism ; Wnt Signaling Pathway ; Mice ; },
abstract = {Precise dorsal-ventral (D-V) patterning of the neural tube (NT) is essential for the development and function of the central nervous system. However, existing models for studying NT D-V patterning and related human diseases remain inadequate. Here, we present organizers derived from pluripotent stem cell aggregate fusion ("ORDER"), a method that establishes opposing BMP and SHH gradients within neural ectodermal cell aggregates. Using this approach, we generated NT organoids with ordered D-V patterning from both zebrafish and human pluripotent stem cells (hPSCs). Single-cell transcriptomic analysis revealed that the synthetic human NT organoids (hNTOs) closely resemble the human embryonic spinal cord at Carnegie stage 12 (CS12) and exhibit greater similarity to human NT than to mouse models. Furthermore, using the hNTO model, we demonstrated the critical role of WNT signaling in regulating intermediate progenitors, modeled TCTN2-related D-V patterning defects, and identified a rescue strategy.},
}
@article {pmid40372852,
year = {2025},
author = {Lo, YT and Maggi, A and Wu, K and Zhong, H and Choi, W and Nguyen, TD and Abedi, A and Agyeman, K and Sakellaridi, S and Reggie Edgerton, V and Kreydin, E and Lee, D and Sideris, C and Liu, CY and Christopoulos, VN},
title = {Exploring the Feasibility of Bidirectional Spinal Cord Machine Interface Through Sensing and Stimulation of Axonal Bundles.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {2004-2012},
doi = {10.1109/TNSRE.2025.3570324},
pmid = {40372852},
issn = {1558-0210},
mesh = {*Axons/physiology ; *Spinal Cord/physiology ; Feasibility Studies ; Action Potentials/physiology ; Animals ; Spinal Cord Injuries/rehabilitation/physiopathology ; Microelectrodes ; Electrodes, Implanted ; Electric Stimulation ; Rats ; Proprioception/physiology ; Rats, Sprague-Dawley ; Male ; Touch/physiology ; Female ; },
abstract = {Spinal cord injury (SCI) patients experience long-term deficits in motor and sensory functions. While brain-machine interface (BMI) has shown great promise for restoring neurological functions after SCI, spinal cord-machine interface (SCMI) offers unique advantages, such as more defined somatotopy and the compact organization of neural elements in the spinal cord. In the current study, we aim to demonstrate the feasibility of sensing and evoking compound action potentials (CAPs) via electrode implantation in spinal cord axonal bundles, an essential prerequisite for advancing SCMI development. To do so, we designed microelectrode arrays (MEA) optimized for recording and stimulation in the spinal cord. For sensory mapping, the MEAs were inserted into the lumbar dorsal column (i.e., the fasciculus gracilis) to determine somatotopic representations corresponding to tactile stimulation across lower body regions and assess proprioceptive signals with varying hip positions. For stimulations, at the L3 level, we delivered electrical pulses both rostrally, along ascending afferent tracts (dorsal column), and caudally, down descending corticospinal tract. We successfully captured axonal CAPs from the dorsal columns with high spatial precision that corresponded to known dermatomal somatotopy. Proprioceptive changes produced by abduction at the hip resulted in modulation of discharge frequency in the dorsal column axons. We demonstrated that stimulation pulses emitted by a caudally placed electrode could be propagated up the ascending fibers and be intercepted by a rostrally placed electrode array along the same axonal tracts. We also confirmed that electrical pulses can be directed down descending corticospinal tracts resulting in specific activations of lower limb muscles. These findings set a critical groundwork for developing closed-loop, bidirectional SCMI systems capable of sensing and modulating spinal cord activity.},
}
@article {pmid40371570,
year = {2025},
author = {Ding, P and Tan, L and Pan, H and Gong, A and Nan, W and Fu, Y},
title = {The Lack of Neurofeedback Training Regulation Guidance and Process Evaluation May be a Source of Controversy in Post-Traumatic Stress Disorder-Neurofeedback Research: A Systematic Review and Statistical Analysis.},
journal = {Brain connectivity},
volume = {},
number = {},
pages = {},
doi = {10.1089/brain.2024.0084},
pmid = {40371570},
issn = {2158-0022},
abstract = {Objectives: Neurofeedback (NF) based on brain-computer interface (BCI) is an important direction in adjunctive interventions for post-traumatic stress disorder (PTSD). However, existing research lacks comprehensive methodologies and experimental designs. There are concerns in the field regarding the effectiveness and mechanistic interpretability of NF, prompting this study to conduct a systematic analysis of primary NF techniques and research outcomes in PTSD modulation. The study aims to explore reasons behind these concerns and propose directions for addressing them. Methods: A search conducted in the Web of Science database up to December 1, 2023, yielded 111 English articles, of which 80 were excluded based on predetermined criteria irrelevant to this study. The remaining 31 original studies were included in the literature review. A checklist was developed to assess the robustness and credibility of these 31 studies. Subsequently, these original studies were classified into electroencephalogram-based NF (EEG-NF) and functional magnetic resonance imaging-based NF (fMRI-NF) based on BCI type. Data regarding target brain regions, target signals, modulation protocols, control group types, assessment methods, data processing strategies, and reported outcomes were extracted and synthesized. Consensus theories from existing research and directions for future improvements in related studies were distilled. Results: Analysis of all included studies revealed that the average sample size of PTSD patients in EEG and fMRI NF studies was 17.4 (SD 7.13) and 14.6 (SD 6.37), respectively. Due to sample and neurofeedback training protocol constraints, 93% of EEG-NF studies and 87.5% of fMRI-NF studies used traditional statistical methods, with minimal utilization of basic machine learning (ML) methods and no studies utilizing deep learning (DL) methods. Apart from approximately 25% of fMRI NF studies supporting exploratory psychoregulatory strategies, the remaining EEG and fMRI studies lacked explicit NF modulation guidance. Only 13% of studies evaluated NF effectiveness methods involving signal classification, decoding during the NF process, and lacking in process monitoring and assessment means. Conclusion: In summary, NF holds promise as an adjunctive intervention technique for PTSD, potentially aiding in symptom alleviation for PTSD patients. However, improvements are necessary in the process evaluation mechanisms for PTSD-NF, clarity in NF modulation guidance, and development of ML/DL methods suitable for PTSD-NF with small sample sizes. To address these challenges, it is crucial to adopt more rigorous methodologies for monitoring NF, and future research should focus on the integration of advanced data analysis techniques to enhance the effectiveness and precision of PTSD-NF interventions. Impact Statement The implications of this study are to address the limited application of Neurofeedback training (NFT) in post-traumatic stress disorder (PTSD) research, where a significant portion of the approaches, foundational research, and conclusions lack consensus. There is a notable absence of retrospective statistical analyses on NFT interventions for PTSD. This study provides a comprehensive statistical analysis and discussion of existing research, offering valuable insights for future studies. The findings hold significance for researchers, clinicians, and practitioners in the field, providing a foundation for informed, evidence-based interventions for PTSD treatment.},
}
@article {pmid40370566,
year = {2025},
author = {Hong, W and Ma, H and Yang, Z and Wang, J and Peng, B and Wang, L and Du, Y and Yang, L and Zhang, L and Li, Z and Huang, H and Zhu, D and Yang, B and He, Q and Wang, J and Weng, Q},
title = {Optineurin restrains CCR7 degradation to guide type II collagen-stimulated dendritic cell migration in rheumatoid arthritis.},
journal = {Acta pharmaceutica Sinica. B},
volume = {15},
number = {3},
pages = {1626-1642},
pmid = {40370566},
issn = {2211-3835},
abstract = {Dendritic cells (DCs) serve as the primary antigen-presenting cells in autoimmune diseases, like rheumatoid arthritis (RA), and exhibit distinct signaling profiles due to antigenic diversity. Type II collagen (CII) has been recognized as an RA-specific antigen; however, little is known about CII-stimulated DCs, limiting the development of RA-specific therapeutic interventions. In this study, we show that CII-stimulated DCs display a preferential gene expression profile associated with migration, offering a new perspective for targeting DC migration in RA treatment. Then, saikosaponin D (SSD) was identified as a compound capable of blocking CII-induced DC migration and effectively ameliorating arthritis. Optineurin (OPTN) is further revealed as a potential SSD target, with Optn deletion impairing CII-pulsed DC migration without affecting maturation. Function analyses uncover that OPTN prevents the proteasomal transport and ubiquitin-dependent degradation of C-C chemokine receptor 7 (CCR7), a pivotal chemokine receptor in DC migration. Optn-deficient DCs exhibit reduced CCR7 expression, leading to slower migration in CII-surrounded environment, thus alleviating arthritis progression. Our findings underscore the significance of antigen-specific DC activation in RA and suggest OPTN is a crucial regulator of CII-specific DC migration. OPTN emerges as a promising drug target for RA, potentially offering significant value for the therapeutic management of RA.},
}
@article {pmid40369268,
year = {2025},
author = {Pan, S and Cai, Y and Liu, R and Jiang, S and Zhao, H and Jiang, J and Lin, Z and Liu, Q and Lu, H and Liang, S and Fan, W and Chen, X and Wu, Y and Wang, F and Chen, Z and Hu, R and Yang, L},
title = {Targeting 5-HT to Alleviate Dose-Limiting Neurotoxicity in Nab-Paclitaxel-Based Chemotherapy.},
journal = {Neuroscience bulletin},
volume = {41},
number = {7},
pages = {1229-1245},
pmid = {40369268},
issn = {1995-8218},
mesh = {*Paclitaxel/adverse effects/toxicity ; Animals ; *Albumins/toxicity/adverse effects ; *Serotonin/blood/metabolism ; Mice ; Humans ; Male ; Female ; *Venlafaxine Hydrochloride/pharmacology/therapeutic use ; *Neurotoxicity Syndromes/drug therapy/etiology/metabolism ; Middle Aged ; Schwann Cells/drug effects/metabolism ; *Peripheral Nervous System Diseases/chemically induced/drug therapy ; *Antineoplastic Agents ; },
abstract = {Chemotherapy-induced peripheral neurotoxicity (CIPN) is a severe dose-limiting adverse event of chemotherapy. Presently, the mechanism underlying the induction of CIPN remains unclear, and no effective treatment is available. In this study, through metabolomics analyses, we found that nab-paclitaxel therapy markedly increased serum serotonin [5-hydroxtryptamine (5-HT)] levels in both cancer patients and mice compared to the respective controls. Furthermore, nab-paclitaxel-treated enterochromaffin (EC) cells showed increased 5-HT synthesis, and serotonin-treated Schwann cells showed damage, as indicated by the activation of CREB3L3/MMP3/FAS signaling. Venlafaxine, an inhibitor of serotonin and norepinephrine reuptake, was found to protect against nerve injury by suppressing the activation of CREB3L3/MMP3/FAS signaling in Schwann cells. Remarkably, venlafaxine was found to significantly alleviate nab-paclitaxel-induced CIPN in patients without affecting the clinical efficacy of chemotherapy. In summary, our study reveals that EC cell-derived 5-HT plays a critical role in nab-paclitaxel-related neurotoxic lesions, and venlafaxine co-administration represents a novel approach to treating chronic cumulative neurotoxicity commonly reported in nab-paclitaxel-based chemotherapy.},
}
@article {pmid40368962,
year = {2025},
author = {Dias, C and Sousa, T and Cruz, A and Costa, D and Mouga, S and Castelhano, J and Pires, G and Castelo-Branco, M},
title = {A role for preparatory midfrontal theta in autism as revealed by a high executive load brain-computer interface reverse spelling task.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {16671},
pmid = {40368962},
issn = {2045-2322},
support = {10.54499/UI/BD/150832/2021, https://doi.org/10.54499/UI/BD/150832/2021//Funda&#xe7;&#xe3;o para a Ci&#xea;ncia e a Tecnologia/ ; CEEC: 2021.01469.CEECIND//Funda&#xe7;&#xe3;o para a Ci&#xea;ncia e a Tecnologia/ ; PTDC/EEI-AUT/30935/2017;//Funda&#xe7;&#xe3;o para a Ci&#xea;ncia e a Tecnologia/ ; UIDB/4950/2020, https://doi.org/10.54499/UIDB/04950/2020//Funda&#xe7;&#xe3;o para a Ci&#xea;ncia e a Tecnologia/ ; PT/FB/BL-2018-306//Funda&#xe7;&#xe3;o Bial/ ; CAIXA Impulse 2024//'la Caixa' Foundation/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Autistic Disorder/physiopathology ; *Theta Rhythm/physiology ; Female ; Adult ; *Executive Function/physiology ; Electroencephalography ; Young Adult ; Memory, Short-Term/physiology ; *Frontal Lobe/physiopathology ; },
abstract = {Midfrontal theta oscillations have been linked to executive function, yet their role in autism-where this function is often compromised-remains unclear. We hypothesized that preparatory increases in theta power may help normalize performance in autism. To test this, we used a challenging interactive executive function task designed to impose a high working memory load and require constant error monitoring. An electroencephalogram (EEG)-based brain-computer interface (BCI) was used to maximize cognitive load and engagement. Neural activity from autistic and non-autistic adults was compared while participants were asked to mentally reverse pseudowords (engaging working memory) and write them using the BCI, which provided real-time performance feedback (maximizing error monitoring). The study focused on theta power modulation during the preparatory (pre-response) and feedback (post-response) periods but also explored the role of posterior alpha oscillations. Results showed similar task performance between groups, but distinct recruitment of brain resources, particularly during the preparatory period. The finding of an increased preparatory theta in autism favors the hypothesis of compensatory recruitment of cognitive control and attentional mechanisms to achieve accurate results.},
}
@article {pmid40367961,
year = {2025},
author = {Partovi, A and Grayden, DB and Burkitt, AN},
title = {POC-CSP: a novel parameterised and orthogonally-constrained neural network layer for learning common spatial patterns (CSP) in EEG signals.},
journal = {Journal of neural engineering},
volume = {22},
number = {3},
pages = {},
doi = {10.1088/1741-2552/add8bc},
pmid = {40367961},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; *Neural Networks, Computer ; *Machine Learning ; Brain-Computer Interfaces ; Adult ; Male ; Imagination/physiology ; },
abstract = {Objective. Common spatial patterns (CSPs) has been established as a powerful feature extraction method in EEG signal processing with machine learning, but it has shortcomings including sensitivity to noise and rigidity in the value of the weights. Our goal was to transform CSP into a trainable machine learning model that can learn from data, be regularized, and be integrated into end-to-end classification networks.Approach. We developed a novel parameterised and orthogonally-constrained neural network layer for learning CSPs (POC-CSP) that maintains CSP's mathematical properties while allowing trainable weights. The layer uses parameterisation based on Lie Group theory to convert constrained optimisation into unconstrained optimisation, enabling integration with standard neural network (NN) training methods. We evaluated the approach on two public motor imagery datasets, focusing on both subject-specific and multi-subject paradigms.Main results. POC-CSP outperformed both conventional CSP and existing NN implementations in subject-specific classification tasks. In a novel multi-subject paradigm, POC-CSP achieved superior generalisation. When fine-tuned with just 50% of a new subject's data, POC-CSP achieved 0.95 average accuracy across subjects, substantially outperforming subject-specific models trained with more data.Significance. These findings demonstrate that combining CSP's proven effectiveness with NNs' flexibility can significantly improve EEG signal processing performance. The ability to generalize across subjects and achieve high accuracy with minimal subject-specific training data makes POC-CSP particularly valuable for practical brain-computer interface applications, where collecting large amounts of training data from each new user is often impractical or unfeasible.},
}
@article {pmid40367953,
year = {2025},
author = {Xu, X and Drougard, N and Roy, RN},
title = {Does topological data analysis work for EEG-based brain-computer interfaces?.},
journal = {Journal of neural engineering},
volume = {22},
number = {3},
pages = {},
doi = {10.1088/1741-2552/add8bd},
pmid = {40367953},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; Adult ; Male ; *Brain/physiology ; Female ; Databases, Factual ; Imagination/physiology ; *Data Analysis ; },
abstract = {Objective.Brain-computer interfaces (BCIs) are systems that establish a direct communication pathway with machines through brain activity only, recorded for example via electroencephalography (EEG). Topological data analysis (TDA) extracts topological features of the shape of the data and showed promising results in various applications. However, the work evaluating TDA systematically on EEG-based BCI is rare. Our study aims to fill this gap.Approach.The hypothesis is that the topology of the EEG dynamics is different under different mental states so that the topological features are discriminant. By adopting a dynamical system point of view, the non-stationary nature of EEG is respected. In practice, topological information is encoded by the persistence diagram. To turn it into a feature vector, some classical vector- and function-based representations are used. Each feature vector is then classified by several basic linear and non-linear classifiers.Main results.A benchmark comparing TDA with the gold standard methods was established on 3 publicly available datasets (2 active BCI datasets based on motor-imagery, 1 passive BCI dataset for mental workload estimation). TDA had significantly lower performance in intra-subject classification, yet comparable and sometimes higher performance in inter-subject classification. The persistence consistently outperformed all other topological features. We explained theoretically the link between persistence and spectral power and demonstrated it experimentally.Significance.To our knowledge, this is the first study that evaluates TDA in both intra- and inter-subject classification on various types of datasets. Insights on the connection between persistence and classical EEG features are also given for the first time.},
}
@article {pmid40367199,
year = {2025},
author = {Yashinski, M},
title = {Neuroprosthesis converts brain activity to speech.},
journal = {Science robotics},
volume = {10},
number = {102},
pages = {eady7192},
doi = {10.1126/scirobotics.ady7192},
pmid = {40367199},
issn = {2470-9476},
mesh = {Humans ; *Speech/physiology ; *Brain-Computer Interfaces ; *Brain/physiology ; *Neural Prostheses ; },
abstract = {A neuroprosthesis decodes short bits of neural activity and synthesizes speech synchronously with a user's vocal intent.},
}
@article {pmid40366622,
year = {2025},
author = {Yang, L and Li, H and Wang, X},
title = {Psilocybin and Obsessive-Compulsive Disorder: Exploring New Therapeutic Horizons.},
journal = {Neuroscience bulletin},
volume = {41},
number = {7},
pages = {1302-1306},
pmid = {40366622},
issn = {1995-8218},
}
@article {pmid40366280,
year = {2025},
author = {Liu, M and Chang, S and Chen, M and Li, P and Roe, AW and Hu, JM},
title = {How shape information is coded by V4 cortical response of macaque monkey.},
journal = {Journal of neurophysiology},
volume = {133},
number = {6},
pages = {2016-2028},
doi = {10.1152/jn.00520.2024},
pmid = {40366280},
issn = {1522-1598},
support = {32471052//MOST | National Natural Science Foundation of China (NSFC)/ ; 32100802//MOST | National Natural Science Foundation of China (NSFC)/ ; },
mesh = {Animals ; Macaca mulatta ; *Visual Cortex/physiology/cytology ; *Neurons/physiology ; Male ; *Form Perception/physiology ; *Pattern Recognition, Visual/physiology ; },
abstract = {Previous neural recording studies have shown that monkey V4 can process shape information across populations of neurons. The responses recorded from each single neuron make it possible to retrieve shape information. However, these studies did not fully characterize the spatial distribution of activity in the cortex. There are multiple types of functional columns (orientation, curvature) in V4; how do these structures respond to different shapes? Here, with intrinsic optical imaging, we explored the cortical responses of V4 to contours (straight and curved) and shapes (circle and square). We found that in V4 the response of neurons to different shapes is highly dependent on the compositional features contained in the shape. A specific local network would have a higher response magnitude to its corresponding shape than other shapes. Meanwhile, the cortical response of V4 exhibits a tolerance to the shift of stimulus location. Our results suggest that two essential cortical response features in V4 are the specificity of the activated response pattern in the cortex and tolerance to the stimulus location variance. These features can help decode shape information from imaging results.NEW &amp; NOTEWORTHY At the cortical response level, the V4 area of the macaque monkey employs two critical principles of shape coding: specificity of the activated response pattern to shape components within the stimuli and tolerance to variations in stimulus location.},
}
@article {pmid40364497,
year = {2025},
author = {Kim, JH and Nam, H and Won, D and Im, CH},
title = {Domain-generalized Deep Learning for Improved Subject-independent Emotion Recognition Based on Electroencephalography.},
journal = {Experimental neurobiology},
volume = {34},
number = {3},
pages = {119-130},
pmid = {40364497},
issn = {1226-2560},
abstract = {Electroencephalography (EEG) provides high temporal resolution and noninvasiveness for a range of practical applications, including emotion recognition. However, inherent variability across subjects poses significant challenges to model generalizability. In this study, we systematically evaluated twelve approaches by combining four domain generalization (DG) techniques, Deep CORAL, GroupDRO, VREx, and DANN, with three representative deep learning architectures (ShallowFBCSPNet, EEGNet, and TSception) to enable improved subject-independent EEG-based emotion recognition. The performances of the DG-integrated deep learning models were quantitatively evaluated using two emotional EEG datasets collected by the authors. Data from each subject were treated as distinct domains in each model. Binary classification tasks were conducted to identify the valence or arousal state of each participant based on a ten-fold cross-validation strategy. The results indicated that the application of DG methods consistently enhanced classification accuracy across datasets. In one dataset, TSception combined with VREx achieved the highest performance for both valence and arousal classifications. In the other dataset, TSception with VREx still yielded the highest valence classification accuracy, while TSception combined with GroupDRO showed the best arousal classification performance among the twelve models, slightly outperforming TSception with VREx. These findings underscore the potential of DG approaches to mitigate distributional shifts caused by intersubject and intersession variabilities to implement robust subject-independent EEG-based emotion recognition systems.},
}
@article {pmid40363359,
year = {2025},
author = {Deng, X and Huo, H and Ai, L and Xu, D and Li, C},
title = {A Novel 3D Approach with a CNN and Swin Transformer for Decoding EEG-Based Motor Imagery Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {9},
pages = {},
pmid = {40363359},
issn = {1424-8220},
support = {No. XTZW2024-KF02//Chongqing Key Laboratory of Germplasm Innovation 755 and Utilization of Native Plants under Grant/ ; },
mesh = {*Electroencephalography/methods ; Humans ; Brain-Computer Interfaces ; *Neural Networks, Computer ; Signal Processing, Computer-Assisted ; *Imagination/physiology ; Signal-To-Noise Ratio ; Movement/physiology ; Deep Learning ; Algorithms ; },
abstract = {Motor imagery (MI) is a crucial research field within the brain-computer interface (BCI) domain. It enables patients with muscle or neural damage to control external devices and achieve movement functions by simply imagining bodily motions. Despite the significant clinical and application value of MI-BCI technology, accurately decoding high-dimensional and low signal-to-noise ratio (SNR) electroencephalography (EEG) signals remains challenging. Moreover, traditional deep learning approaches exhibit limitations in processing EEG signals, particularly in capturing the intrinsic correlations between electrode channels and long-distance temporal dependencies. To address these challenges, this research introduces a novel end-to-end decoding network that integrates convolutional neural networks (CNNs) and a Swin Transformer, aiming at enhancing the classification accuracy of the MI paradigm in EEG signals. This approach transforms EEG signals into a three-dimensional data structure, utilizing one-dimensional convolutions along the temporal dimension and two-dimensional convolutions across the EEG electrode distribution for initial spatio-temporal feature extraction, followed by deep feature exploration using a 3D Swin Transformer module. Experimental results show that on the BCI Competition IV-2a dataset, the proposed method achieves 83.99% classification accuracy, which is significantly better than the existing deep learning methods. This finding underscores the efficacy of combining a CNN and Swin Transformer in a 3D data space for processing high-dimensional, low-SNR EEG signals, offering a new perspective for the future development of MI-BCI. Future research could further explore the applicability of this method across various BCI tasks and its potential clinical implementations.},
}
@article {pmid40363182,
year = {2025},
author = {Hougaard, BI and Knoche, H and Kristensen, MS and Jochumsen, M},
title = {Experience of Virtual Help in a Simulated BCI Stroke Rehabilitation Serious Game and How to Measure It.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {9},
pages = {},
pmid = {40363182},
issn = {1424-8220},
support = {22357//VELUX FONDEN/ ; },
mesh = {Humans ; *Stroke Rehabilitation/methods ; *Brain-Computer Interfaces ; Male ; Female ; Middle Aged ; *Video Games ; Aged ; Adult ; *Stroke/physiopathology ; User-Computer Interface ; },
abstract = {Designers of digital rehabilitation experiences can accommodate error-prone input devices like brain-computer interfaces (BCIs) by incorporating virtual help mechanisms to adjust the difficulty, but it is unclear on what grounds users are willing to accept such help. To study users' experience of virtual help mechanisms, we used three help mechanisms in a blink-controlled game simulating a BCI-based stroke rehabilitation exercise. A mixed-method, simulated BCI study was used to evaluate game help by 19 stroke patients who rated their frustration and perceived control when experiencing moderately high input recognition. None of the help mechanisms affected ratings of frustration, which were low throughout the study, but two mechanisms affected patients' perceived control ratings positively and negatively. Patient ratings were best explained by the amount of positive feedback, including game help, which increased perceived control ratings by 8% and decreased frustration ratings by 3%. The qualitative analysis revealed appeal, interference, self-blame, and prominence as deciding experiential factors of help, but it was unclear how they affected frustration and perceived control ratings. Building upon the results, we redesigned and tested self-reported measures of help quantity, help appeal, irritation, and pacing with game-savvy adults in a follow-up study using the same game. Help quantity appeared larger when game help shielded players from negative feedback, but this did not necessarily appeal to them. Future studies should validate or control for the constructs of perceived help quantity and appeal.},
}
@article {pmid40361409,
year = {2025},
author = {Marín-Liébana, S and Llor, P and Serrano-García, L and Fernández-Murga, ML and Comes-Raga, A and Torregrosa, D and Pérez-García, JM and Cortés, J and Llombart-Cussac, A},
title = {Gene Expression Signatures for Guiding Initial Therapy in ER+/HER2- Early Breast Cancer.},
journal = {Cancers},
volume = {17},
number = {9},
pages = {},
pmid = {40361409},
issn = {2072-6694},
abstract = {In triple-negative (TNBC) and human epidermal growth factor receptor 2-positive (HER2+) breast cancer patients, neoadjuvant systemic therapy is the standard recommendation for tumors larger than 2 cm. Monitoring the response to primary systemic therapy allows for the assessment of treatment effects, the need for breast-conserving surgery (BCS), and the achievement of pathological complete responses (pCRs). In estrogen receptor-positive/HER2-negative (ER+/HER2-) breast cancer, the benefit of neoadjuvant strategies is controversial, as they have shown lower tumor downstaging and pCR rates compared to other breast cancers. In recent decades, several gene expression assays have been developed to tailor adjuvant treatments in ER+/HER2- early breast cancer (EBC) to identify the patients that will benefit the most from adjuvant chemotherapy (CT) and those at low risk who could be spared from undergoing CT. It is still a challenge to identify patients who will benefit from neoadjuvant systemic treatment (CT or endocrine therapy (ET)). Here, we review the published data on the most common gene expression signatures (MammaPrint (MP), BluePrint (BP), Oncotype Dx, PAM50, the Breast Cancer Index (BCI), and EndoPredict (EP)) and their ability to predict the response to neoadjuvant treatment, as well as the possibility of using them on core needle biopsies. Additionally, we review the changes in the gene expression signatures after neoadjuvant treatment, and the ongoing clinical trials related to the utility of gene expression signatures in the neoadjuvant setting.},
}
@article {pmid40360495,
year = {2025},
author = {Li, J and Mo, D and Hu, J and Wang, S and Gong, J and Huang, Y and Li, Z and Yuan, Z and Xu, M},
title = {PEDOT:PSS-based bioelectronics for brain monitoring and modulation.},
journal = {Microsystems &amp; nanoengineering},
volume = {11},
number = {1},
pages = {87},
pmid = {40360495},
issn = {2055-7434},
support = {30802-110690303//Guangdong Science and Technology Department (Science and Technology Department, Guangdong Province)/ ; 2021ZD0204300//National Science Foundation of China | Major Research Plan/ ; MYRGGRG2023-00038-FHS//Universidade de Macau (University of Macau)/ ; 28709-312200502501//Beijing Normal University (BNU)/ ; },
abstract = {The growing demand for advanced neural interfaces that enable precise brain monitoring and modulation has catalyzed significant research into flexible, biocompatible, and highly conductive materials. PEDOT:PSS-based bioelectronic materials exhibit high conductivity, mechanical flexibility, and biocompatibility, making them particularly suitable for integration into neural devices for brain science research. These materials facilitate high-resolution neural activity monitoring and provide precise electrical stimulation across diverse modalities. This review comprehensively examines recent advances in the development of PEDOT:PSS-based bioelectrodes for brain monitoring and modulation, with a focus on strategies to enhance their conductivity, biocompatibility, and long-term stability. Furthermore, it highlights the integration of multifunctional neural interfaces that enable synchronous stimulation-recording architectures, hybrid electro-optical stimulation modalities, and multimodal brain activity monitoring. These integrations enable fundamentally advancing the precision and clinical translatability of brain-computer interfaces. By addressing critical challenges related to efficacy, integration, safety, and clinical translation, this review identifies key opportunities for advancing next-generation neural devices. The insights presented are vital for guiding future research directions in the field and fostering the development of cutting-edge bioelectronic technologies for neuroscience and clinical applications.},
}
@article {pmid40360243,
year = {2025},
author = {Schurzig, D and Iseke, R and Maier, H and Prenzler, NK and Lenarz, T and Ghoncheh, M},
title = {Clinical Evidence on the Influence of Implant Position onto Maximum Output with the Bonebridge Bone Conduction Implant.},
journal = {Otology &amp; neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology},
volume = {46},
number = {6},
pages = {725-732},
doi = {10.1097/MAO.0000000000004533},
pmid = {40360243},
issn = {1537-4505},
mesh = {Humans ; *Bone Conduction/physiology ; Retrospective Studies ; Male ; Female ; Middle Aged ; Adult ; Aged ; *Hearing Aids ; Cochlea ; *Hearing Loss, Conductive/surgery ; Treatment Outcome ; },
abstract = {HYPOTHESIS: In bone conduction implantation, the position of the implant influences the audiological benefit of the patient.<br><br>BACKGROUND: One way of treating hearing loss is the implantation of bone conduction implants (BCIs), which effectively transmit vibrations through the skull bone to the cochlea given that the implant transducer is securely fixated. Laboratory research on the efficacy of bone conduction sound transmission found that a closer proximity of the transducer to the ipsilateral cochlea yields significantly higher cochlear promontory vibrations and hence, higher stimulation efficacy. Up to now, this finding has not been reproduced using clinical data such as the functional or effective gain.<br><br>METHODS: The present, retrospective study was conducted on a cohort of 28 BCI patients to correlate the implantation site of the BC transducer, derived from clinical postoperative imaging and defined in a standardized coordinate system, with maximum output values that are exclusively based on a novel calculation method only employing clinical audiological data.<br><br>RESULTS: It could be shown that the efficacy of BCI stimulation is in fact correlated with the transducer distance to the cochlea, and that this correlation is frequency dependent. Furthermore, the longitudinal distance of the transducer and the ipsilateral external auditory canal is negatively correlated with the maximal output while the sagittal distance is not.<br><br>CONCLUSION: The present study is hence the first one to clinically demonstrate the significance of BCI placement for maximizing patient benefit, which should be considered during the preoperative planning of bone conduction implantation.},
}
@article {pmid40359554,
year = {2025},
author = {Liu, MY and Fang, MZ and Zhang, BH and Dang, CX and Zhang, YS and Wu, L and Liu, B and Li, Z},
title = {Bibliometric analysis of brain-computer interface research in spinal cord injury: current landscape and future directions.},
journal = {International journal of surgery (London, England)},
volume = {111},
number = {7},
pages = {4804-4806},
doi = {10.1097/JS9.0000000000002475},
pmid = {40359554},
issn = {1743-9159},
}
@article {pmid40358727,
year = {2025},
author = {Aamir, A and Siddiqui, M},
title = {Integration of brain-computer interfaces with sacral nerve stimulation: a vision for closed-loop, volitional control of bladder function in neurogenic patients through real-time cortical signal modulation and peripheral neuro-stimulation.},
journal = {World journal of urology},
volume = {43},
number = {1},
pages = {301},
pmid = {40358727},
issn = {1433-8726},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electric Stimulation Therapy/methods ; *Lumbosacral Plexus ; *Urinary Bladder, Neurogenic/therapy/physiopathology ; },
abstract = {Sacral nerve stimulation (SNS) and brain-computer interfaces (BCI) are emerging neuromodulation therapies that offer innovative solutions for chronic neurological disorders. SNS, primarily used in the management of conditions such as urinary incontinence and chronic pelvic pain, demonstrates significant therapeutic potential. In contrast, BCIs are rapidly advancing in their ability to restore lost motor functions and improve the quality of life of patients with severe neurological impairments, such as spinal cord injury and stroke. The integration of SNS and BCI technologies presents a promising avenue for enhancing neuromodulation outcomes by leveraging the potential of both systems. This article explores the combined operation of SNS and BCI, addressing current challenges, future directions, and the potential for these combined therapies to revolutionise the field of functional neuromodulation.},
}
@article {pmid40358723,
year = {2025},
author = {Bénard, A and Maliia, DM and Yochum, M and Köksal-Ersöz, E and Houvenaghel, JF and Wendling, F and Sauleau, P and Benquet, P},
title = {Realistic Subject-Specific Simulation of Resting State Scalp EEG Based on Physiological Model.},
journal = {Brain topography},
volume = {38},
number = {4},
pages = {43},
pmid = {40358723},
issn = {1573-6792},
support = {855109//HORIZON EUROPE Reforming and enhancing the European Research and Innovation system/ ; 855109//HORIZON EUROPE Reforming and enhancing the European Research and Innovation system/ ; 855109//HORIZON EUROPE Reforming and enhancing the European Research and Innovation system/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Brain/physiology ; *Models, Neurological ; Computer Simulation ; *Rest/physiology ; Adult ; Scalp/physiology ; Male ; *Brain Waves/physiology ; Female ; },
abstract = {Electroencephalography (EEG) recordings are widely used in neuroscience to identify healthy individual brain rhythms and to detect alterations associated with various brain diseases. However, understanding the cellular origins of scalp EEG signals and their spatiotemporal changes during the resting state (RS) in humans remains challenging, as cellular-level recordings are typically restricted to animal models. The objective of this study was to simulate individual-specific spatiotemporal features of RS EEG and measure the degree of similarity between real and simulated EEG. Using a physiologically grounded whole-brain computational model (based on known neuronal subtypes and their structural and functional connectivity) that simulates interregional cortical circuitry activity, realistic individual EEG recordings during RS of three healthy subjects were created. The model included interconnected neural mass modules simulating activities of different neuronal subtypes, including pyramidal cells and four types of GABAergic interneurons. High-definition EEG and source localization were used to delineate the cortical extent of alpha and beta-gamma rhythms. To evaluate the realism of the simulated EEG, we developed a similarity index based on cross-correlation analysis in the frequency domain across various bipolar channels respecting standard longitudinal montage. Alpha oscillations were produced by strengthening the somatostatin-pyramidal loop in posterior regions, while beta-gamma oscillations were generated by increasing the excitability of parvalbumin-interneurons on pyramidal neurons in anterior regions. The generation of realistic individual RS EEG rhythms represents a significant advance for research fields requiring data augmentation, including brain-computer interfaces and artificial intelligence training.},
}
@article {pmid40356632,
year = {2025},
author = {Liao, XY and Jiang, YE and Xu, RJ and Qian, TT and Liu, SL and Che, Y},
title = {A bibliometric analysis of electroencephalogram research in stroke: current trends and future directions.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1539736},
pmid = {40356632},
issn = {1664-2295},
abstract = {BACKGROUND: Electroencephalography (EEG) has become an indispensable tool in stroke research for real-time monitoring of neural activity, prognosis prediction, and rehabilitation support. In recent decades, EEG applications in stroke research have expanded, particularly in areas like brain-computer interfaces (BCI) and neurofeedback for motor recovery. However, a comprehensive analysis of research trends in this domain is currently unavailable.<br><br>METHODS: The study collected data from the Web of Science Core Collection database, selecting publications related to stroke and EEG from 2005 to 2024. Visual analysis tools such as VOSviewer and CiteSpace were utilized to build knowledge maps of the research field, analyzing the distribution of publications, authors, institutions, journals, and collaboration networks. Additionally, co-occurrence, clustering, and burst detection of keywords were analyzed in detail.<br><br>RESULTS: A total of 2,931 publications were identified, indicating a consistent increase in EEG research in stroke, with significant growth post-2017. The United States, China, and Germany emerged as the leading contributors, with high collaboration networks among Western institutions. Key research areas included signal processing advancements, EEG applications in seizure risk and consciousness disorder assessment, and EEG-driven rehabilitation techniques. Notably, recent studies have focused on integrating EEG with machine learning and multimodal data for more precise functional evaluations.<br><br>CONCLUSION: The findings reveal that EEG has evolved from a diagnostic tool to a therapeutic support platform in the context of stroke care. The advent of deep learning and multimodal integration has positioned EEG for expanded applications in personalized rehabilitation. It is recommended that future studies prioritize interdisciplinary collaboration and standardized EEG methodologies in order to facilitate clinical adoption and enhance translational potential in stroke management.},
}
@article {pmid40356046,
year = {2025},
author = {Chen, S and Hu, J and Zhang, D and Li, Z and Zheng, Z and Gui, S and He, N},
title = {Preparation and hemostatic evaluation of carboxymethyl chitosan/gelatin/clinodiside a composite sponges.},
journal = {Journal of biomaterials science. Polymer edition},
volume = {36},
number = {15},
pages = {2216-2236},
doi = {10.1080/09205063.2025.2499285},
pmid = {40356046},
issn = {1568-5624},
mesh = {*Chitosan/analogs &amp; derivatives/chemistry ; Animals ; *Gelatin/chemistry ; *Hemostatics/chemistry/pharmacology ; Rabbits ; Rats ; Rats, Sprague-Dawley ; Blood Coagulation/drug effects ; Hemostasis/drug effects ; Male ; Porosity ; Hemolysis/drug effects ; Anti-Bacterial Agents/chemistry/pharmacology ; Biocompatible Materials/chemistry ; Hemorrhage ; },
abstract = {In trauma resuscitation, rapid hemostasis is a top priority for rescuing patients from the risk of hemorrhagic shock and infection. Traditional hemostatic materials are not effective for hemostasis and have some limitations. We added "Duanxue Liu" saponin A (Clinodiside A) to a hemostatic sponge based on carboxymethyl chitosan (CMCS) and gelatin to improve its hemostatic effect. Clinodiside A has hemostatic, anti-inflammatory and antibacterial effects, and its preparation into a sponge can help to improve the coagulation ability, prolong the action time of the drug, increase the bioavailability and improve the stability of the drug. The results showed that the prepared hemostatic sponge had a honeycomb porous structure, strong shape recovery ability, good water absorption and porosity, low hemolysis rate and no obvious cytotoxicity. The results of in vitro coagulation test showed that the coagulation time of GOC, GOC-1, GOC-2 and GOC-3 sponges was shorter than that of the control group, and the BCI index was much lower than that of the commercially available sponges. In the tail-breaking experiment of SD rats, GOC-3 showed the lowest blood loss of 0.2549 g and the hemostasis time of 55 s. In the experiment of rabbit ear artery, GOC-3 showed the lowest blood loss of 98.75 mg and the hemostasis time of 95 s. This indicates that the Clinique A hemostatic sponges have highly efficient hemostatic properties. Therefore, the prepared CMCS/Gel/Clinodiside A sponge has a good application prospect as a hemostatic dressing.},
}
@article {pmid40355527,
year = {2025},
author = {Wu, Y and Liu, Y and Yang, Y and Yao, MS and Yang, W and Shi, X and Yang, L and Li, D and Liu, Y and Yin, S and Lei, C and Zhang, M and Gee, JC and Yang, X and Wei, W and Gu, S},
title = {Author Correction: A concept-based interpretable model for the diagnosis of choroid neoplasias using multimodal data.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {4381},
doi = {10.1038/s41467-025-59792-1},
pmid = {40355527},
issn = {2041-1723},
}
@article {pmid40355001,
year = {2025},
author = {Ognard, J and El Hajj, G and Verma, O and Ghozy, S and Kadirvel, R and Kallmes, DF and Brinjikji, W},
title = {Advances in endovascular brain computer interface: Systematic review and future implications.},
journal = {Journal of neuroscience methods},
volume = {420},
number = {},
pages = {110471},
doi = {10.1016/j.jneumeth.2025.110471},
pmid = {40355001},
issn = {1872-678X},
mesh = {*Brain-Computer Interfaces/trends ; Animals ; Humans ; *Endovascular Procedures/methods/trends ; *Brain/physiology ; Electrodes, Implanted ; },
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) translate neural activity into real-world commands. While traditional invasive BCIs necessitate craniotomy, endovascular BCIs offer a minimally invasive alternative using the venous system for electrode placement.<br><br>NEW METHOD: This systematic review evaluates the technical feasibility, safety, and clinical outcomes of endovascular BCIs, discussing their future implications. A systematic review was conducted per PRISMA guidelines. The search spanned PubMed, Web of Science, and Scopus databases using keywords related to neural interfaces and endovascular approaches. Studies were included if they reported on endovascular BCIs in preclinical or clinical settings. Dual independent screening and extraction focused on electrode material, recording capabilities, safety parameters, and clinical efficacy.<br><br>RESULTS: From 1385 initial publications, 26 met the inclusion criteria. Seventeen studies investigated the Stentrode device. Among the 24 preclinical studies, 16 used ovine or rodent models, and 9 addressed engineering or simulation aspects. Two clinical studies reported six ALS patients successfully using an endovascular BCI for digital communication. Preclinical data established the endovascular ovine model, demonstrating stable neural recordings and vascular changes with long-term implantation. Key challenges include thrombosis risk, long-term electrode stability, and anatomical variability.<br><br>Endovascular BCI reduced invasiveness, improved safety profiles, with comparable neural recording fidelity to invasive methods, and promising preliminary clinical outcomes in severely paralyzed patients.<br><br>CONCLUSIONS: Early results are promising, but clinical data remain scarce. Further research is needed to optimize signal processing, enhance electrode biocompatibility, and refine endovascular procedures for broader clinical applications.},
}
@article {pmid40354812,
year = {2025},
author = {Poli, R and Mercimek, AC and Cinel, C},
title = {Novel sequential BCI speller based on ERPs and event-related slow cortical potentials.},
journal = {Journal of neural engineering},
volume = {22},
number = {3},
pages = {},
doi = {10.1088/1741-2552/add772},
pmid = {40354812},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Humans ; Male ; *Electroencephalography/methods ; Adult ; Female ; Young Adult ; *Evoked Potentials/physiology ; *Event-Related Potentials, P300/physiology ; *Photic Stimulation/methods ; *Cerebral Cortex/physiology ; },
abstract = {Objectives. One of the most effective brain-computer interfaces (BCI) spellers, Donchin and Farwell's matrix speller, uses visual stimulus presentation and the oddball effect, eliciting P300 event-related potentials to rare and randomly presented stimuli of interest. Although proposed almost four decades ago, most BCI spellers still rely on this principle and the original matrix speller design although some of the issues that affect oddball spellers have progressively been addressed over the years with significant, but very incremental, performance improvements. Farwell and Donchin seminal paper suggested the future possibility of abandoning the oddball paradigm, for a regular/periodic presentation pattern which they predicted might produce a contingent negative variation (CNV) and thus improve speller performance. However, this has never been investigated. Building on our past research on a BCI for cursor control which adopted a periodic stimulation protocol, here we explore whether a periodic presentation pattern could be a viable alternative to the oddball paradigm in a BCI speller.Approach. We tested the periodic presentation principle in a BCI speller where 36 letters are organised around a circle and are highlighted sequentially, and compared it to the original matrix speller at two stimulus presentation rates.Main results. Our periodic speller produces not only clear P300s, but also equally clear CNVs, as postulated by Farwell and Donchin, as well as other slow cortical potentials (SCPs). At the higher stimulation rate, this leads to significantly higher AUC, classification accuracy, ITR and utility w.r.t. Donchin's speller.Significance. Our findings suggest that periodic stimulation can not only produce clear P300s but also a variety of event-related SCPs, leading to significant performance improvements over Donchin's paradigm. This work opens new avenues for BCI spelling where event related potentials are combined with naturally-triggered (rather than trained) SCPs, that will hopefully result in more efficient communication systems for individuals with severe motor impairments.},
}
@article {pmid40354807,
year = {2025},
author = {Ladouce, S and Torre Tresols, JJ and Goff, KL and Dehais, F},
title = {EEG-based assessment of long-term vigilance and lapses of attention using a user-centered frequency-tagging approach.},
journal = {Journal of neural engineering},
volume = {22},
number = {3},
pages = {},
doi = {10.1088/1741-2552/add771},
pmid = {40354807},
issn = {1741-2552},
mesh = {Humans ; Male ; *Attention/physiology ; *Electroencephalography/methods ; Female ; Adult ; *Evoked Potentials, Visual/physiology ; Young Adult ; Photic Stimulation/methods ; *Arousal/physiology ; Psychomotor Performance/physiology ; },
abstract = {Objective.Sustaining vigilance over extended periods is crucial for many critical operations but remains challenging due to the cognitive resources required. Fatigue and other factors contribute to fluctuations in vigilance, causing attentional focus to drift from task-relevant information. Such lapses of attention, common in prolonged tasks, lead to decreased performance and missed critical information, with potentially serious consequences. Identifying physiological markers that predict inattention is key to developing preventive strategies.Approach.Previous research has established electroencephalography (EEG) responses to periodic visual stimuli, known as steady-state visual evoked potentials (SSVEP), as sensitive markers of attention. In this study, we evaluated a minimally intrusive SSVEP-based approach for tracking vigilance in healthy participants (N= 16) during two sessions of a 45 min sustained visual attention task (Mackworth's clock task). A 14 Hz frequency-tagging flicker was either superimposed on the task or absent.Main results.Results revealed that SSVEP responses were lower prior to lapses of attention, while other spectral EEG markers, such as frontal theta and parietal alpha activity, did not reliably distinguish between detected and missed attention probes. Importantly, the flicker did not affect task performance or participant experience.Significance.This non-intrusive frequency-tagging method provides a continuous measure of vigilance, effectively detecting attention lapses in prolonged tasks. It holds promise for integration into passive brain-computer interfaces, offering a practical solution for real-time vigilance monitoring in high-stakes settings like air traffic control or driving.},
}
@article {pmid40353311,
year = {2025},
author = {Shah, AM},
title = {Unlocking Naturalistic Speech With Brain-Computer Interface.},
journal = {Artificial organs},
volume = {49},
number = {7},
pages = {1087-1088},
doi = {10.1111/aor.15021},
pmid = {40353311},
issn = {1525-1594},
mesh = {Adult ; Female ; Humans ; Male ; *Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography ; *Speech/physiology ; Clinical Trials as Topic ; },
abstract = {Novel speech brain-computer interface poses the ability to decode detected neural signals in nearly real time. This decreases brain-to-voice latency and has the opportunity to restore naturalistic communication. Trial Registration: ClinicalTrials.gov: NCT03698149.},
}
@article {pmid40352906,
year = {2025},
author = {Besharat, A and Samadzadehaghdam, N and Ghadiri, T},
title = {A novel hybrid method based on task-related component and canonical correlation analyses (H-TRCCA) for enhancing SSVEP recognition.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1544452},
pmid = {40352906},
issn = {1662-4548},
abstract = {INTRODUCTION: Brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEP) rely on the brain's response to visual stimuli. However, accurately recognizing target frequencies using training-based methods remains challenging due to the time-consuming calibration sessions required by subject-specific training methods.<br><br>METHOD: To address this limitation, this study proposes a novel hybrid method called Hybrid task-related component and canonical correlation analysis (H-TRCCA). In the training phase, four spatial filters are derived using canonical correlation analysis (CCA) to maximize the correlation between the training data and reference signals. Additionally, a spatial filter is also computed using task-related component analysis (TRCA). In the test phase, correlation coefficients obtained from the CCA method are clustered using the k-means++ clustering algorithm. The cluster with the highest average correlation identifies the candidate stimuli. Finally, for each candidate, the correlation values are summed and combined with the TRCA-based correlation coefficients.<br><br>RESULTS: The H-TRCCA algorithm was validated using two publicly available benchmark datasets. Experimental results using only two training trials per frequency with 1s data length showed that H-TRCCA achieved average accuracies of 91.44% for Dataset I and 80.46% for Dataset II. Additionally, it achieved maximum average information transfer rates of 188.36 bits/min and 139.96 bits/min for Dataset I and II, respectively.<br><br>DISCUSSION: Remarkably H-TRCCA achieves comparable performance to other methods that require five trials, utilizing only two or three training trials. The proposed H-TRCCA method outperforms state-of-the-art techniques, showing superior performance and robustness with limited calibration data.},
}
@article {pmid40352453,
year = {2025},
author = {Li, Y and Mei, Z and Liu, Z and Li, J and Sun, G and Ong, MEH and Chen, J and Fan, H and Cao, C},
title = {Cardiometabolic multimorbidity and the risk of sudden cardiac death among geriatric community dwellers using longitudinal EHR-derived data.},
journal = {Frontiers in endocrinology},
volume = {16},
number = {},
pages = {1515495},
pmid = {40352453},
issn = {1664-2392},
mesh = {Humans ; Aged ; Male ; Female ; *Multimorbidity ; *Electronic Health Records/statistics &amp; numerical data ; Retrospective Studies ; Aged, 80 and over ; China/epidemiology ; *Death, Sudden, Cardiac/epidemiology/etiology ; *Independent Living/statistics &amp; numerical data ; Risk Factors ; Longitudinal Studies ; *Cardiovascular Diseases/epidemiology ; Prevalence ; *Metabolic Diseases/epidemiology ; },
abstract = {BACKGROUND: Cardiometabolic multimorbidity (CMM) has increased globally in recent years, especially among geriatric community dwellers. However, it is currently unclear how SCD risk is impacted by CMM in older adults. This study aimed to examine the associations between CMM and SCD among geriatric community dwellers in a province of China.<br><br>METHODS: This study was a retrospective, population-based cohort design based on electronic health records (EHRs) of geriatric community dwellers (&#x2265;65 years old) in four towns of Tianjin, China. 55,130 older adults were included in our study. Older adults were categorized into different CMM patterns according to the cardiometabolic disease (CMD) status at baseline. The count of CMDs was also entered as a continuous variable to examine the potential additive effect of CMM on SCD. Cox proportional hazard models were used to evaluate associations between CMM and SCD. The results are expressed as hazard ratios (HRs) and 95% confidence intervals (CIs).<br><br>RESULTS: The prevalence of CMM was approximately 25.3% in geriatric community dwellers. Among participants with CMM, hypertension and diabetes was the most prevalent combination (9,379, 17.0%). The highest crude mortality rates for SCD were 7.5 (2.9, 19.1) per 1000 person-years in older adults with hypertension, coronary heart disease, diabetes and stroke (HR, 4.496; 95% CI, 1.696, 11.917), followed by those with hypertension, coronary heart disease, and stroke (HR, 3.290; 95% CI, 1.056, 10.255). The risks of SCD were significantly increased with increasing numbers of CMDs (HR, 1.787; 95% CI, 1.606, 1.987). The demographic, risk factors, serum measures and ECG-adjusted HR for SCD was 1.488 (1.327, 1.668) for geriatric community dwellers with an increasing number of CMDs.<br><br>CONCLUSION: The risk of SCD varied by the pattern of CMM, and increased with increasing number of CMM among geriatric community dwellers.},
}
@article {pmid40351848,
year = {2025},
author = {Jin, X and Yuan, Y and Chang, C and Wu, X and Tan, X and Liu, Z},
title = {Telemedicine in China: Effective indicators of telemedicine platforms for promoting health and well-being among healthcare consumers.},
journal = {Digital health},
volume = {11},
number = {},
pages = {20552076251341163},
pmid = {40351848},
issn = {2055-2076},
abstract = {OBJECTIVE: Telemedicine platforms played a crucial role during the COVID-19 pandemic, alleviating issues related to the shortage and unequal distribution of healthcare resources. The purpose of this study is to identify key factors affecting the service quality of telemedicine platforms in China, with the dual objectives of advancing patient wellbeing and informing evidence-based service innovations for industry stakeholders.<br><br>METHODS: To quantitatively assess the impact of these key factors on health and wellbeing from the perspective of healthcare consumers, a total of 25,499 valid online reviews were collected from telemedicine platforms. To establish a service quality evaluation framework, this study proposes a novel approach that combines the Servqual quality assessment model with a CNN-BiLSTM deep learning model enhanced by an attention mechanism.<br><br>RESULTS: Analysis of the full sample shows that healthcare consumers are most concerned about the quality of services provided by telemedicine platforms, with the most important being the professional competence of doctors, a critical factor for promoting consumer health and wellbeing. The proposed hybrid deep learning approach demonstrates superior performance in sentiment classification accuracy, outperforming conventional methods by 11.11 percentage points. This methodological innovation enables more precise identification of consumer sentiment patterns across service dimensions.<br><br>CONCLUSION: The novel quality assessment framework introduced here provides actionable insights for advancing telemedicine platforms, driving progress toward precision healthcare and consumer-centric wellbeing. Furthermore, it enables healthcare consumers to select telemedicine services aligned with their personalized needs.},
}
@article {pmid40351570,
year = {2025},
author = {Sercek, I and Sampathila, N and Tasci, I and Ekmekyapar, T and Tasci, B and Barua, PD and Baygin, M and Dogan, S and Tuncer, T and Tan, RS and Acharya, UR},
title = {A new quantum-inspired pattern based on Goldner-Harary graph for automated alzheimer's disease detection.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {71},
pmid = {40351570},
issn = {1871-4080},
abstract = {Alzheimer's disease (AD) is a common cause of dementia. We aimed to develop a computationally efficient yet accurate feature engineering model for AD detection based on electroencephalography (EEG) signal inputs. New method: We retrospectively analyzed the EEG records of 134 AD and 113 non-AD patients. To generate multilevel features, a multilevel discrete wavelet transform was used to decompose the input EEG-signals. We devised a novel quantum-inspired EEG-signal feature extraction function based on 7-distinct different subgraphs of the Goldner-Harary pattern (GHPat), and selectively assigned a specific subgraph, using a forward-forward distance-based fitness function, to each input EEG signal block for textural feature extraction. We extracted statistical features using standard statistical moments, which we then merged with the extracted textural features. Other model components were iterative neighborhood component analysis feature selection, standard shallow k-nearest neighbors, as well as iterative majority voting and greedy algorithm to generate additional voted prediction vectors and select the best overall model results. With leave-one-subject-out cross-validation (LOSO CV), our model attained 88.17% accuracy. Accuracy results stratified by channel lead placement and brain regions suggested P4 and the parietal region to be the most impactful. Comparison with existing methods: The proposed model outperforms existing methods by achieving higher accuracy with a computationally efficient quantum-inspired approach, ensuring robustness and generalizability. Cortex maps were generated that allowed visual correlation of channel-wise results with various brain regions, enhancing model explainability.},
}
@article {pmid40350042,
year = {2025},
author = {Xie, C and Wang, L and Yang, J and Guo, J},
title = {A subject transfer neural network fuses Generator and Euclidean alignment for EEG-based motor imagery classification.},
journal = {Journal of neuroscience methods},
volume = {420},
number = {},
pages = {110483},
doi = {10.1016/j.jneumeth.2025.110483},
pmid = {40350042},
issn = {1872-678X},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Imagination/physiology ; *Neural Networks, Computer ; *Deep Learning ; *Motor Activity/physiology ; Signal Processing, Computer-Assisted ; *Brain/physiology ; },
abstract = {BACKGROUND: Brain-computer interface (BCI) facilitates the connection between human brain and computer, enabling individuals to control external devices indirectly through cognitive processes. Although it has great development prospects, the significant difference in EEG signals among individuals hinders users from further utilizing the BCI system.<br><br>NEW METHOD: Addressing this difference and improving BCI classification accuracy remain key challenges. In this paper, we propose a transfer learning model based on deep learning to transfer the data distribution from the source domain to the target domain, named a subject transfer neural network combining the Generator with Euclidean alignment (ST-GENN). It consists of three parts: 1) Align the original EEG signals in the Euclidean space; 2) Send the aligned data to the Generator to obtain the transferred features; 3) Utilize the Convolution-attention-temporal (CAT) classifier to classify the transferred features.<br><br>RESULTS: The model is validated on BCI competition IV 2a, BCI competition IV 2b and SHU datasets to evaluate its classification performance, and the results are 82.85&#x202f;%, 86.28&#x202f;% and 67.2&#x202f;% for the three datasets, respectively.<br><br>The results have been shown to be robust to subject variability, with the average accuracy of the proposed method outperforming baseline algorithms by ranging from 2.03&#x202f;% to 15.43&#x202f;% on the 2a dataset, from 0.86&#x202f;% to 10.16&#x202f;% on the 2b dataset and from 3.3&#x202f;% to 17.9&#x202f;% on the SHU dataset.<br><br>The advantage of our model lies in its ability to effectively transfer the experience and knowledge of the source domain data to the target domain, thus bridging the gap between them. Our method can improve the practicability of MI-BCI systems.},
}
@article {pmid40349743,
year = {2025},
author = {Dong, T and Lee, HH and Zang, H and Lee, H and Tian, Q and Wan, L and Fan, Q and Huang, S},
title = {In vivo cortical microstructure mapping using high-gradient diffusion MRI accounting for intercompartmental water exchange effects.},
journal = {NeuroImage},
volume = {314},
number = {},
pages = {121258},
pmid = {40349743},
issn = {1095-9572},
support = {U24 NS137077/NS/NINDS NIH HHS/United States ; R01 NS118187/NS/NINDS NIH HHS/United States ; S10 OD032184/OD/NIH HHS/United States ; DP5 OD031854/OD/NIH HHS/United States ; U01 EB026996/EB/NIBIB NIH HHS/United States ; P41 EB030006/EB/NIBIB NIH HHS/United States ; P41 EB015896/EB/NIBIB NIH HHS/United States ; K99 AG073506/AG/NIA NIH HHS/United States ; R21 AG085795/AG/NIA NIH HHS/United States ; },
mesh = {Humans ; *Diffusion Magnetic Resonance Imaging/methods ; *Cerebral Cortex/diagnostic imaging/anatomy &amp; histology ; Adult ; Female ; Male ; White Matter/diagnostic imaging ; *Brain Mapping/methods ; Neurites ; Gray Matter/diagnostic imaging ; Monte Carlo Method ; Water/metabolism ; Models, Neurological ; },
abstract = {In recent years, mapping tissue microstructure in the cortex using high gradient diffusion MRI has received growing attention. The Soma And Neurite Density Imaging (SANDI) explicitly models the soma compartment in the cortex assuming impermeable membranes. As such, it does not account for diffusion time dependence due to water exchange in the estimated microstructural properties, as neurites in gray matter are much less myelinated than in white matter. In this work, we performed a systematic evaluation of an extended SANDI model for in vivo human cortical microstructural mapping that accounts for water exchange effects between the neurite and extracellular compartments using the anisotropic Kärger model. We refer to this model as in vivo SANDIX, adapting the nomenclature from previous publications. As in the original SANDI model, the soma compartment is modeled as an impermeable sphere due to the much smaller surface-to-volume ratio compared to the neurite compartment. A Monte Carlo simulation study was performed to examine the sensitivity of the in vivo SANDIX model to sphere radii, compartment fractions, and water exchange times. The simulation results indicate that the proposed in vivo SANDIX framework can account for the water exchange effect and provide measures of intra-soma and intra-neurite signal fractions without spurious time-dependence in estimated parameters, whereas the measured water exchange times need to be interpreted with caution. The model was then applied to in vivo diffusion MRI data acquired in 13 healthy adults on the 3-Tesla Connectome MRI scanner equipped with 300 mT/m gradients. The in vivo results exhibited patterns that were consistent with corresponding anatomical characteristics in both cortex and white matter. In particular, the estimated water exchange times in gray and white matter were distinct and differentiated between the two tissue types. Our results show the SANDIX approach applied to high-gradient diffusion MRI data achieves cortical microstructure mapping of the in vivo human brain with the evaluation of water exchange effects. This approach potentially provides a more appropriate description of in vivo cortical microstructure for improving data interpretation in future neurobiological studies.},
}
@article {pmid40348851,
year = {2025},
author = {Sun, G and Yu, C and Cai, R and Li, M and Fan, L and Sun, H and Lyu, C and Lin, Y and Gao, L and Wang, KH and Li, X},
title = {Neural representation of self-initiated locomotion in the secondary motor cortex of mice across different environmental contexts.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {725},
pmid = {40348851},
issn = {2399-3642},
support = {ZIA MH002897/ImNIH/Intramural NIH HHS/United States ; 32170991//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32371074//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Animals ; *Motor Cortex/physiology ; *Locomotion/physiology ; Mice ; Male ; *Neurons/physiology ; Mice, Inbred C57BL ; Environment ; },
abstract = {The secondary motor cortex (M2) plays an important role in the adaptive control of locomotor behaviors. However, it is unclear how M2 neurons encode the same type of locomotor control variables in different environmental contexts. Here we image the neuronal activity in M2 with a miniscope while mice are moving freely in each of three environments: a Y-maze, a running-wheel, and an open-field. These animals show distinct locomotor patterns in different environmental contexts. Surprisingly, a large population of M2 neurons are active before starting and after ceasing locomotion, while maintaining decreased neural activity during locomotion. Furthermore, the majority of these neurons are consistently engaged across various contexts, suggesting egocentric voluntary control functions. In contrast, the smaller populations of locomotion-activated M2 neurons are mostly context-specific, suggesting exocentric navigation functions. Thus, our results demonstrate that M2 neurons encode motor control variables for self-initiated locomotor behaviors in both context-dependent and context-independent manners.},
}
@article {pmid40347675,
year = {2025},
author = {Chen, L and Liu, Y and Wang, Z and Zhang, L and Cheng, S and Ming, D},
title = {Using non-invasive brain stimulation to modulate performance in visuomotor rotation adaptation: A scoping review.},
journal = {Cortex; a journal devoted to the study of the nervous system and behavior},
volume = {187},
number = {},
pages = {144-158},
doi = {10.1016/j.cortex.2025.04.010},
pmid = {40347675},
issn = {1973-8102},
mesh = {Humans ; *Psychomotor Performance/physiology ; *Adaptation, Physiological/physiology ; Rotation ; *Transcranial Magnetic Stimulation/methods ; *Brain/physiology ; Motor Cortex/physiology ; Movement/physiology ; },
abstract = {As research on the visuomotor rotation (VMR) adaptation expands its scope from behavioral science to encompass neuropsychological perspectives, an increasing number of studies have employed non-invasive brain stimulation (NIBS) techniques to explore the specific contributions of different neural structures to VMR adaptation. Despite early studies suggesting that cerebellar stimulation influenced the rate of adaptation and that stimulating primary motor cortex led to an enhanced retention of newly learned adaptation, subsequent studies could not always achieve consistent results. To probe this inconsistency, we systematically comb through past studies and extract numerous details, including paradigm designs, context settings, and modulation protocols in this scoping review. In summary, the paradigm design primarily serves two purposes: to dissociate implicit and explicit adaptation and to assess the retention of motor memory, whilst context settings such as apparatus, movement-related parameters and the information provided for subjects may complicate the modulated neuropsychological processes. We also conclude key NIBS parameters such as target regions and timing in stimulation protocols. Furthermore, we recognize the potential of neurophysiological biomarkers to support future VMR studies that incorporate NIBS and advocate for the use of several newly emerging NIBS techniques to enrich the field.},
}
@article {pmid40347660,
year = {2025},
author = {Chen, X and Chen, Y and McNamara, TP},
title = {Processing spatial cue conflict in navigation: Distance estimation.},
journal = {Cognitive psychology},
volume = {158},
number = {},
pages = {101734},
doi = {10.1016/j.cogpsych.2025.101734},
pmid = {40347660},
issn = {1095-5623},
mesh = {Humans ; *Cues ; *Spatial Navigation/physiology ; Male ; Female ; Young Adult ; Adult ; *Conflict, Psychological ; Bayes Theorem ; *Distance Perception/physiology ; *Space Perception/physiology ; Judgment ; Optic Flow ; },
abstract = {Spatial navigation involves the use of various cues. This study examined how cue conflict influences navigation by contrasting landmarks and optic flow. Participants estimated spatial distances under different levels of cue conflict: minimal conflict, large conflict, and large conflict with explicit awareness of landmark instability. Whereas increased cue conflict alone had little behavioral impact, adding explicit awareness reduced reliance on landmarks and impaired the precision of spatial localization based on them. To understand the underlying mechanisms, we tested two cognitive models: a Bayesian causal inference (BCI) model and a non-Bayesian sensory disparity model. The BCI model provided a better fit to the data, revealing two independent mechanisms for reduced landmark reliance: increased sensory noise for unstable landmarks and lower weighting of unstable landmarks when landmarks and optic flow were judged to originate from different causes. Surprisingly, increased cue conflict did not decrease the prior belief in a common cause, even when explicit awareness of landmark instability was imposed. Additionally, cue weighting in the same-cause judgment was determined by bottom-up sensory reliability, while in the different-cause judgment, it correlated with participants' subjective evaluation of cue quality, suggesting a top-down metacognitive influence. The BCI model further identified key factors contributing to suboptimal cue combination in minimal cue conflicts, including the prior belief in a common cause and prior knowledge of the target location. Together, these findings provide critical insights into how navigators resolve conflicting spatial cues and highlight the utility of the BCI model in dissecting cue interaction mechanisms in navigation.},
}
@article {pmid40342556,
year = {2025},
author = {Haseeb, M and Nadeem, R and Sultana, N and Naseer, N and Nazeer, H and Dehais, F},
title = {Monitoring pilots' mental workload in real flight conditions using multinomial logistic regression with a ridge estimator.},
journal = {Frontiers in robotics and AI},
volume = {12},
number = {},
pages = {1441801},
pmid = {40342556},
issn = {2296-9144},
abstract = {Piloting an aircraft is a cognitive task that requires continuous verbal, visual, and auditory attentions (e.g., Air Traffic Control Communication). An increase or decrease in mental workload from a specific level can alter auditory and visual attention, resulting in pilot errors. The objective of this research is to monitor pilots' mental workload using advanced machine learning techniques to achieve improved accuracy compared to previous studies. Electroencephalogram (EEG) data were recorded from 22 pilots operating under visual flight rules (VFR) conditions using a six dry-electrode Enobio Neuroelectrics system, and the Riemannian artifact subspace reconstruction (rASR) filter was used for data cleaning. An information gain (IG) attribute evaluator was used to select 25 optimal features out of 72 power spectral and statistical extracted features. In this study, 15 classifiers were used for classification. Multinomial logistic regression with a ridge estimator was selected, achieving a significant mean accuracy of 84.6% on the dataset from 17 subjects. Data were initially collected from 22 subjects, but 5 were excluded due to data synchronization issues. This work has several limitations, such as the author did not counter balance the order of scenario, could not control all the variables such as wind conditions, and workload was not stationary in each leg of the flight pattern. This study demonstrates that multinomial logistic regression with a ridge estimator shows significant classification accuracy (p < 0.05) and effectively detects pilot mental workload in real flight scenarios.},
}
@article {pmid40341884,
year = {2025},
author = {Bertheau, MAK and Boetzel, C and Herrmann, CS},
title = {Event-related potentials reveal incongruent behavior of autonomous vehicles in the moral machine dilemma.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {16048},
pmid = {40341884},
issn = {2045-2322},
mesh = {Humans ; Male ; Female ; *Evoked Potentials/physiology ; *Morals ; Electroencephalography ; Adult ; Young Adult ; *Decision Making/physiology ; *Artificial Intelligence ; },
abstract = {We investigated event-related potentials (ERPs) in the context of autonomous vehicles (AVs)-specifically in ambiguous, morally challenging traffic situations. In our study, participants (n = 34) observed a putative artificial intelligence (AI) making decisions in a dilemma situation involving an AV, expanding on the Moral Machine (MM) experiment. Additionally to the original MM experiment, we incorporated electroencephalography recordings. We were able to replicate most of the behavioral findings of the original MM: In case of an unavoidable traffic accident, participants consistently favored sparing pedestrians over passengers, more characters over fewer characters, and humans over pets. Beyond that, in the ERP we observed an increased P3 (322-422 ms), and late positive potential (LPP) (500-900 MS) amplitude in fronto-central regions when the putative AI's decision on a moral dilemma was incongruent to the participants' decision. As P3, and LPP are associated with the processing of stimulus significance, our findings suggest that these ERP components could potentially be used to identify critical, or unacceptable situations during human-AI interactions involving moral decision-making. This might be useful in brain computer interfaces research when, classifying single-trial ERP components, to dynamically adopt an AV's behavior.},
}
@article {pmid40341243,
year = {2025},
author = {Lore, S and Poganik, JR and Atala, A and Church, G and Gladyshev, VN and Scheibye-Knudsen, M and Verdin, E},
title = {Replacement as an aging intervention.},
journal = {Nature aging},
volume = {5},
number = {5},
pages = {750-764},
pmid = {40341243},
issn = {2662-8465},
support = {1U01AI180158-01//U.S. Department of Health &amp; Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID)/ ; },
mesh = {Humans ; *Aging/physiology ; Animals ; *Tissue Engineering/methods ; Tissue Scaffolds ; Mice ; },
abstract = {Substantial progress in aging research continues to deepen our understanding of the fundamental mechanisms of aging, yet there is a lack of interventions conclusively shown to attenuate the processes of aging in humans. By contrast, replacement interventions such as joint replacements, pacemaker devices and transplant therapies have a long history of restoring function in injury or disease contexts. Here, we consider biological and synthetic replacement-based strategies as aging interventions. We discuss innovations in tissue engineering, such as the use of scaffolds or bioprinting to generate functional tissues, methods for enhancing donor-recipient compatibility through genetic engineering and recent progress in both cell therapies and xenotransplantation strategies. We explore synthetic approaches including prostheses, external devices and brain-machine interfaces. Additionally, we evaluate the evidence from heterochronic parabiosis experiments in mice and donor-recipient age-mismatched transplants to consider whether systemic benefits could result from personalized replacement approaches. Finally, we outline key challenges and future directions required to advance replacement therapies as viable, scalable and ethical interventions for aging.},
}
@article {pmid40340020,
year = {2025},
author = {Yu, X and Jian, Z and Dang, L and Zhang, X and He, P and Xiong, X and Feng, Y and Rehman, AU},
title = {Chemogenetic modulation in stroke recovery: A promising stroke therapy approach.},
journal = {Brain stimulation},
volume = {18},
number = {4},
pages = {1028-1036},
doi = {10.1016/j.brs.2025.05.003},
pmid = {40340020},
issn = {1876-4754},
mesh = {Humans ; *Stroke/therapy/physiopathology/drug therapy ; Animals ; *Recovery of Function/physiology/drug effects ; *Stroke Rehabilitation/methods ; Neuronal Plasticity/physiology ; Designer Drugs ; Chemogenetics ; },
abstract = {Stroke remains a leading cause of long-term disability and mortality worldwide, necessitating novel therapeutic strategies to enhance recovery. Traditional rehabilitation approaches, including physical therapy and pharmacological interventions, often provide limited functional improvement. Neuromodulation has emerged as a promising strategy to promote post-stroke recovery by enhancing neuroplasticity and functional reorganization. Among various neuromodulatory techniques, chemogenetics, particularly Designer Receptors Exclusively Activated by Designer Drugs (DREADDs), offers precise, cell-type-specific, and temporally controlled modulation of neuronal and glial activity. This review explores the mechanisms and therapeutic potential of chemogenetic modulation in stroke recovery. Preclinical studies have demonstrated that activation of excitatory DREADDs (hM3Dq) in neurons located within the peri-infarct area or contralateral M1 has been shown to enhance neuroplasticity, facilitate axonal sprouting, and lead to improved behavioral recovery following stroke. Conversely, stimulation of inhibitory DREADDs (hM4Di) suppresses stroke-induced excitotoxicity, mitigates peri-infarct spreading depolarizations (PIDs), and modulates neuroinflammatory responses. By targeting specific neuronal and glial populations, chemogenetics enables phase-specific interventions-early inhibition to minimize damage during the acute phase and late excitation to promote plasticity during the recovery phase. Despite its advantages over traditional neuromodulation techniques, such as optogenetics and deep brain stimulation, several challenges remain before chemogenetics can be translated into clinical applications. These include optimizing viral vector delivery, improving ligand specificity, minimizing off-target effects, and ensuring long-term receptor stability. Furthermore, integrating chemogenetics with existing stroke rehabilitation strategies, including brain-computer interfaces and physical therapy, may enhance functional recovery by facilitating adaptive neuroplasticity. Future research should focus on refining chemogenetic tools to enable clinical application. By offering a highly selective, reversible, and minimally invasive approach, chemogenetics holds great potential for revolutionizing post-stroke therapy and advancing personalized neuromodulation strategies.},
}
@article {pmid40338888,
year = {2025},
author = {Faisal, M and Khosa, I and Waris, A and Gilani, SO and Khan, MJ and Hazzazi, F and Ijaz, MA},
title = {Optimizing the impact of time domain segmentation techniques on upper limb EMG decoding using multimodal features.},
journal = {PloS one},
volume = {20},
number = {5},
pages = {e0322580},
pmid = {40338888},
issn = {1932-6203},
mesh = {Humans ; *Electromyography/methods ; *Upper Extremity/physiology ; Male ; Adult ; Female ; Movement/physiology ; Signal Processing, Computer-Assisted ; Support Vector Machine ; Young Adult ; },
abstract = {Neurological disorders, such as stroke, spinal cord injury, and amyotrophic lateral sclerosis, result in significant motor function impairments, affecting millions of individuals worldwide. To address the need for innovative and effective interventions, this study investigates the efficacy of electromyography (EMG) decoding in improving motor function outcomes. While existing literature has extensively explored classifier selection and feature set optimization, the choice of preprocessing technique, particularly time-domain windowing techniques, remains understudied posing a significant knowledge gap. This study presents upper limb movement classification by providing a comprehensive comparison of eight time-domain windowing techniques. For this purpose, the EMG data from volunteers is recorded involving fifteen distinct movements of fingers. The rectangular window technique among others emerged as the most effective, achieving a classification accuracy of 99.98% while employing 40 time-domain features and a L-SVM classifier, among other classifiers. This optimal combination has implications for the development of more accurate and reliable myoelectric control systems. The achieved high classification accuracy demonstrates the feasibility of using surface EMG signals for accurate upper limb movement classification. The study's results have the potential to improve the accuracy and reliability of prosthetic limbs and wearable sensors and inform the development of personalized rehabilitation programs. The findings can contribute to the advancement of human-computer interaction and brain-computer interface technologies.},
}
@article {pmid40336015,
year = {2025},
author = {Yang, P and Zhang, X and Song, H and Zhang, X},
title = {An investigation into mental illness and its comorbidities from the perspective of supervenience physicalism.},
journal = {Philosophy, ethics, and humanities in medicine : PEHM},
volume = {20},
number = {1},
pages = {10},
pmid = {40336015},
issn = {1747-5341},
mesh = {Humans ; *Mental Disorders/psychology ; Comorbidity ; *Spirituality ; Philosophy, Medical ; },
abstract = {The exploration into the origin of human spirituality has always been a hot spot with many unsolved questions in the philosophy of mind, and issues concerning mental illness and its comorbidities are still unclear. In the 1970s, Donald Davidson first proposed anomalous monism with the supervenience concept, a theory that both insists on physicalism and transcends traditional reductionism. This theory provides solid and accessible proof for perceiving the mind-body relationship of spiritual origin in a non-reductionist approach. This paper develops arguments in two aspects. First, three principles of anomalous monism are employed to explore the origin of mental illness. Second, the comorbidity of mental illness is explained with the help of the supervenience theory.},
}
@article {pmid40335704,
year = {2025},
author = {Yang, L and Guo, C and Zheng, Z and Dong, Y and Xie, Q and Lv, Z and Li, M and Lu, Y and Guo, X and Deng, R and Liu, Y and Feng, Y and Mu, R and Zhang, X and Ma, H and Chen, Z and Zhang, Z and Dong, Z and Yang, W and Zhang, X and Cui, Y},
title = {Publisher Correction: Stress dynamically modulates neuronal autophagy to gate depression onset.},
journal = {Nature},
volume = {641},
number = {8065},
pages = {E14},
doi = {10.1038/s41586-025-09086-9},
pmid = {40335704},
issn = {1476-4687},
}
@article {pmid40334964,
year = {2025},
author = {Dong, L and Qi, Y and Luan, M and Liu, Q and Wang, M and Tian, C and Zheng, Y},
title = {A multi-channel implantable micro-magnetic stimulator for synergistic magnetic neuromodulation.},
journal = {Brain research},
volume = {1860},
number = {},
pages = {149679},
doi = {10.1016/j.brainres.2025.149679},
pmid = {40334964},
issn = {1872-6240},
mesh = {Animals ; Mice ; Hippocampus/physiology ; Electrodes, Implanted ; Male ; *Implantable Neurostimulators ; *Deep Brain Stimulation/methods/instrumentation ; Mice, Inbred C57BL ; CA1 Region, Hippocampal/physiology ; Electromagnetic Fields ; Magnetic Fields ; },
abstract = {Micro-magnetic stimulation (μMS) is an emerging technology in magnetic neuromodulation. However, for larger brain structures with complex neural pathways, such as deep brain neural clusters, traditional implantable single-point μMS devices are immobile and incapable of multi-regional magnetic modulation. While multi-channel μMS can effectively address this limitation, its large size, difficulty in implantation, and unclear synergistic modulation patterns restrict its application. To tackle these challenges, this study designs a 4 × 4 array micro-coil structure targeted at the deep hippocampal region of the mouse brain. Numerical simulations were performed to analyze the coupling coefficients among the micro-coils and the distribution of the electromagnetic field in the structure, indicating that, with optimized parameters, the effective magnetic stimulation threshold can be achieved. Based on this, a multi-channel μMS device was fabricated, solving key issues such as waterproofing, biocompatibility, and dual-brain-region implantation of both stimulation and recording electrodes. A multi-point synergistic magnetic stimulation protocol was developed. After determining the synergistic magnetic stimulation parameters and effective target positions through in vitro experiments, real-time monitoring of calcium signal changes in the CA1 region of the hippocampus in mice during synergistic magnetic stimulation was performed. The results demonstrate that synergistic magnetic stimulation significantly enhances synaptic plasticity and calcium signal activity. This validates the feasibility of the implantable multi-channel micro-magnetic stimulator.},
}
@article {pmid40334847,
year = {2025},
author = {Yuan, J and Pan, H and Sun, Y and Wang, Y and Jia, J},
title = {Neural responses to global and local visual information processing provide neural signatures of ADHD symptoms.},
journal = {International journal of psychophysiology : official journal of the International Organization of Psychophysiology},
volume = {212},
number = {},
pages = {112582},
doi = {10.1016/j.ijpsycho.2025.112582},
pmid = {40334847},
issn = {1872-7697},
mesh = {Humans ; *Attention Deficit Disorder with Hyperactivity/physiopathology ; Male ; Female ; Adult ; Electroencephalography ; Young Adult ; *Pattern Recognition, Visual/physiology ; Adolescent ; *Visual Perception/physiology ; },
abstract = {Individuals with ADHD are thought to exhibit a reduced "global bias" in perceptual processing. This bias, found in typically developed individuals, characterizes the tendency to prioritize global over local information processing. However, the relationship between specific ADHD symptoms and global or local processing remains unclear. This study addresses this gap by employing an ensemble perception task with a large sample (N = 465). EEG recordings allowed for the isolation of neural responses to individual and global stimuli using linear regression modeling. The adult ADHD self-report scale was used to assess ADHD symptoms. The results showed a significant association between ensemble perception and early responses to global stimuli. Furthermore, inattention symptoms were associated with early responses to global stimuli, suggesting a reduced global prioritization in individuals with higher inattention scores. Moreover, inattention symptom was associated with later responses to local stimuli, as shown by attenuated neural responses to local stimuli in individuals with more severe symptoms. These findings provide insights that ADHD includes deficits in both global and local processing, challenging earlier theories that focused solely on global processing impairments.},
}
@article {pmid40334819,
year = {2025},
author = {Yu, H and Zhou, X and Ru, Q and Antony, A and Cameron, M and Liu, Y and Klann, IP and Guo, H and Lin, J and Wang, D and Chang, D},
title = {The modulatory effects of persimmon leaf extract on sleep-related neurotransmitters and its potential hypnotic effects.},
journal = {Fitoterapia},
volume = {183},
number = {},
pages = {106576},
doi = {10.1016/j.fitote.2025.106576},
pmid = {40334819},
issn = {1873-6971},
mesh = {Animals ; *Plant Extracts/pharmacology ; Plant Leaves/chemistry ; *Hypnotics and Sedatives/pharmacology ; Humans ; *Neurotransmitter Agents/metabolism ; Mice ; Male ; Rats ; *Sleep/drug effects ; Sleep Initiation and Maintenance Disorders/drug therapy/chemically induced ; Rats, Sprague-Dawley ; gamma-Aminobutyric Acid/metabolism ; Cell Line, Tumor ; Dopamine/metabolism ; Pentobarbital ; Serotonin/metabolism ; },
abstract = {PURPOSE: Persimmon leaf is a traditional herbal medicine with diverse therapeutic applications. This study aimed to explore the effect of persimmon leaf extract (PLE) on the modulation of neurotransmitters involved in sleep regulation and its overall impact on sleep latency and duration.<br><br>METHODS: The key components of PLE were identified by ultra performance liquid chromatography. The modulatory effects of PLE in sleep and wakefulness-related neurotransmitters were studied in human neuroblastoma SH-SY5Y cells. PLE was also investigated in pentobarbital sodium-induced sleep and para-chlorophenylalanine (PCPA)-induced insomnia models in mice and rats.<br><br>RESULTS: PLE induced chloride influx and increased the intracellular production of gamma-aminobutyric acid (GABA), a neurotransmitter crucial for sleep regulation, in SH-SY5Y cells. Furthermore, PLE influenced the cellular expressions of serotonin, dopamine, and adenosine. It increased monoamine oxidase enzyme-A activity and reduced serotonin levels and its metabolites. It induced dopamine biosynthesis and degradation pathways. In the pentobarbital-induced sleep experiment, PLE significantly prolonged total sleep duration and reduced sleep latency in a dose-dependent manner. In the PCPA-induced insomnia model, PLE consistently increased GABA production, and lowered dopamine expression.<br><br>CONCLUSION: PLE exhibited modulatory effects on sleep-related neurotransmitters in vitro, which may also contribute to its hypnotic effects by extending the sleep duration and shortening sleeping latency in vivo.},
}
@article {pmid40334321,
year = {2025},
author = {Chen, X and Jia, T and Wu, D},
title = {Data alignment based adversarial defense benchmark for EEG-based BCIs.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {188},
number = {},
pages = {107516},
doi = {10.1016/j.neunet.2025.107516},
pmid = {40334321},
issn = {1879-2782},
mesh = {*Electroencephalography/methods ; *Brain-Computer Interfaces/standards ; Humans ; Neural Networks, Computer ; Benchmarking ; Machine Learning ; *Computer Security ; Algorithms ; },
abstract = {Machine learning has been extensively applied to signal decoding in electroencephalogram (EEG)-based brain-computer interfaces (BCIs). While most studies have focused on enhancing the accuracy of EEG-based BCIs, more attention should be given to their security. Recent findings reveal that EEG-based BCIs are vulnerable to adversarial attacks. To address this, we present the first adversarial defense benchmark based on data alignment, aiming to enhance both the accuracy and robustness of EEG-based BCIs. This study evaluates nine adversarial defense approaches (including five defense strategies) across five EEG datasets (covering three paradigms), three neural networks, and four experimental scenarios. Our results show for the first time that integrating data augmentation, data alignment, and robust training can further improve both the accuracy and robustness of BCIs compared to using only one or two of them. Furthermore, we provide insights into the characteristics of various adversarial defense approaches based on EEG data alignment, offering valuable guidance for developing more accurate and secure EEG-based BCIs.},
}
@article {pmid40332078,
year = {2025},
author = {Park, BS and Yang, HR and Kang, H and Kim, KK and Kim, YT and Yang, S and Kim, JG},
title = {α2-Adrenergic Receptors in Hypothalamic Dopaminergic Neurons: Impact on Food Intake and Energy Expenditure.},
journal = {International journal of molecular sciences},
volume = {26},
number = {8},
pages = {},
pmid = {40332078},
issn = {1422-0067},
support = {NRF-2021R1C1C2005067//National Research Foundation of Korea/ ; Research Grant (2020)//Incheon National University/ ; },
mesh = {Animals ; *Energy Metabolism/drug effects ; *Hypothalamus/metabolism/cytology/drug effects ; *Dopaminergic Neurons/metabolism/drug effects ; *Receptors, Adrenergic, alpha-2/metabolism/genetics ; Mice ; Male ; *Eating/drug effects ; Mice, Inbred C57BL ; },
abstract = {The adrenergic system plays an active role in modulating synaptic transmission in hypothalamic neurocircuitry. While α2-adrenergic receptors are widely distributed in various organs and are involved in various physiological functions, their specific role in the regulation of energy metabolism in the brain remains incompletely understood. Herein, we investigated the functions of α2-adrenergic receptors in the hypothalamus on energy metabolism in mice. Our study confirmed the expression of α2-adrenergic receptors in hypothalamic dopaminergic neurons and assessed metabolic phenotypes, including food intake and energy expenditure, after treatment with guanabenz, an α2-adrenergic receptor agonist. Guanabenz treatment significantly increased food intake (0.25 ± 0.03 g vs. 0.98 ± 0.05 g, p < 0.001) and body weight (-0.1 ± 0.04 g vs. 0.33 ± 0.03 g, p < 0.001) within 6 h post-treatment. Furthermore, guanabenz markedly elevated energy expenditure parameters, including respiratory exchange ratio (RER, 1.017 ± 0.007 vs. 1.113 ± 0.03, p < 0.01) and carbon dioxide production (1.512 ± 0.018 mL/min vs. 1.635 ± 0.036 mL/min, p < 0.05), compared to vehicle-treated controls. Furthermore, using chemogenetic techniques, we demonstrated that the altered metabolic phenotypes induced by guanabenz treatment were effectively reversed by inhibiting the activity of dopaminergic neurons in the hypothalamic arcuate nucleus (ARC) using a chemogenetic technique. Our findings suggest functional connectivity between hypothalamic α2-adrenergic receptor signals and dopaminergic neurons in metabolic controls.},
}
@article {pmid40329250,
year = {2025},
author = {Karimi, J and Cherono, A and Alegana, V and Mutua, M and Kiarie, H and Muthee, R and Temmerman, M and Gichangi, P},
title = {Geographic inequalities, and social-demographic determinants of reproductive, maternal and child health at sub-national levels in Kenya.},
journal = {BMC public health},
volume = {25},
number = {1},
pages = {1656},
pmid = {40329250},
issn = {1471-2458},
support = {001/WHO_/World Health Organization/International ; 211208/WT_/Wellcome Trust/United Kingdom ; },
mesh = {Humans ; Kenya/epidemiology ; Female ; *Child Health/statistics &amp; numerical data ; *Maternal Health/statistics &amp; numerical data ; Pregnancy ; Adult ; Child ; *Reproductive Health/statistics &amp; numerical data ; Socioeconomic Factors ; Maternal Mortality ; Adolescent ; *Social Determinants of Health/statistics &amp; numerical data ; Young Adult ; },
abstract = {BACKGROUND: Global initiatives have emphasized tracking indicators to monitor progress, particularly in countries with the highest maternal and child mortality. Routine data can be used to monitor indicators for improved target setting at national and subnational levels. Our objective was to assess the geographic inequalities in estimates of reproductive, maternal and child health indicators from routine data at the subnational level in Kenya.<br><br>METHODS: Monthly data from 47 counties clustered in 8 regions, from January 2018 to December 2021 were assembled from the District Health Information Software version 2 (DHIS2) in Kenya. This included women of reproductive age receiving family planning commodities, pregnant women completing four antenatal care visits, deliveries conducted by skilled birth attendants, fully immunized children at 1&#xa0;year and number of maternal deaths at health facilities, from which five indicators were constructed with denominators. A hierarchical Bayesian model was used to generate estimates of the five indicators at the at sub-national levels(counties and sub counties), adjusting for four determinants of health. A reproductive, maternal, and child health (RMCH) index was generated from the 5 indicators to compare overall performance across the continuum of care in reproductive, maternal and child health across the different counties.<br><br>RESULTS: The DHIS2 data quality for the selected 5 indicators was acceptable with detection of less than 3% outliers for the Facility Maternal Mortality Ratio (FMMR) and less than 1% for the other indicators. Overall, counties in the north-eastern, eastern and coastal regions had the lowest RMCH index due to low service coverage and high FMMR. Full immunization coverage at 1&#xa0;year (FIC) had the highest estimate (79.3%, BCI: 77.8-80.5%), while Women of Reproductive age receiving FP commodities had the lowest estimate (38.6%, BCI: 38.2-38.9%). FMMR was estimated at 105.4, (BCI 67.3-177.1)Health facility density was an important determinant in estimating all five indicators. Maternal education was positively correlated with higher FIC coverage, while wealthier sub counties had higher FMMR.<br><br>CONCLUSIONS: Tracking of RMCH indicators revealed geographical inequalities at the County and subcounty level, often masked by national-level estimates. These findings underscore the value of routine monitoring indicators as a potential for evidence-based sub-national planning and precision targeting of interventions to marginalized populations.},
}
@article {pmid40327905,
year = {2025},
author = {Sarankumar, R and Ramkumar, M and Karthik, V and Muthuvel, SK},
title = {Integrating attention networks into a hybrid model for HER2 status prediction in breast cancer.},
journal = {Biochemical and biophysical research communications},
volume = {768},
number = {},
pages = {151856},
doi = {10.1016/j.bbrc.2025.151856},
pmid = {40327905},
issn = {1090-2104},
mesh = {Humans ; *Breast Neoplasms/metabolism/genetics/pathology/diagnosis ; *Receptor, ErbB-2/metabolism/genetics ; Female ; *Neural Networks, Computer ; Algorithms ; Image Processing, Computer-Assisted/methods ; },
abstract = {Breast cancer is one of the most prevalent cancers amongst women, caused by uncontrolled cell growth in breast tissue. Human Epidermal growth factor Receptor 2 (HER2) proteins play a vital role in regulating normal breast cell development and division, and the status is crucial for determining prognosis and treatment strategies. Despite the availability of various techniques to identify the HER2 gene in tumors, the prediction accuracy of existing methods remains insufficient. This research aims to improve HER2 status prediction accuracy by proposing an Enhanced Hybrid Model with Optimized Attention Network (EHMOA-net) for histopathology image analysis. The methodology involves patch segmentation using an Encoder-Decoder-based hybrid weights alignment with Multi-Dilated U-net (EDMDU) model applied to the TCGA dataset, followed by preprocessing through enhanced Macenko stain normalization for segmented patches and images from the BCI dataset. Improved non-subsampled shearlet transform is utilized for feature extraction, and the Hybrid Enhanced Rough k-means clustering and Fuzzy C-Means (HERFCM) algorithm is employed to cluster neighboring image patches based on similar features. Finally, HER2 prediction is performed using nested graph neural networks integrated with a visual attention network. The proposed method, implemented in Python, achieves an accuracy of 97.85 %, surpassing existing techniques. These findings demonstrate the effectiveness of EHMOA-net in improving HER2 prediction accuracy and its potential utility in clinical applications.},
}
@article {pmid40327498,
year = {2025},
author = {Xu, Y and Wang, X and Li, J and Zhang, X and Li, F and Gao, Q and Fu, C and Leng, Y},
title = {A Powered Prosthetic Hand With Vision System for Enhancing the Anthropopathic Grasp.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {1827-1840},
doi = {10.1109/TNSRE.2025.3567392},
pmid = {40327498},
issn = {1558-0210},
mesh = {Humans ; *Hand Strength/physiology ; *Artificial Limbs ; Gestures ; Prosthesis Design ; *Hand/physiology ; Brain-Computer Interfaces ; Algorithms ; Male ; Electromyography ; Adult ; Female ; Biomechanical Phenomena ; Movement ; *Vision, Ocular ; },
abstract = {The anthropomorphic grasping capability of prosthetic hands is critical for enhancing user experience and functional efficiency. Existing prosthetic hands relying on brain-computer interfaces (BCI) and electromyography (EMG) face limitations in achieving natural grasping due to insufficient gesture adaptability and intent recognition. While vision systems enhance object perception, they lack dynamic human-like gesture control during grasping. To address these challenges, we propose a vision-powered prosthetic hand system that integrates two innovations. Spatial Geometry-based Gesture Mapping (SG-GM) dynamically models finger joint angles as polynomial functions of hand-object distance, derived from geometric features of human grasping sequences. These functions enable continuous anthropomorphic gesture transitions, mimicking natural hand movements. Motion Trajectory Regression-based Grasping Intent Estimation (MTR-GIE) predicts user intent in multi-object environments by regressing wrist trajectories and spatially segmenting candidate objects. Experiments with eight daily objects demonstrated high anthropomorphism (similarity coefficient ${R}
^{{2}
}
=0.911$ , root mean squared error $\textit {RMSE}
=2.47 {^{\circ}
}
$), rapid execution ($3.07\pm 0.41$ s), and robust success rates (95.43% single-object; 88.75% multi-object). The MTR-GIE achieved 94.35% intent estimation accuracy under varying object spacing. This work pioneers vision-driven dynamic gesture synthesis for prosthetics, eliminating dependency on invasive sensors and advancing real-world usability.},
}
@article {pmid40326979,
year = {2026},
author = {Du, X and Wang, Y and Wang, X and Tian, X and Jing, W},
title = {Neural circuit mechanisms of epilepsy: Maintenance of homeostasis at the cellular, synaptic, and neurotransmitter levels.},
journal = {Neural regeneration research},
volume = {21},
number = {2},
pages = {455-465},
pmid = {40326979},
issn = {1673-5374},
abstract = {Epilepsy, a common neurological disorder, is characterized by recurrent seizures that can lead to cognitive, psychological, and neurobiological consequences. The pathogenesis of epilepsy involves neuronal dysfunction at the molecular, cellular, and neural circuit levels. Abnormal molecular signaling pathways or dysfunction of specific cell types can lead to epilepsy by disrupting the normal functioning of neural circuits. The continuous emergence of new technologies and the rapid advancement of existing ones have facilitated the discovery and comprehensive understanding of the neural circuit mechanisms underlying epilepsy. Therefore, this review aims to investigate the current understanding of the neural circuit mechanisms in epilepsy based on various technologies, including electroencephalography, magnetic resonance imaging, optogenetics, chemogenetics, deep brain stimulation, and brain-computer interfaces. Additionally, this review discusses these mechanisms from three perspectives: structural, synaptic, and transmitter circuits. The findings reveal that the neural circuit mechanisms of epilepsy encompass information transmission among different structures, interactions within the same structure, and the maintenance of homeostasis at the cellular, synaptic, and neurotransmitter levels. These findings offer new insights for investigating the pathophysiological mechanisms of epilepsy and enhancing its clinical diagnosis and treatment.},
}
@article {pmid40323825,
year = {2025},
author = {Kim, H and Chang, WK and Kim, WS and Jang, JH and Lee, YA and Vermehren, M and Peekhaus, N and Colucci, A and Angerhöfer, C and Hömberg, V and Soekadar, SR and Paik, NJ},
title = {Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {218},
pages = {},
doi = {10.3791/67601},
pmid = {40323825},
issn = {1940-087X},
mesh = {Humans ; *Brain-Computer Interfaces ; *Robotics/methods/instrumentation ; *Stroke Rehabilitation/methods/instrumentation ; *Upper Extremity/physiopathology ; Male ; Female ; Middle Aged ; Electroencephalography/methods ; Activities of Daily Living ; Aged ; Adult ; Electrooculography/methods ; },
abstract = {This study introduces a Brain-Computer Interface (BCI)-controlled upper limb assistive robot for post-stroke rehabilitation. The system utilizes electroencephalogram (EEG) and electrooculogram (EOG) signals to help users assist upper limb function in everyday tasks while interacting with a robotic hand. We evaluated the effectiveness of this BCI-robot system using the Berlin Bimanual Test for Stroke (BeBiTS), a set of 10 daily living tasks involving both hands. Eight stroke patients participated in this study, but only four participants could adapt to the BCI robot system training and perform the postBeBiTS. Notably, when the preBeBiTS score for each item was four or less, the BCI robot system showed greater assistive effectiveness in the postBeBiTS assessment. Furthermore, our current robotic hand does not assist with arm and wrist functions, limiting its use in tasks requiring complex hand movements. More participants are required to confirm the training effectiveness of the BCI-robot system, and future research should consider using robots that can assist with a broader range of upper limb functions. This study aimed to determine the BCI-robot system's ability to assist patients in performing daily living activities.},
}
@article {pmid40321898,
year = {2025},
author = {Zhang, S and Song, Y and Lv, S and Jing, L and Wang, M and Liu, Y and Xu, W and Jiao, P and Zhang, S and Wang, M and Liu, J and Wu, Y and Cai, X},
title = {Electrode Arrays for Detecting and Modulating Deep Brain Neural Information in Primates: A Review.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {6},
number = {},
pages = {0249},
pmid = {40321898},
issn = {2692-7632},
abstract = {Primates possess a more developed central nervous system and a higher level of intelligence than rodents. Detecting and modulating deep brain activity in primates enhances our understanding of neural mechanisms, facilitates the study of major brain diseases, enables brain-computer interactions, and supports advancements in artificial intelligence. Traditional imaging methods such as magnetic resonance imaging, positron emission computed tomography, and scalp electroencephalogram are limited in spatial resolution. They cannot accurately capture deep brain signals from individual neurons. With the progress of microelectromechanical systems and other micromachining technologies, single-neuron level detection and stimulation technology in rodents based on microelectrodes has made important progress. However, compared with rodents, human and nonhuman primates have larger brain volume that needs deeper implantation depth, and the test object has higher safety and device preparation requirements. Therefore, high-resolution devices suitable for long-term detection in the brains of primates are urgently needed. This paper reviewed electrode array devices used for electrophysiological and electrochemical detections in primates' deep brains. The research progress of neural recording and stimulation technologies was introduced from the perspective of electrode type and device structures, and their potential value in neuroscience research and clinical disease treatments was discussed. Finally, it is speculated that future electrodes will have a lot of room for development in terms of flexibility, high resolution, deep brain, and high throughput. The improvements in electrode forms and preparation process will expand our understanding of deep brain neural activities, and bring new opportunities and challenges for the further development of neuroscience.},
}
@article {pmid40321282,
year = {2025},
author = {Forenzo, D and Zhang, Y and Wittenberg, GF and He, B},
title = {Continuous Reaching and Grasping with a BCI Controlled Robotic Arm in Healthy and Stroke-Affected Individuals.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.04.16.25325551},
pmid = {40321282},
abstract = {Recent advancements in signal processing techniques have enabled non-invasive Brain-Computer Interfaces (BCIs) to control assistive devices, like robotic arms, directly with users' EEG signals. However, the applications of these systems are currently limited by the low signal-to-noise ratio and spatial resolution of EEG from which brain intention is decoded. In this study, we propose a motor-imagery (MI) paradigm, inspired by the mechanisms of a computer mouse, that adds an additional "click" signal to an established 2D movement BCI paradigm. The additional output signal increases the degrees of freedom of the BCI system and may enable more complex tasks. We evaluated this paradigm using deep learning (DL) based signal processing on both healthy subjects and stroke-survivors in online BCI tasks derived from two potential applications: clicking on virtual targets and moving physical objects with a robotic arm in a continuous reach-and-grasp task. The results show that subjects were able to control both movement and clicking simultaneously to grab, move, and place up to an average of 7 cups in a 5-minute run using the robotic arm. The proposed paradigm provides an additional degree of freedom to EEG BCIs, and improves upon existing systems by enabling continuous control of reach-and-grasp tasks instead of selecting from a discrete list of predetermined actions. The tasks studied in these experiments show BCIs may be used to control computer cursors or robotic arms for complex real-world or clinical applications in the near future, potentially improving the lives of both healthy individuals and motor-impaired patients.},
}
@article {pmid40318536,
year = {2025},
author = {Tang, E and Li, J and Liu, H and Peng, C and Zhou, D and Hu, S and Chen, H},
title = {Lack of social interaction advantage: A domain-general cognitive alteration in schizophrenia.},
journal = {Journal of psychiatric research},
volume = {186},
number = {},
pages = {434-444},
doi = {10.1016/j.jpsychires.2025.04.030},
pmid = {40318536},
issn = {1879-1379},
mesh = {Humans ; Male ; Female ; Adult ; *Social Interaction ; *Schizophrenia/complications/physiopathology ; *Schizophrenic Psychology ; Young Adult ; Memory, Short-Term/physiology ; Neuropsychological Tests ; Middle Aged ; *Social Perception ; *Cognitive Dysfunction/etiology/physiopathology ; },
abstract = {People with schizophrenia (PSZ) showed preserved ability to unconsciously process simple social information (e.g., face and gaze), but not in higher-order cognition (e.g., memory). It is yet unknown how PSZ process social interactions across different cognitive domains. This study systematically investigated the cognitive characteristics of PSZ during social interaction processing from bottom-up perception to top-down memory, and established correlations with schizophrenic symptoms. In two experiments, social interactions were consistently displayed by face-to-face or back-to-back dyads. Experiment 1 enrolled 30 PSZ and 30 healthy control subjects (HCS) with a breaking continuous flash suppression (b-CFS) paradigm. Experiment 2 recruited 36 PSZ and 36 HCS for two memory tasks, wherein participants restored the between-model distance (working memory task) and recalled the socially bound pairs (long-term memory task). Results indicated that HCS showed advantageous processing of socially interactive stimuli against non-interactive stimuli throughout two experiments, including faster access to visual consciousness, closer spatial distance held in working memory and higher recollection accuracy in long-term memory. However, PSZ did not show any of these advantages, with significant interaction effects for all three tasks (task one: p = .018, ηp[2] = .092; task two: p = .021, ηp[2] = .074; task three: p = .015, ηp[2] = .082). Moreover, correlation analyses indicated that PSZ with more severe negative symptoms (r = -.344, p = .040) or higher medication dosages (r = -.334, p = .046) showed fewer advantages in memorizing socially interactive information. Therefore, social interaction is not prioritized in schizophrenia from bottom-up perception to top-down memory, and the magnitude of such a domain-general cognitive alteration is clinically relevant to symptom severity and medication.},
}
@article {pmid40317785,
year = {2025},
author = {Stieglitz, T and Bersch, I and Mrachacz-Kersting, N and Pasluosta, C},
title = {Differences and Commonalities of Electrical Stimulation Paradigms After Central Paralysis and Amputation.},
journal = {Artificial organs},
volume = {},
number = {},
pages = {},
doi = {10.1111/aor.15017},
pmid = {40317785},
issn = {1525-1594},
abstract = {BACKGROUND: Patients with spinal cord injury (SCI) or with severe brain stroke suffer from life-lasting functional and sensory impairments. Other traumatic injuries such as limb loss after an accident or disease also affect motor function and sensory feedback and impair quality of life in those individuals. Invasive and non-invasive functional electrical stimulation (FES) is a well-established method to partially restore function and sensory feedback of paralyzed and phantom limbs. It is also a supporting technology for the rehabilitation of the neuromuscular system and for complementing assistive devices.<br><br>METHODS: This work reviews the current state-of-the-art of FES as a technology for restoring function and supporting rehabilitation therapy and assistive devices.<br><br>RESULTS: Electrodes, electrical stimulation, use of brain signals for rehabilitation and control, and sensory feedback are covered as parts of the whole. A perspective is given on how clinical and research protocols developed for patients with SCI and brain injuries can be translated to the treatment of patients with an amputation and vice versa. We further elaborate on how motor learning strategies with quantitative electrophysiological and kinematic measurements may help caregivers in the rehabilitation process. Insights from practitioners (collected during a workshop of the IFESS 2025) have been integrated to summarize common needs, open questions, and challenges.<br><br>CONCLUSIONS: The information from the literature and from practitioners was integrated to propose the next steps towards establishing common guidelines and measures of FES in clinical practice towards evidence-driven treatment and objective assessments.},
}
@article {pmid40317558,
year = {2025},
author = {Du, X and Shen, F and Yu, C and Wang, Y and Yu, J and Yao, L and Liu, N and Zhuang, S},
title = {SMYD3 as an Epigenetic Regulator of Renal Tubular Cell Survival and Regeneration Following Acute Kidney Injury in Mice.},
journal = {FASEB journal : official publication of the Federation of American Societies for Experimental Biology},
volume = {39},
number = {9},
pages = {e70533},
doi = {10.1096/fj.202500089R},
pmid = {40317558},
issn = {1530-6860},
support = {82370698//MOST | National Natural Science Foundation of China (NSFC)/ ; 82070700//MOST | National Natural Science Foundation of China (NSFC)/ ; },
mesh = {Animals ; *Acute Kidney Injury/metabolism/pathology/genetics ; Mice ; *Histone-Lysine N-Methyltransferase/metabolism/genetics/antagonists &amp; inhibitors ; *Epigenesis, Genetic ; *Kidney Tubules/metabolism/pathology ; *Regeneration ; Male ; Cell Survival ; Mice, Inbred C57BL ; Apoptosis ; Cell Proliferation ; Reperfusion Injury/metabolism/pathology ; Histones/metabolism ; },
abstract = {The protein SET and MYND-Domain Containing 3 (SMYD3) is a methyltransferase that modifies various non-histone and histone proteins, linking it to tumorigenesis and cyst formation. However, its role in acute kidney injury (AKI) remains unclear. This study investigates the role and mechanism of AKI using a murine model of ischemia-reperfusion (IR)-induced AKI. After IR injury, SMYD3 and H3K4me3 levels increased in the kidneys, correlating with renal dysfunction, tubular cell injury, and apoptosis. Administration of BCI-121, a selective SMYD3 inhibitor, exacerbated IR-induced tubular cell injury and apoptosis, leading to more severe renal dysfunction and pathological changes. Pharmacological inhibition of SMYD3 also impaired the dedifferentiation and proliferation of renal tubular cells, key regenerative processes in injured kidneys, as evidenced by decreased expression of vimentin, snail, proliferating cell nuclear antigen (PCNA), cyclin D1, and retinoblastoma protein (RB). Additionally, SMYD3 inhibition reduced phosphorylation of the epithelial growth factor receptor (EGFR) and AKT, as well as EGFR expression in damaged kidneys. Finally, both BCI-121 and SMYD3 siRNA reduced EGF-induced expression of vimentin, snail, cyclin D1, PCNA, and EGFR, along with phosphorylation of RB and AKT in cultured renal tubular cells. Chip assay indicated that SMYD3 and H3K4me3 are enriched at the promoter of EGFR and SMYD3 inhibition blocked this response. These data suggest that SMYD3 plays an important role as an epigenetic regulator of renal tubular cell survival and regenerative pathways following kidney injury. Targeting SMYD3 or its epigenetic effects could offer therapeutic potential for enhancing kidney regeneration in AKI and related renal diseases.},
}
@article {pmid40315903,
year = {2025},
author = {Chueh, SY and Chen, Y and Subramanian, N and Goolsby, B and Navarro, P and Oweiss, K},
title = {Metaplasticity and continual learning: mechanisms subserving brain computer interface proficiency.},
journal = {Journal of neural engineering},
volume = {22},
number = {3},
pages = {},
pmid = {40315903},
issn = {1741-2552},
mesh = {*Neuronal Plasticity/physiology ; *Brain-Computer Interfaces ; Animals ; Mice ; *Learning/physiology ; Male ; Mice, Inbred C57BL ; Mice, Transgenic ; Optogenetics/methods ; Models, Neurological ; },
abstract = {Objective.Brain computer interfaces (BCIs) require substantial cognitive flexibility to optimize control performance. Remarkably, learning this control is rapid, suggesting it might be mediated by neuroplasticity mechanisms operating on very short time scales. Here, we propose a meta plasticity model of BCI learning and skill consolidation at the single cell and population levels comprised of three elements: (a) behavioral time scale synaptic plasticity (BTSP), (b) intrinsic plasticity (IP) and (c) synaptic scaling (SS) operating at time scales from seconds to minutes to hours and days. Notably, the model is able to explainrepresentational drift-a frequent and widespread phenomenon that adversely affects BCI control and continued use.Approach.We developed an all-optical approach to characterize IP, BTSP and SS with single cell resolution in awake mice using fluorescent two photon (2P) GCaMP7s imaging and optogenetic stimulation of the soma targeted ChRmineKv2.1. We further trained mice on a one-dimensional BCI control task and systematically characterized within session (seconds to minutes) learning as well as across sessions (days and weeks) with different neural ensembles.Main results.On the time scale of seconds, substantial BTSP could be induced and was followed by significant IP over minutes. Over the time scale of days and weeks, these changes could predict BCI control proficiency, suggesting that BTSP and IP might be complemented by SS to stabilize and consolidate BCI control.Significance.Our results provide early experimental support for a meta plasticity model of continual BCI learning and skill consolidation. The model predictions may be used to design and calibrate neural decoders with complete autonomy while considering the temporal and spatial scales of plasticity mechanisms. With the power of modern-day machine learning and artificial Intelligence, fully autonomous neural decoding and adaptation in BCIs might be achieved with minimal to no human intervention.},
}
@article {pmid40315795,
year = {2025},
author = {Chen, TY and Lien, KH and Yeh, KT and Tu, JC and Wai-Yee Ho, V and Chan, KC},
title = {Bonebridge BCI 602 implantation in syndromic and non-syndromic patients with bilateral microtia and aural atresia.},
journal = {International journal of pediatric otorhinolaryngology},
volume = {193},
number = {},
pages = {112370},
doi = {10.1016/j.ijporl.2025.112370},
pmid = {40315795},
issn = {1872-8464},
mesh = {Humans ; *Congenital Microtia/surgery/complications ; Retrospective Studies ; Male ; Female ; *Ear/abnormalities/surgery ; Child ; *Congenital Abnormalities/surgery ; *Bone Conduction ; Adolescent ; Treatment Outcome ; Child, Preschool ; *Prosthesis Implantation/methods ; *Hearing Aids ; },
abstract = {OBJECTIVE: To evaluate the safety and efficacy of Bonebridge bone conduction implant (BCI) 602 implantation in syndromic and non-syndromic patients with bilateral microtia and aural atresia (AA).<br><br>METHODS: This retrospective study included 15 patients (3 syndromic, 12 non-syndromic) with bilateral microtia and AA who underwent BCI 602 implantation at a tertiary medical center between January 2022 and June 2024. Intraoperative and postoperative complications were recorded, with a minimum follow-up of six months. Audiological outcomes, including functional hearing gain (FHG), speech reception threshold (SRT), and word recognition score (WRS), were analyzed.<br><br>RESULTS: No intraoperative complications occurred in any cases. One minor postoperative complication (6.7&#xa0;%) was reported in a non-syndromic patient during follow-up. The mean unaided and aided sound field threshold pure tone averages were 60.3&#xa0;&#xb1;&#xa0;8.7&#xa0;dB HL and 23.8&#xa0;&#xb1;&#xa0;3.9&#xa0;dB HL, respectively, yielding an FHG of 36.6&#xa0;&#xb1;&#xa0;9.3&#xa0;dB HL (p&#xa0;<&#xa0;0.05). SRT improved from 57.0&#xa0;&#xb1;&#xa0;5.9&#xa0;dB HL to 27.0&#xa0;&#xb1;&#xa0;6.5&#xa0;dB HL in quiet and from 0.3&#xa0;&#xb1;&#xa0;8.5&#xa0;dB SNR to -10.7&#xa0;&#xb1;&#xa0;4.2&#xa0;dB SNR in noise. WRS increased from 45.1&#xa0;&#xb1;&#xa0;20.7&#xa0;% to 89.9&#xa0;&#xb1;&#xa0;5.6&#xa0;% in quiet and from 40.9&#xa0;&#xb1;&#xa0;20.9&#xa0;% to 80.9&#xa0;&#xb1;&#xa0;13.8&#xa0;% in noise (p&#xa0;<&#xa0;0.05). Improvements in FHG, SRT, and WRS were comparable between syndromic and non-syndromic groups (p&#xa0;>&#xa0;0.05).<br><br>CONCLUSIONS: The Bonebridge BCI 602 is a safe and effective option for hearing restoration in both syndromic and non-syndromic patients with bilateral microtia and AA. Its compact design enhances surgical safety and minimizes risks to critical structures, particularly in syndromic patients with complex temporal bone anatomy.},
}
@article {pmid40315092,
year = {2025},
author = {Zeng, F and Wen, X and Tang, H and Hu, G and Hou, W and Zhang, X},
title = {Age-Related Changes in Action Observation EEG Response and Its Effect on BCI Performance.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {1805-1816},
doi = {10.1109/TNSRE.2025.3566371},
pmid = {40315092},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Male ; Adult ; Female ; Young Adult ; Aged ; *Aging/physiology ; Middle Aged ; Discriminant Analysis ; Algorithms ; Brain/physiology ; Evoked Potentials, Visual/physiology ; Regression Analysis ; Entropy ; Reproducibility of Results ; Age Factors ; },
abstract = {Action observation-based brain-computer interface (AO-BCI) can simultaneously elicit steady-state motion visual evoked potential in the occipital region and sensorimotor rhythm in the sensorimotor region, demonstrating substantial potential in neurorehabilitation applications. While current AO-BCI research primarily focuses on the younger population, this study conducted a comparative investigation of age-related differences in EEG response to the AO-BCI by enrolling 18 older and 18 younger subjects. We employed task discriminant component analysis (TDCA) to decode observed actions and performed comprehensive analyses of prefrontal EEG responses, i.e. approximate entropy (ApEn), sample entropy (SamEn), and rhythm power ratios (RPR), and the whole-brain functional network. Regression analyses were subsequently conducted to analyze the effects on the classification accuracy. Results revealed significantly diminished TDCA accuracy in older subjects (77.01% $\pm ~14.67$ %) compared to younger subjects (87.22% $\pm ~15.22$ %). Age-dependent EEG responses emerged across multiple dimensions: 1) Prefrontal ApEn, SamEn, and RPR exhibited distinct aging patterns; 2) Brain network analysis uncovered significant intergroup differences in $\alpha $ and $\beta $ band connectivity strength; 3) $\theta $ band network topology demonstrated reduced prefrontal nodal degree along with enhanced global efficiency in older subjects. Regression analysis identified a robust inverse relationship between the $\beta $ / $\theta $ RPR during stimulation and overall accuracy. And the $\beta $ / $\theta $ RPR and the $\beta $ band ApEn might be the main factor that causing individual differences in the identification accuracy in older and younger subjects, respectively. This study provides novel insights into age-related neuro-mechanisms in AO-BCI, establishing quantitative relationships between specific EEG features and BCI performance. These findings would offer guidelines for optimizing AO-BCI in rehabilitation.},
}
@article {pmid40313536,
year = {2025},
author = {Xiong, W and Ma, L and Li, H},
title = {Synthesizing intelligible utterances from EEG of imagined speech.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1565848},
pmid = {40313536},
issn = {1662-4548},
abstract = {Decoding natural language directly from neural activity is of great interest to people with limited communication means. Being a non-invasive and convenient approach, the electroencephalogram (EEG) offers better portability and wider application potentiality. We present an EEG-to-speech system (ETS) that synthesizes audible, and highly understandable language by EEG of imagined speech. Our ETS applies a specially designed X-shape deep neural network (DNN) to realize an End-to-End correspondence between imagined speech EEG and the speech. The system innovatively incorporates dynamic time warping into the network's training process, using actual speech EEG data as a bridge to temporally align imagined speech EEG signals with speech signals. The ETS performance was evaluated on 13 participants who pretraining four Chinese disyllabic words. These words cover all four tones and 40% of the phonemes in Chinese. Our synthesized utterances' average accuracy across all subjects was 91.23%, the average MOS value was 3.50, and the best accuracy was 99% with an MOS value of 3.99. Furthermore, a partial feedback mechanism for DNN and spectral subtraction-based speech enhancement are introduced to improve the quality of generated speech. Our findings prove that non-invasive approaches can be a significant step in regaining verbal language ability.},
}
@article {pmid40313458,
year = {2025},
author = {Xu, Y and Yu, B and Chen, X and Peng, A and Tao, Q and He, Y and Wang, Y and Li, XM},
title = {DSCT: a novel deep-learning framework for rapid and accurate spatial transcriptomic cell typing.},
journal = {National science review},
volume = {12},
number = {5},
pages = {nwaf030},
pmid = {40313458},
issn = {2053-714X},
abstract = {Unraveling complex cell-type-composition and gene-expression patterns at the cellular spatial resolution is crucial for understanding intricate cell functions in the brain. In this study, we developed Deep Neural Network-based Spatial Cell Typing (DSCT)-an innovative framework for spatial cell typing within spatial transcriptomic data sets. This approach utilizes a synergistic integration of an enhanced gene-selection strategy and a lightweight deep neural network for data training, offering a more rapid and accurate solution for the analysis of spatial transcriptomic data. Based on comprehensive analysis, DSCT achieved exceptional accuracy in cell-type identification across various brain regions, species and spatial transcriptomic platforms. It also performed well in mapping finer cell types, thereby showcasing its versatility and adaptability across diverse data sets. Strikingly, DSCT exhibited high efficiency and remarkable processing speed, with fewer computational resource demands. As such, this novel approach opens new avenues for exploring the spatial organization of cell types and gene-expression patterns, advancing our understanding of biological functions and pathologies within the nervous system.},
}
@article {pmid40313281,
year = {2025},
author = {Lee, J and Park, H and Spencer, A and Gong, X and DeNardo, M and Vashahi, F and Pollet, F and Norris, S and Hinton, H and El Fakiri, M and Mehrotra, A and Huang, R and Bar, J and Swann, J and Affonseca, D and Armitage, O and Garry, R and Grumbles, E and Murali, A and Tasserie, J and Fragoso, C and Albouy, R and Couturier, CP and Paulk, AC and Coughlin, B and Cash, SS and Costine-Bartell, B and Baskin, B and Stinson, T and Moradi Chameh, H and Movahed, M and Bazrgar, B and Falby, M and Zhang, D and Valiante, TA and Francis, A and Candanedo, C and Bermudez, R and Liu, J and Ye, T and Le Floch, P},
title = {Clinical translation of ultrasoft Fleuron probes for stable, high-density, and bidirectional brain interfaces.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.04.24.25326126},
pmid = {40313281},
abstract = {Building brain foundation models to capture the underpinning neural dynamics of human behavior requires large functional neural datasets for training, which current implantable Brain-Computer Interfaces (iBCIs) cannot achieve due to the instability of rigid materials in the brain. How can we realize high-density neural recordings with wide brain region access at single-neuron resolution, while maintaining the long-term stability required? In this study, we present a novel approach to overcome these trade-offs, by introducting Fleuron, a family of ultrasoft, ultra-low-k dielectric materials compatible with thin-film scalable microfabrication techniques. We successfully integrate up to 1,024 sites within a single minimally-invasive Fleuron depth electrode. The combination of the novel implant material and geometry enables single-unit level recordings for 18 months in rodent models, and achieves a large number of units detected per electrode across animals. 128-site Fleuron probes, that cover 8x larger tissue volume than state-of-the-art polyimide counterparts, can track over 100 single-units over months. Stability in neural recordings correlates with reduced glial encapsulation compared to polyimide controls up to 9-month post-implantation. Fleuron probes are integrated with a low-power, mixed-signal ASIC to achieve over 1,000 channels electronic interfaces and can be safely implanted in depth using minimally-invasive surgical techniques via a burr hole approach without requiring specialized robotics. Fleuron probes further create a unique contrast in clinical 3T MRI, allowing for post-operative position confirmation. Large-animal and ex vivo human tissue studies confirm safety and functionality in larger brains. Finally, Fleuron probes are used for the first time ever intraoperatively during planned resection surgeries, confirming in-human usability, and demonstrating the potential of the technology for clincical translation in iBCIs.},
}
@article {pmid40313239,
year = {2025},
author = {Sun, S and Li, C and Xie, X and Wan, X and Liu, T and Li, D and Duan, D and Yu, H and Wen, D},
title = {Digital therapeutics for cognitive impairments associated with schizophrenia: our opinion.},
journal = {Frontiers in psychiatry},
volume = {16},
number = {},
pages = {1535309},
pmid = {40313239},
issn = {1664-0640},
}
@article {pmid40311414,
year = {2025},
author = {Pattanayak, S and Dash, P and Satpathi, S and Sahoo, AK and Das, NR and Nayak, B and Sahoo, SK},
title = {Additive manufacturing of 316 L stainless steel orthopedic implant with improved in vitro hemocompatibility and hydrophilicity for osteoinduction in Wistar rat model.},
journal = {Biomaterials advances},
volume = {175},
number = {},
pages = {214322},
doi = {10.1016/j.bioadv.2025.214322},
pmid = {40311414},
issn = {2772-9508},
mesh = {Animals ; *Stainless Steel/chemistry/pharmacology ; Rats, Wistar ; Rats ; Hydrophobic and Hydrophilic Interactions ; Materials Testing ; Male ; *Prostheses and Implants ; *Osseointegration/drug effects ; *Osteogenesis/drug effects ; },
abstract = {Long-term implantation is still challenging for 316 L stainless steel (SS) due to low hydrophilicity and borderline corrosion, which further advances a coating to induce osteoinduction and prevent metallic ions leaching. Here, arc-based direct energy deposition technology is introduced to fabricate 316 L SS via additive manufacturing (AM). The AM 316 L SS are subjected to metallurgical, mechanical, chemical, in vitro and in vivo analyses for their possible orthopedic applications. Compared to commercially available 316 L SS implant, the AM implants encompass γ-austenite phases with δ-ferrite structures that induce pinning dislocations, improve resistance to crack propagation and enhance mechanical performances. The evolution of δ-ferrite structures with higher inter-layer dwell times promotes Cr and Mo content, improving passive layer thickness and thereby enhancing the corrosion resistance, which prevents the release of toxic ions into the bloodstream and cellular metabolism. Additionally, improved BCI with less adherence and activation of platelets on the AM deposits indicates uninterrupted blood flow along the site of implantation and improved thrombo-resistance. The reduction in contact angle (highly hydrophilic) promotes the adsorption of body fluid and proteinaceous materials that boost the adhesion, proliferation, and cytoplasmic extension of cells (from in vitro), marrow spaces, collagen fibers, and tissue adherences (from in vivo). The AM implants do not show any acute toxicity in blood profiles and vital organs (liver and kidney) after long-term implantation in Wistar rats. These peculiarities highlight the hemocompatibility and osteointegration capabilities of AM implants with a faster bone regeneration rate.},
}
@article {pmid40309860,
year = {2025},
author = {Gong, J and Liu, H and Duan, F and Che, Y and Yan, Z},
title = {Research on Adaptive Discriminating Method of Brain-Computer Interface for Motor Imagination.},
journal = {Brain sciences},
volume = {15},
number = {4},
pages = {},
pmid = {40309860},
issn = {2076-3425},
support = {MKF202203//Open Foundation of Engineering Research Center of Big Data Application in Private Health Medicine, Fujian Province University/ ; },
abstract = {(1) Background: Brain-computer interface (BCI) technology represents a cutting-edge field that integrates brain intelligence with machine intelligence. Unlike BCIs that rely on external stimuli, motor imagery-based BCIs (MI-BCIs) generate usable brain signals based on an individual's imagination of specific motor actions. Due to the highly individualized nature of these signals, identifying individuals who are better suited for MI-BCI applications and improving its efficiency is critical. (2) Methods: This study collected four motor imagery tasks (left hand, right hand, foot, and tongue) from 50 healthy subjects and evaluated MI-BCI adaptability through classification accuracy. Functional networks were constructed using the weighted phase lag index (WPLI), and relevant graph theory parameters were calculated to explore the relationship between motor imagery adaptability and functional networks. (3) Results: Research has demonstrated a strong correlation between the network characteristics of tongue imagination and MI-BCI adaptability. Specifically, the nodal degree and characteristic path length in the right hemisphere were found to be significantly correlated with classification accuracy (p < 0.05). (4) Conclusions: The findings of this study offer new insights into the functional network mechanisms of motor imagery, suggesting that tongue imagination holds potential as a predictor of MI-BCI adaptability.},
}
@article {pmid40309849,
year = {2025},
author = {Omer, K and Ferracuti, F and Freddi, A and Iarlori, S and Vella, F and Monteriù, A},
title = {Real-Time Mobile Robot Obstacles Detection and Avoidance Through EEG Signals.},
journal = {Brain sciences},
volume = {15},
number = {4},
pages = {},
pmid = {40309849},
issn = {2076-3425},
abstract = {BACKGROUND/OBJECTIVES: The study explores the integration of human feedback into the control loop of mobile robots for real-time obstacle detection and avoidance using EEG brain-computer interface (BCI) methods. The goal is to assess the possible paradigms applicable to the most current navigation system to enhance safety and interaction between humans and robots.<br><br>METHODS: The research explores passive and active brain-computer interface (BCI) technologies to enhance a wheelchair-mobile robot's navigation. In the passive approach, error-related potentials (ErrPs), neural signals triggered when users comment or perceive errors, enable automatic correction of the robot navigation mistakes without direct input or command from the user. In contrast, the active approach leverages steady-state visually evoked potentials (SSVEPs), where users focus on flickering stimuli to control the robot's movements directly. This study evaluates both paradigms to determine the most effective method for integrating human feedback into assistive robotic navigation. This study involves experimental setups where participants control a robot through a simulated environment, and their brain signals are recorded and analyzed to measure the system's responsiveness and the user's mental workload.<br><br>RESULTS: The results show that a passive BCI requires lower mental effort but suffers from lower engagement, with a classification accuracy of 72.9%, whereas an active BCI demands more cognitive effort but achieves 84.9% accuracy. Despite this, task achievement accuracy is higher in the passive method (e.g., 71% vs. 43% for subject S2) as a single correct ErrP classification enables autonomous obstacle avoidance, whereas SSVEP requires multiple accurate commands.<br><br>CONCLUSIONS: This research highlights the trade-offs between accuracy, mental load, and engagement in BCI-based robot control. The findings support the development of more intuitive assistive robotics, particularly for disabled and elderly users.},
}
@article {pmid40309789,
year = {2025},
author = {Khan, S and Kallis, L and Mee, H and El Hadwe, S and Barone, D and Hutchinson, P and Kolias, A},
title = {Invasive Brain-Computer Interface for Communication: A Scoping Review.},
journal = {Brain sciences},
volume = {15},
number = {4},
pages = {},
pmid = {40309789},
issn = {2076-3425},
abstract = {BACKGROUND: The rapid expansion of the brain-computer interface for patients with neurological deficits has garnered significant interest, and for patients, it provides an additional route where conventional rehabilitation has its limits. This has particularly been the case for patients who lose the ability to communicate. Circumventing neural injuries by recording from the intact cortex and subcortex has the potential to allow patients to communicate and restore self-expression. Discoveries over the last 10-15 years have been possible through advancements in technology, neuroscience, and computing. By examining studies involving intracranial brain-computer interfaces that aim to restore communication, we aimed to explore the advances made and explore where the technology is heading.<br><br>METHODS: For this scoping review, we systematically searched PubMed and OVID Embase. After processing the articles, the search yielded 41 articles that we included in this review.<br><br>RESULTS: The articles predominantly assessed patients who had either suffered from amyotrophic lateral sclerosis, cervical cord injury, or brainstem stroke, resulting in tetraplegia and, in some cases, difficulty speaking. Of the intracranial implants, ten had ALS, six had brainstem stroke, and thirteen had a spinal cord injury. Stereoelectroencephalography was also used, but the results, whilst promising, are still in their infancy. Studies involving patients who were moving cursors on a screen could improve the speed of movement by optimising the interface and utilising better decoding methods. In recent years, intracortical devices have been successfully used for accurate speech-to-text and speech-to-audio decoding in patients who are unable to speak.<br><br>CONCLUSIONS: Here, we summarise the progress made by BCIs used for communication. Speech decoding directly from the cortex can provide a novel therapeutic method to restore full, embodied communication to patients suffering from tetraplegia who otherwise cannot communicate.},
}
@article {pmid40307763,
year = {2025},
author = {Zhang, L and Guan, X and Xue, H and Liu, X and Zhang, B and Liu, S and Ming, D},
title = {Sex-specific patterns in social visual attention among individuals with autistic traits.},
journal = {BMC psychiatry},
volume = {25},
number = {1},
pages = {440},
pmid = {40307763},
issn = {1471-244X},
support = {Grant Nos. 23JCZDJC01030//Natural Science Foundation of Tianjin (Key Program)/ ; 2022YGZD02//Tianjin Education Commission Research Program Project/ ; Grant Nos. 81925020//National Natural Science Foundation of China/ ; },
mesh = {Humans ; Male ; Female ; *Attention/physiology ; Adult ; Young Adult ; *Autism Spectrum Disorder/psychology/physiopathology ; Emotions/physiology ; Sex Factors ; *Autistic Disorder/psychology ; Eye Movements ; *Sex Characteristics ; *Facial Recognition/physiology ; Fixation, Ocular ; Facial Expression ; *Social Perception ; Adolescent ; },
abstract = {BACKGROUND: Autism is a neurodevelopmental condition more prevalent in males, with sex differences emerging in both prevalence and core symptoms. However, most studies investigating behavioral and cognitive features of autism tend to include more male samples, leading to a male-biased understanding. The sex imbalance limits the specificity of these features, especially in female individuals with autism. Hence, it is necessary to explore sex-related differences in behavioral-cognitive traits linked to autism in the general population.<br><br>METHODS: In this study, we designed a dynamic emotion-discrimination task to investigate sex differences in attention to emotional stimuli among the general population with autistic traits. Behavioral and eye movement data were recorded during the task, and the Autism-Spectrum Quotient (AQ) was used to assess autistic traits. Qualitative and quantitative methods were used to analyze gaze patterns in male and female groups. Additionally, correlation analyses were conducted to examine the relationship between AQ scores and proportion of fixation time in both groups.<br><br>RESULTS: Significant sex differences in attention to the eye regions of faces were observed, with females focusing more on the eyes than males. Correlation analyses revealed that, in males, lower eye-looking was associated with higher levels of autistic traits, whereas no such association was found in females.<br><br>CONCLUSIONS: Overall, these results reveal that attention patterns to emotional faces differed between females and males, and autistic traits predicted the trend of eye-looking in males. These findings suggest that sex-related stratification in social attention should be considered in clinical&#xa0;contexts.},
}
@article {pmid40307561,
year = {2025},
author = {, and Ferrante, O and Gorska-Klimowska, U and Henin, S and Hirschhorn, R and Khalaf, A and Lepauvre, A and Liu, L and Richter, D and Vidal, Y and Bonacchi, N and Brown, T and Sripad, P and Armendariz, M and Bendtz, K and Ghafari, T and Hetenyi, D and Jeschke, J and Kozma, C and Mazumder, DR and Montenegro, S and Seedat, A and Sharafeldin, A and Yang, S and Baillet, S and Chalmers, DJ and Cichy, RM and Fallon, F and Panagiotaropoulos, TI and Blumenfeld, H and de Lange, FP and Devore, S and Jensen, O and Kreiman, G and Luo, H and Boly, M and Dehaene, S and Koch, C and Tononi, G and Pitts, M and Mudrik, L and Melloni, L},
title = {Adversarial testing of global neuronal workspace and integrated information theories of consciousness.},
journal = {Nature},
volume = {642},
number = {8066},
pages = {133-142},
pmid = {40307561},
issn = {1476-4687},
mesh = {*Consciousness/physiology ; Humans ; Male ; Female ; Adult ; Magnetoencephalography ; Magnetic Resonance Imaging ; *Information Theory ; Young Adult ; *Models, Neurological ; *Brain/physiology ; Electroencephalography ; *Neurons/physiology ; },
abstract = {Different theories explain how subjective experience arises from brain activity[1,2]. These theories have independently accrued evidence, but have not been directly compared[3]. Here we present an open science adversarial collaboration directly juxtaposing integrated information theory (IIT)[4,5] and global neuronal workspace theory (GNWT)[6-10] via a theory-neutral consortium[11-13]. The theory proponents and the consortium developed and preregistered the experimental design, divergent predictions, expected outcomes and interpretation thereof[12]. Human participants (n = 256) viewed suprathreshold stimuli for variable durations while neural activity was measured with functional magnetic resonance imaging, magnetoencephalography and intracranial electroencephalography. We found information about conscious content in visual, ventrotemporal and inferior frontal cortex, with sustained responses in occipital and lateral temporal cortex reflecting stimulus duration, and content-specific synchronization between frontal and early visual areas. These results align with some predictions of IIT and GNWT, while substantially challenging key tenets of both theories. For IIT, a lack of sustained synchronization within the posterior cortex contradicts the claim that network connectivity specifies consciousness. GNWT is challenged by the general lack of ignition at stimulus offset and limited representation of certain conscious dimensions in the prefrontal cortex. These challenges extend to other theories of consciousness that share some of the predictions tested here[14-17]. Beyond challenging the theories, we present an alternative approach to advance cognitive neuroscience through principled, theory-driven, collaborative research and highlight the need for a quantitative framework for systematic theory testing and building.},
}
@article {pmid40306303,
year = {2025},
author = {Hiep Dinh, T and Kumar Singh, A and Manh Doan, Q and Linh Trung, N and Nguyen, DN and Lin, CT},
title = {An EEG signal smoothing algorithm using upscale and downscale representation.},
journal = {Journal of neural engineering},
volume = {22},
number = {3},
pages = {},
doi = {10.1088/1741-2552/add297},
pmid = {40306303},
issn = {1741-2552},
mesh = {*Algorithms ; *Electroencephalography/methods ; Humans ; Signal-To-Noise Ratio ; *Signal Processing, Computer-Assisted ; Male ; Brain-Computer Interfaces ; Adult ; *Brain/physiology ; Female ; Photic Stimulation/methods ; },
abstract = {Objective.Effective smoothing of electroencephalogram (EEG) signals while maintaining the original signal's features is important in EEG signal analysis and brain-computer interface. This paper proposes a novel EEG signal-smoothing algorithm and its potential application in cognitive conflict (CC) processing.Approach.Instead of being processed in the time domain, the input signal is visualized in increasing line width, the representation frame of which is converted into a binary image. An effective thinning algorithm is employed to obtain a unit-width skeleton as the smoothed signal.Main results.Experimental results on data fitting have verified the effectiveness of the proposed approach on different levels of signal-to-noise (SNR) ratio, especially on high noise levels (SNR⩽5 dB), where our fitting error is only 86.4%-90.4% compared to that of its best counterpart. The potential application of the proposed algorithm in EEG-based CC processing is comprehensively evaluated in a classification and a visual inspection task. The employment of the proposed approach in pre-processing the input data has significantly boosted theF1score of state-of-the-art models by more than 1%. The robustness of our algorithm is also evaluated via a visual inspection task, where specific CC peaks, i.e. the prediction error negativity and error-related positive potential (Pe), can be easily observed at multiple line-width levels, while the insignificant ones are eliminated.Significance.These results demonstrated not only the advance of the proposed approach but also its impact on classification accuracy enhancement.},
}
@article {pmid40305243,
year = {2025},
author = {Yang, X and Li, Y and Zhang, J and Tian, H and Li, S and Pan, G},
title = {EvoMoE: Evolutionary Mixture-of-Experts for SSVEP-EEG Classification With User-Independent Training.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {9},
pages = {6538-6550},
doi = {10.1109/JBHI.2025.3565882},
pmid = {40305243},
issn = {2168-2208},
mesh = {Humans ; *Electroencephalography/methods/classification ; *Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; Algorithms ; Adult ; Brain/physiology ; },
abstract = {The analysis of EEG data in BCI systems captures unique individual characteristics, presenting diverse patterns that deviate from conventional identical distribution assumptions. Therefore, applying AI models directly to brain data becomes challenging due to the non-identical distribution issue. Meanwhile, as user numbers in BCI systems rise, scalable models are crucial to handle the growing data volume. Moreover, the limited availability of individual data necessitates the use of collective data for training, requiring models with strong generalization capabilities. To address these challenges, we propose Evolutionary Mixture of Experts (EvoMoE), a framework leveraging a set of diverse experts to model data from individuals. Users with similar distributions are grouped together, allowing experts to handle EEG data with different distribution types. The gating network of EvoMoE selects experts that closely match the distribution of the current sample, effectively tackling non-identical distribution issues. When encountering an unrecognized distribution, a new expert is introduced to accommodate the new data pattern, ensuring model adaptability. Evaluations on two 40-category BCI Speller datasets demonstrate significant performance improvements over state-of-the-art methods. On the BETA dataset, our online EvoMoE achieves 13.06% increase in accuracy and a 27.24-point increase in high information transfer rate (ITR) compared to the online UI method. The Bench dataset shows 3.64% increase in accuracy and a 10.42-point increase in ITR. These qualities make it a promising solution for practical BCI implementation, while setting the stage for the development of comprehensive biological big models.},
}
@article {pmid40302943,
year = {2025},
author = {Wu, X and Ye, Y and Sun, M and Mei, Y and Ji, B and Wang, M and Song, E},
title = {Recent Progress of Soft and Bioactive Materials in Flexible Bioelectronics.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {6},
number = {},
pages = {0192},
pmid = {40302943},
issn = {2692-7632},
abstract = {Materials that establish functional, stable interfaces to targeted tissues for long-term monitoring/stimulation equipped with diagnostic/therapeutic capabilities represent breakthroughs in biomedical research and clinical medicine. A fundamental challenge is the mechanical and chemical mismatch between tissues and implants that ultimately results in device failure for corrosion by biofluids and associated foreign body response. Of particular interest is in the development of bioactive materials at the level of chemistry and mechanics for high-performance, minimally invasive function, simultaneously with tissue-like compliance and in vivo biocompatibility. This review summarizes the most recent progress for these purposes, with an emphasis on material properties such as foreign body response, on integration schemes with biological tissues, and on their use as bioelectronic platforms. The article begins with an overview of emerging classes of material platforms for bio-integration with proven utility in live animal models, as high performance and stable interfaces with different form factors. Subsequent sections review various classes of flexible, soft tissue-like materials, ranging from self-healing hydrogel/elastomer to bio-adhesive composites and to bioactive materials. Additional discussions highlight examples of active bioelectronic systems that support electrophysiological mapping, stimulation, and drug delivery as treatments of related diseases, at spatiotemporal resolutions that span from the cellular level to organ-scale dimension. Envisioned applications involve advanced implants for brain, cardiac, and other organ systems, with capabilities of bioactive materials that offer stability for human subjects and live animal models. Results will inspire continuing advancements in functions and benign interfaces to biological systems, thus yielding therapy and diagnostics for human healthcare.},
}
@article {pmid40302941,
year = {2025},
author = {Feng, C and Cao, L and Wu, D and Zhang, E and Wang, T and Jiang, X and Chen, J and Wu, H and Lin, S and Hou, Q and Zhu, J and Yang, J and Sawan, M and Zhang, Y},
title = {Acoustic Inspired Brain-to-Sentence Decoder for Logosyllabic Language.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {6},
number = {},
pages = {0257},
pmid = {40302941},
issn = {2692-7632},
abstract = {Recent advances in brain-computer interfaces (BCIs) have demonstrated the potential to decode language from brain activity into sound or text, which has predominantly focused on alphabetic languages, such as English. However, logosyllabic languages, such as Mandarin Chinese, present marked challenges for establishing decoders that cover all characters, due to its unique syllable structures, extended character sets (e.g., over 50,000 characters for Mandarin Chinese), and complex mappings between characters and syllables, thus hindering practical applications. Here, we leverage the acoustic features of Mandarin Chinese syllables, constructing prediction models for syllable components (initials, tones, and finals), and decode speech-related stereoelectroencephalography (sEEG) signals into coherent Chinese sentences. The results demonstrate a high sentence-level offline decoding performance with a median character accuracy of 71.00% over the full spectrum of characters in the best participant. We also verified that incorporating acoustic-related features into the design of prediction models substantially enhances the accuracy of initials, tones, and finals. Moreover, our findings revealed that effective speech decoding also involves subcortical structures like the thalamus in addition to traditional language-related brain regions. Overall, we established a brain-to-sentence decoder for logosyllabic languages over full character set with a large intracranial electroencephalography dataset.},
}
@article {pmid40301357,
year = {2025},
author = {Ni, H and Yang, Y and Zhang, F and Sun, Y and Zheng, Y and Zhu, J and Xu, K},
title = {Dataset of long-term multi-site LFP activity with spontaneous chronic seizures in temporal lobe epilepsy rats.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {709},
pmid = {40301357},
issn = {2052-4463},
support = {82272112//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Animals ; *Epilepsy, Temporal Lobe/physiopathology/chemically induced ; Rats ; *Seizures/physiopathology ; Pilocarpine ; },
abstract = {The characteristics of refractory epilepsy change with disease progression. However, relevant studies are scarce due to the difficulty in obtaining long-term multi-site data from patients with epilepsy. This work aimed to provide a long-term brain electrophysiological dataset of 15 pilocarpine-treated rats with temporal lobe epilepsy (TLE). The dataset was constituted by multi-site local field potential (LFP) signal recorded from 12 sites in the Papez circuit in TLE, including spontaneous seizures and interictal fragments in the chronic period. The LFP data were saved in MATLAB, stored in the Neurodata Without Borders format, and published on the DANDI Archive. We validated the dataset technically through specific signal analysis. In addition, we provided MATLAB codes for basic analyses of this dataset, including power spectral analysis, seizure onset pattern identification, and interictal spike detection. This dataset could reveal how the electrophysiological and epileptic network properties of the brain of rats with chronic TLE changed during epilepsy development, thus help inform the design of adaptive neuromodulation for epilepsy.},
}
@article {pmid40301272,
year = {2025},
author = {Tan, Q and Jia, O and Anderson, BA and Jia, K and Gong, M},
title = {Reward history alters priority map based on spatial relationship, but not absolute location.},
journal = {Psychonomic bulletin &amp; review},
volume = {32},
number = {5},
pages = {2259-2271},
pmid = {40301272},
issn = {1531-5320},
mesh = {Humans ; *Reward ; Adult ; *Attention/physiology ; Young Adult ; Male ; *Space Perception/physiology ; Female ; *Psychomotor Performance/physiology ; },
abstract = {Attention is rapidly directed to stimuli associated with rewards in past experience, independent of current task goals and physical salience of stimuli. However, despite the robust attentional priority given to reward-associated features, studies often indicate negligible priority toward previously rewarded locations. Here, we propose a relational account of value-driven attention, a mechanism that relies on spatial relationship between items to achieve value-guided selections. In three experiments (N = 124), participants were trained to associate specific locations with rewards (e.g., high-reward: top-left; low-reward: top-right). They then performed an orientation-discrimination task where the target's absolute location (top-left or top-right) or spatial relationship ("left of" or "right of") had previously predicted reward. Performance was superior when the target's spatial relationship matched high-reward than low-reward, irrespective of absolute locations. Conversely, the impact of reward was absent when the target matched the absolute location but not the spatial relationship associated with high reward. Our findings challenge the default assumption of location specificity in value-driven attention, demonstrating a generalizable mechanism that humans adopted to integrate value and spatial information into priority maps for adaptive behavior.},
}
@article {pmid40300857,
year = {2025},
author = {Zhang, W and Pan, X and Wang, L and Li, W and Dai, X and Zheng, M and Guo, H and Chen, X and Xu, Y and Wu, H and He, Q and Yang, B and Ding, L},
title = {Selective BCL-2 inhibitor triggers STING-dependent antitumor immunity via inducing mtDNA release.},
journal = {Journal for immunotherapy of cancer},
volume = {13},
number = {4},
pages = {},
pmid = {40300857},
issn = {2051-1426},
mesh = {Animals ; Mice ; Humans ; *Membrane Proteins/metabolism ; *DNA, Mitochondrial/metabolism ; *Proto-Oncogene Proteins c-bcl-2/antagonists &amp; inhibitors ; Mice, Inbred C57BL ; Signal Transduction/drug effects ; Cell Line, Tumor ; *Sulfonamides/pharmacology ; Mice, Inbred BALB C ; Female ; Tumor Microenvironment ; Bridged Bicyclo Compounds, Heterocyclic ; },
abstract = {BACKGROUND: The stimulator of interferon genes (STING) signaling pathway has been demonstrated to propagate the cancer-immunity cycle and remodel the tumor microenvironment and has emerged as an appealing target for cancer immunotherapy. Interest in STING agonist development has increased, and the candidates hold significant promise; however, most are still in the early stages of human clinical trials. We found that ABT-199 activated the STING pathway to enhance the immunotherapeutic effect, and provided a ready-to-use small molecule drug for STING signaling activation.<br><br>METHODS: Phosphorylation of STING, TBK1, and IRF3, as well as activation of the interferon-I (IFN-I) signaling pathway, were detected following ABT-199 treatment in various colorectal cancer cells. C57BL/6J and BALB/c mice with subcutaneous tumors were employed to evaluate the in vivo therapeutic effects of the ABT-199 and anti-PD-L1 combination. Flow cytometry and ELISA were employed to analyze the level and activity of tumor-infiltrating T lymphocytes. Immunofluorescence and quantitative real-time PCR were conducted to assess the source and accumulation of double stranded DNA (dsDNA) in the cytoplasm. Chemical cross-linking assay, co-immunoprecipitation, and CRISPR/Cas9-mediated knockout were performed to investigate the molecular mechanism underlying ABT-199-induced voltage-dependent anion channel protein 1 (VDAC1) oligomerization and mitochondrial DNA (mtDNA) release.<br><br>RESULTS: ABT-199 significantly activated the STING signaling pathway in various colorectal cancer cells, which was evidenced by increased phosphorylation of TBK1 and IRF3, and upregulation of C-C motif chemokine ligand 5 (CCL5), C-X-C motif chemokine ligand 10 (CXCL10), and interferon beta transcription. By promoting chemokine expression and cytotoxic T-cell infiltration, ABT-199 promoted antitumor immunity and synergized with anti-PD-L1 therapy to improve antitumor efficacy. ABT-199 induced mtDNA accumulation in the cytoplasm and triggered STING signaling via the canonical pathway. cGAS or STING-KO models significantly abolished both STING signaling activation and the antitumor efficacy of ABT-199. Mechanically, ABT-199 promoted VDAC1 oligomerization by disturbing the binding between BCL-2 and VDAC1, thereby facilitating mtDNA release into the cytoplasm. ABT-199-triggered STING signaling was attenuated when VADC1 was knocked out. Consistently, the antitumor effect of ABT-199 in vivo was abolished in the absence of VDAC1.<br><br>CONCLUSIONS: Our results identify a ready-to-use small molecule compound for STING activation, reveal the underlying molecular mechanism through which ABT-199 activates the STING signaling pathway, and provide a theoretical basis for the use of ABT-199 in cancer immunotherapy.},
}
@article {pmid40297854,
year = {2025},
author = {Liu, X and Jin, X and Yun, L and Chen, Z},
title = {Prefrontal cortex activity during binocular color fusion and rivalry: an fNIRS study.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1527434},
pmid = {40297854},
issn = {1664-2295},
abstract = {INTRODUCTION: Understanding how the brain processes color information from both the left and right eyes is a significant topic in neuroscience. Binocular color fusion and rivalry, which involve advanced cognitive functions in the prefrontal cortex (PFC), provide a unique perspective for exploring brain activity.<br><br>METHODS: This study used functional near-infrared spectroscopy (fNIRS) to examine PFC activity during binocular color fusion and rivalry conditions. The study included two fNIRS experiments: Experiment 1 employed long-duration (90 s) stimulation to assess brain functional connectivity, while Experiment 2 used short-duration (10 s) repeated stimulation (eight trials), analyzed with a generalized linear model to evaluate brain activation levels. Statistical tests were then conducted to compare the differences in brain functional connectivity strength and activation levels.<br><br>RESULTS: The results indicated that functional connectivity strength was significantly higher during the color fusion condition than the color rivalry condition, and the color rivalry condition was stronger than the Mid-Gray field condition. Additionally, brain activation levels during binocular color fusion were significantly greater, with significant differences concentrated in channel (CH) 12, CH13, and CH14. CH12 is located in the dorsolateral prefrontal cortex, while CH13 and CH14 are in the frontal eye fields, areas associated with higher cognitive functions and visual attention.<br><br>DISCUSSION: These findings suggest that binocular color fusion requires stronger brain integration and higher brain activation levels. Overall, this study demonstrates that color fusion is more cognitively challenging than color rivalry, engaging more attention and executive functions. These results provide theoretical support for the development of color-based brain-computer interfaces and offer new insights into future research on the brain's color-visual information processing mechanisms.},
}
@article {pmid40297442,
year = {2025},
author = {Padmaja, GKR and Bhagat, NA and Balasubramani, PP},
title = {Assessing the utility of Fronto-Parietal and Cingulo-Opercular networks in predicting the trial success of brain-machine interfaces for upper extremity stroke rehabilitation.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
pmid = {40297442},
support = {R01 NS081854/NS/NINDS NIH HHS/United States ; },
abstract = {For stroke participants undergoing motor rehabilitation, brain-machine/computer interfaces (BMI/BCI) can potentially improve the efficacy of robotic or exoskeleton-based therapies by ensuring patient engagement and active participation, through monitoring of motor intent. In such interventions, exploring the network-level understanding of the source space, in terms of various cognitive dimensions such as executive control versus reward processing is fruitful in both improving the existing therapy protocols as well as understanding the subject-level differences. This contrasts to traditional approaches that predominantly investigate rehabilitation from resting state data. Moreover, conventional BMIs used for stroke rehabilitation barely accommodate people suffering from moderate to severe cognitive impairments. In this first-of-the-kind study, we explore the cognitive dimensions of a BMI trial by probing the networks that are core to the BMI performance and propose a network connectivity-based measurement with the potential to characterize the cognitive impairments in patients for closed-loop intervention. Specifically, we tease apart the extent of cognitive evaluation versus executive control aspects of impairments in these patients, by measuring the activation power of a major cognitive evaluation network- the Cingulo-Opercular Network (CON) and a major executive control circuit- the Fronto-Parietal network (FPN), and the connectivity between FPN-CON. We test our hypothesis in a previously collected dataset of electroencephalography (EEG) and structural imaging performed on stroke patients with upper limb impairments, while they underwent an exoskeleton-based BMI intervention for about 12 sessions over 4 weeks. Our logistic regression modeling results suggest that the connectivity between FPN and CON networks and their source powers predict trial failure accurately to about 84.2%. In the future, we aim to integrate these observations into a closed-loop design to adaptively control the cognitive difficulty and passively increase the subject's motivation and attention factor for effective BMI learning.},
}
@article {pmid40296528,
year = {2025},
author = {Yang, C and Wang, H and Wang, K and Cao, Z and Ren, F and Zhou, G and Chen, Y and Sun, B},
title = {Silk Fibroin-Based Biomemristors for Bionic Artificial Intelligence Robot Applications.},
journal = {ACS nano},
volume = {19},
number = {18},
pages = {17173-17198},
doi = {10.1021/acsnano.5c02480},
pmid = {40296528},
issn = {1936-086X},
mesh = {*Fibroins/chemistry ; *Robotics/instrumentation ; *Bionics ; *Artificial Intelligence ; Humans ; Animals ; Wearable Electronic Devices ; },
abstract = {In the emerging fields of flexible electronics and bioelectronics, protein-based materials have attracted widespread attention due to their biocompatibility, biodegradability, and processability. Among these materials, silk fibroin (SF), a protein derived from natural silk, has demonstrated significant potential in biomedical applications such as medical sensing and bone tissue engineering, as well as in the development of advanced biosensors. This is primarily due to its highly ordered β-sheet structure, mechanical properties, and processability. Furthermore, SF-based memristors provided a material choice for producing flexible wearable, and even implantable bioelectronic devices, which are expected to advance intelligent health monitoring, electronic skin (e-skin), brain-computer interface (BCI), and other frontier bioelectronic technologies. This review systematically summarizes the latest research progress in SF-based memristors concerning structural design, performance optimization, device integration, and application prospects, particularly highlighting their potential applications in neuromorphic computing and memristive sensors. Concurrently, we objectively analyzed the challenges currently faced by SF-based memristors and prospectively discussed their future development trends. This review provides a theoretical foundation and technological roadmap for biomaterials-based memristor devices, aiming to realize applications in flexible electronics and bioelectronics.},
}
@article {pmid40295498,
year = {2025},
author = {Fang, Z and Sims, CR},
title = {Humans learn generalizable representations through efficient coding.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {3989},
pmid = {40295498},
issn = {2041-1723},
support = {2024M761999//China Postdoctoral Science Foundation/ ; },
mesh = {Humans ; *Reinforcement, Psychology ; Reward ; *Generalization, Psychological/physiology ; Male ; Female ; *Learning/physiology ; Adult ; Young Adult ; },
abstract = {Reinforcement learning theory explains human behavior as driven by the goal of maximizing reward. Conventional approaches, however, offer limited insights into how people generalize from past experiences to new situations. Here, we propose refining the classical reinforcement learning framework by incorporating an efficient coding principle, which emphasizes maximizing reward using the simplest necessary representations. This refined framework predicts that intelligent agents, constrained by simpler representations, will inevitably: 1) distill environmental stimuli into fewer, abstract internal states, and 2) detect and utilize rewarding environmental features. Consequently, complex stimuli are mapped to compact representations, forming the foundation for generalization. We tested this idea in two experiments that examined human generalization. Our findings reveal that while conventional models fall short in generalization, models incorporating efficient coding achieve human-level performance. We argue that the classical RL objective, augmented with efficient coding, represents a more comprehensive computational framework for understanding human behavior in both learning and generalization.},
}
@article {pmid40294568,
year = {2025},
author = {Pan, L and Wang, K and Huang, Y and Sun, X and Meng, J and Yi, W and Xu, M and Jung, TP and Ming, D},
title = {Enhancing motor imagery EEG classification with a Riemannian geometry-based spatial filtering (RSF) method.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {188},
number = {},
pages = {107511},
doi = {10.1016/j.neunet.2025.107511},
pmid = {40294568},
issn = {1879-2782},
mesh = {*Electroencephalography/methods/classification ; Humans ; *Imagination/physiology ; *Brain-Computer Interfaces ; Algorithms ; Movement/physiology ; Signal Processing, Computer-Assisted ; *Brain/physiology ; Signal-To-Noise Ratio ; },
abstract = {Motor imagery (MI) refers to the mental simulation of movements without physical execution, and it can be captured using electroencephalography (EEG). This area has garnered significant research interest due to its substantial potential in brain-computer interface (BCI) applications, especially for individuals with physical disabilities. However, accurate classification of MI EEG signals remains a major challenge due to their non-stationary nature, low signal-to-noise ratio, and sensitivity to both external and physiological noise. Traditional classification methods, such as common spatial pattern (CSP), often assume that the data is stationary and Gaussian, which limits their applicability in real-world scenarios where these assumptions do not hold. These challenges highlight the need for more robust methods to improve classification accuracy in MI-BCI systems. To address these issues, this study introduces a Riemannian geometry-based spatial filtering (RSF) method that projects EEG signals into a lower-dimensional subspace, maximizing the Riemannian distance between covariance matrices from different classes. By leveraging the inherent geometric properties of EEG data, RSF enhances the discriminative power of the features while maintaining robustness against noise. The performance of RSF was evaluated in combination with ten commonly used MI decoding algorithms, including CSP with linear discriminant analysis (CSP-LDA), Filter Bank CSP (FBCSP), Minimum Distance to Riemannian Mean (MDM), Tangent Space Mapping (TSM), EEGNet, ShallowConvNet (sCNN), DeepConvNet (dCNN), FBCNet, Graph-CSPNet, and LMDA-Net, using six publicly available MI-BCI datasets. The results demonstrate that RSF significantly improves classification accuracy and reduces computational time, particularly for deep learning models with high computational complexity. These findings underscore the potential of RSF as an effective spatial filtering approach for MI EEG classification, providing new insights and opportunities for the development of robust MI-BCI systems. The code for this research is available at https://github.com/PLC-TJU/RSF.},
}
@article {pmid40292964,
year = {2025},
author = {Kasawala, E and Mouli, S},
title = {Dual-Mode Visual System for Brain-Computer Interfaces: Integrating SSVEP and P300 Responses.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {6},
pages = {},
pmid = {40292964},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Event-Related Potentials, P300/physiology ; Electroencephalography/methods ; Algorithms ; Photic Stimulation ; Male ; Adult ; Signal Processing, Computer-Assisted ; Female ; Young Adult ; },
abstract = {In brain-computer interface (BCI) systems, steady-state visual-evoked potentials (SSVEP) and P300 responses have achieved widespread implementation owing to their superior information transfer rates (ITR) and minimal training requirements. These neurophysiological signals have exhibited robust efficacy and versatility in external device control, demonstrating enhanced precision and scalability. However, conventional implementations predominantly utilise liquid crystal display (LCD)-based visual stimulation paradigms, which present limitations in practical deployment scenarios. This investigation presents the development and evaluation of a novel light-emitting diode (LED)-based dual stimulation apparatus designed to enhance SSVEP classification accuracy through the integration of both SSVEP and P300 paradigms. The system employs four distinct frequencies-7 Hz, 8 Hz, 9 Hz, and 10 Hz-corresponding to forward, backward, right, and left directional controls, respectively. Oscilloscopic verification confirmed the precision of these stimulation frequencies. Real-time feature extraction was accomplished through the concurrent analysis of maximum Fast Fourier Transform (FFT) amplitude and P300 peak detection to ascertain user intent. Directional control was determined by the frequency exhibiting maximal amplitude characteristics. The visual stimulation hardware demonstrated minimal frequency deviation, with error differentials ranging from 0.15% to 0.20% across all frequencies. The implemented signal processing algorithm successfully discriminated between all four stimulus frequencies whilst correlating them with their respective P300 event markers. Classification accuracy was evaluated based on correct task intention recognition. The proposed hybrid system achieved a mean classification accuracy of 86.25%, coupled with an average ITR of 42.08 bits per minute (bpm). These performance metrics notably exceed the conventional 70% accuracy threshold typically employed in BCI system evaluation protocols.},
}
@article {pmid40292808,
year = {2025},
author = {Gao, D and Wang, Y and Fu, P and Qiu, J and Li, H},
title = {Modeling and Parameter Analysis of Basic Single Channel Neuron Mass Model for SSVEP.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {6},
pages = {},
pmid = {40292808},
issn = {1424-8220},
mesh = {*Evoked Potentials, Visual/physiology ; Humans ; Brain-Computer Interfaces ; *Neurons/physiology ; *Models, Neurological ; Visual Cortex/physiology ; Electroencephalography ; Photic Stimulation ; },
abstract = {While steady-state visual evoked potentials (SSVEPs) are widely used in brain-computer interfaces (BCIs) due to their robustness to rhythmic visual stimuli, their generation mechanisms remain poorly understood. Challenges such as experimental complexity, inter-subject variability, and limited physiological interpretability hinder the development of efficient BCI systems. This study employed a single-channel neural mass model (NMM) of V1 cortical dynamics to investigate the biophysical underpinnings of SSVEP generation. By systematically varying synaptic gain, time constants, and external input parameters, we simulated δ/α/γ band oscillations and analyzed their generation principles. The model demonstrates that synaptic gain controls oscillation amplitude and harmonic content, and time constants determine signal decay kinetics and frequency precision, while input variance modulates harmonic stability. Our results reveal how V1 circuitry generates frequency-locked SSVEP responses through excitatory-inhibitory interactions and dynamic filtering mechanisms. This computational framework successfully reproduces fundamental SSVEP characteristics without requiring multi-subject experimental data, offering new insights into the physiological basis of SSVEP-based brain-computer interfaces.},
}
@article {pmid40292646,
year = {2025},
author = {Jeon, Y and Kim, M and Song, KH},
title = {Development of Hydrogels Fabricated via Stereolithography for Bioengineering Applications.},
journal = {Polymers},
volume = {17},
number = {6},
pages = {},
pmid = {40292646},
issn = {2073-4360},
support = {Incheon National University (International Cooperative) Research Grant in 2020//Incheon National University/ ; },
abstract = {The architectures of hydrogels fabricated with stereolithography (SLA) 3D printing systems have played various roles in bioengineering applications. Typically, the SLA systems successively illuminated light to a layer of photo-crosslinkable hydrogel precursors for the fabrication of hydrogels. These SLA systems can be classified into point-scanning types and digital micromirror device (DMD) types. The point-scanning types form layers of hydrogels by scanning the precursors with a focused light, while DMD types illuminate 2D light patterns to the precursors to form each hydrogel layer at once. Overall, SLA systems were cost-effective and allowed the fabrication of hydrogels with good shape fidelity and uniform mechanical properties. As a result, hydrogel constructs fabricated with the SLA 3D printing systems were used to regenerate tissues and develop lab-on-a-chip devices and native tissue-like models.},
}
@article {pmid40292025,
year = {2025},
author = {Pawlak, WA and Howard, N},
title = {Neuromorphic algorithms for brain implants: a review.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1570104},
pmid = {40292025},
issn = {1662-4548},
abstract = {Neuromorphic computing technologies are about to change modern computing, yet most work thus far has emphasized hardware development. This review focuses on the latest progress in algorithmic advances specifically for potential use in brain implants. We discuss current algorithms and emerging neurocomputational models that, when implemented on neuromorphic hardware, could match or surpass traditional methods in efficiency. Our aim is to inspire the creation and deployment of models that not only enhance computational performance for implants but also serve broader fields like medical diagnostics and robotics inspiring next generations of neural implants.},
}
@article {pmid40290410,
year = {2025},
author = {Ng, JY},
title = {Exploring the intersection of brain-computer interfaces and traditional, complementary, and integrative medicine.},
journal = {Integrative medicine research},
volume = {14},
number = {2},
pages = {101142},
pmid = {40290410},
issn = {2213-4220},
abstract = {Brain-computer interfaces (BCIs) represent a transformative innovation in healthcare, enabling direct communication between the brain and external devices. This educational article explores the potential intersection of BCIs and traditional, complementary, and integrative medicine (TCIM). BCIs have shown promise in enhancing mind-body practices such as meditation, while their integration with energy-based therapies may offer novel insights and measurable outcomes. Emerging advancements, including artificial intelligence-enhanced BCIs, hold potential for improving personalization and expanding the therapeutic efficacy of TCIM interventions. Despite these opportunities, integrating BCIs with TCIM presents considerable ethical, cultural, and practical challenges. Concerns related to informed consent, cultural sensitivity, data privacy, accessibility, and regulatory frameworks must be addressed to ensure responsible implementation. Interdisciplinary collaboration among relevant stakeholders, including TCIM and conventional practitioners, researchers, and policymakers among other relevant stakeholders is crucial for developing integrative healthcare models that balance innovation with patient safety and respect for diverse healing traditions. Future directions include expanding evidence bases to validate TCIM practices through BCI-enhanced research, fostering equitable access to neurotechnological advancements, and promoting global ethical guidelines to navigate complex sociocultural dynamics. BCIs have the potential to revolutionize TCIM, offering novel solutions for complex health challenges and fostering a more inclusive, integrative approach to healthcare, provided that they are utilized responsibly and ethically.},
}
@article {pmid40289727,
year = {2025},
author = {Lin, X and Zhang, X and Chen, J and Liu, J},
title = {Material Selection and Device Design of Scalable Flexible Brain-Computer Interfaces: A Balance Between Electrical and Mechanical Performance.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {37},
number = {26},
pages = {e2413938},
doi = {10.1002/adma.202413938},
pmid = {40289727},
issn = {1521-4095},
support = {DMR-2011754//Directorate for Engineering/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; Equipment Design ; Electric Conductivity ; *Mechanical Phenomena ; Brain/physiology ; Animals ; },
abstract = {Brain-computer interfaces (BCIs) hold the potential to revolutionize brain function restoration, enhance human capability, and advance our understanding of cognitive mechanisms by directly linking neural signals with hardware. However, the mechanical mismatch between conventional rigid BCIs and soft brain tissue limits long-term interface stability. Next-generation BCIs must achieve long-term biocompatibility while maintaining high performance, enabling the integration of millions of sensors within tissue-level flexible and soft, stable neural interfaces. Lithographic fabrication techniques provide scalable thin-film flexible electronics, but traditional electronic materials often fail to meet the unique requirements of BCIs. This review examines the selection of materials and device design for flexible BCIs, starting with an analysis of intrinsic material properties-Young's modulus, electrical conductivity and dielectric constant. It then explores the integration of material selection with electrode design to optimize electrical circuits and assess key mechanical factors. Next, the correlation between electrical and mechanical performance is analyzed to guide material selection and device design. Finally, recent advances in neural probes are reviewed, highlighting improvements in signal quality, recording stability, and scalability. This review focuses on scalable, lithography-based BCIs, aiming to identify optimal materials and designs for long-term, reliable neural recordings.},
}
@article {pmid40289349,
year = {2025},
author = {Pitt, KM and Boster, JB},
title = {Identifying P300 brain-computer interface training strategies for AAC in children: a focus group study.},
journal = {Augmentative and alternative communication (Baltimore, Md. : 1985)},
volume = {},
number = {},
pages = {1-10},
pmid = {40289349},
issn = {1477-3848},
support = {R21 DC021496/DC/NIDCD NIH HHS/United States ; },
abstract = {The integration of Brain-Computer Interface (BCI) technology into Augmentative and Alternative Communication (AAC) systems introduces new complexities in training, particularly for children with diverse cognitive, sensory, motor, and linguistic abilities. Effective AAC training is crucial for enabling individuals to achieve personal goals and enhance social participation. This study aimed to explore potential training strategies for children using P300 based BCI-AAC systems through focus group discussions with experts in AAC and BCI technologies. Participants identified six key themes for effective training: (1) Scaffolding-developing adaptive systems tailored to each child's developmental level, including preteaching, visual display adaptations, and gamification; (2) Verbal Instructions-emphasizing the use of clear, simple language and spoken prompts; (3) Feedback-incorporating immediate feedback and biofeedback methods to reinforce learning; (4) Positioning-ensuring proper trunk stability and addressing electrode placement; (5) Modeling and Physical Supports-using physical cues and demonstrating BCI-AAC use; and (6) Considerations for Visual Impairment-accommodating cortical visual impairment (CVI) with suitable stimuli and environmental adjustments. These insights offer an initial foundation for identifying P300 BCI-AAC training strategies for children. Further systematic research with end users, support networks, and professionals is needed to validate, refine, and expand interventions that support diverse communication needs.},
}
@article {pmid40289107,
year = {2025},
author = {Zhi, Y and Guo, Y and Li, S and He, X and Wei, H and Laster, K and Wu, Q and Zhao, D and Xie, J and Ruan, S and Lemoine, NR and Li, H and Dong, Z and Liu, K},
title = {FBL promotes hepatocellular carcinoma tumorigenesis and progression by recruiting YY1 to enhance CAD gene expression.},
journal = {Cell death &amp; disease},
volume = {16},
number = {1},
pages = {348},
pmid = {40289107},
issn = {2041-4889},
mesh = {*Carcinoma, Hepatocellular/genetics/pathology/metabolism ; *Liver Neoplasms/genetics/pathology/metabolism ; Humans ; Animals ; *YY1 Transcription Factor/metabolism/genetics ; Gene Expression Regulation, Neoplastic/drug effects ; Mice ; *Carcinogenesis/genetics/pathology/drug effects ; Disease Progression ; Cell Line, Tumor ; Cell Proliferation/drug effects/genetics ; Male ; Mice, Nude ; Mice, Knockout ; },
abstract = {Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. Accumulating evidence suggests that epigenetic dysregulation contributes to the initiation and progression of HCC. We aimed to investigate key epigenetic regulators that contribute to tumorigenesis and progression, providing a theoretical basis for targeted therapy for HCC. We performed a comprehensive epigenetic analysis of differentially expressed genes in LIHC from the TCGA database. We identified fibrillarin (FBL), an rRNA 2'-O-methyltransferase, as an essential contributor to HCC. A series of in vitro and in vivo biological experiments were performed to investigate the potential mechanisms of FBL. FBL knockdown suppressed the proliferation of HCC cells. In vivo studies using cell-derived xenograft (CDX), patient-derived xenograft (PDX), and diethylnitrosamine (DEN)-induced HCC models in Fbl liver-specific knockout mice demonstrated the critical role of FBL in HCC carcinogenesis and progression. Mechanistically, FBL regulates the expression of CAD in HCC cells by recruiting YY1 to the CAD promoter region. We also revealed that fludarabine phosphate is a novel inhibitor of FBL and can inhibit HCC growth in vitro and in vivo. The antitumor activity of lenvatinib has been shown to be synergistically enhanced by fludarabine phosphate. Our study highlights the cancer-promoting role of the FBL-YY1-CAD axis in HCC and identifies fludarabine phosphate as a novel inhibitor of FBL. A schematic diagram depicting the FBL-YY1-CAD signaling pathway and its regulatory role in HCC progression.},
}
@article {pmid40288968,
year = {2025},
author = {Pan, L and Sun, X and Wang, K and Cao, Y and Xu, M and Ming, D},
title = {[Cross-session motor imagery-electroencephalography decoding with Riemannian spatial filtering and domain adaptation].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {2},
pages = {272-279},
pmid = {40288968},
issn = {1001-5515},
mesh = {*Electroencephalography/methods ; *Brain-Computer Interfaces ; Humans ; *Imagination/physiology ; *Signal Processing, Computer-Assisted ; Movement/physiology ; Signal-To-Noise Ratio ; Deep Learning ; Algorithms ; },
abstract = {Motor imagery (MI) is a mental process that can be recognized by electroencephalography (EEG) without actual movement. It has significant research value and application potential in the field of brain-computer interface (BCI) technology. To address the challenges posed by the non-stationary nature and low signal-to-noise ratio of MI-EEG signals, this study proposed a Riemannian spatial filtering and domain adaptation (RSFDA) method for improving the accuracy and efficiency of cross-session MI-BCI classification tasks. The approach addressed the issue of inconsistent data distribution between source and target domains through a multi-module collaborative framework, which enhanced the generalization capability of cross-session MI-EEG classification models. Comparative experiments were conducted on three public datasets to evaluate RSFDA against eight existing methods in terms of classification accuracy and computational efficiency. The experimental results demonstrated that RSFDA achieved an average classification accuracy of 79.37%, outperforming the state-of-the-art deep learning method Tensor-CSPNet (76.46%) by 2.91% (P < 0.01). Furthermore, the proposed method showed significantly lower computational costs, requiring only approximately 3 minutes of average training time compared to Tensor-CSPNet's 25 minutes, representing a reduction of 22 minutes. These findings indicate that the RSFDA method demonstrates superior performance in cross-session MI-EEG classification tasks by effectively balancing accuracy and efficiency. However, its applicability in complex transfer learning scenarios remains to be further investigated.},
}
@article {pmid40288455,
year = {2025},
author = {Tang, Y and Tang, Z and Zhou, Y and Luo, Y and Wen, X and Yang, Z and Jiang, T and Luo, N},
title = {A systematic review of resting-state functional-MRI studies in the diagnosis, comorbidity and treatment of postpartum depression.},
journal = {Journal of affective disorders},
volume = {383},
number = {},
pages = {153-166},
doi = {10.1016/j.jad.2025.04.142},
pmid = {40288455},
issn = {1573-2517},
mesh = {Humans ; *Depression, Postpartum/therapy/diagnostic imaging/physiopathology/diagnosis ; Female ; *Magnetic Resonance Imaging ; *Brain/diagnostic imaging/physiopathology ; Comorbidity ; },
abstract = {BACKGROUND: Postpartum depression (PPD) is a common and serious mental health problem that affects many new mothers and their families worldwide. In recent years, there has been an increasing number of studies using magnetic resonance techniques (MRI), particularly functional MRI (fMRI), to explore the neuroimaging biomarker of this disease.<br><br>METHODS: PubMed database was used to search for English literature focusing on resting-state fMRI and PPD published up to June 2024.<br><br>RESULTS: After screening, 17 studies were finally identified, among which all 17 studies reported abnormal regions or connectivity compared to health controls (HC), 4 studies reported results considering the differences between PPD and PPD with anxiety (PPD-A), and 2 studies reported biomarkers for the treatment of PPD. The existing studies indicate that PPD is characterized by functional impairments in multiple brain regions, especially the medial prefrontal cortex (MPFC), precentral gyrus and cerebellum. Abnormal functional connectivity has been widely reported in the dorsomedial prefrontal cortex (dmPFC), anterior cingulate cortex (ACC) and the orbitofrontal cortex (OFC). However, none of the four comorbidity studies identified overlapping discriminative biomarkers between PPD and PPD-A. Additionally, the two treatment-related studies consistently reported functional improvements in the amygdala after effective treatment.<br><br>CONCLUSION: The affected brain regions were highly overlapped with major depressive disorder (MDD), suggesting that PPD may be categorized as a potential subtype of MDD. Considering the negative effects of medication on PPD, future efforts should focus on developing non-pharmacological therapies, such as transcranial magnetic stimulation (TMS) and acupuncture, to support women with PPD in overcoming this unique and important phase.},
}
@article {pmid40288311,
year = {2025},
author = {Li, L and Jiang, C},
title = {Electrodeposited coatings for neural electrodes: A review.},
journal = {Biosensors &amp; bioelectronics},
volume = {282},
number = {},
pages = {117492},
doi = {10.1016/j.bios.2025.117492},
pmid = {40288311},
issn = {1873-4235},
mesh = {Humans ; *Coated Materials, Biocompatible/chemistry ; *Biosensing Techniques/instrumentation/methods ; Electrodes ; *Electroplating/methods ; Brain-Computer Interfaces ; Equipment Design ; Animals ; *Neurons/physiology ; Electrodes, Implanted ; },
abstract = {Neural electrodes play a pivotal role in ensuring safe stimulation and high-quality recording for various bioelectronics such as neuromodulation devices and brain-computer interfaces. With the miniaturization of electrodes and the increasing demand for multi-functionality, the incorporation of coating materials via electrodeposition to enhance electrodes performance emerges as a highly effective strategy. These coatings not only substantially improve the stimulation and recording performance of electrodes but also introduce additional functionalities. This review began by outlining the application scenarios and critical requirements of neural electrodes. It then delved into the deposition principles and key influencing factors. Furthermore, the advancements in the electrochemical performance and adhesion stability of these coatings were reviewed. Ultimately, the latest innovative works in the electrodeposited coating applications were highlighted, and future perspectives were summarized.},
}
@article {pmid40287824,
year = {2025},
author = {Kumar, R and Waisberg, E and Ong, J and Lee, AG},
title = {The potential power of Neuralink - how brain-machine interfaces can revolutionize medicine.},
journal = {Expert review of medical devices},
volume = {22},
number = {6},
pages = {521-524},
doi = {10.1080/17434440.2025.2498457},
pmid = {40287824},
issn = {1745-2422},
}
@article {pmid40287725,
year = {2025},
author = {Zhang, X and Xie, L and Liu, W and Liang, S and Huang, L and Wang, M and Tian, L and Zhang, L and Liang, Z and Li, H and Huang, G},
title = {Exoskeleton-guided passive movement elicits standardized EEG patterns for generalizable BCIs in stroke rehabilitation.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {97},
pmid = {40287725},
issn = {1743-0003},
support = {62201356//National Natural Science Foundation of China/ ; 62276169//National Natural Science Foundation of China/ ; 62271326//National Natural Science Foundation of China/ ; 2023SHIBS0003//Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions/ ; JCYJ20210324134401004//Shenzhen Science and Technology Innovation Program/ ; JCYJ20241202124222027//Shenzhen Science and Technology Innovation Program/ ; C2401028//Shenzhen Medical Research Foundation/ ; },
mesh = {Humans ; *Stroke Rehabilitation/methods ; *Brain-Computer Interfaces ; Male ; Female ; *Electroencephalography/methods ; Middle Aged ; *Exoskeleton Device ; Movement/physiology ; Adult ; Aged ; Algorithms ; Stroke/physiopathology ; Evoked Potentials/physiology ; Motor Cortex/physiopathology ; },
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) hold significant potential for post-stroke motor recovery, yet active movement-based BCIs face limitations in generalization due to inter-subject variability. This study investigates passive movement-based BCIs, driven by exoskeleton-guided rehabilitation, to address these challenges by evaluating electroencephalogram (EEG) responses and algorithmic generalization in both healthy subjects and stroke patients.<br><br>METHODS: EEG signals were recorded from 20 healthy subjects and 10 stroke patients during voluntary and passive hand movements. Time and time-frequency domain analyses were performed to examine the event-related potential (ERP), event-related desynchronization (ERD), and synchronization (ERS) patterns. The performance of two BCI algorithms, Common Spatial Patterns (CSP) and EEGNet, was evaluated in both within-subject and cross-subject decoding tasks.<br><br>RESULTS: Time-domain and time-frequency analyses revealed that passive movements elicited stronger, more consistent ERPs in healthy subjects, particularly in bilateral motor cortices (contralateral: - 7.29 &#xb1; 4.51 &#xa0;&#x3bc;V; ipsilateral: - 4.33 &#xb1; 3.69 &#xa0;&#x3bc;V). Stroke patients exhibited impaired mu/beta ERD/ERS in the affected hemisphere during voluntary movements but demonstrated EEG patterns during passive movements resembling those of healthy subjects. Machine learning evaluation highlighted EEGNet's superior performance, achieving 84.19% accuracy in classifying affected vs. unaffected movements in patients, surpassing healthy subject left-right discrimination (58.38%). Cross-subject decoding further validated passive movement efficacy, with EEGNet attaining 86.00% (healthy) and 72.63% (stroke) accuracy, outperforming traditional CSP methods.<br><br>CONCLUSIONS: These findings underscore that passive movement elicits consistent neural responses, thereby enhancing the generalizability of decoding algorithms for stroke patients. By integrating exoskeleton-evoked proprioceptive feedback, this paradigm reduces inter-subject variability and improves clinical feasibility. Future work should explore the application of exoskeletons in the combination of active and passive movement for stroke rehabilitation.},
}
@article {pmid40281719,
year = {2025},
author = {Wan, X and Liu, Z and Yao, Y and Wan Hasan, WZ and Liu, T and Duan, D and Xie, X and Wen, D},
title = {Data Uncertainty (DU)-Former: An Episodic Memory Electroencephalography Classification Model for Pre- and Post-Training Assessment.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {12},
number = {4},
pages = {},
pmid = {40281719},
issn = {2306-5354},
support = {62206014//National Natural Science Foundation of China/ ; 62276022//National Natural Science Foundation of China/ ; 2023YFF1203702//National Key Research and Development Program of China/ ; },
abstract = {Episodic memory training plays a crucial role in cognitive enhancement, particularly in addressing age-related memory decline and cognitive disorders. Accurately assessing the effectiveness of such training requires reliable methods to capture changes in memory function. Electroencephalography (EEG) offers an objective way of evaluating neural activity before and after training. However, EEG classification in episodic memory assessment remains challenging due to the variability in brain responses, individual differences, and the complex temporal-spatial dynamics of neural signals. Traditional EEG classification methods, such as Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs), face limitations when applied to episodic memory training assessment, struggling to extract meaningful features and handle the inherent uncertainty in EEG signals. To address these issues, this paper introduces DU-former, which improves feature extraction and enhances the model's robustness against noise. Specifically, data uncertainty (DU) explicitly handles data uncertainty by modeling input features as Gaussian distributions within the reparameterization module. One branch predicts the mean through convolution and normalization, while the other estimates the variance via average pooling and normalization. These values are then used for Gaussian reparameterization, enabling the model to learn more robust feature representations. This approach allows the model to remain stable when dealing with complex or noisy data. To validate the method, an episodic memory training experiment was designed with 17 participants who underwent 28 days of training. Behavioral data showed a significant reduction in task completion time. Object recognition accuracy also improved, as indicated by the higher proportion of correctly identified target items in the episodic memory testing game. Furthermore, EEG data collected before and after the training were used to evaluate the DU-former's performance, demonstrating significant improvements in classification accuracy. This paper contributes by introducing uncertainty learning and proposing a more efficient and robust method for EEG signal classification, demonstrating superior performance in episodic memory assessment.},
}
@article {pmid40281692,
year = {2025},
author = {Acuña Luna, KP and Hernandez-Rios, ER and Valencia, V and Trenado, C and Peñaloza, C},
title = {Deep Learning-Enhanced Motor Training: A Hybrid VR and Exoskeleton System for Cognitive-Motor Rehabilitation.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {12},
number = {4},
pages = {},
pmid = {40281692},
issn = {2306-5354},
abstract = {This research explored the integration of the real-time machine learning classification of motor imagery data with a brain-machine interface, leveraging prefabricated exoskeletons and an EEG headset integrated with virtual reality (VR). By combining these technologies, the study aimed to develop practical and scalable therapeutic applications for rehabilitation and daily motor training. The project showcased an optimized system designed to assess and train cognitive-motor functions in elderly individuals. Key innovations included a motor imagery EEG acquisition protocol for data classification and a machine learning framework leveraging deep learning with a wavelet packet transform for feature extraction. Comparative analyses were conducted with traditional models such as Support Vector Machines (SVMs), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks. The performance was further enhanced through a random hyperparameter search, optimizing feature extraction and learning parameters to achieve high classification accuracy (89.23%). A novel VR fishing game was developed to dynamically respond to EEG outputs, enabling the performance of interactive motor imagery tasks in coordination with upper limb exoskeleton arms. While clinical testing is ongoing, the system demonstrates potential for increasing ERD/ERS polarization rates in alpha and beta waves among elderly users after several weeks of training. This integrated approach offers a tangible step forward in creating effective, user-friendly solutions for motor function rehabilitation.},
}
@article {pmid40281628,
year = {2025},
author = {Atkinson, C and Lombardi, L and Lang, M and Keesey, R and Hawthorn, R and Seitz, Z and Leuthardt, EC and Brunner, P and Seáñez, I},
title = {Development and evaluation of a non-invasive brain-spine interface using transcutaneous spinal cord stimulation.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {95},
pmid = {40281628},
issn = {1743-0003},
support = {K12 HD073945/HD/NICHD NIH HHS/United States ; U24 NS109103/NS/NINDS NIH HHS/United States ; K12-HD073945//National Institute of Child Health and Human Development/ ; K01 NS127936/NS/NINDS NIH HHS/United States ; P41-EB018783/EB/NIBIB NIH HHS/United States ; P41 EB018783/EB/NIBIB NIH HHS/United States ; U24-NS109103/NH/NIH HHS/United States ; K01-NS127936/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; Male ; Female ; Adult ; Electroencephalography ; *Spinal Cord Stimulation/methods ; *Brain-Computer Interfaces ; Middle Aged ; *Spinal Cord Injuries/rehabilitation/physiopathology ; Movement/physiology ; Young Adult ; *Transcutaneous Electric Nerve Stimulation/methods ; Sensorimotor Cortex/physiology ; Discriminant Analysis ; },
abstract = {Motor rehabilitation is a therapeutic process to facilitate functional recovery in people with spinal cord injury (SCI). However, its efficacy is limited to areas with remaining sensorimotor function. Spinal cord stimulation (SCS) creates a temporary prosthetic effect that may allow further rehabilitation-induced recovery in individuals without remaining sensorimotor function, thereby extending the therapeutic reach of motor rehabilitation to individuals with more severe injuries. In this work, we report our first steps in developing a non-invasive brain-spine interface (BSI) based on electroencephalography (EEG) and transcutaneous spinal cord stimulation (tSCS). The objective of this study was to identify EEG-based neural correlates of lower limb movement in the sensorimotor cortex of unimpaired individuals (N = 17) and to quantify the performance of a linear discriminant analysis (LDA) decoder in detecting movement onset from these neural correlates. Our results show that initiation of knee extension was associated with event-related desynchronization in the central-medial cortical regions at frequency bands between 4 and 44 Hz. Our neural decoder using µ (8-12 Hz), low β (16-20 Hz), and high β (24-28 Hz) frequency bands achieved an average area under the curve (AUC) of 0.83 ± 0.06 s.d. (n = 7) during a cued movement task offline. Generalization to imagery and uncued movement tasks served as positive controls to verify robustness against movement artifacts and cue-related confounds, respectively. With the addition of real-time decoder-modulated tSCS, the neural decoder performed with an average AUC of 0.81 ± 0.05 s.d. (n = 9) on cued movement and 0.68 ± 0.12 s.d. (n = 9) on uncued movement. Our results suggest that the decrease in decoder performance in uncued movement may be due to differences in underlying cortical strategies between conditions. Furthermore, we explore alternative applications of the BSI system by testing neural decoders trained on uncued movement and imagery tasks. By developing a non-invasive BSI, tSCS can be timed to be delivered only during voluntary effort, which may have implications for improving rehabilitation.},
}
@article {pmid40280929,
year = {2025},
author = {Bai, Y and Tang, Q and Zhao, R and Liu, H and Zhang, S and Guo, M and Guo, M and Wang, J and Wang, C and Xing, M and Ni, G and Ming, D},
title = {TMNRED, A Chinese Language EEG Dataset for Fuzzy Semantic Target Identification in Natural Reading Environments.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {701},
pmid = {40280929},
issn = {2052-4463},
mesh = {Humans ; *Electroencephalography ; *Reading ; *Semantics ; China ; *Language ; *Natural Language Processing ; East Asian People ; },
abstract = {Semantic understanding is central to advanced cognitive functions, and the mechanisms by which the brain processes language information are still being explored. Existing EEG datasets often lack natural reading data specific to Chinese, limiting research on Chinese semantic decoding and natural language processing. This study aims to construct a Chinese natural reading EEG dataset, TMNRED, for semantic target identification in natural reading environments. TMNRED was collected from 30 participants reading sentences sourced from public internet resources and media reports. Each participant underwent 400-450 trials in a single day, resulting in a dataset with over 10 hours of continuous EEG data and more than 4000 trials. This dataset provides valuable physiological data for studying Chinese semantics and developing more accurate Chinese natural language processing models.},
}
@article {pmid40280532,
year = {2025},
author = {Gazerani, P},
title = {The neuroplastic brain: current breakthroughs and emerging frontiers.},
journal = {Brain research},
volume = {1858},
number = {},
pages = {149643},
doi = {10.1016/j.brainres.2025.149643},
pmid = {40280532},
issn = {1872-6240},
mesh = {Humans ; *Neuronal Plasticity/physiology ; *Brain/physiology ; Animals ; Brain-Computer Interfaces ; Neurogenesis/physiology ; },
abstract = {Neuroplasticity, the brain's capacity to reorganize itself by forming new neural connections, is central to modern neuroscience. Once believed to occur only during early development, research now shows that plasticity continues throughout the lifespan, supporting learning, memory, and recovery from injury or disease. Substantial progress has been made in understanding the mechanisms underlying neuroplasticity and their therapeutic applications. This overview article examines synaptic plasticity, structural remodeling, neurogenesis, and functional reorganization, highlighting both adaptive (beneficial) and maladaptive (harmful) processes across different life stages. Recent strategies to harness neuroplasticity, ranging from pharmacological agents and lifestyle interventions to cutting-edge technologies like brain-computer interfaces (BCIs) and targeted neuromodulation are evaluated in light of current empirical evidence. Contradictory findings in the literature are addressed, and methodological limitations that hamper widespread clinical adoption are discussed. The ethical and societal implications of deploying novel neuroplasticity-based interventions, including issues of equitable access, data privacy, and the blurred line between treatment and enhancement, are then explored in a structured manner. By integrating mechanistic insights, empirical data, and ethical considerations, the aim is to provide a comprehensive and balanced perspective for researchers, clinicians, and policymakers working to optimize brain health across diverse populations.},
}
@article {pmid40280369,
year = {2025},
author = {Zhang, Y and Gao, Y and Zhou, J and Zhang, Z and Feng, M and Liu, Y},
title = {Advances in brain-computer interface controlled functional electrical stimulation for upper limb recovery after stroke.},
journal = {Brain research bulletin},
volume = {226},
number = {},
pages = {111354},
doi = {10.1016/j.brainresbull.2025.111354},
pmid = {40280369},
issn = {1873-2747},
mesh = {Humans ; *Brain-Computer Interfaces ; *Stroke Rehabilitation/methods ; *Upper Extremity/physiopathology ; *Stroke/physiopathology/therapy ; *Recovery of Function/physiology ; *Electric Stimulation Therapy/methods ; },
abstract = {Stroke often results in varying degrees of functional impairment, significantly affecting patients' quality of daily life. In recent years, brain-computer interface-controlled functional electrical stimulation has offered new therapeutic approaches for post-stroke rehabilitation. This paper reviews the application of BCI-FES in the recovery of upper limb function after stroke and explores its underlying mechanisms. By analyzing relevant studies, the aim is to provide a theoretical basis for rehabilitating upper limb function post-stroke, promote BCI-FES, and offer guidance for future clinical practice.},
}
@article {pmid40280291,
year = {2025},
author = {Han, MJ and Oh, Y and Ann, Y and Kang, S and Baeg, E and Hong, SJ and Sohn, H and Kim, SG},
title = {Whole-brain effective connectivity of the sensorimotor system using 7 T fMRI with electrical microstimulation in non-human primates.},
journal = {Progress in neurobiology},
volume = {250},
number = {},
pages = {102760},
doi = {10.1016/j.pneurobio.2025.102760},
pmid = {40280291},
issn = {1873-5118},
mesh = {Animals ; Magnetic Resonance Imaging/methods ; Electric Stimulation ; *Somatosensory Cortex/physiology/diagnostic imaging ; *Motor Cortex/physiology/diagnostic imaging ; Male ; Brain Mapping ; *Sensorimotor Cortex/physiology/diagnostic imaging ; Neural Pathways/physiology/diagnostic imaging ; Macaca mulatta ; },
abstract = {The sensorimotor system is a crucial interface between the brain and the environment, and it is endowed with multiple computational mechanisms that enable efficient behaviors. For example, predictive processing via an efference copy of a motor command has been proposed as one of the key computations used to compensate for the sensory consequence of movement. However, the neural pathways underlying this process remain unclear, particularly regarding whether the M1-to-S1 pathway plays a dominant role in predictive processing and how its influence compares to that of other pathways. In this study, we present a causally inferable input-output map of the sensorimotor effective connectivity that we made by combining ultrahigh-field functional MRI, electrical microstimulation of the S1/M1 cortex, and dynamic causal modeling for the whole sensorimotor network in anesthetized primates. We investigated how motor signals from M1 are transmitted to S1 at the circuit level, either via direct cortico-cortical projections or indirectly via subcortical structures such as the thalamus. Across different stimulation conditions, we observed a robust asymmetric connectivity from M1 to S1 that was also the most prominent output from M1. In the thalamus, we identified distinct activations: M1 stimulation showed connections to the anterior part of ventral thalamic nuclei, whereas S1 was linked to the more posterior regions of the ventral thalamic nuclei. These findings suggest that the cortico-cortical projection from M1 to S1, rather than the cortico-thalamic loop, plays a dominant role in transmitting movement-related information. Together, our detailed dissection of the sensorimotor circuitry underscores the importance of M1-to-S1 connectivity in sensorimotor coordination.},
}
@article {pmid40280150,
year = {2025},
author = {Singer-Clark, T and Hou, X and Card, NS and Wairagkar, M and Iacobacci, C and Peracha, H and Hochberg, LR and Stavisky, SD and Brandman, DM},
title = {Speech motor cortex enables BCI cursor control and click.},
journal = {Journal of neural engineering},
volume = {22},
number = {3},
pages = {},
pmid = {40280150},
issn = {1741-2552},
support = {DP2 DC021055/DC/NIDCD NIH HHS/United States ; },
mesh = {Humans ; Male ; Middle Aged ; Amyotrophic Lateral Sclerosis/physiopathology ; *Brain-Computer Interfaces ; Electrodes, Implanted ; Electroencephalography/methods ; *Motor Cortex/physiology ; *Speech/physiology ; },
abstract = {Objective.Decoding neural activity from ventral (speech) motor cortex is known to enable high-performance speech brain-computer interface (BCI) control. It was previously unknown whether this brain area could also enable computer control via neural cursor and click, as is typically associated with dorsal (arm and hand) motor cortex.Approach.We recruited a clinical trial participant with amyotrophic lateral sclerosis and implanted intracortical microelectrode arrays in ventral precentral gyrus (vPCG), which the participant used to operate a speech BCI in a prior study. We developed a cursor BCI driven by the participant's vPCG neural activity, and evaluated performance on a series of target selection tasks.Main results.The reported vPCG cursor BCI enabled rapidly-calibrating (40 s), accurate (2.90 bits per second) cursor control and click. The participant also used the BCI to control his own personal computer independently.Significance.These results suggest that placing electrodes in vPCG to optimize for speech decoding may also be a viable strategy for building a multi-modal BCI which enables both speech-based communication and computer control via cursor and click. (BrainGate2 ClinicalTrials.gov ID NCT00912041).},
}
@article {pmid40280132,
year = {2025},
author = {Zhou, N and Chen, J and Hu, M and Wen, N and Cai, W and Li, P and Zhao, L and Meng, Y and Zhao, D and Yang, X and Liu, S and Huang, F and Zhao, C and Feng, X and Jiang, Z and Xie, E and Pan, H and Cen, Z and Chen, X and Luo, W and Tang, B and Min, J and Wang, F and Yang, J and Xu, H},
title = {SLC7A11 is an unconventional H[+] transporter in lysosomes.},
journal = {Cell},
volume = {188},
number = {13},
pages = {3441-3458.e25},
doi = {10.1016/j.cell.2025.04.004},
pmid = {40280132},
issn = {1097-4172},
mesh = {*Lysosomes/metabolism ; Humans ; Animals ; *Amino Acid Transport System y+/metabolism/genetics ; Mice ; Ferroptosis ; Parkinson Disease/metabolism/pathology ; Hydrogen-Ion Concentration ; alpha-Synuclein/metabolism ; Neurons/metabolism ; Protons ; Cystine/metabolism ; Glutamic Acid/metabolism ; },
abstract = {Lysosomes maintain an acidic pH of 4.5-5.0, optimal for macromolecular degradation. Whereas proton influx is produced by a V-type H[+] ATPase, proton efflux is mediated by a fast H[+] leak through TMEM175 channels, as well as an unidentified slow pathway. A candidate screen on an orphan lysosome membrane protein (OLMP) library enabled us to discover that SLC7A11, the protein target of the ferroptosis-inducing compound erastin, mediates a slow lysosomal H[+] leak through downward flux of cystine and glutamate, two H[+] equivalents with uniquely large but opposite concentration gradients across lysosomal membranes. SLC7A11 deficiency or inhibition caused lysosomal over-acidification, reduced degradation, accumulation of storage materials, and ferroptosis, as well as facilitated α-synuclein aggregation in neurons. Correction of abnormal lysosomal acidity restored lysosome homeostasis and prevented ferroptosis. These studies have revealed an unconventional H[+] transport conduit that is integral to lysosomal flux of protonatable metabolites to regulate lysosome function, ferroptosis, and Parkinson's disease (PD) pathology.},
}
@article {pmid40280131,
year = {2025},
author = {Xin, Q and Wang, J and Zheng, J and Tan, Y and Jia, X and Ni, Z and Xu, Z and Feng, J and Wu, Z and Li, Y and Li, XM and Ma, H and Hu, H},
title = {Neuron-astrocyte coupling in lateral habenula mediates depressive-like behaviors.},
journal = {Cell},
volume = {188},
number = {12},
pages = {3291-3309.e24},
doi = {10.1016/j.cell.2025.04.010},
pmid = {40280131},
issn = {1097-4172},
mesh = {Animals ; *Astrocytes/metabolism ; *Habenula/metabolism/cytology ; Mice ; *Neurons/metabolism ; *Depression/metabolism/physiopathology ; Locus Coeruleus/metabolism ; Male ; Norepinephrine/metabolism ; Mice, Inbred C57BL ; Calcium/metabolism ; Glutamic Acid/metabolism ; Calcium Signaling ; Stress, Psychological ; Adenosine Triphosphate/metabolism ; },
abstract = {The lateral habenula (LHb) neurons and astrocytes have been strongly implicated in depression etiology, but it was not clear how the two dynamically interact during depression onset. Here, using multi-brain-region calcium photometry recording in freely moving mice, we discover that stress induces a most rapid astrocytic calcium rise and a bimodal neuronal response in the LHb. LHb astrocytic calcium requires the α1A-adrenergic receptor and depends on a recurrent neural network between the LHb and locus coeruleus (LC). Through the gliotransmitter glutamate and ATP/adenosine, LHb astrocytes mediate the second-wave LHb neuronal activation and norepinephrine (NE) release. Activation or inhibition of LHb astrocytic calcium signaling facilitates or prevents stress-induced depressive-like behaviors, respectively. These results identify a stress-induced positive feedback loop in the LHb-LC axis, with astrocytes being a critical signaling relay. The identification of this prominent neuron-glia interaction may shed light on stress management and depression prevention.},
}
@article {pmid40280000,
year = {2025},
author = {Jiang, Y and Zhou, C and Zhao, J and Ren, X and Wang, Q and Ni, P and Li, T},
title = {Generation and Characterization of a Human-Derived iPSC line from a female child with First-Episode of sporadic schizophrenia.},
journal = {Stem cell research},
volume = {86},
number = {},
pages = {103713},
doi = {10.1016/j.scr.2025.103713},
pmid = {40280000},
issn = {1876-7753},
mesh = {Humans ; *Induced Pluripotent Stem Cells/metabolism/cytology/pathology ; Kruppel-Like Factor 4 ; Female ; *Schizophrenia/pathology/metabolism ; Cell Differentiation ; Cell Line ; Child ; Cellular Reprogramming ; Animals ; Leukocytes, Mononuclear/metabolism/cytology ; },
abstract = {Schizophrenia is a highly heritable neurodevelopmental disorder. In this study, peripheral blood mononuclear cells (PBMCs) were obtained from a female child diagnosed with first-episode of sporadic schizophrenia. Induced pluripotent stem cells (iPSCs) were generated by introducing the reprogramming factors OCT4, SOX2, NANOG, LIN28, c-MYC, KLF4, and SV40LT. The iPSC line was confirmed through karyotyping and the expression of key pluripotency markers. These cells demonstrated the ability to differentiate into all three germ layers in vivo.},
}
@article {pmid40279233,
year = {2025},
author = {Aung, HW and Jiao Li, J and An, Y and Su, SW},
title = {A Real-Time Framework for EEG Signal Decoding With Graph Neural Networks and Reinforcement Learning.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {36},
number = {9},
pages = {17047-17058},
doi = {10.1109/TNNLS.2025.3558171},
pmid = {40279233},
issn = {2162-2388},
mesh = {Humans ; *Electroencephalography/methods ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; *Reinforcement, Psychology ; Signal Processing, Computer-Assisted ; Algorithms ; Imagination/physiology ; Machine Learning ; Graph Neural Networks ; },
abstract = {Brain-computer interfaces (BCIs) rely on accurately decoding electroencephalography (EEG) motor imagery (MI) signals for effective device control. Graph neural networks (GNNs) outperform convolutional neural networks (CNNs) in this regard, by leveraging the spatial relationships between EEG electrodes through adjacency matrices. The EEG graph lottery ticket framework, EEG_GLT-Net, featuring the state-of-the-art (SOTA) EEG_GLT adjacency matrix method, has notably enhanced EEG MI signal classification, evidenced by an average accuracy of 83.95% across 20 subjects on the PhysioNet dataset. This significantly exceeds the 76.10% accuracy rate achieved using the Pearson correlation coefficient (PCC) method in the same framework. In this research, we advance the field by applying a reinforcement learning (RL) approach to the classification of EEG MI signals. Our innovative method empowers the RL agent, enabling not only the classification of EEG MI data points with higher accuracy but effective identification of EEG MI data points that are less distinct. We present the EEG_RL-Net, an enhancement of the EEG_GLT-Net framework, which incorporates the trained EEG_GCN Block from EEG_GLT-Net at an adjacency matrix density of 13.39% alongside the RL-centric dueling deep Q network (Dueling DQN) block. The EEG_RL-Net model showcases exceptional classification performance, achieving an unprecedented average accuracy of 96.40% across 20 subjects within 25 ms. This model illustrates the transformative effect of the RL in EEG MI time point classification.},
}
@article {pmid40277624,
year = {2025},
author = {Zhang, W and Tang, X and Dang, X and Wang, M},
title = {A Capsule Decision Neural Network Based on Transfer Learning for EEG Signal Classification.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {10},
number = {4},
pages = {},
pmid = {40277624},
issn = {2313-7673},
abstract = {Transfer learning is the act of using the data or knowledge in a problem to help solve different but related problems. In a brain computer interface (BCI), it is important to deal with individual differences between topics and/or tasks. A kind of capsule decision neural network (CDNN) based on transfer learning is proposed. In order to solve the problem of feature distortion caused by EEG feature extraction algorithm, a deep capsule decision network was constructed. The architecture includes multiple primary capsules to form a hidden layer, and the connection between the advanced capsule and the primary capsule is determined by the neural decision routing algorithm. Unlike the dynamic routing algorithm that iteratively calculates the similarity between primary capsules and advanced capsules, the neural decision network computes the relationship between each capsule in the deep and shallow hidden layers in a probabilistic manner. At the same time, the distribution of the EEG covariance matrix is aligned in Riemann space, and the regional adaptive method is further introduced to improve the independent decoding ability of the capsule decision neural network for the subject's EEG signals. Experiments on two motor imagery EEG datasets show that CDNN outperforms several of the most advanced transfer learning methods.},
}
@article {pmid40277574,
year = {2025},
author = {Jiao, P and Jia, Q and Li, S and Shan, J and Xu, W and Wang, Y and Liu, Y and Wang, M and Song, Y and Zhang, Y and Yu, Y and Wang, M and Cai, X},
title = {Distinct Neural Activities in Hippocampal Subregions Revealed Using a High-Performance Wireless Microsystem with PtNPs/PEDOT:PSS-Enhanced Microelectrode Arrays.},
journal = {Biosensors},
volume = {15},
number = {4},
pages = {},
pmid = {40277574},
issn = {2079-6374},
support = {2022YFC2402501//the National Key Research and Development Program of China/ ; 2022YFC2402503//the National Key Research and Development Program of China/ ; 2022YFB3205602//the National Key Research and Development Program of China/ ; T2293730//the National Natural Science Foundation of China/ ; T2293731//the National Natural Science Foundation of China/ ; 61960206012//the National Natural Science Foundation of China/ ; 62121003//the National Natural Science Foundation of China/ ; 62333020//the National Natural Science Foundation of China/ ; 62171434//the National Natural Science Foundation of China/ ; 2021ZD02016030//Major Program of Scientific and Technical Innovation 2030/ ; PTYQ2024BJ0009//the Scientific Instrument Developing Project of the Chinese Academy of Sciences/ ; },
mesh = {Animals ; Microelectrodes ; *Wireless Technology ; Rats ; *Hippocampus/physiology ; Platinum/chemistry ; Polymers/chemistry ; Polystyrenes/chemistry ; Metal Nanoparticles/chemistry ; Bridged Bicyclo Compounds, Heterocyclic/chemistry ; *Biosensing Techniques ; Rats, Sprague-Dawley ; Male ; },
abstract = {Wireless microsystems for neural signal recording have emerged as a solution to overcome the limitations of tethered systems, which restrict the mobility of subjects and introduce noise interference. However, existing microsystems often face data throughput, signal processing, and long-distance wireless transmission challenges. This study presents a high-performance wireless microsystem capable of 32-channel, 30 kHz real-time recording, featuring Field Programmable Gate Array (FPGA)-based signal processing to reduce transmission load. The microsystem is integrated with platinum nanoparticles/poly (3,4-ethylenedioxythiophene) polystyrene sulfonate-enhanced microelectrode arrays for improved signal quality. A custom NeuroWireless platform was developed for seamless data reception and storage. Experimental validation in rats demonstrated the microsystem's ability to detect spikes and local field potentials from the hippocampal CA1 and CA2 subregions. Comparative analysis of the neural signals revealed distinct activity patterns between these subregions. The wireless microsystem achieves high accuracy and throughput over distances up to 30 m, demonstrating its resilience and potential for neuroscience research. This work provides a compact, adaptable solution for multi-channel neural signal detection and offers a foundation for future applications in brain-computer interfaces.},
}
@article {pmid40277527,
year = {2025},
author = {Tavakolidakhrabadi, A and Stark, M and Küenzi, A and Carrara, S and Bessire, C},
title = {Optimized Microfluidic Biosensor for Sensitive C-Reactive Protein Detection.},
journal = {Biosensors},
volume = {15},
number = {4},
pages = {},
pmid = {40277527},
issn = {2079-6374},
support = {52116.1 IP-LS//Innosuisse - Swiss Innovation Agency/ ; },
mesh = {*C-Reactive Protein/analysis ; *Biosensing Techniques ; Metal Nanoparticles/chemistry ; Gold/chemistry ; Immunoassay ; Humans ; Point-of-Care Testing ; Microfluidics ; },
abstract = {Lateral flow immunoassays (LFIAs) were integrated into microfluidic chips and tested to enhance point-of-care testing (POCT), with the aim of improving sensitivity and expanding the range of CRP detection. The microfluidic approach improves upon traditional methods by precisely controlling fluid speed, thus enhancing sensitivity and accuracy in CRP measurements. The microfluidic approach also enables a one-step detection system, eliminating the need for buffer solution steps and reducing the nitrocellulose (NC) pad area to just the detection test line. This approach minimizes the non-specific binding of conjugated antibodies to unwanted areas of the NC pad, eliminating the need to block those areas, which enhances the sensitivity of detection. The gold nanoparticle method detects CRP in the high-sensitivity range of 1-10 μg/mL, which is suitable for chronic disease monitoring. To broaden the CRP detection range, including infection levels beyond 10 μg/mL, fluorescent labels were introduced, extending the measuring range from 1 to 70 μg/mL. Experimental results demonstrate that integrating microfluidic technology significantly enhances operational efficiency by precisely controlling the flow rate and optimizing the mixing efficiency while reducing fabrication resources by eliminating the need for separate pads, making these methods suitable for resource-limited settings. Microfluidics also provides greater control over fluid dynamics compared to traditional LFIA methods, which contributes to enhanced detection sensitivity even with lower sample volumes and no buffer solution, helping to enhance the usability of POCT. These findings highlight the potential to develop accessible, accurate, and cost-effective diagnostic tools essential for timely medical interventions at the POC.},
}
@article {pmid40275590,
year = {2025},
author = {Haghani Dogahe, M and Mahan, MA and Zhang, M and Bashiri Aliabadi, S and Rouhafza, A and Karimzadhagh, S and Feizkhah, A and Monsef, A and Habibi Roudkenar, M},
title = {Advancing Prosthetic Hand Capabilities Through Biomimicry and Neural Interfaces.},
journal = {Neurorehabilitation and neural repair},
volume = {39},
number = {6},
pages = {481-494},
doi = {10.1177/15459683251331593},
pmid = {40275590},
issn = {1552-6844},
mesh = {Humans ; *Hand/physiology ; *Brain-Computer Interfaces ; *Artificial Limbs ; *Prosthesis Design ; *Biomimetics/methods ; },
abstract = {Background and ObjectivesProsthetic hand development is undergoing a transformative phase, blending biomimicry and neural interface technologies to redefine functionality and sensory feedback. This article explores the symbiotic relationship between biomimetic design principles and neural interface technology (NIT) in advancing prosthetic hand capabilities.MethodsDrawing inspiration from biological systems, researchers aim to replicate the intricate movements and capabilities of the human hand through innovative prosthetic designs. Central to this endeavor is NIT, facilitating seamless communication between artificial devices and the human nervous system. Recent advances in fabrication methods have propelled brain-computer interfaces, enabling precise control of prosthetic hands by decoding neural activity.ResultsAnatomical complexities of the human hand underscore the importance of understanding biomechanics, neuroanatomy, and control mechanisms for crafting effective prosthetic solutions. Furthermore, achieving the goal of a fully functional cyborg hand necessitates a multidisciplinary approach and biomimetic design to replicate the body's inherent capabilities. By incorporating the expertise of clinicians, tissue engineers, bioengineers, electronic and data scientists, the next generation of the implantable devices is not only anatomically and biomechanically accurate but also offer intuitive control, sensory feedback, and proprioception, thereby pushing the boundaries of current prosthetic technology.ConclusionBy integrating machine learning algorithms, biomechatronic principles, and advanced surgical techniques, prosthetic hands can achieve real-time control while restoring tactile sensation and proprioception. This manuscript contributes novel approaches to prosthetic hand development, with potential implications for enhancing the functionality, durability, and safety of the prosthetic limb.},
}
@article {pmid40274133,
year = {2025},
author = {Meng, L and Wang, D and Ma, J and Shi, Y and Zhao, H and Wang, Y and Shi, Q and Zhu, X and Ming, D},
title = {Unraveling Parkinson's disease motor subtypes: A deep learning approach based on spatiotemporal dynamics of EEG microstates.},
journal = {Neurobiology of disease},
volume = {210},
number = {},
pages = {106915},
doi = {10.1016/j.nbd.2025.106915},
pmid = {40274133},
issn = {1095-953X},
mesh = {Humans ; *Parkinson Disease/physiopathology/classification/complications/diagnosis ; Male ; *Electroencephalography/methods ; Female ; *Deep Learning ; Aged ; Middle Aged ; *Tremor/physiopathology ; *Brain/physiopathology ; *Gait Disorders, Neurologic/physiopathology/etiology ; Postural Balance/physiology ; },
abstract = {BACKGROUND: Despite prior studies on early-stage Parkinson's disease (PD) brain connectivity and temporal patterns, differences between tremor-dominant (TD) and postural instability/gait difficulty (PIGD) motor subtypes remain poorly understood. Our study aims to understand the contribution of altered brain network dynamics to heterogeneous motor phenotypes in PD for improving personalized treatment.<br><br>METHODS: Electroencephalography (EEG) microstate dynamics were firstly used to capture spatiotemporal brain network changes. A deep learning model was developed to classify PD motor subtypes where spatial variability and electrode location data were incorporated into the analysis.<br><br>RESULTS: Compared to healthy individuals, both PD-TD and PD-PIGD patients showed increased local segregation of brain regions. The PD-PIGD subtype had more severe and extensive disorganization in microstate A dynamics, suggesting greater disruption in auditory and motor-related networks. Incorporating spatial information significantly improved the accuracy of subtype classification, with an AUC of 0.972, indicating that EEG microstate dynamic spatial patterns reflect distinct PD motor pathologies. The increased spatial variability in the PD-PIGD group was more closely associated with motor impairments.<br><br>CONCLUSIONS: This study presents a novel framework for differentiating PD motor subtypes and emphasizes dynamic brain network features as potential markers for understanding motor symptom variability in PD, which may contribute to the development of personalized treatment strategies.<br><br>TRIAL REGISTRATION: ChiCTR2300067657.},
}
@article {pmid40273947,
year = {2025},
author = {Ahmadi, H and Mesin, L},
title = {Universal semantic feature extraction from EEG signals: a task-independent framework.},
journal = {Journal of neural engineering},
volume = {22},
number = {3},
pages = {},
doi = {10.1088/1741-2552/add08f},
pmid = {40273947},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; *Semantics ; *Brain-Computer Interfaces ; Evoked Potentials, Visual/physiology ; Neural Networks, Computer ; Male ; Adult ; Female ; Imagination/physiology ; },
abstract = {Objective.Extracting universal, task-independent semantic features from electroencephalography (EEG) signals remains an open challenge. Traditional approaches are often task-specific, limiting their generalization across different EEG paradigms. This study aims to develop a robust, unsupervised framework for learning high-level, task-independent neural representations.Approach.We propose a novel framework integrating convolutional neural networks, AutoEncoders, and Transformers to extract both low-level spatiotemporal patterns and high-level semantic features from EEG signals. The model is trained in an unsupervised manner to ensure adaptability across diverse EEG paradigms, including motor imagery (MI), steady-state visually evoked potentials (SSVEPs), and event-related potentials (ERPs, specifically P300). Extensive analyses, including clustering, correlation, and ablation studies, are conducted to validate the quality and interpretability of the extracted features.Main results.Our method achieves state-of-the-art performance, with average classification accuracies of 83.50% and 84.84% on MI datasets (BCICIV_2a and BCICIV_2b), 98.41% and 99.66% on SSVEP datasets (Lee2019-SSVEP and Nakanishi2015), and an average AUC of 91.80% across eight ERP datasets. t-distributed stochastic neighbor embedding and clustering analyses reveal that the extracted features exhibit enhanced separability and structure compared to raw EEG data. Correlation studies confirm the framework's ability to balance universal and subject-specific features, while ablation results highlight the near-optimality of the selected model configuration.Significance.This work establishes a universal framework for task-independent semantic feature extraction from EEG signals, bridging the gap between conventional feature engineering and modern deep learning methods. By providing robust, generalizable representations across diverse EEG paradigms, this approach lays the foundation for advanced brain-computer interface applications, cross-task EEG analysis, and future developments in semantic EEG processing.},
}
@article {pmid40271395,
year = {2025},
author = {Han, CH and Kim, SU and Lim, KS and Jung, YJ and Lee, S and Kim, SH and Hwang, HJ},
title = {Evaluation of a silicone-based flexible dry electrode for measuring human bioelectrical signals.},
journal = {Biomedical engineering letters},
volume = {15},
number = {3},
pages = {563-574},
pmid = {40271395},
issn = {2093-985X},
abstract = {The development of conductive polymer-based dry electrodes with high conductivity is promising for practical applications in daily life due to their biocompatibility, flexibility, lightweight, and comfort. The objective of this study is to demonstrate the feasibility of using a novel silicone-based dry electrode for measuring various bioelectrical signals.The silicone-based electrode, manufactured using an optimized polymer matrix, combines high conductivity with flexibility, ensuring superior wearability and reliable bioelectrical signal monitoring. To evaluate its performance, we compared its impedance and flexibility with those of a commercial electrode. Additionally, its compatibility for measuring biological signals was assessed through performance comparisons across various bioelectrical signals. Fourteen healthy participants performed three experimental paradigms: (1) eyes closed and open to measure alpha electroencephalography (EEG) as well as resting-state electrocardiography (ECG), (2) eye blinking to measure electrooculography (EOG), and (3) wrist movement to measure electromyography (EMG). All bioelectrical signals were measured simultaneously using both the silicone-based dry electrode and a commercial dry electrode. The performance of the silicone-based dry electrode was evaluated by comparing the signal quality of both electrodes. The silicone-based dry electrode exhibited lower electrical impedance (39.43 kΩ on average, p = 0.0058) and greater flexibility (Young's modulus: silicone 1.51 ± 0.10 MPa vs. commercial 2.46 ± 0.38 MPa) compared to the commercial dry electrode. Overall, there were minimal differences in signal quality between the two electrodes: i) EEG (α power SNR: silicone 1.39 ± 0.34 vs. commercial 1.36 ± 0.29), ii) ECG (R-peak recall: 99.20 ± 2.50%, correlation coefficient: 0.96 ± 0.08), iii) EOG (eye blink recall: 100.00%, correlation coefficient: 0.98 ± 0.03), and iv) EMG (no significant difference in SNR values). These findings indicate that the developed electrode not only ensures superior flexibility but also maintains compatible electrical properties for measuring various bioelectrical signals.},
}
@article {pmid40270987,
year = {2025},
author = {Jiang, Z and Hu, K and Qu, J and Bian, Z and Yu, D and Zhou, J},
title = {Recognition of MI-EEG signals using extended-LSR-based inductive transfer learning.},
journal = {Frontiers in neuroinformatics},
volume = {19},
number = {},
pages = {1559335},
pmid = {40270987},
issn = {1662-5196},
abstract = {INTRODUCTION: Motor imagery electroencephalographic (MI-EEG) signal recognition is used in various brain-computer interface (BCI) systems. In most existing BCI systems, this identification relies on classification algorithms. However, generally, a large amount of subject-specific labeled training data is required to reliably calibrate the classification algorithm for each new subject. To address this challenge, an effective strategy is to integrate transfer learning into the construction of intelligent models, allowing knowledge to be transferred from the source domain to enhance the performance of models trained in the target domain. Although transfer learning has been implemented in EEG signal recognition, many existing methods are designed specifically for certain intelligent models, limiting their application and generalization.<br><br>METHODS: To broaden application and generalization, an extended-LSR-based inductive transfer learning method is proposed to facilitate transfer learning across various classical intelligent models, including neural networks, Takagi-SugenoKang (TSK) fuzzy systems, and kernel methods.<br><br>RESULTS AND DISCUSSION: The proposed method not only promotes the transfer of valuable knowledge from the source domain to improve learning performance in the target domain when target domain training data are insufficient but also enhances application and generalization by incorporating multiple classic base models. The experimental results demonstrate the effectiveness of the proposed method in MI-EEG signal recognition.},
}
@article {pmid40270567,
year = {2025},
author = {Hammond, L and Rowley, D and Tuck, C and Floreani, ED and Wieler, A and Kim, VS and Bahari, H and Andersen, J and Kirton, A and Kinney-Lang, E},
title = {BCI move: exploring pediatric BCI-controlled power mobility.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1456692},
pmid = {40270567},
issn = {1662-5161},
abstract = {INTRODUCTION: Children and young people (CYP) with severe physical disabilities often experience barriers to independent mobility, placing them at risk for developmental impairments and restricting their independence and participation. Pilot work suggests that brain-computer interface (BCIs) could enable powered mobility control for children with motor disabilities. We explored how severely disabled CYP could use BCI to achieve individualized, functional power mobility goals and acquire power mobility skills. We also explored the practicality of pediatric BCI-enabled power mobility.<br><br>METHODS: Nine CYP aged 7-17 years with severe physical disabilities and their caregivers participated in up to 12 BCI-enabled power mobility training sessions focused on a personalized power mobility goal. Goal achievement was assessed using the Canadian Occupational Performance Measure (COPM) and Goal Attainment Scaling (GAS). The Assessment for Learning Powered Mobility (ALP) was used to measure session-by-session power mobility skill acquisition. BCI set-up and calibration metrics, perceived workload, and participant engagement were also reported.<br><br>RESULTS: Significant improvements in COPM performance (Z&#x202f;=&#x202f;-2.869, adjusted p&#x202f;=&#x202f;0.012) and satisfaction scores (Z&#x202f;=&#x202f;-2.809, adjusted p&#x202f;=&#x202f;0.015) and GAS T scores (Z&#x202f;=&#x202f;-2.805, p&#x202f;=&#x202f;0.005) were observed following the intervention. ALP scores displayed a small but significant increase over time (R [2]&#x202f;=&#x202f;0.07-0.19; adjusted p&#x202f;=&#x202f;<0.001-0.039), with 7/9 participants achieving increased overall ALP scores following the intervention. Setup and calibration times were practical although calibration consistency was highly variable. Participants reported moderate workload with no significant change over time (R [2]&#x202f;=&#x202f;0.00-0.13; adjusted p&#x202f;=&#x202f;0.006-1.000), although there was a trend towards increased frustration over time(R [2]&#x202f;=&#x202f;0.13; adjusted p&#x202f;=&#x202f;0.006).<br><br>DISCUSSION: Participants were highly engaged throughout the intervention. BCI-enabled power mobility appears to help CYP with severe physical disabilities achieve personalized power mobility goals and acquire power mobility skills. BCI-enabled power mobility training also appears to be practical, but BCI performance optimization and skill acquisition may be needed to translate this technology into clinical use.},
}
@article {pmid40269846,
year = {2025},
author = {Mansour, S and Giles, J and Nair, KPS and Marshall, R and Ali, A and Arvaneh, M},
title = {A clinical trial evaluating feasibility and acceptability of a brain-computer interface for telerehabilitation in patients with stroke.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {91},
pmid = {40269846},
issn = {1743-0003},
support = {MC-PC-19051//UK Medical Research Council/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Stroke Rehabilitation/methods ; Male ; Female ; Middle Aged ; Feasibility Studies ; *Telerehabilitation ; Aged ; Upper Extremity/physiopathology ; Adult ; Stroke/physiopathology ; *Electric Stimulation Therapy/methods ; Patient Acceptance of Health Care ; },
abstract = {BACKGROUND: We have created a groundbreaking telerehabilitation system known as Tele BCI-FES. This innovative system merges brain-computer interface (BCI) and functional electrical stimulation (FES) technologies to rehabilitate upper limb function following a stroke. Our system pioneers the concept of allowing patients to undergo BCI therapy from the comfort of their homes, while ensuring supervised therapy and real-time adjustment capabilities. In this paper, we introduce our single-arm clinical trial, which evaluates the feasibility and acceptance of this proposed system as a telerehabilitation solution for upper extremity recovery in stroke survivors.<br><br>METHOD: The study involved eight chronic patients with stroke and their caregivers who were recruited to attend nine home-based Tele BCI-FES sessions (three sessions per week) while receiving remote support from the research team. The primary outcomes of this study were recruitment and retention rates, as well as participants perception on the adoption of technology. The secondary outcomes involved assessing improvements in upper extremity function using the Fugl-Meyer Assessment for Upper Extremity (FMA_UE) and the Leeds Arm Spasticity Impact Scale.<br><br>RESULTS: Seven chronic patients with stroke completed the home-based Tele BCI-FES sessions, with high retention (87.5%) and recruitment rates (86.7%). Although participants provided mixed feedback on setup ease, they found the system progressively easier to use, and the setup process became more efficient with continued sessions. Participants suggested modifications to enhance user experience. Following the intervention, a significant increase in FMA_UE scores was observed, with an average improvement of 3.83 points (p = 0.032). The observed improvement of 3.83 points in the FMA-UE score approaches the reported Minimal clinically important difference of 4.25 points for patients with chronic stroke.<br><br>CONCLUSION: This study serves as a proof of concept, showcasing the feasibility and acceptability of the proposed Tele BCI-FES system for rehabilitating the upper extremities of stroke survivors. While some participants demonstrated significant improvements in FMA-UE scores, these findings are not generalizable, as they were derived from a small-scale feasibility study. The results should be interpreted cautiously within the study's specific context. Additionally, the intervention was not compared to other therapeutic approaches, limiting conclusions regarding its relative effectiveness. To further validate the efficacy of the proposed Tele BCI-FES system, it is essential to conduct additional research with larger sample sizes and extended rehabilitation sessions. Moreover, future studies should include comparisons with other therapeutic approaches to better evaluate the relative effectiveness of this intervention. Trial registration This clinical study is registered at clinicaltrials.gov https://clinicaltrials.gov/study/NCT05215522 under the study identifier (NCT05215522) and registered with the ISRCTN registry https://doi.org/10.1186/ISRCTN42991002 (ISRCTN42991002).},
}
@article {pmid40264507,
year = {2025},
author = {Decker, J and Daeglau, M and Zich, C and Kranczioch, C},
title = {Nature documentaries vs. quiet rest: no evidence for an impact on event-related desynchronization during motor imagery and neurofeedback.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1539172},
pmid = {40264507},
issn = {1662-5161},
abstract = {Motor imagery (MI) in combination with neurofeedback (NF) has emerged as a promising approach in motor neurorehabilitation, facilitating brain activity modulation and promoting motor learning. Although MI-NF has been demonstrated to enhance motor performance and cortical plasticity, its efficacy varies considerably across individuals. Various context factors have been identified as influencing neurophysiological outcomes in motor execution and MI, however, their specific impact on event-related desynchronization (ERD), a key neurophysiological marker in NF, remains insufficiently understood. Previous research suggested that declarative interference following MI-NF may serve as a context factor hindering the progression of ERD. Yet, no significant changes in ERD within the mu and beta (8-30 Hz) frequency bands were observed across blocks in either a declarative interference or a control condition. This raises the question of whether the absence of ERD modulation could be attributed to the break task that was common to both declarative interference and control condition: watching nature documentaries immediately after MI blocks. To investigate this, we conducted a follow-up study replicating the original methodology while collecting new data. We compared NF-MI-ERD between groups with and without nature documentaries as a post-MI condition. Participants completed three sessions of kinesthetic MI-NF training involving a finger-tapping task over two consecutive days, with quiet rest as the post-MI condition (group quiet rest). 64-channel EEG data were analyzed from 17 healthy participants (8 females, 18-35 years, M and SD: 25.2 ± 4.2 years). Data were compared to a previously recorded dataset (group documentaries), in which 17 participants (10 females, 23-32 years, M and SD: 25.8 ± 2.5 years) watched nature documentaries after MI blocks. The results showed no significant main effects for blocks or group, though a session-by-group interaction was observed. Post-hoc tests, however, did not reveal significant differences in ERD development between the groups across individual blocks. These findings do not provide evidence that nature documentaries used as a post-MI condition negatively affect across-block development of NF-MI-ERD. This study highlights the importance of exploring additional context factors in MI-NF training to better understand their influence on ERD development.},
}
@article {pmid40263649,
year = {2025},
author = {Li, X and Dai, P and Yuan, Y},
title = {[Perioperative safety assessment and complications follow-up of simultaneous bilateral cochlear implantation in young infants].},
journal = {Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery},
volume = {39},
number = {5},
pages = {413-418;424},
pmid = {40263649},
issn = {2096-7993},
mesh = {Humans ; *Cochlear Implantation/adverse effects/methods ; Infant ; *Postoperative Complications ; *Hearing Loss, Sensorineural/surgery ; Follow-Up Studies ; Male ; Perioperative Period ; Female ; Cochlear Implants ; },
abstract = {Objective:To evaluate the perioperative safety and long-term complications of simultaneous bilateral cochlear implantation（BCI） in young infants, providing reference data for clinical BCI in young children. Methods:Seventy-four infants aged 6-23 months with congenital severe to profound sensorineural hearing loss who were candidates for cochlear implantation at the Department of Otolaryngology, Chinese PLA General Hospital between August 2018 and August 2019 were consecutively enrolled. Parents made the decision to implant either unilaterally or bilaterally. Participants were divided into unilateral cochlear implantation（UCI） group（before and after 12 months of age） and simultaneous BCI group（before and after 12 months of age）. Safety indicators, including perioperative risk variables, complications, and other postoperative adverse events were monitored, with complications followed up for 5-6 years. Comparisons were made between the BCI and UCI, as well as between implantation before and after 12 months of age regarding perioperative safety and long-term complications. Results:A total of 40 BCI patients（23 before 12 months, 17 after 12 months） and 34 UCI patients（20 before 12 months, 14 after 12 months） were included in the study. Regarding perioperative risk variables, the BCI group showed significantly longer anesthesia duration, operative time, and greater blood loss compared to the UCI group, though less than twice that of the UCI group; no anesthetic complications occurred in either group; and there was no significant difference in postoperative hospital stay between the groups. Regarding surgical complications during the 5-year follow-up period, the BCI group experienced 7 complications（2 major, 5 minor）, while the UCI group had 7 complications（1 major, 6 minor）, with no statistical differences between groups. Regarding other postoperative adverse events, the BCI group demonstrated significantly higher total adverse event rates than the UCI group（80.0% vs 38.2%）, with higher rates of moderate to severe anemia（60.0% vs 20.6%） and lower mean hemoglobin levels[（92.35±12.14） g/L vs（102.39±13.09） g/L]. No significant differences were found in postoperative fever rates（50.0% vs 52.9%） or C-reactive protein levels between groups. Within the BCI group, patients implanted before 12 months indicated notably higher rates of total adverse events（91.3% vs 64.7%）, high fever（26.1% vs 0）, and moderate to severe anemia（78.3% vs 35.3%） compared to those implanted after 12 months. Conclusion:Simultaneous BCI in young children under 2 years of age demonstrates controllable overall risks. Compared to UCI, while it shows no increase in anesthetic or surgical complications, it presents higher perioperative risks and adverse event rates, especially in patients implanted before 12 months of age, warranting special attention from medical staff.},
}
@article {pmid40262425,
year = {2025},
author = {Bao, X and Feng, X and Huang, H and Li, M and Chen, D and Wang, Z and Li, J and Huang, Q and Cai, Y and Li, Y},
title = {Day-night hyperarousal in tinnitus patients.},
journal = {Sleep medicine},
volume = {131},
number = {},
pages = {106519},
doi = {10.1016/j.sleep.2025.106519},
pmid = {40262425},
issn = {1878-5506},
mesh = {Humans ; *Tinnitus/physiopathology/complications ; Male ; Female ; Electroencephalography ; Middle Aged ; *Wakefulness/physiology ; Adult ; *Sleep Stages/physiology ; *Arousal/physiology ; },
abstract = {Tinnitus, which affects 12-30 % of the population, is associated with sleep disturbances and daytime dysfunction, yet the neural mechanisms that link wake-up states remain unclear. This study investigated electroencephalographic (EEG) characteristics of 51 tinnitus patients and 51 controls across wakefulness (eyes-open, eyes-closed, mental arithmetic) and sleep stages (N1, N2, N3, REM) to clarify day-night pathological mechanisms. The key findings showed persistent hyperarousal in tinnitus: wakefulness revealed enhanced gamma power (30-45 Hz) in eyes-closed and task states, while sleep demonstrated elevated gamma/beta power across all stages accompanied by reduced delta/theta power in deep sleep (N2/N3).). An analysis of sleep structure indicates impaired stability in maintaining the N2 stage among tinnitus patients, corroborating a reduction in N3 duration and an increased proportion of the N2 stage. From the wake states to the sleep stages, group × state interactions for the delta/theta power suggest an impaired state regulation capacity in tinnitus patients. Correlation clustering further revealed aberrant integration of wake-related gamma/beta activity into non-rapid eye movement sleep, indicating neuroplastic overgeneralization of wake hyperarousal into sleep. These results extend the so-called loss-of-inhibition theory to sleep, proposing that deficient low-frequency oscillations fail to suppress hyperarousal, impairing sleep-dependent neuroplasticity, and perpetuating daytime symptoms. Furthermore, this study establishes sleep as a critical therapeutic target to interrupt the 24-h dysfunctional cycle of tinnitus.},
}
@article {pmid40262392,
year = {2025},
author = {Li, X and Dong, X and Wang, J and Mao, H and Tu, X and Li, W and He, J and Li, Q and Zhang, P},
title = {Deep transfer learning-based decoder calibration for intracortical brain-machine interfaces.},
journal = {Computers in biology and medicine},
volume = {192},
number = {Pt A},
pages = {110231},
doi = {10.1016/j.compbiomed.2025.110231},
pmid = {40262392},
issn = {1879-0534},
mesh = {*Brain-Computer Interfaces ; Animals ; *Deep Learning ; Macaca mulatta ; Calibration ; Neural Networks, Computer ; Male ; *Signal Processing, Computer-Assisted ; },
abstract = {Intracortical brain-machine interfaces (iBMIs) aim to establish a communication path between the brain and external devices. However, in the daily use of iBMIs, the non-stationarity of recorded neural signals necessitates frequent recalibration of the iBMI decoder to maintain decoding performance, which requires collecting and labeling a large amount of new data. To address this challenge and minimize the time needed for decoder recalibration, we proposed an active learning domain adversarial neural network (AL-DANN). This model leveraged a substantial volume of historical data alongside a small amount of current data (four samples per category) to calibrate the decoder. By incorporating domain adversarial and active learning strategies, the model effectively transferred knowledge from historical data to new data, reducing the demand for new samples. We validated the proposed method using neural signals recorded from three monkeys performing different movements in a classification task or a regression task. The results showed that the AL-DANN outperformed existing state-of-the-art methods. Impressively, it required only four new samples per category for decoder recalibration, leading to a recalibration time reduction of over 80 %. To our knowledge, this is the first study to incorporate deep transfer learning into iBMI decoder calibration, highlighting the significant potential of applying deep learning technologies in iBMIs.},
}
@article {pmid40262075,
year = {2025},
author = {Shao, WW and Shao, Q and Xu, HH and Qiao, GJ and Wang, RX and Ma, ZY and Meng, WW and Yang, ZB and Zang, YL and Li, XH},
title = {Repetitive training enhances the pattern recognition capability of cultured neural networks.},
journal = {PLoS computational biology},
volume = {21},
number = {4},
pages = {e1013043},
pmid = {40262075},
issn = {1553-7358},
mesh = {Animals ; *Nerve Net/physiology ; *Neural Networks, Computer ; Neurons/physiology ; Computational Biology ; Rats ; Microelectrodes ; *Pattern Recognition, Physiological/physiology ; Cells, Cultured ; *Pattern Recognition, Automated/methods ; },
abstract = {Cultured neural networks in vitro have demonstrated the biocomputing capability to recognize patterns. However, the underlying mechanisms behind information processing and pattern recognition remain less understood. Here, we developed an in vitro neural network integrated with microelectrode arrays (MEAs) to explore the network's classification capability and elucidate the mechanisms underlying this classification. After applying different stimulation patterns using MEAs, the network exhibited structural alterations and distinct electrical responses that recognized various stimulation patterns. Alongside the reshaping of network structures, repeated training increased recognition accuracy for each stimulation pattern. Additionally, it was reported for the first time that spontaneous networks after stimulation are more closely related to the structures of evoked networks. This work provides new insights into the structural changes underlying information processing and contributes to our understanding of how cultured neural networks respond to different patterns.},
}
@article {pmid40261790,
year = {2025},
author = {Luo, J and Liu, Q and Tai, P and Li, G and Li, Y},
title = {A Multi-Level Integrated EEG-Channel Selection Method Based on the Lateralization Index.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {1586-1599},
doi = {10.1109/TNSRE.2025.3563416},
pmid = {40261790},
issn = {1558-0210},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Support Vector Machine ; Algorithms ; Male ; Adult ; Female ; Young Adult ; Movement/physiology ; Reproducibility of Results ; *Functional Laterality/physiology ; Least-Squares Analysis ; },
abstract = {The importance of optimizing channel selection for portable brain-computer interface (BCI) technology is increasingly recognized. Effective channel selection reduces computational load and enhances user experience by making BCI systems more comfortable and easier to use. A significant challenge lies in reducing the number of electrodes without compromising decoding accuracy. Although some methods have been proposed in previous studies, these often increase computational load and overlook the importance of channel selection across different subjects. Therefore, we propose a novel Multi-level Integrated EEG-Channel Selection method based on the Lateralization Index (MLI-ECS-LI). This method leverages the lateralization index in selecting important channels and can achieve the channel selection for the cross-tasks and the cross-subjects scenarios. To evaluate the effectiveness of the proposed method, the time and frequency domain features from selected channels were extracted. Three widely used classifiers, Least Squares Support Vector Machine (LSSVM), Random Forest (RF), and Support Vector Machine (SVM) were used to classify movement types based on these features. Compared to the conventional condition (C1-C6), the average decoding accuracies across 21 healthy subjects demonstrated an improved performance of 6.6%, 4.9%, 6.9% (LSSVM); 3.8%, 2.8%, 4.5%(RF); and 7.6%, 5.6%, 9.2%(SVM) via using the channels selected from the conditions of the single task, the cross-tasks, and the cross-subjects scenarios, respectively. These results demonstrated the potential of the proposed method in improving the utility of the portable Motor Imagery Brain-Computer Interface (MI-BCI) and effectiveness in practical applications.},
}
@article {pmid40260641,
year = {2025},
author = {Xu, Y and Li, YL and Yu, G and Ou, Z and Yao, S and Li, Y and Huang, Y and Chen, J and Ding, Q},
title = {Effect of Brain Computer Interface Training on Frontoparietal Network Function for Young People: A Functional Near-Infrared Spectroscopy Study.},
journal = {CNS neuroscience &amp; therapeutics},
volume = {31},
number = {4},
pages = {e70400},
pmid = {40260641},
issn = {1755-5949},
support = {2024A04J3082//Guangzhou Science and Technology Program/ ; A2024500//Guangdong Medical Research Foundation/ ; 82102678//National Natural Science Foundation of China/ ; },
mesh = {Humans ; Spectroscopy, Near-Infrared ; Male ; Female ; *Parietal Lobe/physiology/diagnostic imaging ; *Brain-Computer Interfaces ; Young Adult ; *Frontal Lobe/physiology/diagnostic imaging ; Adult ; Attention/physiology ; *Nerve Net/physiology/diagnostic imaging ; Executive Function/physiology ; },
abstract = {AIMS: Inattention in young people is one of the main reasons for their declining learning ability. Frontoparietal networks (FPNs) are associated with attention and executive function. Brain computer interface (BCI) training has been applied in neurorehabilitation, but there is a lack of research on its application to cognition. This study aimed to investigate the effect of BCI on the attention network in healthy young adults.<br><br>METHODS: Twenty-seven healthy people performed BCI training for 5 consecutive days. An attention network test (ANT) was performed at baseline and immediately after the fifth day of training and included simultaneous functional near-infrared spectroscopy recording.<br><br>RESULTS: BCI performance improved significantly after BCI training (p&#x2009;=&#x2009;0.005). The efficiencies of the alerting and executive control networks were enhanced after BCI training (p&#x2009;=&#x2009;0.032 and 0.003, respectively). The functional connectivity in the bilateral prefrontal cortices and the right posterior parietal cortex increased significantly after BCI training (p&#x2009;<&#x2009;0.05).<br><br>CONCLUSION: Our findings suggested that repetitive BCI training could improve attention and induce lasting neuroplastic changes in FPNs. It might be a promising rehabilitative strategy for clinical populations with attention deficits. The right PPC may also be an effective target for neuromodulation in diseases with attention deficits.},
}
@article {pmid40260139,
year = {2025},
author = {Zhang, T and Wang, N and Chai, X and He, Q and Cao, T and Yuan, L and Lan, Q and Yang, Y and Zhao, J},
title = {Evaluation of pressure-induced pain in patients with disorders of consciousness based on functional near infrared spectroscopy.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1542691},
pmid = {40260139},
issn = {1664-2295},
abstract = {OBJECTIVE: This study aimed to investigate the brain's hemodynamic responses (HRO) and functional connectivity in patients with disorders of consciousness (DoC) in response to acute pressure pain stimulation using near-infrared spectroscopy (NIRS).<br><br>METHODS: Patients diagnosed with DoC underwent pressure stimulation while brain activity was measured using NIRS. Changes in oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) concentrations were monitored across several regions of interest (ROIs), including the primary somatosensory cortex (PSC), primary motor cortex (PMC), dorsolateral prefrontal cortex (dPFC), somatosensory association cortex (SAC), temporal gyrus (TG), and frontopolar area (FPA). Functional connectivity was assessed during pre-stimulation, stimulation, and post-stimulation phases.<br><br>RESULTS: No significant changes in HbO or HbR concentrations were observed during the stimulation vs. baseline or stimulation vs. post-stimulation comparisons, indicating minimal activation of the targeted brain regions in response to the pressure stimulus. However, functional connectivity between key regions, particularly the PSC, PMC, and dPFC, showed significant enhancement during the stimulation phase (r > 0.9, p < 0.001), suggesting greater coordination among sensory, motor, and cognitive regions. These changes in connectivity were not accompanied by significant activation in pain-related brain areas.<br><br>CONCLUSION: Although pain-induced brain activation was minimal in patients with DoC, enhanced functional connectivity during pain stimulation suggests that the brain continues to process pain information through coordinated activity between regions. The findings highlight the importance of assessing functional connectivity as a potential method for evaluating pain processing in patients with DoC.},
}
@article {pmid40257892,
year = {2025},
author = {Hashemi, M and Depannemaecker, D and Saggio, M and Triebkorn, P and Rabuffo, G and Fousek, J and Ziaeemehr, A and Sip, V and Athanasiadis, A and Breyton, M and Woodman, M and Wang, H and Petkoski, S and Sorrentino, P and Jirsa, V},
title = {Principles and Operation of Virtual Brain Twins.},
journal = {IEEE reviews in biomedical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/RBME.2025.3562951},
pmid = {40257892},
issn = {1941-1189},
abstract = {Current clinical methods often overlook individual variability by relying on population-wide trials, while mechanismbased trials remain underutilized in neuroscience due to the brain's complexity. This situation may change through the use of a Virtual Brain Twin (VBT), which is a personalized digital replica of an individual's brain, integrating structural and functional brain data into advanced computational models and inference algorithms. By bridging the gap between molecular mechanisms, whole-brain dynamics, and imaging data, VBTs enhance the understanding of (patho)physiological mechanisms, advancing insights into both healthy and disordered brain function. Central to VBT is the network modeling that couples mesoscopic representation of neuronal activity through white matter connectivity, enabling the simulation of brain dynamics at a network level. This transformative approach provides interpretable predictive capabilities, supporting clinicians in personalizing treatments and optimizing interventions. This Review outlines the key components of VBT development, covering the conceptual, mathematical, technical, and clinical aspects. We describe the stages of VBT construction-from anatomical coupling and modeling to simulation and Bayesian inference-and demonstrate their applications in resting-state, healthy aging, multiple sclerosis, and epilepsy. Finally, we discuss potential extensions to other neurological disorders, such as Parkinson's disease, and explore future applications in consciousness research and brain-computer interfaces, paving the way for advancements in personalized medicine and brainmachine integration.},
}
@article {pmid40257874,
year = {2025},
author = {Li, S and Liu, G and Feng, F and Chang, Z and Li, W and Duan, F},
title = {An Interventional Brain-Computer Interface for Long-Term EEG Collection and Motion Classification of a Quadruped Mammal.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {1633-1642},
doi = {10.1109/TNSRE.2025.3562922},
pmid = {40257874},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Animals ; *Electroencephalography/methods ; Sheep ; Movement/physiology ; Algorithms ; Motor Cortex/physiology ; Electrodes, Implanted ; Deep Learning ; Neural Networks, Computer ; Motion ; Walking/physiology ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interfaces (BCI) acquire electroencephalogram (EEG) signals to effectively address postoperative motor dysfunction in stroke patients by discerning their motor intentions during significant movements. Traditionally, noninvasive BCIs have been constrained by limitations in their usage environments; whereas, invasive BCIs damage neural permanently. Therefore, we proposed a novel interventional BCI, in which electrodes are implanted along the veins into the brain to acquire intracerebral EEG signals without an open craniotomy. We collect EEG signals from the primary motor cortex in the superior sagittal sinus of sheep during three different significant movements: laying down; standing; and walking. The first three month data are used to train the neural network, and The fourth month of data were used to validate. The deep learning model achieved an 86% accuracy rate in classifying motion states in validation. Furthermore, the results of the power spectral density (PSD) show that the signal power in the main frequency band did not decrease over a period of five months, which demonstrates that the interventional BCI has the ability to effectively capture EEG signals over long periods of time.},
}
@article {pmid40257872,
year = {2025},
author = {Huang, S and Liu, Y and Wang, Z and Wu, W and Guo, J and Xu, W and Ming, D},
title = {Enhanced Brain Functional Interaction Following BCI-Guided Supernumerary Robotic Finger Training Based on Sixth-Finger Motor Imagery.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {1519-1528},
doi = {10.1109/TNSRE.2025.3562700},
pmid = {40257872},
issn = {1558-0210},
mesh = {Humans ; Male ; *Brain-Computer Interfaces ; Female ; Magnetic Resonance Imaging ; *Fingers/physiology ; Adult ; *Imagination/physiology ; Young Adult ; *Robotics/methods ; *Brain/physiology/diagnostic imaging ; Motor Cortex/physiology ; Brain Mapping ; Psychomotor Performance/physiology ; Neuronal Plasticity ; },
abstract = {Supernumerary robotic finger (SRF) has shown unique advantages in the field of motor augmentation and rehabilitation, while the development of brain computer interface (BCI) technology has provided the possibility for direct control of SRF. However, the neuroplasticity effects of BCI-actuated SRF (BCI-SRF) training based on the "six finger" motor imagery paradigm are still unclear. This study recruited 20 healthy right-handed participants and randomly assigned them to either a BCI-SRF training group or a sham SRF training group. During the testing phase before and after 4 weeks of training, all participants were tested for SRF-finger opposition sequence behavior, resting state fMRI (rs-fMRI), and task-based fMRI (tb-fMRI). The results showed that compared with the Sham group, the BCI-SRF group improved the accuracy rate of the SRF-finger opposition sequence by 132%. The activation analysis of tb-fMRI before and after training revealed a significant increase in left middle frontal gyrus only in the BCI-SRF group. In addition, the BCI-SRF group showed an increase in FC between the right primary motor cortex and left cerebellum inferior lobe, as well as between the left middle frontal gyrus and the right precuneus lobe after training, while there was no significant change in the Sham group. In addition, only the BCI-SRF group showed a significant increase in clustering coefficients after training. Moreover, the increase in the clustering coefficients of the two groups is positively correlated with the improvement of the accuracy of the SRF-finger opposition sequences. These results indicate that the integration of BCI and SRF significantly regulates the functional interaction between motor learning and cognitive imagery brain regions, enhances the integration and processing ability of brain networks for local information, and improves human-machine interaction behavioral performance.},
}
@article {pmid40254808,
year = {2025},
author = {Wang, H and Wang, X},
title = {Exploring the Role of Psychedelics in Modulating Ego and Treating Neuropsychiatric Disorders.},
journal = {ACS chemical neuroscience},
volume = {16},
number = {9},
pages = {1636-1638},
doi = {10.1021/acschemneuro.5c00247},
pmid = {40254808},
issn = {1948-7193},
mesh = {Humans ; *Hallucinogens/therapeutic use/pharmacology ; *Mental Disorders/drug therapy ; *Ego ; *Brain/drug effects ; Animals ; },
abstract = {This viewpoint explores the therapeutic potential of psychedelics in treating neuropsychiatric disorders, particularly through the modulation of brain entropy and the experience of ego dissolution. Psychedelics disrupt rigid neural patterns, facilitating enhanced connectivity and fostering profound emotional breakthroughs that may alleviate symptoms of disorders like depression, anxiety, PTSD, and addiction. Despite their promising potential, the clinical application of psychedelics presents significant challenges, including the need for careful patient screening, managing adverse experiences, and addressing ethical considerations, all of which are essential for their safe integration into therapy.},
}
@article {pmid40254540,
year = {2025},
author = {Fu, Q and Tong, L and Zhang, H and Xu, H},
title = {Multimodal Imaging Diagnosis of Apical Ventricular Aneurysm With Thrombosis Resulting From Blunt Myocardial Injury: A Case Report.},
journal = {Journal of clinical ultrasound : JCU},
volume = {53},
number = {6},
pages = {1398-1402},
doi = {10.1002/jcu.24026},
pmid = {40254540},
issn = {1097-0096},
support = {20210101260JC//the Science and Technology Development Program of the Jilin Province/ ; },
mesh = {Humans ; Male ; *Multimodal Imaging/methods ; *Heart Aneurysm/etiology/diagnostic imaging/diagnosis ; *Heart Ventricles/diagnostic imaging/injuries ; *Thrombosis/etiology/diagnostic imaging/diagnosis ; Echocardiography ; *Wounds, Nonpenetrating/complications ; Accidents, Traffic ; Diagnosis, Differential ; Magnetic Resonance Imaging ; Electrocardiography ; },
abstract = {This article presents the case of a male patient who sustained blunt myocardial injury following a traffic accident. A series of diagnostic imaging procedures were conducted on the patient, including electrocardiography, echocardiography, computed tomography angiography, and cardiac magnetic resonance imaging, which demonstrated edema in a portion of the myocardium and the formation of a ventricular aneurysm with thrombus in the left ventricular apex. After 6 months and 1 year, echocardiography demonstrated no detection of thrombus, but the apical left ventricular aneurysm was not significantly different from the anterior film, leading to a final clinical diagnosis of blunt cardiac injury (BCI).},
}
@article {pmid40253420,
year = {2025},
author = {Wang, B and Zhang, X and Zhang, L and Kong, XZ},
title = {A naturalistic fMRI dataset in response to public speaking.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {659},
pmid = {40253420},
issn = {2052-4463},
support = {32171031//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Magnetic Resonance Imaging ; Female ; Male ; *Speech ; Young Adult ; Adult ; *Brain/physiology ; *Communication ; },
abstract = {Public speaking serves as a powerful tool for informing, inspiring, persuading, motivating, or entertaining an audience. While some speeches effectively engage audience and disseminate knowledge, others fail to resonate. This dataset presents functional magnetic resonance imaging (fMRI) data from 31 participants (14 females; age: 22.29 ± 2.84 years) who viewed two informative speeches with varying effectiveness, selected from YiXi talks (similar to TED Talks), and matched in length and topic. A total of 22 participants (10 females; age: 22.64 ± 2.77 years) who completed the full task were included in the validation analyses. A comprehensive validation process, involving behavioral data analysis and head motion assessment, confirmed the quality of the fMRI dataset. While previous analyses have used inter-subject correlation to examine neural synchronization during the reception of informative public speaking, this dataset can be utilized for a variety of analyses to further elucidate the neural mechanisms underlying audience engagement and effective communication.},
}
@article {pmid40253415,
year = {2025},
author = {He, T and Wei, M and Wang, R and Wang, R and Du, S and Cai, S and Tao, W and Li, H},
title = {VocalMind: A Stereotactic EEG Dataset for Vocalized, Mimed, and Imagined Speech in Tonal Language.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {657},
pmid = {40253415},
issn = {2052-4463},
support = {62271432//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Electroencephalography ; *Speech ; *Language ; Brain-Computer Interfaces ; },
abstract = {Speech BCIs based on implanted electrodes hold significant promise for enhancing spoken communication through high temporal resolution and invasive neural sensing. Despite the potential, acquiring such data is challenging due to its invasive nature, and publicly available datasets, particularly for tonal languages, are limited. In this study, we introduce VocalMind, a stereotactic electroencephalography (sEEG) dataset focused on Mandarin Chinese, a tonal language. This dataset includes sEEG-speech parallel recordings from three distinct speech modes, namely vocalized speech, mimed speech, and imagined speech, at both word and sentence levels, totaling over one hour of intracranial neural recordings related to speech production. This paper also presents a baseline model as the reference model for future studies, at the same time, ensuring the integrity of the dataset. The diversity of tasks and the substantial data volume provide a valuable resource for developing advanced algorithms for speech decoding, thereby advancing BCI research for spoken communication.},
}
@article {pmid40253381,
year = {2025},
author = {Xue, S and Jin, B and Jiang, J and Guo, L and Zhou, J and Wang, C and Liu, J},
title = {A multi-subject and multi-session EEG dataset for modelling human visual object recognition.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {663},
pmid = {40253381},
issn = {2052-4463},
support = {U21B2043, 62206279//National Natural Science Foundation of China (National Science Foundation of China)/ ; U21B2043, 62206279//National Natural Science Foundation of China (National Science Foundation of China)/ ; U21B2043, 62206279//National Natural Science Foundation of China (National Science Foundation of China)/ ; U21B2043, 62206279//National Natural Science Foundation of China (National Science Foundation of China)/ ; U21B2043, 62206279//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Electroencephalography ; Brain-Computer Interfaces ; Machine Learning ; *Visual Perception ; *Pattern Recognition, Visual ; Algorithms ; },
abstract = {We share a multi-subject and multi-session (MSS) dataset with 122-channel electroencephalographic (EEG) signals collected from 32 human participants. The data was obtained during serial visual presentation experiments in two paradigms. Dataset of first paradigm consists of around 800,000 trials presenting stimulus sequences at 5 Hz. Dataset of second paradigm comprises around 40,000 trials displaying each image for 1 second. Each participant completed between 1 to 5 sessions on different days, and each session lasted for approximately 1.5 hours of EEG recording. The stimulus set used in the experiments included 10,000 images, with 500 images per class, manually selected from PASCAL and ImageNet image databases. The MSS dataset can be useful for various studies, including but not limited to (1) exploring the characteristics of EEG visual response, (2) comparing the differences in EEG response of different visual paradigms, and (3) designing machine learning algorithms for cross-subject and cross-session brain-computer interfaces (BCIs) using EEG data from multiple subjects and sessions.},
}
@article {pmid40252874,
year = {2025},
author = {Ye, Z and Lv, C and Zhou, H and Bao, Y and Hong, T and He, Q and Hu, Y},
title = {Neural substrates of attack event prediction in video games: the role of ventral posterior cingulate cortex and theory of mind network.},
journal = {NeuroImage},
volume = {312},
number = {},
pages = {121228},
doi = {10.1016/j.neuroimage.2025.121228},
pmid = {40252874},
issn = {1095-9572},
mesh = {Humans ; *Video Games/psychology ; Male ; *Gyrus Cinguli/physiology/diagnostic imaging ; Young Adult ; Magnetic Resonance Imaging ; *Theory of Mind/physiology ; Adult ; Female ; *Anticipation, Psychological/physiology ; Brain Mapping/methods ; *Nerve Net/physiology/diagnostic imaging ; Psychomotor Performance/physiology ; },
abstract = {Action anticipation, the ability to observe actions and predict the intent of others, plays a crucial role in social interaction and fields such as electronic sports. However, the neural mechanisms underlying the inference of purpose from action observation remain unclear. In this study, we conducted an fMRI experiment using video game combat scenarios to investigate the neural correlates of action anticipation and its relationship with task performance. The results showed that the higher level of ability to infer the purpose from action observation during experiment associates with higher level of proficiency in real world electric gaming competition. The action anticipation task activates visual streams, fronto-parietal network, and the ventral posterior cingulate cortex (vPCC), a key hub in the theory of mind network. The strength of vPCC activation during action anticipation, but not movement direction judgment, was positively correlated with gaming proficiency. Finite impulse response analysis revealed distinct dynamic response profiles in the vPCC compared to other theory of mind regions. These findings suggest that theory of mind ability may be an important factor influencing individual competitive performance, with the vPCC serving as a core neural substrate for inferring purpose from action observation.},
}
@article {pmid40250541,
year = {2025},
author = {Liu, Y and Wang, M and Rao, H},
title = {Common neural activations of creativity and exploration: A meta-analysis of task-based fMRI studies.},
journal = {Neuroscience and biobehavioral reviews},
volume = {174},
number = {},
pages = {106158},
doi = {10.1016/j.neubiorev.2025.106158},
pmid = {40250541},
issn = {1873-7528},
mesh = {*Creativity ; Humans ; Magnetic Resonance Imaging ; *Brain/physiology/diagnostic imaging ; *Thinking/physiology ; Brain Mapping ; },
abstract = {Creativity is a common, complex, and multifaceted cognitive activity with significant implications for technological progress, social development, and human survival. Understanding the neurocognitive mechanisms underlying creative thought is essential for fostering individual creativity. While previous studies have demonstrated that exploratory behavior positively influences creative performance, few studies investigated the relationship between creativity and exploration at the neural level. To address this gap, we conducted a quantitative meta-analysis comprising 80 creativity experiments (1850 subjects) and 23 exploration experiments (646 subjects) to examine potential shared neural activations between creativity and exploration. Furthermore, we analyzed the neural similarities and differences among three forms of creative thinking-divergent thinking (DT), convergent thinking (CT), and artistic creativity-and their relationship with exploration. The conjunction analysis of creativity and exploration revealed significant activations in the bilateral IFJ and left preSMA. Further conjunction analyses revealed that both CT and artistic creativity exhibited common neural activations with exploration, with CT co-activating the left IFJ and artistic creativity co-activating both the right IFJ and left preSMA, while DT did not. Additionally, the conjunction analyses across the three forms of creativity did not identify shared neural activations. Further functional decoding analyses of the overlapping brain regions associated with CT and exploration, as well as artistic creativity and exploration, revealed correlations with inhibitory control mechanisms. These results enhance our understanding of the role of exploration in the creative thinking process and provide valuable insights for developing strategies to foster innovative thinking.},
}
@article {pmid40249697,
year = {2025},
author = {Cao, B and Li-Ting Tsai, C and Zhou, N and Do, T and Lin, CT},
title = {A Novel 3D Paradigm for Target Expansion of Augmented Reality SSVEP.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {1562-1573},
doi = {10.1109/TNSRE.2025.3562217},
pmid = {40249697},
issn = {1558-0210},
mesh = {Humans ; *Evoked Potentials, Visual/physiology ; Male ; *Brain-Computer Interfaces ; *Augmented Reality ; Electroencephalography/methods ; Female ; Adult ; Young Adult ; Algorithms ; Photic Stimulation/methods ; Rotation ; Visual Perception/physiology ; Reproducibility of Results ; },
abstract = {Steady-State Visual Evoked Potentials (SSVEP) have proven to be practical in Brain-Computer Interfaces (BCI), particularly when integrated with augmented reality (AR) for real-world application. However, unlike conventional computer screen-based SSVEP (CS-SSVEP), which benefits from stable experimental environments, AR-based SSVEP (AR-SSVEP) systems are susceptible to the interference of real-world environment and device instability. Particularly, the performance of AR-SSVEP significantly declines as the target frequency increases. Therefore, our study introduced a 3D paradigm that combines flicker frequency with rotation patterns as stimuli, enabling expansion of target sets without additional frequencies. In the proposed design, in addition to the conventional frequency-based SSVEP feature, bio-marker elicited by visual perception of rotation was investigated. An experimental comparison between this novel 3D paradigm and a traditional 2D approach, which increases targets by adding frequencies, reveals significant advantages. The 12-class 3D paradigm achieved an accuracy of 76.5% and an information transfer rate (ITR) of 70.42 bits/min using 1-second EEG segments. In contrast, the 2D paradigm exhibited a lower performance with 72.07% accuracy and 62.28 bits/min ITR. The result underscores the 3D paradigm's superiority in enhancing the practical applications of SSVEP-based BCIs in AR settings, especially with shorter time windows, by effectively expanding target recognition without compromising system efficiency.},
}
@article {pmid40247883,
year = {2025},
author = {Uszko, JM and Schroeder, JC and Eichhorn, SJ and Patil, AJ and Hall, SR},
title = {Morphological control of cuprate superconductors using sea sponges as templates.},
journal = {RSC advances},
volume = {15},
number = {14},
pages = {11189-11193},
pmid = {40247883},
issn = {2046-2069},
abstract = {Functional porous superconducting sponges, consisting of YBa2Cu3O6+δ (YBCO) and Bi2Sr2CaCu2O8+δ (BSCCO), were created by biotemplating with natural sea sponges. Naturally occurring calcium in the spongin fibers was utilized to dope YBCO and to form BSCCO without adding any external calcium source. The sample morphology was confirmed with scanning electron microscopy, and the sample composition was confirmed with energy-dispersive X-ray spectroscopy, powder electron diffraction and high-resolution transmission electron microscopy. The YBCO sponge exhibited a critical temperature (T c) of approximately 70 K, and the BSCCO sponge showed a T c of 77 K. This proof-of-concept study demonstrates the feasibility of using sea sponges as a greener, more sustainable template for superconductor synthesis.},
}
@article {pmid40247859,
year = {2025},
author = {Kuo, YT and Wang, HL and Chen, BW and Wang, CF and Lo, YC and Lin, SH and Chen, PC and Chen, YY},
title = {Degradation-aware neural imputation: Advancing decoding stability in brain machine interfaces.},
journal = {APL bioengineering},
volume = {9},
number = {2},
pages = {026106},
pmid = {40247859},
issn = {2473-2877},
abstract = {Neural signal degradation poses a significant challenge in maintaining stable performance when decoding motor tasks using multiunit activity (MUA) and local field potential (LFP) signals in the implantable brain machine interface (iBMI) applications. Effective methods for imputing degraded or missing signals are essential to restore neural signal integrity, thereby improving decoding accuracy and system robustness over long-term recordings with fluctuating signal quality. This study introduces a confidence-weighted Bayesian linear regression (CW-BLR) approach to impute neural signals affected by degradation, enhancing the robustness and consistency of decoding. The performance of CW-BLR was compared to traditional methods-mean imputation (Mean-imp) and Gaussian-mixture-model-based expectation-maximization (GMM-EM)-using a kernel-sliced inverse regression (kSIR) decoder to evaluate decoding outcomes. Four Wistar rats were trained to perform a forelimb-reaching task while neural activity (MUA and LFPs) was recorded over 27 days. CW-BLR imputed signals degraded during days 8-27. Decoding performance was evaluated using kSIR and compared with Mean-imp and GMM-EM. CW-BLR demonstrated superior performance by effectively preserving both temporal and spatial dependencies within the neural signals. CW-BLR-imputed data significantly improved decoding accuracy over traditional imputation methods, with the kSIR decoder showing consistently higher performance, particularly in maintaining signal quality from the degraded period. CW-BLR offers a robust and effective imputation framework for iBMI applications, addressing signal degradation challenges and maintaining accurate decoding over prolonged recordings. By utilizing confidence-based quality metrics, CW-BLR surpasses traditional methods, providing stable neural decoding across fluctuating signal quality scenarios.},
}
@article {pmid40246195,
year = {2025},
author = {Isis Yonza, AK and Tao, L and Zhang, X and Postnov, D and Kucharz, K and Lind, B and Asiminas, A and Han, A and Sonego, V and Kim, K and Cai, C},
title = {Spatially and temporally mismatched blood flow and neuronal activity by high-intensity intracortical microstimulation.},
journal = {Brain stimulation},
volume = {18},
number = {3},
pages = {885-896},
doi = {10.1016/j.brs.2025.04.015},
pmid = {40246195},
issn = {1876-4754},
mesh = {Animals ; *Cerebrovascular Circulation/physiology ; *Neurons/physiology ; Mice ; *Neurovascular Coupling/physiology ; Mice, Transgenic ; *Cerebral Cortex/physiology/blood supply ; *Electric Stimulation/methods ; Calcium/metabolism ; },
abstract = {INTRODUCTION: Intracortial microstimulation (ICMS) is widely used in neuroprosthetic brain-machine interfacing, particularly in restoring lost sensory and motor functions. Spiking neuronal activity requires increased cerebral blood flow to meet local metabolic demands, a process conventionally denoted as neurovascular coupling (NVC). However, it is unknown precisely how and to what extent ICMS elicits NVC and how the neuronal and blood flow responses to ICMS correlate. Suboptimal NVC by ICMS may compromise oxygen and energy delivery to the activated neurons thus impair neuroprosthetic functionality.<br><br>MATERIAL AND METHOD: We used wide-field imaging (WFI), laser speckle imaging (LSI) and two-photon microscopy (TPM) to study living, transgenic mice expressing calcium (Ca[2+]) fluorescent indicators in either neurons or vascular mural cells (VMC), as well as to measure vascular inner lumen diameters.<br><br>RESULT: By testing a range of stimulation amplitudes and examining cortical tissue responses at different distances from the stimulating electrode tip, we found that high stimulation intensities (&#x2265;50&#xa0;&#x3bc;A) elicited a spatial and temporal neurovascular decoupling in regions most adjacent to electrode tip (<200&#xa0;&#x3bc;m), with significantly delayed onset times of blood flow responses to ICMS and compromised maximum blood flow increases. We attribute these effects respectively to delayed Ca[2+] signalling and decreased Ca[2+] sensitivity in VMCs.<br><br>CONCLUSION: Our study offers new insights into ICMS-associated neuronal and vascular physiology with potentially critical implications towards the optimal design of ICMS in neuroprosthetic therapies: low intensities preserve NVC; high intensities disrupt NVC responses and potentially precipitate blood supply deficits.},
}
@article {pmid40246009,
year = {2025},
author = {Ren, J and Wang, Y and Wang, Y and Zhang, Y and Xing, M and Deng, S and Tong, S and Wang, L and Zheng, C and Yang, J and Ni, G and Ming, D},
title = {Dynamic changes of hippocampal dendritic spines in Alzheimer's disease mice among the different stages.},
journal = {Experimental neurology},
volume = {390},
number = {},
pages = {115266},
doi = {10.1016/j.expneurol.2025.115266},
pmid = {40246009},
issn = {1090-2430},
mesh = {Animals ; *Dendritic Spines/pathology/metabolism ; *Alzheimer Disease/pathology/genetics/metabolism ; *Hippocampus/pathology ; Mice, Transgenic ; Mice ; Amyloid beta-Peptides/toxicity ; Disease Progression ; Disease Models, Animal ; Peptide Fragments/toxicity ; Amyloid beta-Protein Precursor/genetics ; Male ; Mice, Inbred C57BL ; Presenilin-1/genetics ; },
abstract = {Alzheimer's disease (AD) is characterized by the accumulation of amyloid-β (Aβ) peptides and a progressive decline in cognitive function. Hippocampus as a crucial brain area for learning and memory, is also adversely affected by AD's pathology. The accumulation of Aβ is often associated with the loss of dendritic spines of the hippocampus. However, the dynamic alterations in dendritic spines throughout AD progression are not fully understood. To investigate it, we conducted in-vivo imaging in two mouse models representing the early and late stages of AD pathology: young mice injected with Aβ1-42 oligomers and APP/PS1 transgenic mice. In the early-stage AD model, imaging was conducted at third- and fifth- weeks post-injection. In the late-stage AD model, a four-month imaging began at 14 months old. The imaging results showed spine elimination in both models. Notably, acute Aβ exposure was linked to heightened spine loss on secondary dendrites, while in the late stage the primary effect was on tertiary dendrites. Concurrently, with the metabolism of Aβ, cognition recovered to some extent by five weeks post Aβ1-42 exposure. These findings suggested that dendritic spine plasticity was impaired during the development of AD, as evidenced by increasing spine loss at different levels. However, the cognitive recovery observed in early-stage AD model mice may indicate a compensatory structural reorganization, highlighting the potential of early intervention to mitigate disease progression. Our results provide novel insights into the neurotoxic effects of Aβ1-42 and may contribute to the development of therapeutic strategies for AD.},
}
@article {pmid40245876,
year = {2025},
author = {Zhao, D and Dong, G and Pei, W and Gao, X and Wang, Y},
title = {Comparisons of stimulus paradigms for SSVEP-based brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adce32},
pmid = {40245876},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; Male ; *Photic Stimulation/methods ; *Electroencephalography/methods ; Female ; Adult ; Young Adult ; },
abstract = {Objective.With the recent development of visual evoked potential (VEP) based brain-computer interfaces (BCIs), the stimulus paradigm has been continuously innovated, in which the pursuit of higher BCI performance and better user experience has become indispensable.Approach.To optimize the stimulus paradigm, a 12-target online BCI system was designed in this study by adopting flicker for steady-state VEPs, Newton's ring for steady-state motion VEP, and frame rate based video stimulus, respectively. The signal characteristics of VEP, classification accuracy, and user experience of the three stimulus paradigms were quantitatively evaluated and compared.Main results.The online information transfer rates for the three stimulus paradigms were 53.77 bits min[-1], 51.41 ± 3.55 bits min[-1], and 52.07 ± 3.09 bits min[-1], respectively. The video stimulus had a significantly better user experience, while the flicker stimulus showed the worst.Significance.These results demonstrate the advantage of the proposed video stimulus paradigm and have significant theoretical and applied implications for developing VEP-based BCI systems.},
}
@article {pmid40245253,
year = {2025},
author = {Singh, K and Lin, CC and Huang, WH and Lei, WL and Chiueh, H and Wang, YH and Chang, PH and Lin, RZ and Huang, WC},
title = {Ultrabioconformal, Self-Healable, and Antioxidized Polydopamine-Inspired Nanowire Hydrogels Enable Resolving Power in Forehead and Ear Electroencephalograms for Brain Function Assessment.},
journal = {ACS applied materials &amp; interfaces},
volume = {17},
number = {17},
pages = {24887-24900},
pmid = {40245253},
issn = {1944-8252},
mesh = {*Electroencephalography/methods/instrumentation ; *Polymers/chemistry ; *Indoles/chemistry ; *Hydrogels/chemistry ; *Nanowires/chemistry ; Humans ; *Brain/physiology ; Forehead/physiology ; Silver/chemistry ; Platinum/chemistry ; Electric Conductivity ; Ear/physiology ; Electrodes ; },
abstract = {Continuous brain function monitoring by high-performance electroencephalogram (EEG) suggests a high impact for advancing precision personalized medication of neurodevelopmental or neurodegenerative disorders. Forehead and ear EEGs are nonhairy recording strategies that allow the recording of brain activity using only a few electrodes. However, they require well-designed electrodes that are easy and comfortable to carry while simultaneously performing durable high-quality EEG acquisition. Herein, we propose a new ultrabiocompliant EEG sensor that enables seamless contact to surfaces of both earhole and forehead, while permitting prolonged and high-quality EEG signal identification. Bioinspired polydopamine/platinum-silver nanowires, called PDA-Ag@Pt NWs, are synthesized with noticeable performances in electrical conductivity, antioxidation ability, cytocompatibility, and adhesion. PDA-Ag@Pt NWs can promote synchronic gelation and interlinks within polydopamine-polyacrylamide (PDA-PAM) hydrogels, in turn leading to the one-step formation of a nanowire/hydrogel matrix, called PDA-PAM/NW, as an electrode patch in the presence of adhesive and self-healing capabilities. Combined with a self-designed signal processor, a portable electrophysiological signal recording system was realized. The PDA-PAM/NW electrode patch outperformed commercial electrodes in terms of reliability and resolution for electrocardiography (ECG), electromyography (EMG), and electroencephalography (EEG) recording. In addition, through brain cognitive assessment by frontal- and ear-EEG recording, the ultrathin design and comfortable adhesion of PDA-PAM/NW electrodes make participants comfortable over time, subsequently providing the identification of the brain activity in high resolution. This work underscores the potential of the ultrabiocompliant and durable patch in the development of comfy, long-lasting, and high-performance wearable brain-machine interfaces for the revolution in neuroscience.},
}
@article {pmid40245060,
year = {2025},
author = {Akhter, J and Nazeer, H and Naseer, N and Naeem, R and Kallu, KD and Lee, J and Ko, SY},
title = {Improved performance of fNIRS-BCI by stacking of deep learning-derived frequency domain features.},
journal = {PloS one},
volume = {20},
number = {4},
pages = {e0314447},
pmid = {40245060},
issn = {1932-6203},
mesh = {Humans ; Spectroscopy, Near-Infrared/methods ; *Deep Learning ; *Brain-Computer Interfaces ; Male ; Adult ; Algorithms ; Female ; Neural Networks, Computer ; Young Adult ; Hand Strength/physiology ; },
abstract = {The functional near-infrared spectroscopy-based brain-computer interface (fNIRS-BCI) systems recognize patterns in brain signals and generate control commands, thereby enabling individuals with motor disabilities to regain autonomy. In this study hand gripping data is acquired using fNIRS neuroimaging system, preprocessing is performed using nirsLAB and features extraction is performed using deep learning (DL) Algorithms. For feature extraction and classification stack and fft methods are proposed. Convolutional neural networks (CNN), long short-term memory (LSTM), and bidirectional long-short-term memory (Bi-LSTM) are employed to extract features. The stack method classifies these features using a stack model and the fft method enhances features by applying fast Fourier transformation which is followed by classification using a stack model. The proposed methods are applied to fNIRS signals from twenty participants engaged in a two-class hand-gripping motor activity. The classification performance of the proposed methods is compared with conventional CNN, LSTM, and Bi-LSTM algorithms and one another. The proposed fft and stack methods yield 90.11% and 87.00% classification accuracies respectively, which are significantly higher than those achieved by CNN (85.16%), LSTM (79.46%), and Bi-LSTM (81.88%) conventional algorithms. The results show that the proposed stack and fft methods can be effectively used for the classification of the two and three-class problems in fNIRS-BCI applications.},
}
@article {pmid40244939,
year = {2025},
author = {Noel, JP and Bockbrader, M and Bertoni, T and Colachis, S and Solca, M and Orepic, P and Ganzer, PD and Haggard, P and Rezai, A and Blanke, O and Serino, A},
title = {Neuronal responses in the human primary motor cortex coincide with the subjective onset of movement intention in brain-machine interface-mediated actions.},
journal = {PLoS biology},
volume = {23},
number = {4},
pages = {e3003118},
pmid = {40244939},
issn = {1545-7885},
support = {K99 NS128075/NS/NINDS NIH HHS/United States ; R00 NS128075/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Motor Cortex/physiology ; *Intention ; Movement/physiology ; Male ; Adult ; Female ; *Neurons/physiology ; Electric Stimulation ; Quadriplegia/physiopathology ; },
abstract = {Self-initiated behavior is accompanied by the experience of intending our actions. Here, we leverage the unique opportunity to examine the full intentional chain-from intention to action to environmental effects-in a tetraplegic person outfitted with a primary motor cortex (M1) brain-machine interface (BMI) generating real hand movements via neuromuscular electrical stimulation (NMES). This combined BMI-NMES approach allowed us to selectively manipulate each element of the intentional chain (intention, action, effect) while probing subjective experience and performing extra-cellular recordings in human M1. Behaviorally, we reveal a novel form of intentional binding: motor intentions are reflected in a perceived temporal attraction between the onset of intentions and that of actions. Neurally, we demonstrate that evoked spiking activity in M1 largely coincides in time with the onset of the experience of intention and that M1 spike counts and the onset of subjective intention may co-vary on a trial-by-trial basis. Further, population-level dynamics, as indexed by a decoder instantiating movement, reflect intention-action temporal binding. The results fill a significant knowledge gap by relating human spiking activity in M1 with the onset of subjective intention and complement prior human intracranial work examining pre-motor and parietal areas.},
}
@article {pmid40242802,
year = {2025},
author = {Iosif, R and Skrbinšek, T and Erős, N and Konec, M and Boljte, B and Jan, M and Promberger-Fürpass, B},
title = {Wolf Population Size and Composition in One of Europe's Strongholds, the Romanian Carpathians.},
journal = {Ecology and evolution},
volume = {15},
number = {4},
pages = {e71200},
pmid = {40242802},
issn = {2045-7758},
abstract = {Strategies of coexistence with large carnivores should integrate scientific evidence, population monitoring providing an opportunity for advancing outdated management paradigms. We estimated wolf population density and social dynamics across a 1400 km[2] area in a data-poor region of the Romanian Carpathians. Across three consecutive years (2017-2018 until 2019-2020), we collected and genotyped 505 noninvasive DNA wolf samples (scat, hair and urine) to identify individuals, reconstruct pedigrees, and check for the presence of hybridization with domestic dogs. We identified 27 males, 20 females, and one F1 wolf-dog hybrid male. We delineated six wolf packs, with pack size varying between two and seven individuals, and documented yearly changes in pack composition. Using a spatial capture-recapture approach, we estimated population density at 2.35 wolves/100 km[2] (95% BCI = 1.68-3.03) and population abundance at 70 individuals (95% BCI = 49-89). Noninvasive DNA data collection coupled with spatial capture-recapture has the potential to inform on wolf population size and dynamics at broader spatial scales, across different sampling areas representative of the diverse Carpathian landscapes, and across different levels of human impact, supporting wildlife decision making in one of Europe's main strongholds for large carnivores.},
}
@article {pmid40242584,
year = {2025},
author = {Hu, S and Lin, C and Wang, H and Wang, X},
title = {Psychedelics and Eating Disorders: Exploring the Therapeutic Potential for Anorexia Nervosa and Beyond.},
journal = {ACS pharmacology &amp; translational science},
volume = {8},
number = {4},
pages = {910-916},
pmid = {40242584},
issn = {2575-9108},
abstract = {Anorexia nervosa (AN) is a severe psychiatric disorder characterized by extreme food restriction, an intense fear of weight gain, and a distorted body image, leading to significant morbidity and mortality. Conventional treatments such as cognitive-behavioral therapy (CBT) and pharmacotherapy often prove inadequate, especially in severe cases, highlighting the need for novel therapeutic approaches. Recent research into psychedelics, such as psilocybin and 3,4-methylenedioxymethamphetamine (MDMA), offers promising avenues for treating anorexia nervosa by targeting its neurobiological and psychological underpinnings. These psychedelics disrupt maladaptive neural circuits, enhance cognitive flexibility, and facilitate emotional processing, offering potential relief for patients unresponsive to traditional therapies. Early studies have shown positive outcomes with psychedelics, including reductions in anorexia nervosa symptoms and improvements in psychological well-being. However, further research is needed to establish their long-term safety, efficacy, and integration into clinical practice. Addressing the legal, ethical, and safety challenges will be crucial in determining whether psychedelics can transform the treatment landscape for anorexia nervosa and other eating disorders.},
}
@article {pmid40242456,
year = {2025},
author = {Yan, W and Luo, Q and Du, C},
title = {Channel component correlation analysis for multi-channel EEG feature component extraction.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1522964},
pmid = {40242456},
issn = {1662-4548},
abstract = {INTRODUCTION: Electroencephalogram (EEG) analysis has shown significant research value for brain disease diagnosis, neuromodulation and brain-computer interface (BCI) application. The analysis and processing of EEG signals is complex since EEG are nonstationary, nonlinear, and often contaminated by intense background noise. Principal component analysis (PCA) and independent component analysis (ICA), as the commonly used methods for multi-dimensional signal feature component extraction, still have some limitations in terms of performance and calculation.<br><br>METHODS: In this study, channel component correlation analysis (CCCA) method was proposed to extract feature components of multi-channel EEG. Firstly, empirical wavelet transform (EWT) decomposed each channel signal into different frequency bands, and reconstructed them into a multi-dimensional signal. Then the objective optimization function was constructed by maximizing the covariance between multi-dimensional signals. Finally the feature components of multi-channel EEG were extracted using the calculated weight coefficient.<br><br>RESULTS: The results showed that the CCCA method could find the most relevant frequency band between multi-channel EEG. Compared with PCA and ICA methods, CCCA could extract the common components of multi-channel EEG more effectively, which is of great significance for the accurate analysis of EEG.<br><br>DISCUSSION: The CCCA method proposed in this study has shown excellent performance in the feature component extraction of multi-channel EEG and could be considered for practical engineering applications.},
}
@article {pmid40241786,
year = {2025},
author = {Hernández-Gloria, JJ and Jaramillo-Gonzalez, A and Savić, AM and Mrachacz-Kersting, N},
title = {Toward brain-computer interface speller with movement-related cortical potentials as control signals.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1539081},
pmid = {40241786},
issn = {1662-5161},
abstract = {Brain Computer Interface spellers offer a promising alternative for individuals with Amyotrophic Lateral Sclerosis (ALS) by facilitating communication without relying on muscle activity. This study assessed the feasibility of using movement related cortical potentials (MRCPs) as a control signal for a Brain-Computer Interface speller in an offline setting. Unlike motor imagery-based BCIs, this study focused on executed movements. Fifteen healthy subjects performed three spelling tasks that involved choosing specific letters displayed on a computer screen by performing a ballistic dorsiflexion of the dominant foot. Electroencephalographic signals were recorded from 10 sites centered around Cz. Three conditions were tested to evaluate MRCP performance under varying task demands: a control condition using repeated selections of the letter "O" to isolate movement-related brain activity; a phrase spelling condition with structured text ("HELLO IM FINE") to simulate a meaningful spelling task with moderate cognitive load; and a random condition using a randomized sequence of letters to introduce higher task complexity by removing linguistic or semantic context. The success rate, defined as the presence of an MRCP, was manually determined. It was approximately 69% for both the control and phrase conditions, with a slight decrease in the random condition, likely due to increased task complexity. Significant differences in MRCP features were observed between conditions with Laplacian filtering, whereas no significant differences were found in single-site Cz recordings. These results contribute to the development of MRCP-based BCI spellers by demonstrating their feasibility in a spelling task. However, further research is required to implement and validate real-time applications.},
}
@article {pmid40241515,
year = {2025},
author = {Jin, F and Li, M and Yang, L and Yang, L and Shang, Z},
title = {Exploring value learning in pigeons: the role of dual pathways in the basal ganglia and synaptic plasticity.},
journal = {The Journal of experimental biology},
volume = {228},
number = {9},
pages = {},
doi = {10.1242/jeb.249507},
pmid = {40241515},
issn = {1477-9145},
support = {62301496//National Natural Science Foundation of China/ ; GZC20232447//National Postdoctoral Researcher Program/ ; 252102210008//Key Scientific and Technological Projects of Henan Province/ ; 252102311095//Key Scientific and Technological Projects of Henan Province/ ; },
mesh = {Animals ; *Columbidae/physiology ; *Basal Ganglia/physiology ; *Neuronal Plasticity/physiology ; *Learning/physiology ; Reinforcement, Psychology ; Choice Behavior ; Models, Neurological ; },
abstract = {Understanding value learning in animals is a key focus in cognitive neuroscience. Current models used in research are often simple, and while more complex models have been proposed, it remains unclear which assumptions align with actual value-learning strategies of animals. This study investigated the computational mechanisms behind value learning in pigeons using a free-choice task. Three models were constructed based on different assumptions about the role of the basal ganglia's dual pathways and synaptic plasticity in value computation, followed by model comparison and neural correlation analysis. Among the three models tested, the dual-pathway reinforcement learning model with Hebbian rules most closely matched the pigeons' behavior. Furthermore, the striatal gamma band connectivity showed the highest correlation with the values estimated by this model. Additionally, enhanced beta band connectivity in the nidopallium caudolaterale supported value learning. This study provides valuable insights into reinforcement learning mechanisms in non-human animals.},
}
@article {pmid40240152,
year = {2025},
author = {Ma, YN and Karako, K and Song, P and Hu, X and Xia, Y},
title = {Integrative neurorehabilitation using brain-computer interface: From motor function to mental health after stroke.},
journal = {Bioscience trends},
volume = {19},
number = {3},
pages = {243-251},
doi = {10.5582/bst.2025.01109},
pmid = {40240152},
issn = {1881-7823},
mesh = {Humans ; *Brain-Computer Interfaces ; *Stroke Rehabilitation/methods ; Neurofeedback ; *Stroke/psychology/physiopathology ; Mental Health ; *Neurological Rehabilitation/methods ; Electroencephalography ; Artificial Intelligence ; Virtual Reality ; Cognition ; },
abstract = {Stroke remains a leading cause of mortality and long-term disability worldwide, frequently resulting in impairments in motor control, cognition, and emotional regulation. Conventional rehabilitation approaches, while partially effective, often lack individualization and yield suboptimal outcomes. In recent years, brain-computer interface (BCI) technology has emerged as a promising neurorehabilitation tool by decoding neural signals and providing real-time feedback to enhance neuroplasticity. This review systematically explores the use of BCI systems in post-stroke rehabilitation, focusing on three core domains: motor function, cognitive capacity, and emotional regulation. This review outlines the neurophysiological principles underpinning BCI-based motor rehabilitation, including neurofeedback training, Hebbian plasticity, and multimodal feedback strategies. It then examines recent advances in upper limb and gait recovery using BCI integrated with functional electrical stimulation (FES), robotics, and virtual reality (VR). Moreover, it highlights BCI's potential in cognitive and language rehabilitation through EEG-based neurofeedback and the integration of artificial intelligence (AI) and immersive VR environments. In addition, it discusses the role of BCI in monitoring and regulating post-stroke emotional disorders via closed-loop systems. While promising, BCI technologies face challenges related to signal accuracy, device portability, and clinical validation. Future research should prioritize multimodal integration, AI-driven personalization, and large-scale randomized trials to establish long-term efficacy. This review underscores BCI's transformative potential in delivering intelligent, personalized, and cross-domain rehabilitation solutions for stroke survivors.},
}
@article {pmid40239679,
year = {2025},
author = {Faes, A and Calvo Merino, E and Branco, MP and Van Hoylandt, A and Keirse, E and Theys, T and Ramsey, NF and Van Hulle, MM},
title = {Decoding sign language finger flexions from high-density electrocorticography using graph-optimized block term tensor regression.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adcd9e},
pmid = {40239679},
issn = {1741-2552},
mesh = {Humans ; *Electrocorticography/methods ; *Sign Language ; *Fingers/physiology ; Male ; Gestures ; Female ; Adult ; Regression Analysis ; Middle Aged ; Movement/physiology ; },
abstract = {Objective.A novel method is introduced to regress over the sign language finger movements from human electrocorticography (ECoG) recordings.Approach.The proposed graph-optimized block-term tensor regression (Go-BTTR) method consists of two components: a deflation-based regression model that sequentially Tucker-decomposes multiway ECoG data into a series of blocks, and a causal graph process (CGP) that accounts for the complex relationship between finger movements when expressing sign language gestures. Prior to each regression block, CGP is applied to decide which fingers should be kept separate or grouped and should therefore be referred to BTTR or its extended version eBTTR, respectively.Main results.Two ECoG datasets were used, one recorded in five patients expressing four hand gestures of the American sign language alphabet, and another in two patients expressing all gestures of the Flemish sign language alphabet. As Go-BTTR combines fingers in a flexible way, it can better account for the nonlinear relationship ECoG exhibits when expressing hand gestures, including unintentional finger co-activations. This is reflected by the superior joint finger trajectory predictions compared to eBTTR, and predictions that are on par with BTTR in single-finger scenarios. For the American sign language alphabet (Utrecht dataset), the average correlation across all fingers for all subjects was 0.73 for Go-BTTR, 0.719 for eBTTR and 0.70 for BTTR. For the Flemish sign language alphabet (Leuven dataset), the average correlation across all fingers for all subjects was 0.37 for Go-BTTR, 0.34 for eBTTR and 0.33 for BTTR.Significance.Our findings show that Go-BTTR is capable of decoding complex hand gestures taken from the sign language alphabet. Go-BTTR also demonstrates computational efficiency, providing a notable benefit when intracranial electrodes are inserted during a patient's pre-surgical evaluation. This efficiency helps reduce the time required for developing and testing a brain-computer interface solution.},
}
@article {pmid40238867,
year = {2025},
author = {Li, X and Deng, Z and Zhang, W and Zhou, W and Liu, X and Quan, H and Li, J and Li, P and Li, Y and Hu, C and Li, F and Niu, L and Tian, Z and Meng, L and Zheng, H},
title = {Oscillating microbubble array-based metamaterials (OMAMs) for rapid isolation of high-purity exosomes.},
journal = {Science advances},
volume = {11},
number = {16},
pages = {eadu8915},
pmid = {40238867},
issn = {2375-2548},
mesh = {*Microbubbles ; *Exosomes/metabolism/chemistry ; Humans ; Acoustics ; },
abstract = {Exosomes secreted by cells hold substantial potential for disease diagnosis and treatment. However, the rapid isolation of high-purity exosomes and their subpopulations from biofluids (e.g., undiluted whole blood) remains challenging. This study presents oscillating microbubble array-based metamaterials (OMAMs) for enabling the rapid isolation of high-purity exosomes and their subpopulations from biofluids without labeling or preprocessing. Particularly, leveraging acoustically excited microbubble oscillation, OMAMs can generate numerous acoustofluidic traps for filtering in-fluid micro/nanoparticles, thus allowing for removing bioparticles larger than exosomes to obtain high-purity (93%) exosomes from undiluted whole blood in ~3 minutes. Moreover, exosome subpopulations in different size ranges can be isolated by tuning the microbubble oscillation amplitude. Additionally, as each oscillating microbubble functions as an ultradeep subwavelength (~λ/186) acoustic amplifier and a nonlinear source, OMAMs can generate high-resolution complex acoustic energy patterns and tune the patterns by activating different-sized microbubbles at their distinct resonance frequencies.},
}
@article {pmid40236895,
year = {2025},
author = {Wen, D and Xing, Y and Yao, Y and Liang, G and Xing, Y and Jung, TP and Yu, H and Xie, X and Wan, X and Liu, T and Duan, D and Li, D and Zhou, Y},
title = {Transforming long-term adjunctive therapy for cognitive impairment: the role of multimodal self-adaptive digital medicine.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1571817},
pmid = {40236895},
issn = {1664-2295},
}
@article {pmid40236742,
year = {2025},
author = {Wei, B and Cheng, S and Feng, Y},
title = {Neural personal information and its legal protection: evidence from China.},
journal = {Journal of law and the biosciences},
volume = {12},
number = {1},
pages = {lsaf006},
doi = {10.1093/jlb/lsaf006},
pmid = {40236742},
issn = {2053-9711},
abstract = {The rapid advancements in neuroscience highlight the pressing need to safeguard neural personal information (NPI). China has achieved significant progress in brain-computer interface technology and its clinical applications. Considering the intrinsic vulnerability of NPI and the paucity of legal scrutiny concerning NPI breaches, a thorough assessment of the adequacy of China's personal information protection legislation is essential. This analysis contends that NPI should be classified as sensitive personal information. The absence of bespoke provisions for NPI in current legislation underscores persistent challenges in its protection. To address these gaps, this work proposes the establishment of a concentric-circle hard-soft law continuum to support a hybrid governance model for NPI, rooted in fundamental human rights principles.},
}
@article {pmid40236412,
year = {2025},
author = {Jude, JJ and Levi-Aharoni, H and Acosta, AJ and Allcroft, SB and Nicolas, C and Lacayo, BE and Card, NS and Wairagkar, M and Brandman, DM and Stavisky, SD and Willett, FR and Williams, ZM and Simeral, JD and Hochberg, LR and Rubin, DB},
title = {An intuitive, bimanual, high-throughput QWERTY touch typing neuroprosthesis for people with tetraplegia.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.04.01.25324990},
pmid = {40236412},
support = {I01 RX003803/RX/RRD VA/United States ; I50 RX002864/RX/RRD VA/United States ; U01 NS123101/NS/NINDS NIH HHS/United States ; R01 DC014034/DC/NIDCD NIH HHS/United States ; I01 RX004820/RX/RRD VA/United States ; K23 DC021297/DC/NIDCD NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; },
abstract = {Recognizing keyboard typing as a familiar, high information rate communication paradigm, we developed an intracortical brain computer interface (iBCI) typing neuroprosthesis providing bimanual QWERTY keyboard functionality for people with paralysis. Typing with this iBCI involves only attempted finger movements, which are decoded accurately with as few as 30 calibration sentences. Sentence decoding is improved using a 5-gram language model. This typing neuroprosthesis performed well for two iBCI clinical trial participants with tetraplegia - one with ALS and one with spinal cord injury. Typing speed is user-regulated, reaching 110 characters per minute, resulting in 22 words per minute with a word error rate of 1.6%. This resembles able-bodied typing accuracy and provides higher throughput than current state-of-the-art hand motor iBCI decoding. In summary, a typing neuroprosthesis decoding finger movements, provides an intuitive, familiar, and easy-to-learn paradigm for individuals with impaired communication due to paralysis.},
}
@article {pmid40236074,
year = {2025},
author = {Busch, EL and Fincke, EC and Lajoie, G and Krishnaswamy, S and Turk-Browne, NB},
title = {Accelerated learning of a noninvasive human brain-computer interface via manifold geometry.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.03.29.646109},
pmid = {40236074},
issn = {2692-8205},
abstract = {Brain-computer interfaces (BCIs) promise to restore and enhance a wide range of human capabilities. However, a barrier to the adoption of BCIs is how long it can take users to learn to control them. We hypothesized that human BCI learning could be accelerated by leveraging the naturally occurring geometric structure of brain activity, or its intrinsic manifold, extracted using a data-diffusion process. We trained participants on a noninvasive BCI that allowed them to gain real-time control of an avatar in a virtual reality game by modulating functional magnetic resonance imaging (fMRI) activity in brain regions that support spatial navigation. We then perturbed the mapping between fMRI activity patterns and the movement of the avatar to test our manifold hypothesis. When the new mapping respected the intrinsic manifold, participants succeeded in regaining control of the BCI by aligning their brain activity within the manifold. When the new mapping did not respect the intrinsic manifold, participants could not learn to control the avatar again. These findings show that the manifold geometry of brain activity constrains human learning of a complex cognitive task in higher-order brain regions. Manifold geometry may be a critical ingredient for unlocking the potential of future human neurotechnologies.},
}
@article {pmid40235786,
year = {2025},
author = {Wang, LP and Yang, C and Fu, JY and Zhang, XY and Shen, XM and Shi, NL and Wu, HL and Gao, XR},
title = {A preliminary study of steady-state visually-evoked potential-based non-invasive brain-computer interface technology as a communication aid for patients with amyotrophic lateral sclerosis.},
journal = {Quantitative imaging in medicine and surgery},
volume = {15},
number = {4},
pages = {3469-3479},
pmid = {40235786},
issn = {2223-4292},
abstract = {BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that affects motor neurons, leading to severe disability and ultimately death. Communication difficulties are common in ALS patients as the disease progresses; thus, alternative communication aids need to be explored. This study sought to examine the use and effect of steady-state visually-evoked potential (SSVEP)-based non-invasive brain-computer interface (BCI) technology as a communication aid for patients with ALS and to examine possible influencing factors.<br><br>METHODS: In total, 12 patients with ALS were selected, and a 40-character target selection was performed using SSVEP-based non-invasive BCI technology. The patients were presented with specific visual stimuli, and nine-lead electroencephalogram (EEG) signals in the occipital region were acquired when the patients were looking at the target. Using the feature recognition analysis method, the final output was the characters recognized by the patients. The basic clinical data of the patients (e.g., age, gender, course of disease, affected area, and ALS functional scale score) were collected, and the BCI accuracy rate, information transmission rate, and average SSVEP recognition time were calculated.<br><br>RESULTS: The results revealed that the recognition efficiency of the ALS patients varied. The accuracy potential increased as the stimulus duration extended, highlighting the possibility for improvement via further optimization. The results also showed that the experimental design schedules typically used for healthy individuals may not be entirely suitable for ALS patients, which presents an exciting opportunity to tailor future studies to better meet the unique needs of ASL patients. Further, the results revealed the necessity of using customized experimental schedules in future studies, which could lead to more relevant and effective data collection for ALS patients.<br><br>CONCLUSIONS: The study found that SSVEP-based non-invasive BCI technology has promising potential as a communication aid for ALS patients. While further algorithm optimization and comprehensive studies with larger sample sizes are necessary, the initial findings are encouraging, and could lead to the development of more effective communication solutions that are specifically tailored to address the challenges faced by ALS patients.},
}
@article {pmid40234729,
year = {2025},
author = {Webster, P},
title = {Can AI-powered brain-computer interfaces boost human intelligence?.},
journal = {Nature medicine},
volume = {31},
number = {4},
pages = {1045-1047},
doi = {10.1038/s41591-025-03641-7},
pmid = {40234729},
issn = {1546-170X},
}
@article {pmid40234486,
year = {2025},
author = {Zhao, W and Zhang, B and Zhou, H and Wei, D and Huang, C and Lan, Q},
title = {Multi-scale convolutional transformer network for motor imagery brain-computer interface.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {12935},
pmid = {40234486},
issn = {2045-2322},
support = {3502Z202374054//Natural Science Foundation of Xiamen, China/ ; 2023J01785//Natural Science Foundation of Fujian Province of China/ ; JAT191153 and JAT201045//Education and Scientific Research Project for Young and Middle-aged Teachers of Fujian Province of China/ ; CKZ24016//Jimei University Chengyi College Provincial and Ministerial-Level Scientific Research Cultivation Project/ ; CKZ24016//Jimei University Chengyi College Provincial and Ministerial-Level Scientific Research Cultivation Project/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography/methods ; *Neural Networks, Computer ; *Imagination/physiology ; *Brain/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interface (BCI) systems allow users to communicate with external devices by translating neural signals into real-time commands. Convolutional neural networks (CNNs) have been effectively utilized for decoding motor imagery electroencephalography (MI-EEG) signals in BCIs. However, traditional CNN-based methods face challenges such as individual variability in EEG signals and the limited receptive fields of CNNs. This study presents the Multi-Scale Convolutional Transformer (MSCFormer) model that integrates multiple CNN branches for multi-scale feature extraction and a Transformer module to capture global dependencies, followed by a fully connected layer for classification. The multi-branch multi-scale CNN structure effectively addresses individual variability in EEG signals, enhancing the model's generalization capabilities, while the Transformer encoder strengthens global feature integration and improves decoding performance. Extensive experiments on the BCI IV-2a and IV-2b datasets show that MSCFormer achieves average accuracies of 82.95% (BCI IV-2a) and 88.00% (BCI IV-2b), with kappa values of 0.7726 and 0.7599 in five-fold cross-validation, surpassing several state-of-the-art methods. These results highlight MSCFormer's robustness and accuracy, underscoring its potential in EEG-based BCI applications. The code has been released in https://github.com/snailpt/MSCFormer .},
}
@article {pmid40234393,
year = {2025},
author = {Bertoni, T and Noel, JP and Bockbrader, M and Foglia, C and Colachis, S and Orset, B and Evans, N and Herbelin, B and Rezai, A and Panzeri, S and Becchio, C and Blanke, O and Serino, A},
title = {Pre-movement sensorimotor oscillations shape the sense of agency by gating cortical connectivity.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {3594},
pmid = {40234393},
issn = {2041-1723},
support = {R00 NS128075/NS/NINDS NIH HHS/United States ; 163951//Schweizerischer Nationalfonds zur F&#xf6;rderung der Wissenschaftlichen Forschung (Swiss National Science Foundation)/ ; },
mesh = {Humans ; *Motor Cortex/physiology ; Male ; Adult ; Female ; Electroencephalography ; Young Adult ; Movement/physiology ; Brain-Computer Interfaces ; Alpha Rhythm/physiology ; Hand/physiology ; Sense of Agency ; },
abstract = {Our sense of agency, the subjective experience of controlling our actions, is a crucial component of self-awareness and motor control. It is thought to originate from the comparison between intentions and actions across broad cortical networks. However, the underlying neural mechanisms are still not fully understood. We hypothesized that oscillations in the theta-alpha range, thought to orchestrate long-range neural connectivity, may mediate sensorimotor comparisons. To test this, we manipulated the relation between intentions and actions in a tetraplegic user of a brain machine interface (BMI), decoding primary motor cortex (M1) activity to restore hand functionality. We found that the pre-movement phase of low-alpha oscillations in M1 predicted the participant's agency judgements. Further, using EEG-BMI in healthy participants, we found that pre-movement alpha oscillations in M1 and supplementary motor area (SMA) correlated with agency ratings, and with changes in their functional connectivity with parietal, temporal and prefrontal areas. These findings argue for phase-driven gating as a key mechanism for sensorimotor integration and sense of agency.},
}
@article {pmid40232894,
year = {2025},
author = {Wang, D and Wei, Q},
title = {SMANet: A Model Combining SincNet, Multi-Branch Spatial-Temporal CNN, and Attention Mechanism for Motor Imagery BCI.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {1497-1508},
doi = {10.1109/TNSRE.2025.3560993},
pmid = {40232894},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography ; *Neural Networks, Computer ; *Imagination/physiology ; *Attention ; Algorithms ; Deep Learning ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; Movement ; Male ; Adult ; },
abstract = {Building a brain-computer interface (BCI) based on motor imagery (MI) requires accurately decoding MI tasks, which poses a significant challenge due to individual discrepancy among subjects and low signal-to-noise ratio of EEG signals. We propose an end-to-end deep learning model, Sinc-multibranch-attention network (SMANet), which combines a SincNet, a multibranch spatial-temporal convolutional neural network (MBSTCNN), and an attention mechanism for MI-BCI classification. Firstly, Sinc convolution is utilized as a band-pass filter bank for data filtering; Second, pointwise convolution facilitates the effective integration of feature information across different frequency ranges, thereby enhancing the overall feature expression capability; Next, the resulting data are fed into the MBSTCNN to learn a deep feature representation. Thereafter, the outputs of the MBSTCNN are concatenated and then passed through an efficient channel attention (ECA) module to enhance local channel feature extraction and calibrate feature mapping. Ultimately, the feature maps yielded by ECA are classified using a fully connected layer. This model SMANet enhances discriminative features through a multi-objective optimization scheme that integrates cross-entropy loss and central loss. The experimental outcomes reveal that our model attains an average accuracy of 80.21% on the four-class MI dataset (BCI Competition IV 2a), 84.02% on the two-class MI dataset (BCI Competition IV 2b), and 72.70% on the two-class MI dataset (OpenBMI). These results are superior to those of the current state-of-the-art methods. The SMANet is capable to effectively decoding the spatial-spectral-temporal information of EEG signals, thereby enhancing the performance of MI-BCIs.},
}
@article {pmid40231563,
year = {2025},
author = {Park, K and Hong, J and Shin, H and Choi, J and Xu, D and Lee, J and Ryu, J and Kim, S and Jeong, H and Choe, J and Yang, S and Yang, S and Ahn, JH},
title = {2D Material-Based Injectable Sensor for Minimally-Invasive Cerebral Blood Flow Monitoring.},
journal = {Small (Weinheim an der Bergstrasse, Germany)},
volume = {21},
number = {30},
pages = {e2501744},
doi = {10.1002/smll.202501744},
pmid = {40231563},
issn = {1613-6829},
support = {20012355//Ministry of Trade, Industry and Energy/ ; },
mesh = {*Cerebrovascular Circulation/physiology ; Graphite/chemistry ; Injections ; Monitoring, Physiologic/methods/instrumentation ; Animals ; Temperature ; Molybdenum/chemistry ; },
abstract = {Monitoring cerebral blood flow is an important method for diagnosing and treating brain diseases. Thermal transport caused by blood flow provides valuable information for detecting abnormalities in blood flow. Here, a minimally invasive, injectable blood flow sensor is reported, consisting of a flexible, graphene-based thin film heater and MoS2-based temperature sensor array integrated on a mesh-structured polymer substrate. Upon injection through a small skull hole in the skull, the device unfolds and achieves conformal contact on the cortical surface, aligning with the target vessel. By measuring temperature variations in response to the heater activation, the injectable sensor continuously monitors blood flow changes in the underlying vessel. This approach offers a new potential for cerebral blood flow sensing via minimally invasive implantation.},
}
@article {pmid40230710,
year = {2025},
author = {Finney, JN and Levy, IR and Chandrasekaran, S and Collinger, JL and Boninger, ML and Gaunt, RA and Helm, ER and Fisher, LE},
title = {Techniques to mitigate lead migration for percutaneous trials of cervical spinal cord stimulation.},
journal = {Frontiers in surgery},
volume = {12},
number = {},
pages = {1458572},
pmid = {40230710},
issn = {2296-875X},
abstract = {INTRODUCTION: Epidural spinal cord stimulation (SCS) is a clinical neuromodulation technique that is commonly used to treat neuropathic pain, with patients typically undergoing a one-week percutaneous trial phase before permanent implantation. Traditional SCS involves stimulation of the thoracic spinal cord, but there has been substantial recent interest in cervical SCS to treat upper extremity pain, restore sensation from the missing hand after amputation, or restore motor function to paretic limbs in people with stroke and spinal cord injury. Because of the additional mobility of the neck, as compared to the trunk, lead migration can be a major challenge for cervical SCS, especially during the percutaneous trial phase. The objective of this study was to optimize the implantation procedure of cervical SCS leads to minimize lead migration and increase lead stability during SCS trials.<br><br>METHODS: In this study, four subjects underwent percutaneous placement of three SCS leads targeting the cervical spinal segments as part of a clinical trial aiming to restore sensation for people with upper-limb amputation. The leads were maintained for up to 29 days and weekly x-ray imaging was used to measure rostrocaudal and mediolateral lead migration based on bony landmarks.<br><br>RESULTS AND DISCUSSION: Lead migration was primarily confined to the rostrocaudal axis with the most migration occurring during the first week. Iterative improvements to the implantation procedure were implemented to increase lead stability across subjects. There was a decrease in lead migration for patients who had more rostral placement of the SCS leads. The average migration from the day of lead implant to lead removal was 29.7&#x2005;mm for Subject 1 (lead placement ranging from T3-T4 to T1-T2), 41.9&#x2005;mm for Subject 2 (T2-T3 to C7-T1), 1.9&#x2005;mm for Subject 3 (T1-T2 to C7-T1), and 16.6&#x2005;mm for Subject 4 (T1-T2 to C7-T1). We found that initial placement of spinal cord stimulator leads in the cervical epidural space as rostral as possible was critical to minimizing subsequent rostrocaudal lead migration.},
}
@article {pmid40228689,
year = {2025},
author = {Hesam-Shariati, N and Alexander, L and Stapleton, F and Newton-John, T and Lin, CT and Zahara, P and Chen, KY and Restrepo, S and Skinner, IW and McAuley, JH and Moseley, GL and Jensen, MP and Gustin, SM},
title = {The effect of an EEG neurofeedback intervention for corneal neuropathic pain: A single-case experimental design with multiple baselines.},
journal = {The journal of pain},
volume = {32},
number = {},
pages = {105394},
doi = {10.1016/j.jpain.2025.105394},
pmid = {40228689},
issn = {1528-8447},
mesh = {Humans ; *Neurofeedback/methods ; Male ; *Neuralgia/therapy ; Female ; *Electroencephalography/methods ; Middle Aged ; Adult ; Pain Measurement ; Single-Case Studies as Topic ; },
abstract = {Corneal neuropathic pain is a complex condition, rarely responsive to current treatments. This trial investigated the potential effect of a novel home-based self-directed EEG neurofeedback intervention on corneal neuropathic pain using a multiple-baseline single-case experimental design. Four Participants completed a predetermined baseline of 7, 10, 14, and 17 days, randomly assigned to each participant, followed by 20 intervention sessions over four weeks. Two one-week follow-ups occurred immediately and five weeks post-intervention during which participants were encouraged to practice mental strategies. Daily pain severity and pain interference observations were the outcome measures, while anxiety, depression, or sleep problems were the generalisation measures. The results showed a medium effect on pain severity and interference across participants when comparing baseline to five-week post-intervention according to Tau-U effect sizes. At the individual level, both Tau-U and NAP effect sizes indicated significant reductions in pain severity and interference for three participants when comparing baseline to five-week post-intervention. However, the reductions indicated by the visual inspection were not considered clinically meaningful. This single-case experimental design study raises the possibility that the intervention may improve pain severity and interference for some individuals; variability in outcomes highlights the need for future research to better understand individual responses and optimize the intervention effect. REGISTRATION: Australian New Zealand Clinical Trial Registry ACTRN12623000173695 PERSPECTIVE: This trial demonstrates the potential of EEG neurofeedback to reduce pain severity and interference in individuals with corneal neuropathic pain. It also highlights user preferences for technology-based interventions, emphasizing ease of use, accessibility, and self-administration to enhance adherence, especially for individuals with limited mobility or restricted healthcare access.},
}
@article {pmid40228398,
year = {2025},
author = {Grigoryan, KA and Mueller, K and Wagner, M and Masri, D and Pine, KJ and Villringer, A and Sehm, B},
title = {Short-term BCI intervention enhances functional brain connectivity associated with motor performance in chronic stroke.},
journal = {NeuroImage. Clinical},
volume = {46},
number = {},
pages = {103772},
pmid = {40228398},
issn = {2213-1582},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; Middle Aged ; *Stroke Rehabilitation/methods ; Magnetic Resonance Imaging ; *Stroke/physiopathology/diagnostic imaging ; Aged ; Cross-Over Studies ; Adult ; Longitudinal Studies ; Chronic Disease ; *Nerve Net/physiopathology/diagnostic imaging ; *Brain/physiopathology/diagnostic imaging ; *Default Mode Network/physiopathology/diagnostic imaging ; },
abstract = {BACKGROUND: Evidence suggests that brain-computer interface (BCI)-based rehabilitation strategies show promise in overcoming the limited recovery potential in the chronic phase of stroke. However, the specific mechanisms driving motor function improvements are not fully understood.<br><br>OBJECTIVE: We aimed at elucidating the potential functional brain connectivity changes induced by BCI training in participants with chronic stroke.<br><br>METHODS: A longitudinal crossover design was employed with two groups of participants over the span of 4&#xa0;weeks to allow for within-subject (n&#xa0;=&#xa0;21) and cross-group comparisons. Group 1 (n&#xa0;=&#xa0;11) underwent a 6-day motor imagery-based BCI training during the second week, whereas Group 2 (n&#xa0;=&#xa0;10) received the same training during the third week. Before and after each week, both groups underwent resting state functional MRI scans (4 for Group 1 and 5 for Group 2) to establish a baseline and monitor the effects of BCI training.<br><br>RESULTS: Following BCI training, an increased functional connectivity was observed between the medial prefrontal cortex of the default mode network (DMN) and motor-related areas, including the premotor cortex, superior parietal cortex, SMA, and precuneus. Moreover, these changes were correlated with the increased motor function as confirmed with upper-extremity Fugl-Meyer assessment scores, measured before and after the training.<br><br>CONCLUSIONS: Our findings suggest that BCI training can enhance brain connectivity, underlying the observed improvements in motor function. They provide a basis for developing novel rehabilitation approaches using non-invasive brain stimulation for targeting functionally relevant brain regions, thereby augmenting BCI-induced neuroplasticity and enhancing motor recovery.},
}
@article {pmid40227907,
year = {2025},
author = {Zhong, Y and Wang, Y and Farina, D and Yao, L},
title = {A Closed-Loop Tactile Stimulation Training Protocol for Motor Imagery-Based BCI: Boosting BCI Performance for BCI-Deficiency Users.},
journal = {IEEE transactions on bio-medical engineering},
volume = {72},
number = {10},
pages = {3019-3029},
doi = {10.1109/TBME.2025.3560713},
pmid = {40227907},
issn = {1558-2531},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Adult ; Male ; *Imagination/physiology ; Female ; *Touch/physiology ; *Neurofeedback/methods ; Young Adult ; Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) enable users to control and communicate with the external environment. However, a significant challenge in BCI research is the occurrence of "BCI-illiteracy" or "BCI-deficiency", where a notable percentage of users (estimated at 15 to 30%) are unable to achieve successful BCI control. For those users, they are struggling to generate stable and distinguishable brain activity patterns, which are essential for BCI control. Existing neurofeedback training protocols, often rely on the trial-and-error process, which is time-consuming and inefficient, particularly for these low-performing users.<br><br>METHODS: To address this issue, we propose a closed-loop tactile stimulation training protocol, in which tactile stimulation training is incorporated within the closed neurofeedback loop, providing users with explicit guidance on how to correctly perform MI tasks. When a subject performs an incorrect MI trial, tactile-assisted MI training is provided to guide the user toward the correct brain state, while no training is given during correct performance.<br><br>RESULTS: The results from our study demonstrated that the proposed training protocol significantly enhances BCI decoding performance, with an improvement of 16.9%. Moreover, the BCI-deficiency rate was reduced by 61.5%. Further analysis revealed that the training process also led to enhanced motor imagery-related cortical activation.<br><br>CONCLUSION: The proposed training protocol significantly improved BCI decoding performance, enabling previously BCI-deficient users to surpass the 70% control threshold.<br><br>SIGNIFICANCE: This study demonstrates the effectiveness of closed-loop tactile-assisted training in enhancing BCI accessibility and efficiency, paving the way for more inclusive neurofeedback-based BCI training strategies.},
}
@article {pmid40227903,
year = {2025},
author = {Mai, X and Meng, J and Ding, Y and Zhu, X and Guan, C},
title = {SRRNet: Unseen SSVEP Response Regression From Stimulus for Cross-Stimulus Transfer in SSVEP-BCIs.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {1460-1472},
doi = {10.1109/TNSRE.2025.3560434},
pmid = {40227903},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; Male ; Adult ; Electroencephalography/methods ; Female ; Algorithms ; Young Adult ; Photic Stimulation ; Regression Analysis ; Calibration ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; },
abstract = {The prolonged calibration time required by steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) poses a significant challenge to real-life applications. Cross-stimulus transfer emerges as a promising solution, wherein a model trained on a subset of classes (seen classes) can predict both seen and unseen classes. Existing approaches extracted common components from SSVEP templates of seen classes to construct templates for unseen classes; however, they are limited by the class-specific activities and noise contained in these components, leading to imprecise templates that degrade classification performance. To address this issue, this study proposed an SSVEP Response Regression Network (SRRNet), which learned the regression mapping between sine-cosine reference signals and SSVEP templates using seen class data. This network reconstructed SSVEP templates for unseen classes utilizing their corresponding sine-cosine signals. Additionally, an SSVEP template regressing and spatial filtering (SRSF) framework was introduced, where both test data and SSVEP templates were projected by task-related component analysis (TRCA) spatial filters, and correlations were computed for target prediction. Comparative evaluations on two public datasets revealed that our method significantly outperformed state-of-the-art methods, elevating the information transfer rate (ITR) from 173.33 bits/min to 203.79 bits/min. By effectively modeling the regression from sine-cosine reference signals to SSVEP templates, SRRNet can construct SSVEP templates for unseen classes without training samples from those classes. By integrating regressed SSVEP templates with spatial filtering-based methods, our method enhances cross-stimulus transfer performance in SSVEP-BCIs, thus advancing their practical applicability. The code is available at https://github.com/MaiXiming/SRRNet.},
}
@article {pmid40227525,
year = {2025},
author = {Yan, L and Liu, Z and Wang, J and Yu, L},
title = {Integrating Hard Silicon for High-Performance Soft Electronics via Geometry Engineering.},
journal = {Nano-micro letters},
volume = {17},
number = {1},
pages = {218},
pmid = {40227525},
issn = {2150-5551},
abstract = {Soft electronics, which are designed to function under mechanical deformation (such as bending, stretching, and folding), have become essential in applications like wearable electronics, artificial skin, and brain-machine interfaces. Crystalline silicon is one of the most mature and reliable materials for high-performance electronics; however, its intrinsic brittleness and rigidity pose challenges for integrating it into soft electronics. Recent research has focused on overcoming these limitations by utilizing structural design techniques to impart flexibility and stretchability to Si-based materials, such as transforming them into thin nanomembranes or nanowires. This review summarizes key strategies in geometry engineering for integrating crystalline silicon into soft electronics, from the use of hard silicon islands to creating out-of-plane foldable silicon nanofilms on flexible substrates, and ultimately to shaping silicon nanowires using vapor-liquid-solid or in-plane solid-liquid-solid techniques. We explore the latest developments in Si-based soft electronic devices, with applications in sensors, nanoprobes, robotics, and brain-machine interfaces. Finally, the paper discusses the current challenges in the field and outlines future research directions to enable the widespread adoption of silicon-based flexible electronics.},
}
@article {pmid40226198,
year = {2025},
author = {Nirabi, A and Rahman, FA and Habaebi, MH and Sidek, KA and Yusoff, S},
title = {Cognitive load assessment through EEG: A dataset from arithmetic and Stroop tasks.},
journal = {Data in brief},
volume = {60},
number = {},
pages = {111477},
pmid = {40226198},
issn = {2352-3409},
abstract = {This study introduces a thoughtfully curated dataset comprising electroencephalogram (EEG) recordings designed to unravel mental stress patterns through the perspective of cognitive load. The dataset incorporates EEG signals obtained from 15 subjects, with a gender distribution of 8 females and 7 males, and a mean age of 21.5 years [1]. Recordings were collected during the subjects' engagement in diverse tasks, including the Stroop color-word test and arithmetic problem-solving tasks. The recordings are categorized into four classes representing varying levels of induced mental stress: normal, low, mid, and high. Each task was performed for a duration of 10-20 s, and three trials were conducted for comprehensive data collection. Employing an OpenBCI device with an 8-channel Cyton board, the EEG captures intricate responses of the frontal lobe to cognitive challenges posed by the Stroop and Arithmetic Tests, recorded at a sampling rate of 250 Hz. The proposed dataset serves as a valuable resource for advancing research in the realm of brain-computer interfaces and offers insights into identifying EEG patterns associated with stress. The proposed dataset serves as a valuable resource for researchers, offering insights into identifying EEG patterns that correlate with different stress states. By providing a solid foundation for the development of algorithms capable of detecting and classifying stress levels, the dataset supports innovations in non-invasive monitoring tools and contributes to personalized healthcare solutions that can adapt to the cognitive states of users. This study's foundation is crucial for advancing stress classification research, with significant implications for cognitive function and well-being.},
}
@article {pmid40225841,
year = {2025},
author = {Kashou, N},
title = {Editorial: New horizons in stroke management.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1587791},
doi = {10.3389/fnhum.2025.1587791},
pmid = {40225841},
issn = {1662-5161},
}
@article {pmid40225573,
year = {2025},
author = {Bai, R and Jia, Y and Wang, B and Wang, Z and Han, G and Liang, L and Chen, L and Ming, Y and Zhu, G and Hsu, YC and Zhao, P and Zhang, Y and Liu, Z and Liu, C and Li, Z and Liu, Y},
title = {In vivo spatiotemporal mapping of proliferation activity in gliomas via water-exchange dynamic contrast-enhanced MRI.},
journal = {Theranostics},
volume = {15},
number = {10},
pages = {4693-4707},
pmid = {40225573},
issn = {1838-7640},
mesh = {*Glioma/diagnostic imaging/pathology ; *Magnetic Resonance Imaging/methods ; Animals ; Humans ; *Cell Proliferation ; Temozolomide/pharmacology ; Cell Line, Tumor ; *Contrast Media ; Aquaporin 4/metabolism ; *Brain Neoplasms/diagnostic imaging/pathology ; *Water/metabolism ; Mice ; },
abstract = {Proliferation activity mapping is crucial for the guidance of first biopsy and treatment evaluation of gliomas due to the highly heterogenous nature of glioma tumor. Here we propose and demonstrate an ease-of-use way of in vivo spatiotemporal mapping of proliferation activity by simply tracking transmembrane water dynamics with magnetic resonance imaging (MRI). Specifically, we demonstrated that proliferation activity can accelerate the transmembrane water transport in glioma cells. Method: The transmembrane water-efflux rate (k io) measured by water-exchange dynamic contrast-enhanced (DCE) MRI. Immunofluorescence, immunohistochemistry, and immunocytochemistry staining were used to validate results obtained from the in vivo imaging studies. Results: In glioma cell cultures, k io precisely followed the dynamic changes of proliferation activity in growth cycles and response to temozolomide (TMZ) treatment. In both animal glioma model and human glioma, k io linearly and strongly correlated with the spatial heterogeneity of intra-tumoral proliferation activity. More importantly, proliferation activity predicted by the single MRI parameter k io is much more accurate than those predicted by state-of-the-art methods using multimodal standard MRIs and advanced machine learning. Upregulated aquaporin 4 (AQP4) expression were observed in most proliferating glioma cells and the knockout of AQP4 could largely slow down proliferation activity, suggesting AQP4 is the potential molecule connecting MRI-k io with proliferation activity. Conclusion: This study provides an ease-of-use, accurate, and non-invasive imaging method for the spatiotemporal monitoring of proliferation activity in glioma.},
}
@article {pmid40223771,
year = {2025},
author = {Kapur, A and Van Til, M and Daignault-Newton, S and Seibel, C and Nagpal, S and Ippolito, GM and Smith, AL and Lucioni, A and Lee, U and Suskind, A and Anger, J and Chung, D and Reynolds, WS and Cameron, AP and Tenggardjaja, C and Padmanabhan, P and Brucker, BM and , },
title = {Association Between Urodynamic Findings and Urinary Retention After Onabotulinumtoxin A for Idiopathic Overactive Bladder.},
journal = {Neurourology and urodynamics},
volume = {44},
number = {5},
pages = {1022-1030},
doi = {10.1002/nau.70050},
pmid = {40223771},
issn = {1520-6777},
support = {//This secondary analysis did not receive any external sources of funding. Funding for the primary analysis which utilized the same original data set as the current study was Society of Urodynamics, Female Pelvic Medicine and Urogenital Reconstruction Foundation (SUFU); National Institutes of Health, Grant/Award Number: UL1TR002240./ ; },
mesh = {Humans ; Female ; *Urinary Bladder, Overactive/drug therapy/physiopathology ; Male ; *Urodynamics/drug effects ; *Botulinum Toxins, Type A/adverse effects/administration &amp; dosage ; *Urinary Retention/physiopathology/chemically induced/epidemiology ; Middle Aged ; Retrospective Studies ; Aged ; Adult ; *Neuromuscular Agents/adverse effects ; Risk Factors ; },
abstract = {INTRODUCTION: Onabotulinumtoxin A (BTX-A) is a minimally invasive therapy for idiopathic overactive bladder (iOAB). Incomplete bladder emptying is a known risk of the procedure, with an overall rate as high as 20% in male and female patients. Risk factors for incomplete bladder emptying after BTX-A have been reported in the literature, but are widely variable amongst studies and therefore patients at increased risk of this adverse effect cannot easily be identified by clinicians. The aim of this study was to evaluate whether pre-procedure urodynamics (UDS) findings are associated with incomplete bladder emptying after intradetrusor BTX-A injection for iOAB.<br><br>METHODS: Data were analyzed from the SUFU Research Network (SURN) multi-institutional retrospective database. Men and women undergoing first-time injection of 100 units BTX-A for iOAB in 2016 were included. Subjects were excluded if they did not have record of pre-procedure and post-procedure (within 1 month) post-void residual volume (PVR). The primary outcome was incidence of urinary retention within 1 month after BTX-A, defined as PVR&#x2009;>&#x2009;300&#x2009;mL and/or initiation of self-catheterization or indwelling catheter. We assessed the association of pre-procedure UDS parameters with urinary retention using Wilcoxon rank tests, Fisher's exact test, and chi-squared tests.<br><br>RESULTS: A total of 167 subjects (141 women, 26 men) were included. Ninety-nine subjects (59%) had urodynamic data. Thirty-seven subjects (22%) had urinary retention within 1 month of BTX-A. There were no significant differences in age, gender, race, or body mass index between the retention and non-retention groups. There was no statistically significant difference in median Qmax between those who did and did not have postprocedure retention (10.0 vs. 14.3&#x2009;mL/s respectively, p&#x2009;=&#x2009;0.06). Mean PVR at the start of UDS was not statistically significant when comparing the retention and non-retention groups (22.5 vs. 10.0&#x2009;mL respectively, p&#x2009;=&#x2009;0.70). Bladder outlet obstruction index (BOOI), bladder contractility index (BCI), and presence of detrusor overactivity (DO) were not found to be associated with posttreatment retention.<br><br>CONCLUSION: This retrospective multi-institutional cohort study revealed that of patients who receive UDS before BTX-A, there are no significant UDS parameters or baseline demographic factors associated with incomplete bladder emptying after intradetrusor BTX-A injections for iOAB. Future studies that focus on better defining objective evidence-based predictors of incomplete emptying after BTX are needed to optimize patient perception of efficacy and satisfaction with this therapy.},
}
@article {pmid40223534,
year = {2025},
author = {Jung, M and Abu Shihada, J and Decke, S and Koschinski, L and Graff, PS and Maruri Pazmino, S and Höllig, A and Koch, H and Musall, S and Offenhäusser, A and Rincón Montes, V},
title = {Flexible 3D Kirigami Probes for In Vitro and In Vivo Neural Applications.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {37},
number = {24},
pages = {e2418524},
pmid = {40223534},
issn = {1521-4095},
support = {VH-NG-1611//Helmholtz Association/ ; GRK2610//Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)/ ; 424556709//Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)/ ; GRK2416//Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)/ ; 368482240//Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)/ ; },
mesh = {Animals ; Microelectrodes ; *Neurons/physiology/cytology ; Mice ; Rats ; },
abstract = {3D microelectrode arrays (MEAs) are gaining popularity as brain-machine interfaces and platforms for studying electrophysiological activity. Interactions with neural tissue depend on the electrochemical, mechanical, and spatial features of the recording platform. While planar or protruding 2D MEAs are limited in their ability to capture neural activity across layers, existing 3D platforms still require advancements in manufacturing scalability, spatial resolution, and tissue integration. In this work, a customizable, scalable, and straightforward approach to fabricate flexible 3D kirigami MEAs containing both surface and penetrating electrodes, designed to interact with the 3D space of neural tissue, is presented. These novel probes feature up to 512 electrodes distributed across 128 shanks in a single flexible device, with shank heights reaching up to 1 mm. The 3D kirigami MEAs are successfully deployed in several neural applications, both in vitro and in vivo, and identified spatially dependent electrophysiological activity patterns. Flexible 3D kirigami MEAs are therefore a powerful tool for large-scale electrical sampling of complex neural tissues while improving tissue integration and offering enhanced capabilities for analyzing neural disorders and disease models where high spatial resolution is required.},
}
@article {pmid40223097,
year = {2025},
author = {Wu, Y and Liu, Y and Yang, Y and Yao, MS and Yang, W and Shi, X and Yang, L and Li, D and Liu, Y and Yin, S and Lei, C and Zhang, M and Gee, JC and Yang, X and Wei, W and Gu, S},
title = {A concept-based interpretable model for the diagnosis of choroid neoplasias using multimodal data.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {3504},
pmid = {40223097},
issn = {2041-1723},
support = {F30 MD020264/MD/NIMHD NIH HHS/United States ; 62236009//National Science Foundation of China | Key Programme/ ; },
mesh = {Humans ; *Choroid Neoplasms/diagnosis/diagnostic imaging ; *Melanoma/diagnosis/diagnostic imaging ; Machine Learning ; Artificial Intelligence ; Female ; Uveal Melanoma ; Male ; *Uveal Neoplasms/diagnostic imaging/diagnosis ; Hemangioma/diagnosis/diagnostic imaging ; Middle Aged ; Diagnosis, Differential ; Multimodal Imaging/methods ; Adult ; },
abstract = {Diagnosing rare diseases remains a critical challenge in clinical practice, often requiring specialist expertise. Despite the promising potential of machine learning, the scarcity of data on rare diseases and the need for interpretable, reliable artificial intelligence (AI) models complicates development. This study introduces a multimodal concept-based interpretable model tailored to distinguish uveal melanoma (0.4-0.6 per million in Asians) from hemangioma and metastatic carcinoma following the clinical practice. We collected a comprehensive dataset on Asians to date on choroid neoplasm imaging with radiological reports, encompassing over 750 patients from 2013 to 2019. Our model integrates domain expert insights from radiological reports and differentiates between three types of choroidal tumors, achieving an F1 score of 0.91. This performance not only matches senior ophthalmologists but also improves the diagnostic accuracy of less experienced clinicians by 42%. The results underscore the potential of interpretable AI to enhance rare disease diagnosis and pave the way for future advancements in medical AI.},
}
@article {pmid40223012,
year = {2025},
author = {Lorente-Piera, J and Manrique-Huarte, R and Picciafuoco, S and Lima, JP and Calavia, D and Serra, V and Manrique, M},
title = {Optimization of surgical interventions in auditory rehabilitation for chronic otitis media: comparative between passive middle ear implants, bone conduction implants, and active middle ear systems.},
journal = {European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery},
volume = {282},
number = {9},
pages = {4513-4525},
pmid = {40223012},
issn = {1434-4726},
mesh = {Humans ; Female ; Male ; *Bone Conduction ; Retrospective Studies ; *Otitis Media/surgery/complications/rehabilitation ; Middle Aged ; Adult ; Chronic Disease ; *Ossicular Prosthesis ; *Tympanoplasty/methods ; Treatment Outcome ; Aged ; Speech Perception ; Adolescent ; Young Adult ; Hearing Aids ; },
abstract = {INTRODUCTION: In otology consultations, patients with chronic otitis media (COM) often present as candidates for various hearing rehabilitation options. Selecting the most suitable approach requires careful consideration of patient preferences and expectations, the risk of disease progression, and the integrity of the bone conduction pathway. This study aims to evaluate and compare postoperative hearing outcomes in COM patients undergoing tympanoplasty (with or without passive middle ear implants), bone conduction systems (BCI), or active middle ear implants (AMEI). The objective is to assess the effectiveness of each surgical approach in hearing rehabilitation, considering the type and severity of hearing loss as well as the duration of the disease.<br><br>METHODS: Retrospective data analysis in a tertiary referral center studying average PTA across six different frequencies, speech perception at 65&#xa0;dB, influence of Eustachian tube dysfunction, reintervention rate and adverse effects, and the influence of disease duration on functional outcomes via linear regression analysis.<br><br>RESULTS: 116 patients underwent surgery due to COM between 1998 and 2024. With a slight female predominance (54.31%). AMEIs and bone conduction devices provided the highest amplification in terms of PTA and speech discrimination, with a lower reintervention rate when comparing both groups with passive middle ear implants, OR in BCI group of 0.30 (0.10; 0.89, p&#x2009;=&#x2009;0.030), OR in VSB group of 0.15 (0.04; 0.56, p&#x2009;=&#x2009;0.005). It was also observed that a longer evolution time could be associated with greater auditory gain, with a p-value&#x2009;=&#x2009;0.033.<br><br>CONCLUSIONS: The selection of each treatment option primarily depends on bone conduction thresholds, along with surgical risk, patient preferences, and MRI compatibility. In our study, AMEIs demonstrated the highest functional gain in terms of speech discrimination and frequency-specific amplification, followed by BCI. These findings support the use of implantable hearing solutions as effective alternatives for auditory rehabilitation in COM patients.},
}
@article {pmid40222332,
year = {2025},
author = {Choi, JY and Kim, YJ and Shin, JS and Choi, E and Kim, Y and Kim, MG and Kim, YT and Park, BS and Kim, JK and Kim, JG},
title = {Integrative metabolic profiling of hypothalamus and skeletal muscle in a mouse model of cancer cachexia.},
journal = {Biochemical and biophysical research communications},
volume = {763},
number = {},
pages = {151766},
doi = {10.1016/j.bbrc.2025.151766},
pmid = {40222332},
issn = {1090-2104},
mesh = {Animals ; *Cachexia/metabolism/etiology/pathology ; *Hypothalamus/metabolism ; *Muscle, Skeletal/metabolism ; Mice ; Mice, Inbred C57BL ; Disease Models, Animal ; Energy Metabolism ; Male ; *Carcinoma, Lewis Lung/metabolism/complications ; Metabolomics ; *Metabolome ; },
abstract = {Cancer cachexia is a multifactorial metabolic syndrome characterized by progressive weight loss, muscle wasting, and systemic inflammation. Despite its clinical significance, the underlying mechanisms linking central and peripheral metabolic changes remain incompletely understood. In this study, we employed a murine model of cancer cachexia induced by intraperitoneal injection of Lewis lung carcinoma (LLC1) cells to investigate tissue-specific metabolic adaptations. Cachectic mice exhibited reduced food intake, body weight loss, impaired thermoregulation, and decreased energy expenditure. Metabolomic profiling of serum, skeletal muscle, and hypothalamus revealed distinct metabolic shifts, with increased fatty acid and ketone body utilization and altered amino acid metabolism. Notably, hypothalamic metabolite changes diverged from peripheral tissues, showing decreased neurotransmitter-related metabolites and enhanced lipid-based energy signatures. Gene expression analysis further confirmed upregulation of glycolysis- and lipid oxidation-related genes in both hypothalamus and muscle. These findings highlight coordinated yet compartmentalized metabolic remodeling in cancer cachexia and suggest that hypothalamic adaptations may play a central role in the systemic energy imbalance associated with cachexia progression.},
}
@article {pmid40221457,
year = {2025},
author = {Rybář, M and Poli, R and Daly, I},
title = {Simultaneous EEG and fNIRS recordings for semantic decoding of imagined animals and tools.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {613},
pmid = {40221457},
issn = {2052-4463},
mesh = {Humans ; *Electroencephalography ; *Brain-Computer Interfaces ; Spectroscopy, Near-Infrared ; *Semantics ; *Imagination ; Animals ; Male ; Female ; Adult ; Brain/physiology ; },
abstract = {Semantic neural decoding aims to identify which semantic concepts an individual focuses on at a given moment based on recordings of their brain activity. We investigated the feasibility of semantic neural decoding to develop a new type of brain-computer interface (BCI) that allows direct communication of semantic concepts, bypassing the character-by-character spelling used in current BCI systems. We provide data from our study to differentiate between two semantic categories of animals and tools during a silent naming task and three intuitive sensory-based imagery tasks using visual, auditory, and tactile perception. Participants were instructed to visualize an object (animal or tool) in their minds, imagine the sounds produced by the object, and imagine the feeling of touching the object. Simultaneous electroencephalography (EEG) and near-infrared spectroscopy (fNIRS) signals were recorded from 12 participants. Additionally, EEG signals were recorded from 7 other participants in a follow-up experiment focusing solely on the auditory imagery task. These datasets can serve as a valuable resource for researchers investigating semantic neural decoding, brain-computer interfaces, and mental imagery.},
}
@article {pmid40219565,
year = {2025},
author = {Liu, N and Man, L and He, F and Huang, G and Zhai, J},
title = {[Correlation between urination intermittences and urodynamic parameters in benign prostatic hyperplasia patients].},
journal = {Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences},
volume = {57},
number = {2},
pages = {328-333},
pmid = {40219565},
issn = {1671-167X},
mesh = {*Prostatic Hyperplasia/complications/physiopathology ; Humans ; Male ; *Urodynamics ; *Urination ; Retrospective Studies ; Middle Aged ; Aged ; Aged, 80 and over ; *Urinary Bladder/physiopathology ; *Urination Disorders/etiology ; },
abstract = {OBJECTIVE: To explore the impact factors and the clinical significance of the urination intermittences in benign prostatic hyperplasia (BPH) patients.<br><br>METHODS: A retrospective study was performed in BPH patients who underwent urodynamic studies in Beijing Jishuitan Hospital form January 2016 to June 2021. The patients were aged 45 to 84 years with a median age of 63 years, and all the patients had no previous history of neurological disease and had no positive findings in neurological examinations. All the patients had free uroflometry followed by urethral catheterization and urodynamic tests. The voiding work of bladder was calculated using the detrusor power curve method, and the voiding power of bladder and the voiding energy consumption were also calculated. The frequency of urination intermittences generated in uroflometry was also recorded and the patients were divided into different groups according to it. The detrusor pressure at maximal flow rate (PdetQmax), the maximal flow rate (Qmax), the bladder contractile index (BCI), the bladder outlet obstruction index (BOOI), the voiding work, the voiding power, and the voiding energy consumption were compared among the different groups. Multiva-riate analyses associated with presence of urination intermittences were performed using step-wise Logistic regressions.<br><br>RESULTS: There were 272 patients included in this study, of whom, 179 had no urination intermittence (group A), 46 had urination intermittence for only one time (group B), 22 had urination intermittence for two times (group C), and 25 had urination intermittence for three times and more (group D). The BCI were 113.4&#xb1;28.2, 101.0&#xb1;30.2, 83.3&#xb1;30.2, 81.0&#xb1;30.5 in groups A, B, C, and D, respectively; The voiding power were (29.2&#xb1;14.8) mW, (16.4&#xb1;9.6) mW, (14.5&#xb1;7.1) mW, (8.5&#xb1;5.0) mW in groups A, B, C, and D, respectively, and the differences were significant (P < 0.05). The BOOI were 41.6&#xb1;29.3, 46.4&#xb1;31.0, 41.4&#xb1;29.0, 42.7&#xb1;22.8 in groups A, B, C, and D, respectively; The voiding energy consumption were (5.41&#xb1;2.21) J/L, (4.83&#xb1;2.31) J/L, (5.02&#xb1;2.54) J/L, (4.39&#xb1;2.03) J/L in groups A, B, C, and D, respectively, and the differences were insignificant (P>0.05). Among the patients, 179 cases were negative in presence of urination intermittences and 93 cases were positive. Step-wise Logistic regression analysis showed that bladder power (OR=0.814, 95%CI: 0.765-0.866, P < 0.001), BCI (OR=1.023, 95%CI: 1.008-1.038, P=0.003), and bladder work (OR=2.232, 95%CI: 1.191-4.184, P=0.012) were independent risk factors for urination intermittences in the BPH patients.<br><br>CONCLUSION: The presence of urination intermittences in the BPH patients was mainly influenced by bladder contractile functions, and was irrelevant to the degree of bladder outlet obstruction. The increase of frequency of urination intermittences seemed to be a sign of the decrease of the bladder contractile functions in the BPH patients.},
}
@article {pmid40218833,
year = {2025},
author = {Ranjbar Koleibi, E and Lemaire, W and Koua, K and Benhouria, M and Bostani, R and Serri Mazandarani, M and Gauthier, LP and Besrour, M and Ménard, J and Majdoub, M and Gosselin, B and Roy, S and Fontaine, R},
title = {Design and Implementation of a Low-Power Biopotential Amplifier in 28 nm CMOS Technology with a Compact Die-Area of 2500 μm[2] and an Ultra-High Input Impedance.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {7},
pages = {},
pmid = {40218833},
issn = {1424-8220},
abstract = {Neural signal recording demands compact, low-power, high-performance amplifiers, to enable large-scale, multi-channel electrode arrays. This work presents a bioamplifier optimized for action potential detection, designed using TSMC 28 nm HPC CMOS technology. The amplifier integrates an active low-pass filter, eliminating bulky DC-blocking capacitors and significantly reducing the size and power consumption. It achieved a high input impedance of 105.5 GΩ, ensuring minimal signal attenuation. Simulation and measurement results demonstrated a mid-band gain of 58 dB, a -3 dB bandwidth of 7 kHz, and an input-referred noise of 11.1 μVrms, corresponding to a noise efficiency factor (NEF) of 8.4. The design occupies a compact area of 2500 μm2, making it smaller than previous implementations for similar applications. Additionally, it operates with an ultra-low power consumption of 3.4 μW from a 1.2 V supply, yielding a power efficiency factor (PEF) of 85 and an area efficiency factor of 0.21. These features make the proposed amplifier well suited for multi-site in-skull neural recording systems, addressing critical constraints regarding miniaturization and power efficiency.},
}
@article {pmid40218817,
year = {2025},
author = {Andreev, A and Cattan, G and Congedo, M},
title = {The Riemannian Means Field Classifier for EEG-Based BCI Data.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {7},
pages = {},
pmid = {40218817},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Algorithms ; Signal Processing, Computer-Assisted ; },
abstract = {: A substantial amount of research has demonstrated the robustness and accuracy of the Riemannian minimum distance to mean (MDM) classifier for all kinds of EEG-based brain-computer interfaces (BCIs). This classifier is simple, fully deterministic, robust to noise, computationally efficient, and prone to transfer learning. Its training is very simple, requiring just the computation of a geometric mean of a symmetric positive-definite (SPD) matrix per class. We propose an improvement of the MDM involving a number of power means of SPD matrices instead of the sole geometric mean. By the analysis of 20 public databases, 10 for the motor-imagery BCI paradigm and 10 for the P300 BCI paradigm, comprising 587 individuals in total, we show that the proposed classifier clearly outperforms the MDM, approaching the state-of-the art in terms of performance while retaining the simplicity and the deterministic behavior. In order to promote reproducible research, our code will be released as open source.},
}
@article {pmid40218770,
year = {2025},
author = {Gómez-Morales, &#xd3;W and Collazos-Huertas, DF and Álvarez-Meza, AM and Castellanos-Dominguez, CG},
title = {EEG Signal Prediction for Motor Imagery Classification in Brain-Computer Interfaces.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {7},
pages = {},
pmid = {40218770},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; *Imagination/physiology ; Algorithms ; Male ; Adult ; *Brain/physiology ; Female ; },
abstract = {Brain-computer interfaces (BCIs) based on motor imagery (MI) generally require EEG signals recorded from a large number of electrodes distributed across the cranial surface to achieve accurate MI classification. Not only does this entail long preparation times and high costs, but it also carries the risk of losing valuable information when an electrode is damaged, further limiting its practical applicability. In this study, a signal prediction-based method is proposed to achieve high accuracy in MI classification using EEG signals recorded from only a small number of electrodes. The signal prediction model was constructed using the elastic net regression technique, allowing for the estimation of EEG signals from 22 complete channels based on just 8 centrally located channels. The predicted EEG signals from the complete channels were used for feature extraction and MI classification. The results obtained indicate a notable efficacy of the proposed prediction method, showing an average performance of 78.16% in classification accuracy. The proposed method demonstrated superior performance compared to the traditional approach that used few-channel EEG and also achieved better results than the traditional method based on full-channel EEG. Although accuracy varies among subjects, from 62.30% to an impressive 95.24%, these data indicate the capability of the method to provide accurate estimates from a reduced set of electrodes. This performance highlights its potential to be implemented in practical MI-based BCI applications, thereby mitigating the time and cost constraints associated with systems that require a high density of electrodes.},
}
@article {pmid40218647,
year = {2025},
author = {Xu, H and Hassan, SA and Haider, W and Sun, Y and Yu, X},
title = {A Frequency-Shifting Variational Mode Decomposition-Based Approach to MI-EEG Signal Classification for BCIs.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {7},
pages = {},
pmid = {40218647},
issn = {1424-8220},
support = {U2033202, U1333119//National Natural Science Foundation of China and Civil Aviation Administration of China/ ; 52172387//National Natural Science Foundation of China/ ; ILA22032-1A//Fundamental Research Funds for the Central Universities/ ; 2022Z071052001//Aeronautical Science Foundation of China/ ; 2022JGZ14//Northwestern Polytechnical University/ ; },
mesh = {*Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; Neural Networks, Computer ; *Brain-Computer Interfaces ; Algorithms ; Brain/physiology ; },
abstract = {Electroencephalogram (EEG) signal analysis is crucial for understanding neural activity and advancing diagnostics in neurology. However, traditional signal decomposition (SD) techniques are hindered by two critical issues, mode mixing and mode aliasing, that compromise the quality of the decomposed signal. These challenges result in poor signal integrity, which significantly affects the accuracy of subsequent EEG interpretations and classifications. As EEG analysis is widely used in diagnosing conditions such as epilepsy, brain injuries, and sleep disorders, the impact of these shortcomings can be far-reaching, leading to misdiagnoses or delayed treatments. Despite extensive research on SD techniques, these issues remain largely unresolved, emphasizing the urgent need for a more reliable and precise approach. This study proposes a novel solution through the frequency-shifting variational mode decomposition (FS-VMD) method, which overcomes the limitations of traditional SD techniques by providing better resolution of intrinsic mode functions (IMFs). The FS-VMD method works by extracting and shifting the fundamental frequency of the EEG signal to a lower frequency range, followed by an iterative decomposition process that enhances signal clarity and reduces mode aliasing. By integrating advanced feature selection techniques and classifiers such as support vector machines (SVM), convolutional neural networks (CNN), and feature-weighted k-nearest neighbors (FWKNN), this approach offers a significant improvement in classification accuracy, with SVM achieving up to 99.99% accuracy in the 18-channel EEG setup with a standard deviation of 0.25. The results demonstrate that FS-VMD can address the critical issues of mode mixing and aliasing, providing a more accurate and efficient solution for EEG signal analysis and diagnostics.},
}
@article {pmid40215960,
year = {2025},
author = {Chen, LN and Zhou, H and Xi, K and Cheng, S and Liu, Y and Fu, Y and Ma, X and Xu, P and Ji, SY and Wang, WW and Shen, DD and Zhang, H and Shen, Q and Chai, R and Zhang, M and Yang, L and Han, F and Mao, C and Cai, X and Zhang, Y},
title = {Proton perception and activation of a proton-sensing GPCR.},
journal = {Molecular cell},
volume = {85},
number = {8},
pages = {1640-1657.e8},
doi = {10.1016/j.molcel.2025.02.030},
pmid = {40215960},
issn = {1097-4164},
mesh = {Humans ; *Receptors, G-Protein-Coupled/metabolism/chemistry/genetics/ultrastructure ; *Protons ; Cryoelectron Microscopy ; HEK293 Cells ; Hydrophobic and Hydrophilic Interactions ; Histidine/metabolism/chemistry ; Hydrogen Bonding ; Protein Binding ; Models, Molecular ; Protein Conformation ; Hydrogen-Ion Concentration ; },
abstract = {Maintaining pH at cellular, tissular, and systemic levels is essential for human health. Proton-sensing GPCRs regulate physiological and pathological processes by sensing the extracellular acidity. However, the molecular mechanism of proton sensing and activation of these receptors remains elusive. Here, we present cryoelectron microscopy (cryo-EM) structures of human GPR4, a prototypical proton-sensing GPCR, in its inactive and active states. Our studies reveal that three extracellular histidine residues are crucial for proton sensing of human GPR4. The binding of protons induces substantial conformational changes in GPR4's ECLs, particularly in ECL2, which transforms from a helix-loop to a β-turn-β configuration. This transformation leads to the rearrangements of H-bond network and hydrophobic packing, relayed by non-canonical motifs to accommodate G proteins. Furthermore, the antagonist NE52-QQ57 hinders human GPR4 activation by preventing hydrophobic stacking rearrangement. Our findings provide a molecular framework for understanding the activation mechanism of a human proton-sensing GPCR, aiding future drug discovery.},
}
@article {pmid40213917,
year = {2025},
author = {Wang, N and Wang, Y and Guo, M and Wang, L and Wang, X and Zhu, N and Yang, J and Wang, L and Zheng, C and Ming, D},
title = {Dynamic gamma modulation of hippocampal place cells predominates development of theta sequences.},
journal = {eLife},
volume = {13},
number = {},
pages = {},
pmid = {40213917},
issn = {2050-084X},
support = {2022ZD0205000//National Science and Technology Innovation 2030 Major Project of China/ ; T2322021//National Natural Science Foundation of China/ ; 82271218//National Natural Science Foundation of China/ ; 12271272//National Natural Science Foundation of China/ ; 81925020//National Natural Science Foundation of China/ ; 82371886//National Natural Science Foundation of China/ ; 82202797//National Natural Science Foundation of China/ ; LG-TKN-202204-01//Space Brain Project from Lingang Laboratory/ ; 2022M712365//China Postdoctoral Science Foundation/ ; },
mesh = {Animals ; *Theta Rhythm/physiology ; Rats ; *Gamma Rhythm/physiology ; *Hippocampus/physiology/cytology ; *Place Cells/physiology ; Male ; Rats, Long-Evans ; },
abstract = {The experience-dependent spatial cognitive process requires sequential organization of hippocampal neural activities by theta rhythm, which develops to represent highly compressed information for rapid learning. However, how the theta sequences were developed in a finer timescale within theta cycles remains unclear. In this study, we found in rats that sweep-ahead structure of theta sequences developing with exploration was predominantly dependent on a relatively large proportion of FG-cells, that is a subset of place cells dominantly phase-locked to fast gamma rhythms. These ensembles integrated compressed spatial information by cells consistently firing at precessing slow gamma phases within the theta cycle. Accordingly, the sweep-ahead structure of FG-cell sequences was positively correlated with the intensity of slow gamma phase precession, in particular during early development of theta sequences. These findings highlight the dynamic network modulation by fast and slow gamma in the development of theta sequences which may further facilitate memory encoding and retrieval.},
}
@article {pmid40212471,
year = {2025},
author = {Feng, J and Li, Y and Huang, Z and Chen, Y and Lu, S and Hu, R and Hu, Q and Chen, Y and Wang, X and Fan, Y and He, J},
title = {Heterogeneous transfer learning model for improving the classification performance of fNIRS signals in motor imagery among cross-subject stroke patients.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1555690},
pmid = {40212471},
issn = {1662-5161},
abstract = {INTRODUCTION: Motor imagery functional near-infrared spectroscopy (MI-fNIRS) offers precise monitoring of neural activity in stroke rehabilitation, yet accurate cross-subject classification remains challenging due to limited training samples and significant inter-subject variability. This study proposes a Cross-Subject Heterogeneous Transfer Learning Model (CHTLM) to enhance the generalization of MI-fNIRS signal classification in stroke patients.<br><br>METHODS: CHTLM leverages labeled electroencephalogram (EEG) data from healthy individuals as the source domain. An adaptive feature matching network aligns task-relevant feature maps and convolutional layers between source (EEG) and target (fNIRS) domains. Multi-scale fNIRS features are extracted, and a sparse Bayesian extreme learning machine classifies the fused deep learning features.<br><br>RESULTS: Experiments utilized two MI-fNIRS datasets from eight stroke patients pre- and post-rehabilitation. CHTLM achieved average accuracies of 0.831 (pre-rehabilitation) and 0.913 (post-rehabilitation), with mean AUCs of 0.887 and 0.930, respectively. Compared to five baselines, CHTLM improved accuracy by 8.6-10.5% pre-rehabilitation and 11.3-15.7% post-rehabilitation.<br><br>DISCUSSION: The model demonstrates robust cross-subject generalization by transferring task-specific knowledge from heterogeneous EEG data while addressing domain discrepancies. Its performance gains post-rehabilitation suggest clinical potential for monitoring recovery progress. CHTLM advances MI-fNIRS-based brain-computer interfaces in stroke rehabilitation by mitigating data scarcity and variability challenges.},
}
@article {pmid40210930,
year = {2025},
author = {Qi, W and Zhang, Y and Su, Y and Hui, Z and Li, S and Wang, H and Zhang, J and Shi, K and Wang, M and Zhou, L and Zhu, D},
title = {Exploring cortical excitability in children with cerebral palsy through lower limb robot training based on MI-BCI.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {12285},
pmid = {40210930},
issn = {2045-2322},
mesh = {Humans ; *Cerebral Palsy/physiopathology/rehabilitation ; Child ; Male ; Female ; *Brain-Computer Interfaces ; Child, Preschool ; Electroencephalography ; *Robotics/methods ; *Lower Extremity/physiopathology ; },
abstract = {This study aims to compare brain activity differences under the motor imagery-brain-computer interface (MI-BCI), motor imagery (MI), and resting (REST) paradigms through EEG microstate and functional connectivity (FC) analysis, providing a theoretical basis for applying MI-BCI in the rehabilitation of children with cerebral palsy (CP). This study included 30 subjects aged 4-6 years with GMFCS II-III grade, diagnosed with CP and classified as spastic diplegia. They sequentially completed EEG signal acquisition under REST, MI, and MI-BCI conditions. Clustering analysis was used to analyze EEG microstates and extract EEG microstate temporal parameters. Additionally, the strength of brain FC in different frequency bands was analyzed to compare the differences under various conditions. Four microstate classes (A-D) were identified to best explain the datasets of three groups. Compared to REST, the average duration and coverage rate of microstate D under MI and MI-BCI significantly increased (P < 0.05), while their frequency and the coverage rate and frequency of microstate A decreased. Compared to MI, the average duration of microstate C under MI-BCI significantly decreased (P < 0.05), while the frequency of microstate B significantly increased (P < 0.05). Additionally, the transition probability results showed that other microstates under REST had a higher transition probability to microstate A, while under MI and MI-BCI, other microstates had a higher transition probability to microstate D. The brain network results revealed significant differences in brain network connectivity among REST, MI, and MI-BCI across different frequency bands. No FC differences were found between REST, MI, and MI-BCI in the α2 frequency band. In the δ and γ frequency bands, MI and MI-BCI both had greater inter-electrode connectivity strength than REST. In the θ frequency band, REST had greater inter-electrode connectivity strength than MI-BCI, while MI-BCI had greater inter-electrode connectivity strength than both REST and MI. In the α1 frequency band, MI-BCI had greater inter-electrode connectivity strength than REST, and in the β frequency band, MI-BCI had greater inter-electrode connectivity strength than MI. MI-BCI can significantly alter the brain activity patterns of children with CP, particularly by enhancing the activity intensity of EEG microstates related to attention, motor planning, and execution, as well as the brain FC strength in different frequency bands. It holds high application value in the lower limb motor rehabilitation of children with CP.},
}
@article {pmid40210429,
year = {2025},
author = {Benioudakis, ES and Kalaitzaki, A and Karlafti, E and Kapageridou, E and Ahanov, O and Kontoninas, Z and Savopoulos, C and Didangelos, T},
title = {Psychometric Properties and Dimensionality of the Greek Version of the Hypoglycemic Confidence Scale.},
journal = {Journal of nursing measurement},
volume = {33},
number = {2},
pages = {312-319},
doi = {10.1891/JNM-2024-0108},
pmid = {40210429},
issn = {1945-7049},
mesh = {Humans ; *Psychometrics/standards ; Greece ; Female ; Adult ; Male ; Middle Aged ; Reproducibility of Results ; *Diabetes Mellitus, Type 1/psychology/drug therapy ; *Hypoglycemia/psychology ; Surveys and Questionnaires ; Young Adult ; Adolescent ; Translations ; Translating ; },
abstract = {Background and purpose: The prevalence of type 1 diabetes mellitus (T1D) is rising at an alarming rate and is projected to continue increasing in the coming years. The primary approach to preventing diabetes-related complications in individuals with T1D is the exogenous administration of insulin. However, this method can sometimes lead to hypoglycemia, a condition with a wide range of symptoms, including loss of consciousness, seizures, coma, and, in severe cases, death. This study aims to present the psychometric properties of the Greek translation of the Hypoglycemic Confidence Scale (HCS). The HCS measures an individual's sense of personal strength and comfort based on the belief that they possess the necessary resources to manage and prevent hypoglycemia-related complications. Methods: We conducted a forward and backward translation, along with a cultural adaptation, of the HCS into Greek. The psychometric properties of the scale were evaluated through confirmatory factor analysis. To assess the reliability, we calculated the intraclass correlation coefficient, while internal consistency was measured using Cronbach's alpha. Construct validity was evaluated through convergent and divergent validity, comparing the HCS-Gr with the Diabetes Quality of Life Brief Clinical Inventory (DQoL-BCI) and hemoglobin A1C levels. Differential validity was assessed using the known-groups method. Results: Ninety-seven adults with T1D, aged between 18 and 57 years (mean age: 38.6 ± 11.7), completed the HCS-Gr. The two structures of the HCS-Gr demonstrated strong internal consistency, with Cronbach's α values of 0.87 for the eight-item version and 0.86 for the nine-item version. Convergent validity was supported by moderate negative correlations between both HCS-Gr versions and the DQoL-BCI subscales and total score. The HCS-Gr also showed satisfactory test-retest reliability and differential validity, confirming its robustness as a psychometric tool. Conclusions: The HCS-Gr is a valid and reliable tool for assessing confidence (or self-efficacy) in managing hypoglycemic situations among individuals with T1D in Greece.},
}
@article {pmid40209829,
year = {2025},
author = {Ruiz Ibán, MA and García Navlet, M and Moros Marco, S and Diaz Heredia, J and Hernando, A and Ruiz Díaz, R and Vaquero Comino, C and Alvarez Villar, S and Avila Lafuente, JL},
title = {Augmentation of a Posterosuperior Cuff Repair With a Bovine Bioinductive Collagen Implant Shows a Lower Retear Rate but Similar Outcomes Compared With No Augmentation: 2-Year Results of a Randomized Controlled Trial.},
journal = {Arthroscopy : the journal of arthroscopic &amp; related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association},
volume = {41},
number = {10},
pages = {3869-3879},
doi = {10.1016/j.arthro.2025.03.057},
pmid = {40209829},
issn = {1526-3231},
mesh = {Humans ; Male ; Female ; Middle Aged ; *Rotator Cuff Injuries/surgery/diagnostic imaging ; *Collagen/therapeutic use ; Treatment Outcome ; Cattle ; Aged ; Animals ; Follow-Up Studies ; Arthroscopy/methods ; Magnetic Resonance Imaging ; Rotator Cuff/surgery ; },
abstract = {PURPOSE: To assess the clinical and radiologic outcomes of the addition of a bioinductive collagen implant (BCI) to repair of medium to large posterosuperior rotator cuff tears at 24-month follow-up.<br><br>METHODS: This study was an update of a randomized controlled trial that was extended from 1- to 2-year follow-up. A total of 124 subjects with symptomatic full-thickness posterosuperior rotator cuff tears with a fatty infiltration grade of 2 or less per the Goutallier classification were randomized into 2 groups in which a transosseous-equivalent repair was performed alone (control group) or with a BCI applied over the repair (BCI group). The outcomes reassessed at 2-year follow-up were as follows: Sugaya grade, retear rate and tendon thickness on magnetic resonance imaging (MRI), and clinical outcomes (pain level, EQ-5D-5L score, American Shoulder and Elbow Surgeons [ASES] score, and Constant-Murley score [CMS]).<br><br>RESULTS: There were no relevant differences in preoperative characteristics between the groups. There were no additional complications or reinterventions in the second year of follow-up. Of 124 randomized patients (59 male and 55 female patients; mean age: 58.1 years [standard deviation (SD), 7.35 years]), 114 (91.9%) underwent MRI evaluation at 25.4 months (SD, 1.95 months) after surgery. There was a lower retear rate (12.3% [7 of 57]) in the BCI group compared with the control group (35.1% [20 of 57]) (P = .004; relative risk of retear, 0.35 [95% confidence interval, 0.16-0.76]). The Sugaya grade was also better in the BCI group (2.58 [SD, 1.07] vs 3.14 [SD, 1.19]; P = .020). Clinical follow-up was performed in 114 of 124 patients (91.9%) at 25.8 months (SD, 2.75 months) and showed improvements in both groups (P < .001), with 87% achieving the minimal clinically important difference for the CMS and 90% doing so for the ASES score; however, there were no differences between the groups. Among subjects who underwent both MRI and clinical assessment (n = 112), those with an intact tendon presented better CMS values (P = .035), ASES scores (P = .015), and pain scores (P = .006) than those with a failed repair.<br><br>CONCLUSIONS: Augmentation of a transosseous-equivalent repair with a BCI in posterosuperior rotator cuff tears clearly reduces the retear rate at 2-year follow-up without increased complication rates and with similar clinical outcomes. Subjects with failed repairs had poorer clinical outcomes.<br><br>LEVEL OF EVIDENCE: Level I, randomized controlled trial.},
}
@article {pmid40209163,
year = {2025},
author = {Kurmanavičiūtė, D and Kataja, H and Parkkonen, L},
title = {Comparing MEG and EEG measurement set-ups for a brain-computer interface based on selective auditory attention.},
journal = {PloS one},
volume = {20},
number = {4},
pages = {e0319328},
pmid = {40209163},
issn = {1932-6203},
mesh = {Humans ; *Magnetoencephalography/methods ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Male ; Female ; Adult ; *Attention/physiology ; Young Adult ; Evoked Potentials, Auditory/physiology ; *Auditory Perception/physiology ; Support Vector Machine ; Acoustic Stimulation ; Algorithms ; },
abstract = {Auditory attention modulates auditory evoked responses to target vs. non-target sounds in electro- and magnetoencephalographic (EEG/MEG) recordings. Employing whole-scalp MEG recordings and offline classification algorithms has been shown to enable high accuracy in tracking the target of auditory attention. Here, we investigated the decrease in accuracy when moving from the whole-scalp MEG to lower channel count EEG recordings and when training the classifier only from the initial or middle part of the recording instead of extracting training trials throughout the recording. To this end, we recorded simultaneous MEG (306 channels) and EEG (64 channels) in 18 healthy volunteers while presented with concurrent streams of spoken "Yes"/"No" words and instructed to attend to one of them. We then trained support vector machine classifiers to predict the target of attention from unaveraged trials of MEG/EEG. Classifiers were trained on 204 MEG gradiometers or on EEG with 64, 30, nine or three channels with trials extracted randomly across or only from the beginning of the recording. The highest classification accuracy, 73.2% on average across the participants for one-second trials, was obtained with MEG when the training trials were randomly extracted throughout the recording. With EEG, the accuracy was 69%, 69%, 66%, and 61% when using 64, 30, nine, and three channels, respectively. When training the classifiers with the same amount of data but extracted only from the beginning of the recording, the accuracy dropped by 11%-units on average, causing the result from the three-channel EEG to fall below the chance level. The combination of five consecutive trials partially compensated for this drop such that it was one to 5%-units. Although moving from whole-scalp MEG to EEG reduces classification accuracy, usable auditory-attention-based brain-computer interfaces can be implemented with a small set of optimally placed EEG channels.},
}
@article {pmid40206150,
year = {2025},
author = {Zhang, H and Wang, X and Chen, G and Zhang, Y and Jian, X and He, F and Xu, M and Ming, D},
title = {Noninvasive Intracranial Source Signal Localization and Decoding with High Spatiotemporal Resolution.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {6},
number = {},
pages = {0206},
pmid = {40206150},
issn = {2692-7632},
abstract = {High spatiotemporal resolution of noninvasive electroencephalography (EEG) signals is an important prerequisite for fine brain-computer manipulation. However, conventional scalp EEG has a low spatial resolution due to the volume conductor effect, making it difficult to accurately identify the intent of brain-computer manipulation. In recent years, transcranial focused ultrasound modulated EEG technology has increasingly become a research hotspot, which is expected to acquire noninvasive acoustoelectric coupling signals with a high spatial and temporal resolution. In view of this, this study established a transcranial focused ultrasound numerical simulation model and experimental platform based on a real brain model and a 128-array phased array, further constructed a 3-dimensional transcranial multisource dipole localization and decoding numerical simulation model and experimental platform based on the acoustic field platform, and developed a high-precision localization and decoding algorithm. The results show that the simulation-guided phased-array acoustic field experimental platform can achieve accurate focusing in both pure water and transcranial conditions within a safe threshold, with a modulation range of 10 mm, and the focal acoustic pressure can be enhanced by more than 200% compared with that of transducer self-focusing. In terms of dipole localization decoding results, the proposed algorithm in this study has a localization signal-to-noise ratio of 24.18 dB, which is 50.59% higher than that of the traditional algorithm, and the source signal decoding accuracy is greater than 0.85. This study provides a reliable experimental basis and technical support for high-spatiotemporal-resolution noninvasive EEG signal acquisition and precise brain-computer manipulation.},
}
@article {pmid40205860,
year = {2025},
author = {Sun, Y and Yu, N and Chen, G and Liu, T and Wen, S and Chen, W},
title = {What Else Is Happening to the Mirror Neurons?-A Bibliometric Analysis of Mirror Neuron Research Trends and Future Directions (1996-2024).},
journal = {Brain and behavior},
volume = {15},
number = {4},
pages = {e70486},
pmid = {40205860},
issn = {2162-3279},
support = {21BZX005//National Social Science Fund of China/ ; 21NDQN281YB//Philosophy and Social Sciences Project of Zhejiang Province/ ; 23QNYC19ZD//Special Project for Cultivating Leading Talents in Philosophy and Social Sciences of Zhejiang Province (Cultivation of Young Talents)/ ; },
mesh = {Animals ; Humans ; *Bibliometrics ; Brain/physiology ; *Mirror Neurons/physiology ; Neurosciences/trends ; },
abstract = {BACKGROUND: Since its discovery in the late 20th century, research on mirror neurons has become a pivotal area in neuroscience, linked to various cognitive and social functions. This bibliometric analysis explores the research trajectory, key research topics, and future trends in the field of mirror neuron research.<br><br>METHODS: We searched the Web of Science Core Collection (WoSCC) database for publications from 1996 to 2024 on mirror neuron research. Statistical and visualization analyses were performed using CiteSpace and VOSviewer.<br><br>RESULTS: Publication output on mirror neurons peaked in 2013 and remained active. High-impact journals such as Science, Brain, Neuron, PNAS, and NeuroImage frequently feature findings on the mirror neuron system, including its distribution, neural coding, and roles in intention understanding, affective empathy, motor learning, autism, and neurological disorders. Keyword clustering reveals major directions in cognitive neuroscience, motor neuroscience, and neurostimulation, whereas burst detection underscores the emerging significance of brain-computer interfaces (BCIs). Research methodologies have been evolving from traditional electrophysiological recordings to advanced techniques such as functional magnetic resonance imaging, transcranial magnetic stimulation, and BCIs, highlighting a dynamic, multidisciplinary progression.<br><br>CONCLUSIONS: This study identifies key areas associated with mirror neurons and anticipates that future work will integrate findings with artificial intelligence, clinical interventions, and novel neuroimaging techniques, providing new perspectives on complex socio-cognitive issues and their applications in both basic science and clinical practice.},
}
@article {pmid40205038,
year = {2025},
author = {Yang, L and Guo, C and Zheng, Z and Dong, Y and Xie, Q and Lv, Z and Li, M and Lu, Y and Guo, X and Deng, R and Liu, Y and Feng, Y and Mu, R and Zhang, X and Ma, H and Chen, Z and Zhang, Z and Dong, Z and Yang, W and Zhang, X and Cui, Y},
title = {Stress dynamically modulates neuronal autophagy to gate depression onset.},
journal = {Nature},
volume = {641},
number = {8062},
pages = {427-437},
pmid = {40205038},
issn = {1476-4687},
mesh = {*Autophagy/drug effects/physiology ; Animals ; Mice ; *Neurons/pathology/drug effects/metabolism ; *Stress, Psychological/pathology/physiopathology/drug therapy/complications ; Antidepressive Agents/pharmacology/therapeutic use ; Male ; *Depression/pathology/drug therapy/physiopathology ; Habenula/drug effects/pathology/cytology/physiopathology ; Synaptic Transmission/drug effects ; Neuronal Plasticity/drug effects ; Homeostasis/drug effects ; Chronic Disease ; Mice, Inbred C57BL ; },
abstract = {Chronic stress remodels brain homeostasis, in which persistent change leads to depressive disorders[1]. As a key modulator of brain homeostasis[2], it remains elusive whether and how brain autophagy is engaged in stress dynamics. Here we discover that acute stress activates, whereas chronic stress suppresses, autophagy mainly in the lateral habenula (LHb). Systemic administration of distinct antidepressant drugs similarly restores autophagy function in the LHb, suggesting LHb autophagy as a common antidepressant target. Genetic ablation of LHb neuronal autophagy promotes stress susceptibility, whereas enhancing LHb autophagy exerts rapid antidepressant-like effects. LHb autophagy controls neuronal excitability, synaptic transmission and plasticity by means of on-demand degradation of glutamate receptors. Collectively, this study shows a causal role of LHb autophagy in maintaining emotional homeostasis against stress. Disrupted LHb autophagy is implicated in the maladaptation to chronic stress, and its reversal by autophagy enhancers provides a new antidepressant strategy.},
}
@article {pmid40204716,
year = {2025},
author = {Amann, LK and Casasnovas, V and Gail, A},
title = {Visual target and task-critical feedback uncertainty impair different stages of reach planning in motor cortex.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {3372},
pmid = {40204716},
issn = {2041-1723},
support = {H2020-FETPROACT-16 732266 WP1//European Commission (EC)/ ; ZN3422//Nieders&#xe4;chsische Ministerium f&#xfc;r Wissenschaft und Kultur (Lower Saxony Ministry of Science and Culture)/ ; SFB-889 C4//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; SFB 1690 B09//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; },
mesh = {Animals ; Macaca mulatta ; Male ; *Motor Cortex/physiology ; Uncertainty ; *Feedback, Sensory/physiology ; *Psychomotor Performance/physiology ; Movement/physiology ; Brain-Computer Interfaces ; Hand/physiology ; Visual Perception/physiology ; },
abstract = {Sensory uncertainty jeopardizes accurate movement. During reaching, visual uncertainty can affect the estimation of hand position (feedback) and the desired movement endpoint (target). While impairing motor learning, it is unclear how either form of uncertainty affects cortical reach goal encoding. We show that reach trajectories vary more with higher visual uncertainty of the target, but not the feedback. Accordingly, cortical motor goal activities in male rhesus monkeys are less accurate during planning and movement initiation under target but not feedback uncertainty. Yet, when monkeys critically depend on visual feedback to conduct reaches via a brain-computer interface, then visual feedback uncertainty impairs reach accuracy and neural motor goal encoding around movement initiation. Neural state space analyses reveal a dimension that separates population activity by uncertainty level in all tested conditions. Our findings demonstrate that while both target and feedback uncertainty always reflect in neural activity, uncertain feedback only deteriorates neural reach goal information and behavior when it is task-critical, i.e., when having to rely on the sensory feedback and no other more reliable sensory modalities are available. Further, uncertain target and feedback impair reach goal encoding in a time-dependent manner, suggesting that they are integrated during different stages of reach planning.},
}
@article {pmid40204228,
year = {2025},
author = {Hasegawa, R and Poulin, R},
title = {Effect of parasite infections on fish body condition: a systematic review and meta-analysis.},
journal = {International journal for parasitology},
volume = {55},
number = {8-9},
pages = {417-426},
doi = {10.1016/j.ijpara.2025.03.002},
pmid = {40204228},
issn = {1879-0135},
mesh = {Animals ; *Fish Diseases/parasitology/pathology ; *Fishes/parasitology ; *Parasitic Diseases, Animal/parasitology/pathology ; Host-Parasite Interactions ; },
abstract = {Using host body condition indices (BCIs) based on the relationship between host body mass and length is a general and pervasive approach to assess the negative effects of parasites on host health. Although many researchers, especially fish biologists and fisheries managers, commonly utilize BCIs, the overall general patterns among BCI - infection relationships remain unclear. Here, we first systematically reviewed 985 fish BCI - infection relationships from 216 publications and investigated the factors affecting the strength and directionality of effects in BCI - infection relationships. We specifically predicted that the BCI measure used, parasite taxonomic group, and the infection measure used would influence the observed effect size and directionality of BCI - infection relationships. We found that most studies were heavily biased towards specific BCI measures such as Fulton's BCI and Relative BCI. Furthermore, studies using Fulton's BCI were more likely to report significant results compared with those using other BCI measures, suggesting that index choice could lead to an overestimation of the negative effects of parasites. Our meta-regressions uncovered that the use of parasite intensity as an infection measure and studies based on experimental rather than natural infections were more likely to report significant negative effects, however there were no differences among parasite taxonomic groups. Surprisingly, many studies, especially field studies, did not report significant negative correlations between BCI and infection, contrary to widespread expectations among researchers that parasites would negatively affect fish health. We discuss potential mechanisms underlying these results. Finally, we make several recommendations for the use of BCI - infection relationships in future studies.},
}
@article {pmid40204168,
year = {2025},
author = {Tan, H and Hu, YT and Goudswaard, A and Li, YJ and Balesar, R and Swaab, D and Bao, AM},
title = {Increased oxytocin/vasopressin ratio in bipolar disorder in a cohort of human postmortem adults.},
journal = {Neurobiology of disease},
volume = {209},
number = {},
pages = {106904},
doi = {10.1016/j.nbd.2025.106904},
pmid = {40204168},
issn = {1095-953X},
mesh = {Humans ; *Oxytocin/metabolism ; Female ; Male ; *Bipolar Disorder/metabolism/pathology ; Middle Aged ; Adult ; *Supraoptic Nucleus/metabolism ; *Vasopressins/metabolism ; *Paraventricular Hypothalamic Nucleus/metabolism ; Cohort Studies ; Depressive Disorder, Major/metabolism ; Aged ; *Arginine Vasopressin/metabolism ; },
abstract = {Bipolar disorder (BD) and major depressive disorder (MDD) share some common characteristics in stress-related brain circuits, but they also exhibit distinct symptoms. Our previous postmortem research on the immunoreactivity (ir) levels of neuropeptide oxytocin (OT) in the hypothalamic paraventricular nucleus (OT[PVN]) and some clinical research on plasma OT levels suggested that increased levels of OT is a potential trait marker for BD. However, dysregulation of the related neuropeptide arginine vasopressin (AVP), that often shows opposite effects for stress responses compared to OT has not been investigated in BD. Moreover, it remains so far unknown what the contribution may be of OT produced in the hypothalamic supraoptic nucleus (SON), another major source of OT (OT[SON]). Therefore, in the present postmortem study, alterations in levels of OT-ir and for the first time in AVP-ir were determined in the SON and PVN among patients with BD, MDD, and matched controls. We observed a significantly increased OT[PVN]-ir but relatively stable AVP[PVN]-ir in male BD, and a significantly decreased AVP[PVN]-ir but relatively stable OT[PVN]-ir in female BD patients. A significantly increased ratio of OT-ir/AVP-ir was observed only in BD patients in both, the PVN and SON. No significant changes in OT-ir or AVP-ir were found in MDD patients compared with controls. Our data illustrate a clear disease- and sex-specificity of the OT and AVP changes in BD. In addition, since increased AVP-ir was observed in female BD patients with lithium nephropathy, increased AVP may have a direct effect on symptoms of BD.},
}
@article {pmid40203859,
year = {2025},
author = {Pang, Y and Wang, X and Zhao, Z and Han, C and Gao, N},
title = {Multi-view collaborative ensemble classification for EEG signals based on 3D second-order difference plot and CSP.},
journal = {Physics in medicine and biology},
volume = {70},
number = {8},
pages = {},
doi = {10.1088/1361-6560/adcafa},
pmid = {40203859},
issn = {1361-6560},
mesh = {*Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; *Imaging, Three-Dimensional/methods ; Brain-Computer Interfaces ; },
abstract = {Objective.EEG signal analysis methods based on electrical source imaging (ESI) technique have significantly improved classification accuracy and response time. However, for the refined and informative source signals, the current studies have not fully considered their dynamic variability in feature extraction and lacked an effective integration of their dynamic variability and spatial characteristics. Additionally, the adaptability and complementarity of classifiers have not been considered comprehensively. These two aspects lead to the issue of insufficient decoding of source signals, which still limits the application of brain-computer interface (BCI). To address these challenges, this paper proposes a multi-view collaborative ensemble classification method for EEG signals based on three-dimensional second-order difference plot (3D SODP) and common spatial pattern.Approach.First, EEG signals are mapped to the source domain using the ESI technique, and then the source signals in the region of interest are obtained. Next, features from three viewpoints of the source signals are extracted, including 3D SODP features, spatial features, and the weighted fusion of both. Finally, the extracted multi-view features are integrated with subject-specific sub-classifier combination, and a voting mechanism is used to determine the final classification.Main results.The results show that the proposed method achieves classification accuracy of 81.3% and 82.6% respectively in two sessions of the OpenBMI dataset, which is nearly 5% higher than the state-of-the-art method, and maintains the analysis response time required for online BCI.Significance.This paper employs multi-view feature extraction to fully capture the characteristics of the source signals and enhances feature utilization through collaborative ensemble classification. The results demonstrate high accuracy and robust performance, providing a novel approach for online BCI.},
}
@article {pmid40203855,
year = {2025},
author = {Yin, S and Yue, Z and Qu, H and Wang, J and Shi, B and Zhang, J},
title = {Enhancing lower-limb motor imagery using a paradigm with visual and spatiotemporal tactile synchronized stimulation.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adcaec},
pmid = {40203855},
issn = {1741-2552},
mesh = {Humans ; Male ; Female ; *Imagination/physiology ; Adult ; *Brain-Computer Interfaces ; *Lower Extremity/physiology ; Young Adult ; *Photic Stimulation/methods ; *Touch/physiology ; *Motor Cortex/physiology ; Physical Stimulation/methods ; Electroencephalography/methods ; Psychomotor Performance/physiology ; },
abstract = {Objective.Vibrotactile stimulation (VS) has been widely used as an appropriate motor imagery (MI) guidance strategy to improve MI performance. However, most VS induced by a single vibrator cannot provide spatiotemporal information of tactile sensation associated with the visual guidance of the imagined motion process, not vividly providing MI guidance for subjects.Approach.This paper proposed a paradigm with visual and spatiotemporal tactile synchronized stimulation (VSTSS) to provide vivid MI guidance to help subjects perform lower-limb MI tasks and improve MI-based brain-computer interface (MI-BCI) performance, with a focus on poorly performing subjects. The proposed paradigm provided subjects with the natural spatiotemporal tactile sensation associated with the visual guidance of the foot movement process during MI. Fourteen healthy subjects were recruited to participate in the MI and Rest tasks and divided into good and poor performers. Furthermore, electrophysiological features and classification performance were analyzed to assess motor cortical activation and MI-BCI performance under no VS (NVS), VS, and VSTSS.Main results.The phenomenon of event-related desynchronization (ERD) in the sensorimotor cortex during MI under the VSTSS was more pronounced compared to the NVS and VS. Specifically, the VSTSS could improve the average ERD values in the motor cortex during the task segment by 34.70% and 14.28% than the NVS and VS in the alpha rhythm for poor performers, respectively. Additionally, the VSTSS could significantly enhance the classification accuracy between the MI and Rest tasks by 12.52% and 4.05% compared to NVS and VS for poor performers, respectively.Significance.The proposed paradigm could enhance motor cortical activation during MI and improve classification performance by providing vivid MI guidance for subjects, offering a promise for the application of lower-limb MI-BCI in stroke rehabilitation in the future.},
}
@article {pmid40203854,
year = {2025},
author = {Collinger, JL and Vansteensel, MJ and Mrachacz-Kersting, N and Mattia, D and Valeriani, D and Vaughan, TM},
title = {Special issue on brain-computer interfaces: highlighting research from the 10th International Brain-Computer Interface Meeting.},
journal = {Journal of neural engineering},
volume = {22},
number = {3},
pages = {},
doi = {10.1088/1741-2552/adcaed},
pmid = {40203854},
issn = {1741-2552},
}
@article {pmid40203098,
year = {2025},
author = {Sîmpetru, RC and Braun, DI and Simon, AU and März, M and Cnejevici, V and de Oliveira, DS and Weber, N and Walter, J and Franke, J and Höglinger, D and Prahm, C and Ponfick, M and Del Vecchio, A},
title = {MyoGestic: EMG interfacing framework for decoding multiple spared motor dimensions in individuals with neural lesions.},
journal = {Science advances},
volume = {11},
number = {15},
pages = {eads9150},
pmid = {40203098},
issn = {2375-2548},
mesh = {Humans ; Male ; Female ; Young Adult ; Adult ; Middle Aged ; *Electromyography/instrumentation ; *Spinal Cord Injuries/rehabilitation ; *Stroke Rehabilitation/instrumentation ; *Amputation, Surgical/rehabilitation ; *Brain-Computer Interfaces ; Machine Learning ; Psychomotor Performance ; Intention ; Software Validation ; },
abstract = {Restoring motor function in individuals with spinal cord injuries (SCIs), strokes, or amputations is a crucial challenge. Recent studies show that spared motor neurons can still be voluntarily controlled using surface electromyography (EMG), even without visible movement. To harness these signals, we developed a wireless, high-density EMG bracelet and a software framework, MyoGestic. Our system enables rapid adaptation of machine learning models to users' needs, allowing real-time decoding of spared motor dimensions. In our study, we successfully decoded motor intent from two participants with traumatic SCI, two with spinal stroke, and three with amputations in real time, achieving multiple controllable motor dimensions within minutes. The decoded neural signals could control a digitally rendered hand, an orthosis, a prosthesis, or a two-dimensional cursor. MyoGestic's participant-centered approach allows a collaborative and iterative development of myocontrol algorithms, bridging the gap between researcher and participant, to advance intuitive EMG interfaces for neural lesions.},
}
@article {pmid40199879,
year = {2025},
author = {Zhao, Y and Wu, JT and Feng, JB and Cai, XY and Wang, XT and Wang, L and Xie, W and Gu, Y and Liu, J and Chen, W and Zhou, L and Shen, Y},
title = {Dual and plasticity-dependent regulation of cerebello-zona incerta circuits on anxiety-like behaviors.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {3339},
pmid = {40199879},
issn = {2041-1723},
support = {2021ZD0204000//Ministry of Science and Technology of the People&apos;s Republic of China (Chinese Ministry of Science and Technology)/ ; 2023YFE0206800//Ministry of Science and Technology of the People&apos;s Republic of China (Chinese Ministry of Science and Technology)/ ; 2021ZD0204000//Ministry of Science and Technology of the People&apos;s Republic of China (Chinese Ministry of Science and Technology)/ ; 81625006//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31820103005//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32200620//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32225021//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32170976//National Natural Science Foundation of China (National Science Foundation of China)/ ; LY21C090003//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
mesh = {Animals ; *Anxiety/physiopathology/metabolism ; Male ; *Neuronal Plasticity/physiology ; Mice ; *Zona Incerta/physiology/physiopathology ; *Cerebellum/physiology ; Mice, Inbred C57BL ; *Cerebellar Nuclei/physiology ; Neurons/physiology/metabolism ; Behavior, Animal/physiology ; Glutamic Acid/metabolism ; Neural Pathways ; Synaptic Transmission/physiology ; },
abstract = {Clinical observation has identified cerebellar cognitive affective syndrome, which is characterized by various non-motor dysfunctions such as social disorders and anxiety. Increasing evidence has revealed reciprocal mono-/poly-synaptic connections of cerebello-cerebral circuits, forming the concept of the cerebellar connectome. In this study, we demonstrate that neurons in the cerebellar nuclei (CN) of male mice project to a subset of zona incerta (ZI) neurons through long-range glutamatergic and GABAergic transmissions, both capable of encoding acute stress. Furthermore, activating or inhibiting glutamatergic and GABAergic transmissions in the CN → ZI pathway can positively or negatively regulate anxiety and place preference through presynaptic plasticity-dependent mechanisms, as well as mediate motor-induced alleviation of anxiety. Our data support the close relationship between the cerebellum and emotional processes and suggest that targeting cerebellar outputs may be an effective approach for treating anxiety.},
}
@article {pmid40199863,
year = {2025},
author = {Guttmann-Flury, E and Sheng, X and Zhu, X},
title = {Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {587},
pmid = {40199863},
issn = {2052-4463},
support = {91948302//National Natural Science Foundation of China (National Science Foundation of China)/ ; 91948302//National Natural Science Foundation of China (National Science Foundation of China)/ ; 91948302//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Electroencephalography ; *Brain-Computer Interfaces ; *Eye-Tracking Technology ; *Eye Movements ; Blinking ; Video Recording ; },
abstract = {In Brain-Computer Interface (BCI) research, the detailed study of blinks is crucial. They can be considered as noise, affecting the efficiency and accuracy of decoding users' cognitive states and intentions, or as potential features, providing valuable insights into users' behavior and interaction patterns. We introduce a large dataset capturing electroencephalogram (EEG) signals, eye-tracking, high-speed camera recordings, as well as subjects' mental states and characteristics, to provide a multifactor analysis of eye-related movements. Four paradigms - motor imagery, motor execution, steady-state visually evoked potentials, and P300 spellers - are selected due to their capacity to evoke various sensory-motor responses and potential influence on ocular activity. This online-available dataset contains over 46 hours of data from 31 subjects across 63 sessions, totaling 2520 trials for each of the first three paradigms, and 5670 for P300. This multimodal and multi-paradigms dataset is expected to allow the development of algorithms capable of efficiently handling eye-induced artifacts and enhancing task-specific classification. Furthermore, it offers the opportunity to evaluate the cross-paradigm robustness involving the same participants.},
}
@article {pmid40198632,
year = {2025},
author = {Ueda, M and Ueno, K and Inoue, T and Sakiyama, M and Shiroma, C and Ishii, R and Naito, Y},
title = {Detection of motor-related mu rhythm desynchronization by ear EEG.},
journal = {PloS one},
volume = {20},
number = {4},
pages = {e0321107},
pmid = {40198632},
issn = {1932-6203},
mesh = {Humans ; Male ; *Electroencephalography/methods/instrumentation ; Female ; Adult ; Young Adult ; Hand/physiology ; Movement/physiology ; *Ear/physiology ; Brain-Computer Interfaces ; },
abstract = {Event-related desynchronization (ERD) of the mu rhythm (8-13 Hz) is an important indicator of motor execution, neurofeedback, and brain-computer interface in EEG. This study investigated the feasibility of an ear electroencephalography (EEG) device monitoring mu-ERD during hand grasp and release movements. The EEG data of the right hand movement and the eye opened resting condition were measured with an ear EEG device. We calculated and compared mu rhythm power and time-frequency data from 20 healthy participants during right hand movement and eye opened resting. Our results showed a significant difference of mean mu rhythm power between the eye opened rest condition and the right hand movement condition and significant suppression in the 9-12.5 Hz frequency band in the time-frequency data. These results support the utility of ear EEG in detecting motor activity-related mu-ERD. Ear EEG could be instrumental in refining rehabilitation strategies by providing in-situ assessment of motor function and tailored feedback.},
}
@article {pmid40198304,
year = {2025},
author = {Wang, Z and Li, A and Wang, Z and Zhou, T and Xu, T and Hu, H},
title = {BSAN: A Self-Adapted Motor Imagery Decoding Framework Based on Contextual Information.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {8},
pages = {5610-5621},
doi = {10.1109/JBHI.2025.3557499},
pmid = {40198304},
issn = {2168-2208},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Imagination/physiology ; *Signal Processing, Computer-Assisted ; *Neural Networks, Computer ; Adult ; Male ; Algorithms ; Female ; Brain/physiology ; },
abstract = {In motor imagery (MI) decoding, it still remains challenging to excavate enough contextual information of MI in different brain regions and to bridge the cross-session variance in feature distributions. In light of these issues, our study presents an innovative Bi-Stream Adaptation Network (BSAN) to bolster network efficacy, aiming to improve MI-based brain-computer interface (BCI) robustness across sessions. Our framework consists of the Bi-attention module, feature extractor, classifier, and Bi-discriminator. Precisely, we devise the Bi-attention module to reveal granular context information of MI with performing multi-scale convolutions asymptotically. Then, after features extraction, Bi-discriminator is involved to align the features from different MI sessions such that a uniform and accurate representation of neural patterns is achieved. By such a workflow, the proposed BSAN allows for the effective fusion of context coherence and session-invariance within the network architecture, therefore diminishing the reliance of redundant MI trials for MI-BCI re-calibration. To empirically substantiate BSAN, comprehensive experiments are conducted based on two public MI datasets. With average accuracies of 78.97% and 83.79% on two public datasets, and an inference time of 2.99 ms on CPU-only devices, it is believed that our approach has the potential to accelerate the practical deployment of MI-BCI.},
}
@article {pmid40197656,
year = {2025},
author = {Won, C and Cho, S and Jang, KI and Park, JU and Cho, JH and Lee, T},
title = {Emerging fiber-based neural interfaces with conductive composites.},
journal = {Materials horizons},
volume = {12},
number = {13},
pages = {4545-4572},
doi = {10.1039/d4mh01854k},
pmid = {40197656},
issn = {2051-6355},
mesh = {Electric Conductivity ; Humans ; *Brain-Computer Interfaces ; Animals ; Polymers/chemistry ; },
abstract = {Neural interfaces that enable bidirectional communication between neural systems and external devices are crucial for treating neurological disorders and advancing brain-machine interfaces. Key requirements for these neural interfaces are the ability to modulate electrophysiological activity without causing tissue damage in the nerve system and long-term usability. Recent advances in biomedical neural electrodes aim to reduce mechanical mismatch between devices and surrounding tissues/organs while maintaining their electrical conductivity. Among these, fiber electrodes stand out as essential candidates for future neural interfaces owing to their remarkable flexibility, controllable scalability, and facile integration with systems. Herein, we introduce fiber-based devices with conductive composites, along with their fabrication technologies, and integration strategies for future neural interfaces. Compared to conventional neural electrodes, fiber electrodes readily combine with conductive materials such as metal nanoparticles, carbon-based nanomaterials, and conductive polymers. Their fabrication technologies enable high electrical performance without sacrificing mechanical properties. In addition, the neural modulation techniques of fiber electrodes; electrical, optical, and chemical, and their applications in central and peripheral nervous systems are carefully discussed. Finally, current limitations and potential advancements in fiber-based neural interfaces are highlighted for future innovations.},
}
@article {pmid40196469,
year = {2025},
author = {Tor, A and Clarke, SE and Bray, IE and Nuyujukian, P},
title = {Material Damage to Multielectrode Arrays after Electrolytic Lesioning is in the Noise.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {40196469},
issn = {2692-8205},
support = {R01 NS123517/NS/NINDS NIH HHS/United States ; R01 NS130789/NS/NINDS NIH HHS/United States ; U19 NS118284/NS/NINDS NIH HHS/United States ; },
abstract = {The quality of stable long-term recordings from chronically implanted electrode arrays is essential for experimental neuroscience and brain-computer interfaces. This work uses scanning electron microscopy (SEM) to image and analyze eight 96-channel Utah arrays previously implanted in motor cortical regions of four subjects (subject H = 2242 days implanted, F = 1875, U = 2680, C = 594), providing important contributions to a growing body of long-term implant research leveraging this imaging technology. Four of these arrays have been used in electrolytic lesioning experiments (H = 10 lesions, F = 1, U = 4, C = 1), a novel electrolytic perturbation technique using small direct currents. In addition to surveying physical damage, such as biological debris and material deterioration, this work also analyzes whether electrolytic lesioning created damage beyond what is typical for these arrays. Each electrode was scored in six damage categories, identified from the literature: abnormal debris, metal coating cracks, silicon tip breakage, parylene C delamination, parylene C cracks, and shank fracture. This analysis confirms previous results that observed damage on explanted arrays is more severe on the outer-edge electrodes versus inner electrodes. These findings also indicate that are no statistically significant differences between the damage observed on normal electrodes versus electrodes used for electrolytic lesioning. This work provides evidence that electrolytic lesioning does not significantly affect the quality of chronically implanted electrode arrays and can be a useful tool in understanding perturbations to neural systems. Finally, this work also includes the largest collection of single-electrode SEM images for previously implanted multielectrode Utah arrays, spanning eleven different intact arrays and one broken array. As the clinical relevance of chronically implanted electrodes with single-neuron resolution continues to grow, these images may be used to provide the foundation for a larger public database and inform further electrode design and analyses.},
}
@article {pmid40196347,
year = {2025},
author = {Yu, H and Mu, Q and Wang, Z and Guo, Y and Zhao, J and Wang, G and Wang, Q and Meng, X and Dong, X and Wang, S and Sun, J},
title = {A study on early diagnosis for fracture non-union prediction using deep learning and bone morphometric parameters.},
journal = {Frontiers in medicine},
volume = {12},
number = {},
pages = {1547588},
pmid = {40196347},
issn = {2296-858X},
abstract = {BACKGROUND: Early diagnosis of non-union fractures is vital for treatment planning, yet studies using bone morphometric parameters for this purpose are scarce. This study aims to create a fracture micro-CT image dataset, design a deep learning algorithm for fracture segmentation, and develop an early diagnosis model for fracture non-union.<br><br>METHODS: Using fracture animal models, micro-CT images from 12 rats at various healing stages (days 1, 7, 14, 21, 28, and 35) were analyzed. Fracture lesion frames were annotated to create a high-resolution dataset. We proposed the Vision Mamba Triplet Attention and Edge Feature Decoupling Module UNet (VM-TE-UNet) for fracture area segmentation. And we extracted bone morphometric parameters to establish an early diagnostic evaluation system for the non-union of fractures.<br><br>RESULTS: A dataset comprising 2,448 micro-CT images of the rat fracture lesions with fracture Region of Interest (ROI), bone callus and healing characteristics was established and used to train and test the proposed VM-TE-UNet which achieved a Dice Similarity Coefficient of 0.809, an improvement over the baseline's 0.765, and reduced the 95th Hausdorff Distance to 13.1. Through ablation studies, comparative experiments, and result analysis, the algorithm's effectiveness and superiority were validated. Significant differences (p < 0.05) were observed between the fracture and fracture non-union groups during the inflammatory and repair phases. Key indices, such as the average CT values of hematoma and cartilage tissues, BS/TS and BS/TV of mineralized cartilage, BS/TV of osteogenic tissue, and BV/TV of osteogenic tissue, align with clinical methods for diagnosing fracture non-union by assessing callus presence and local soft tissue swelling. On day 14, the early diagnosis model achieved an AUC of 0.995, demonstrating its ability to diagnose fracture non-union during the soft-callus phase.<br><br>CONCLUSION: This study proposed the VM-TE-UNet for fracture areas segmentation, extracted micro-CT indices, and established an early diagnostic model for fracture non-union. We believe that the prediction model can effectively screen out samples of poor fracture rehabilitation caused by blood supply limitations in rats 14 days after fracture, rather than the widely accepted 35 or 40 days. This provides important reference for the clinical prediction of fracture non-union and early intervention treatment.},
}
@article {pmid40196232,
year = {2025},
author = {Yang, Y and Zhao, H and Hao, Z and Shi, C and Zhou, L and Yao, X},
title = {Recognition of brain activities via graph-based long short-term memory-convolutional neural network.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1546559},
pmid = {40196232},
issn = {1662-4548},
abstract = {INTRODUCTION: Human brain activities are always difficult to recognize due to its diversity and susceptibility to disturbance. With its unique capability of measuring brain activities, magnetoencephalography (MEG), as a high temporal and spatial resolution neuroimaging technique, has been used to identify multi-task brain activities. Accurately and robustly classifying motor imagery (MI) and cognitive imagery (CI) from MEG signals is a significant challenge in the field of brain-computer interface (BCI).<br><br>METHODS: In this study, a graph-based long short-term memory-convolutional neural network (GLCNet) is proposed to classify the brain activities in MI and CI tasks. It was characterized by implementing three modules of graph convolutional network (GCN), spatial convolution and long short-term memory (LSTM) to effectively extract time-frequency-spatial features simultaneously. For performance evaluation, our method was compared with six benchmark algorithms of FBCSP, FBCNet, EEGNet, DeepConvNets, Shallow ConvNet and MEGNet on two public datasets of MEG-BCI and BCI competition IV dataset 3.<br><br>RESULTS: The results demonstrated that the proposed GLCNet outperformed other models with the average accuracies of 78.65% and 65.8% for two classification and four classification on the MEG-BCI dataset, respectively.<br><br>DISCUSSION: It was concluded that the GLCNet enhanced the model's adaptability in handling individual variability with robust performance. This would contribute to the exploration of brain activates in neuroscience.},
}
@article {pmid40195935,
year = {2025},
author = {Hasegawa, R and Poulin, R},
title = {Cause or consequence? Exploring authors' interpretations of correlations between fish body condition and parasite infection.},
journal = {Journal of fish biology},
volume = {107},
number = {2},
pages = {658-661},
pmid = {40195935},
issn = {1095-8649},
support = {202460294//Japan Society for the Promotion of Science/ ; JP22KJ0086//Japan Society for the Promotion of Science/ ; },
abstract = {We reviewed 194 publications that reported relationships between fish body condition indices (BCIs) and parasite infections, and examined the authors' intention behind this cross-sectional analysis, that is, whether authors interpreted the negative correlations as the negative effects of parasites or as fish with poor BCIs being more susceptible to infections. While 89% of studies only considered parasite infections as causes of poor BCI, studies acknowledging the opposite or bidirectional causal links were rare. We recommend considering both possibilities in any given fish host and parasite association.},
}
@article {pmid40195900,
year = {2025},
author = {Shin, H and Kim, K and Lee, J and Nam, J and Baeg, E and You, C and Choi, H and Kim, M and Chung, CK and Kim, JG and Ahn, JH and Han, M and Kim, J and Yang, S and Lee, SQ and Yang, S},
title = {A Wireless Cortical Surface Implant for Diagnosing and Alleviating Parkinson's Disease Symptoms in Freely Moving Animals.},
journal = {Advanced healthcare materials},
volume = {14},
number = {17},
pages = {e2405179},
pmid = {40195900},
issn = {2192-2659},
support = {//High Risk, High Return Research Program/ ; //ETRI grant (23YB1210, Collective Behavioral Modelling in Socially Interacting Group)/ ; },
mesh = {Animals ; *Parkinson Disease/diagnosis/therapy/physiopathology ; *Wireless Technology ; *Motor Cortex/physiopathology ; Electrodes, Implanted ; Male ; Rats ; Graphite/chemistry ; Disease Models, Animal ; },
abstract = {Parkinson's disease (PD), one of the most common neurodegenerative diseases, is involved in motor abnormality, primarily arising from the degeneration of dopaminergic neurons. Previous studies have examined the electrotherapeutic effects of PD using various methodological contexts, including live conditions, wireless control, diagnostic/therapeutic aspects, removable interfaces, or biocompatible materials, each of which is separately utilized for testing the diagnosis or alleviation of various brain diseases. Here, a cortical surface implant designed to improve motor function in freely moving PD animals is presented. This implant, a minimally invasive system equipped with a graphene electrode array, is the first integrated system to exhibit biocompatibility, wearability, removability, target specificity, and wireless control. The implant positioned at the motor cortical surface activates the motor cortex to maximize therapeutic effects and minimize off-target effects while monitoring motor activities. In PD animals, cortical motor surface stimulation restores motor function and brain waves, which corresponds to potentiated synaptic responses. Furthermore, these changes are associated with the upregulation of metabotropic glutamate receptor 5 (mGluR5, Grm5) and D5 dopamine receptor (D5R, Drd5) genes in the glutamatergic synapse. The newly designed wireless neural implant demonstrates capabilities in both real-time diagnostics and targeted therapeutics, suggesting its potential as a wireless system for biomedical devices for patients with PD and other neurodegenerative diseases.},
}
@article {pmid40195429,
year = {2025},
author = {Ming, Z and Yu, W and Fan, J and Ling, G and Fengming, C and Wei, T},
title = {Efficacy of kinesthetic motor imagery based brain computer interface combined with tDCS on upper limb function in subacute stroke.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {11829},
pmid = {40195429},
issn = {2045-2322},
support = {XWRCHT20220045//the Xuzhou Key Medical Talents Project/ ; No.52375224//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Transcranial Direct Current Stimulation/methods ; Male ; Female ; *Upper Extremity/physiopathology ; *Brain-Computer Interfaces ; Middle Aged ; *Stroke Rehabilitation/methods ; *Stroke/physiopathology/therapy ; Aged ; Electroencephalography ; *Kinesthesis/physiology ; Treatment Outcome ; Adult ; },
abstract = {This study investigates whether the combined effect of kinesthetic motor imagery-based brain computer interface (KI-BCI) and transcranial direct current stimulation (tDCS) on upper limb function in subacute stroke patients is more effective than using KI-BCI or tDCS alone. Forty-eight subacute stroke survivors were randomized to the KI-BCI, tDCS, or BCI-tDCS group. The KI-BCI group performed 30 min of KI-BCI training. Patients in tDCS group received 30 min of tDCS. Patients in BCI-tDCS group received 15 min of tDCS and 15 min of KI-BCI. The treatment cycle was five times a week, for four weeks. After all intervention, the Fugl-Meyer Assessment-Upper Extremity, Motor Status Scale, and the Modified Barthel Index scores of the KI-BCI group were superior to those of the tDCS group. The BCI-tDCS group was superior to the tDCS group in terms of the Motor Status Scale. Although quantitative EEG showed no significant group differences, the quantitative EEG indices in the tDCS group were significantly lower than before treatment. In conclusion, after treatment, although all intervention strategies improved upper limb motor function and daily living abilities in subacute stroke patients, KI-BCI demonstrated significantly better efficacy than tDCS. Under the same total treatment duration, the combined use of tDCS and KI-BCI did not achieve the hypothesized optimal outcome. Notably, tDCS reduced QEEG indices, possibly indicating favorable future outcomes in future.Trial registry number: ChiCTR2000034730.},
}
@article {pmid40194524,
year = {2025},
author = {Johnson, TR and Haddix, CA and Ajiboye, AB and Taylor, DM},
title = {Simplified control of neuromuscular stimulation systems for restoration of reach with limb stiffness as a modifiable degree of freedom.},
journal = {Journal of neural engineering},
volume = {22},
number = {3},
pages = {},
pmid = {40194524},
issn = {1741-2552},
support = {R01 NS119160/NS/NINDS NIH HHS/United States ; T32 AR007505/AR/NIAMS NIH HHS/United States ; },
mesh = {Humans ; Animals ; Macaca mulatta ; *Muscle, Skeletal/physiology/innervation ; *Arm/physiology/innervation ; Computer Simulation ; *Electric Stimulation Therapy/methods ; Biomechanical Phenomena ; },
abstract = {Objective.Brain-controlled functional electrical stimulation (FES) of the upper limb has been used to restore arm function to paralyzed individuals in the lab. Able-bodied individuals naturally modulate limb stiffness throughout movements and in anticipation of perturbations. Our goal is to develop, via simulation, a framework for incorporating stiffness modulation into the currently-used 'lookup-table-based' FES control systems while addressing several practical issues: (1) optimizing stimulation across muscles with overlap in function, (2) coordinating stimulation across joints, and (3) minimizing errors due to fatigue. Our calibration process also needs to account for when current spread causes additional muscles to become activated.Approach.We developed an analytical framework for building a lookup-table-based FES controller and simulated the clinical process of calibrating and using the arm. A computational biomechanical model of a human paralyzed arm responding to stimulation was used for simulations with six muscles controlling the shoulder and elbow in the horizontal plane. Both joints had multiple muscles with overlapping functional effects, as well as biarticular muscles to reflect complex interactions between joints. Performance metrics were collectedin silico,and real-time use was demonstrated with a Rhesus macaque using its cortical signals to control the computational arm model in real time.Main results.By explicitly including stiffness as a definable degree of freedom in the lookup table, our analytical approach was able to achieve all our performance criteria. While using more empirical data during controller parameterization produced more accurate lookup tables, interpolation between sparsely sampled points (e.g. 20° angular intervals) still produced good results with median endpoint position errors of less than 1 cm-a range that should be easy to correct for with real-time visual feedback.Significance.Our simplified process for generating an effective FES controller now makes translating upper limb FES systems into mainstream clinical practice closer to reality.},
}
@article {pmid40193612,
year = {2025},
author = {Kim, H and Kim, JH and Lee, YJ and Lee, J and Han, H and Yi, H and Kim, H and Kim, H and Kang, TW and Chung, S and Ban, S and Lee, B and Lee, H and Im, CH and Cho, SJ and Sohn, JW and Yu, KJ and Kang, TJ and Yeo, WH},
title = {Motion artifact-controlled micro-brain sensors between hair follicles for persistent augmented reality brain-computer interfaces.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {122},
number = {15},
pages = {e2419304122},
pmid = {40193612},
issn = {1091-6490},
support = {ECCS-2025462//NSF (NSF)/ ; P0017303//Korea Institute for Advancement of Technology (KIAT)/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Hair Follicle/physiology ; Electroencephalography/methods/instrumentation ; *Augmented Reality ; Artifacts ; *Brain/physiology ; Motion ; Algorithms ; Electrodes ; Evoked Potentials, Visual/physiology ; },
abstract = {Modern brain-computer interfaces (BCI), utilizing electroencephalograms for bidirectional human-machine communication, face significant limitations from movement-vulnerable rigid sensors, inconsistent skin-electrode impedance, and bulky electronics, diminishing the system's continuous use and portability. Here, we introduce motion artifact-controlled micro-brain sensors between hair strands, enabling ultralow impedance density on skin contact for long-term usable, persistent BCI with augmented reality (AR). An array of low-profile microstructured electrodes with a highly conductive polymer is seamlessly inserted into the space between hair follicles, offering high-fidelity neural signal capture for up to 12 h while maintaining the lowest contact impedance density (0.03 kΩ·cm[-2]) among reported articles. Implemented wireless BCI, detecting steady-state visually evoked potentials, offers 96.4% accuracy in signal classification with a train-free algorithm even during the subject's excessive motions, including standing, walking, and running. A demonstration captures this system's capability, showing AR-based video calling with hands-free controls using brain signals, transforming digital communication. Collectively, this research highlights the pivotal role of integrated sensors and flexible electronics technology in advancing BCI's applications for interactive digital environments.},
}
@article {pmid40193313,
year = {2025},
author = {Estivalet, KM and Pettenuzzo, TSA and Mazzilli, NL and Ferreira, LF and Cechetti, F},
title = {The use of brain-machine interface, motor imagery, and action observation in the rehabilitation of individuals with Parkinson's disease: A protocol study for a randomized clinical trial.},
journal = {PloS one},
volume = {20},
number = {4},
pages = {e0315148},
pmid = {40193313},
issn = {1932-6203},
mesh = {Aged ; Female ; Humans ; Male ; Middle Aged ; *Brain-Computer Interfaces ; Cognition ; *Imagery, Psychotherapy/methods ; *Parkinson Disease/rehabilitation/physiopathology ; Single-Blind Method ; Upper Extremity/physiopathology ; },
abstract = {BACKGROUND: Parkinson's disease (PD) is a neurodegenerative condition that impacts motor planning and control of the upper limbs (UL) and leads to cognitive impairments. Rehabilitation approaches, including motor imagery (MI) and action observation (AO), along with the use of brain-machine interfaces (BMI), are essential in the PD population to enhance neuroplasticity and mitigate symptoms.<br><br>OBJECTIVE: To provide a description of a rehabilitation protocol for evaluating the effects of isolated and combined applications of MI and action observation (AO), along with BMI, on upper limb (UL) motor changes and cognitive function in PD.<br><br>METHODS: This study provides a detailed protocol for a single-blinded, randomized clinical trial. After selection, participants will be randomly assigned to one of five experimental groups. Each participant will be assessed at three points: pre-intervention, post-intervention, and at a follow-up four weeks after the intervention ends. The intervention consists of 10 sessions, each lasting approximately 60 minutes.<br><br>EXPECTED RESULTS: The primary outcome expected is an improvement in the Test d'&#xc9;valuation des Membres Sup&#xe9;rieurs de Personnes &#xc2;g&#xe9;es score, accompanied by a reduction in task execution time. Secondary outcomes include motor symptoms in the upper limbs, assessed via the Unified Parkinson's Disease Rating Scale - Part III and the 9-Hole Peg Test; cognitive function, assessed with the PD Cognitive Rating Scale; and occupational performance, assessed with the Canadian Occupational Performance Measure.<br><br>DISCUSSION: This study protocol is notable for its intensive daily sessions. Both MI and AO are low-cost, enabling personalized interventions that physiotherapists and occupational therapists can readily replicate in practice. While BMI use does require professionals to acquire an exoskeleton, the protocol ensures the distinctiveness of the interventions and, to our knowledge, is the first to involve individuals with PD.<br><br>TRIAL REGISTRATION: ClinicalTrials.gov NCT05696925.},
}
@article {pmid40191683,
year = {2025},
author = {Miri, M and Abootalebi, V and Saeedi-Sourck, H and Van De Ville, D and Behjat, H},
title = {Graphs Constructed from Instantaneous Amplitude and Phase of Electroencephalogram Successfully Differentiate Motor Imagery Tasks.},
journal = {Journal of medical signals and sensors},
volume = {15},
number = {},
pages = {7},
pmid = {40191683},
issn = {2228-7477},
abstract = {BACKGROUND: Accurate classification of electroencephalogram (EEG) signals is challenging given the nonlinear and nonstationary nature of the data as well as subject-dependent variations. Graph signal processing (GSP) has shown promising results in the analysis of brain imaging data.<br><br>METHODS: In this article, a GSP-based approach is presented that exploits instantaneous amplitude and phase coupling between EEG time series to decode motor imagery (MI) tasks. A graph spectral representation of the Hilbert-transformed EEG signals is obtained, in which simultaneous diagonalization of covariance matrices provides the basis of a subspace that differentiates two classes of right hand and right foot MI tasks. To determine the most discriminative subspace, an exploratory analysis was conducted in the spectral domain of the graphs by ranking the graph frequency components using a feature selection method. The selected features are fed into a binary support vector machine that predicts the label of the test trials.<br><br>RESULTS: The performance of the proposed approach was evaluated on brain-computer interface competition III (IVa) dataset.<br><br>CONCLUSIONS: Experimental results reflect that brain functional connectivity graphs derived using the instantaneous amplitude and phase of the EEG signals show comparable performance with the best results reported on these data in the literature, indicating the efficiency of the proposed method compared to the state-of-the-art methods.},
}
@article {pmid40189874,
year = {2025},
author = {Li, X and Zhang, J and Shi, B and Li, Y and Wang, Y and Shuai, K and Li, Y and Ming, G and Song, T and Pei, W and Sun, B},
title = {Freestanding Transparent Organic-Inorganic Mesh E-Tattoo for Breathable Bioelectrical Membranes with Enhanced Capillary-Driven Adhesion.},
journal = {ACS applied materials &amp; interfaces},
volume = {17},
number = {15},
pages = {22337-22351},
doi = {10.1021/acsami.5c00565},
pmid = {40189874},
issn = {1944-8252},
mesh = {Humans ; *Wearable Electronic Devices ; *Tattooing ; Brain-Computer Interfaces ; },
abstract = {The electronic tattoo (e-tattoo), a cutting-edge wearable sensor technology adhered to human skin, has garnered significant attention for its potential in brain-computer interfaces (BCIs) and routine health monitoring. Conventionally, flexible substrates with adhesion force on dewy surfaces pursue seamless contact with skin, employing compact airtight substrates, hindering air circulation between skin and the surrounding environment, and compromising long-term wearing comfort. To address these challenges, we have developed a freestanding transparent e-tattoo featuring flexible serpentine mesh bridges with a unique full-breathable multilayer structure. The mesh e-tattoo demonstrates remarkable ductility and air permeability while maintaining robust electronic properties, even after significant mechanical deformation. Furthermore, it exhibits an impressive visible-light transmittance of up to 95%, coupled with a low sheet resistance of 0.268 Ω sq[-1], ensuring both optical clarity and electrical efficiency. By increasing the number of menisci between the mesh e-tattoo and the skin, the total adhesion force increases due to the cumulative capillary-driven effect. We also successfully demonstrated high-quality bioelectric signal collections. In particular, the controlling virtual reality (VR) objects using electrooculogram (EOG) signals collected by mesh e-tattoos were achieved to demonstrate their potential for human-computer interactions (HCIs). This freestanding transparent e-tattoo with a fully breathable mesh structure represents a significant advancement in flexible electrodes for bioelectrical signal monitoring applications.},
}
@article {pmid40189123,
year = {2025},
author = {Wang, F and Ren, J and Cai, Q and Liang, R and Wang, L and Yang, Q and Tian, Y and Zheng, C and Yang, J and Ming, D},
title = {Theta-gamma phase-amplitude coupling as a promising neurophysiological biomarker for evaluating the efficacy of low-intensity focused ultrasound stimulation on vascular dementia treatment.},
journal = {Experimental neurology},
volume = {389},
number = {},
pages = {115237},
doi = {10.1016/j.expneurol.2025.115237},
pmid = {40189123},
issn = {1090-2430},
mesh = {Animals ; *Dementia, Vascular/therapy/physiopathology ; Male ; Rats ; *Gamma Rhythm/physiology ; *Theta Rhythm/physiology ; Rats, Sprague-Dawley ; Hippocampus/physiopathology ; *Ultrasonic Therapy/methods ; Biomarkers ; Treatment Outcome ; Cerebrovascular Circulation/physiology ; },
abstract = {Low-intensity focused ultrasound stimulation (LIFUS) has garnered attention for its potential in vascular dementia (VD) treatment. However, the lack of sufficient data supporting its efficacy and elucidating its mechanisms of action limits its further clinical translation and application. Considerable researches support the idea that LIFUS can improve the disturbance of neural oscillation modes caused by a variety of neurological diseases. However, the effect of LIFUS on neural oscillation modes in VD remains unclear. Therefore, this study aims to investigate the therapeutic effects of LIFUS on neural oscillation modes in VD. To achieve this purpose, the VD model was established via the bilateral common carotid artery occlusion, followed by two weeks of LIFUS treatment targeting the bilateral hippocampus. The therapeutic effects of LIFUS were evaluated by behavioral tests and cerebral blood flow measurement. Electrophysiological signals were recorded from the hippocampal CA1 and CA3 and medial prefrontal cortex (mPFC). The results indicated LIFUS could effectively improve cognitive dysfunction in VD rats. The underlying electrophysiological mechanisms involved the restoration of phase-amplitude coupling (PAC) of theta-gamma oscillations within both the CA3-CA1 local circuit and the hippocampus-mPFC cross-brain circuit. Classification results based on PAC characteristics suggested that PAC metrics are effective for evaluating the efficacy of LIFUS in treating VD, with optimal recognition performance observed in the hippocampus-mPFC cross-brain circuit. Our findings provide neuroelectrophysiological insights into the mechanisms of LIFUS in VD treatment and propose a promising diagnostic biomarker for evaluating LIFUS efficacy in future applications.},
}
@article {pmid40187178,
year = {2025},
author = {S, P and M, S},
title = {Design of asynchronous low-complexity SSVEP-based brain control interface speller.},
journal = {Computers in biology and medicine},
volume = {190},
number = {},
pages = {110062},
doi = {10.1016/j.compbiomed.2025.110062},
pmid = {40187178},
issn = {1879-0534},
mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; Male ; Adult ; Female ; *Signal Processing, Computer-Assisted ; Young Adult ; Wireless Technology ; },
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) provide a transformative solution, addressing communication challenges for individuals with speech impairments or neuromuscular disorders. The real-time wireless asynchronous BCI speller system utilizes electroencephalography (EEG) signals, tapping the brain's electrical activity for effective communication.<br><br>METHODS: Users interact with a screen featuring flickering stimuli, each representing cursor movement and character selection. The system includes cursor movements, displays selected characters, and produces an audio output of the complete word. Users generate real-time SSVEP responses captured wirelessly through an EEG acquisition system by directing attention to the stimulus. The single-channel EEG signal is wirelessly transmitted to a Raspberry Pi processing module through Wi-Fi. The EEG signals are decoded using modified power spectral density (PSD) analysis to identify the user's focus, maneuvering the cursor for character selection.<br><br>RESULTS: In experiments with ten subjects, the single-channel asynchronous low-complexity BCI speller system achieved 95.2% SSVEP identification accuracy with a detection time of 1.05 s for selecting each character/target and an information transfer rate (ITR) of 119.82 bits/min.<br><br>CONCLUSION: This underscores its efficacy in enabling individuals to spell words and communicate efficiently. The proposed real-time wireless BCI speller system is an effective tool for communication-challenged individuals, enhancing communication efficiency through brain signals.},
}
@article {pmid40183071,
year = {2025},
author = {Muthukrishnan, SP and Atyabi, A},
title = {Editorial: Neural mechanisms of motor planning in assisted voluntary movement.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1582214},
pmid = {40183071},
issn = {1662-5161},
}
@article {pmid40182217,
year = {2025},
author = {Wirawan, IMA and Paramarta, K},
title = {Acquisition Of Balinese Imagined Spelling using Electroencephalogram (BISE) Dataset.},
journal = {Data in brief},
volume = {60},
number = {},
pages = {111454},
pmid = {40182217},
issn = {2352-3409},
abstract = {One of the main goals of today's technology is to create a connected environment between humans and technological devices to perform daily physical activities. However, users with speech disorders cannot use this application. Loss of verbal communication can be caused by injuries and neurodegenerative diseases that affect motor production, speech articulation, and language comprehension. To overcome this problem, Brain-Computer Interfaces (BCI) use EEG signals as assistive technology to provide a new communication channel for individuals who cannot communicate due to loss of motor control. Of the several BCI studies that use EEG signals, no studies have studied Balinese characters. As a first step, this study examines the acquisition of EEG signal data for Balinese character recognition. There are several stages in obtaining EEG signal data for Balinese character spelling imagination in this study: preparation of research documents, preparation of stimulus media, submission of ethical permits, determination of participants, recording process, data presentation, and publication of datasets. The result datasets from this study are in the form of raw data, and data was analyzed for 18 Balinese and 6 vowel characters, both spelling and imagined.},
}
@article {pmid40182177,
year = {2025},
author = {Paillard, J and Hipp, JF and Engemann, DA},
title = {GREEN: A lightweight architecture using learnable wavelets and Riemannian geometry for biomarker exploration with EEG signals.},
journal = {Patterns (New York, N.Y.)},
volume = {6},
number = {3},
pages = {101182},
pmid = {40182177},
issn = {2666-3899},
abstract = {Spectral analysis using wavelets is widely used for identifying biomarkers in EEG signals. Recently, Riemannian geometry has provided an effective mathematical framework for predicting biomedical outcomes from multichannel electroencephalography (EEG) recordings while showing concord with neuroscientific domain knowledge. However, these methods rely on handcrafted rules and sequential optimization. In contrast, deep learning (DL) offers end-to-end trainable models achieving state-of-the-art performance on various prediction tasks but lacks interpretability and interoperability with established neuroscience concepts. We introduce Gabor Riemann EEGNet (GREEN), a lightweight neural network that integrates wavelet transforms and Riemannian geometry for processing raw EEG data. Benchmarking on six prediction tasks across four datasets with over 5,000 participants, GREEN outperformed non-deep state-of-the-art models and performed favorably against large DL models while using orders-of-magnitude fewer parameters. Computational experiments showed that GREEN facilitates learning sparse representations without compromising performance. By integrating domain knowledge, GREEN combines a desirable complexity-performance trade-off with interpretable representations.},
}
@article {pmid40181137,
year = {2025},
author = {Wang, G and Wang, W and Wang, Z and Huang, S and Liu, Y and Ming, D},
title = {The sixth finger illusion induced by palm outside stroking shows stable ownership and independence.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {11447},
pmid = {40181137},
issn = {2045-2322},
support = {2023YFC3603800//National Key Research and Development Program of China/ ; 62273251//National Natural Science Foundation of China/ ; MSV202418//Research Project of State Key Laboratory of Mechanical System and Vibration/ ; 21JCYBJC00520//Natural Science Foundation of Tianjin Municipality/ ; },
mesh = {Humans ; *Illusions/physiology ; Male ; Female ; *Fingers/physiology ; Adult ; Young Adult ; *Hand/physiology ; *Touch Perception/physiology ; },
abstract = {Recently, the sixth finger illusion has been widely studied for body representation. It remains unclear how the stroking area, visual effects and the number of trials affect the illusion. We recruited 80 participants to conduct five trials by stroking the palm outside or little finger outside in conditions with and without wearing supernumerary rubber finger. The results show the stroking area has a greater impact on the intensity and independence of the illusion. And the palm outside can induce a stronger and more independent illusion. In addition, the sixth finger illusion induced by these four conditions was significantly influenced by the number of trials, and there is a significant enhancement in the intensity of the illusion induced by the palm outside as the number of trials increases. These indicate that stroking the outer lateral side of the palm can induce a relatively stronger and more independent sixth finger illusion, and the intensity of it reaches a steady state after three trials when wearing a supernumerary rubber finger and five trials when not wearing a supernumerary rubber finger. This study adds evidence to the research on multisensory integration and sensory feedback of the supernumerary robotic fingers.},
}
@article {pmid40181122,
year = {2025},
author = {Rawat, K and Sharma, T},
title = {An enhanced CNN-Bi-transformer based framework for detection of neurological illnesses through neurocardiac data fusion.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {11379},
pmid = {40181122},
issn = {2045-2322},
mesh = {Humans ; Electroencephalography/methods ; Deep Learning ; *Nervous System Diseases/diagnosis/physiopathology ; Electrocardiography ; *Neural Networks, Computer ; *Mental Disorders/diagnosis/physiopathology ; Adult ; Male ; Female ; },
abstract = {Classical approaches to diagnosis frequently rely on self-reported symptoms or clinician observations, which can make it difficult to examine mental health illnesses due to their subjective and complicated nature. In this work, we offer an innovative methodology for predicting mental illnesses such as epilepsy, sleep disorders, bipolar disorder, eating disorders, and depression using a multimodal deep learning framework that integrates neurocardiac data fusion. The proposed framework combines MEG, EEG, and ECG signals to create a more comprehensive understanding of brain and cardiac function in individuals with mental disorders. The multimodal deep learning approach uses an integrated CNN-Bi-Transformer, i.e., CardioNeuroFusionNet, which can process multiple types of inputs simultaneously, allowing for the fusion of various modalities and improving the performance of the predictive representation. The proposed framework has undergone testing on data from the Deep BCI Scalp Database and was further validated on the Kymata Atlas dataset to assess its generalizability. The model achieved promising results with high accuracy (98.54%) and sensitivity (97.77%) in predicting mental problems, including neurological and psychiatric conditions. The neurocardiac data fusion has been found to provide additional insights into the relationship between brain and cardiac function in neurological conditions, which could potentially lead to more accurate diagnosis and personalized treatment options. The suggested method overcomes the shortcomings of earlier studies, which tended to concentrate on single-modality data, lacked thorough neurocardiac data fusion, and made use of less advanced machine learning algorithms. The comprehensive experimental findings, which provide an average improvement in accuracy of 2.72%, demonstrate that the suggested work performs better than other cutting-edge AI techniques and generalizes effectively across diverse datasets.},
}
@article {pmid40180157,
year = {2025},
author = {Liang, W and Xu, R and Wang, X and Cichocki, A and Jin, J},
title = {Enhancing robustness of spatial filters in motor imagery based brain-computer interface via temporal learning.},
journal = {Journal of neuroscience methods},
volume = {418},
number = {},
pages = {110441},
doi = {10.1016/j.jneumeth.2025.110441},
pmid = {40180157},
issn = {1872-678X},
mesh = {*Brain-Computer Interfaces ; Humans ; *Imagination/physiology ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; *Brain/physiology ; *Motor Activity/physiology ; },
abstract = {BACKGROUND: In motor imagery-based brain-computer interface (MI-BCI) EEG decoding, spatial filtering play a crucial role in feature extraction. Recent studies have emphasized the importance of temporal filtering for extracting discriminative features in MI tasks. While many efforts have been made to optimize feature extraction externally, stabilizing features from spatial filtering remains underexplored.<br><br>NEW METHOD: To address this problem, we propose an approach to improve the robustness of temporal features by minimizing instability in the temporal domain. Specifically, we utilize Jensen-Shannon divergence to quantify temporal instability and integrate decision variables to construct an objective function that minimizes this instability. Our method enhances the stability of variance and mean values in the extracted features, improving the identification of discriminative features and reducing the effects of instability.<br><br>RESULTS: The proposed method was applied to spatial filtering models, and tested on two publicly datasets as well as a self-collected dataset. Results demonstrate that the proposed method significantly boosts classification accuracy, confirming its effectiveness in enhancing temporal feature stability.<br><br>We compared our method with spatial filtering methods, and the-state-of-the-art models. The proposed approach achieves the highest accuracy, with 92.43&#x202f;% on BCI competition III IVa dataset, 84.45&#x202f;% on BCI competition IV 2a dataset, and 73.18&#x202f;% on self-collected dataset.<br><br>CONCLUSIONS: Enhancing the instability of temporal features contributes to improved MI-BCI performance. This not only improves classification performance but also provides a stable foundation for future advancements. The proposed method shows great potential for EEG decoding.},
}
@article {pmid40179638,
year = {2025},
author = {Thielen, J and Tangermann, M and Aarnoutse, EJ and Ramsey, NF and Vansteensel, MJ},
title = {Towards an sEEG-based BCI using code-modulated VEP: A case study showing the influence of electrode location on decoding efficiency.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {173},
number = {},
pages = {213-215},
doi = {10.1016/j.clinph.2025.03.034},
pmid = {40179638},
issn = {1872-8952},
}
@article {pmid40177878,
year = {2025},
author = {Bhamidipaty, V and Botchu, B and Bhamidipaty, DL and Guntoory, I and Iyengar, KP},
title = {ChatGPT for speech-impaired assistance.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {20},
number = {6},
pages = {1575-1577},
doi = {10.1080/17483107.2025.2483300},
pmid = {40177878},
issn = {1748-3115},
mesh = {Humans ; Communication Devices for People with Disabilities ; *Generative Artificial Intelligence/trends ; *Language Disorders/rehabilitation ; Neural Prostheses ; *Speech Disorders/rehabilitation ; Speech Therapy/methods/trends ; *Speech-Language Pathology/instrumentation/methods/trends ; },
abstract = {BACKGROUND: Speech and language impairments, though often used interchangeably, are two very distinct types of challenges. A speech impairment may lead to impaired ability to produce speech sounds whilst communication may be affected due to lack of fluency or articulation of words. Consequently this may affect a person's ability to articulate may affect academic achievement, social development and progress in life. ChatGPT (Generative Pretrained Transformer) is an open access AI (Artificial Intelligence) tool developed by Open AI® based on Large language models (LLMs) with the ability to respond to human prompts to generate texts using Supervised and Unsupervised Machine Learning (ML) Algorithms. This article explores the current role and future perspectives of ChatGPT AI Tool for Speech-Impaired Assistance.<br><br>METHODS: A cumulative search strategy using databases of PubMed, Google Scholar, Scopus and grey literature was conducted to generate this narrative review.<br><br>RESULTS: A spectrum of Enabling Technologies for Speech &amp; Language Impairment have been explored. Augmentative and Alternative Communication technology (AAC), Integration with Neuroprosthesis technology and Speech therapy applications offer considerable potential to aid speech and language impaired individuals.<br><br>CONCLUSION: Current applications of AI, ChatGPT and other LLM's offer promising solutions in enhancing communication in people affected by Speech and Language impairment. However, further research and development is required to ensure affordability, accessibility and authenticity of these AI Tools in clinical Practice.},
}
@article {pmid40175961,
year = {2025},
author = {Guo, X and Deng, R and Lai, J and Hu, S},
title = {Is muscarinic receptor agonist effective and tolerant for schizophrenia?.},
journal = {BMC psychiatry},
volume = {25},
number = {1},
pages = {323},
pmid = {40175961},
issn = {1471-244X},
mesh = {Humans ; *Schizophrenia/drug therapy ; *Muscarinic Agonists/adverse effects/therapeutic use ; Randomized Controlled Trials as Topic ; *Antipsychotic Agents/therapeutic use/adverse effects ; },
abstract = {BACKGROUND: Several randomized clinical trials (RCTs) have recently examined the efficacy and tolerability of muscarinic receptor agonists in schizophrenia. However, whether therapeutics targeting muscarinic receptors improve symptom management and reduce side effects remains systemically unexplored.<br><br>METHODS: Embase, PubMed, and Web of Science were searched from inception until Jan 9, 2025. Altogether, the efficacy and safety outcomes of four RCTs (397 individuals in the muscarinic receptor agonists group, and 374 in the placebo control group) were meta-analyzed. To compare scores of positive and negative syndrome scale (PANSS), response rate, discontinuation rate, and adverse events with muscarinic receptor agonists vs. placebo in patients with schizophrenia, scale changes were pooled as mean difference (MD) for continuous outcomes and risk ratio (RR) for categorical outcomes.<br><br>RESULTS: It revealed that muscarinic receptor agonists were superior to placebo in terms of decrease in the total PANSS score (MD, -&#x2009;9.92; 95% CI, -12.46 to -7.37; I[2]&#x2009;=&#x2009;0%), PANSS positive symptom subscore (MD, -&#x2009;3.21; 95% CI, -4.02 to -2.40; I[2]&#x2009;=&#x2009;0%), and PANSS negative symptom subscore (MD, -1.79; 95% CI, -2.47 to -1.11; I[2]&#x2009;=&#x2009;48%). According to the study-defined response rate, the pooled muscarinic receptor agonists vs. placebo RR was 2.08 (95% CI, 1.59 to 2.72; I[2]&#x2009;=&#x2009;0%). No significance was found in the discontinuation rate. Muscarinic receptor agonists were associated with a higher risk of nausea (RR&#x2009;=&#x2009;4.61, 95% CI, 2.65 to 8.02; I[2]&#x2009;=&#x2009;3%), and in particular, xanomeline-trospium was associated with risks of dyspepsia, vomiting, and constipation.<br><br>CONCLUSIONS: The findings highlighted an efficacy advantage with tolerated adverse event profiles for muscarinic receptor agonists in schizophrenia.},
}
@article {pmid40175631,
year = {2025},
author = {Qi, Y and Zhu, X and Xiong, X and Yang, X and Ding, N and Wu, H and Xu, K and Zhu, J and Zhang, J and Wang, Y},
title = {Human motor cortex encodes complex handwriting through a sequence of stable neural states.},
journal = {Nature human behaviour},
volume = {9},
number = {6},
pages = {1260-1271},
pmid = {40175631},
issn = {2397-3374},
support = {62276228//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62336007//National Natural Science Foundation of China (National Science Foundation of China)/ ; LR24F020002//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
mesh = {Humans ; *Handwriting ; *Motor Cortex/physiology ; Male ; Adult ; *Neurons/physiology ; Female ; Models, Neurological ; *Psychomotor Performance/physiology ; Young Adult ; },
abstract = {How the human motor cortex (MC) orchestrates sophisticated sequences of fine movements such as handwriting remains a puzzle. Here we investigate this question through Utah array recordings from human MC during attempted handwriting of Chinese characters (n = 306, each consisting of 6.3 ± 2.0 strokes). We find that MC activity evolves through a sequence of states corresponding to the writing of stroke fragments during complicated handwriting. The directional tuning curve of MC neurons remains stable within states, but its gain or preferred direction strongly varies across states. By building models that can automatically infer the neural states and implement state-dependent directional tuning, we can significantly better explain the firing pattern of individual neurons and reconstruct recognizable handwriting trajectories with 69% improvement compared with baseline models. Our findings unveil that skilled and sophisticated movements are encoded through state-specific neural configurations.},
}
@article {pmid40175376,
year = {2025},
author = {Dong, L and Ke, Y and Zhu, X and Liu, S and Ming, D},
title = {Long-term cognitive and neurophysiological effects of mental rotation training.},
journal = {NPJ science of learning},
volume = {10},
number = {1},
pages = {16},
pmid = {40175376},
issn = {2056-7936},
support = {81741139//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Mental rotation, a crucial aspect of spatial cognition, can be improved through repeated practice. However, the long-term effects of combining training with non-invasive brain stimulation and its neurophysiological correlates are not well understood. This study examined the lasting effects of a 10-day mental rotation training with high-definition transcranial direct current stimulation (HD-tDCS) on behavioral and neural outcomes in 34 healthy participants. Participants were randomly assigned to the Active and Shan groups, with equal group sizes. Mental rotation tests and EEG recordings were conducted at baseline, 1 day, 20 days, and 90 days post-training. Although HD-tDCS showed no significant effect, training led to improved accuracy, faster response times, and enhanced task-evoked EEG responses, with benefits lasting up to 90 days. Notably, task-evoked EEG responses remained elevated 20 days post-training. Individual differences, such as gender and baseline performance, influenced the outcomes. These results emphasize the potential of mental rotation training for cognitive enhancement and suggest a need for further investigation into cognition-related neuroplasticity.},
}
@article {pmid40174604,
year = {2025},
author = {Kojima, S and Eren Kortenbach, B and Aalberts, C and Miloševska, S and de Wit, K and Zheng, R and Kanoh, S and Musso, M and Tangermann, M},
title = {Influence of pitch modulation on event-related potentials elicited by Dutch word stimuli in a brain-computer interface language rehabilitation task.},
journal = {Journal of neural engineering},
volume = {22},
number = {3},
pages = {},
doi = {10.1088/1741-2552/adc83d},
pmid = {40174604},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; Adult ; *Acoustic Stimulation/methods ; *Pitch Perception/physiology ; Electroencephalography/methods ; Middle Aged ; Netherlands ; Language ; *Evoked Potentials/physiology ; *Language Therapy/methods ; *Evoked Potentials, Auditory/physiology ; },
abstract = {Objective.Recently, a novel language training using an auditory brain-computer interface (BCI) based on electroencephalogram recordings has been proposed for chronic stroke patients with aphasia. Tested with native German patients, it has shown significant and medium to large effect sizes in improving multiple aspects of language. During the training, the auditory BCI system delivers word stimuli using six spatially arranged loudspeakers. As delivering the word stimuli via headphones reduces spatial cues and makes the attention to target words more difficult, we investigate the influence of added pitch information. While pitch modulations have shown benefits for tone stimuli, they have not yet been investigated in the context of language stimuli.Approach.The study translated the German experimental setup into Dutch. Seventeen native Dutch speakers participated in a single session of an exploratory study. An incomplete Dutch sentence cued them to listen to a target word embedded into a sequence of comparable non-target words while an electroencephalogram was recorded. Four conditions were compared within-subject to investigate the influence of pitch modulation: presenting the words spatially from six loudspeakers without (6D) and with pitch modulation (6D-Pitch), via stereo headphones with simulated spatial cues and pitch modulation (Stereo-Pitch), and via headphones without spatial cues or pitch modulation (Mono).Main results.Comparing the 6D conditions of both language setups, the Dutch setup could be validated. For the Dutch setup, the binary AUC classification score in the 6D and the 6D-Pitch condition were 0.75 and 0.76, respectively, and adding pitch information did not significantly alter the binary classification accuracy of the event-related potential responses. The classification scores in the 6D condition and the Stereo-Pitch condition were on the same level.Significance.The competitive performance of pitch-modulated word stimuli suggests that the complex hardware setup of the 6D condition could be replaced by a headphone condition. If future studies with aphasia patients confirm the effectiveness and higher usability of a headphone-based language rehabilitation training, a simplified setup could be implemented more easily outside of clinics to deliver frequent training sessions to patients in need.},
}
@article {pmid40174326,
year = {2025},
author = {Wan, P and Xue, H and Zhang, S and Kong, W and Shao, W and Wen, B and Zhang, D},
title = {Image by co-reasoning: A collaborative reasoning-based implicit data augmentation method for dual-view CEUS classification.},
journal = {Medical image analysis},
volume = {102},
number = {},
pages = {103557},
doi = {10.1016/j.media.2025.103557},
pmid = {40174326},
issn = {1361-8423},
mesh = {Humans ; Ultrasonography/methods ; *Liver Neoplasms/diagnostic imaging ; *Breast Neoplasms/diagnostic imaging ; *Contrast Media ; Machine Learning ; Female ; Retrospective Studies ; *Image Interpretation, Computer-Assisted/methods ; Algorithms ; },
abstract = {Dual-view contrast-enhanced ultrasound (CEUS) data are often insufficient to train reliable machine learning models in typical clinical scenarios. A key issue is that limited clinical CEUS data fail to cover the underlying texture variations for specific diseases. Implicit data augmentation offers a flexible way to enrich sample diversity, however, inter-view semantic consistency has not been considered in previous studies. To address this issue, we propose a novel implicit data augmentation method for dual-view CEUS classification, which performs a sample-adaptive data augmentation with collaborative semantic reasoning across views. Specifically, the method constructs a feature augmentation distribution for each ultrasound view of an individual sample, accounting for intra-class variance. To maintain semantic consistency between the augmented views, plausible semantic changes in one view are transferred from similar instances in the other view. In this retrospective study, we validate the proposed method on the dual-view CEUS datasets of breast cancer and liver cancer, obtaining the superior mean diagnostic accuracy of 89.25% and 95.57%, respectively. Experimental results demonstrate its effectiveness in improving model performance with limited clinical CEUS data. Code: https://github.com/wanpeng16/CRIDA.},
}
@article {pmid40173067,
year = {2025},
author = {Tian, M and Li, S and Xu, R and Cichocki, A and Jin, J},
title = {An Interpretable Regression Method for Upper Limb Motion Trajectories Detection With EEG Signals.},
journal = {IEEE transactions on bio-medical engineering},
volume = {72},
number = {10},
pages = {2961-2971},
doi = {10.1109/TBME.2025.3557255},
pmid = {40173067},
issn = {1558-2531},
mesh = {Humans ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; *Upper Extremity/physiology ; Brain-Computer Interfaces ; Movement/physiology ; Male ; Algorithms ; Adult ; Regression Analysis ; Biomechanical Phenomena/physiology ; Female ; Young Adult ; },
abstract = {OBJECTIVE: The motion trajectory prediction (MTP) based brain-computer interface (BCI) leverages electroencephalography (EEG) signals to reconstruct the three-dimensional trajectory of upper limb motion, which is pivotal for the advancement of prosthetic devices that can assist motor-disabled individuals. Most research focused on improving the performance of regression models while neglecting the correlation between the implicit information extracted from EEG features across various frequency bands with limb kinematics. Current work aims to identify key channels that capture information related to various motion execution movements from different frequency bands and reconstruct three-dimensional motion trajectories based on EEG features.<br><br>METHODS: We propose an interpretable motion trajectory regression framework that extracts bandpower features from different frequency bands and concatenates them into multi-band fusion features. The extreme gradient boosting regression model with Bayesian optimization and Shapley additive explanation methods are introduced to provide further explanation.<br><br>RESULTS: The experimental results demonstrate that the proposed method achieves a mean Pearson correlation coefficient (PCC) value of 0.452, outperforming traditional regression models.<br><br>CONCLUSION: Our findings reveal that the contralateral side contributes the most to motion trajectory regression than the ipsilateral side which improves the clarity and interpretability of the motion trajectory regression model. Specifically, the feature from channel C5 in the Mu band is crucial for the movement of the right hand, while the feature from channel C3 in the Beta band plays a vital role.<br><br>SIGNIFICANCE: This work provides a novel perspective on the comprehensive study of movement disorders.},
}
@article {pmid40172963,
year = {2025},
author = {Yang, Z and Zheng, Y and Ma, D and Wang, L and Zhang, J and Song, T and Wang, Y and Zhang, Y and Nan, F and Su, N and Gao, Z and Guo, J},
title = {Phosphatidylinositol 4,5-bisphosphate activation mechanism of human KCNQ5.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {122},
number = {14},
pages = {e2416738122},
pmid = {40172963},
issn = {1091-6490},
support = {2020YFA0908501//MOST | National Key Research and Development Program of China (NKPs)/ ; 32371204//MOST | National Natural Science Foundation of China (NSFC)/ ; },
mesh = {Humans ; *Phosphatidylinositol 4,5-Diphosphate/metabolism/chemistry ; *KCNQ Potassium Channels/metabolism/chemistry/genetics ; Cryoelectron Microscopy ; Protein Binding ; Ion Channel Gating ; Models, Molecular ; Protein Domains ; Protein Conformation ; Membrane Potentials ; },
abstract = {The human voltage-gated potassium channels KCNQ2, KCNQ3, and KCNQ5 can form homo- and heterotetrameric channels that are responsible for generating the neuronal M current and maintaining the membrane potential stable. Activation of KCNQ channels requires both the depolarization of membrane potential and phosphatidylinositol 4,5-bisphosphate (PIP2). Here, we report cryoelectron microscopy structures of the human KCNQ5-calmodulin (CaM) complex in the apo, PIP2-bound, and both PIP2- and the activator HN37-bound states in either a closed or an open conformation. In the closed conformation, a PIP2 molecule binds in the middle of the groove between two adjacent voltage-sensing domains (VSDs), whereas in the open conformation, one additional PIP2 binds to the interface of VSD and the pore domain, accompanying structural rearrangement of the cytosolic domain of KCNQ and CaM. The structures, along with electrophysiology analyses, reveal the two different binding modes of PIP2 and elucidate the PIP2 activation mechanism of KCNQ5.},
}
@article {pmid40172828,
year = {2025},
author = {Li, M and Zhao, Q and Zhang, T and Ge, J and Wang, J and Xu, G},
title = {A Personalized Predictor of Motor Imagery Ability Based on Multi-frequency EEG Features.},
journal = {Neuroscience bulletin},
volume = {41},
number = {7},
pages = {1198-1212},
pmid = {40172828},
issn = {1995-8218},
mesh = {Humans ; *Imagination/physiology ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Male ; Female ; Adult ; Young Adult ; *Brain/physiology ; Movement/physiology ; *Motor Activity/physiology ; Psychomotor Performance/physiology ; },
abstract = {A brain-computer interface (BCI) based on motor imagery (MI) provides additional control pathways by decoding the intentions of the brain. MI ability has great intra-individual variability, and the majority of MI-BCI systems are unable to adapt to this variability, leading to poor training effects. Therefore, prediction of MI ability is needed. In this study, we propose an MI ability predictor based on multi-frequency EEG features. To validate the performance of the predictor, a video-guided paradigm and a traditional MI paradigm are designed, and the predictor is applied to both paradigms. The results demonstrate that all subjects achieved > 85% prediction precision in both applications, with a maximum of 96%. This study indicates that the predictor can accurately predict the individuals' MI ability in different states, provide the scientific basis for personalized training, and enhance the effect of MI-BCI training.},
}
@article {pmid40172544,
year = {2025},
author = {Li, H and Li, C and Zhao, H and Li, Q and Zhao, Y and Gong, J and Li, G and Yu, H and Tian, Q and Liu, Z and Han, F},
title = {Flexible fibrous electrodes for implantable biosensing.},
journal = {Nanoscale},
volume = {17},
number = {16},
pages = {9870-9894},
doi = {10.1039/d4nr04542d},
pmid = {40172544},
issn = {2040-3372},
mesh = {*Biosensing Techniques/instrumentation ; Humans ; *Electrodes, Implanted ; Animals ; Electrochemical Techniques ; },
abstract = {Flexible fibrous electrodes have emerged as a promising technology for implantable biosensing applications, offering significant advancements in the monitoring and manipulation of biological signals. This review systematically explores the key aspects of flexible fibrous electrodes, including the materials, structural designs, and fabrication methods. A detailed discussion of electrode performance metrics is provided, covering factors such as conductivity, stretchability, axial channel count, and implantation duration. The diverse applications of these electrodes in electrophysiological signal monitoring, electrochemical sensing, tissue strain monitoring, and in vivo electrical stimulation are reviewed, highlighting their potential in biomedical settings. Finally, the review discusses the eight major challenges currently faced by implantable fibrous electrodes and explores future development directions, providing critical technical analysis and potential solutions for the advancement of next-generation flexible implantable fiber-based biosensors.},
}
@article {pmid40172075,
year = {2025},
author = {Wu, X and Liang, C and Bustillo, J and Kochunov, P and Wen, X and Sui, J and Jiang, R and Yang, X and Fu, Z and Zhang, D and Calhoun, VD and Qi, S},
title = {The Impact of Atlas Parcellation on Functional Connectivity Analysis Across Six Psychiatric Disorders.},
journal = {Human brain mapping},
volume = {46},
number = {5},
pages = {e70206},
pmid = {40172075},
issn = {1097-0193},
support = {R01 NS114628/NS/NINDS NIH HHS/United States ; R01 MH116948/MH/NIMH NIH HHS/United States ; R01 EB015611/EB/NIBIB NIH HHS/United States ; BE2023668//Jiangsu Provincial Key Research and Development Program/ ; RF1 NS114628/NS/NINDS NIH HHS/United States ; U01 MH108148/MH/NIMH NIH HHS/United States ; RF1 MH123163/MH/NIMH NIH HHS/United States ; S10 OD023696/OD/NIH HHS/United States ; BK20220889//Natural Science Foundation of Jiangsu Province/ ; 62376124//National Natural Science Foundation of China/ ; },
mesh = {Humans ; Male ; Female ; *Mental Disorders/diagnostic imaging/physiopathology/pathology ; *Magnetic Resonance Imaging/methods ; *Brain/physiopathology/diagnostic imaging ; Adult ; *Atlases as Topic ; Neural Pathways/physiopathology/diagnostic imaging ; Young Adult ; *Connectome/methods ; Middle Aged ; *Brain Mapping/methods ; Image Processing, Computer-Assisted ; Bipolar Disorder/diagnostic imaging/physiopathology ; },
abstract = {Neuropsychiatric disorders are associated with altered functional connectivity (FC); however, the reported regional patterns of functional alterations suffered from low replicability and high variability. This is partly because of differences in the atlas and delineation techniques used to measure FC-related deficits within/across disorders. We systematically investigated the impact of the brain parcellation approach on the FC-based brain network analysis. We focused on identifying the replicable FCs using three structural brain atlases, including Automated Anatomical Labeling (AAL), Brainnetome atlas (BNA) and HCP_MMP_1.0, and four functional brain parcellation approaches: Yeo-Networks (Yeo), Gordon parcel (Gordon) and two Schaefer parcelletions, among correlation, group difference, and classification tasks in six neuropsychiatric disorders: attention deficit and hyperactivity disorder (ADHD, n = 340), autism spectrum disorder (ASD, n = 513), schizophrenia (SZ, n = 200), schizoaffective disorder (SAD, n = 142), bipolar disorder (BP, n = 172), and major depression disorder (MDD, n = 282). Our cross-atlas/disorder analyses demonstrated that frontal-related FC deficits were reproducible in all disorders, independent of the atlasing approach; however, replicable FC extraction in other areas and the classification accuracy were affected by the parcellation schema. Overall, functional atlases with finer granularity performed better in classification tasks. Specifically, the Schaefer atlases generated the most repeatable FC deficit patterns across six illnesses. These results indicate that frontal-related FCs may serve as potential common and robust neuro-abnormalities across 6 psychiatric disorders. Furthermore, in order to improve the replicability of rsfMRI-based FC analyses, this study suggests the use of functional templates at larger granularity.},
}
@article {pmid40171943,
year = {2025},
author = {Lin, C and Zhou, X and Li, M and Zhang, C and Zhai, H and Li, H and Wang, H and Wang, X},
title = {S-ketamine Alleviates Neuroinflammation and Attenuates Lipopolysaccharide-Induced Depression Via Targeting SIRT2.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {12},
number = {23},
pages = {e2416481},
pmid = {40171943},
issn = {2198-3844},
support = {2021ZD0203000//STI2030-Major Projects/ ; 2021ZD0203003//STI2030-Major Projects/ ; T2341003//National Natural Science Foundation of China/ ; 22207105//National Natural Science Foundation of China/ ; 2023C038-3//Jilin Provincial Development and Reform Commission/ ; BMI2400014//Open Research Fund of the State Key Laboratory of Brain-Machine Intelligence, Zhejiang University/ ; },
mesh = {*Ketamine/pharmacology/therapeutic use ; Animals ; *Sirtuin 2/metabolism/drug effects ; Mice ; Lipopolysaccharides ; *Depression/drug therapy/metabolism/chemically induced ; Male ; Disease Models, Animal ; *Neuroinflammatory Diseases/drug therapy/metabolism ; Mice, Inbred C57BL ; *Inflammation/drug therapy/metabolism ; Prefrontal Cortex/metabolism/drug effects ; },
abstract = {Depression, a pervasive mental health condition, has increasingly been linked to neuroinflammation, as evidenced by elevated levels of pro-inflammatory markers such as TNF-α and IL-1β observed in patients, which underscores the role of inflammation in its pathophysiology. This study investigates the differential effects of S-ketamine (S-KET) and R-ketamine (R-KET) on inflammation-induced depression using a lipopolysaccharide (LPS)-induced mouse model. Results showed that S-KET, but not R-KET, significantly alleviated depressive-like behaviors and reduced levels of pro-inflammatory factors in the medial prefrontal cortex (mPFC). Activity-based protein profiling identified SIRT2 as a key intracellular target of S-KET, with direct binding observed at the Q167 residue, whereas R-KET showed no such binding. S-KET enhanced SIRT2 interaction with NF-κB subunit p65, reducing its acetylation and suppressing pro-inflammatory gene expression, effects not seen with R-KET. In vitro studies with RNA interference and the SIRT2 inhibitor AK-7, along with in vivo pharmacological blockade, confirmed that SIRT2 is crucial for the anti-inflammatory and antidepressant actions of S-KET. These findings suggest that SIRT2 mediates the therapeutic effects of S-KET, highlighting its potential as a target for treating inflammation-associated depression. This study provides novel insights into the stereospecific actions of ketamine enantiomers and the promise of targeting SIRT2 for neuroinflammatory depression.},
}
@article {pmid40170996,
year = {2025},
author = {Ping, A and Wang, J and Ángel García-Cabezas, M and Li, L and Zhang, J and Gothard, KM and Zhu, J and Roe, AW},
title = {Brainwide mesoscale functional networks revealed by focal infrared neural stimulation of the amygdala.},
journal = {National science review},
volume = {12},
number = {4},
pages = {nwae473},
pmid = {40170996},
issn = {2053-714X},
support = {R01 MH121706/MH/NIMH NIH HHS/United States ; },
abstract = {The primate amygdala serves to evaluate the emotional content of sensory inputs and modulate emotional and social behaviors; it modulates cognitive, multisensory and autonomic circuits predominantly via the basal, lateral and central nuclei, respectively. Recent evidence has suggested the mesoscale (millimeter-scale) nature of intra-amygdala functional organization. However, the connectivity patterns by which these mesoscale regions interact with brainwide networks remain unclear. Using infrared neural stimulation of single mesoscale sites coupled with mapping in ultrahigh field 7-T functional magnetic resonance imaging, we have discovered that these mesoscale sites exert influence over a surprisingly extensive scope of the brain. Our findings strongly indicate that mesoscale sites within the amygdala modulate brainwide networks through a 'one-to-many' (integral) way. Meanwhile, these connections exhibit a point-to-point (focal) topography. Our work provides new insights into the functional architecture underlying emotional and social behavioral networks, thereby opening up possibilities for individualized modulation of psychological disorders.},
}
@article {pmid40169544,
year = {2025},
author = {Zhu, M and Peng, J and Wang, M and Lin, S and Zhang, H and Zhou, Y and Dai, X and Zhao, H and Yu, YQ and Shen, L and Li, XM and Chen, J},
title = {Transcriptomic and spatial GABAergic neuron subtypes in zona incerta mediate distinct innate behaviors.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {3107},
pmid = {40169544},
issn = {2041-1723},
support = {81870898//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Animals ; *GABAergic Neurons/metabolism/physiology ; Mice ; *Transcriptome ; *Zona Incerta/metabolism/cytology/physiology ; Male ; *Behavior, Animal/physiology ; Mice, Inbred C57BL ; *Instinct ; Optogenetics ; Female ; Mice, Transgenic ; },
abstract = {Understanding the anatomical connection and behaviors of transcriptomic neuron subtypes is critical to delineating cell type-specific functions in the brain. Here we integrated single-nucleus transcriptomic sequencing, in vivo circuit mapping, optogenetic and chemogenetic approaches to dissect the molecular identity and function of heterogeneous GABAergic neuron populations in the zona incerta (ZI) in mice, a region involved in modulating various behaviors. By microdissecting ZI for transcriptomic and spatial gene expression analyses, our results revealed two non-overlapping Ecel1- and Pde11a-expressing GABAergic neurons with dominant expression in the rostral and medial zona incerta (ZIr[Ecel1] and ZIm[Pde11a]), respectively. The GABAergic projection from ZIr[Ecel1] to periaqueductal gray mediates self-grooming, while the GABAergic projection from ZIm[Pde11a] to the oral part of pontine reticular formation promotes transition from sleep to wakefulness. Together, our results revealed the molecular markers, spatial organization and specific neuronal circuits of two discrete GABAergic projection neuron populations in segregated subregions of the ZI that mediate distinct innate behaviors, advancing our understanding of the functional organization of the brain.},
}
@article {pmid40168986,
year = {2025},
author = {Wang, P and Han, L and Wang, L and Tao, Q and Guo, Z and Luo, T and He, Y and Xu, Z and Yu, J and Liu, Y and Wu, Z and Xu, B and Jin, B and Wei, Y and Yang, Y and Cheng, M and Jiang, Y and Tian, C and Zheng, H and Fan, Z and Jiang, P and Gao, Y and Wu, J and Wang, S and Sun, B and Fang, Z and Lei, J and Luo, B and Wen, H and Peng, G and Tang, Y and Yang, T and Chen, J and Zhuang, Z and Su, X and Pan, C and Zhu, K and Shen, Y and Liu, S and Bao, A and Yao, J and Wang, J and Xu, X and Li, XM and Liu, L and Duan, S and Zhang, J},
title = {Molecular pathways and diagnosis in spatially resolved Alzheimer's hippocampal atlas.},
journal = {Neuron},
volume = {113},
number = {13},
pages = {2123-2140.e9},
doi = {10.1016/j.neuron.2025.03.002},
pmid = {40168986},
issn = {1097-4199},
mesh = {Humans ; *Alzheimer Disease/metabolism/diagnosis/pathology/genetics ; *Hippocampus/metabolism/pathology ; Male ; Aged ; Female ; Astrocytes/metabolism ; Microglia/metabolism/pathology ; Aged, 80 and over ; Transcriptome ; Neurons/metabolism/pathology ; Plaque, Amyloid/pathology/metabolism ; Amyloid beta-Peptides/metabolism ; },
abstract = {We employed Stereo-seq combined with single-nucleus RNA sequencing (snRNA-seq) to investigate the gene expression and cell composition changes in human hippocampus with or without Alzheimer's disease (AD). The transcriptomic map, with single-cell precision, unveiled AD-associated alterations with spatial specificity, which include the following: (1) elevated synapse pruning gene expression in the fimbria of AD, with disrupted microglia-astrocyte communication likely leading to disorganized synaptic structure; (2) a globally increased energy generation in the cornu ammonis (CA) region, with varying degrees across its subregions; (3) a significant reduction in the number of CA1 neurons in AD, while CA4 neurons remained largely unaffected, potentially due to gene alterations in CA4 conferring resilience to AD; and (4) aggravated amyloid-beta (Aβ) plaques in CA1 and stratum lucidum, radiatum, and moleculare (SLRM), and integration of Stereo-seq map with Aβ staining revealed a sequential enrichment of microglia and astrocytes around Aβ plaques. Finally, reduced brain-derived extracellular vesicles carrying cholecystokinin (CCK) and peripheral myelin protein 2 (PMP2) in AD plasma highlighted their diagnostic potential for clinical applications.},
}
@article {pmid40168808,
year = {2025},
author = {Denis-Robichaud, J and Barbeau-Grégoire, N and Gauthier, ML and Dufour, S and Roy, JP and Buczinski, S and Dubuc, J},
title = {Validity of purulent vaginal discharge, esterase, luminometry, and three bacteriological tests for diagnosing uterine infection in dairy cows using Bayesian latent class analysis.},
journal = {Preventive veterinary medicine},
volume = {239},
number = {},
pages = {106521},
doi = {10.1016/j.prevetmed.2025.106521},
pmid = {40168808},
issn = {1873-1716},
mesh = {Animals ; Cattle ; Female ; *Cattle Diseases/diagnosis/microbiology ; Bayes Theorem ; *Vaginal Discharge/veterinary/diagnosis/microbiology ; Sensitivity and Specificity ; Cross-Sectional Studies ; Latent Class Analysis ; Prospective Studies ; *Uterine Diseases/veterinary/diagnosis/microbiology ; Esterases/analysis ; *Bacteriological Techniques/veterinary ; Reproducibility of Results ; },
abstract = {This prospective cross-sectional study aimed to evaluate the ability of laboratory bacterial culture, Petrifilm, Tri-Plate, luminometry, purulent vaginal discharge (PVD), and esterase to correctly identify uterine infection in dairy cows, and to assess these tests' usefulness in different situations. We sampled dairy cows between 29 and 43 days in milk in seven farms. We considered all six tests imperfect to identify uterine infection and used Bayesian latent class analyses to estimate their sensitivity and specificity. We created ten scenarios, including tests alone, in series, or in parallel, and we calculated predictive values and misclassification cost terms (MCTs). All estimates are presented with 95 % Bayesian credibility intervals (BCI). A total of 326 uterine samples were collected. The laboratory culture had the best validity (sensitivity = 0.87, 95 % BCI = 0.77-0.97; specificity = 0.71, 95 % BCI = 0.58-0.86). The other tests had similar specificity but lower sensitivity, with PVD having the lowest sensitivity (0.05, 95 % BCI = 0.01-0.10). If treating a healthy cow was considered worse than leaving a cow with a uterine infection untreated, luminometry yielded an MCT similar to the laboratory culture. These findings highlight that the on-farm tools currently used to identify cows that could benefit from intrauterine antimicrobial treatment do not identify uterine infection accurately. While the laboratory culture was the most accurate test, it cannot easily be implemented on farms. Luminometry's validity was good, but additional research is necessary to understand how it can be implemented to improve judicious intrauterine antimicrobial use.},
}
@article {pmid40166115,
year = {2025},
author = {Yu, Z and Yang, B and Wei, P and Xu, H and Shan, Y and Fan, X and Zhang, H and Wang, C and Wang, J and Yu, S and Zhao, G},
title = {Critical biomarkers for responsive deep brain stimulation and responsive focal cortex stimulation in epilepsy field.},
journal = {Fundamental research},
volume = {5},
number = {1},
pages = {103-114},
pmid = {40166115},
issn = {2667-3258},
abstract = {To derive critical signal features from intracranial electroencephalograms of epileptic patients in order to design instructions for feedback-type electrical stimulation systems. The Detrended Fluctuation Analysis (DFA) exponent is chosen as the classification exponent, and the disparities between indicators representing distinct seizure states and the classification efficacy of rudimentary machine learning models are computed. The DFA exponent exhibited a statistically significant variation among the pre-ictal, ictal period, and post-ictal stages. The Linear Discriminant Analysis model demonstrates the highest accuracy among the three basic machine learning models, whereas the Naive Bayesian model necessitates the least amount of computational and storage space. The set of DFA exponents is employed as an intermediary variable in the machine learning process. The resultant model possesses the capability to function as a feedback trigger program for electrical stimulation systems of the feedback variety, specifically within the domain of neural modulation in epilepsy.},
}
@article {pmid40166113,
year = {2025},
author = {Sun, Y and Chen, X and Liu, B and Liang, L and Wang, Y and Gao, S and Gao, X},
title = {Signal acquisition of brain-computer interfaces: A medical-engineering crossover perspective review.},
journal = {Fundamental research},
volume = {5},
number = {1},
pages = {3-16},
pmid = {40166113},
issn = {2667-3258},
abstract = {Brain-computer interface (BCI) technology represents a burgeoning interdisciplinary domain that facilitates direct communication between individuals and external devices. The efficacy of BCI systems is largely contingent upon the progress in signal acquisition methodologies. This paper endeavors to provide an exhaustive synopsis of signal acquisition technologies within the realm of BCI by scrutinizing research publications from the last ten years. Our review synthesizes insights from both clinical and engineering viewpoints, delineating a comprehensive two-dimensional framework for understanding signal acquisition in BCIs. We delineate nine discrete categories of technologies, furnishing exemplars for each and delineating the salient challenges pertinent to these modalities. This review furnishes researchers and practitioners with a broad-spectrum comprehension of the signal acquisition landscape in BCI, and deliberates on the paramount issues presently confronting the field. Prospective enhancements in BCI signal acquisition should focus on harmonizing a multitude of disciplinary perspectives. Achieving equilibrium between signal fidelity, invasiveness, biocompatibility, and other pivotal considerations is imperative. By doing so, we can propel BCI technology forward, bolstering its effectiveness, safety, and dependability, thereby contributing to an auspicious future for human-technology integration.},
}
@article {pmid40164913,
year = {2025},
author = {Cheng, YA and Sanayei, M and Chen, X and Jia, K and Li, S and Fang, F and Watanabe, T and Thiele, A and Zhang, RY},
title = {A neural geometry approach comprehensively explains apparently conflicting models of visual perceptual learning.},
journal = {Nature human behaviour},
volume = {9},
number = {5},
pages = {1023-1040},
pmid = {40164913},
issn = {2397-3374},
support = {32100901//National Natural Science Foundation of China (National Science Foundation of China)/ ; R01EY019466//U.S. Department of Health &amp; Human Services | NIH | National Eye Institute (NEI)/ ; 32441102//National Natural Science Foundation of China (National Science Foundation of China)/ ; T2421004//National Natural Science Foundation of China (National Science Foundation of China)/ ; R01 EY019466/EY/NEI NIH HHS/United States ; 3230085//National Natural Science Foundation of China (National Science Foundation of China)/ ; BCS-2241417//NSF | Directorate for Social, Behavioral &amp; Economic Sciences | Division of Behavioral and Cognitive Sciences (Behavioral &amp; Cognitive Sciences)/ ; 31930053//National Natural Science Foundation of China (National Science Foundation of China)/ ; G0700976//RCUK | Medical Research Council (MRC)/ ; },
mesh = {Humans ; *Visual Perception/physiology ; *Learning/physiology ; Animals ; *Models, Neurological ; Neural Networks, Computer ; Neuronal Plasticity/physiology ; Male ; },
abstract = {Visual perceptual learning (VPL), defined as long-term improvement in a visual task, is considered a crucial tool for elucidating underlying visual and brain plasticity. Previous studies have proposed several neural models of VPL, including changes in neural tuning or in noise correlations. Here, to adjudicate different models, we propose that all neural changes at single units can be conceptualized as geometric transformations of population response manifolds in a high-dimensional neural space. Following this neural geometry approach, we identified neural manifold shrinkage due to reduced trial-by-trial population response variability, rather than tuning or correlation changes, as the primary mechanism of VPL. Furthermore, manifold shrinkage successfully explains VPL effects across artificial neural responses in deep neural networks, multivariate blood-oxygenation-level-dependent signals in humans and multiunit activities in monkeys. These converging results suggest that our neural geometry approach comprehensively explains a wide range of empirical results and reconciles previously conflicting models of VPL.},
}
@article {pmid40164740,
year = {2025},
author = {Littlejohn, KT and Cho, CJ and Liu, JR and Silva, AB and Yu, B and Anderson, VR and Kurtz-Miott, CM and Brosler, S and Kashyap, AP and Hallinan, IP and Shah, A and Tu-Chan, A and Ganguly, K and Moses, DA and Chang, EF and Anumanchipalli, GK},
title = {A streaming brain-to-voice neuroprosthesis to restore naturalistic communication.},
journal = {Nature neuroscience},
volume = {28},
number = {4},
pages = {902-912},
pmid = {40164740},
issn = {1546-1726},
support = {5U01DC018671//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; F30DC021872//U.S. Department of Health &amp; Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Speech/physiology ; *Voice/physiology ; Adult ; Female ; Communication Devices for People with Disabilities ; *Sensorimotor Cortex/physiology ; Communication ; Paralysis/physiopathology/rehabilitation ; },
abstract = {Natural spoken communication happens instantaneously. Speech delays longer than a few seconds can disrupt the natural flow of conversation. This makes it difficult for individuals with paralysis to participate in meaningful dialogue, potentially leading to feelings of isolation and frustration. Here we used high-density surface recordings of the speech sensorimotor cortex in a clinical trial participant with severe paralysis and anarthria to drive a continuously streaming naturalistic speech synthesizer. We designed and used deep learning recurrent neural network transducer models to achieve online large-vocabulary intelligible fluent speech synthesis personalized to the participant's preinjury voice with neural decoding in 80-ms increments. Offline, the models demonstrated implicit speech detection capabilities and could continuously decode speech indefinitely, enabling uninterrupted use of the decoder and further increasing speed. Our framework also successfully generalized to other silent-speech interfaces, including single-unit recordings and electromyography. Our findings introduce a speech-neuroprosthetic paradigm to restore naturalistic spoken communication to people with paralysis.},
}
@article {pmid40163319,
year = {2025},
author = {van Balen, B},
title = {Somatosensory Feedback in BCIs: Why Aiming for Naturalness Raises Ethical Concerns.},
journal = {AJOB neuroscience},
volume = {},
number = {},
pages = {1-15},
doi = {10.1080/21507740.2025.2478427},
pmid = {40163319},
issn = {2150-7759},
abstract = {Recent developments in the domain of bi-directional Brain-Computer Interface (BCI) technology are directed at generating naturalistic sensory perceptual experiences for disabled people. I argue that conceptualizing and operationalizing "naturalness" in this context has profound impact on disabled people and their experiences. I ask (1) what does it mean to have a "natural" perceptual experience and (2) should the bi-directional BCI-community strive for naturalness in this context? Inspired by phenomenological and 4E-cognition approaches to perception, I argue that the terms "natural" and "naturalness" should not be used in this context because of (1) polysemicity and (2) an implicit bias favoring able-bodied perception over disabled perception. I offer the phenomenological concept of transparency as a positive alternative to denote the underlying goal of embodiment and effortless use. I cash out methodological ramifications of my argument for research in bi-directional BCIs and plea for a transdisciplinary dialogue between end-users, phenomenologists and neuroscientists.},
}
@article {pmid40162212,
year = {2025},
author = {Wang, D and Ramesh, R and Azgomi, HF and Louie, K and Balakid, J and Marks, J},
title = {At-Home Movement State Classification Using Totally Implantable Bidirectional Cortical-Basal Ganglia Neural Interface.},
journal = {Research square},
volume = {},
number = {},
pages = {},
pmid = {40162212},
issn = {2693-5015},
support = {R01 NS130183/NS/NINDS NIH HHS/United States ; },
abstract = {Movement decoding from invasive human recordings typically relies on a distributed system employing advanced machine learning algorithms programmed into an external computer for state classification. These brain-computer interfaces are limited to short-term studies in laboratory settings that may not reflect behavior and neural states in the real world. The development of implantable devices with sensing capabilities is revolutionizing the study and treatment of brain circuits. However, it is unknown whether these devices can decode natural movement state from recorded neural activity or accurately classify states in real-time using on-board algorithms. Here, using a totally implanted sensing-enabled neurostimulator to perform long-term, at-home recordings from the motor cortex and pallidum of four subjects with Parkinson's disease, we successfully identified highly sensitive and specific personalized signatures of gait state, as determined by wearable sensors. Additionally, we demonstrated the feasibility of using at-home data to generate biomarkers compatible with the classifier embedded on-board the neurostimulator. These findings offer a pipeline for ecologically valid movement biomarker identification that can advance therapy across a variety of diseases.},
}
@article {pmid40162168,
year = {2025},
author = {Yang, H and Jiang, L},
title = {Regulating neural data processing in the age of BCIs: Ethical concerns and legal approaches.},
journal = {Digital health},
volume = {11},
number = {},
pages = {20552076251326123},
pmid = {40162168},
issn = {2055-2076},
abstract = {Brain-computer interfaces (BCIs) have seen increasingly fast growth under the help from AI, algorithms, and cloud computing. While providing great benefits for both medical and educational purposes, BCIs involve processing of neural data which are uniquely sensitive due to their most intimate nature, posing unique risks and ethical concerns especially related to privacy and safe control of our neural data. In furtherance of human right protection such as mental privacy, data laws provide more detailed and enforceable rules for processing neural data which may balance the tension between privacy protection and need of the public for wellness promotion and scientific progress through data sharing. This article notes that most of the current data laws like GDPR have not covered neural data clearly, incapable of providing full protection in response to its specialty. The new legislative reforms in the U.S. states of Colorado and California made pioneering advances to incorporate neural data into data privacy laws. Yet regulatory gaps remain as such reforms have not provided special additional rules for neural data processing. Potential problems such as static consent, vague research exceptions, and loopholes in regulating non-personal neural data need to be further addressed. We recommend relevant improved measures taken through amending data laws or making special data acts.},
}
@article {pmid40161643,
year = {2025},
author = {Haro, S and Beauchene, C and Quatieri, TF and Smalt, CJ},
title = {A Brain-Computer Interface for Improving Auditory Attention in Multi-Talker Environments.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {40161643},
issn = {2692-8205},
support = {T32 DC000038/DC/NIDCD NIH HHS/United States ; },
abstract = {OBJECTIVE: There is significant research in accurately determining the focus of a listener's attention in a multi-talker environment using auditory attention decoding (AAD) algorithms. These algorithms rely on neural signals to identify the intended speaker, assuming that these signals consistently reflect the listener's focus. However, some listeners struggle with this competing talkers task, leading to suboptimal tracking of the desired speaker due to potential interference from distractors. The goal of this study was to enhance a listener's attention to the target speaker in real time and investigate the underlying neural bases of this improvement.<br><br>APPROACH: This paper describes a closed-loop neurofeedback system that decodes the auditory attention of the listener in real time, utilizing data from a non-invasive, wet electroencephalography (EEG) brain-computer interface (BCI). Fluctuations in the listener's real-time attention decoding accuracy was used to provide acoustic feedback. As accuracy improved, the ignored talker in the two-talker listening scenario was attenuated; making the desired talker easier to attend to due to the improved attended talker signal-to-noise ratio (SNR). A one-hour session was divided into a 10-minute decoder training phase, with the rest of the session allocated to observing changes in neural decoding.<br><br>RESULTS: In this study, we found evidence of suppression of (i.e., reduction in) neural tracking of the unattended talker when comparing the first and second half of the neurofeedback session (p = 0.012). We did not find a statistically significant increase in the neural tracking of the attended talker.<br><br>SIGNIFICANCE: These results establish a single session performance benchmark for a time-invariant, non-adaptive attended talker linear decoder utilized to extract attention from a listener integrated within a closed-loop neurofeedback system. This research lays the engineering and scientific foundation for prospective multi-session clinical trials of an auditory attention training paradigm.},
}
@article {pmid40161457,
year = {2025},
author = {Ren, X and Wang, Y and Li, X and Wang, X and Liu, Z and Yang, J and Wang, L and Zheng, C},
title = {Attenuated heterogeneity of hippocampal neuron subsets in response to novelty induced by amyloid-β.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {56},
pmid = {40161457},
issn = {1871-4080},
abstract = {Alzheimer's disease (AD) patients exhibited episodic memory impairments including location-object recognition in a spatial environment, which was also presented in animal models with amyloid-β (Aβ) accumulation. A potential cellular mechanism was the unstable representation of spatial information and lack of discrimination ability of novel stimulus in the hippocampal place cells. However, how the firing characteristics of different hippocampal subsets responding to diverse spatial information were interrupted by Aβ accumulation remains unclear. In this study, we observed impaired novel object-location recognition in Aβ-treated Long-Evans rats, with larger receptive fields of place cells in hippocampal CA1, compared with those in the saline-treated group. We identified two subsets of place cells coding object information (ObjCell) and global environment (EnvCell) during the task, with firing heterogeneity in response to introduced novel information. ObjCells displayed a dynamic representation responding to the introduction of novel information, while EnvCells exhibited a stable representation to support the recognition of the familiar environment. However, the dynamic firing patterns of these two subsets of cells were disrupted to present attenuated heterogeneity under Aβ accumulation. The impaired spatial representation novelty information could be due to the disturbed gamma modulation of neural activities. Taken together, these findings provide new evidence for novelty recognition impairments of AD rats with spatial representation dysfunctions of hippocampal subsets.},
}
@article {pmid40159374,
year = {2025},
author = {Phang, CR and Hirata, A},
title = {Shared autonomy between human electroencephalography and TD3 deep reinforcement learning: A multi-agent copilot approach.},
journal = {Annals of the New York Academy of Sciences},
volume = {1546},
number = {1},
pages = {157-172},
doi = {10.1111/nyas.15322},
pmid = {40159374},
issn = {1749-6632},
support = {KAKENHI 21H04956//Japan Society for the Promotion of Science/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Deep Learning ; *Reinforcement, Psychology ; Algorithms ; Brain/physiology ; Reinforcement Machine Learning ; },
abstract = {Deep reinforcement learning (RL) algorithms enable the development of fully autonomous agents that can interact with the environment. Brain-computer interface (BCI) systems decipher human implicit brain signals regardless of the explicit environment. We proposed a novel integration technique between deep RL and BCI to improve beneficial human interventions in autonomous systems and the performance in decoding brain activities by considering environmental factors. Shared autonomy was allowed between the action command decoded from the electroencephalography (EEG) of the human agent and the action generated from the twin delayed DDPG (TD3) agent for a given complex environment. Our proposed copilot control scheme with a full blocker (Co-FB) significantly outperformed the individual EEG (EEG-NB) or TD3 control. The Co-FB model achieved a higher target-approaching score, lower failure rate, and lower human workload than the EEG-NB model. We also proposed a disparity d $d$ -index to evaluate the effect of contradicting agent decisions on the control accuracy and authority of the copilot model. We observed that shifting control authority to the TD3 agent improved performance when BCI decoding was not optimal. These findings indicate that the copilot system can effectively handle complex environments and that BCI performance can be improved by considering environmental factors.},
}
@article {pmid40153908,
year = {2025},
author = {Liu, J and Yang, X and Musmar, B and Hasan, DM},
title = {Trans-arterial approach for neural recording and stimulation: Present and future.},
journal = {Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia},
volume = {135},
number = {},
pages = {111180},
doi = {10.1016/j.jocn.2025.111180},
pmid = {40153908},
issn = {1532-2653},
mesh = {Humans ; *Brain-Computer Interfaces/trends ; Electrodes, Implanted ; *Endovascular Procedures/methods ; *Brain/physiology ; Electroencephalography/methods ; },
abstract = {Neural recording and stimulation are fundamental techniques used for brain computer interfaces (BCIs). BCIs have significant potential for use in a range of brain disorders. However, for most BCIs, electrode implantation requires invasive craniotomy procedures, which have a risk of infection, hematoma, and immune responses. Such drawbacks may limit the extensive application of BCIs. There has been a rapid increase in the development of endovascular technologies and devices. Indeed, in a clinical trial, stent electrodes have been endovascularly implanted via a venous approach and provided an effective endovascular BCI to help disabled patients. Several authors have reviewed the use of endovascular recordings or endovascular BCIs. However, there is limited information on the use of trans-arterial BCIs. Herein, we reviewed the literature on the use of trans-arterial neural recording and stimulation for BCIs, and discuss their potential in terms of anatomical features, device innovations, and clinical applications. Although the use of trans-arterial recording and stimulation in the brain remains challenging, we believe it has high potential for both scientists and physicians.},
}
@article {pmid40152578,
year = {2025},
author = {Li, K and Cui, Y},
title = {The Emerging Role of Astrocytes in Learning and Memory Recall.},
journal = {Journal of integrative neuroscience},
volume = {24},
number = {3},
pages = {38721},
doi = {10.31083/JIN38721},
pmid = {40152578},
issn = {0219-6352},
support = {2022ZD0211700//STI2030-Major projects/ ; 32371057//National Natural Science Foundation of China/ ; },
}
@article {pmid40151645,
year = {2025},
author = {Iwama, S and Ueno, T and Fujimaki, T and Ushiba, J},
title = {Enhanced human sensorimotor integration via self-modulation of the somatosensory activity.},
journal = {iScience},
volume = {28},
number = {4},
pages = {112145},
pmid = {40151645},
issn = {2589-0042},
abstract = {Motor performance improvement through self-modulation of brain activity has been demonstrated through neurofeedback. However, the sensorimotor plasticity induced through the training remains unclear. Here, we combined individually tailored closed-loop neurofeedback, neurophysiology, and behavioral assessment to characterize how the training can modulate the somatosensory system and improve performance. The real-time neurofeedback of human electroencephalogram (EEG) signals enhanced participants' self-modulation ability of intrinsic neural oscillations in the primary somatosensory cortex (S1) within 30 min. Further, the short-term reorganization in S1 was corroborated by the post-training changes in somatosensory evoked potential (SEP) amplitude of the early component from S1. Meanwhile those derived from peripheral and spinal sensory fibers were maintained (N9 and N13 components), suggesting that the training manipulated S1 activities. Behavioral evaluation demonstrated improved performance during keyboard touch-typing indexed by resolved speed-accuracy trade-off. Collectively, our results provide evidence that neurofeedback training induces functional reorganization of S1 and sensorimotor function.},
}
@article {pmid40149743,
year = {2025},
author = {Zheng, J and Li, Y and Chen, L and Wang, F and Gu, B and Sun, Q and Gao, X and Zhou, F},
title = {Effects of Packet Loss on Neural Decoding Effectiveness in Wireless Transmission.},
journal = {Brain sciences},
volume = {15},
number = {3},
pages = {},
pmid = {40149743},
issn = {2076-3425},
support = {2021ZD0200405//National Key R&amp;D Program of China/ ; 2024M752811//China Postdoctoral Science Foundation under Grant Number/ ; 202204A09//Key Agricultural and Social Development Projects of Hangzhou/ ; },
abstract = {BACKGROUND: In brain-computer interfaces, neural decoding plays a central role in translating neural signals into meaningful physical actions. These signals are transmitted to processors for decoding via wired or wireless channels; however, they are often subject to data loss, commonly referred to as "packet loss". Despite their importance, the effects of different types and degrees of packet loss on neural decoding have not yet been comprehensively studied. Understanding these effects is critical for advancing neural signal processing.<br><br>METHODS: This study addresses this gap by constructing four distinct packet loss models that simulate the congestion, distribution, and burst loss scenarios. Using macaque superior arm movement decoding experiments, we analyzed the effects of the aforementioned packet loss types on decoding performance across six parameters (position, velocity, and acceleration in the x and y dimensions). The performance was assessed using the R2 metric and statistical comparisons across different loss scenarios.<br><br>RESULTS: Our results indicate that sudden, consecutive packet loss significantly degraded decoding performance. For the same packet loss probability, burst loss led to the largest decrease in the R2 value. Notably, when the packet loss rate reached 10%, the decoding performance for acceleration dropped to 73% of the original R2 value. On the other hand, when the packet loss rate was within 2%, the neural signal decoding results across all packet loss models remained largely unaffected. However, as the packet loss rate increased, the impact became more pronounced. These findings highlight the varying degrees to which different packet loss models affect decoding outcomes.<br><br>CONCLUSIONS: This study quantitatively evaluated the relationship between packet loss and neural decoding outcomes, highlighting the differential effects of loss patterns on decoding parameters, and it proposed some methods and devices to solve the problem of packet loss. These findings offer valuable insights for the development of resilient neural signal acquisition and processing systems capable of mitigating the impact of packet loss.},
}
@article {pmid40149575,
year = {2025},
author = {Calderone, A and Manuli, A and Arcadi, FA and Militi, A and Cammaroto, S and Maggio, MG and Pizzocaro, S and Quartarone, A and De Nunzio, AM and Calabrò, RS},
title = {The Impact of Visualization on Stroke Rehabilitation in Adults: A Systematic Review of Randomized Controlled Trials on Guided and Motor Imagery.},
journal = {Biomedicines},
volume = {13},
number = {3},
pages = {},
pmid = {40149575},
issn = {2227-9059},
abstract = {Background/Objectives: Guided imagery techniques, which include mentally picturing motions or activities to help motor recovery, are an important part of neuroplasticity-based motor therapy in stroke patients. Motor imagery (MI) is a kind of guided imagery in neurorehabilitation that focuses on mentally rehearsing certain motor actions in order to improve performance. This systematic review aims to evaluate the current evidence on guided imagery techniques and identify their therapeutic potential in stroke motor rehabilitation. Methods: Randomized controlled trials (RCTs) published in the English language were identified from an online search of PubMed, Web of Science, Embase, EBSCOhost, and Scopus databases without a specific search time frame. The inclusion criteria take into account guided imagery interventions and evaluate their impact on motor recovery through validated clinical, neurophysiological, or functional assessments. This review has been registered on Open OSF with the following number: DOI 10.17605/OSF.IO/3D7MF. Results: This review synthesized 41 RCTs on MI in stroke rehabilitation, with 996 participants in the intervention group and 757 in the control group (average age 50-70, 35% female). MI showed advantages for gait, balance, and upper limb function; however, the RoB 2 evaluation revealed 'some concerns' related to allocation concealment, blinding, and selective reporting issues. Integrating MI with gait training or action observation (AO) seems to improve motor recovery, especially in balance and walking. Technological methods like brain-computer interfaces (BCIs) and hybrid models that combine MI with circuit training hold potential for enhancing functional mobility and motor results. Conclusions: Guided imagery shows promise as a beneficial adjunct in stroke rehabilitation, with the potential to improve motor recovery across several domains such as gait, upper limb function, and balance.},
}
@article {pmid40147158,
year = {2025},
author = {Tang, J and Xi, X and Wang, T and Li, L and Yang, J},
title = {Evaluation of the impacts of neuromuscular electrical stimulation based on cortico-muscular-cortical functional network.},
journal = {Computer methods and programs in biomedicine},
volume = {265},
number = {},
pages = {108735},
doi = {10.1016/j.cmpb.2025.108735},
pmid = {40147158},
issn = {1872-7565},
mesh = {Humans ; Electromyography ; *Muscle, Skeletal/physiology/physiopathology ; Male ; *Electric Stimulation ; Female ; Middle Aged ; Stroke/physiopathology ; *Motor Cortex/physiopathology ; *Electric Stimulation Therapy ; Adult ; Stroke Rehabilitation ; *Cerebral Cortex ; Nerve Net ; },
abstract = {BACKGROUND AND OBJECTIVE: Neuromuscular electrical stimulation (NMES) has been extensively applied for recovery of motor functions. However, its impact on the cortical network changes related to muscle activity remains unclear, which is crucial for understanding the changes in the collaborative working patterns within the sensory-motor control system post-stroke.<br><br>METHODS: In this research, we have integrated cortico-muscular interactions, intercortical interactions, and intramuscular interactions to propose a novel closed-loop network structure, namely the cortico-muscular-cortical functional network (CMCFN). The framework is endowed with the capability to distinguish the directionality of causal interactions and local frequency band characteristics through transfer spectral entropy (TSE). Subsequently, the CMCFN is applied to stroke patients to elucidate the potential influence of NMES on cortical physiological function changes during motor induction.<br><br>RESULTS: The results indicate that short-term modulation by NMES significantly enhanced the cortico-muscular interactions of the contralateral cerebral hemisphere and the affected upper limb (p < 0.001), while coexistence of facilitatory and inhibitory effects is observed in the intermuscular coupling across different electromyography (EMG) signals. Furthermore, following NMES treatment, the connectivity of the brain functional network is significantly strengthened, particularly in the &#x3b3; frequency band (30-45 Hz), with marked improvements in the clustering coefficient and shortest path length (p < 0.001).<br><br>CONCLUSIONS: As a new framework, CMCFN offers a novel perspective for studying motor cortical networks related to muscle activity.},
}
@article {pmid40145943,
year = {2025},
author = {Jilderda, MF and Zhang, Y and Rebattu, V and Salunga, R and Mesker, W and Wong, J and de Munck, L and Fornander, T and Nordenskjöld, B and Stål, O and Anderson, AKL and Bastiaannet, E and Treuner, K and Liefers, GJ},
title = {Identification of Early-Stage Breast Cancer with a Minimal Risk of Recurrence by the Breast Cancer Index.},
journal = {Clinical cancer research : an official journal of the American Association for Cancer Research},
volume = {31},
number = {11},
pages = {2222-2229},
pmid = {40145943},
issn = {1557-3265},
support = {//BioTheranostics (BioTheranostics Inc)/ ; },
mesh = {Humans ; Female ; *Breast Neoplasms/pathology/diagnosis/drug therapy/therapy ; Middle Aged ; Aged ; Prognosis ; *Neoplasm Recurrence, Local/pathology ; Neoplasm Staging ; Tamoxifen/therapeutic use ; Netherlands ; Prospective Studies ; Risk Factors ; Chemotherapy, Adjuvant ; Adult ; Registries ; },
abstract = {PURPOSE: This study assessed the prognostic ability of the breast cancer index (BCI) to identify patients at a minimal risk (<5%) of 10-year distant recurrence (DR) who are unlikely to benefit from adjuvant endocrine therapy.<br><br>EXPERIMENTAL DESIGN: This prospective translational study included postmenopausal patients with early-stage, hormone receptor-positive N0 breast cancer from the Stockholm (STO-3) trial who underwent surgery alone ("untreated") or surgery plus adjuvant tamoxifen ("treated") and from the Netherlands Cancer Registry (surgery alone). The primary endpoint was time to DR. An adjusted BCI model with an additional cutpoint was developed, which stratified patients into four prognostic risk groups.<br><br>RESULTS: Across cohorts, 16% to 22% of patients were classified as minimal risk of 10-year DR. In the Stockholm untreated cohort (n = 283), risks in the minimal-, low-, intermediate-, and high-risk groups were 2.3%, 15.5% [hazard ratio, 4.71 (95% confidence interval, 1.09-20.29) vs. minimal risk], 19.8% [6.97 (1.61-30.18)], and 35.9% [13.21 (3.07-56.76)], respectively (P < 0.001). In the Stockholm treated cohort (n = 317), risks were 4.3%, 5.0% [1.16 (0.35-3.85)], 11.7% [2.45 (0.74-8.14)], and 21.1% [5.27 (1.72-16.16); P < 0.001]. In the Netherlands Cancer Registry cohort (n = 1245), risks were 4.5%, 7.5% [subdistribution hazard ratio, 1.67 (95% confidence interval, 0.81-3.45)], 10.3% [2.40 (1.14-5.03)], and 13.1% [3.13 (1.50-6.55); P = 0.005]. BCI risk scores provided additional independent information over standard prognostic factors (likelihood ratio, &#x3c7;2 = 7.98; P = 0.004).<br><br>CONCLUSIONS: The adjusted BCI model identified women with early-stage, hormone receptor-positive N0 breast cancer at a minimal risk of DR who may consider de-escalating adjuvant endocrine therapy.},
}
@article {pmid40144587,
year = {2025},
author = {Marzulli, M and Bleuzé, A and Saad, J and Martel, F and Ciuciu, P and Aksenova, T and Struber, L},
title = {Classifying mental motor tasks from chronic ECoG-BCI recordings using phase-amplitude coupling features.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1521491},
pmid = {40144587},
issn = {1662-5161},
abstract = {INTRODUCTION: Phase-amplitude coupling (PAC), the modulation of high-frequency neural oscillations by the phase of slower oscillations, is increasingly recognized as a marker of goal-directed motor behavior. Despite this interest, its specific role and potential value in decoding attempted motor movements remain unclear.<br><br>METHODS: This study investigates whether PAC-derived features can be leveraged to classify different motor behaviors from ECoG signals within Brain-Computer Interface (BCI) systems. ECoG data were collected using the WIMAGINE implant during BCI experiments with a tetraplegic patient performing mental motor tasks. The data underwent preprocessing to extract complex neural oscillation features (amplitude, phase) through spectral decomposition techniques. These features were then used to quantify PAC by calculating different coupling indices. PAC metrics served as input features in a machine learning pipeline to evaluate their effectiveness in predicting mental tasks (idle state, right-hand movement, left-hand movement) in both offline and pseudo-online modes.<br><br>RESULTS: The PAC features demonstrated high accuracy in distinguishing among motor tasks, with key classification features highlighting the coupling of theta/low-gamma and beta/high-gamma frequency bands.<br><br>DISCUSSION: These preliminary findings hold significant potential for advancing our understanding of motor behavior and for developing optimized BCI systems.},
}
@article {pmid40144585,
year = {2025},
author = {Saad, J and Evans, A and Jaoui, I and Roux-Sibillon, V and Hardy, E and Anghel, L},
title = {Comparison metrics and power trade-offs for BCI motor decoding circuit design.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1547074},
pmid = {40144585},
issn = {1662-5161},
abstract = {Brain signal decoders are increasingly being used in early clinical trials for rehabilitation and assistive applications such as motor control and speech decoding. As many Brain-Computer Interfaces (BCIs) need to be deployed in battery-powered or implantable devices, signal decoding must be performed using low-power circuits. This paper reviews existing hardware systems for BCIs, with a focus on motor decoding, to better understand the factors influencing the power and algorithmic performance of such systems. We propose metrics to compare the energy efficiency of a broad range of on-chip decoding systems covering Electroencephalography (EEG), Electrocorticography (ECoG), and Microelectrode Array (MEA) signals. Our analysis shows that achieving a given classification rate requires an Input Data Rate (IDR) that can be empirically estimated, a finding that is helpful for sizing new BCI systems. Counter-intuitively, our findings show a negative correlation between the power consumption per channel (PpC) and the Information Transfer Rate (ITR). This suggests that increasing the number of channels can simultaneously reduce the PpC through hardware sharing and increase the ITR by providing new input data. In fact, for EEG and ECoG decoding circuits, the power consumption is dominated by the complexity of signal processing. To better understand how to minimize this power consumption, we review the optimizations used in state-of-the-art decoding circuits.},
}
@article {pmid40143846,
year = {2025},
author = {Andronache, C and Curǎvale, D and Nicolae, IE and Neacşu, AA and Nicolae, G and Ivanovici, M},
title = {Tackling the possibility of extracting a brain digital fingerprint based on personal hobbies predilection.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1487175},
pmid = {40143846},
issn = {1662-4548},
abstract = {In an attempt to create a more familiar brain-machine interaction for biometric authentication applications, we investigated the efficiency of using the users' personal hobbies, interests, and memory collections. This approach creates a unique and pleasant experience that can be later utilized within an authentication protocol. This paper presents a new EEG dataset recorded while subjects watch images of popular hobbies, pictures with no point of interest and images with great personal significance. In addition, we propose several applications that can be tackled with our newly collected dataset. Namely, our study showcases 4 types of applications and we obtain state-of-the-art level results for all of them. The tackled tasks are: emotion classification, category classification, authorization process, and person identification. Our experiments show great potential for using EEG response to hobby visualization for people authentication. In our study, we show preliminary results for using predilection for personal hobbies, as measured by EEG, for identifying people. Also, we propose a novel authorization process paradigm using electroencephalograms. Code and dataset are available here.},
}
@article {pmid40141951,
year = {2025},
author = {Aydin, S and Melek, M and Gökrem, L},
title = {A Safe and Efficient Brain-Computer Interface Using Moving Object Trajectories and LED-Controlled Activation.},
journal = {Micromachines},
volume = {16},
number = {3},
pages = {},
pmid = {40141951},
issn = {2072-666X},
support = {2023/86//Tokat Gaziosmanpa&#x15f;a &#xdc;niversitesi/ ; },
abstract = {Nowadays, brain-computer interface (BCI) systems are frequently used to connect individuals who have lost their mobility with the outside world. These BCI systems enable individuals to control external devices using brain signals. However, these systems have certain disadvantages for users. This paper proposes a novel approach to minimize the disadvantages of visual stimuli on the eye health of system users in BCI systems employing visual evoked potential (VEP) and P300 methods. The approach employs moving objects with different trajectories instead of visual stimuli. It uses a light-emitting diode (LED) with a frequency of 7 Hz as a condition for the BCI system to be active. The LED is assigned to the system to prevent it from being triggered by any involuntary or independent eye movements of the user. Thus, the system user will be able to use a safe BCI system with a single visual stimulus that blinks on the side without needing to focus on any visual stimulus through moving balls. Data were recorded in two phases: when the LED was on and when the LED was off. The recorded data were processed using a Butterworth filter and the power spectral density (PSD) method. In the first classification phase, which was performed for the system to detect the LED in the background, the highest accuracy rate of 99.57% was achieved with the random forest (RF) classification algorithm. In the second classification phase, which involves classifying moving objects within the proposed approach, the highest accuracy rate of 97.89% and an information transfer rate (ITR) value of 36.75 (bits/min) were achieved using the RF classifier.},
}
@article {pmid40140571,
year = {2025},
author = {Gazit Shimoni, N and Tose, AJ and Seng, C and Jin, Y and Lukacsovich, T and Yang, H and Verharen, JPH and Liu, C and Tanios, M and Hu, E and Read, J and Tang, LW and Lim, BK and Tian, L and Földy, C and Lammel, S},
title = {Changes in neurotensin signalling drive hedonic devaluation in obesity.},
journal = {Nature},
volume = {641},
number = {8065},
pages = {1238-1247},
pmid = {40140571},
issn = {1476-4687},
support = {R01 DA042889/DA/NIDA NIH HHS/United States ; R01 DA049787/DA/NIDA NIH HHS/United States ; P30 EY003176/EY/NEI NIH HHS/United States ; S10 RR026866/RR/NCRR NIH HHS/United States ; U01 NS113295/NS/NINDS NIH HHS/United States ; R01 NS121231/NS/NINDS NIH HHS/United States ; U01 NS120820/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Neurotensin/metabolism/genetics/deficiency ; *Obesity/metabolism/physiopathology/psychology ; Mice ; Ventral Tegmental Area/cytology/metabolism/physiology ; Diet, High-Fat/adverse effects ; Nucleus Accumbens/cytology/metabolism ; Male ; *Signal Transduction ; *Feeding Behavior/physiology/psychology ; Optogenetics ; Receptors, Neurotensin/metabolism/antagonists &amp; inhibitors ; Mice, Inbred C57BL ; Neurons/metabolism ; *Pleasure ; },
abstract = {Calorie-rich foods, particularly those that are high in fat and sugar, evoke pleasure in both humans and animals[1]. However, prolonged consumption of such foods may reduce their hedonic value, potentially contributing to obesity[2-4]. Here we investigated this phenomenon in mice on a chronic high-fat diet (HFD). Although these mice preferred high-fat food over regular chow in their home cages, they showed reduced interest in calorie-rich foods in a no-effort setting. This paradoxical decrease in hedonic feeding has been reported previously[3-7], but its neurobiological basis remains unclear. We found that in mice on regular diet, neurons in the lateral nucleus accumbens (NAcLat) projecting to the ventral tegmental area (VTA) encoded hedonic feeding behaviours. In HFD mice, this behaviour was reduced and uncoupled from neural activity. Optogenetic stimulation of the NAcLat→VTA pathway increased hedonic feeding in mice on regular diet but not in HFD mice, though this behaviour was restored when HFD mice returned to a regular diet. HFD mice exhibited reduced neurotensin expression and release in the NAcLat→VTA pathway. Furthermore, neurotensin knockout in the NAcLat and neurotensin receptor blockade in the VTA each abolished optogenetically induced hedonic feeding behaviour. Enhancing neurotensin signalling via overexpression normalized aspects of diet-induced obesity, including weight gain and hedonic feeding. Together, our findings identify a neural circuit mechanism that links the devaluation of hedonic foods with obesity.},
}
@article {pmid40139011,
year = {2025},
author = {Luo, C and Zhu, X and Zhang, Y and Wen, Y and Wan, L and Qian, Z},
title = {Competitive electrochemical immunosensor for trace phosphorylated Tau181 analysis in plasma: Toward point-of-care technologies of Alzheimer's disease.},
journal = {Talanta},
volume = {292},
number = {},
pages = {128009},
doi = {10.1016/j.talanta.2025.128009},
pmid = {40139011},
issn = {1873-3573},
mesh = {*tau Proteins/blood/immunology ; *Alzheimer Disease/blood/diagnosis ; Humans ; Phosphorylation ; *Electrochemical Techniques/methods ; Immunoassay/methods ; *Biosensing Techniques/methods ; *Point-of-Care Systems ; Electrodes ; Limit of Detection ; Biomarkers/blood ; Horseradish Peroxidase/chemistry ; },
abstract = {Accurate detection of core Alzheimer's disease (AD) biomarkers in biofluids is crucial for identifying preclinical AD and predicting disease progression. Phosphorylated tau 181 (p-tau181), a key biomarker, holds promise for early diagnosis. This work presents a sensitive and rapid electrochemical immunosensor (EC-iSensor) based on screen-printed electrodes (SPEs) for p-tau181 quantification. Employing a competitive immunoassay format, the EC-iSensor utilizes biotinylated p-tau181 as a competitor against the target analyte for binding to immobilized capture antibodies. Signal transduction is achieved via horseradish peroxidase (HRP) and tetramethylbenzidine (TMB) substrate. The EC-iSensor exhibits a low detection limit of 1.91 fg/mL and a wide dynamic range spanning 6.97 fg/mL to 100 ng/mL in PBS. Furthermore, successful detection of p-tau181 in blood samples from AD patients demonstrated its practical applicability. This cost-effective SPE-based EC-iSensor offers a simple and highly sensitive platform for p-tau181 detection, presenting potential for point-of-care technologies (POCT) of AD.},
}
@article {pmid40138736,
year = {2025},
author = {Yang, W and Wang, X and Qi, W and Wang, W},
title = {LGFormer: integrating local and global representations for EEG decoding.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adc5a3},
pmid = {40138736},
issn = {1741-2552},
mesh = {*Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; *Brain/physiology ; Brain-Computer Interfaces ; },
abstract = {Objective.Electroencephalography (EEG) decoding is challenging because of its temporal variability and low signal-to-noise ratio, which complicate the extraction of meaningful information from signals. Although convolutional neural networks (CNNs) effectively extract local features from EEG signals, they are constrained by restricted receptive fields. In contrast, transformers excel at capturing global dependencies through self-attention mechanisms but often require extensive training data and computational resources, which limits their efficiency on EEG datasets with limited samples.Approach.In this paper, we propose LGFormer, a hybrid network designed to efficiently learn both local and global representations for EEG decoding. LGFormer employs a deep attention module to extract global information from EEG signals, dynamically adjusting the focus of CNNs. Subsequently, LGFormer incorporates a local-enhanced transformer, combining the strengths of CNNs and transformers to achieve multiscale perception from local to global. Despite integrating multiple advanced techniques, LGFormer maintains a lightweight design and training efficiency.Main results.LGFormer achieves state-of-the-art performance within 200 training epochs across four public datasets, including motor imagery, cognitive workload, and error-related negativity decoding tasks. Additionally, we propose a novel spatial and temporal attention visualization method, revealing that LGFormer captures discriminative spatial and temporal features, enhancing model interpretability and providing insights into its decision-making process.Significance.In summary, LGFormer demonstrates superior performance while maintaining high training efficiency across different tasks, highlighting its potential as a versatile and practical model for EEG decoding.},
}
@article {pmid40136841,
year = {2025},
author = {Mohamed, AK and Aharonson, V},
title = {Single-Trial Electroencephalography Discrimination of Real, Regulated, Isometric Wrist Extension and Wrist Flexion.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {10},
number = {3},
pages = {},
pmid = {40136841},
issn = {2313-7673},
support = {99207 and 117965//National Research Foundation of South Africa/ ; },
abstract = {Improved interpretation of electroencephalography (EEG) associated with the neural control of essential hand movements, including wrist extension (WE) and wrist flexion (WF), could improve the performance of brain-computer interfaces (BCIs). These BCIs could control a prosthetic or orthotic hand to enable motor-impaired individuals to regain the performance of activities of daily living. This study investigated the interpretation of neural signal patterns associated with kinematic differences between real, regulated, isometric WE and WF movements from recorded EEG data. We used 128-channel EEG data recorded from 14 participants performing repetitions of the wrist movements, where the force, speed, and range of motion were regulated. The data were filtered into four frequency bands: delta and theta, mu and beta, low gamma, and high gamma. Within each frequency band, independent component analysis was used to isolate signals originating from seven cortical regions of interest. Features were extracted from these signals using a time-frequency algorithm and classified using Mahalanobis distance clustering. We successfully classified bilateral and unilateral WE and WF movements, with respective accuracies of 90.68% and 69.80%. The results also demonstrated that all frequency bands and regions of interest contained motor-related discriminatory information. Bilateral discrimination relied more on the mu and beta bands, while unilateral discrimination favoured the gamma bands. These results suggest that EEG-based BCIs could benefit from the extraction of features from multiple frequencies and cortical regions.},
}
@article {pmid40136836,
year = {2025},
author = {Rusev, G and Yordanov, S and Nedelcheva, S and Banderov, A and Sauter-Starace, F and Koprinkova-Hristova, P and Kasabov, N},
title = {Decoding Brain Signals in a Neuromorphic Framework for a Personalized Adaptive Control of Human Prosthetics.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {10},
number = {3},
pages = {},
pmid = {40136836},
issn = {2313-7673},
support = {101070891/01.10.2022//European Commission under the HORIZON-EIC action/ ; },
abstract = {Current technological solutions for Brain-machine Interfaces (BMI) achieve reasonable accuracy, but most systems are large in size, power consuming and not auto-adaptive. This work addresses the question whether current neuromorphic technologies could resolve these problems? The paper proposes a novel neuromorphic framework of a BMI system for prosthetics control via decoding Electro Cortico-Graphic (ECoG) brain signals. It includes a three-dimensional spike timing neural network (3D-SNN) for brain signals features extraction and an on-line trainable recurrent reservoir structure (Echo state network (ESN)) for Motor Control Decoding (MCD). A software system, written in Python using NEST Simulator SNN library is described. It is able to adapt continuously in real time in supervised or unsupervised mode. The proposed approach was tested on several experimental data sets acquired from a tetraplegic person. First simulation results are encouraging, showing also the need for a further improvement via multiple hyper-parameters tuning. Its future implementation on a neuromorphic hardware platform that is smaller in size and significantly less power consuming is discussed too.},
}
@article {pmid40136825,
year = {2025},
author = {Li, H and Wang, Y and Fu, P},
title = {A Novel Multi-Dynamic Coupled Neural Mass Model of SSVEP.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {10},
number = {3},
pages = {},
pmid = {40136825},
issn = {2313-7673},
support = {2024JC-YBON-0659//Natural Science Basic Research Program of Shaanxi Province/ ; TC2023JC16//Basic Research Programs of Taicang/ ; },
abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) leverage high-speed neural synchronization to visual flicker stimuli for efficient device control. While SSVEP-BCIs minimize user training requirements, their dependence on physical EEG recordings introduces challenges, such as inter-subject variability, signal instability, and experimental complexity. To overcome these limitations, this study proposes a novel neural mass model for SSVEP simulation by integrating frequency response characteristics with dual-region coupling mechanisms. Specific parallel linear transformation functions were designed based on SSVEP frequency responses, and weight coefficient matrices were determined according to the frequency band energy distribution under different visual stimulation frequencies in the pre-recorded SSVEP signals. A coupled neural mass model was constructed by establishing connections between occipital and parietal regions, with parameters optimized through particle swarm optimization to accommodate individual differences and neuronal density variations. Experimental results demonstrate that the model achieved a high-precision simulation of real SSVEP signals across multiple stimulation frequencies (10 Hz, 11 Hz, and 12 Hz), with maximum errors decreasing from 2.2861 to 0.8430 as frequency increased. The effectiveness of the model was further validated through the real-time control of an Arduino car, where simulated SSVEP signals were successfully classified by the advanced FPF-net model and mapped to control commands. This research not only advances our understanding of SSVEP neural mechanisms but also releases the user from the brain-controlled coupling system, thus providing a practical framework for developing more efficient and reliable BCI-based systems.},
}
@article {pmid40135785,
year = {2025},
author = {Chen, J and Liu, B and Peng, G and Zhou, L and Tan, C and Qin, J and Li, J and Hong, Z and Wu, Y and Lu, M and Cai, F and Huang, Y},
title = {Achieving High-Performance Transcranial Ultrasound Transmission Through Mie and Fano Resonance in Flexible Metamaterials.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {12},
number = {19},
pages = {e2500170},
pmid = {40135785},
issn = {2198-3844},
support = {2021YFB3801802//National Key R&amp;D Program of China/ ; 2022YFB3204300//National Key R&amp;D Program of China/ ; 226-2024-00120//Fundamental Research Funds for the Central Universities/ ; 2023R5231//Special Support Plan for High Level Talents in Zhejiang Province/ ; },
mesh = {Humans ; *Skull/diagnostic imaging ; },
abstract = {Transcranial ultrasound holds great potential in medical applications. However, the effective transmission of ultrasound through the skull remains challenging due to the acoustic impedance mismatch, as well as the non-uniform thickness, and the curved surface. To overcome these challenges, this work introduces an innovative Mie-resonance flexible metamaterial (MRFM), which consists of periodically arranged low-speed micropillars embedded within a high-speed flexible substrate. The MRFM generates Mie-resonance, which couples with the skull to form Fano resonance, thereby enhancing ultrasound transmittance through the skull. Simulation results demonstrate that the proposed resonance solution significantly increases transcranial ultrasound transmittance from 33.7% to 75.2% at 0.309 MHz. For the fabrication of the MRFM, porous nickel foam is used as the Mie micropillars, and agarose hydrogel serves as the flexible substrate. Experimental results demonstrate enhanced ultrasound transmittance from 20.6% to 73.3% at 0.33 MHz with the MRFM, which shows good agreement with the simulation results, further validating the effectiveness of the design. The simplicity, tunability, and flexibility of the MRFM represent a significant breakthrough, addressing the limitations of conventional rigid metamaterials. This work lays a solid theoretical and experimental foundation for advancing the clinical application of transcranial ultrasound stimulation and neuromodulation.},
}
@article {pmid40134759,
year = {2025},
author = {Sakel, M and Ozolins, CA and Saunders, K and Biswas, R},
title = {A home-based EEG neurofeedback treatment for chronic neuropathic pain-a pilot study.},
journal = {Frontiers in pain research (Lausanne, Switzerland)},
volume = {6},
number = {},
pages = {1479914},
pmid = {40134759},
issn = {2673-561X},
abstract = {OBJECTIVE: This study assessed the effect of an 8-week home-based neurofeedback intervention in chronic neuropathic pain patients.<br><br>SUBJECTS/PATIENTS: A cohort of eleven individuals with chronic neuropathic pain receiving treatment within the NHS framework.<br><br>METHODS: Participants were trained to operate a home-based neurofeedback system. Each received a portable Axon system for one week of electroencephalogram (EEG) baselines, followed by an 8-week neurofeedback intervention, and subsequent 12 weeks of follow-up EEG baselines. Primary outcome measures included changes in the Brief Pain Inventory and Visual Analogue Pain Scale at post-intervention, and follow-ups compared with the baseline. Secondary outcomes included changes in depression, anxiety, stress, pain catastrophizing, central sensitization, sleep quality, and quality of life. EEG activities were monitored throughout the trial.<br><br>RESULTS: Significant improvements were noted in pain scores, with all participants experiencing overall pain reduction. Clinically significant pain improvement (&#x2265;30%) was reported by 5 participants (56%). Mood scores showed a significant decrease in depression (p&#x2009;<&#x2009;0.05), and pain catastrophizing (p&#x2009;<&#x2009;0.05) scores improved significantly at post-intervention, with continued improvement at the first-month follow-up.<br><br>CONCLUSION: The findings indicate that an 8-week home-based neurofeedback intervention improved pain and psychological well-being in this sample of chronic neuropathic pain patients. A randomized controlled trial is required to replicate these results in a larger cohort. Clinical Trial Registration: https://clinicaltrials.gov/study/NCT05464199, identifier: (NCT05464199).},
}
@article {pmid40133571,
year = {2025},
author = {Xu, X and Sha, L and Basang, S and Peng, A and Zhou, X and Liu, Y and Li, Y and Chen, L},
title = {Mortality in patients with epilepsy: a systematic review.},
journal = {Journal of neurology},
volume = {272},
number = {4},
pages = {291},
pmid = {40133571},
issn = {1432-1459},
support = {Grant Recipient//Science and Technology Major Project of Sichuan Province/ ; },
mesh = {Humans ; *Epilepsy/mortality ; *Sudden Unexpected Death in Epilepsy/epidemiology ; Female ; Male ; Global Burden of Disease ; },
abstract = {BACKGROUND: Epilepsy is linked to a significantly higher risk of death, yet public awareness remains low. This study aims to investigate mortality characteristics, to reduce epilepsy-related deaths and improve prevention strategies.<br><br>METHODS: This study systematically reviews mortality data in relevant literature from PubMed and Embase up until June 2024. Data quality is assessed using the Newcastle-Ottawa Scale, and analysis includes trends, regional differences, and the economic impact of premature death. Global Burden of Disease (GBD) data are used to validate trends. In addition, a review of guidelines and expert statements on sudden unexpected death in epilepsy (SUDEP) is included to explore intervention strategies and recommendations.<br><br>RESULTS: Annual mortality rates of epilepsy have gradually declined, mainly due to improvements in low-income countries, while high-income regions have experienced an upward trend. Male patients exhibit higher mortality rates than females. Age-based analysis shows that the elderly contributes most to this increase due to chronic conditions such as cardiovascular disease and pneumonia related to epilepsy. This may be a key factor contributing to the increased mortality among epilepsy patients in aging high-income regions. Accidents and suicides are more prevalent in low-income regions. The highest mortality risks occur in the early years post-diagnosis and during prolonged, uncontrolled epilepsy. SUDEP remains a leading cause of death.<br><br>CONCLUSION: This study highlights the impact of gender, region, and disease duration on epilepsy mortality. Future research should focus on elderly epilepsy patients mortality characteristics and personalized interventions for SUDEP.},
}
@article {pmid40129720,
year = {2025},
author = {Daly, I and Matran-Fernandez, A and Lebedev, MA and Kübler, A and Valeriani, D},
title = {Editorial: Datasets for brain-computer interface applications, volume II.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1569216},
pmid = {40129720},
issn = {1662-4548},
}
@article {pmid40129499,
year = {2025},
author = {Maltezou-Papastylianou, C and Scherer, R and Paulmann, S},
title = {How do voice acoustics affect the perceived trustworthiness of a speaker? A systematic review.},
journal = {Frontiers in psychology},
volume = {16},
number = {},
pages = {1495456},
pmid = {40129499},
issn = {1664-1078},
abstract = {Trust is a multidimensional and dynamic social and cognitive construct, considered the glue of society. Gauging someone's perceived trustworthiness is essential for forming and maintaining healthy relationships across various domains. Humans have become adept at inferring such traits from speech for survival and sustainability. This skill has extended to the technological space, giving rise to humanlike voice technologies. The inclination to assign personality traits to these technologies suggests that machines may be processed along similar social and vocal dimensions as human voices. Given the increasing prevalence of voice technology in everyday tasks, this systematic review examines the factors in the psychology of voice acoustics that influence listeners' trustworthiness perception of speakers, be they human or machine. Overall, this systematic review has revealed that voice acoustics impact perceptions of trustworthiness in both humans and machines. Specifically, combining multiple acoustic features through multivariate methods enhances interpretability and yields more balanced findings compared to univariate approaches. Focusing solely on isolated features like pitch often yields inconclusive results when viewed collectively across studies without considering other factors. Crucially, situational, or contextual factors should be utilised for enhanced interpretation as they tend to offer more balanced findings across studies. Moreover, this review has highlighted the significance of cross-examining speaker-listener demographic diversity, such as ethnicity and age groups; yet, the scarcity of such efforts accentuates the need for increased attention in this area. Lastly, future work should involve listeners' own trust predispositions and personality traits with ratings of trustworthiness perceptions.},
}
@article {pmid40128831,
year = {2025},
author = {Liu, XY and Wang, WL and Liu, M and Chen, MY and Pereira, T and Doda, DY and Ke, YF and Wang, SY and Wen, D and Tong, XG and Li, WG and Yang, Y and Han, XD and Sun, YL and Song, X and Hao, CY and Zhang, ZH and Liu, XY and Li, CY and Peng, R and Song, XX and Yasi, A and Pang, MJ and Zhang, K and He, RN and Wu, L and Chen, SG and Chen, WJ and Chao, YG and Hu, CG and Zhang, H and Zhou, M and Wang, K and Liu, PF and Chen, C and Geng, XY and Qin, Y and Gao, DR and Song, EM and Cheng, LL and Chen, X and Ming, D},
title = {Recent applications of EEG-based brain-computer-interface in the medical field.},
journal = {Military Medical Research},
volume = {12},
number = {1},
pages = {14},
pmid = {40128831},
issn = {2054-9369},
support = {2021YFF1200602//The National Key R&amp;D Program of China/ ; 0401260011//The National Science Fund for Excellent Overseas Scholars/ ; c02022088//The National Defense Science and Technology Innovation Fund of Chinese Academy of Sciences/ ; 82472098//National Natural Science Foundation of China (General Program)/ ; 82202798//The National Natural Science Foundation of China/ ; 22YF1404200//The Shanghai Sailing Program/ ; },
mesh = {Humans ; *Brain-Computer Interfaces/trends/standards ; *Electroencephalography/methods/instrumentation/trends ; },
abstract = {Brain-computer interfaces (BCIs) represent an emerging technology that facilitates direct communication between the brain and external devices. In recent years, numerous review articles have explored various aspects of BCIs, including their fundamental principles, technical advancements, and applications in specific domains. However, these reviews often focus on signal processing, hardware development, or limited applications such as motor rehabilitation or communication. This paper aims to offer a comprehensive review of recent electroencephalogram (EEG)-based BCI applications in the medical field across 8 critical areas, encompassing rehabilitation, daily communication, epilepsy, cerebral resuscitation, sleep, neurodegenerative diseases, anesthesiology, and emotion recognition. Moreover, the current challenges and future trends of BCIs were also discussed, including personal privacy and ethical concerns, network security vulnerabilities, safety issues, and biocompatibility.},
}
@article {pmid40127544,
year = {2025},
author = {Davis, KC and Wyse-Sookoo, KR and Raza, F and Meschede-Krasa, B and Prins, NW and Fisher, L and Brown, EN and Cajigas, I and Ivan, ME and Jagid, JR and Prasad, A},
title = {5-year follow-up of a fully implanted brain-computer interface in a spinal cord injury patient.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
pmid = {40127544},
issn = {1741-2552},
support = {R25 NS108937/NS/NINDS NIH HHS/United States ; T32 GM112601/GM/NIGMS NIH HHS/United States ; T32 GM145462/GM/NIGMS NIH HHS/United States ; },
mesh = {Humans ; *Brain-Computer Interfaces/trends ; *Electrocorticography/methods/instrumentation/trends ; *Electrodes, Implanted/trends ; Follow-Up Studies ; *Spinal Cord Injuries/rehabilitation/physiopathology/diagnosis ; },
abstract = {Spinal cord injury (SCI) affects over 250 000 individuals in the US. Brain-computer interfaces (BCIs) may improve quality of life by controlling external devices. Invasive intracortical BCIs have shown promise in clinical trials but degrade in the chronic period and tether patients to acquisition hardware. Alternatively, electrocorticography (ECoG) records data from electrodes on the cortex,and studies evaluating fully implanted BCI-ECoG systems are scarce. Objective. We seek to address this need using a fully implanted ECoG-based BCI that allows for home use in SCI.Approach.The patient used a long-term BCI system, initially controlling an functional electrical stimulation orthosis in the lab and later using an external mechanical orthosis at home. To evaluate its long-term viability, electrode contact impedance, signal quality, and decoder performance were measured. Signal quality was assessed using signal-to-noise ratio and maximum bandwidth of the signal. Decoder performance was monitored using the area under the receiver operator characteristic curve (AUROC).Main results.The study analyzed data from the patient's home environment over 54 months, revealing that the device was used at home for 38 ± 24 min on average daily. After six months, we observed stable event-related desynchronization that aided in determining the onset of motor intention. The decoder's average AUROC across months was 0.959. Importantly, 40 months of the data collected was gather from the subject's home or community environment. The results indicate long-term ECoG recordings were stable for motor-imagery classification and motor control in the community environment in a case of an individual with SCI.Significance.This study presents the long-term feasibility and viability of an ECoG-based BCI system that persists in the home environment in a case of SCI. Future research should explore larger electrode counts with more participants to confirm this stability. Understanding these trends is crucial for clinical utility and chronic viability in broader patient populations.},
}
@article {pmid40127541,
year = {2025},
author = {Marissens Cueva, V and Bougrain, L and Lotte, F and Rimbert, S},
title = {Reliable predictor of BCI motor imagery performance using median nerve stimulation.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adc48d},
pmid = {40127541},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Humans ; *Imagination/physiology ; Male ; Female ; Adult ; Electroencephalography/methods ; *Median Nerve/physiology ; Young Adult ; *Psychomotor Performance/physiology ; },
abstract = {Objective.Predicting performance in brain-computer interfaces (BCIs) is crucial for enhancing user experience, optimizing training and identifying the most efficient BCI approach for each individual.Approach.This study explores the use of median nerve stimulation (MNS) as a predictor of motor imagery (MI)-BCI performance. MNS induces event related (de)synchronization (ERD/ERS) patterns in the brain that are similar to those generated during MI tasks, providing a non-invasive, user-independent, and easy-to-setup method for performance prediction.Main results.Our proposed predictor, based on the minimum value of the ERD induced by the MNS, not only exhibits a robust correlation with the MI-BCI performance accuracy (rho = -0.71,p<0.001), but also effectively predicts this performance with a significant correlation (rho = 0.61, mean absolute error = 9.0,p<0.01). These results demonstrate its validity as a reliable predictor of MI-BCI performance.Significance.By systematically analyzing patterns induced by MNS and correlating them with subsequent MI-BCI task performance, we aim to establish a robust predictive method of motor activity to each individual only based on MNS, making it possible, among other things, to passively predict BCI deficiency or proficiency, and to potentially adapt BCI parameters for an efficient BCI experience or BCI-based recovery.},
}
@article {pmid40127535,
year = {2025},
author = {Ofer, A and Ophir, A and Yoav, N and Roman, R and Oren, S},
title = {Supervised autoencoder denoiser for non-stationarity in multi-session EEG-based BCI.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adc48e},
pmid = {40127535},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; Imagination/physiology ; Signal-To-Noise Ratio ; Algorithms ; Adult ; Male ; Autoencoder ; },
abstract = {Objective.Non-stationarity in electroencephalogram (EEG) signals poses significant challenges for the performance and implementation of brain-computer interfaces (BCIs).Approach.In this study, we propose a novel method for cross-session BCI tasks that employs a supervised autoencoder to reduce session-specific information while preserving task-related signals. Our approach compresses high-dimensional EEG inputs and reconstructs them, thereby mitigating non-stationary variability in the data. In addition to unsupervised minimization of the reconstruction error, the objective function of the network includes two supervised terms to ensure that the latent representations exclude session identity information and are optimized for subsequent classification.Main results.Evaluation across three different motor imagery datasets demonstrates that our approach effectively addresses domain adaptation challenges, outperforming both naïve cross-session and within-session methods.Significance.Our method eliminates the need for data from new sessions, making it fully unsupervised concerning new session data and reducing the necessity for recalibration with each session. Furthermore, the reduction of session-specific information in the reconstructed signals indicates that our approach effectively denoises non-stationary signals, thereby enhancing the accuracy of BCI models. Future applications could extend this model to a broader range of BCI tasks and explore the residual signals to investigate sources of non-stationary brain components and other cognitive processes.},
}
@article {pmid40125566,
year = {2025},
author = {Hong, W and Mao, L and Lin, K and Huang, C and Su, Y and Zhang, S and Wang, C and Wang, D and Song, J and Chen, Z},
title = {Accurate and Noninvasive Dysphagia Assessment via a Soft High-Density sEMG Electrode Array Conformal to the Submental and Infrahyoid Muscles.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {12},
number = {25},
pages = {e2500472},
pmid = {40125566},
issn = {2198-3844},
support = {12402202//National Natural Science Foundation of China/ ; 12302223//National Natural Science Foundation of China/ ; 12225209//National Natural Science Foundation of China/ ; 12321002//National Natural Science Foundation of China/ ; 2021C03050//Zhejiang Key Research and Development Program of China/ ; },
mesh = {*Deglutition Disorders/diagnosis/physiopathology ; Humans ; *Electromyography/methods/instrumentation ; Electrodes ; Male ; *Deglutition/physiology ; Female ; Adult ; *Neck Muscles/physiopathology ; Middle Aged ; },
abstract = {Accurate, noninvasive dysphagia assessment is important for rehabilitation therapy but current clinical diagnostic methods are either invasive or subjective. Surface electromyography (sEMG) that monitors muscle activity during swallowing, offers a promising alternative. However, existing sEMG electrode arrays for dysphagia assessment remain challenging in combining the advantages of a large coverage area and strong compliance to the entire swallowing muscles. Here, we report a stretchable, breathable, large-area high-density sEMG (HD-sEMG) electrode array, which enables intimate contact to complex surface of the submental and infrahyoid muscles to detect high-fidelity HD-sEMG signals during swallowing. The electrode array features a 64-channel soft on-skin sensing array for comprehensive data capture, and a stiff connector for simple and reliable connection to an external acquisition setup. Systemically experimental studies revealed the easy operability of the soft HD-sEMG electrode array for effortless integration with the skin, as well as the excellent mechanical and electrical characteristics even subject to substantial skin deformations. By comparing HD-sEMG signals collected from 38 participants, three objective indicators for quantitative dysphagia evaluation were discussed. Finally, a machine learning model was developed to accurately and automatically classify the severity of dysphagia, and the factors affecting the recognition accuracy of the model were discussed in depth.},
}
@article {pmid40124055,
year = {2025},
author = {Lee, H and Lee, S and Lee, S and Lee, J and Chou, N and Shin, H},
title = {A Highly Efficient Low-Cost Flexible Neural Probe for Scalable Mass Fabrication.},
journal = {ACS omega},
volume = {10},
number = {10},
pages = {10733-10740},
pmid = {40124055},
issn = {2470-1343},
abstract = {Neural probes capable of the precise recording and control of brain signals are essential tools for brain-computer interfaces and neuroscience research. However, conventional neural probes have not been widely adopted due to the high costs associated with semiconductor fabrication and complex packaging procedures. Herein, we present a breakthrough in this area in the form of a highly efficient flexible neural probe with a production cost of only 1.5 dollars per unit that can be mass-produced (1000 units within 3 days). The probe design is based on a standardized flexible printed circuit board (PCB) process that ensures large-scale producibility and minimizes device performance variation. The device features four independent neural probes that enable flexible targeting of multiple brain regions and a reusable interface PCB that minimizes packaging complexity. The neural signal recording performance of the fabricated probe is comparable to that of traditional silicon-based probes and is scalable with eight electrodes capable of simultaneous measurements. We anticipate that our innovative device will significantly improve the accessibility of neuroscience research.},
}
@article {pmid40122923,
year = {2025},
author = {Yang, B and Rong, F and Xie, Y and Li, D and Zhang, J and Li, F and Shi, G and Gao, X},
title = {A multi-day and high-quality EEG dataset for motor imagery brain-computer interface.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {488},
pmid = {40122923},
issn = {2052-4463},
support = {62376149//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography ; Imagination ; Male ; },
abstract = {A key challenge in developing a robust electroencephalography (EEG)-based brain-computer interface (BCI) is obtaining reliable classification performance across multiple days. In particular, EEG-based motor imagery (MI) BCI faces large variability and low signal-to-noise ratio. To address these issues, collecting a large and reliable dataset is critical for learning of cross-session and cross-subject patterns while mitigating EEG signals inherent instability. In this study, we obtained a comprehensive MI dataset from the 2019 World Robot Conference Contest-BCI Robot Contest. We collected EEG data from 62 healthy participants across three recording sessions. This experiment includes two paradigms: (1) two-class tasks: left and right hand-grasping, (2) three-class tasks: left and right hand-grasping, and foot-hooking. The dataset comprises raw data, and preprocessed data. For the two-class data, an average classification accuracy of 85.32% was achieved using EEGNet, while the three-class data achieved an accuracy of 76.90% using deepConvNet. Different researchers can reuse the dataset according to their needs. We hope that this dataset will significantly advance MI-BCI research, particularly in addressing cross-session and cross-subject challenges.},
}
@article {pmid40122080,
year = {2025},
author = {Branco, MP and Verberne, MSW and van Balen, BJ and Bekius, A and Leinders, S and Ketelaar, M and Geytenbeek, J and van Driel-Boerrigter, M and Willems-Op Het Veld, M and Rabbie-Baauw, K and Vansteensel, MJ},
title = {Stakeholder's perspective on brain-computer interfaces for children and young adults with cerebral palsy.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {},
number = {},
pages = {1-11},
doi = {10.1080/17483107.2025.2481426},
pmid = {40122080},
issn = {1748-3115},
abstract = {Communication Brain-Computer Interfaces (cBCIs) are a promising tool for people with motor and speech impairment, in particular for children and young adults with communication impairments, for example due to cerebral palsy (CP). Here we aimed to create a solid basis for the user-centered design of cBCIs for children and young adults with severe CP by investigating the perspectives of their parents/caregivers and health care professionals on communication and cBCIs. We conducted an online survey on 1) current communication problems and usability of used aids, 2) interest in cBCIs, and 3) preference for specific types of cBCIs. A total of 19 parents/caregivers and 36 health care professionals who interacted directly with children and young adults (8-25 years old) with severe CP, corresponding to Gross Motor Function Classification System level IV or V, participated. Both groups of respondents indicated that motor impairment occurred the most frequently and had the greatest impact on communication. The currently used communication aids included mainly no/low-tech aids and high-tech aids. The majority of health care professionals and parents/caregivers reported an interest in cBCIs, with a slight preference for implanted electrodes over non-implanted ones, and no preference for either of the two proposed mental BCI control strategies. Results indicate that cBCIs should be considered for a subpopulation of children and young adults with severe CP, and that in the development of cBCIs for this group both visual stimuli and sensorimotor rhythms, as well as the use of implanted electrodes, should be considered.},
}
@article {pmid40121857,
year = {2025},
author = {Zhou, H and Qiao, K and Rao, L and Zhai, HJ},
title = {Nanosilica cross-linked polyurethane hybrid hydrogels to stabilize the silicone rubber based invasive electrode-neural tissue interface.},
journal = {Colloids and surfaces. B, Biointerfaces},
volume = {251},
number = {},
pages = {114643},
doi = {10.1016/j.colsurfb.2025.114643},
pmid = {40121857},
issn = {1873-4367},
mesh = {*Hydrogels/chemistry/pharmacology ; Animals ; Rats ; PC12 Cells ; *Polyurethanes/chemistry ; *Silicon Dioxide/chemistry ; *Silicone Elastomers/chemistry ; Electrodes ; *Cross-Linking Reagents/chemistry ; Surface Properties ; Cell Adhesion/drug effects ; Particle Size ; },
abstract = {An unstable electrode-neural tissue interface induced by tissue inflammatory response hinders the application of invasive brain-computer interfaces (BCIs). In this work, nanosilica cross-linked polyurethane (SiO2/PU) hybrid hydrogels were prepared to serve as the coatings and to modify silicone rubber (SR), which is a commonly used encapsulation material of invasive electrodes for neural recording/stimulation. The hydrophilicity, swelling ratio, and bulk ionic conductivity of SiO2/PU hybrid hydrogels were tailored by incorporating different amount of SiO2 serving as the cross-linking agent. Correspondingly, the optimized SiO2/PU hybrid hydrogel coatings have less impact on the electrochemical properties of invasive electrodes relative to PU hydrogel. Cell affinity assays with rat pheochromocytoma cells reveal that coatings made of SiO2/PU hybrid hydrogels are more effective in enhancing their adhesion and neurite outgrowth than those made of PU hydrogels. The adsorption amount of non-specific proteins on SR is significantly reduced by 81.6 % and 92.6 % upon coating with PU hydrogels and SiO2/PU hybrid hydrogels, respectively. Histological assessment indicates that the SR implants with a SiO2/PU hybrid hydrogel coating provoke the mildest tissue response. Collectively, the SiO2/PU hybrid hydrogel is highly promising for the stabilization of electrode-neural tissue interface, which is crucial for the development of invasive BCIs.},
}
@article {pmid40119207,
year = {2025},
author = {Cheng, M and Lu, D and Li, K and Wang, Y and Tong, X and Qi, X and Yan, C and Ji, K and Wang, J and Wang, W and Lv, H and Zhang, X and Kong, W and Zhang, J and Ma, J and Li, K and Wang, Y and Feng, J and Wei, P and Li, Q and Shen, C and Fu, XD and Ma, Y and Zhang, X},
title = {Author Correction: Mitochondrial respiratory complex IV deficiency recapitulates amyotrophic lateral sclerosis.},
journal = {Nature neuroscience},
volume = {28},
number = {4},
pages = {913},
doi = {10.1038/s41593-025-01941-2},
pmid = {40119207},
issn = {1546-1726},
}
@article {pmid40118966,
year = {2025},
author = {Hussain, SAH and Raza, I and Hussain, SA and Jamal, MH and Gulrez, T and Zia, A},
title = {A mental state aware brain computer interface for adaptive control of electric powered wheelchair.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {9880},
pmid = {40118966},
issn = {2045-2322},
mesh = {Humans ; *Brain-Computer Interfaces ; *Wheelchairs ; Electroencephalography ; Machine Learning ; Male ; Adult ; Female ; Emotions ; Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interfaces (BCI) provide a mobility solution for patients with various disabilities. However, BCI systems require further research to enhance their performance while incorporating the physical and behavioral states of patients into the system. As the principal users of a BCI system, patients with disabilities are emotionally sensitive, so a BCI device that adaptively adjusts to the psychological effects of the patient could provide the foundation for refining BCI applications. This paper focuses on the collection and realization of human electroencephalogram (EEG) signals data, obtained as a response to different psychological effects of sound stimuli. Filtration and pre-processing of the data set are achieved using the frequency-based distribution of EEG signals. Different machine learning tools and techniques are evaluated and applied to abstracted powerbands of psychological signals. The experimental results show that the proposed system predicts mental states with an average accuracy of 74.26%. In addition, an automated BCI system is developed to control an electric wheelchair (EPW) while responding to the mental state of the user with a contingency mechanism. The results show that such a system could be designed to make BCI systems more reliable, safe, adaptable, and responsive to emotions for sensitive paralytic patients. The system also shows a satisfactory True Positive Rate (TPR) and False Positive Rate (FPR) with an average time of 8.4 s to generate the interpretable brain signal from the user.},
}
@article {pmid40118477,
year = {2025},
author = {Ren, Y and Kang, YN and Cao, SY and Meng, F and Zhang, J and Liao, R and Li, X and Chen, Y and Wen, Y and Wu, J and Xia, W and Xu, L and Wen, S and Liu, H and Li, Y and Gu, J and Lv, Q},
title = {Evaluating the performance of large language models in health education for patients with ankylosing spondylitis/spondyloarthritis: a cross-sectional, single-blind study in China.},
journal = {BMJ open},
volume = {15},
number = {3},
pages = {e097528},
pmid = {40118477},
issn = {2044-6055},
mesh = {Humans ; Cross-Sectional Studies ; *Spondylitis, Ankylosing/therapy ; Single-Blind Method ; China ; Male ; Female ; Adult ; *Patient Education as Topic/methods ; Middle Aged ; *Health Education/methods ; Large Language Models ; },
abstract = {OBJECTIVES: To evaluate the potential of large language models (LLMs) in health education for patients with ankylosing spondylitis (AS)/spondyloarthritis (SpA), focusing on the accuracy of information transmission, patient acceptance and performance differences between different models.<br><br>DESIGN: Cross-sectional, single-blind study.<br><br>SETTING: Multiple centres in China.<br><br>PARTICIPANTS: 182 volunteers, including 4 rheumatologists and 178 patients with AS/SpA.<br><br>Scientificity, precision and accessibility of the content of the answers provided by LLMs; patient acceptance of the answers.<br><br>RESULTS: LLMs performed well in terms of scientificity, precision and accessibility, with ChatGPT-4o and Kimi models outperforming traditional guidelines. Most patients with AS/SpA showed a higher level of understanding and acceptance of the responses from LLMs.<br><br>CONCLUSIONS: LLMs have significant potential in medical knowledge transmission and patient education, making them promising tools for future medical practice.},
}
@article {pmid40117671,
year = {2025},
author = {Revechkis, B and Aflalo, TN and Pouratian, N and Rosario, E and Ouellette, DS and Zhang, C and Pejsa, K},
title = {Effector specificity in human posterior parietal neurons and local field potentials during movement in virtual reality and online brain control.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
pmid = {40117671},
issn = {1741-2552},
support = {R01 EY015545/EY/NEI NIH HHS/United States ; },
mesh = {Humans ; *Virtual Reality ; *Parietal Lobe/physiology ; *Neurons/physiology ; Movement/physiology ; Male ; *Brain-Computer Interfaces ; Quadriplegia/physiopathology/rehabilitation ; Psychomotor Performance/physiology ; Adult ; Female ; Electrodes, Implanted ; },
abstract = {Objective. Neural prosthetics represent a significant opportunity for control of external effectors like artificial limbs and computer devices as well as a means for interacting with virtual reality. Prior studies have shown posterior parietal cortex (PPC) to be a viable source of signals for the purposes of decoding motor intentions given its representation of both visual inputs and motor outputs. Additionally, signals in parietal cortex have been shown to be associated with tool use the body schema. We investigated if more realistic movement effectors in virtual reality might elicit stronger signals at the single neuron level in parietal cortex.Approach. A quadriplegic human subject was implanted with multi-electrode recording arrays in the PPC. Neural spiking and local field potentials were recorded during attempted movement in a computer-rendered, stereoscopic, 3D virtual environment. Tuning to different movement effectors was examined using a first-person movement generation task in addition to closed loop control performance.Main results. We found single neurons and simultaneously recorded field potentials in a quadriplegic patient exhibited enhanced responses during attempted (rather than passively observed) movement of a realistic and 'attached' 3D arm relative to either a visually similar but 'detached' 2D arm or a non-anthropomorphic abstract effector. These preferences were found despite the patient having lost motor function years prior. These differences did not effect performance during closed loop brain control of the movement effectors.Significance. In human parietal cortex, single neuron activity and local field potentials responded preferentially to visually guided attempted movement of a realistic arm rather than abstract effector. However, this tuning did not affect closed loop brain control in a virtual reality environment when preceded by a text-based decoder training paradigm.},
}
@article {pmid40117159,
year = {2025},
author = {Xia, Y and Chen, J and Li, J and Gong, T and Vidal-Rosas, EE and Loureiro, R and Cooper, RJ and Zhao, H},
title = {A Deep-Learning Empowered, Real-Time Processing Platform of fNIRS/DOT for Brain Computer Interfaces and Neurofeedback.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {1220-1230},
pmid = {40117159},
issn = {1558-0210},
support = {/WT_/Wellcome Trust/United Kingdom ; },
mesh = {*Brain-Computer Interfaces ; Spectroscopy, Near-Infrared/methods ; Humans ; *Neurofeedback/methods ; Algorithms ; *Deep Learning ; *Tomography, Optical/methods ; Male ; Adult ; Artifacts ; Brain/diagnostic imaging/physiology ; Calibration ; Female ; Young Adult ; Image Processing, Computer-Assisted/methods ; Computer Systems ; },
abstract = {Brain-Computer Interfaces (BCI) and Neurofeedback (NFB) approaches, which both rely on real-time monitoring of brain activity, are increasingly being applied in rehabilitation, assistive technology, neurological diseases and behavioral disorders. Functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) are promising techniques for these applications due to their non-invasiveness, portability, low cost, and relatively high spatial resolution. However, real-time processing of fNIRS/DOT data remains a significant challenge as it requires establishing a baseline of the measurement, simultaneously performing real-time motion artifact (MA) correction across all channels, and (in the case of DOT) addressing the time-consuming process of image reconstruction. This study proposes a real-time processing system for fNIRS/DOT that integrates baseline calibration, denoising autoencoder (DAE) based MA correction model with a sliding window strategy, and a pre-calculated inverse Jacobian matrix to streamline the reconstructed 3D brain hemodynamics. The DAE model was trained on an extensive whole-head high-density DOT (HD-DOT) dataset and tested on separate motor imagery dataset augmented with artificial MA. The system demonstrated the capability to simultaneously process approximately 750 channels in real-time. Our results show that the DAE-based MA correction method outperformed traditional MA correction in terms of mean squared error and correlation to the known MA-free data while maintaining low latency, which is critical for effective BCI and NFB applications. The system's high-channel, real-time processing capability provides channel-wise oxygenation information and functional 3D imaging, making it well-suited for fNIRS/DOT applications in BCI and NFB, particularly in movement-intensive scenarios such as motor rehabilitation and assistive technology for mobility support.},
}
@article {pmid40115973,
year = {2025},
author = {Chen, W and Chen, H and Ruan, H and Jiang, W and Chen, C and Xu, M and Xu, Y and Chen, H and Yu, Z and Chen, S},
title = {Identification of Adolescents With Major Depressive Disorder Using Random Forest Based on Nocturnal Heart Rate Variability.},
journal = {Psychophysiology},
volume = {62},
number = {3},
pages = {e70049},
doi = {10.1111/psyp.70049},
pmid = {40115973},
issn = {1469-8986},
support = {A20240472//Hangzhou Municipal General Medical and Health Plan/ ; },
mesh = {Humans ; *Depressive Disorder, Major/diagnosis/physiopathology ; *Heart Rate/physiology ; Adolescent ; Male ; Female ; Electrocardiography ; Algorithms ; Bayes Theorem ; Reproducibility of Results ; Polysomnography ; Random Forest ; },
abstract = {Major depressive disorder (MDD) in adolescents is often underdiagnosed, with the current diagnosis predominantly relying on subjective assessment. Sleep disturbance and reduced heart rate variability (HRV) have been typically observed in adolescents with MDD. This study aimed to develop an automatic classification model based on nocturnal HRV features to identify adolescent MDD. Sixty-three subjects, including depressed adolescents and healthy controls, participated in the study and completed a three-night sleep electrocardiogram (ECG) monitoring, yielding 160 overnight RR interval time series and 7520 5-min short-term segments for analysis. Nineteen HRV features were extracted from the time domain, frequency domain, and nonlinear dynamics. The Bayesian-optimized random forest (BO-RF) algorithm was applied as the classifier, with performance evaluated using ten-fold cross-validation. The impact of data accumulation on the reliability of identification using short-term data and the importance of features were also examined. The BO-RF classifier based on long-term features achieved a noteworthy predictive accuracy of 80.6%, and the performance of the classifier using short-term data showed a significant improvement when more segment outcomes from the same night were included, ultimately achieving an accuracy of 75.0%. The Poincaré plot-derived features, especially heart rate asymmetry (HRA) features such as C1d, significantly contributed to distinguishing depressed adolescents from healthy subjects. Nocturnal HRV features can effectively differentiate adolescents with MDD from healthy controls. This study provides a promising diagnostic approach for adolescent MDD, with the potential to be integrated into wearable devices for broader application.},
}
@article {pmid40115889,
year = {2025},
author = {Li, J and Hu, B and Guan, ZH},
title = {AM-MTEEG: multi-task EEG classification based on impulsive associative memory.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1557287},
pmid = {40115889},
issn = {1662-4548},
abstract = {Electroencephalogram-based brain-computer interfaces (BCIs) hold promise for healthcare applications but are hindered by cross-subject variability and limited data. This article proposes a multi-task (MT) classification model, AM-MTEEG, which integrates deep learning-based convolutional and impulsive networks with bidirectional associative memory (AM) for cross-subject EEG classification. AM-MTEEG deals with the EEG classification of each subject as an independent task and utilizes common features across subjects. The model is built with a convolutional encoder-decoder and a population of impulsive neurons to extract shared features across subjects, as well as a Hebbian-learned bidirectional associative memory matrix to classify EEG within one subject. Experimental results on two BCI competition datasets demonstrate that AM-MTEEG improves average accuracy over state-of-the-art methods and reduces performance variance across subjects. Visualization of neuronal impulses in the bidirectional associative memory network reveal a precise mapping between hidden-layer neuron activities and specific movements. Given four motor imagery categories, the reconstructed waveforms resemble the real event-related potentials, highlighting the biological interpretability of the model beyond classification.},
}
@article {pmid40115888,
year = {2025},
author = {Liu, J and Xie, J and Zhang, H and Yang, H and Shao, Y and Chen, Y},
title = {Improvement of BCI performance with bimodal SSMVEPs: enhancing response intensity and reducing fatigue.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1506104},
pmid = {40115888},
issn = {1662-4548},
abstract = {Steady-state visual evoked potential (SSVEP) is a widely used brain-computer interface (BCI) paradigm, valued for its multi-target capability and limited EEG electrode requirements. Conventional SSVEP methods frequently lead to visual fatigue and decreased recognition accuracy because of the flickering light stimulation. To address these issues, we developed an innovative steady-state motion visual evoked potential (SSMVEP) paradigm that integrated motion and color stimuli, designed specifically for augmented reality (AR) glasses. Our study aimed to enhance SSMVEP response intensity and reduce visual fatigue. Experiments were conducted under controlled laboratory conditions. EEG data were analyzed using the deep learning algorithm of EEGNet and fast Fourier transform (FFT) to calculate the classification accuracy and assess the response intensity. Experimental results showed that the bimodal motion-color integrated paradigm significantly outperformed single-motion SSMVEP and single-color SSVEP paradigms, respectively, achieving the highest accuracy of 83.81% ± 6.52% under the medium brightness (M) and area ratio of C of 0.6. Enhanced signal-to-noise ratio (SNR) and reduced visual fatigue were also observed, as confirmed by objective measures and subjective reports. The findings verified the bimodal paradigm as a novel application in SSVEP-based BCIs, enhancing both brain response intensity and user comfort.},
}
@article {pmid40115887,
year = {2025},
author = {Schreiner, L and Wipprecht, A and Olyanasab, A and Sieghartsleitner, S and Pretl, H and Guger, C},
title = {Brain-computer-interface-driven artistic expression: real-time cognitive visualization in the pangolin scales animatronic dress and screen dress.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1516776},
pmid = {40115887},
issn = {1662-5161},
abstract = {This paper explores the intersection of brain-computer interfaces (BCIs) and artistic expression, showcasing two innovative projects that merge neuroscience with interactive wearable technology. BCIs, traditionally applied in clinical settings, have expanded into creative domains, enabling real-time monitoring and representation of cognitive states. The first project showcases a low-channel BCI Screen Dress, utilizing a 4-channel electroencephalography (EEG) headband to extract an engagement biomarker. The engagement is visualized through animated eyes on small screens embedded in a 3D-printed dress, which dynamically responds to the wearer's cognitive state. This system offers an accessible approach to cognitive visualization, leveraging real-time engagement estimation and demonstrating the effectiveness of low-channel BCIs in artistic applications. In contrast, the second project involves an ultra-high-density EEG (uHD EEG) system integrated into an animatronic dress inspired by pangolin scales. The uHD EEG system drives physical movements and lighting, visually and kinetically expressing different EEG frequency bands. Results show that both projects have successfully transformed brain signals into interactive, wearable art, offering a multisensory experience for both wearers and audiences. These projects highlight the vast potential of BCIs beyond traditional clinical applications, extending into fields such as entertainment, fashion, and education. These innovative wearable systems underscore the ability of BCIs to expand the boundaries of creative expression, turning the wearer's cognitive processes into art. The combination of neuroscience and fashion tech, from simplified EEG headsets to uHD EEG systems, demonstrates the scalability of BCI applications in artistic domains.},
}
@article {pmid40115885,
year = {2025},
author = {Liu, J and Li, Y and Zhao, D and Zhong, L and Wang, Y and Hao, M and Ma, J},
title = {Efficacy and safety of brain-computer interface for stroke rehabilitation: an overview of systematic review.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1525293},
pmid = {40115885},
issn = {1662-5161},
abstract = {BACKGROUND: Stroke is a major global health challenge that significantly influences public health. In stroke rehabilitation, brain-computer interfaces (BCI) offer distinct advantages over traditional training programs, including improved motor recovery and greater neuroplasticity. Here, we provide a first re-evaluation of systematic reviews and meta-analyses to further explore the safety and clinical efficacy of BCI in stroke rehabilitation.<br><br>METHODS: A standardized search was conducted in major databases up to October 2024. We assessed the quality of the literature based on the following aspects: AMSTAR-2, PRISMA, publication year, study design, homogeneity, and publication bias. The data were subsequently visualized as radar plots, enabling a comprehensive and rigorous evaluation of the literature.<br><br>RESULTS: We initially identified 908 articles and, after removing duplicates, we screened titles and abstracts of 407 articles. A total of 18 studies satisfied inclusion criteria were included. The re-evaluation showed that the quality of systematic reviews and meta-analyses concerning stroke BCI training is moderate, which can provide relatively good evidence.<br><br>CONCLUSION: It has been proven that BCI-combined treatment can improve upper limb motor function and the quality of daily life for stroke patients, especially those in the subacute phase, demonstrating good safety. However, its effects on improving speech function, lower limb motor function, and long-term outcomes require further evidence. Multicenter, long-term follow-up studies are needed to increase the reliability of the results.<br><br>CLINICAL TRIAL REGISTRATION: https://www.crd.york.ac.uk/PROSPERO/view/CRD42024562114, CRD42023407720.},
}
@article {pmid40112995,
year = {2025},
author = {Huang, T and Ma, Y and Chen, H and Zhang, S and Liu, L and Chen, M and Jia, R and Lin, L and Ullah, MW and Fan, Y},
title = {A silk nanofiber and hyaluronic acid composite hemostatic sponge for compressible hemostasis.},
journal = {International journal of biological macromolecules},
volume = {307},
number = {Pt 4},
pages = {142262},
doi = {10.1016/j.ijbiomac.2025.142262},
pmid = {40112995},
issn = {1879-0003},
mesh = {*Hyaluronic Acid/chemistry/pharmacology ; *Nanofibers/chemistry ; *Hemostasis/drug effects ; *Hemostatics/chemistry/pharmacology ; *Silk/chemistry ; Animals ; Humans ; Biocompatible Materials/chemistry/pharmacology ; Materials Testing ; },
abstract = {Uncontrolled traumatic blood loss is a leading cause of hemorrhagic shock and death, highlighting the critical need for compressible and rapid hemostatic first-aid materials. In this study, silk nanofibers (MA-SNFs) were prepared through maleic acid (MA) hydrolysis decorated with enriched carboxyl groups. The MA-SNFs were then combined with hyaluronic acid (HA) through EDC/NHS crosslinking to form a porous sponge (i.e., MA-SNF/HA) through freeze-drying. The fabricated MA-SNF/HA sponges demonstrated excellent blood compatibility (hemolysis ratio < 5 %), outstanding hemocompatibility (blood clotting index (BCI) < 35 % within 60 s), and good cytocompatibility (cell viability >85 %). Among the different sponges prepared, M4-H6 (MA-SNFs: HA = 4:6) exhibited the best liquid reabsorption capacity after 80 % compression, outperforming M6-H4 and M5-H5 sponges. Furthermore, M4-H6 sponge absorbed liquid rapidly (~30 s) while matching the liquid absorption capacity of commercial gelatin sponge (GS), which require over 5 min for similar absorption (2232.84 ± 141.69 %). These findings suggest that M4-H6 sponge is highly suitable for compressible hemostasis applications and provide further insights into its potential hemostatic mechanism.},
}
@article {pmid40112909,
year = {2025},
author = {Wang, J and Guo, M and Zhang, J and Bai, Y and Ni, G},
title = {Early audiovisual integration in target processing under continuous noise: Behavioral and EEG evidence.},
journal = {Neuropsychologia},
volume = {211},
number = {},
pages = {109128},
doi = {10.1016/j.neuropsychologia.2025.109128},
pmid = {40112909},
issn = {1873-3514},
mesh = {Humans ; Electroencephalography ; Male ; *Visual Perception/physiology ; Female ; Young Adult ; *Auditory Perception/physiology ; Reaction Time/physiology ; Photic Stimulation ; Acoustic Stimulation ; Adult ; Evoked Potentials/physiology ; *Noise ; *Brain/physiology ; Brain Mapping ; },
abstract = {Multisensory integration is interconnected across various information reception. The stage and mechanism of brain response to audiovisual integration have not been fully understood. In this study, we designed audiovisual and unisensory experiments to investigate task performance and electrophysiological characteristics associated with audiovisual integration in a continuous background interference environment using materials collected from the underwater environment. Behavioral results showed that the reaction time (RT) was shorter, and the accuracy was higher in the audiovisual experiment. The cumulative distribution function (CDF) results of RT indicated that audiovisual integration supported the co-activation model. Event-related potential (ERP) results revealed shorter latency of the P1 and N1 components in the audiovisual experiment. Microstate analysis indicated that the parietal-occipital area may play a key role in audiovisual integration. Moreover, event-related spectral perturbation (ERSP) results demonstrated the critical role of low-frequency oscillation in audiovisual integration at the early stage. Our findings support the view that the beneficial effect of audiovisual integration is predominantly upon the early stage of neural information processing, including task-independent information.},
}
@article {pmid40112768,
year = {2025},
author = {Yang, HR and Han, MR and Oh, EY and Choi, JY and Choi, JY and Kim, Y and Kim, YT and Kang, H and Kim, JG},
title = {Role of cold-inducible RNA-binding protein in hypothalamic regulation of feeding behavior during fasting and cold exposure.},
journal = {Biochemical and biophysical research communications},
volume = {757},
number = {},
pages = {151616},
doi = {10.1016/j.bbrc.2025.151616},
pmid = {40112768},
issn = {1090-2104},
mesh = {Animals ; *Fasting/physiology ; *RNA-Binding Proteins/metabolism/genetics ; *Hypothalamus/metabolism/physiology ; *Cold Temperature ; Male ; *Feeding Behavior/physiology ; Mice ; Mice, Inbred C57BL ; Neurons/metabolism ; Agouti-Related Protein/metabolism ; Eating ; },
abstract = {Appetite regulation is a complex process that is critical for maintaining energy balance and is governed by intricate molecular and cellular mechanisms in the hypothalamus. RNA-binding proteins play vital roles in the post-transcriptional regulation of mRNA and influence feeding behavior and energy metabolism. This study explored the role of cold-inducible RNA-binding protein (Cirbp) in hypothalamic neurons under metabolic stress conditions, such as fasting and cold exposure. Next-generation sequencing (NGS) of the hypothalami from fasted mice identified 67 differentially expressed RNA-binding proteins, with Cirbp and RNA-binding motif protein 3 (Rbm3) being significantly upregulated. Immunohistochemical analysis confirmed increased Cirbp expression in the arcuate nucleus (ARC) and dorsomedial hypothalamus during fasting, indicating responsiveness to metabolic cues. Ribo-Tag analysis of agouti-related protein (AgRP) neurons demonstrated elevated Cirbp expression levels in response to fasting, linking it to hunger-regulating pathways. Intracerebroventricular injection of Cirbp antisense oligodeoxynucleotides (AS ODN) reduced Cirbp expression, leading to a decrease in food intake and a reduction in body weight, highlighting the functional role of Cirbp in appetite regulation. Cold exposure also induced Cirbp expression in the ARC, which correlated with an increase in food intake. Blockade of Cirbp by AS ODN treatment attenuated cold-induced food intake, indicating that Cirbp plays a specific role in regulating feeding behavior during cold stress. This suggests that Cirbp is a key mediator in hypothalamic responses to metabolic stress, influencing feeding behavior through its regulatory functions in AgRP neurons. Further exploration of Cirbp mechanisms may offer insights into therapeutic strategies for energy balance disorders, such as obesity and anorexia.},
}
@article {pmid40111769,
year = {2025},
author = {Wang, X and Liu, A and Cui, H and Chen, X and Wang, K and Chen, X},
title = {GZSL-Lite: A Lightweight Generalized Zero-Shot Learning Network for SSVEP-Based BCIs.},
journal = {IEEE transactions on bio-medical engineering},
volume = {72},
number = {9},
pages = {2840-2850},
doi = {10.1109/TBME.2025.3553204},
pmid = {40111769},
issn = {1558-2531},
mesh = {*Electroencephalography/methods ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Humans ; *Signal Processing, Computer-Assisted ; Adult ; *Machine Learning ; Male ; *Neural Networks, Computer ; Female ; Algorithms ; Young Adult ; },
abstract = {Generalized zero-shot learning (GZSL) networks offer promising avenues for the development of user-friendly steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs), aiming to alleviate the training burden on users. These networks only require the user to provide training data from partial classes during training, yet they demonstrate the capability to classify all classes during testing. However, these GZSL networks have a large number of trainable parameters, resulting in long training times and difficulty to practicalize. In this study, we proposed a dual-attention structure to construct a lightweight GZSL network, termed GZSL-Lite. We first embedded the input training data-constructed class templates, manually constructed sine templates, and electroencephalogram (EEG) signals using convolution-based networks. The embedding part uses the same network weights to embed the features across different stimulus frequencies while reducing the depth of the network. After embedding, two branches of the dual-attention use class and sine templates to guide the feature extraction of the EEG signal with the attention mechanism, respectively. Compared to the networks that extract all features equally, dual-attention focuses only on EEG features relative to templates, which helps classification with fewer parameters. Finally, we used depthwise convolutional blocks to output classification results. Experimental evaluations conducted on two publicly available datasets demonstrate the efficacy of the proposed network. Comparative analysis reveals a remarkable reduction in trainable parameters to less than 1% of the SOTA counterpart, concurrently showing significant performance improvement.},
}
@article {pmid40111392,
year = {2025},
author = {Tang, P and Jing, P and Luo, Z and Liu, K and Tan, W and Yao, Q and Qiu, Z and Liu, Y and Dou, Q and Yan, X},
title = {Modulating Ionic Hysteresis to Selective Interaction Mechanism toward Transition from Supercapacitor-Memristor to Supercapacitor-Diode.},
journal = {Nano letters},
volume = {25},
number = {13},
pages = {5415-5424},
doi = {10.1021/acs.nanolett.5c00596},
pmid = {40111392},
issn = {1530-6992},
abstract = {The emerging ion-confined transport supercapacitors, including supercapacitor-diodes (CAPodes) and supercapacitor-memristors (CAPistors), offer potential for neuromorphic computing, brain-computer interface, signal propagation, and logic operations. This study reports a novel transition from CAPistor to CAPode via electrochemical cycling of a ZIF-7 electrode. X-ray absorption fine structure (XAFS) and electrochemical analyses reveal a shift from "ionic hysteresis" to "ionic selective interaction" in an alkaline electrolyte, elucidating the evolution of ionic devices. The CAPodes exhibit high rectification ratios, long cycling stability, and effective current blocking in reverse bias. Additionally, they are demonstrated in ionic logic circuits ("AND" and "OR" gates), with comparisons to traditional electronic diodes. This work advances the development of functional supercapacitors and iontronic devices for future capacitive computing architectures.},
}
@article {pmid40110612,
year = {2025},
author = {Jain, S and Srivastava, R},
title = {Multi-modality NDE fusion using encoder-decoder networks for identify multiple neurological disorders from EEG signals.},
journal = {Technology and health care : official journal of the European Society for Engineering and Medicine},
volume = {33},
number = {5},
pages = {2431-2451},
doi = {10.1177/09287329241291334},
pmid = {40110612},
issn = {1878-7401},
mesh = {Humans ; *Electroencephalography/methods ; *Neural Networks, Computer ; *Nervous System Diseases/diagnosis ; *Signal Processing, Computer-Assisted ; Wavelet Analysis ; },
abstract = {BACKGROUND: The complexity and diversity of brain activity patterns make it difficult to accurately diagnose neurological disorders such epilepsy, Parkinson's disease, schizophrenia, stroke, and Alzheimer's disease. Integrated and effective analysis of multiple data sources is often beyond the scope of traditional diagnostic procedures. With the use of multi-modal data, recent developments in neural network approaches present encouraging opportunities for raising diagnostic accuracy.<br><br>OBJECTIVES: A novel approach has been proposed toward the integration of different Nondestructive Evaluation data with EEG signals for improving the diagnosis of neurological disorders such as stroke, epilepsy, Parkinson's disease, and schizophrenia, by leveraging advanced neural network techniques in order to improve the identification and correlation of shared latent features across heterogeneous NDE datasets.<br><br>METHODS: We determined the 2D scalogram images using a specific encoder-decoder neural network after transforming the EEG signals using wavelet signal processing. Several NDE data types can be easily integrated for thorough analysis due to this network's ability to extract and correlate important aspects from each form of data. Aiming to uncover common patterns indicating of neurological disorders, the technique was evaluated on datasets containing EEG signals and corresponding NDE data.<br><br>RESULTS: Our method demonstrated a significant improvement in diagnostic accuracy and efficiency. The encoder-decoder network effectively identified shared latent features across the heterogeneous NDE datasets, leading to more precise and reliable diagnoses. The fusion of multi-modality NDE data with EEG signals provided a robust framework for the automatic identification of multiple neurological disorders.<br><br>CONCLUSIONS: This innovative approach represents a substantial advancement in the field of neurological disorder diagnosis. By integrating diverse NDE data with EEG signals through advanced neural network techniques, we have developed a method that enhances the accuracy and efficiency of diagnosing multiple neurological conditions. This fusion of multi-modality data has the potential to revolutionize current diagnostic practices in neurology, paving the way for more precise and automated identification of neurological disorders.},
}
@article {pmid40110537,
year = {2025},
author = {Zhou, Y and Xu, X and Zhang, D},
title = {Cognitive load recognition in simulated flight missions: an EEG study.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1542774},
pmid = {40110537},
issn = {1662-5161},
abstract = {Cognitive load recognition (CLR) utilizing EEG signals has experienced significant advancement in recent years. However, current load-eliciting paradigms often rely on simplistic cognitive tasks such as arithmetic calculations, failing to adequately replicate real-world scenarios and lacking applicability. This study explores simulated flight missions over time to better reflect operational environments and investigate temporal dynamics of multiple load states. Thirty-six participants were recruited to perform simulated flight tasks with varying cognitive load levels of low, medium, and high. Throughout the experiments, we collected EEG load data from three sessions, pre- and post-task resting-state EEG data, subjective ratings, and objective performance metrics. Then, we employed several deep convolutional neural network (CNN) models, utilizing raw EEG data as model input, to assess cognitive load levels with six classification designs. Key findings from the study include (1) a notable distinction between resting-state and post-fatigue EEG data; (2) superior performance of shallow CNN models compared to more complex ones; and (3) temporal dynamics decline in CLR as the missions progressed. This paper establishes a potential foundation for assessing cognitive states during intricate simulated tasks across different individuals.},
}
@article {pmid40109751,
year = {2025},
author = {Wang, X and Lin, C and Wang, X},
title = {Psychedelics and Pro-Social Behaviors: A Perspective on Autism Spectrum Disorders.},
journal = {ACS pharmacology &amp; translational science},
volume = {8},
number = {3},
pages = {903-906},
pmid = {40109751},
issn = {2575-9108},
abstract = {Autism Spectrum Disorders (ASD) are complex neurodevelopmental conditions characterized by deficits in social interaction, communication, and repetitive behaviors. This viewpoint explores the potential mechanisms through which psychedelics such as lysergic acid diethylamide (LSD), psilocybin, and 3,4-methylenedioxymethamphetamine (MDMA) may positively influence pro-social behaviors, focusing on their implications for individuals with ASD.},
}
@article {pmid40109747,
year = {2025},
author = {Li, H and Wang, H and Wang, X},
title = {Psychedelics and the Autonomic Nervous System: A Perspective on Their Interplay and Therapeutic Potential.},
journal = {ACS pharmacology &amp; translational science},
volume = {8},
number = {3},
pages = {899-902},
pmid = {40109747},
issn = {2575-9108},
abstract = {Psychedelics, known for their therapeutic potential in psychiatric disorders, interact with the autonomic nervous system in ways that are not well understood. This viewpoint examines the complex relationships between psychedelics and autonomic functions, focusing on sympathetic and parasympathetic modulation. We propose a research framework to elucidate how these interactions influence cardiovascular health and contribute to therapeutic outcomes.},
}
@article {pmid40109736,
year = {2025},
author = {Li, H and Wang, X},
title = {Exploring End-of-Life Experiences and Consciousness through the Lens of Psychedelics.},
journal = {ACS pharmacology &amp; translational science},
volume = {8},
number = {3},
pages = {907-909},
pmid = {40109736},
issn = {2575-9108},
abstract = {Exploring dying through the lens of psychedelic experiences offers transformative perspectives on the end-of-life process, potentially alleviating existential distress and enriching the quality of life for those nearing death. Their potential in palliative care, therapy, and spiritual exploration is increasingly recognized, promising to revolutionize end-of-life understanding and care.},
}
@article {pmid40109381,
year = {2025},
author = {Lloyd, S and Bonventre, C},
title = {Habilitation beyond the Bionic Metaphor: Producing Deafnesses of the Future.},
journal = {Science, technology &amp; human values},
volume = {50},
number = {2},
pages = {336-363},
pmid = {40109381},
issn = {0162-2439},
abstract = {In this article, we travel back to the early days of experimental use of cochlear implants (CIs) in the 1970s, when unsettled expectations of the device and broad investigations of its effects began to settle and center on speech outcomes. We describe how this attention to speech outcomes coalesced into specific understandings of what CIs do, and how implicit or explicit understandings of CIs as bionic devices that normalize hearing influenced research on and expectations of CIs into the present. We conclude that accumulated evidence about what is known and unknown about experiences and materialities with CIs calls for a decisive break from the metaphor of the bionic ear. This shift would create a space to reconsider the "deafness of history and the present," as well as experiences of brain-computer interfaces that are inclusive of nonnormative life. This article is based on fieldwork in research and clinical facilities in Australia, Canada, and the United States. It included forty-three interviews with clinical experts and leading researchers in the fields of audiology, psychoacoustics, and neuroscience, among them scientists involved in the development and commercialization of one of the first CIs.},
}
@article {pmid40109135,
year = {2025},
author = {Sheng, T and Li, J and Zheng, L and Du, N and Xie, M and Wang, X and Gao, X and Huang, M and Wen, S and Liu, W and Guo, Y and Yao, Y and Shao, X and Liu, L and Xu, J and Wang, Y and Zhang, M},
title = {An Expandable Brain-Machine Interface Enabled by Origami Materials and Structures for Tracking Epileptic Traveling Waves.},
journal = {Advanced healthcare materials},
volume = {14},
number = {11},
pages = {e2404947},
doi = {10.1002/adhm.202404947},
pmid = {40109135},
issn = {2192-2659},
support = {2023YFB4705500//National Key Research and Development Program of China/ ; 62350710211//National Natural Science Foundation of China/ ; 1S24080//Beijing Natural Science Foundation/ ; },
mesh = {*Brain-Computer Interfaces ; Animals ; Rats ; *Electrocorticography/methods ; *Epilepsy/physiopathology ; Electrodes, Implanted ; *Brain/physiopathology ; Male ; Rats, Sprague-Dawley ; Seizures/physiopathology ; },
abstract = {Tracking neural activities across multiple brain regions remains a daunting challenge due to the non-negligible skull injuries during implantations of large-area electrocorticography (ECoG) grids and the limited spatial accessibility of conventional rectilinear depth probes. Here, a multiregion Brain-machine Interface (BMI) is proposed comprising an expandable bio-inspired origami ECoG electrode covering cortical areas larger than the cranial window, and an expandable origami depth probe capable of reaching multiple deep brain regions beyond a single implantation axis. Using the proposed BMI, it is observed that, in rat models of focal seizures, cortical multiband epileptiform activities mainly manifest as expanding traveling waves outward from a cortical source.},
}
@article {pmid40108144,
year = {2025},
author = {Qi, Z and Liu, H and Jin, F and Wang, Y and Lu, X and Liu, L and Yang, Z and Fan, L and Song, M and Zuo, N and Jiang, T},
title = {A wearable repetitive transcranial magnetic stimulation device.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {2731},
pmid = {40108144},
issn = {2041-1723},
support = {2021ZD0200200//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; 82202253//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31620103905//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {*Transcranial Magnetic Stimulation/instrumentation/methods ; Humans ; *Wearable Electronic Devices ; Male ; Adult ; Female ; Walking/physiology ; Motor Cortex/physiology ; Equipment Design ; },
abstract = {Repetitive transcranial magnetic stimulation (rTMS) is widely used to treat various neuropsychiatric disorders and to explore the brain, but its considerable power consumption and large size limit its potential for broader utility, such as applications in free behaviors and in home and community settings. We addressed this challenge through lightweight magnetic core coil designs and high-power-density, high-voltage pulse driving techniques and successfully developed a battery-powered wearable rTMS device. The combined weight of the stimulator and coil is only 3 kg. The power consumption was reduced to 10% of commercial rTMS devices even though the stimulus intensity and repetition frequency are comparable. We demonstrated the effectiveness of this device during free walking, showing that neural activity associated with the legs can enhance the cortex excitability associated with the arms. This advancement allows for high-frequency rTMS modulation during free behaviors and enables convenient home and community rTMS treatments.},
}
@article {pmid40107265,
year = {2025},
author = {Jiang, R and Tian, Y and Yuan, X and Guo, F},
title = {Regulation of pre-dawn arousal in Drosophila by a pair of trissinergic descending neurons of the visual and circadian networks.},
journal = {Current biology : CB},
volume = {35},
number = {8},
pages = {1750-1764.e3},
doi = {10.1016/j.cub.2025.02.056},
pmid = {40107265},
issn = {1879-0445},
mesh = {Animals ; *Circadian Rhythm/physiology ; *Drosophila Proteins/metabolism/genetics ; *Drosophila melanogaster/physiology ; *Neurons/physiology ; *Arousal/physiology ; },
abstract = {Circadian neurons form a complex neural network that generates circadian oscillations. How the circadian neural network transmits circadian signals to other brain regions, thereby regulating the activity patterns in fruit flies, is not well known. Using the FlyWire database, we identified a cluster of descending neurons, DNp27, which is densely connected with key circadian neurons and the visual circuit, projecting extensively across the brain. DNp27 receives excitatory inputs from the circadian neurons DN3s at night and photo-inhibitory signals predominantly during the day, resulting in calcium oscillations that peak in the early morning and dip at dusk. Experimental manipulation of DNp27 revealed its role in activity regulation: artificial activation of DNp27 decreased flies' activity, while ablation or silencing led to an advance in the morning anticipatory peak. Similar alterations in the morning peak were observed following pan-neuronal knockdown of either Trissin or TrissinR, suggesting the involvement of this neuropeptide signaling pathway in DNp27 function. Moreover, neural circuitry and connectivity analyses indicate that DNp27 may regulate circadian neurons via extra-clock electrical oscillators (xCEOs). Lastly, we found that DNp27 modulates arousal thresholds by inhibiting light-responsive activity in the central brain, thereby promoting sleep stability, particularly in the pre-dawn period. Together, these findings suggest that DNp27 plays a crucial role in maintaining stable sleep patterns.},
}
@article {pmid40106898,
year = {2025},
author = {Hobbs, TG and Greenspon, CM and Verbaarschot, C and Valle, G and Hughes, CL and Boninger, ML and Bensmaia, SJ and Gaunt, RA},
title = {Biomimetic stimulation patterns drive natural artificial touch percepts using intracortical microstimulation in humans.},
journal = {Journal of neural engineering},
volume = {22},
number = {3},
pages = {},
doi = {10.1088/1741-2552/adc2d4},
pmid = {40106898},
issn = {1741-2552},
mesh = {Adult ; Female ; Humans ; Male ; Middle Aged ; *Biomimetics/methods ; Electric Stimulation/methods ; Electrodes, Implanted ; *Somatosensory Cortex/physiology ; Spinal Cord Injuries/physiopathology ; *Touch/physiology ; *Touch Perception/physiology ; },
abstract = {Objective.Intracortical microstimulation (ICMS) of human somatosensory cortex evokes tactile percepts that people describe as originating from their own body, but are not always described as feeling natural. It remains unclear whether stimulation parameters such as amplitude, frequency, and spatiotemporal patterns across electrodes can be chosen to increase the naturalness of these artificial tactile percepts.Approach.In this study, we investigated whether biomimetic stimulation patterns-ICMS patterns that reproduce essential features of natural neural activity-increased the perceived naturalness of ICMS-evoked sensations compared to a non-biomimetic pattern in three people with cervical spinal cord injuries. All participants had electrode arrays implanted in their somatosensory cortices. Rather than qualitatively asking which pattern felt more natural, participants directly compared natural residual percepts, delivered by mechanical indentation on a sensate region of their hand, to artificial percepts evoked by ICMS and were asked whether linear non-biomimetic or biomimetic stimulation felt most like the mechanical indentation.Main results.We show that simple biomimetic ICMS, which modulated the stimulation amplitude on a single electrode, was perceived as being more like a mechanical indentation reference on 32% of the electrodes. We also tested an advanced biomimetic stimulation scheme that captured more of the spatiotemporal dynamics of cortical activity using co-modulated stimulation amplitudes and frequencies across four electrodes. Here, ICMS felt more like the mechanical reference for 75% of the electrode groups. Finally, biomimetic stimulus trains required less charge than their non-biomimetic counterparts to create an intensity-matched sensation.Significance.We conclude that ICMS encoding schemes that mimic naturally occurring neural spatiotemporal activation patterns in the somatosensory cortex feel more like an actual touch than non-biomimetic encoding schemes. This also suggests that using key elements of neuronal activity can be a useful conceptual guide to constrain the large stimulus parameter space when designing future stimulation strategies. This work is a part of Clinical Trial NCT01894802.},
}
@article {pmid40106847,
year = {2025},
author = {Wen, B and Su, L and Zhang, Y and Wang, A and Zhao, H and Wu, J and Gan, Z and Zhang, L and Kang, X},
title = {Fabrication of micro-wire stent electrode as a minimally invasive endovascular neural interface for vascular electrocorticography using laser ablation method.},
journal = {Biomedical physics &amp; engineering express},
volume = {11},
number = {3},
pages = {},
doi = {10.1088/2057-1976/adc266},
pmid = {40106847},
issn = {2057-1976},
mesh = {Animals ; Rats ; *Stents ; *Electrocorticography/instrumentation/methods ; *Laser Therapy/methods/instrumentation ; Rats, Sprague-Dawley ; Electrodes ; Male ; *Endovascular Procedures/instrumentation ; *Minimally Invasive Surgical Procedures ; Equipment Design ; Electrodes, Implanted ; },
abstract = {Objective. Minimally invasive endovascular stent electrode is a currently emerging technology in neural engineering with minimal damage to the neural tissue. However, the typical stent electrode still requires resistive welding and is relatively large, limiting its application mainly on the large animal or thick vessels. In this study, we investigated the feasibility of laser ablation of micro-wire stent electrode with a diameter as small as 25μmand verified it in the superior sagittal sinus (SSS) of a rat.Approach. We have developed a laser ablation technology to expose the electrode sites of the micro-wire on both sides without damaging the wire itself. During laser ablation, we applied a new method to fix and realign the micro-wires. The micro-wire stent electrode was fabricated by carefully assemble the micro-wire and stent. We tested the electrochemical performances of the electrodes as a neural interface. Finally, we deployed the stent electrode in a rat to verified the feasibility.Main result. Based on the proposed micro-wire stent electrode, we demonstrated that the stent electrode could be successfully deployed in a rat. With the benefit of the smaller design and laser fabrication technology, it can be fitted into a catheter with an inner diameter of 0.6mm. The vascular electrocorticography can be detected during the acute recording, making it promising in the application of small animals and thin vessels.Significance. The method we proposed combines the advantages of endovascular micro-wire electrode and stent, helping make the electrodes smaller. This study provided an alternative method for deploying micro-wire electrodes into thinner vessels as an endovascular neural interface.},
}
@article {pmid40106436,
year = {2025},
author = {Wang, Y and Chen, Z and Liang, K and Wang, W and Hu, Z and Mao, Y and Liang, X and Jiang, L and Liu, Z and Ma, Z},
title = {AGO2 mediates immunotherapy failure via suppressing tumor IFN-gamma response-dependent CD8[+] T cell immunity.},
journal = {Cell reports},
volume = {44},
number = {4},
pages = {115445},
doi = {10.1016/j.celrep.2025.115445},
pmid = {40106436},
issn = {2211-1247},
mesh = {*Interferon-gamma/metabolism/pharmacology/immunology ; *CD8-Positive T-Lymphocytes/immunology ; Humans ; *Argonaute Proteins/metabolism/genetics ; *Immunotherapy/methods ; Animals ; MicroRNAs/metabolism/genetics ; Cell Line, Tumor ; Mice ; STAT1 Transcription Factor/metabolism ; *Neoplasms/immunology/therapy ; Female ; Mice, Inbred C57BL ; },
abstract = {Interferon-gamma (IFN-γ), a cytokine essential for activating cellular immune responses, plays a crucial role in cancer immunosurveillance and the clinical success of immune checkpoint blockade therapy. In this study, we show that Argonaute 2 (AGO2), a key mediator in small RNA-guided gene regulation, inversely correlates with tumor responsiveness to IFN-γ and the efficacy of immunotherapy. Mechanistically, IFN-γ upregulates miR-1246 expression in tumor cells, enhancing its interaction with AGO2. This miR-1246-AGO2 complex disrupts IFN-γ-mediated signal transducer and activator of transcription 1 (STAT1) phosphorylation by stabilizing protein tyrosine phosphatase non-receptor 6 (PTPN6) mRNA, thereby suppressing the expression of downstream C-X-C motif chemokine ligands (CXCLs), IFN-stimulated genes (ISGs), and human leukocyte antigen (HLA) molecules, which collectively contribute to tumor immune evasion. In preclinical cancer models, inhibiting AGO2 with BCI-137 or targeting miR-1246 with its antagomir re-sensitizes tumor cells to IFN-γ, leading to the enhanced recruitment, activation, and cytotoxicity of CD8[+] T cells and ultimately improving immunotherapy efficacy.},
}
@article {pmid40104767,
year = {2025},
author = {Memmott, T and Klee, D and Smedemark-Margulies, N and Oken, B},
title = {Artifact filtering application to increase online parity in a communication BCI: progress toward use in daily-life.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1551214},
pmid = {40104767},
issn = {1662-5161},
abstract = {A significant challenge in developing reliable Brain-Computer Interfaces (BCIs) is the presence of artifacts in the acquired brain signals. These artifacts may lead to erroneous interpretations, poor fitting of models, and subsequent reduced online performance. Furthermore, BCIs in a home or hospital setting are more susceptible to environmental noise. Artifact handling procedures aim to reduce signal interference by filtering, reconstructing, and/or eliminating unwanted signal contaminants. While straightforward conceptually and largely undisputed as essential, suitable artifact handling application in BCI systems remains unsettled and may reduce performance in some cases. A potential confound that remains unexplored in the majority of BCI studies using these procedures is the lack of parity with online usage (e.g., online parity). This manuscript compares classification performance between frequently used offline digital filtering, using the whole dataset, and an online digital filtering approach where the segmented data epochs that would be used during closed-loop control are filtered instead. In a sample of healthy adults (n = 30) enrolled in a BCI pilot study to integrate new communication interfaces, there were significant benefits to model performance when filtering with online parity. While online simulations indicated similar performance across conditions in this study, there appears to be no drawback to the approach with greater online parity.},
}
@article {pmid40103837,
year = {2024},
author = {Yektaeian Vaziri, A and Makkiabadi, B},
title = {Accelerated algorithms for source orientation detection and spatiotemporal LCMV beamforming in EEG source localization.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1505017},
pmid = {40103837},
issn = {1662-4548},
abstract = {This paper illustrates the development of two efficient source localization algorithms for electroencephalography (EEG) data, aimed at enhancing real-time brain signal reconstruction while addressing the computational challenges of traditional methods. Accurate EEG source localization is crucial for applications in cognitive neuroscience, neurorehabilitation, and brain-computer interfaces (BCIs). To make significant progress toward precise source orientation detection and improved signal reconstruction, we introduce the Accelerated Linear Constrained Minimum Variance (ALCMV) beamforming toolbox and the Accelerated Brain Source Orientation Detection (AORI) toolbox. The ALCMV algorithm speeds up EEG source reconstruction by utilizing recursive covariance matrix calculations, while AORI simplifies source orientation detection from three dimensions to one, reducing computational load by 66% compared to conventional methods. Using both simulated and real EEG data, we demonstrate that these algorithms maintain high accuracy, with orientation errors below 0.2% and signal reconstruction accuracy within 2%. These findings suggest that the proposed toolboxes represent a substantial advancement in the efficiency and speed of EEG source localization, making them well-suited for real-time neurotechnological applications.},
}
@article {pmid40102969,
year = {2025},
author = {Jeong, H and Song, M and Jang, SH and Kim, J},
title = {Investigating the cortical effect of false positive feedback on motor learning in motor imagery based rehabilitative BCI training.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {61},
pmid = {40102969},
issn = {1743-0003},
support = {2022 R1 A2 C1008150//National Research Foundation of Korea/ ; NRCTR-EX23004//Translational Research Program for Rehabilitation Robots/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; Adult ; *Stroke Rehabilitation ; Electroencephalography ; *Learning/physiology ; Middle Aged ; *Imagination/physiology ; Spectroscopy, Near-Infrared ; Neuronal Plasticity/physiology ; *Motor Cortex/physiology ; Stroke/physiopathology ; Young Adult ; False Positive Reactions ; },
abstract = {BACKGROUND: Motor imagery-based brain-computer interface (MI-BCI) is a promising solution for neurorehabilitation. Many studies proposed that reducing false positive (FP) feedback is crucial for inducing neural plasticity by BCI technology. However, the effect of FP feedback on cortical plasticity induction during MI-BCI training is yet to be investigated.<br><br>OBJECTIVE: This study aims to validate the hypothesis that FP feedback affects the cortical plasticity of the user's MI during MI-BCI training by first comparing two different asynchronous MI-BCI paradigms (with and without FP feedback), and then comparing its effectiveness with that of conventional motor learning methods (passive and active training).<br><br>METHODS: Twelve healthy volunteers and four patients with stroke participated in the study. We implemented two electroencephalogram-driven asynchronous MI-BCI systems with different feedback conditions. The feedback was provided by a hand exoskeleton robot performing hand open/close task. We assessed the hemodynamic responses in two different feedback conditions and compared them with two conventional motor learning methods using functional near-infrared spectroscopy with an event-related design. The cortical effects of FP feedback were analyzed in different paradigms, as well as in the same paradigm via statistical analysis.<br><br>RESULTS: The MI-BCI without FP feedback paradigm induced higher cortical activation in MI, focusing on the contralateral motor area, compared to the paradigm with FP feedback. Additionally, within the same paradigm providing FP feedback, the task period immediately following FP feedback elicited a lower hemodynamic response in the channel located over the contralateral motor area compared to the MI-BCI paradigm without FP feedback (p&#x2009;=&#x2009;0.021 for healthy people; p&#x2009;=&#x2009;0.079 for people with stroke). In contrast, task trials where there was no FP feedback just before showed a higher hemodynamic response, similar to the MI-BCI paradigm without FP feedback (p&#x2009;=&#x2009;0.099 for healthy people, p&#x2009;=&#x2009;0.084 for people with stroke).<br><br>CONCLUSIONS: FP feedback reduced cortical activation for the users during MI-BCI training, suggesting a potential negative effect on cortical plasticity. Therefore, minimizing FP feedback may enhance the effectiveness of rehabilitative MI-BCI training by promoting stronger cortical activation and plasticity, particularly in the contralateral motor area.},
}
@article {pmid40102931,
year = {2025},
author = {Wu, X and Hu, Z and Yue, H and Wang, C and Li, J and Yang, Y and Luan, Z and Wang, L and Shen, Y and Gu, Y},
title = {Enhancing myelinogenesis through LIN28A rescues impaired cognition in PWMI mice.},
journal = {Stem cell research &amp; therapy},
volume = {16},
number = {1},
pages = {141},
pmid = {40102931},
issn = {1757-6512},
support = {2017YFA0104200//National Key Research and Development Program of China/ ; 32071021//National Natural Science Foundation of China/ ; 32225021//China National Funds for Distinguished Young Scientists/ ; },
mesh = {Animals ; *RNA-Binding Proteins/metabolism/genetics ; Mice ; *Myelin Sheath/metabolism ; Cell Differentiation ; Oligodendrocyte Precursor Cells/metabolism/cytology ; Oligodendroglia/metabolism ; Mice, Knockout ; Animals, Newborn ; Cognition ; Disease Models, Animal ; },
abstract = {BACKGROUND: In premature newborn infants, preterm white matter injury (PWMI) causes motor and cognitive disabilities. Accumulating evidence suggests that PWMI may result from defected differentiation of oligodendrocyte precursor cells (OPCs) and impaired maturation of oligodendrocytes. However, the underlying mechanisms remain unclear.<br><br>METHODS: Using RNAscope, we analyzed the expression level of RNA-binding protein LIN28A in individual OPCs. Knockout of one or both alleles of Lin28a in OPCs was achieved by administrating tamoxifen to NG2[CreER]::Ai14::Lin28a[flox/+] or NG2[CreER]::Ai14::Lin28a[flox/flox] mice. Lentivirus expressing FLEX-Lin28a was used in NG2[CreER] mice to overexpress LIN28A in OPCs. A series of behavioral tests were performed to assess the cognitive functions of mice. Two-tailed unpaired t-tests was carried out for statistical analysis between groups.<br><br>RESULTS: We found that the expression of Lin28a was decreased in OPCs in a PWMI mouse model. Knockout of one or both alleles of Lin28a in OPCs postnatally resulted in reduced OPC differentiation, decreased myelinogenesis and impaired cognitive functions. Supplementing LIN28A in OPCs postnatally was able to promote OPC differentiation and enhance myelinogenesis, thus rescuing the cognitive functions in PWMI mice.<br><br>CONCLUSION: Our study reveals that LIN28A is critical in regulating postnatal myelinogenesis. Overexpression of LIN28A in OPCs rescues cognitive deficits in PWMI mice by promoting myelinogenesis, thus providing a potential strategy for the treatment of PWMI.},
}
@article {pmid40101709,
year = {2025},
author = {Chen, Q and Zhu, L and Zhang, S and Qiao, S and Ding, ZJ and Zheng, SJ and Guo, J and Su, N},
title = {Structures and mechanisms of the ABC transporter ABCB1 from Arabidopsis thaliana.},
journal = {Structure (London, England : 1993)},
volume = {33},
number = {5},
pages = {903-915.e5},
doi = {10.1016/j.str.2025.02.008},
pmid = {40101709},
issn = {1878-4186},
mesh = {*Arabidopsis/metabolism/chemistry ; *Arabidopsis Proteins/chemistry/metabolism/genetics ; Cryoelectron Microscopy ; *Brassinosteroids/metabolism/chemistry ; Models, Molecular ; Protein Binding ; *ATP Binding Cassette Transporter, Subfamily B/chemistry/metabolism ; *ATP-Binding Cassette Transporters/chemistry/metabolism ; Biological Transport ; Protein Conformation ; Binding Sites ; },
abstract = {The Arabidopsis thaliana auxin transporter ABCB1 plays a fundamental role in the regulation of plant growth and development. While its homolog ABCB19 was previously shown to transport brassinosteroids (BR), another class of essential hormones, the ability of ABCB1 to mediate BR transport has remained unexplored. In this study we show that ABCB1 also transports brassinosteroids with an in vitro brassinolide (BL) transport assay. Using single-particle cryo-electron microscopy, we determined ABCB1 structures in multiple inward-facing conformations in the apo state, ANP-bound state, BL-bound state, and the both BL- and ANP-bound state. BL binds to the large cavity of two transmembrane domains, inducing a slight conformational change. Additionally, we obtained the structure of ABCB1 in an outward-facing conformation. By comparing these different conformations, we elucidated the possible mechanism of hormone transport by ABCB1. These high-resolution structures help us to understand the structural basis for hormone recognition and transport mechanisms of ABCB1.},
}
@article {pmid40101581,
year = {2025},
author = {Abdelaty, MM and Rushdi, MA and Rasmy, ME and Annaby, MH},
title = {Graph vertex and spectral features for EEG-based motor imagery classification.},
journal = {Computers in biology and medicine},
volume = {189},
number = {},
pages = {109944},
doi = {10.1016/j.compbiomed.2025.109944},
pmid = {40101581},
issn = {1879-0534},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Imagination/physiology ; *Signal Processing, Computer-Assisted ; Male ; Adult ; Support Vector Machine ; Female ; Algorithms ; },
abstract = {Motor imagery (MI) patterns play a vital role in brain-computer interface (BCI) systems, enabling control of external devices without relying on peripheral nerves or muscles. These patterns are typically classified by analyzing the associated electroencephalogram (EEG) signals. In this work, we introduce a novel MI classification approach based on multilevel graph-theoretic modeling of multichannel EEG signals. Multivariate autoregressive modeling and coherence analysis are firstly employed to construct directed graph signals to represent the relationships among EEG channels and capture the complex correlations inherent in MI patterns. Spatial graph vertex features are thus extracted as well as graph Fourier transform coefficients. Moreover, multilevel generalizations of vertex-domain features are thus defined where edges of graph signals are pruned according to different thresholds, vertex features are extracted for each threshold level, and then all features are combined into a multilevel hierarchical graph descriptor. These graph-theoretic descriptors could be fused with different variants of common spatial patterns for improved discriminability on MI classification tasks. Different feature combinations are used to train k-nearest neighbor classifiers, support vector machines, and random forests for MI pattern classification. The proposed method demonstrates competitive performance compared to the FWCSP and SCSP methods on Dataset 2a of the BCI Competition IV, as well as robust results on Dataset 1 from the same competition. Overall, the findings highlight the potential of multilevel spatial and spectral graph features in leveraging the correlation among EEG channels towards enhanced MI classification performance.},
}
@article {pmid40101362,
year = {2025},
author = {Arpaia, P and Esposito, A and Galasso, E and Galdieri, F and Natalizio, A},
title = {A wearable brain-computer interface to play an endless runner game by self-paced motor imagery.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adc205},
pmid = {40101362},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Imagination/physiology ; Female ; *Wearable Electronic Devices ; Adult ; Young Adult ; Electroencephalography/methods/instrumentation ; *Running/physiology/psychology ; *Video Games ; *Psychomotor Performance/physiology ; },
abstract = {Objective.A wearable brain-computer interface is proposed and validated experimentally in relation to the real-time control of an endless runner game by self-paced motor imagery(MI).Approach.Electroencephalographic signals were recorded via eight wet electrodes. The processing pipeline involved a filter-bank common spatial pattern approach and the combination of three binary classifiers exploiting linear discriminant analysis. This enabled the discrimination between imagining left-hand, right-hand, and no movement. Each mental task corresponded to an avatar horizontal motion within the game. Twenty-three healthy subjects participated to the experiments and their data are made publicly available. A custom metric was proposed to assess avatar control performance during the gaming phase. The game consisted of two levels, and after each, participants completed a questionnaire to self-assess their engagement and gaming experience.Main results.The mean classification accuracies resulted 73%, 73%, and 67% for left-rest, right-rest, and left-right discrimination, respectively. In the gaming phase, subjects with higher accuracies for left-rest and right-rest pair exhibited higher performance in terms of the custom metric. Correlation of the offline and real-time performance was investigated. The left-right MI did not correlate to the gaming phase performance due to the poor mean accuracy of the calibration. Finally, the engagement questionnaires revealed that level 1 and level 2 were not perceived as frustrating, despite the increasing difficulty.Significance.The work contributes to the development of wearable and self-paced interfaces for real-time control. These enhance user experience by guaranteeing a more natural interaction with respect to synchronous neural interfaces. Moving beyond benchmark datasets, the work paves the way to future applications on mobile devices for everyday use.},
}
@article {pmid40101262,
year = {2025},
author = {Choubey, C and Dhanalakshmi, M and Karunakaran, S and Londhe, GV and Vimal, V and Kirubakaran, MK},
title = {Optimizing Bioimaging: Quantum Computing-Inspired Bald Eagle Search Optimization for Motor Imaging EEG Feature Selection.},
journal = {Clinical EEG and neuroscience},
volume = {},
number = {},
pages = {15500594251325273},
doi = {10.1177/15500594251325273},
pmid = {40101262},
issn = {2169-5202},
abstract = {One of the most important objectives in brain-computer interfaces (BCI) is to identify a subset of characteristics that represents the electroencephalographic (EEG) signal while eliminating elements that are duplicate or irrelevant. Neuroscientific research is advanced by bioimaging, especially in the field of BCI. In this work, a novel quantum computing-inspired bald eagle search optimization (QC-IBESO) method is used to improve the effectiveness of motor imagery EEG feature selection. This method can prevent the dimensionality curse and improve the classification accuracy of the system by lowering the dimensionality of the dataset. The dataset that was used in the assessment is from BCI Competition-III IV-A. To normalize the EEG data, Z-score normalization is used in the preprocessing stage. Principal component analysis reduces dimensionality and preserves important information during feature extraction. In the context of motor imagery, the QC-IBESO approach is utilized to select certain EEG characteristics for bioimaging. This facilitates the exploration of intricate search spaces and improves the detection of critical EEG signals related to motor imagery. The study contrasts the suggested approach with conventional methods like neural networks, support vector machines and logistic regression. To evaluate the efficacy of the suggested strategy in contrast to current techniques, performance measures such as F1-score, precision, accuracy and recall are computed. This work advances the field of feature selection techniques in bioimaging and opens up a novel and intriguing direction for the investigation of quantum-inspired optimization in neuroimaging.},
}
@article {pmid40100843,
year = {2025},
author = {Russo, JS and Mahoney, T and Kokorin, K and Reynolds, A and Lin, CS and John, SE and Grayden, DB},
title = {Towards developing brain-computer interfaces for people with Multiple Sclerosis.},
journal = {PloS one},
volume = {20},
number = {3},
pages = {e0319811},
pmid = {40100843},
issn = {1932-6203},
mesh = {Humans ; *Brain-Computer Interfaces ; *Multiple Sclerosis/physiopathology ; Male ; Female ; Adult ; Middle Aged ; Surveys and Questionnaires ; },
abstract = {BACKGROUND: Multiple Sclerosis (MS) can be a severely disabling condition that leads to various neurological symptoms. A Brain-Computer Interface (BCI) may substitute some lost function; however, there is a lack of BCI research in people with MS. Present BCI designs have also overlooked the unique pathological changes associated with MS and have not considered needs of users within their home environments. To progress this research area effectively and efficiently, we aimed to evaluate user needs and assess the feasibility and user-centric requirements of a BCI for people with MS. We hypothesised that (i) people with MS would be interested in adopting BCI technology and (ii) those with reduced independence would prefer a higher-performing invasive BCI.<br><br>METHODS: We conducted an online survey of people with MS to describe user preferences and establish the initial steps of user-centred design. The survey aimed to understand their interest in BCI applications, bionic applications, device preferences, and development considerations and related these to symptoms and assistance needs.<br><br>RESULTS: We demonstrated widespread interest for BCI applications in all stages of MS, with a preference for a non-invasive (n = 12) or minimally invasive (n = 15) BCI over carer assistance (n = 6). Descriptive analysis indicated that level of independence did not influence preference towards the higher performing but highly invasive BCI.<br><br>CONCLUSIONS: The needs of end users reported in this study are crucial for efficient development of BCI systems that can be effectively translated into the home environment. Considering the potential to enhance independence and quality of life for people living with MS, the results emphasise the importance of user-centred design for future advancement of BCIs that account for the unique pathological changes associated with MS.},
}
@article {pmid40100695,
year = {2025},
author = {Guo, Z and Xu, L and Tan, W and Chen, F},
title = {Impact of Generation Rate of Speech Imagery on Neural Activity and BCI Decoding Performance: A fNIRS Study.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {1180-1190},
doi = {10.1109/TNSRE.2025.3552606},
pmid = {40100695},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; Spectroscopy, Near-Infrared/methods ; Male ; Female ; Adult ; *Speech/physiology ; *Imagination/physiology ; Stroke Rehabilitation ; Brain/physiology ; Young Adult ; Algorithms ; Brain Mapping ; Electroencephalography ; Healthy Volunteers ; Neuronal Plasticity ; },
abstract = {Brain-computer interface (BCI) enables stroke patients to actively modulate neural activity, fostering neuroplasticity and thereby accelerating the recovery process. Due to being portable, non-invasive, and safe, functional near-infrared spectroscopy (fNIRS) has become one of the most widely used neuroimaging techniques. Current BCI research primarily focuses on improving the decoding performance. However, a key aspect of stroke rehabilitation lies in inducing stronger cortical activations in the damaged brain areas, thereby accelerating the recovery of brain functions. This study investigated the regulatory mechanism of the generation rate of speech imagery on neural activity and its impact on BCI decoding performance based on fNIRS. As the generation rate increased from 1 word/4 s to 1 word/2 s, and finally to 1 word/1 s, neural activity in speech-related brain regions steadily enhanced. Correspondingly, the accuracy of detecting speech imagery tasks increased from 83.83% to 85.39%, and ultimately showed a significant improvement, reaching 88.28%. Additionally, the differences in neural activities between the "yes" and "no" speech imagery tasks became more pronounced as the generation rate increased, leading to an improvement in classification performance from 62.81% to 65.78%, and ultimately to 67.50%. This study demonstrates that the neural activity level of most speech-related brain regions during speech imagery enhanced as the generation rate increased. Therefore, accelerating the generation rate of speech imagery induces stronger neural activity and more distinct response patterns between different tasks, which holds the potential to facilitate the development of a BCI feedback system with higher neuroplasticity induction and improved decoding performance.},
}
@article {pmid40100694,
year = {2025},
author = {Yan, W and Lin, Y and Chen, YF and Wang, Y and Wang, J and Zhang, M},
title = {Enhancing Neuroplasticity for Post-Stroke Motor Recovery: Mechanisms, Models, and Neurotechnology.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {1156-1168},
doi = {10.1109/TNSRE.2025.3551753},
pmid = {40100694},
issn = {1558-0210},
mesh = {Humans ; *Neuronal Plasticity/physiology ; *Stroke Rehabilitation/methods ; *Recovery of Function/physiology ; Brain-Computer Interfaces ; Stroke/physiopathology ; Robotics ; Models, Neurological ; },
abstract = {Stroke remains a significant global health challenge, imposing substantial socioeconomic burdens. Post-stroke neurorehabilitation aims to maximize functional recovery and mitigate persistent disability through effective neuromodulation, while many patients experience prolonged recovery periods with suboptimal outcomes. This review explores innovative neurotechnologies and therapeutic strategies enhancing neuroplasticity for post-stroke motor recovery, with a particular focus on the subacute and chronic phases. We examine key neuroplasticity mechanisms and rehabilitation models informing neurotechnology use, including the vicariation model, the interhemispheric competition model, and the bimodal balance-recovery model. Building on these theoretical foundations, current neurotechnologies are categorized into endogenous drivers of neuroplasticity (e.g., task-oriented training, brain-computer interfaces) and exogenous drivers (e.g., brain stimulation, muscular electrical stimulation, robot-assisted passive movement). However, most approaches lack tailored adjustments combining volitional behavior with brain neuromodulation. Given the heterogeneous effects of current neurotechnologies, we propose that future directions should focus on personalized rehabilitation strategies and closed-loop neuromodulation. These advanced approaches may provide deeper insights into neuroplasticity and potentially expand recovery possibilities for stroke patients.},
}
@article {pmid40100553,
year = {2025},
author = {Zhou, H and Yan, ZN and Gao, WH and Lv, XX and Luo, R and Hoellwarth, JS and He, L and Yang, JM and Zhang, JY and Wang, HL and Xie, Y and Chen, XL and Xue, MD and Fang, Y and Duan, YY and Li, RY and Wang, XD and Wang, RL and Xie, M and Huang, L and Liu, PR and Ye, ZW},
title = {Construction of a Multimodal 3D Atlas for a Micrometer-Scale Brain-Computer Interface Based on Mixed Reality.},
journal = {Current medical science},
volume = {45},
number = {2},
pages = {194-205},
pmid = {40100553},
issn = {2523-899X},
support = {No.82172524//the National Natural Science Foundation of China/ ; No.81974355//the National Natural Science Foundation of China/ ; No.2020021105012440//National Innovation Platform Development Program/ ; 2021BEA161//Major Program of Hubei Province/ ; JD2023BAA005//Major Key Project of Hubei Province/ ; No.2024XHYN047//Wuhan Union Hospital Free Innovation Preliminary Research Fund/ ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Rats ; Rats, Sprague-Dawley ; *Imaging, Three-Dimensional/methods ; *Brain/diagnostic imaging/physiology ; Magnetic Resonance Imaging/methods ; X-Ray Microtomography ; *Multimodal Imaging/methods ; Skull/diagnostic imaging ; *Visual Cortex/diagnostic imaging/physiology ; Male ; },
abstract = {OBJECTIVE: To develop a multimodal imaging atlas of a rat brain-computer interface (BCI) that incorporates brain, arterial, bone tissue and a BCI device using mixed reality (MR) for three-dimensional (3D) visualization.<br><br>METHODS: An invasive BCI was implanted in the left visual cortex of 4-week-old Sprague-Dawley rats. Multimodal imaging techniques, including micro-CT and 9.0&#xa0;T MRI, were used to acquire images of the rat cranial bone structure, vascular distribution, brain tissue functional zones, and BCI device before and after implantation. Using 3D-slicer software, the images were fused through spatial transformations, followed by image segmentation and 3D model reconstruction. The HoloLens platform was employed for MR visualization.<br><br>RESULTS: This study constructed a multimodal imaging atlas for rats that included the skull, brain tissue, arterial tissue, and BCI device coupled with MR technology to create an interactive 3D anatomical model.<br><br>CONCLUSIONS: This multimodal 3D atlas provides an objective and stable reference for exploring complex relationships between brain tissue structure and function, enhancing the understanding of the operational principles of BCIs. This is the first multimodal 3D imaging atlas related to a BCI created using Sprague-Dawley rats.},
}
@article {pmid40100543,
year = {2025},
author = {Gao, J and Tang, H and Wang, Z and Li, Y and Luo, N and Song, M and Xie, S and Shi, W and Yan, H and Lu, L and Yan, J and Li, P and Song, Y and Chen, J and Chen, Y and Wang, H and Liu, W and Li, Z and Guo, H and Wan, P and Lv, L and Yang, Y and Wang, H and Zhang, H and Wu, H and Ning, Y and Zhang, D and Jiang, T},
title = {Graph Neural Networks and Multimodal DTI Features for Schizophrenia Classification: Insights from Brain Network Analysis and Gene Expression.},
journal = {Neuroscience bulletin},
volume = {41},
number = {6},
pages = {933-950},
pmid = {40100543},
issn = {1995-8218},
mesh = {Humans ; *Schizophrenia/classification/diagnostic imaging/genetics/pathology ; *Diffusion Tensor Imaging/methods ; Male ; Female ; Adult ; *Brain/diagnostic imaging/pathology/metabolism ; Young Adult ; Middle Aged ; White Matter/diagnostic imaging/pathology ; Gene Expression ; *Nerve Net/diagnostic imaging ; Graph Neural Networks ; },
abstract = {Schizophrenia (SZ) stands as a severe psychiatric disorder. This study applied diffusion tensor imaging (DTI) data in conjunction with graph neural networks to distinguish SZ patients from normal controls (NCs) and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features, achieving an accuracy of 73.79% in distinguishing SZ patients from NCs. Beyond mere discrimination, our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis. These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers, providing novel insights into the neuropathological basis of SZ. In summary, our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.},
}
@article {pmid40099835,
year = {2025},
author = {Jin, J and Xiao, Q and Liu, Y and Xu, T and Shen, Q},
title = {Test-retest reliability of decisions under risk with outcome evaluation: evidence from behavioral and event-related potentials (ERPs) measures in 2 monetary gambling tasks.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {35},
number = {3},
pages = {},
doi = {10.1093/cercor/bhaf058},
pmid = {40099835},
issn = {1460-2199},
support = {2022KFKT005//Open Research Fund/ ; 22dz2261100//Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior/ ; 23NDJC124YB//Zhejiang Province Philosophy and Social Science Planning Project/ ; 41005067//Fundamental Research Funds for the Central Universities/ ; 72371165//National Natural Science Foundation of China/ ; },
mesh = {Humans ; Male ; Female ; *Gambling/psychology/physiopathology ; Electroencephalography ; *Evoked Potentials/physiology ; Young Adult ; Adult ; *Risk-Taking ; Reproducibility of Results ; *Decision Making/physiology ; Reward ; *Brain/physiology ; },
abstract = {The balance between potential gains and losses under risk, the stability of risk propensity, the associated reward processing, and the prediction of subsequent risk behaviors over time have become increasingly important topics in recent years. In this study, we asked participants to carry out 2 risk tasks with outcome evaluation-the monetary gambling task and mixed lottery task twice, with simultaneous recording of behavioral and electroencephalography data. Regarding risk behavior, we observed both individual-specific risk attitudes and outcome-contingent risky inclination following a loss outcome, which remained stable across sessions. In terms of event-related potential (ERP) results, low outcomes, compared to high outcomes, induced a larger feedback-related negativity, which was modulated by the magnitude of the outcome. Similarly, high outcomes evoked a larger deflection of the P300 compared to low outcomes, with P300 amplitude also being sensitive to outcome magnitude. Intraclass correlation coefficient analyses indicated that both the feedback-related negativity and P300 exhibited modest to good test-retest reliability across both tasks. Regarding choice prediction, we found that neural responses-especially those following a loss outcome-predicted subsequent risk-taking behavior at the single-trial level for both tasks. Therefore, this study extends our understanding of the reliability of risky preferences in gain-loss trade-offs.},
}
@article {pmid40096442,
year = {2025},
author = {Shi, X and Zhai, X and Wang, R and Le, Y and Fu, S and Liu, N},
title = {Task Planning of Multiple Unmanned Aerial Vehicles Based on Minimum Cost and Maximum Flow.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {5},
pages = {},
pmid = {40096442},
issn = {1424-8220},
abstract = {With the rapid development of UAV technology, UAV delivery has gained attention for its potential to reduce labor costs. However, limitations in load capacity and energy restrict UAVs' distribution capabilities. This paper proposes a cooperative delivery scheme combining traditional trucks and UAVs to extend UAV coverage and improve delivery completion rates. For densely distributed depots in wide-area regions, we develop algorithms for task allocation and path planning in a truck-independent UAV system. Specifically, a minimum-cost, maximum-flow model is constructed to obtain sub-paths covering all delivery tasks, and resource tree-based algorithms are used to construct global paths for UAVs and trucks. Simulation results show that our algorithms reduce total energy consumption by 11.53% and 9.15% under different task points, which suggests that our proposed method can significantly enhance delivery efficiency, offering a promising solution for future logistics operations.},
}
@article {pmid40096214,
year = {2025},
author = {Bouchane, M and Guo, W and Yang, S},
title = {Hybrid CNN-GRU Models for Improved EEG Motor Imagery Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {5},
pages = {},
pmid = {40096214},
issn = {1424-8220},
support = {236Z0105G//Hebei Central Leading Local Science and Technology Development Foundation/ ; },
mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; *Imagination/physiology ; Signal Processing, Computer-Assisted ; Movement/physiology ; Algorithms ; },
abstract = {Brain-computer interfaces (BCIs) based on electroencephalography (EEG) enable neural activity interpretation for device control, with motor imagery (MI) serving as a key paradigm for decoding imagined movements. Efficient feature extraction from raw EEG signals is essential to improve classification accuracy while minimizing reliance on extensive preprocessing. In this study, we introduce new hybrid architectures to enhance MI classification using data augmentation and a limited number of EEG channels. The first model combines a shallow convolutional neural network and a gated recurrent unit (CNN-GRU), while the second incorporates a convolutional neural network with a bidirectional gated recurrent unit (CNN-Bi-GRU). Evaluated using the publicly available PhysioNet dataset, the CNN-GRU classifier achieved peak mean accuracy rates of 99.71%, 99.73%, 99.61%, and 99.86% for tasks involving left fist (LF), right fist (RF), both fists (LRF), and both feet (BF), respectively. The experimental results provide compelling evidence that our proposed models outperform current state-of-the-art methods, underscoring their efficiency on small-scale EEG datasets. The CNN-GRU and CNN-Bi-GRU architectures exhibit superior predictive reliability, offering a faster, cost-effective solution for user-adaptable MI-BCI applications.},
}
@article {pmid40096116,
year = {2025},
author = {González-España, JJ and Sánchez-Rodríguez, L and Pacheco-Ramírez, MA and Feng, J and Nedley, K and Chang, SH and Francisco, GE and Contreras-Vidal, JL},
title = {At-Home Stroke Neurorehabilitation: Early Findings with the NeuroExo BCI System.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {5},
pages = {},
pmid = {40096116},
issn = {1424-8220},
support = {1827769//National Science Foundation/ ; 2137255//NSF Industry-University Cooperative Research Center for Building Reliable Advances and Innovations in Neurotechnology (IUCRC BRAIN)/ ; },
mesh = {Humans ; *Stroke Rehabilitation/methods ; *Brain-Computer Interfaces ; Middle Aged ; Male ; *Stroke/physiopathology ; Female ; Aged ; *Neurological Rehabilitation/methods ; Adult ; Exoskeleton Device ; Robotics ; },
abstract = {BACKGROUND: Democratized access to safe and effective robotic neurorehabilitation for stroke survivors requires innovative, affordable solutions that can be used not only in clinics but also at home. This requires the high usability of the devices involved to minimize costs associated with support from physical therapists or technicians.<br><br>METHODS: This paper describes the early findings of the NeuroExo brain-machine interface (BMI) with an upper-limb robotic exoskeleton for stroke neurorehabilitation. This early feasibility study consisted of a six-week protocol, with an initial training and BMI calibration phase at the clinic followed by 60 sessions of neuromotor therapy at the homes of the participants. Pre- and post-assessments were used to assess users' compliance and system performance.<br><br>RESULTS: Participants achieved a compliance rate between 21% and 100%, with an average of 69%, while maintaining adequate signal quality and a positive perceived BMI performance during home usage with an average Likert scale score of four out of five. Moreover, adequate signal quality was maintained for four out of five participants throughout the protocol. These findings provide valuable insights into essential components for comprehensive rehabilitation therapy for stroke survivors. Furthermore, linear mixed-effects statistical models showed a significant reduction in trial duration (p-value < 0.02) and concomitant changes in brain patterns (p-value < 0.02).<br><br>CONCLUSIONS: the analysis of these findings suggests that a low-cost, safe, simple-to-use BMI system for at-home stroke rehabilitation is feasible.},
}
@article {pmid40096108,
year = {2025},
author = {Alexopoulou, A and Pergantis, P and Koutsojannis, C and Triantafillou, V and Drigas, A},
title = {Non-Invasive BCI-VR Applied Protocols as Intervention Paradigms on School-Aged Subjects with ASD: A Systematic Review.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {5},
pages = {},
pmid = {40096108},
issn = {1424-8220},
mesh = {Humans ; *Autism Spectrum Disorder/physiopathology/therapy ; Child ; Adolescent ; *Brain-Computer Interfaces ; *Virtual Reality ; Cognition/physiology ; Electroencephalography ; Male ; },
abstract = {This paper aims to highlight non-invasive BCI-VR applied protocols as intervention paradigms on school-aged subjects with ASD. Computer-based interventions are considered appropriate for users with ASD as concentration on a screen reduces other stimuli from the environment that are likely to be distracting or disruptive. Since there are no social conditions for engagement in such processes and the responses of computing systems do not hold surprises for users, as the outputs are fully controlled, they are ideal for ASD subjects. Children and adolescents with ASD, when supported by BCI interventions through virtual reality applications, especially appear to show significant improvements in core symptoms, such as cognitive and social deficits, regardless of their age or IQ. We examined nine protocols applied from 2016 to 2023, focusing on the BCI paradigms, the procedure, and the outcomes. Our study is non-exhaustive but representative of the state of the art in the field. As concluded by the research, BCI-VR applied protocols have no side effects and are rather easy to handle and maintain, and despite the fact that there are research limitations, they hold promise as a tool for improving social and cognitive skills in school-aged individuals with ASD.},
}
@article {pmid40096020,
year = {2025},
author = {Vafaei, E and Hosseini, M},
title = {Transformers in EEG Analysis: A Review of Architectures and Applications in Motor Imagery, Seizure, and Emotion Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {5},
pages = {},
pmid = {40096020},
issn = {1424-8220},
mesh = {*Electroencephalography/methods ; Humans ; *Emotions/physiology ; *Seizures/physiopathology/diagnosis ; Machine Learning ; Signal Processing, Computer-Assisted ; Algorithms ; Imagination/physiology ; },
abstract = {Transformers have rapidly influenced research across various domains. With their superior capability to encode long sequences, they have demonstrated exceptional performance, outperforming existing machine learning methods. There has been a rapid increase in the development of transformer-based models for EEG analysis. The high volumes of recently published papers highlight the need for further studies exploring transformer architectures, key components, and models employed particularly in EEG studies. This paper aims to explore four major transformer architectures: Time Series Transformer, Vision Transformer, Graph Attention Transformer, and hybrid models, along with their variants in recent EEG analysis. We categorize transformer-based EEG studies according to the most frequent applications in motor imagery classification, emotion recognition, and seizure detection. This paper also highlights the challenges of applying transformers to EEG datasets and reviews data augmentation and transfer learning as potential solutions explored in recent years. Finally, we provide a summarized comparison of the most recent reported results. We hope this paper serves as a roadmap for researchers interested in employing transformer architectures in EEG analysis.},
}
@article {pmid40096019,
year = {2025},
author = {Koo, BH and Siu, HC and Newman, DJ and Roche, ET and Petersen, LG},
title = {Utilization of Classification Learning Algorithms for Upper-Body Non-Cyclic Motion Prediction.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {5},
pages = {},
pmid = {40096019},
issn = {1424-8220},
mesh = {Adult ; Female ; Humans ; Male ; *Deep Learning ; Electromyography/methods ; Exoskeleton Device ; Motion ; *Movement/physiology ; Muscle, Skeletal/physiology ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; },
abstract = {This study explores two methods of predicting non-cyclic upper-body motions using classification algorithms. Exoskeletons currently face challenges with low fluency, hypothesized to be in part caused by the lag in active control innate in many leader-follower paradigms seen in today's systems, leading to energetic inefficiencies and discomfort. To address this, we employ k-nearest neighbor (KNN) and deep learning models to predict motion characteristics, such as magnitude and category, from surface electromyography (sEMG) signals. Data were collected from six muscles located around the elbow. The sEMG signals were processed to identify significant activation changes. Two classification approaches were utilized: a KNN algorithm that categorizes motion based on the slopes of processed sEMG signals at change points and a deep neural network employing continuous categorization. Both methods demonstrated the capability to predict future voluntary non-cyclic motions up to and beyond commonly acknowledged electromechanical delay times, with the deep learning model able to predict, with certainty at or beyond 90%, motion characteristics even prior to myoelectric activation of the muscles involved. Our findings indicate that these classification algorithms can be used to predict upper-body non-cyclic motions to potentially increase machine interfacing fluency. Further exploration into regression-based prediction models could enhance the precision of these predictions, and further work could explore their effects on fluency when utilized in a tandem or wearable robotic application.},
}
@article {pmid40095842,
year = {2025},
author = {Tian, B and Zhang, S and Xue, D and Chen, S and Zhang, Y and Peng, K and Wang, D},
title = {Decoding Intrinsic Fluctuations of Engagement From EEG Signals During Fingertip Motor Tasks.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {1271-1283},
doi = {10.1109/TNSRE.2025.3551819},
pmid = {40095842},
issn = {1558-0210},
mesh = {Humans ; *Electroencephalography/methods ; Male ; *Fingers/physiology ; Adult ; Female ; Young Adult ; Machine Learning ; Algorithms ; Virtual Reality ; Brain-Computer Interfaces ; Psychomotor Performance/physiology ; Reproducibility of Results ; Motor Skills/physiology ; Signal Processing, Computer-Assisted ; Movement/physiology ; },
abstract = {Maintaining a high mental engagement is critical for motor rehabilitation interventions. Achieving a flow experience, often conceptualized as a highly engaged mental state, is an ideal goal for motor rehabilitation tasks. This paper proposes a virtual reality-based fine fingertip motor task in which the difficulty is maintained to match individual abilities. The aim of this study is to decode the intrinsic fluctuations of flow experience from electroencephalogram (EEG) signals during the execution of a motor task, addressing a gap in flow research that overlooks these fluctuations. To resolve the conflict between sparse self-reported flow sampling and the high dimensionality of neural signals, we use motor behavioral measures to represent flow and label the EEG data, thereby increasing the number of samples. A machine learning-based neural decoder is then established to classify each trial into high-flow or low-flow using spectral power and coherence features extracted from the EEG signals. Cross-validation reveals that the classification accuracy of the neural decoder can exceed 80%. Notably, we highlight the contributions of high-frequency bands in EEG activities to flow decoding. Additionally, EEG feature analyses reveal significant increases in the power of parietal-occipital electrodes and global coherence values, specifically in the alpha and beta bands, during high-flow durations. This study validates the feasibility of decoding the intrinsic flow fluctuations during fine motor task execution with a high accuracy. The methodology and findings in this work lay a foundation for future applications in manipulating flow experience and enhancing engagement levels in motor rehabilitation practice.},
}
@article {pmid40095346,
year = {2025},
author = {Li, J and Shao, N and Zhang, Y and Liu, X and Zhang, H and Tian, L and Piatkevich, KD and Zhang, D and Lee, HJ},
title = {Screening of Vibrational Spectroscopic Voltage Indicator by Stimulated Raman Scattering Microscopy.},
journal = {Small methods},
volume = {9},
number = {9},
pages = {e2402124},
doi = {10.1002/smtd.202402124},
pmid = {40095346},
issn = {2366-9608},
support = {82372011//National Natural Science Foundation of China/ ; 12074339//National Natural Science Foundation of China/ ; 32050410298//National Natural Science Foundation of China/ ; 32171093//National Natural Science Foundation of China/ ; LZ25H180001//Natural Science Foundation of Zhejiang Province/ ; 2025ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024YFA1408900//National Key Research and Development Program of China/ ; 28961//Brain and Behavior Research Foundation/ ; },
mesh = {*Spectrum Analysis, Raman/methods ; Humans ; Animals ; Membrane Potentials/physiology ; Vibration ; HEK293 Cells ; Rhodopsin/metabolism/chemistry ; },
abstract = {Genetically encoded voltage indicators (GEVIs) have significantly advanced voltage imaging, offering spatial details at cellular and subcellular levels not easily accessible with electrophysiology. In addition to fluorescence imaging, certain chemical bond vibrations are sensitive to membrane potential changes, presenting an alternative imaging strategy; however, challenges in signal sensitivity and membrane specificity highlight the need to develop vibrational spectroscopic GEVIs (vGEVIs) in mammalian cells. To address this need, a vGEVI screening approach is developed that employs hyperspectral stimulated Raman scattering (hSRS) imaging synchronized with an induced transmembrane voltage (ITV) stimulation, revealing unique spectroscopic signatures of sensors expressed on membranes. Specifically, by screening various rhodopsin-based voltage sensors in live mammalian cells, a characteristic peak associated with retinal bound to the sensor is identified in one of the GEVIs, Archon, which exhibited a 70 cm[-1] red shift relative to the membrane-bound retinal. Notably, this peak is responsive to changes in membrane potential. Overall, hSRS-ITV presents a promising platform for screening vGEVIs, paving the way for advancements in vibrational spectroscopic voltage imaging.},
}
@article {pmid40093990,
year = {2025},
author = {Yang, KC and Xu, Y and Lin, Q and Tang, LL and Zhong, JW and An, HN and Zeng, YQ and Jia, K and Jin, Y and Yu, G and Gao, F and Zhao, L and Tong, LS},
title = {Explainable deep learning algorithm for identifying cerebral venous sinus thrombosis-related hemorrhage (CVST-ICH) from spontaneous intracerebral hemorrhage using computed tomography.},
journal = {EClinicalMedicine},
volume = {81},
number = {},
pages = {103128},
pmid = {40093990},
issn = {2589-5370},
abstract = {BACKGROUND: Misdiagnosis of hemorrhage secondary to cerebral venous sinus thrombosis (CVST-ICH) as arterial-origin spontaneous intracerebral hemorrhage (sICH) can lead to inappropriate treatment and the potential for severe adverse outcomes. The current practice for identifying CVST-ICH involves venography, which, despite being increasingly utilized in many centers, is not typically used as the initial imaging modality for ICH patients. The study aimed to develop an explainable deep learning model to quickly identify ICH caused by CVST based on non-contrast computed tomography (NCCT).<br><br>METHODS: The study population included patients diagnosed with CVST-ICH and other spontaneous ICH from January 2016 to March 2023&#xa0;at the Second Affiliated Hospital of Zhejiang University, Taizhou First People's Hospital, Taizhou Hospital, Quzhou Second People's Hospital, and Longyan First People's Hospital. A transfer learning-based 3D U-Net with segmentation and classification was proposed and developed only on admission plain CT. Model performance was assessed using the area under the curve (AUC), sensitivity, and specificity metrics. For further evaluation, the average diagnostic performance of nine doctors on plain CT was compared with model assistance. Interpretability methods, including Grad-CAM++, SHAP, IG, and occlusion, were employed to understand the model's attention.<br><br>FINDINGS: An internal dataset was constructed using propensity score matching based on age, initially including 102 CVST-ICH patients (median age: 44 [29, 61] years) and 683 sICH patients (median age: 65 [52, 73] years). After matching, 102 CVST-ICH patients and 306 sICH patients (median age: 50 [40, 62] years) were selected. An external dataset consisted of 38 CVST-ICH and 119 sICH patients from four other hospitals. Validation showed AUC 0&#xb7;94, sensitivity 0&#xb7;96, and specificity 0&#xb7;8 for the internal testing subset; AUC 0&#xb7;85, sensitivity 0&#xb7;87, and specificity 0&#xb7;82 for the external dataset, respectively. The discrimination performance of nine doctors interpreting CT images significantly improved with the assistance of the proposed model (accuracy 0&#xb7;79 vs 0&#xb7;71, sensitivity 0&#xb7;88 vs 0&#xb7;81, specificity 0&#xb7;75 vs 0&#xb7;68, p&#xa0;<&#xa0;0&#xb7;05). Interpretability methods highlighted the attention of model to the features of hemorrhage edge appearance.<br><br>INTERPRETATION: The present model demonstrated high-performing and robust results on discrimination between CVST-ICH and spontaneous ICH, and aided doctors' diagnosis in clinical practice as well. Prospective validation with larger-sample size is required.<br><br>FUNDING: The work was funded by the National Key R&amp;D Program of China (2023YFE0118900), National Natural Science Foundation of China (No.81971155 and No.81471168), the Science and Technology Department of Zhejiang Province (LGJ22H180004), Medical and Health Science and Technology Project of Zhejiang Province (No.2022KY174), the 'Pioneer' R&amp;D Program of Zhejiang (No. 2024C03006 and No. 2023C03026) and the MOE Frontier Science Center for Brain Science &amp; Brain-Machine Integration, Zhejiang University.},
}
@article {pmid40092993,
year = {2025},
author = {Wen, J and Li, Y and Deng, W and Li, Z},
title = {Central nervous system and immune cells interactions in cancer: unveiling new therapeutic avenues.},
journal = {Frontiers in immunology},
volume = {16},
number = {},
pages = {1528363},
pmid = {40092993},
issn = {1664-3224},
mesh = {Humans ; *Neoplasms/immunology/therapy/metabolism/pathology ; *Central Nervous System/immunology/metabolism ; Animals ; Tumor Microenvironment/immunology ; *Cell Communication/immunology ; Signal Transduction ; Neuroimmunomodulation ; },
abstract = {Cancer remains a leading cause of mortality worldwide. Despite significant advancements in cancer research, our understanding of its complex developmental pathways remains inadequate. Recent research has clarified the intricate relationship between the central nervous system (CNS) and cancer, particularly how the CNS influences tumor growth and metastasis via regulating immune cell activity. The interactions between the central nervous system and immune cells regulate the tumor microenvironment via various signaling pathways, cytokines, neuropeptides, and neurotransmitters, while also incorporating processes that alter the tumor immunological landscape. Furthermore, therapeutic strategies targeting neuro-immune cell interactions, such as immune checkpoint inhibitors, alongside advanced technologies like brain-computer interfaces and nanodelivery systems, exhibit promise in improving treatment efficacy. This complex bidirectional regulatory network significantly affects tumor development, metastasis, patient immune status, and therapy responses. Therefore, understanding the mechanisms regulating CNS-immune cell interactions is crucial for developing innovative therapeutic strategies. This work consolidates advancements in CNS-immune cell interactions, evaluates their potential in cancer treatment strategies, and provides innovative insights for future research and therapeutic approaches.},
}
@article {pmid40092069,
year = {2025},
author = {Sayal, A and Direito, B and Sousa, T and Singer, N and Castelo-Branco, M},
title = {Music in the loop: a systematic review of current neurofeedback methodologies using music.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1515377},
pmid = {40092069},
issn = {1662-4548},
abstract = {Music, a universal element in human societies, possesses a profound ability to evoke emotions and influence mood. This systematic review explores the utilization of music to allow self-control of brain activity and its implications in clinical neuroscience. Focusing on music-based neurofeedback studies, it explores methodological aspects and findings to propose future directions. Three key questions are addressed: the rationale behind using music as a stimulus, its integration into the feedback loop, and the outcomes of such interventions. While studies emphasize the emotional link between music and brain activity, mechanistic explanations are lacking. Additionally, there is no consensus on the imaging or behavioral measures of neurofeedback success. The review suggests considering whole-brain neural correlates of music stimuli and their interaction with target brain networks and reward mechanisms when designing music-neurofeedback studies. Ultimately, this review aims to serve as a valuable resource for researchers, facilitating a deeper understanding of music's role in neurofeedback and guiding future investigations.},
}
@article {pmid40091139,
year = {2025},
author = {Thamaraimanalan, T and Gopal, D and Vignesh, S and Kishore Kumar, K},
title = {Exploiting adaptive neuro-fuzzy inference systems for cognitive patterns in multimodal brain signal analysis.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {9029},
pmid = {40091139},
issn = {2045-2322},
mesh = {Humans ; *Fuzzy Logic ; Electroencephalography/methods ; *Brain/physiology ; *Cognition/physiology ; Principal Component Analysis ; Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; Algorithms ; },
abstract = {The analysis of cognitive patterns through brain signals offers critical insights into human cognition, including perception, attention, memory, and decision-making. However, accurately classifying these signals remains a challenge due to their inherent complexity and non-linearity. This study introduces a novel method, PCA-ANFIS, which integrates Principal Component Analysis (PCA) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), to enhance cognitive pattern recognition in multimodal brain signal analysis. PCA reduces the dimensionality of EEG data while retaining salient features, enabling computational efficiency. ANFIS combines the adaptability of neural networks with the interpretability of fuzzy logic, making it well-suited to model the non-linear relationships within brain signals. Performance metrics of our proposed method, such as accuracy, sensitivity, and computational efficiency. These additions highlight the effectiveness of the method and provide a concise summary of the findings. The proposed method achieves superior classification performance, with an unprecedented accuracy of 99.5%, significantly outperforming existing approaches. Comprehensive experiments were conducted using a diverse multimodal EEG dataset, demonstrating the method's robustness and sensitivity. The integration of PCA and ANFIS addresses key challenges in multimodal brain signal analysis, such as EEG artifact contamination and non-stationarity, ensuring reliable feature extraction and classification. This research has significant implications for both cognitive neuroscience and clinical practice. By advancing the understanding of cognitive processes, the PCA-ANFIS method facilitates accurate diagnosis and treatment of cognitive disorders and neurological conditions. Future work will focus on testing the approach with larger and more diverse datasets and exploring its applicability in domains such as neurofeedback, neuromarketing, and brain-computer interfaces. This study establishes PCA-ANFIS as a capable tool for the precise and efficient classification of cognitive patterns in brain signal processing.},
}
@article {pmid40089693,
year = {2025},
author = {Khamisa, N and Madala, S and Fonka, CB},
title = {Burnout among South African nurses during the peak of COVID-19 pandemic: a holistic investigation.},
journal = {BMC nursing},
volume = {24},
number = {1},
pages = {290},
pmid = {40089693},
issn = {1472-6955},
abstract = {BACKGROUND: The wellbeing of health care workers (HCWs) has been an ongoing challenge, especially within low and middle-income countries (LMICs) such as South Africa. Evidence suggesting that HCWs are increasingly stressed and burned out is cause for concern. Nurses in particular have been impacted physically, mentally and psychosocially during the recent COVID-19 pandemic. This may leave a disproportionate consequence, affecting various aspects of their wellbeing, thereby justifying a need for a more holistic investigation of the wellbeing of South African nurses and their coping mechanisms during the peak of the pandemic.<br><br>METHODS: This was a cross-sectional study design. Online self-reported questionnaires were administered in six hospitals, sampled purposively and conveniently from three South African provinces. Using STATA 18.0, the Wilcoxon Ranksum test at 5% alpha compared the wellbeing and coping mechanisms of nursing staff and nursing management during COVID-19's peak. Univariable and multivariable linear regression analyses were performed to determine factors associated with burnout in nurses, at a 95% confidence interval (CI). Validated scales measuring burnout, coping, resilience, as well as mental and physical health were utilised.<br><br>RESULTS: Of 139 participants, 112(97.4%) were females, with 91(82%) and 20(18%) being nursing staff and management respectively. The median age of the participants was 43.3 years (n&#x2009;=&#x2009;112), with a practising duration of 12 years (n&#x2009;=&#x2009;111). There was a significant difference in the burnout score between nursing staff and nursing management (p&#x2009;=&#x2009;0.028). In the univariable linear regression model, burnout was significantly (p&#x2009;<&#x2009;0.05) associated with the Brief COPE Inventory (BCI), Conor-Davidson Resilience Scale (CDRS), Global Mental and Health Scale (GMHS), Global Physical and Health Scale (GPHS) and Hospital Anxiety and Depression Scale (HADS), as well as occupation. In the multivariable linear regression model, burnout was significantly associated with the CDRS [Coeff.=0.7, 95%CI 0.4; 0.9], GMHS [Coeff.=-2.4, 95%CI -3.2; -1.6], GPHS [Coeff.2.1, 95%CI 1.3; 2.9], and HADS [Coeff.=0.7, 95%CI 0.2; 1.2].<br><br>CONCLUSION: Investigating multiple aspects of wellbeing in this study, it's shown that coping and resilience may not be key factors in promoting the wellbeing of South African nurses. However, effective mental health interventions are crucial and should be prioritised to mitigate burnout during future health emergencies. Future studies examining the associations between general health, coping and resilience may help generate further evidence towards holistic interventions aimed at promoting nurses' wellbeing.<br><br>CLINICAL TRIAL NUMBER: Not applicable.},
}
@article {pmid40089573,
year = {2025},
author = {Sivasakthivel, R and Rajagopal, M and Anitha, G and Loganathan, K and Abbas, M and Ksibi, A and Rao, KS},
title = {Simulating online and offline tasks using hybrid cheetah optimization algorithm for patients affected by neurodegenerative diseases.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {8951},
pmid = {40089573},
issn = {2045-2322},
mesh = {Humans ; Male ; *Algorithms ; Female ; *Brain-Computer Interfaces ; *Neurodegenerative Diseases/physiopathology ; Adult ; Neural Networks, Computer ; Electroencephalography ; Middle Aged ; },
abstract = {Brain-Computer Interface (BCI) is a versatile technique to offer better communication system for people affected by the locked-in syndrome (LIS).In the current decade, there has been a growing demand for improved care and services for individuals with neurodegenerative diseases. To address this barrier, the current work is designed with four states of BCI for paralyzed persons using Welch Power Spectral Density (W-PSD). The features extracted from the signals were trained with a hybrid Feed Forward Neural Network Cheetah Optimization Algorithm (FFNNCOA) in both offline and online modes. Totally, eighteen subjects were involved in this study. The study proved that the offline analysis phase outperformed than the online phase in the real-time. The experiment was achieved the accuracies of 95.56% and 93.88% for men and female respectively. Furthermore, the study confirms that the subject's performance in the offline can manage the task more easily than in online mode.},
}
@article {pmid40086264,
year = {2025},
author = {Jiang, Y and Zhou, C and Zhao, J and Ren, X and Wang, Q and Ni, P and Li, T},
title = {Derivation of human-derived iPSC line from a male adolescent with first-episode of sporadic schizophrenia.},
journal = {Stem cell research},
volume = {85},
number = {},
pages = {103694},
doi = {10.1016/j.scr.2025.103694},
pmid = {40086264},
issn = {1876-7753},
mesh = {Humans ; *Induced Pluripotent Stem Cells/metabolism/cytology/pathology ; Male ; Kruppel-Like Factor 4 ; *Schizophrenia/pathology/metabolism ; Adolescent ; Cell Differentiation ; Cell Line ; Animals ; Cellular Reprogramming ; Leukocytes, Mononuclear/metabolism/cytology ; Mice ; },
abstract = {Schizophrenia is considered to be a neurodevelopmental disorder with high heritability. In this study, peripheral blood mononuclear cells (PBMCs) were collected from a male adolescent diagnosed with first-episode of sporadic schizophrenia. Induced pluripotent stem cells (iPSCs) were generated by reprogramming using the factors OCT4, SOX2, NANOG, LIN28, c-MYC, KLF4, and SV40LT. The generated iPSC line was validated by karyotype analysis and expression of pluripotency markers. These iPSCs were capable of differentiating into derivatives of all three germ layers in vivo.},
}
@article {pmid40085468,
year = {2025},
author = {Chen, J and Yang, H and Xia, Y and Gong, T and Thomas, A and Liu, J and Chen, W and Carlson, T and Zhao, H},
title = {Simultaneous Mental Fatigue and Mental Workload Assessment With Wearable High-Density Diffuse Optical Tomography.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {1242-1251},
pmid = {40085468},
issn = {1558-0210},
support = {/WT_/Wellcome Trust/United Kingdom ; },
mesh = {Humans ; *Tomography, Optical/methods/instrumentation ; *Mental Fatigue/diagnostic imaging/psychology/diagnosis ; Male ; Adult ; *Workload/psychology ; *Wearable Electronic Devices ; Machine Learning ; Female ; Brain-Computer Interfaces ; Spectroscopy, Near-Infrared ; Young Adult ; Reproducibility of Results ; Support Vector Machine ; Algorithms ; Brain/diagnostic imaging ; Imaging, Three-Dimensional ; },
abstract = {Accurately assessing mental states-such as mental workload and fatigue- is crucial for ensuring the reliability and effectiveness of brain-computer interface (BCI)-based applications. Relying on signals from a limited brain region with low spatial resolution may fail to capture the full scope of relevant information. To address this, high-density diffuse optical tomography (HD-DOT), an emerging form of functional near-infrared spectroscopy (fNIRS) was employed in this study, which provides higher spatial resolution for hemodynamic measurements and enables the reconstruction of 3D brain images. An experiment protocol was designed to investigate both mental workload and fatigue, two critical components of cognitive state that often fluctuate concurrently in real-world scenarios. Machine learning methods were applied for subject-specific classification, achieving 95.14% mean accuracy for fatigue/non-fatigue and 97.93% for four n-back tasks using Random Forest, outperforming Support Vector Machines. These results highlight the transformative potential of HD-DOT in advancing multifaceted cognitive state assessment, paving the way for more precise, adaptable, and powerful BCI applications.},
}
@article {pmid40085464,
year = {2025},
author = {Zhang, C and Pan, W and Santina, CD},
title = {NiSNN-A: Noniterative Spiking Neural Network With Attention With Application to Motor Imagery EEG Classification.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {36},
number = {8},
pages = {14298-14312},
doi = {10.1109/TNNLS.2025.3538335},
pmid = {40085464},
issn = {2162-2388},
mesh = {*Electroencephalography/classification/methods ; Humans ; *Neural Networks, Computer ; *Imagination/physiology ; Algorithms ; *Attention/physiology ; Neurons/physiology ; Brain/physiology ; Deep Learning ; Brain-Computer Interfaces ; },
abstract = {Motor imagery (MI), an important category in electroencephalogram (EEG) research, often intersects with scenarios demanding low energy consumption, such as portable medical devices and isolated environment operations. Traditional deep learning (DL) algorithms, despite their effectiveness, are characterized by significant computational demands accompanied by high energy usage. As an alternative, spiking neural networks (SNNs), inspired by the biological functions of the brain, emerge as a promising energy-efficient solution. However, SNNs typically exhibit lower accuracy than their counterpart convolutional neural networks (CNNs). Although attention mechanisms successfully increase network accuracy by focusing on relevant features, their integration in the SNN framework remains an open question. In this work, we combine the SNN and the attention mechanisms for the EEG classification, aiming to improve precision and reduce energy consumption. To this end, we first propose a noniterative leaky integrate-and-fire (NiLIF) neuron model, overcoming the gradient issues in traditional SNNs that use iterative LIF neurons for long time steps. Then, we introduce the sequence-based attention mechanisms to refine the feature map. We evaluated the proposed noniterative SNN with attention (NiSNN-A) model on two MI EEG datasets, OpenBMI and BCIC IV 2a. Experimental results demonstrate that: 1) our model outperforms other SNN models by achieving higher accuracy and 2) our model increases energy efficiency compared with the counterpart CNN models (i.e., by 2.13 times) while maintaining comparable accuracy.},
}
@article {pmid40084138,
year = {2025},
author = {Kaiju, T and Inoue, M and Hirata, M and Suzuki, T},
title = {Compact and low-power wireless headstage for electrocorticography recording of freely moving primates in a home cage.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1491844},
pmid = {40084138},
issn = {1662-4548},
abstract = {OBJECTIVE: Wireless electrocorticography (ECoG) recording from unrestrained nonhuman primates during behavioral tasks is a potent method for investigating higher-order brain functions over extended periods. However, conventional wireless neural recording devices have not been optimized for ECoG recording, and few devices have been tested on freely moving primates engaged in behavioral tasks within their home cages.<br><br>METHODS: We developed a compact, low-power, 32-channel wireless ECoG headstage specifically designed for neuroscience research. To evaluate its efficacy, we established a behavioral task setup within a home cage environment.<br><br>RESULTS: The developed headstage weighed merely 1.8 g and had compact dimensions of 25 mm &#xd7; 16 mm &#xd7; 4 mm. It was efficiently powered by a 100-mAh battery (weighing 3 g), enabling continuous recording for 8.5 h. The device successfully recorded data from an unrestrained monkey performing a center-out joystick task within its home cage.<br><br>CONCLUSION: The device demonstrated excellent capability for recording ECoG data from freely moving primates in a home cage environment. This versatile device enhances task design freedom, decrease researchers' workload, and enhances data collection efficiency.},
}
@article {pmid40083893,
year = {2025},
author = {Gordienko, Y and Gordienko, N and Taran, V and Rojbi, A and Telenyk, S and Stirenko, S},
title = {Effect of natural and synthetic noise data augmentation on physical action classification by brain-computer interface and deep learning.},
journal = {Frontiers in neuroinformatics},
volume = {19},
number = {},
pages = {1521805},
pmid = {40083893},
issn = {1662-5196},
abstract = {Analysis of electroencephalography (EEG) signals gathered by brain-computer interface (BCI) recently demonstrated that deep neural networks (DNNs) can be effectively used for investigation of time sequences for physical actions (PA) classification. In this study, the relatively simple DNN with fully connected network (FCN) components and convolutional neural network (CNN) components was considered to classify finger-palm-hand manipulations each from the grasp-and-lift (GAL) dataset. The main aim of this study was to imitate and investigate environmental influence by the proposed noise data augmentation (NDA) of two kinds: (i) natural NDA by inclusion of noise EEG data from neighboring regions by increasing the sampling size N and the different offset values for sample labeling and (ii) synthetic NDA by adding the generated Gaussian noise. The natural NDA by increasing N leads to the higher micro and macro area under the curve (AUC) for receiver operating curve values for the bigger N values than usage of synthetic NDA. The detrended fluctuation analysis (DFA) was applied to investigate the fluctuation properties and calculate the correspondent Hurst exponents H for the quantitative characterization of the fluctuation variability. H values for the low time window scales (< 2 s) are higher in comparison with ones for the bigger time window scales. For example, H more than 2-3 times higher for some PAs, i.e., it means that the shorter EEG fragments (< 2 s) demonstrate the scaling behavior of the higher complexity than the longer fragments. As far as these results were obtained by the relatively small DNN with the low resource requirements, this approach can be promising for porting such models to Edge Computing infrastructures on devices with the very limited computational resources.},
}
@article {pmid40083152,
year = {2025},
author = {Liu, H and Bai, Y and Zheng, Q and Zhao, R and Guo, M and Zhu, J and Ni, G},
title = {Effects of spatial separation and background noise on brain functional connectivity during auditory selective spatial attention.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {35},
number = {3},
pages = {},
doi = {10.1093/cercor/bhaf054},
pmid = {40083152},
issn = {1460-2199},
support = {2022BKY056//Tianjin Research Innovation Project for Postgraduate Students/ ; 2023YFF1203500//National Key Research and Development Program of China/ ; },
mesh = {Humans ; Male ; *Attention/physiology ; Female ; *Brain/physiology/diagnostic imaging ; *Auditory Perception/physiology ; Young Adult ; Adult ; Acoustic Stimulation ; Noise ; Brain Mapping ; *Space Perception/physiology ; Magnetic Resonance Imaging ; Neural Pathways/physiology ; Signal-To-Noise Ratio ; },
abstract = {Auditory selective spatial attention (ASSA) plays an important role in "cocktail party" scenes, but the effects of spatial separation between target and distractor sources and background noise on the associated brain responses have not been thoroughly investigated. This study utilized the multilayer time-varying brain network to reveal the effect patterns of different separation degrees and signal-to-noise ratio (SNR) levels on brain functional connectivity during ASSA. Specifically, a multilayer time-varying brain network with six time-windows equally divided by each epoch was constructed to investigate the segregation and integration of brain functional connectivity. The results showed that the inter-layer connectivity strength was consistently lower than the intra-layer connectivity strength for various separation degrees and SNR levels. Moreover, the connectivity strength of the multilayer time-varying brain network increased with decreasing separation degrees and initially increased and subsequently decreased with decreasing SNR levels. The second time-window of the network showed the most significant variation under some conditions and was determined as the core layer. The topology within the core layer was mainly reflected in the connectivity between the frontal and parietal-occipital cortices. In conclusion, these results suggest that spatial separation and background noise significantly modulate brain functional connectivity during ASSA.},
}
@article {pmid40083123,
year = {2025},
author = {Meng, M and Chen, G and Chen, S and Ma, Y and Gao, Y and Luo, Z},
title = {DGPDR: discriminative geometric perception dimensionality reduction of SPD matrices on Riemannian manifold for EEG classification.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-11},
doi = {10.1080/10255842.2025.2476184},
pmid = {40083123},
issn = {1476-8259},
abstract = {Manifold learning with Symmetric Positive Definite (SPD) matrices has demonstrated potential for classifying Electroencephalography (EEG) in Brain-Computer Interface (BCI) applications. However, SPD matrices may lead to crucial information loss of EEG signals. This paper proposes a dimensionality reduction method based on discriminative geometric perception on the Riemannian manifold to enhance SPD matrix discriminability. Experiments on BCI Competition IV Dataset 1 and Dataset 2a show the proposed method improves accuracy by 5.0% and 19.38% respectively, demonstrating that applying discriminative geometric perception can effectively maintain robust performance associated with the dimensionality-reduced SPD matrix.},
}
@article {pmid40082683,
year = {2025},
author = {Verwoert, M and Amigó-Vega, J and Gao, Y and Ottenhoff, MC and Kubben, PL and Herff, C},
title = {Whole-brain dynamics of articulatory, acoustic and semantic speech representations.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {432},
pmid = {40082683},
issn = {2399-3642},
support = {17619//Nederlandse Organisatie voor Wetenschappelijk Onderzoek (Netherlands Organisation for Scientific Research)/ ; },
mesh = {Humans ; Male ; *Speech/physiology ; Female ; *Brain/physiology ; Adult ; Semantics ; Young Adult ; *Speech Acoustics ; },
abstract = {Speech production is a complex process that traverses several representations, from the meaning of spoken words (semantic), through the movement of articulatory muscles (articulatory) and, ultimately, to the produced audio waveform (acoustic). In this study, we identify how these different representations of speech are spatially and temporally distributed throughout the depth of the brain. Intracranial neural data is recorded from 15 participants, across 1647 electrode contacts, while overtly speaking 100 unique words. We find a bilateral spatial distribution for all three representations, with a more widespread and temporally dynamic distribution in the left compared to the right hemisphere. The articulatory and acoustic representations share a similar spatial distribution surrounding the Sylvian fissure, while the semantic representation is more widely distributed across the brain in a mostly distinct network. These results highlight the distributed nature of the speech production neural process and the potential of non-motor representations for speech brain-computer interfaces.},
}
@article {pmid40082601,
year = {2025},
author = {Wang, Y and Yang, Z and Shi, X and Han, H and Li, AN and Zhang, B and Yuan, W and Sun, YH and Li, XM and Lian, H and Li, MD},
title = {Investigating the effect of Arvcf reveals an essential role on regulating the mesolimbic dopamine signaling-mediated nicotine reward.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {429},
pmid = {40082601},
issn = {2399-3642},
mesh = {Animals ; *Reward ; *Nicotine/pharmacology ; *Dopamine/metabolism ; Dopaminergic Neurons/metabolism/drug effects ; Mice ; *Signal Transduction/drug effects ; Ventral Tegmental Area/metabolism/drug effects ; Male ; Nucleus Accumbens/metabolism/drug effects ; Mice, Knockout ; Mice, Inbred C57BL ; },
abstract = {The mesolimbic dopamine system is crucial for drug reinforcement and reward learning, leading to addiction. We previously demonstrated that Arvcf was associated significantly with nicotine and alcohol addiction through genome-wide association studies. However, the role and mechanisms of Arvcf in dopamine-mediated drug reward processes were largely unknown. In this study, we first showed that Arvcf mediates nicotine-induced reward behavior by using conditioned place preference (CPP) model on Arvcf-knockout (Arvcf-KO) animal model. Then, we revealed that Arvcf was mainly expressed in VTA dopaminergic neurons whose expression could be upregulated by nicotine treatment. Subsequently, our SnRNA-seq analysis revealed that Arvcf was directly involved in dopamine biosynthesis in VTA dopaminergic neurons. Furthermore, we found that Arvcf-KO led to a significant reduction in both the dopamine synthesis and release in the nucleus accumbens (NAc) on nicotine stimulation. Specifically, we demonstrated that inhibition of Arvcf in VTA dopaminergic neurons decreased dopamine release within VTA-NAc circuit and suppressed nicotine reward-related behavior, while overexpression of Arvcf led to the opposite results. Taken together, these findings highlight the role of Arvcf in regulating dopamine signaling and reward learning, and its enhancement of dopamine release in the VTA-NAc circuit as a novel mechanism for nicotine reward.},
}
@article {pmid40082534,
year = {2025},
author = {Muniyandi, AP and Padmanandam, K and Subbaraj, K and Khadidos, AO and Khadidos, AO and Deepa, N and Selvarajan, S},
title = {An intelligent emotion prediction system using improved sand cat optimization technique based on EEG signals.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {8782},
pmid = {40082534},
issn = {2045-2322},
mesh = {Humans ; Algorithms ; Brain/physiology ; *Electroencephalography/methods ; *Emotions/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {Emotion recognition and prediction plays a vital role in human-computer interaction (HCI), offering more potential for efficient intuitive and adaptive systems. This presents an innovative and efficient approach for emotion prediction from electroencephalogram (EEG) signals by using an Improved Sand Cat Optimization (ISCO) technique to enhance prediction accuracy and efficiency. EEG signals directly indicates the brain activity and these signals are rich and reliable source of data for capturing emotional states. The proposed method is improved by adapting the Cat movement which uses convex lens opposition based learning technique and this will enhance the Cat movement towards target. The proposed method converges to target identification quickly for achieving efficient emotion prediction by extending the exiting Sand Cat Optimization algorithm. The algorithm has been evaluated by using openly available EEG signals dataset, which contains 2132 labelled records of three categories of emotional classes. The performance of the proposed method is compared with other nature inspired optimization algorithms such as Practical Swam Optimization (PSO), Artificial Rabbit Optimization (ARO), Artificial Bee Colony Optimization (ABCO), and Cat Optimization (CO) algorithm. The experimental evaluation shows that the proposed technique outperforms and showcases significant improvements in emotion prediction with accuracy of 97.5% compared to the other bioinspired optimization techniques. This research article has a scope to contribute to the advancement of emotion prediction system in the field of mental health care monitoring, HCI systems, gaming systems, and affective computing.},
}
@article {pmid40081769,
year = {2025},
author = {Yin, Y and Cao, Y and Zhou, Y and Xu, Z and Luo, P and Yang, B and He, Q and Yan, H and Yang, X},
title = {Downregulation of DDIT4 levels with borneol attenuates hepatotoxicity induced by gilteritinib.},
journal = {Biochemical pharmacology},
volume = {236},
number = {},
pages = {116869},
doi = {10.1016/j.bcp.2025.116869},
pmid = {40081769},
issn = {1873-2968},
mesh = {*Chemical and Drug Induced Liver Injury/metabolism/prevention &amp; control/drug therapy ; Animals ; *Down-Regulation/drug effects/physiology ; Humans ; *Aniline Compounds/toxicity ; *Pyrazines/toxicity ; *Transcription Factors/antagonists &amp; inhibitors/metabolism/genetics ; Male ; Mice ; Dose-Response Relationship, Drug ; Hepatocytes/drug effects/metabolism ; Protein Kinase Inhibitors/toxicity ; Hep G2 Cells ; Apoptosis/drug effects ; },
abstract = {Gilteritinib, a multi-target kinase inhibitor, is currently used as standard therapy for acute myeloid leukemia. However, approximately half of the patients encounter liver-related adverse effects during the treatment with gilteritinib, which limiting its clinical applications. The underlying mechanisms of gilteritinib-induced hepatotoxicity and the development of strategies to prevent this toxicity are not well-reported. In our study, we utilized JC-1 dye, and MitoSOX to demonstrate that gilteritinib treatment leads to hepatocytes undergoing p53-mediated mitochondrial apoptosis. Furthermore, qRT-PCR analysis revealed that DNA damage-inducible transcript 4 (DDIT4), a downstream target of p53, was upregulated following gilteritinib administration and was identified as a key factor in gilteritinib-induced hepatotoxicity. After drug screening and western blot analysis, borneol, a bicyclic monoterpenoid, was found to decrease the protein level of DDIT4. This is the first compound found to downregulate DDIT4 levels and ameliorate hepatic injury caused by gilteritinib. Our findings suggest that high levels of DDIT4 are the primary driver behind gilteritinib-induced liver injury, and that borneol could potentially be a clinically safe and feasible therapeutic strategy by inhibiting DDIT4 levels.},
}
@article {pmid40081503,
year = {2025},
author = {Xu, F and Lou, Y and Deng, Y and Lun, Z and Zhao, P and Yan, D and Han, Z and Wu, Z and Feng, C and Chen, L and Leng, J},
title = {Motor imagery EEG decoding based on TS-former for spinal cord injury patients.},
journal = {Brain research bulletin},
volume = {224},
number = {},
pages = {111298},
doi = {10.1016/j.brainresbull.2025.111298},
pmid = {40081503},
issn = {1873-2747},
mesh = {Humans ; *Electroencephalography/methods ; *Spinal Cord Injuries/physiopathology/rehabilitation ; *Imagination/physiology ; Neural Networks, Computer ; Adult ; Machine Learning ; Male ; Brain-Computer Interfaces ; Female ; Signal Processing, Computer-Assisted ; Middle Aged ; Motor Activity/physiology ; },
abstract = {Traditional machine learning methods struggle with efficiency when processing large-scale data, while deep learning approaches, such as convolutional neural networks (CNN) and long short-term memory networks (LSTM), exhibit certain limitations when handling long-duration sequences. The choice of convolutional kernel size needs to be determined after several experiments, and LSTM has difficulty capturing effective information from long-time sequences. In this paper, we propose a transfer learning (TL) method based on Transformer, which constructs a new network architecture for feature extraction and classification of electroencephalogram (EEG) signals in the time-space domain, named TS-former. The frequency and spatial domain information of EEG signals is extracted using the Filter Bank Common Spatial Pattern (FBCSP), and the resulting features are subsequently processed by the Transformer to capture temporal patterns. The input features are processed by the Transformer using a multi-head attention mechanism, and the final classification outputs are generated through a fully connected layer. A classification model is pre-trained using fine-tuning techniques. When performing a new classification task, only some layers of the model are modified to adapt it to the new data and achieve good classification results. The experiments are conducted on a motor imagery (MI) EEG dataset from 16 spinal cord injury (SCI) patients. After training the model using a ten-time ten-fold cross-validation method, the average classification accuracy reached 95.09 %. Our experimental results confirm a new approach to build a brain-computer interface (BCI) system for rehabilitation training of SCI patients.},
}
@article {pmid40079091,
year = {2025},
author = {Zhang, Y and Coid, J},
title = {Testing syndemic models along pathways to psychotic spectrum disorder: implications for population-level preventive interventions.},
journal = {Psychological medicine},
volume = {55},
number = {},
pages = {e85},
pmid = {40079091},
issn = {1469-8978},
mesh = {Humans ; Male ; Adult ; Cross-Sectional Studies ; *Psychotic Disorders/epidemiology/prevention &amp; control/etiology/psychology ; Middle Aged ; *Syndemic ; *Substance-Related Disorders/epidemiology/psychology ; *Adverse Childhood Experiences/statistics &amp; numerical data ; *Violence/statistics &amp; numerical data/psychology ; Young Adult ; *Sexual Behavior/statistics &amp; numerical data ; United Kingdom/epidemiology ; Adolescent ; Crime/statistics &amp; numerical data/psychology ; },
abstract = {BACKGROUND: Population-level preventive interventions are urgently needed and may be effective for psychosis due to social determinants. We tested three syndemic models along pathways from childhood adversity (CA) to psychotic spectrum disorder (PSD) and their implications for prevention.<br><br>METHODS: Cross-sectional data from 7461 British men surveyed in 5 population subgroups. We tested interactions on both additive and multiplicative scales for a syndemic of violence/criminality (VC), sexual behavior (SH), and substance misuse (SM) according to the presence of CA and adult traumatic life events; mediation analysis of path models; and partial least squares path modeling, with PSD as outcome.<br><br>RESULTS: Multiplicative synergistic interactions were found between VC, SH, and SM among men, who experienced CA and traumatic adult life events. However, when disaggregated, only SM mediated the pathway from CA to PSD. Path modeling showed traumatic life events acted on PSD through the syndemic and had no direct effect on PSD. Higher syndemic scores and living in areas of deprivation characterized men with PSD and CA.<br><br>CONCLUSIONS: Our findings support a broad division of PSD into cases due to (i) biological/inherent causes, and (ii) social determinants, the latter including a syndemic pathway determined by CA. Preventive strategies should focus primarily on preventing adverse effects of CA on developmental pathways which result in PSD. Single component prevention strategies may prevent triggering effects of SM on PSD during adolescence/early adulthood among vulnerable individuals due to CA. Future research should determine applicability and transferability of interventions based on these findings to different populations, specifically those experiencing syndemics.},
}
@article {pmid40078487,
year = {2025},
author = {Li, M and Yu, P and Shen, Y},
title = {A spatial and temporal transformer-based EEG emotion recognition in VR environment.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1517273},
pmid = {40078487},
issn = {1662-5161},
abstract = {With the rapid development of deep learning, Electroencephalograph(EEG) emotion recognition has played a significant role in affective brain-computer interfaces. Many advanced emotion recognition models have achieved excellent results. However, current research is mostly conducted in laboratory settings for emotion induction, which lacks sufficient ecological validity and differs significantly from real-world scenarios. Moreover, emotion recognition models are typically trained and tested on datasets collected in laboratory environments, with little validation of their effectiveness in real-world situations. VR, providing a highly immersive and realistic experience, is an ideal tool for emotional research. In this paper, we collect EEG data from participants while they watched VR videos. We propose a purely Transformer-based method, EmoSTT. We use two separate Transformer modules to comprehensively model the temporal and spatial information of EEG signals. We validate the effectiveness of EmoSTT on a passive paradigm collected in a laboratory environment and an active paradigm emotion dataset collected in a VR environment. Compared with state-of-the-art methods, our method achieves robust emotion classification performance and can be well transferred between different emotion elicitation paradigms.},
}
@article {pmid40074408,
year = {2025},
author = {Zhang, C and Pu, Y and Kong, XZ},
title = {Latent dimensions of brain asymmetry.},
journal = {Handbook of clinical neurology},
volume = {208},
number = {},
pages = {37-45},
doi = {10.1016/B978-0-443-15646-5.00027-0},
pmid = {40074408},
issn = {0072-9752},
mesh = {Humans ; *Brain/physiology/diagnostic imaging/anatomy &amp; histology ; *Functional Laterality/physiology ; },
abstract = {Functional lateralization represents a fundamental aspect of brain organization, where certain cognitive functions are specialized in one hemisphere over the other. Deviations from typical patterns of lateralization often manifest in various brain disorders, such as autism spectrum disorder, schizophrenia, and dyslexia. However, despite its importance, uncovering the intrinsic properties of brain lateralization and its underlying structural basis remains challenging. On the one hand, functional lateralization has long been oversimplified, often reduced to a unidimensional perspective. For instance, individuals are sometimes labeled as left-brained or right-brained based on specific behavioral measures like handedness and language lateralization. Such a perspective disregards the nuanced subtypes of lateralization, each potentially attributed to distinct factors and associated with unique functional correlates. On the other hand, traditional studies of brain structural asymmetry have typically focused on localized analyses of homologous regions in the two hemispheres. This perspective fails to capture the inherent interplay between brain regions, resulting in an overly complex depiction of structural asymmetry. Such conceptual and methodological discrepancies between studies of functional lateralization and structural asymmetry pose significant obstacles to establishing meaningful links between them. To address this gap, a shift toward uncovering the dimensional structure of brain asymmetry has been proposed. This chapter introduces the concept of latent dimensions of brain asymmetry and provides an up-to-date overview of studies regarding dimensions of functional lateralization and structural asymmetry in the human brain. By transcending the traditional analysis and employing multivariate pattern techniques, these studies offer valuable insights into our understanding of the intricate organizational principles governing the human brain's lateralized functions.},
}
@article {pmid40073454,
year = {2025},
author = {Pei, Y and Zhao, S and Xie, L and Ji, B and Luo, Z and Ma, C and Gao, K and Wang, X and Sheng, T and Yan, Y and Yin, E},
title = {Toward the enhancement of affective brain-computer interfaces using dependence within EEG series.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adbfc0},
pmid = {40073454},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Male ; Adult ; Female ; *Emotions/physiology ; Young Adult ; *Affect/physiology ; *Brain/physiology ; },
abstract = {In recent years, electroencephalogram (EEG)-based affective brain-computer interfaces (aBCI) has made remarkable advances.Objective. However, a subtle but crucial problem caused by the sliding window method has long been overlooked, which is the serious quantity mismatch between stimuli and short-term EEG frames. This may be an important factor limiting the performance of aBCIs.Approach. We refer to this mismatch as the quantity-independence imbalance (Q/I imbalance) and we propose the weak independence hypothesis to explain the mismatch. To validate this hypothesis and explore the effects of the Q/I imbalance on short-term EEG frames, we design four experiments from four perspectives, which are visualization, cross-validation, randomness test, and redundancy test.Main results. Inspired by validation experiments, we propose an inference correction (IC) method to enhancing the emotional predictions by leveraging the majority of the classifier's outputs. The proposed IC method is evaluated on two datasets involving 60 subjects using both intra-subject and inter-subject validation protocols. Our IC achieves a significant improvement of 14.97% in classification accuracy.Significance. This study promotes the understanding of the time-dependent nature of EEG signals in aBCI.},
}
@article {pmid40073451,
year = {2025},
author = {Dong, Y and Zheng, L and Pei, W and Gao, X and Wang, Y},
title = {A 240-target VEP-based BCI system employing narrow-band random sequences.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adbfc1},
pmid = {40073451},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; Male ; Adult ; Female ; *Photic Stimulation/methods ; Young Adult ; Neural Networks, Computer ; Algorithms ; },
abstract = {Objective.In the field of brain-computer interface (BCI), achieving high information transfer rates (ITR) with a large number of targets remains a challenge. This study aims to address this issue by developing a novel code-modulated visual evoked potential (c-VEP) BCI system capable of handling an extensive instruction set while maintaining high performance.Approach. We propose a c-VEP BCI system that employs narrow-band random sequences as visual stimuli and utilizes a convolutional neural network (CNN)-based EEG2Code decoding algorithm. This algorithm predicts corresponding stimulus sequences from EEG data and achieves efficient and accurate classification.Main results.Offline experiments which conducted in a sequential paradigm, resulted in an average accuracy of 87.66% and a simulated ITR of 260.14 bits/min. In online experiments, the system demonstrated an accuracy of 76.27% and an ITR of 213.80 bits/min in a cued spelling task.Significance.This work represents an advancement in c-VEP BCI systems, offering one of the largest known instruction set in VEP-based BCIs and demonstrating robust performance metrics. The proposed system is potential for more practical and efficient BCI applications.},
}
@article {pmid40072857,
year = {2025},
author = {Ji, D and Huang, Y and Chen, Z and Zhou, X and Wang, J and Xiao, X and Xu, M and Ming, D},
title = {Enhanced Spatial Division Multiple Access BCI Performance via Incorporating MEG With EEG.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {1202-1211},
doi = {10.1109/TNSRE.2025.3550653},
pmid = {40072857},
issn = {1558-0210},
mesh = {Humans ; *Electroencephalography/methods ; *Magnetoencephalography/methods ; *Brain-Computer Interfaces ; Algorithms ; Male ; Adult ; Female ; Young Adult ; Signal Processing, Computer-Assisted ; Reproducibility of Results ; Photic Stimulation ; },
abstract = {Spatial division multiple access (SDMA) is a way of encoding BCI systems based on spatial distribution of brain signal characteristics. However, SDMA-BCI based on EEG had poor system performance limited by spatial resolution. MEG-EEG fusion modality analysis can help solve this problem. According retina-cortical relationship, this study used stimulus out of the central visual field and tiny fixation points to construct a 16-command SDMA coded MEG-EEG fusion modality BCI system. We achieved this by synchronously acquiring MEG and EEG signals from 10 subjects. We compared the spatiotemporal features between MEG and EEG by analyzing signals in the occipital region. We fused MEG and EEG modalities without any signal processing and used the multi-class discriminative canonical pattern matching (Multi-DCPM) algorithm to evaluate and compare the system performance of EEG, MEG, and MEG-EEG fusion modalities. The result showed that MEG and EEG had obvious differences in spatial distribution characteristics. MEG improves offline classification accuracy of the 16 fixation points by 27.81% over EEG at 4s data length. Specially, the MEG-EEG fusion modality achieves an impressive average offline accuracy of 91.71%, which was a significant improvement over MEG (p<0.01, ANOVA). The MEG-EEG fusion modality achieved average information transfer rate (ITR) of 60.74 bits/min with a data length of 1 s, which was a 14% improvement over MEG. The MEG-EEG fusion modality significantly enhanced the spatial features and performance of SDMA-encoded BCIs. These results highlight the potential and feasibility of MEG-EEG fusion modality BCI, and provide theoretical insights and practical value for promoting the further development and application of SDMA in BCI.},
}
@article {pmid40072172,
year = {2025},
author = {Zhang, C and Zhang, C and Liu, Y},
title = {Progress in the Development of Flexible Devices Utilizing Protein Nanomaterials.},
journal = {Nanomaterials (Basel, Switzerland)},
volume = {15},
number = {5},
pages = {},
pmid = {40072172},
issn = {2079-4991},
support = {52473014//National Natural Science Foundation of China/ ; },
abstract = {Flexible devices are soft, lightweight, and portable, making them suitable for large-area applications. These features significantly expand the scope of electronic devices and demonstrate their unique value in various fields, including smart wearable devices, medical and health monitoring, human-computer interaction, and brain-computer interfaces. Protein materials, due to their unique molecular structure, biological properties, sustainability, self-assembly ability, and good biocompatibility, can be applied in electronic devices to significantly enhance the sensitivity, stability, mechanical strength, energy density, and conductivity of the devices. Protein-based flexible devices have become an important research direction in the fields of bioelectronics and smart wearables, providing new material support for the development of more environmentally friendly and reliable flexible electronics. Currently, many proteins, such as silk fibroin, collagen, ferritin, and so on, have been used in biosensors, memristors, energy storage devices, and power generation devices. Therefore, in this paper, we provide an overview of related research in the field of protein-based flexible devices, including the concept and characteristics of protein-based flexible devices, fabrication materials, fabrication processes, characterization, and evaluation, and we point out the future development direction of protein-based flexible devices.},
}
@article {pmid40071135,
year = {2025},
author = {Kobayashi, N and Ino, M},
title = {Parameter optimization of 3D convolutional neural network for dry-EEG motor imagery brain-machine interface.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1469244},
pmid = {40071135},
issn = {1662-4548},
abstract = {Easing the behavioral restrictions of those in need of care not only improves their own quality of life (QoL) but also reduces the burden on care workers and may help reduce the number of care workers in countries with declining birthrates. The brain-machine interface (BMI), in which appliances and machines are controlled only by brain activity, can be used in nursing care settings to alleviate behavioral restrictions and reduce stress for those in need of care. It is also expected to reduce the workload of care workers. In this study, we focused on motor imagery (MI) classification by deep-learning to construct a system that can identify MI obtained by electroencephalography (EEG) measurements with high accuracy and a low latency response. By completing the system on the edge, the privacy of personal MI data can be ensured, and the system is ubiquitous, which improves user convenience. On the other hand, however, the edge is limited by hardware resources, and the implementation of models with a huge number of parameters and high computational cost, such as deep-learning, on the edge is challenging. Therefore, by optimizing the MI measurement conditions and various parameters of the deep-learning model, we attempted to reduce the power consumption and improve the response latency of the system by minimizing the computational cost while maintaining high classification accuracy. In addition, we investigated the use of a 3-dimension convolutional neural network (3D CNN), which can retain spatial locality as a feature to further improve the classification accuracy. We propose a method to maintain a high classification accuracy while enabling processing on the edge by optimizing the size and number of kernels and the layer structure. Furthermore, to develop a practical BMI system, we introduced dry electrodes, which are more comfortable for daily use, and optimized the number of parameters and memory consumption size of the proposed model to maintain classification accuracy even with fewer electrodes, less recall time, and a lower sampling rate. Compared to EEGNet, the proposed 3D CNN reduces the number of parameters, the number of multiply-accumulates, and memory footprint by approximately 75.9%, 16.3%, and 12.5%, respectively, while maintaining the same level of classification accuracy with the conditions of eight electrodes, 3.5 seconds sample window size, and 125 Hz sampling rate in 4-class dry-EEG MI.},
}
@article {pmid40070670,
year = {2025},
author = {Pan, H and Tang, C and Song, C and Li, J},
title = {Analysis of clinical efficacy of sacral magnetic stimulation for the treatment of detrusor underactivity.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1499310},
pmid = {40070670},
issn = {1664-2295},
abstract = {OBJECTIVE: The objective of this study was to investigate the effectiveness and safety of sacral magnetic stimulation (SMS) in the management of detrusor underactivity (DU).<br><br>METHODS: We retrospectively analyzed 66 patients with detrusor underactivity treated at Hangzhou Third People's Hospital from January 2020 to October 2024, divided into two groups (33 cases each). Both groups had confirmed detrusor underactivity via urodynamic studies. The control group received conventional treatment (medication, catheterization, bladder training), while the observation group received SMS therapy. Urination diaries, urodynamic parameters and self-rating anxiety scale (SAS) were collected before and after the 4-week treatment to evaluate SMS efficacy and safety.<br><br>RESULTS: All patients in the observation group completed the course of sacral magnetic stimulation without experiencing any serious complications. After treatment, the observation group showed a significant reduction in the number of daily urinations, nocturnal urinations, SAS score and residual urine volume (RUV) (p <&#x202f;0.05) compared with the control group. There was no statistically significant difference in maximum cystometric capacity (MCC) (p >&#x202f;0.05). However, improvements were observed in SAS score, Detrusor Pressure at Maximum Flow (Pdet), Bladder Contractility Index (BCI), Maximum urinary Flow Rate (Qmax) and Average Urinary Flow Rate (Qavg) (p <&#x202f;0.05). The effective rate in the observation group was 78.78%, significantly higher than that in the control group (p <&#x202f;0.05). Although there was a slight decrease in the effective rate during the 6-month follow-up, the difference was not statistically significant (p >&#x202f;0.05).<br><br>CONCLUSION: In conclusion, sacral magnetic stimulation therapy has demonstrated effectiveness in improving urinary function in patients with detrusor underactivity while maintaining a high level of safety.},
}
@article {pmid40069360,
year = {2025},
author = {Cheng, M and Lu, D and Li, K and Wang, Y and Tong, X and Qi, X and Yan, C and Ji, K and Wang, J and Wang, W and Lv, H and Zhang, X and Kong, W and Zhang, J and Ma, J and Li, K and Wang, Y and Feng, J and Wei, P and Li, Q and Shen, C and Fu, XD and Ma, Y and Zhang, X},
title = {Mitochondrial respiratory complex IV deficiency recapitulates amyotrophic lateral sclerosis.},
journal = {Nature neuroscience},
volume = {28},
number = {4},
pages = {748-756},
pmid = {40069360},
issn = {1546-1726},
support = {2019YFA0508701//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; 2022YFA1303300//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; },
mesh = {*Amyotrophic Lateral Sclerosis/genetics/pathology/metabolism ; Animals ; Rats ; Motor Neurons/metabolism/pathology ; *Electron Transport Complex IV/genetics ; *Mitochondria/metabolism/genetics ; Humans ; Disease Models, Animal ; Mutation/genetics ; },
abstract = {Amyotrophic lateral sclerosis (ALS) is categorized into ~10% familial and ~90% sporadic cases. While familial ALS is caused by mutations in many genes of diverse functions, the underlying pathogenic mechanisms of ALS, especially in sporadic ALS (sALS), are largely unknown. Notably, about half of the cases with sALS showed defects in mitochondrial respiratory complex IV (CIV). To determine the causal role of this defect in ALS, we used transcription activator-like effector-based mitochondrial genome editing to introduce mutations in CIV subunits in rat neurons. Our results demonstrate that neuronal CIV deficiency is sufficient to cause a number of ALS-like phenotypes, including cytosolic TAR DNA-binding protein 43 redistribution, selective motor neuron loss and paralysis. These results highlight CIV deficiency as a potential cause of sALS and shed light on the specific vulnerability of motor neurons, marking an important advance in understanding and therapeutic development of sALS.},
}
@article {pmid40068721,
year = {2025},
author = {Kopalli, SR and Shukla, M and Jayaprakash, B and Kundlas, M and Srivastava, A and Jagtap, J and Gulati, M and Chigurupati, S and Ibrahim, E and Khandige, PS and Garcia, DS and Koppula, S and Gasmi, A},
title = {Artificial intelligence in stroke rehabilitation: From acute care to long-term recovery.},
journal = {Neuroscience},
volume = {572},
number = {},
pages = {214-231},
doi = {10.1016/j.neuroscience.2025.03.017},
pmid = {40068721},
issn = {1873-7544},
mesh = {Humans ; *Stroke Rehabilitation/methods ; *Artificial Intelligence ; *Recovery of Function/physiology ; *Stroke/therapy/diagnosis ; Brain-Computer Interfaces ; },
abstract = {Stroke is a leading cause of disability worldwide, driving the need for advanced rehabilitation strategies. The integration of Artificial Intelligence (AI) into stroke rehabilitation presents significant advancements across the continuum of care, from acute diagnosis to long-term recovery. This review explores AI's role in stroke rehabilitation, highlighting its impact on early diagnosis, motor recovery, and cognitive rehabilitation. AI-driven imaging techniques, such as deep learning applied to CT and MRI scans, improve early diagnosis and identify ischemic penumbra, enabling timely, personalized interventions. AI-assisted decision support systems optimize acute stroke treatment, including thrombolysis and endovascular therapy. In motor rehabilitation, AI-powered robotics and exoskeletons provide precise, adaptive assistance, while AI-augmented Virtual and Augmented Reality environments offer immersive, tailored recovery experiences. Brain-Computer Interfaces utilize AI for neurorehabilitation through neural signal processing, supporting motor recovery. Machine learning models predict functional recovery outcomes and dynamically adjust therapy intensities. Wearable technologies equipped with AI enable continuous monitoring and real-time feedback, facilitating home-based rehabilitation. AI-driven tele-rehabilitation platforms overcome geographic barriers by enabling remote assessment and intervention. The review also addresses the ethical, legal, and regulatory challenges associated with AI implementation, including data privacy and technical integration. Future research directions emphasize the transformative potential of AI in stroke rehabilitation, with case studies and clinical trials illustrating the practical benefits and efficacy of AI technologies in improving patient recovery.},
}
@article {pmid40068108,
year = {2025},
author = {Li, Y and Li, H and Wang, H and Wang, X},
title = {Utilizing Caenorhabditis Elegans as a Rapid and Precise Model for Assessing Amphetamine-Type Stimulants: A Novel Approach to Evaluating New Psychoactive Substances Activity and Mechanisms.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {12},
number = {17},
pages = {e2500808},
pmid = {40068108},
issn = {2198-3844},
support = {T2341003//National Natural Science Foundation of China/ ; T2394480(T2394483)//National Natural Science Foundation of China/ ; 2021ZD0203000(2021ZD0203003)//STI2030-Major Projects/ ; 20240304115SF//Science and Technology Development Plan Project of Jilin Province/ ; XDB0450102//Strategic Priority Research Program of the Chinese Academy of Sciences/ ; BMI2400014//Open Research Fund of the State Key Laboratory of Brain-Machine Intelligence, Zhejiang University/ ; },
mesh = {Animals ; *Caenorhabditis elegans/drug effects ; *Central Nervous System Stimulants/pharmacology ; *Amphetamines/pharmacology ; *Amphetamine/pharmacology ; *Psychotropic Drugs/pharmacology ; Methamphetamine/pharmacology ; },
abstract = {The surge of new psychoactive substances (NPS) poses significant public health challenges due to their unregulated status and diverse effects. However, existing in vivo models for evaluating their activities are limited. To address this gap, this study utilizes the model organism Caenorhabditis elegans (C. elegans) to evaluate the activity of amphetamine-type stimulants (ATS) and their analogs. The swimming-induced paralysis (SWIP) assay is employed to measure the acute responses of C. elegans to various ATS, including amphetamine (AMPH), methamphetamine (METH), 3,4-methylenedioxymethamphetamine (MDMA) and their enantiomers. The findings reveal distinct responses in wild-type and mutant C. elegans, highlighting the roles of dopaminergic and serotonergic pathways, particularly DOP-3 and SER-4 receptors. The assay also revealed that C. elegans can distinguish between the chiral forms of ATS. Additionally, structural activity relationships (SAR) are observed, with meta-R amphetamines showing more pronounced effects than ortho-R and para-R analogs. This study demonstrates the utility of C. elegans in rapidly assessing ATS activity and toxicity, providing a cost-effective and precise method for high-throughput testing of NPS. These results contribute to a better understanding of ATS pharmacology and offer a valuable framework for future research and potential regulatory applications.},
}
@article {pmid40067734,
year = {2025},
author = {Wang, X and Qi, W and Yang, W and Wang, W},
title = {Cholesky Space for Brain-Computer Interfaces.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {36},
number = {8},
pages = {15424-15435},
doi = {10.1109/TNNLS.2025.3542801},
pmid = {40067734},
issn = {2162-2388},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Algorithms ; Imagination/physiology ; Emotions/physiology ; *Brain/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interfaces (BCIs) based on electroencephalogram (EEG) enable direct interactions between the brain and external environments, with applications in medical rehabilitation, motor substitution, gaming, and entertainment. Traditional methods that model the non-Euclidean characteristics of EEG signals demonstrate robustness and high performance, but they suffer from significant computational costs and are typically restricted to a single BCI paradigm. This article addresses these limitations by utilizing a diffeomorphism from Riemannian manifolds to the Cholesky space, which simplifies the solution process and enables application across multiple BCI paradigms. Our proposed Cholesky space-based model, CSNet, achieves state-of-the-art (SOTA) performance in motor imagery (MI) decoding and emotion recognition and demonstrates competitive performance in error-related negativity (ERN) decoding, all without the need for data preprocessing. Furthermore, our runtime comparison shows that the Cholesky space method is more efficient than the method based on the Riemannian manifold as the matrix dimension increases. To enhance the interpretability of CSNet, we perform t-distributed stochastic neighbor embedding (t-SNE) visualization for MI, frequency band energy visualization for emotion recognition, and temporal importance visualization for ERN. The results indicate that CSNet effectively learns discriminative features, identifies important frequency bands, and focuses on important temporal features. The CSNet effectively captures the non-Euclidean characteristics of EEG signals across various BCI paradigms, while mitigating high computational costs, making it a promising candidate for future BCI algorithms. The code for this study is publicly available at: https://github.com/XingfuWang/CSNet.},
}
@article {pmid40067717,
year = {2025},
author = {Du, Y and Chen, J and Liu, Z and Wong, N and Zhang, C and Ding, Z and Liu, J and Ngai, ECH},
title = {Valence-Arousal Disentangled Representation Learning for Emotion Recognition in SSVEP-Based BCIs.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {7},
pages = {4820-4833},
doi = {10.1109/JBHI.2025.3549727},
pmid = {40067717},
issn = {2168-2208},
mesh = {Humans ; *Emotions/physiology/classification ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Adult ; *Signal Processing, Computer-Assisted ; *Arousal/physiology ; Electroencephalography/methods ; Male ; Female ; *Machine Learning ; Young Adult ; Algorithms ; },
abstract = {Steady state visually evoked potential (SSVEP)-based brain-computer interfaces (BCIs), which are widely used in rehabilitation and disability assistance, can benefit from real-time emotion recognition to enhance human-machine interaction. However, the learned discri-minative latent representations in SSVEP-BCIs may generalize in an unintended direction, which can lead to reduced accuracy in detecting emotional states. In this paper, we introduce a Valence-Arousal Disentangled Representation Learning (VADL) method, drawing inspir-ation from the classical two-dimensional emotional model, to enhance the performance and generalization of emotion recognition within SSVEP-BCIs. VADL distinctly disentangles the latent variables of valence and arousal information to improve accuracy. It utilizes the structured state space duality model to thoroughly extract global emotional features. Additionally, we propose a Multisubject Gradient Blending training strategy that individually tailors the learning pace of reconstruction and discrimination tasks within VADL on-the-fly. To verify the feasibility of our method, we have developed a comprehensive database comprising 23 subjects, in which both the emotional states and SSVEPs were effectively elicited. Experimental results indicate that VADL surpasses existing state-of-the-art benchmark algorithms.},
}
@article {pmid40064104,
year = {2025},
author = {Rodríguez-García, ME and Carino-Escobar, RI and Carrillo-Mora, P and Hernandez-Arenas, C and Ramirez-Nava, AG and Pacheco-Gallegos, MDR and Valdés-Cristerna, R and Cantillo-Negrete, J},
title = {Neuroplasticity changes in cortical activity, grey matter, and white matter of stroke patients after upper extremity motor rehabilitation via a brain-computer interface therapy program.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adbebf},
pmid = {40064104},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Neuronal Plasticity/physiology ; *Stroke Rehabilitation/methods ; Female ; *Upper Extremity/physiopathology/physiology ; Middle Aged ; *White Matter/diagnostic imaging/physiopathology/physiology ; Aged ; *Stroke/physiopathology/diagnostic imaging ; Electroencephalography/methods ; *Gray Matter/diagnostic imaging/physiopathology/physiology ; Adult ; *Cerebral Cortex/diagnostic imaging/physiopathology/physiology ; Magnetic Resonance Imaging ; Diffusion Tensor Imaging ; },
abstract = {Objective. Upper extremity (UE) motor function loss is one of the most impactful consequences of stroke. Recently, brain-computer interface (BCI) systems have been utilized in therapy programs to enhance UE motor recovery after stroke, widely attributed to neuroplasticity mechanisms. However, the effect that the BCI's closed-loop feedback can have in these programs is unclear. The aim of this study was to quantitatively assess and compare the neuroplasticity effects elicited in stroke patients by a UE motor rehabilitation BCI therapy and by its sham-BCI counterpart.Approach. Twenty patients were randomly assigned to either the experimental group (EG), who controlled the BCI system via UE motor intention, or the control group (CG), who received random feedback. The elicited neuroplasticity effects were quantified using asymmetry metrics derived from electroencephalography (EEG), functional magnetic resonance imaging (fMRI), and diffusion tensor imaging (DTI) data acquired before, at the middle, and at the end of the intervention, alongside UE sensorimotor function evaluations. These asymmetry indexes compare the affected and unaffected hemispheres and are robust to lesion location variability.Main results. Most patients from the EG presented brain activity lateralisation to one brain hemisphere, as described by EEG (8 patients) and fMRI (6 patients) metrics. Conversely, the CG showed less pronounced lateralisations, presenting primarily bilateral activity patterns. DTI metrics showed increased white matter integrity in half of the EG patients' unaffected hemisphere, and in all but 2 CG patients' affected hemisphere. Individual patient analysis suggested that lesion location was relevant since functional and structural lateralisations occurred towards different hemispheres depending on stroke site.Significance. This study shows that a BCI intervention can elicit more pronounced neuroplasticity-related lateralisations than a sham-BCI therapy. These findings could serve as future biomarkers, helping to better select patients and increasing the impact that a BCI intervention can achieve. Clinical trial: NCT04724824.},
}
@article {pmid40064095,
year = {2025},
author = {Russo, JS and Shiels, TA and Lin, CS and John, SE and Grayden, DB},
title = {Feasibility of source-level motor imagery classification for people with multiple sclerosis.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adbec1},
pmid = {40064095},
issn = {1741-2552},
mesh = {Humans ; Male ; *Multiple Sclerosis/physiopathology/diagnosis ; *Imagination/physiology ; Female ; *Brain-Computer Interfaces ; Adult ; Middle Aged ; Feasibility Studies ; *Electroencephalography/methods/classification ; Movement/physiology ; },
abstract = {Objective.There is limited work investigating brain-computer interface (BCI) technology in people with multiple sclerosis (pwMS), a neurodegenerative disorder of the central nervous system. Present work is limited to recordings at the scalp, which may be significantly altered by changes within the cortex due to volume conduction. The recordings obtained from the sensors, therefore, combine disease-related alterations and task-relevant neural signals, as well as signals from other regions of the brain that are not relevant. The current study aims to unmix signals affected by multiple sclerosis (MS) progression and BCI task-relevant signals using estimated source activity to improve classification accuracy.Approach.Data was collected from eight participants with a range of MS severity and ten neurotypical participants. This dataset was used to report the classification accuracy of imagined movements of the hands and feet at the sensor-level and the source-level in the current study.K-means clustering of equivalent current dipoles was conducted to unmix temporally independent signals. The location of these dipoles was compared between MS and control groups and used for classification of imagined movement. Linear discriminant analysis classification was performed at each time-frequency point to highlight differences in frequency band delay.Main Results.Source-level signal acquisition significantly improved decoding accuracy of imagined movement vs rest and movement vs movement classification in pwMS and controls. There was no significant difference found in alpha (7-13 Hz) and beta (13-30 Hz) band classification delay between the neurotypical control and MS group, including imagery of limbs with weakness or paralysis.Significance.This study is the first to demonstrate the advantages of source-level analysis for BCI applications in pwMS. The results highlight the potential for enhanced clinical outcomes and emphasize the need for longitudinal studies to assess the impact of MS progression on BCI performance, which is crucial for effective clinical translation of BCI technology.},
}
@article {pmid40063703,
year = {2025},
author = {Ahmed, AAA and Alegret, N and Almeida, B and Alvarez-Puebla, R and Andrews, AM and Ballerini, L and Barrios-Capuchino, JJ and Becker, C and Blick, RH and Bonakdar, S and Chakraborty, I and Chen, X and Cheon, J and Chilla, G and Coelho Conceicao, AL and Delehanty, J and Dulle, M and Efros, AL and Epple, M and Fedyk, M and Feliu, N and Feng, M and Fernández-Chacón, R and Fernandez-Cuesta, I and Fertig, N and Förster, S and Garrido, JA and George, M and Guse, AH and Hampp, N and Harberts, J and Han, J and Heekeren, HR and Hofmann, UG and Holzapfel, M and Hosseinkazemi, H and Huang, Y and Huber, P and Hyeon, T and Ingebrandt, S and Ienca, M and Iske, A and Kang, Y and Kasieczka, G and Kim, DH and Kostarelos, K and Lee, JH and Lin, KW and Liu, S and Liu, X and Liu, Y and Lohr, C and Mailänder, V and Maffongelli, L and Megahed, S and Mews, A and Mutas, M and Nack, L and Nakatsuka, N and Oertner, TG and Offenhäusser, A and Oheim, M and Otange, B and Otto, F and Patrono, E and Peng, B and Picchiotti, A and Pierini, F and Pötter-Nerger, M and Pozzi, M and Pralle, A and Prato, M and Qi, B and Ramos-Cabrer, P and Genger, UR and Ritter, N and Rittner, M and Roy, S and Santoro, F and Schuck, NW and Schulz, F and Şeker, E and Skiba, M and Sosniok, M and Stephan, H and Wang, R and Wang, T and Wegner, KD and Weiss, PS and Xu, M and Yang, C and Zargarian, SS and Zeng, Y and Zhou, Y and Zhu, D and Zierold, R and Parak, WJ},
title = {Interfacing with the Brain: How Nanotechnology Can Contribute.},
journal = {ACS nano},
volume = {19},
number = {11},
pages = {10630-10717},
pmid = {40063703},
issn = {1936-086X},
support = {R01 MH111872/MH/NIMH NIH HHS/United States ; R01 MH094730/MH/NIMH NIH HHS/United States ; R01 DA045550/DA/NIDA NIH HHS/United States ; R03 NS118156/NS/NINDS NIH HHS/United States ; R21 AT010933/AT/NCCIH NIH HHS/United States ; R61 MH135106/MH/NIMH NIH HHS/United States ; },
mesh = {*Nanotechnology/methods ; Humans ; *Brain-Computer Interfaces ; *Brain/physiology ; Nanostructures/chemistry ; Animals ; },
abstract = {Interfacing artificial devices with the human brain is the central goal of neurotechnology. Yet, our imaginations are often limited by currently available paradigms and technologies. Suggestions for brain-machine interfaces have changed over time, along with the available technology. Mechanical levers and cable winches were used to move parts of the brain during the mechanical age. Sophisticated electronic wiring and remote control have arisen during the electronic age, ultimately leading to plug-and-play computer interfaces. Nonetheless, our brains are so complex that these visions, until recently, largely remained unreachable dreams. The general problem, thus far, is that most of our technology is mechanically and/or electrically engineered, whereas the brain is a living, dynamic entity. As a result, these worlds are difficult to interface with one another. Nanotechnology, which encompasses engineered solid-state objects and integrated circuits, excels at small length scales of single to a few hundred nanometers and, thus, matches the sizes of biomolecules, biomolecular assemblies, and parts of cells. Consequently, we envision nanomaterials and nanotools as opportunities to interface with the brain in alternative ways. Here, we review the existing literature on the use of nanotechnology in brain-machine interfaces and look forward in discussing perspectives and limitations based on the authors' expertise across a range of complementary disciplines─from neuroscience, engineering, physics, and chemistry to biology and medicine, computer science and mathematics, and social science and jurisprudence. We focus on nanotechnology but also include information from related fields when useful and complementary.},
}
@article {pmid40063028,
year = {2025},
author = {Ben-Zion, Z and Simon, AJ and Rosenblatt, M and Korem, N and Duek, O and Liberzon, I and Shalev, AY and Hendler, T and Levy, I and Harpaz-Rotem, I and Scheinost, D},
title = {Connectome-Based Predictive Modeling of PTSD Development Among Recent Trauma Survivors.},
journal = {JAMA network open},
volume = {8},
number = {3},
pages = {e250331},
pmid = {40063028},
issn = {2574-3805},
support = {R01 MH103287/MH/NIMH NIH HHS/United States ; },
mesh = {Humans ; *Stress Disorders, Post-Traumatic/diagnostic imaging/physiopathology/etiology/diagnosis ; Female ; Male ; Adult ; *Connectome/methods ; *Survivors/psychology ; Magnetic Resonance Imaging ; Middle Aged ; Longitudinal Studies ; Israel ; Prognosis ; Brain/diagnostic imaging/physiopathology ; },
abstract = {IMPORTANCE: The weak link between subjective symptom-based diagnostics for posttraumatic psychopathology and objective neurobiological indices hinders the development of effective personalized treatments.<br><br>OBJECTIVE: To identify early neural networks associated with posttraumatic stress disorder (PTSD) development among recent trauma survivors.<br><br>This prognostic study used data from the Neurobehavioral Moderators of Posttraumatic Disease Trajectories (NMPTDT) large-scale longitudinal neuroimaging dataset of recent trauma survivors. The NMPTDT study was conducted from January 20, 2015, to March 11, 2020, and included adult civilians who were admitted to a general hospital emergency department in Israel and screened for early PTSD symptoms indicative of chronic PTSD risk. Enrolled participants completed comprehensive clinical assessments and functional magnetic resonance imaging (fMRI) scans at 1, 6, and 14 months post trauma. Data were analyzed from September 2023 to March 2024.<br><br>EXPOSURE: Traumatic events included motor vehicle incidents, physical assaults, robberies, hostilities, electric shocks, fires, drownings, work accidents, terror attacks, or large-scale disasters.<br><br>MAIN OUTCOMES AND MEASURES: Connectome-based predictive modeling (CPM), a whole-brain machine learning approach, was applied to resting-state and task-based fMRI data collected at 1 month post trauma. The primary outcome measure was PTSD symptom severity across the 3 time points, assessed with the Clinician-Administered PTSD Scale for DSM-5 (CAPS-5). Secondary outcomes included Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (DSM-5) PTSD symptom clusters (intrusion, avoidance, negative alterations in mood and cognition, hyperarousal).<br><br>RESULTS: A total of 162 recent trauma survivors (mean [SD] age,&#x2009;33.9&#x2009;[11.5] years; 80 women [49.4%] and 82 men [50.6%]) were included at 1 month post trauma. Follow-up assessments were completed by 136 survivors (84.0%) at 6 months and by 133 survivors (82.1%) at 14 months post trauma. Among the 162 recent trauma survivors, CPM significantly predicted PTSD severity at 1 month (&#x3c1;&#x2009;=&#x2009;0.18, P&#x2009;<&#x2009;.001) and 14 months (&#x3c1;&#x2009;=&#x2009;0.24, P&#x2009;<&#x2009;.001) post trauma, but not at 6 months post trauma (&#x3c1;&#x2009;=&#x2009;0.03, P&#x2009;=&#x2009;.39). The most predictive edges at 1 month included connections within and between the anterior default mode, motor sensory, and salience networks. These networks, with the additional contribution of the central executive and visual networks, were predictive of symptoms at 14 months. CPM predicted avoidance and negative alterations in mood and cognition at 1 month, but it predicted intrusion and hyperarousal symptoms at 14 months.<br><br>CONCLUSIONS AND RELEVANCE: In this prognostic study of recent trauma survivors, individual differences in large-scale neural networks shortly after trauma were associated with variability in PTSD symptom trajectories over the first year following trauma exposure. These findings suggest that CPM may identify potential targets for interventions.},
}
@article {pmid40062568,
year = {2025},
author = {Pilipović, K and Parpura, V},
title = {The potential of single-walled carbon nanotube-based therapeutic platforms targeting astrocytes.},
journal = {Nanomedicine (London, England)},
volume = {20},
number = {11},
pages = {1209-1211},
pmid = {40062568},
issn = {1748-6963},
}
@article {pmid40061257,
year = {2025},
author = {Song, Y and Han, L and Zhang, T and Xu, B},
title = {Multiscale fusion enhanced spiking neural network for invasive BCI neural signal decoding.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1551656},
pmid = {40061257},
issn = {1662-4548},
abstract = {Brain-computer interfaces (BCIs) are an advanced fusion of neuroscience and artificial intelligence, requiring stable and long-term decoding of neural signals. Spiking Neural Networks (SNNs), with their neuronal dynamics and spike-based signal processing, are inherently well-suited for this task. This paper presents a novel approach utilizing a Multiscale Fusion enhanced Spiking Neural Network (MFSNN). The MFSNN emulates the parallel processing and multiscale feature fusion seen in human visual perception to enable real-time, efficient, and energy-conserving neural signal decoding. Initially, the MFSNN employs temporal convolutional networks and channel attention mechanisms to extract spatiotemporal features from raw data. It then enhances decoding performance by integrating these features through skip connections. Additionally, the MFSNN improves generalizability and robustness in cross-day signal decoding through mini-batch supervised generalization learning. In two benchmark invasive BCI paradigms, including the single-hand grasp-and-touch and center-and-out reach tasks, the MFSNN surpasses traditional artificial neural network methods, such as MLP and GRU, in both accuracy and computational efficiency. Moreover, the MFSNN's multiscale feature fusion framework is well-suited for the implementation on neuromorphic chips, offering an energy-efficient solution for online decoding of invasive BCI signals.},
}
@article {pmid40060989,
year = {2025},
author = {Huang, J and Huang, L and Li, Y and Fang, F},
title = {A Bibliometric Analysis of the Application of Brain-Computer Interface in Rehabilitation Medicine Over the Past 20 Years.},
journal = {Journal of multidisciplinary healthcare},
volume = {18},
number = {},
pages = {1297-1317},
pmid = {40060989},
issn = {1178-2390},
abstract = {OBJECTIVE: This study aims to conduct a bibliometric analysis of the application of brain- computer interface (BCI) in rehabilitation medicine, assessing the current state, developmental trends, and future potential of this field. By systematically analyzing relevant literature, we seek to identify key research themes and enhance understanding of BCI technology in rehabilitation.<br><br>METHODS: We utilized bibliometric analysis tools such as VOSviewer and CiteSpace to screen and analyze 426 relevant articles from the Web of Science Core Collection (WoSCC) database. We quantitatively evaluated citation patterns, publication trends, and the collaboration networks of research institutions and authors to uncover research hotspots and frontier dynamics in the field.<br><br>RESULTS: The findings indicate a continuous increase in research publications since 2003, with a notable peak occurring between 2019 and 2021. The analysis revealed that motor imagery, motor recovery, and signal processing are the predominant research themes. Furthermore, the United States and China are leading in the publication volume related to BCI and rehabilitation medicine. Key research institutions include the University of T&#xfc;bingen and the New York State Department of Health, with significant contributions from scholars like Niels Birbaumer.<br><br>CONCLUSION: Although the current research on BCI in rehabilitation medicine shows significant potential and efficacy, further exploration of certain research directions is needed, along with the promotion of interdisciplinary collaboration to comprehensively address complex real-world issues such as motor function impairment. Future research should focus on optimizing training models, enhancing technical feasibility, and exploring home rehabilitation applications to facilitate the broader adoption of BCI technology in rehabilitation medicine.},
}
@article {pmid40060849,
year = {2025},
author = {Castañeda-Valencia, G and Gama, LF and Panneerselvam, M and Vaiss, VS and Guedes, IA and Dardenne, LE and Costa, LT},
title = {Methodological Approach Based on Structural Parameters, Vibrational Frequencies, and MMFF94 Bond Charge Increments for Platinum-Based Compounds.},
journal = {ACS omega},
volume = {10},
number = {8},
pages = {8314-8335},
pmid = {40060849},
issn = {2470-1343},
abstract = {In this work, we performed a comprehensive benchmark study for the ground state of five small- and medium-sized platinum derivatives, PtH, PtCl, [PtCl4][2-], [Pt(NH3)4][2+], and cis-[Pt(NH3)2Cl2], in the gas phase and two cisplatin polymorphs in the solid phase. The benchmark encompassed 16 density functionals, including nonhybrids, hybrids, and double hybrids. Furthermore, Hartree-Fock (HF) and Post-HF by Møller-Plesset MP2 methods were also tested. Additionally, 11 basis sets were explored, comparing relativistic all-electron and RECP approaches. Our results indicate that the methodologies best suited for predicting structural parameters do not excel in predicting vibrational frequencies and vice versa. In the context of this theoretical framework, we also examine the derivation of partial atomic charges and bond charge increments (bci) as fundamental parameters within the MMFF94 classical force field. Our results show that the partial atomic charges of CHELPG present a slight charge fluctuation in Pt for all investigated levels of theory, and this behavior reproduces well the soft acid definition for Pt[2+], giving the best description of the chemical environment of platinum in the cisplatin complex. The average calculated bci values effectively capture the atomic charge variations in the chemical environment of Pt in the investigated species. The developed bci optimization tool, based on MMFF94, was implemented using a Python code made available at https://github.com/molmodcs/bci_solver. This methodology will be further implemented in the DockThor receptor-ligand docking program, allowing future molecular docking validations involving ligand compounds containing Pt atoms.},
}
@article {pmid40060267,
year = {2025},
author = {Shawki, N and Napoli, A and Vargas-Irwin, CE and Thompson, CK and Donoghue, JP and Serruya, MD},
title = {Neural signal analysis in chronic stroke: advancing intracortical brain-computer interface design.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1544397},
pmid = {40060267},
issn = {1662-5161},
abstract = {INTRODUCTION: Intracortical Brain-computer interfaces (iBCIs) are a promising technology to restore function after stroke. It remains unclear whether iBCIs will be able to use the signals available in the neocortex overlying stroke affecting the underlying white matter and basal ganglia.<br><br>METHODS: Here, we decoded both local field potentials (LFPs) and spikes recorded from intracortical electrode arrays in a person with chronic cerebral subcortical stroke performing various tasks with his paretic hand, with and without a powered orthosis. Analysis of these neural signals provides an opportunity to explore the electrophysiological activities of a stroke affected brain and inform the design of medical devices that could restore function.<br><br>RESULTS: The frequency domain analysis showed that as the distance between an array and the stroke site increased, the low frequency power decreased, and high frequency power increased. Coordinated cross-channel firing of action potentials while attempting a motor task and cross-channel simultaneous low frequency bursts while relaxing were also observed. Using several offline analysis techniques, we propose three features for decoding motor movements in stroke-affected brains.<br><br>DISCUSSION: Despite the presence of unique activities that were not reported in previous iBCI studies with intact brain functions, it is possible to decode motor intents from the neural signals collected from a subcortical stroke-affected brain.},
}
@article {pmid40059723,
year = {2025},
author = {Han, JS and Jeon, MC and Lee, CM and Velasco, GC and Park, SY and Park, SN},
title = {Comparing Tinnitus Suppression in Asymmetric Hearing Loss and Single-Sided Deafness: Cochlear Versus Bone Conduction Implants.},
journal = {The Laryngoscope},
volume = {135},
number = {7},
pages = {2547-2557},
pmid = {40059723},
issn = {1531-4995},
support = {RS-2024-00340841//National Research Foundation of Korea/ ; },
mesh = {Humans ; *Tinnitus/etiology/surgery ; Female ; Retrospective Studies ; Male ; Middle Aged ; Bone Conduction ; *Hearing Loss, Unilateral/complications/surgery/rehabilitation ; *Cochlear Implants ; Adult ; Treatment Outcome ; Aged ; },
abstract = {OBJECTIVES: Implantable hearing devices, such as cochlear implants (CI) and bone conduction implants (BCI), are options for hearing rehabilitation in patients with asymmetric hearing loss (AHL) and single-sided deafness (SSD). This study aimed to compare the effects of CI and BCI on tinnitus in AHL/SSD patients with tinnitus.<br><br>METHODS: This retrospective study enrolled adult AHL/SSD patients with significant tinnitus who underwent CI or BCI placement between 2017 and 2023. Clinical characteristics, preoperative and postoperative audiologic test results, and tinnitus questionnaires (tinnitus handicap inventory, THI; visual analog scale, VAS) were collected and analyzed.<br><br>RESULTS: Of 33 AHL/SSD patients with significant tinnitus (THI &#x2265;&#x2009;18), 16 received CI and 17 BCI. In the CI group, all four VAS scores (loudness, awareness, annoyance, and effect on life) and THI scores significantly improved. In the BCI group, annoyance and effect on life categories of VAS and THI scores significantly improved, while VAS loudness and awareness remained similar. Linear mixed model analysis showed that the decrease in VAS loudness, awareness, and annoyance scores was significantly greater in the CI group compared to the BCI group. The CI group showed a significantly higher tinnitus cure rate (62.5.0%) compared with the BCI group (11.8%) at 6-months postoperative.<br><br>CONCLUSION: Both CI and BCI effectively improved tinnitus in AHL/SSD patients with tinnitus. However, CI is considered the first-line therapeutic option for tinnitus due to its stronger effect on tinnitus suppression as well as the higher cure rate in SSD/AHL patients with tinnitus.},
}
@article {pmid40059266,
year = {2025},
author = {Meng, J and Wei, Y and Mai, X and Li, S and Wang, X and Luo, R and Ji, M and Zhu, X},
title = {Paradigms and methods of noninvasive brain-computer interfaces in motor or communication assistance and rehabilitation: a systematic review.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {63},
number = {8},
pages = {2209-2233},
pmid = {40059266},
issn = {1741-0444},
mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography/methods ; *Communication Devices for People with Disabilities ; Brain/physiology ; *Rehabilitation/methods ; Spectroscopy, Near-Infrared ; },
abstract = {Noninvasive brain-computer interfaces (BCIs) have rapidly developed over the past decade. This new technology utilizes magneto-electrical recording or hemodynamic imaging approaches to acquire neurophysiological signals noninvasively, such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). These noninvasive signals have different temporal resolutions ranging from milliseconds to seconds and various spatial resolutions ranging from centimeters to millimeters. Thanks to these neuroimaging technologies, various BCI modalities like steady-state visual evoked potential (SSVEP), P300, and motor imagery (MI) could be proposed to rehabilitate or assist patients' lost function of mobility or communication. This review focuses on the recent development of paradigms, methods, and applications of noninvasive BCI for motor or communication assistance and rehabilitation. The selection of papers follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), obtaining 223 research articles since 2016. We have observed that EEG-based BCI has gained more research focus due to its low cost and portability, as well as more translational studies in rehabilitation, robotic device control, etc. In the past decade, decoding approaches such as deep learning and source imaging have flourished in BCI. Still, there are many challenges to be solved to date, such as designing more convenient electrodes, improving the decoding accuracy and efficiency, designing more applicable systems for target patients, etc., before this new technology matures enough to benefit clinical users.},
}
@article {pmid40058464,
year = {2025},
author = {Zhao, Z and Li, Y and Peng, Y and Camilleri, K and Kong, W},
title = {Multi-view graph fusion of self-weighted EEG feature representations for speech imagery decoding.},
journal = {Journal of neuroscience methods},
volume = {418},
number = {},
pages = {110413},
doi = {10.1016/j.jneumeth.2025.110413},
pmid = {40058464},
issn = {1872-678X},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Speech/physiology ; *Imagination/physiology ; Signal Processing, Computer-Assisted ; *Brain/physiology ; Adult ; Male ; Female ; },
abstract = {BACKGROUND: Electroencephalogram (EEG)-based speech imagery is an emerging brain-computer interface paradigm, which enables the speech disabled to naturally and intuitively communicate with external devices or other people. Currently, speech imagery research decoding performance is limited. One of the reasons is that there is still no consensus on which domain features are more discriminative.<br><br>NEW METHOD: To adaptively capture the complementary information from different domain features, we treat each domain as a view and propose a multi-view graph fusion of self-weighted EEG feature representations (MVGSF) model by learning a consensus graph from multi-view EEG features, based on which the imagery intentions can be effectively decoded. Considering that different EEG features in each view have different discriminative abilities, the view-dependent feature importance exploration strategy is incorporated in MVGSF.<br><br>RESULTS: (1) MVGSF exhibits outstanding performance on two public speech imagery datasets (2) The learned consensus graph from multi-view features effectively characterizes the relationships of EEG samples in a progressive manner. (3) Some task-related insights are explored including the feature importance-based identification of critical EEG channels and frequency bands in speech imagery decoding.<br><br>We compared MVGSF with single-view counterparts, other multi-view models, and state-of-the-art models. MVGSF achieved the highest accuracy, with average accuracies of 78.93% on the 2020IBCIC3 dataset and 53.85% on the KaraOne dataset.<br><br>CONCLUSIONS: MVGSF effectively integrates features from multiple domains to enhance decoding capabilities. Furthermore, through the learned feature importance, MVGSF has made certain contributions to identify the EEG spatial-frequency patterns in speech imagery decoding.},
}
@article {pmid40057290,
year = {2025},
author = {Chuang, CH and Chang, KY and Huang, CS and Bessas, AM},
title = {Augmenting brain-computer interfaces with ART: An artifact removal transformer for reconstructing multichannel EEG signals.},
journal = {NeuroImage},
volume = {310},
number = {},
pages = {121123},
doi = {10.1016/j.neuroimage.2025.121123},
pmid = {40057290},
issn = {1095-9572},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Artifacts ; *Signal Processing, Computer-Assisted ; Algorithms ; Signal-To-Noise Ratio ; *Brain/physiology ; },
abstract = {Artifact removal in electroencephalography (EEG) is a longstanding challenge that significantly impacts neuroscientific analysis and brain-computer interface (BCI) performance. Tackling this problem demands advanced algorithms, extensive noisy-clean training data, and thorough evaluation strategies. This study presents the Artifact Removal Transformer (ART), an innovative EEG denoising model employing transformer architecture to adeptly capture the transient millisecond-scale dynamics characteristic of EEG signals. Our approach offers a holistic, end-to-end denoising solution that simultaneously addresses multiple artifact types in multichannel EEG data. We enhanced the generation of noisy-clean EEG data pairs using an independent component analysis, thus fortifying the training scenarios critical for effective supervised learning. We performed comprehensive validations using a wide range of open datasets from various BCI applications, employing metrics like mean squared error and signal-to-noise ratio, as well as sophisticated techniques such as source localization and EEG component classification. Our evaluations confirm that ART surpasses other deep-learning-based artifact removal methods, setting a new benchmark in EEG signal processing. This advancement not only boosts the accuracy and reliability of artifact removal but also promises to catalyze further innovations in the field, facilitating the study of brain dynamics in naturalistic environments.},
}
@article {pmid40054446,
year = {2025},
author = {Natraj, N and Seko, S and Abiri, R and Miao, R and Yan, H and Graham, Y and Tu-Chan, A and Chang, EF and Ganguly, K},
title = {Sampling representational plasticity of simple imagined movements across days enables long-term neuroprosthetic control.},
journal = {Cell},
volume = {188},
number = {5},
pages = {1208-1225.e32},
pmid = {40054446},
issn = {1097-4172},
support = {DP2 HD087955/HD/NICHD NIH HHS/United States ; R01 HD111562/HD/NICHD NIH HHS/United States ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Neuronal Plasticity/physiology ; Female ; Adult ; Movement/physiology ; Electrocorticography ; *Imagination/physiology ; Quadriplegia/physiopathology ; Young Adult ; Robotics ; },
abstract = {The nervous system needs to balance the stability of neural representations with plasticity. It is unclear what the representational stability of simple well-rehearsed actions is, particularly in humans, and their adaptability to new contexts. Using an electrocorticography brain-computer interface (BCI) in tetraplegic participants, we found that the low-dimensional manifold and relative representational distances for a repertoire of simple imagined movements were remarkably stable. The manifold's absolute location, however, demonstrated constrained day-to-day drift. Strikingly, neural statistics, especially variance, could be flexibly regulated to increase representational distances during BCI control without somatotopic changes. Discernability strengthened with practice and was BCI-specific, demonstrating contextual specificity. Sampling representational plasticity and drift across days subsequently uncovered a meta-representational structure with generalizable decision boundaries for the repertoire; this allowed long-term neuroprosthetic control of a robotic arm and hand for reaching and grasping. Our study offers insights into mesoscale representational statistics that also enable long-term complex neuroprosthetic control.},
}
@article {pmid40051983,
year = {2025},
author = {Lingelbach, K and Rips, J and Karstensen, L and Mathis-Ullrich, F and Vukelić, M},
title = {Evaluating robotic actions: spatiotemporal brain dynamics of performance assessment in robot-assisted laparoscopic training.},
journal = {Frontiers in neuroergonomics},
volume = {6},
number = {},
pages = {1535799},
pmid = {40051983},
issn = {2673-6195},
abstract = {INTRODUCTION: Enhancing medical robot training traditionally relies on explicit feedback from physicians to identify optimal and suboptimal robotic actions during surgery. Passive brain-computer interfaces (BCIs) offer an emerging alternative by enabling implicit brain-based performance evaluations. However, effectively decoding these evaluations of robot performance requires a comprehensive understanding of the spatiotemporal brain dynamics identifying optimal and suboptimal robot actions within realistic settings.<br><br>METHODS: We conducted an electroencephalographic study with 16 participants who mentally assessed the quality of robotic actions while observing simulated robot-assisted laparoscopic surgery scenarios designed to approximate real-world conditions. We aimed to identify key spatiotemporal dynamics using the surface Laplacian technique and two complementary data-driven methods: a mass-univariate permutation-based clustering and multivariate pattern analysis (MVPA)-based temporal decoding. A second goal was to identify the optimal time interval of evoked brain signatures for single-trial classification.<br><br>RESULTS: Our analyses revealed three distinct spatiotemporal brain dynamics differentiating the quality assessment of optimal vs. suboptimal robotic actions during video-based laparoscopic training observations. Specifically, an enhanced left fronto-temporal current source, consistent with P300, LPP, and P600 components, indicated heightened attentional allocation and sustained evaluation processes during suboptimal robot actions. Additionally, amplified current sinks in right frontal and mid-occipito-parietal regions suggested prediction-based processing and conflict detection, consistent with the oERN and interaction-based ERN/N400. Both mass-univariate clustering and MVPA provided convergent evidence supporting these neural distinctions.<br><br>DISCUSSION: The identified neural signatures propose that suboptimal robotic actions elicit enhanced, sustained brain dynamics linked to continuous attention allocation, action monitoring, conflict detection, and ongoing evaluative processing. The findings highlight the importance of prioritizing late evaluative brain signatures in BCIs to classify robotic actions reliably. These insights have significant implications for advancing machine-learning-based training paradigms.},
}
@article {pmid40051611,
year = {2025},
author = {Martínez-Cagigal, V and Thielen, J and Hornero, R and Desain, P},
title = {Editorial: The role of code-modulated evoked potentials in next-generation brain-computer interfacing.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1548183},
pmid = {40051611},
issn = {1662-5161},
}
@article {pmid40051554,
year = {2025},
author = {Teman, SJ and Atwood, TC and Converse, SJ and Fry, TL and Laidre, KL},
title = {Measuring polar bear health using allostatic load.},
journal = {Conservation physiology},
volume = {13},
number = {1},
pages = {coaf013},
pmid = {40051554},
issn = {2051-1434},
abstract = {The southern Beaufort Sea polar bear sub-population (Ursus maritimus) has been adversely affected by climate change and loss of sea ice habitat. Even though the sub-population is likely decreasing, it remains difficult to link individual polar bear health and physiological change to sub-population effects. We developed an index of allostatic load, which represents potential physiological dysregulation. The allostatic load index included blood- and hair-based analytes measured in physically captured southern Beaufort bears in spring. We examined allostatic load in relation to bear body condition, age, terrestrial habitat use and, over time, for bear demographic groups. Overall, allostatic load had no relationship with body condition. However, allostatic load was higher in adult females without cubs that used terrestrial habitats the prior year, indicating potential physiological dysregulation with land use. Allostatic load declined with age in adult females without cubs. Sub-adult males demonstrated decreased allostatic load over time. Our study is one of the first attempts to develop a health scoring system for free-ranging polar bears, and our findings highlight the complexity of using allostatic load as an index of health in a wild species. Establishing links between individual bear health and population dynamics is important for advancing conservation efforts.},
}
@article {pmid40050621,
year = {2025},
author = {Cui, H and Xiao, Y and Yang, Y and Pei, M and Ke, S and Fang, X and Qiao, L and Shi, K and Long, H and Xu, W and Cai, P and Lin, P and Shi, Y and Wan, Q and Wan, C},
title = {A bioinspired in-materia analog photoelectronic reservoir computing for human action processing.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {2263},
pmid = {40050621},
issn = {2041-1723},
support = {92364106//National Natural Science Foundation of China (National Science Foundation of China)/ ; 92364204//National Natural Science Foundation of China (National Science Foundation of China)/ ; BK20220121//Natural Science Foundation of Jiangsu Province (Jiangsu Provincial Natural Science Foundation)/ ; },
mesh = {Humans ; Algorithms ; Transistors, Electronic ; *Biomimetics/methods ; },
abstract = {Current computer vision is data-intensive and faces bottlenecks in shrinking computational costs. Incorporating physics into a bioinspired visual system is promising to offer unprecedented energy efficiency, while the mismatch between physical dynamics and bioinspired algorithms makes the processing of real-world samples rather challenging. Here, we report a bioinspired in-materia analogue photoelectronic reservoir computing for dynamic vision processing. Such system is built based on InGaZnO photoelectronic synaptic transistors as the reservoir and a TaOX-based memristor array as the output layer. A receptive field inspired encoding scheme is implemented, simplifying the feature extraction process. High recognition accuracies (>90%) on four motion recognition datasets are achieved based on such system. Furthermore, falling behaviors recognition is also verified by our system with low energy consumption for processing per action (~45.78 μJ) which outperforms most previous reports on human action processing. Our results are of profound potential for advancing computer vision based on neuromorphic electronics.},
}
@article {pmid40049535,
year = {2025},
author = {Kong, L and Zhang, Q and Wang, H and Xu, Y and Xu, C and Chen, Y and Lu, J and Hu, S},
title = {Exploration of the optimized portrait of omega-3 polyunsaturated fatty acids in treating depression: A meta-analysis of randomized-controlled trials.},
journal = {Journal of affective disorders},
volume = {379},
number = {},
pages = {489-501},
doi = {10.1016/j.jad.2025.03.006},
pmid = {40049535},
issn = {1573-2517},
mesh = {Humans ; *Fatty Acids, Omega-3/therapeutic use ; Randomized Controlled Trials as Topic ; *Depressive Disorder/drug therapy ; Treatment Outcome ; },
abstract = {BACKGROUND: According to previous studies, omega-3 polyunsaturated fatty acids (PUFAs) are controversial for the efficacy of treating depression.<br><br>AIMS: This meta-analysis aims to investigate whether omega-3 PUFAs are able to treat depression, and find out the most beneficial clinical portrait.<br><br>METHODS: More than two reviewers searched six registries, and 36 studies were eventually considered eligible. The PRISMA guidelines were used for data extraction, Cochrane Handbook for quality assessment, and random effects model for data pooling.<br><br>OUTCOMES: Significant heterogeneity and publication bias were observed. According to the results, significant efficacy was detected in the overall analysis [SMD&#xa0;=&#xa0;-0.26, 95&#xa0;% CI&#xa0;=&#xa0;(-0.41, -0.11)] and several subgroups, while total daily dosage might be a potential heterogeneity source (P&#xa0;<&#xa0;0.05). No between-group difference was observed in the rate of response [RR&#xa0;=&#xa0;0.99, 95&#xa0;% CI&#xa0;=&#xa0;(0.82, 1.20)], remission [RR&#xa0;=&#xa0;1.17, 95&#xa0;% CI&#xa0;=&#xa0;(0.92, 1.48)], and adverse events [RR&#xa0;=&#xa0;1.07, 95&#xa0;% CI&#xa0;=&#xa0;(0.90, 1.29)]. Total daily intake of eicosapentaenoic acid (EPA) and remission rate conformed to linear correlation (P&#xa0;<&#xa0;0.05).<br><br>CONCLUSIONS: 1) Omega-3 PUFAs might be effective in treating depression; 2) For Asian patients with mild to moderate depression and no other baseline medication, over 8&#xa0;weeks of omega-3 PUFAs 1000-1500&#xa0;mg/day with ratio of EPA/docosahexaenoic acid (DHA) between 1:1 and 2:1 might benefit the most; 3) Omega-3 PUFAs are no superior than placebo in rates of response, remission, and adverse events. Although several limitations exist, the evidence-based information provides guidance for clinical practice and directions for further research.<br><br>PROSPERO REGISTRATION NUMBER: CRD42023464823.},
}
@article {pmid40049458,
year = {2025},
author = {Premchand, B and Toe, KK and Wang, C and Wan, KR and Selvaratnam, T and Toh, VE and Ng, WH and Libedinsky, C and Chen, W and Lim, R and Cheng, MY and Gao, Y and Ang, KK and So, RQY},
title = {Comparing a BCI communication system in a patient with Multiple System Atrophy, with an animal model.},
journal = {Brain research bulletin},
volume = {223},
number = {},
pages = {111289},
doi = {10.1016/j.brainresbull.2025.111289},
pmid = {40049458},
issn = {1873-2747},
mesh = {*Brain-Computer Interfaces ; *Multiple System Atrophy/physiopathology ; Animals ; Humans ; Electroencephalography/methods ; Disease Models, Animal ; Neural Networks, Computer ; Male ; Communication Devices for People with Disabilities ; Machine Learning ; Macaca mulatta ; Female ; },
abstract = {Paralysis affects many people worldwide, and the people affected often suffer from impaired communication. We developed a microelectrode-based Brain-Computer Interface (BCI) for enabling communication in patients affected by paralysis, and implanted it in a patient with Multiple System Atrophy (MSA), a neurodegenerative disease that causes widespread neural symptoms including paralysis. To verify the effectiveness of the BCI system, it was also tested by implanting it in a non-human primate (NHP). Data from the human and NHP were used to train binary classifiers two different types of machine learning models: a Linear Discriminant Analysis (LDA) model, and a Long Short-Term Memory (LSTM)-based Artificial Neural Network (ANN). The LDA model performed at up to 72.7 % accuracy for binary decoding in the human patient, however, performance was highly variable and was much lower on most recording days. The BCI system was able to accurately decode movement vs non-movement in the NHP (accuracy using LDA: 82.7 ± 3.3 %, LSTM: 83.7 ± 2.2 %, 95 % confidence intervals), however it was not able to with recordings from the human patient (accuracy using LDA: 47.0 ± 5.1 %, LSTM: 44.6 ± 9.9 %, 95 % confidence intervals). We discuss how neurodegenerative diseases such as MSA can impede BCI-based communication, and postulate on the mechanisms by which this may occur.},
}
@article {pmid40049046,
year = {2025},
author = {Chai, C and Yang, X and Zheng, Y and Bin Heyat, MB and Li, Y and Yang, D and Chen, YH and Sawan, M},
title = {Multimodal fusion of magnetoencephalography and photoacoustic imaging based on optical pump: Trends for wearable and noninvasive Brain-Computer interface.},
journal = {Biosensors &amp; bioelectronics},
volume = {278},
number = {},
pages = {117321},
doi = {10.1016/j.bios.2025.117321},
pmid = {40049046},
issn = {1873-4235},
mesh = {*Brain-Computer Interfaces ; Humans ; *Magnetoencephalography/instrumentation/methods ; *Wearable Electronic Devices ; *Photoacoustic Techniques/instrumentation/methods ; Electroencephalography/instrumentation ; *Brain/physiology/diagnostic imaging ; *Biosensing Techniques/instrumentation ; Spectroscopy, Near-Infrared ; Equipment Design ; },
abstract = {Wearable noninvasive brain-computer interface (BCI) technologies, such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), have experienced significant progress since their inception. However, these technologies have not achieved revolutionary advancements, largely because of their inherently low signal-to-noise ratio and limited penetration depth. In recent years, the application of quantum-theory-based optically pumped (OP) technologies, particularly optical pumped magnetometers (OPMs) for magnetoencephalography (MEG) and photoacoustic imaging (PAI) utilizing OP pulsed laser sources, has opened new avenues for development in noninvasive BCIs. These advanced technologies have garnered considerable attention owing to their high sensitivity in tracking neural activity and detecting blood oxygen saturation. This paper represents the first attempt to discuss and compare technologies grounded in OP theory by examining the technical advantages of OPM-MEG and PAI over traditional EEG and fNIRS. Furthermore, the paper investigates the theoretical and structural feasibility of hardware reuse in OPM-MEG and PAI applications.},
}
@article {pmid40048825,
year = {2025},
author = {Kacker, K and Chetty, N and Feldman, AK and Bennett, J and Yoo, PE and Fry, A and Lacomis, D and Harel, NY and Nogueira, RG and Majidi, S and Opie, NL and Collinger, JL and Oxley, TJ and Putrino, DF and Weber, DJ},
title = {Motor activity in gamma and high gamma bands recorded with a Stentrode from the human motor cortex in two people with ALS.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
pmid = {40048825},
issn = {1741-2552},
support = {UH3 NS120191/NS/NINDS NIH HHS/United States ; },
mesh = {Female ; Humans ; Male ; Middle Aged ; *Amyotrophic Lateral Sclerosis/physiopathology/diagnosis ; *Brain-Computer Interfaces ; *Electrocorticography/methods/instrumentation ; Electrodes, Implanted ; *Gamma Rhythm/physiology ; *Motor Activity/physiology ; *Motor Cortex/physiopathology/physiology ; Movement/physiology ; *Stents ; Clinical Trials as Topic ; },
abstract = {Objective.This study examined the strength and stability of motor signals in low gamma and high gamma bands of vascular electrocorticograms (vECoG) recorded with endovascular stent-electrode arrays (Stentrodes) implanted in the superior sagittal sinus of two participants with severe paralysis due to amyotrophic lateral sclerosis.Approach.vECoG signals were recorded from two participants in the COMMAND trial, an Early Feasibility Study of the Stentrode brain-computer interface (BCI) (NCT05035823). The participants performed attempted movements of their ankles or hands. The signals were band-pass filtered to isolate low gamma (30-70 Hz) and high gamma (70-200 Hz) components. The strength of vECoG motor activity was measured as signal-to-noise ratio (SNR) and the percentage change in signal amplitude between the rest and attempted movement epochs, which we termed depth of modulation (DoM). We trained and tested classifiers to evaluate the accuracy and stability of detecting motor intent.Main results.Both low gamma and high gamma were modulated during attempted movements. For Participant 1, the average DoM across channels and sessions was 125.41 ± 17.53% for low gamma and 54.23 ± 4.52% for high gamma, with corresponding SNR values of 6.75 ± 0.37 dB and 3.69 ± 0.28 dB. For Participant 2, the average DoM was 22.77 ± 4.09% for low gamma and 22.53 ± 2.04% for high gamma, with corresponding SNR values of 1.72 ± 0.25 dB and 1.73 ± 0.13 dB. vECoG amplitudes remained significantly different between rest and move periods over the 3 month testing period, with >90% accuracy in discriminating attempted movement from rest epochs for both participants. For Participant 1, the average DoM was strongest during attempted movements of both ankles, while for Participant 2, the DoM was greatest for attempted movement of the right hand. The overall classification accuracy was 91.43% for Participant 1 and 70.37% for Participant 2 in offline decoding of multiple attempted movements and rest conditions.Significance.By eliminating the need for open brain surgery, the Stentrode offers a promising BCI alternative, potentially enhancing access to BCIs for individuals with severe motor impairments. This study provides preliminary evidence that the Stentrode can detect discriminable signals indicating motor intent, with motor signal modulation observed over the 3 month testing period reported here.},
}
@article {pmid40048822,
year = {2025},
author = {Berger, LM and Wood, G and Kober, SE},
title = {Manipulating cybersickness in virtual reality-based neurofeedback and its effects on training performance.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adbd76},
pmid = {40048822},
issn = {1741-2552},
mesh = {Humans ; Male ; Female ; *Neurofeedback/methods/physiology ; *Virtual Reality ; Adult ; Young Adult ; *Brain-Computer Interfaces/psychology ; Electroencephalography/methods ; *Motion Sickness/physiopathology/psychology ; *Psychomotor Performance/physiology ; },
abstract = {Objective. Virtual reality (VR) serves as a modern and powerful tool to enrich neurofeedback (NF) and brain-computer interface (BCI) applications as well as to achieve higher user motivation and adherence to training. However, between 20%-80% of all the users develop symptoms of cybersickness (CS), namely nausea, oculomotor problems or disorientation during VR interaction, which influence user performance and behavior in VR. Hence, we investigated whether CS-inducing VR paradigms influence the success of a NF training task.Approach. We tested 39 healthy participants (20 female) in a single-session VR-based NF study. One half of the participants was presented with a high CS-inducing VR-environment where movement speed, field of view and camera angle were varied in a CS-inducing fashion throughout the session and the other half underwent NF training in a less CS-inducing VR environment, where those parameters were held constant. The NF training consisted of 6 runs of 3 min each, in which participants should increase their sensorimotor rhythm (SMR, 12-15 Hz) while keeping artifact control frequencies constant (Theta 4-7 Hz, Beta 16-30 Hz). Heart rate and subjectively experienced CS were also assessed.Main results. The high CS-inducing condition tended to lead to more subjectively experienced CS nausea symptoms than the low CS-inducing condition. Further, women experienced more CS, a higher heart rate and showed a worse NF performance compared to men. However, the SMR activity during the NF training was comparable between both the high and low CS-inducing groups. Both groups were able to increase their SMR across feedback runs, although, there was a tendency of higher SMR power for male participants in the low CS group.Significance. Hence, sickness symptoms in VR do not necessarily impair NF/BCI training success. This takes us one step further in evaluating the practicability of VR in BCI and NF applications. Nevertheless, inter-individual differences in CS susceptibility should be taken into account for VR-based NF applications.},
}
@article {pmid40048236,
year = {2025},
author = {Zhang, Y and Hedley, FE and Zhang, RY and Jin, J},
title = {Toward quantitative cognitive-behavioral modeling of psychopathology: An active inference account of social anxiety disorder.},
journal = {Journal of psychopathology and clinical science},
volume = {134},
number = {4},
pages = {363-388},
doi = {10.1037/abn0000972},
pmid = {40048236},
issn = {2769-755X},
support = {//National Key R&amp;D Program of China/ ; //National Natural Science Foundation of China/ ; //Zhejiang University; State Key Laboratory of Brain-Machine Intelligence/ ; //Ministry of Education; Key Laboratory of Brain-Machine Intelligence for Information Behavior/ ; },
mesh = {Humans ; *Phobia, Social/physiopathology/psychology ; *Models, Psychological ; *Cognitive Behavioral Therapy ; },
abstract = {Understanding psychopathological mechanisms is a central goal in clinical science. While existing theories have demonstrated high research and clinical utility, they have provided limited quantitative explanations of mechanisms. Previous computational modeling studies have primarily focused on isolated factors, posing challenges for advancing clinical theories holistically. To address this gap and leverage the strengths of clinical theories and computational modeling in a synergetic manner, it is crucial to develop quantitative models that integrate major factors proposed by comprehensive theoretical models. In this study, using social anxiety disorder (SAD) as an example, we present a novel approach to formalize conceptual models by combining cognitive-behavioral theory (CBT) with active inference modeling, an innovative computational approach that elucidates human cognition and action. This CBT-informed active inference model integrates multiple mechanistic factors of SAD in a quantitative manner. Through a series of simulations, we systematically examined the effects of these factors on the belief about social threat and tendency of engaging in safety behaviors. The resultant model inherits the conceptual comprehensiveness of CBT and the quantitative rigor of active inference modeling, delineating previously elusive pathogenetic pathways and enabling the formulation of concrete model predictions for future research. Overall, this research presents a novel quantitative model of SAD that unifies major mechanistic factors proposed by CBT and active inference modeling. It highlights the feasibility and potential of integrating clinical theory and computational modeling to advance our understanding of psychopathology. (PsycInfo Database Record (c) 2025 APA, all rights reserved).},
}
@article {pmid40047565,
year = {2025},
author = {Haghighi, P and Jeakle, EN and Sturgill, BS and Abbott, JR and Solis, E and Devata, VS and Vijayakumar, G and Hernandez-Reynoso, AG and Cogan, SF and Pancrazio, JJ},
title = {Enhanced Performance of Novel Amorphous Silicon Carbide Microelectrode Arrays in Rat Motor Cortex.},
journal = {Micromachines},
volume = {16},
number = {2},
pages = {},
pmid = {40047565},
issn = {2072-666X},
support = {2R01TW104344-22S5/NH/NIH HHS/United States ; },
abstract = {Implantable microelectrode arrays (MEAs) enable the recording of electrical activity from cortical neurons for applications that include brain-machine interfaces. However, MEAs show reduced recording capabilities under chronic implantation conditions. This has largely been attributed to the brain's foreign body response, which is marked by neuroinflammation and gliosis in the immediate vicinity of the MEA implantation site. This has prompted the development of novel MEAs with either coatings or architectures that aim to reduce the tissue response. The present study examines the comparative performance of multi-shank planar, silicon-based devices and low-flexural-rigidity amorphous silicon carbide (a-SiC) MEAs that have a similar architecture but differ with respect to the shank cross-sectional area. Data from a-SiC arrays were previously reported in a prior study from our group. In a manner consistent with the prior work, larger cross-sectional area silicon-based arrays were implanted in the motor cortex of female Sprague-Dawley rats and weekly recordings were made for 16 weeks after implantation. Single unit metrics from the recordings were compared over the implantation period between the device types. Overall, the expression of single units measured from a-SiC devices was significantly higher than for silicon-based MEAs throughout the implantation period. Immunohistochemical analysis demonstrated reduced neuroinflammation and gliosis around the a-SiC MEAs compared to silicon-based devices. Our findings demonstrate that the a-SiC MEAs with a smaller shank cross-sectional area can record single unit activity with more stability and exhibit a reduced inflammatory response compared to the silicon-based device employed in this study.},
}
@article {pmid40046512,
year = {2025},
author = {Fang, K and Wang, Z and Tang, Y and Guo, X and Li, X and Wang, W and Liu, B and Dai, Z},
title = {Dynamically Controlled Flight Altitudes in Robo-Pigeons via Locus Coeruleus Neurostimulation.},
journal = {Research (Washington, D.C.)},
volume = {8},
number = {},
pages = {0632},
pmid = {40046512},
issn = {2639-5274},
abstract = {Robo-pigeons, a novel class of hybrid robotic systems developed using brain-computer interface technology, hold marked promise for search and rescue missions due to their superior load-bearing capacity and sustained flight performance. However, current research remains largely confined to laboratory environments, and precise control of their flight behavior, especially flight altitude regulation, in a large-scale spatial range outdoors continues to pose a challenge. Herein, we focus on overcoming this limitation by using electrical stimulation of the locus coeruleus (LoC) nucleus to regulate outdoor flight altitude. We investigated the effects of varying stimulation parameters, including stimulation frequency (SF), interstimulus interval (ISI), and stimulation cycles (SC), on the flight altitude of robo-pigeons. The findings indicate that SF functions as a pivotal switch controlling the ascending and descending flight modes of the robo-pigeons. Specifically, 60 Hz stimulation effectively induced an average ascending flight of 12.241 m with an 87.72% success rate, while 80 Hz resulted in an average descending flight of 15.655 m with a 90.52% success rate. SF below 40 Hz did not affect flight altitude change, whereas over 100 Hz caused unstable flights. The number of SC was directly correlated with the magnitude of altitude change, enabling quantitative control of flight behavior. Importantly, electrical stimulation of the LoC nucleus had no significant effects on flight direction. This study is the first to establish that targeted variation of electrical stimulation parameters within the LoC nucleus can achieve precise altitude control in robo-pigeons, providing new insights for advancing the control of flight animal-robot systems in real-world applications.},
}
@article {pmid40044089,
year = {2025},
author = {Jialin, A and Zhang, HG and Wang, XH and Wang, JF and Zhao, XY and Wang, C and Cao, MN and Li, XJ and Li, Y and Cao, LL and Zhong, BL and Deng, W},
title = {Cortical activation patterns in generalized anxiety and major depressive disorders measured by multi-channel near-infrared spectroscopy.},
journal = {Journal of affective disorders},
volume = {379},
number = {},
pages = {549-558},
doi = {10.1016/j.jad.2025.02.116},
pmid = {40044089},
issn = {1573-2517},
mesh = {Humans ; *Depressive Disorder, Major/physiopathology/diagnosis/psychology ; Spectroscopy, Near-Infrared/methods ; Male ; Female ; *Anxiety Disorders/physiopathology/diagnosis/psychology ; Adult ; *Prefrontal Cortex/physiopathology ; Temporal Lobe/physiopathology ; Middle Aged ; Generalized Anxiety Disorder ; },
abstract = {BACKGROUND: Major depressive disorder (MDD) and generalized anxiety disorder (GAD) are highly prevalent mental disorders in psychiatry, but their overlapping symptoms often complicate precise diagnoses. This study aims to explore differential brain activation patterns in healthy controls (HC), MDD, and GAD groups through functional near-infrared spectroscopy (fNIRS) during the verbal fluency task (VFT) to enhance the accuracy of clinical diagnoses.<br><br>METHODS: This study recruited 30 patients with MDD, 45 patients with GAD, and 34 demographically matched HCs. Hemodynamic changes in the prefrontal cortex (PFC) and temporal lobes were measured using a 48-channel fNIRS during the VFT task. Demographics information, clinical characteristics and VFT performance were also recorded.<br><br>RESULTS: Compared to HCs, both MDD and GAD share a neurobiological phenotype of hypoactivation in the dorsolateral prefrontal cortex (DLPFC) and medial prefrontal cortex (mPFC) during VFT. Moreover, MDD patients exhibited significantly greater hypoactivation in the left DLPFC and mPFC than GAD patients.<br><br>CONCLUSIONS: Although both GAD and MDD patients exhibit disrupted cortical function, the impairment is less severe in GAD. These findings provide preliminary neurophysiological evidence supporting the utility of the fNIRS-VFT paradigm in differentiating GAD from MDD. This approach may complement traditional diagnostic methods, inform targeted interventions, and ultimately enhance patient outcomes.},
}
@article {pmid40044088,
year = {2025},
author = {Liang, S and Gao, Y and Palaniyappan, L and Song, XM and Zhang, T and Han, JF and Tan, ZL and Li, T},
title = {Transcriptional substrates of cortical thickness alterations in anhedonia of major depressive disorder.},
journal = {Journal of affective disorders},
volume = {379},
number = {},
pages = {118-126},
doi = {10.1016/j.jad.2025.03.003},
pmid = {40044088},
issn = {1573-2517},
mesh = {Humans ; *Depressive Disorder, Major/genetics/pathology/diagnostic imaging/psychology ; *Anhedonia/physiology ; Male ; Female ; Adult ; *Cerebral Cortex/pathology/diagnostic imaging ; Middle Aged ; Magnetic Resonance Imaging ; Neuroimaging ; *Brain Cortical Thickness ; Gene Expression ; Transcriptome ; },
abstract = {BACKGROUND: Anhedonia is a core symptom of major depressive disorder (MDD), which has been shown to be associated with abnormalities in cortical morphology. However, the correlation between cortical thickness (CT) changes with anhedonia in MDD and gene expression remains unclear.<br><br>METHODS: We investigated the link between brain-wide gene expression and CT correlates of anhedonia in individuals with MDD, using 7 Tesla neuroimaging and a publicly available transcriptomic dataset. The interest-activity score was used to evaluation MDD with high anhedonia (HA) and low anhedonia (LA). Nineteen patients with HA, nineteen patients with LA, and twenty healthy controls (HC) were enrolled. We investigated CT alterations of anhedonia subgroups relative to HC and related cortical gene expression, enrichment and specific cell types. We further used Neurosynth and von Economo-Koskinas atlas to assess the meta-analytic cognitive functions and cytoarchitectural variation associated with anhedonia-related cortical changes.<br><br>RESULTS: Both patient subgroups exhibited widespread CT reduction, with HA manifesting more pronounced changes. Gene expression related to anhedonia had significant spatial correlations with CT differences. Transcriptional signatures related to anhedonia-associated cortical thinning were connected to mitochondrial dysfunction and enriched in adipogenesis, oxidative phosphorylation, mTORC1 signaling pathways, involving neurons, astrocytes, and oligodendrocytes. These CT alterations were significantly correlated with meta-analytic terms involving somatosensory processing and pain perception. HA had reduced CT within the somatomotor and ventral attention networks, and in agranular cortical regions.<br><br>LIMITATIONS: These include measuring anhedonia using interest-activity score and employing a cross-sectional design.<br><br>CONCLUSIONS: This study sheds light on the molecular basis underlying gene expression associated with anhedonia in MDD, suggesting directions for targeted therapeutic interventions.},
}
@article {pmid40043367,
year = {2025},
author = {Demchenko, I and Shavit, T and Benyamini, M and Zacksenhouse, M},
title = {Self-correcting brain computer interface based on classification of multiple error-related potentials.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adbcda},
pmid = {40043367},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods/classification ; Male ; Adult ; Female ; Young Adult ; *Evoked Potentials/physiology ; Psychomotor Performance/physiology ; },
abstract = {Objective.Electroencephalogram (EEG) based brain-computer interfaces (BCIs) have shown tremendous promise in facilitating direct non-invasive brain-control over external devices. However, their practical application is hampered due to errors in command interpretation. A promising strategy for improving BCI accuracy is based on detecting error-related potentials (ErrPs), which are EEG potentials evoked in response to errors. Thus, performance can be improved by undoing actions that evoke potentials that the BCI detects as ErrPs. To achieve further improvement, we aimed to classify the type of error and correct, rather than just undo, erroneous actions. The objectives of this study are to develop an error classifier (EC) and to investigate the hypothesis that correcting the actions according to the EC decisions improves performance.Approach.To evaluate our hypothesis we developed a BCI application to control the pose of virtual hands with three possible commands: change the pose of either the right or left hand and maintain pose. Thus, when an action elicits an ErrP, the identity of the correct command is still undecided. The self-correcting BCI included an EC and was developed in three phases: hand control, initial brain control and self-correcting brain control. The first two phases were conducted by 22 participants, and half of them (n= 11) also completed the last phase.Main results.Detecting the type of error and correcting actions accordingly improved the success rate of the self-correcting BCI for each participant (n= 11), with a significant average improvement of 6.6%and best improvement of 13.5%.Significance.Self-correction, based on an EC, was demonstrated to improve the accuracy of BCIs for three commands. Thus, our work presents a significant step toward the development of more reliable and user-friendly non-invasive BCIs.},
}
@article {pmid40043361,
year = {2025},
author = {Luca, IS and Vuckovic, A},
title = {How are opposite neurofeedback tasks represented at cortical and corticospinal tract levels?.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adbcdb},
pmid = {40043361},
issn = {1741-2552},
mesh = {Humans ; *Neurofeedback/methods/physiology ; *Pyramidal Tracts/physiology ; Male ; Female ; Adult ; Young Adult ; *Evoked Potentials, Motor/physiology ; Electroencephalography/methods ; *Motor Cortex/physiology ; *Cerebral Cortex/physiology ; },
abstract = {Objective.The study objective was to characterise indices of learning and patterns of connectivity in two neurofeedback (NF) paradigms that modulate mu oscillations in opposite directions, and the relationship with change in excitability of the corticospinal tract (CST).Approach.Forty-three healthy volunteers participated in 3 NF sessions for upregulation (N = 24) or downregulation (N = 19) of individual alpha (IA) power at central location Cz. Brain signatures from multichannel electroencephalogram (EEG) were analysed, including oscillatory (power, spindles), non-oscillatory components (Hurst exponent), and effective connectivity directed transfer function (DTF) of participants who were successful at enhancing or suppressing IA power at Cz. CST excitability was studied through leg motor-evoked potential, tested before and after the last NF session. We assessed whether participants modulated widespread alpha or central mu rhythm through the use of current source density derivation (CSD), and related the change in activity in mu and upper half of mu band, to CST excitability change.Main results.In the last session, IA/mu power suppression was achieved by 79% of participants, while 63% enhanced IA. CSD-EEG revealed that mu power was upregulated through an increase in the incidence rate of bursts of alpha band activity, while downregulation involved changes in oscillation amplitude and temporal patterns. Neuromodulation also influenced frequencies adjacent to the targeted band, indicating the use of common mental strategies within groups. DTF analysis showed, for both groups, significant connectivity between structures commonly associated with motor imagery tasks, known to modulate the excitability of the motor cortex, although most connections did not remain significant after correcting for multiple comparisons. CST excitability modulation was related to the absolute amplitude of upper mu modulation, rather than the modulation direction.Significance.The upregulation and downregulation of IA/mu power during NF, with respect to baseline were achieved via distinct mechanisms involving oscillatory and non-oscillatory EEG features. Mu enhancement and suppression post-NF and during the last NF block with respect to the baseline, respectively corresponded to opposite trends in motor-evoked potential changes post-NF. The ability of NF to modulate CST excitability could be a valuable rehabilitation tool for central nervous system disorders (stroke, spinal cord injury), where increased excitability and neural plasticity are desired. This work may inform future neuromodulation protocols, and may improve NF training effectiveness by rewarding certain EEG signatures.},
}
@article {pmid40043320,
year = {2025},
author = {Alcolea, PI and Ma, X and Bodkin, K and Miller, LE and Danziger, ZC},
title = {Less is more: selection from a small set of options improves BCI velocity control.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
pmid = {40043320},
issn = {1741-2552},
support = {R01 NS109257/NS/NINDS NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Animals ; Humans ; Macaca mulatta ; Algorithms ; Male ; Female ; Adult ; Electroencephalography/methods ; Psychomotor Performance/physiology ; },
abstract = {Objective.Decoding algorithms used in invasive brain-computer interfaces (iBCIs) typically convert neural activity into continuously varying velocity commands. We hypothesized that putting constraints on which decoded velocity commands are permissible could improve user performance. To test this hypothesis, we designed the discrete direction selection (DDS) decoder, which uses neural activity to select among a small menu of preset cursor velocities.Approach. We tested DDS in a closed-loop cursor control task against many common continuous velocity decoders in both a human-operated real-time iBCI simulator (the jaBCI) and in a monkey using an iBCI. In the jaBCI, we compared performance across four visits by each of 48 naïve, able-bodied human subjects using either DDS, direct regression with assist (an affine map from neural activity to cursor velocity, DR-A), ReFIT, or the velocity Kalman Filter (vKF). In a follow up study to verify the jaBCI results, we compared a monkey's performance using an iBCI with either DDS or the Wiener filter decoder (a direct regression decoder that includes time history, WF).Main Result. In the jaBCI, DDS substantially outperformed all other decoders with 93% mean targets hit per visit compared to DR-A, ReFIT, and vKF with 56%, 39%, and 26% mean targets hit, respectively. With the iBCI, the monkey achieved a 61% success rate with DDS and a 37% success rate with WF.Significance. Discretizing the decoded velocity with DDS effectively traded high resolution velocity commands for less tortuous and lower noise trajectories, highlighting the potential benefits of discretization in simplifying online BCI control.},
}
@article {pmid40043184,
year = {2025},
author = {Chandler, JA},
title = {Inferring Mental States from Brain Data: Ethico-legal Questions about Social Uses of Brain Data.},
journal = {The Hastings Center report},
volume = {55},
number = {1},
pages = {22-32},
pmid = {40043184},
issn = {1552-146X},
support = {//ERANET-Neuron Program/ ; /CAPMC/CIHR/Canada ; },
mesh = {Humans ; *Brain/physiology ; *Privacy/legislation &amp; jurisprudence ; },
abstract = {Neurotechnologies that collect and interpret data about brain activity are already in use for medical and nonmedical applications. Refinements of existing noninvasive techniques and the discovery of new ones will likely encourage broader uptake. The increased collection and use of brain data and, in particular, their use to infer the existence of mental states have led to questions about whether mental privacy may be threatened. It may be threatened if the brain data actually support inferences about the mind or if decisions are made about a person in the belief that the inferences are justified. This article considers the chain of inferences lying between data about neural activity and a particular mental state as well as the ethico-legal issues raised by making these inferences, focusing here on what the threshold of reliability should be for using brain data to infer mental states.},
}
@article {pmid40043182,
year = {2025},
author = {Yang, Y and Wang, Y and Wang, X},
title = {Harnessing psychedelics for stroke recovery: therapeutic potential and mechanisms.},
journal = {Brain : a journal of neurology},
volume = {148},
number = {6},
pages = {1862-1865},
doi = {10.1093/brain/awaf093},
pmid = {40043182},
issn = {1460-2156},
support = {2021ZD0203000//STI2030-Major Projects/ ; 2021ZD0203003//STI2030-Major Projects/ ; BMI2400014//Zhejiang University/ ; },
}
@article {pmid40042910,
year = {2025},
author = {Xie, B and Xiong, T and Guo, G and Pan, C and Ma, W and Yu, P},
title = {Bioinspired ion-shuttling memristor with both neuromorphic functions and ion selectivity.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {122},
number = {10},
pages = {e2417040122},
pmid = {40042910},
issn = {1091-6490},
support = {22125406 22074149//MOST | National Natural Science Foundation of China (NSFC)/ ; },
abstract = {The fluidic memristor has attracted growing attention as a promising candidate for neuromorphic computing and brain-computer interfaces. However, a fluidic memristor with ion selectivity as that of natural ion channels remains a key challenge. Herein, inspired by the structure of natural biomembranes, we developed an ion-shuttling memristor (ISM) by utilizing organic solvents and artificial carriers to emulate ion channels embedded in biomembranes, which exhibited both neuromorphic functions and ion selectivity. Pinched hysteresis I-V loop curve, scan rate dependency, and distinctive impedance spectra confirmed the memristive characteristics of the as-prepared device. Moreover, the memory mechanism was discussed theoretically and validated by finite-element modeling. The ISM features multiple neuromorphic functions, such as paired-pulse facilitation, paired-pulse depression, and learning-experience behavior. More importantly, the ion selectivity of the ISM was observed, which allowed further emulation of ion-selective neural functions like resting membrane potential. Benefiting from the structural similarity to membrane-embedded ion channels, the ISM opens the door for ion-based neuromorphic computing and sophisticated chemical regulation by manipulating multifarious ions with neuromorphic functions.},
}
@article {pmid40042891,
year = {2025},
author = {Gielas, AM},
title = {Man, Hibernating Animals, and Poikilothermic Fish: The Present and Future of BCI Technology.},
journal = {Journal of special operations medicine : a peer reviewed journal for SOF medical professionals},
volume = {25},
number = {1},
pages = {50-54},
doi = {10.55460/FA29-NVKE},
pmid = {40042891},
issn = {1553-9768},
mesh = {*Brain-Computer Interfaces/trends ; Humans ; *Military Personnel ; Animals ; },
abstract = {In 2024 and early 2025, several successful surgeries involving brain-computer interfaces (BCIs) gained media attention, including those conducted by Elon Musk's company Neuralink, which implanted BCIs in three paralyzed volunteers, allowing them to control computers through thought alone. While the concept of merging humans with machines dates back to the 1960s, BCI technology has now entered the clinical trial stage, with a focus on restoring communication, mobility, and sensation in individuals with severe disabilities and neurodegenerative disorders. For over two decades, BCIs have also been explored as tools to enhance the cognitive and physical abilities of military personnel. However, before Special Operations Forces (SOF) medical staff encounter BCIs in an enhancement capacity, they are likely to first come across them in medical settings. This article provides an overview of BCI technology, focusing on 1) how it works, 2) its potential significance for injured SOF servicemembers, 3) current challenges, and 4) its potential to enhance SOF in the future.},
}
@article {pmid40040918,
year = {2025},
author = {Jiang, Y and Liu, YL and Zhou, X and Shu, QQ and Dong, L and Xu, Z and Wan, JQ},
title = {A retrospective study of the Dual-channels Bolus Contrast Injection (Dc-BCI) technique during endovascular mechanical thrombectomy in the management of acute ischemic stroke due to large-vessel occlusion: a technical report.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1508976},
pmid = {40040918},
issn = {1664-2295},
abstract = {Endovascular mechanical thrombectomy (EMT) is an effective treatment for acute ischemic stroke and identifying the precise thrombus size remains key to a successful EMT. However, no imaging modality has been able to provide this information simultaneously and efficiently in an emergency setting. The present study introduces a novel technique named dual-channel bolus contrast injection (Dc-BCI) for determining thrombus size and location during EMT. In the in vitro study, the Dc-BCI demonstrated an accurate projection of the thrombus size, as the actual thrombus diameter (R[2] = 0.92, p < 0.01) and length (R[2] = 0.94, p < 0.01) exhibited a high degree of correlation with that of obtained from Dc-BCI. Consequently, between February 2023 and August 2024, 87 patients diagnosed with acute cerebral large vessel occlusions were enrolled in the study and received EMT for the treatment of acute cerebral large vessel occlusions. The Dc-BCI was successfully performed in all patients to measure the diameter and length of the thrombus. These information were used to select an appropriate stent-retriever for EMT. The restoration of blood flow was achieved in 84 patients (96.6%) to an mTICI score of 2b/3. Additionally, a low incidence of postoperative complications was observed (e.g., subarachnoid hemorrhage 8% and cerebral hemorrhage 5.7%). In conclusion, it can be posited that the Dc-BCI has the potential to enhance the outcomes of EMT, as it is capable of revealing the thrombus size information, which optimizes the interaction between the stent retriever and the thrombus, while simultaneously reducing the risk of vascular injury that is associated with the prolonged use of the stent retriever.},
}
@article {pmid40040909,
year = {2025},
author = {Tekin, U and Dener, M},
title = {A bibliometric analysis of studies on artificial intelligence in neuroscience.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1474484},
pmid = {40040909},
issn = {1664-2295},
abstract = {The incorporation of artificial intelligence (AI) into neuroscience has the potential to significantly enhance our comprehension of brain function and facilitate more effective diagnosis and treatment of neurological disorders. Artificial intelligence (AI) techniques, particularly deep learning and machine learning, offer transformative solutions by improving the analysis of complex neural data, facilitating early diagnosis, and enabling personalized treatment approaches. A bibliometric analysis is a method that employs quantitative techniques for the examination of scientific literature, with the objective of identifying trends in research, evaluating the impact of influential studies, and mapping the networks of collaboration. In light of the accelerated growth and interdisciplinary scope of AI applications in neuroscience, a bibliometric analysis is vital for mapping the landscape, identifying pivotal contributions, and underscoring emerging areas of interest. This study aims to address this need by examining 1,208 studies published between 1983 and 2024 from the Web of Science database. The analysis reveals a notable surge in publications since the mid-2010s, with substantial advancements in neurological imaging, brain-computer interfaces (BCI), and the diagnosis and treatment of neurological diseases. The analysis underscores the pioneering role of countries such as the United States, China, and the United Kingdom in this field and highlights the prevalence of international collaboration. This study offers a comprehensive overview of the current state and future directions of AI applications in neuroscience, as well as an examination of the transformative potential of AI in advancing neurological research and healthcare. It is recommended that future research address the ethical issues, data privacy concerns, and interpretability of AI models in order to fully capitalize on the benefits of AI in neuroscience.},
}
@article {pmid40040811,
year = {2025},
author = {Feng, X and Bao, X and Huang, H and Wang, Z and Hu, W and Xue, C and Song, Z and Cai, Y and Huang, Q and Li, Y},
title = {Frontal gamma-alpha ratio reveals neural oscillatory mechanism of attention shifting in tinnitus.},
journal = {iScience},
volume = {28},
number = {3},
pages = {111929},
pmid = {40040811},
issn = {2589-0042},
abstract = {In clinical practice, the symptoms of tinnitus patients can be temporarily alleviated by diverting their attention away from disturbing sounds. However, the precise mechanisms through which this alleviation occurs are still not well understood. Here, we aimed to directly evaluate the role of attention in tinnitus alleviation by conducting distraction tasks with multilevel loads and resting-state tests among 52 adults with tinnitus and 52 healthy controls. We demonstrated that the abnormal neural oscillations in tinnitus subjects, reflected in an altered gamma/alpha ratio index in the frontal lobe, could be regulated by attention shifting in a linear manner for which the regulatory effect increased with the load of distraction. Quantitative measures of the regulation significantly correlated with symptom severity. Altogether, our work provides proof-of-concept for the role of attention in tinnitus perception and lays a solid foundation to support evidence-based applications of attention shifting in clinical interventions for tinnitus.},
}
@article {pmid40040231,
year = {2024},
author = {Chaudhry, ZA and Baxter, RH and Fu, JL and Wang, PT and Sohn, WJ and Do, AH},
title = {Feasibility of Immersive Virtual Reality Feedback for Enhancing Learning in Brain-Computer Interface Control of Ambulation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782667},
pmid = {40040231},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; *Virtual Reality ; *Learning ; Male ; *Walking/physiology ; Electroencephalography ; Feasibility Studies ; Adult ; Female ; Feedback ; },
abstract = {After prolonged paralysis, paraplegic spinal cord injury (SCI) patients typically lose the ability to generate the expected electroencephalogram (EEG) α/β modulation associated with leg movements. Brain computer interface (BCI)-controlled ambulation devices have emerged as a way to restore brain-controlled walking, but this loss of EEG signal modulation may impede the ability to operate such systems and prolonged training may be necessary to restore this physiologic phenomenon. To address this issue, this study explores the use of immersive virtual reality (VR) in providing more convincing feedback to enhance learning within a BCI training paradigm. Here, an EEG-based BCI-controlled walking simulator with an environment composed of 10 designated stop zones along a linear course was used to test this concept. Able-bodied subjects were tasked with using idling or kinesthetic motor imagery (KMI) of gait to control an avatar to either dwell at each designated stop for 5 s or advance along the course respectively. Subject performance was measured using a composite score per run and learning rate across runs. Composite scores were calculated as the geometric mean of two subscores: a stop score (reflecting the number of successful stops), and a time score (reflecting how fast the course was completed). The learning rate was calculated as the slope of the composite scores across all runs. A random walk procedure was performed to determine the statistical likelihood that each BCI run was purposeful (p≤ 0.001). Three able-bodied subjects were recruited (2 in immersive VR group and 1 in non-immersive VR group), and operated the simulator for up to 4 separate visits. The immersive VR group achieved an average composite score of 60.4% ± 12.9, while the non-VR group had an average composite score of 79.0% ± 12.2. The learning rate was 1.07%/run and 0.42%/run for the immersive and non-immersive VR groups, respectively. Purposeful control was attained in a higher proportion of runs for the immersive VR group than in the non-immersive VR group. Although limited by small sample size, this study demonstrates a conceptual framework of implementing immersive VR feedback using more convincing sensory feedback to aid training with BCI devices. Future work will test this protocol in SCI patients and with larger sample size.},
}
@article {pmid40040213,
year = {2024},
author = {Okitsu, K and Isezaki, T and Obara, K and Nishimura, Y},
title = {Enhancing Brain Machine Interface Decoding Accuracy through Domain Knowledge Integration.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782166},
pmid = {40040213},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Animals ; Torque ; Humans ; Algorithms ; Macaca mulatta ; Wrist/physiology ; Muscle, Skeletal/physiology ; },
abstract = {This paper introduces a novel decoding approach for Brain Machine Interface (BMI) that enhances the estimation accuracy and stability of muscle activity by incorporating domain knowledge of motor control. Our approach uniquely integrates domain knowledge, focusing on the relationship between torque direction and muscle activity in isometric wrist tasks. We demonstrate the effectiveness of our approach through decoding analysis with non-human primates performing a wrist torque tracking task. By implementing a Kalman filter augmented with models of muscle activity and torque for specific movement directions, we show significant improvements compared to vanilla Kalman filter in the accuracy of muscle activity estimation. The proposed approach presents a promising direction for enhancing the performance of BMI by leveraging domain-specific insights into motor control.},
}
@article {pmid40040208,
year = {2024},
author = {Li, D and Shin, HB and Yin, K and Lee, SW},
title = {Domain-Incremental Learning Framework for Continual Motor Imagery EEG Classification Task.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10781886},
pmid = {40040208},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; Humans ; Brain-Computer Interfaces ; *Imagination/physiology ; *Machine Learning ; Algorithms ; Signal Processing, Computer-Assisted ; },
abstract = {Due to inter-subject variability in electroencephalogram (EEG) signals, the generalization ability of many existing brain-computer interface (BCI) models is significantly limited. Although transfer learning (TL) offers a temporary solution, in scenarios requiring sustained knowledge transfer, the performance of TL-based models gradually declines as the number of transfers increases-a phenomenon known as catastrophic forgetting. To address this issue, we introduce a novel domain-incremental learning framework for the continual motor imagery (MI) EEG classification. Specifically, to learn and retain common features between subjects, we separate latent representations into subject-invariant and subject-specific features through adversarial training, while also proposing an extensible architecture to preserve features that are easily forgotten. Additionally, we incorporate a memory replay mechanism to reinforce previously acquired knowledge. Through extensive experiments, we demonstrate our framework's effectiveness in mitigating forgetting within the continual MI-EEG classification task.},
}
@article {pmid40040206,
year = {2024},
author = {Huang, CM and Lai, WL and Yang, CC and Hsieh, YJ and Wu, CM and Lee, CH},
title = {EEG Channel Localization and Selection via Training with Noise Injection for BCI Applications.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782774},
pmid = {40040206},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; Humans ; Algorithms ; *Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; Electrodes ; },
abstract = {Electroencephalography (EEG) is crucial for monitoring brain activity in neuroscience and clinical applications. However, the multitude of channels recorded by scalp electrodes poses challenges, including impractical usage and high model complexity. This paper addresses the challenges of high dimensionality in EEG data and introduces an innovative EEG channel selection algorithm, LSvT-NI, based on model training and noise injection, achieving substantial reductions in channels, model size, and complexity while maintaining high classification accuracy. Validated through experiments on EEGNet and the BCI Competition IV 2a dataset, the algorithm proves beneficial for practical and cost-efficient scenarios. Specifically, experiments on the BCI Competition IV 2a dataset demonstrate that LSvT-NI with white noise and pink noise at 5dB SNR achieves a remarkable 77.3% and 72.7% reduction in channels, along with 11.7% and 11% reductions in model size, and 86.9% and 71.8% in computation complexity.},
}
@article {pmid40040181,
year = {2024},
author = {Sartipi, S and Cetin, M},
title = {Robust EEG-based Emotion Recognition Using an Inception and Two-sided Perturbation Model.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782060},
pmid = {40040181},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; *Emotions/physiology ; Humans ; Algorithms ; Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; Deep Learning ; },
abstract = {Automated emotion recognition using electroencephalogram (EEG) signals has gained substantial attention. Although deep learning approaches exhibit strong performance, they often suffer from vulnerabilities to various perturbations, like environmental noise and adversarial attacks. In this paper, we propose an Inception feature generator and two-sided perturbation (INC-TSP) approach to enhance emotion recognition in brain-computer interfaces. INC-TSP integrates the Inception module for EEG data analysis and employs two-sided perturbation (TSP) as a defensive mechanism against input perturbations. TSP introduces worst-case perturbations to the model's weights and inputs, reinforcing the model's elasticity against adversarial attacks. The proposed approach addresses the challenge of maintaining accurate emotion recognition in the presence of input uncertainties. We validate INC-TSP in a subject-independent three-class emotion recognition scenario, demonstrating robust performance.},
}
@article {pmid40040170,
year = {2024},
author = {Meng, J and Yang, M and Zhang, S and Xu, M and Meng, L and Ming, D},
title = {An online brain-computer interface for a precise positioning of target based on rapid serial visual presentation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782815},
pmid = {40040170},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Male ; Female ; Adult ; Evoked Potentials/physiology ; Young Adult ; Signal Processing, Computer-Assisted ; },
abstract = {The brain-computer interface (BCI) based on rapid serial visual presentation (RSVP) provides a novel approach for efficiently optimizing traditional machine-based target detection, revealing a broad application prospect in security, entrainment, monitoring, etc. A bottleneck of current RSVP-BCI is that its detectable result is limited to a binary way, i.e., target vs. non-target, more detailed and important information about targets, such as the precise position, remains undetectable. To solve this problem, this study investigated the relationship between targets positions (up, down, left, right) and electroencephalogram (EEG) characteristics, and tested the separability of EEGs induced by the four targets positions in an online RSVP-BCI. Twelve healthy subjects participated in this study, event-related potential (ERP), topographies, laterality index (LI), discriminant canonical pattern matching (DCPM) methods were used to analyzed the EEG data. Consequently, left-right targets induced ipsilateral ERPs between bilateral hemispheres; when targets appeared at up and down positions, opposite ERPs were found between frontal and occipital areas; up-down and left-right difference reached its maximum in the 140~190ms and 190~240ms temporal window, respectively. Single-trial classification showed five-class balanced accuracy (BACC) (non-target, target at up/ down/ left/ right position) was 71.02% and 67.91% for offline and online sessions, respectively. The results provide new understanding of the RSVP features for developing BCIs.},
}
@article {pmid40040166,
year = {2024},
author = {Zhuo, F and Lv, B and Tang, F},
title = {Time Window Optimization for Riemannian Geometry-based Motor Imagery EEG Classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10782640},
pmid = {40040166},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; Algorithms ; Signal Processing, Computer-Assisted ; *Imagination/physiology ; Time Factors ; },
abstract = {The existing Riemannian geometry-based approaches for brain computer interface (BCI) employ fixed time windows. However, the inherent variability and dynamic changes among subjects necessitate robust and adaptive solutions for time window optimization. Recognizing the current limitations of Riemannian classifiers, we propose a time window selection confidence metric (TWSCM) based on Riemannian geometry. This metric operates on the manifold of symmetric positive definite (SPD) matrices, providing a theoretically grounded and computationally efficient approach for time window optimization. The optimization process is unsupervised, which is able to deal with the online scenario without training labels. Experimental results on the BCI competition IV dataset IIa demonstrate that the classification performance is significantly improved for most subjects. The average performance over six subjects improved by 7.52%. The simulated online experiment shows enhanced performance in comparison to baseline experiments without time window optimization. Additionally, an in-depth analysis of TWSCM provides insights into performance variations among subjects. Overall, this paper introduces the first time window optimization method within the Riemannian geometric framework, presenting an effective and interpretable approach for optimizing time windows in motor imagery classification, providing a novel and promising perspective in EEG signal analysis.},
}
@article {pmid40040155,
year = {2024},
author = {Huang, J and Tostado-Marcos, P and Narasimha, SM and Patel, AN and Arneodo, EM and Gentner, TQ and Mishne, G and Gilja, V},
title = {Guiding Brain-to-Vocalization Decoder Design Using Structured Generalization Error.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782761},
pmid = {40040155},
issn = {2694-0604},
mesh = {Humans ; *Vocalization, Animal/physiology ; Animals ; *Brain/physiology ; Finches/physiology ; *Brain-Computer Interfaces ; },
abstract = {State-of-the-art intracortical neuroprostheses currently enable communication at 60+ words per minute for anarthric individuals by training on over 10K sentences to account for phoneme variability in different word contexts. There is limited understanding about whether this performance can be maintained in decoding naturalistic speech with 40K+ word vocabularies across elicited, spontaneous, and conversational speech contexts. We introduce a vocal-unit-level generalization test to explicitly evaluate neural decoder performance with an expanded and more diverse behavioral repertoire. Tested on neural decoders modeling zebra finch vocalization, an analog to human vocal production, we compare three decoders with different input types: spike trains, neural factors, and firing rates. The factors and rates are latent neural features inferred using trained Latent Factor Analysis via Dynamical Systems (LFADS) models that capture the population neural dynamics during vocal production. While the conventional random holdout generalization error measure is similar for all three decoders, factor- and rate-based decoders outperform spike-based decoders when testing vocal-unit-holdout generalization error. These results suggest the later models better adapt to flexible vocalization inference when trained with partial observation of data variation, motivating further exploration of decoders incorporating latent neural and vocalization dynamics.},
}
@article {pmid40040137,
year = {2024},
author = {Idowu, OP and Kinney-Lang, E and Gulamhusein, A and Irvine, B and Kirton, A and Abou-Zeid, H},
title = {Profiling a Raspberry Pi-Based Motor Imagery Classification to Facilitate At-Home BCI for Children with Disabilities.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-7},
doi = {10.1109/EMBC53108.2024.10781873},
pmid = {40040137},
issn = {2694-0604},
mesh = {Humans ; Child ; *Brain-Computer Interfaces ; Neural Networks, Computer ; *Children with Disabilities ; Electroencephalography ; Machine Learning ; Algorithms ; },
abstract = {There has been incremental progress in moving BCI out of the laboratory environment and into the homes of those who would benefit most, especially children living with severe physical disabilities. Practical issues, such as available computational resources and long calibration times, have slowed down the adoption of such systems. To develop an efficient and scalable machine learning framework consistent with early approaches that facilitate at-home BCI use, this study provides valuable insights into measuring the behavioral characteristics of a Raspberry Pi 4 (RPi4) during the operation and execution of standard BCI processes, including the training and evaluation of classifier models. The results, which evaluated ten standard classifiers, including the Riemannian Geometry (RG) framework and more advanced deep learning approaches like Artificial Neural Network (ANN), were profiled on RPi4. These were compared to Desktop and MacBook computations for metrics such as training time, inference time, peak memory, and incremental memory usage, with computational bottlenecks identified. Our assessment revealed comparable performance metrics (84.3% of accuracy, recall, and f1_score, and 84.7% precision) for the neural network models despite the lower computational resources. Profiling results, including 1.74 sec training time, 0.405 sec inference time, 1154.9 MiB peak memory, and 405.2 MiB incremental memory usage, also demonstrated that the RPi4 is a potentially viable device for low-cost BCI systems. However, high-resource demanding classifiers such as ANN may need to be carefully considered in their implementation, which, in turn, will scale down the potential cost and complexity of adopting practical, impactful at-home BCI systems.},
}
@article {pmid40040110,
year = {2024},
author = {Wang, Z and Liu, Y and Wu, W and Huang, S and An, X and Ming, D},
title = {EEG Pattern Comparison and Classification Performance of Motor Imagery Between Supernumerary and Inherent Limbs.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782967},
pmid = {40040110},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Imagination/physiology ; Male ; Adult ; Brain-Computer Interfaces ; Movement/physiology ; *Extremities/physiology ; Female ; },
abstract = {Adding supernumerary robotic limbs (SRLs) to humans and controlling them directly through the brain are main goals for movement augmentation. However, whether neural patterns that are distinct from the traditional inherent limbs motor imagery (MI) paradigm can be extracted, which is essential for the high-dimensional control of external equipment. In this study, a novel type of MI paradigm based on SRLs was proposed, consisting of "the sixth-finger", "the third-arm" and "the third-leg", and validated the distinctness of EEG response patterns between the novel and the traditional (hand, arm and leg) MI paradigm. The results showed that imagining extra limbs induced more obvious event-related desynchronization (ERD) phenomenon in sensorimotor areas compared to imagining inherent limbs. Classification results indicate well separable performance among different mental tasks (all above 86%, with a maximum of 90.5%). This work proposed a novel type of MI paradigm, and offered new way for widening the control bandwidth of the BCI system.},
}
@article {pmid40040096,
year = {2024},
author = {Lim, EY and Yin, K and Shin, HB and Lee, SW},
title = {Baseline-Guided Representation Learning for Noise-Robust EEG Signal Classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10781970},
pmid = {40040096},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; Humans ; Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; Algorithms ; Deep Learning ; *Machine Learning ; },
abstract = {Brain-computer interfaces (BCIs) suffer from limited accuracy due to noisy electroencephalography (EEG) signals. Existing denoising methods often remove artifacts such as eye movement or use techniques such as linear detrending, which inadvertently discard crucial task-relevant information. To address this issue, we present BGNet, a novel deep learning framework that leverages underutilized baseline EEG signals for dynamic noise mitigation and robust feature extraction to improve motor imagery (MI) EEG classification. Our approach employs data augmentation to strengthen model robustness, an autoencoder to extract features from baseline and MI signals, a feature alignment module to separate specific task and noise, and a classifier. We achieve state-of-the-art performance, an improvement of 5.9% and 3.7% on the BCIC IV 2a and 2b datasets, respectively. The qualitative analysis of our learned features proves superior representational power over baseline models, a critical aspect in dealing with noisy EEG signals. Our findings demonstrate the efficacy of readily available baseline signals in enhancing performance, opening possibilities for simplified BCI systems in brain-based communication applications.},
}
@article {pmid40040056,
year = {2024},
author = {Huang, S and Liu, Y and Xu, W and Wang, Z and Ming, D},
title = {Enhancement of Functional Connectivity in Frontal-Parietal Regions After BCI-Actuated Supernumerary Robotic Finger Training.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10781807},
pmid = {40040056},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; *Fingers/physiology ; Magnetic Resonance Imaging ; Male ; *Robotics ; *Frontal Lobe/physiology ; Adult ; *Parietal Lobe/physiology ; Female ; Young Adult ; },
abstract = {The supernumerary robotic finger (SRF) can expand human hand abilities to achieve motor augmentation, and integrate with brain computer interface (BCI) to free the occupation of inherent body degrees of freedom. However, the neuro remodeling mechanisms of brain-actuated SRF training is not clear. In this study, a BCI-actuated SRF was used to investigate the concurrent changes in behavior and brain activity. After 4 weeks BCI-SRF training, the novel sequence operation accuracy rate enhanced by more than 350% compared with innate finger training (IFT). Task-based fMRI showed a significant increase in lateral activation of sensorimotor cortex and found a significant activation change in S1M1_L area. Moreover, BCI-SRF training significantly increase functional connectivity (FC) between S1M1_L and Frontal_Mid_L compared with IFT at post stage. And this FC increase in frontal-parietal is also significant at post vs pre in BCI-SRF group and significantly correlated with the improvement of motor sequence accuracy rate. Our findings provide useful insights into the enhanced human-machine interaction and this efficacy exhibited significant potential for clinical rehabilitation application.},
}
@article {pmid40040035,
year = {2024},
author = {Norouzi, M and Amirani, MZ and Shahriari, Y and Abiri, R},
title = {Precision Enhancement in Sustained Visual Attention Training Platforms: Offline EEG Signal Analysis for Classifier Fine-Tuning.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10782784},
pmid = {40040035},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Attention/physiology ; Support Vector Machine ; *Brain-Computer Interfaces ; Male ; *Signal Processing, Computer-Assisted ; Adult ; Female ; Young Adult ; Algorithms ; Evoked Potentials/physiology ; },
abstract = {In this study, a novel open-source brain-computer interface (BCI) platform was developed to decode scalp electroencephalography (EEG) signals associated with sustained attention. The EEG signal collection was conducted using a wireless headset during a sustained visual attention task, where participants were instructed to discriminate between composite images superimposed with scenes and faces, responding only to the relevant subcategory while ignoring the irrelevant ones. Seven volunteers participated in this experiment. The data collected were subjected to analyses through event-related potential (ERP), Hilbert Transform, and Wavelet Transform to extract temporal and spectral features. For each participant, utilizing its extracted features, personalized Support Vector Machine (SVM) and Random Forest (RF) models with tuned hyperparameters were developed. The models aimed to decode the participant's attentional state towards the face and scene stimuli. The SVM models achieved a higher average accuracy of 80% and an Area Under the Curve (AUC) of 0.86, while the RF models showed an average accuracy of 78% and AUC of 0.8. This work suggests potential applications for the evaluation of visual attention and the development of closed-loop brainwave regulation systems in the future.},
}
@article {pmid40040033,
year = {2024},
author = {Ferdous, TR and Pollonini, L and Francis, JT},
title = {Enhancing Auditory BCI Performance: Incorporation of Connectivity Analysis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10782147},
pmid = {40040033},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Support Vector Machine ; *Brain/physiology ; Acoustic Stimulation ; Signal Processing, Computer-Assisted ; },
abstract = {Brain connectivity analysis to classify auditory stimuli applicable to invasive auditory BCI technology, particularly intracranial electroencephalography (iEEG) remains an exciting frontier. This study revealed insights into brain network dynamics, improving analysis precision to distinguish related auditory stimuli such as speech and music. We thereby contribute to advancing auditory BCI systems to bridge the gap between noninvasive and invasive BCI by utilizing noninvasive BCI methodological frameworks to invasive BCI (iEEG) data. We focused on the viability of using connectivity matrices in BCI calculated across brain waves such as alpha, beta, theta, and gamma. The research highlights that the traditional machine learning classifier, Support Vector Machine (SVM), demonstrates exceptional capabilities in handling brain connectivity data, exhibiting an outstanding 97% accuracy in classifying brain states, surpassing previous relevant studies with an improvement of 9.64% The results are significant as we show that neural activity in the gamma band provides the best classification performance using connectivity matrices calculated with Phase Locking Values and Coherence methods.},
}
@article {pmid40039972,
year = {2024},
author = {Ye, H and Goerttler, S and He, F},
title = {EEG-GMACN: Interpretable EEG Graph Mutual Attention Convolutional Network.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782694},
pmid = {40039972},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; *Neural Networks, Computer ; Algorithms ; Electrodes ; Brain/physiology ; Brain-Computer Interfaces ; },
abstract = {Electroencephalogram (EEG) is a valuable technique to record brain electrical activity through electrodes placed on the scalp. Analyzing EEG signals contributes to the understanding of neurological conditions and developing brain-computer interface. Graph Signal Processing (GSP) has emerged as a promising method for EEG spatial-temporal analysis, by further considering the topological relationships between electrodes. However, existing GSP studies lack interpretability of electrode importance and the credibility of prediction confidence. This work proposes an EEG Graph Mutual Attention Convolutional Network (EEG-GMACN), by introducing an 'Inverse Graph Weight Module' to output interpretable electrode graph weights, enhancing the clinical credibility and interpretability of EEG classification results. Additionally, we incorporate a mutual attention mechanism module into the model to improve its capability to distinguish critical electrodes and introduce credibility calibration to assess the uncertainty of prediction results. This study enhances the transparency and effectiveness of EEG analysis, paving the way for its widespread use in clinical and neuroscience research.},
}
@article {pmid40039945,
year = {2024},
author = {Kanda, T and Isezaki, T and Okitsu, K},
title = {A Study on Changes in Estimation Accuracy for EEG Data During Calibration and Operation in MI-BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782616},
pmid = {40039945},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; Calibration ; Male ; Signal Processing, Computer-Assisted ; Adult ; Algorithms ; Female ; Deep Learning ; },
abstract = {Changes in psychological factors have been suggested to cause variations in brain-computer interface (BCI) performance. More specifically, differences in psychological variables between the calibration and operation phases may cause a decrease in accuracy during operation, presenting a potential challenge for the adoption of BCI technology. The purpose of this study is to analyze the differences in accuracy between the calibration and operation phases of a BCI using a deep learning model. We structured tasks to simulate the calibration and operation phases, and participants performed motor imagery tasks under both conditions. The analysis revealed a significant decrease in accuracy for data obtained under the operation condition, highlighting the need for techniques capable of adapting to the electroencephalography signal data produced when users execute operations.},
}
@article {pmid40039943,
year = {2024},
author = {Wang, J and Li, X and Huang, Y and Xiao, D and Fan, Y and Huang, W and Hu, Y},
title = {Patient-Involved Validation of A Somatosensory ERP-BCI Facilitated by Electric Stimulation for Stroke Rehabilitation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-6},
doi = {10.1109/EMBC53108.2024.10781706},
pmid = {40039943},
issn = {2694-0604},
mesh = {Humans ; *Stroke Rehabilitation ; *Brain-Computer Interfaces ; Electroencephalography ; *Electric Stimulation ; *Evoked Potentials, Somatosensory ; Male ; Stroke/physiopathology ; Neural Networks, Computer ; Female ; Adult ; },
abstract = {Brain-computer interface (BCI) is emerging as an effective complementary solution in the field of rehabilitation for the interaction between patients and robotic assistive devices. Specifically, the somatosensory event-related potentials (ERP) BCI has unique advantage for post-stroke motor rehabilitation scenarios and has been proven feasible on healthy subjects. We conducted the first patient-involved somatosensory ERP-BCI experiment with electric stimulation to evaluate its feasibility for real-world clinical usage. In the experiment, participant selectively attended to electric stimuli applied on either left or right wrist, which represented the operation of robot-assisted exercise of corresponding hand. An integrated platform that included exercise, stimulation, and electroencephalography (EEG) sampling modules was used. For evaluation, we used convolutional neural network (CNN) with transformer module to construct subject-specific intent decoder. The network demonstrated on average 58.95% accuracy in classifying target response from a single ERP trial. When using the classification from multiple consecutive trials, the decoder achieved a maximum of 80.12% mean accuracy in recognizing participants intent, and the highest rate from a single participant was 97.21%. The best information transfer rate (ITR) achieved was 1.956 Bit/min. These results demonstrated that the proposed BCI paradigm could be a valid choice for stroke rehabilitation. In the next stage, we anticipate the involvement of larger patient population, real-time feedback training, and the subsequent quantified motor function recovery results.},
}
@article {pmid40039935,
year = {2024},
author = {Han, HT and Kim, SJ and Lee, DH and Lee, SW},
title = {Proxy-based Masking Module for Revealing Relevance of Characteristics in Motor Imagery.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782698},
pmid = {40039935},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Imagination/physiology ; *Signal Processing, Computer-Assisted ; Movement/physiology ; Algorithms ; },
abstract = {Brain-computer interface (BCI) has been developed for communication between users and external devices by reflecting users' status and intentions. Motor imagery (MI) is one of the BCI paradigms for controlling external devices by imagining muscle movements. MI-based EEG signals generally tend to contain signals with sparse MI characteristics (sparse MI signals). When conducting domain adaptation (DA) on MI signals with sparse MI signals, it could interrupt the training process. In this paper, we proposed the proxy-based masking module (PMM) for masking sparse MI signals within MI signals. The proposed module was designed to suppress the amplitude of sparse MI signals using the negative similarity-based mask generated between the proxy of rest signals and the feature vectors of MI signals. We attached our proposed module to the conventional DA methods (i.e., the DJDAN, the MAAN, and the DRDA) to verify the effectiveness in the cross-subject environment on dataset 2a of BCI competition IV. When our proposed module was attached to each conventional DA method, the average accuracy was improved by much as 4.67 %, 0.76 %, and 1.72 %, respectively. Hence, we demonstrated that our proposed module could emphasize the information related to MI characteristics. The code of our implementation is accessible on GitHub.[1].},
}
@article {pmid40039926,
year = {2024},
author = {Zhong, Y and Yao, L and Wang, Y},
title = {Enhanced BCI Performance using Diffusion Model for EEG Generation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10782900},
pmid = {40039926},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; Algorithms ; Signal Processing, Computer-Assisted ; Motor Cortex/physiology ; },
abstract = {In the realm of Motor Imagery (MI)-based Brain-Computer Interface (BCI), the widespread adoption of deep learning-based algorithms has resulted in an increased demand for a larger training sample size, thereby placing a heightened burden on users. This study advocates the utilization of one of the most advanced generative models, the denoising diffusion probabilistic model (DDPM), for the artificial synthesis of Electroencephalogram (EEG) raw signals. The quality of the generated EEG signals is evaluated through both qualitative and quantitative analyses. Through dimensionality reduction projection, we observed a notable similarity in the data distributions between the generated EEG signals and real EEG signals. Additionally, spectral analysis indicates a striking similarity in energy distribution between the two, accompanied by the presence of an event-related synchronization (ERS) phenomenon in the generated EEG signals. Quantitative analysis reveals that the accuracy of generated EEG signals for left and right-hand motor imagery tasks is 89.81 ± 2.11%, with discriminative information related to classes predominantly concentrated in the motor-sensory cortex area and alpha-beta frequency band. Furthermore, the integration of generated EEG samples contributes to a 3.17% improvement in the classification performance of BCI-deficiency subjects. These artificially generated EEG signals exhibit promising potential for application in calibrating MI-BCI deep learning models, thereby alleviating the burden on participants.},
}
@article {pmid40039924,
year = {2024},
author = {Hoshino, T and Kanoga, S and Aoyama, A},
title = {Channel- and Label-Flip Data Augmentation for Motor Imagery-Based Brain-Computer Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782028},
pmid = {40039924},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography/methods ; Machine Learning ; *Imagination/physiology ; Algorithms ; Movement/physiology ; Male ; Signal Processing, Computer-Assisted ; Adult ; Female ; Hand/physiology ; },
abstract = {Achieving high classification accuracy in motor-imagery-based brain-computer interfaces (BCIs) requires substantial amounts of training data. A challenge arises because of the impracticality of measuring large amounts of data from users. Data augmentation (DA) has emerged as a promising solution for this challenge. We propose a novel DA method called channel&amp;label-flip DA that involves not only flipping channels but also flipping class labels. This method is based on the neuroscience finding that motor imageries of left- and right-hand movements are roughly symmetrical. The efficiency of the proposed method was evaluated using the OpenBMI dataset, which comprises electroencephalograms collected from 54 participants engaged in left- and right-hand motor imagery tasks. To compare the impact on classifiers, we employed three classical machine learning models utilizing filter bank common spatial pattern features, along with a deep learning-based model that uses raw signal input. As a result, the channel&amp;label-flip DA improved the classification accuracy on average, whereas simple flipping of the channels reduced the classification accuracy compared to the case without DA.},
}
@article {pmid40039912,
year = {2024},
author = {Tan, J and Wang, Y},
title = {Dynamic Inverse Reinforcement Learning for Feedback-driven Reward Estimation in Brain Machine Interface Tasks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782800},
pmid = {40039912},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; *Reward ; Algorithms ; *Reinforcement, Psychology ; *Machine Learning ; Feedback ; },
abstract = {Reinforcement learning (RL)-based brain machine interfaces (BMIs) provide a promising solution for paralyzed people. Enhancing the decoding performance of RL-based BMIs relies on the design of effective reward signals. Inverse reinforcement learning (IRL) offers an approach to infer subjects' own evaluation from the observed behavior. However, applying IRL to extract reward information in complex BMI tasks requires consideration of the dynamics of subjects' goal during the control process. This dynamic nature of subjects' evaluation requires the IRL method to be able to estimate a time varying reward function. Previous IRL methods applied in BMI systems only estimated a static reward function. Existing IRL algorithms for dynamic reward estimation employ optimization methods to approximate the reward map for each state at each time, which demands substantial amounts of data to achieve convergence. In this paper, we propose a dynamic IRL method to estimate the feedback-driven reward of subjects during BMI tasks. We utilize a state-observation model to continuously infer the reward value for each state, with sensory feedback serving as the external input to model the transition process of the reward. We evaluate our proposed method on a simulated BMI fetch task, which is a multistep task with a time varying reward function. Our method demonstrates improved reward estimation close to the ground truth value, and it significantly outperforms the existing dynamic IRL method when the map size exceeds 25(p<0.01). These preliminary results suggests that the dynamic IRL method for feedback-driven reward estimation holds potential for improving the design of RL-based BMIs.},
}
@article {pmid40039908,
year = {2024},
author = {Patel, K and Safavi, F and Chandramouli, R and Vinjamuri, R},
title = {Transformer-Based Emotion Recognition with EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10781700},
pmid = {40039908},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; Humans ; *Emotions/physiology ; *Signal Processing, Computer-Assisted ; Algorithms ; Brain-Computer Interfaces ; Male ; Adult ; Female ; Arousal ; },
abstract = {Emotion recognition via electroencephalography (EEG) has emerged as a pivotal domain in biomedical signal processing, offering valuable insights into affective states. This paper presents a novel approach utilizing a tailored Transformer-based model to predict valence and arousal levels from EEG signals. Diverging from traditional Transformers handling singular sequential data, our model adeptly accommodates multiple EEG channels concurrently, enhancing its ability to discern intricate temporal patterns across the brain. The modified Transformer architecture enables comprehensive exploration of spatiotemporal dynamics linked with emotional states. Demonstrating robust performance, the model achieves mean accuracies of 92.66% for valence and 91.17% for arousal prediction, validated through 10-fold cross-validation across subjects on the DEAP dataset. Trained for subject-specific analysis, our methodology offers promising avenues for enhancing understanding and applications in emotion recognition through EEG. This research contributes to a broader discourse in biomedical signal processing, paving the way for refined methodologies in decoding neural correlates of emotions with implications across various domains including brain-computer interfaces, and human-robot interaction.},
}
@article {pmid40039888,
year = {2024},
author = {Sturgill, BS and Jiang, MS and Jeakle, EN and Smith, TJ and Hoeferlin, GF and Duncan, J and Thai, TTD and Hess, JL and Alam, NN and Hernandez-Reynoso, AG and Capadona, JR and Pancrazio, JJ},
title = {Antioxidant Coated Microelectrode Arrays: Effects on Putative Inhibitory and Excitatory Neurons.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10781940},
pmid = {40039888},
issn = {2694-0604},
mesh = {Microelectrodes ; Animals ; *Neurons/drug effects/physiology/cytology ; *Antioxidants/pharmacology/chemistry ; Rats ; Coated Materials, Biocompatible ; Reactive Oxygen Species/metabolism ; Metalloporphyrins/pharmacology/chemistry ; },
abstract = {Intracortical microelectrode arrays (MEAs) are used to record neural activity in vivo at single-cell resolution for both neuroscience studies and for engineering restorative devices such as brain-computer interfaces (BCIs). The recording performance of these devices are known to degrade over weeks to months after implantation due, in part, to neuroinflammation and oxidative stress. Characterizing and mitigating the degradation of recording performance is of particular interest for chronic applications. Literature suggests that inhibitory neurons may be more susceptible to oxidative stress than excitatory neurons. In this study, we classify recorded neural signals as either putative inhibitory or excitatory based on their waveform characteristics and aim to identify if one preferentially benefits from the use of a Mn(III)tetrakis94-benzoic acid)porphyrin (MnTBAP) coating to reduce reactive oxygen species, which we have previously demonstrated improves chronic neural recordings. In this study, we found that the MnTBAP coating affects these two classes of neurons differently, depending on the cortical depth. The MnTBAP coating improves the number of putative inhibitory signals recorded on the middle electrode sites (L5) and putative excitatory units on the superficial (L2/3 &amp; L4) electrode sites. Our results suggest that decreases in recording performance may be influenced by both cortical depth and neuronal cell type. Furthermore, we show that the benefits of a MnTBAP coating to chronic neural recordings differ between putative inhibitory and excitatory neurons with a depth dependence.},
}
@article {pmid40039787,
year = {2024},
author = {Won, E and Lim, S and Kim, Y and Dong, SY},
title = {Toward the TCN-based Real-Time BCI System for Target Detection.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782928},
pmid = {40039787},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography ; Signal Processing, Computer-Assisted ; Algorithms ; Deep Learning ; Neural Networks, Computer ; },
abstract = {This study focuses on developing a real-time Brain-Computer Interface (BCI) system, specifically designed for military applications, to enhance target detection in rapid serial visual presentation (RSVP) tasks. The proposed BCI system utilizes electroencephalogram (EEG) signals based on dry electrodes, known for their exceptional temporal resolution, to identify swiftly specific target symbols within sequences of visual stimuli. Leveraging deep learning techniques, particularly Temporal Convolutional Networks (TCN), this study demonstrates the accuracy and efficiency improvement in target detection for RSVP tasks. According to our findings, the adaptability and efficacy of TCN in handling temporal dynamics of EEG signals exhibit outstanding performance in target detection, thus offering the potential for accurate and efficient real-time BCI system.},
}
@article {pmid40039782,
year = {2024},
author = {Park, JH and Lee, SH and Lee, SW},
title = {Towards EEG-based Talking-face Generation for Brain Signal-driven Dynamic Communication.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10781922},
pmid = {40039782},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Brain/physiology ; *Speech/physiology ; *Face ; *Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; *Communication ; Algorithms ; },
abstract = {Research on decoding speech or generating images from human brain activity holds intriguing potential as neuroprosthesis for patients and innovative communication tools for general users. However, previous studies have been constrained in generating fragmented or abstract outputs, rendering them less applicable for serving as an alternative form of communication. In this paper, we propose an integrated framework that synthesizes speech from non-invasive speech-related brain signals and generates a talking-face that performs "lip-sync" using intermediate input decoded from brain signals. For realistic and dynamic brain signal-mediated communication, we generated a personalized talking-face by utilizing various forms of target data such as a real face or an avatar. Additionally, we performed a denoising process to enhance the quality of synthesized voices from brain signals, and to minimize unnecessary facial movements according to the noise. Therefore, clear and natural talking-faces, applicable to both real faces and avatars, could be generated from noisy brain signals, enabling dynamic communication. These findings serve as a pivotal contribution to the advancement of brain signal-driven face-to-face communication through the provision of integrated speech and visual interfaces. This represents a significant step towards the development of a more intuitive and dynamic brain-computer interface communication system.},
}
@article {pmid40039768,
year = {2024},
author = {He, F and Zhang, S and Yang, M and Meng, J and Xu, M and Meng, L and Ming, D},
title = {Prediction errors from distinct perspectives induce separable EEG features for brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10781933},
pmid = {40039768},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Male ; Female ; Algorithms ; Adult ; Young Adult ; Evoked Potentials/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {The ability to efficiently detect error is fundamental for human adaptive behaviors, and plays an increasingly crucial role in developing more intelligent brain-computer interface (BCI). Error-related potential (ErrP), which can reflect prediction error, has been widely used by the BCI to read whether outcomes accord with users' expectation or not. However, current ErrP-BCI cannot distinguish the prediction error is induced by user's own (first-person perspective, 1PP) or other's (third-person perspective, 3PP) wrong action, hindering it from being applied in social interactions. This study used virtual reality (VR) to make subjects aware of prediction errors from the first- or third-person perspective, and recorded electroencephalogram (EEG) data of 22 healthy subjects. Event-related potential (ERP), event-related spectral perturbation (ERSP), inter-trial coherence (ITC) and shrinkage discriminant canonical pattern matching (SKDCPM) algorithm were used to investigate EEG features and the separability of prediction errors induced by distinct perspectives. Consequently, ErrP induced by the 1PP emerged significantly earlier than that of 3PP, and caused greater ERSP and ITC in the prefrontal region in the theta and alpha bands. Decoding result achieved 76.4%± 9.13% accuracy for the two types of errors (1PP-incorrect vs 3PP-incorrect). This study fills in the fine-grained classification of different error types and provides a finer metric for the systematic error correction efficiency of two-person collaborative brain control, which is the basis for future human-machine hybrid intelligence.},
}
@article {pmid40039762,
year = {2024},
author = {Li, M and Wang, M and Wang, Y},
title = {An Adaptive Superposition Point Process Model with Neuronal Encoding Engagement Identification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10781885},
pmid = {40039762},
issn = {2694-0604},
mesh = {*Neurons/physiology ; *Brain-Computer Interfaces ; Animals ; *Models, Neurological ; Humans ; Algorithms ; },
abstract = {Neuronal encoding is realized by modulating firing rates in response to various encoding factors, including external stimuli, behaviors, and complex neural interactions. The neuronal encoding engagement of various factors are dynamic, which reflects how neurons aggregate different information. The process of uncovering the extent to which these factors contribute to neuronal encoding over time is neuronal encoding engagement identification. Brain-machine interface (BMI) establishes a closed-loop framework to investigate how neurons response to different encoding factors. Since neurons don't fully participate in encoding one specific factor, accurate encoding engagement identification contributes to leveraging encoding and decoding in more naturalistic BMI application scenarios. However, previous works focus on modeling and estimating tuning properties instead of analyzing the neuronal information aggregation. We develop a dual adaptive superposition point process filter (DASPPF), which explicitly incorporates various encoding factors. DASPPF not only enables decoding kinematics but also identifies the engagement of individual kinematics encoding and functional neural connectivity encoding. DASPPF is validated on numerical simulations of monkey circle-tracking tasks. The proposed method can effectively promote decoding performance and uncover how neurons engage themselves in different effects with point process observation, which may help enhance the development of neurotechnologies.},
}
@article {pmid40039684,
year = {2024},
author = {Tang, Y and Robinson, N and Fu, X and Thomas, KP and Wai, AAP and Guan, C},
title = {Reconstruction of Continuous Hand Grasp Movement from EEG Using Deep Learning.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10781850},
pmid = {40039684},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography ; *Deep Learning ; *Hand Strength/physiology ; Movement/physiology ; *Hand/physiology ; Brain-Computer Interfaces ; Male ; Algorithms ; Adult ; Signal Processing, Computer-Assisted ; Female ; },
abstract = {Brain-Computer Interface (BCI) is a promising neu-rotechnology offering non-muscular control of external devices, such as neuroprostheses and robotic exoskeletons. A new yet under-explored BCI control paradigm is Motion Trajectory Prediction (MTP). While MTP provides continuous control signals suitable for high-precision tasks, its feasibility and applications are challenged by the low signal-to-noise ratio, especially in noninvasive settings. Previous research has predominantly focused on kinematic reconstruction of upper (e.g., arm reaching) and lower limbs (e.g., gait). However, finger movements have received much less attention, despite their crucial role in daily activities. To address this gap, our study explores the potential of noninvasive Electroencephalography (EEG) for reconstructing finger movements, specifically during hand grasping actions. A new experimental paradigm to collect multichannel EEG data from 20 healthy subjects, while performing full, natural hand opening and closing movements, was designed. Employing state-of-the-art deep learning algorithms, continuous decoding models were constructed for eight key finger joints. The Convolutional Neural Network with Attention approach achieved an average decoding performance of r=0.63. Furthermore, a post-hoc metric was proposed for hand grasp cycle detection, and 83.5% of hand grasps were successfully detected from the reconstructed motion signals, which can potentially serve as a new BCI command. Explainable AI algorithm was also applied to analyze the topographical relevance of trained features. Our findings demonstrate the feasibility of using EEG to reconstruct hand joint movements and highlight the potential of MTP-BCI in control and rehabilitation applications.},
}
@article {pmid40039675,
year = {2024},
author = {Tang, C and Jiang, D and Chen, B},
title = {MEG Channel Selection Using Copula Entropy-Based Transfer Entropy for Motor Imagery BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782066},
pmid = {40039675},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; *Magnetoencephalography/methods ; Humans ; Entropy ; Algorithms ; *Imagination/physiology ; Signal Processing, Computer-Assisted ; Movement/physiology ; },
abstract = {Multi-channel magnetoencephalography (MEG) data provides high spatiotemporal resolution for motor imagery (MI)-based brain-machine interfaces (BCIs). However, not all channels contribute to the performance of BCIs. Taking into account the importance of specific channels in measuring their causal relationships with other channels during MI tasks, a novel channel selection method using copula entropy-based transfer entropy (CTE) is proposed to select task-relevant channels. Experiments on a publicly available dataset validate the effectiveness of the proposed methods. Compared to using all channels, channel selection based on CTE can significantly (p < 0.05) improve single-session classification accuracy and greatly reduce the number of MEG channels. Cross-session classification also outperforms the competing method.},
}
@article {pmid40039672,
year = {2024},
author = {Irvine, B and Abou-Zeid, H and Kirton, A and Kinney-Lang, E},
title = {Benchmarking motor imagery algorithms for pediatric users of brain-computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782164},
pmid = {40039672},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; Child ; *Algorithms ; Adolescent ; Child, Preschool ; Male ; Benchmarking ; Female ; *Imagination/physiology ; Electroencephalography ; },
abstract = {Brain-computer interfaces (BCIs) can enable opportunities for self-expression and life participation for children with severe neurological disabilities. Unfortunately, the development and evaluation of state-of-the-art algorithms has largely neglected pediatric users. This work tests 12 state-of-the-art algorithms for motor imagery classification on three datasets of typically developing pediatric users (n=94 ages 5-17). When all datasets were combined, there were no significant differences between most non-deep learning algorithms, with all having a mean AUC score of 0.64 or 0.65. All the non-deep learning algorithms significantly outperformed the deep learning algorithms, which can be partially attributed to a lack of hyperparameter tuning. The best of the deep learning algorithms was ShallowConvNet, with a mean AUC score of 0.57. Of the algorithms tested, only the filter bank common spatial pattern (FBCSP) and ShallowConvNet exhibited significant age effects. This general lack of age effects, combined with examples of children as young as 6 having AUC scores as high as 0.8, provides evidence that young children are capable of producing measurable motor imagery activations. The age effects that were present for some algorithms suggest that the changing EEG patterns associated with development could have a measurable impact on classification algorithm outcomes, and such algorithms should be evaluated to ensure that they are not performing disproportionately poorly for younger children. This work serves as a first step towards ensuring that the state-of-the-art improvements in BCI classification can be evaluated, and where necessary, adapted to meet the needs of pediatric users.},
}
@article {pmid40039651,
year = {2024},
author = {Kolbl, N and Tziridis, K and Krauss, P and Schilling, A},
title = {Methodological Considerations in the Analysis of Acoustically Evoked Neural Signals: A Comparative Study of Active EEG, Passive EEG and MEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-7},
doi = {10.1109/EMBC53108.2024.10782081},
pmid = {40039651},
issn = {2694-0604},
mesh = {*Magnetoencephalography/methods ; Humans ; *Electroencephalography/methods ; Brain-Computer Interfaces ; Male ; *Evoked Potentials, Auditory/physiology ; Adult ; Female ; Acoustic Stimulation ; Signal Processing, Computer-Assisted ; *Brain/physiology ; },
abstract = {Analyzing and deciphering brain signals on a single trial base is the main goal of brain-computer interface (BCI) research as well as neurolinguistics. In the present study, we have evaluated the efficacy of three neuroimaging techniques-active electroencephalography (EEG), passive EEG, and magnetoencephalography (MEG)-in capturing and evaluating brain activity in response to auditory stimuli. The main goals of our research included two primary components: first, to identify ROIs, and second, to determine the appropriate number of stimulus samples needed to achieve a meaningful level of reliability. To estimate this number of measurement repetitions we performed step-wise sub-sampling combined with permutation testing. This involved a detailed comparison of event-related potentials resp. fields (ERPs, ERFs) elicited by auditory stimuli such as acoustic clicks and continuous speech. Our results show that active EEG outperformed passive EEG and MEG in sensor space. However, MEG demonstrated superior signal localization in source space. These results also highlight the complexity of developing real-time speech BCIs.},
}
@article {pmid40039633,
year = {2024},
author = {Lu, JB and Tsao, Y and Wang, YT},
title = {Design and Evaluate Semi-dry Watermill-like EEG Electrodes.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782960},
pmid = {40039633},
issn = {2694-0604},
mesh = {*Electroencephalography/instrumentation ; Electrodes ; Humans ; Equipment Design ; Brain-Computer Interfaces ; Male ; Adult ; Water/chemistry ; },
abstract = {Semi-dry electrodes act as the middle ground between wet and dry electrodes as they not only have similar contact features (equivalent circuit) with Ag/AgCl-based wet electrodes but also carry the conduct material in their cavity or sponge (e.g. absorb saline water) for long-term brain-computer interface(BCI) applications. However, the trade off between hair-layer penetration and dose control of conductive material is challenging e.g. two electrodes might be bridged when the headset continuously presses or squeezes the reservoir and electrolyte flows on the scalp. The goal of this study is to design, prototype, and evaluate watermill-like electroencephalogram (EEG) electrodes that aim to simultaneously overcome two issues: hair-layer penetration and dose control of conductive material. Two electrode profiles, straight and spiral, were 3D printed, coated and evaluated with participants' EEGs. Without any help from skilled technicians, the self-wearing mechanical design allows users to wear and acquire their EEGs in few minutes. In addition, the refillable reservoir enable the possibility for long-term BCI applications. The results show that the proposed electrodes can read neural activities on the hair-covered area. Furthermore, straight profile electrodes outperform the spiral profile in the steady-state visually evoked potential (SSVEP) response. In sum, the watermill-like EEG electrodes can shorten the preparation time as well as the dose control of conductive material for naive users. The results suggest the proposed electrodes might open opportunities for BCI users to develop real-world BCI applications in the future.},
}
@article {pmid40039626,
year = {2024},
author = {Lin, X and Eldele, E and Chen, Z and Wu, M and Ng, HW and Guan, C},
title = {Bi-hemisphere Interaction Convolutional Neural Network for Motor Imagery Classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782755},
pmid = {40039626},
issn = {2694-0604},
mesh = {Humans ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; Electroencephalography ; Signal Processing, Computer-Assisted ; Brain/physiology ; *Imagination/physiology ; Algorithms ; Convolutional Neural Networks ; },
abstract = {Decoding EEG-based, Motor Imagery Brain-Computer Interfaces (MI-BCI) in a subject-independent manner is very challenging due to high dimensionality of the EEG signal, and high inter-subject variability. In recent years, Convolutional neural networks (CNNs) have significantly enhanced decoding accuracy. Nevertheless, the majority of these CNN designs did not explicitly incorporate the inter-hemisphere functional connections, omitting crucial spatial information. Notably, in binary MI decoding of the left-hand versus right-hand, the Event-Related Desynchronization is observed in the contralateral hemisphere. Building upon this concept and various Neuroscience research, we have designed a CNN architecture that forges a functional connection between the two hemispheres. Specifically, we applied the Channel Average Referencing to one hemisphere and compared the output with all channels of the opposite hemisphere. Then, we utilized the cosine similarity to identify the most correlated channels and combined with them the original hemisphere for spatial filtering to learn the inter-hemispheric connections. This innovative technique aligns more closely with the actual brain functionality. Our method has demonstrated superior results on the Cho2017 and OpenBMI datasets, underscoring its effectiveness.},
}
@article {pmid40039616,
year = {2024},
author = {Guerrero-Mendez, CD and Rivera-Flor, H and Villa-Parra, AC and Bastos-Filho, TF},
title = {Exploring Novel Practical Approach to Post-Stroke Upper-Limb Neurorehabilitation Based on Complex Motor Imagery Tasks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-6},
doi = {10.1109/EMBC53108.2024.10782286},
pmid = {40039616},
issn = {2694-0604},
mesh = {Humans ; *Stroke Rehabilitation ; *Upper Extremity/physiopathology ; Male ; Female ; Electroencephalography ; *Stroke/physiopathology ; Middle Aged ; *Neurological Rehabilitation/methods ; *Imagination ; *Imagery, Psychotherapy/methods ; Adult ; Aged ; Movement ; },
abstract = {Motor imagery (MI) is one of the main strategies for upper-limb movement rehabilitation in post-stroke individuals. Promising results of MI applied for rehabilitation have been reported in the literature. However, there is currently a need related to the recovery of movements aimed to Activities of Daily Living (ADLs) for individuals with severe motor impairments. Therefore, this study presents the evaluation of a novel MI protocol for post-stroke upper-limb neurorehabilitation using complex tasks related to the manipulation of a drinking cup. The protocol is based on the Action Observation (AO), which was used under a first-person 2D virtual reality. Subjects had to simultaneously imagine the movements presented in AO for the manipulation of a cup varying in four positions. EEG signals were recorded from 16 channels located mainly in the motor cortex of the brain. Two computational strategies based on Riemannian Geometry (RG) with and without Feature Selection (FS) using Pair-Wise Feature Proximity (PWFP) were implemented for the binary identification of each complex MI-Task vs. MI-Rest. This approach was evaluated on 30 healthy individuals and 2 post-stroke individuals. Using Linear Discriminant Analysis (LDA) as a classifier, the results report a maximum accuracy of 0.78 for both healthy and post-stroke individuals, and a minimum FPR of 0.21 and 0.13 for healthy and post-stroke individuals, respectively. This highlights the potential use of this type of paradigms for the implementation of more robust BCI systems that allow the rehabilitation of movements close to ADLs. Therefore, complex MI tasks may be a suitable variant for rehabilitation in post-stroke individuals.},
}
@article {pmid40039598,
year = {2024},
author = {Noble, SC and Ward, T and Ringwood, JV},
title = {Assessing the Impact of Environment and Electrode Configuration on P300 Speller Performance and EEG Signal Quality.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782158},
pmid = {40039598},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/instrumentation/methods ; Electrodes ; Male ; Adult ; *Brain-Computer Interfaces ; *Event-Related Potentials, P300/physiology ; Female ; *Signal Processing, Computer-Assisted ; Young Adult ; *Environment ; },
abstract = {Recent years have seen extensive use of brain-computer interfaces (BCIs) using electroencephalography (EEG). A critical element in BCI research is electrode selection, which influences performance, experiment duration, resource utilization, and consequently, cost. Electrode choice is partly dictated by the study location, as environmental electrical noise can impact EEG signal quality. This study evaluates the performance of a P300 speller and EEG signal quality using 4-, 6-, 8-, and 16-electrode configurations in two different office environments. Ten healthy adults participated in a single session, using a P300 speller to spell three words with each electrode set. Participants were split between two locations, with five individuals in each. Significant performance disparities were observed between the locations. Notably, within each location, the performance differences among 4-, 6-, and 8-electrode sets were minimal; only the 16-electrode set outperformed the others in both settings. The location associated with poorer performances also exhibited lower P300 amplitudes and higher levels of mains electricity noise.},
}
@article {pmid40039596,
year = {2024},
author = {Ben Ticha, MB and Ran, X and Roussel, P and Bocquelet, F and Le Godais, G and Aubert, M and Costecalde, T and Struber, L and Zhang, S and Charvet, G and Kahane, P and Chabardes, S and Yvert, B},
title = {A Vision Transformer Architecture For Overt Speech Decoding From ECoG Data.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10781877},
pmid = {40039596},
issn = {2694-0604},
mesh = {Humans ; *Speech/physiology ; *Brain-Computer Interfaces ; Algorithms ; Neural Networks, Computer ; *Electrocorticography/methods ; Signal Processing, Computer-Assisted ; },
abstract = {Speech Brain-Computer Interfaces rely on decoding algorithms that transform neural activity into speech. A current challenge is to achieve intelligible speech synthesis in real time from continuous ongoing brain activity, ideally without the need of language models that prevent free-speech production. As a first step toward this goal, we introduce here an encoder-decoder architecture, in which neural data is first encoded into a latent space using a multi-layer vision transformer (ViT), and then these latent variables are converted into acoustic coefficients using a bidirectional LSTM recurrent network. This network is compared to a more conventional architecture where the encoding is performed using a convolutional neural network. Moreover, we introduce a new data-driven data augmentation strategy based on Dynamic Time Warping (DTW) to increase a training dataset based on the intrinsic variability of its input neural features. On two ECoG datasets obtained in participants performing an overt speech task, we found that ViT-encoding outperforms CNN-encoding to predict produced speech offline and that DTW-based data augmentation also improves decoding performance.},
}
@article {pmid40039592,
year = {2024},
author = {Schrag, E and Comaduran-Marquez, D and Kirton, A and Kinney-Lang, E},
title = {Textured Stimuli Comfort and Response in SSVEP-Based Brain Computer Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782587},
pmid = {40039592},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; Adult ; *Evoked Potentials, Visual/physiology ; Male ; Female ; Young Adult ; Electroencephalography ; *Photic Stimulation/methods ; Signal-To-Noise Ratio ; },
abstract = {State of the art steady-state visual evoked potential (SSVEP) brain computer interface (BCI) stimuli are commonly high-contrast, solid color flashing objects which can contribute to visual discomfort and fatigue. The use of low-contrast, textured flashing stimuli is proposed as a more comfortable alternative stimulus presentation paradigm. Eight participants (aged 19-35) were presented with four textured stimuli at varying frequencies, alongside standard stimuli. Results indicate significant effects of stimulus type as well as an interaction between frequency and channel subset on signal-to-noise ratio (SNR) values. Comfort scores consistently favored textured stimuli over high-contrast options at all frequencies The observed lack of SNR differences between stimulus conditions supports the feasibility of using textured stimuli in BCIs. This study lays a foundation for developing comfortable and effective BCI systems. The promising results of textured stimuli suggest a potential alternative for SSEVP-based BCI systems, emphasizing the importance of balancing neural responses and user comfort in stimulus design.},
}
@article {pmid40039576,
year = {2024},
author = {Jiang, R and Qiu, S and Wang, Y and Zhang, C and He, H},
title = {Evaluation of EEG and MEG responses during Fine Motor Imagery from the same limb.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10782038},
pmid = {40039576},
issn = {2694-0604},
mesh = {Humans ; *Magnetoencephalography/methods ; *Electroencephalography/methods ; Male ; Brain-Computer Interfaces ; Adult ; Movement/physiology ; *Imagination/physiology ; Female ; *Extremities/physiology ; },
abstract = {Motor imagery (MI) is an important brain-computer interface (BCI) paradigm. BCI systems based on fine MI can provide an intuitive control pathway of the outer device. Electroencephalography (EEG) is a widely used modality for MI due to its high temporal resolution and portability. Magnetoencephalography (MEG) has high spatial and temporal resolution, which has received more and more attention. This study designed four kinds of MI tasks of different joints from the same upper limb, including finger, wrist, elbow, and shoulder joints, and additionally added a resting task. The EEG and MEG signals of eight subjects were acquired synchronously. Analysis was conducted on the EEG and MEG data to find the time, time-frequency, and spatial difference between MI tasks of different joints from the same limb. The induced event-related desynchronization (ERD) in EEG signals at the electrode position of the left motor area are more broad and stronger in the alpha frequency band than that in MEG signals during fine MI tasks. From the topographical distribution, different MI tasks affects the area and intensity of the activated area, and topographical distribution of MEG signals in different MI tasks are more discriminative than that of EEG signals. Moreover, the analysis of movement-related cortical potentials (MRCP) showed that significant negative potentials were detected near the onset of the motor imagery events and there is a significant difference in temporal dimension between magnetoencephalogram and electroencephalogram signals. The work implies that there exist the separable differences between EEG and MEG during fine MI tasks, which can be utilized to build a multimodal classification method for fine MI-BCI systems.},
}
@article {pmid40039573,
year = {2024},
author = {Delavari, F and Santaniello, S},
title = {Role of Scalp EEG Brain Connectivity in Motor Imagery Decoding for BCI Applications.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10781532},
pmid = {40039573},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; *Brain/physiology ; *Scalp/physiology ; *Imagination/physiology ; Signal Processing, Computer-Assisted ; Algorithms ; },
abstract = {Brain Connectivity (BC) features of multichannel EEG have been proposed for Motor Imagery (MI) decoding in Brain-Computer Interface applications, but the advantages of BC features vs. single-channel features are unclear. Here, we consider three BC features, i.e., Phase Locking Value (PLV), Granger Causality, and weighted Phase Lag Index, and investigate the relationship between the most central nodes in BC-based networks and the most influential EEG channels in single-channel classification based on common spatial pattern filtering. Then, we compare the accuracy of MI decoders that use BC features in source vs. sensor space. Applied to the BCI Competition VI Dataset 2a (left- vs. right-hand MI decoding), our study found that PLV in sensor space achieves the highest classification accuracy among BC features and has similar performance compared to single-channel features, while the transition from sensor to source space reduces the average accuracy of BC features. Across all BC measures, the network topology is similar in left- vs. right-hand MI tasks, and the most central nodes in BC-based networks partially overlap with the most influential channels in single-channel classification.},
}
@article {pmid40039515,
year = {2024},
author = {Marquez, DC and Minhas, A and Kinney-Lang, E and Kirton, A},
title = {Automated Hyper-Parameter Optimization for Eye Movement Artifact Removal.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782395},
pmid = {40039515},
issn = {2694-0604},
mesh = {*Artifacts ; Humans ; *Electroencephalography/methods ; *Eye Movements/physiology ; *Brain-Computer Interfaces ; Algorithms ; Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interface (BCI) systems allow users to control external devices with their brain waves. However, electroencephalography (EEG) signals used by most BCI systems are prone to artifacts from various sources (e.g., muscle activity, eye movements, and electrical interference). These artifacts can degrade the performance and usability of BCI systems. Many tools exist to eliminate these artifacts. However, not all methods are automated, and some might require tuning certain hyper-parameters for optimal performance. We propose a method to automatically optimize the hyper-parameters of an eye blink artifact removal tool to improve the removal of artifacts in resting state EEG. We use a subset of eye movement artifacts to optimize the hyper-parameters using the EEG Quality Index (EQI) as the objective function. The optimized hyper-parameters are then used in a test artifact to quantify the improvement of the EQI. Results show improvement in the EQI when compared to the default artifact removal hyper-parameters, and raw EEG traces. We conclude that our method can provide a personalized and robust artifact removal solution for BCI users with complex needs.},
}
@article {pmid40039504,
year = {2024},
author = {Osborn, LE and Christie, B and McMullen, DP and Thomas, TM and Thompson, MC and Nickl, RW and Pawar, AS and Wester, BA and Cantarero, GL and Celnik, PA and Crone, NE and Fifer, MS and Tenore, FV},
title = {Artificial touch feedback using microstimulation of human somatosensory cortex to convey grip force from a robotic hand.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782061},
pmid = {40039504},
issn = {2694-0604},
mesh = {Humans ; *Robotics/instrumentation ; *Hand Strength/physiology ; *Somatosensory Cortex/physiology/physiopathology ; *Touch/physiology ; *Hand/physiology ; *Electric Stimulation ; Brain-Computer Interfaces ; Male ; *Feedback, Sensory ; Adult ; },
abstract = {Invasive brain-machine interfaces can help restore function through the control of external devices while the addition of intracortical microstimulation (ICMS) can elicit sensations of touch and help provide further benefits for individuals living with sensorimotor deficits. However, the extent of tactile information that can be conveyed through ICMS has not been fully explored. In a human participant with spinal cord injury and chronically implanted microelectrode arrays, we used ICMS to the somatosensory cortex to provide grip force feedback in the hands during grasping of objects with varying stiffness with a robotic arm. Using only ICMS-evoked touch sensations, the participant was able to identify between two and three objects with an accuracy of 92% and 67%, respectively. In a compliant grasping task with the goal of grasping a delicate object without crushing it, objects were deformed on average only 2.8 mm with ICMS-based touch feedback compared to 8.7 mm without. These results demonstrate that ICMS-evoked touch sensations to the hands can be used to provide force-based feedback for perceiving object properties and enable more precise grasping during closed-loop control of a robotic limb through a cortical interface.},
}
@article {pmid40039500,
year = {2024},
author = {Teymourlouei, A and Hu, M and Gentili, R and Reggia, J},
title = {Functional Connectivity Methods for Multi-Class Mental Workload Classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782848},
pmid = {40039500},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Workload ; Support Vector Machine ; *Brain/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; Male ; },
abstract = {Recently, significant attention has been drawn to the ability of network-based features to classify EEG signals reflecting varying levels of mental workload. Such features are based on methods of functional connectivity (FC), which quantify the statistical relationship between EEG electrode potentials. Here, we compare three FC-based feature extraction methods for the classification of mental workload from the Multi-Attribute Task Battery. The approaches used are weighted phase lag index (WPLI), imaginary coherence (IC), and layer entanglement (LE). WPLI and IC are popular methods for FC analysis. LE is a new approach which was introduced in recent literature. When classifying between three levels of workload, a support vector machine classifier achieved an 88% average (person-dependent) accuracy using all FC methods together, 89% using only the LE method, 67% with the IC method, and 61% with the WPLI method. When classifying between two levels of workload, these scores improve to 97%, 97%, 86%, and 81%, respectively. These results support and extend the findings of prior work and suggest that LE-based methods may enable accurate mental workload prediction which is suitable for passive brain-computer interfaces.},
}
@article {pmid40039452,
year = {2024},
author = {N, GR and Guha, D and Mahadevappa, M},
title = {EEG Artifact Removal using Stacked Multi-Head Attention Transformer Architecture.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-6},
doi = {10.1109/EMBC53108.2024.10782044},
pmid = {40039452},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; *Artifacts ; Humans ; *Signal Processing, Computer-Assisted ; Deep Learning ; Algorithms ; Signal-To-Noise Ratio ; },
abstract = {This study presents a transformer attention model with stacked multi-head attention layer designed to remove noise from electroencephalogram (EEG) signals, specifically addressing the problem of signal distortion caused by artifacts such as ocular and muscular noise. This is a crucial step in improving the efficacy of EEG, for disease diagnostics and BCI applications. Deep learning (DL) models have been increasingly employed for denoising EEG data in recent years, demonstrating comparable performance to classical approaches. However, the current models have been unsuccessful in capturing temporal long-term dependencies to efficiently eliminating ocular and muscular abnormalities. In this study, we address those challenges faced in the DL models by introducing multiple multi-head attention layers in the transformer model, which surpass the performance measures of previous works in EEGdenoiseNet dataset.},
}
@article {pmid40039435,
year = {2024},
author = {Chen, J and Xia, Y and Thomas, A and Carlson, T and Zhao, H},
title = {Mental Fatigue Classification with High-Density Diffuse Optical Tomography: A Feasibility Study.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10782566},
pmid = {40039435},
issn = {2694-0604},
mesh = {Humans ; *Tomography, Optical/methods ; Feasibility Studies ; *Mental Fatigue/diagnosis/classification ; Support Vector Machine ; Spectroscopy, Near-Infrared/methods ; Male ; Adult ; Female ; },
abstract = {High-Density Diffuse Optical Tomography (HD-DOT) presents as a promising tool for not only clinical use but also daily monitoring of mental states. This study employed wearable HD-DOT to evaluate mental fatigue, specifically examining the differences in functional near-infrared spectroscopy (fNIRS) data between states of low and high fatigue among healthy participants for data collection. Data processing involved filtering, channel selection, and dimensionality reduction through Uniform Manifold Approximation (UMAP) and Projection, followed by classification using Support Vector Machines (SVM). We developed two models to assess the accuracy and generalizability of our findings: one based on individually tailored models and another employing a leave-one-participant-out cross-validation strategy. We evaluated different kernel functions, resulting in various accuracy, F1 score, and Area Under the Curve (AUC) metrics. The study achieved an average accuracy of approximately 90% for participant-specific classifiers, underscoring the effectiveness of our approach to differentiate between low and high states of mental fatigue. Our analyses led to a robust model demonstrating high classification accuracy, proving its suitability and potential for real-time Brain-Computer Interface (BCI) applications.},
}
@article {pmid40039431,
year = {2024},
author = {Ziegelman, L and Hernandez, ME},
title = {Application of a Neural ODE to Classify Motion Control Strategy using EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782326},
pmid = {40039431},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; Male ; Adult ; Female ; Motion ; Movement/physiology ; Algorithms ; Young Adult ; },
abstract = {Speed-accuracy trade offs exist in a variety of functional tasks, which may require differences in control strategies in future neuroprosthetic devices. It is the goal of this work to evaluate the predictability of different motor control strategies during wrist rotation tasks. Participants were asked to perform a series of discrete wrist rotations. This motion data was clustered into segments of either speed or range of motion oriented control strategy, controlling for age cohort and motion type. Competing neural ordinary differential equation (NODE) and random forest (RF) models were evaluated to explore the feasibility of classifying control strategy using cortical data alone. In comparison to traditional ML techniques, such as RF models, the NODE model provided achieved comparable classification accuracy at a fraction of the time. Furthermore, the use of a single motor cluster or two frontal clusters provided similar accuracy to the full data from 4 clusters, which may due to increased information from these cortical areas. This study provided a promising initial demonstration of the benefits of NODE models for future brain-computer-interface applications that require near real-time classification.},
}
@article {pmid40039415,
year = {2024},
author = {Flores, C and Casas, P and de Carvalho, SN and Attux, R},
title = {Advancing SSVEP-BCI Decoding: Cross-Subject Transfer Learning and Short Calibrated Approach with ELM-AE.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10782739},
pmid = {40039415},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Calibration ; Electroencephalography/methods ; *Machine Learning ; Algorithms ; Signal Processing, Computer-Assisted ; Male ; Adult ; },
abstract = {The Steady-State Visually Evoked Potential (SSVEP) is a robust paradigm for developing a high-speed Brain-Computer Interface (BCI). However, one of the challenges of BCI is to face the variability of EEG signals between subjects to reduce or eliminate the time calibration process for a new subject (target subject). Some approaches propose linearly transforming; however, it limits the ability to capture complex and nonlinear relationships in data. This study presents a method for performing a Nonlinear Transformation (NLT) using an Extreme Learning Machine Autoencoder (ELM-AE) on SSVEP trials. To improve the NLT, it maps each trial from the existing subjects (source subjects) to one or a few templates from the target subject. This approach can enhance cross-subject recognition classification, reducing the calibration time for the target subject. Our results reported that, for one template, NLT and LST achieved 84.23% and 82.19% average recognition accuracy, respectively. Thus, our results reported that the recognition accuracy of NLT outperformed LST for all template sizes across all 35 subjects. These results demonstrated the feasibility of the NLT using one or a few templates for rapid calibration for the target subject.},
}
@article {pmid40039404,
year = {2024},
author = {Rabbito, R and Cinanni, A and Bussi, L and Guiot, C and Roatta, S},
title = {A neuro-feedback prototype based on transcranial Doppler ultrasound for brain computer interface applications.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10782446},
pmid = {40039404},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; *Ultrasonography, Doppler, Transcranial/methods ; Male ; *Neurofeedback/methods ; Adult ; Female ; *Brain/physiology ; },
abstract = {This study proposes a TCD-based neurofeedback system designed to visualize interhemispheric hemodynamic imbalance based on the bilateral monitoring of middle cerebral arteries (MCAs). The difference between cerebral blood velocities collected from the right and left side is calculated in real time and used to drive the horizontal position of the ball displayed on a screen. With this visual feedback, the user may see how different thoughts impact on the position of the ball and possibly acquire and improve control of the ball through progressive training. Four healthy volunteers participated in a preliminary assessment conducted over four training sessions, on average demonstrating increased control over the ball movement. The results provide a proof of concept of the methodology, confirm the feasibility of the approach. The system's novelty lies in its simplicity, cost-effectiveness, and focus on cerebral lateralization, which make TCD an intriguing alternative to other neurofeedback systems, typically based on EEG, fMRI or fNIRS. The results encourage larger sample size, investigations on the TCD-based neurofeedback's therapeutic and rehabilitative potential.},
}
@article {pmid40039400,
year = {2024},
author = {Kondo, S and Tanaka, H},
title = {High-Frequency SSVEP-BCI Stimulation Frequency Optimization Based on BCI accuracy.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782291},
pmid = {40039400},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; Algorithms ; Electroencephalography/methods ; Male ; Adult ; },
abstract = {This study investigates optimization of the stimulation frequency of blinking stimuli used for steady-state visual evoked potential (SSVEP) brain-computer interface (BCI) for individuals. Heare, we set the target BCI accuracy to 90%, and we propose and evaluate an efficient algorithm to search for stimulation frequencies that satisfy the accuracy target for each subject. The results of a four-input SSVEP-BCI operation experiment with various stimulation frequencies indicate that the experimental system obtained optimal stimulation frequency for the subject based on BCI accuracy. However, we found that the optimization time was greater for subjects who are not proficient at BCI operations, which caused subject fatigue.},
}
@article {pmid40039379,
year = {2024},
author = {Lim, J and Wang, PT and Joon Sohn, W and Serrano-Amenos, C and Ibrahim, M and Lin, D and Thaploo, S and Shaw, SJ and Armacost, M and Gong, H and Lee, B and Lee, D and Andersen, RA and Heydari, P and Liu, CY and Nenadic, Z and Do, AH},
title = {Early feasibility of an embedded bi-directional brain-computer interface for ambulation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10782271},
pmid = {40039379},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; *Walking/physiology ; Feasibility Studies ; Exoskeleton Device ; Robotics ; Spinal Cord Injuries/physiopathology ; },
abstract = {Current treatments for paraplegia induced by spinal cord injury (SCI) are often limited by the severity of the injury. The accompanying loss of sensory and motor functions often results in reliance on wheelchairs, which in turn causes reduced quality of life and increased risk of co-morbidities. While brain-computer interfaces (BCIs) for ambulation have shown promise in restoring or replacing lower extremity motor functions, none so far have simultaneously implemented sensory feedback functions. Additionally, many existing BCIs for ambulation rely on bulky external hardware that make them ill-suited for non-research set-tings. Here, we present an embedded bi-directional BCI (BDBCI), that restores motor function by enabling neural control over a robotic gait exoskeleton (RGE) and delivers sensory feedback via direct cortical electrical stimulation (DCES) in response to RGE leg swing. A first demonstration with this system was performed with a single subject implanted with electrocorticography electrodes, achieving an average lag-optimized cross-correlation of 0.80±0.08 between cues and decoded states over 5 runs.},
}
@article {pmid40039323,
year = {2024},
author = {Kasprzak, H and Niewinska, N and Komendzinski, T and Otake-Matsuura, M and Rutkowski, TM},
title = {Improving the Classification of Olfactory Brain-Computer Interface Responses by Combining EEG and EBG Signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782826},
pmid = {40039323},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; Algorithms ; Signal Processing, Computer-Assisted ; *Smell/physiology ; Male ; Adult ; },
abstract = {The sense of smell, or olfaction, can enhance brain-computer interfaces (BCIs). Different scents can be assigned to specific commands to allow users to interact with technology naturally, but challenges remain. Accurate odor delivery systems and robust algorithms for detecting and interpreting brain activity patterns are necessary. We propose combining electroencephalography (EEG) and electrobulbography (EBG) to improve classification accuracy. Our pilot study shows promising results for a new olfactory brain-computer interface (BCI) modality that combines common spatial pattern (CSP) filtration applied to EEG and EBG to classify responses to six scent stimuli in a classical oddball paradigm.},
}
@article {pmid40039276,
year = {2024},
author = {Lim, RY and Jiang, M and Ang, KK and Lin, X and Guan, C},
title = {Brain-Computer-Brain system for individualized transcranial alternating current stimulation with concurrent EEG recording: a healthy subject pilot study.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782251},
pmid = {40039276},
issn = {2694-0604},
mesh = {Humans ; *Transcranial Direct Current Stimulation/methods ; *Electroencephalography/methods ; Pilot Projects ; *Brain-Computer Interfaces ; Male ; Adult ; Female ; Motor Cortex/physiology ; Young Adult ; *Brain/physiology ; },
abstract = {In this study, we introduce a novel brain-computer-brain (BCB) system to investigate the aftereffects of individualized, task-dependent transcranial alternating current stimulation (tACS) delivered to the motor cortex. While previous studies utilized either a generic stimulation frequency or matched it to an individual's resting frequency (e.g. individual alpha frequency, iAF), our study employed a trial-by-trial tACS stimulation design wherein the stimulation frequency delivered matches the individual's peak motor imagery (MI) performance frequency. 14 healthy subjects participated in both tACS and tACS-sham on separate days in a within-subject, randomized controlled design. We found that active tACS delivered to subjects receiving alpha (α)-tACS resulted in a decline in MI performance while that with tACS-sham did not differ significantly from baseline. However, subjects receiving beta (β)-tACS showed no significant difference in effect for both active tACS and tACS-sham conditions. These findings indirectly corroborated with that from literature advocating the notion of α tACS as functionally inhibitory; hence the consequential deterioration of MI performance observed only in α-tACS subjects. A more conclusive analysis will be conducted once more data is collected from this ongoing study.Clinical Relevance: The results gathered suggest the differential functional significance of α- and β-tACS in an individualized MI task-specific tACS delivery to the motor cortex with concurrent EEG recording. Although insignificant at the point of data analysis where sample size is small in this ongoing study, tACS-sham (30 Hz) seemed to potentially modulate neural oscillations in the direction of improving MI performance. These findings can inform future tACS study designs based on a system with personalized stimulation delivery for MI task investigations within laboratory and clinical settings - potentially beneficial towards upper limb stroke rehabilitation.},
}
@article {pmid40039273,
year = {2024},
author = {Jiang, H and Xiao, X and Mei, J and Xu, M and Wang, K and Ming, D},
title = {A Novel Real-time Algorithm Based on Phase-Locked Data Alignment for Continuously Controlled SSVEP-BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782824},
pmid = {40039273},
issn = {2694-0604},
mesh = {*Algorithms ; Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; Male ; Adult ; Signal Processing, Computer-Assisted ; Female ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) show good performance. However, algorithms always decode segments of electroencephalogram (EEG) and can only satisfy discrete output instructions, which limit the real-time continuous control of the BCI system. This article proposes a novel algorithm for SSVEP-BCI that can translate continuous EEG into control commands, achieving real-time monitoring of user intentions.<br><br>METHODS: A phase synchronicity maximum strategy has been employed in this algorithm, which could capture a fixed-duration SSVEP epoch near any given moment, ensuring each trial is aligned with the phase of the potential corresponding template. Then, the algorithm utilized an update strategy of a small-step sliding window to recognize and output commands in approximately real-time.<br><br>RESULTS: We constructed an SSVEP-BCI system with continuous stimulation and recruited nine subjects. The results showed that the algorithm proposed in this study efficiently decoded continuously evoked SSVEP signals. The BCI's online average accuracy and ITR were 92.03% and 143.38 bits/min, respectively.<br><br>SIGNIFICANCE: The proposed algorithm can decode SSVEP at any time theoretically, which improves command output density as well as maintains high recognition accuracy. This study provides novel methods for real-time control of external devices using SSVEP-BCIs and helps to develop BCIs that are more compatible with human control habits.},
}
@article {pmid40039271,
year = {2024},
author = {Umezawa, K and Isezaki, T and Okitsu, K and Yokoyama, O and Suzuki, M and Nishimura, Y},
title = {Refined Force Estimation in Monkey's Pinching Tasks Through Integrated EMG and ECoG Data: A Kalman Filter Method.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782574},
pmid = {40039271},
issn = {2694-0604},
mesh = {*Electromyography/methods ; Animals ; *Brain-Computer Interfaces ; *Electrocorticography/methods ; Algorithms ; Macaca mulatta ; },
abstract = {In the development of brain-computer interfaces (BCIs), precise decoding of motor outputs is crucial. This study presents an enhanced Kalman filter approach that integrates electromyography (EMG) with electrocorticography (ECoG) to improve force estimation in pinching tasks. By incorporating EMG data as a state variable in the filter, we aim to account for musculoskeletal dynamics, enhancing the accuracy of force predictions. This integration significantly improves the decoding performance, particularly during dynamic force phases. The results confirm the importance of embedding musculoskeletal dynamics into ECoG-based BCIs, which may help improve prosthetic control and motor rehabilitation for people with motor impairments.},
}
@article {pmid40039264,
year = {2024},
author = {Li, M and Pun, SH and Chen, F},
title = {Cross-paradigm data alignment to improve the calibration of asynchronous BCI systems in EEG-based speech imagery.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10781999},
pmid = {40039264},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; Calibration ; *Speech/physiology ; Algorithms ; Male ; Adult ; Signal Processing, Computer-Assisted ; },
abstract = {The brain-computer interfaces (BCIs) based on speech imagery with asynchronous (self-paced) paradigms enable users to directly access and manipulate BCIs with more freedom. Compared with the indirect BCIs with traditional synchronous (cue-based) paradigms, the calibration time of asynchronous paradigms was much longer and with the unbalanced number of task states and idle states. This work aimed to improve the calibration of asynchronous BCI systems by applying a data alignment (DA) approach on cue-based and self-paced paradigms. The cue-based paradigm was regarded as the calibration paradigm and the self-paced paradigm was the testing paradigm. The data alignment approach based on the parallel transport mapped their features on the same tangent space. The logistic regression was used as the classifier to classify task states and idle states. The average result with DA was 7.52% higher than that without DA (baseline), which were 78.45% and 70.92%, respectively. Specially, the best classification accuracy was for 91.82% with DA, and the largest improvement in accuracy was 22.92%. These results suggest that it is practical to use a synchronous paradigm as calibration paradigm in asynchronous BCI systems and the data alignment approach has positive impacts on the classification of task states and idle states.},
}
@article {pmid40039208,
year = {2024},
author = {Geng, Y and Yang, B and Ke, S and Chang, L and Zhang, J and Zheng, Y},
title = {Motor Imagery Decoding from EEG under Visual Distraction via Feature Map Attention EEGNet.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10781898},
pmid = {40039208},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Male ; *Imagination/physiology ; *Attention/physiology ; Adult ; Female ; Signal Processing, Computer-Assisted ; Algorithms ; Young Adult ; },
abstract = {The investigation of motor imagery (MI)-based brain-computer interface (BCI) is vital to the domains of human-computer interaction and rehabilitation. Few existing studies on electroencephalogram(EEG) signals decoding based on MI consider any distractions. However, it is difficult for users to do a single MI task in real life, which is especially affected by visual distraction. In this paper, we aim to investigate the effects of visual distraction on MI decoding performance. We first design a noval MI paradigm under visual distraction and observe distinct patterns of event-related desynchronization (ERD) and event-related synchronization (ERS) in MI under visual distraction. Then, we propose a robust decoding method of MI under visual distraction from EEG signals by using the feature map attention EEGNet (named FMA-EEGNet) and use EEG data under conditions without and with distraction to compare the decoding performance of five methods (including the proposed method and other methods). The results demonstrate that FMA-EEGNet achieved mean accuracy of 89.1% and 82.2% without and with visual distraction, respectively, indicating superior performance compared to other methods while exhibiting minimal degradation in performance. This work contributes significantly to the advancement of practical applications in MI-BCI technology.},
}
@article {pmid40039201,
year = {2024},
author = {Li, Q and Zhang, Z and Shi, M and Tao, X},
title = {Multi-channel Neural Signal Recording System for an Implantable Brain-Computer Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10782545},
pmid = {40039201},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted/instrumentation ; Humans ; *Brain/physiology ; Equipment Design ; *Electroencephalography/instrumentation/methods ; },
abstract = {Simultaneous recordings of neural activity at massive scope, in the long term, and under bio-safety conditions, could provide crucial information, which helps in better understanding the operation mechanism of the brain and promotes the clinical application evolution for the brain-computer interface. For this purpose, a multi-channel neural signal recording system is presented, which can record up to 2048-channel neural signals by multiple connections of a customized collection system. The system consists of a sensor array module, a central controller module, and an upper computer module. Using the modular design method, the sensor array module can be contrived by changing the number of channels. The single-channel data acquisition module has a sampling resolution of 16 bits, and a sampling rate of 30 KSamples/s. The central controller module can establish a connection between the sensor array module and the upper computer module, and control their operations. The upper computer module can display the data results. The system verifies the performance of the multi-channel data acquisition through the analog neural signal.},
}
@article {pmid40039185,
year = {2024},
author = {Raghavan, V and Patel, P and He, X and Mesgarani, N},
title = {Decoding auditory attention for real-time BCI control.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-6},
pmid = {40039185},
issn = {2694-0604},
support = {R01 DC014279/DC/NIDCD NIH HHS/United States ; R01 DC018805/DC/NIDCD NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography/methods ; *Attention/physiology ; Male ; Adult ; Acoustic Stimulation ; *Auditory Perception/physiology ; },
abstract = {Brain-Computer Interfaces (BCI) facilitate interaction with devices, enhancing the quality of life for individuals with disabilities and offering a more direct method for controlling smart devices. Auditory BCIs commonly utilize event-related potentials (ERPs) necessitating a sequential presentation of choices through auditory stimuli. However, such methods impose constraints on the achievable Information Transfer Rate (ITR) compared to visual BCIs due to extended stimulus presentation times. Here, we introduce an auditory BCI approach in which the selective representation of attended speech in a listener's brain enables the decoding of one target sound source from the background. The simultaneous delivery of options in our proposed method reduces presentation durations by 2.5x compared to previous auditory BCI paradigms. This approach yields an average ITR exceeding 17 bits/min, with the best subject surpassing 33 bits/min. By outdoing current state-of-the-art auditory BCI paradigms, our research represents a significant advancement in the development of practical auditory BCI technologies.},
}
@article {pmid40039137,
year = {2024},
author = {Cinquetti, E and Siviero, I and Babiloni, F and Menegaz, G and Storti, SF},
title = {Passive BCI Towards Health and Safety in Industry: Forecasting Human Vigilance 5.5 s Ahead.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782689},
pmid = {40039137},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography ; *Brain-Computer Interfaces ; Forecasting ; *Industry ; Male ; Adult ; *Safety ; *Arousal/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interfaces based on electroencephalography (EEG) recordings are gaining increasing interest in the industrial domain, aiming to enhance health, safety and performance by optimizing the cognitive load of industrial operators and facilitating human-robot interactions. This study introduces a novel experimental protocol and analysis pipeline for predicting vigilance degradation during repetitive tasks. A dataset was recorded from 10 volunteers who observed a robotic arm executing three distinct movements. The EEG power spectrum was analyzed over time using the continuous wavelet transform. Upon verifying the increased amplitude of EEG oscillations in the 8-12 Hz frequency band, we forecast its behaviour, comparing the vector autoregressive model with two deep learning recurrent architectures. The proposed encoder-decoder gated recurrent unit model obtained accurate forecasts (mean absolute error = 0.048, R[2] = 0.726) up to 5.5 s into the future. The findings suggested the feasibility of vigilance monitoring in the Industry 5.0 framework, proposing a strategy to prevent human accidents and performance decline during monotonous activities.},
}
@article {pmid40039126,
year = {2024},
author = {Amrani, H and Micucci, D and Nalin, M and Napoletano, P and Rizzi, I},
title = {EEG Acquisition and Motor Imagery Classification for Robotic Control.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782723},
pmid = {40039126},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; Humans ; *Robotics ; *Brain-Computer Interfaces ; Support Vector Machine ; *Imagination/physiology ; Signal Processing, Computer-Assisted ; Movement/physiology ; Machine Learning ; },
abstract = {The adoption of brain-computer interfaces (BCIs) has significantly increased in various application domains, particularly in the field of controlling robotic systems through motor imagery. The article contributes in two primary ways: 1) validating the effectiveness of using a minimally invasive electroencephalography (EEG) device combined with machine learning techniques to control fundamental movements in a robotic setting, and 2) demonstrating these findings practically through the construction of a robotic vehicle. In this vehicle, tasks involving motor imagery align directly with control commands for the vehicle. To validate our approach, we identified four-class and two-class classification tasks. The signals have been acquired from a portable EEG device equipped with eight dry electrodes. We employed sliding window strategies to segment the data, along with feature extraction using the Common Spatial Pattern (CSP) method. Classification modules were implemented based on Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) models. The experimentation involved five participants, each with their own personalized model. While the accuracy of results in the four-class tasks is not notably high, the outcomes in binary classification tasks are promising, boasting an average accuracy of approximately 61%. Results suggest a promising potential for this approach in the realm of robot control, particularly when employing dry-electrode EEG devices.},
}
@article {pmid40039119,
year = {2024},
author = {Rajpura, P and Meena, YK},
title = {Towards Optimising EEG Decoding using Post-hoc Explanations and Domain Knowledge.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10781846},
pmid = {40039119},
issn = {2694-0604},
mesh = {*Electroencephalography ; Validation Studies as Topic ; *Brain-Computer Interfaces ; Models, Neurological ; Humans ; },
abstract = {Decoding Electoencephalography (EEG) during motor imagery is pivotal for the Brain-Computer Interface (BCI) system, influencing its overall performance significantly. As end-to-end data-driven learning methods advance, the challenge lies in balancing model complexity with the need for human interpretability and trust. Despite strides in EEG-based BCIs, challenges like artefacts and low signal-to-noise ratio emphasise the ongoing importance of model transparency. This work proposes using post-hoc explanations to interpret model outcomes and validate them against domain knowledge. Leveraging the GradCAM post-hoc explanation technique on the EEG motor movement/imagery dataset, this work demonstrates that relying solely on accuracy metrics may be inadequate to ensure BCI performance and acceptability. A model trained using all EEG channels of the dataset achieves 72.60% accuracy, while a model trained with motor-imagery/movement-relevant channel data has a statistically insignificant decrease of 1.75%. However, the relevant features for both are very different based on neurophysiological facts. This work demonstrates that integrating domain-specific knowledge with Explainable AI (XAI) techniques emerges as a promising paradigm for validating the neurophysiological basis of model outcomes in BCIs. Our results reveal the significance of neurophysiological validation in evaluating BCI performance, highlighting the potential risks of exclusively relying on performance metrics when selecting models for dependable and transparent BCIs.},
}
@article {pmid40039013,
year = {2024},
author = {Cetera, A and Rabiee, A and Ghafoori, S and Shahriari, Y and Abiri, R},
title = {Classification of Emerging Neural Activity from Planning to Grasp Execution using a Novel EEG-Based BCI Platform.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10782523},
pmid = {40039013},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Hand Strength/physiology ; Support Vector Machine ; Male ; Adult ; Motor Cortex/physiology ; Signal Processing, Computer-Assisted ; Female ; },
abstract = {There have been different reports of developing Brain-Computer Interface (BCI) platforms to investigate the noninvasive electroencephalography (EEG) signals associated with plan-to-grasp tasks in humans. However, these reports were unable to clearly show evidence of emerging neural activity from the planning (observation) phase - dominated by the vision cortices - to grasp execution - dominated by the motor cortices. In this study, we developed a novel vision-based-grasping BCI platform that distinguishes different grip types (power and precision) through the phases of plan-to-grasp tasks using EEG signals. Using our platform and extracting features from Filter Bank Common Spatial Patterns (FBCSP), we show that frequency-band specific EEG contains discriminative spatial patterns present in both the observation and movement phases. Support Vector Machine (SVM) classification (power vs precision) yielded high accuracy percentages of 74% and 68% for the observation and movement phases in the alpha band, respectively.},
}
@article {pmid40038999,
year = {2024},
author = {Farabbi, A and Mainardi, L},
title = {Advancing Brain-Computer Interface Systems: Asynchronous Classification of Error Potentials.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782785},
pmid = {40038999},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Discriminant Analysis ; Algorithms ; Signal Processing, Computer-Assisted ; Brain/physiology ; },
abstract = {This paper explores the paradigm shift in the classification of Error-Related Potentials (ErrP) in Brain-Computer Interfaces (BCIs) by introducing an asynchronous approach. Traditional synchronous methods, relying on precise temporal alignment between stimuli presentation and neural responses, face challenges in real-world scenarios with human response variability.The proposed asynchronous classification liberates BCI systems from strict temporal constraints, allowing for a more natural interaction paradigm. The study introduces an innovative ensemble method comprising Linear Discriminant Analysis (LDA) and EEGNet for asynchronous ErrP classification.The method is evaluated on EEG data from the BNCI Horizon 2020 dataset, demonstrating high balanced accuracy. While the introduction of EEGNet refines the classification, reducing false positives, challenges persist in achieving a balanced trade-off between precision and recall.The findings suggest the ensemble method's potential for practical applications, emphasizing the need for further refinement and exploration of advanced techniques in asynchronous ErrP classification.},
}
@article {pmid40038985,
year = {2024},
author = {Lee, KY and Chang, KY and Hsu, HC and Tseng, YT and Wei, CS and Lin, SS and Chuang, CH},
title = {Utilizing Motor-Imagery Brain-Computer Interfaces for the Assessment of Developmental Coordination Disorder in Children.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10781534},
pmid = {40038985},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; *Motor Skills Disorders/diagnosis/physiopathology ; Child ; *Electroencephalography/methods ; Male ; Support Vector Machine ; Female ; *Imagination ; },
abstract = {Developmental Coordination Disorder (DCD) is a neurodevelopmental disorder characterized by significant motor difficulties that affect daily life. Current assessment methods primarily focus on behavioral analysis, lacking in neuroscientific metrics for a comprehensive evaluation. This study introduced an electroencephalography-based motor imagery brain-computer interface classification system for evaluating children with DCD. A key of this system was the implementation of entropy-based data screening, which markedly enhanced classification performance. Notably, using mu band power in a support vector machine achieved an accuracy rate of 79.0%. These findings pave the way for developing a tool that could assist professionals in identifying children potentially affected by DCD.},
}
@article {pmid40038962,
year = {2024},
author = {Sreekantham, S and Chetty, N and Weber, DJ},
title = {Detecting and Eliminating Cardiac Artifact from Endovascular EEG Signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782938},
pmid = {40038962},
issn = {2694-0604},
mesh = {Humans ; *Artifacts ; *Electroencephalography/methods/instrumentation ; Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; *Heart/physiology ; Electrocardiography/methods ; },
abstract = {Paralysis is a debilitating condition that affects more than 5.4 million people in the U.S. In severe cases, the paralyzed patient is incapable of communication. Restoring this communication is a primary goal of caretakers and is critical to improving the patient's quality of life. Brain-computer interfaces (BCIs) that directly access signals from the motor cortex are a promising method of circumventing the condition causing paralysis, typically using machine learning (ML) to predict motor intent from brain signals. However, BCIs are highly invasive and subjects have primarily been limited to patients with mild to moderate paralysis. The Stentrode is a novel technology that records electroencephalographic (EEG) signals via an electrode array placed endovascularly in the superior sagittal sinus. The first clinical trials of this technology aim to enable digital communication for severely paralyzed patients, translating brain signals from attempted movements into computer control inputs like mouse clicks. However, recordings of EEG are often contaminated with artifacts, including biopotentials arising from other excitable tissues, such as the heart and skeletal muscle. This study characterizes the electrocardiographic (ECG) artifact detected in the Stentrode recordings and proposes an automated Independent Component Analysis (ICA) method for removing this artifact. We compare the effectiveness of this method to previous methods for removal. Quantifying and eliminating the cardiac artifact is critical to accurately decode signals from the motor cortex and restore patients' ability to communicate.},
}
@article {pmid40038951,
year = {2024},
author = {Song, Z and Zhang, X and Tan, J and Wang, M and Wang, Y},
title = {Facilitating Knowledge Transfer: An Approach for Matching Neural Patterns between Motor Tasks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10781969},
pmid = {40038951},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Rats ; Animals ; Algorithms ; Humans ; },
abstract = {Brain-machine interface (BMI) holds great promise for restoring the impaired motor functions of individuals. In real-life scenarios, BMI users often face the challenge of quickly learning new tasks to adapt to complex environments. Consequently, it becomes essential to investigate the transferability of knowledge (neural-action mapping) of the decoder gained from previously learned tasks to new tasks. This paper introduces an approach for matching neural patterns between motor tasks to facilitate knowledge transfer, which is a key step in facilitating knowledge transfer. We project neural data into a 6D jPCA feature space and observe that neural patterns associated with the same action are preserved in the last four dimensions. By utilizing the decoder trained from the previous task, we obtain a prior estimate of the matched class. This prior estimate is further refined by clustering the neural patterns in the first two dimensions, as the data demonstrates distinct cluster shapes. To validate our approach, we conducted an experiment where a rat learned two related motor tasks sequentially. The preliminary results showed that our proposed method achieved an accuracy of 87.04% in estimating the matched class compared to the ground truth. In contrast, utilizing the decoder trained from the previous task within the entire jPCA space resulted in a significantly lower accuracy of merely 39.8%. These findings highlight the efficacy of our proposed method in matching neural patterns between motor tasks, thus facilitating knowledge transfer.},
}
@article {pmid40038942,
year = {2024},
author = {Rabiee, A and Ghafoori, S and Cetera, A and Shahriari, Y and Abiri, R},
title = {Wavelet Analysis of Noninvasive EEG Signals Discriminates Complex and Natural Grasp Types.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10782674},
pmid = {40038942},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Wavelet Analysis ; *Hand Strength/physiology ; Brain-Computer Interfaces ; Male ; Signal Processing, Computer-Assisted ; Adult ; Algorithms ; Machine Learning ; Female ; },
abstract = {This research aims to decode hand grasps from Electroencephalograms (EEGs) for dexterous neuroprosthetic development and Brain-Computer Interface (BCI) applications, especially for patients with motor disorders. Particularly, it focuses on distinguishing two complex natural power and precision grasps in addition to a neutral condition as a no-movement condition using a new EEG-based BCI platform and wavelet signal processing. Wavelet analysis involved generating time-frequency and topographic maps from wavelet power coefficients. Then, by using machine learning techniques with novel wavelet features, we achieved high average accuracies: 85.16% for multiclass, 95.37% for No-Movement vs Power, 95.40% for No-Movement vs Precision, and 88.07% for Power vs Precision, demonstrating the effectiveness of these features in EEG-based grasp differentiation. In contrast to previous studies, a critical part of our study was permutation feature importance analysis, which highlighted key features for grasp classification. It revealed that the most crucial brain activities during grasping occur in the motor cortex, within the alpha and beta frequency bands. These insights demonstrate the potential of wavelet features in real-time neuroprosthetic technology and BCI applications.},
}
@article {pmid40037510,
year = {2025},
author = {Bjånes, DA and Kellis, S and Nickl, R and Baker, B and Aflalo, T and Bashford, L and Chivukula, S and Fifer, MS and Osborn, LE and Christie, B and Wester, BA and Celnik, PA and Kramer, D and Pejsa, K and Crone, NE and Anderson, WS and Pouratian, N and Lee, B and Liu, CY and Tenore, FV and Rieth, L and Andersen, RA},
title = {Quantifying physical degradation alongside recording and stimulation performance of 980 intracortical microelectrodes chronically implanted in three humans for 956-2130 days.},
journal = {Acta biomaterialia},
volume = {198},
number = {},
pages = {188-206},
pmid = {40037510},
issn = {1878-7568},
support = {U01 NS098975/NS/NINDS NIH HHS/United States ; UG1 EY032039/EY/NEI NIH HHS/United States ; UG3 NS107688/NS/NINDS NIH HHS/United States ; U01 NS123127/NS/NINDS NIH HHS/United States ; R01 EY015545/EY/NEI NIH HHS/United States ; },
mesh = {Microelectrodes ; Humans ; *Electrodes, Implanted ; Male ; *Brain-Computer Interfaces ; Female ; Iridium/chemistry ; Middle Aged ; Platinum/chemistry ; },
abstract = {The clinical success of brain computer interfaces (BCI) depends on overcoming both biological and material challenges to ensure a long-term stable connection for neural recording and stimulation. This study systematically quantified damage that microelectrodes sustained during chronical implantation in three people with tetraplegia for 956-2130 days. Using scanning electron microscopy (SEM), we imaged 980 microelectrodes from eleven Neuroport arrays tipped with platinum (Pt, n = 8) and sputtered iridium oxide film (SIROF, n = 3). Arrays were implanted/explanted from posterior parietal, motor and somatosensory cortices across three clinical sites (Caltech/UCLA, Caltech/USC, APL/Johns Hopkins). From the electron micrographs, we quantified and correlated physical damage with functional outcomes measured in vivo, prior to explant (recording quality, noise, impedance and stimulation ability). Despite greater physical degradation, SIROF electrodes were twice as likely to record neural activity than Pt (measured by SNR). For SIROF, 1 kHz impedance significantly correlated with all physical damage metrics, recording metrics, and stimulation performance, suggesting a reliable measurement of in vivo degradation. We observed a new degradation type, primarily on stimulated electrodes ("pockmarked" vs "cracked") electrodes; however, no significant degradation due to stimulation or amount of charge delivered. We hypothesize erosion of the silicon shank accelerates damage to the electrode / tissue interface, following damage to the tip metal. These findings link quantitative measurements to the microelectrodes' physical condition and their capacity to record/stimulate. These data could lead to improved manufacturing processes or novel electrode designs to improve long-term performance of BCIs, making them vitally important as multi-year clinical trials of BCIs are becoming more common. STATEMENT OF SIGNIFICANCE: Long-term performance stability of the electrode-tissue interface is essential for clinical viability of brain computer interface (BCI) devices; currently, materials degradation is a critical component for performance loss. Across three human participants, ten micro-electrode arrays (plus one control) were implanted for 956-2130 days. Using scanning electron microscopy (SEM), we analyzed degradation of 980 electrodes, comparing two types of commonly implanted electrode tip metals: Platinum (Pt) and Sputtered Iridium Oxide Film (SIROF). We correlated observed degradation with in vivo electrode performance: recording (signal-to-noise ratio, noise, impedance) and stimulation (evoked somatosensory percepts). We hypothesize penetration of the electrode tip by biotic processes leads to erosion of the supporting silicon core, which then accelerates further tip metal damage. These data could lead to improved manufacturing processes or novel electrode designs towards the goal of a stable BCI electrical interface, spanning a multi-decade participant lifetime.},
}
@article {pmid40037493,
year = {2025},
author = {Tang, G and Chen, B and Wu, M and Sun, L and Fan, R and Hou, R and Liu, W and Kang, J and Li, Y and Wang, M and Zhang, Y and Lu, N and Guo, W and Zhang, Y and Li, X and Wei, W and Yu, H and Li, T},
title = {Effectiveness of mindfulness-based cognitive therapy for treating generalized anxiety disorder and the moderating influence of abuse during childhood: A randomized controlled trial.},
journal = {Journal of affective disorders},
volume = {379},
number = {},
pages = {510-518},
doi = {10.1016/j.jad.2025.02.103},
pmid = {40037493},
issn = {1573-2517},
mesh = {Humans ; *Anxiety Disorders/therapy/psychology ; Female ; Male ; *Mindfulness/methods ; *Cognitive Behavioral Therapy/methods ; Adult ; Treatment Outcome ; Child ; Middle Aged ; *Child Abuse/psychology ; Surveys and Questionnaires ; Generalized Anxiety Disorder ; },
abstract = {BACKGROUND: Mindfulness-based cognitive therapy (MBCT) has emerged as a promising intervention for generalized anxiety disorder (GAD). This study evaluated MBCT's effectiveness for GAD and examined whether childhood maltreatment moderates its impact.<br><br>METHODS: Individuals with GAD were randomized to receive one of two 8-week interventions, either MBCT in-person or psychoeducation on-line (n&#xa0;=&#xa0;27 per group). At baseline and after 4 and 8&#xa0;weeks of intervention, both groups were assessed using the Beck Anxiety Inventory and Penn State Worry Questionnaire as well as several secondary questionnaires. Changes in the severity of anxiety and worry over time, as determined using linear mixed modeling, were compared between the two groups as a whole and among subgroups stratified according to type of maltreatment in childhood.<br><br>RESULTS: Among all participants, severity of worry decreased significantly more in the MBCT group than in the psychoeducation group, whereas severity of anxiety decreased to a similar extent in the two groups. Among individuals who had experienced emotional abuse in childhood, MBCT reduced the severity of anxiety significantly more than psychoeducation. In fact, MBCT was significantly more effective against anxiety in individuals who had experienced emotional abuse than in those who had not.<br><br>CONCLUSIONS: MBCT might be effective in alleviating worry symptoms in GAD, while its effectiveness against anxiety symptoms appears to be influenced by the history of maltreatment, particularly emotional abuse.<br><br>TRIAL REGISTRATION: ChiCTR2400087188 (Chictr.org).},
}
@article {pmid40036596,
year = {2025},
author = {Chen, Q and Huang, X and Ju, Z and Lin, H and Tang, H and Guo, C and Fan, F and Zhao, X and Ma, Y and Luo, Y and Li, W and Zhong, W and Zhao, D},
title = {A Triband Metasurface Covering Visible, Midwave Infrared, and Long-Wave Infrared for Optical Security.},
journal = {Nano letters},
volume = {25},
number = {11},
pages = {4459-4466},
doi = {10.1021/acs.nanolett.5c00083},
pmid = {40036596},
issn = {1530-6992},
abstract = {The independent manipulation of light across multiple wavelength bands provides new opportunities for optical security. Although dual-band optical encryption methods in the visible (VIS) and infrared bands have been developed, achieving synchronized and synergistic optical security across the VIS, midwave infrared (MWIR), and long-wave infrared (LWIR) bands remains a significant challenge. Here, we experimentally demonstrate a triband metasurface that covers the VIS, MWIR, and LWIR bands. While VIS imaging is achieved by structural color, MWIR, and LWIR imaging are achieved by selective emissivity structures, with MWIR/LWIR emissivities in the MWIR imaging region of 0.81/0.17, and in the LWIR imaging region of 0.21/0.83. Importantly, the MWIR and LWIR information is completely hidden in the VIS band. We also validate the ability of metasurface to encode complex information and information-misleading encryption. This work introduces new approaches for enhancing optical security and holds significant potential for applications such as anticounterfeiting and thermal camouflage.},
}
@article {pmid40036537,
year = {2025},
author = {Ravi, A and Wolfe, P and Tung, J and Jiang, N},
title = {Signal Characteristics, Motor Cortex Engagement, and Classification Performance of Combined Action Observation, Motor Imagery and SSMVEP (CAMS) BCI.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {1004-1013},
doi = {10.1109/TNSRE.2025.3544479},
pmid = {40036537},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; *Motor Cortex/physiology ; Male ; Female ; *Imagination/physiology ; Adult ; Young Adult ; Electroencephalography/methods ; *Evoked Potentials, Visual/physiology ; Movement/physiology ; Algorithms ; Reproducibility of Results ; Gait/physiology ; Healthy Volunteers ; },
abstract = {Motor imagery (MI)-based Brain-Computer Interfaces (BCIs) have shown promise in engaging the motor cortex for recovery. However, individual responses to MI-based BCIs are highly variable and relatively weak. Conversely, combined action observation (AO) and motor imagery (MI) paradigms have demonstrated stronger responses compared to AO or MI alone, along with enhanced cortical excitability. In this study, a novel BCI called Combined AO, MI, and Steady-State Motion Visual Evoked Potential (SSMVEP) (CAMS) was proposed. CAMS was designed based on gait observation and imagination. Twenty-five healthy volunteers participated in the study with CAMS serving as the intervention and SSMVEP checkerboard as the control condition. We hypothesized the CAMS intervention can induce observable increases in the negativity of the movement-related cortical potential (MRCP) associated with ankle dorsiflexion. MRCP components, including Bereitschaftspotential, were measured pre- and post-intervention. Additionally, the signal characteristics of the visual and motor responses were quantified. Finally, a two-class visual BCI classification performance was assessed. A consistent increase in negativity was observed across all MRCP components in signals over the primary motor cortex, compared to the control condition. CAMS visual BCI achieved a median accuracy of 83.8%. These findings demonstrate the ability of CAMS BCI to enhance cortical excitability in relation to movement preparation and execution. The CAMS stimulus not only evokes SSMVEP-like activity and sensorimotor rhythm but also enhances the MRCP. These findings contribute to the understanding of CAMS paradigm in enhancing cortical excitability, consistent and reliable classification performance holding promise for motor rehabilitation outcomes and future BCI design considerations.},
}
@article {pmid40036449,
year = {2025},
author = {Kim, MK and Shin, HB and Cho, JH and Lee, SW},
title = {Developing Brain-Based Bare-Handed Human-Machine Interaction via On-Skin Input.},
journal = {IEEE transactions on cybernetics},
volume = {55},
number = {4},
pages = {1554-1567},
doi = {10.1109/TCYB.2025.3533088},
pmid = {40036449},
issn = {2168-2275},
mesh = {Humans ; Male ; *Brain-Computer Interfaces ; Female ; Adult ; *Touch/physiology ; Young Adult ; Gestures ; *Signal Processing, Computer-Assisted ; Deep Learning ; },
abstract = {Developing natural, intuitive, and human-centric input systems for mobile human-machine interaction (HMI) poses significant challenges. Existing gaze or gesture-based interaction systems are often constrained by their dependence on continuous visual engagement, limited interaction surfaces, or cumbersome hardware. To address these challenges, we propose MetaSkin, a novel neurohaptic interface that uniquely integrates neural signals with on-skin interaction for bare-handed, eyes-free interaction by exploiting human's natural proprioceptive capabilities. To support the interface, we developed a deep learning framework that employs multiscale temporal-spectral feature representation and selective feature attention to effectively decode neural signals generated by on-skin touch and motion gestures. In experiments with 12 participants, our method achieved offline accuracies of 81.95% for touch location discrimination, 71.00% for motion type identification, and 46.08% for 10-class touch-motion classification. In pseudo-online settings, accuracies reached 99.43% for touch onset detection, and 80.34% and 67.02% for classification of touch location and motion type, respectively. Neurophysiological analyses revealed distinct neural activation patterns in the sensorimotor cortex, underscoring the efficacy of our multiscale approach in capturing rich temporal and spectral dynamics. Future work will focus on optimizing the system for diverse user populations and dynamic environments, with a long-term goal of advancing human-centered, neuroadaptive interfaces for next-generation HMI systems. This work represents a significant step toward a paradigm shift in design of brain-computer interfaces, bridging sensory and motor paradigms for building more sophisticated systems.},
}
@article {pmid40035637,
year = {2025},
author = {Sun, B and Zhang, X and Zhang, X and Xu, B and Wang, Y},
title = {Data collection, enhancement, and classification of functional near-infrared spectroscopy motor execution and imagery.},
journal = {The Review of scientific instruments},
volume = {96},
number = {3},
pages = {},
doi = {10.1063/5.0236392},
pmid = {40035637},
issn = {1089-7623},
mesh = {Spectroscopy, Near-Infrared/methods/instrumentation ; Humans ; Brain-Computer Interfaces ; *Imagination/physiology ; Signal Processing, Computer-Assisted ; Male ; Electroencephalography ; Brain/physiology ; Adult ; },
abstract = {Recognition and execution of motor imagery play a key role in brain-computer interface (BCI) and are prerequisites for converting thoughts into executable instructions. However, to date, data acquired through commonly used electroencephalography (EEG) methods are very sensitive to motion interference, which will affect the accuracy of the data classification. The emerging functional near-infrared spectroscopy (fNIRS) technique, while overcoming the drawbacks of EEG's susceptibility to interference and difficulty in detecting motor signals, has less publicly available data. In this paper, we designed a motor execution and imagery experiment based on a wearable fNIRS device to acquire brain signals and proposed a modified Kolmogorov-Arnold network (named SE-KAN) for recognizing fNIRS signals corresponding to the task. Due to the small number of subjects in this experiment, the Wasserstein generative adversarial network was used to enhance the data processing. For the fNIRS data recognition task, the SE-KAN method achieved 96.36 ± 2.43% single-subject accuracy and 84.72 ± 3.27% cross-subject accuracy. It is believed that the dataset and method of this paper will help the development of BCI.},
}
@article {pmid40035554,
year = {2025},
author = {Khan, WU and Shen, Z and Mugo, SM and Wang, H and Zhang, Q},
title = {Implantable hydrogels as pioneering materials for next-generation brain-computer interfaces.},
journal = {Chemical Society reviews},
volume = {54},
number = {6},
pages = {2832-2880},
doi = {10.1039/d4cs01074d},
pmid = {40035554},
issn = {1460-4744},
mesh = {*Hydrogels/chemistry ; *Brain-Computer Interfaces ; Humans ; Biocompatible Materials/chemistry ; Electrodes, Implanted ; Animals ; Brain/physiology ; },
abstract = {Use of brain-computer interfaces (BCIs) is rapidly becoming a transformative approach for diagnosing and treating various brain disorders. By facilitating direct communication between the brain and external devices, BCIs have the potential to revolutionize neural activity monitoring, targeted neuromodulation strategies, and the restoration of brain functions. However, BCI technology faces significant challenges in achieving long-term, stable, high-quality recordings and accurately modulating neural activity. Traditional implantable electrodes, primarily made from rigid materials like metal, silicon, and carbon, provide excellent conductivity but encounter serious issues such as foreign body rejection, neural signal attenuation, and micromotion with brain tissue. To address these limitations, hydrogels are emerging as promising candidates for BCIs, given their mechanical and chemical similarities to brain tissues. These hydrogels are particularly suitable for implantable neural electrodes due to their three-dimensional water-rich structures, soft elastomeric properties, biocompatibility, and enhanced electrochemical characteristics. These exceptional features make them ideal for signal recording, neural modulation, and effective therapies for neurological conditions. This review highlights the current advancements in implantable hydrogel electrodes, focusing on their unique properties for neural signal recording and neuromodulation technologies, with the ultimate aim of treating brain disorders. A comprehensive overview is provided to encourage future progress in this field. Implantable hydrogel electrodes for BCIs have enormous potential to influence the broader scientific landscape and drive groundbreaking innovations across various sectors.},
}
@article {pmid40035293,
year = {2025},
author = {Schilling, KG and Grussu, F and Ianus, A and Hansen, B and Howard, AFD and Barrett, RLC and Aggarwal, M and Michielse, S and Nasrallah, F and Syeda, W and Wang, N and Veraart, J and Roebroeck, A and Bagdasarian, AF and Eichner, C and Sepehrband, F and Zimmermann, J and Soustelle, L and Bowman, C and Tendler, BC and Hertanu, A and Jeurissen, B and Verhoye, M and Frydman, L and van de Looij, Y and Hike, D and Dunn, JF and Miller, K and Landman, BA and Shemesh, N and Anderson, A and McKinnon, E and Farquharson, S and Dell'Acqua, F and Pierpaoli, C and Drobnjak, I and Leemans, A and Harkins, KD and Descoteaux, M and Xu, D and Huang, H and Santin, MD and Grant, SC and Obenaus, A and Kim, GS and Wu, D and Le Bihan, D and Blackband, SJ and Ciobanu, L and Fieremans, E and Bai, R and Leergaard, TB and Zhang, J and Dyrby, TB and Johnson, GA and Cohen-Adad, J and Budde, MD and Jelescu, IO},
title = {Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 2-Ex vivo imaging: Added value and acquisition.},
journal = {Magnetic resonance in medicine},
volume = {93},
number = {6},
pages = {2535-2560},
pmid = {40035293},
issn = {1522-2594},
support = {R01 EB031954/EB/NIBIB NIH HHS/United States ; 202788/Z/16/A/WT_/Wellcome Trust/United Kingdom ; R01EB017230/NH/NIH HHS/United States ; R01NS109090/NH/NIH HHS/United States ; P30 DA048742/DA/NIDA NIH HHS/United States ; R01EB031954/NH/NIH HHS/United States ; K01EB032898/NH/NIH HHS/United States ; R56EB031765/NH/NIH HHS/United States ; 203139/A/16/Z/WT_/Wellcome Trust/United Kingdom ; R01 CA160620/CA/NCI NIH HHS/United States ; R01NS125020/NH/NIH HHS/United States ; R01 EB017230/EB/NIBIB NIH HHS/United States ; R01CA160620/NH/NIH HHS/United States ; R01EB019980/NH/NIH HHS/United States ; R01EB031765/NH/NIH HHS/United States ; 203139/Z/16/Z/WT_/Wellcome Trust/United Kingdom ; R01 EB019980/EB/NIBIB NIH HHS/United States ; K01 EB032898/EB/NIBIB NIH HHS/United States ; R01 AG057991/AG/NIA NIH HHS/United States ; /WT_/Wellcome Trust/United Kingdom ; R01 EB031765/EB/NIBIB NIH HHS/United States ; R01 NS109090/NS/NINDS NIH HHS/United States ; R01 NS125020/NS/NINDS NIH HHS/United States ; R01AG057991/NH/NIH HHS/United States ; R56 EB031765/EB/NIBIB NIH HHS/United States ; },
mesh = {Animals ; Humans ; *Brain/diagnostic imaging ; *Diffusion Magnetic Resonance Imaging/methods ; *Image Processing, Computer-Assisted/methods ; Reproducibility of Results ; Signal-To-Noise Ratio ; },
abstract = {The value of preclinical diffusion MRI (dMRI) is substantial. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages including higher SNR and spatial resolution compared to in vivo studies, and enabling more advanced diffusion contrasts for improved microstructure and connectivity characterization. Another major advantage of ex vivo dMRI is the direct comparison with histological data, as a crucial methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work represents "Part 2" of a three-part series of recommendations and considerations for preclinical dMRI. We describe best practices for dMRI of ex vivo tissue, with a focus on the value that ex vivo imaging adds to the field of dMRI and considerations in ex vivo image acquisition. We first give general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in specimens and models and discuss why some may be more or less appropriate for different studies. We then give guidelines for ex vivo protocols, including tissue fixation, sample preparation, and MR scanning. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. An overarching goal herein is to enhance the rigor and reproducibility of ex vivo dMRI acquisitions and analyses, and thereby advance biomedical knowledge.},
}
@article {pmid40034942,
year = {2025},
author = {Tang, X and Fan, D and Wang, X and Xing, Z and Yu, S and Wang, A and Yu, H},
title = {Exploring how sensory dominance modulated by modality-specific expectation: an event-related potential study.},
journal = {Frontiers in psychology},
volume = {16},
number = {},
pages = {1548100},
pmid = {40034942},
issn = {1664-1078},
abstract = {The Colavita visual dominance effect refers to the phenomenon in which tend to respond only or preferentially to visual stimuli of bimodal audiovisual stimulus. Previous evidence has indicated that sensory dominance can be modulated by top-down expectation. However, it remains unclear how expectations directed toward a single sensory modality influence Colavita visual dominance at the electrophysiology level. Using event-related potential (ERP) measurements, we investigated how modality expectation modulates sensory dominance by manipulating the different unimodal target probabilities used in previous related Colavita studies. For the behavioral results, a significantly larger visual dominance effect was found when the modality expectation was directed to the visual sensory condition (40% V:10% A). Further ERPs results revealed that the mean amplitude of P2 (200-250 ms) in the central-parietal region was larger in the visual precedence auditory response (V_A) type than in the auditory precedence visual response (A_V) type when modality expectation was directed to visual sensory stimuli (40% V:10% A). In contrast, the mean amplitude of N2 (290-330 ms) in the frontal region was larger for the V_A type than in the A_V type when modality expectation was directed to the auditory sensory stimuli (10% V:40% A). Additionally, for the A_V type N1 (150-170 ms) in the frontal region was larger in visual versus auditory expectation condition. Overall, the study tentatively suggested that increasing unimodal target probability may lead to greater top-down expectation direct to target modality stimulus, and then sensory dominance emerges in the late phase when participant response to visual stimuli of bimodal audiovisual stimulus.},
}
@article {pmid40034836,
year = {2025},
author = {Alsuradi, H and Hong, J and Sarmadi, A and Volcic, R and Salam, H and Atashzar, SF and Khorrami, F and Eid, M},
title = {BandFocusNet: A Lightweight Model for Motor Imagery Classification of a Supernumerary Thumb in Virtual Reality.},
journal = {IEEE open journal of engineering in medicine and biology},
volume = {6},
number = {},
pages = {305-311},
pmid = {40034836},
issn = {2644-1276},
abstract = {Objective: Human movement augmentation through supernumerary effectors is an emerging field of research. However, controlling these effectors remains challenging due to issues with agency, control, and synchronizing movements with natural limbs. A promising control strategy for supernumerary effectors involves utilizing electroencephalography (EEG) through motor imagery (MI) functions. In this work, we investigate whether MI activity associated with a supernumerary effector could be reliably differentiated from that of a natural one, thus addressing the concern of concurrency. Twenty subjects were recruited to participate in a two-fold experiment in which they observed movements of natural and supernumerary thumbs, then engaged in MI of the observed movements, conducted in a virtual reality setting. Results: A lightweight deep-learning model that accounts for the temporal, spatial and spectral nature of the EEG data is proposed and called BandFocusNet, achieving an average classification accuracy of 70.9% using the leave-one-subject-out cross validation method. The trustworthiness of the model is examined through explainability analysis, and influential regions-of-interests are cross-validated through event-related-spectral-perturbation (ERSPs) analysis. Explainability results showed the importance of the right and left frontal cortical regions, and ERSPs analysis showed an increase in the delta and theta powers in these regions during the MI of the natural thumb but not during the MI of the supernumerary thumb. Conclusion: Evidence in the literature indicates that such activation is observed during the MI of natural effectors, and its absence could be interpreted as a lack of embodiment of the supernumerary thumb.},
}
@article {pmid40034215,
year = {2025},
author = {Li, K and Li, M and Liu, W and Wu, Y and Li, F and Xie, J and Zhou, S and Wang, S and Guo, Y and Pan, J and Wang, X},
title = {Electroencephalographic differences between waking and sleeping periods in patients with prolonged disorders of consciousness at different levels of consciousness.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1521355},
pmid = {40034215},
issn = {1662-5161},
abstract = {OBJECTIVE: This study aimed to explore differences in sleep electroencephalogram (EEG) patterns in individuals with prolonged disorders of consciousness, utilizing polysomnography (PSG) to assist in distinguishing between the vegetative state (VS)/unresponsive wakefulness syndrome (UWS) and the minimally conscious state (MCS), thereby reducing misdiagnosis rates and enhancing the quality of medical treatment.<br><br>METHODS: A total of 40 patients with prolonged disorders of consciousness (pDOC; 27 patients in the VS/UWS and 13 in the MCS) underwent polysomnography. We analyzed differential EEG indices between VS/UWS and MCS groups and performed correlation analyses between these indices and the Coma Recovery Scale-Revised (CRS-R) scores. The diagnostic accuracy of the differential indices was evaluated using receiver operating characteristic (ROC) curves.<br><br>RESULTS: 1. The fractal dimension (Higuchi's fractal dimension (HFD)) of patients in the MCS tended to be higher than that of patients in the VS/UWS across all phases, with a significant difference only in the waking phase (p <&#x202f;0.05). The HFD in the waking phase was positively correlated with the CRS-R score and exhibited the highest diagnostic accuracy at 88.3%. The Teager-Kaiser energy operator (TKEO) also showed higher levels in patients in the MCS compared to those in the VS/UWS, significantly so in the NREM2 phase (p <&#x202f;0.05), with a positive correlation with the CRS-R score and diagnostic accuracy of 75.2%. The &#x3b4;-band power spectral density [PSD(&#x3b4;)] in the patients in the MCS was lower than that in those in the VS/UWS, significantly so in the waking phase (p <&#x202f;0.05), and it was negatively correlated with the CRS-R score, with diagnostic accuracy of 71.5%.<br><br>CONCLUSION: Polysomnography for the VS/UWS and MCS revealed significant differences, aiding in distinguishing between the two patient categories and reducing misdiagnosis rates. Notably, the HFD and PSD(&#x3b4;) showed significantly better performance during wakefulness compared to sleep, while the TKEO was more prominent in the NREM2 stage. Notably, the HFD exhibited a robust correlation with the CRS-R scores, the highest diagnostic accuracy, and immense promise in the clinical diagnosis of prolonged disorders of consciousness.},
}
@article {pmid40033447,
year = {2025},
author = {Li, D and Li, R and Song, Y and Qin, W and Sun, G and Liu, Y and Bao, Y and Liu, L and Jin, L},
title = {Effects of brain-computer interface based training on post-stroke upper-limb rehabilitation: a meta-analysis.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {44},
pmid = {40033447},
issn = {1743-0003},
mesh = {Humans ; *Brain-Computer Interfaces ; *Stroke Rehabilitation/methods ; *Upper Extremity/physiopathology ; Stroke/physiopathology ; Randomized Controlled Trials as Topic ; },
abstract = {BACKGROUND: Previous research has used the brain-computer interface (BCI) to promote upper-limb motor rehabilitation. However, the results of these studies were variable, leaving efficacy unclear.<br><br>OBJECTIVES: This review aims to evaluate the effects of BCI-based training on post-stroke upper-limb rehabilitation and identify potential factors that may affect the outcome.<br><br>DESIGN: A meta-analysis including all available randomized-controlled clinical trials (RCTs) that reported the efficacy of BCI-based training on upper-limb motor rehabilitation after stroke.<br><br>DATA SOURCES AND METHODS: We searched PubMed, Cochrane Library, and Web of Science before September 15, 2024, for relevant studies. The primary efficacy outcome was the Fugl-Meyer Assessment-Upper extremity (FMA-UE). RevMan 5.4.1 with a random effect model was used for data synthesis and analysis. Mean difference (MD) and 95% confidence interval (95%CI) were calculated.<br><br>RESULTS: Twenty-one RCTs (n&#x2009;=&#x2009;886 patients) were reviewed in the meta-analysis. Compared with control, BCI-based training exerted significant effects on FMA-UE (MD&#x2009;=&#x2009;3.69, 95%CI 2.41-4.96, P&#x2009;<&#x2009;0.00001, moderate-quality evidence), Wolf Motor Function Test (WMFT) (MD&#x2009;=&#x2009;5.00, 95%CI 2.14-7.86, P&#x2009;=&#x2009;0.0006, low-quality evidence), and Action Research Arm Test (ARAT) (MD&#x2009;=&#x2009;2.04, 95%CI 0.25-3.82, P&#x2009;=&#x2009;0.03, high-quality evidence). Additionally, BCI-based training was effective on FMA-UE for both subacute (MD&#x2009;=&#x2009;4.24, 95%CI 1.81-6.67, P&#x2009;=&#x2009;0.0006) and chronic patients (MD&#x2009;=&#x2009;2.63, 95%CI 1.50-3.76, P&#x2009;<&#x2009;0.00001). BCI combined with functional electrical stimulation (FES) (MD&#x2009;=&#x2009;4.37, 95%CI 3.09-5.65, P&#x2009;<&#x2009;0.00001), robots (MD&#x2009;=&#x2009;2.87, 95%CI 0.69-5.04, P&#x2009;=&#x2009;0.010), and visual feedback (MD&#x2009;=&#x2009;4.46, 95%CI 0.24-8.68, P&#x2009;=&#x2009;0.04) exhibited significant effects on FMA-UE. BCI combined with FES significantly improved FMA-UE for both subacute (MD&#x2009;=&#x2009;5.31, 95%CI 2.58-8.03, P&#x2009;=&#x2009;0.0001) and chronic patients (MD&#x2009;=&#x2009;3.71, 95%CI 2.44-4.98, P&#x2009;<&#x2009;0.00001), and BCI combined with robots was effective for chronic patients (MD&#x2009;=&#x2009;1.60, 95%CI 0.15-3.05, P&#x2009;=&#x2009;0.03). Better results may be achieved with daily training sessions ranging from 20 to 90&#xa0;min, conducted 2-5 sessions per week for 3-4 weeks.<br><br>CONCLUSIONS: BCI-based training may be a reliable rehabilitation program to improve upper-limb motor impairment and function.<br><br>TRIAL REGISTRATION: PROSPERO registration ID: CRD42022383390.},
}
@article {pmid40033324,
year = {2025},
author = {Patarini, F and Tamburella, F and Pichiorri, F and Mohebban, S and Bigioni, A and Ranieri, A and Di Tommaso, F and Tagliamonte, NL and Serratore, G and Lorusso, M and Ciaramidaro, A and Cincotti, F and Scivoletto, G and Mattia, D and Toppi, J},
title = {Correction: On the role of visual feedback and physiotherapist-patient interaction in robot-assisted gait training: an eye-tracking and HD-EEG study.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {46},
pmid = {40033324},
issn = {1743-0003},
}
@article {pmid40033273,
year = {2025},
author = {Zhang, B and Liu, S and Chen, S and Liu, X and Ke, Y and Qi, S and Wei, X and Ming, D},
title = {Disrupted small-world architecture and altered default mode network topology of brain functional network in college students with subclinical depression.},
journal = {BMC psychiatry},
volume = {25},
number = {1},
pages = {193},
pmid = {40033273},
issn = {1471-244X},
support = {2023YFF1203700//National Key Research and Development Program of China/ ; 81925020//National Natural Science Foundation of China/ ; },
mesh = {Humans ; Male ; Female ; Young Adult ; Adult ; Universities ; *Students/psychology ; *Depression/diagnostic imaging/physiopathology/psychology ; Case-Control Studies ; *Magnetic Resonance Imaging/methods ; *Neuroimaging/methods ; *Nerve Net/diagnostic imaging/physiopathology ; Cerebral Cortex/diagnostic imaging/physiopathology ; *Default Mode Network/diagnostic imaging/physiopathology ; },
abstract = {BACKGROUND: Subclinical depression (ScD), serving as a significant precursor to depression, is a prevalent condition in college students and imposes a substantial health service burden. However, the brain network topology of ScD remains poorly understood, impeding our comprehension of the neuropathology underlying ScD.<br><br>METHODS: Functional networks of individuals with ScD (n&#x2009;=&#x2009;26) and healthy controls (HCs) (n&#x2009;=&#x2009;33) were constructed based on functional magnetic resonance imaging data. These networks were then optimized using a small-worldness and modular similarity-based network thresholding method to ensure the robustness of functional networks. Subsequently, graph-theoretic methods were employed to investigated both global and nodal topological metrics of these functional networks.<br><br>RESULTS: Compared to HCs, individuals with ScD exhibited significantly higher characteristic path length, clustering coefficient, and local efficiency, as well as a significantly lower global efficiency. Additionally, significantly lower nodal centrality metrics were found in the default mode network (DMN) regions (anterior cingulate cortex, superior frontal gyrus, precuneus) and occipital lobe in ScD, and the nodal efficiency of the left precuneus was negatively correlated with the severity of depression.<br><br>CONCLUSIONS: Altered global metrics indicate a disrupted small-world architecture and a typical shift toward regular configuration of functional networks in ScD, which may result in lower efficiency of information transmission in the brain of ScD. Moreover, lower nodal centrality in DMN regions suggest that DMN dysfunction is a neuroimaging characteristic shared by both ScD and major depressive disorder, and might serve as a vital factor promoting the development of depression.},
}
@article {pmid40033004,
year = {2025},
author = {Li, XY and Rao, Y and Li, GH and He, L and Wang, Y and He, W and Fang, P and Pei, C and Xi, L and Xie, H and Lu, YR},
title = {Single-nucleus RNA sequencing uncovers metabolic dysregulation in the prefrontal cortex of major depressive disorder patients.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {7418},
pmid = {40033004},
issn = {2045-2322},
support = {2023YFC2506200//National Key Research and Development Program of China/ ; 2023ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024SSYS0016//Key Research and Development Program of Zhejiang Province/ ; },
mesh = {Humans ; *Depressive Disorder, Major/metabolism/genetics ; Male ; Female ; *Prefrontal Cortex/metabolism ; Adult ; Middle Aged ; Sequence Analysis, RNA/methods ; Case-Control Studies ; Single-Cell Analysis ; Oxidative Phosphorylation ; },
abstract = {Major depressive disorder (MDD) is a widespread psychiatric condition, recognized as the third leading cause of global disease burden in 2008. In the context of MDD, alterations in synaptic transmission within the prefrontal cortex (PFC) are associated with PFC hypoactivation, a key factor in cognitive function and mood regulation. Given the high energy demands of the central nervous system, these synaptic changes suggest a metabolic imbalance within the PFC of MDD patients. However, the cellular mechanisms underlying this metabolic dysregulation remain not fully elucidated. This study employs single-nucleus RNA sequencing (snRNA-seq) data to predict metabolic alterations in the dorsolateral PFC (DLPFC) of MDD patients. Our analysis revealed cell type-specific metabolic patterns, notably the disruption of oxidative phosphorylation and carbohydrate metabolism in the DLPFC of MDD patients. Gene set enrichment analysis based on human phenotype ontology predicted alterations in serum lactate levels in MDD patients, corroborated by the observed decrease in lactate levels in MDD patients compared to 47 age-matched healthy controls (HCs). This transcriptional analysis offers novel insights into the metabolic disturbances associated with MDD and the energy dynamics underlying DLPFC hypoactivation. These findings are instrumental for comprehending the pathophysiology of MDD and may guide the development of innovative therapeutic strategies.},
}
@article {pmid40032982,
year = {2025},
author = {Wu, F and Chen, Y and Chen, X and Tong, D and Zhou, J and Du, Z and Yao, C and Yang, Y and Du, A and Ma, G},
title = {Nematode serine protease inhibitor SPI-I8 negatively regulates host NF-κB signalling by hijacking MKRN1-mediated polyubiquitination of RACK1.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {356},
pmid = {40032982},
issn = {2399-3642},
support = {32473050//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32202829//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32172877//National Natural Science Foundation of China (National Science Foundation of China)/ ; LQ23C180006//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
mesh = {Animals ; *NF-kappa B/metabolism ; *Receptors for Activated C Kinase/metabolism/genetics ; Mice ; Ubiquitination ; *Helminth Proteins/metabolism/genetics/pharmacology ; Signal Transduction ; *Serine Proteinase Inhibitors/metabolism/pharmacology ; Humans ; *Ubiquitin-Protein Ligases/metabolism ; Haemonchus/metabolism ; Mice, Inbred C57BL ; Colitis/chemically induced ; },
abstract = {Parasitic roundworms are remarkable for their ability to manipulate host immune systems and ameliorate inflammatory diseases. Although much is known about the nature of nematode effectors in immune modulation, little is known about the action mode of these molecules. Here, we report that a serine protease inhibitor SPI-I8 in the extracellular vesicles of blood-feeding nematodes like Ancylostoma ceylanicum, Haemonchus contortus and Nippostrongylus brasiliensis, effectively halts excessive inflammatory responses in vitro and in vivo. We demonstrate that H. contortus SPI-I8 promotes the role of a negative regulator of RACK1 and enhances the effects of RACK1 on tumor necrosis factor (TNF)-α-IκB kinases (IKKs)-nuclear factor kappa beta (NF-κB) axis in mammalian cells, by hijacking E3 ubiquitin protein ligase MKRN1-mediated polyubiquitination of RACK1. Administration of recombinant N. brasiliensis SPI-I8 effectively protects mice from dextran sulfate sodium (DSS)-induced colitis and lipopolysaccharide (LPS)-induced sepsis. Considering the structural and functional conservation of SPI-I8s among Strongylida nematodes and the conservation of interactive mediators (i.e., MKRN1 and RACK1) among mammals, our findings provide insights into the host-parasite interface where parasitic roundworms secret molecules to suppress host inflammatory responses. Harnessing these findings should underpin the exploitation of nematode's immunomodulators to relief excessive inflammation associated diseases in animals and humans.},
}
@article {pmid40032521,
year = {2025},
author = {Sharma, D and Lupkin, SM and McGinty, VB},
title = {Orbitofrontal High-Gamma Reflects Spike-Dissociable Value and Decision Mechanisms.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {45},
number = {20},
pages = {},
pmid = {40032521},
issn = {1529-2401},
support = {K01 DA036659/DA/NIDA NIH HHS/United States ; },
mesh = {Animals ; Male ; *Decision Making/physiology ; Macaca mulatta ; *Prefrontal Cortex/physiology ; *Gamma Rhythm/physiology ; *Action Potentials/physiology ; *Neurons/physiology ; },
abstract = {The orbitofrontal cortex (OFC) plays a crucial role in value-based decisions. While much is known about how OFC neurons represent values, far less is known about information encoded in OFC local field potentials (LFPs). LFPs are important because they can reflect subthreshold activity not directly coupled to spiking and because they are potential targets for less invasive forms of brain-machine interface (BMI). We recorded neural activity in the OFC of male macaques performing a two-option value-based decision task. We compared the value- and decision-coding properties of high-gamma LFPs (HG, 50-150 Hz) to the coding properties of spiking multiunit activity (MUA) recorded concurrently on the same electrodes. HG and MUA both represented the values of decision targets, but HG signals had value-coding features that were distinct from concurrently measured MUA. On average HG amplitude increased monotonically with value, whereas in MUA the value encoding was net neutral on average. HG encoded a signal consistent with a comparison between target values, a signal which was negligible in MUA. In individual channels, HG could predict choice outcomes more accurately than MUA; however, when channels were combined in a population-based decoder, MUA was more accurate than HG. In summary, HG signals reveal value-coding features in OFC that could not be observed from spiking activity, including representation of value comparisons and more accurate behavioral predictions. These results have implications for the role of OFC in value-based decisions and suggest that high-frequency LFPs may be a viable-or even preferable-target for BMIs to assist cognitive function.},
}
@article {pmid40031838,
year = {2025},
author = {Lopez-Gordo, MA and Geirnaert, S and Bertrand, A},
title = {Unsupervised Accuracy Estimation for Brain-Computer Interfaces Based on Selective Auditory Attention Decoding.},
journal = {IEEE transactions on bio-medical engineering},
volume = {72},
number = {8},
pages = {2388-2399},
doi = {10.1109/TBME.2025.3542253},
pmid = {40031838},
issn = {1558-2531},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Algorithms ; *Attention/physiology ; *Signal Processing, Computer-Assisted ; *Auditory Perception/physiology ; Adult ; Male ; *Unsupervised Machine Learning ; Female ; Young Adult ; },
abstract = {OBJECTIVE: Selective auditory attention decoding (AAD) algorithms process brain data such as electroencephalography to decode to which of multiple competing sound sources a person attends. Example use cases are neuro-steered hearing aids or communication via brain-computer interfaces (BCI). Recently, it has been shown that it is possible to train such AAD decoders based on stimulus reconstruction in an unsupervised setting, where no ground truth is available regarding which sound source is attended. In many practical scenarios, such ground-truth labels are absent, making it, moreover, difficult to quantify the accuracy of the decoders. In this paper, we aim to develop a completely unsupervised algorithm to estimate the accuracy of correlation-based AAD algorithms during a competing talker listening task.<br><br>METHODS: We use principles of digital communications by modeling the AAD decision system as a binary phase-shift keying channel with additive white gaussian noise.<br><br>RESULTS: We show that the proposed unsupervised performance estimation technique can accurately determine the AAD accuracy in a transparent-for-the-user way, for different amounts of training and estimation data and decision window lengths. Furthermore, since different applications demand different targeted accuracies, our approach can estimate the minimal amount of training required for any given target accuracy.<br><br>CONCLUSION: Our proposed estimation technique accurately predicts the performance of a correlation-based AAD algorithm without access to ground-truth labels.<br><br>SIGNIFICANCE: In neuro-steered hearing aids, the accuracy estimates provided by our approach could support time-adaptive decoding, dynamic gain control, and neurofeedback. In BCIs, it could support a robust communication paradigm with accuracy feedback for caregivers.},
}
@article {pmid40031638,
year = {2025},
author = {Ma, Z and Yang, X and Meng, J and Wang, K and Xu, M and Ming, D},
title = {Decoding Arm Movement Direction Using Ultra-High-Density EEG.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {6},
pages = {4035-4045},
doi = {10.1109/JBHI.2025.3545856},
pmid = {40031638},
issn = {2168-2208},
mesh = {Humans ; *Electroencephalography/methods ; *Arm/physiology ; Movement/physiology ; *Signal Processing, Computer-Assisted ; Male ; Adult ; Female ; Brain-Computer Interfaces ; Young Adult ; Principal Component Analysis ; Algorithms ; },
abstract = {Detecting arm movement direction is significant for individuals with upper-limb motor disabilities to restore independent self-care abilities. It involves accurately decoding the fine movement patterns of the arm, which has become feasible using invasive brain-computer interfaces (BCIs). However, it is still a significant challenge for traditional electroencephalography (EEG) based BCIs to decode multi-directional arm movements effectively. This study designed an ultra-high-density (UHD) EEG system to decode multi-directional arm movements. The system contains 200 electrodes with an interval of about 4 mm. We analyzed the patterns of the UHD EEG signals induced by arm movements in different directions. To extract discriminative features from UHD EEG, we proposed a spatial filtering method combining principal component analysis (PCA) and discriminative spatial pattern (DSP). We collected EEG signals from five healthy subjects (two left-handed and three right-handed) to verify the system's feasibility. The movement-related cortical potentials (MRCPs) showed a certain degree of separability both in waveforms and spatial patterns for arm movements in different directions. This study achieved an average classification accuracy of 63.15 (8.71)% for both arms (eight-class task) with a peak accuracy of 77.24%. For the dominant arm (four-class task), we obtained an average accuracy of 75.31 (9.21)% with a peak accuracy of 85.00%. For the first time, this study simultaneously decodes multi-directional movements of both arms using UHD EEG. This study provides a promising approach for detecting information about arm movement directions, which is significant for the development of BCIs.},
}
@article {pmid40031623,
year = {2025},
author = {Li, M and Chen, S and Zhang, X and Wang, Y},
title = {Neural Correlation Integrated Adaptive Point Process Filtering on Population Spike Trains.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {1014-1025},
doi = {10.1109/TNSRE.2025.3545206},
pmid = {40031623},
issn = {1558-0210},
mesh = {Animals ; Rats ; Algorithms ; *Action Potentials/physiology ; *Brain-Computer Interfaces ; Models, Neurological ; Bayes Theorem ; Computer Simulation ; *Neurons/physiology ; Neural Networks, Computer ; Normal Distribution ; Reproducibility of Results ; Motor Cortex/physiology ; },
abstract = {Brain encodes information through neural spiking activities that modulate external environmental stimuli and underlying internal states. Population of neurons coordinate through functional connectivity to plan movement trajectories and accurately activate neuromuscular activities. Motor Brain-machine interface (BMI) is a platform to study the relationship between behaviors and neural ensemble activities. In BMI, point process filters model directly on spike timings to extract underlying states such as motion intents from observed multi-neuron spike trains. However, these methods assume the encoded information from individual neurons is conditionally independent, which leads to less precise estimation. It is necessary to incorporate functional neural connectivity into a point process filter to improve the state estimation. In this paper, we propose a neural correlation integrated adaptive point process filter (CIPPF) that can incorporate the information from functional neural connectivity from population spike trains in a recursive Bayesian framework. Functional neural connectivity information is approximated by an artificial neural network to provide extra updating information for the posterior estimation. Gaussian approximation is applied on the probability distribution to obtain a closed-form solution. Our proposed method is validated on both simulation and real data collected from the rat two-lever discrimination task. Due to the simultaneous modeling of functional neural connectivity and single neuronal tuning properties, the proposed method shows better decoding performance. This suggests the possibility to improve BMI performance by processing the coordinated neural population activities.},
}
@article {pmid40031582,
year = {2025},
author = {Berg, GLWV and Rohr, V and Platt, D and Blankertz, B},
title = {A New Canonical Log-Euclidean Kernel for Symmetric Positive Definite Matrices for EEG Analysis (Oct 2024).},
journal = {IEEE transactions on bio-medical engineering},
volume = {72},
number = {3},
pages = {1000-1007},
doi = {10.1109/TBME.2024.3483936},
pmid = {40031582},
issn = {1558-2531},
mesh = {*Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; Algorithms ; Brain-Computer Interfaces ; Brain/physiology ; },
abstract = {OBJECTIVE: Working with the Riemannian manifold of symmetric positive-definite (SPD) matrices has become popular in electroencephalography (EEG) analysis. Frequently selected for its speed property is the manifold geometry provided by the log-euclidean Riemannian metric. However, the kernels used in the log-euclidean framework are not canonically based on the underlying geometry. Therefore, we introduce a new canonical log-euclidean (CLE) kernel.<br><br>METHODS: We used the log-euclidean metric tensor on the SPD manifold to derive the CLE kernel. We compared it with existing kernels, namely the affine-invariant, log-euclidean, and Gaussian log-euclidean kernel. For comparison, we tested the kernels on two paradigms: classification and dimensionality reduction. Each paradigm was evaluated on five open-access brain-computer interface datasets with motor-imagery tasks across multiple sessions. Performance was measured as balanced classification accuracy using a leave-one-session-out cross-validation. Dimensionality reduction performance was measured using AUClogRNX.<br><br>RESULTS: The CLE kernel itself is simple and easily turned into code, which is provided in addition to all the analytical solutions to relevant equations in the log-euclidean framework. The CLE kernel significantly outperformed existing log-euclidean kernels in classification tasks and was several times faster than the affine-invariant kernel for most datasets.<br><br>CONCLUSION: We found that adhering to the geometrical structure significantly improves the accuracy over two commonly used log-euclidean kernels while keeping the speed advantages of the log-euclidean framework.<br><br>SIGNIFICANCE: The CLE provides a good choice as a kernel in time-critical applications and fills a gap in the kernel methods of the log-euclidean framework.},
}
@article {pmid40031574,
year = {2025},
author = {Zhang, H and Xie, J and Zhao, C and Jin, Z and Du, F and Chen, Y and Xu, G and Tao, Q and Li, M},
title = {A Novel Spatial Auditory Brain-Computer Interface Based on Low-Frequency Periodic Auditory Motion Stimulation Paradigm.},
journal = {IEEE transactions on bio-medical engineering},
volume = {72},
number = {8},
pages = {2484-2495},
doi = {10.1109/TBME.2025.3544646},
pmid = {40031574},
issn = {1558-2531},
mesh = {*Brain-Computer Interfaces ; Humans ; Male ; *Acoustic Stimulation/methods ; *Evoked Potentials, Auditory/physiology ; Adult ; Female ; Young Adult ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVE: This study aims to improve the performance of auditory brain-computer interfaces (BCIs) by developing two-target and three-target paradigms based on steady-state motion auditory evoked potential (SSMAEP) using low-frequency stimuli in a spatial audio environment. SSMAEP is elicited by auditory stimuli exhibited by periodic and discrete changes in auditory spatial position.<br><br>METHODS: We designed a periodic auditory motion stimulation paradigm to evoke SSMAEP. Two-target and three-target SSMAEP-BCIs were developed. For the two-target SSMAEP-BCI, two periodic auditory motion stimuli with different motion frequencies were located on the left (2 Hz) and right (1.6 Hz) sides of the head, respectively. For the three-target SSMAEP-BCI, three periodic auditory motion stimuli with different motion frequencies were located on the front (2 Hz), left (2.4 Hz) and right (1.6 Hz) sides of the head, respectively.<br><br>RESULTS: SSMAEP amplitudes were modulated by auditory selective attention. In the two-target BCI, the offline experiments showed a peak average information transfer rate (ITR) of 7.70 bits/min, while the online experiments had a mean accuracy of 82.83% and an ITR of 4.41 bits/min. The three-target BCI achieved a peak ITR of 12.04 bits/min offline, with an online mean accuracy of 80.45% and an ITR of 7.05 bits/min.<br><br>CONCLUSION: The study confirms the feasibility and enhanced performance of spatial low-frequency SSMAEP-BCIs.<br><br>SIGNIFICANCE: This novel approach to SSMAEP-BCI offers a promising direction for enhancing auditory BCI performance, potentially improving user experience and application in complex environments.},
}
@article {pmid40031548,
year = {2025},
author = {Liu, K and Xing, X and Yang, T and Yu, Z and Xiao, B and Wang, G and Wu, W},
title = {DMSACNN: Deep Multiscale Attentional Convolutional Neural Network for EEG-Based Motor Decoding.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {7},
pages = {4884-4896},
doi = {10.1109/JBHI.2025.3546288},
pmid = {40031548},
issn = {2168-2208},
mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; Imagination/physiology ; Convolutional Neural Networks ; },
abstract = {OBJECTIVE: Accurate decoding of electroencephalogram (EEG) signals has become more significant for the brain-computer interface (BCI). Specifically, motor imagery and motor execution (MI/ME) tasks enable the control of external devices by decoding EEG signals during imagined or real movements. However, accurately decoding MI/ME signals remains a challenge due to the limited utilization of temporal information and ineffective feature selection methods.<br><br>METHODS: This paper introduces DMSACNN, an end-to-end deep multiscale attention convolutional neural network for MI/ME-EEG decoding. DMSACNN incorporates a deep multiscale temporal feature extraction module to capture temporal features at various levels. These features are then processed by a spatial convolutional module to extract spatial features. Finally, a local and global feature fusion attention module is utilized to combine local and global information and extract the most discriminative spatiotemporal features.<br><br>MAIN RESULTS: DMSACNN achieves impressive accuracies of 78.20%, 96.34% and 70.90% for hold-out analysis on the BCI-IV-2a, High Gamma and OpenBMI datasets, respectively, outperforming most of the state-of-the-art methods.<br><br>CONCLUSION AND SIGNIFICANCE: These results highlight the potential of DMSACNN in robust BCI applications. Our proposed method provides a valuable solution to improve the accuracy of the MI/ME-EEG decoding, which can pave the way for more efficient and reliable BCI systems.},
}
@article {pmid40031523,
year = {2024},
author = {Wang, J and Wang, Z and Xu, T and Zhou, T and Zhao, X and Hu, H},
title = {SS-MSDA: Streamlined Sample-level Multi-source Domain Adaptation for EEG Emotion Recognition.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10781531},
pmid = {40031523},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; Algorithms ; *Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; },
abstract = {Affective EEG-based Brain-Computer Interface (BCI) offers extensive prospects. Yet, it grapples with notable challenges in consistently achieving accurate emotion recognition among new subjects. Mitigating this matter, Multi-Source Domain Adaptation (MSDA) has been advanced. However, they exhibit performance that falls short of expectations, necessitate complex preparations and lack solid theoretical underpinnings. Concerning these insufficiencies, we propose an innovative MSDA algorithm, effectively narrowing the Wasserstein Distance between identified subdomain and the target domain, thereby theoretically constraining the upper bound of emotion classification error. Compared with baseline model on the emotional EEG dataset SEED,SS-MSDA achieved an increase in recognition accuracy ranging from 1[~]14% (average improvement of 7.2%) across subjects, demonstrating superior performance over Domain Adaption (DA) benchmarks. Moreover, it significantly reduced the preparation time by over 99.8%, along with its minimal computational costs, thus being exceptionally apt for practical applications. Finally, the algorithm extends the theory of MSDA for affective BCI. The significance of this algorithm lies in its potential to improve the recognition accuracy of existing emotion recognition algorithms on new subjects, without the need for pre-training and data pre-collection. Moreover, it provides a novel theoretical perspective on the methods for constraining the error upper bound of the classifier.},
}
@article {pmid40031513,
year = {2024},
author = {Li, R and Zhao, X and Wang, Z and Xu, G and Hu, H and Zhou, T and Xu, T},
title = {Narrowband-Enhanced Method for Improving Frequency Recognition in SSVEP-BCIs.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782374},
pmid = {40031513},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; Algorithms ; },
abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCI) provide a non-invasive and effective means for communication and control, which fundamentally rely on the feature of frequency information. However, filter banks in conventional spatial filter classification methods do not effectively utilize narrowband information. This study proposed a narrowband-enhanced filter bank canonical correlation analysis (NE-FBCCA) to integrate narrowband signal processing with a broadband filter bank analysis. By employing adaptive signal decomposition via multivariate fast iterative filtering (MvFIF), the specific component corresponding to the stimulus frequency can be strengthened separately. To validate the efficacy of this method, we conducted a performance evaluation using public SSVEP datasets. The results demonstrate a notable enhancement of reconstructed EEG signals in the signal-to-noise ratio (SNR) of stimulus frequency responses. Furthermore, there are significant improvements observed in classification accuracy and ITRs when compared to standard canonical correlation analysis (CCA) and filter bank CCA (FBCCA) approaches. This study provides a narrowband signal processing strategy for SSVEP responses and shows its potential to improve the performance of SSVEP-based BCI systems.},
}
@article {pmid40031504,
year = {2024},
author = {Kang, H and Bao, N and Liu, H and Dong, C and Lei, D and Chen, X},
title = {A Method of Cross-Subject Transfer Learning for Ultra Short Time SSVEP Classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-7},
doi = {10.1109/EMBC53108.2024.10782593},
pmid = {40031504},
issn = {2694-0604},
mesh = {Humans ; *Evoked Potentials, Visual/physiology ; Electroencephalography ; *Brain-Computer Interfaces ; Algorithms ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; *Machine Learning ; Time Factors ; },
abstract = {The steady-state visual evoked potentials (SSVEP) based brain-computer interfaces (BCIs) require extensive training data for efficient classification, but existing algorithms struggle with ultra short time inputs (less than 0.2 seconds), limiting the feasibility of real-time systems. This paper proposes a novel method CSA-GSDANN consisting of CSA and GSDANN. GSDANN improves SSVEP feature extraction performance in ultra short time input scenarios by applying cross-subject transfer learning techniques, combining a Global Attention Mechanism (GAM) and an optimized SSVEPNet and pre-training method CSA selects the most suitable source subject based on accuracy and aligns it with the target subject to address the inter-subject variability. The proposed CSA-GSDANN method adopts a Domain Adversarial Neural Network (DANN) framework, which integrates an enhanced SSVEPNet algorithm with an attention mechanism to extract features from electroencephalogram (EEG) data within and across subjects. The extracted features undergo domain-adversarial transfer learning. The final stage involves frequency signal classification using a constrained convolutional network. The evaluation of the CSA-GSDANN method on the IMUT dataset containing 12 subjects shows significant improvements. A comparative analysis against eight mainstream deep learning and traditional algorithms demonstrates an average accuracy enhancement of 4.23% and an average Information Transfer Rate (ITR) improvement of 50.482 bits/min compared to state-of-the-art classification algorithms under short time (0.2s) EEG inputs, substantially improving SSVEP classification performance.},
}
@article {pmid40031501,
year = {2024},
author = {Li, Z and Shi, K and Li, W and Mu, F and Zhang, J and Huang, R and Cheng, H},
title = {A Dynamic Evaluation-Denoising Network for Motion Artifacts Removal from Single-Channel EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-6},
doi = {10.1109/EMBC53108.2024.10782860},
pmid = {40031501},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; *Artifacts ; Humans ; Signal-To-Noise Ratio ; *Signal Processing, Computer-Assisted ; Algorithms ; *Motion ; Brain-Computer Interfaces ; Neural Networks, Computer ; },
abstract = {Brain-computer interfaces (BCIs) have gained significant attention in rehabilitation research as a critical step in investigating neural remodeling techniques. However, most existing methods usually overlook the randomness and diversity of motion artifacts, thereby lacking the desired generalization ability and denoising precision, which limits their practical application. To address these limitations, we propose a Dynamic Evaluation Denoising Network (DED-Net) that incorporates an evaluation model with cross-domain feature fusion for artifact detection and classification. Then dynamically selecting Bidirectional Long Short-Term Memory (Bi-LSTM) networks with varying parameters for artifact removal, which achieves superior performance compared to state-of-the-art methods. Our experiment on a semi-simulated dataset constructed by EEGdenoiseNET demonstrates that the performance of DED-Net is advanced over the state-of-the-art method, i.e., SDNet, in terms of the signal-to-noise rate (SNR) and correlation coefficient (CC). Using our method, SNR and CC are 6.0597 dB and 95.28%, respectively increasing by 20.48% and 3.15%. Experiments on real EEG data demonstrate the superior performance of the proposed method in reconstructing EEG signals, in terms of the intent recognition tasks, achieving a remarkable accuracy of 88.89%, outperforming other methods.},
}
@article {pmid40031496,
year = {2024},
author = {Tai, P and Yang, J and Qi, S and Li, G and Fu, Y and Li, Y},
title = {Exploring the Relationship Between Imitated and Associated Mechanism on Performance of Visual Imagery Brain-Computer Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10782491},
pmid = {40031496},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Electroencephalography/methods ; Female ; *Imagination/physiology ; Adult ; Young Adult ; *Brain/physiology ; },
abstract = {Visual Imagery (VI) can be defined as the manipulation of visual information derived from memory rather than perception. Currently, the brain responses underlying imitation and associative VI are not clear. In this study, we explore the differences from imitation to associative VI on the brain responses based on EEG signals. In this study, eight participants were instructed to observe visual cues from three predefined images or characters (tree, computer, or sphere), and then imagine the same cues. The results indicate that there is a significant difference in power intensity among electrode channels in the occipital lobe, posterior parietal lobe, and temporal lobe during the imagination phase between imitative tasks and associative tasks, as revealed by t-tests (p < 0.05, rejecting the null hypothesis). Overall, imitation mechanisms and associative mechanisms represent the short-term memory (STM) and long-term memory (LTM) features of objects. This study addresses a crucial research gap in VI, as there is currently a scarcity of formal simultaneous comparisons in the existing literature.},
}
@article {pmid40031495,
year = {2024},
author = {Liu, H and Yang, B and Guan, S and Rong, F and Guo, M and Fang, Y and Liu, B and Gao, Y and Gu, Y},
title = {MSDAC: A multi-source domain adversarial framework for motion prediction in intracortical brain-computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782649},
pmid = {40031495},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Animals ; Algorithms ; Humans ; *Motion ; Signal Processing, Computer-Assisted ; Macaca mulatta ; },
abstract = {Intracortical brain-computer interfaces (iBCIs) restore motor function in patients with paralysis by converting neural activity into control signals for external devices. However, the frequent recalibration required by current decoding methods due to turnover and loss of recording neurons poses a challenge for achieving stable online decoding. To address these issues, we propose a multi-source domain adversarial classification (MSDAC) framework for cross-day decoding that utilizes an out-of-distribution (OOD) generalization approach. This framework divides the historical data into source domains by date and employs adversarial networks to minimize the distribution discrepancies among multiple source domains, thereby achieving robust domain-invariant characteristics and superior performance on unseen test data. The MSDAC framework was evaluated using five months of monkey center-out neural activity data and demonstrated exceptional performance. Without relying on test day data for model calibration or parameter updating, the MSDAC achieved an average decoding accuracy of 84.38% (day-5 to day-150, 27968 trials). These results underscore that the MSDAC-based decoding framework can be an ideal choice for establishing stable iBCI systems.},
}
@article {pmid40031494,
year = {2024},
author = {Yu, S and Liang, F and Zhang, Y and Chen, L and Dong, L and Guo, Z and Jie, J and Wang, X and Yin, M},
title = {A Three-stage Strategy Significantly Improves Hand Movement Direction Decoding of a Single Neural Unit.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10781633},
pmid = {40031494},
issn = {2694-0604},
mesh = {*Hand/physiology ; Movement/physiology ; *Brain-Computer Interfaces ; Animals ; Macaca mulatta ; *Neurons/physiology ; Algorithms ; Action Potentials/physiology ; },
abstract = {Invasive brain-computer interfaces (iBCI) can record multiple neural signals with the highest temporal and spatial resolution. However, the number of available neural units decreases with the increase in implantation time, which affects the stability of the iBCI system's control. Meanwhile, most current studies utilize a population of neural units to decode a single instruction, which limits the ability to decode multi-tasks simultaneously in complex scenarios. Using a few number of neural units, possibly even a single one, to perform single-task decoding is expected to enable the simultaneous control of multitasks. Herein, a three-stage strategy is proposed to accurately decode the direction of a monkey's hand movement in a Center-out task using spiking activities from a single neural unit. First, the optimal decoding window was selected based on the time course of decoding performance. Second, a firing rate variance ratio is proposed to choose the optimal neural unit from all units. Last, hard voting is employed to classify hand movements based on a single neural unit. The results indicated that decoding with a chosen neural unit and an optimal decoding window leads to a classification accuracy of 82.0%, which is nearly equivalent to that of multi-unit decoding (84.41%). This study provides insights into controlling multiple degrees of freedom with fewer neural units in iBCI control.},
}
@article {pmid40031491,
year = {2024},
author = {Ye, Y and Mu, X and Pan, T and Li, Y and Wei, L and Fan, X and Wei, L},
title = {Cross-subject EEG-based Motor Imagery Recognition for Patient's Rehabilitation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10782932},
pmid = {40031491},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Imagination/physiology ; Brain-Computer Interfaces ; Algorithms ; Signal Processing, Computer-Assisted ; },
abstract = {Motor imagery (MI), a kind of psychological representation without actual action, has garnered increasing attention in rehabilitation. However, the inherent differences between patients and healthy persons hinder rehabilitation by reducing the accuracy of cross-subject MI recognition. Although unsupervised domain adaptation (UDA) methods have mitigated individual differences, they still suffer from challenges in terms of selecting confusing source domains and accurately classifying MI samples at the boundary. To address these challenges, we propose a novel UDA framework with a causal graphical model and label similarity clustering. The causal graphical model is employed to estimate the similarity of EEG signals, enabling the causal selection to effectively avoid confusing healthy persons' data. In addition, label similarity clustering mechanism is utilized to establish a distinct boundary, thereby enhancing the classification accuracy. The experimental results demonstrate that our approach outperforms baseline 10.10% and 16.27% on BCI IV-2a&amp;2b, separately. MI is expected to aid rehabilitation through precise recognition and active support.},
}
@article {pmid40031490,
year = {2024},
author = {Wang, X and Lai, YH and Chen, F},
title = {Intended Speech Classification with EEG Signals Based on a Temporal Attention Mechanism: A Study of Mandarin Vowels.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782383},
pmid = {40031490},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Speech/physiology ; Brain-Computer Interfaces ; Male ; *Signal Processing, Computer-Assisted ; *Language ; Female ; Adult ; Young Adult ; Brain/physiology ; *Attention/physiology ; China ; },
abstract = {Speech brain-machine interfaces (BCIs) offer an effective means for patients with voice disorders to communicate, and research on decoding electroencephalography (EEG) signals related to intended speech can help to understand the mechanisms of language production in the brain. This study classified the intended speech EEG signals of four Chinese vowels, utilizing a dataset collected from 10 participants. A proposed TA-EEGNet model was employed, incorporating a temporal attention module. The model achieved an accuracy of 49.47%, surpassing other prevalent EEG classification models. The average accuracy in the binary classification of vowels was 69.83%. The vowels /u/ and /ü/ were classified with the lowest accuracy, suggesting difficulties in classifying vowels with similar articulatory movements based on intended speech EEG signals. Furthermore, the research analyzed the classification performance using data of different brain regions. The results showed that the auditory cortex, Broca's and Wernicke's areas, prefrontal cortex, and motor cortex outperformed the sensory cortex, indicating their contributions in the intended speech process of Mandarin vowels. Results also showed left hemisphere dominance. These findings contribute to the study of the neural mechanisms underlying speech production and articulatory movements, emphasizing the potential of speech BCIs to improve communication for people with speech disorders.},
}
@article {pmid40031482,
year = {2024},
author = {Zhang, Y and Yang, S and Cauwenberghs, G and Jung, TP},
title = {From Word Embedding to Reading Embedding Using Large Language Model, EEG and Eye-tracking.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10781627},
pmid = {40031482},
issn = {2694-0604},
mesh = {Humans ; *Reading ; *Electroencephalography/methods ; *Eye-Tracking Technology ; *Brain-Computer Interfaces ; Comprehension ; Male ; Large Language Models ; },
abstract = {Reading comprehension, a fundamental cognitive ability essential for knowledge acquisition, is a complex skill, with a notable number of learners lacking proficiency in this domain. This study introduces innovative tasks for Brain-Computer Interface (BCI), predicting the relevance of words or tokens read by individuals to the target inference words. We use state-of-the-art Large Language Models (LLMs) to guide a new reading embedding representation in training. This representation, integrating EEG and eye-tracking biomarkers through an attention-based transformer encoder, achieved a mean 5-fold cross-validation accuracy of 68.7% across nine subjects using a balanced sample, with the highest single-subject accuracy reaching 71.2%. This study pioneers the integration of LLMs, EEG, and eye-tracking for predicting human reading comprehension at the word level. We fine-tune the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model for word embedding, devoid of information about the reading tasks. Despite this absence of task-specific details, the model effortlessly attains an accuracy of 92.7%, thereby validating our findings from LLMs. This work represents a preliminary step toward developing tools to assist reading. The code and data are available in github.},
}
@article {pmid40031471,
year = {2024},
author = {Gui, Z and Liu, Y and Qiu, S and Zhang, Y and Dong, K and Ming, D},
title = {Electrical stimulation-based paradigm to enhance lower limb motor imagery: initial validation in stroke patients.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782372},
pmid = {40031471},
issn = {2694-0604},
mesh = {Humans ; *Lower Extremity/physiopathology ; Male ; Female ; *Stroke/physiopathology ; Middle Aged ; Brain-Computer Interfaces ; *Stroke Rehabilitation ; *Electric Stimulation/methods ; Aged ; Motor Cortex/physiopathology ; *Imagination ; *Imagery, Psychotherapy/methods ; },
abstract = {Lower limb motor dysfunction is a prevalent complication of stroke that significantly impacts patients' quality of life. Current research indicates that motor imagery-based brain-computer interface (BCI-MI) training can assist stroke patients in enhancing motor function and reconstructing neural pathways. Nevertheless, 40% of stroke patients struggle with effective motor imagery (MI), leading to challenges in applying lower limb MI in clinical settings. Electrical stimulation (ES) has demonstrated the ability to induce muscle contractions, generating a kinesthetic illusion that effectively guides subjects in performing MI. However, the existing study lacks clarity regarding the effectiveness of the ES-MI paradigm in improving lower limb MI in stroke patients. To address this gap, we recruited seven stroke patients to participate in an experiment involving the ES-MI enhancement paradigm, aiming to validate its performance in stroke patients. The results revealed that the ES-MI paradigm augmented the activation of the motor cortex in the lower limb and reactivated dormant areas, suggesting that MI training based on the ES-MI paradigm holds promise for enhancing neural remodeling effects in stroke patients. Additionally, the paradigm enhanced the classification accuracy of SVM(+1.17%), KNN(+0.93%), RF(+7.13%), LDA(+5.29%), and EEGNet(+0.96%), indicating potential improvements in the efficiency and quality of human-robot interaction in brain-controlled lower limb rehabilitation robots.},
}
@article {pmid40031469,
year = {2024},
author = {Zhou, W and Zhao, X and Zhou, T and Wang, Z and Xu, T and Hu, H},
title = {Enhancing Detection of SSVEP-based BCIs Using Adjacent Frequencies Fusion Method.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782332},
pmid = {40031469},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; Algorithms ; Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interfaces (BCIs) have emerged as transformative technologies, enabling direct communication between the human brain and external devices. Steady-state visual evoked potentials (SSVEP) have gained particular attention due to their potential in BCIs. Current decoding algorithms do not take advantage of the correlation coefficients of adjacent frequencies. We propose adjacent frequencies fusion filter bank canonical correlation analysis (AFF-FBCCA), which enhances accuracy and robustness by utilizing information from adjacent frequencies. This weighted fusion aims to capitalize on the inherent similarity between electroencephalogram (EEG) signals at closely spaced frequencies. The determination of weight coefficients, incorporating dynamic adjustments based on the time window, further contributes to the adaptability of AFF-FBCCA. The proposed method is validated using public benchmark datasets. The results show that AFF-FBCCA is always superior to standard FBCCA in terms of classification accuracy and information transfer rate (ITR) in all test time windows. This method maintains the advantage of training-free and provides a more accurate and user-friendly solution for SSVEP-based BCI.},
}
@article {pmid40031462,
year = {2024},
author = {Liu, H and Wang, Z and Li, R and Zhao, X and Xu, T and Zhou, T and Hu, H},
title = {A Novel SSVEP Modulation Method Utilizing VR-Based Binocular Vision.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10781783},
pmid = {40031462},
issn = {2694-0604},
mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Vision, Binocular/physiology ; Electroencephalography/methods ; Algorithms ; *Virtual Reality ; Brain-Computer Interfaces ; Male ; Adult ; Female ; Young Adult ; },
abstract = {This paper presents a novel method for modulating steady-state visual evoked potentials (SSVEP) based on binocular vision in virtual reality (VR). The method involves displaying monocular frequencies in the left and right view of VR to encode nine binocular targets using only two frequencies. We constructed a VR-BCI system and validated the effectiveness of this binocular-encoded paradigm through the task-related component analysis (TRCA) algorithm, which is a supervised approach based on individual templates. The results showed a recognition accuracy of 79.05% and an information transfer rate (ITR) of 43.38 bits/min with a data length of 2 seconds. The electroencephalography (EEG) responses of the binocular combinations exhibited unique characteristics compared to traditional SSVEP, suggesting potential for further optimization in terms of performance. This proposed method reduces the frequency requirements for encoding SSVEP-speller and highlights the potential of VR-BCI in utilizing binocular characteristics, which could contribute to the practicality and high-speed implementation of SSVEP-based brain-computer interface (BCI) systems.},
}
@article {pmid40031461,
year = {2024},
author = {Li, A and Wang, Z and Zhao, X and Xu, T and Zhou, T and Hu, H},
title = {Enhancing Word-Level Imagined Speech BCI Through Heterogeneous Transfer Learning.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782407},
pmid = {40031461},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography/methods ; *Speech/physiology ; *Imagination/physiology ; *Machine Learning ; Algorithms ; Signal Processing, Computer-Assisted ; },
abstract = {In this study, we proposed a novel heterogeneous transfer learning approach named Focused Speech Feature Transfer Learning (FSFTL), aimed at enhancing the performance of electroencephalogram (EEG)-based word-level Imagined Speech (IS) Brain-Computer Interface (BCI). In IS BCI, the classification accuracy for imagining specific words is relatively low due to the inherent complexity in high-level feature variations. However, the binary classification accuracy for IS/rest is significantly higher. FSFTL leverages the refined feature focusing capability of the binary IS/Rest classification task to effectively locate relevant features for the word-level task. The feature extractor in the IS/Rest model demonstrates robust decoding ability for low-level IS features in EEG signals. We applied this high-performance yet low-resolution feature extractor to a public dataset for five-word IS task. The classifier was retrained to handle an increased number of classification categories, and the feature extractor was further fine-tuned to accommodate higher-level classification tasks. Before the experiment, we aligned the data from the two datasets to maintain the versatility of the feature extractor. Our proposed FSFTL approach was compared with existing EEG models, showing a significant improvement. The FSFTL approach outperformed the backbone strategy with a 6% increase in mean accuracy across all fifteen subjects. This study highlights the commonality of features in EEG data of IS and their transferability across various datasets and tasks, which is beneficial for improving the decoding ability of word-level IS BCI.},
}
@article {pmid40031460,
year = {2024},
author = {Yang, J and Kulwa, F and Liu, X and Lu, Y and Fu, Y and Li, G and Huai, Y and Zhang, X and Li, Y},
title = {CEBRA Method: Decoding Brain Activity for Advanced Brain-Computer Interface Technology.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-6},
doi = {10.1109/EMBC53108.2024.10782428},
pmid = {40031460},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Brain/physiology/physiopathology ; Male ; Support Vector Machine ; Female ; Middle Aged ; Algorithms ; Adult ; Signal Processing, Computer-Assisted ; },
abstract = {The emerging neurorehabilitation technology, Brain-Computer Interface (BCI), provides a novel prospect for stroke recovery. However, decoding brain activity during the movement present substantial challenges, and feature extraction is crutial to build a better decoder. In this study the CEBRA method were employed to extract features first based on electroencephalogram(EEG) data during Motor Execution(ME) and Motor lmagery (Ml) tasks for 20 participants (including 10 stroke patients). The results revealed that, in MI tasks, CEBRA-RF (Random Forests) achieved an average classification accuracy of 91.33%, with an average F1-score of 91.19%, and CEBRA-SVM (Support Vector Machine) achieved an average classification accuracy of 91.32%, with an average F1-score of 90.83%. Compared to other conventional feature extraction methods, CEBRA shows significantly higher accuracy (t-test, p<0.01). However, in ME tasks, CEBRA-RF achieved an average classification accuracy of 75.67%, with an average F1-score of 75.39%, and CEBRA-SVM achieved an average classification accuracy of 76.13%, with an average F1-score of 75.80%. Nevertheless, no significant differences were observed compared to other feature extraction methods. These findings demonstrate the potential of CEBRA in decoding patients' brain activity. The results of this study hold promise in addressing the current challenges of low decoding accuracy in BCI systems, offering a new approach for designing BCI-assisted rehabilitation systems for stroke patients.},
}
@article {pmid40031451,
year = {2024},
author = {Luo, H and Zhao, X and Zhou, T and Wang, Z and Xu, T and Hu, H},
title = {EEG Emotion Recognition Based on 3D-CTransNet.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782401},
pmid = {40031451},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; Humans ; *Emotions/physiology ; Algorithms ; *Signal Processing, Computer-Assisted ; *Neural Networks, Computer ; Brain-Computer Interfaces ; Deep Learning ; },
abstract = {Emotion recognition is of great significance for brain-computer interface and emotion computing, and EEG plays a key role in this field. However, the current design of brain computer interface deep learning model is faced with algorithmic or structural constraints, and it is difficult to recognize the complex features in EEG signals with long-term dynamic changes. To solve this issue, a hybrid CNN-Transformer structure using 3D data input is proposed and named 3D-CTransNet in this paper, which solves the problem of performance degradation of the traditional CNN-LSTM hybrid structure in the recognition of long sequence signals. At the same time, the self attention mechanism and parallel mode introduced by Transformer improve the recognition accuracy and processing speed. In addition, the 3D data feature map based on electrode position mapping effectively retains the spatial characteristics of EEG signals, which makes CNN better combine the time domain and spatial domain. Finally, the Valence-Arousal classification training of emotion is carried out on the public dataset DEAP, and the classification accuracy is 97.04%, which is about 5% higher than that of the hybrid CNN-LSTM model.},
}
@article {pmid40031446,
year = {2025},
author = {Wei, Z and Lin, Y and Chen, J and Pan, S and Gao, X},
title = {Effects of 3D Stimuli With Frequency Ranges, Patterns, and Shapes on SSVEP-BCI Performance in Virtual Reality.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {890-899},
doi = {10.1109/TNSRE.2025.3544308},
pmid = {40031446},
issn = {1558-0210},
mesh = {Humans ; *Virtual Reality ; *Brain-Computer Interfaces ; Male ; *Evoked Potentials, Visual/physiology ; Female ; Adult ; Young Adult ; Electroencephalography ; *Photic Stimulation/methods ; User-Computer Interface ; Algorithms ; Surveys and Questionnaires ; Healthy Volunteers ; },
abstract = {Traditional steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) systems offer stability and simplicity in evoking brain responses, but their practical utility is limited by immovable screens for visual stimuli. Virtual Reality (VR) technology provides a more natural and immersive environment to evoke SSVEP signals. However, the design methods for visual stimuli in VR environments remain to be explored, especially under the stereoscopic vision conditions. This study investigated the effects of 3D stimuli with frequency ranges, patterns, and shapes on the performance and user experiences of VR-SSVEP. There were four patterns including three-dimensional (3D) flicker, two-dimensional (2D) flicker, 3D checkerboard, and 3D quick response (QR) code with four shapes comprising cube, sphere, cylinder, and cone at low (9-15Hz), medium (18-24Hz), and high frequencies (30-36Hz). Both offline and online experiments were conducted to analyze the effects of different parameter combinations on SSVEP-BCI performance, and a questionnaire was exploited to evaluate user experiences. Compared to high frequency range, the low and medium frequency ranges had better performance and lower user experiences. 3D checkerboard and 3D QR code patterns showed significantly better user experiences than 3D and 2D flickers for all frequency ranges. With a high level of classification performance, 3D checkerboard and 3D QR code patterns in medium frequency range could synthetically enhance the system performance and user experiences. These results could provide significant value for SSVEP-BCI application in VR environments.},
}
@article {pmid40031345,
year = {2025},
author = {Liu, G and Zhang, R and Tian, L and Zhou, W},
title = {Fine-Grained Spatial-Frequency-Time Framework for Motor Imagery Brain-Computer Interface.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {6},
pages = {4121-4133},
doi = {10.1109/JBHI.2025.3536212},
pmid = {40031345},
issn = {2168-2208},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; *Imagination/physiology ; Male ; Adult ; Algorithms ; Female ; Young Adult ; },
abstract = {The Motor Imagery Brain-Computer Interfaces (MI-BCIs) have shown considerable promise for applications in neural rehabilitation. However, improving the practicality and interpretability of MI-BCIs remains a critical challenge. Unlike previous methods that focus generally on either spatial, frequency, or temporal domains with coarse-grained segmentation schemes, this study proposes a novel fine-grained spatial-frequency-time (FGSFT) framework, aiming to enhance the efficiency and reliability of MI-BCIs. Multi-channel MI EEG recordings are firstly processed through multiscale time-frequency segmentation and spatial segmentation schemes, yielding fine-grained spatial-frequency-time segments (SFTSs). The key SFTSs are then selected with a tailored wrapper-based feature selection approach. Discriminative MI EEG features are extracted using a divergence-based common spatial pattern algorithm with intra-class regularization and classified using an efficient linear support vector machine (SVM). The proposed framework was evaluated on the BCI IV IIa and SDU-MI datasets, demonstrating state-of-the-art performance in terms of information transfer rate (ITR) Meanwhile, the proposed spatial segmentation strategy can significantly improve the performance of MI-BCIs when using a larger number of electrodes. Additionally, the fine-grained Motor Imagery Time-Frequency Reaction Map (MI-TFRM) and time-frequency topographical map can be obtained with the proposed framework enabling visualization of the subject-specific dynamic neural process during motor imagery tasks, facilitating the devising of personalized MI-BCIs. The FGSFT framework significantly advances the accuracy, ITR, and interoperability of MI-BCIs, paving the way for future neuroscientific research and clinical applications in neural rehabilitation and assistive technologies.},
}
@article {pmid40031268,
year = {2025},
author = {Gao, X and Gui, K and Wu, X and Metcalfe, B and Zhang, D},
title = {Effects of Different Preprocessing Pipelines on Motor Imagery-Based Brain-Computer Interfaces.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {5},
pages = {3343-3355},
doi = {10.1109/JBHI.2025.3532771},
pmid = {40031268},
issn = {2168-2208},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; *Imagination/physiology ; Adult ; Male ; Female ; Young Adult ; Machine Learning ; Algorithms ; Brain/physiology ; },
abstract = {In recent years, brain-computer interfaces (BCIs) leveraging electroencephalography (EEG) signals for the control of external devices have garnered increasing attention. The information transfer rate of BCI has been significantly improved by a lot of cutting-edge methods. The exploration of effective preprocessing in brain-computer interfaces, particularly in terms of identifying suitable preprocessing methods and determining the optimal sequence for their application, remains an area ripe for further investigation. To address this gap, this study explores a range of preprocessing techniques, including but not limited to independent component analysis, surface Laplacian, bandpass filtering, and baseline correction, examining their potential contributions and synergies in the context of BCI applications. In this extensive research, a variety of preprocessing pipelines were rigorously tested across four EEG data sets, all of which were pertinent to motor imagery-based BCIs. These tests incorporated five EEG machine learning models, working in tandem with the preprocessing methods discussed earlier. The study's results highlighted that baseline correction and bandpass filtering consistently provided the most beneficial preprocessing effects. From the perspective of online deployment, after testing and time complexity analysis, this study recommends baseline correction, bandpass filtering and surface Laplace as more suitable for online implementation. An interesting revelation of the study was the enhanced effectiveness of the surface Laplacian algorithm when used alongside algorithms that focus on spatial information. Using appropriate processing algorithms, we can even achieve results (92.91% and 88.11%) that exceed the SOTA feature extraction methods in some cases. Such findings are instrumental in offering critical insights for the selection of effective preprocessing pipelines in EEG signal decoding. This, in turn, contributes to the advancement and refinement of brain-computer interface technologies.},
}
@article {pmid40031262,
year = {2025},
author = {Chen, P and Liu, X and Ma, C and Wang, H and Yang, X and Grebogi, C and Gu, X and Gao, Z},
title = {Unsupervised Domain Adaptation With Synchronized Self-Training for Cross- Domain Motor Imagery Recognition.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {5},
pages = {3664-3677},
doi = {10.1109/JBHI.2025.3525577},
pmid = {40031262},
issn = {2168-2208},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Imagination/physiology ; *Signal Processing, Computer-Assisted ; Adult ; Algorithms ; Male ; *Unsupervised Machine Learning ; Female ; Young Adult ; },
abstract = {Robust decoding performance is essential for the practical deployment of brain-computer interface (BCI) systems. Existing EEG decoding models often rely on large amounts of annotated data collected through specific experimental setups, which fail to address the heterogeneity of data distributions across different domains. This limitation hinders BCI systems from effectively managing the complexity and variability of real-world data. To overcome these challenges, we propose Synchronized Self-Training Domain Adaptation (SSTDA) for cross-domain motor imagery classification. Specifically, SSTDA leverages labeled signals from a source domain and applies self-training to unlabeled signals from a target domain, enabling the simultaneous training of a more robust classifier. The raw EEG signals are mapped into a latent space by a feature extractor for discriminative representation learning. A domain-shared latent space is then learned by optimizing the feature extractor with both source and target samples, using an easy-tohard self-training process. We validate the method with extensive experiments on two public motor imagery datasets: Dataset IIa of BCI Competition IV and the High Gamma dataset. In the inter-subject task, our method achieves classification accuracies of 64.43% and 80.40%, respectively. It also outperforms existing methods in the inter-session task. Moreover, we develope a new six-class motor imagery dataset and achieve test accuracies of 77.09% and 80.18% across different datasets. All experimental results demonstrate that our SSTDA outperforms existing algorithms in inter-session, inter-subject, and inter-dataset validation protocols, highlighting its capability to learn discriminative, domain-invariant representations that enhance EEG decoding performance.},
}
@article {pmid40031240,
year = {2025},
author = {Kim, CU and Park, S and Im, CH},
title = {Performance Enhancement of an SSVEP-Based Brain-Computer Interface in Augmented Reality through Adaptive Color Adjustment of Visual Stimuli for Optimal Background Contrast.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3530421},
pmid = {40031240},
issn = {1558-0210},
abstract = {The aim of this study is to develop a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) system with enhanced performance in an augmented reality (AR) environment by dynamically adjusting colors of visual stimuli to contrast with the background seen through the transparent display. Our proposed method extracts the average color value from the area surrounding the visual stimulus location. It then calculates the contrast value using the HSV color model and applies this to the stimulus color. In an offline experiment, we determined the optimal visual stimulus presentation strategy by comparing the performances of three different methods for determining the colors of visual stimuli in an AR environment. We then evaluated the feasibility of the proposed strategy through online experiments conducted in both indoor and outdoor conditions. The classification performance of the SSVEP-BCI system in an AR environment based on our proposed stimulus presentation strategy was 95.0% for a window size of 3.5 s in offline experiments performed with 17 participants. This was significantly higher than the performance of the conventional black-and-white color strategy. Additionally, it was confirmed by the online experiments that there was no large performance degradation between indoor and outdoor uses.},
}
@article {pmid40031192,
year = {2025},
author = {Li, Y and Wang, Y and Lei, B and Wang, S},
title = {SCDM: Unified Representation Learning for EEG-to-fNIRS Cross-Modal Generation in MI-BCIs.},
journal = {IEEE transactions on medical imaging},
volume = {44},
number = {6},
pages = {2384-2394},
doi = {10.1109/TMI.2025.3532480},
pmid = {40031192},
issn = {1558-254X},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Spectroscopy, Near-Infrared/methods ; *Signal Processing, Computer-Assisted ; Algorithms ; Brain/physiology/diagnostic imaging ; Adult ; Male ; },
abstract = {Hybrid motor imagery brain-computer interfaces (MI-BCIs), which integrate both electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals, outperform those based solely on EEG. However, simultaneously recording EEG and fNIRS signals is highly challenging due to the difficulty of colocating both types of sensors on the same scalp surface. This physical constraint complicates the acquisition of high-quality hybrid signals, thereby limiting the widespread application of hybrid MI-BCIs. To address this issue, this study proposes the spatio-temporal controlled diffusion model (SCDM) as a framework for cross-modal generation from EEG to fNIRS. The model utilizes two core modules, the spatial cross-modal generation (SCG) module and the multi-scale temporal representation (MTR) module, which adaptively learn the respective latent temporal and spatial representations of both signals in a unified representation space. The SCG module further maps EEG representations to fNIRS representations by leveraging their spatial relationships. Experimental results show high similarity between synthetic and real fNIRS signals. The joint classification performance of EEG and synthetic fNIRS signals is comparable to or even better than that of EEG with real fNIRS signals. Furthermore, the synthetic signals exhibit similar spatio-temporal features to real signals while preserving spatial relationships with EEG signals. To our knowledge, it is the first work that an end-to-end framework is proposed to achieve cross-modal generation from EEG to fNIRS. Experimental results suggest that the SCDM may represent a promising paradigm for the acquisition of hybrid EEG-fNIRS signals in MI-BCI systems.},
}
@article {pmid40031188,
year = {2025},
author = {Yu, H and Zeng, F and Liu, D and Wang, J and Liu, J},
title = {Neural Manifold Decoder for Acupuncture Stimulations With Representation Learning: An Acupuncture-Brain Interface.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {6},
pages = {4147-4160},
doi = {10.1109/JBHI.2025.3530922},
pmid = {40031188},
issn = {2168-2208},
mesh = {Humans ; *Electroencephalography/methods ; *Acupuncture Therapy/methods ; Male ; Adult ; *Brain-Computer Interfaces ; Female ; *Signal Processing, Computer-Assisted ; *Deep Learning ; Young Adult ; *Brain/physiology ; Neural Networks, Computer ; },
abstract = {Acupuncture stimulations in somatosensory system can modulate spatiotemporal brain activity and improve cognitive functions of patients with neurological disorders. The correlation between these somatosensory stimulations and dynamical brain responses is still unclear. We proposed a deep learning framework using electroencephalographic activity of stimulated subjects to decode the needling processes of various acupuncture manipulations performed on Zusanli acupoint. Contrastive representation learning integrated with domain adaptation strategy was applied to estimate 3D hand postures and hand joint motion trajectories of acupuncturist with video recordings, by which finite dimensional representations of behavior manifolds for needling operations were inferred. Distinct transition dynamics of behavior manifold were observed for acupuncture with lifting-thrusting and twisting-rotating manipulations. Moreover, latent neural manifolds of acupuncture evoked EEG signals were estimated in low dimensional state space of brain activities with unsupervised manifold learning, which can reliably represent acupuncture stimulations. Furthermore, a nonlinear decoder based on neural networks was designed to transform neural manifolds to behavior manifolds and further predict acupuncture manipulation as well as needling process. Experimental results demonstrated a high performance of the proposed decoding framework for four types of acupuncture manipulations with a precision of 92.42%. The EEG decoder provides an acupuncture-brain interface linking somatosensory stimulations with neural representations, an effective scheme for revealing clinical efficacy of acupuncture treatment.},
}
@article {pmid40031046,
year = {2025},
author = {Li, R and Wang, Z and Zhao, X and Xu, G and Hu, H and Zhou, T and Xu, T},
title = {Amplitude Modulation Depth Coding Method for SSVEP-based Brain-computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3528409},
pmid = {40031046},
issn = {1558-0210},
abstract = {In steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), the limited availability of frequency resources inherently constrains the scale of the instruction set, presenting a substantial challenge for efficient communication. As the number of stimuli increases, the comfort level of the stimulus interface also becomes increasingly demanding due to the expanded flickering area. To address these issues, we proposed a novel amplitude modulation depth coding (AMDC) method that employs Amplitude Shift Keying (ASK) technique to modulate the luminance level of stimuli dynamically. Each stimulus with a single carrier frequency was assigned a specific binary sequence to operate two modulation depths. Two experiments were conducted to comprehensively assess the effectiveness of this approach. In Experiment 1, the time-frequency responses at two modulation depths across different frequencies were examined. A 36-target paradigm based on AMDC strategy was designed and evaluated in terms of user experience and classification performance in Experiment 2. The results show that the proposed paradigm obtains an average classification accuracy of 81.7 ± 12.6% with an average information transfer rate (ITR) of 45.4 ± 11.5 bits/min. Moreover, it significantly reduces flicker perception and improves comfort level compared to traditional SSVEP stimuli with uniform modulation depth. Given its capability to improve coding efficiency for a single frequency and improve user experience, this method shows promising potential for application in large-scale command SSVEP-based BCI systems.},
}
@article {pmid40030956,
year = {2025},
author = {Wu, J and He, F and Xiao, X and Gao, R and Meng, L and Liu, X and Xu, M and Ming, D},
title = {SSVEP enhancement in mixed reality environment for brain-computer interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3526950},
pmid = {40030956},
issn = {1558-0210},
abstract = {Expanding the application possibilities of brain-computer interfaces (BCIs) is possible through their implementation in mixed reality (MR) environments. However, visual stimuli are displayed against a realistic scene in the MR environment, which degrades BCI performance. The purpose of this study was to optimize stimulus colors in order to improve the MR-BCI system's performance. In the MR environment, a 10-command SSVEP-BCI was deployed. Various stimulus colors and background colors for the BCI system were tested and optimized in offline and online experiments. Color contrast ratios (CCRs) between stimulus and background colors were introduced to assess the performance difference among all conditions. Additionally, we proposed a cross-correlation task-related component analysis based on simulated annealing (SA-xTRCA), which can increase the signal-to-noise ratio (SNR) and detection accuracy by aligning SSVEP trials. The results of an offline experiment showed that the background and stimulus colors had a significant interaction effect that can impact system performance. A possible nonlinear relationship between CCR value and SSVEP detection accuracy exists. Online experiment results demonstrated that the system performed best with polychromatic stimulus on the colored background. The proposed SA-xTRCA significantly outperformed the other four traditional algorithms. The online average information transfer rate (ITR) achieved 57.58 ± 5.31 bits/min. This study proved that system performance can be effectively enhanced by optimizing stimulus color based on background color. In MR environments, CCR can be used as a quantitative criterion for choosing stimulus colors in BCI system design.},
}
@article {pmid40030957,
year = {2025},
author = {Zhang, Y and Zhang, C and Jiang, R and Qiu, S and He, H},
title = {A Distribution Adaptive Feedback Training Method to Improve Human Motor Imagery Ability.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3527629},
pmid = {40030957},
issn = {1558-0210},
abstract = {A brain-computer interface (BCI) based on motor imagery (MI) can translate users' subjective movement-related mental state without external stimulus, which has been successfully used for replacing and repairing motor function. In contrast with studies about decoding methods, less work was reported about training users to improve the performance of MI-BCIs. This study aimed to develop a novel MI feedback training method to enhance the ability of humans to use the MI-BCI system. In this study, an adaptive MI feedback training method was proposed to improve the effectiveness of the training process. The method updated the feedback model during training process and assigned different weights to the samples to better adapt the changes in the distribution of the Electroencephalograms (EEGs). An online feedback training system was established. Each of ten subjects participated in a three-day experiment involving three different feedback methods: no feedback algorithm update, feedback algorithm update, and feedback algorithm update using the proposed adaptive method. Comparison experiments were conducted on three different feedback methods. The experimental results showed that the feedback algorithm using the proposed method can most quickly improve the MI classification accuracy and has the largest increase in accuracy. This indicates that the proposed method can enhance the effectiveness of feedback training and improve the practicality of MI-BCI systems.},
}
@article {pmid40030935,
year = {2025},
author = {Meng, J and Li, X and Li, S and Fan, X and Xu, M and Ming, D},
title = {High-Frequency Power Reflects Dual Intentions of Time and Movement for Active Brain-Computer Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3529997},
pmid = {40030935},
issn = {1558-0210},
abstract = {Active brain-computer interface (BCI) provides a natural way for direct communications between the brain and devices. However, its detectable intention is very limited, let alone of detecting dual intentions from a single electroencephalography (EEG) feature. This study aims to develop time-based active BCI, and further investigate the feasibility of detecting time-movement dual intentions using a single EEG feature. A time-movement synchronization experiment was designed, which contained the intentions of both time (500 ms vs. 1000 ms) and movement (left vs. right). Behavioural and EEG data of 22 healthy participants were recorded and analyzed in both the before (BT) and after (AT) timing prediction training sessions. Consequently, compared to the BT sessions, AT sessions led to substantially smaller absolute deviation time behaviourally, along with larger high-frequency event-related desynchronization (ERD) in frontal-motor areas, and significantly improved decoding accuracy of time. Moreover, AT sessions achieved enhanced motor-related contralateral dominance of event-related potentials (ERP) and ERDs than the BT, which illustrated a synergistic relationship between the two intentions. The feature of 20-60 Hz power can simultaneously reflect the time and movement intentions, achieving a 73.27% averaged four-classification accuracy (500 ms-left vs. 500 ms-right vs. 1000 ms-left vs.1000 ms-right), with the highest up to 93.81%. The results initiatively verified the dual role of high-frequency (20-60 Hz) power in representing both the time and movement intentions. It not only broadens the detectable intentions of active BCI, but also enables it to read user's mind concurrently from two information dimensions.},
}
@article {pmid40030934,
year = {2025},
author = {Mahmoudi, A and Khosrotabar, M and Gramann, K and Rinderknecht, S and Sharbafi, MA},
title = {Using passive BCI for personalization of assistive wearable devices: a proof-of-concept study.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3530154},
pmid = {40030934},
issn = {1558-0210},
abstract = {Assistive wearable devices can significantly enhance the quality of life for individuals with movement impairments, aid the rehabilitation process, and augment movement abilities of healthy users. However, personalizing the assistance to individual preferences and needs remains a challenge. Brain-Computer Interface (BCI) offers a promising solution for this personalization problem. The overarching goal of this study is to investigate the feasibility of utilizing passive BCI technology to personalize the assistance provided by a knee exoskeleton. Participants performed seated knee flexion-extension tasks while wearing a one-degree-of-freedom knee exoskeleton with varying levels of applied force. Their brain activities were recorded throughout the movements using electroencephalography (EEG). EEG spectral bands from several brain regions were compared between the conditions with the lowest and highest exoskeleton forces to identify statistically significant changes. A Naive Bayes classifier was trained on these spectral features to distinguish between the two conditions. Statistical analysis revealed significant increases in δ and θ activity and decreases in α and β activity in the frontal, motor, and occipital cortices. These changes suggest heightened attention, concentration, and motor engagement when the task became more difficult. The trained Naive Bayes classifier achieved an average accuracy of approximately 72% in distinguishing between the two conditions. The outcomes of our study demonstrate the potential of passive BCI in personalizing assistance provided by wearable devices. Future research should further explore integrating passive BCI into assistive wearable devices to enhance user experience.},
}
@article {pmid40030668,
year = {2025},
author = {Perna, A and Orban, G and Berdondini, L and Ribeiro, JF},
title = {Force Measurements to Advance the Design and Implantation of CMOS-Based Neural Probes.},
journal = {IEEE transactions on bio-medical engineering},
volume = {72},
number = {5},
pages = {1731-1739},
doi = {10.1109/TBME.2024.3519763},
pmid = {40030668},
issn = {1558-2531},
mesh = {*Electrodes, Implanted/adverse effects ; Equipment Design ; Microelectrodes ; Animals ; *Brain-Computer Interfaces ; Brain/physiology ; Semiconductors ; Male ; Rats ; Foreign-Body Reaction ; },
abstract = {OBJECTIVE: Tissue penetrating active neural probes provide large and densely packed microelectrode arrays for the fine-grained investigation of brain circuits and for advancing brain-machine interfaces (BMIs). To improve the electrical interfacing performances of such stiff silicon devices, which typically elicit a vigorous foreign body reaction (FBR), here we perform insertion force measurements and derive probe layout and implantation procedure optimizations.<br><br>METHODS: We performed in-vivo insertion force measurements to evaluate the impact of probe design and implantation speed on mechanically induced trauma and iatrogenic injury. Because acute damage constitutes the initial trigger of FBR, these experiments allow to characterize and minimize device invasiveness.<br><br>RESULTS: Probe sharpness outweighs cross-sectional dimensions during the dimpling stage of the implantation, when the device compresses the brain before penetration. Insertion speed does not display a major effect on dimpling magnitude. A slow speed, however, significantly increases dimpling duration.<br><br>CONCLUSION: It is crucial to use sharp devices to reduce mechanical and ischemic damage. Although slow insertion speeds typically improve the quality of acute electrophysiological recordings, we show that slow speeds should only be used upon penetration in the brain parenchyma and not during the dimpling stage. A closed-loop implantation procedure can be used to set the appropriate speed in the different insertion stages.<br><br>SIGNIFICANCE: We provide new evidence on the impact of probe layout and insertion speed on insertion force, with implications on the design and implantation procedure for minimally invasive CMOS neural probes. A novel closed-loop methodology to optimize device implantation and reduce FBR is proposed.},
}
@article {pmid40030619,
year = {2024},
author = {Tantawanich, P and Phunruangsakao, C and Izumi, SI and Hayashibe, M},
title = {A Systematic Review of Bimanual Motor Coordination in Brain-Computer Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2024.3522168},
pmid = {40030619},
issn = {1558-0210},
abstract = {Advancements in neuroscience and artificial intelligence are propelling rapid progress in brain-computer interfaces (BCIs). These developments hold significant potential for decoding motion intentions from brain signals, enabling direct control commands without reliance on conventional neural pathways. Growing interest exists in decoding bimanual motor tasks, crucial for activities of daily living. This stems from the need to restore motor function, especially in individuals with deficits. This review aims to summarize neurological advancements in bimanual BCIs, encompassing neuroimaging techniques, experimental paradigms, and analysis algorithms. Thirty-six articles were reviewed, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The literature search result revealed diverse experimental paradigms, protocols, and research directions, including enhancing the decoding accuracy, advancing versatile prosthesis robots, and enabling real-time applications. Notably, within BCI studies on bimanual movement coordination, a shared objective is to achieve naturalistic movement and practical applications with neurorehabilitation potential.},
}
@article {pmid40030617,
year = {2025},
author = {Jiang, Y and Li, K and Liang, Y and Chen, D and Tan, M and Li, Y},
title = {Daily Assistance for Amyotrophic Lateral Sclerosis Patients Based on a Wearable Multimodal Brain-Computer Interface Mouse.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {150-161},
doi = {10.1109/TNSRE.2024.3520984},
pmid = {40030617},
issn = {1558-0210},
mesh = {*Amyotrophic Lateral Sclerosis/rehabilitation/physiopathology ; *Brain-Computer Interfaces ; Humans ; Middle Aged ; Male ; Female ; *Wearable Electronic Devices ; Wheelchairs ; Aged ; Adult ; Equipment Design ; Electroencephalography ; Communication Devices for People with Disabilities ; User-Computer Interface ; Activities of Daily Living ; },
abstract = {Amyotrophic lateral sclerosis (ALS) is a chronic, progressive neurodegenerative disease that mainly causes damage to upper and lower motor neurons. This leads to a progressive deterioration in the voluntary mobility of the upper and lower extremities in ALS patients, which underscores the pressing need for an assistance system to facilitate communication and body movement without relying on neuromuscular function. In this paper, we developed a daily assistance system for ALS patients based on a wearable multimodal brain-computer interface (BCI) mouse. The system comprises two subsystems: a mouse system assisting the upper extremity and a wheelchair system based on the mouse system assisting the lower extremity. By wearing a BCI headband, ALS patients can control a computer cursor on the screen with slight head rotation and eye blinking, and further operate a computer and drive a wheelchair with specially designed graphical user interfaces (GUIs). We designed operating tasks that simulate daily needs and invited ALS patients to perform the tasks. In total, 15 patients with upper extremity limitations performed the mouse system task and 9 patients with lower extremity mobility issues performed the wheelchair system task. To our satisfaction, all the participants fully accomplished the tasks and average accuracies of 83.9% and 87.0% for the two tasks were achieved. Furthermore, workload evaluation using NASA Task Load Index (NASA-TLX) revealed that the participants experienced a low workload when using the system. The experimental results demonstrate that the proposed system provides ALS patients with effective daily assistance and shows promising long-term application prospects.},
}
@article {pmid40030607,
year = {2025},
author = {Gao, Z and Xu, B and Wang, X and Zhang, W and Ping, J and Li, H and Song, A},
title = {Multilayer Brain Networks for Enhanced Decoding of Natural Hand Movements and Kinematic Parameters.},
journal = {IEEE transactions on bio-medical engineering},
volume = {72},
number = {5},
pages = {1708-1719},
doi = {10.1109/TBME.2024.3519348},
pmid = {40030607},
issn = {1558-2531},
mesh = {Humans ; *Hand/physiology ; Biomechanical Phenomena/physiology ; Male ; Adult ; Female ; Movement/physiology ; Young Adult ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; *Brain/physiology ; Hand Strength/physiology ; },
abstract = {Decoding natural hand movements using Movement-Related Cortical Potentials (MRCPs) features is crucial for the natural control of neuroprosthetics. However, current research has primarily focused on the characteristics of individual channels or on brain networks within a single frequency or time segment, overlooking the potential of brain networks across multiple time-frequency domains. To address this problem, our study investigates the application of multilayer brain networks (MBNs) in decoding natural hand movements and kinematic parameters, using a combination of MRCPs features and MBNs metrics. Based on grasp taxonomy, we selected four natural movements for our study: Large Diameter (LD), Sphere 3-Finger (SF), Precision Disk (PD), and Parallel Extension (PE), each incorporating two levels of speed and force parameters. The results demonstrate that a combination of MRCPs features and MBNs metrics can successfully decode not only the types of movements and kinematic parameters but also differentiate between different grasp taxonomy characteristics, such as the number of fingers exerting force and the type of grasp. In terms of movement type, we achieved a peak four-class accuracy of 60.56%. For grasp type and number of fingers exerting force, binary classification peak accuracies reached 79.25% and 79.28%, respectively. In the case of kinematic parameters, the Precision Disk movement exhibited the highest binary classification peak accuracy at 84.65%. Moreover, our research also found the changes and patterns in brain region connectivity across both time and frequency domains. We believe that our research highlights the potential of MBNs to enhance the functionality of Brain-Computer Interface (BCI) systems for more intuitive control mechanisms and contributes valuable insights into the brain's operational mechanisms.},
}
@article {pmid40030575,
year = {2025},
author = {Xu, L and Jiang, X and Wang, R and Lin, P and Yang, Y and Leng, Y and Zheng, W and Ge, S},
title = {Decoding SSVEP Via Calibration-Free TFA-Net: A Novel Network Using Time-Frequency Features.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {4},
pages = {2400-2412},
doi = {10.1109/JBHI.2024.3510740},
pmid = {40030575},
issn = {2168-2208},
mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; *Neural Networks, Computer ; Adult ; Algorithms ; Male ; Female ; Young Adult ; Deep Learning ; },
abstract = {Brain-computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) signals offer high information transfer rates and non-invasive brain-to-device connectivity, making them highly practical. In recent years, deep learning techniques, particularly convolutional neural network (CNN) architectures, have gained prominence in EEG (e.g., SSVEP) decoding because of their nonlinear modeling capabilities and autonomy from manual feature extraction. However, most studies using CNNs employ temporal signals as the input and cannot directly mine the implicit frequency information, which may cause crucial frequency details to be lost and challenges in decoding. By contrast, the prevailing supervised recognition algorithms rely on a lengthy calibration phase to enhance algorithm performance, which could impede the popularization of SSVEP based BCIs. To address these problems, this study proposes the Time-Frequency Attention Network (TFA-Net), a novel CNN model tailored for SSVEP signal decoding without the calibration phase. Additionally, we introduce the Frequency Attention and Channel Recombination modules to enhance ability of TFA-Net to infer finer frequency-wise attention and extract features efficiently from SSVEP in the time-frequency domain. Classification results on a public dataset demonstrated that the proposed TFA-Net outperforms all the compared models, achieving an accuracy of 79.00% $\pm$ 0.27% and information transfer rate of 138.82 $\pm$ 0.78 bits/min with a 1-s data length. TFA-Net represents a novel approach to SSVEP identification as well as time-frequency signal analysis, offering a calibration-free solution that enhances the generalizability and practicality of SSVEP based BCIs.},
}
@article {pmid40030518,
year = {2025},
author = {Meng, J and Li, S and Li, G and Luo, R and Sheng, X and Zhu, X},
title = {Improving Reliability of Life Applications Using Model-Based Brain Switches via SSVEP.},
journal = {IEEE transactions on bio-medical engineering},
volume = {72},
number = {5},
pages = {1636-1644},
doi = {10.1109/TBME.2024.3516733},
pmid = {40030518},
issn = {1558-2531},
mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; Male ; *Signal Processing, Computer-Assisted ; Adult ; Female ; Reproducibility of Results ; Young Adult ; *Brain/physiology ; *Models, Neurological ; Signal-To-Noise Ratio ; },
abstract = {The brain switch improves the reliability of asynchronous brain-computer interface (aBCI) systems by switching the control state of the BCI system. Traditional brain switch research focuses on extracting advanced electroencephalography (EEG) features. However, a low signal-to-noise ratio (SNR) of EEG signals resulted in limited feature information and low performance of brain switches. Here, we design a virtual physical system to build the brain switch, allowing users to trigger the system through periodic brainwave modulation, fully integrating limited feature information and improving reliability. Furthermore, we designed multiple experiments to validate the effectiveness of the proposed brain switch based on steady-state visual evoked potentials (SSVEP). The results verified the performance of SSVEP brain switches based on virtual physical systems, improving the reliability of brain switches to 0.1 FP/h or even better with acceptable triggering time and calibration-free for most subjects. This represents that the proposed virtual physical model-based brain switch can utilize SSVEP features and output the reliable commands required to control external devices, promoting BCI real applications.},
}
@article {pmid40030472,
year = {2025},
author = {Ke, Y and Chen, X and Xu, W and Wang, T and Shen, S and Ming, D},
title = {High-Frequency SSVEP-BCI With Row-Column Dual-Frequency Encoding and Decoding Strategy for Reduced Training Data.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {3},
pages = {1897-1908},
doi = {10.1109/JBHI.2024.3514794},
pmid = {40030472},
issn = {2168-2208},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Evoked Potentials, Visual/physiology ; *Signal Processing, Computer-Assisted ; Male ; Adult ; Female ; Young Adult ; Algorithms ; Photic Stimulation ; },
abstract = {Steady-state visual evoked potentials (SSVEP)-based brain-computer interfaces (BCIs) have the potential to be utilized in various fields due to their high accuracies and information transfer rates (ITR). High-frequency (HF) visual stimuli have shown promise in reducing visual fatigue and enhancing user comfort. However, these HF-SSVEP-BCIs often face limitations in the number of commands and typically require extensive individual training data to achieve high performance. In this study, we proposed a row-column dual-frequency encoding and decoding method using HF stimulation to develop a comfortable BCI system that supports multiple commands and reduces training costs. We arranged 20 targets in a matrix of five rows and four columns, with each target modulated by left-and-right field stimulation using two frequency-phase combinations. Targets in each row or column share a unique frequency-phase combination, allowing EEG data from the same row or column to be used collectively to train a row/column index decoding model for target identification. To evaluate the performance of our method, we constructed a 20-target asynchronous robotic arm control system with the adaptive window method. With only four training trials per target, the online system achieved an ITR of 105.14 ± 14.15 bits/min, a true positive rate of 98.18 ± 2.87%, a false positive rate of 7.39 ± 6.73%, and a classification accuracy of 91.88 ± 5.75%, with an average data length of 925.70 ± 45.44 ms. These results indicate that the proposed protocol can deliver accurate and rapid command outputs for a comfortable SSVEP-based BCI with minimal training data and fewer frequencies.},
}
@article {pmid40030451,
year = {2025},
author = {Zhang, X and Wei, W and Qiu, S and Li, X and Wang, Y and He, H},
title = {Enhancing SSVEP-Based BCI Performance via Consensus Information Transfer Among Subjects.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {36},
number = {8},
pages = {13833-13847},
doi = {10.1109/TNNLS.2024.3506998},
pmid = {40030451},
issn = {2162-2388},
mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Electroencephalography/methods ; Neural Networks, Computer ; Adult ; Algorithms ; *Consensus ; Male ; Photic Stimulation/methods ; Young Adult ; Female ; },
abstract = {The brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) has received considerable attention for its high communication speed. While large datasets provide an important opportunity to enhance decoding accuracies, the key challenge lies in the exploration of existing data to extract valuable information based on the distinctive characteristics of brain responses. In this study, we introduce ConsenNet, a framework designed to enhance SSVEP classification performance by leveraging information from the diverse perspectives of existing subjects. First, this study exploits the diversity of existing subjects to generate new samples, which retain both task-related components and variability. This effectively enhances the network generalization capability on new subjects. Second, the structured knowledge that encapsulates the interrelationships between categories has been constructed and then transferred from the teacher network to the student network, guiding the student network to extract invariant features across subjects. Finally, our model incorporates a small amount of new subject data for model calibration in the final stage. Offline experiments conducted on three public datasets demonstrate the superiority of ConsenNet over 19 methods compared in this study, while online experiments validate its feasibility for real-world applications.},
}
@article {pmid40030403,
year = {2024},
author = {Tan, J and Zhang, X and Wu, S and Song, Z and Wang, Y},
title = {Hidden Brain State-based Internal Evaluation Using Kernel Inverse Reinforcement Learning in Brain-machine Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2024.3503713},
pmid = {40030403},
issn = {1558-0210},
abstract = {Reinforcement learning (RL)-based brain machine interfaces (BMIs) assist paralyzed people in controlling neural prostheses without the need for real limb movement as supervised signals. The design of reward signal significantly impacts the learning efficiency of the RL-based decoders. Existing reward designs in the RL-based BMI framework rely on external rewards or manually labeled internal rewards, unable to accurately extract subjects' internal evaluation. In this paper, we propose a hidden brain state-based kernel inverse reinforcement learning (HBS-KIRL) method to accurately infer the subject-specific internal evaluation from neural activity during the BMI task. The state-space model is applied to project the neural state into low-dimensional hidden brain state space, which greatly reduces the exploration dimension. Then the kernel method is applied to speed up the convergence of policy, reward, and Q-value networks in reproducing kernel Hilbert space (RKHS). We tested our proposed algorithm on the data collected from the medial prefrontal cortex (mPFC) of rats when they were performing a two-lever-discrimination task. We assessed the state-value estimation performance of our proposed method and compared it with naïve IRL and PCA-based IRL. To validate that the extracted internal evaluation could contribute to the decoder training, we compared the decoding performance of decoders trained by different reward models, including manually designed reward, naïve IRL, PCA-IRL, and our proposed HBS-KIRL. The results show that the HBS-KIRL method can give a stable and accurate estimation of state-value distribution with respect to behavior. Compared with other methods, the decoder guided by HBS-KIRL achieves consistent and better decoding performance over days. This study reveals the potential of applying the IRL method to better extract subject-specific evaluation and improve the BMI decoding performance.},
}
@article {pmid40030380,
year = {2025},
author = {Chen, D and Cao, C and Gong, J and Huang, J and Xiao, J and Huang, Q and Guo, Y and Li, Y},
title = {Decoding Single-Pellet Retrieval Task From Local Field Potentials in Pre- and Post-Stroke Motor Areas: Insights Into Interhemispheric Connectivity Difference.},
journal = {IEEE transactions on bio-medical engineering},
volume = {72},
number = {4},
pages = {1316-1327},
doi = {10.1109/TBME.2024.3499319},
pmid = {40030380},
issn = {1558-2531},
mesh = {Animals ; Rats ; *Motor Cortex/physiopathology/physiology ; *Stroke/physiopathology ; *Brain-Computer Interfaces ; Male ; *Signal Processing, Computer-Assisted ; Rats, Sprague-Dawley ; Forelimb/physiopathology/physiology ; Stroke Rehabilitation ; },
abstract = {OBJECTIVE: Intracortical brain-machine interfaces (iBMIs) hold promise for restoring communication and movement in stroke-paralyzed individuals. Recent studies have demonstrated the potential of using local field potentials (LFPs) for decoding single-pellet retrieval (SPR) tasks in iBMIs. However, most research has relied on LFPs from healthy rats rather than those affected by stroke. This study aimed to investigate the feasibility of utilizing LFPs from both the right and left (stroke) cortical forelimb areas (CFAs) for the SPR tasks decoding under both pre- and post-stroke conditions.<br><br>METHODS: LFPs were recorded via microelectrode arrays implanted into CFAs of eight rats trained to perform the SPR tasks. The relative spectral power method was used to represent frequency information, and random forest classification differentiated SPR tasks from resting states. We also assessed interhemispheric connectivity, including correlation, coherence, and phase-amplitude coupling (PAC), to compare differences between the SPR tasks and the resting states under both pre- and post-stroke conditions.<br><br>RESULTS: Our findings indicated that the relative PS method with LFPs achieves 87.10% 9.2% accuracy in post-stoke SPR decoding, where high gamma is crucial. Additionally, we observed changes in PACs from the right to the left sensorimotor cortex post-stroke during the SPR tasks compared to the resting states.<br><br>SIGNIFICANCE: Our work provides a comprehensive insight into the role of different frequency band from LFPs in motor function recovery mechanisms, highlighting the importance of the high gamma in motor function. This research lays the foundation for developing post-stoke SPR-related BMIs.},
}
@article {pmid40030325,
year = {2025},
author = {Saadatmand, H and Akbarzadeh-T, MR},
title = {Multiobjective Evolutionary Sequential Channel/ Feature Selection for EEG Motor Imagery Analysis.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {4},
pages = {2546-2556},
doi = {10.1109/JBHI.2024.3508277},
pmid = {40030325},
issn = {2168-2208},
mesh = {*Electroencephalography/methods ; Humans ; Algorithms ; *Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; *Imagination/physiology ; Fuzzy Logic ; },
abstract = {Motor imagery (MI) analysis from EEG signals constitutes a class of emerging brain-computer interface (BCI) applications that face EEG's predominant complexities arising from the multitude of channels and the vast number of possible features. This study presents a two-step multiobjective set-based integer-coded fuzzy-initialized evolutionary algorithm (MOSIFE) for efficient EEG-based MI signal analysis. The two-step process is a non-dominant wrapper strategy that sequentially identifies the optimal channels and the minimal set of features, thereby reducing MI's combinatorial search complexity. We also employ a reptile-based search algorithm (RSA), a recent metaheuristic for efficient search in multimodal continuous domains, to optimize the classifier's hyper-parameters. The proposed MOSIFE-RSA algorithm is benchmarked against 12 representative algorithms on four standard BCI Competition databases, including IV-I, III-IVa, III-IIIa, and II. The results show that MOSIFE-RSA improves accuracy by 20%, with channel selection contributing as much as 15% and feature selection as much as 5% towards these results. Furthermore, it reduces computational complexity by 81% through channel selection and 16% through feature selection, demonstrating its effectiveness in advancing EEG-based MI signal analysis. This research has practical implications for developing more accurate and efficient brain-computer interface systems.},
}
@article {pmid40030277,
year = {2025},
author = {Ding, Y and Li, Y and Sun, H and Liu, R and Tong, C and Liu, C and Zhou, X and Guan, C},
title = {EEG-Deformer: A Dense Convolutional Transformer for Brain-Computer Interfaces.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {3},
pages = {1909-1918},
doi = {10.1109/JBHI.2024.3504604},
pmid = {40030277},
issn = {2168-2208},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; *Neural Networks, Computer ; Adult ; Brain/physiology ; Male ; Young Adult ; },
abstract = {Effectively learning the temporal dynamics in electroencephalogram (EEG) signals is challenging yet essential for decoding brain activities using brain-computer interfaces (BCIs). Although Transformers are popular for their long-term sequential learning ability in the BCI field, most methods combining Transformers with convolutional neural networks (CNNs) fail to capture the coarse-to-fine temporal dynamics of EEG signals. To overcome this limitation, we introduce EEG-Deformer, which incorporates two main novel components into a CNN-Transformer: (1) a Hierarchical Coarse-to-Fine Transformer (HCT) block that integrates a Fine-grained Temporal Learning (FTL) branch into Transformers, effectively discerning coarse-to-fine temporal patterns; and (2) a Dense Information Purification (DIP) module, which utilizes multi-level, purified temporal information to enhance decoding accuracy. Comprehensive experiments on three representative cognitive tasksâcognitive attention, driving fatigue, and mental workload detectionâconsistently confirm the generalizability of our proposed EEG-Deformer, demonstrating that it either outperforms or performs comparably to existing state-of-the-art methods. Visualization results show that EEG-Deformer learns from neurophysiologically meaningful brain regions for the corresponding cognitive tasks.},
}
@article {pmid40030249,
year = {2024},
author = {Kokorin, K and Zehra, SR and Mu, J and Yoo, P and Grayden, DB and John, SE},
title = {Semi-Autonomous Continuous Robotic Arm Control Using an Augmented Reality Brain-Computer Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2024.3500217},
pmid = {40030249},
issn = {1558-0210},
abstract = {Noninvasive augmented-reality (AR) brain-computer interfaces (BCIs) that use steady-state visually evoked potentials (SSVEPs) typically adopt a fully-autonomous goal-selection framework to control a robot, where automation is used to compensate for the low information transfer rate of the BCI. This scheme improves task performance but users may prefer direct control (DC) of robot motion. To provide users with a balance of autonomous assistance and manual control, we developed a shared control (SC) system for continuous control of robot translation using an SSVEP AR-BCI, which we tested in a 3D reaching task. The SC system used the BCI input and robot sensor data to continuously predict which object the user wanted to reach, generated an assistance signal, and regulated the level of assistance based on prediction confidence. Eighteen healthy participants took part in our study and each completed 24 reaching trials using DC and SC. Compared to DC, SC significantly improved (paired two-tailed t-test, Holm-corrected α<0.05) mean task success rate (p<0.0001, μ=36.1%, 95% CI [25.3%, 46.9%]), normalised reaching trajectory length (p<0.0001, μ=-26.8%, 95% CI [-36.0%, -17.7%]), and participant workload (p=0.02, μ=-11.6, 95% CI [-21.1, -2.0]) measured with the NASA Task Load Index. Therefore, users of SC can control the robot effectively, while experiencing increased agency. Our system can personalise assistive technology by providing users with the ability to select their preferred level of autonomous assistance.},
}
@article {pmid40030248,
year = {2024},
author = {Ke, S and Yang, B and Qin, Y and Rong, F and Zhang, J and Zheng, Y},
title = {FACT-Net: a Frequency Adapter CNN with Temporal-periodicity Inception for Fast and Accurate MI-EEG Decoding.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2024.3499998},
pmid = {40030248},
issn = {1558-0210},
abstract = {Motor imagery brain-computer interface (MI-BCI) based on non-invasive electroencephalogram (EEG) signals is a typical paradigm of BCI. However, existing decoding methods face significant challenges in terms of signal decoding accuracy, real-time processing, and deployment. To overcome these challenges, we propose FACT-Net, an innovative deep-learning network for the fast and accurate decoding of MI-EEG signals. FACT-Net incorporates a Frequency Adapter (FA) module designed for processing the frequency features of MI-EEG data, as well as a Temporal-Periodicity Inception (TPI) module specifically for handling global periodic signals in MI. To evaluate the proposed model, we conduct the experiments on the cross-day dataset collected from 67 subjects and the BCIC-IV-2a dataset. The FACT-Net achieved an accuracy of 48.32% and 80.67% higher than the state-of-the-art (SOTA) approaches, demonstrating excellent performance in MI decoding. Additionally, it exhibits exceptional memory efficiency and inference time, indicating significant potential for practical applications. We anticipate that FACT-Net will set a new baseline for MI-EEG decoding. The code is available in https://github.com/Ktn1ga/EEG_FACT.},
}
@article {pmid40030247,
year = {2024},
author = {Rao, Z and Zhu, J and Lu, Z and Zhang, R and Li, K and Guan, Z and Li, Y},
title = {A Wearable Brain-Computer Interface with Fewer EEG Channels for Online Motor Imagery Detection.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2024.3502135},
pmid = {40030247},
issn = {1558-0210},
abstract = {Motor imagery-based brain-computer interfaces (MI-BCIs) have significant potential for neurorehabilitation and motor recovery. However, most BCI systems employ multi-channel electroencephalogram (EEG) recording devices, during which the pre-experimental preparation and post-experimental hair cleaning are time-consuming and inconvenient for stroke patients, and potentially affect their motivation for rehabilitation training. In this paper, we introduced a wearable MI-BCI system for online MI classification using a wireless headband device with four EEG channels to reduce setup time while enhancing portability. To validate the performance of the system in decoding MI-EEG signals, extensive experiments and comparisons were performed on sixty-six healthy subjects. Specifically, an offline and an online experiment with forty-six subjects were conducted, with the system achieving average offline and online accuracies of 85.21% and 76.54%, respectively. Furthermore, a comparison experiment involving another twenty subjects showed that the online performance of our headband device (77.84%) was comparable to that of a mature commercial Neuroscan device (76.50%). Compared to several existing portable systems, our wearable system achieved superior performance with fewer channels and was validated on a larger number of subjects. These results demonstrated that our wearable BCI system can reduce preparation time, enhance portability, and meet the classification performance requirements for BCI-based rehabilitation intervention, indicating its substantial potential for large-scale clinical applications in enhancing motor recovery of stroke patients.},
}
@article {pmid40030217,
year = {2025},
author = {Xu, M and Jiao, J and Chen, D and Ding, Y and Chen, Q and Wu, J and Gu, P and Pan, Y and Peng, X and Xiao, N and Yang, B and Li, Q and Guo, J},
title = {REI-Net: A Reference Electrode Standardization Interpolation Technique Based 3D CNN for Motor Imagery Classification.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {3},
pages = {2136-2147},
doi = {10.1109/JBHI.2024.3498916},
pmid = {40030217},
issn = {2168-2208},
mesh = {Humans ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; Electrodes ; *Neural Networks, Computer ; Brain-Computer Interfaces ; Algorithms ; *Imagination/physiology ; Adult ; Male ; },
abstract = {High-quality scalp EEG datasets are extremely valuable for motor imagery (MI) analysis. However, due to electrode size and montage, different datasets inevitably experience channel information loss, posing a significant challenge for MI decoding. A 2D representation that focuses on the time domain may loss the spatial information in EEG. In contrast, a 3D representation based on topography may suffer from channel loss and introduce noise through different padding methods. In this paper, we propose a framework called Reference Electrode Standardization Interpolation Network (REI-Net). Through an interpolation of 3D representation, REI-Net retains the temporal information in 2D scalp EEG while improving the spatial resolution within a certain montage. Additionally, to overcome the data variability caused by individual differences, transfer learning is employed to enhance the decoding robustness. Our approach achieves promising performance on two widely-recognized MI datasets, with an accuracy of 77.99% on BCI-C IV-2a and an accuracy of 63.94% on Kaya2018. The proposed algorithm outperforms the SOTAs leading to more accurate and robust results.},
}
@article {pmid40030141,
year = {2025},
author = {Pankka, H and Lehtinen, J and Ilmoniemi, RJ and Roine, T},
title = {Enhanced EEG Forecasting: A Probabilistic Deep Learning Approach.},
journal = {Neural computation},
volume = {37},
number = {4},
pages = {793-814},
doi = {10.1162/neco_a_01743},
pmid = {40030141},
issn = {1530-888X},
mesh = {*Electroencephalography/methods ; Humans ; *Deep Learning ; Forecasting/methods ; *Brain/physiology ; Neural Networks, Computer ; Brain-Computer Interfaces ; },
abstract = {Forecasting electroencephalography (EEG) signals, that is, estimating future values of the time series based on the past ones, is essential in many real-time EEG-based applications, such as brain-computer interfaces and closed-loop brain stimulation. As these applications are becoming more and more common, the importance of a good prediction model has increased. Previously, the autoregressive model (AR) has been employed for this task; however, its prediction accuracy tends to fade quickly as multiple steps are predicted. We aim to improve on this by applying probabilistic deep learning to make robust longer-range forecasts. For this, we applied the probabilistic deep neural network model WaveNet to forecast resting-state EEG in theta- (4-7.5 Hz) and alpha-frequency (8-13 Hz) bands and compared it to the AR model. WaveNet reliably predicted EEG signals in both theta and alpha frequencies 150 ms ahead, with mean absolute errors of 1.0 ± 1.1 µV (theta) and 0.9 ± 1.1 µV (alpha), and outperformed the AR model in estimating the signal amplitude and phase. Furthermore, we found that the probabilistic approach offers a way of forecasting even more accurately while effectively discarding uncertain predictions. We demonstrate for the first time that probabilistic deep learning can be used to forecast resting-state EEG time series. In the future, the developed model can enhance the real-time estimation of brain states in brain-computer interfaces and brain stimulation protocols. It may also be useful for answering neuroscientific questions and for diagnostic purposes.},
}
@article {pmid40027274,
year = {2024},
author = {Cai, Y and Li, Q and Wesselmann, U and Zhao, C},
title = {Exosomal Bupivacaine: Integrating Nerve Barrier Penetration Capability and Sustained Drug Release for Enhanced Potency in Peripheral Nerve Block and Reduced Toxicity.},
journal = {Advanced functional materials},
volume = {34},
number = {42},
pages = {},
pmid = {40027274},
issn = {1616-301X},
support = {R61 NS123196/NS/NINDS NIH HHS/United States ; },
abstract = {Peripherally injected local anesthetics exhibit limited ability to penetrate peripheral nerve barriers (PNBs), which limits their effectiveness in peripheral nerve block and increases the risk of adverse effects. In this work, we demonstrated that exosomes derived from Human Embryo Kidney (HEK) 293 cells can effectively traverse the perineurium, which is the rate-limiting barrier within PNBs that local anesthetics need to cross before acting on axons. Based on this finding, we use these exosomes as a carrier for bupivacaine (BUP), a local anesthetic commonly used in clinical settings. The in vitro assessments revealed that the prepared exosomal bupivacaine (BUP@EXO) achieves a BUP loading capacity of up to 82.33% and sustained release of BUP for over 30 days. In rats, a single peripheral injection of BUP@EXO, containing 0.75 mg of BUP, which is ineffective for BUP alone, induced a 2-hour sensory nerve blockade without significant motor impairments. Increasing the BUP dose in BUP@EXO to 2.5 mg, a highly toxic dose for BUP alone, extended the sensory nerve blockade to 12 hours without causing systemic cardiotoxicity and local neurotoxicity and myotoxicity.},
}
@article {pmid40026891,
year = {2025},
author = {Moreno-Alcayde, Y and Ruotsalo, T and Leiva, LA and Traver, VJ},
title = {Brainsourcing for temporal visual attention estimation.},
journal = {Biomedical engineering letters},
volume = {15},
number = {2},
pages = {311-326},
pmid = {40026891},
issn = {2093-985X},
abstract = {The concept of temporal visual attention in dynamic contents, such as videos, has been much less studied than its spatial counterpart, i.e., visual salience. Yet, temporal visual attention is useful for many downstream tasks, such as video compression and summarisation, or monitoring users' engagement with visual information. Previous work has considered quantifying a temporal salience score from spatio-temporal user agreements from gaze data. Instead of gaze-based or content-based approaches, we explore to what extent only brain signals can reveal temporal visual attention. We propose methods for (1) computing a temporal visual salience score from salience maps of video frames; (2) quantifying the temporal brain salience score as a cognitive consistency score from the brain signals from multiple observers; and (3) assessing the correlation between both temporal salience scores, and computing its relevance. Two public EEG datasets (DEAP and MAHNOB) are used for experimental validation. Relevant correlations between temporal visual attention and EEG-based inter-subject consistency were found, as compared with a random baseline. In particular, effect sizes, measured with Cohen's d, ranged from very small to large in one dataset, and from medium to very large in another dataset. Brain consistency among subjects watching videos unveils temporal visual attention cues. This has relevant practical implications for analysing attention for visual design in human-computer interaction, in the medical domain, and in brain-computer interfaces at large.},
}
@article {pmid40026370,
year = {2025},
author = {Pišot, R and Marušič, U and Šlosar, L},
title = {Addressing the Paradox of Rest with Innovative Technologies.},
journal = {Zdravstveno varstvo},
volume = {64},
number = {2},
pages = {68-72},
pmid = {40026370},
issn = {0351-0026},
abstract = {The paradox of rest lies in its dual nature: essential for recovery yet potentially harmful when prolonged. Prolonged physical inactivity (PI) significantly contributes to non-communicable diseases (NCDs). Studies show nearly a third of adults worldwide were insufficiently active in 2022, with the economic costs of PI projected to reach INT$520 billion by 2030. Bedrest models have illuminated the rapid onset of insulin resistance, general functional decline and muscle atrophy associated with PI, particularly in hospitalised older adults. Innovative technologies, such as extended reality (XR), offer promising solutions for mitigating the effects of PI and can enhance non-physical rehabilitation techniques such as motor imagery and action observation. These technologies provide immersive, personalised therapeutic experiences that engage multiple senses, transforming passive recovery into an active process and addressing both the physical and cognitive consequences of inactivity. Results of bedrest study showed significant preservation of muscle mass, improved strength and enhanced insulin sensitivity in the intervention group compared to controls. These findings highlight the potential of XR-based strategies in addressing structural and functional declines during inactivity. As part of the Interreg VI-A Italia-Slovenija project X-BRAIN.net, advanced XR-equipped active rooms were developed to aid post-stroke rehabilitation in acute care settings. XR technologies, particularly VR, have shown promise in providing dynamic and adaptable therapeutic environments that facilitate early and targeted interventions. Future advancements focus on integrating XR with brain-computer interfaces (BCIs) and synchronised visual-haptic neurofeedback, enhancing sensorimotor cortical activation and improving rehabilitation outcomes. Comprehensive multimodal approaches, including nutritional, physical and non-physical interventions, are emerging as effective strategies to personalise and optimise patient recovery.},
}
@article {pmid40026248,
year = {2025},
author = {Ge, Q and Yang, J and Huang, F and Dai, X and Chen, C and Guo, J and Wang, M and Zhu, M and Shao, Y and Xia, Y and Zhou, Y and Peng, J and Deng, S and Shi, J and Hu, Y and Zhang, H and Wang, Y and Wang, X and Li, XM and Chen, Z and Shu, Y and Zhu, JM and Zhang, J and Shen, Y and Duan, S and Xu, S and Shen, L and Chen, J},
title = {Multimodal single-cell analyses reveal molecular markers of neuronal senescence in human drug-resistant epilepsy.},
journal = {The Journal of clinical investigation},
volume = {135},
number = {5},
pages = {},
pmid = {40026248},
issn = {1558-8238},
mesh = {*Drug Resistant Epilepsy/genetics/metabolism/pathology ; *Cellular Senescence/genetics ; Single-Cell Gene Expression Analysis ; *Pyramidal Cells/metabolism/pathology ; Cerebral Cortex/cytology/metabolism/pathology ; Case-Control Studies ; Humans ; Male ; Female ; Infant ; Child, Preschool ; Child ; Adolescent ; Young Adult ; Adult ; Animals ; Mice ; Disease Models, Animal ; Biomarkers/analysis/metabolism ; },
abstract = {The histopathological neurons in the brain tissue of drug-resistant epilepsy exhibit aberrant cytoarchitecture and imbalanced synaptic circuit function. However, the gene expression changes of these neurons remain unknown, making it difficult to determine the diagnosis or to dissect the mechanism of drug-resistant epilepsy. By integrating whole-cell patch clamp recording and single-cell RNA-seq approaches, we identified a transcriptionally distinct subset of cortical pyramidal neurons. These neurons highly expressed genes CDKN1A (P21), CCL2, and NFKBIA, which associate with mTOR pathway, inflammatory response, and cellular senescence. We confirmed the expression of senescent marker genes in a subpopulation of cortical pyramidal neurons with enlarged soma size in the brain tissue of drug-resistant epilepsy. We further revealed the expression of senescent cell markers P21, P53, COX2, γ-H2AX, and β-Gal, and reduction of nuclear integrity marker Lamin B1 in histopathological neurons in the brain tissue of patients with drug-resistant epilepsy with different pathologies, but not in control brain tissue with no history of epilepsy. Additionally, chronic, but not acute, epileptic seizures induced senescent marker expression in cortical neurons in mouse models of drug-resistant epilepsy. These results provide important molecular markers for histopathological neurons and what we believe to be new insights into the pathophysiological mechanisms of drug-resistant epilepsy.},
}
@article {pmid40025635,
year = {2025},
author = {Chen, J and Cheng, Y and Chen, L and Yang, B},
title = {Human papillomavirus (HPV) prediction for oropharyngeal cancer based on CT by using off-the-shelf features: A dual-dataset study.},
journal = {Journal of applied clinical medical physics},
volume = {26},
number = {5},
pages = {e70061},
pmid = {40025635},
issn = {1526-9914},
mesh = {Humans ; *Oropharyngeal Neoplasms/virology/diagnostic imaging ; *Tomography, X-Ray Computed/methods ; *Papillomavirus Infections/virology/diagnostic imaging/complications ; *Neural Networks, Computer ; *Papillomaviridae/isolation &amp; purification ; Male ; Female ; *Image Processing, Computer-Assisted/methods ; Algorithms ; Human Papillomavirus Viruses ; },
abstract = {BACKGROUND: This study aims to develop a novel predictive model for determining human papillomavirus (HPV) presence in oropharyngeal cancer using computed tomography (CT). Current image-based HPV prediction methods are hindered by high computational demands or suboptimal performance.<br><br>METHODS: To address these issues, we propose a methodology that employs a Siamese Neural Network architecture, integrating multi-modality off-the-shelf features-handcrafted features and 3D deep features-to enhance the representation of information. We assessed the incremental benefit of combining 3D deep features from various networks and introduced manufacturer normalization. Our method was also designed for computational efficiency, utilizing transfer learning and allowing for model execution on a single-CPU platform. A substantial dataset comprising 1453 valid samples was used as internal validation, a separate independent dataset for external validation.<br><br>RESULTS: Our proposed model achieved superior performance compared to other methods, with an average area under the receiver operating characteristic curve (AUC) of 0.791 [95% (confidence interval, CI), 0.781-0.809], an average recall of 0.827 [95% CI, 0.798-0.858], and an average accuracy of 0.741 [95% CI, 0.730-0.752], indicating promise for clinical application. In the external validation, proposed method attained an AUC of 0.581 [95% CI, 0.560-0.603] and same network architecture with pure deep features achieved an AUC of 0.700 [95% CI, 0.682-0.717]. An ablation study confirmed the effectiveness of incorporating manufacturer normalization and the synergistic effect of combining different feature sets.<br><br>CONCLUSION: Overall, our proposed model not only outperforms existing counterparts for HPV status prediction but is also computationally accessible for use on a single-CPU platform, which reduces resource requirements and enhances clinical usability.},
}
@article {pmid40022809,
year = {2025},
author = {Luo, TJ and Wu, T},
title = {Sum of similarity-regularized squared correlations for enhancing SSVEP detection.},
journal = {Artificial intelligence in medicine},
volume = {162},
number = {},
pages = {103100},
doi = {10.1016/j.artmed.2025.103100},
pmid = {40022809},
issn = {1873-2860},
mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; Calibration ; Algorithms ; },
abstract = {A brain-computer interface (BCI) provides a direct control pathway between human brain and external devices. Steady-state visual evoked potential based BCI (SSVEP-BCI) has been proven to be a valuable solution due to its advantages of high information transfer rate (ITR) and minimal calibration requirement. Recently, some methods have been proposed based on calibration-training techniques to compute optimal spatial filters from covariances, and have achieved good detection performance. However, these methods ignore the temporally-varying and spatially-coupled characteristics of the EEG signals, which is essentially an important clue for enhancing ITR. More importantly, existing methods cannot well deal with intrinsic noise components of electroencephalogram (EEG) signals, greatly affecting their detection performance. In this paper, we propose a novel method, termed as Sum of Similarity-Regularized Squared Correlations (SSRSC), which is extended and regularized from the sum of squared correlations. We simultaneously compute the squared correlations for both calibration data and sine-cosine harmonics templates, and mitigate variations by the similarity regularization. Moreover, we extend the SSRSC by adopting the ranking weighted ensemble strategy, termed as weSSCOR. Extensive experiments have been conducted on two benchmark SSVEP datasets, and the results demonstrated that the proposed SSRSC/weSSRSC can significantly improve accuracy and ITR of SSVEP detection with less calibration data, which has great potential in designing high ITR SSVEP-BCIs with less calibration efforts.},
}
@article {pmid40020404,
year = {2025},
author = {Wang, R and Fang, T and Zhang, Y and Cheng, Y and Wang, C and Chen, Y and Fan, Q and Zhao, X and Ming, D},
title = {The overgrowth of structure-function coupling in premature brain during infancy.},
journal = {Developmental cognitive neuroscience},
volume = {72},
number = {},
pages = {101535},
pmid = {40020404},
issn = {1878-9307},
mesh = {Humans ; *Infant, Premature/growth &amp; development/physiology ; Magnetic Resonance Imaging ; Infant, Newborn ; Male ; *Brain/growth &amp; development/diagnostic imaging/physiology ; Female ; Infant ; *Child Development/physiology ; },
abstract = {Although the rapid growth of brain structure and function during infancy has been well documented, relatively little is known about how these two developmental processes couple-an aspect that exhibits distinct patterns in adult brain. In this study, the multimodal MRI data from the dHCP database were used to investigate the coupling between brain structure and function in infants, with a particular focus on how prematurity influences this relationship. A similar pattern of the coupling distribution between preterm and full-term infants was identified with coupling index varying across unimodal cortices such as visual and sensorimotor regions and transmodal cortices including default mode network. Notably, a widespread overgrowth of structure-function coupling and a slow developmental trajectory towards full-term infants in preterm infants at term-equivalent age were found. Collectively, the study quantified the development of structure-function relationships in preterm infants, offering new insights into the information transmission processes and developmental patterns of the early-life brain.},
}
@article {pmid40014925,
year = {2025},
author = {Dold, M and Pereira, J and Sajonz, B and Coenen, VA and Thielen, J and Janssen, MLF and Tangermann, M},
title = {Dareplane: a modular open-source software platform for BCI research with application in closed-loop deep brain stimulation.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adbb20},
pmid = {40014925},
issn = {1741-2552},
mesh = {*Deep Brain Stimulation/methods/instrumentation ; *Brain-Computer Interfaces ; Humans ; *Software ; Electroencephalography/methods ; Parkinson Disease/therapy/physiopathology ; },
abstract = {Objective.This work introduces Dareplane, a modular and broad technology-agnostic open source software platform for brain-computer interface (BCI) research with an application focus on adaptive deep brain stimulation (aDBS). One difficulty for investigating control approaches for aDBS resides with the complex setups required for aDBS experiments, a challenge Dareplane tries to address.Approach.The key features of the platform are presented and the composition of modules into a full experimental setup is discussed in the context of a Python-based orchestration module. The performance of a typical experimental setup on Dareplane for aDBS is evaluated in three benchtop experiments, covering (a) an easy-to-replicate setup using an Arduino microcontroller, (b) a setup with hardware of an implantable pulse generator, and (c) a setup using an established and CE certified external neurostimulator. The full technical feasibility of the platform in the aDBS context is demonstrated in a first closed-loop session with externalized leads on a patient with Parkinson's disease receiving DBS treatment and further in a non-invasive BCI speller application using code-modulated visual evoked potential (c-VEP).Main results.The platform is implemented and open-source accessible onhttps://github.com/bsdlab/Dareplane. Benchtop results show that performance of the platform is sufficient for current aDBS latencies, and the platform could successfully be used in the aDBS experiment. The timing-critical c-VEP speller could be successfully implemented on the platform achieving expected information transfer rates.Significance.The Dareplane platform supports aDBS setups, and more generally the research on neurotechnological systems such as BCIs. It provides a modular, technology-agnostic, and easy-to-implement software platform to make experimental setups more resilient and replicable.},
}
@article {pmid40013095,
year = {2025},
author = {Yuan, Z and Huang, Z and Li, C and Li, S and Ren, Q and Xia, X and Jiang, Q and Zhang, D and Zhu, Q and Meng, X},
title = {Multimodal fusion model for diagnosing mild cognitive impairment in unilateral middle cerebral artery steno-occlusive disease.},
journal = {Frontiers in aging neuroscience},
volume = {17},
number = {},
pages = {1527323},
pmid = {40013095},
issn = {1663-4365},
abstract = {OBJECTIVES: To propose a multimodal functional brain network (FBN) and structural brain network (SBN) topological feature fusion technique based on resting-state functional magnetic resonance imaging (rs-fMRI), diffusion tensor imaging (DTI), 3D-T1-weighted imaging (3D-T1WI), and demographic characteristics to diagnose mild cognitive impairment (MCI) in patients with unilateral middle cerebral artery (MCA) steno-occlusive disease.<br><br>METHODS: The performances of different algorithms on the MCI dataset were evaluated using 5-fold cross-validation. The diagnostic results of the multimodal performance were evaluated using t-distributed stochastic neighbor embedding (t-SNE) analysis. The four-modal analysis method proposed in this study was applied to identify brain regions and connections associated with MCI, thus confirming its validity.<br><br>RESULTS: Based on the fusion of the topological features of the multimodal FBN and SBN, the accuracy for the diagnosis of MCI in patients with unilateral MCA steno-occlusive disease reached 90.00%. The accuracy, recall, sensitivity, and F1-score were higher than those of the other methods, as was the diagnostic efficacy (AUC = 0.9149).<br><br>CONCLUSION: The multimodal FBN and SBN topological feature fusion technique, which incorporates rs-fMRI, DTI, 3D-T1WI, and demographic characteristics, obtains the most discriminative features of MCI in patients with unilateral MCA steno-occlusive disease and can effectively identify disease-related brain areas and connections. Efficient automated diagnosis facilitates the early and accurate detection of MCI and timely intervention and treatment to delay or prevent disease progression.},
}
@article {pmid40011760,
year = {2025},
author = {Qin, R and Zhang, Y and Shi, J and Wu, P and An, C and Li, Z and Liu, N and Wan, Z and Hua, T and Li, X and Lou, J and Yin, W and Chen, W},
title = {TCR catch bonds nonlinearly control CD8 cooperation to shape T cell specificity.},
journal = {Cell research},
volume = {35},
number = {4},
pages = {265-283},
pmid = {40011760},
issn = {1748-7838},
support = {T2394511//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31971237//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12172371//National Natural Science Foundation of China (National Science Foundation of China)/ ; T2394512//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32101052//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12102389//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12272216//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32090044//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12272348//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31600751//National Natural Science Foundation of China (National Science Foundation of China)/ ; KJ2070000094//Chinese Academy of Sciences (CAS)/ ; KY9100000092//University of Science and Technology of China (USTC)/ ; },
mesh = {Humans ; *Receptors, Antigen, T-Cell/chemistry/genetics/immunology ; *Lymphocyte Activation ; *T-Lymphocytes/immunology ; *CD8 Antigens/chemistry/genetics/immunology ; Ligands ; Molecular Docking Simulation ; Protein Structure, Quaternary ; Protein Binding ; Molecular Dynamics Simulation ; Protein Engineering ; },
abstract = {Naturally evolved T-cell receptors (TCRs) exhibit remarkably high specificity in discriminating non-self antigens from self-antigens under dynamic biomechanical modulation. In contrast, engineered high-affinity TCRs often lose this specificity, leading to cross-reactivity with self-antigens and off-target toxicity. The underlying mechanism for this difference remains unclear. Our study reveals that natural TCRs exploit mechanical force to form optimal catch bonds with their cognate antigens. This process relies on a mechanically flexible TCR-pMHC binding interface, which enables force-enhanced CD8 coreceptor binding to MHC-α1α2 domains through sequential conformational changes induced by force in both the MHC and CD8. Conversely, engineered high-affinity TCRs create rigid, tightly bound interfaces with cognate pMHCs of their parental TCRs. This rigidity prevents the force-induced conformational changes necessary for optimal catch-bond formation. Paradoxically, these high-affinity TCRs can form moderate catch bonds with non-stimulatory pMHCs of their parental TCRs, leading to off-target cross-reactivity and reduced specificity. We have also developed comprehensive force-dependent TCR-pMHC kinetics-function maps capable of distinguishing functional and non-functional TCR-pMHC pairs and identifying toxic, cross-reactive TCRs. These findings elucidate the mechano-chemical basis of the specificity of natural TCRs and highlight the critical role of CD8 in targeting cognate antigens. This work provides valuable insights for engineering TCRs with enhanced specificity and potency against non-self antigens, particularly for applications in cancer immunotherapy and infectious disease treatment, while minimizing the risk of self-antigen cross-reactivity.},
}
@article {pmid40010648,
year = {2025},
author = {Spadacenta, S and Dicke, PW and Thier, P},
title = {Minimally invasive electrocorticography (ECoG) recording in common marmosets.},
journal = {Journal of neuroscience methods},
volume = {417},
number = {},
pages = {110409},
doi = {10.1016/j.jneumeth.2025.110409},
pmid = {40010648},
issn = {1872-678X},
mesh = {Animals ; Callithrix ; *Electrocorticography/methods/instrumentation ; Electrodes, Implanted ; Male ; Brain Mapping/methods ; Female ; *Cerebral Cortex/physiology ; *Brain/physiology ; },
abstract = {BACKGROUND: Electrocorticography (ECoG) provides a valuable compromise between spatial and temporal resolution for recording brain activity with excellent signal quality, crucial for presurgical epilepsy mapping and advancing neuroscience, including brain-machine interface development. ECoG is particularly effective in the common marmoset (Callithrix jacchus), whose lissencephalic (unfolded) brain surface provides broad cortical access. One of the key advantages of ECoG recordings is the ability to study interactions between distant brain regions. Traditional methods rely on large electrode arrays, necessitating extensive trepanations and a trade-off between size and electrode spacing.<br><br>NEW METHOD: This study introduces a refined ECoG technique for examining interactions among multiple cortical areas in marmosets, combining circumscribed trepanations with high-density electrode arrays at specific sites of interest.<br><br>Standard ECoG techniques typically require large electrode arrays and extensive trepanation, which heighten surgical risks and the likelihood of infection, while potentially compromising spatial resolution. In contrast, our method facilitates detailed and stable recordings across multiple cortical areas with minimized invasiveness and reduced complication risks, all while preserving high spatial resolution.<br><br>RESULTS: Two adult marmosets underwent ECoG implantation in frontal, temporal, and parietal regions. Postoperative monitoring confirmed rapid recovery, long-term health, and stable, high-quality neural recordings during various behavioral tasks.<br><br>CONCLUSIONS: This refined ECoG method enhances the study of cortical interactions in marmosets while minimizing surgical invasiveness and complication risks. It offers potential for broader application in other species and opens new avenues for long-term data collection, ultimately advancing both neuroscience and brain-machine interface research.},
}
@article {pmid40010602,
year = {2025},
author = {OuYang, Z and Yang, R and Wang, Y},
title = {Hotspots and Trends in Spinal Cord Stimulation Research for Spinal Cord Injury: A Bibliometric Analysis with Emphasis on Motor Recovery (2014-2024).},
journal = {World neurosurgery},
volume = {197},
number = {},
pages = {123832},
doi = {10.1016/j.wneu.2025.123832},
pmid = {40010602},
issn = {1878-8769},
mesh = {Humans ; *Bibliometrics ; Biomedical Research/trends ; Cross-Sectional Studies ; *Recovery of Function/physiology ; *Spinal Cord Injuries/therapy/physiopathology ; *Spinal Cord Stimulation/trends/methods ; },
abstract = {BACKGROUND: Spinal cord stimulation (SCS) has emerged as a key therapeutic strategy for enhancing motor recovery in spinal cord injury (SCI). This study employs bibliometric analysis to explore research trends and hotspots in SCS for motor recovery, highlighting advances and emerging directions over the past decade.<br><br>METHODS: This cross-sectional bibliometric study retrieved publications on SCS for motor recovery from the Web of Science Core Collection database (2014-2024). Key information, including annual publication trends, contributing countries, institutions, authors, journals, keywords, and highly cited references, was analyzed using CiteSpace and VOSviewer.<br><br>RESULTS: A total of 1033 publications were analyzed, demonstrating exponential growth in SCS research since 2014. The United States and Switzerland were identified as leading contributors, with prominent institutions such as the Swiss Federal Institute of Technology and the University of California System driving advancements. Key authors included Gr&#xe9;goire Courtine and Susan J. Harkema. Research themes have evolved through four phases: foundational studies on spinal cord mechanisms, exploration of neural circuits, application of electrical stimulation for motor recovery, and advancements in noninvasive therapies such as transcutaneous SCS. Highly cited journals, including Nature and Lancet, have published transformative studies, underscoring the field's clinical and academic significance.<br><br>CONCLUSIONS: This bibliometric analysis provides a comprehensive overview of SCS research for motor recovery post-SCI over the past decade. Interdisciplinary collaboration and technological innovation have positioned SCS as a cornerstone of SCI rehabilitation. Future efforts should focus on optimizing approaches, leveraging advanced imaging and artificial intelligence technologies, and broadening rehabilitation goals to improve outcomes for SCI patients.},
}
@article {pmid40009882,
year = {2025},
author = {Ding, L and Zou, Q and Zhu, J and Wang, Y and Yang, Y},
title = {Dynamical intracranial EEG functional network controllability localizes the seizure onset zone and predicts the epilepsy surgical outcome.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adba8d},
pmid = {40009882},
issn = {1741-2552},
mesh = {Humans ; *Electrocorticography/methods ; Treatment Outcome ; *Drug Resistant Epilepsy/surgery/physiopathology/diagnosis ; Machine Learning ; *Epilepsy/surgery/physiopathology/diagnosis ; Male ; *Seizures/physiopathology/surgery/diagnosis ; Female ; Adult ; *Nerve Net/physiopathology ; *Electroencephalography/methods ; Young Adult ; Predictive Value of Tests ; },
abstract = {Objective. Seizure onset zone (SOZ) localization and SOZ resection outcome prediction are critical for the surgical treatment of drug-resistant epilepsy but have mainly relied on manual inspection of intracranial electroencephalography (iEEG) monitoring data, which can be both inaccurate and time-consuming. Therefore, automating SOZ localization and surgical outcome prediction by using appropriate iEEG neural features and machine learning models has become an emerging topic. However, current channel-wise local features, graph-theoretic network features, and system-theoretic network features cannot fully capture the spatial, temporal, and neural dynamical aspects of epilepsy, hindering accurate SOZ localization and surgical outcome prediction.Approach. Here, we develop a method for computing dynamical functional network controllability from multi-channel iEEG signals, which from a control-theoretic viewpoint, has the ability to simultaneously capture the spatial, temporal, functional, and dynamical aspects of epileptic brain networks. We then apply multiple machine learning models to use iEEG functional network controllability for localizing SOZ and predicting surgical outcomes in drug-resistant epilepsy patients and compare with existing neural features. We finally combine iEEG functional network controllability with representative local, graph-theoretic, and system-theoretic features to leverage complementary information for further improving performance.Main results. We find that iEEG functional network controllability at SOZ channels is significantly higher than that of other channels. We further show that machine learning models using iEEG functional network controllability successfully localize SOZ and predict surgical outcomes, significantly outperforming existing local, graph-theoretic, and system-theoretic features. We finally demonstrate that there exists complementary information among different types of neural features and fusing them further improves performance.Significance. Our results suggest that iEEG functional network controllability is an effective feature for automatic SOZ localization and surgical outcome prediction in epilepsy treatment.},
}
@article {pmid40009879,
year = {2025},
author = {Yan, Y and Li, J and Yin, M},
title = {EEG-based recognition of hand movement and its parameter.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adba8a},
pmid = {40009879},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; *Hand/physiology ; Movement/physiology ; Male ; Adult ; *Brain-Computer Interfaces ; Female ; Young Adult ; Neural Networks, Computer ; },
abstract = {Objecitve. Brain-computer interface is a cutting-edge technology that enables interaction with external devices by decoding human intentions, and is highly valuable in the fields of medical rehabilitation and human-robot collaboration. The technique of decoding motor intent for motor execution (ME) based on electroencephalographic (EEG) signals is in the feasibility study stage by now. There are still insufficient studies on the accuracy of ME EEG signal recognition in between-subjects classification to reach the level of realistic applications. This paper aims to investigate EEG signal-based hand movement recognition by analyzing low-frequency time-domain information.Approach. Experiments with four types of hand movements, two force parameter (picking up and pushing) tasks, and a four-target directional displacement task were designed and executed, and the EEG data from thirteen healthy volunteers was collected. Sliding window approach is used to expand the dataset in order to address the issue of EEG signal overfitting. Furtherly, Convolutional Neural Network (CNN)-Bidirectional Long Short-Term Memory Network (BiLSTM) model, an end-to-end serial combination of a BiLSTM and (CNN) is constructed to classify and recognize the hand movement based on the raw EEG data.Main results. According to the experimental results, the model is able to categorize four types of hand movements, picking up movements, pushing movements, and four target direction displacement movements with an accuracy of 99.14% ± 0.49%, 99.29% ± 0.11%, 99.23% ± 0.60%, and 98.11% ± 0.23%, respectively.Significance. Furthermore, comparative tests conducted with alternative deep learning models (LSTM, CNN, EEGNet, CNN-LSTM) demonstrates that the CNN-BiLSTM model is with practicable accuracy in terms of EEG-based hand movement recognition and its parameter decoding.},
}
@article {pmid40008568,
year = {2025},
author = {Jelescu, IO and Grussu, F and Ianus, A and Hansen, B and Barrett, RLC and Aggarwal, M and Michielse, S and Nasrallah, F and Syeda, W and Wang, N and Veraart, J and Roebroeck, A and Bagdasarian, AF and Eichner, C and Sepehrband, F and Zimmermann, J and Soustelle, L and Bowman, C and Tendler, BC and Hertanu, A and Jeurissen, B and Verhoye, M and Frydman, L and van de Looij, Y and Hike, D and Dunn, JF and Miller, K and Landman, BA and Shemesh, N and Anderson, A and McKinnon, E and Farquharson, S and Dell'Acqua, F and Pierpaoli, C and Drobnjak, I and Leemans, A and Harkins, KD and Descoteaux, M and Xu, D and Huang, H and Santin, MD and Grant, SC and Obenaus, A and Kim, GS and Wu, D and Le Bihan, D and Blackband, SJ and Ciobanu, L and Fieremans, E and Bai, R and Leergaard, TB and Zhang, J and Dyrby, TB and Johnson, GA and Cohen-Adad, J and Budde, MD and Schilling, KG},
title = {Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 1: In vivo small-animal imaging.},
journal = {Magnetic resonance in medicine},
volume = {93},
number = {6},
pages = {2507-2534},
pmid = {40008568},
issn = {1522-2594},
support = {R01 EB031954/EB/NIBIB NIH HHS/United States ; R01 MH092535/MH/NIMH NIH HHS/United States ; U54 AG054349/AG/NIA NIH HHS/United States ; R01 EB017230/EB/NIBIB NIH HHS/United States ; R01 EB019980/EB/NIBIB NIH HHS/United States ; R01 NS119605/NS/NINDS NIH HHS/United States ; P30 DA048742/DA/NIDA NIH HHS/United States ; R56 EB031765/EB/NIBIB NIH HHS/United States ; P41 EB017183/EB/NIBIB NIH HHS/United States ; K01 EB032898/EB/NIBIB NIH HHS/United States ; R01 AG057991/AG/NIA NIH HHS/United States ; R01 EB031765/EB/NIBIB NIH HHS/United States ; R01 NS109090/NS/NINDS NIH HHS/United States ; R01 NS125020/NS/NINDS NIH HHS/United States ; R01 CA160620/CA/NCI NIH HHS/United States ; P50 MH096889/MH/NIMH NIH HHS/United States ; },
mesh = {Animals ; *Diffusion Magnetic Resonance Imaging/methods ; *Image Processing, Computer-Assisted/methods ; Brain/diagnostic imaging ; Software ; Mice ; Reproducibility of Results ; },
abstract = {Small-animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected considerations and recommendations from the diffusion community on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We, then, give recommendations for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including preprocessing, model-fitting, and tractography. Finally, we provide an online resource that lists publicly available preclinical dMRI datasets and software packages to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. Although we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.},
}
@article {pmid40008460,
year = {2025},
author = {Schilling, KG and Howard, AFD and Grussu, F and Ianus, A and Hansen, B and Barrett, RLC and Aggarwal, M and Michielse, S and Nasrallah, F and Syeda, W and Wang, N and Veraart, J and Roebroeck, A and Bagdasarian, AF and Eichner, C and Sepehrband, F and Zimmermann, J and Soustelle, L and Bowman, C and Tendler, BC and Hertanu, A and Jeurissen, B and Verhoye, M and Frydman, L and van de Looij, Y and Hike, D and Dunn, JF and Miller, K and Landman, BA and Shemesh, N and Anderson, A and McKinnon, E and Farquharson, S and Dell'Acqua, F and Pierpaoli, C and Drobnjak, I and Leemans, A and Harkins, KD and Descoteaux, M and Xu, D and Huang, H and Santin, MD and Grant, SC and Obenaus, A and Kim, GS and Wu, D and Le Bihan, D and Blackband, SJ and Ciobanu, L and Fieremans, E and Bai, R and Leergaard, TB and Zhang, J and Dyrby, TB and Johnson, GA and Cohen-Adad, J and Budde, MD and Jelescu, IO},
title = {Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 3-Ex vivo imaging: Data processing, comparisons with microscopy, and tractography.},
journal = {Magnetic resonance in medicine},
volume = {93},
number = {6},
pages = {2561-2582},
pmid = {40008460},
issn = {1522-2594},
support = {34824//Canada Foundation for Innovation/ ; R01 EB031954/EB/NIBIB NIH HHS/United States ; PCEFP2_194260//Eccellenza Fellowship/ ; R01EB019980/NH/NIH HHS/United States ; R01EB017230/NH/NIH HHS/United States ; R01NS109090/NH/NIH HHS/United States ; K01 EB032898/EB/NIBIB NIH HHS/United States ; CIHRFDN-143263//Canadian Institute of Health Research/ ; //Research Center of Excellence of the University of Antwerp/ ; P30 DA048742/DA/NIDA NIH HHS/United States ; FDN-143263//CIHR/ ; R01 CA160620/CA/NCI NIH HHS/United States ; 202788/Z/16/A/WT_/Wellcome Trust/United Kingdom ; P30DA048742/DA/NIDA NIH HHS/United States ; R01 EB017230/EB/NIBIB NIH HHS/United States ; R01CA160620/NH/NIH HHS/United States ; 101044180/ERC_/European Research Council/International ; 32454//Canada Foundation for Innovation/ ; 203139/Z/16/Z/WT_/Wellcome Trust/United Kingdom ; R01 EB019980/EB/NIBIB NIH HHS/United States ; //NSERC/ ; 322736//Fonds de Recherche du Qu&#xe9;bec - Sant&#xe9;/ ; FWO//Research Foundation Flanders/ ; /SNSF_/Swiss National Science Foundation/Switzerland ; R01EB031954/NH/NIH HHS/United States ; RGPIN-2019-07244//Natural Sciences and Engineering Research Council of Canada/ ; K01EB032898/NH/NIH HHS/United States ; 203139/A/16/Z/WT_/Wellcome Trust/United Kingdom ; R01EB031765/EB/NIBIB NIH HHS/United States ; R56EB031765/EB/NIBIB NIH HHS/United States ; /WT_/Wellcome Trust/United Kingdom ; R01 EB031765/EB/NIBIB NIH HHS/United States ; R01 NS109090/NS/NINDS NIH HHS/United States ; R01 NS125020/NS/NINDS NIH HHS/United States ; 5886,35450//Quebec BioImaging Network/ ; LCF/BQ/PR22/11920010//la Caixa/ ; R01AG057991/NH/NIH HHS/United States ; R56 EB031765/EB/NIBIB NIH HHS/United States ; 12M3119N//Research Foundation Flanders/ ; P41 EB017183/EB/NIBIB NIH HHS/United States ; R01NS125020/NH/NIH HHS/United States ; 2020 BP 00117//Government of Catalonia/ ; },
mesh = {*Diffusion Magnetic Resonance Imaging/methods ; Animals ; *Brain/diagnostic imaging ; *Image Processing, Computer-Assisted/methods ; *Diffusion Tensor Imaging/methods ; *Microscopy/methods ; Humans ; Signal-To-Noise Ratio ; },
abstract = {Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitate high spatial resolution and high SNR images, cutting-edge diffusion contrasts, and direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work concludes a three-part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre-processing, data processing, and comparisons with microscopy. In each section, we attempt to provide guidelines and recommendations but also highlight areas for which no guidelines exist (and why), and where future work should lie. We end by providing guidelines on code sharing and data sharing and point toward open-source software and databases specific to small animal and ex vivo imaging.},
}
@article {pmid40007882,
year = {2025},
author = {Crell, MR and Kostoglou, K and Sterk, K and Müller-Putz, GR},
title = {A novel paradigm for fast training data generation in asynchronous movement-based BCIs.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1540155},
pmid = {40007882},
issn = {1662-5161},
abstract = {INTRODUCTION: Movement-based brain-computer interfaces (BCIs) utilize brain activity generated during executed or attempted movement to provide control over applications. By relying on natural movement processes, these BCIs offer a more intuitive control compared to other BCI systems. However, non-invasive movement-based BCIs utilizing electroencephalographic (EEG) signals usually require large amounts of training data to achieve suitable accuracy in the detection of movement intent. Additionally, patients with movement impairments require cue-based paradigms to indicate the start of a movement-related task. Such paradigms tend to introduce long delays between trials, thereby extending training times. To address this, we propose a novel experimental paradigm that enables the collection of 300 cued movement trials in 18 min.<br><br>METHODS: By obtaining measurements from ten participants, we demonstrate that the data produced by this paradigm exhibits characteristics similar to those observed during self-paced movement.<br><br>RESULTS AND DISCUSSION: We also show that classifiers trained on this data can be used to accurately detect executed movements with an average true positive rate of 31.8% at a maximum rate of 1.0 false positives per minute.},
}
@article {pmid40007617,
year = {2025},
author = {Wu, H and Feng, E and Yin, H and Zhang, Y and Chen, G and Zhu, B and Yue, X and Zhang, H and Liu, Q and Xiong, L},
title = {Biomaterials for neuroengineering: applications and challenges.},
journal = {Regenerative biomaterials},
volume = {12},
number = {},
pages = {rbae137},
pmid = {40007617},
issn = {2056-3418},
abstract = {Neurological injuries and diseases are a leading cause of disability worldwide, underscoring the urgent need for effective therapies. Neural regaining and enhancement therapies are seen as the most promising strategies for restoring neural function, offering hope for individuals affected by these conditions. Despite their promise, the path from animal research to clinical application is fraught with challenges. Neuroengineering, particularly through the use of biomaterials, has emerged as a key field that is paving the way for innovative solutions to these challenges. It seeks to understand and treat neurological disorders, unravel the nature of consciousness, and explore the mechanisms of memory and the brain's relationship with behavior, offering solutions for neural tissue engineering, neural interfaces and targeted drug delivery systems. These biomaterials, including both natural and synthetic types, are designed to replicate the cellular environment of the brain, thereby facilitating neural repair. This review aims to provide a comprehensive overview for biomaterials in neuroengineering, highlighting their application in neural functional regaining and enhancement across both basic research and clinical practice. It covers recent developments in biomaterial-based products, including 2D to 3D bioprinted scaffolds for cell and organoid culture, brain-on-a-chip systems, biomimetic electrodes and brain-computer interfaces. It also explores artificial synapses and neural networks, discussing their applications in modeling neural microenvironments for repair and regeneration, neural modulation and manipulation and the integration of traditional Chinese medicine. This review serves as a comprehensive guide to the role of biomaterials in advancing neuroengineering solutions, providing insights into the ongoing efforts to bridge the gap between innovation and clinical application.},
}
@article {pmid40007259,
year = {2025},
author = {Sirakov, A and Ninov, K and Sirakova, K and Sirakov, SS},
title = {Blazing the trail! Commentary on "Intra-arterial lidocaine administration in middle meningeal artery for short-term treatment of subarachoid hemorrhage-related headaches" by Qureshi et al.},
journal = {Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences},
volume = {},
number = {},
pages = {15910199251324039},
pmid = {40007259},
issn = {2385-2011},
abstract = {In their recently published INR study, Qureshi et al. present their results on intra-arterial lidocaine administration in the middle meningeal artery for the short-term treatment of subarachnoid hemorrhage (SAH)-related headaches. The authors demonstrate that their proposed intra-arterial treatment consistently alleviates headaches in patients with SAH. The purpose of this commentary is to commend the authors on their paper and the notable results they have achieved. It is always pleasant to encounter studies that not only make it to the "Latest Online" section of neurointerventional journals but also push the boundaries, advancing our understanding and care for patients in the most meaningful ways. There is no doubt that our field has witnessed remarkable progress and an expanding spectrum of interventions that endovascular neuroservices can offer. Several therapeutic approaches have emerged from similarly constructive articles, including intra-arterial chemotherapy for malignant cerebral tumors, innovative treatments for cerebrospinal fluid-venous fistulas, hydrocephalus, and chronic subdural hematomas, as well as the implantation of brain-computer interface devices.},
}
@article {pmid40006451,
year = {2025},
author = {Zaidi, SR and Khan, NA and Hasan, MA},
title = {Bridging Neuroscience and Machine Learning: A Gender-Based Electroencephalogram Framework for Guilt Emotion Identification.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {4},
pages = {},
pmid = {40006451},
issn = {1424-8220},
mesh = {Humans ; *Electroencephalography/methods ; Male ; Female ; *Emotions/physiology ; *Machine Learning ; *Guilt ; Adult ; *Neurosciences/methods ; Young Adult ; Brain/physiology ; Sex Factors ; Support Vector Machine ; },
abstract = {This study explores the link between the emotion "guilt" and human EEG data, and investigates the influence of gender differences on the expression of guilt and neutral emotions in response to visual stimuli. Additionally, the stimuli used in the study were developed to ignite guilt and neutral emotions. Two emotions, "guilt" and "neutral", were recorded from 16 participants after these emotions were induced using storyboards as pictorial stimuli. These storyboards were developed based on various guilt-provoking events shared by another group of participants. In the pre-processing step, collected data were de-noised using bandpass filters and ICA, then segmented into smaller sections for further analysis. Two approaches were used to feed these data to the SVM classifier. First, the novel approach employed involved feeding the data to SVM classifier without computing any features. This method provided an average accuracy of 83%. In the second approach, data were divided into Alpha, Beta, Gamma, Theta and Delta frequency bands using Discrete Wavelet Decomposition. Afterward, the computed features, including entropy, Hjorth parameters and Band Power, were fed to SVM classifiers. This approach achieved an average accuracy of 63%. The findings of both classification methodologies indicate that females are more expressive in response to depicted stimuli and that their brain cells exhibit higher feature values. Moreover, females displayed higher accuracy than males in all bands except the Delta band.},
}
@article {pmid40006375,
year = {2025},
author = {Wu, Y and Cao, P and Xu, M and Zhang, Y and Lian, X and Yu, C},
title = {Adaptive GCN and Bi-GRU-Based Dual Branch for Motor Imagery EEG Decoding.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {4},
pages = {},
pmid = {40006375},
issn = {1424-8220},
support = {CEIEC-2023-ZM02-0090//Industrial Internet identification analysis System/ ; },
mesh = {*Electroencephalography/methods ; Humans ; Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Algorithms ; Neural Networks, Computer ; },
abstract = {Decoding motor imagery electroencephalography (MI-EEG) signals presents significant challenges due to the difficulty in capturing the complex functional connectivity between channels and the temporal dependencies of EEG signals across different periods. These challenges are exacerbated by the low spatial resolution and high signal redundancy inherent in EEG signals, which traditional linear models struggle to address. To overcome these issues, we propose a novel dual-branch framework that integrates an adaptive graph convolutional network (Adaptive GCN) and bidirectional gated recurrent units (Bi-GRUs) to enhance the decoding performance of MI-EEG signals by effectively modeling both channel correlations and temporal dependencies. The Chebyshev Type II filter decomposes the signal into multiple sub-bands giving the model frequency domain insights. The Adaptive GCN, specifically designed for the MI-EEG context, captures functional connectivity between channels more effectively than conventional GCN models, enabling accurate spatial-spectral feature extraction. Furthermore, combining Bi-GRU and Multi-Head Attention (MHA) captures the temporal dependencies across different time segments to extract deep time-spectral features. Finally, feature fusion is performed to generate the final prediction results. Experimental results demonstrate that our method achieves an average classification accuracy of 80.38% on the BCI-IV Dataset 2a and 87.49% on the BCI-I Dataset 3a, outperforming other state-of-the-art decoding approaches. This approach lays the foundation for future exploration of personalized and adaptive brain-computer interface (BCI) systems.},
}
@article {pmid40003400,
year = {2025},
author = {Teteh-Brooks, DK and Ericson, M and Bethea, TN and Dawkins-Moultin, L and Sarkaria, N and Bailey, J and Llanos, AAM and Montgomery, S},
title = {Validating the Black Identity, Hair Product Use, and Breast Cancer Scale (BHBS) Among Black Breast Cancer Survivors.},
journal = {International journal of environmental research and public health},
volume = {22},
number = {2},
pages = {},
pmid = {40003400},
issn = {1660-4601},
support = {K01 MD018417/MD/NIMHD NIH HHS/United States ; L60 CA253971/CA/NCI NIH HHS/United States ; R01 CA217841/CA/NCI NIH HHS/United States ; R43 MD017966/MD/NIMHD NIH HHS/United States ; },
mesh = {Humans ; *Breast Neoplasms/epidemiology/ethnology/psychology ; Female ; *Cancer Survivors/statistics &amp; numerical data/psychology ; Middle Aged ; *Black or African American/psychology/statistics &amp; numerical data ; Adult ; *Hair Preparations/adverse effects ; Aged ; Surveys and Questionnaires ; },
abstract = {UNLABELLED: Personal care products containing toxic chemicals (e.g., endocrine-disrupting chemicals) may increase breast cancer risk, especially for Black women who use these products more than other racial groups. There are limited tools that examine the intersections of identity, behaviors, and attitudes surrounding product use, perceived safety, and breast cancer risk; thus, the Black Identity, Hair Product Use, and Breast Cancer Scale (BHBS) was developed to bridge this gap. While initial validations lacked diverse survivor representation, this study seeks to validate the BHBS among Black survivors.<br><br>METHODS: This study is a part of the Bench to Community Initiative (BCI), where respondents (n = 167) completed a 41-item survey including the BHBS between 2020 and 2022. The use of Principal Component Analysis (PCA) and confirmatory factor analysis (CFA) established the underlying component structures and model fit. CFA measures used to confirm component structures included the Root Mean Square Error of Approximation, the Comparative Fit Index, and the Tucker Lewis Index.<br><br>RESULTS: Black survivors on average were diagnosed with breast cancer before age 40 (37.41 &#xb1; 8.8) with Stage 1 (45%) disease. Sixty-three percent of the total variance resulted in a two-component structure. Subscale 1 (S1) measures the sociocultural perspectives about hair and identity (28% of the total variance; &#x3b1; = 0.73; 95% CI = 0.71-0.82). Subscale 2 (S2) can be used to assess perceived breast cancer risk related to hair product use (35% of the total variance; &#x3b1; = 0.86; 95% CI = 0.81-0.94). The two-component structure was confirmed with Root Mean Square Error of Approximation = 0.034, Comparative Fit Index = 0.93, and Tucker Lewis Index = 0.89.<br><br>DISCUSSION/CONCLUSIONS: The BHBS is a valid tool to measure identity, attitudes, and behaviors about product use and breast cancer risk among survivors. Hair is a significant cultural identity expression, and the health effects of styling products should be considered in future interventions.},
}
@article {pmid40002607,
year = {2025},
author = {Halkiopoulos, C and Gkintoni, E and Aroutzidis, A and Antonopoulou, H},
title = {Advances in Neuroimaging and Deep Learning for Emotion Detection: A Systematic Review of Cognitive Neuroscience and Algorithmic Innovations.},
journal = {Diagnostics (Basel, Switzerland)},
volume = {15},
number = {4},
pages = {},
pmid = {40002607},
issn = {2075-4418},
abstract = {Background/Objectives: The following systematic review integrates neuroimaging techniques with deep learning approaches concerning emotion detection. It, therefore, aims to merge cognitive neuroscience insights with advanced algorithmic methods in pursuit of an enhanced understanding and applications of emotion recognition. Methods: The study was conducted following PRISMA guidelines, involving a rigorous selection process that resulted in the inclusion of 64 empirical studies that explore neuroimaging modalities such as fMRI, EEG, and MEG, discussing their capabilities and limitations in emotion recognition. It further evaluates deep learning architectures, including neural networks, CNNs, and GANs, in terms of their roles in classifying emotions from various domains: human-computer interaction, mental health, marketing, and more. Ethical and practical challenges in implementing these systems are also analyzed. Results: The review identifies fMRI as a powerful but resource-intensive modality, while EEG and MEG are more accessible with high temporal resolution but limited by spatial accuracy. Deep learning models, especially CNNs and GANs, have performed well in classifying emotions, though they do not always require large and diverse datasets. Combining neuroimaging data with behavioral and cognitive features improves classification performance. However, ethical challenges, such as data privacy and bias, remain significant concerns. Conclusions: The study has emphasized the efficiencies of neuroimaging and deep learning in emotion detection, while various ethical and technical challenges were also highlighted. Future research should integrate behavioral and cognitive neuroscience advances, establish ethical guidelines, and explore innovative methods to enhance system reliability and applicability.},
}
@article {pmid40002501,
year = {2025},
author = {Wu, C and Yao, B and Zhang, X and Li, T and Wang, J and Pu, J},
title = {The Application of Entropy in Motor Imagery Paradigms of Brain-Computer Interfaces.},
journal = {Brain sciences},
volume = {15},
number = {2},
pages = {},
pmid = {40002501},
issn = {2076-3425},
support = {RS2024X007//Key Project of Construction of Drug Regulatory Science System/ ; 2021ZD0200406//STI 2030-MajorProjects under grant/ ; 2021-I2M-1-042, 2021-I2M-1-058//the Medical and Health Innovation Project/ ; 20JCJQIC00230//the Tianjin Outstanding Youth Fund Project/ ; },
abstract = {Background: In motor imagery brain-computer interface (MI-BCI) research, electroencephalogram (EEG) signals are complex and nonlinear. This complexity and nonlinearity render signal processing and classification challenging when employing traditional linear methods. Information entropy, with its intrinsic nonlinear characteristics, effectively captures the dynamic behavior of EEG signals, thereby addressing the limitations of traditional methods in capturing linear features. However, the multitude of entropy types leads to unclear application scenarios, with a lack of systematic descriptions. Methods: This study conducted a review of 63 high-quality research articles focused on the application of entropy in MI-BCI, published between 2019 and 2023. It summarizes the names, functions, and application scopes of 13 commonly used entropy measures. Results: The findings indicate that sample entropy (16.3%), Shannon entropy (13%), fuzzy entropy (12%), permutation entropy (9.8%), and approximate entropy (7.6%) are the most frequently utilized entropy features in MI-BCI. The majority of studies employ a single entropy feature (79.7%), with dual entropy (9.4%) and triple entropy (4.7%) being the most prevalent combinations in multiple entropy applications. The incorporation of entropy features can significantly enhance pattern classification accuracy (by 8-10%). Most studies (67%) utilize public datasets for classification verification, while a minority design and conduct experiments (28%), and only 5% combine both methods. Conclusions: Future research should delve into the effects of various entropy features on specific problems to clarify their application scenarios. As research methodologies continue to evolve and advance, entropy features are poised to play a significant role in a wide array of fields and contexts.},
}
@article {pmid40002462,
year = {2025},
author = {Wu, P and Fei, K and Chen, B and Pan, L},
title = {MSEI-ENet: A Multi-Scale EEG-Inception Integrated Encoder Network for Motor Imagery EEG Decoding.},
journal = {Brain sciences},
volume = {15},
number = {2},
pages = {},
pmid = {40002462},
issn = {2076-3425},
support = {61773078//the National Natural Science Foundation of China/ ; },
abstract = {BACKGROUND: Due to complex signal characteristics and distinct individual differences, the decoding of a motor imagery electroencephalogram (MI-EEG) is limited by the unsatisfactory performance of suboptimal traditional models.<br><br>METHODS: A subject-independent model named MSEI-ENet is proposed for multiple-task MI-EEG decoding. It employs a specially designed multi-scale structure EEG-inception module (MSEI) for comprehensive feature learning. The encoder module further helps to detect discriminative information by its multi-head self-attention layer with a larger receptive field, which enhances feature representation and improves recognition efficacy.<br><br>RESULTS: The experimental results on Competition IV dataset 2a showed that our proposed model yielded an overall accuracy of 94.30%, MF1 score of 94.31%, and Kappa of 0.92.<br><br>CONCLUSIONS: A performance comparison with state-of-the-art methods demonstrated the effectiveness and generalizability of the proposed model on challenging multi-task MI-EEG decoding.},
}
@article {pmid40002457,
year = {2025},
author = {Gu, H and Chen, T and Ma, X and Zhang, M and Sun, Y and Zhao, J},
title = {CLTNet: A Hybrid Deep Learning Model for Motor Imagery Classification.},
journal = {Brain sciences},
volume = {15},
number = {2},
pages = {},
pmid = {40002457},
issn = {2076-3425},
abstract = {BACKGROUND: Brain-computer interface (BCI) technology opens up new avenues for human-machine interaction and rehabilitation by connecting the brain to machines. Electroencephalography (EEG)-based motor imagery (MI) classification is a key component of BCI technology, which is capable of translating neural activity in the brain into commands for controlling external devices. Despite the great potential of BCI technology, the challenges of extracting and decoding brain signals limit its wide application.<br><br>METHODS: To address this challenge, this study proposes a novel hybrid deep learning model, CLTNet, which focuses on solving the feature extraction problem to improve the classification of MI-EEG signals. In the preliminary feature extraction stage, CLTNet uses a convolutional neural network (CNN) to extract time series, channel, and spatial features of EEG signals to obtain important local information. In the deep feature extraction stage, the model combines the long short-term memory (LSTM) network and the Transformer module to capture time-series data and global dependencies in the EEG. The LSTM explains the dynamics of the brain activity, while the Transformer's self-attention mechanism reveals the global features of the time series. Ultimately, the CLTNet model classifies motor imagery EEG signals through a fully connected layer.<br><br>RESULTS: The model achieved an average accuracy of 83.02% and a Kappa value of 0.77 on the BCI IV 2a dataset, and 87.11% and a Kappa value of 0.74 on the BCI IV 2b dataset, both of which outperformed the traditional methods.<br><br>CONCLUSIONS: The innovation of the CLTNet model is that it integrates multiple network architectures, which offers a more comprehensive understanding of the characteristics of the EEG signals during motor imagery, providing a more comprehensive perspective and establishing a new benchmark for future research in this area.},
}
@article {pmid40002431,
year = {2025},
author = {Yang, Y and Li, M and Liu, J},
title = {Generative Diffusion-Based Task Incremental Learning Method for Decoding Motor Imagery EEG.},
journal = {Brain sciences},
volume = {15},
number = {2},
pages = {},
pmid = {40002431},
issn = {2076-3425},
support = {62173010//Mingai Li/ ; },
abstract = {BACKGROUND/OBJECTIVES: Motor neurorehabilitation can be realized by gradually learning diverse motor imagery (MI) tasks. EEG-based brain-computer interfaces (BCIs) provide an effective solution. Nevertheless, existing MI decoding methods cannot balance plasticity for unseen tasks and stability for old tasks. This paper proposes a generative diffusion-based task Incremental Learning (IL) method called GD-TIL.<br><br>METHODS: First, data augmentation is employed to increase data diversity by segmenting and recombining EEG signals. Second, to capture temporal-spatial features (TSFs) from different temporal resolutions, a multi-scale temporal-spatial feature extractor (MTSFE) is developed via integrating multiscale temporal-spatial convolutions, a dual-branch pooling operation, multiple multi-head self-attention mechanisms, and a dynamic convolutional encoder. The proposed self-supervised task generalization (SSTG) mechanism introduces a regularization constraint to guide MTSFE and unified classifier updating, which combines labels and semantic similarity between the augmentation with original views to enhance model generalizability for unseen tasks. In the IL phase, a prototype-guided generative replay module (PGGR) is used to generate old tasks' TSFs by training a lightweight diffusion model based on the prototype and label of each task. Furthermore, the generated TSF is merged with a new TSF to fine-tune the convolutional encoder and update the classifier and PGGR. Finally, GD-TIL is evaluated on a self-collected ADL-MI dataset with two MI pairs and a public dataset with four MI tasks.<br><br>RESULTS: The continuous decoding accuracy reaches 80.20% and 81.32%, respectively. The experimental results exhibit the excellent plasticity and stability of GD-TIL, even beating the state-of-the-art IL methods.<br><br>CONCLUSIONS: Our work illustrates the potential of MI-based BCI and generative AI for continuous neurorehabilitation.},
}
@article {pmid40001978,
year = {2025},
author = {Al-Nafjan, A and Alshehri, H and Aldayel, M},
title = {Objective Pain Assessment Using Deep Learning Through EEG-Based Brain-Computer Interfaces.},
journal = {Biology},
volume = {14},
number = {2},
pages = {},
pmid = {40001978},
issn = {2079-7737},
support = {(13461-imamu-2023-IMIU-R-3-1-HW-)//The Research, Development, and Innovation Authority (RDIA)-Kingdom of Saudi Arabia/ ; },
abstract = {Objective pain measurements are essential in clinical settings for determining effective treatment strategies. This study aims to utilize brain-computer interface technology for reliable pain classification and detection. We developed an electroencephalography-based pain detection system comprising two main components: (1) pain/no-pain detection and (2) pain severity classification across three levels: low, moderate, and high. Deep learning models, including convolutional neural networks and recurrent neural networks, were employed to classify the wavelet features extracted through time-frequency domain analysis. Furthermore, we compared the performance of our system against conventional machine learning models, such as support vector machines and random forest classifiers. Our deep learning approach outperformed the baseline models, achieving accuracies of 91.84% for pain/no-pain detection and 87.94% for pain severity classification, respectively.},
}
@article {pmid40000219,
year = {2024},
author = {He, W and Wang, D and Meng, Q and He, F and Xu, M and Ming, D},
title = {[Applications and prospects of electroencephalography technology in neurorehabilitation assessment and treatment].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {41},
number = {6},
pages = {1271-1278},
pmid = {40000219},
issn = {1001-5515},
mesh = {Humans ; *Electroencephalography/methods ; Brain-Computer Interfaces ; *Neurological Rehabilitation/methods ; Transcranial Magnetic Stimulation ; Transcranial Direct Current Stimulation ; *Nervous System Diseases/rehabilitation/diagnosis ; Epilepsy/diagnosis ; },
abstract = {With the high incidence of neurological diseases such as stroke and mental illness, rehabilitation treatments for neurological disorders have received widespread attention. Electroencephalography (EEG) technology, despite its excellent temporal resolution, has historically been limited in application due to its insufficient spatial resolution, and is mainly confined to preoperative assessment, intraoperative monitoring, and epilepsy detection. However, traditional constraints of EEG technology are being overcome with the popularization of EEG technology with high-density over 64-lead, the application of innovative analysis techniques and the integration of multimodal techniques, which are significantly broadening its applications in clinical settings. These advancements have not only reinforced the irreplaceable role of EEG technology in neurorehabilitation assessment, but also expanded its therapeutic potential through its combined use with technologies such as transcranial magnetic stimulation, transcranial electrical stimulation and brain-computer interfaces. This article reviewed the applications, advancements, and future prospects of EEG technology in neurorehabilitation assessment and treatment. Advancements in technology and interdisciplinary collaboration are expected to drive new applications and innovations in EEG technology within the neurorehabilitation field, providing patients with more precise and personalized rehabilitation strategies.},
}
@article {pmid40000217,
year = {2024},
author = {Yang, H and Li, T and Zhao, L and Chen, X and Pan, J and Fu, Y},
title = {[An emerging major: brain-computer interface major].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {41},
number = {6},
pages = {1257-1264},
pmid = {40000217},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces/trends ; Humans ; Electroencephalography ; *User-Computer Interface ; },
abstract = {Brain-computer interface (BCI) is a revolutionizing technology that disrupts traditional human-computer interaction by establishing direct communication and control between the brain and computer, bypassing the peripheral nervous and muscular systems. With the rapid advancement of BCI technology, growing application demands, and an increasing need for specialized BCI professionals, a new academic major-BCI major-has gradually emerged. However, few studies to date have discussed the interdisciplinary nature and training framework of this emerging major. To address this gap, this paper first introduced the application demands of BCI, including the demand for BCI technology in both medical and non-medical fields. The paper also described the interdisciplinary nature of the BCI major and the urgent need for specialized professionals in this field. Subsequently, a training program of the BCI major was presented, with careful consideration of the multidisciplinary nature of BCI research and development, along with recommendations for curriculum structure and credit distribution. Additionally, the facing challenges of the construction of the BCI major were analyzed, and suggested strategies for addressing these challenges were offered. Finally, the future of the BCI major was envisioned. It is hoped that this paper will provide valuable reference for the development and construction of the BCI major.},
}
@article {pmid40000203,
year = {2024},
author = {Wu, X and Chu, Y and Zhao, X and Zhao, Y},
title = {[Three-dimensional convolutional neural network based on spatial-spectral feature pictures learning for decoding motor imagery electroencephalography signal].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {41},
number = {6},
pages = {1145-1152},
pmid = {40000203},
issn = {1001-5515},
mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; *Imagination/physiology ; Signal Processing, Computer-Assisted ; Brain/physiology ; Convolutional Neural Networks ; },
abstract = {The brain-computer interface (BCI) based on motor imagery electroencephalography (EEG) shows great potential in neurorehabilitation due to its non-invasive nature and ease of use. However, motor imagery EEG signals have low signal-to-noise ratios and spatiotemporal resolutions, leading to low decoding recognition rates with traditional neural networks. To address this, this paper proposed a three-dimensional (3D) convolutional neural network (CNN) method that learns spatial-frequency feature maps, using Welch method to calculate the power spectrum of EEG frequency bands, converted time-series EEG into a brain topographical map with spatial-frequency information. A 3D network with one-dimensional and two-dimensional convolutional layers was designed to effectively learn these features. Comparative experiments demonstrated that the average decoding recognition rate reached 86.89%, outperforming traditional methods and validating the effectiveness of this approach in motor imagery EEG decoding.},
}
@article {pmid40000192,
year = {2025},
author = {Chen, Z and Huang, Y and Yu, H and Cao, C and Xu, M and Ming, D},
title = {[Research progress on the characteristics of magnetoencephalography signals in depression].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {1},
pages = {189-196},
pmid = {40000192},
issn = {1001-5515},
mesh = {Humans ; *Magnetoencephalography/methods ; *Brain/physiopathology ; *Depression/physiopathology/diagnosis ; Electroencephalography ; Magnetic Resonance Imaging ; },
abstract = {Depression, a mental health disorder, has emerged as one of the significant challenges in the global public health domain. Investigating the pathogenesis of depression and accurately assessing the symptomatic changes are fundamental to formulating effective clinical diagnosis and treatment strategies. Utilizing non-invasive brain imaging technologies such as functional magnetic resonance imaging and scalp electroencephalography, existing studies have confirmed that the onset of depression is closely associated with abnormal neural activities and altered functional connectivity in multiple brain regions. Magnetoencephalography, unaffected by tissue conductivity and skull thickness, boasts high spatial resolution and signal-to-noise ratio, offering unique advantages and significant value in revealing the abnormal brain mechanisms and neural characteristics of depression. This review, starting from the rhythmic characteristics, nonlinear dynamic features, and connectivity characteristics of magnetoencephalography in depression patients, revisits the research progress on magnetoencephalography features related to depression, discusses current issues and future development trends, and provides insights for the study of pathophysiological mechanisms, as well as for clinical diagnosis and treatment of depression.},
}
@article {pmid40000170,
year = {2025},
author = {Zhang, Y and Zhang, C and Sun, S and Xu, G},
title = {[Research on motor imagery recognition based on feature fusion and transfer adaptive boosting].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {1},
pages = {9-16},
pmid = {40000170},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; Humans ; Support Vector Machine ; *Algorithms ; Neural Networks, Computer ; *Imagination/physiology ; *Pattern Recognition, Automated/methods ; Electroencephalography ; Wavelet Analysis ; },
abstract = {This paper proposes a motor imagery recognition algorithm based on feature fusion and transfer adaptive boosting (TrAdaboost) to address the issue of low accuracy in motor imagery (MI) recognition across subjects, thereby increasing the reliability of MI-based brain-computer interfaces (BCI) for cross-individual use. Using the autoregressive model, power spectral density and discrete wavelet transform, time-frequency domain features of MI can be obtained, while the filter bank common spatial pattern is used to extract spatial domain features, and multi-scale dispersion entropy is employed to extract nonlinear features. The IV-2a dataset from the 4 [th] International BCI Competition was used for the binary classification task, with the pattern recognition model constructed by combining the improved TrAdaboost integrated learning algorithm with support vector machine (SVM), k nearest neighbor (KNN), and mind evolutionary algorithm-based back propagation (MEA-BP) neural network. The results show that the SVM-based TrAdaboost integrated learning algorithm has the best performance when 30% of the target domain instance data is migrated, with an average classification accuracy of 86.17%, a Kappa value of 0.723 3, and an AUC value of 0.849 8. These results suggest that the algorithm can be used to recognize MI signals across individuals, providing a new way to improve the generalization capability of BCI recognition models.},
}
@article {pmid39999624,
year = {2025},
author = {King, SE and Waddell, JT and McDonald, AE and Corbin, WR},
title = {Are you feeling what I'm feeling? Momentary interactions between personal and perceived peer subjective response predict craving and continued drinking in young adults.},
journal = {Drug and alcohol dependence},
volume = {270},
number = {},
pages = {112601},
pmid = {39999624},
issn = {1879-0046},
support = {F31 AA030167/AA/NIAAA NIH HHS/United States ; T32 DA039772/DA/NIDA NIH HHS/United States ; },
mesh = {Humans ; Female ; *Craving ; Adult ; *Peer Group ; Male ; *Alcohol Drinking/psychology ; Young Adult ; Adolescent ; Ecological Momentary Assessment ; },
abstract = {BACKGROUND: Subjective response to alcohol is a robust predictor of alcohol outcomes. It is possible that the perceived subjective response of others may influence concurrent experiences of one's own subjective response. However, no studies have examined how the perceived subjective response of others might interact with personal subjective response and how such interactions may influence levels of craving and subsequent drinking.<br><br>METHOD: Emerging adults (ages 18-25, N&#x2009;=&#x2009;131, 53.4&#x2009;% female) completed 21 days of ecological momentary assessments. During drinking events (N&#x2009;=&#x2009;1335) both personal and perceived peer subjective response (four domains encompassing high- and low-arousal positive &amp; negative effects) were assessed at drink initiation and two subsequent surveys 60 and 120min later. Current craving and drinking quantity since last report were also collected. Three-level multilevel structural equation models with Bayesian estimation tested indirect relations between subjective response and drinking continuation via craving and whether perceived subjective response moderated such relations.<br><br>RESULTS: Levels of both personal (b=0.029,95&#x2009;%BCI:[0.012,0.053]) and perceived (b=0.027,95&#x2009;%BCI:[0.012,0.051]) experiences of alcohol's rewarding, stimulating effects indirectly predicted drinking continuation via increased craving, and relations were potentiated when perceptions of peer reward were highest (b=0.015,95&#x2009;%BCI:[0.008,0.020]). Personal experiences of alcohol's relaxing, calming effects indirectly predicted a lower likelihood of drinking continuation via decreased craving (b=-0.017,95&#x2009;%BCI:[-0.036,-0.003]) whereas perceived effects directly predicted lower likelihoods of drinking (b=-0.133,95&#x2009;%CI:[-0.239, -0.031]).<br><br>CONCLUSION: Results suggest both personal and perceived peer subjective response independently influence drinking behavior even when controlling for one another. Targeted interventions focused on altering interpretations of peer subjective effects may be effective at reducing momentary risk.},
}
@article {pmid39997141,
year = {2025},
author = {Ha, J and Park, S and Han, Y and Kim, L},
title = {Hybrid BCI for Meal-Assist Robot Using Dry-Type EEG and Pupillary Light Reflex.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {10},
number = {2},
pages = {},
pmid = {39997141},
issn = {2313-7673},
support = {RS-2024-00340293//the National Research Foundation of Korea (NRF)/ ; },
abstract = {Brain-computer interface (BCI)-based assistive technologies enable intuitive and efficient user interaction, significantly enhancing the independence and quality of life of elderly and disabled individuals. Although existing wet EEG-based systems report high accuracy, they suffer from limited practicality. This study presents a hybrid BCI system combining dry-type EEG-based flash visual-evoked potentials (FVEP) and pupillary light reflex (PLR) designed to control an LED-based meal-assist robot. The hybrid system integrates dry-type EEG and eyewear-type infrared cameras, addressing the preparation challenges of wet electrodes, while maintaining practical usability and high classification performance. Offline experiments demonstrated an average accuracy of 88.59% and an information transfer rate (ITR) of 18.23 bit/min across the four target classifications. Real-time implementation uses PLR triggers to initiate the meal cycle and EMG triggers to detect chewing, indicating the completion of the cycle. These features allow intuitive and efficient operation of the meal-assist robot. This study advances the BCI-based assistive technologies by introducing a hybrid system optimized for real-world applications. The successful integration of the FVEP and PLR in a meal-assisted robot demonstrates the potential for robust and user-friendly solutions that empower the users with autonomy and dignity in their daily activities.},
}
@article {pmid39997117,
year = {2025},
author = {Premchand, B and Zhang, Z and Ang, KK and Yu, J and Tan, IO and Lam, JPW and Choo, AXY and Sidarta, A and Kwong, PWH and Chung, LHC},
title = {A Personalized Multimodal BCI-Soft Robotics System for Rehabilitating Upper Limb Function in Chronic Stroke Patients.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {10},
number = {2},
pages = {},
pmid = {39997117},
issn = {2313-7673},
support = {//Institute for Infocomm Research/ ; Research Grant RRG4/2008//Rehabilitation Research Institute of Singapore/ ; },
abstract = {Multimodal brain-computer interfaces (BCIs) that combine electrical features from electroencephalography (EEG) and hemodynamic features from functional near-infrared spectroscopy (fNIRS) have the potential to improve performance. In this paper, we propose a multimodal EEG- and fNIRS-based BCI system with soft robotic (BCI-SR) components for personalized stroke rehabilitation. We propose a novel method of personalizing rehabilitation by aligning each patient's specific abilities with the treatment options available. We collected 160 single trials of motor imagery using the multimodal BCI from 10 healthy participants. We identified a confounding effect of respiration in the fNIRS signal data collected. Hence, we propose to incorporate a breathing sensor to synchronize motor imagery (MI) cues with the participant's respiratory cycle. We found that implementing this respiration synchronization (RS) resulted in less dispersed readings of oxyhemoglobin (HbO). We then conducted a clinical trial on the personalized multimodal BCI-SR for stroke rehabilitation. Four chronic stroke patients were recruited to undergo 6 weeks of rehabilitation, three times per week, whereby the primary outcome was measured using upper-extremity Fugl-Meyer Motor Assessment (FMA) and Action Research Arm Test (ARAT) scores on weeks 0, 6, and 12. The results showed a striking coherence in the activation patterns in EEG and fNIRS across all patients. In addition, FMA and ARAT scores were significantly improved on week 12 relative to the pre-trial baseline, with mean gains of 8.75 ± 1.84 and 5.25 ± 2.17, respectively (mean ± SEM). These improvements were all better than the Standard Arm Therapy and BCI-SR group when retrospectively compared to previous clinical trials. These results suggest that personalizing the rehabilitation treatment leads to improved BCI performance compared to standard BCI-SR, and synchronizing motor imagery cues to respiration increased the consistency of HbO levels, leading to better motor imagery performance. These results showed that the proposed multimodal BCI-SR holds promise to better engage stroke patients and promote neuroplasticity for better motor improvements.},
}
@article {pmid39996608,
year = {2025},
author = {Hjortkjær, J and Wong, DDE and Catania, A and Märcher-Rørsted, J and Ceolini, E and Fuglsang, SA and Kiselev, I and Di Liberto, G and Liu, SC and Dau, T and Slaney, M and de Cheveigné, A},
title = {Real-time control of a hearing instrument with EEG-based attention decoding.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ad867c},
pmid = {39996608},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods/instrumentation ; *Attention/physiology ; *Brain-Computer Interfaces ; *Speech Perception/physiology ; *Hearing Aids ; Acoustic Stimulation/methods ; Computer Systems ; },
abstract = {Enhancing speech perception in everyday noisy acoustic environments remains an outstanding challenge for hearing aids. Speech separation technology is improving rapidly, but hearing devices cannot fully exploit this advance without knowing which sound sources the user wants to hear. Even with high-quality source separation, the hearing aid must know which speech streams to enhance and which to suppress. Advances in EEG-based decoding of auditory attention raise the potential of neurosteering, in which a hearing instrument selectively enhances the sound sources that a hearing-impaired listener is focusing their attention on. Here, we present and discuss a real-time brain-computer interface system that combines a stimulus-response model based on canonical correlation analysis for real-time EEG attention decoding, coupled with a multi-microphone hardware platform enabling low-latency real-time speech separation through spatial beamforming. We provide an overview of the system and its various components, discuss prospects and limitations of the technology, and illustrate its application with case studies of listeners steering acoustic feedback of competing speech streams via real-time attention decoding. A software implementation code of the system is publicly available for further research and explorations.},
}
@article {pmid39996071,
year = {2025},
author = {Samadi, E and Rahatabad, FN and Nasrabadi, AM and Dabanlou, NJ},
title = {Brain analysis to approach human muscles synergy using deep learning.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {44},
pmid = {39996071},
issn = {1871-4080},
abstract = {Brain signals and muscle movements have been analyzed using electroencephalogram (EEG) data in several studies. EEG signals contain a lot of noise, such as electromyographic (EMG) waves. Further studies have been done to improve the quality of the results, though it is thought that the combination of these two signals can lead to a significant improvement in the synergistic analysis of muscle movements and muscle connections. Using graph theory, this study examined the interaction of EMG and EEG signals during hand movement and estimated the synergy between muscle and brain signals. Mapping of the brain diagram was also developed to reconstruct the muscle signals from the muscle connections in the brain diagram. The proposed method included noise removal from EEG and EMG signals, graph feature analysis from EEG, and synergy calculation from EMG. Two methods were used to estimate synergy. In the first method, after calculating the brain connections, the features of the communication graph were extracted and then synergy estimating was made with neural networks. In the second method, a convolutional network created a transition from the matrix of brain connections to the synergistic EMG signal. This study reached the high correlation values of 99.8% and maximum MSE error of 0.0084. Compared to other graph-based methods, this method based on regression analysis had a very significant performance. This research can lead to the improvement of rehabilitation methods and brain-computer interfaces.},
}
@article {pmid39994209,
year = {2025},
author = {Guan, S and Dong, T and Cong, LK},
title = {Method for EEG signal recognition based on multi-domain feature fusion and optimization of multi-kernel extreme learning machine.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {6601},
pmid = {39994209},
issn = {2045-2322},
support = {20220508014RC//Project supported by Jilin Provincial Science and Technology Development Plan Project/ ; },
abstract = {In response to the current issues of one-sided effective feature extraction and low classification accuracy in multi-class motor imagery recognition, this study proposes an Electroencephalogram (EEG) signal recognition method based on multi-domain feature fusion and optimized multi-kernel extreme learning machine. Firstly, the EEG signals are preprocessed using the Improved Comprehensive Ensemble Empirical Mode Decomposition (ICEEMD) algorithm combined with the Pearson correlation coefficient to eliminate noise and interference. Secondly, multivariate autoregressive (MVAR) model, wavelet packet decomposition, and Riemannian geometry methods are used to extract features from the time domain, frequency domain, and spatial domain, respectively, to construct a joint time-frequency-space feature vector. Subsequently, kernel principal component analysis (KPCA) is employed to fuse and reduce the dimensionality of the joint features, resulting in a reduced-dimensional fused feature vector. Finally, these feature vectors are input into a Radius-incorporated multi-kernel extreme learning machine (RIO-MKELM) for classification. The experimental results indicate that through multi-domain feature fusion and the incorporation of radius in a multi-kernel extreme learning machine, feature selection can be performed more effectively, eliminating redundant or irrelevant features and retaining the most useful information for classification. This approach enhances classification accuracy and other evaluation metrics, with the final classification accuracy reaching 95.49%, sensitivity at 97.88%, specificity at 98.12%, recall at 97.88%, and F1 Score at 96.67%. The findings of this study are of significant importance for the development and practical application of brain-computer interface (BCI) systems.},
}
@article {pmid39993988,
year = {2025},
author = {Guo, J and Yang, J and Li, Y},
title = {[Analysis of Brain-Computer Interface Technology in the Medical Field and the Regulation of the US FDA].},
journal = {Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation},
volume = {49},
number = {1},
pages = {96-102},
doi = {10.12455/j.issn.1671-7104.240187},
pmid = {39993988},
issn = {1671-7104},
mesh = {*Brain-Computer Interfaces ; United States ; United States Food and Drug Administration ; Humans ; Electroencephalography ; },
abstract = {Brain-computer interface (BCI) technology is an innovative and cutting-edge medical advancement that enables direct interaction between the brain and external devices, facilitating the reconstruction of daily functions for patients or serving as a method for neuro-regulation therapy. Although this technology offers a broad range of clinical applications, there are problems as potential risks, individual variations, and the need for long-term monitoring of its effects during utilization. Consequently, the comprehensive evaluation of its safety and effectiveness poses a considerable challenge for regulatory agencies. This study provides a concise introduction to the development history and various types of BCI technology, followed by a summary of the regulatory situation for different types of BCI medical devices in the United States. Furthermore, the regulatory requirements imposed by the US FDA on this product category are analyzed. Finally, the article concludes by presenting a summary and future perspective on the current development of BCI technology, with the aim of offering beneficial insights and guidance for the regulation of BCI medical devices.},
}
@article {pmid39993333,
year = {2025},
author = {Kunigk, NG and Schone, HR and Gontier, C and Hockeimer, W and Tortolani, AF and Hatsopoulos, NG and Downey, JE and Chase, SM and Boninger, ML and Dekleva, BD and Collinger, JL},
title = {Motor somatotopy impacts imagery strategy success in human intracortical brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
pmid = {39993333},
issn = {1741-2552},
support = {R01 NS121079/NS/NINDS NIH HHS/United States ; UH3 NS107714/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Motor Cortex/physiology ; Male ; *Imagination/physiology ; Adult ; Female ; Movement/physiology ; Electrodes, Implanted ; *Brain Mapping/methods ; Middle Aged ; Spinal Cord Injuries/physiopathology/rehabilitation ; Arm/physiology ; },
abstract = {Objective:The notion of a somatotopically organized motor cortex, with movements of different body parts being controlled by spatially distinct areas of cortex, is well known. However, recent studies have challenged this notion and suggested a more distributed representation of movement control. This shift in perspective has significant implications, particularly when considering the implantation location of electrode arrays for intracortical brain-computer interfaces (iBCIs). We sought to evaluate whether the location of neural recordings from the precentral gyrus, and thus the underlying somatotopy, has any impact on the imagery strategies that can enable successful iBCI control.Approach:Three individuals with a spinal cord injury were enrolled in an ongoing clinical trial of an iBCI. Participants had two intracortical microelectrode arrays implanted in the arm and/or hand areas of the precentral gyrus based on presurgical functional imaging. Neural data were recorded while participants attempted to perform movements of the hand, wrist, elbow, and shoulder.Main results:We found that electrode arrays that were located more medially recorded significantly more activity during attempted proximal arm movements (elbow, shoulder) than did lateral arrays, which captured more activity related to attempted distal arm movements (hand, wrist). We also evaluated the relative contribution from the two arrays implanted in each participant to decoding accuracy during calibration of an iBCI decoder for translation and grasping tasks. For both task types, imagery strategy (e.g. reaching vs wrist movements) had a significant impact on the relative contributions of each array to decoding. Overall, we found some evidence of broad tuning to arm and hand movements; however, there was a clear bias in the amount of information accessible about each movement type in spatially distinct areas of cortex.Significance:These results demonstrate that classical concepts of somatotopy can have real consequences for iBCI use, and highlight the importance of considering somatotopy when planning iBCI implantation.},
}
@article {pmid39992333,
year = {2024},
author = {Qin, HJ and Liu, YY and Fu, EH and Liu, YW and Tian, ZL and Dong, HW and Liu, TA and Zou, DH and Cheng, YB and Liu, NG},
title = {Intelligent Recognition and Segmentation of Blunt Craniocerebral Injury CT Images Based on DeepLabV3+ Model.},
journal = {Fa yi xue za zhi},
volume = {40},
number = {5},
pages = {419-429},
doi = {10.12116/j.issn.1004-5619.2024.440801},
pmid = {39992333},
issn = {1004-5619},
mesh = {Humans ; *Tomography, X-Ray Computed/methods ; *Neural Networks, Computer ; *Deep Learning ; *Head Injuries, Closed/diagnostic imaging ; Skull Fractures/diagnostic imaging ; *Craniocerebral Trauma/diagnostic imaging ; Forensic Medicine/methods ; *Image Processing, Computer-Assisted/methods ; Brain Contusion/diagnostic imaging ; Hematoma, Subdural/diagnostic imaging ; Hematoma, Epidural, Cranial/diagnostic imaging ; },
abstract = {OBJECTIVES: To achieve intelligent recognition and segmentation of common craniocerebral injuries (hereinafter referred to as "segmentation") by training convolutional neural network DeepLabV3+ model based on CT images of blunt craniocerebral injury (BCI), and to explore the value of deep learning in automated diagnosis of BCI in forensic medicine.<br><br>METHODS: A total of 5 486 CT images of BCI from living persons were collected as the training set, validation set and test set for model training and performance evaluation. Another 255 CT images of BCI and 156 normal craniocerebral CT images from living persons were collected as the blind test set to evaluate the ability of the model to segment the five types of craniocerebral injuries including scalp hematoma, skull fracture, epidural hematoma, subdural hematoma, and brain contusion. Another 340 BCI and 120 normal craniocerebral CT images from cadavers were collected as the new blind test set to explore the application value of the model trained by living CT images in the segmentation of BCI in cadavers. The five types CT images of all BCI except the blind test set were manually labeled; then, each dataset was inputted into the model to train the model. The performance of the model was evaluated and optimized based on the loss function and accuracy curves of the training set and validation set, and the generalization ability was evaluated based on the Dice value of the test set. According to the accuracy, precision and F1 value of the blind test set, the segmentation performance of the model for five types of BCI was evaluated.<br><br>RESULTS: After training and optimizing the model, the average Dice values of the final optimal model to scalp hematoma, skull fracture, epidural hematoma, subdural hematoma and brain contusion segmentation were 0.766 4, 0.812 3, 0.938 7, 0.782 7 and 0.858 1, respectively, all greater than 0.75, meeting the expected requirements. External validation showed that the F1 values were 93.02%, 89.80%, 87.80%, 92.93% and 86.57% in living CT images, respectively; 83.92%, 44.90%, 76.47%, 64.29% and 48.89% in cadaveric CT images, respectively. The above suggested that the model was able to accurately segment various types of craniocerebral injury on living CT images, while its segmentation ability was relatively poor on cadaveric CT images, but still able to accurately segment scalp hematoma, epidural hematoma and subdural hematoma.<br><br>CONCLUSIONS: Deep learning model trained on CT images can be used for BCI segmentation. However, the direct use of living persons' BCI models for the identification of cadaveric BCI has some limitations. This study provides a new approach for intelligent segmentation of virtual anatomical data for BCI.},
}
@article {pmid39992067,
year = {2025},
author = {Wan, C and Zhang, W and Nie, Y and Qian, Y and Wang, J and Xu, H and Li, Z and Su, B and Zhang, Y and Li, Y},
title = {Impact of motor imagery-based brain-computer interface combined with virtual reality on enhancing attention, executive function, and lower-limb function in stroke: A pilot study.},
journal = {PM &amp; R : the journal of injury, function, and rehabilitation},
volume = {17},
number = {7},
pages = {811-821},
doi = {10.1002/pmrj.13324},
pmid = {39992067},
issn = {1934-1563},
support = {No.303103136AA22//National Clinical Medical Research Centre Cultivation Program of Nanjing/ ; No.ST242102//Major sports research projects of Jiangsu Sports Bureau/ ; No.JBGS202414//Jiangsu Province Hospital clinical diagnosis and treatment of technological innovation "Open bidding for selecting the best candidates" project/ ; },
mesh = {Humans ; Male ; Female ; *Stroke Rehabilitation/methods ; Pilot Projects ; *Brain-Computer Interfaces ; Middle Aged ; *Attention/physiology ; *Lower Extremity/physiopathology ; *Executive Function/physiology ; *Virtual Reality ; *Stroke/physiopathology/psychology/diagnosis ; Aged ; Feasibility Studies ; Recovery of Function ; Adult ; *Imagery, Psychotherapy/methods ; *Imagination/physiology ; },
abstract = {BACKGROUND: Brain-computer interface combined with virtual reality (BCI-VR) can reduce the difficulty of motor imagery execution and improve training performance. Few studies have focused on the effects of BCI-VR on attention, executive function, and lower-limb function in stroke.<br><br>OBJECTIVE: To evaluate feasibility and preliminary efficacy of BCI-VR pedaling training on the attention, executive function, and lower-extremity function in people after stroke. It will also provide data support for future research, especially sample size calculations.<br><br>DESIGN: A single group before-after trial design was used. All participants had a stable level of function over a 2-week period to ensure that their functional recovery was all attributable to BCI-VR training.<br><br>SETTING: The study was conducted in a specialized rehabilitation hospital.<br><br>PARTICIPANTS: Twelve participants with stroke, a certain level of motor imagery ability, capable of walking 10 meters continuously.<br><br>INTERVENTIONS: All participants received a 4-week BCI-VR pedaling training program, 5&#x2009;days per week, 30&#x2009;minutes each session.<br><br>OUTCOME MEASURES: Primary outcomes are feasibility and safety. Secondary outcomes were lower-extremity mobility, attention, and executive functions.<br><br>RESULTS: Twelve patients were recruited from inpatient rehabilitation and nine completed the study (six males/three females; 56.6&#x2009;&#xb1;&#x2009;11.6&#x2009;years). Recruitment and retention rates were 34% and 75%, respectively. Excellent adherence rate (97.7%) was obtained. No adverse events or equipment issues were reported. Following the intervention, significant improvements were found in the lower-extremity strength, balance, walking stability, attention, and general cognitive function (p&#x2009;<&#x2009;.05). A significant correlation was found between improved Berg balance scale change values and symbol digit modalities test change values (p&#x2009;<&#x2009;.05, r&#x2009;=&#x2009;0.677).<br><br>CONCLUSIONS: BCI-VR pedaling training provides a depth of feasibility and safety data, methodological detail, and preliminary results. This could provide a useful basis for future studies of BCI-VR pedaling training for stroke rehabilitation.<br><br>CLINICALTRIALS: gov registration number: ChiCTR2300071522 (http://www.chictr.org.cn).},
}
@article {pmid39990438,
year = {2025},
author = {Lin, Z and Marin-Llobet, A and Baek, J and He, Y and Lee, J and Wang, W and Zhang, X and Lee, AJ and Liang, N and Du, J and Ding, J and Li, N and Liu, J},
title = {Spike sorting AI agent.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {39990438},
issn = {2692-8205},
support = {DP1 DK130673/DK/NIDDK NIH HHS/United States ; R01 LM014465/LM/NLM NIH HHS/United States ; },
abstract = {Spike sorting is a fundamental process for decoding neural activity, involving preprocessing, spike detection, feature extraction, clustering, and validation. However, conventional spike sorting methods are highly fragmented, labor-intensive, and heavily reliant on expert manual curation, limiting their scalability and reproducibility. This challenge has become more pressing with advances in neural recording technology, such as high-density Neuropixels for large-scale neural recording or flexible electrodes for long-term stable recording over months to years. The volume and complexity of these datasets make manual curation infeasible, requiring an automated and scalable solution. Here, we introduce SpikeAgent, a multimodal large language model (LLM)-based AI agent that automates and standardizes the entire spike sorting pipeline. Unlike traditional approaches, SpikeAgent integrates multiple LLM backends, coding functions, and established algorithms, autonomously performing spike sorting with reasoning-based decision-making and real-time interaction with intermediate results. It generates interpretable reports, providing transparent justifications for each sorting decision, enhancing transparency and reliability. We benchmarked SpikeAgent against human experts across various neural recording technology, demonstrating its versatility and ability to achieve curation consistency that are equal to, or even higher than human experts. It also drastically reduces the expertise barrier and accelerates the curation and validation time by orders of magnitude. Moreover, it enables automated interpretability of the neural spiking data, which cannot be achieved by any conventional methods. SpikeAgent presents a paradigm shift in processing signals for neuroscience and brain-computer interfaces, while laying the ground for AI agent-augmented science across various domains.},
}
@article {pmid39990331,
year = {2025},
author = {Pescatore, CRC and Zhang, H and Hadjinicolaou, AE and Paulk, AC and Rolston, JD and Richardson, RM and Williams, ZM and Cai, J and Cash, SS},
title = {Decoding semantics from natural speech using human intracranial EEG.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {39990331},
issn = {2692-8205},
support = {R01 DC019653/DC/NIDCD NIH HHS/United States ; U01 NS098968/NS/NINDS NIH HHS/United States ; },
abstract = {Brain-computer interfaces (BCIs) hold promise for restoring natural language production capabilities in patients with speech impairments, potentially enabling smooth conversation that conveys meaningful information via synthesized words. While considerable progress has been made in decoding phonetic features of speech, our ability to extract lexical semantic information (i.e. the meaning of individual words) from neural activity remains largely unexplored. Moreover, most existing BCI research has relied on controlled experimental paradigms rather than natural conversation, limiting our understanding of semantic decoding in ecological contexts. Here, we investigated the feasibility of decoding lexical semantic information from stereo-electroencephalography (sEEG) recordings in 14 participants during spontaneous conversation. Using multivariate pattern analysis, we were able to decode word level semantic features during language production with an average accuracy of 21% across all participants compared to a chance level of 10%. This semantic decoding remained robust across different semantic representations while maintaining specificity to semantic features. Further, we identified a distributed left-lateralized network spanning precentral gyrus, pars triangularis, and middle temporal cortex, with low-frequency oscillations showing stronger contributions. Together, our results establish the feasibility of extracting word meanings from neural activity during natural speech production and demonstrate the potential for decoding semantic content from unconstrained speech.},
}
@article {pmid39989959,
year = {2025},
author = {Shoffstall, A and Li, L and Hartzler, A and Menendez-Lustri, D and Zhang, J and Chen, A and Lam, D and Traylor, B and Quill, E and Hoeferlin, G and Pawlowski, C and Bruckman, M and Gupta, SA and Capadona, J},
title = {Dexamethasone-loaded platelet-inspired nanoparticles improve intracortical microelectrode recording performance.},
journal = {Research square},
volume = {},
number = {},
pages = {},
pmid = {39989959},
issn = {2693-5015},
support = {I01 RX003420/RX/RRD VA/United States ; R01 HL121212/HL/NHLBI NIH HHS/United States ; T32 EB004314/EB/NIBIB NIH HHS/United States ; },
abstract = {Long-term robust intracortical microelectrode (IME) neural recording quality is negatively affected by the neuroinflammatory response following microelectrode insertion. This adversely impacts brain-machine interface (BMI) performance for patients with neurological disorders or amputations. Recent studies suggest that the leakage of blood-brain barrier (BBB) and microhemorrhage caused by the IME insertions lead to the increased neuroinflammation and reduced neural recording performance. Additionally, a sustained presence of activated platelets and coagulation factors is found near the insertion site. Thus, we hypothesized that the systemic administration of dexamethasone sodium phosphate-loaded platelet-inspired nanoparticle (SPPINDEX) can improve the neural recording performance of intracortical microelectrodes (IMEs) by promoting hemostasis, facilitating blood-brain barrier (BBB) healing, and achieving implant-targeted drug delivery. Leveraging the hemostatic and coagulation factor-binding properties of the platelet-inspired nanoparticle (PIN) drug delivery platform, SPPINDEX treatment can initially attenuate the invasion of neuroinflammatory triggers into the brain parenchyma caused by insertion-induced microhemorrhages or a compromised BBB. Furthermore, targeted delivery of the anti-inflammatory drug dexamethasone sodium phosphate (DEXSP) to the implant site via these nanoparticles can attenuate ongoing neuroinflammation, enhancing overall therapeutic efficacy. Weekly treatment with SPPINDEX for 8 weeks significantly improved the recording capabilities of IMEs compared to platelet-inspired nanoparticles alone (PIN), free dexamethasone sodium phosphate (Free DEXSP), and a diluent control trehalose buffer (TH), as assessed through extracellular single-unit recordings. Immunohistochemical analyses of neuron density, activated microglia/macrophage density, astrocyte density, and BBB permeability suggest that the improved neural recording performance may be attributed to reduced neuron degeneration, activated microglia and astrocytes at the implant interface caused by the decreased infiltration of blood-derived proteins that trigger neuroinflammation and the therapeutic effects from DEXSP. Overall, SPPINDEX treatment promotes an anti-inflammatory environment that improves neuronal density and enhances recording performance.},
}
@article {pmid39988822,
year = {2025},
author = {Park, J and Ahn, J and Choi, J and Kim, J},
title = {Mol-AIR: Molecular Reinforcement Learning with Adaptive Intrinsic Rewards for Goal-Directed Molecular Generation.},
journal = {Journal of chemical information and modeling},
volume = {65},
number = {5},
pages = {2283-2296},
pmid = {39988822},
issn = {1549-960X},
mesh = {*Drug Discovery/methods ; Artificial Intelligence ; Deep Learning ; *Reward ; *Machine Learning ; },
abstract = {Optimizing techniques for discovering molecular structures with desired properties is crucial in artificial intelligence (AI)-based drug discovery. Combining deep generative models with reinforcement learning has emerged as an effective strategy for generating molecules with specific properties. Despite its potential, this approach is ineffective in exploring the vast chemical space and optimizing particular chemical properties. To overcome these limitations, we present Mol-AIR, a reinforcement learning-based framework using adaptive intrinsic rewards for effective goal-directed molecular generation. Mol-AIR leverages the strengths of both history-based and learning-based intrinsic rewards by exploiting random distillation network and counting-based strategies. In benchmark tests, Mol-AIR demonstrates improved performance over existing approaches in generating molecules having the desired properties, including penalized LogP, QED, and celecoxib similarity, without any prior knowledge. We believe that Mol-AIR represents a significant advancement in drug discovery, offering a more efficient path to discovering novel therapeutics.},
}
@article {pmid39987217,
year = {2025},
author = {Wang, X and Chen, S and Wang, K and Cao, L},
title = {Predicted action-effects shape action representation through pre-activation of alpha oscillations.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {275},
pmid = {39987217},
issn = {2399-3642},
support = {32271078//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; Male ; Female ; Adult ; Young Adult ; *Alpha Rhythm/physiology ; Attention/physiology ; *Feedback, Sensory/physiology ; *Psychomotor Performance/physiology ; *Visual Cortex/physiology ; },
abstract = {Actions are typically accompanied by sensory feedback (or action-effects). Action-effects, in turn, influence the action. Theoretical accounts of action control assume a pre-activation of action-effects prior to action execution. Here we show that when participants were asked to report the time of their voluntary keypress using the position of a fast-rotating clock hand, a predictable action-effect (i.e. a 250 ms delayed sound after keypress) led to a shift of visuospatial attention towards the clock hand position of action-effect onset, thus demonstrating an influence of action-effects on action representation. Importantly, the attention shift occurred about 1 second before the action execution, which was further preceded and predicted by a lateralisation of alpha oscillations in the visual cortex. Our results indicate that when the spatial location is the key feature of action-effects, the neural implementation of the action-effect pre-activation is achieved through alpha lateralisation.},
}
@article {pmid39986990,
year = {2025},
author = {Ding, N},
title = {Sequence chunking through neural encoding of ordinal positions.},
journal = {Trends in cognitive sciences},
volume = {29},
number = {7},
pages = {641-654},
doi = {10.1016/j.tics.2025.01.014},
pmid = {39986990},
issn = {1879-307X},
mesh = {Humans ; *Brain/physiology ; Cues ; Animals ; },
abstract = {Grouping sensory events into chunks is an efficient strategy to integrate information across long sequences such as speech, music, and complex movements. Although chunks can be constructed based on diverse cues (e.g., sensory features, statistical patterns, internal knowledge) recent studies have consistently demonstrated that the chunks constructed by different cues are all tracked by low-frequency neural dynamics. Here, I review evidence that chunking cues drive low-frequency activity in modality-dependent networks, which interact to generate chunk-tracking activity in broad brain areas. Functionally, this work suggests that a core computation underlying sequence chunking may assign each event its ordinal position within a chunk and that this computation is causally implemented by chunk-tracking neural activity during predictive sequence chunking.},
}
@article {pmid39986550,
year = {2025},
author = {Zhao, W and Rao, J and Wang, R and Chai, Y and Mao, T and Quan, P and Deng, Y and Chen, W and Wang, S and Guo, B and Zhang, Q and Rao, H},
title = {Test-retest reliability of coupling between cerebrospinal fluid flow and global brain activity after normal sleep and sleep deprivation.},
journal = {NeuroImage},
volume = {309},
number = {},
pages = {121097},
doi = {10.1016/j.neuroimage.2025.121097},
pmid = {39986550},
issn = {1095-9572},
mesh = {Humans ; *Sleep Deprivation/physiopathology/cerebrospinal fluid ; Male ; Adult ; Female ; Reproducibility of Results ; Young Adult ; *Brain/physiology/diagnostic imaging/physiopathology ; *Sleep/physiology ; *Glymphatic System/physiology/diagnostic imaging/physiopathology ; *Cerebrospinal Fluid/physiology ; Magnetic Resonance Imaging/methods ; *Nerve Net/diagnostic imaging ; },
abstract = {The glymphatic system (GS) plays a key role in maintaining brain homeostasis by clearing metabolic waste during sleep, with the coupling between global blood-oxygen-level-dependent (gBOLD) and cerebrospinal fluid (CSF) signals serving as a potential marker for glymphatic clearance function. However, the test-retest reliability and spatial heterogeneity of gBOLD-CSF coupling after different sleep conditions remain unclear. In this study, we assessed the test-retest reliability of gBOLD-CSF coupling following either normal sleep or total sleep deprivation (TSD) in 64 healthy adults under controlled laboratory conditions. The reliability was high after normal sleep (ICC = 0.763) but decreased following TSD (ICC = 0.581). Moreover, spatial heterogeneity was evident in participants with normal sleep, with lower-order networks (visual, somatomotor, and attention) showing higher ICC values compared to higher-order networks (default-mode, limbic, and frontoparietal). This spatial variation was less distinct in the TSD group. These results demonstrate the robustness of the gBOLD-CSF coupling method and emphasize the significance of considering sleep history in glymphatic function research.},
}
@article {pmid39985774,
year = {2025},
author = {Zhang, D and Wang, Z and Qian, Y and Zhao, Z and Liu, Y and Lu, J and Li, Y},
title = {Protocol to perform offline ECoG brain-to-text decoding for natural tonal sentences.},
journal = {STAR protocols},
volume = {6},
number = {1},
pages = {103650},
pmid = {39985774},
issn = {2666-1667},
mesh = {Humans ; *Electrocorticography/methods ; *Brain/physiology ; Language ; Speech/physiology ; },
abstract = {Here, we present a protocol to decode Mandarin sentences from invasive neural recordings using a brain-to-text framework. We describe steps for preparing materials, including designing the sentence corpus and setting up electrocorticography (ECoG) recording systems. We then detail procedures for decoding, such as data preprocessing, selection of speech-responsive electrodes, speech detection, syllable and tone decoding, and language modeling. We also outline performance evaluation metrics. For complete details on the use and execution of this protocol, please refer to Zhang et al.[1].},
}
@article {pmid39984530,
year = {2025},
author = {Liu, Y and Gui, Z and Yan, D and Wang, Z and Gao, R and Han, N and Chen, J and Wu, J and Ming, D},
title = {Lower limb motor imagery EEG dataset based on the multi-paradigm and longitudinal-training of stroke patients.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {314},
pmid = {39984530},
issn = {2052-4463},
support = {62273251//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Electroencephalography ; *Brain-Computer Interfaces ; *Stroke/physiopathology ; *Stroke Rehabilitation ; *Lower Extremity/physiopathology ; Male ; Middle Aged ; Female ; Aged ; },
abstract = {Motor dysfunction is one of the most significant sequelae of stroke, with lower limb impairment being a major concern for stroke patients. Motor imagery (MI) technology based on brain-computer interface (BCI) offers promising rehabilitation potential for stroke patients by activating motor-related brain areas. However, developing a robust BCI-MI system and uncovering the underlying mechanisms of neural plasticity during stroke recovery through such systems requires large-scale datasets. These datasets are particularly needed for accurate lower limb MI in stroke patients and for longitudinal data reflecting the rehabilitation process. This study addresses this gap by collecting EEG data from 27 stroke patients, covering two enhanced paradigms and three different time points. The dataset includes raw EEG signals, preprocessed data, and patient information. An initial analysis using CSP-SVM on the dataset yielded an average classification accuracy of 80.50%. We anticipate that this dataset will facilitate research into brain neuroplasticity in stroke patients, aid in the development of decoding algorithms for lower limb stroke, and contribute to the establishment of comprehensive stroke rehabilitation systems.},
}
@article {pmid39983772,
year = {2025},
author = {Wang, M and Zhang, Y and Wang, A and Gan, Z and Zhang, L and Kang, X},
title = {Soft neural interface with color adjusted PDMS encapsulation layer for spinal cord stimulation.},
journal = {Journal of neuroscience methods},
volume = {417},
number = {},
pages = {110402},
doi = {10.1016/j.jneumeth.2025.110402},
pmid = {39983772},
issn = {1872-678X},
mesh = {Animals ; *Dimethylpolysiloxanes/chemistry ; Mice ; *Spinal Cord Stimulation/instrumentation/methods ; Electrodes, Implanted ; Color ; *Spinal Cord/physiology ; Microtechnology/methods ; },
abstract = {BACKGROUND: Spinal cord stimulation (SCS) plays a crucial role in treating various neurological diseases. Utilizing soft spinal cord electrodes in SCS allows for a better fit with the physiological structure of the spinal cord and reduces tissue damage. Polydimethylsiloxane (PDMS) has emerged as an ideal material for soft bioelectronics. However, micromachining soft PDMS bioelectronics devices with low thermal effects and high uniformity remains challenging.<br><br>NEW METHOD: Here, we demonstrated a fully laser-micromachined soft neural interface for SCS. The native and color adjusted PDMS with variable absorbance characteristics were investigated in laser processing. In addition, we systematically evaluated the impact of electrode sizes on the electrochemical performance of neural interface. By fitting the equivalent circuit model, the electrochemical process of neural interface was revealed and the performance of the electrode was evaluated. The biocompatibility of color adjusted PDMS was confirmed by cytotoxicity assays. Finally, we validated the neural interface in mice.<br><br>RESULTS: Color adjusted PDMS has good biocompatibility and can significantly reduce the damage caused by thermal effects, enhancing the electrochemical performance of bioelectronic devices. The soft neural interface with color adjusted PDMS encapsulation layer can activate the motor function safely.<br><br>The fully laser-micromachined soft neural interface was proposed for the first time. Compared with existing methods, this method showed low thermal effects, high uniformity, and could be easily scaled up.<br><br>CONCLUSIONS: The fully laser-micromachined soft neural interface device with color adjusted PDMS encapsulation layer shows great promise for applications in SCS.},
}
@article {pmid39983236,
year = {2025},
author = {Phang, CR and Hirata, A},
title = {Explainable multiscale temporal convolutional neural network model for sleep stage detection based on electroencephalogram activities.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adb90c},
pmid = {39983236},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; *Neural Networks, Computer ; *Sleep Stages/physiology ; Polysomnography/methods ; Male ; Adult ; Female ; Convolutional Neural Networks ; },
abstract = {Objective.Humans spend a significant portion of their lives in sleep (an essential driver of body metabolism). Moreover, as sleep deprivation could cause various health complications, it is crucial to develop an automatic sleep stage detection model to facilitate the tedious manual labeling process. Notably, recently proposed sleep staging algorithms lack model explainability and still require performance improvement.Approach.We implemented multiscale neurophysiology-mimicking kernels to capture sleep-related electroencephalogram (EEG) activities at varying frequencies and temporal lengths; the implemented model was named 'multiscale temporal convolutional neural network (MTCNN).' Further, we evaluated its performance using an open-source dataset (Sleep-EDF Database Expanded comprising 153 d of polysomnogram data).Main results.By investigating the learned kernel weights, we observed that MTCNN detected the EEG activities specific to each sleep stage, such as the frequencies, K-complexes, and sawtooth waves. Furthermore, regarding the characterization of these neurophysiologically significant features, MTCNN demonstrated an overall accuracy (OAcc) of 91.12% and a Cohen kappa coefficient of 0.86 in the cross-subject paradigm. Notably, it demonstrated an OAcc of 88.24% and a Cohen kappa coefficient of 0.80 in the leave-few-days-out analysis. Our MTCNN model also outperformed the existing deep learning models in sleep stage classification even when it was trained with only 16% of the total EEG data, achieving an OAcc of 85.62% and a Cohen kappa coefficient of 0.75 on the remaining 84% of testing data.Significance.The proposed MTCNN enables model explainability and it can be trained with lesser amount of data, which is beneficial to its application in the real-world because large amounts of training data are not often and readily available.},
}
@article {pmid39983235,
year = {2025},
author = {T A, S and R, S and Vinod, AP and Alladi, S},
title = {On the feasibility of an online brain-computer interface-based neurofeedback game for enhancing attention and working memory in stroke and mild cognitive impairment patients.},
journal = {Biomedical physics &amp; engineering express},
volume = {11},
number = {2},
pages = {},
doi = {10.1088/2057-1976/adb8ef},
pmid = {39983235},
issn = {2057-1976},
mesh = {Humans ; *Neurofeedback/methods ; Male ; *Cognitive Dysfunction/physiopathology/therapy/rehabilitation ; *Memory, Short-Term ; *Brain-Computer Interfaces ; *Attention ; Female ; Electroencephalography ; *Stroke/physiopathology ; Middle Aged ; Aged ; Feasibility Studies ; },
abstract = {Background. Neurofeedback training (NFT) using Electroencephalogram-based Brain Computer Interface (EEG-BCI) is an emerging therapeutic tool for enhancing cognition.Methods. We developed an EEG-BCI-based NFT game for enhancing attention and working memory of stroke and Mild cognitive impairment (MCI) patients. The game involves a working memory task during which the players memorize locations of images in a matrix and refill them correctly using their attention levels. The proposed NFT was conducted across fifteen participants (6 Stroke, 7 MCI, and 2 non-patients). The effectiveness of the NFT was evaluated using the percentage of correctly filled matrix elements and EEG-based attention score. EEG varitions during working memory tasks were also investigated using EEG topographs and EEG-based indices.Results. The EEG-based attention score showed an enhancement ranging from 4.29-32.18% in the Stroke group from the first session to the third session, while in the MCI group, the improvement ranged from 4.32% to 48.25%. We observed significant differences in EEG band powers during working memory operation between the stroke and MCI groups.Significance. The proposed neurofeedback game operates based on attention and aims to improve multiple cognitive functions, including attention and working memory, in patients with stroke and MCI.Conclusions. The experimental results on the effect of NFT in patient groups demonstrated that the proposed neurofeedback game has the potential to enhance attention and memory skills in patients with neurological disorders. A large-scale study is needed in the future to prove the efficacy on a wider population.},
}
@article {pmid39981822,
year = {2025},
author = {Cai, Y and Li, Q and Banga, AK and Wesselmann, U and Zhao, C},
title = {Tetrodotoxin Delivery Pen Safely Uses Potent Natural Neurotoxin to Manage Severe Cutaneous Pain.},
journal = {Advanced healthcare materials},
volume = {14},
number = {9},
pages = {e2401549},
pmid = {39981822},
issn = {2192-2659},
support = {R61 NS123196/NS/NINDS NIH HHS/United States ; R01 GM144388/GM/NIGMS NIH HHS/United States ; R15GM139193/GM/NIGMS NIH HHS/United States ; R15 GM139193/GM/NIGMS NIH HHS/United States ; R61NS123196/NS/NINDS NIH HHS/United States ; R01GM144388/GM/NIGMS NIH HHS/United States ; },
mesh = {Animals ; *Tetrodotoxin/administration &amp; dosage/pharmacology/therapeutic use ; Rats ; Rats, Sprague-Dawley ; Male ; *Skin/drug effects ; *Neurotoxins/pharmacology/administration &amp; dosage ; *Drug Delivery Systems ; *Pain/drug therapy ; Sodium Dodecyl Sulfate/chemistry ; },
abstract = {Clinically available therapies often inadequately address severe chronic cutaneous pain due to short anesthetic duration, insufficient intensity, or side effects. This study introduces a pen device delivering tetrodotoxin (TTX), a potent neurotoxin targeting nerve voltage-gated sodium channels, as a safe and effective topical anesthetic to treat severe chronic cutaneous pain. Chemical permeation enhancers, such as sodium dodecyl sulfate (SDS) and limonene (LIM), are incorporated to enhance TTX skin permeability. The device ensures precise TTX dosing down to the nanogram level, essential to avoid TTX overdose. In rats, the pen device treatment produces TTX-dose-dependent anesthetic effectiveness. An administration of 900 ng of TTX with SDS and LIM to the rat back skin produces a 393.25% increase (measurement limit) in the nociceptive skin pressure threshold, and the hypoalgesia lasts for 11.25 h, outperforming bupivacaine (28 µg), of which are 25.24% and under 1 h. Moreover, the pen device provides on-demand therapy for multiple treatments, consistently achieving prolonged anesthesia over ten sessions (1 treatment per day) without noted toxicity. Furthermore, a single topical administration of 16 µg of TTX exhibits no TTX-related toxicity in rats. The TTX delivery pen paves the way for clinical trials, offering a promising solution for severe cutaneous pain.},
}
@article {pmid39981403,
year = {2025},
author = {Liao, W and Miao, Z and Liang, S and Zhang, L and Li, C},
title = {A composite improved attention convolutional network for motor imagery EEG classification.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1543508},
pmid = {39981403},
issn = {1662-4548},
abstract = {INTRODUCTION: A brain-computer interface (BCI) is an emerging technology that aims to establish a direct communication pathway between the human brain and external devices. Motor imagery electroencephalography (MI-EEG) signals are analyzed to infer users' intentions during motor imagery. These signals hold potential for applications in rehabilitation training and device control. However, the classification accuracy of MI-EEG signals remains a key challenge for the development of BCI technology.<br><br>METHODS: This paper proposes a composite improved attention convolutional network (CIACNet) for MI-EEG signals classification. CIACNet utilizes a dual-branch convolutional neural network (CNN) to extract rich temporal features, an improved convolutional block attention module (CBAM) to enhance feature extraction, temporal convolutional network (TCN) to capture advanced temporal features, and multi-level feature concatenation for more comprehensive feature representation.<br><br>RESULTS: The CIACNet model performs well on both the BCI IV-2a and BCI IV-2b datasets, achieving accuracies of 85.15 and 90.05%, respectively, with a kappa score of 0.80 on both datasets. These results indicate that the CIACNet model's classification performance exceeds that of four other comparative models.<br><br>CONCLUSION: Experimental results demonstrate that the proposed CIACNet model has strong classification capabilities and low time cost. Removing one or more blocks results in a decline in the overall performance of the model, indicating that each block within the model makes a significant contribution to its overall effectiveness. These results demonstrate the ability of the CIACNet model to reduce time costs and improve performance in motor imagery brain-computer interface (MI-BCI) systems, while also highlighting its practical applicability.},
}
@article {pmid39981127,
year = {2025},
author = {Ghosh, S and Yadav, RK and Soni, S and Giri, S and Muthukrishnan, SP and Kumar, L and Bhasin, S and Roy, S},
title = {Decoding the brain-machine interaction for upper limb assistive technologies: advances and challenges.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1532783},
pmid = {39981127},
issn = {1662-5161},
abstract = {Understanding how the brain encodes upper limb movements is crucial for developing control mechanisms in assistive technologies. Advances in assistive technologies, particularly Brain-machine Interfaces (BMIs), highlight the importance of decoding motor intentions and kinematics for effective control. EEG-based BMI systems show promise due to their non-invasive nature and potential for inducing neural plasticity, enhancing motor rehabilitation outcomes. While EEG-based BMIs show potential for decoding motor intention and kinematics, studies indicate inconsistent correlations with actual or planned movements, posing challenges for achieving precise and reliable prosthesis control. Further, the variability in predictive EEG patterns across individuals necessitates personalized tuning to improve BMI efficiency. Integrating multiple physiological signals could enhance BMI precision and reliability, paving the way for more effective motor rehabilitation strategies. Studies have shown that brain activity adapts to gravitational and inertial constraints during movement, highlighting the critical role of neural adaptation to biomechanical changes in creating control systems for assistive devices. This review aims to provide a comprehensive overview of recent progress in deciphering neural activity patterns associated with both physiological and assisted upper limb movements, highlighting avenues for future exploration in neurorehabilitation and brain-machine interface development.},
}
@article {pmid39980021,
year = {2025},
author = {Angulo-Sherman, IN and León-Domínguez, U and Martinez-Torteya, A and Fragoso-González, GA and Martínez-Pérez, MV},
title = {Proficiency in motor imagery is linked to the lateralization of focused ERD patterns and beta PDC.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {30},
pmid = {39980021},
issn = {1743-0003},
support = {Not applicable//Universidad de Monterrey/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Imagination/physiology ; Male ; Electroencephalography ; Adult ; Female ; *Functional Laterality/physiology ; Young Adult ; *Beta Rhythm/physiology ; Movement/physiology ; Motor Cortex/physiology ; Psychomotor Performance/physiology ; },
abstract = {BACKGROUND: Motor imagery based brain-computer interfaces (MI-BCIs) are systems that detect the mental rehearsal of movement from brain activity signals (EEG) for controlling devices that can potentiate motor neurorehabilitation. Considering the problem that MI proficiency requires training and it is not always achieved, EEG desirable features should be investigated to propose indicators of successful MI training.<br><br>METHODS: Nine healthy right-handed subjects trained with a MI-BCI for four sessions. In each session, EEG was recorded for 30 trials that consisted of a rest and a dominant-hand MI sequence, which were used for calibrating the system. Then, the subject participated in 160 trials in which a cursor was displaced on a screen by performing MI or relaxing to hit a target. The session's accuracy was calculated. For each trial from the calibration phase of the first session, the power spectral density (PSD) and the partial directed coherence (PDC) of the rest and MI EEG segments were obtained to estimate the event-related synchronization changes (ERS) and the connectivity patterns of the &#x3b8; , &#x3b1; , &#x3b2; and &#x3b3; bands that are associated with high BCI control (accuracy above 70% in at least one session). Finally, t-tests and rank-sum tests (p < 0.05 , with Benjamini-Hochberg correction) were used to compare the ERS/ERD and PDC values of subjects with high and low accuracy, respectively.<br><br>RESULTS: Proficient users showed greater &#x3b1; ERD on the right-hand motor cortex (left hemisphere). Furthermore, the &#x3b2; PDC related to the ipsilateral motor cortex is commonly weakened during motor imagery, while the contralateral motor cortex &#x3b3; PDC is enhanced.<br><br>CONCLUSIONS: Motor imagery proficiency is related to the focused and lateralized event-related &#x3b1; desynchronization patterns and the lateralization of &#x3b2; and &#x3b3; PDC. Future analysis of these features could allow complimenting the information for assessment of subject-specific BCI control and the prediction of the effectiveness of motor-imagery training.},
}
@article {pmid39979463,
year = {2025},
author = {Bhadra, K and Giraud, AL and Marchesotti, S},
title = {Learning to operate an imagined speech Brain-Computer Interface involves the spatial and frequency tuning of neural activity.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {271},
pmid = {39979463},
issn = {2399-3642},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; Electroencephalography ; Adult ; *Speech/physiology ; *Imagination/physiology ; Young Adult ; *Learning ; *Brain/physiology ; },
abstract = {Brain-Computer Interfaces (BCI) will revolutionize the way people with severe impairment of speech production can communicate. While current efforts focus on training classifiers on vast amounts of neurophysiological signals to decode imagined speech, much less attention has been given to users' ability to adapt their neural activity to improve BCI-control. To address whether BCI-control improves with training and characterize the underlying neural dynamics, we trained 15 healthy participants to operate a binary BCI system based on electroencephalography (EEG) signals through syllable imagery for five consecutive days. Despite considerable interindividual variability in performance and learning, a significant improvement in BCI-control was globally observed. Using a control experiment, we show that a continuous feedback about the decoded activity is necessary for learning to occur. Performance improvement was associated with a broad EEG power increase in frontal theta activity and focal enhancement in temporal low-gamma activity, showing that learning to operate an imagined-speech BCI involves dynamic changes in neural features at different spectral scales. These findings demonstrate that combining machine and human learning is a successful strategy to enhance BCI controllability.},
}
@article {pmid39979351,
year = {2025},
author = {Gupta, E and Sivakumar, R},
title = {Response coupling with an auxiliary neural signal for enhancing brain signal detection.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {6227},
pmid = {39979351},
issn = {2045-2322},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Evoked Potentials, Visual/physiology ; *Brain/physiology ; Male ; Adult ; Female ; Young Adult ; Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interfaces (BCIs) offer an implicit, non-linguistic communication channel between users and machines. Despite their potential, BCIs are far from becoming a mainstream communication modality like text and speech. While non-invasive BCIs, such as Electroencephalography, are favored for their ease of use, their broader adoption is limited by challenges related to signal noise, artifacts, and variability across users. In this paper, we propose a novel method called response coupling, aimed at enhancing brain signal detection and reliability by pairing a brain signal with an artificially induced auxiliary signal and leveraging their interaction. Specifically, we use error-related potentials (ErrPs) as the primary signal and steady-state visual evoked potentials (SSVEPs) as the auxiliary signal. SSVEPs, known for their phase-locked responses to rhythmic stimuli, are selected because rhythmic neural activity plays a critical role in sensory and cognitive processes, with evidence suggesting that reinforcing these oscillations can improve neural performance. By exploring the interaction between these two signals, we demonstrate that response coupling significantly improves the detection accuracy of ErrPs, especially in the parietal and occipital regions. This method introduces a new paradigm for enhancing BCI performance, where the interaction between a primary and an auxiliary signal is harnessed to enhance the detection performance. Additionally, the phase-locking properties of SSVEPs allow for unsupervised rejection of suboptimal data, further increasing BCI reliability.},
}
@article {pmid39979293,
year = {2025},
author = {Hoeferlin, GF and Grabinski, SE and Druschel, LN and Duncan, JL and Burkhart, G and Weagraff, GR and Lee, AH and Hong, C and Bambroo, M and Olivares, H and Bajwa, T and Coleman, J and Li, L and Memberg, W and Sweet, J and Hamedani, HA and Acharya, AP and Hernandez-Reynoso, AG and Donskey, C and Jaskiw, G and Ricky Chan, E and Shoffstall, AJ and Bolu Ajiboye, A and von Recum, HA and Zhang, L and Capadona, JR},
title = {Bacteria invade the brain following intracortical microelectrode implantation, inducing gut-brain axis disruption and contributing to reduced microelectrode performance.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {1829},
pmid = {39979293},
issn = {2041-1723},
support = {R01 NS131502/NS/NINDS NIH HHS/United States ; R25 CA221718/CA/NCI NIH HHS/United States ; T32 EB004314/EB/NIBIB NIH HHS/United States ; },
mesh = {Animals ; Microelectrodes/adverse effects ; Mice ; *Electrodes, Implanted/adverse effects/microbiology ; *Brain/microbiology/drug effects ; Anti-Bacterial Agents/pharmacology ; *Gastrointestinal Microbiome/drug effects ; Blood-Brain Barrier/microbiology ; Male ; Mice, Inbred C57BL ; *Brain-Computer Interfaces ; *Bacteria/genetics/isolation &amp; purification/drug effects/classification ; *Brain-Gut Axis ; },
abstract = {Brain-machine interface performance can be affected by neuroinflammatory responses due to blood-brain barrier (BBB) damage following intracortical microelectrode implantation. Recent findings suggest that certain gut bacterial constituents might enter the brain through damaged BBB. Therefore, we hypothesized that damage to the BBB caused by microelectrode implantation could facilitate microbiome entry into the brain. In our study, we found bacterial sequences, including gut-related ones, in the brains of mice with implanted microelectrodes. These sequences changed over time. Mice treated with antibiotics showed a reduced presence of these bacteria and had a different inflammatory response, which temporarily improved microelectrode recording performance. However, long-term antibiotic use worsened performance and disrupted neurodegenerative pathways. Many bacterial sequences found were not present in the gut or in unimplanted brains. Together, the current study established a paradigm-shifting mechanism that may contribute to chronic intracortical microelectrode recording performance and affect overall brain health following intracortical microelectrode implantation.},
}
@article {pmid39978072,
year = {2025},
author = {Lim, MJR and Lo, JYT and Tan, YY and Lin, HY and Wang, Y and Tan, D and Wang, E and Naing Ma, YY and Wei Ng, JJ and Jefree, RA and Tseng Tsai, Y},
title = {The state-of-the-art of invasive brain-computer interfaces in humans: a systematic review and individual patient meta-analysis.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adb88e},
pmid = {39978072},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces/trends ; Electrodes, Implanted/trends ; },
abstract = {Objective.Invasive brain-computer interfaces (iBCIs) have evolved significantly since the first neurotrophic electrode was implanted in a human subject three decades ago. Since then, both hardware and software advances have increased the iBCI performance to enable tasks such as decoding conversations in real-time and manipulating external limb prostheses with haptic feedback. In this systematic review, we aim to evaluate the advances in iBCI hardware, software and functionality and describe challenges and opportunities in the iBCI field.Approach.Medline, EMBASE, PubMed and Cochrane databases were searched from inception until 13 April 2024. Primary studies reporting the use of iBCI in human subjects to restore function were included. Endpoints extracted include iBCI electrode type, iBCI implantation, decoder algorithm, iBCI effector, testing and training methodology and functional outcomes. Narrative synthesis of outcomes was done with a focus on hardware and software development trends over time. Individual patient data (IPD) was also collected and an IPD meta-analysis was done to identify factors significant to iBCI performance.Main results.93 studies involving 214 patients were included in this systematic review. The median task performance accuracy for cursor control tasks was 76.00% (Interquartile range [IQR] = 21.2), for motor tasks was 80.00% (IQR = 23.3), and for communication tasks was 93.27% (IQR = 15.3). Current advances in iBCI software include use of recurrent neural network architectures as decoders, while hardware advances such as intravascular stentrodes provide a less invasive alternative for neural recording. Challenges include the lack of standardized testing paradigms for specific functional outcomes and issues with portability and chronicity limiting iBCI usage to laboratory settings.Significance.Our systematic review demonstrated the exponential rate at which iBCIs have evolved over the past two decades. Yet, more work is needed for widespread clinical adoption and translation to long-term home-use.},
}
@article {pmid39976033,
year = {2025},
author = {Li, L and Li, B and Wang, G and Li, S and Li, X and Santos, J and González, AM and Guo, L and Tu, Y and Qin, Y},
title = {Research on Precision Medicine AI Algorithm for Neuro Immune Gastrointestinal Diseases based on Quantum Biochemistry and Computational Cancer Genetics.},
journal = {Current pharmaceutical biotechnology},
volume = {},
number = {},
pages = {},
doi = {10.2174/0113892010348489241210060447},
pmid = {39976033},
issn = {1873-4316},
abstract = {OBJECTIVE: The objective of this study is to conduct network toxicology analysis based on smoking habits and develop a simpler and more effective toxicology product ingestion control system.<br><br>BACKGROUND: Smoking behavior can affect the pathogenesis and prognosis of neuroimmune gastrointestinal diseases.<br><br>AIMS: The purpose of developing tools to assist clinical practice is to avoid the harm of cigarettes to the human body.<br><br>METHODS: Molecular dynamics method was used to elucidate the biophysical mechanism of TP53 gene mutation caused by harmful ingredients, and the signaling pathway of midbrain edge excitation was determined by molecular dynamics of nicotine and dopamine receptor D3. The possible involvement of nicotine in neuronal damage was determined through the molecular interaction between nicotine and ACHE. Molecular pathways were analyzed based on the aforementioned biological principles, developed artificial intelligence systems and brain computer interface systems.<br><br>RESULTS: Several signaling pathways were elucidated, and effective AI algorithms were developed.<br><br>CONCLUSION: The accuracy of artificial intelligence systems is over 70%. This study provides clinical doctors with a new precision medicine strategy and tool to regulate patient behavior and reduce disease risk. Other: This project was approved by the Ethics Committee of Chifeng Cancer Hospital and reported to the WHO.},
}
@article {pmid39975237,
year = {2025},
author = {Cubillos, LH and Kelberman, MM and Mender, MJ and Hite, A and Temmar, H and Willsey, M and Kumar, NG and Kung, TA and Patil, PG and Chestek, C and Krishnan, C},
title = {Exploring Synergies in Brain-Machine Interfaces: Compression vs. Performance.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {39975237},
issn = {2692-8205},
support = {R01 NS105132/NS/NINDS NIH HHS/United States ; T32 NS007222/NS/NINDS NIH HHS/United States ; },
abstract = {Individuals with severe neurological injuries often rely on assistive technologies, but current methods have limitations in accurately decoding multi-degree-of-freedom (DoF) movements. Intracortical brain-machine interfaces (iBMIs) use neural signals to provide a more natural control method, but currently struggle with higher-DoF movements-something the brain handles effortlessly. It has been theorized that the brain simplifies high-DoF movement through muscle synergies, which link multiple muscles to function as a single unit. These synergies have been studied using dimensionality reduction techniques like principal component analysis (PCA), non-negative matrix factorization (NMF), and demixed PCA (dPCA) and successfully used to reduce noise and improve offline decoder stability in non-invasive applications. However, their effectiveness in improving decoding and generalizability for implanted recordings across varied tasks is unclear. Here, we evaluated if brain and muscle synergies can enhance iBMI performance in non-human primates performing a two-DoF finger task. Specifically, we tested if PCA, dPCA, and NMF could compress and denoise brain and muscle data and improve decoder generalization across tasks. Our results showed that while all methods effectively compressed data with minimal loss in decoding accuracy, none improved performance through denoising. Additionally, none of the methods enhanced generalization across tasks. These findings suggest that while dimensionality reduction can aid data compression, alone it may not reveal the "true" control space needed to improve decoder performance or generalizability. Further research is required to determine whether synergies are the optimal control framework or if alternative approaches are required to enhance decoder robustness in iBMI applications.},
}
@article {pmid39974888,
year = {2025},
author = {Letner, JG and Lam, JLW and Copenhaver, MG and Barrow, M and Patel, PR and Richie, JM and Lee, J and Kim, HS and Cai, D and Weiland, JD and Phillips, J and Blaauw, D and Chestek, CA},
title = {A method for efficient, rapid, and minimally invasive implantation of individual non-functional motes with penetrating subcellular-diameter carbon fiber electrodes into rat cortex.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {39974888},
issn = {2692-8205},
support = {R01 NS118606/NS/NINDS NIH HHS/United States ; RF1 MH120005/MH/NIMH NIH HHS/United States ; RF1 NS128667/NS/NINDS NIH HHS/United States ; UF1 NS107659/NS/NINDS NIH HHS/United States ; },
abstract = {OBJECTIVE: Distributed arrays of wireless neural interfacing chips with 1-2 channels each, known as "neural dust", could enhance brain machine interfaces (BMIs) by removing the wired connection through the scalp and increasing biocompatibility with their submillimeter size. Although several approaches for neural dust have emerged, a procedure for implanting them in batches that builds upon the safety and performance of currently used electrodes remains to be demonstrated.<br><br>APPROACH: Here, we demonstrate the feasibility of implanting batches of wireless motes that rest on the cortical surface with carbon fiber electrodes of subcellular diameter (6.8-8.4 &#x3bc;m) that penetrate to a target brain depth of 1 mm without insertion aids. To simulate their implantation, we assembled more than 230 mechanically equivalent motes and affixed them to insertion tools with polyethylene glycol (PEG), a quickly dissolvable and biocompatible material. Then, we implanted mote grids of multiple configurations into rat cortex in vivo and evaluated insertion success and their arrangement on the brain surface using photos and videos captured during their implantation.<br><br>MAIN RESULTS: When placing motes onto the insertion device, we found that they aggregated in molten PEG such that the array pitch was only 5% wider than the dimensions of the mote bases themselves (240 &#xd7; 240 &#x3bc;m). Overall, we found that motes with this arrangement could be inserted into rat cortex with a high success rate, as 171/186 (92%) motes in 4&#xd7;4 (N=4) and 5&#xd7;5 (N=5) square grid configurations were successfully inserted using the insertion device alone. After implantation, measurements of how much motes tilted (22&#xb1;9&#xb0;, X&#x304;&#xb1;S) and had been displaced relative to their original positions were smaller than those measured for devices implanted inside the brain in the literature.<br><br>SIGNIFICANCE: Collectively, these data establish the viability of safely implementing motes with ultrasmall electrodes and epicortically-situated chips for use in future BMIs.},
}
@article {pmid39973870,
year = {2025},
author = {Kanagaluru, V and M, S},
title = {Artificial intelligence based BCI using SSVEP signals with single channel EEG.},
journal = {Technology and health care : official journal of the European Society for Engineering and Medicine},
volume = {33},
number = {4},
pages = {1905-1916},
doi = {10.1177/09287329241302740},
pmid = {39973870},
issn = {1878-7401},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Evoked Potentials, Visual/physiology ; *Artificial Intelligence ; Support Vector Machine ; Algorithms ; Machine Learning ; Discriminant Analysis ; Adult ; *Signal Processing, Computer-Assisted ; Male ; Decision Trees ; Female ; },
abstract = {BackgroundBrain-Computer Interfaces (BCIs) enable direct communication between the brain and external devices. Steady-state visual-evoked potentials (SSVEPs) are particularly useful in BCIs because of their rapid communication capabilities and minimal calibration requirements. Although SSVEP-based BCIs are highly effective, traditional classification methods face challenges in maintaining high accuracy with minimal EEG channels, especially in real-world applications. There is a growing need for improved classification techniques to enhance performance and efficiency.ObjectiveThe aim of this research is to improve the classification of SSVEP signals using machine-learning algorithms. This involves extracting dominant frequency features from SSVEP data and applying classifiers such as Decision Tree (DT), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM) to achieve high accuracy while reducing the number of EEG channels required, making the method practical for BCI applications.MethodsSSVEP data were collected from the Benchmark Dataset at Tsinghua BCI Lab using 64 EEG channels per subject. The Oz channel was selected as the dominant channel for analysis. Wavelet decomposition (db4) was used to extract frequency features in the range 7.8 Hz to 15.6 Hz. The frequency of the maximum amplitude within a 5-s window was extracted as the key feature, and machine learning models (DT, LDA, and SVM) were applied to classify these features.ResultsThe proposed method achieved a high classification accuracy, with 95.8% for DT and 96.7% for both LDA and SVM. These results show significant improvement over existing methods, indicating the potential of this approach for BCI applications.ConclusionThis study demonstrates that SSVEP classification using machine-learning models improves accuracy and efficiency. The use of wavelet decomposition for feature extraction and machine learning for classification offers a robust method for SSVEP-based BCIs. This method is promising for assistive technologies and other BCI applications.},
}
@article {pmid39970032,
year = {2025},
author = {M, AL and R, R},
title = {Comprehensive analysis of prefrontal cortex-directional rhythms categorization for rehabilitation.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-12},
doi = {10.1080/10255842.2025.2467460},
pmid = {39970032},
issn = {1476-8259},
abstract = {Prefrontal Cortex-Directional Rhythms (PFC-DR) classification plays a significant role in Brain-Computer Interface (BCI) research since it is crucial for the effective rehabilitation of injured voluntary movements. The primary aims of this study are to conduct a thorough examination of traditional classification techniques, while emphasizing the significance of radial basis functions within support vector machine (RBF-SVM) based approaches in the context of BCI systems. Consequently, in contrast to existing machine learning-based approaches, this generalized RBF-SVM classifier effectively identified observed data with an overall 96.91% accuracy validated with a 10-fold repeated random train test split cross validation technique using confusion matrix analysis.},
}
@article {pmid39969013,
year = {2025},
author = {Magalhães, SS and Lucas-Ochoa, AM and Gonzalez-Cuello, AM and Fernández-Villalba, E and Pereira Toralles, MB and Herrero, MT},
title = {The mind-machine connection: adaptive information processing and new technologies promoting mental health in older adults.},
journal = {The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry},
volume = {},
number = {},
pages = {10738584251318948},
doi = {10.1177/10738584251318948},
pmid = {39969013},
issn = {1089-4098},
abstract = {The human brain demonstrates an exceptional adaptability, which encompasses the ability to regulate emotions, exhibit cognitive flexibility, and generate behavioral responses, all supported by neuroplasticity. Brain-computer interfaces (BCIs) employ adaptive algorithms and machine learning techniques to adapt to variations in the user's brain activity, allowing for customized interactions with external devices. Older adults may experience cognitive decline, which could affect the ability to learn and adapt to new technologies such as BCIs, but both (human brain and BCI) demonstrate adaptability in their responses. The human brain is skilled at quickly switching between tasks and regulating emotions, while BCIs can modify signal-processing algorithms to accommodate changes in brain activity. Furthermore, the human brain and BCI participate in knowledge acquisition; the first one strengthens cognitive abilities through exposure to new experiences, and the second one improves performance through ongoing adjustment and improvement. Current research seeks to incorporate emotional states into BCI systems to improve the user experience, despite the exceptional emotional regulation abilities of the human brain. The implementation of BCIs for older adults could be more effective, inclusive, and beneficial in improving their quality of life. This review aims to improve the understanding of brain-machine interfaces and their implications for mental health in older adults.},
}
@article {pmid39968680,
year = {2025},
author = {Hao, W and Yang, S and Sheng, Y and Ye, C and Han, L and Zhou, Z and Cui, W},
title = {Efficient expression of recombinant proteins in Bacillus subtilis using a rewired gene circuit of quorum sensing.},
journal = {Biotechnology progress},
volume = {41},
number = {3},
pages = {e70007},
doi = {10.1002/btpr.70007},
pmid = {39968680},
issn = {1520-6033},
support = {32171420//National Natural Science Foundation of China/ ; KLIB-KF202307//Open Project of Key Laboratory of Industrial Biotechnology, Ministry of Education/ ; 2023YFC3402402//National Key Research and Development Program of China/ ; },
mesh = {*Bacillus subtilis/genetics/metabolism ; *Quorum Sensing/genetics ; *Recombinant Proteins/genetics/metabolism/biosynthesis ; Promoter Regions, Genetic ; Bacterial Proteins/genetics/metabolism ; *Gene Regulatory Networks/genetics ; Gene Expression Regulation, Bacterial ; Aliivibrio fischeri/genetics ; },
abstract = {Bacillus subtilis is a favored chassis for high productivity of several high value-added product in synthetic biology. Efficient production of recombinant proteins is critical but challenging using this chassis because these expression systems in use, such as constitutive and inducible expression systems, demand for coordination of cell growth with production and addition of chemical inducers. These systems compete for intracellular resources with the host, eventually resulting in dysfunction of cell survival. To overcome the problem, in this study, LuxRI quorum sensing (QS) system from Aliivibrio fischeri was functionally reconstituted in B. subtilis for achieving coordinated protein overproduction with cell growth in a cell-density-dependent manner. Furthermore, the output-controlling promoter, PluxI, was engineered through two rounds of evolution, by which we identified four mutants, P22, P47, P56, and P58 that exhibited elevated activity compared to the original PluxI. By incorporating a strong terminator (TB5) downstream of the target gene further enhanced expression level. The expression level of this system surpasses commonly used promoter-based systems in B. subtilis like P43 and PylbP. The LuxRI QS system proves to be a potent platform for recombinant protein overproduction in B. subtilis.},
}
@article {pmid39965558,
year = {2025},
author = {Persson, AC and Eeg-Olofsson, M and Sadeghi, A and Lepp, M and Persson, AC},
title = {Patients' Experiences of an Active Transcutaneous Implant: The Bone Conduction Implant.},
journal = {Audiology &amp; neuro-otology},
volume = {30},
number = {4},
pages = {335-346},
pmid = {39965558},
issn = {1421-9700},
mesh = {Humans ; Female ; Male ; *Bone Conduction/physiology ; Middle Aged ; Adult ; *Hearing Aids/psychology ; Qualitative Research ; Aged ; Quality of Life ; Self Concept ; *Hearing Loss, Conductive/rehabilitation/psychology ; },
abstract = {UNLABELLED: <p>Introduction: The aim of this qualitative study was to explore and describe patients' experiences of using and living with the bone conduction implant (BCI).<br><br>METHODS: Semi-structured interviews were conducted with 10 BCI users and analyzed according to the phenomenographic approach.<br><br>RESULTS: Four conceptual themes were formed during the analysis; (1) conceptions of the process receiving the BCI, (2) conceptions of handling the BCI on a daily basis, (3) conceptions of hearing with the BCI, and (4) conceptions of health care issues related to the BCI. The participants' statements include experiences of improved hearing and self-esteem by using the BCI. Noisy situations and not being able to hear in daily life situations causes frustrations. The participants described anxiety about consequences following an MRI examination. The audio processor is easy to handle but the fact that it is not waterproof raise concerns. Despite some frustration and concerns, participants state that the audio processor has become a part of them, and they cannot imagine being without it.<br><br>CONCLUSION: The ability to hear and communicate with other people has a great impact on the participants' daily life quality, and their statements show the importance hearing has on their lives and how they perceive themselves. The BCI seems to be a good hearing rehabilitation alternative for the participants, and they state that the audio processor is easy to use and handle. </p>.},
}
@article {pmid39963381,
year = {2025},
author = {Wang, N and He, Y and Zhu, S and Liu, D and Chai, X and He, Q and Cao, T and He, J and Li, J and Si, J and Yang, Y and Zhao, J},
title = {Functional near-infrared spectroscopy for the assessment and treatment of patients with disorders of consciousness.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1524806},
pmid = {39963381},
issn = {1664-2295},
abstract = {BACKGROUND: Advances in neuroimaging have significantly enhanced our understanding of brain function, providing critical insights into the diagnosis and management of disorders of consciousness (DoC). Functional near-infrared spectroscopy (fNIRS), with its real-time, portable, and noninvasive imaging capabilities, has emerged as a promising tool for evaluating functional brain activity and nonrecovery potential in DoC patients. This review explores the current applications of fNIRS in DoC research, identifies its limitations, and proposes future directions to optimize its clinical utility.<br><br>AIM: This review examines the clinical application of fNIRS in monitoring DoC. Specifically, it investigates the potential value of combining fNIRS with brain-computer interfaces (BCIs) and closed-loop neuromodulation systems for patients with DoC, aiming to elucidate mechanisms that promote neurological recovery.<br><br>METHODS: A systematic analysis was conducted on 155 studies published between January 1993 and October 2024, retrieved from the Web of Science Core Collection database.<br><br>RESULTS: Analysis of 21 eligible studies on neurological diseases involving 262 DoC patients revealed significant findings. The prefrontal cortex was the most frequently targeted brain region. fNIRS has proven crucial in assessing brain functional connectivity and activation, facilitating the diagnosis of DoC. Furthermore, fNIRS plays a pivotal role in diagnosis and treatment through its application in neuromodulation techniques such as deep brain stimulation (DBS) and spinal cord stimulation (SCS).<br><br>CONCLUSION: As a noninvasive, portable, and real-time neuroimaging tool, fNIRS holds significant promise for advancing the assessment and treatment of DoC. Despite limitations such as low spatial resolution and the need for standardized protocols, fNIRS has demonstrated its utility in evaluating residual brain activity, detecting covert consciousness, and monitoring therapeutic interventions. In addition to assessing consciousness levels, fNIRS offers unique advantages in tracking hemodynamic changes associated with neuroregulatory treatments, including DBS and SCS. By providing real-time feedback on cortical activation, fNIRS facilitates optimizing therapeutic strategies and supports individualized treatment planning. Continued research addressing its technical and methodological challenges will further establish fNIRS as an indispensable tool in the diagnosis, prognosis, and treatment monitoring of DoC patients.},
}
@article {pmid39957485,
year = {2025},
author = {Yi, L and Jiang, T and Ren, R and Cao, J and Edel, JB and Ivanov, AP and Tang, L},
title = {Quantum Mechanical Tunnelling Probes with Redox Cycling for Ultra-Sensitive Detection of Biomolecules.},
journal = {Angewandte Chemie (International ed. in English)},
volume = {64},
number = {23},
pages = {e202501941},
doi = {10.1002/anie.202501941},
pmid = {39957485},
issn = {1521-3773},
support = {21874119//National Natural Science Foundation of China/ ; 62127818//National Natural Science Foundation of China/ ; LR22F050003//Zhejiang Provincial Outstanding Youth Science Foundation/ ; 724300//European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme/ ; 875525//European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme/ ; 600322/05//Analytical Chemistry Trust Fund grant/ ; EP/V049070/1//Engineering and Physical Sciences Research Council/ ; },
mesh = {Oxidation-Reduction ; *Electrochemical Techniques/methods ; *Quantum Theory ; Electrodes ; Ferrocyanides/chemistry/analysis ; Ferricyanides/chemistry/analysis ; *Biosensing Techniques/methods ; Limit of Detection ; },
abstract = {Quantum mechanical tunnelling sensors (QMTs) have emerged as a promising technology for next-generation single-molecule detection. Furthermore, QMT sensors can be combined with redox species resulting in repeated oxidation and reduction (redox cycling).. We developed robust QMT probes with electrode gap distances below 2 nm. Using the generator-collector (GC) mode, we verified that redox cycling of the ferrocyanide/ferricyanide (Fe(CN)6 [3-/4-]) couple occurs both in the tunnelling regime and on the electrode surface. Our findings indicated that the current enhancement is affected by both the gap distance and surface modifications of the probes. These QMT probes exhibited remarkable sensitivity, capable of detecting Fe(CN)6 [3-/4-] concentrations down to sub-picomolar levels. Utilising this ability to modulate redox reactions, we adapted the QMT probes to serve as electrochemical sensors for detecting viral proteins. By modifying the electrode surfaces, our functionalised QMT probes achieved sub-pM detection limits with high selectivity in biofluids such as nasopharyngeal secretions. These findings highlight the potential of QMT probes to develop into a new class of electrochemical tunnelling sensors, offering significant advancements in biomedical diagnostics.},
}
@article {pmid39957341,
year = {2025},
author = {Zhai, H and Li, P and Wang, H and Wang, X},
title = {Temperature and steric hindrance-regulated selective synthesis of ketamine derivatives and 2-aryl-cycloketone-1-carboxamides via nucleophilic substitution and Favorskii rearrangement.},
journal = {Organic &amp; biomolecular chemistry},
volume = {23},
number = {11},
pages = {2704-2711},
doi = {10.1039/d4ob02039a},
pmid = {39957341},
issn = {1477-0539},
abstract = {A selective temperature and steric hindrance-regulated method for nucleophilic substitution or Favorskii rearrangement reactions of 2-aryl-2-bromo-cycloketones with aliphatic amines has been developed to prepare ketamine derivatives and 2-aryl-cycloketone-1-carboxamides. In the presence of secondary amines or ortho-substituted 2-aryl-2-bromocycloketones, steric hindrance directs the Favorskii rearrangement to occur. Conversely, with primary amines, the product ratio of nucleophilic substitution to Favorskii rearrangement is temperature-dependent, with higher temperatures favoring the Favorskii rearrangement. At lower temperatures (-25 °C or below), nucleophilic substitution predominates, yielding ketamine derivatives in yields of 60% to 85%. This method effectively utilizes temperature and steric hindrance to control the reaction pathway and optimize product formation.},
}
@article {pmid39957214,
year = {2025},
author = {Chinta, B and Pampana, M and M, M},
title = {An efficient deep learning approach for automatic speech recognition using EEG signals.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-21},
doi = {10.1080/10255842.2025.2456982},
pmid = {39957214},
issn = {1476-8259},
abstract = {Electroencephalogram (EEG) signals enhance human-machine interaction but pose challenges in speech recognition due to noise and complexity. This study proposes an Efficient Deep Learning Approach (EDLA) integrating the Gannet Optimization Algorithm (GOA) and Elman Recurrent Neural Network (ERNN) for speaker identification. EEG data is preprocessed using a Savitzky-Golay filter, and key features are selected via recursive feature elimination. Evaluated on the Kara One dataset, EDLA achieves 95.2% accuracy, outperforming baseline methods. This framework advances EEG based speech recognition aiding brain-computer interfaces and assistive technologies for individuals with speech disorders.},
}
@article {pmid39956854,
year = {2025},
author = {Zou, W and Fan, Y and Liu, J and Cheng, H and Hong, H and Al-Sheikh, U and Li, S and Zhu, L and Li, R and He, L and Tang, YQ and Zhao, G and Zhang, Y and Wang, F and Zhan, R and Zheng, X and Kang, L},
title = {Anoctamin-1 is a core component of a mechanosensory anion channel complex in C. elegans.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {1680},
pmid = {39956854},
issn = {2041-1723},
support = {P40 OD010440/OD/NIH HHS/United States ; 31771113//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31471023//National Natural Science Foundation of China (National Science Foundation of China)/ ; LZ22C090001//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; 2021ZD0203303//Ministry of Science and Technology of the People&apos;s Republic of China (Chinese Ministry of Science and Technology)/ ; },
mesh = {Animals ; *Caenorhabditis elegans/genetics/metabolism/physiology ; *Caenorhabditis elegans Proteins/metabolism/genetics ; *Mechanotransduction, Cellular/physiology ; Humans ; *Chloride Channels/metabolism/genetics ; *Anoctamin-1/metabolism/genetics ; Neurons/metabolism ; Animals, Genetically Modified ; Neoplasm Proteins ; },
abstract = {Mechanotransduction channels are widely expressed in both vertebrates and invertebrates, mediating various physiological processes such as touch, hearing and blood-pressure sensing. While previously known mechanotransduction channels in metazoans are primarily cation-selective, we identified Anoctamin-1 (ANOH-1), the C. elegans homolog of mammalian calcium-activated chloride channel ANO1/TMEM16A, as an essential component of a mechanosensory channel complex that contributes to the nose touch mechanosensation in C. elegans. Ectopic expression of either C. elegans or human Anoctamin-1 confers mechanosensitivity to touch-insensitive neurons, suggesting a cell-autonomous role of ANOH-1/ANO1 in mechanotransduction. Additionally, we demonstrated that the mechanosensory function of ANOH-1/ANO1 relies on CIB (calcium- and integrin- binding) proteins. Thus, our results reveal an evolutionarily conserved chloride channel involved in mechanosensory transduction in metazoans, highlighting the importance of anion channels in mechanosensory processes.},
}
@article {pmid39954182,
year = {2025},
author = {Jangir, G and Joshi, N and Purohit, G},
title = {Harnessing the synergy of statistics and deep learning for BCI competition 4 dataset 4: a novel approach.},
journal = {Brain informatics},
volume = {12},
number = {1},
pages = {5},
pmid = {39954182},
issn = {2198-4018},
abstract = {Human brain signal processing and finger's movement coordination is a complex mechanism. In this mechanism finger's movement is mostly performed for every day's task. It is well known that to capture such movement EEG or ECoG signals are used. In this order to find the patterns from these signals is important. The BCI competition 4 dataset 4 is one such standard dataset of ECoG signals for individual finger movement provided by University of Washington, USA. In this work, this dataset is, statistically analyzed to understand the nature of data and outliers in it. Effectiveness of pre-processing algorithm is then visualized. The cleaned dataset has dual polarity and gaussian distribution nature which makes Tanh activation function suitable for the neural network BC4D4 model. BC4D4 uses Convolutional neural network for feature extraction, dense neural network for pattern identification and incorporating dropout &amp; regularization making the proposed model more resilient. Our model outperforms the state of the art work on the dataset 4 achieving 0.85 correlation value that is 1.85X (Winner of BCI competition 4, 2012) &amp; 1.25X (Finger Flex model, 2022).},
}
@article {pmid39953165,
year = {2025},
author = {Jordan, S and Buchmann, M and Loss, J and Okan, O},
title = {[Health literacy and health behaviour-insights into a developing field of research and action for public health].},
journal = {Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz},
volume = {68},
number = {3},
pages = {255-263},
pmid = {39953165},
issn = {1437-1588},
mesh = {Humans ; *Biomedical Research/trends ; Germany ; *Health Behavior ; *Health Literacy/trends ; *Health Promotion/trends ; *Health Services Research/trends ; *Public Health/trends ; },
abstract = {The research and action field of health literacy and health behaviour is increasingly differentiating. General health literacy is established and focuses on population-based studies. Specific health literacy for health behaviour offers topic-related starting points for interventions and public health strategies.There are various concepts, definitions and measurement instruments for general health literacy and specific health literacy in the areas of nutrition and physical activity. These differ in terms of the levels of action and areas of application of health literacy.Most studies show a positive association between health literacy and various health behaviours. Higher health literacy is more often associated with improved health-promoting behaviour. This applies to both general as well as specific health literacy regarding nutrition and exercise (physical activity). Some studies found no correlation for certain behaviours, while others only found correlations for certain groups, which may be due to the different measuring instruments and research contexts. This points to the importance of always considering the interaction between behaviour and circumstances in order to improve the fit between the individual and the everyday demands of dealing with health information.The behavioural and cultural insights (BCI) approach can provide insights into how to promote health literacy with regard to various health behaviours, individual barriers and facilitators that arise from life circumstances and conditions, and that take social practice into account. BCI and health literacy complement each other and have the potential to make strategies for improving health behaviour more effective and targeted.},
}
@article {pmid39950750,
year = {2024},
author = {Jiang, E and Huang, T and Yin, X},
title = {A combination of deep learning models and type-2 fuzzy for EEG motor imagery classification through spatiotemporal-frequency features.},
journal = {Journal of medical engineering &amp; technology},
volume = {48},
number = {7},
pages = {262-275},
doi = {10.1080/03091902.2025.2463577},
pmid = {39950750},
issn = {1464-522X},
mesh = {*Electroencephalography/methods ; Humans ; *Fuzzy Logic ; *Deep Learning ; Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Bayes Theorem ; Algorithms ; },
abstract = {Developing a robust and effective technique is crucial for interpreting a user's brainwave signals accurately in the realm of biomedical signal processing. The variability and uncertainty present in EEG patterns over time, compounded by noise, pose notable challenges, particularly in mental tasks like motor imagery. Introducing fuzzy components can enhance the system's ability to withstand noisy environments. The emergence of deep learning has significantly impacted artificial intelligence and data analysis, prompting extensive exploration into assessing and understanding brain signals. This work introduces a hybrid series architecture called FCLNET, which combines Compact-CNN to extract frequency and spatial features alongside the LSTM network for temporal feature extraction. The activation functions in the CNN architecture were implemented using type-2 fuzzy functions to tackle uncertainties. Hyperparameters of the FCLNET model are tuned by the Bayesian optimisation algorithm. The efficacy of this approach is assessed through the BCI Competition IV-2a database and the BCI Competition IV-1 database. By incorporating type-2 fuzzy activation functions and employing Bayesian optimisation for tuning, the proposed architecture indicates good classification accuracy compared to the literature. Outcomes showcase the exceptional achievements of the FCLNET model, suggesting that integrating fuzzy units into other classifiers could lead to advancements in motor imagery-based BCI systems.},
}
@article {pmid39949790,
year = {2025},
author = {Ding, F and Ying, Y and Jin, Y and Guo, X and Xu, Y and Yu, Z and Jiang, H},
title = {Reduced frontotemporal connectivity during a verbal fluency task in patients with anxiety, sleep, and major depressive disorders.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1542346},
pmid = {39949790},
issn = {1664-2295},
abstract = {BACKGROUND: It has been well established that psychiatric disorders are often accompanied by cognitive dysfunction. Previous studies have investigated the verbal fluency task (VFT) for detecting executive function impairment in different psychiatric disorders, but the sensitivity and specificity of this task in different psychiatric disorders have not been explored. Furthermore, clarifying the mechanisms underlying variations in executive function impairments across multiple psychiatric disorders will enhance our comprehension of brain activity alternations among these disorders. Therefore, this study combined the VFT and the functional near-infrared spectroscopy (fNIRS) to investigate the neural mechanisms underlying the impairment of executive function across psychiatric disorders including anxiety disorder (AD), sleep disorder (SD) and major depressive disorder (MDD).<br><br>METHODS: Two hundred and eight participants were enrolled including 52 AD, 52 SD, 52 MDD and 52 healthy controls (HCs). All participants completed the VFT while being monitored using fNIRS to measure changes in brain oxygenated hemoglobin (Oxy-Hb).<br><br>RESULTS: Our results demonstrated that MDD, AD and SD exhibited decreased overall connectivity strength, as well as reduced connected networks involving the frontal and temporal regions during the VFT comparing to HC. Furthermore, the MDD group showed a reduction in connected networks, specifically in the left superior temporal gyrus and precentral gyrus, compared to the AD group.<br><br>CONCLUSION: Our study offers neural evidence that the VFT combined with fNIRS could effectively detect executive function impairment in different psychiatric disorders.},
}
@article {pmid39948616,
year = {2025},
author = {Gedela, NSS and Radawiec, RD and Salim, S and Richie, J and Chestek, C and Draelos, A and Pelled, G},
title = {In vivo electrophysiology recordings and computational modeling can predict octopus arm movement.},
journal = {Bioelectronic medicine},
volume = {11},
number = {1},
pages = {4},
pmid = {39948616},
issn = {2332-8886},
support = {UF1 NS115817/NS/NINDS NIH HHS/United States ; },
abstract = {The octopus has many features that make it advantageous for revealing principles of motor circuits and control and predicting behavior. Here, an array of carbon electrodes providing single-unit electrophysiology recordings were implanted into the octopus anterior nerve cord. The number of spikes and arm movements in response to stimulation at different locations along the arm were recorded. We observed that the number of spikes occurring within the first 100 ms after stimulation were predictive of the resultant movement response. Machine learning models showed that temporal electrophysiological features could be used to predict whether an arm movement occurred with 88.64% confidence, and if it was a lateral arm movement or a grasping motion with 75.45% confidence. Both supervised and unsupervised methods were applied to gain streaming measurements of octopus arm movements and how their motor circuitry produces rich movement types in real time. For kinematic analysis, deep learning models and unsupervised dimensionality reduction identified a consistent set of features that could be used to distinguish different types of arm movements. The neural circuits and the computational models identified here generated predictions for how to evoke a particular, complex movement in an orchestrated sequence for an individual motor circuit. This study demonstrates how real-time motor behaviors can be predicted and distinguished, contributing to the development of brain-machine interfaces. The ability to accurately model and predict complex movement patterns has broad implications for advancing technologies in robotics, neuroprosthetics, and artificial intelligence, paving the way for more sophisticated and adaptable systems.},
}
@article {pmid39947717,
year = {2025},
author = {Huang-Fu, HQ and Wang, L and Karmacharya, B and Koirala, UK and Ke, CW and Liang, D and Hao, C and Lai, YS},
title = {Spatial profiling of geographical accessibility to maternal healthcare and coverage of maternal health service utilisation in Nepal: a geospatial analysis based on demographic and health survey.},
journal = {BMJ global health},
volume = {10},
number = {2},
pages = {},
pmid = {39947717},
issn = {2059-7908},
mesh = {Humans ; Nepal ; *Health Services Accessibility/statistics &amp; numerical data ; Female ; *Maternal Health Services/statistics &amp; numerical data ; Adult ; Spatial Analysis ; Pregnancy ; Health Surveys ; Bayes Theorem ; Young Adult ; Prenatal Care/statistics &amp; numerical data ; Middle Aged ; Adolescent ; *Patient Acceptance of Health Care/statistics &amp; numerical data ; Delivery, Obstetric/statistics &amp; numerical data ; },
abstract = {BACKGROUND: Information on geographical accessibility to maternal healthcare (MHC) and coverage of maternal health service utilisation at high spatial resolution in Nepal are important for evidence-based health planning.<br><br>METHODS: Based on the Nepal Health Facility Registry dataset in 2022, we measured the geographical accessibility to MHC facilities across Nepal. Using data from 2022 Nepal Demographic and Health Survey and other sources, we assessed the relationships between geographical accessibility and the utilisation of the three major healthcare services (ie, four or more antenatal care (ANC) visits, institutional delivery and postnatal care (PNC) check-up), by applying Bayesian geostatistical models. High-resolution maps on coverage of the above services were produced.<br><br>RESULTS: The geographical accessibility showed high in the central and southern Terai belt but low in the northern mountains, with average travel-mode adjusted travel time for ANC, institutional delivery and PNC 26.74, 40.72 and 29.09&#x2009;min, respectively. Negative correlations were found between geographical accessibility with four or more ANC visits (OR 0.76, 95% Bayesian credible interval, BCI 0.65 to 0.90), institutional delivery (OR 0.76, 95% BCI 0.64 to 0.90) and PNC check-up (OR 0.87, 95% BCI 0.76 to 0.99), respectively. Population-weighted coverages for four or more ANC visits, institutional delivery and PNC check-up were estimated 83.25% (95% BCI 80.43% to 85.35%), 84.26% (95% BCI 81.30% to 86.08%) and 73.19% (95% BCI 69.43% to 76.09%), respectively, across Nepal. The northern mountains and southeastern Terai showed low coverage for the three healthcare services, while the central, eastern and western hilly regions exhibited good coverage.<br><br>CONCLUSION: Geographical accessibility is important in utilisation of maternal health services in Nepal. The high-resolution maps enable an evidence-based assessment for better health planning.},
}
@article {pmid39947551,
year = {2025},
author = {Lyu, Y and Yu, L and Qi, L and Meng, J and Wang, Y and Liu, Y and Xue, T and Zhi, C},
title = {Construction of 3D-fabric-based triple-decker agar/sodium alginate/Ca[2+] dual-network composite for wound dressing.},
journal = {International journal of biological macromolecules},
volume = {304},
number = {Pt 2},
pages = {140883},
doi = {10.1016/j.ijbiomac.2025.140883},
pmid = {39947551},
issn = {1879-0003},
mesh = {*Alginates/chemistry ; *Bandages ; *Agar/chemistry ; *Wound Healing/drug effects ; Anti-Bacterial Agents/pharmacology/chemistry ; *Calcium/chemistry ; Escherichia coli/drug effects ; Animals ; Staphylococcus aureus/drug effects ; Humans ; Polyurethanes/chemistry ; Nanofibers/chemistry ; },
abstract = {This study designed a novel multifunctional Janus structure dressing (DNCD dressing) composed of spacer fabric, agar/sodium alginate/calcium ion dual-network aerogel, methylene blue, and AgNO3-added thermoplastic polyurethane nanofiber membrane. The unidirectional liquid transport and absorbency tests prove that the DNCD dressing can unidirectionally transport liquids within just two seconds and possesses a liquid absorption ratio of 875.3 %. The unique open structure formed by the spacer fabric and liquid transport channels provides excellent air permeability as well as a suitable water vapor transmission rate, reaching 584.96 mm/s, 10.3 L/min, and 1104.82 g/m[2]/24 h, respectively. The exceptional compressive strength (216.78 kPa) and compressive modulus (515.23 kPa) of the dressing can provide protection for the wound. Antibacterial tests demonstrate that the silver ion-added DNCD dressing can eradicate >99 % of Escherichia coli and Staphylococcus aureus, while the added methylene blue can effectively monitor the survival status of bacteria. The low BCI value and the hemolysis ratio of <5 % indicate that the DNCD dressing has a certain hemostatic ability and does not cause hemolysis. The results of cytotoxicity tests and full-thickness skin defect models show that the DNCD dressing has good cytocompatibility and the potential to promote wound healing.},
}
@article {pmid39947091,
year = {2025},
author = {Zhao, H and Xu, S},
title = {Associations between panic buying and choice overload during the public health crisis in China: Testing sequential mediation models.},
journal = {Acta psychologica},
volume = {254},
number = {},
pages = {104800},
doi = {10.1016/j.actpsy.2025.104800},
pmid = {39947091},
issn = {1873-6297},
mesh = {Humans ; China ; Male ; Female ; *COVID-19/psychology ; Adult ; *Choice Behavior ; *Panic ; Cross-Sectional Studies ; Young Adult ; *Consumer Behavior ; Middle Aged ; *Public Health ; *Fear/psychology ; Adolescent ; Surveys and Questionnaires ; Mediation Analysis ; Motivation ; },
abstract = {Using the COVID-19 pandemic in China as the background, the current study investigated the association between panic buying behavior and consumers' choice overload during the public health crisis, and provided the empirical evidence on the dual sequential mediating pathways from the theoretical perspective of Protection Motivation Theory and Compensatory Control Theory. Through the cross-sectional online anonymous survey method, 492 samples were collected during the COVID-19 pandemic in China when the lockdown measure and the static management were implemented. Our results identified that the fear of being infected and the perceived threat sequentially mediated the effect of the panic buying on the choice overload, and the fear of being infected and the perceived control sequentially mediated the effect of the panic buying on the choice overload during the public health crisis. Machine learning algorithms also further identified the predictive effect of all feature variables on the choice overload during the public health crisis. Our findings provided a new perspective on the understanding of consumers' behavior during the public health crisis, and further extended the application of the Protection Motivation Theory and the Compensatory Control Theory to the consumer behavior research.},
}
@article {pmid39947044,
year = {2025},
author = {Zhu, L and Xin, Y and Yang, Y and Kong, W},
title = {A multi-layer EEG fusion decoding method with channel selection for multi-brain motor imagery.},
journal = {Computer methods and programs in biomedicine},
volume = {262},
number = {},
pages = {108595},
doi = {10.1016/j.cmpb.2025.108595},
pmid = {39947044},
issn = {1872-7565},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Brain/physiology ; Discriminant Analysis ; Algorithms ; *Imagination ; Signal Processing, Computer-Assisted ; },
abstract = {Traditional motor imagery-based single-brain computer interfaces(BCIs) face inherent limitations, such as unstable signals and low recognition accuracy. In contrast, multi-brain BCIs offer a promising solution by leveraging group electroencephalography (EEG) data. This paper presents a novel multi-layer EEG fusion method with channel selection for motor imagery-based multi-brain BCIs. We utilize mutual information convergent cross-mapping (MCCM) to identify channels that the represent causal relationships between brains; this strategy is combined with multiple linear discriminant analysis (MLDA) for decoding intentions via both data-layer and decision-layer strategies. Our experimental results demonstrate that the proposed method improves the accuracy of multi-brain motor imagery decoding by approximately 10% over that of the traditional methods, with a further 3%-5% accuracy increase due to the effective channel selection mechanism.},
}
@article {pmid39946849,
year = {2025},
author = {Zhou, Z and Hu, Z and Lyu, H},
title = {A 0.53-μW/channel calibration-free spike detection IC with 98.8-%-accuracy based on stationary wavelet transforms and Teager energy operators.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adb5c4},
pmid = {39946849},
issn = {1741-2552},
mesh = {*Wavelet Analysis ; *Action Potentials/physiology ; Humans ; Animals ; *Neurons/physiology ; Calibration ; *Brain-Computer Interfaces ; Algorithms ; },
abstract = {Objective. The brain-computer interface is currently experiencing a surge in the number of recording channels, resulting in a vast amount of raw data. It has become crucial to reliably detect neural spikes from a large population of neurons in the presence of noise, in order to constrain the transmission bandwidth.Approach. We investigate various time-frequency analysis methods for spike detection, followed by an exploration of energy operators amplifying spikes and signal statistics for adaptive thresholding. Subsequently, we introduce a precise and computationally efficient spike detection module, leveraging stationary wavelet transform (SWT), Teager energy operator, and root-mean-square calculator. This module is capable of autonomously adapting to different levels of noise. The SWT effectively eliminates high-frequency noise, enhancing the performance of the energy operators. The hardware computational process is simplified through the use of the lifting scheme and a channel-interleaving architecture.Main results. We evaluate the proposed spike detector with adaptive threshold on the publicly available WaveClus datasets. The detector achieves an average accuracy of 98.84%. The application-specific integrated circuit (ASIC) implementation results of the spike detector demonstrate an optimized interleaving channel of 8. In a 65 nm technology, the 8-channel spike detector consumes a power of 0.532μW Ch[-1]and occupies an area of 0.00645 mm[2]Ch[-1], operating at a 1.2 V supply voltage.Significance. The proposed spike detection processor offers one of the highest accuracies among state-of-the-art spike detection methods. Importantly, the ASIC explores the considerations in the scalability and hardware costs. The proposed design provides a systematic solution on spike detection with adaptive thresholding, offering a high accuracy while maintaining low power and area consumptions.},
}
@article {pmid39946786,
year = {2025},
author = {Dutta, S and Goswami, S and Debnath, S and Adhikary, S and Majumder, A},
title = {MusicalBSI - musical genres responses to fMRI signals analysis with prototypical model agnostic meta-learning for brain state identification in data scarce environment.},
journal = {Computers in biology and medicine},
volume = {188},
number = {},
pages = {109795},
doi = {10.1016/j.compbiomed.2025.109795},
pmid = {39946786},
issn = {1879-0534},
mesh = {Humans ; *Magnetic Resonance Imaging/methods ; *Music ; *Brain/diagnostic imaging/physiology ; Male ; Female ; Adult ; Cerebrovascular Circulation/physiology ; },
abstract = {Functional magnetic resonance imaging is a popular non-invasive brain-computer interfacing technique to monitor brain activities corresponding to several physical or neurological responses by measuring blood flow changes at different brain parts. Recent studies have shown that blood flow within the brain can have signature activity patterns in response to various musical genres. However, limited studies exist in the state of the art for automatized recognition of the musical genres from functional magnetic resonance imaging. This is because the feasibility of obtaining these kinds of data is limited, and currently available open-sourced data is insufficient to build an accurate deep-learning model. To solve this, we propose a prototypical model agnostic meta-learning framework for accurately classifying musical genres by studying blood flow dynamics using functional magnetic resonance imaging. A test with open-sourced data collected from 20 human subjects with consent for 6 different mental states resulted in up to 97.25 ± 1.38% accuracy by training with only 30 samples surpassing state-of-the-art methods. Further, a detailed evaluation of the performances confirms the model's reliability.},
}
@article {pmid39946752,
year = {2025},
author = {Gherman, DE and Krol, LR and Klug, M and Zander, TO},
title = {An investigation of a passive BCI's performance for different body postures and presentation modalities.},
journal = {Biomedical physics &amp; engineering express},
volume = {11},
number = {2},
pages = {},
doi = {10.1088/2057-1976/adb58b},
pmid = {39946752},
issn = {2057-1976},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Posture/physiology ; Male ; Adult ; Virtual Reality ; Female ; *Brain/physiology ; User-Computer Interface ; Young Adult ; },
abstract = {Passive brain-computer interfaces (passive BCIs, pBCIs) enable computers to unobtrusively decipher aspects of a user's mental state in real time from recordings of brain activity, e.g. electroencephalography (EEG). When used during human-computer interaction (HCI), this allows a computer to dynamically adapt for enhancing the subjective user experience. For transitioning from controlled laboratory environments to practical applications, understanding BCI performance in real contexts is of utmost importance. Here, Virtual Reality (VR) can play a unique role: both as a fully controllable simulation of a realistic environment and as an independent, increasingly popular real application. Given the potential of VR as a dynamic and controllable environment, and the capability of pBCIs to enable novel modes of interaction, it is tempting to envision a future where pBCI and VR are seamlessly integrated. However, the simultaneous use of these two technologies-both of which are head-mounted-presents new challenges. Due to their immediate proximity, electromagnetic artifacts can arise, contaminating the EEG. Furthermore, the active movements promoted by VR can induce mechanical and muscular artifacts in the EEG. The varying body postures and display preferences of users further complicate the practical application of pBCIs. To address these challenges, the current study investigates the influence of body posture (sitting Versus standing) and display media (computer screen Versus VR) on the performance of a pBCI in assessing cognitive load. Our results show that these conditions indeed led to some changes in the EEG data; nevertheless, the ability of pBCIs to detect cognitive load remained largely unaffected. However, when a classifier trained in one context (body posture or modality) was applied to another (e.g., cross-task application), reductions in classification accuracy were observed. As HCI moves towards increasingly adaptive and more interactive designs, these findings support the expansive potential of pBCIs in VR contexts.},
}
@article {pmid39945188,
year = {2025},
author = {Zhang, R and Sui, L and Shen, C and Xu, L and Cao, J},
title = {EEG-based real-time BCI system using drones for attention visualization.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-9},
doi = {10.1080/10255842.2025.2459272},
pmid = {39945188},
issn = {1476-8259},
abstract = {Attention management is crucial for cognitive development, especially in children. This study presents a novel brain-computer interface (BCI) system that uses EEG signals to classify attention states. It analyzes these signals using a waveform ratio feature extraction method and visualizes attention levels through a drone's altitude. The system provides real-time feedback via a GUI and incorporates gamified elements like drone control to enhance engagement and training efficacy. Experimental results show that positive response mechanisms significantly improve focus and motivation, demonstrating the system's potential to transform traditional attention training methods.},
}
@article {pmid39943246,
year = {2025},
author = {Zare, S and Beaber, SI and Sun, Y},
title = {NeuroFlex: Feasibility of EEG-Based Motor Imagery Control of a Soft Glove for Hand Rehabilitation.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {3},
pages = {},
pmid = {39943246},
issn = {1424-8220},
support = {2135620//National Science Foundation/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Hand/physiology/physiopathology ; Brain-Computer Interfaces ; Feasibility Studies ; Movement/physiology ; Hand Strength/physiology ; Robotics ; Deep Learning ; Male ; },
abstract = {Motor impairments resulting from neurological disorders, such as strokes or spinal cord injuries, often impair hand and finger mobility, restricting a person's ability to grasp and perform fine motor tasks. Brain plasticity refers to the inherent capability of the central nervous system to functionally and structurally reorganize itself in response to stimulation, which underpins rehabilitation from brain injuries or strokes. Linking voluntary cortical activity with corresponding motor execution has been identified as effective in promoting adaptive plasticity. This study introduces NeuroFlex, a motion-intent-controlled soft robotic glove for hand rehabilitation. NeuroFlex utilizes a transformer-based deep learning (DL) architecture to decode motion intent from motor imagery (MI) EEG data and translate it into control inputs for the assistive glove. The glove's soft, lightweight, and flexible design enables users to perform rehabilitation exercises involving fist formation and grasping movements, aligning with natural hand functions for fine motor practices. The results show that the accuracy of decoding the intent of fingers making a fist from MI EEG can reach up to 85.3%, with an average AUC of 0.88. NeuroFlex demonstrates the feasibility of detecting and assisting the patient's attempted movements using pure thinking through a non-intrusive brain-computer interface (BCI). This EEG-based soft glove aims to enhance the effectiveness and user experience of rehabilitation protocols, providing the possibility of extending therapeutic opportunities outside clinical settings.},
}
@article {pmid39941230,
year = {2025},
author = {Alshehri, H and Al-Nafjan, A and Aldayel, M},
title = {Decoding Pain: A Comprehensive Review of Computational Intelligence Methods in Electroencephalography-Based Brain-Computer Interfaces.},
journal = {Diagnostics (Basel, Switzerland)},
volume = {15},
number = {3},
pages = {},
pmid = {39941230},
issn = {2075-4418},
support = {(13461-imamu-2023-IMIU-R-3-1-HW-)//The Research, Development, and Innovation Authority 611 (RDIA) - Kingdom of Saudi Arabia/ ; },
abstract = {Objective pain evaluation is crucial for determining appropriate treatment strategies in clinical settings. Studies have demonstrated the potential of using brain-computer interface (BCI) technology for pain classification and detection. Collating knowledge and insights from prior studies, this review explores the extensive work on pain detection based on electroencephalography (EEG) signals. It presents the findings, methodologies, and advancements reported in 20 peer-reviewed articles that utilize machine learning and deep learning (DL) approaches for EEG-based pain detection. We analyze various ML and DL techniques, support vector machines, random forests, k-nearest neighbors, and convolution neural network recurrent neural networks and transformers, and their effectiveness in decoding pain neural signals. The motivation for combining AI with BCI technology lies in the potential for significant advancements in the real-time responsiveness and adaptability of these systems. We reveal that DL techniques effectively analyze EEG signals and recognize pain-related patterns. Moreover, we discuss advancements and challenges associated with EEG-based pain detection, focusing on BCI applications in clinical settings and functional requirements for effective pain classification systems. By evaluating the current research landscape, we identify gaps and opportunities for future research to provide valuable insights for researchers and practitioners.},
}
@article {pmid39937921,
year = {2025},
author = {Qiu, S and Tang, Y and Yu, H and Xie, H and Dreher, JC and Hu, Y and Zhou, X},
title = {Toward a computational understanding of bribe-taking behavior.},
journal = {Annals of the New York Academy of Sciences},
volume = {1545},
number = {1},
pages = {5-15},
doi = {10.1111/nyas.15294},
pmid = {39937921},
issn = {1749-6632},
support = {32200853//National Natural Science Foundation of China/ ; 71942001//National Natural Science Foundation of China/ ; 2021ZD0200500//STI2023-Major Projects/ ; 23ZR1418400//National Science Foundation of Shanghai/ ; 2022ECNUXWK-XK003//Fundamental Research Funds for the Central Universities/ ; ANR-16-IDEX-0005//Agence Nationale de la Recherche/ ; ANR-11-LABX-0042//Agence Nationale de la Recherche/ ; ANR-11-IDEX-007//Agence Nationale de la Recherche/ ; ANR-21-CE37-0032//Agence Nationale de la Recherche/ ; },
mesh = {Humans ; *Decision Making ; Morals ; },
abstract = {Understanding how corrupt behavior occurs is a critical issue at the intersection of behavioral ethics, social psychology, and other related social sciences, laying the foundation for establishing effective anticorruption policies. Despite a substantial body of studies focused on bribe-taking behavior-a typical form of corruption-and its modulators, its underlying psychological processes remain poorly understood. Drawing inspiration from recent literature on neuroeconomics and moral decision-making, we argue that bribe-taking decision-making involves a value-based computational process that can be characterized by a computational framework. We show how this framework advances our understanding of bribe-taking decision-making by (1) clarifying how the cost-benefit tradeoff determines the decision to accept or reject a bribe and its neural foundations, (2) improving the prediction of bribe-taking behaviors across contexts and individuals, and (3) enhancing our comprehension of individual differences in bribe-taking behaviors. Moreover, we delineate how this framework can benefit future research on bribery by examining the mechanisms through which various modulators impact the bribe-taking behaviors or the computational processes underlying more intricate forms of corrupt behaviors. We also discussed its potential fusion with artificial intelligence techniques in offering insights for understanding cognitive processes underlying bribe-taking behaviors and designing anticorruption strategies.},
}
@article {pmid39935682,
year = {2025},
author = {Yasuhara, M and Nambu, I},
title = {Error-related potentials during multitasking involving sensorimotor control: an ERP and offline decoding study for brain-computer interface.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1516721},
pmid = {39935682},
issn = {1662-5161},
abstract = {Humans achieve efficient behaviors by perceiving and responding to errors. Error-related potentials (ErrPs) are electrophysiological responses that occur upon perceiving errors. Leveraging ErrPs to improve the accuracy of brain-computer interfaces (BCIs), utilizing the brain's natural error-detection processes to enhance system performance, has been proposed. However, the influence of external and contextual factors on the detectability of ErrPs remains poorly understood, especially in multitasking scenarios involving both BCI operations and sensorimotor control. Herein, we hypothesized that the difficulty in sensorimotor control would lead to the dispersion of neural resources in multitasking, resulting in a reduction in ErrP features. To examine this, we conducted an experiment in which participants were instructed to keep a ball within a designated area on a board, while simultaneously attempting to control a cursor on a display through motor imagery. The BCI provided error feedback with a random probability of 30%. Three scenarios-without a ball (single-task), lightweight ball (easy-task), and heavyweight ball (hard-task)-were used for the characterization of ErrPs based on the difficulty of sensorimotor control. In addition, to examine the impact of multitasking on ErrP-BCI performance, we analyzed single-trial classification accuracy offline. Contrary to our hypothesis, varying the difficulty of sensorimotor control did not result in significant changes in ErrP features. However, multitasking significantly affected ErrP classification accuracy. Post-hoc analyses revealed that the classifier trained on single-task ErrPs exhibited reduced accuracy under hard-task scenarios. To our knowledge, this study is the first to investigate how ErrPs are modulated in a multitasking environment involving both sensorimotor control and BCI operation in an offline framework. Although the ErrP features remained unchanged, the observed variation in accuracy suggests the need to design classifiers that account for task load even before implementing a real-time ErrP-based BCI.},
}
@article {pmid39935311,
year = {2025},
author = {Wen, X and Xue, P and Zhu, M and Zhong, J and Yu, W and Ma, S and Liu, Y and Liu, P and Jing, B and Yang, M and Mo, X and Zhang, D},
title = {Alteration in Cortical Structure Mediating the Impact of Blood Oxygen-Carrying Capacity on Gross Motor Skills in Infants With Complex Congenital Heart Disease.},
journal = {Human brain mapping},
volume = {46},
number = {3},
pages = {e70155},
pmid = {39935311},
issn = {1097-0193},
support = {62476129//National Natural Science Foundation of China/ ; 81970265//National Natural Science Foundation of China/ ; 82270310//National Natural Science Foundation of China/ ; NZ2024040//Fundamental Research Funds for the Central Universities/ ; },
mesh = {Humans ; *Heart Defects, Congenital/physiopathology/diagnostic imaging/complications ; Male ; Female ; Infant ; *Oxygen/blood ; *Cerebral Cortex/diagnostic imaging/growth &amp; development/physiopathology/pathology ; *Motor Skills/physiology ; Magnetic Resonance Imaging ; Child, Preschool ; },
abstract = {Congenital heart disease (CHD) is the most common congenital anomaly, leading to an increased risk of neurodevelopmental abnormalities in many children with CHD. Understanding the neurological mechanisms behind these neurodevelopmental disorders is crucial for implementing early interventions and treatments. In this study, we recruited 83 infants aged 12-26.5 months with complex CHD, along with 86 healthy controls (HCs). We collected multimodal data to explore the abnormal patterns of cerebral cortex development and explored the complex interactions among blood oxygen-carrying capacity, cortical development, and gross motor skills. We found that, compared to healthy infants, those with complex CHD exhibit significant reductions in cortical surface area development, particularly in the default mode network. Most of these developmentally abnormal brain regions are significantly correlated with the blood oxygen-carrying capacity and gross motor skills of infants with CHD. Additionally, we further discovered that the blood oxygen-carrying capacity of infants with CHD can indirectly predict their gross motor skills through cortical structures, with the left middle temporal area and left inferior temporal area showing the greatest mediation effects. This study identified biomarkers for neurodevelopmental disorders and highlighted blood oxygen-carrying capacity as an indicator of motor development risk, offering new insights for the clinical management CHD.},
}
@article {pmid39935127,
year = {2025},
author = {Xu, Q and Wang, L and Xi, Y and Ruan, T and Cao, J and Xu, M and Zheng, K and Du, Z and Wei, N and Wang, X and Yang, B and Liu, J},
title = {An Efficient MEMS Microelectrode Array with Reliable Interelectrode Insulation Processes for In Vivo Neural Recording.},
journal = {Small (Weinheim an der Bergstrasse, Germany)},
volume = {21},
number = {10},
pages = {e2407950},
doi = {10.1002/smll.202407950},
pmid = {39935127},
issn = {1613-6829},
support = {2022ZD0208601//STI 2030-Major Projects/ ; 2022ZD0208600//STI 2030-Major Projects/ ; 2022YFB3207301//National Key R&amp;D Program of China/ ; 2022YFF120301//National Key R&amp;D Program of China/ ; XDA25040100//Strategic Priority Research Program of Chinese Academy of Sciences/ ; XDA25040200//Strategic Priority Research Program of Chinese Academy of Sciences/ ; XDA25040300//Strategic Priority Research Program of Chinese Academy of Sciences/ ; 62474109 42127807-03//National Natural Science Foundation of China/ ; 2021SHZDZX//Shanghai Municipal Science and Technology Major Project/ ; 21TQ1400203//Shanghai Pilot Program for Basic Research-Shanghai Jiao Tong University/ ; SL2023ZD205//Oceanic Interdisciplinary Program of Shanghai Jiao Tong University/ ; 21&#xd7;010301627//SJTU Trans-med Award/ ; //Fundamental Research Funds for the Central Universities/ ; },
mesh = {Microelectrodes ; Animals ; Mice ; *Micro-Electrical-Mechanical Systems/instrumentation/methods ; *Neurons/physiology ; },
abstract = {Microelectrode arrays, particularly Utah arrays, offer irreplaceable advantages in clinical applications and play a crucial role in advancing brain-computer interactions. However, the glass-fused monolithic structure of Utah arrays limits functional expansion, and the glass insulation process is complex, costly, and time-intensive. This paper presents a microelectrode array with a simple and time-saving fabrication process, utilizing low-resistance silicon and borosilicate glass wafers as electrodes and insulation substrates, respectively. The utilization of the anodic bonding process improves production efficiency and enhances process compatibility. A one-step static wet etching process is used to form microneedle morphology to further simplify the fabrication process. Sputtered iridium oxide, as the electrode interface material, significantly reduces electrochemical impedance, and cellular experiments have confirmed its non-cytotoxicity. Moreover, the implantation into the primary visual cortex of mice has demonstrated the ability of the electrode to record in vivo electrical signals within 15 days. Movement trajectory experiments demonstrate that the mice exhibit good behavior activities following electrode implantation. The bonded microelectrode array (BMEA) presented in this work provides a universal and effective tool for neural recording, with prospective applications in multi-physiological monitoring and microelectromechanical system integration.},
}
@article {pmid39935067,
year = {2025},
author = {Yao, Y and Ahnood, A and Chambers, A and Tong, W and Prawer, S},
title = {Nitrogen-Doped Ultrananocrystalline Diamond - Optoelectronic Biointerface for Wireless Neuronal Stimulation.},
journal = {Advanced healthcare materials},
volume = {14},
number = {9},
pages = {e2403901},
pmid = {39935067},
issn = {2192-2659},
support = {2029454//National Health and Medical Research Council/ ; DE220100302//Australian Research Council/ ; DE210102750//Australian Research Council/ ; },
mesh = {Animals ; *Diamond/chemistry ; *Nitrogen/chemistry ; Rats ; *Neurons/physiology ; Retina ; *Wireless Technology ; Semiconductors ; Light ; },
abstract = {This study presents a semiconducting optoelectronic system for light-controlled non-genetic neuronal stimulation using visible light. The system architecture is entirely wireless, comprising a thin film of nitrogen-doped ultrananocrystalline diamond directly grown on a semiconducting silicon substrate. When immersed in a physiological medium and subjected to pulsed illumination in the visible (595 nm) or near-infrared wavelength (808 nm) range, charge accumulation at the device-medium interface induces a transient ionic displacement current capable of electrically stimulating neurons with high temporal resolution. With a measured photoresponsivity of 7.5 mA W[-1], the efficacy of this biointerface is demonstrated through optoelectronic stimulation of degenerate rat retinas using 595 nm irradiation, pulse durations of 50-500 ms, and irradiance levels of 1.1-4.3 mW mm[-2], all below the safe ocular threshold. This work presents the pioneering utilization of a diamond-based optoelectronic platform, capable of generating sufficiently large photocurrents for neuronal stimulation in the retina.},
}
@article {pmid39934334,
year = {2025},
author = {Chen, Y and Hu, J and Zhao, P and Fang, J and Kuang, Y and Liu, Z and Dong, S and Yao, W and Ding, Y and Wang, X and Pan, Y and Wu, J and Zhao, J and Yang, J and Xu, Z and Liu, X and Zhang, Y and Wu, C and Zhang, L and Fan, M and Feng, S and Hong, Z and Yan, Z and Xia, H and Tang, K and Yang, B and Liu, W and Sun, Q and Mei, K and Zou, W and Huang, Y and Feng, D and Yi, C},
title = {Rpl12 is a conserved ribophagy receptor.},
journal = {Nature cell biology},
volume = {27},
number = {3},
pages = {477-492},
pmid = {39934334},
issn = {1476-4679},
support = {32122028//National Natural Science Foundation of China (National Science Foundation of China)/ ; 92254307//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32070739//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32100600//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32370822//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31970919//National Natural Science Foundation of China (National Science Foundation of China)/ ; LR21C070001//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
mesh = {Animals ; Drosophila melanogaster/metabolism/genetics ; *Caenorhabditis elegans/metabolism/genetics ; *Ribosomal Proteins/metabolism/genetics ; *Ribosomes/metabolism/genetics ; *Caenorhabditis elegans Proteins/metabolism/genetics ; *Autophagy ; Drosophila Proteins/metabolism/genetics ; RNA, Ribosomal/metabolism/genetics ; Phosphorylation ; Humans ; Autophagy-Related Proteins/metabolism/genetics ; Longevity ; },
abstract = {Ribophagy is a selective autophagic process that regulates ribosome turnover. Although NUFIP1 has been identified as a mammalian receptor for ribophagy, its homologues do not exist in yeast and nematodes. Here we demonstrate that Rpl12, a ribosomal large subunit protein, functions as a conserved ribophagy receptor in multiple organisms. Disruption of Rpl12-Atg8s binding leads to significant accumulation of ribosomal proteins and rRNA, while Atg1-mediated Rpl12 phosphorylation enhances its association with Atg11, thus triggering ribophagy during starvation. Ribophagy deficiency accelerates cell death induced by starvation and pathogen infection, leading to impaired growth and development and a shortened lifespan in both Caenorhabditis elegans and Drosophila melanogaster. Moreover, ribophagy deficiency results in motor impairments associated with ageing, while the overexpression of RPL12 significantly improves movement defects induced by starvation, ageing and Aβ accumulation in fly models. Our findings suggest that Rpl12 functions as a conserved ribophagy receptor vital for ribosome metabolism and cellular homeostasis.},
}
@article {pmid39933011,
year = {2025},
author = {Jin, X and Jiang, M and Qian, L and Tao, L and Yang, Y and Xing, L and Qian, Z and Li, W},
title = {Effect of 433 MHz double-slot microwave antennas for double-zone ablation in ex vivo swine liver experiment.},
journal = {PloS one},
volume = {20},
number = {2},
pages = {e0315678},
pmid = {39933011},
issn = {1932-6203},
mesh = {Animals ; *Microwaves/therapeutic use ; *Liver/surgery/radiation effects ; Swine ; *Ablation Techniques/instrumentation ; Finite Element Analysis ; },
abstract = {PURPOSE: To evaluate the effects of axial length and slot-to-slot distance of double-slot microwave antenna (DSMA) with frequency of 433 MHz on the size and shape of ablation zones created under different input microwave powers.<br><br>MATERIALS AND METHODS: The design of double slot microwave antennas (DSMAs) with axial lengths (70&#x2009;mm, 30&#x2009;mm) and slot-to-slot distance (49&#x2009;mm, 10&#x2009;mm) were optimized by numerical simulation and ex vivo liver experiments. Finite-element method simulations and forty ablations of swine liver were employed to obtain the temperature distributions within liver tissue using DSMAs at the 433 MHz operating frequency in a range of heating powers (20, 30, 40 and 50W) for 600&#x2009;s. The dependence of the effectiveness of MWA on the axial length and slot-to-slot distance of antenna as well as the input power was further evaluated by analyzing morphologic characteristics of ablated zone.<br><br>RESULTS: Two-zone ablation was achieved by two types of double-slot antennas in our study with frequency of 433 MHz, and the observed shapes of ex vivo experimental ablation zones were in good agreement with patterns predicted by simulation models. The ablation zone exhibited a 'gourd' shape after the treatment using the antenna with longer axial length and slot-to-slot distance, while the short antenna caused a guitar-shape ablation in liver tissue after MWA.<br><br>CONCLUSION: The dedicated design of our DSMAs with a frequency of 433 MHz could enable new ablation shapes with controllable dimensions, which can be applied to the clinical treatment of MWA for gourd-shaped liver tumors and other long-shaped tumors. Furthermore, research can be conducted on how to design the antenna as flexible and use it for the treatment of pulmonary nodules or varicose veins.},
}
@article {pmid39930808,
year = {2025},
author = {Cao, M and Zhu, S and Tang, E and Xue, C and Li, K and Yu, H and Zhong, T and Li, T and Chen, H and Deng, W},
title = {Neural correlates of emotional processing in trauma-related narratives.},
journal = {Psychological medicine},
volume = {55},
number = {},
pages = {e33},
pmid = {39930808},
issn = {1469-8978},
mesh = {Humans ; *Stress Disorders, Post-Traumatic/physiopathology/diagnostic imaging ; Male ; Adult ; Female ; *Emotions/physiology ; Case-Control Studies ; Spectroscopy, Near-Infrared ; *Prefrontal Cortex/physiopathology/diagnostic imaging ; Narration ; Middle Aged ; Natural Language Processing ; },
abstract = {BACKGROUND: Post-traumatic stress disorder (PTSD) is a mental health condition caused by the dysregulation or overgeneralization of memories related to traumatic events. Investigating the interplay between explicit narrative and implicit emotional memory contributes to a better understanding of the mechanisms underlying PTSD.<br><br>METHODS: This case-control study focused on two groups: unmedicated patients with PTSD and a trauma-exposed control (TEC) group who did not develop PTSD. Experiments included real-time measurements of blood oxygenation changes using functional near-infrared spectroscopy during trauma narration and processing of emotional and linguistic data through natural language processing (NLP).<br><br>RESULTS: Real-time fNIRS monitoring showed that PTSD patients (mean [SD] Oxy-Hb activation, 0.153 [0.084], 95% CI 0.124 to 0.182) had significantly higher brain activity in the left anterior medial prefrontal cortex (L-amPFC) within 10&#xa0;s after expressing negative emotional words compared with the control group (0.047 [0.026], 95% CI 0.038 to 0.056; p&#xa0;<&#xa0;0.001). In the control group, there was a significant time-series correlation between the use of negative emotional memory words and activation of the L-amPFC (latency 3.82&#xa0;s, slope&#xa0;=&#xa0;0.0067, peak value&#xa0;=&#xa0;0.184, difference&#xa0;=&#xa0;0.273; Spearman's r&#xa0;=&#xa0;0.727, p&#xa0;<&#xa0;0.001). In contrast, the left anterior cingulate prefrontal cortex of PTSD patients remained in a state of high activation (peak value&#xa0;=&#xa0;0.153, difference&#xa0;=&#xa0;0.084) with no apparent latency period.<br><br>CONCLUSIONS: PTSD patients display overactivity in pathways associated with rapid emotional responses and diminished regulation in cognitive processing areas. Interventions targeting these pathways may alleviate symptoms of PTSD.},
}
@article {pmid39927921,
year = {2025},
author = {Chen, J and Ke, Y and Ni, G and Liu, S and Ming, D},
title = {Tonic and Event-Related Phasic Transcutaneous Auricular Vagus Nerve Stimulation Alters Pupil Responses in the Change-Detection Task.},
journal = {Neuromodulation : journal of the International Neuromodulation Society},
volume = {28},
number = {4},
pages = {652-662},
doi = {10.1016/j.neurom.2025.01.003},
pmid = {39927921},
issn = {1525-1403},
mesh = {Humans ; Male ; Female ; *Transcutaneous Electric Nerve Stimulation/methods ; Young Adult ; *Vagus Nerve Stimulation/methods ; *Pupil/physiology ; Adult ; Cross-Over Studies ; *Memory, Short-Term/physiology ; Heart Rate/physiology ; Electrocardiography ; },
abstract = {BACKGROUND: Transcutaneous auricular vagus nerve stimulation (taVNS) has emerged as a potential modulator of cognitive behavior by activating the locus ceruleus-noradrenaline (LC-NA) system. Previous studies explored both phasic and tonic taVNS by investigating their impact on LC-NA markers such as pupil dilation and heart rate variability (HRV).<br><br>OBJECTIVE: Inconsistencies persist in the identification of reliable markers for assessing the effects of taVNS on noradrenergic activity. Furthermore, it remains unclear whether the effects of taVNS extend beyond pure vagal nerve responses, particularly in specific cognitive domains such as working memory. In the present study, we investigated the effects of taVNS on working memory capacity and LC-NA markers using a change-detection task.<br><br>MATERIALS AND METHODS: Twenty-two healthy, right-handed university students participated in a sham-controlled, randomized cross-over experiment with four sessions. We applied two types of phasic and event-related stimulation (Pre-event and Event-synchronous), tonic stimulation (Pre-task), and sham stimulation across different sessions. Pupil size and electrocardiogram data were recorded during the tasks.<br><br>RESULTS: taVNS did not significantly modulate behavioral performance on the change-detection task, specifically working memory capacity. However, both tonic and event-related phasic taVNS significantly influenced the pupillary response during the task. In addition, the Pre-task condition of the taVNS affected the low-frequency parameter of HRV.<br><br>CONCLUSIONS: Our findings suggest that tonic and event-related phasic taVNS may modulate noradrenergic activity, as evidenced by pupil responses and HRV changes during the change-detection task. This study provides new evidence regarding the impact of taVNS on cognitive tasks, thus supporting the development of noninvasive neuromodulation interventions.},
}
@article {pmid39926788,
year = {2024},
author = {Sah, SK and Taksande, V and Jadhav, D and Maurya, AT},
title = {Exploring the Impact of Brain-Computer Interfaces on Health Care: Innovations, Challenges, and Future Prospects: A Review Article.},
journal = {Journal of pharmacy &amp; bioallied sciences},
volume = {16},
number = {Suppl 4},
pages = {S3037-S3040},
pmid = {39926788},
issn = {0976-4879},
abstract = {Brain-Computer Interfaces (BCIs) are an innovative technology that methods with a great possibility to revolutionize the sphere of medicine with the help of integration of human brain and external devices. In this article, we discuss how BCIs can be incorporated into hospitals and civil rehabilitation centers, possibly for rehabilitation, communication, and cognitive treatments. This review aims to discuss the advancement, usefulness, difficulties, and potential in regards to the use of BCIs in healthcare. We describe trends in the development of BCIs from simple experimental paradigms to multimedia advanced devices and their usage in clinical practice: assistive technology in patients with motor disorders, neurorehabilitation of post-stroke patients, and cognitive prosthesis for humans with neurodegenerative diseases. The article also emphasizes on present-day issues including signal quality, comfort level of the users, and the ethical parameter of the technique along with the research going on and future work streams. Thus, by evaluating the modern developments in the field and highlighting the existing problems, this article will try to give a briefing on the current stage of application of BCIs in the sphere of healthcare.},
}
@article {pmid39926014,
year = {2025},
author = {Li, C and Xu, Y and Feng, T and Wang, M and Zhang, X and Zhang, L and Cheng, R and Chen, W and Chen, W and Zhang, S},
title = {Fusion of EEG and EMG signals for detecting pre-movement intention of sitting and standing in healthy individuals and patients with spinal cord injury.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1532099},
pmid = {39926014},
issn = {1662-4548},
abstract = {INTRODUCTION: Rehabilitation devices assist individuals with movement disorders by supporting daily activities and facilitating effective rehabilitation training. Accurate and early motor intention detection is vital for real-time device applications. However, traditional methods of motor intention detection often rely on single-mode signals, such as EEG or EMG alone, which can be limited by low signal quality and reduced stability. This study proposes a multimodal fusion method based on EEG-EMG functional connectivity to detect sitting and standing intentions before movement execution, enabling timely intervention and reducing latency in rehabilitation devices.<br><br>METHODS: Eight healthy subjects and five spinal cord injury (SCI) patients performed cue-based sit-to-stand and stand-to-sit transition tasks while EEG and EMG data were recorded simultaneously. We constructed EEG-EMG functional connectivity networks using data epochs from the 1.5-s period prior to movement onset. Pairwise spatial filters were then designed to extract discriminative spatial network topologies. Each filter paired with a support vector machine classifier to classify future movements into one of three classes: sit-to-stand, stand-to-sit, or rest. The final prediction was determined using a majority voting scheme.<br><br>RESULTS: Among the three functional connectivity methods investigated-coherence, Pearson correlation coefficient and mutual information (MI)-the MI-based EEG-EMG network showed the highest decoding performance (94.33%), outperforming both EEG (73.89%) and EMG (89.16%). The robustness of the fusion method was further validated through a fatigue training experiment with healthy subjects. The fusion method achieved 92.87% accuracy during the post-fatigue stage, with no significant difference compared to the pre-fatigue stage (p&#x202f;>&#x202f;0.05). Additionally, the proposed method using pre-movement windows achieved accuracy comparable to trans-movement windows (p&#x202f;>&#x202f;0.05 for both pre- and post-fatigue stages). For the SCI patients, the fusion method showed improved accuracy, achieving 87.54% compared to single- modality methods (EEG: 83.03%, EMG: 84.13%), suggesting that the fusion method could be promising for practical rehabilitation applications.<br><br>CONCLUSION: Our results demonstrated that the proposed multimodal fusion method significantly enhances the performance of detecting human motor intentions. By enabling early detection of sitting and standing intentions, this method holds the potential to offer more accurate and timely interventions within rehabilitation systems.},
}
@article {pmid39925722,
year = {2025},
author = {Radwan, YA and Ahmed Mohamed, E and Metwalli, D and Barakat, M and Ahmed, A and Kiroles, AE and Selim, S},
title = {Stochasticity as a solution for overfitting-A new model and comparative study on non-invasive EEG prospects.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1484470},
pmid = {39925722},
issn = {1662-5161},
abstract = {The potential and utility of inner speech is pivotal for developing practical, everyday Brain-Computer Interface (BCI) applications, as it represents a type of brain signal that operates independently of external stimuli however it is largely underdeveloped due to the challenges faced in deciphering its signals. In this study, we evaluated the behaviors of various Machine Learning (ML) and Deep Learning (DL) models on a publicly available dataset, employing popular preprocessing methods as feature extractors to enhance model training. We face significant challenges like subject-dependent variability, high noise levels, and overfitting. To address overfitting in particular, we propose using "BruteExtraTree": a new classifier which relies on moderate stochasticity inherited from its base model, the ExtraTreeClassifier. This model not only matches the best DL model, ShallowFBCSPNet, in the subject-independent scenario in our experiments scoring 32% accuracy, but also surpasses the state-of-the-art by achieving 46.6% average per-subject accuracy in the subject-dependent case. Our results on the subject-dependent case show promise on the possibility of a new paradigm for using inner speech data inspired from LLM pretraining but we also highlight the crucial need for a drastic change in data recording or noise removal methods to open the way for more practical accuracies in the subject-independent case.},
}
@article {pmid39923980,
year = {2025},
author = {Ghasimi, A and Shamekhi, S},
title = {Enhanced EEG-based cognitive workload detection using RADWT and machine learning.},
journal = {Neuroscience},
volume = {569},
number = {},
pages = {231-244},
doi = {10.1016/j.neuroscience.2025.01.068},
pmid = {39923980},
issn = {1873-7544},
mesh = {Humans ; *Electroencephalography/methods ; *Cognition/physiology ; *Wavelet Analysis ; *Brain/physiology ; *Machine Learning ; Support Vector Machine ; Male ; Adult ; Female ; Young Adult ; *Workload ; },
abstract = {Understanding cognitive workload improves learning performance and provides insights into human cognitive processes. Estimating cognitive workload finds practical applications in adaptive learning systems, brain-computer interfaces, and cognitive monitoring. In this work, different levels of cognitive workload are investigated, and a classification approach based on the Rational-Dilation Wavelet Transform (RADWT) is proposed. RADWT excels at capturing the oscillatory behavior of EEG signal sub-bands, offering high precision through its ability to adaptively analyze both temporal and spectral dynamics. Different classifications of machine learning and feature selection techniques were evaluated to get optimum classification accuracy and identify the most effective combination of features for the used dataset. The analysis shows that the most relevant brain region in differentiating cognitive workload levels is the frontal region, along with alpha and theta rhythm sub-bands. Integrating RADWT with a Linear Support Vector Machine (LSVM) and minimum Redundancy Maximum Relevance (mRMR) feature selection method yields notable classification accuracy. Concretely, the model yields accuracies of 96.6% for 0-back vs.3-back, 94.9% for 0-back vs 2-back, 92.3% for 2-back vs 3-back, and 81.7% for the three-class scenario. These results confirm the validity of the method proposed for estimating cognitive workload using the RADWT- and machine learning-based approach. The results also offer insights into neural mechanisms and a foundation for advanced applications in adaptive systems, brain-computer interfaces, and cognitive monitoring.},
}
@article {pmid39921840,
year = {2025},
author = {Huang, Y and Wang, J and Liu, N and Xu, H},
title = {Zona Incerta: A Bridge for Infant-Mother Interaction.},
journal = {Neuroscience bulletin},
volume = {41},
number = {5},
pages = {921-924},
pmid = {39921840},
issn = {1995-8218},
}
@article {pmid39921681,
year = {2025},
author = {Liyanagedera, ND and Bareham, CA and Kempton, H and Guesgen, HW},
title = {Novel machine learning-driven comparative analysis of CSP, STFT, and CSP-STFT fusion for EEG data classification across multiple meditation and non-meditation sessions in BCI pipeline.},
journal = {Brain informatics},
volume = {12},
number = {1},
pages = {4},
pmid = {39921681},
issn = {2198-4018},
support = {1839RF//Neurological Foundation of New Zealand/ ; MAU2011//Royal Society of New Zealand Marsden Fast-Start/ ; },
abstract = {This study focuses on classifying multiple sessions of loving kindness meditation (LKM) and non-meditation electroencephalography (EEG) data. This novel study focuses on using multiple sessions of EEG data from a single individual to train a machine learning pipeline, and then using a new session data from the same individual for the classification. Here, two meditation techniques, LKM-Self and LKM-Others were compared with non-meditation EEG data for 12 participants. Among many tested, three BCI pipelines we built produced promising results, successfully detecting features in meditation/ non-meditation EEG data. While testing different feature extraction algorithms, a common neural network structure was used as the classification algorithm to compare the performance of the feature extraction algorithms. For two of those pipelines, Common Spatial Patterns (CSP) and Short Time Fourier Transform (STFT) were successfully used as feature extraction algorithms where both these algorithms are significantly new for meditation EEG. As a novel concept, the third BCI pipeline used a feature extraction algorithm that fused the features of CSP and STFT, achieving the highest classification accuracies among all tested pipelines. Analyses were conducted using EEG data of 3, 4 or 5 sessions, totaling 3960 tests on the entire dataset. At the end of the study, when considering all the tests, the overall classification accuracy using SCP alone was 67.1%, and it was 67.8% for STFT alone. The algorithm combining the features of CSP and STFT achieved an overall classification accuracy of 72.9% which is more than 5% higher than the other two pipelines. At the same time, the highest mean classification accuracy for the 12 participants was achieved using the pipeline with the combination of CSP STFT algorithm, reaching 75.5% for LKM-Self/ non-meditation for the case of 5 sessions of data. Additionally, the highest individual classification accuracy of 88.9% was obtained by the participant no. 14. Furthermore, the results showed that the classification accuracies for all three pipelines increased with the number of training sessions increased from 2 to 3 and then to 4. The study was successful in classifying a new session of EEG meditation/ non-meditation data after training machine learning algorithms using a different set of session data, and this achievement will be beneficial in the development of algorithms that support meditation.},
}
@article {pmid39918096,
year = {2025},
author = {Chen, L and Yang, Z and Ji, S and Song, T and Li, H and Tang, Y and Chen, Y and Li, Y},
title = {Comparing the Risk of Epilepsy in Patients With Simple Congenital Heart Diseases: A Prospective Cohort Study.},
journal = {CNS neuroscience &amp; therapeutics},
volume = {31},
number = {2},
pages = {e70230},
pmid = {39918096},
issn = {1755-5949},
support = {2022-I2M-C&amp;T-B-100//the Chinese Academy of Medical Sciences by Medical and Health Technology Innovation Project/ ; 2024NSFSC1652//the support from the Natural Science Foundation of Sichuan/ ; },
mesh = {Humans ; Female ; Male ; *Epilepsy/epidemiology/diagnosis ; *Heart Defects, Congenital/epidemiology/complications ; Prospective Studies ; Adult ; Adolescent ; Cohort Studies ; Young Adult ; China/epidemiology ; Middle Aged ; Child ; Incidence ; Risk Factors ; Follow-Up Studies ; Child, Preschool ; },
abstract = {AIMS: Simple congenital heart diseases (CHD) are associated with various central nervous system diseases, including epilepsy. This study aimed to compare the risk of epilepsy in patients with different types of simple CHD.<br><br>METHODS: In this prospective cohort study, from January 2008 to June 2022, patients with atrial septal defect (ASD), patent foramen ovale (PFO), ventricular septal defect (VSD), and patent ductus arteriosus (PDA) were recruited at the Registration Center of CHD in West China Hospital. Follow-up was conducted yearly until the diagnosis of epilepsy, loss to follow-up, or end of study. The outcomes included a comparison of epilepsy incidence according to different simple CHD types and a risk assessment of developing epilepsy. Multivariable Poisson regression was performed to adjusted factors of demographics and disease history.<br><br>RESULTS: Of 10,914 patients who met the inclusion criteria, 108 were diagnosed with epilepsy at an average follow-up of 2.19&#x2009;years. Epilepsy incidence in patients with PFO, VSD, PDA, and ASD was 8.58/1000, 4.85/1000, 3.98/1000, and 2.63/1000 person-years, respectively. Compared with ASD patients (reference group), the risk ratios (95% confidence intervals) in patients with PFO, VSD, and PDA were 3.28 (2.00-5.43), 1.47 (0.79-2.68), and 1.46 (0.70-2.82), respectively. Subgroup analyses determined that patients with simple CHD who underwent CHD surgery demonstrated a lower risk of epilepsy than those who did not.<br><br>CONCLUSION: Among the major types of simple CHD, PFO was associated with a significantly higher risk of epilepsy, while the risk was reduced in those who underwent PFO closure procedures.},
}
@article {pmid39914028,
year = {2025},
author = {Cernera, S and Gemicioglu, T and Berezutskaya, J and Csaky, R and Verwoert, M and Polyakov, D and Papadopoulos, S and Spagnolo, V and Astudillo, JG and Kumar, S and Alawieh, H and Kelly, D and Keough, JRG and Minhas, A and Dold, M and Han, Y and McClanahan, A and Mustafa, M and Gonzalez-Espana, JJ and Garro, F and Vujic, A and Kacker, K and Kapeller, C and Geukes, S and Verbaarschot, C and Wimmer, M and Sultana, M and Ahmadi, S and Herff, C and Sburlea, AI and Jeunet, C and Thompson, DE and Semprini, M and Andersen, R and Stavisky, S and Kinney-Lang, E and Lotte, F and Thielen, J and Chen, X and Peterson, V and Gunduz, A and Vaughan, T and Valeriani, D},
title = {Master classes of the tenth international brain-computer interface meeting: showcasing the research of BCI trainees.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
pmid = {39914028},
issn = {1741-2552},
support = {P41 EB018783/EB/NIBIB NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces/trends ; Humans ; Electroencephalography/methods ; },
abstract = {The Tenth International brain-computer interface (BCI) meeting was held June 6-9, 2023, in the Sonian Forest in Brussels, Belgium. At that meeting, 21 master classes, organized by the BCI Society's Postdoc &amp; Student Committee, supported the Society's goal of fostering learning opportunities and meaningful interactions for trainees in BCI-related fields. Master classes provide an informal environment where senior researchers can give constructive feedback to the trainee on their chosen and specific pursuit. The topics of the master classes span the whole gamut of BCI research and techniques. These include data acquisition, neural decoding and analysis, invasive and noninvasive stimulation, and ethical and transitional considerations. Additionally, master classes spotlight innovations in BCI research. Herein, we discuss what was presented within the master classes by highlighting each trainee and expert researcher, providing relevant background information and results from each presentation, and summarizing discussion and references for further study.},
}
@article {pmid39914006,
year = {2025},
author = {Bakas, S and Ludwig, S and Adamos, DA and Laskaris, N and Panagakis, Y and Zafeiriou, S},
title = {Latent alignment in deep learning models for EEG decoding.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/adb336},
pmid = {39914006},
issn = {1741-2552},
mesh = {*Deep Learning ; Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Male ; Adult ; Female ; Imagination/physiology ; Event-Related Potentials, P300/physiology ; Sleep Stages/physiology ; },
abstract = {Objective. Brain-computer interfaces (BCIs) face a significant challenge due to variability in electroencephalography (EEG) signals across individuals. While recent approaches have focused on standardizing input signal distributions, we propose that aligning distributions in the deep learning model's feature space is more effective for classification.Approach. We introduce the Latent Alignment method, which won the Benchmarks for EEG Transfer Learning competition. This method can be formulated as a deep set architecture applied to trials from a given subject, introducing deep sets to EEG decoding for the first time. We compare Latent Alignment to recent statistical domain adaptation techniques, carefully considering class-discriminative artifacts and the impact of class distributions on classification performance.Main results. Our experiments across motor imagery, sleep stage classification, and P300 event-related potential tasks validate Latent Alignment's effectiveness. We identify a trade-off between improved classification accuracy when alignment is performed at later modeling stages and increased susceptibility to class imbalance in the trial set used for statistical computation.Significance. Latent Alignment offers consistent improvements to subject-independent deep learning models for EEG decoding when relevant practical considerations are addressed. This work advances our understanding of statistical alignment techniques in EEG decoding and provides insights for their effective implementation in real-world BCI applications, potentially facilitating broader use of BCIs in healthcare, assistive technologies, and beyond. The model code is available athttps://github.com/StylianosBakas/LatentAlignment.},
}
@article {pmid39913287,
year = {2025},
author = {Ma, Q and Tian, JL and Lou, Y and Guo, R and Ma, XR and Wu, JB and Yang, J and Tang, BJ and Li, S and Qiu, M and Duan, S and Zhao, JW and Zhang, J and Xu, ZZ},
title = {Oligodendrocytes drive neuroinflammation and neurodegeneration in Parkinson's disease via the prosaposin-GPR37-IL-6 axis.},
journal = {Cell reports},
volume = {44},
number = {2},
pages = {115266},
doi = {10.1016/j.celrep.2025.115266},
pmid = {39913287},
issn = {2211-1247},
mesh = {Animals ; *Oligodendroglia/metabolism/pathology ; *Parkinson Disease/pathology/metabolism ; *Receptors, G-Protein-Coupled/metabolism/genetics ; *Interleukin-6/metabolism ; Mice ; Humans ; Dopaminergic Neurons/metabolism/pathology ; *Saposins/metabolism ; Mice, Inbred C57BL ; *Neuroinflammatory Diseases/pathology/metabolism ; Male ; Signal Transduction ; *Nerve Degeneration/pathology/metabolism ; Disease Models, Animal ; },
abstract = {Parkinson's disease (PD) is a common neurodegenerative disease and is difficult to treat due to its elusive mechanisms. Recent studies have identified a striking association between oligodendrocytes and PD progression, yet how oligodendrocytes regulate the pathogenesis of PD is still unknown. Here, we show that G-protein-coupled receptor 37 (GPR37) is upregulated in oligodendrocytes of the substantia nigra and that prosaposin (PSAP) secretion is increased in parkinsonian mice. The released PSAP can induce interleukin (IL)-6 upregulation and secretion from oligodendrocytes via a GPR37-dependent pathway, resulting in enhanced neuroinflammation, dopamine neuron degeneration, and behavioral deficits. GPR37 deficiency in oligodendrocytes prevents neurodegeneration in multiple PD models. Finally, the hallmarks of the PSAP-GPR37-IL-6 axis are observed in patients with PD. Thus, our results reveal that dopaminergic neurons interact with oligodendrocytes via secreted PSAP, and our findings identify the PSAP-GPR37-IL-6 axis as a driver of PD pathogenesis and a potential therapeutic target that might alleviate PD progression in patients.},
}
@article {pmid39912868,
year = {2025},
author = {Chen, P and Zhang, B and He, E and Xiao, Y and Liu, F and Lin, P and Wang, Z and Pan, G},
title = {Towards scalable memristive hardware for spiking neural networks.},
journal = {Materials horizons},
volume = {12},
number = {9},
pages = {2820-2839},
doi = {10.1039/d4mh01676a},
pmid = {39912868},
issn = {2051-6355},
abstract = {Spiking neural networks (SNNs) represent a promising frontier in artificial intelligence (AI), offering event-driven, energy-efficient computation that mimics rich neural dynamics in the brain. However, running large-scale SNNs on mainstream computing hardware faces significant challenges to efficiently emulate these dynamical processes using synchronized and logical chips. Memristor based systems have recently demonstrated great potential for AI acceleration, sparking speculations and explorations of using these emerging devices for SNN tasks. This paper reviews the promises and challenges of memristive devices in SNN implementations, and our discussions are focused on the scaling and integration of neuronal and synaptic devices. We survey recent progress in device and circuit development, discuss possible pathways for chip-level integration, and finally probe into hardware-oriented algorithm designs. This review offers a system-level perspective on implementing scalable memristor based SNN platforms.},
}
@article {pmid39912736,
year = {2025},
author = {Huang, Z and Mei, T and Zhu, X and Xiao, K},
title = {Ionic Device: From Neuromorphic Computing to Interfacing with the Brain.},
journal = {Chemistry, an Asian journal},
volume = {20},
number = {7},
pages = {e202401170},
doi = {10.1002/asia.202401170},
pmid = {39912736},
issn = {1861-471X},
support = {B2401005//Shenzhen Medical Research Fund/ ; 22275079//National Natural Science Foundation of China/ ; 22474053//National Natural Science Foundation of China/ ; KQTD20221101093559017//Shenzhen Science and Technology Program/ ; JCYJ20230807093205011//Shenzhen Science and Technology Program/ ; 2024A1515012600//Guangdong Basic and Applied Basic Research Foundation/ ; 2022B1212010003//Guangdong Provincial Key Laboratory of Advanced Biomaterials/ ; //Starting Grant from Southern University of Science and Technology/ ; G03050K002//High level of special funds/ ; },
mesh = {Humans ; *Neural Networks, Computer ; *Brain/physiology ; Ions/chemistry ; Algorithms ; },
abstract = {In living organisms, the modulation of ion conductivity in ion channels of neuron cells enables intelligent behaviors, such as generating, transmitting, and storing neural signals. Drawing inspiration from these natural processes, researchers have fabricated ionic devices that replicate the functions of the nervous system. However, this field remains in its infancy, necessitating extensive foundational research in ionic device preparation, algorithm development, and biological interaction. This review summarizes recently developed neuromorphic ionic devices into three categories based on the materials states: liquid, semi-solid, and solid. The neural network algorithms embedded in these devices for neuromorphic computing are introduced, and future directions for the development of bidirectional human-computer interaction and hybrid human-computer intelligence are discussed.},
}
@article {pmid39911854,
year = {2025},
author = {Wu, X and Chu, Y and Li, Q and Luo, Y and Zhao, Y and Zhao, X},
title = {AMEEGNet: attention-based multiscale EEGNet for effective motor imagery EEG decoding.},
journal = {Frontiers in neurorobotics},
volume = {19},
number = {},
pages = {1540033},
pmid = {39911854},
issn = {1662-5218},
abstract = {Recently, electroencephalogram (EEG) based on motor imagery (MI) have gained significant traction in brain-computer interface (BCI) technology, particularly for the rehabilitation of paralyzed patients. But the low signal-to-noise ratio of MI EEG makes it difficult to decode effectively and hinders the development of BCI. In this paper, a method of attention-based multiscale EEGNet (AMEEGNet) was proposed to improve the decoding performance of MI-EEG. First, three parallel EEGNets with fusion transmission method were employed to extract the high-quality temporal-spatial feature of EEG data from multiple scales. Then, the efficient channel attention (ECA) module enhances the acquisition of more discriminative spatial features through a lightweight approach that weights critical channels. The experimental results demonstrated that the proposed model achieves decoding accuracies of 81.17, 89.83, and 95.49% on BCI-2a, 2b and HGD datasets. The results show that the proposed AMEEGNet effectively decodes temporal-spatial features, providing a novel perspective on MI-EEG decoding and advancing future BCI applications.},
}
@article {pmid39909352,
year = {2025},
author = {Li, X and Wei, W and Qian, L and Li, X and Li, M and Kakkos, I and Wang, Q and Yu, H and Guo, W and Ma, X and Matsopoulos, GK and Zhao, L and Deng, W and Sun, Y and Li, T},
title = {Individualized prediction of multi-domain intelligence quotient in bipolar disorder patients using resting-state functional connectivity.},
journal = {Brain research bulletin},
volume = {222},
number = {},
pages = {111238},
doi = {10.1016/j.brainresbull.2025.111238},
pmid = {39909352},
issn = {1873-2747},
mesh = {Humans ; *Bipolar Disorder/physiopathology/psychology/diagnostic imaging ; Female ; Male ; Adult ; Magnetic Resonance Imaging/methods ; *Intelligence/physiology ; *Connectome/methods ; Middle Aged ; Intelligence Tests ; *Brain/physiopathology/diagnostic imaging ; Nerve Net/physiopathology/diagnostic imaging ; Young Adult ; Neural Pathways/physiopathology ; },
abstract = {BACKGROUND: Although accumulating studies have explored the neural underpinnings of intelligence quotient (IQ) in patients with bipolar disorder (BD), these studies utilized a classification/comparison scheme that emphasized differences between BD and healthy controls at a group level. The present study aimed to infer BD patients' IQ scores at the individual level using a prediction model.<br><br>METHODS: We applied a cross-validated Connectome-based Predictive Modeling (CPM) framework using resting-state fMRI functional connectivity (FCs) to predict BD patients' IQ scores, including verbal IQ (VIQ), performance IQ (PIQ), and full-scale IQ (FSIQ). For each IQ domain, we selected the FCs that contributed to the predictions and described their distribution across eight widely-recognized functional networks. Moreover, we further explored the overlapping patterns of the contributed FCs for different IQ domains.<br><br>RESULTS: The CPM achieved statistically significant prediction performance for three IQ domains in BD patients. Regarding the contributed FCs, we observed a widespread distribution of internetwork FCs across somatomotor, visual, dorsal attention, and ventral attention networks, demonstrating their correspondence with aberrant FCs correlated to cognition deficits in BD patients. A convergent pattern in terms of contributed FCs for different IQ domains was observed, as evidenced by the shared-FCs with a leftward hemispheric dominance.<br><br>CONCLUSIONS: The present study preliminarily explored the feasibility of inferring individual IQ scores in BD patients using the FCs-based CPM framework. It is a step toward the development of applicable techniques for quantitative and objective cognitive assessment in BD patients and contributes novel insights into understanding the complex neural mechanisms underlying different IQ domains.},
}
@article {pmid39908388,
year = {2025},
author = {Xiao, Y and Liu, Y and Zhang, B and Chen, P and Zhu, H and He, E and Zhao, J and Huo, W and Jin, X and Zhang, X and Jiang, H and Ma, D and Zheng, Q and Tang, H and Lin, P and Kong, W and Pan, G},
title = {Bio-plausible reconfigurable spiking neuron for neuromorphic computing.},
journal = {Science advances},
volume = {11},
number = {6},
pages = {eadr6733},
pmid = {39908388},
issn = {2375-2548},
mesh = {*Neurons/physiology ; *Action Potentials/physiology ; *Models, Neurological ; *Neural Networks, Computer ; Humans ; },
abstract = {Biological neurons use diverse temporal expressions of spikes to achieve efficient communication and modulation of neural activities. Nonetheless, existing neuromorphic computing systems mainly use simplified neuron models with limited spiking behaviors due to high cost of emulating these biological spike patterns. Here, we propose a compact reconfigurable neuron design using the intrinsic dynamics of a NbO2-based spiking unit and excellent tunability in an electrochemical memory (ECRAM) to emulate the fast-slow dynamics in a bio-plausible neuron. The resistance of the ECRAM was effective in tuning the temporal dynamics of the membrane potential, contributing to flexible reconfiguration of various bio-plausible firing modes, such as phasic and burst spiking, and exhibiting adaptive spiking behaviors in changing environment. We used the bio-plausible neuron model to build spiking neural networks with bursting neurons and demonstrated improved classification accuracies over simplified models, showing great promises for use in more bio-plausible neuromorphic computing systems.},
}
@article {pmid39908280,
year = {2025},
author = {El-Osta, A and Al Ammouri, M and Khan, S and Altalib, S and Karki, M and Riboli-Sasco, E and Majeed, A},
title = {Community perspectives regarding brain-computer interfaces: A cross-sectional study of community-dwelling adults in the UK.},
journal = {PLOS digital health},
volume = {4},
number = {2},
pages = {e0000524},
pmid = {39908280},
issn = {2767-3170},
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) represent a ground-breaking advancement in neuroscience, facilitating direct communication between the brain and external devices. This technology has the potential to significantly improve the lives of individuals with neurological disorders by providing innovative solutions for rehabilitation, communication and personal autonomy. However, despite the rapid progress in BCI technology and social media discussions around Neuralink, public perceptions and ethical considerations concerning BCIs-particularly within community settings in the UK-have not been thoroughly investigated.<br><br>OBJECTIVE: The primary aim of this study was to investigate public knowledge, attitudes and perceptions regarding BCIs including ethical considerations. The study also explored whether demographic factors were related to beliefs about BCIs increasing inequalities, support for strict regulations, and perceptions of appropriate fields for BCI design, testing and utilization in healthcare.<br><br>METHODS: This cross-sectional study was conducted between 1 December 2023 and 8 March 2024. The survey included 29 structured questions covering demographics, awareness of BCIs, ethical considerations and willingness to use BCIs for various applications. The survey was distributed via the Imperial College Qualtrics platform. Participants were recruited primarily through Prolific Academic's panel and personal networks. Data analysis involved summarizing responses using frequencies and percentages, with chi-squared tests to compare groups. All data were securely stored and pseudo-anonymized to ensure confidentiality.<br><br>RESULTS: Of the 950 invited respondents, 846 participated and 806 completed the survey. The demographic profile was diverse, with most respondents aged 36-45 years (26%) balanced in gender (52% female), and predominantly identifying as White (86%). Most respondents (98%) had never used BCIs, and 65% were unaware of them prior to the survey. Preferences for BCI types varied by condition. Ethical concerns were prevalent, particularly regarding implantation risks (98%) and costs (92%). Significant associations were observed between demographic variables and perceptions of BCIs regarding inequalities, regulation and their application in healthcare. Conclusion: Despite strong interest in BCIs, particularly for medical applications, ethical concerns, safety and privacy issues remain significant highlighting the need for clear regulatory frameworks and ethical guidelines, as well as educational initiatives to improve public understanding and trust. Promoting public discourse and involving stakeholders including potential users, ethicists and technologists in the design process through co-design principles can help align technological development with public concerns whilst also helping developers to proactively address ethical dilemmas.},
}
@article {pmid39908266,
year = {2025},
author = {Liu, X and Zhi, H and Czosnyka, M and Robba, C and Czosnyka, Z and Summers, JL and Yu, H and Tong, X and Gao, G and Xiao, G and Yu, K and Xing, Y and Mao, R and Yin, S and Chao, Y and Li, H and Pu, K and Feng, K and Pang, M and Ming, D},
title = {Advancing Hydrocephalus Management: Pathogenesis Insights, Therapeutic Innovations, and Emerging Challenges.},
journal = {Aging and disease},
volume = {},
number = {},
pages = {},
doi = {10.14336/AD.2024.1434},
pmid = {39908266},
issn = {2152-5250},
abstract = {Hydrocephalus is a prevalent neurological disorder, particularly impactful in older adults, characterized by high incidence and numerous complications that impose a significant burden on healthcare systems. This review aims to provide a comprehensive description of hydrocephalus pathogenesis, focusing on cellular and molecular insights derived from animal models. We also present the latest advances in hydrocephalus research and highlight potential therapeutic targets. Lastly, the review advocates the integration of findings from both animal and human studies to achieve better outcomes and examines the potential of emerging technologies. We wish to raise public attention about this disease in an aging society. Current animal models for hydrocephalus involve acquired hydrocephalus models and genetic/congenital hydrocephalus models. Studies from animals have shown that the main mechanisms of models can be broadly classified into nine types. A variety of drug-targeted therapy methods and non-surgical treatment methods have been used in clinical practice. But current treatment approaches primarily focus on symptomatic relief and intracranial pressure control rather than addressing the underlying pathological mechanisms. We call for the development of more accurate and representative animal models to achieve better outcomes and examine the potential of emerging technologies, such as artificial intelligence and neuroimaging. In summary, this review synthesizes recent findings in hydrocephalus research, identifies promising therapeutic targets and interventions, and critically evaluates the limitations of current research paradigms, aiming to align preclinical studies with clinical endpoints. Continued studies and multidisciplinary collaboration are essential to develop effective interventions and facilitate new treatments into bedside.},
}
@article {pmid39904453,
year = {2025},
author = {Liu, B and Wang, Y and Gao, L and Cai, Z},
title = {Enhanced electroencephalogram signal classification: A hybrid convolutional neural network with attention-based feature selection.},
journal = {Brain research},
volume = {1851},
number = {},
pages = {149484},
doi = {10.1016/j.brainres.2025.149484},
pmid = {39904453},
issn = {1872-6240},
mesh = {*Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; Brain-Computer Interfaces ; Brain/physiology ; *Attention/physiology ; Signal Processing, Computer-Assisted ; Imagination/physiology ; Deep Learning ; Male ; Convolutional Neural Networks ; },
abstract = {Accurate recognition and classification of motor imagery electroencephalogram (MI-EEG) signals are crucial for the successful implementation of brain-computer interfaces (BCI). However, inherent characteristics in original MI-EEG signals, such as nonlinearity, low signal-to-noise ratios, and large individual variations, present significant challenges for MI-EEG classification using traditional machine learning methods. To address these challenges, we propose an automatic feature extraction method rooted in deep learning for MI-EEG classification. First, original MI-EEG signals undergo noise reduction through discrete wavelet transform and common average reference. To reflect the regularity and specificity of brain neural activities, a convolutional neural network (CNN) is used to extract the time-domain features of MI-EEG. We also extracted spatial features to reflect the activity relationships and connection states of the brain in different regions. This process yields time series containing spatial information, focusing on enhancing crucial feature sequences through talking-heads attention. Finally, more abstract spatial-temporal features are extracted using a temporal convolutional network (TCN), and classification is done through a fully connected layer. Validation experiments based on the BCI Competition IV-2a dataset show that the enhanced EEG model achieves an impressive average classification accuracy of 85.53% for each subject. Compared with CNN, EEGNet, CNN-LSTM and EEG-TCNet, the classification accuracy of this model is improved by 11.24%, 6.90%, 11.18% and 6.13%, respectively. Our work underscores the potential of the proposed model to enhance intention recognition in MI-EEG significantly.},
}
@article {pmid39904383,
year = {2025},
author = {Shi, CY and Zhang, HT and Tang, Z},
title = {Large-sized trees regulating the structural diversity-productivity relationships through shaping different productive processes in a tropical forest.},
journal = {Proceedings. Biological sciences},
volume = {292},
number = {2040},
pages = {20242202},
pmid = {39904383},
issn = {1471-2954},
support = {//National Natural Science Foundation of China/ ; },
mesh = {*Trees/growth &amp; development/physiology ; *Rainforest ; Tropical Climate ; *Biodiversity ; Biomass ; *Forests ; Colorado ; },
abstract = {Forest structural diversity, a measurement indicating the spatial and size distribution of individual trees, is critical for forest productivity, which stems from the combination of different ecological processes, such as tree mortality, recruitment and growth. Here, we evaluated the relationship between structural diversity and productivity caused by different ecological processes, and tested the roles of different-sized trees in influencing this relationship in a Forest Global Earth Observatory (ForestGEO) rainforest site on the Barro Colorado Island between 2000 and 2015. Generally, we found a negative relationship between structural diversity and forest productivity. Specifically, tree mortality-induced productivity loss increased, while tree recruitment-induced productivity gain decreased, with structural diversity. In addition, the structural diversity-productivity relationship varied with tree size, which was negative for small trees but positive for large trees. Furthermore, we revealed the important role of large-sized trees, which significantly promoted structural diversity but decreased productivity through increasing biomass loss. By disentangling the components of productivity, our results provide insights on the mechanism of the relationship between structural diversity and productivity, and highlight the role of large trees in shaping this relationship.},
}
@article {pmid39902757,
year = {2025},
author = {Wang, J and Luo, Y and Wang, H and Wang, L and Zhang, L and Gan, Z and Kang, X},
title = {FLANet: A multiscale temporal convolution and spatial-spectral attention network for EEG artifact removal with adversarial training.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/adae34},
pmid = {39902757},
issn = {1741-2552},
mesh = {*Electroencephalography/methods ; Humans ; *Artifacts ; *Neural Networks, Computer ; Signal-To-Noise Ratio ; *Attention/physiology ; Signal Processing, Computer-Assisted ; *Deep Learning ; },
abstract = {Objective.Denoising artifacts, such as noise from muscle or cardiac activity, is a crucial and ubiquitous concern in neurophysiological signal processing, particularly for enhancing the signal-to-noise ratio in electroencephalograph (EEG) analysis. Novel methods based on deep learning demonstrate a notably prominent effect compared to traditional denoising approaches. However, those still suffer from certain limitations. Some methods often neglect the multi-domain characteristics of the artifact signal. Even among those that do consider these, there are deficiencies in terms of efficiency, effectiveness and computation cost.Approach.In this study, we propose a multiscale temporal convolution and spatial-spectral attention network with adversarial training for automatically filtering artifacts, named filter artifacts network (FLANet). The multiscale convolution module can extract sufficient temporal information and the spatial-spectral attention network can extract not only non-local similarity but also spectral dependencies. To make data denoising more efficient and accurate, we adopt adversarial training with novel loss functions to generate outputs that are closer to pure signals.Main results.The results show that the method proposed in this paper achieves great performance in artifact removal and valid information preservation on EEG signals contaminated by different types of artifacts. This approach enables a more optimal trade-off between denoising efficacy and computational overhead.Significance.The proposed artifact removal framework facilitates the implementation of an efficient denoising method, contributing to the advancement of neural analysis and neural engineering, and can be expected to be applied to clinical research and to realize novel human-computer interaction systems.},
}
@article {pmid39901197,
year = {2025},
author = {Wang, S and Ma, R and Gao, C and Tian, YN and Hu, RG and Zhang, H and Li, L and Li, Y},
title = {Unraveling the function of TSC1-TSC2 complex: implications for stem cell fate.},
journal = {Stem cell research &amp; therapy},
volume = {16},
number = {1},
pages = {38},
pmid = {39901197},
issn = {1757-6512},
support = {82073832//National Natural Science Foundation of China/ ; 82204885//National Natural Science Foundation of China/ ; 81973792//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Tuberous Sclerosis Complex 2 Protein/metabolism/genetics ; *Tuberous Sclerosis Complex 1 Protein/metabolism/genetics ; *Tuberous Sclerosis/genetics/metabolism/pathology ; Cell Differentiation ; Animals ; Signal Transduction ; *Stem Cells/metabolism/cytology ; TOR Serine-Threonine Kinases/metabolism ; },
abstract = {BACKGROUND: Tuberous sclerosis complex is a genetic disorder caused by mutations in the TSC1 or TSC2 genes, affecting multiple systems. These genes produce proteins that regulate mTORC1 activity, essential for cell function and metabolism. While mTOR inhibitors have advanced treatment, maintaining long-term therapeutic success is still challenging. For over 20 years, significant progress has linked TSC1 or TSC2 gene mutations in stem cells to tuberous sclerosis complex symptoms.<br><br>METHODS: A comprehensive review was conducted using databases like Web of Science, Google Scholar, PubMed, and Science Direct, with search terms such as "tuberous sclerosis complex," "TSC1," "TSC2," "stem cell," "proliferation," and "differentiation." Relevant literature was thoroughly analyzed and summarized to present an updated analysis of the TSC1-TSC2 complex's role in stem cell fate determination and its implications for tuberous sclerosis complex.<br><br>RESULTS: The TSC1-TSC2 complex plays a crucial role in various stem cells, such as neural, germline, nephron progenitor, intestinal, hematopoietic, and mesenchymal stem/stromal cells, primarily through the mTOR signaling pathway.<br><br>CONCLUSIONS: This review aims shed light on the role of the TSC1-TSC2 complex in stem cell fate, its impact on health and disease, and potential new treatments for tuberous sclerosis complex.},
}
@article {pmid39900901,
year = {2025},
author = {Derosiere, G and Shokur, S and Vassiliadis, P},
title = {Reward signals in the motor cortex: from biology to neurotechnology.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {1307},
pmid = {39900901},
issn = {2041-1723},
mesh = {*Motor Cortex/physiology ; *Reward ; Humans ; Brain-Computer Interfaces ; Animals ; Movement/physiology ; Learning/physiology ; },
abstract = {Over the past decade, research has shown that the primary motor cortex (M1), the brain's main output for movement, also responds to rewards. These reward signals may shape motor output in its final stages, influencing movement invigoration and motor learning. In this Perspective, we highlight the functional roles of M1 reward signals and propose how they could guide advances in neurotechnologies for movement restoration, specifically brain-computer interfaces and non-invasive brain stimulation. Understanding M1 reward signals may open new avenues for enhancing motor control and rehabilitation.},
}
@article {pmid39899980,
year = {2025},
author = {Gusman, JT and Hosman, T and Crawford, R and Singer-Clark, T and Kapitonava, A and Kelemen, JN and Hahn, N and Henderson, JM and Hochberg, LR and Simeral, JD and Vargas-Irwin, CE},
title = {Multi-gesture drag-and-drop decoding in a 2D iBCI control task.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
pmid = {39899980},
issn = {1741-2552},
support = {I01 RX003803/RX/RRD VA/United States ; I01 RX002295/RX/RRD VA/United States ; UH2 NS095548/NS/NINDS NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; R25 GM125500/GM/NIGMS NIH HHS/United States ; I50 RX002864/RX/RRD VA/United States ; },
mesh = {Humans ; *Gestures ; *Brain-Computer Interfaces ; Male ; Adult ; Female ; *Motor Cortex/physiology ; *Psychomotor Performance/physiology ; Young Adult ; Quadriplegia/rehabilitation/physiopathology ; Hand/physiology ; Movement/physiology ; },
abstract = {Objective. Intracortical brain-computer interfaces (iBCIs) have demonstrated the ability to enable point and click as well as reach and grasp control for people with tetraplegia. However, few studies have investigated iBCIs during long-duration discrete movements that would enable common computer interactions such as 'click-and-hold' or 'drag-and-drop'.Approach. Here, we examined the performance of multi-class and binary (attempt/no-attempt) classification of neural activity in the left precentral gyrus of two BrainGate2 clinical trial participants performing hand gestures for 1, 2, and 4 s in duration. We then designed a novel 'latch decoder' that utilizes parallel multi-class and binary decoding processes and evaluated its performance on data from isolated sustained gesture attempts and a multi-gesture drag-and-drop task.Main results. Neural activity during sustained gestures revealed a marked decrease in the discriminability of hand gestures sustained beyond 1 s. Compared to standard direct decoding methods, the Latch decoder demonstrated substantial improvement in decoding accuracy for gestures performed independently or in conjunction with simultaneous 2D cursor control.Significance. This work highlights the unique neurophysiologic response patterns of sustained gesture attempts in human motor cortex and demonstrates a promising decoding approach that could enable individuals with tetraplegia to intuitively control a wider range of consumer electronics using an iBCI.},
}
@article {pmid39899514,
year = {2025},
author = {Chandravadia, N and Pendekanti, S and Roberts, D and Tran, R and Panchavati, S and Arnold, C and Pouratian, N and Speier, W},
title = {Comparing P300 flashing paradigms in online typing with language models.},
journal = {PloS one},
volume = {20},
number = {2},
pages = {e0303390},
pmid = {39899514},
issn = {1932-6203},
mesh = {Humans ; *Event-Related Potentials, P300/physiology ; *Language ; *Brain-Computer Interfaces ; Male ; Female ; Adult ; Young Adult ; },
abstract = {The P300 Speller is a brain-computer interface system that allows victims of motor neuron diseases to regain the ability to communicate by typing characters into a computer by thought. Since the system has a relatively slow typing speed, different stimulus presentation paradigms have been proposed designed to allow users to input information faster by reducing the number of required stimuli or increase signal fidelity. This study compares the typing speeds of the Row-Column, Checkerboard, and Combinatorial Paradigms to examine how their performance compares in online and offline settings. When the different flashing patterns were tested in conjunction with other established optimization techniques such as language models and dynamic stopping, they did not make a significant impact on P300 speller performance. This result could indicate that further performance improvements on the system lie beyond optimizing flashing patterns.},
}
@article {pmid39898793,
year = {2025},
author = {Perkins, SM and Amematsro, EA and Cunningham, J and Wang, Q and Churchland, MM},
title = {An emerging view of neural geometry in motor cortex supports high-performance decoding.},
journal = {eLife},
volume = {12},
number = {},
pages = {},
pmid = {39898793},
issn = {2050-084X},
support = {1R01NS135240/NH/NIH HHS/United States ; T32 NS064929/NS/NINDS NIH HHS/United States ; T32 MH126036/MH/NIMH NIH HHS/United States ; R01 NS135240/NS/NINDS NIH HHS/United States ; DP2 NS083037/NS/NINDS NIH HHS/United States ; SCGB//Simons Foundation/ ; Graduate Fellowship//National Science Foundation/ ; },
mesh = {*Motor Cortex/physiology/anatomy &amp; histology ; *Brain-Computer Interfaces ; Machine Learning ; Humans ; *Neurons/physiology ; },
abstract = {Decoders for brain-computer interfaces (BCIs) assume constraints on neural activity, chosen to reflect scientific beliefs while yielding tractable computations. Recent scientific advances suggest that the true constraints on neural activity, especially its geometry, may be quite different from those assumed by most decoders. We designed a decoder, MINT, to embrace statistical constraints that are potentially more appropriate. If those constraints are accurate, MINT should outperform standard methods that explicitly make different assumptions. Additionally, MINT should be competitive with expressive machine learning methods that can implicitly learn constraints from data. MINT performed well across tasks, suggesting its assumptions are well-matched to the data. MINT outperformed other interpretable methods in every comparison we made. MINT outperformed expressive machine learning methods in 37 of 42 comparisons. MINT's computations are simple, scale favorably with increasing neuron counts, and yield interpretable quantities such as data likelihoods. MINT's performance and simplicity suggest it may be a strong candidate for many BCI applications.},
}
@article {pmid39898755,
year = {2025},
author = {Andre, V and Abdel-Mottaleb, M and Shotbolt, M and Chen, S and Ramezini, Z and Zhang, E and Conlan, S and Telisman, O and Liang, P and Bryant, JM and Chomko, R and Khizroev, S},
title = {Foundational insights for theranostic applications of magnetoelectric nanoparticles.},
journal = {Nanoscale horizons},
volume = {10},
number = {4},
pages = {699-718},
pmid = {39898755},
issn = {2055-6764},
mesh = {*Theranostic Nanomedicine/methods ; Humans ; *Nanoparticles/chemistry/therapeutic use ; Drug Delivery Systems ; Neoplasms/diagnosis/therapy/drug therapy ; Animals ; Electroporation ; },
abstract = {Reviewing emerging biomedical applications of MagnetoElectric NanoParticles (MENPs), this paper presents basic physics considerations to help understand the possibility of future theranostic applications. Currently emerging applications include wireless non-surgical neural modulation and recording, functional brain mapping, high-specificity cell electroporation for targeted cancer therapies, targeted drug delivery, early screening and diagnostics, and others. Using an ab initio analysis, each application is discussed from the perspective of its fundamental limitations. Furthermore, the review identifies the most eminent challenges and offers potential engineering solutions on the pathway to implement each application and combine the therapeutic and diagnostic capabilities of the nanoparticles.},
}
@article {pmid39896750,
year = {2025},
author = {Angulo, IN and Iáñez, E and Ubeda, A},
title = {Editorial: Recent applications of noninvasive physiological signals and artificial intelligence.},
journal = {Frontiers in neuroinformatics},
volume = {19},
number = {},
pages = {1543103},
pmid = {39896750},
issn = {1662-5196},
}
@article {pmid39896041,
year = {2025},
author = {Sarikaya, MA and Ince, G},
title = {Improved BCI calibration in multimodal emotion recognition using heterogeneous adversarial transfer learning.},
journal = {PeerJ. Computer science},
volume = {11},
number = {},
pages = {e2649},
pmid = {39896041},
issn = {2376-5992},
abstract = {The use of brain-computer interface (BCI) technology to identify emotional states has gained significant interest, especially with the rise of virtual reality (VR) applications. However, the extensive calibration required for precise emotion recognition models presents a significant challenge, particularly for sensitive groups such as children, elderly, and patients. This study presents a novel approach that utilizes heterogeneous adversarial transfer learning (HATL) to synthesize electroencephalography (EEG) data from various other signal modalities, reducing the need for lengthy calibration phases. We benchmark the efficacy of three generative adversarial network (GAN) architectures, such as conditional GAN (CGAN), conditional Wasserstein GAN (CWGAN), and CWGAN with gradient penalty (CWGAN-GP) within this framework. The proposed framework is rigorously tested on two conventional open sourced datasets, SEED-V and DEAP. Additionally, the framework was applied to an immersive three-dimensional (3D) dataset named GraffitiVR, which we collected to capture the emotional and behavioral reactions of individuals experiencing urban graffiti in a VR environment. This expanded application provides insights into emotion recognition frameworks in VR settings, providing a wider range of contexts for assessing our methodology. When the accuracy of emotion recognition classifiers trained with CWGAN-GP-generated EEG data combined with non-EEG sensory data was compared against those trained using a combination of real EEG and non-EEG sensory data, the accuracy ratios were 93% on the SEED-V dataset, 99% on the DEAP dataset, and 97% on the GraffitiVR dataset. Moreover, in the GraffitiVR dataset, using CWGAN-GP-generated EEG data with non-EEG sensory data for emotion recognition models resulted in up to a 30% reduction in calibration time compared to classifiers trained on real EEG data with non-EEG sensory data. These results underscore the robustness and versatility of the proposed approach, significantly enhancing emotion recognition processes across a variety of environmental settings.},
}
@article {pmid39890467,
year = {2025},
author = {Ouchi, T and Scholl, LR and Rajeswaran, P and Canfield, RA and Smith, LI and Orsborn, AL},
title = {Mapping Eye, Arm, and Reward Information in Frontal Motor Cortices Using Electrocorticography in Nonhuman Primates.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {45},
number = {12},
pages = {},
pmid = {39890467},
issn = {1529-2401},
support = {K12 HD073945/HD/NICHD NIH HHS/United States ; P51 OD010425/OD/NIH HHS/United States ; R01 NS134634/NS/NINDS NIH HHS/United States ; TL1 TR002318/TR/NCATS NIH HHS/United States ; },
mesh = {Animals ; Male ; *Arm/physiology ; Brain Mapping ; *Eye ; Macaca mulatta ; *Motor Cortex/physiology ; Movement/physiology ; *Ocular Physiological Phenomena ; *Reward ; Electrocorticography ; Space Perception ; },
abstract = {Goal-directed reaches give rise to dynamic neural activity across the brain as we move our eyes and arms and process outcomes. High spatiotemporal resolution mapping of multiple cortical areas will improve our understanding of how these neural computations are spatially and temporally distributed across the brain. In this study, we used micro-electrocorticography (µECoG) recordings in two male monkeys performing visually guided reaches to map information related to eye movements, arm movements, and receiving rewards over primary motor cortex, premotor cortex, frontal eye field, and dorsolateral prefrontal cortex. Time-frequency and decoding analyses revealed that eye and arm movement information shifts across brain regions during a reach, likely reflecting shifts from planning to execution. Although eye and arm movement temporally overlapped, phase clustering analyses enabled us to resolve differences in eye and arm information across brain regions. This analysis revealed that eye and arm information spatially overlapped in motor cortex, which we further confirmed by demonstrating that arm movement decoding performance from motor cortex activity was impacted by task-irrelevant eye movements. Phase clustering analyses also identified reward-related activity in the prefrontal and premotor cortex. Our results demonstrate µECoG's strengths for functional mapping and provide further detail on the spatial distribution of eye, arm, and reward information processing distributed across frontal cortices during reaching. These insights advance our understanding of the overlapping neural computations underlying coordinated movements and reveal opportunities to leverage these signals to enhance future brain-computer interfaces.},
}
@article {pmid39889306,
year = {2025},
author = {Li, D and Huang, Y and Luo, R and Zhao, L and Xiao, X and Wang, K and Yi, W and Xu, M and Ming, D},
title = {Enhancing detection of SSVEPs using discriminant compacted network.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/adb0f2},
pmid = {39889306},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Evoked Potentials, Visual/physiology ; *Deep Learning ; *Neural Networks, Computer ; Photic Stimulation/methods ; Male ; Adult ; Discriminant Analysis ; Female ; },
abstract = {Objective. Steady-state visual evoked potential-based brain-computer interfaces (SSVEP-BCIs) have gained significant attention due to their simplicity, high signal to noise ratio and high information transfer rates (ITRs). Currently, accurate detection is a critical issue for enhancing the performance of SSVEP-BCI systems.Approach.This study proposed a novel decoding method called Discriminant Compacted Network (Dis-ComNet), which exploited the advantages of both spatial filtering and deep learning (DL). Specifically, this study enhanced SSVEP features using global template alignment and discriminant spatial pattern, and then designed a compacted temporal-spatio module (CTSM) to extract finer features. The proposed method was evaluated on a self-collected high-frequency dataset, a public Benchmark dataset and a public wearable dataset.Main Results.The results showed that Dis-ComNet significantly outperformed state-of-the-art spatial filtering methods, DL methods, and other fusion methods. Remarkably, Dis-ComNet improved the classification accuracy by 3.9%, 3.5%, 3.2%, 13.3%, 17.4%, 37.5%, and 2.5% when comparing with eTRCA, eTRCA-R, TDCA, DNN, EEGnet, Ensemble-DNN, and TRCA-Net respectively in the high-frequency dataset. The achieved results were 4.7%, 4.6%, 23.6%, 52.5%, 31.7%, and 7.0% higher than those of eTRCA, eTRCA-R, DNN, EEGnet, Ensemble-DNN, and TRCA-Net, respectively, and were comparable to those of TDCA in Benchmark dataset. The accuracy of Dis-ComNet in the wearable dataset was 9.5%, 7.1%, 36.1%, 26.3%, 15.7% and 4.7% higher than eTRCA, eTRCA-R, DNN, EEGnet, Ensemble-DNN, and TRCA-Net respectively, and comparable to TDCA. Besides, our model achieved the ITRs up to 126.0 bits/min, 236.4 bits/min and 103.6 bits/min in the high-frequency, Benchmark and the wearable datasets respectively.Significance.This study develops an effective model for the detection of SSVEPs, facilitating the development of high accuracy SSVEP-BCI systems.},
}
@article {pmid39887443,
year = {2025},
author = {Li, Z and Liang, C and He, Q and Feiweier, T and Hsu, YC and Li, J and Bai, R},
title = {Comparison of water exchange measurements between filter-exchange imaging and diffusion time-dependent kurtosis imaging in the human brain.},
journal = {Magnetic resonance in medicine},
volume = {93},
number = {6},
pages = {2357-2369},
doi = {10.1002/mrm.30454},
pmid = {39887443},
issn = {1522-2594},
support = {82111530201//National Natural Science Foundation of China (NSFC)/ ; 82222032//National Natural Science Foundation of China (NSFC)/ ; 82172050//National Natural Science Foundation of China (NSFC)/ ; 2022ZD0206000//the STI2030-Major Projects of China/ ; },
mesh = {Humans ; Male ; Adult ; *Brain/metabolism/diagnostic imaging ; Female ; *Body Water/metabolism ; *Diffusion Magnetic Resonance Imaging/methods ; Reproducibility of Results ; *Image Interpretation, Computer-Assisted/methods ; Algorithms ; *Water/metabolism ; },
abstract = {PURPOSE: Filter-exchange imaging (FEXI) and diffusion time (t)-dependent kurtosis imaging (DKI(t)) are two diffusion-based methods that have been proposed for in vivo measurements of water exchange rates. Few studies have directly compared these methods. We aimed to investigate whether FEXI and DKI(t) yield comparable water exchange measurements in the human brain in vivo.<br><br>METHODS: Eight healthy volunteers underwent multiple-direction FEXI and DKI(t) acquisitions on a 3T scanner. We performed region of interest (ROI) analysis to determine correlations between FEXI-derived apparent exchange rate (AXR) and DKI(t)-derived reciprocal of exchange time (1 / &#x3c4; ex $$ 1/{\tau}
_{ex}
$$).<br><br>RESULTS: In both white matter (WM) and gray matter (GM), DKI(t) revealed substantial diffusion-time dependence of diffusivity and kurtosis. However, at t&#x2009;&#x2265;&#x2009;100&#x2009;ms, the diffusivity showed weak time dependence. In WM, this time dependence may be due to water exchange between myelin water and "free" water with different T1 values, although other factors, such as remaining restrictive effects from microstructural barriers, cannot be excluded. We found a significant correlation between DKI(t)-derived 1 / &#x3c4; ex $$ 1/{\tau}
_{ex}
$$ and FEXI-derived AXR in the axial direction within WM. No such correlation was present in GM, although both values showed similar ranges.<br><br>CONCLUSION: These results suggest that FEXI and DKI(t) could be sensitive to the same water exchange process only when the diffusion time in DKI(t) is sufficiently long, and only in WM. In both GM and WM, the restrictive effect of microstructure is non-negligible, especially at short diffusion times (<100&#x2009;ms).},
}
@article {pmid39886803,
year = {2025},
author = {Ali, HS and Ismail, AI and El-Rabaie, EM and Abd El-Samie, FE},
title = {Diagonal loading common spatial patterns with Pearson correlation coefficient based feature selection for efficient motor imagery classification.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-15},
doi = {10.1080/10255842.2025.2457122},
pmid = {39886803},
issn = {1476-8259},
abstract = {The conversion of a person's intentions into device commands through the use of brain-computer interface (BCI) is a feasible communication method for individuals with nervous system disorders. While common spatial pattern (CSP) is commonly used for feature extraction in BCIs, it has limitations. It is known for its susceptibility to noise and tendency to overfit. Moreover, high-dimensional, and irrelevant features can make it harder for a classifier to learn effectively. To address these challenges, exploring potential solutions is crucial. This paper introduces Regularized CSP with diagonal loading (DL-CSP) and Pearson correlation coefficient (PCC) based feature selection to extract the most discriminative motor imagery EEG (MI-EEG) features. Three classifiers in an ensemble are considered; bidirectional long short-term memory (Bi-LSTM), K-nearest neighbors (KNN) and naïve Bayes (NB). Decision level fusion through majority voting is exploited to leverage diverse perspectives and increase the overall system robustness. Experiments have been implemented using three publicly available datasets for MI classification; BCI competition IV-IIA (data-1), BCI Competition III-IVa (data-2), and a stroke patients' dataset (data-3). The accuracy achieved, according to the results, is 86.96% for data-1, 91.70% for data-2, and 85.75% for data-3. These percentages outperform the accuracy achieved by any state-of-the-art techniques.},
}
@article {pmid39886225,
year = {2025},
author = {Huang, J and Zhang, L and Shao, N and Zhang, Y and Xu, Y and Zhou, Y and Zhang, D and Zhang, J and Lee, HJ},
title = {Lipid Metabolic Heterogeneity during Early Embryogenesis Revealed by Hyper-3D Stimulated Raman Imaging.},
journal = {Chemical &amp; biomedical imaging},
volume = {3},
number = {1},
pages = {15-24},
pmid = {39886225},
issn = {2832-3637},
abstract = {Studying embryogenesis is fundamental to understanding developmental biology and reproductive medicine. Its process requires precise spatiotemporal regulations in which lipid metabolism plays a crucial role. However, the spatial dynamics of lipid species at the subcellular level remains obscure due to technical limitations. To address this challenge, we developed a hyperspectral 3D imaging and analysis method based on stimulated Raman scattering microscopy (hyper-3D SRS) to quantitatively assess lipid profiles in individual embryos through submicrometer resolution (x-y), 3D optical sectioning (z), and chemical bond-selective (Ω) imaging. Using hyper-3D SRS, individual lipid droplets (LDs) in single cells were identified and quantified. Our findings revealed that the LD profiles within a single embryo are not uniform, even as early as the 2-cell stage. Notably, we also discovered a dynamic relationship between the LD size and unsaturation degree as embryos develop, indicating diverse lipid metabolism during early development. Furthermore, abnormal LDs were observed in oocytes of a progeria mouse model, suggesting that LDs could serve as a potential biomarker for assessing oocyte/embryo quality. Overall, our results highlight the potential of hyper-3D SRS as a noninvasive method for studying lipid content, composition, and subcellular distribution in embryos. This technique provides valuable insights into lipid metabolism during embryonic development and has the potential for clinical applications in evaluating oocyte/embryo quality.},
}
@article {pmid39886177,
year = {2024},
author = {Murthy, V and Kashid, SR and Pal, M and Vadassery, A and Maitre, P and Arora, A and Singh, P and Joshi, A and Bakshi, G and Prakash, G},
title = {Prospective comparative study of quality of life in patients with bladder cancer undergoing cystectomy with ileal conduit or bladder preservation.},
journal = {BMJ oncology},
volume = {3},
number = {1},
pages = {e000435},
pmid = {39886177},
issn = {2752-7948},
abstract = {OBJECTIVE: To compare health-related quality of life (HRQOL) in patients undergoing radical cystectomy with ileal conduit (RC) or bladder preservation (BP) with (chemo)radiotherapy for bladder cancer.<br><br>METHODS AND ANALYSIS: Patients with bladder cancer, stage cT1-T4, cN0-N1, M0 with a minimum follow-up of 6 months from curative treatment (RC or BP) and without disease were eligible for inclusion. Two HRQOL instruments were administered: Bladder Cancer Index (BCI) for bladder cancer-specific HRQOL and European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30). The mean QOL scores across various domains and specific questions were compared between the two treatment groups using an independent t-test.<br><br>RESULTS: Out of the 104 enrolled patients, 56 underwent RC and 48 opted for BP, with 95 (91.3%) being male. The median time from treatment completion to QOL assessment was 22 months (IQR 10-56). The median age for the entire cohort was 62 years (IQR 55-68), 65.5 years (IQR 55-71) in BP and 59.5 years (IQR 55-66) in RC. There was no significant difference in mean BCI urinary and bowel scores in function or bother subdomains between the two groups. Overall, BCI sexual scores were low in both groups but significantly better after BP (BPmean 56.9, RCmean 41.5, p=0.01). Mean scores for sexual function subdomain were BPmean 38.4 and RCmean 25 (p=0.07) and for sexual bother were BPmean 81 RCmean 62 (p=0.02). The EORTC QLQ-C30 outcomes did not show a significant difference in either group.<br><br>CONCLUSION: The BP group showed significantly better results in the sexual domain compared with the RC group. Both groups had good QOL in terms of urinary and bowel functions.},
}
@article {pmid39885677,
year = {2025},
author = {Sara Wilson, K and Saravanan, KK},
title = {Artificial intelligent based control strategy for reach and grasp of multi-objects using brain-controlled robotic arm system.},
journal = {Network (Bristol, England)},
volume = {36},
number = {3},
pages = {1253-1281},
doi = {10.1080/0954898X.2025.2453620},
pmid = {39885677},
issn = {1361-6536},
mesh = {Humans ; *Robotics/methods/instrumentation ; Electroencephalography/methods ; *Hand Strength/physiology ; *Brain/physiology ; *Artificial Intelligence ; *Brain-Computer Interfaces ; *Arm/physiology ; Algorithms ; },
abstract = {Brain-controlled robotic arm systems are designed to provide a method of communication and control for individuals with limited mobility or communication abilities. These systems can be beneficial for people who have suffered from a spinal cord injury, stroke, or neurological disease that affects their motor abilities. The ability of a person to control a robotic arm to reach and grasp multiple objects using their brain signals. This technology involves the use of an electroencephalogram (EEG) cap that captures the electrical activity in the user's brain, which is then processed by an artificial intelligent to translate it into commands that control the movements of the robotic arm. With this technology, individuals who are unable to move their limbs due to paralysis or other conditions can still perform daily activities such as feeding themselves, drinking from a glass, or grasping objects. In this paper, we propose an artificial intelligent-based control strategy for reach and grasp of multi-objects using brain-controlled robotic arm system. The proposed control strategy consists of threefold process: feature extraction, feature optimization, and control strategy classification. Initially, we design an improved ResNet pre-trained architecture for deep feature extraction from the given EEG signal.},
}
@article {pmid39883960,
year = {2025},
author = {Tortolani, AF and Kunigk, NG and Sobinov, AR and Boninger, ML and Bensmaia, SJ and Collinger, JL and Hatsopoulos, NG and Downey, JE},
title = {How different immersive environments affect intracortical brain computer interfaces.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
pmid = {39883960},
issn = {1741-2552},
support = {R01 NS130302/NS/NINDS NIH HHS/United States ; T32 NS121763/NS/NINDS NIH HHS/United States ; UH3 NS107714/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Female ; Humans ; Male ; *Brain-Computer Interfaces ; Electrodes, Implanted ; Electroencephalography/methods ; *Environment ; *Motor Cortex/physiology ; *Virtual Reality ; },
abstract = {Objective. As brain-computer interface (BCI) research advances, many new applications are being developed. Tasks can be performed in different virtual environments, and whether a BCI user can switch environments seamlessly will influence the ultimate utility of a clinical device.Approach. Here we investigate the importance of the immersiveness of the virtual environment used to train BCI decoders on the resulting decoder and its generalizability between environments. Two participants who had intracortical electrodes implanted in their precentral gyrus used a BCI to control a virtual arm, both viewed immersively through virtual reality goggles and at a distance on a flat television monitor.Main results. Each participant performed better with a decoder trained and tested in the environment they had used the most prior to the study, one for each environment type. The neural tuning to the desired movement was minimally influenced by the immersiveness of the environment. Finally, in further testing with one of the participants, we found that decoders trained in one environment generalized well to the other environment, but the order in which the environments were experienced within a session mattered.Significance. Overall, experience with an environment was more influential on performance than the immersiveness of the environment, but BCI performance generalized well after accounting for experience.Clinical Trial: NCT01894802.},
}
@article {pmid39883957,
year = {2025},
author = {Lutes, NA and Sriram Siddhardh Nadendla, V and Krishnamurthy, K},
title = {Few-shot transfer learning for individualized braking intent detection on neuromorphic hardware.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/adb079},
pmid = {39883957},
issn = {1741-2552},
mesh = {Humans ; *Neural Networks, Computer ; Electroencephalography/methods ; *Intention ; *Automobile Driving/psychology ; *Transfer, Psychology/physiology ; Male ; Adult ; Machine Learning ; Female ; },
abstract = {Objective.This work explores use of a few-shot transfer learning method to train and implement a convolutional spiking neural network (CSNN) on a BrainChip Akida AKD1000 neuromorphic system-on-chip for developing individual-level, instead of traditionally used group-level, models using electroencephalographic data. The efficacy of the method is studied on an advanced driver assist system related task of predicting braking intention.Approach.Data are collected from participants operating an NVIDIA JetBot on a testbed simulating urban streets for three different scenarios. Participants receive a braking indicator in the form of: (1) an audio countdown in a nominal baseline, stress-free environment; (2) an audio countdown in an environment with added elements of physical fatigue and active cognitive distraction; (3) a visual cue given through stoplights in a stress-free environment. These datasets are then used to develop individual-level models from group-level models using a few-shot transfer learning method, which involves: (1) creating a group-level model by training a CNN on group-level data followed by quantization and recouping any performance loss using quantization-aware retraining; (2) converting the CNN to be compatible with Akida AKD1000 processor; and (3) training the final decision layer on individual-level data subsets to create individual-customized models using an online Akida edge-learning algorithm.Main results.Efficacy of the above methodology to develop individual-specific braking intention predictive models by rapidly adapting the group-level model in as few as three training epochs while achieving at least 90% accuracy, true positive rate and true negative rate is presented. Further, results show the energy-efficiency of the neuromorphic hardware through a power reduction of over 97% with only a 1.3 × increase in latency when using the Akida AKD1000 processor for network inference compared to an Intel Xeon central processing unit. Similar results were obtained in a subsequent ablation study using a subset of five out of 19 channels.Significance.Especially relevant to real-time applications, this work presents an energy-efficient, few-shot transfer learning method that is implemented on a neuromorphic processor capable of training a CSNN as new data becomes available, operating conditions change, or to customize group-level models to yield personalized models unique to each individual.},
}
@article {pmid39883956,
year = {2025},
author = {Sultana, M and Gheorghe, L and Perdikis, S},
title = {EEG correlates of acquiring race driving skills.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/adb077},
pmid = {39883956},
issn = {1741-2552},
mesh = {Humans ; *Automobile Driving/psychology/education ; Male ; *Electroencephalography/methods ; Female ; Adult ; Transcranial Direct Current Stimulation/methods ; Young Adult ; *Motor Skills/physiology ; *Brain/physiology ; *Learning/physiology ; *Psychomotor Performance/physiology ; },
abstract = {Objective. Race driving is a complex motor task that involves multiple concurrent cognitive processes in different brain regions coordinated to maintain and optimize speed and control. Delineating the neuroplasticity accompanying the acquisition of complex and fine motor skills such as racing is crucial to elucidate how these are gradually encoded in the brain and inform new training regimes. This study aims, first, to identify the neural correlates of learning to drive a racing car using non-invasive electroencephalography (EEG) imaging and longitudinal monitoring. Second, we gather evidence on the potential role of transcranial direct current stimulation (tDCS) in enhancing the training outcome of race drivers.Approach. We collected and analyzed multimodal experimental data, including drivers' EEG and telemetry from a driving simulator to identify neuromarkers of race driving proficiency and assess the potential to improve training through anodal tDCS.Main results. Our findings indicate that theta-band EEG rhythms and alpha-band effective functional connectivity between frontocentral and occipital cortical areas are significant neuromarkers for acquiring racing skills. We also observed signs of a potential tDCS effect in accelerating the learning process.SignificanceThese results provide a foundation for future research to develop innovative race-driving training protocols using neurotechnology.},
}
@article {pmid39883372,
year = {2025},
author = {Xiong, H and Yan, Y and Chen, Y and Liu, J},
title = {Graph convolution network-based eeg signal analysis: a review.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {63},
number = {6},
pages = {1609-1625},
pmid = {39883372},
issn = {1741-0444},
mesh = {*Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; *Neural Networks, Computer ; Brain/physiology ; Algorithms ; Brain-Computer Interfaces ; },
abstract = {With the advancement of artificial intelligence technology, more and more effective methods are being used to identify and classify Electroencephalography (EEG) signals to address challenges in healthcare and brain-computer interface fields. The applications and major achievements of Graph Convolution Network (GCN) techniques in EEG signal analysis are reviewed in this paper. Through an exhaustive search of the published literature, a module-by-module discussion is carried out for the first time to address the current research status of GCN. An exhaustive classification of methods and a systematic analysis of key modules, such as brain map construction, node feature extraction, and GCN architecture design, are presented. In addition, we pay special attention to several key research issues related to GCN. This review enhances the understanding of the future potential of GCN in the field of EEG signal analysis. At the same time, several valuable development directions are sorted out for researchers in related fields, such as analysing the applicability of different GCN layers, building task-oriented GCN models, and improving adaptation to limited data.},
}
@article {pmid39882377,
year = {2024},
author = {Ma, Y and Huang, J and Liu, C and Shi, M},
title = {A portable EEG signal acquisition system and a limited-electrode channel classification network for SSVEP.},
journal = {Frontiers in neurorobotics},
volume = {18},
number = {},
pages = {1502560},
pmid = {39882377},
issn = {1662-5218},
abstract = {Brain-computer interfaces (BCIs) have garnered significant research attention, yet their complexity has hindered widespread adoption in daily life. Most current electroencephalography (EEG) systems rely on wet electrodes and numerous electrodes to enhance signal quality, making them impractical for everyday use. Portable and wearable devices offer a promising solution, but the limited number of electrodes in specific regions can lead to missing channels and reduced BCI performance. To overcome these challenges and enable better integration of BCI systems with external devices, this study developed an EEG signal acquisition platform (Gaitech BCI) based on the Robot Operating System (ROS) using a 10-channel dry electrode EEG device. Additionally, a multi-scale channel attention selection network based on the Squeeze-and-Excitation (SE) module (SEMSCS) is proposed to improve the classification performance of portable BCI devices with limited channels. Steady-state visual evoked potential (SSVEP) data were collected using the developed BCI system to evaluate both the system and network performance. Offline data from ten subjects were analyzed using within-subject and cross-subject experiments, along with ablation studies. The results demonstrated that the SEMSCS model achieved better classification performance than the comparative reference model, even with a limited number of channels. Additionally, the implementation of online experiments offers a rational solution for controlling external devices via BCI.},
}
@article {pmid39880895,
year = {2025},
author = {Haghi, B and Aflalo, T and Kellis, S and Guan, C and Gamez de Leon, JA and Huang, AY and Pouratian, N and Andersen, RA and Emami, A},
title = {Author Correction: Enhanced control of a brain-computer interface by tetraplegic participants via neural-network-mediated feature extraction.},
journal = {Nature biomedical engineering},
volume = {9},
number = {6},
pages = {986},
doi = {10.1038/s41551-025-01355-2},
pmid = {39880895},
issn = {2157-846X},
support = {R01 EY015545/EY/NEI NIH HHS/United States ; UG1 EY032039/EY/NEI NIH HHS/United States ; },
}
@article {pmid39880008,
year = {2025},
author = {Loss, J and Betsch, C and Ellermann, C and Ewert, B and Grill, E and Jenny, MA and Jordan, S and Kubiak, T and Mata, J and Rebitschek, FG and Rehfuess, EA and Sniehotta, F},
title = {[Characteristics of the "Behavioural and cultural insights (BCI)" approach in Public Health - a consensus paper of the network "Behavioural Science Connect"].},
journal = {Gesundheitswesen (Bundesverband der Arzte des Offentlichen Gesundheitsdienstes (Germany))},
volume = {87},
number = {4},
pages = {273-281},
doi = {10.1055/a-2528-8276},
pmid = {39880008},
issn = {1439-4421},
mesh = {Germany ; *Public Health ; Humans ; *Health Promotion/organization &amp; administration ; *Health Behavior ; *Behavioral Sciences/organization &amp; administration ; },
abstract = {Public health interventions are often intended to make it easier for people to adopt health-related behaviours, e. g., by changing the social environment and material living conditions. With the concept of "Behavioral and Cultural Insights" (BCI), the World Health Organization (WHO) has established an approach that aims at a better understanding of health-related behaviours, in order to be able to develop corresponding public health interventions in a more targeted manner. The focus is on the empirical identification of individuals as well as cultural, social and environmental barriers to and facilitators of health behaviour.The BCI approach can be used to plan preventive measures in a more evidence-based and needs-oriented manner. To this end, the current article outlines some basic features of BCI which should be taken into account for integrating the concept into a contemporary understanding of "new public health". This includes social and individual factors influencing health as well as social inequalities in health.First, the article distinguishes the BCI concept from the approach of behavioural economics (e. g., nudging). To illustrate its potential for population health and health equity, the article then explains that BCI-based measures that (a) focus on both behaviour and environment, (b) aim at health equity, (c) are developed and implemented in a participatory manner, and (d) follow the logic of the well-established Public Health Action Cycle. For BCI, it is crucial to systematically identify and analyse the factors influencing human behaviour in everyday life. BCI-based interventions must also consider the characteristics of complex interventions and be tailored to local conditions and the cultural diversity of specific population groups.The BCI approach has many similarities with other approaches of quality-assured and needs-oriented prevention measures. The focus on a systematic identification of barriers and facilitators offers an important added value in the planning of public health measures. Research into BCI and their use in prevention should be expanded in Germany.},
}
@article {pmid39878111,
year = {2025},
author = {Badr, Y and AlSawaftah, N and Husseini, GA},
title = {User-Centered Design of Neuroprosthetics: Advancements and Limitations.},
journal = {CNS &amp; neurological disorders drug targets},
volume = {24},
number = {6},
pages = {409-421},
pmid = {39878111},
issn = {1996-3181},
support = {FRG20-L-E48, eFRG18-BBRCEN-03//American University of Sharjah Faculty Research Grants/ ; MRG-57-2019-2020//Sheikh Hamdan Award for Medical Sciences/ ; },
mesh = {Humans ; *User-Centered Design ; *Neural Prostheses ; Brain-Computer Interfaces ; },
abstract = {Neurological conditions resulting from severe spinal cord injuries, brain injuries, and other traumatic incidents often lead to the loss of essential bodily functions, including sensory and motor capabilities. Traditional prosthetic devices, though standard, have limitations in delivering the required dexterity and functionality. The advent of neuroprosthetics marks a paradigm shift, aiming to bridge the gap between prosthetic devices and the human nervous system. This review paper explores the evolution of neuroprosthetics, categorizing devices into sensory and motor neuroprosthetics and emphasizing their significance in addressing specific challenges. The discussion section delves into long-term challenges in clinical practice, encompassing device durability, ethical considerations, and issues of accessibility and affordability. Furthermore, the paper proposes potential solutions with a specific focus on enhancing sensory experiences and the importance of user-friendly interfaces. In conclusion, this paper offers a comprehensive overview of the current state of neuroprosthetics, outlining future research and development directions to guide advancements in the field.},
}
@article {pmid39875198,
year = {2025},
author = {Chen, S and Jiang, D and Li, M and Xuan, X and Li, H},
title = {Brain-Computer Interface and Electrochemical Sensor Based on Boron-Nitrogen Co-Doped Graphene-Diamond Microelectrode for EEG and Dopamine Detection.},
journal = {ACS sensors},
volume = {10},
number = {2},
pages = {868-880},
doi = {10.1021/acssensors.4c02461},
pmid = {39875198},
issn = {2379-3694},
mesh = {*Dopamine/analysis ; *Graphite/chemistry ; *Electroencephalography/instrumentation/methods ; *Boron/chemistry ; Microelectrodes ; *Nitrogen/chemistry ; *Electrochemical Techniques/instrumentation/methods ; Humans ; *Brain-Computer Interfaces ; Limit of Detection ; },
abstract = {The simultaneous detection of electroencephalography (EEG) signals and neurotransmitter levels plays an important role as biomarkers for the assessment and monitoring of emotions and cognition. This paper describes the development of boron and nitrogen codoped graphene-diamond (BNGrD) microelectrodes with a diameter of only 200 μm for sensing EEG signals and dopamine (DA) levels, which have been developed for the first time. The optimized BNGrD microelectrode responded sensitively to both EEG and DA signals, with a signal-to-noise ratio of 9 dB for spontaneous EEG signals and a limit of detection as low as 124 nM for DA. Furthermore, the BNGrD microelectrodes demonstrate excellent repeatability, reproducibility, and stability for the detection of EEG and dopamine. These results indicate that the BNGrD microelectrode creates suitable conditions for establishing a correlation between the EEG signals and neurotransmitters. A flexible printed circuit board with BNGrD microelectrodes for an eight-channel EEG headband, portable EEG collector, and light stimulation glasses are designed. The self-designed EEG collector adopts a split design strategy of digital and analog signal modules and uses miniaturized impedance-matched BNGrD microelectrodes, which effectively reduce the noise of the electrophysiological signals. The BNGrD microelectrode-based portable EEG/electrochemical analysis system detects EEG signals and DA levels in a noninvasive and minimally invasive manner and has application prospects in remote online diagnosis and treatment of patients with emotional and cognition-related diseases.},
}
@article {pmid39875073,
year = {2025},
author = {Géraudie, A and De Rossi, P and Canney, M and Carpentier, A and Delatour, B},
title = {Effects of blood-brain barrier opening using ultrasound on tauopathies: A systematic review.},
journal = {Journal of controlled release : official journal of the Controlled Release Society},
volume = {379},
number = {},
pages = {1029-1044},
doi = {10.1016/j.jconrel.2025.01.056},
pmid = {39875073},
issn = {1873-4995},
mesh = {*Blood-Brain Barrier/metabolism ; Animals ; Humans ; *Tauopathies/therapy/metabolism/pathology ; tau Proteins/metabolism ; *Ultrasonic Waves ; },
abstract = {UNLABELLED: Blood-brain barrier opening with ultrasound can potentiate drug efficacy in the treatment of brain pathologies and also provides therapeutic effects on its own. It is an innovative tool to transiently, repeatedly and safely open the barrier, with studies showing beneficial effects in both preclinical models for Alzheimer's disease and recent clinical studies. The first preclinical and clinical work has mainly shown a decrease in amyloid burden in mice models and in patients. However, Alzheimer's disease pathology also encompasses tauopathy, which is closely related to cognitive decline, making it a crucial therapeutic target. The effects of blood-brain barrier opening with ultrasound have been rarely assessed on tau and are still unclear.<br><br>METHODS: This systematic review, conducted through searches using Pubmed, Embase, Web of Science and Cochrane Central databases, extracted results of 15 studies reporting effects of blood-brain barrier opening using ultrasound on tau proteins.<br><br>RESULTS: This review of the literature indicates that blood-brain barrier opening using ultrasound can decrease the extent of the tau pathology or potentialize the effect of a therapeutic drug. However, selected studies report paradoxically that blood-brain barrier opening can increase tau pathology burden and induce brain damage.<br><br>DISCUSSION: Apparent discrepancies between reports could originate from the variability in protocols or analytical methods that may impact the effects of blood-brain barrier opening with ultrasound on tauopathies, glial populations, tissue integrity and functional outcomes.<br><br>CONCLUSION: This calls for a better standardization effort combined with improved methodologies allowing between-studies comparisons, and for further understanding of the effects of blood-brain barrier opening on tau pathology as an essential prerequisite before translation to clinic.},
}
@article {pmid39874948,
year = {2025},
author = {Tian, F and Liu, Y and Chen, M and Schriver, KE and Roe, AW},
title = {Selective activation of mesoscale functional circuits via multichannel infrared stimulation of cortical columns in ultra-high-field 7T MRI.},
journal = {Cell reports methods},
volume = {5},
number = {1},
pages = {100961},
pmid = {39874948},
issn = {2667-2375},
mesh = {*Magnetic Resonance Imaging/methods ; Humans ; *Visual Cortex/physiology/diagnostic imaging ; Animals ; Cats ; *Infrared Rays ; Photic Stimulation/methods ; Brain Mapping/methods ; Visual Prosthesis ; Male ; },
abstract = {To restore vision in the blind, advances in visual cortical prosthetics (VCPs) have offered high-channel-count electrical interfaces. Here, we design a 100-fiber optical bundle interface apposed to known feature-specific (color, shape, motion, and depth) functional columns that populate the visual cortex in humans, primates, and cats. Based on a non-viral optical stimulation method (INS, infrared neural stimulation; 1,875 nm), it can deliver dynamic patterns of stimulation, is non-penetrating and non-damaging to tissue, and is movable and removable. In addition, its magnetic resonance (MR) compatibility (INS-fMRI) permits systematic mapping of brain-wide circuits. In the MRI, we illustrate (1) the single-point activation of functionally specific networks, (2) shifting cortical networks activated via shifting points of stimulation, and (3) "moving dot" stimulation-evoked activation of higher-order motion-selective areas. We suggest that, by mimicking patterns of columnar activation normally activated by visual stimuli, a columnar VCP opens doors for the planned activation of feature-specific circuits and their associated visual percepts.},
}
@article {pmid39874664,
year = {2025},
author = {Qin, Y and Li, B and Wang, W and Shi, X and Peng, C and Wang, X and Wang, H},
title = {ECA-FusionNet: a hybrid EEG-fNIRS signals network for MI classification.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/adaf58},
pmid = {39874664},
issn = {1741-2552},
mesh = {*Electroencephalography/methods/classification ; Humans ; Spectroscopy, Near-Infrared/methods/classification ; *Brain-Computer Interfaces ; *Imagination/physiology ; *Brain/physiology ; *Neural Networks, Computer ; Movement/physiology ; },
abstract = {Objective. Among all BCI paradigms, motion imagery (MI) has gained favor among researchers because it allows users to control external devices by imagining movements rather than actually performing actions. This property holds important promise for clinical applications, especially in areas such as stroke rehabilitation. Electroencephalogram (EEG) signals and functional near-infrared spectroscopy (fNIRS) signals are two of the more popular neuroimaging techniques for obtaining MI signals from the brain. However, the performance of MI-based unimodal classification methods is low due to the limitations of EEG or fNIRS.Approach. In this paper, we propose a new multimodal fusion classification method capable of combining the potential complementary advantages of EEG and fNIRS. First, we propose a feature extraction network capable of extracting spatio-temporal features from EEG-based and fNIRS-based MI signals. Then, we successively fused the EEG and fNIRS at the feature-level and the decision-level to improve the adaptability and robustness of the model.Main results. We validate the performance of ECA-FusionNet on a publicly available EEG-fNIRS dataset. The results show that ECA-FusionNet outperforms unimodal classification methods, as well as existing fusion classification methods, in terms of classification accuracy for MI.Significance. ECA-FusionNet may provide a useful reference for the field of multimodal fusion classification.},
}
@article {pmid39874652,
year = {2025},
author = {Ziebell, P and Modde, A and Roland, E and Eidel, M and Vansteensel, MJ and Mrachacz-Kersting, N and Vaughan, TM and Kübler, A},
title = {Designing an online BCI forum: insights from researchers and end-users.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/adaf57},
pmid = {39874652},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces/psychology/trends ; Humans ; Male ; Female ; *Research Personnel/psychology ; Adult ; Middle Aged ; Communication Devices for People with Disabilities ; },
abstract = {Objective.Brain-computer interfaces (BCIs) can support non-muscular communication and device control for severely paralyzed people. However, efforts that directly involve potential or actual end-users and address their individual needs are scarce, demonstrating a translational gap. An online BCI forum supported by the BCI Society could initiate and sustainably strengthen interactions between BCI researchers and end-users to bridge this gap.Approach.We interviewed six severely paralyzed individuals and surveyed 121 BCI researchers to capture their opinions and wishes concerning an online BCI forum. Data were analyzed with a mixed-method quantitative and qualitative content analysis.Main results.All end-users and most researchers (83%) reported an interest in participating in an online BCI forum. Rating questions and open comments to identify design aspects included what should be featured most prominently, how people would get engaged in the online BCI forum, and which pitfalls should be considered.Significance.Responses support establishing an online BCI forum to serve as a meaningful resource for the entire BCI community.},
}
@article {pmid39874276,
year = {2025},
author = {Longo, L and Reilly, RB},
title = {onEEGwaveLAD: A fully automated online EEG wavelet-based learning adaptive denoiser for artefacts identification and mitigation.},
journal = {PloS one},
volume = {20},
number = {1},
pages = {e0313076},
pmid = {39874276},
issn = {1932-6203},
mesh = {*Electroencephalography/methods ; Humans ; *Artifacts ; Signal-To-Noise Ratio ; Male ; Adult ; *Signal Processing, Computer-Assisted ; Brain/physiology ; Female ; Algorithms ; Brain-Computer Interfaces ; Wavelet Analysis ; Young Adult ; },
abstract = {Electroencephalographic signals are obtained by amplifying and recording the brain's spontaneous biological potential using electrodes positioned on the scalp. While proven to help find changes in brain activity with a high temporal resolution, such signals are contaminated by non-stationary and frequent artefacts. A plethora of noise reduction techniques have been developed, achieving remarkable performance. However, they often require multi-channel information and additional reference signals, are not fully automated, require human intervention and are mostly offline. With the popularity of Brain-Computer Interfaces and the application of Electroencephalography in daily activities and other ecological settings, there is an increasing need for robust, online, near real-time denoising techniques, without additional reference signals, that is fully automated and does not require human supervision nor multi-channel information. This research contributes to the body of knowledge by introducing onEEGwaveLAD, a novel, fully automated, ONline, EEG wavelet-based Learning Adaptive Denoiser pipeline for artefact identification and reduction. It is a specific framework that can be instantiated for various types of artefacts paving the path towards real-time denoising. As the first of its kind, it is described and instantiated for the particular problem of blink detection and reduction, and evaluated across a general and a specific analysis of the signal to noise ratio across 30 participants.},
}
@article {pmid39873797,
year = {2025},
author = {Mohan, A and Anand, RS},
title = {Classification of Imagined Speech Signals Using Functional Connectivity Graphs and Machine Learning Models.},
journal = {Brain topography},
volume = {38},
number = {2},
pages = {25},
pmid = {39873797},
issn = {1573-6792},
mesh = {Humans ; *Machine Learning ; Electroencephalography/methods ; *Imagination/physiology ; *Speech/physiology ; Brain-Computer Interfaces ; *Brain/physiology ; Neural Networks, Computer ; Adult ; Male ; Female ; },
abstract = {EEG involves recording electrical activity generated by the brain through electrodes placed on the scalp. Imagined speech classification has emerged as an essential area of research in brain-computer interfaces (BCIs). Despite significant advances, accurately classifying imagined speech signals remains challenging due to their complex and non-stationary nature. Existing approaches often struggle with low signal-to-noise ratios and high inter-subject variability. A proposed method named imagined speech functional connectivity graph (ISFCG) is implemented to deal with these issues. The functional connectivity graphs capture the complex relationships between brain regions during imagined speech tasks. These graphs are then used to extract features that serve as inputs to various machine-learning models. The ISFCG provides an alternative representation of imagined speech signals, focusing on brain connectivity features to enhance the analysis and classification process. Also, a convolutional neural network (CNN) is proposed to learn features from these complex graphs, leading to improved classification accuracy. Experimental results on a benchmark dataset demonstrate the effectiveness of our method.},
}
@article {pmid39872211,
year = {2025},
author = {Wang, L and Zhang, C and Hao, Z and Yao, S and Bai, L and Oliveira, JM and Wang, P and Zhang, K and Zhang, C and He, J and Reis, RL and Li, D},
title = {Bioaugmented design and functional evaluation of low damage implantable array electrodes.},
journal = {Bioactive materials},
volume = {47},
number = {},
pages = {18-31},
pmid = {39872211},
issn = {2452-199X},
abstract = {Implantable neural electrodes are key components of brain-computer interfaces (BCI), but the mismatch in mechanical and biological properties between electrode materials and brain tissue can lead to foreign body reactions and glial scarring, and subsequently compromise the long-term stability of electrical signal transmission. In this study, we proposed a new concept for the design and bioaugmentation of implantable electrodes (bio-array electrodes) featuring a heterogeneous gradient structure. Different composite polyaniline-gelatin-alginate based conductive hydrogel formulations were developed for electrode surface coating. In addition, the design, materials, and performance of the developed electrode was optimized through a combination of numerical simulations and physio-chemical characterizations. The long-term biological performance of the bio-array electrodes were investigated in vivo using a C57 mouse model. It was found that compared to metal array electrodes, the surface charge of the bio-array electrodes increased by 1.74 times, and the impedance at 1 kHz decreased by 63.17 %, with a doubling of the average capacitance. Long-term animal experiments showed that the bio-array electrodes could consistently record 2.5 times more signals than those of the metal array electrodes, and the signal-to-noise ratio based on action potentials was 2.1 times higher. The study investigated the mechanisms of suppressing the scarring effect by the bioaugmented design, revealing reduces brain damage as a result of the interface biocompatibility between the bio-array electrodes and brain tissue, and confirmed the long-term in vivo stability of the bio-array electrodes.},
}
@article {pmid39870740,
year = {2025},
author = {Hu, D and Sato, T and Rockland, KS and Tanifuji, M and Tanigawa, H},
title = {Relationship between functional structures and horizontal connections in macaque inferior temporal cortex.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {3436},
pmid = {39870740},
issn = {2045-2322},
support = {31872776//National Natural Science Foundation of China/ ; 2018YFA0701402//National Key Research and Development Program of China/ ; },
mesh = {Animals ; *Temporal Lobe/physiology ; Macaca mulatta ; Male ; Visual Cortex/physiology ; Photic Stimulation ; Visual Pathways/physiology ; Brain Mapping ; Axons/physiology ; Macaca ; },
abstract = {Horizontal connections in anterior inferior temporal cortex (ITC) are thought to play an important role in object recognition by integrating information across spatially separated functional columns, but their functional organization remains unclear. Using a combination of optical imaging, electrophysiological recording, and anatomical tracing, we investigated the relationship between stimulus-response maps and patterns of horizontal axon terminals in the macaque ITC. In contrast to the "like-to-like" connectivity observed in the early visual cortex, we found that horizontal axons in ITC do not preferentially connect sites with similar object selectivity. While some axon terminal patches shared responsiveness to specific visual features with the injection site, many connected to regions with different selectivity. Our results suggest that horizontal connections in anterior ITC exhibit diverse functional connectivity, potentially supporting flexible integration of visual information for advanced object recognition processes.},
}
@article {pmid39870272,
year = {2025},
author = {Abedi, M and Arbabi, M and Gholampour, R and Amini, J and Barandeh, Z and Hosseini, S and Abedi, A and Gholibegloo, E and Zomorrodian, H and Raoufi, M},
title = {Zinc oxide nanoparticle-embedded tannic acid/chitosan-based sponge: A highly absorbent hemostatic agent with enhanced antimicrobial activity.},
journal = {International journal of biological macromolecules},
volume = {300},
number = {},
pages = {140337},
doi = {10.1016/j.ijbiomac.2025.140337},
pmid = {39870272},
issn = {1879-0003},
mesh = {*Zinc Oxide/chemistry/pharmacology ; *Chitosan/chemistry ; Animals ; *Hemostatics/chemistry/pharmacology ; *Tannins/chemistry/pharmacology ; Rats ; *Nanoparticles/chemistry ; *Anti-Infective Agents/pharmacology/chemistry ; Male ; Escherichia coli/drug effects ; Polyphenols ; },
abstract = {This study reports the development of a highly absorbent Chitosan (CS)/Tannic Acid (TA) sponge, synthesized via chemical cross-linking with Epichlorohydrin (ECH) and integrated with zinc oxide nanoparticles (ZnO NPs) as a novel hemostatic anti-infection agent. The chemical properties of the sponges were characterized using Fourier-transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), and zeta potential measurements. Morphological and elemental analyses conducted through scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDAX) revealed a uniform distribution of ZnO NPs, with particle sizes below 20 nm. Compression tests indicated that the incorporation of ECH enhanced the compressive strength of the TA/CS sample, increasing from 0.614 MPa to 1.03 MPa for TA/CS-ECH and 1.16 MPa for ZnO@TA/CS-ECH, while preserving its flexibility. ZnO@TA/CS-ECH sponges exhibited high swelling ratios, consistent with their mesoporous structure revealed by porosity analysis. MTT assays confirmed that the addition of ECH did not adversely affect the biocompatibility of the final ZnO@TA/CS-ECH sample. Hemostatic performance was assessed through prothrombin time (PT), activated partial thromboplastin time (aPTT), blood clotting index (BCI), blood clotting time (BCT) assays, and platelet adhesion imaging. ZnO@TA/CS-ECH significantly reduced the BCT of untreated blood from 349 to 49 s, outperforming Celox™ (182 s). This performance was further confirmed using a rat liver hemostatic model. Moreover, ZnO@TA/CS-ECH demonstrated substantial antimicrobial activity against E. coli, S. aureus, and C. albicans, comparable to standard antibiotics and antifungals. These findings suggest that the three-dimensional ZnO@TA/CS-ECH sponge holds promise in managing infected bleeding and inspiring the next-generation of hemostatic agents.},
}
@article {pmid39870046,
year = {2025},
author = {Zhang, L and Zhang, H and Yan, S and Li, R and Yao, D and Hu, Y and Zhang, R},
title = {Improving pre-movement patterns detection with multi-dimensional EEG features for readiness potential decrease.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/adaef2},
pmid = {39870046},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; Movement/physiology ; Male ; *Brain-Computer Interfaces ; Female ; Algorithms ; Adult ; Support Vector Machine ; Young Adult ; *Pattern Recognition, Automated/methods ; },
abstract = {Objective.The readiness potential (RP) is an important neural characteristic in motor preparation-based brain-computer interface. In our previous research, we observed a significant decrease of the RP amplitude in some cases, which severely affects the pre-movement patterns detection. In this paper, we aimed to improve the accuracy (Acc) of pre-movement patterns detection in the condition of RP decrease.Approach.We analyzed multi-dimensional EEG features in terms of time-frequency, brain networks, and cross-frequency coupling (CFC). And, a multi-dimensional Electroencephalogram feature combination (MEFC) algorithm was proposed. The features used include: (1) waveforms of the RP; (2) energy in alpha and beta bands; (3) brain network in alpha and beta bands; and (4) CFC value between 2 and 10 Hz.Main results.By employing support vector machines, the MEFC method achieved an average recognition rate of 88.9% and 85.5% under normal and RP decrease conditions, respectively. Compared to classical algorithm, the average Acc for both tasks improved by 7.8% and 8.8% respectively.Significance.This method can effectively improve the Acc of pre-movement patterns decoding in the condition of RP decrease.},
}
@article {pmid39870043,
year = {2025},
author = {Candelori, B and Bardella, G and Spinelli, I and Ramawat, S and Pani, P and Ferraina, S and Scardapane, S},
title = {Spatio-temporal transformers for decoding neural movement control.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/adaef0},
pmid = {39870043},
issn = {1741-2552},
mesh = {Animals ; *Motor Cortex/physiology ; Movement/physiology ; Macaca mulatta ; *Neural Networks, Computer ; *Neurons/physiology ; Action Potentials/physiology ; Male ; Psychomotor Performance/physiology ; Spatio-Temporal Analysis ; Models, Neurological ; },
abstract = {Objective. Deep learning tools applied to high-resolution neurophysiological data have significantly progressed, offering enhanced decoding, real-time processing, and readability for practical applications. However, the design of artificial neural networks to analyze neural activityin vivoremains a challenge, requiring a delicate balance between efficiency in low-data regimes and the interpretability of the results.Approach. To address this challenge, we introduce a novel specialized transformer architecture to analyze single-neuron spiking activity. The model is tested on multi-electrode recordings from the dorsal premotor cortex of non-human primates performing a motor inhibition task.Main results. The proposed architecture provides an early prediction of the correct movement direction, achieving accurate results no later than 230 ms after the Go signal presentation across animals. Additionally, the model can forecast whether the movement will be generated or withheld before a stop signal, unattended, is actually presented. To further understand the internal dynamics of the model, we compute the predicted correlations between time steps and between neurons at successive layers of the architecture, with the evolution of these correlations mirrors findings from previous theoretical analyses.Significance. Overall, our framework provides a comprehensive use case for the practical implementation of deep learning tools in motor control research, highlighting both the predictive capabilities and interpretability of the proposed architecture.},
}
@article {pmid39870041,
year = {2025},
author = {Konrad, P and Gelman, KR and Lawrence, J and Bhatia, S and Jacqueline, D and Sharma, R and Ho, E and Byun, YW and Mermel, CH and Rapoport, BI},
title = {First-in-human experience performing high-resolution cortical mapping using a novel microelectrode array containing 1024 electrodes.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adaeed},
pmid = {39870041},
issn = {1741-2552},
mesh = {Humans ; Microelectrodes ; Male ; Female ; *Brain Mapping/instrumentation/methods ; Middle Aged ; Adult ; *Electrocorticography/instrumentation/methods ; *Cerebral Cortex/physiology ; *Electrodes, Implanted ; Brain Neoplasms/surgery/physiopathology ; Brain-Computer Interfaces ; Intraoperative Neurophysiological Monitoring/instrumentation/methods ; Equipment Design ; },
abstract = {Objective.Localization of function within the brain and central nervous system is an essential aspect of clinical neuroscience. Classical descriptions of functional neuroanatomy provide a foundation for understanding the functional significance of identifiable anatomic structures. However, individuals exhibit substantial variation, particularly in the presence of disorders that alter tissue structure or impact function. Furthermore, functional regions do not always correspond to identifiable structural features. Understanding function at the level of individual patients-and diagnosing and treating such patients-often requires techniques capable of correlating neural activity with cognition, behavior, and experience in anatomically precise ways.Approach. Recent advances in brain-computer interface technology have given rise to a new generation of electrophysiologic tools for scalable, nondestructive functional mapping with spatial precision in the range of tens to hundreds of micrometers, and temporal resolutions in the range of tens to hundreds of microseconds. Here we describe our initial intraoperative experience with novel, thin-film arrays containing 1024 surface microelectrodes for electrocorticographic mapping in a first-in-human study.Main results. Eight patients undergoing standard electrophysiologic cortical mapping during resection of eloquent-region brain tumors consented to brief sessions of concurrent mapping (micro-electrocorticography) using the novel arrays. Four patients underwent motor mapping using somatosensory evoked potentials (SSEPs) while under general anesthesia, and four underwent awake language mapping, using both standard paradigms and the novel microelectrode array. SSEP phase reversal was identified in the region predicted by conventional mapping, but at higher resolution (0.4 mm) and as a contour rather than as a point. In Broca's area (confirmed by direct cortical stimulation), speech planning was apparent in the micro-electrocorticogram as high-amplitude beta-band activity immediately prior to the articulatory event.Significance. These findings support the feasibility and potential clinical utility of incorporating micro-electrocorticography into the intraoperative workflow for systematic cortical mapping of functional brain regions.},
}
@article {pmid39869167,
year = {2025},
author = {Sun, L and Duan, S},
title = {The Paraventricular Hypothalamus: A Sorting Center for Visceral and Somatic Pain.},
journal = {Neuroscience bulletin},
volume = {41},
number = {4},
pages = {731-733},
pmid = {39869167},
issn = {1995-8218},
}
@article {pmid39866662,
year = {2025},
author = {Chen, L and Hu, Y and Wang, Z and Zhang, L and Jian, C and Cheng, S and Ming, D},
title = {Effects of transcutaneous auricular vagus nerve stimulation (taVNS) on motor planning: a multimodal signal study.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {35},
pmid = {39866662},
issn = {1871-4080},
abstract = {Motor planning plays a pivotal role in daily life. Transcutaneous auricular vagus nerve stimulation (taVNS) has been demonstrated to enhance decision-making efficiency, illustrating its potential use in cognitive modulation. However, current research primarily focuses on behavioral and single-modal electrophysiological signal, such as electroencephalography (EEG) and electrocardiography (ECG). To investigate the effect of taVNS on motor planning, a total of 21 subjects were recruited for this study and were divided into two groups: active group (n = 10) and sham group (n = 11). Each subject was required to be involved in a single-blind, sham-controlled, between-subject end-state comfort (ESC) experiment. The study compared behavioral indicators and electrophysiological features before and following taVNS. The results indicated a notable reduction in reaction time and an appreciable increase in the proportion of end-state comfort among the participants following taVNS, accompanied by notable alterations in motor-related cortical potential (MRCP) amplitude, low-frequency power of HRV (LF), and cortico-cardiac coherence, particularly in the parietal and occipital regions. These findings show that taVNS may impact the brain and heart, potentially enhancing their interaction, and improve participants' ability of motor planning.},
}
@article {pmid39866661,
year = {2025},
author = {Mathumitha, R and Maryposonia, A},
title = {Emotion analysis of EEG signals using proximity-conserving auto-encoder (PCAE) and ensemble techniques.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {32},
pmid = {39866661},
issn = {1871-4080},
abstract = {Emotion recognition plays a crucial role in brain-computer interfaces (BCI) which helps to identify and classify human emotions as positive, negative, and neutral. Emotion analysis in BCI maintains a substantial perspective in distinct fields such as healthcare, education, gaming, and human-computer interaction. In healthcare, emotion analysis based on electroencephalography (EEG) signals is deployed to provide personalized support for patients with autism or mood disorders. Recently, several deep learning (DL) based approaches have been developed for accurate emotion recognition tasks. Yet, previous works often struggle with poor recognition accuracy, high dimensionality, and high computational time. This research work designed an innovative framework named Proximity-conserving Auto-encoder (PCAE) for accurate emotion recognition based on EEG signals and resolves challenges faced by traditional emotion analysis techniques. For preserving local structures among the EEG data and reducing dimensionality, the proposed PCAE approach is introduced and it captures the essential features related to emotional states. The EEG data are collected from the EEG Brainwave dataset using a Muse EEG headband and applying preprocessing steps to enhance signal quality. The proposed PCAE model incorporates multiple convolution and deconvolution layers for encoding and decoding and deploys a Local Proximity Preservation Layer for preserving local correlations in the latent space. In addition, it develops a Proximity-conserving Squeeze-and-Excitation Auto-encoder (PC-SEAE) model to further improve the feature extraction ability of the PCAE technique. The proposed PCAE technique utilizes Maximum Mean Discrepancy (MMD) regularization to decrease the distribution discrepancy between input data and the extracted features. Moreover, the proposed model designs an ensemble model for emotion categorization that incorporates a one-versus-support vector machine (SVM), random forest (RF), and Long Short-Term Memory (LSTM) networks by utilizing each classifier's strength to enhance classification accuracy. Further, the performance of the proposed PCAE model is evaluated using diverse performance measures and the model attains outstanding results including accuracy, precision, and Kappa coefficient of 98.87%, 98.69%, and 0.983 respectively. This experimental validation proves that the proposed PCAE framework provides a significant contribution to accurate emotion recognition and classification systems.},
}
@article {pmid39866660,
year = {2025},
author = {Liu, H and Jin, X and Liu, D and Kong, W and Tang, J and Peng, Y},
title = {Joint disentangled representation and domain adversarial training for EEG-based cross-session biometric recognition in single-task protocols.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {31},
pmid = {39866660},
issn = {1871-4080},
abstract = {The increasing adoption of wearable technologies highlights the potential of electroencephalogram (EEG) signals for biometric recognition. However, the intrinsic variability in cross-session EEG data presents substantial challenges in maintaining model stability and reliability. Moreover, the diversity within single-task protocols complicates achieving consistent and generalized model performance. To address these issues, we propose the Joint Disentangled Representation with Domain Adversarial Training (JDR-DAT) framework for EEG-based cross-session biometric recognition within single-task protocols. The JDR-DAT framework disentangles identity-specific features through mutual information estimation and incorporates domain adversarial training to enhance longitudinal robustness. Extensive experiments on longitudinal EEG data from two publicly available single-task protocol datasets-RSVP-based (Rapid Serial Visual Presentation) and MI-based (Motor Imagery)-demonstrate the efficacy of the JDR-DAT framework, with the proposed method achieving average accuracies of 85.83% and 96.72%, respectively.},
}
@article {pmid39866658,
year = {2025},
author = {Zhou, Y and Song, Y and Song, X and He, F and Xu, M and Ming, D},
title = {Review of directional leads, stimulation patterns and programming strategies for deep brain stimulation.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {33},
pmid = {39866658},
issn = {1871-4080},
abstract = {Deep brain stimulation (DBS) is a well-established treatment for both neurological and psychiatric disorders. Directional DBS has the potential to minimize stimulation-induced side effects and maximize clinical benefits. Many new directional leads, stimulation patterns and programming strategies have been developed in recent years. Therefore, it is necessary to review new progress in directional DBS. This paper summarizes progress for directional DBS from the perspective of directional DBS leads, stimulation patterns, and programming strategies which are three key elements of DBS systems. Directional DBS leads are reviewed in electrode design and volume of tissue activated visualization strategies. Stimulation patterns are reviewed in stimulation parameters and advances in stimulation patterns. Programming strategies are reviewed in computational modeling, monopolar review, direction indicators and adaptive DBS. This review will provide a comprehensive overview of primary directional DBS leads, stimulation patterns and programming strategies, making it helpful for those who are developing DBS systems.},
}
@article {pmid39862529,
year = {2025},
author = {Lan, Z and Li, Z and Yan, C and Xiang, X and Tang, D and Wu, M and Chen, Z},
title = {RMKD: Relaxed matching knowledge distillation for short-length SSVEP-based brain-computer interfaces.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {185},
number = {},
pages = {107133},
doi = {10.1016/j.neunet.2025.107133},
pmid = {39862529},
issn = {1879-2782},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Evoked Potentials, Visual/physiology ; Algorithms ; *Brain/physiology ; },
abstract = {Accurate decoding of electroencephalogram (EEG) signals in the shortest possible time is essential for the realization of a high-performance brain-computer interface (BCI) system based on the steady-state visual evoked potential (SSVEP). However, the degradation of decoding performance of short-length EEG signals is often unavoidable due to the reduced information, which hinders the development of BCI systems in real-world applications. In this paper, we propose a relaxed matching knowledge distillation (RMKD) method to transfer both feature-level and logit-level knowledge in a relaxed manner to improve the decoding performance of short-length EEG signals. Specifically, the long-length EEG signals and short-length EEG signals are decoded into the frequency representation by the teacher and student models, respectively. At the feature-level, the frequency-masked generation distillation is designed to improve the representation ability of student features by forcing the randomly masked student features to generate full teacher features. At the logit-level, the non-target class knowledge distillation and the inter-class relation distillation are combined to mitigate loss conflicts by imitating the distribution of non-target classes and preserve the inter-class relation in the prediction vectors of the teacher and student models. We conduct comprehensive experiments on two public SSVEP datasets in the subject-independent scenario with six different signal lengths. The extensive experimental results demonstrate that the proposed RMKD method has significantly improved the decoding performance of short-length EEG signals in SSVEP-based BCI systems.},
}
@article {pmid39860813,
year = {2025},
author = {Mallat, S and Hkiri, E and Albarrak, AM and Louhichi, B},
title = {A Synergy of Convolutional Neural Networks for Sensor-Based EEG Brain-Computer Interfaces to Enhance Motor Imagery Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {2},
pages = {},
pmid = {39860813},
issn = {1424-8220},
support = {IMSIU-RG23077//Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University, Saudi Arabia/ ; },
mesh = {Humans ; Brain/physiology ; *Brain-Computer Interfaces ; *Convolutional Neural Networks ; *Electroencephalography/methods ; *Imagination/physiology ; Neural Networks, Computer ; },
abstract = {Enhancing motor disability assessment and its imagery classification is a significant concern in contemporary medical practice, necessitating reliable solutions to improve patient outcomes. One promising avenue is the use of brain-computer interfaces (BCIs), which establish a direct communication pathway between users and machines. This technology holds the potential to revolutionize human-machine interaction, especially for individuals diagnosed with motor disabilities. Despite this promise, extracting reliable control signals from noisy brain data remains a critical challenge. In this paper, we introduce a novel approach leveraging the collaborative synergy of five convolutional neural network (CNN) models to improve the classification accuracy of motor imagery tasks, which are essential components of BCI systems. Our method demonstrates exceptional performance, achieving an accuracy of 79.44% on the BCI Competition IV 2a dataset, surpassing existing state-of-the-art techniques in using multiple CNN models. This advancement offers significant promise for enhancing the efficacy and versatility of BCIs in a wide range of real-world applications, from assistive technologies to neurorehabilitation, thereby providing robust solutions for individuals with motor disabilities.},
}
@article {pmid39860606,
year = {2025},
author = {Baruch, Y and Barba, M and Cola, A and Frigerio, M},
title = {The Role of Anterior Vaginal Prolapse in Co-Existent Underactive Overactive Bladder Syndrome-A Retrospective Cohort Study.},
journal = {Journal of clinical medicine},
volume = {14},
number = {2},
pages = {},
pmid = {39860606},
issn = {2077-0383},
abstract = {Background: CUOB (co-existent underactive overactive bladder) syndrome is a clinical entity that embraces storage and emptying symptoms, not strictly correlated with urodynamic findings. We assessed the differences between patients diagnosed with CUOB with/without cystocele. Methods: The study group was allocated from 2000 women who underwent urodynamic studies between 2008 and 2016. The demographic and clinical data of 369 patients with complaints consistent with CUOB were retrieved. The study group was subdivided using the Pelvic Organ Prolapse Quantification System. The International Consultation on Incontinence Questionnaire Short Form (ICIQ-UI SF) was used to quantify LUTS severity. Results: A total of 185 women had no or grade I cystocele (group 1), and 185 had grade II or III cystocele (group 2). No difference in mean age was computed. Patients from group 1 had a higher BMI (27 vs. 25, p = 0.02). Risk factors for prolapse, such as parity (1.7 vs. 2.1, p = 0.001) and maximal birthweight (3460 g vs. 3612 g, p = 0.049), were higher in group 2. Pelvic Organ Prolapse symptoms were 4.5 times more frequent in group 2 [n = 36/185 (19.5%) vs. n = 162/184 (88%) p < 0.001]. The rate of stress (70.8% vs. 55.4%, p = 0.002) and urge (64.9% vs. 50%, p = 0.04), urinary incontinence, and ICIQ-UI-SF scores (8 vs. 5, p < 0.001) were higher in group 1. Qmax measured lower in group 2 (17 vs. 15 mL/s, p = 0.008). Detrusor pressure at maximum flow was identical (24 cm H2O). The Bladder Contractility Index (BCI) was higher in group 1 (108 vs. 96.5, p = 0.017), and weak contraction (BCI < 100) was more common in group 2 (73/185; 39.5% vs. 95/184; 52.7%, p = 0.011). Conclusions: Based on our results, we assume that CUOB could be further subdivided based on its association with cystocele. The effect of prolapse repair in women with CUOB and cystocele remains to be evaluated in order to afford better counseling in the future.},
}
@article {pmid39860555,
year = {2025},
author = {Onciul, R and Tataru, CI and Dumitru, AV and Crivoi, C and Serban, M and Covache-Busuioc, RA and Radoi, MP and Toader, C},
title = {Artificial Intelligence and Neuroscience: Transformative Synergies in Brain Research and Clinical Applications.},
journal = {Journal of clinical medicine},
volume = {14},
number = {2},
pages = {},
pmid = {39860555},
issn = {2077-0383},
abstract = {The convergence of Artificial Intelligence (AI) and neuroscience is redefining our understanding of the brain, unlocking new possibilities in research, diagnosis, and therapy. This review explores how AI's cutting-edge algorithms-ranging from deep learning to neuromorphic computing-are revolutionizing neuroscience by enabling the analysis of complex neural datasets, from neuroimaging and electrophysiology to genomic profiling. These advancements are transforming the early detection of neurological disorders, enhancing brain-computer interfaces, and driving personalized medicine, paving the way for more precise and adaptive treatments. Beyond applications, neuroscience itself has inspired AI innovations, with neural architectures and brain-like processes shaping advances in learning algorithms and explainable models. This bidirectional exchange has fueled breakthroughs such as dynamic connectivity mapping, real-time neural decoding, and closed-loop brain-computer systems that adaptively respond to neural states. However, challenges persist, including issues of data integration, ethical considerations, and the "black-box" nature of many AI systems, underscoring the need for transparent, equitable, and interdisciplinary approaches. By synthesizing the latest breakthroughs and identifying future opportunities, this review charts a path forward for the integration of AI and neuroscience. From harnessing multimodal data to enabling cognitive augmentation, the fusion of these fields is not just transforming brain science, it is reimagining human potential. This partnership promises a future where the mysteries of the brain are unlocked, offering unprecedented advancements in healthcare, technology, and beyond.},
}
@article {pmid39858677,
year = {2024},
author = {Wen, B and Shen, L and Kang, X},
title = {Laser Welding of Micro-Wire Stent Electrode as a Minimally Invasive Endovascular Neural Interface.},
journal = {Micromachines},
volume = {16},
number = {1},
pages = {},
pmid = {39858677},
issn = {2072-666X},
support = {61904038//National Natural Science Foundation of China/ ; 2021YFC0122700//National Key Research and Development Program of China/ ; 2021SHZDZX0103//Shanghai Municipal Science and Technology Major Project/ ; },
abstract = {Minimally invasive endovascular stent electrodes are an emerging technology in neural engineering, designed to minimize the damage to neural tissue. However, conventional stent electrodes often rely on resistive welding and are relatively bulky, restricting their use primarily to large animals or thick blood vessels. In this study, the feasibility is explored of fabricating a laser welding stent electrode as small as 300 μm. A high-precision laser welding technique was developed to join micro-wire electrodes without compromising structural integrity or performance. To ensure consistent results, a novel micro-wire welding with platinum pad method was introduced during the welding process. The fabricated electrodes were integrated with stent structures and subjected to detailed electrochemical performance testing to evaluate their potential as neural interface components. The laser-welded endovascular stent electrodes exhibited excellent electrochemical properties, including low impedance and stable charge transfer capabilities. At the same time, in this study, a simulation is conducted of the electrode distribution and arrangement on the stent structure, optimizing the utilization of the available surface area for enhanced functionality. These results demonstrate the potential of the fabricated electrodes for high-performance neural interfacing in endovascular applications. The approach provided a promising solution for advancing endovascular neural engineering technologies, particularly in applications requiring compact electrode designs.},
}
@article {pmid39856396,
year = {2025},
author = {Qin, Y and Zhang, L and Yu, B},
title = {A cross-domain-based channel selection method for motor imagery.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {63},
number = {6},
pages = {1765-1775},
pmid = {39856396},
issn = {1741-0444},
support = {51977020//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Imagination/physiology ; Signal Processing, Computer-Assisted ; Algorithms ; Brain/physiology ; Discriminant Analysis ; Male ; Adult ; },
abstract = {Selecting channels for motor imagery (MI)-based brain-computer interface (BCI) systems can not only enhance the portability of the systems, but also improve the decoding performance. Hence, we propose a cross-domain-based channel selection (CDCS) approach, which effectively minimizes the number of EEG channels used while maintaining high accuracy in MI recognition. The EEG source imaging (ESI) technique is employed to map scalp EEG into the cortical source domain. We divide the equivalent dipoles in the source domain into different regions by k-means clustering. Then, we calculate the band energy (5-40 Hz) of time series of dipoles in these regions by power spectral density (PSD), and the regions with the highest and lowest band energy are selected as the region of interests (ROIs) in the source domain. Subsequently, Pearson correlation coefficients between the dipole time series in ROIs and scalp EEG are used as the criterion for channel selection and a multi-trial-sorting-based channel selection strategy is proposed. Finally, we propose the CDCS-based MI classification framework, where common spatial pattern is applied to extract features and linear discriminant analysis is used to identify MI tasks. The CDCS method demonstrated significant improvement in decoding accuracy on two public datasets, achieving increases of 18.51% and 13.37% compared to all-channel method, and 10.74% and 3.43% compared to the three-channel method. The experimental results validated that CDCS is effective in selecting important channels.},
}
@article {pmid39856240,
year = {2025},
author = {Zhou, Y and Tang, X and Zhang, D and Lee, HJ},
title = {Machine learning empowered coherent Raman imaging and analysis for biomedical applications.},
journal = {Communications engineering},
volume = {4},
number = {1},
pages = {8},
pmid = {39856240},
issn = {2731-3395},
support = {82372011//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12074339//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {In situ and in vivo visualization and analysis of functional, endogenous biomolecules in living systems have generated a pivotal impact in our comprehension of biology and medicine. An increasingly adopted approach involves the utilization of molecular vibrational spectroscopy, which delivers notable advantages such as label-free imaging, high spectral density, high sensitivity, and molecule specificity. Nonetheless, analyzing and processing the intricate, multi-dimensional imaging data to extract interpretable and actionable information poses a fundamental obstacle. In contrast to conventional multivariate methods, machine learning has recently gained considerable attention for its capability of discerning essential features from massive datasets. Here, we present a comprehensive review of the latest advancements in the application of machine learning in the molecular spectroscopic imaging fields. We also discuss notable attributes of spectroscopic imaging modalities and explore their broader impact on other imaging techniques.},
}
@article {pmid39856041,
year = {2025},
author = {Lai, QL and Cai, MT and Li, EC and Fang, GL and Shen, CH and Xu, YF and Qiao, S and Wang, JJ and Weng, QJ and Zhang, YX},
title = {Neurofilament light chain levels in neuronal surface antibody-associated autoimmune encephalitis: a systematic review and meta-analysis.},
journal = {Translational psychiatry},
volume = {15},
number = {1},
pages = {25},
pmid = {39856041},
issn = {2158-3188},
support = {LQ23H090004//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; 82222069//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82073857//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Neurofilament Proteins/blood/cerebrospinal fluid ; *Encephalitis/blood/cerebrospinal fluid/immunology ; *Autoantibodies/blood/cerebrospinal fluid ; *Hashimoto Disease/blood/cerebrospinal fluid/immunology ; Biomarkers/cerebrospinal fluid/blood ; },
abstract = {BACKGROUND: Neuronal surface antibody-associated autoimmune encephalitis (NSAE) is a group of neuro-inflammatory disorders that is mediated by autoantibodies against the cell-surface and synaptic antigens. Studies have explored the role of neurofilament light chain (NfL) in NSAE and provided inconsistent data. We performed a systematic review and meta-analysis to evaluate the NfL levels in the serum and cerebrospinal fluid (CSF) of patients with NSAE.<br><br>METHODS: The National Center for Biotechnology Information (NCBI, PubMed), Web of Knowledge, and the Cochrane Library databases were searched for studies reporting NfL levels in patients with NSAE. Random-effects meta-analysis was used to pool results across studies.<br><br>RESULTS: Thirteen studies were included in the final systematic review and meta-analysis. The serum NfL levels were significantly higher in patients with NSAE compared to unaffected controls (standardized mean difference [SMD]&#x2009;=&#x2009;0.909, 95% confidence interval [CI]: 0.536-1.282). Similarly, the CSF NfL levels were elevated in patients with NSAE (SMD&#x2009;=&#x2009;0.897, 95% CI: 0.508-1.286). The serum and CSF NfL levels were not significantly correlated with disease severity, prognosis, and abnormalities in magnetic resonance imaging, electroencephalography, and CSF.<br><br>CONCLUSIONS: NfL levels in the serum and CSF were higher in patients with NSAE compared to unaffected controls. However, the NfL levels were not shown to be significantly associated with clinical or paraclinical features.},
}
@article {pmid39855567,
year = {2025},
author = {Wei, Y and Zhao, L and Wei, J and Yu, X and Wei, L and Ni, R and Li, T},
title = {Hippocampal transcriptome analysis in ClockΔ19 mice identifies pathways associated with glial cell differentiation and myelination.},
journal = {Journal of affective disorders},
volume = {376},
number = {},
pages = {280-293},
doi = {10.1016/j.jad.2025.01.039},
pmid = {39855567},
issn = {1573-2517},
mesh = {Animals ; *Hippocampus/metabolism/pathology ; Mice ; *Neuroglia/metabolism ; *Cell Differentiation/genetics ; *CLOCK Proteins/genetics ; *Bipolar Disorder/genetics/metabolism ; Gene Expression Profiling ; Disease Models, Animal ; *Myelin Sheath/genetics ; Transcriptome ; Gene Regulatory Networks ; },
abstract = {BACKGROUND: ClockΔ19 mice demonstrate behavioral characteristics and neurobiological changes that closely resemble those observed in bipolar disorder (BD). Notably, abnormalities in the hippocampus have been observed in patients with BD, yet direct molecular investigation of human hippocampal tissue remains challenging due to its limited accessibility.<br><br>METHODS: To model BD, Clock&#x394;19 mice were employed. Weighted gene co-expression network analysis (WGCNA) was utilized to identify mutation-related modules, and changes in cell populations were determined using the computational deconvolution CIBERSORTx. Furthermore, GeneMANIA and protein-protein interactions (PPIs) were leveraged to construct a comprehensive interaction network.<br><br>RESULTS: 174 differentially expressed genes (DEGs) were identified, revealing abnormalities in rhythmic processes, mitochondrial metabolism, and various cell functions including morphology, differentiation, and receptor activity. Analysis identified 5 modules correlated with the mutation, with functional enrichment highlighting disturbances in rhythmic processes and neural cell differentiation due to the mutation. Furthermore, a decrease in neural stem cells (NSC), and an increase in astrocyte-restricted precursors (ARP), ependymocytes (EPC), and hemoglobin-expressing vascular cells (Hb-VC) in the mutant mice were observed. A network comprising 12 genes that link rhythmic processes to neural cell differentiation in the hippocampus was also identified.<br><br>LIMITATIONS: This study focused on the hippocampus of mice, hence the applicability of these findings to human patients warrants further exploration.<br><br>CONCLUSION: The Clock&#x394;19 mutation may disrupt circadian rhythm, myelination, and the differentiation of neural stem cells (NSCs) into glial cells. These abnormalities are linked to altered expression of key genes, including DPB, CIART, NR1D1, GFAP, SLC20A2, and KL. Furthermore, interactions between SLC20A2 and KL might provide a connection between circadian rhythm regulation and cell type transitions.},
}
@article {pmid39854845,
year = {2025},
author = {Bellicha, A and Struber, L and Pasteau, F and Juillard, V and Devigne, L and Karakas, S and Chabardes, S and Babel, M and Charvet, G},
title = {Depth-sensor-based shared control assistance for mobility and object manipulation: toward long-term home-use of BCI-controlled assistive robotic devices.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/adae36},
pmid = {39854845},
issn = {1741-2552},
mesh = {Adult ; Female ; Humans ; Male ; *Brain-Computer Interfaces ; Electroencephalography/methods/instrumentation ; Equipment Design ; Quadriplegia/rehabilitation ; *Robotics/instrumentation/methods ; *Self-Help Devices ; Wheelchairs ; },
abstract = {Objective.Assistive robots can be developed to restore or provide more autonomy for individuals with motor impairments. In particular, power wheelchairs can compensate lower-limb impairments, while robotic manipulators can compensate upper-limbs impairments. Recent studies have shown that Brain-Computer Interfaces (BCI) can be used to operate this type of devices. However, activities of daily living and long-term use in real-life contexts such as home require robustness and adaptability to complex, changing and cluttered environments which can be problematic given the neural signals that do not always allow a safe and efficient use. This article describes assist-as-needed sensor-based shared control (SC) methods relying on the blending of BCI and depth-sensor-based control.Approach.The proposed assistance targets the BCI-teleoperation of effectors for tasks that answer mobility and manipulation needs in a at-home context. The assistance provided by the proposed methods was evaluated through a wheelchair mobility and reach-and-grasp laboratory-based experiments in a controlled environment, as part of a clinical trial with a quadriplegic patient implanted with a wireless 64-channel ElectroCorticoGram recording implant named WIMAGINE.Main results.Results showed that the proposed methods can assist BCI users in both tasks. Indeed, the time to perform the tasks and the number of changes of mental tasks were reduced. Moreover, unwanted actions, such as wheelchair collisions with the environment, and gripper opening that could result in the fall of the object were avoided.Significance.The proposed methods are steps toward at-home use of BCI-teleoperated assistive robots. Indeed, the proposed SC methods improved the performance of the two assistive devices.Clinical trial, registration number: NCT02550522.},
}
@article {pmid39854835,
year = {2025},
author = {Choi, BJ and Liu, J},
title = {A low-cost transhumeral prosthesis operated via an ML-assisted EEG-head gesture control system.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/adae35},
pmid = {39854835},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods/economics/instrumentation ; *Gestures ; *Machine Learning ; *Head/physiology ; *Artificial Limbs/economics ; Male ; *Prosthesis Design/economics/methods ; Adult ; Neural Networks, Computer ; },
abstract = {Objective.Key challenges in upper limb prosthetics include a lack of effective control systems, the often invasive surgical requirements of brain-controlled limbs, and prohibitive costs. As a result, disuse rates remain high despite potential for increased quality of life. To address these concerns, this project developed a low cost, noninvasive transhumeral neuroprosthesis-operated via a combination of electroencephalography (EEG) signals and head gestures.Approach.To address the shortcomings of current noninvasive neural monitoring techniques-namely, single-channel EEG-we leveraged machine learning (ML), creating a neural network-based EEG interpretation algorithm. ML generation was guided by two underlying goals: (1) to improve overall system performance by combining discrete models using a prediction voting scheme, and (2) to favor modeldiversitywithin these new neural network ensembles, as opposed to individual modelperformance. EEG data from eight frequency bands was collected from human subjects to train a ML algorithm employing a hierarchical mixture-of-experts structure. We also implemented head gesture-based control to assist in the generation of additional stable classes for the control system.Main results.The final model performs competitively with existing EEG interpretation systems. Inertial measurement unit (IMU)-based head gestures supplement the neural control system, with 270° actuation of synovial elbow and radial wrist joints driven by intuitive corresponding head gestures. The brain-controlled prosthesis presented in this study costs US$300 to manufacture and achieved competitive performance on a Box and Block Test.Significance.These results suggest proof-of-concept for potential application as an alternative to current prosthetics, but it is important to note that the demonstration in this study remains exploratory. Future work includes broader clinical testing and exploring further uses for the developed ML system.},
}
@article {pmid39854573,
year = {2025},
author = {Eckert, AL and Fuehrer, E and Schmitter, C and Straube, B and Fiehler, K and Endres, D},
title = {Modelling sensory attenuation as Bayesian causal inference across two datasets.},
journal = {PloS one},
volume = {20},
number = {1},
pages = {e0317924},
pmid = {39854573},
issn = {1932-6203},
mesh = {Adult ; Female ; Humans ; Male ; Young Adult ; Bayes Theorem ; Computer Simulation ; Markov Chains ; *Movement/physiology ; Touch/physiology ; },
abstract = {INTRODUCTION: To interact with the environment, it is crucial to distinguish between sensory information that is externally generated and inputs that are self-generated. The sensory consequences of one's own movements tend to induce attenuated behavioral- and neural responses compared to externally generated inputs. We propose a computational model of sensory attenuation (SA) based on Bayesian Causal Inference, where SA occurs when an internal cause for sensory information is inferred.<br><br>METHODS: Experiment 1investigates sensory attenuation during a stroking movement. Tactile stimuli on the stroking finger were suppressed, especially when they were predictable. Experiment 2 showed impaired delay detection between an arm movement and a video of the movement when participants were moving vs. when their arm was moved passively. We reconsider these results from the perspective of Bayesian Causal Inference (BCI). Using a hierarchical Markov Model (HMM) and variational message passing, we first qualitatively capture patterns of task behavior and sensory attenuation in simulations. Next, we identify participant-specific model parameters for both experiments using optimization.<br><br>RESULTS: A sequential BCI model is well equipped to capture empirical patterns of SA across both datasets. Using participant-specific optimized model parameters, we find a good agreement between data and model predictions, with the model capturing both tactile detections in Experiment 1 and delay detections in Experiment 2.<br><br>DISCUSSION: BCI is an appropriate framework to model sensory attenuation in humans. Computational models of sensory attenuation may help to bridge the gap across different sensory modalities and experimental paradigms and may contribute towards an improved description and understanding of deficits in specific patient groups (e.g. schizophrenia).},
}
@article {pmid39851820,
year = {2024},
author = {Yu, W and Wu, C},
title = {Development and Validation of the Interpreting Learning Engagement Scale (ILES).},
journal = {Behavioral sciences (Basel, Switzerland)},
volume = {15},
number = {1},
pages = {},
pmid = {39851820},
issn = {2076-328X},
support = {20CG40//Shanghai Chenguang Talent Program/ ; 2022KFKT009//Open project of Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior/ ; B202205//open project of Key Laboratory of Multilingual Education with AI/ ; C2022411//Shanghai Education and Science Research Project/ ; 2019114002//Shanghai International Studies University Planned Project/ ; },
abstract = {This study developed and validated the Interpreting Learning Engagement Scale (ILES), which was designed to measure the engagement of students in the interpreting learning context. Recognizing the crucial role of learning engagement in academic success and the acquisition of interpreting skills, which demands considerable cognitive effort and active involvement, this research addresses the gap in empirical studies on engagement within the field of interpreting. The ILES, comprising 18 items across four dimensions (behavioral, emotional, cognitive, and agentic engagement), was validated with data collected from a cohort of 306 students from five universities in China. The study employed exploratory and confirmatory factor analyses to establish the scale's theoretical underpinnings and provided further reliability and validity evidence, demonstrating its adequate psychometric properties. Additionally, the scale's scores showed a significant correlation with grit, securing the external validity of the ILES. This study not only contributes a validated instrument for assessing student engagement in interpreting learning but also provides implications for promoting engagement through potential interventions, with the ultimate aim of achieving high levels of interpreting competence.},
}
@article {pmid39851633,
year = {2024},
author = {Ding, X and Zhang, Z and Wang, K and Xiao, X and Xu, M},
title = {A Lightweight Network with Domain Adaptation for Motor Imagery Recognition.},
journal = {Entropy (Basel, Switzerland)},
volume = {27},
number = {1},
pages = {},
pmid = {39851633},
issn = {1099-4300},
abstract = {Brain-computer interfaces (BCI) are an effective tool for recognizing motor imagery and have been widely applied in the motor control and assistive operation domains. However, traditional intention-recognition methods face several challenges, such as prolonged training times and limited cross-subject adaptability, which restrict their practical application. This paper proposes an innovative method that combines a lightweight convolutional neural network (CNN) with domain adaptation. A lightweight feature extraction module is designed to extract key features from both the source and target domains, effectively reducing the model's parameters and improving the real-time performance and computational efficiency. To address differences in sample distributions, a domain adaptation strategy is introduced to optimize the feature alignment. Furthermore, domain adversarial training is employed to promote the learning of domain-invariant features, significantly enhancing the model's cross-subject generalization ability. The proposed method was evaluated on an fNIRS motor imagery dataset, achieving an average accuracy of 87.76% in a three-class classification task. Additionally, lightweight experiments were conducted from two perspectives: model structure optimization and data feature selection. The results demonstrated the potential advantages of this method for practical applications in motor imagery recognition systems.},
}
@article {pmid39851418,
year = {2025},
author = {Zhu, X and Meng, M and Yan, Z and Luo, Z},
title = {Motor Imagery EEG Classification Based on Multi-Domain Feature Rotation and Stacking Ensemble.},
journal = {Brain sciences},
volume = {15},
number = {1},
pages = {},
pmid = {39851418},
issn = {2076-3425},
support = {62271181 and 62171171//National Natural Science Foundation of China/ ; },
abstract = {BACKGROUND: Decoding motor intentions from electroencephalogram (EEG) signals is a critical component of motor imagery-based brain-computer interface (MI-BCIs). In traditional EEG signal classification, effectively utilizing the valuable information contained within the electroencephalogram is crucial.<br><br>OBJECTIVES: To further optimize the use of information from various domains, we propose a novel framework based on multi-domain feature rotation transformation and stacking ensemble for classifying MI tasks.<br><br>METHODS: Initially, we extract the features of Time Domain, Frequency domain, Time-Frequency domain, and Spatial Domain from the EEG signals, and perform feature selection for each domain to identify significant features that possess strong discriminative capacity. Subsequently, local rotation transformations are applied to the significant feature set to generate a rotated feature set, enhancing the representational capacity of the features. Next, the rotated features were fused with the original significant features from each domain to obtain composite features for each domain. Finally, we employ a stacking ensemble approach, where the prediction results of base classifiers corresponding to different domain features and the set of significant features undergo linear discriminant analysis for dimensionality reduction, yielding discriminative feature integration as input for the meta-classifier for classification.<br><br>RESULTS: The proposed method achieves average classification accuracies of 92.92%, 89.13%, and 86.26% on the BCI Competition III Dataset IVa, BCI Competition IV Dataset I, and BCI Competition IV Dataset 2a, respectively.<br><br>CONCLUSIONS: Experimental results show that the method proposed in this paper outperforms several existing MI classification methods, such as the Common Time-Frequency-Spatial Patterns and the Selective Extract of the Multi-View Time-Frequency Decomposed Spatial, in terms of classification accuracy and robustness.},
}
@article {pmid39851397,
year = {2024},
author = {Suresh, RE and Zobaer, MS and Triano, MJ and Saway, BF and Grewal, P and Rowland, NC},
title = {Exploring Machine Learning Classification of Movement Phases in Hemiparetic Stroke Patients: A Controlled EEG-tDCS Study.},
journal = {Brain sciences},
volume = {15},
number = {1},
pages = {},
pmid = {39851397},
issn = {2076-3425},
support = {P20 GM109040/GM/NIGMS NIH HHS/United States ; 5 P20 GM109040//Center of Biomedical Research Excellence (COBRE) in Stroke Recovery-Junior Investigator Research Project. Source: National Institutes of Health/ ; },
abstract = {BACKGROUND/OBJECTIVES: Noninvasive brain stimulation (NIBS) can boost motor recovery after a stroke. Certain movement phases are more responsive to NIBS, so a system that auto-detects these phases would optimize stimulation timing. This study assessed the effectiveness of various machine learning models in identifying movement phases in hemiparetic individuals undergoing simultaneous NIBS and EEG recordings. We hypothesized that transcranial direct current stimulation (tDCS), a form of NIBS, would enhance EEG signals related to movement phases and improve classification accuracy compared to sham stimulation.<br><br>METHODS: EEG data from 10 chronic stroke patients and 11 healthy controls were recorded before, during, and after tDCS. Eight machine learning algorithms and five ensemble methods were used to classify two movement phases (hold posture and reaching) during each of these periods. Data preprocessing included z-score normalization and frequency band power binning.<br><br>RESULTS: In chronic stroke participants who received active tDCS, the classification accuracy for hold vs. reach phases increased from pre-stimulation to the late intra-stimulation period (72.2% to 75.2%, p < 0.0001). Late active tDCS surpassed late sham tDCS classification (75.2% vs. 71.5%, p < 0.0001). Linear discriminant analysis was the most accurate (74.6%) algorithm with the shortest training time (0.9 s). Among ensemble methods, low gamma frequency (30-50 Hz) achieved the highest accuracy (74.5%), although this result did not achieve statistical significance for actively stimulated chronic stroke participants.<br><br>CONCLUSIONS: Machine learning algorithms showed enhanced movement phase classification during active tDCS in chronic stroke participants. These results suggest their feasibility for real-time movement detection in neurorehabilitation, including brain-computer interfaces for stroke recovery.},
}
@article {pmid39851395,
year = {2024},
author = {Pan, S and Shen, T and Lian, Y and Shi, L},
title = {A Task-Related EEG Microstate Clustering Algorithm Based on Spatial Patterns, Riemannian Distance, and a Deep Autoencoder.},
journal = {Brain sciences},
volume = {15},
number = {1},
pages = {},
pmid = {39851395},
issn = {2076-3425},
support = {23CJL006//National Social Science Fund of China/ ; },
abstract = {BACKGROUND: The segmentation of electroencephalography (EEG) signals into a limited number of microstates is of significant importance in the field of cognitive neuroscience. Currently, the microstate analysis algorithm based on global field power has demonstrated its efficacy in clustering resting-state EEG. The task-related EEG was extensively analyzed in the field of brain-computer interfaces (BCIs); however, its primary objective is classification rather than segmentation.<br><br>METHODS: We propose an innovative algorithm for analyzing task-related EEG microstates based on spatial patterns, Riemannian distance, and a modified deep autoencoder. The objective of this algorithm is to achieve unsupervised segmentation and clustering of task-related EEG signals.<br><br>RESULTS: The proposed algorithm was validated through experiments conducted on simulated EEG data and two publicly available cognitive task datasets. The evaluation results and statistical tests demonstrate its robustness and efficiency in clustering task-related EEG microstates.<br><br>CONCLUSIONS: The proposed unsupervised algorithm can autonomously discretize EEG signals into a finite number of microstates, thereby facilitating investigations into the temporal structures underlying cognitive processes.},
}
@article {pmid39850117,
year = {2025},
author = {Mahheidari, N and Alizadeh, M and Kamalabadi Farahani, M and Arabpour, Z and Rezaei Kolarijani, N and Djalilian, AR and Salehi, M},
title = {Regeneration of the skin wound by two different crosslinkers: In vitro and in vivo studies.},
journal = {Iranian journal of basic medical sciences},
volume = {28},
number = {2},
pages = {194-208},
pmid = {39850117},
issn = {2008-3866},
abstract = {OBJECTIVES: For designing a suitable hydrogel, two crosslinked Alginate/ Carboxymethyl cellulose (Alg/CMC) hydrogel, using calcium chloride (Ca[2+]) and glutaraldehyde (GA) as crosslinking agents were synthesized and compared.<br><br>MATERIALS AND METHODS: All samples were characterized by Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), Blood compatibility (BC), Blood clotting index (BCI), weight loss (WL), water absorption (WA), pH, and Electrochemical Impedance Spectroscopy (EIS). Cell viability and cell migration were investigated using the MTT assay and the wound scratch test, respectively. Besides, the wound healing potential of prepared hydrogels was evaluated on the rat models with full-thickness skin excision. To further investigation, TGF &#x3b2;1, IGF-I, COL1, ACT-A (alfa-SMA), and GAPDH expression levels were also reported by RT-PCR.<br><br>RESULTS: Water absorption and weight loss properties were compared between different crosslinker agents, and the most nontoxic crosslinker concentration was determined. We have shown that GA (20 &#xb5;l/ml) and Ca[2+] (50 or 75 mM) enhanced the physical stability of Alg-CMC hydrogel, and they are nontoxic and suitable crosslinkers for wound dressing applications. Although in vivo assessments indicated that the GA (20 &#xb5;l/ml) had a cytotoxic effect on tissue repair, Ca[2+] (75 mM) boosted the wound healing process. Further, RT-PCR results revealed that TGF &#x3b2;1, IGF-I, COL1, ACT-A (alfa-SMA), and GAPDH expression levels were increased in GA (20 &#xb5;l/ml). Moreover, this trend is the opposite in the Ca[2+] (75 mM) treatment groups.<br><br>CONCLUSION: This research shows that Ca[2+] (75 mM) boosts tissue regeneration and wound healing process.},
}
@article {pmid39850073,
year = {2024},
author = {He, J and Huang, Z and Li, Y and Shi, J and Chen, Y and Jiang, C and Feng, J},
title = {Single-channel attention classification algorithm based on robust Kalman filtering and norm-constrained ELM.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1481493},
pmid = {39850073},
issn = {1662-5161},
abstract = {INTRODUCTION: Attention classification based on EEG signals is crucial for brain-computer interface (BCI) applications. However, noise interference and real-time signal fluctuations hinder accuracy, especially in portable single-channel devices. This study proposes a robust Kalman filtering method combined with a norm-constrained extreme learning machine (ELM) to address these challenges.<br><br>METHODS: The proposed method integrates Discrete Wavelet Transformation (DWT) and Independent Component Analysis (ICA) for noise removal, followed by a robust Kalman filter enhanced with convex optimization to preserve critical EEG components. The norm-constrained ELM employs L1/L2 regularization to improve generalization and classification performance. Experimental data were collected using a Schulte Grid paradigm and TGAM sensors, along with publicly available datasets for validation.<br><br>RESULTS: The robust Kalman filter demonstrated superior denoising performance, achieving an average AUC of 0.8167 and a maximum AUC of 0.8678 on self-collected datasets, and an average AUC of 0.8344 with a maximum of 0.8950 on public datasets. The method outperformed traditional Kalman filtering, LMS adaptive filtering, and TGAM's eSense algorithm in both noise reduction and attention classification accuracy.<br><br>DISCUSSION: The study highlights the effectiveness of combining advanced signal processing and machine learning techniques to improve the robustness and generalization of EEG-based attention classification. Limitations include the small sample size and limited demographic diversity, suggesting future research should expand participant groups and explore broader applications, such as mental health monitoring and neurofeedback.},
}
@article {pmid39846559,
year = {2025},
author = {Pantaleo, A and Curatoli, L and Cavallaro, G and Auricchio, D and Murri, A and Quaranta, N},
title = {Unilateral Versus Bilateral Cochlear Implants in Adults: A Cross-Sectional Questionnaire Study Across Multiple Hearing Domains.},
journal = {Audiology research},
volume = {15},
number = {1},
pages = {},
pmid = {39846559},
issn = {2039-4330},
abstract = {AIM: The aim of this study was to assess the subjective experiences of adults with different cochlear implant (CI) configurations-unilateral cochlear implant (UCI), bilateral cochlear implant (BCI), and bimodal stimulation (BM)-focusing on their perception of speech in quiet and noisy environments, music, environmental sounds, people's voices and tinnitus.<br><br>METHODS: A cross-sectional survey of 130 adults who had undergone UCI, BCI, or BM was conducted. Participants completed a six-item online questionnaire, assessing difficulty levels and psychological impact across auditory domains, with responses measured on a 10-point scale. Statistical analyses were performed to compare the subjective experiences of the three groups.<br><br>RESULTS: Patients reported that understanding speech in noise and tinnitus perception were their main concerns. BCI users experienced fewer difficulties with understanding speech in both quiet (p < 0.001) and noisy (p = 0.008) environments and with perceiving non-vocal sounds (p = 0.038) compared to UCI and BM users; no significant differences were found for music perception (p = 0.099), tinnitus perception (p = 0.397), or voice naturalness (p = 0.157). BCI users also reported less annoyance in quiet (p = 0.004) and noisy (p = 0.047) environments, and in the perception of voices (p = 0.009) and non-vocal sounds (p = 0.019). Tinnitus-related psychological impact showed no significant differences between groups (p = 0.090).<br><br>CONCLUSIONS: Although speech perception in noise and tinnitus remain major problems for CI users, the results of our study suggest that bilateral cochlear implantation offers significant subjective advantages over unilateral implantation and bimodal stimulation in adults, particularly in difficult listening environments.},
}
@article {pmid39844431,
year = {2025},
author = {Guo, M and Han, X and Liu, H and Zhu, J and Zhang, J and Bai, Y and Ni, G},
title = {MI-Mamba: A hybrid motor imagery electroencephalograph classification model with Mamba's global scanning.},
journal = {Annals of the New York Academy of Sciences},
volume = {1544},
number = {1},
pages = {242-253},
doi = {10.1111/nyas.15288},
pmid = {39844431},
issn = {1749-6632},
support = {2023YFF1203500//National Key Research and Development Program of China/ ; },
mesh = {*Electroencephalography/methods/classification ; Humans ; Neural Networks, Computer ; Brain-Computer Interfaces ; Deep Learning ; *Imagination/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {Deep learning has revolutionized electroencephalograph (EEG) decoding, with convolutional neural networks (CNNs) being a predominant tool. However, CNNs struggle with long-term dependencies in sequential EEG data. Models like long short-term memory and transformers improve performance but still face challenges of computational efficiency and long sequences. Mamba, a state space model-based method, excels in modeling long sequences. To overcome the limitations of existing EEG decoding models and exploit Mamba's potential in EEG analysis, we propose MI-Mamba, a model integrating CNN with Mamba for motor imagery (MI) data decoding. MI-Mamba processes multi-channel EEG signals through a single convolutional layer to capture spatial features in the local temporal domain, followed by a Mamba module that processes global temporal features. A fully connected, layer-based classifier is used to derive classification results. Evaluated on two public MI datasets, MI-Mamba achieves 80.59% accuracy in the four-class MI task of the BCI Competition IV 2a dataset and 84.42% in the two-class task of the BCI Competition IV 2b dataset, while reducing parameter count by nearly six times compared to the most advanced previous models. These results highlight MI-Mamba's effectiveness in MI decoding and its potential as a new backbone for general EEG decoding.},
}
@article {pmid39843750,
year = {2025},
author = {O'Connell, KS and Koromina, M and van der Veen, T and Boltz, T and David, FS and Yang, JMK and Lin, KH and Wang, X and Coleman, JRI and Mitchell, BL and McGrouther, CC and Rangan, AV and Lind, PA and Koch, E and Harder, A and Parker, N and Bendl, J and Adorjan, K and Agerbo, E and Albani, D and Alemany, S and Alliey-Rodriguez, N and Als, TD and Andlauer, TFM and Antoniou, A and Ask, H and Bass, N and Bauer, M and Beins, EC and Bigdeli, TB and Pedersen, CB and Boks, MP and Børte, S and Bosch, R and Brum, M and Brumpton, BM and Brunkhorst-Kanaan, N and Budde, M and Bybjerg-Grauholm, J and Byerley, W and Cabana-Domínguez, J and Cairns, MJ and Carpiniello, B and Casas, M and Cervantes, P and Chatzinakos, C and Chen, HC and Clarence, T and Clarke, TK and Claus, I and Coombes, B and Corfield, EC and Cruceanu, C and Cuellar-Barboza, A and Czerski, PM and Dafnas, K and Dale, AM and Dalkner, N and Degenhardt, F and DePaulo, JR and Djurovic, S and Drange, OK and Escott-Price, V and Fanous, AH and Fellendorf, FT and Ferrier, IN and Forty, L and Frank, J and Frei, O and Freimer, NB and Fullard, JF and Garnham, J and Gizer, IR and Gordon, SD and Gordon-Smith, K and Greenwood, TA and Grove, J and Guzman-Parra, J and Ha, TH and Hahn, T and Haraldsson, M and Hautzinger, M and Havdahl, A and Heilbronner, U and Hellgren, D and Herms, S and Hickie, IB and Hoffmann, P and Holmans, PA and Huang, MC and Ikeda, M and Jamain, S and Johnson, JS and Jonsson, L and Kalman, JL and Kamatani, Y and Kennedy, JL and Kim, E and Kim, J and Kittel-Schneider, S and Knowles, JA and Kogevinas, M and Kranz, TM and Krebs, K and Kushner, SA and Lavebratt, C and Lawrence, J and Leber, M and Lee, HJ and Liao, C and Lucae, S and Lundberg, M and MacIntyre, DJ and Maier, W and Maihofer, AX and Malaspina, D and Manchia, M and Maratou, E and Martinsson, L and Mattheisen, M and McGregor, NW and McInnis, MG and McKay, JD and Medeiros, H and Meyer-Lindenberg, A and Millischer, V and Morris, DW and Moutsatsou, P and Mühleisen, TW and O'Donovan, C and Olsen, CM and Panagiotaropoulou, G and Papiol, S and Pardiñas, AF and Park, HY and Perry, A and Pfennig, A and Pisanu, C and Potash, JB and Quested, D and Rapaport, MH and Regeer, EJ and Rice, JP and Rivera, M and Schulte, EC and Senner, F and Shadrin, A and Shilling, PD and Sigurdsson, E and Sindermann, L and Sirignano, L and Siskind, D and Slaney, C and Sloofman, LG and Smeland, OB and Smith, DJ and Sobell, JL and Soler Artigas, M and Stein, DJ and Stein, F and Su, MH and Sung, H and Świątkowska, B and Terao, C and Tesfaye, M and Tesli, M and Thorgeirsson, TE and Thorp, JG and Toma, C and Tondo, L and Tooney, PA and Tsai, SJ and Tsermpini, EE and Vawter, MP and Vedder, H and Vreeker, A and Walters, JTR and Winsvold, BS and Witt, SH and Won, HH and Ye, R and Young, AH and Zandi, PP and Zillich, L and , and Adolfsson, R and Alda, M and Alfredsson, L and Backlund, L and Baune, BT and Bellivier, F and Bengesser, S and Berrettini, WH and Biernacka, JM and Boehnke, M and Børglum, AD and Breen, G and Carr, VJ and Catts, S and Cichon, S and Corvin, A and Craddock, N and Dannlowski, U and Dikeos, D and Etain, B and Ferentinos, P and Frye, M and Fullerton, JM and Gawlik, M and Gershon, ES and Goes, FS and Green, MJ and Grigoroiu-Serbanescu, M and Hauser, J and Henskens, FA and Hjerling-Leffler, J and Hougaard, DM and Hveem, K and Iwata, N and Jones, I and Jones, LA and Kahn, RS and Kelsoe, JR and Kircher, T and Kirov, G and Kuo, PH and Landén, M and Leboyer, M and Li, QS and Lissowska, J and Lochner, C and Loughland, C and Luykx, JJ and Martin, NG and Mathews, CA and Mayoral, F and McElroy, SL and McIntosh, AM and McMahon, FJ and Medland, SE and Melle, I and Milani, L and Mitchell, PB and Morken, G and Mors, O and Mortensen, PB and Müller-Myhsok, B and Myers, RM and Myung, W and Neale, BM and Nievergelt, CM and Nordentoft, M and Nöthen, MM and Nurnberger, JI and O'Donovan, MC and Oedegaard, KJ and Olsson, T and Owen, MJ and Paciga, SA and Pantelis, C and Pato, CN and Pato, MT and Patrinos, GP and Pawlak, JM and Ramos-Quiroga, JA and Reif, A and Reininghaus, EZ and Ribasés, M and Rietschel, M and Ripke, S and Rouleau, GA and Roussos, P and Saito, T and Schall, U and Schalling, M and Schofield, PR and Schulze, TG and Scott, LJ and Scott, RJ and Serretti, A and Smoller, JW and Squassina, A and Stahl, EA and Stefansson, H and Stefansson, K and Stordal, E and Streit, F and Sullivan, PF and Turecki, G and Vaaler, AE and Vieta, E and Vincent, JB and Waldman, ID and Weickert, CS and Weickert, TW and Werge, T and Whiteman, DC and Zwart, JA and Edenberg, HJ and McQuillin, A and Forstner, AJ and Mullins, N and Di Florio, A and Ophoff, RA and Andreassen, OA and , },
title = {Genomics yields biological and phenotypic insights into bipolar disorder.},
journal = {Nature},
volume = {639},
number = {8056},
pages = {968-975},
pmid = {39843750},
issn = {1476-4687},
support = {U01 MH085520/MH/NIMH NIH HHS/United States ; R01 MH124839/MH/NIMH NIH HHS/United States ; U01 MH094421/MH/NIMH NIH HHS/United States ; 001/WHO_/World Health Organization/International ; U01 MH109528/MH/NIMH NIH HHS/United States ; },
mesh = {Female ; Humans ; Male ; *Bipolar Disorder/genetics/classification/pathology/physiopathology ; Black or African American/genetics ; Chromosome Mapping ; Cohort Studies ; Genetic Predisposition to Disease ; Genome-Wide Association Study ; *Genomics ; Hispanic or Latino/genetics ; Phenotype ; White ; White People/genetics ; },
abstract = {Bipolar disorder is a leading contributor to the global burden of disease[1]. Despite high heritability (60-80%), the majority of the underlying genetic determinants remain unknown[2]. We analysed data from participants of European, East Asian, African American and Latino ancestries (n = 158,036 cases with bipolar disorder, 2.8 million controls), combining clinical, community and self-reported samples. We identified 298 genome-wide significant loci in the multi-ancestry meta-analysis, a fourfold increase over previous findings[3], and identified an ancestry-specific association in the East Asian cohort. Integrating results from fine-mapping and other variant-to-gene mapping approaches identified 36 credible genes in the aetiology of bipolar disorder. Genes prioritized through fine-mapping were enriched for ultra-rare damaging missense and protein-truncating variations in cases with bipolar disorder[4], highlighting convergence of common and rare variant signals. We report differences in the genetic architecture of bipolar disorder depending on the source of patient ascertainment and on bipolar disorder subtype (type I or type II). Several analyses implicate specific cell types in the pathophysiology of bipolar disorder, including GABAergic interneurons and medium spiny neurons. Together, these analyses provide additional insights into the genetic architecture and biological underpinnings of bipolar disorder.},
}
@article {pmid39842626,
year = {2025},
author = {Xu, X and Gong, Q and Wang, XD},
title = {MK-801 attenuates one-trial tolerance in the elevated plus maze via the thalamic nucleus reuniens.},
journal = {Neuropharmacology},
volume = {268},
number = {},
pages = {110318},
doi = {10.1016/j.neuropharm.2025.110318},
pmid = {39842626},
issn = {1873-7064},
mesh = {Animals ; *Dizocilpine Maleate/pharmacology ; Male ; *Excitatory Amino Acid Antagonists/pharmacology ; *Midline Thalamic Nuclei/drug effects/metabolism/physiology ; Mice ; *Drug Tolerance/physiology ; Anxiety/drug therapy ; Proto-Oncogene Proteins c-fos/metabolism ; Mice, Inbred C57BL ; Elevated Plus Maze Test ; Anti-Anxiety Agents/pharmacology ; Receptors, N-Methyl-D-Aspartate/antagonists &amp; inhibitors ; *Maze Learning/drug effects/physiology ; Diazepam/pharmacology ; },
abstract = {Anxiety, a future-oriented negative emotional state, is characterized by heightened arousal and vigilance. The elevated plus maze (EPM) test is a widely used assay of anxiety-related behaviors in rodents and shows a phenomenon where animals with prior test experience tend to avoid open arms in retest sessions. While this one-trial tolerance (OTT) phenomenon limits the reuse of the EPM test, the potential mechanisms remain unsolved. Here, we found that neither anxiogenic factors like acute restraint stress nor anxiolytic factors like diazepam (2 mg/kg) influenced the emergence of the OTT phenomenon in mice in the EPM test. In contrast, OTT was markedly attenuated by MK-801 (0.1 mg/kg), a non-competitive N-methyl-D-aspartate receptor antagonist. Through the use of c-fos mapping, MK-801 was found to increase neuronal activation in the thalamic nucleus reuniens (Re). Moreover, chemogenetic inactivation of Re neurons could prevent the effects of MK-801. Our findings suggest the Re as a crucial brain region in emotional adaptation in the EPM and shed light on the experimental design optimization and mechanistic investigation of anxiety-related behaviors.},
}
@article {pmid39841068,
year = {2025},
author = {Zhang, J and Yang, X and Liang, Z and Lou, H and Cui, T and Shen, C and Gao, Z},
title = {A brain-computer interface system for lower-limb exoskeletons based on motor imagery and stacked ensemble approach.},
journal = {The Review of scientific instruments},
volume = {96},
number = {1},
pages = {},
doi = {10.1063/5.0232481},
pmid = {39841068},
issn = {1089-7623},
mesh = {*Brain-Computer Interfaces ; Humans ; *Exoskeleton Device ; *Lower Extremity/physiology ; Support Vector Machine ; Algorithms ; *Imagination ; Male ; Adult ; Electroencephalography ; Female ; },
abstract = {Existing lower limb exoskeletons (LLEs) have demonstrated a lack of sufficient patient involvement during rehabilitation training. To address this issue and better incorporate the patient's motion intentions, this paper proposes an online brain-computer interface (BCI) system for LLE based motor imagery and stacked ensemble. The establishment of this online BCI system enables a comprehensive closed-loop control process, which includes the collection and decoding of brain signals, robotic control, and real-time feedback mechanisms. Additionally, an online experimental protocol that integrates visual and proprioceptive feedback is developed. To enhance decoding precision, we proposed a novel classification algorithm based on the stacking technique, termed weighted random forests-support vector machines (WRF-SVM). In this algorithm, WRFs function as the base learning models, while SVMs act as the meta-learning layer. To assess the efficacy of the BCI system and the classification algorithm, eight subjects were recruited for testing. The outcomes of both online and offline experiments exhibit high classification accuracy, confirming the viability and utility of the BCI system. We are confident that our approach holds significant promise for practical applications in the field of LLE technology.},
}
@article {pmid39840009,
year = {2024},
author = {Yang, XY and Huang, S and Fu, QJ and Galvin, J and Chen, B and Liu, JS and Tao, DD},
title = {Preliminary evaluation of the FastCAP for users of the Nurotron cochlear implant.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1523212},
pmid = {39840009},
issn = {1662-4548},
abstract = {BACKGROUND: Electrically evoked compound action potential (ECAP) can be used to measure the auditory nerve's response to electrical stimulation in cochlear implant (CI) users. In the Nurotron CI system, extracting the ECAP waveform from the stimulus artifact is time-consuming.<br><br>METHOD: We developed a new paradigm ("FastCAP") for use with Nurotron CI devices. In electrically evoked compound action potential in fast mode (FastCAP), N recordings are averaged directly on the CI hardware before data transmission, significantly reducing data transmission time. FastCAPs and ECAPs were measured across five electrodes and four stimulation levels per electrode. The FastCAP stimulation rate (33.3 Hz) is also faster than the ECAP rate (2.5 Hz).<br><br>RESULTS: Results showed strong correlations between ECAPs and FastCAPs for N1 latency (r = 0.84, p < 0.001) and N1 amplitude (r = 0.97, p < 0.001). Test-retest reliability for FastCAPs was also high, with intraclass correlation coefficients of r = 0.87 for N1 latency (p < 0.001) and r = 0.96 for N1 amplitude (p < 0.001). The mean test time was 46.9 &#xb1; 1.4 s for the FastCAP and 340.3 &#xb1; 6.3 s for the ECAP. The FastCAP measurement time was significantly shorter than the ECAP measurement time (W = -210.0, p < 0.001). FastCAP thresholds were significantly correlated with behavioral thresholds in 7/20 participants and with comfortable loudness levels in 11/20 participants. The time required to measure FastCAPs was significantly lower than that for ECAPs. The FastCAP paradigm maintained the accuracy and reliability the ECAP measurements while offering a significant reduction in time requirements.<br><br>CONCLUSION: This preliminary evaluation suggests that the FastCAP could be an effective clinical tool to optimize CI processor settings (e.g., threshold stimulation levels) in users of the Nurotron CI device.},
}
@article {pmid39839676,
year = {2024},
author = {Kusano, K and Hayashi, M and Iwama, S and Ushiba, J},
title = {Improved motor imagery skills after repetitive passive somatosensory stimulation: a parallel-group, pre-registered study.},
journal = {Frontiers in neural circuits},
volume = {18},
number = {},
pages = {1510324},
pmid = {39839676},
issn = {1662-5110},
mesh = {Humans ; Male ; Female ; Adult ; *Imagination/physiology ; *Brain-Computer Interfaces ; Electroencephalography ; *Motor Skills/physiology ; Electric Stimulation/methods ; Young Adult ; Somatosensory Cortex/physiology ; *Sensorimotor Cortex/physiology ; },
abstract = {INTRODUCTION: Motor-imagery-based Brain-Machine Interface (MI-BMI) has been established as an effective treatment for post-stroke hemiplegia. However, the need for long-term intervention can represent a significant burden on patients. Here, we demonstrate that motor imagery (MI) instructions for BMI training, when supplemented with somatosensory stimulation in addition to conventional verbal instructions, can help enhance MI capabilities of healthy participants.<br><br>METHODS: Sixteen participants performed MI during scalp EEG signal acquisition before and after somatosensory stimulation to assess MI-induced cortical excitability, as measured using the event-related desynchronization (ERD) of the sensorimotor rhythm (SMR). The non-dominant left hand was subjected to neuromuscular electrical stimulation above the sensory threshold but below the motor threshold (St-NMES), along with passive movement stimulation using an exoskeleton. Participants were randomly divided into an intervention group, which received somatosensory stimulation, and a control group, which remained at rest without stimulation.<br><br>RESULTS: The intervention group exhibited a significant increase in SMR-ERD compared to the control group, indicating that somatosensory stimulation contributed to improving MI ability.<br><br>DISCUSSION: This study demonstrates that somatosensory stimulation, combining electrical and mechanical stimuli, can improve MI capability and enhance the excitability of the sensorimotor cortex in healthy individuals.},
}
@article {pmid39837322,
year = {2025},
author = {Coulter, ME and Gillespie, AK and Chu, J and Denovellis, EL and Nguyen, TTK and Liu, DF and Wadhwani, K and Sharma, B and Wang, K and Deng, X and Eden, UT and Kemere, C and Frank, LM},
title = {Closed-loop modulation of remote hippocampal representations with neurofeedback.},
journal = {Neuron},
volume = {113},
number = {6},
pages = {949-961.e3},
pmid = {39837322},
issn = {1097-4199},
support = {/HHMI/Howard Hughes Medical Institute/United States ; F32 MH123003/MH/NIMH NIH HHS/United States ; },
mesh = {Animals ; *Hippocampus/physiology ; *Neurofeedback/methods/physiology ; Rats ; Male ; Rats, Long-Evans ; Reward ; Cues ; *Place Cells/physiology ; },
abstract = {Humans can remember specific remote events without acting on them and influence which memories are retrieved based on internal goals. However, animal models typically present sensory cues to trigger memory retrieval and then assess retrieval based on action. Thus, it is difficult to determine whether measured neural activity patterns relate to the cue(s), the memory, or the behavior. We therefore asked whether retrieval-related neural activity could be generated in animals without cues or a behavioral report. We focused on hippocampal "place cells," which primarily represent the animal's current location (local representations) but can also represent locations away from the animal (remote representations). We developed a neurofeedback system to reward expression of remote representations and found that rats could learn to generate specific spatial representations that often jumped directly to the experimenter-defined target location. Thus, animals can deliberately engage remote representations, enabling direct study of retrieval-related activity in the brain.},
}
@article {pmid39834791,
year = {2025},
author = {Alotaibi, S and Alotaibi, MM and Alghamdi, FS and Alshehri, MA and Bamusa, KM and Almalki, ZF and Alamri, S and Alghamdi, AJ and Alhazmi, M and Osman, H and Khandaker, MU},
title = {The role of fMRI in the mind decoding process in adults: a systematic review.},
journal = {PeerJ},
volume = {13},
number = {},
pages = {e18795},
pmid = {39834791},
issn = {2167-8359},
mesh = {Humans ; *Magnetic Resonance Imaging/methods ; Adult ; *Brain/physiology/diagnostic imaging ; Brain Mapping/methods ; *Theory of Mind/physiology ; },
abstract = {BACKGROUND: Functional magnetic resonance imaging (fMRI) has revolutionized our understanding of brain activity by non-invasively detecting changes in blood oxygen levels. This review explores how fMRI is used to study mind-reading processes in adults.<br><br>METHODOLOGY: A systematic search was conducted across Web of Science, PubMed, and Google Scholar. Studies were selected based on strict inclusion and exclusion criteria: peer-reviewed; published between 2000 and 2024 (in English); focused on adults; investigated mind-reading (mental state decoding, brain-computer interfaces) or related processes; and employed various mind-reading techniques (pattern classification, multivariate analysis, decoding algorithms).<br><br>RESULTS: This review highlights the critical role of fMRI in uncovering the neural mechanisms of mind-reading. Key brain regions involved include the superior temporal sulcus (STS), medial prefrontal cortex (mPFC), and temporoparietal junction (TPJ), all crucial for mentalizing (understanding others' mental states).<br><br>CONCLUSIONS: This review emphasizes the importance of fMRI in advancing our knowledge of how the brain interprets and processes mental states. It offers valuable insights into the current state of mind-reading research in adults and paves the way for future exploration in this field.},
}
@article {pmid39834638,
year = {2024},
author = {Liu, J and Yang, Z and Wang, Y and Wang, L and Li, Z},
title = {Editorial: Micro/nano devices and technologies for neural science and medical applications.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {12},
number = {},
pages = {1545853},
pmid = {39834638},
issn = {2296-4185},
}
@article {pmid39833722,
year = {2025},
author = {Kocher, HM and , and , and , and Sasieni, P and Corrie, P and McNamara, MG and Sarker, D and Froeling, FEM and Christie, A and Gillmore, R and Khan, K and Propper, D},
title = {Study protocol: multi-centre, randomised controlled clinical trial exploring stromal targeting in locally advanced pancreatic cancer; STARPAC2.},
journal = {BMC cancer},
volume = {25},
number = {1},
pages = {106},
pmid = {39833722},
issn = {1471-2407},
support = {MR/S036601/1/MRC_/Medical Research Council/United Kingdom ; PCRFTB//Pancreatic Cancer Research Fund/ ; },
mesh = {Humans ; *Pancreatic Neoplasms/drug therapy/pathology/mortality ; *Antineoplastic Combined Chemotherapy Protocols/therapeutic use ; Deoxycytidine/analogs &amp; derivatives/administration &amp; dosage ; Paclitaxel/administration &amp; dosage ; Gemcitabine ; Tretinoin/administration &amp; dosage ; *Carcinoma, Pancreatic Ductal/drug therapy/pathology ; Albumins/administration &amp; dosage ; Randomized Controlled Trials as Topic ; Multicenter Studies as Topic ; Clinical Trials, Phase II as Topic ; Stromal Cells/drug effects/pathology ; Male ; },
abstract = {BACKGROUND: Pancreatic cancer (PDAC: pancreatic ductal adenocarcinoma, the commonest form), a lethal disease, is best treated with surgical excision but is feasible in less than a fifth of patients. Around a third of patients presentlocally advanced, inoperable, non-metastatic (laPDAC), whose stadrd of care is palliative chemotherapy; a small minority are down-sized sufficiently to enable surgical excision. We propose a phase II clinical trial to test whether a combination of standard chemotherapy (gemcitabine &amp; nab-Paclitaxel: GEM-NABP) and repurposing All Trans Retinoic Acid (ATRA) to target the stroma may extend progression-free survival and enable successful surgical resection for patients with laPDAC, since data from phase IB clinical trial demonstrate safety of GEM-NABP-ATRA combination to patients with advanced PDAC with potential therapeutic benefit.<br><br>METHODS: Patients with laPDAC will receive at least six cycles of GEM-NABP with 1:1 randomisation to receive this with or without ATRA to assess response, until progression or intolerance. Those with stable/responding disease may undergo surgical resection. Primary endpoint is progression free survival (PFS) defined as the time from the date of randomisation to the date of first documented tumour progression (response evaluation criteria in solid tumours [RECIST] v1.1) or death from any cause, whichever occurs first. Secondary endpoints include objective response rate (ORR), overall survival (OS), safety and tolerability, surgical resection rate, R0 surgical resection rate and patient reported outcome measures (PROMS) as measured by questionnaire EQ-5D-5L. Exploratory endpoints include a decrease or increase in CA19-9 and serum Vitamin A over time correlated with ORR, PFS, and OS.<br><br>DISCUSSION: STARPAC2 aims to assess the role of stromal targeting in laPDAC.<br><br>TRIAL REGISTRATION: EudraCT: 2019-004231-23;&#xa0;NCT04241276; ISRCTN11503604.},
}
@article {pmid39833405,
year = {2025},
author = {Willsey, MS and Shah, NP and Avansino, DT and Hahn, NV and Jamiolkowski, RM and Kamdar, FB and Hochberg, LR and Willett, FR and Henderson, JM},
title = {A high-performance brain-computer interface for finger decoding and quadcopter game control in an individual with paralysis.},
journal = {Nature medicine},
volume = {31},
number = {1},
pages = {96-104},
pmid = {39833405},
issn = {1546-170X},
support = {R01 DC014034/DC/NIDCD NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; U01-DC017844//U.S. Department of Health &amp; Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; R01-DC014034//U.S. Department of Health &amp; Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Fingers/physiopathology/physiology ; *Video Games ; *Quadriplegia/rehabilitation/physiopathology ; Male ; *Paralysis/physiopathology/rehabilitation ; Spinal Cord Injuries/physiopathology/rehabilitation/complications ; Adult ; },
abstract = {People with paralysis express unmet needs for peer support, leisure activities and sporting activities. Many within the general population rely on social media and massively multiplayer video games to address these needs. We developed a high-performance, finger-based brain-computer-interface system allowing continuous control of three independent finger groups, of which the thumb can be controlled in two dimensions, yielding a total of four degrees of freedom. The system was tested in a human research participant with tetraplegia due to spinal cord injury over sequential trials requiring fingers to reach and hold on targets, with an average acquisition rate of 76 targets per minute and completion time of 1.58 ± 0.06 seconds-comparing favorably to prior animal studies despite a twofold increase in the decoded degrees of freedom. More importantly, finger positions were then used to control a virtual quadcopter-the number-one restorative priority for the participant-using a brain-to-finger-to-computer interface to allow dexterous navigation around fixed- and random-ringed obstacle courses. The participant expressed or demonstrated a sense of enablement, recreation and social connectedness that addresses many of the unmet needs of people with paralysis.},
}
@article {pmid39833404,
year = {2025},
author = {Ramsey, NF and Vansteensel, MJ},
title = {The expanding repertoire of brain-computer interfaces.},
journal = {Nature medicine},
volume = {31},
number = {1},
pages = {31-32},
pmid = {39833404},
issn = {1546-170X},
}
@article {pmid39833218,
year = {2025},
author = {Sun, Z and Huang, J and Ma, X and Liang, J and Sun, C and Hu, L and He, H and Yu, G},
title = {A Low-Field MRI Dataset For Spatiotemporal Analysis of Developing Brain.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {109},
pmid = {39833218},
issn = {2052-4463},
support = {62076218//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {*Magnetic Resonance Imaging ; Humans ; *Brain/growth &amp; development/diagnostic imaging ; Infant ; Spatio-Temporal Analysis ; Infant, Newborn ; },
abstract = {Recently, imaging investigation of brain development has increasingly captured the attention of researchers and clinicians in an attempt to understand the link between the brain and behavioral changes. Although high-field MR imaging of infants is feasible, the necessary customizations have limited its accessibility, affordability, and reproducibility. Low-field MR, as an emerging solution for scrutinizing developing brain, has exhibited its unique advantages in safety, portability, and cost-effectiveness. The presented low-field infant structural MR data aims to manifest the feasibility of using low-field MR image to exam brain structural changes during early life in infants. The dataset comprises 100 T2 weighed MR images from infants with in-plane resolution of ~0.85 mm and ~6 mm slice thickness. To demonstrate the potential utility, we conducted atlas-based whole brain segmentations and volumetric quantifications to analyze brain development features in first 10 week in postnatal life. This dataset addresses the scarcity of a large, extended-span infant brain dataset that restricts the further tracking of infant brain development trajectories and the development of routine low-field MR imaging pipelines.},
}
@article {pmid39833018,
year = {2025},
author = {Pickles, J and Griffiths, L and McCloskey, AP and Vasey, N and Lim, E and Rathbone, AP},
title = {Developing a behaviour change intervention using information about greenhouse gas emissions to reduce liquid antibiotic prescribing.},
journal = {Research in social &amp; administrative pharmacy : RSAP},
volume = {21},
number = {4},
pages = {277-286},
doi = {10.1016/j.sapharm.2025.01.006},
pmid = {39833018},
issn = {1934-8150},
mesh = {Humans ; *Anti-Bacterial Agents/administration &amp; dosage/therapeutic use ; *Greenhouse Gases ; *Practice Patterns, Physicians' ; England ; Climate Change ; Male ; Primary Health Care ; Female ; Motivation ; },
abstract = {INTRODUCTION: The determinants of antimicrobial prescribing often involve social influence, which can be harnessed through behaviour change techniques (BCTs). While previous studies have used BCTs to address antimicrobial resistance, there is a lack of evidence regarding their application to address climate change-related issues in antibiotic prescribing. This study aimed to develop a behaviour change intervention (BCI) using information about greenhouse gas emissions to reduce liquid antibiotic prescribing.<br><br>METHODS: A convenience sample of participants from a primary care practice in North East England participated in semi-structured interviews. The intervention design was guided by the Theoretical Domains Framework (TDF) and the Capability, Opportunity, Motivation - Behaviour (COM-B) model. Data were analysed thematically, mapped to the TDF, and used to refine the BCI.<br><br>FINDINGS: Participants identified motivating factors related to high rates of liquid prescribing, climate change, and solid oral dosage form (pill) aversion. The broader context of practice, such as initiatives reduce cost and improve sustainability, provided opportunities for intervention. Participants demonstrated the capability to change prescribing behaviours and expressed willingness to share resources within their teams.<br><br>CONCLUSION: This study underscores the potential of BCIs using greenhouse gas emissions data to reduce liquid antibiotic prescribing. Further research should focus on implementing and evaluating these interventions in practice settings.},
}
@article {pmid39832646,
year = {2025},
author = {Chen, Q and Pan, C and Shen, Y and Pan, Q and Zhang, Q and Wang, J and Hu, Y and Xu, H and Gong, M and Jia, K},
title = {Atypical subcortical involvement in emotional face processing in major depressive disorder with and without comorbid social anxiety.},
journal = {Journal of affective disorders},
volume = {374},
number = {},
pages = {531-539},
doi = {10.1016/j.jad.2025.01.081},
pmid = {39832646},
issn = {1573-2517},
mesh = {Humans ; *Depressive Disorder, Major/physiopathology/psychology/epidemiology ; Female ; Male ; Adult ; *Facial Expression ; *Facial Recognition/physiology ; *Emotions/physiology ; *Phobia, Social/physiopathology/psychology ; Middle Aged ; Comorbidity ; Young Adult ; },
abstract = {Previous research on major depressive disorder (MDD) has largely focused on cognitive biases and abnormalities in cortico-limbic circuitry during emotional face processing. However, it remains unclear whether these abnormalities start at early perceptual stages via subcortical pathways and how comorbid social anxiety influences this process. Here, we investigated subcortical mechanisms in emotional face processing using a psychophysical method that measures monocular advantage (i.e., superior discrimination performance when two stimuli are presented to the same eye than to different eyes). Participants included clinical patients diagnosed with MDD (n = 32), patients with MDD comorbid with social anxiety (comorbid MDD-SAD, n = 32), and a control group of healthy participants (HC, n = 32). We assessed monocular advantage across different emotions (neutral, sad, angry) and among groups. Results indicated that individuals with MDD showed a stronger monocular advantage for sad expressions compared to neutral and angry expressions. In contrast, HC and comorbid MDD-SAD groups showed a greater monocular advantage for neural over negative expressions. Cross-group comparisons revealed that MDD group had a stronger monocular advantage for sad expressions than both HC and comorbid MDD-SAD groups. Additionally, self-reported depressive symptoms were positively correlated with monocular advantage for sad expressions, while social anxiety symptoms were negatively correlated with monocular advantage for negative expressions. These findings suggest atypical early perceptual processing of sadness in individuals with MDD via subcortical mechanisms, with comorbid social anxiety potentially counteracting this effect. This study may inform novel interventions targeting sensory processing and expand beyond cognitive bias modification.},
}
@article {pmid39832388,
year = {2025},
author = {Haddix, C and Bates, M and Garcia-Pava, S and Salmon Powell, E and Sawaki, L and Sunderam, S},
title = {Electroencephalogram features reflect effort corresponding to graded finger extension: implications for hemiparetic stroke.},
journal = {Biomedical physics &amp; engineering express},
volume = {11},
number = {2},
pages = {},
doi = {10.1088/2057-1976/adabeb},
pmid = {39832388},
issn = {2057-1976},
mesh = {Humans ; *Electroencephalography/methods ; *Fingers/physiopathology ; *Stroke/physiopathology/complications ; Male ; *Paresis/physiopathology ; Female ; Brain-Computer Interfaces ; Middle Aged ; Electromyography ; Aged ; Adult ; Movement ; },
abstract = {Brain-computer interfaces (BCIs) offer disabled individuals the means to interact with devices by decoding the electroencephalogram (EEG). However, decoding intent in fine motor tasks can be challenging, especially in stroke survivors with cortical lesions. Here, we attempt to decode graded finger extension from the EEG in stroke patients with left-hand paresis and healthy controls. Participants extended their fingers to one of four levels: low, medium, high, or 'no-go' (none), while hand, muscle (electromyography: EMG), and brain (EEG) activity were monitored. Event-related desynchronization (ERD) was measured as the change in 8-30 Hz EEG power during movement. Classifiers were trained on EEG features, EMG power, or both (EEG+EMG) to decode finger extension, and accuracy assessed via four-fold cross-validation for each hand of each participant. Mean accuracy exceeded chance (25%) for controls (n = 11) at 62% for EMG, 60% for EEG, and 71% for EEG+EMG on the left hand; and 67%, 60%, and 74%, respectively, on the right hand. Accuracies were similar on the unimpaired right hand for the stroke group (n = 3): 61%, 68%, and 78%, respectively. But on the paretic left hand, EMG only discriminated no-go from movement above chance (41%); in contrast, EEG gave 65% accuracy (68% for EEG+EMG), comparable to the non-paretic hand. The median ERD was significant (p < 0.01) over the cortical hand area in both groups and increased with each level of finger extension. But while the ERD favored the hemisphere contralateral to the active hand as expected, it was ipsilateral for the left hand of stroke due to the lesion in the right hemisphere, which may explain its discriminative ability. Hence, the ERD captures effort in finger extension regardless of success or failure at the task; and harnessing residual EMG improves the correlation. This marker could be leveraged in rehabilitative protocols that focus on fine motor control.},
}
@article {pmid39832179,
year = {2025},
author = {Jiang, M and Tan, Y and Wang, X and Hao, Y},
title = {The Phonograms' Genuine-Character Status: What the Embedded Semantic Radicals' Semantic Activation Live by.},
journal = {Brain and behavior},
volume = {15},
number = {1},
pages = {e70277},
pmid = {39832179},
issn = {2162-3279},
mesh = {*Semantics ; Humans ; Male ; Female ; Young Adult ; Adult ; *Language ; *Phonetics ; },
abstract = {BACKGROUND: In Chinese phonogram processing studies, it is widely accepted that both character and non-character semantic radicals could be semantically activated. However, little attention was paid to the underlying workings that enabled the semantic radicals' semantic activation.<br><br>PURPOSE: The present study aimed to address the above issue by conducting two experiments.<br><br>METHODS: Experiment 1 was committed to confirming whether both character and non-character semantic radicals could be semantically activated when embedded in genuine Chinese phonograms. Experiment 2 was devoted to exploring whether the same semantic radicals could also be semantically activated when incorporated in Chinese pseudo-characters.<br><br>RESULTS: Results demonstrated that both character and non-character semantic radicals embedded in the genuine phonograms were semantically activated, but those placed in the pseudo-characters underwent no semantic activation, suggesting that the semantic activation of semantic radicals was genuine-character status-dependent, irrespective of the semantic radicals' characterhood.<br><br>CONCLUSION: It seems that the genuine-character status and the meaning of the host phonogram have strong sway on the semantic activation of semantic radicals.},
}
@article {pmid39830198,
year = {2024},
author = {Liu, D and Wang, N and Song, M and Chai, X and He, Q and Cao, T and Kong, D and Song, Z and Zhang, G and Liu, L and Wang, X and Chen, G and Yin, S and Yang, Y and Zhao, J},
title = {Global glucose metabolism rate as diagnostic marker for disorder of consciousness of patients: quantitative FDG-PET study.},
journal = {Frontiers in neurology},
volume = {15},
number = {},
pages = {1425271},
pmid = {39830198},
issn = {1664-2295},
abstract = {OBJECTIVE: This study was to employ 18F-flurodeoxyglucose (FDG-PET) to evaluate the resting-state brain glucose metabolism in a sample of 46 patients diagnosed with disorders of consciousness (DoC). The aim was to identify objective quantitative metabolic indicators and predictors that could potentially indicate the level of awareness in these patients.<br><br>METHODS: A cohort of 46 patients underwent Coma Recovery Scale-Revised (CRS-R) assessments in order to distinguish between the minimally conscious state (MCS) and the unresponsive wakefulness syndrome (UWS). Additionally, resting-state FDG-PET data were acquired from both the patient group and a control group consisting of 10 healthy individuals. The FDG-PET data underwent reorientation, spatial normalization to a stereotaxic space, and smoothing. The normalization procedure utilized a customized template following the methodology outlined by Phillips et al. Mean cortical metabolism of the overall sample was utilized for distinguishing between UWS and MCS, as well as for predicting the outcome at a 1-year follow-up through the application of receiver operating characteristic (ROC) analysis.<br><br>RESULTS: We used Global Glucose Metabolism as the Diagnostic Marker. A one-way ANOVA revealed that there was a statistically significant difference in cortical metabolic index between two groups (F(2, 53)&#x202f;=&#x202f;7.26, p&#x202f;<&#x202f;0.001). Multiple comparisons found that the mean of cortical metabolic index was significantly different between MCS (M&#x202f;=&#x202f;4.19, SD&#x202f;=&#x202f;0.64) and UWS group (M&#x202f;=&#x202f;2.74, SD&#x202f;=&#x202f;0.94,p&#x202f;<&#x202f;0.001). Also, the mean of cortical metabolic index was significantly different between MCS and healthy group (M&#x202f;=&#x202f;7.88, SD&#x202f;=&#x202f;0.80,p&#x202f;<&#x202f;0.001). Using the above diagnostic criterion, the diagnostic accuracy yielded an area under the curve (AUC) of 0.89 across the pooled cohort (95%CI 0.79-0.99). There was an 85% correct classification between MCS and UWS, with 88% sensitivity and 81% specificity for MCS. The best classification rate in the derivation cohort was achieved at a metabolic index of 3.32 (41% of the mean cortical metabolic index in healthy controls).<br><br>CONCLUSION: Our findings demonstrate that conscious awareness requires a minimum of 41% of normal cortical activity, as indicated by metabolic rates.},
}
@article {pmid39825141,
year = {2025},
author = {Oby, ER and Degenhart, AD and Grigsby, EM and Motiwala, A and McClain, NT and Marino, PJ and Yu, BM and Batista, AP},
title = {Dynamical constraints on neural population activity.},
journal = {Nature neuroscience},
volume = {28},
number = {2},
pages = {383-393},
pmid = {39825141},
issn = {1546-1726},
support = {R01 HD071686/HD/NICHD NIH HHS/United States ; R01 NS105318/NS/NINDS NIH HHS/United States ; NCS BCS1533672//National Science Foundation (NSF)/ ; R01 MH118929/MH/NIMH NIH HHS/United States ; T32 NS086749/NS/NINDS NIH HHS/United States ; NS105318//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; R01 NS129584/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Macaca mulatta ; *Neurons/physiology ; *Motor Cortex/physiology ; *Models, Neurological ; Brain-Computer Interfaces ; Male ; *Nerve Net/physiology ; *Action Potentials/physiology ; Time Factors ; },
abstract = {The manner in which neural activity unfolds over time is thought to be central to sensory, motor and cognitive functions in the brain. Network models have long posited that the brain's computations involve time courses of activity that are shaped by the underlying network. A prediction from this view is that the activity time courses should be difficult to violate. We leveraged a brain-computer interface to challenge monkeys to violate the naturally occurring time courses of neural population activity that we observed in the motor cortex. This included challenging animals to traverse the natural time course of neural activity in a time-reversed manner. Animals were unable to violate the natural time courses of neural activity when directly challenged to do so. These results provide empirical support for the view that activity time courses observed in the brain indeed reflect the underlying network-level computational mechanisms that they are believed to implement.},
}
@article {pmid39824976,
year = {2025},
author = {Zhao, J and Shen, Q and Yong, X and Li, X and Tian, X and Sun, S and Xu, Z and Zhang, X and Zhang, L and Yang, H and Shao, Z and Xu, H and Jiang, Y and Zhang, Y and Yan, W},
title = {Cryo-EM reveals cholesterol binding in the lysosomal GPCR-like protein LYCHOS.},
journal = {Nature structural &amp; molecular biology},
volume = {32},
number = {5},
pages = {896-904},
pmid = {39824976},
issn = {1545-9985},
mesh = {Cryoelectron Microscopy ; *Cholesterol/metabolism/chemistry ; *Lysosomes/metabolism ; Humans ; Protein Binding ; Models, Molecular ; *Receptors, G-Protein-Coupled/metabolism/chemistry/ultrastructure ; },
abstract = {Cholesterol plays a pivotal role in modulating the activity of mechanistic target of rapamycin complex 1 (mTOR1), thereby regulating cell growth and metabolic homeostasis. LYCHOS, a lysosome-localized G-protein-coupled receptor-like protein, emerges as a cholesterol sensor and is capable of transducing the cholesterol signal to affect the mTORC1 function. However, the precise mechanism by which LYCHOS recognizes cholesterol remains unknown. Here, using cryo-electron microscopy, we determined the three-dimensional structural architecture of LYCHOS in complex with cholesterol molecules, revealing a unique arrangement of two sequential structural domains. Through a comprehensive analysis of this structure, we elucidated the specific structural features of these two domains and their collaborative role in the process of cholesterol recognition by LYCHOS.},
}
@article {pmid39824751,
year = {2025},
author = {Cong, L and Wang, X and Wang, J and Liu, W and Xu, W and Zhang, S and Xu, S},
title = {Three-Dimensional SERS-Active Hydrogel Microbeads Enable Highly Sensitive Homogeneous Phase Detection of Alkaline Phosphatase in Biosystems.},
journal = {ACS applied materials &amp; interfaces},
volume = {17},
number = {4},
pages = {5933-5941},
doi = {10.1021/acsami.4c18139},
pmid = {39824751},
issn = {1944-8252},
mesh = {*Alkaline Phosphatase/blood/analysis/metabolism ; Humans ; Spectrum Analysis, Raman/methods ; *Microspheres ; *Hydrogels/chemistry ; Metal Nanoparticles/chemistry ; Silver/chemistry ; Hep G2 Cells ; },
abstract = {Alkaline phosphatase (ALP) is a biomarker for many diseases, and monitoring its activity level is important for disease diagnosis and treatment. In this study, we used the microdroplet technology combined with an in situ laser-induced polymerization method to prepare the Ag nanoparticle (AgNP) doped hydrogel microbeads (HMBs) with adjustable pore sizes that allow small molecules to enter while blocking large molecules. The AgNPs embedded in the hydrogel microspheres can provide SERS activity, improving the SERS signal of small molecules that diffuse to the AgNPs. A specific hydrolysis reaction of ALP on 5-bromo-4-chloro-3-indolylphosphate (BCIP) was introduced and itsproduct 5,5'-dibromo-4,4'-dichloro-1H,1H-[2,2']bisindolyl-3,3'-dione (BCI) was employed to assess ALP activity due to its highly resonance Raman activity. The sensing platform was applied to model ALP activity in serum and evaluate ALP inhibitors. The SERS assay showed higher sensitivity than UV-vis absorption spectroscopy, with the lowest detectable ALP concentration of 1.0 × 10[-20] M. In addition, the ALP activity in HepG2 cells was evaluated using this sensing platform, showing lower ALP-expressing activity than that of controls in response to hypoxia and iron metastasis. This SERS-activated HMB shows great potential in detecting ALP and is expected to help analyze complex clinical samples.},
}
@article {pmid39823647,
year = {2025},
author = {Ma, X and Rizzoglio, F and Bodkin, KL and Miller, LE},
title = {Unsupervised, piecewise linear decoding enables an accurate prediction of muscle activity in a multi-task brain computer interface.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
pmid = {39823647},
issn = {1741-2552},
support = {R01 NS053603/NS/NINDS NIH HHS/United States ; R01 NS074044/NS/NINDS NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; *Motor Cortex/physiology ; Animals ; Electromyography/methods ; *Muscle, Skeletal/physiology ; Macaca mulatta ; Male ; Algorithms ; *Unsupervised Machine Learning ; },
abstract = {Objective.Creating an intracortical brain computer interface (iBCI) capable of seamless transitions between tasks and contexts would greatly enhance user experience. However, the nonlinearity in neural activity presents challenges to computing a global iBCI decoder. We aimed to develop a method that differs from a globally optimized decoder to address this issue.Approach.We devised an unsupervised approach that relies on the structure of a low-dimensional neural manifold to implement a piecewise linear decoder. We created a distinctive dataset in which monkeys performed a diverse set of tasks, some trained, others innate, while we recorded neural signals from the motor cortex (M1) and electromyographs (EMGs) from upper limb muscles. We used both linear and nonlinear dimensionality reduction techniques to discover neural manifolds and applied unsupervised algorithms to identify clusters within those spaces. Finally, we fit a linear decoder of EMG for each cluster. A specific decoder was activated corresponding to the cluster each new neural data point belonged to.Main results.We found clusters in the neural manifolds corresponding with the different tasks or task sub-phases. The performance of piecewise decoding improved as the number of clusters increased and plateaued gradually. With only two clusters it already outperformed a global linear decoder, and unexpectedly, it outperformed even a global recurrent neural network decoder with 10-12 clusters.Significance.This study introduced a computationally lightweight solution for creating iBCI decoders that can function effectively across a broad range of tasks. EMG decoding is particularly challenging, as muscle activity is used, under varying contexts, to control interaction forces and limb stiffness, as well as motion. The results suggest that a piecewise linear decoder can provide a good approximation to the nonlinearity between neural activity and motor outputs, a result of our increased understanding of the structure of neural manifolds in motor cortex.},
}
@article {pmid39821163,
year = {2025},
author = {Zou, Y and Xu, J and Chen, Y},
title = {Volatility, correlation and risk spillover effect between freight rates in BCI and BPI markets: Evidence from static and dynamic GARCH-Copula and dynamic CoVaR models.},
journal = {PloS one},
volume = {20},
number = {1},
pages = {e0315167},
pmid = {39821163},
issn = {1932-6203},
mesh = {*Commerce/economics ; Humans ; *Ships/economics ; *Models, Economic ; },
abstract = {The dry bulk shipping market plays a crucial role in global trade. To examine the volatility, correlation, and risk spillover between freight rates in the BCI and BPI markets, this paper employs the GARCH-Copula-CoVaR model. We analyze the dynamic behavior of the secondary market freight index for dry bulk cargo, highlighting its performance in a complex financial environment and offering empirical support for the shipping industry and financial markets. The findings reveal that: (1) There are significant differences in correlation across various routes, with the correlation between BCI and BPI routes fluctuating over time. Among all route combinations, C5 and P3A_03 exhibit the highest positive correlation. (2) A one-way risk spillover exists between P1A_03 an C5, while two-way positive risk spillover is observed between other routes. This suggests that when a risk materializes on a specific route, other routes are also exposed to potential risks, with varying intensities of spillover. (3) The distance and geographical location of routes may be key factors influencing the differing intensities of risk spillover. This highlights the need to consider the geographical characteristics of routes in understanding risk transmission. This paper aims to provide risk management strategies based on these empirical findings, assisting shipping companies and investors in developing more effective responses to market volatility.},
}
@article {pmid39820611,
year = {2025},
author = {D, J and K C, S},
title = {Parallel convolutional neural network and empirical mode decomposition for high accuracy in motor imagery EEG signal classification.},
journal = {PloS one},
volume = {20},
number = {1},
pages = {e0311942},
pmid = {39820611},
issn = {1932-6203},
mesh = {Humans ; Algorithms ; Brain-Computer Interfaces ; *Convolutional Neural Networks ; *Electroencephalography/methods ; *Imagination/physiology ; *Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; },
abstract = {In recent years, the utilization of motor imagery (MI) signals derived from electroencephalography (EEG) has shown promising applications in controlling various devices such as wheelchairs, assistive technologies, and driverless vehicles. However, decoding EEG signals poses significant challenges due to their complexity, dynamic nature, and low signal-to-noise ratio (SNR). Traditional EEG pattern recognition algorithms typically involve two key steps: feature extraction and feature classification, both crucial for accurate operation. In this work, we propose a novel method that addresses these challenges by employing empirical mode decomposition (EMD) for feature extraction and a parallel convolutional neural network (PCNN) for feature classification. This approach aims to mitigate non-stationary issues, improve performance speed, and enhance classification accuracy. We validate the effectiveness of our proposed method using datasets from the BCI competition IV, specifically datasets 2a and 2b, which contain motor imagery EEG signals. Our method focuses on identifying two- and four-class motor imagery EEG signal classifications. Additionally, we introduce a transfer learning technique to fine-tune the model for individual subjects, leveraging important features extracted from a group dataset. Our results demonstrate that the proposed EMD-PCNN method outperforms existing approaches in terms of classification accuracy. We conduct both qualitative and quantitative analyses to evaluate our method. Qualitatively, we employ confusion matrices and various performance metrics such as specificity, sensitivity, precision, accuracy, recall, and f1-score. Quantitatively, we compare the classification accuracies of our method with those of existing approaches. Our findings highlight the superiority of the proposed EMD-PCNN method in accurately classifying motor imagery EEG signals. The enhanced performance and robustness of our method underscore its potential for broader applicability in real-world scenarios.},
}
@article {pmid39819752,
year = {2025},
author = {Chen, J and Chen, X and Wang, R and Le, C and Khalilian-Gourtani, A and Jensen, E and Dugan, P and Doyle, W and Devinsky, O and Friedman, D and Flinker, A and Wang, Y},
title = {Transformer-based neural speech decoding from surface and depth electrode signals.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
pmid = {39819752},
issn = {1741-2552},
support = {R01 DC018805/DC/NIDCD NIH HHS/United States ; R01 NS109367/NS/NINDS NIH HHS/United States ; R01 NS115929/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; *Electrocorticography/methods/instrumentation ; *Speech/physiology ; *Electrodes, Implanted ; Male ; Deep Learning ; Female ; Brain-Computer Interfaces ; Electroencephalography/methods ; Adult ; },
abstract = {Objective.This study investigates speech decoding from neural signals captured by intracranial electrodes. Most prior works can only work with electrodes on a 2D grid (i.e. Electrocorticographic (ECoG) or ECoG array) and data from a single patient. We aim to design a deep-learning model architecture that can accommodate both surface ECoG and depth (stereotactic EEG or sEEG) electrodes. The architecture should allow training on data from multiple participants with large variability in electrode placements. The model should not have subject-specific layers and the trained model should perform well on participants unseen during training.Approach.We propose a novel transformer-based model architecture named SwinTW that can work with arbitrarily positioned electrodes by leveraging their 3D locations on the cortex rather than their positions on a 2D grid. We train subject-specific models using data from a single participant and multi-subject models exploiting data from multiple participants.Main results.The subject-specific models using only low-density 8 × 8 ECoG data achieved high decoding Pearson Correlation Coefficient with ground truth spectrogram (PCC = 0.817), overN= 43 participants, significantly outperforming our prior convolutional ResNet model and the 3D Swin transformer model. Incorporating additional strip, depth, and grid electrodes available in each participant (N= 39) led to further improvement (PCC = 0.838). For participants with only sEEG electrodes (N= 9), subject-specific models still enjoy comparable performance with an average PCC = 0.798. A single multi-subject model trained on ECoG data from 15 participants yielded comparable results (PCC = 0.837) as 15 models trained individually for these participants (PCC = 0.831). Furthermore, the multi-subject models achieved high performance on unseen participants, with an average PCC = 0.765 in leave-one-out cross-validation.Significance.The proposed SwinTW decoder enables future speech decoding approaches to utilize any electrode placement that is clinically optimal or feasible for a particular participant, including using only depth electrodes, which are more routinely implanted in chronic neurosurgical procedures. The success of the single multi-subject model when tested on participants within the training cohort demonstrates that the model architecture is capable of exploiting data from multiple participants with diverse electrode placements. The architecture's flexibility in training with both single-subject and multi-subject data, as well as grid and non-grid electrodes, ensures its broad applicability. Importantly, the generalizability of the multi-subject models in our study population suggests that a model trained using paired acoustic and neural data from multiple patients can potentially be applied to new patients with speech disability where acoustic-neural training data is not feasible.},
}
@article {pmid39819697,
year = {2025},
author = {Xue, YY and Zhang, ZS and Lin, RR and Huang, HF and Zhu, KQ and Chen, DF and Wu, ZY and Tao, QQ},
title = {Correction: CD2AP deficiency aggravates Alzheimer's disease phenotypes and pathology through p38 MAPK activation.},
journal = {Translational neurodegeneration},
volume = {14},
number = {1},
pages = {3},
pmid = {39819697},
issn = {2047-9158},
}
@article {pmid39819671,
year = {2025},
author = {H Liu, D and Kumar, S and Alawieh, H and Samuel Racz, F and Del R Millán, J},
title = {Personalizedµ-transcranial alternating current stimulation improves online brain-computer interface control.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ada980},
pmid = {39819671},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Transcranial Direct Current Stimulation/methods ; Adult ; Female ; Electroencephalography/methods ; Young Adult ; *Imagination/physiology ; Single-Blind Method ; Online Systems ; *Precision Medicine/methods ; },
abstract = {Objective.A motor imagery (MI)-based brain-computer interface (BCI) enables users to engage with external environments by capturing and decoding electroencephalography (EEG) signals associated with the imagined movement of specific limbs. Despite significant advancements in BCI technologies over the past 40 years, a notable challenge remains: many users lack BCI proficiency, unable to produce sufficiently distinct and reliable MI brain patterns, hence leading to low classification rates in their BCIs. The objective of this study is to enhance the online performance of MI-BCIs in a personalized, biomarker-driven approach using transcranial alternating current stimulation (tACS).Approach.Previous studies have identified that the peak power spectral density value in sensorimotor idling rhythms is a neural correlate of participants' upper limb MI-BCI performances. In this active-controlled, single-blind study, we applied 20 min of tACS at the participant-specific, peakµfrequency in resting-state sensorimotor rhythms (SMRs), with the goal of enhancing resting-stateµSMRs.Main results.After tACS, we observed significant improvements in event-related desynchronizations (ERDs) ofµSMRs, and in the performance of an online MI-BCI that decodes left versus right hand commands in healthy participants (N= 10)-but not in an active control-stimulation control group (N= 10). Lastly, we showed a significant correlation between the resting-stateµSMRs andµERD, offering a mechanistic interpretation behind the observed changes in online BCI performances.Significance.Our research lays the groundwork for future non-invasive interventions designed to enhance BCI performances, thereby improving the independence and interactions of individuals who rely on these systems.},
}
@article {pmid39819299,
year = {2025},
author = {Tsai, PC and Akpan, A and Tang, KT and Lakany, H},
title = {Brain computer interfaces for cognitive enhancement in older people - challenges and applications: a systematic review.},
journal = {BMC geriatrics},
volume = {25},
number = {1},
pages = {36},
pmid = {39819299},
issn = {1471-2318},
mesh = {Humans ; *Brain-Computer Interfaces/trends ; *Cognitive Dysfunction/rehabilitation/psychology/therapy ; Aged ; *Neurofeedback/methods ; Electroencephalography/methods ; *Cognition/physiology ; },
abstract = {BACKGROUND: Brain-computer interface (BCI) offers promising solutions to cognitive enhancement in older people. Despite the clear progress received, there is limited evidence of BCI implementation for rehabilitation. This systematic review addresses BCI applications and challenges in the standard practice of EEG-based neurofeedback (NF) training in healthy older people or older people with mild cognitive impairment (MCI).<br><br>METHODS: Articles were searched via MEDLINE, PubMed, SCOPUS, SpringerLink, and Web of Science. 16 studies between 1st January 2010 to 1st November 2024 are included after screening using PRISMA. The risk of bias, system design, and neurofeedback protocols are reviewed.<br><br>RESULTS: The successful BCI applications in NF trials in older people were biased by the randomisation process and outcome measurement. Although the studies demonstrate promising results in effectiveness of research-grade BCI for cognitive enhancement in older people, it is premature to make definitive claims about widespread BCI usability and applicability.<br><br>SIGNIFICANCE: This review highlights the common issues in the field of EEG-based BCI for older people. Future BCI research could focus on trial design and BCI performance gaps between the old and the young to develop a robust BCI system that compensates for age-related declines in cognitive and motor functions.},
}
@article {pmid39818889,
year = {2025},
author = {Marasco, PD},
title = {Navigating the complexity of touch.},
journal = {Science (New York, N.Y.)},
volume = {387},
number = {6731},
pages = {248-249},
doi = {10.1126/science.adu7929},
pmid = {39818889},
issn = {1095-9203},
mesh = {Humans ; *Brain-Computer Interfaces ; *Somatosensory Cortex/physiology ; *Touch/physiology ; *Touch Perception ; *Deep Brain Stimulation/methods ; },
abstract = {Precise cortical microstimulation improves tactile experience in brain-machine interfaces.},
}
@article {pmid39818881,
year = {2025},
author = {Valle, G and Alamri, AH and Downey, JE and Lienkämper, R and Jordan, PM and Sobinov, AR and Endsley, LJ and Prasad, D and Boninger, ML and Collinger, JL and Warnke, PC and Hatsopoulos, NG and Miller, LE and Gaunt, RA and Greenspon, CM and Bensmaia, SJ},
title = {Tactile edges and motion via patterned microstimulation of the human somatosensory cortex.},
journal = {Science (New York, N.Y.)},
volume = {387},
number = {6731},
pages = {315-322},
pmid = {39818881},
issn = {1095-9203},
support = {R35 NS122333/NS/NINDS NIH HHS/United States ; UH3 NS107714/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Female ; Humans ; Male ; Young Adult ; Bionics ; Cervical Cord/injuries ; *Deep Brain Stimulation/methods ; Hand/physiology ; *Somatosensory Cortex/physiology ; *Touch/physiology ; *Touch Perception/physiology ; Trauma, Nervous System/physiopathology/therapy ; *Brain-Computer Interfaces ; },
abstract = {Intracortical microstimulation (ICMS) of somatosensory cortex evokes tactile sensations whose properties can be systematically manipulated by varying stimulation parameters. However, ICMS currently provides an imperfect sense of touch, limiting manual dexterity and tactile experience. Leveraging our understanding of how tactile features are encoded in the primary somatosensory cortex (S1), we sought to inform individuals with paralysis about local geometry and apparent motion of objects on their skin. We simultaneously delivered ICMS through electrodes with spatially patterned projected fields (PFs), evoking sensations of edges. We then created complex PFs that encode arbitrary tactile shapes and skin indentation patterns. By delivering spatiotemporally patterned ICMS, we evoked sensation of motion across the skin, the speed and direction of which could be controlled. Thus, we improved individuals' tactile experience and use of brain-controlled bionic hands.},
}
@article {pmid39818280,
year = {2025},
author = {Li, C and Ma, K and Li, S and Meng, X and Wang, R and Zhang, D and Zhu, Q},
title = {Multi-channel spatio-temporal graph attention contrastive network for brain disease diagnosis.},
journal = {NeuroImage},
volume = {307},
number = {},
pages = {121013},
doi = {10.1016/j.neuroimage.2025.121013},
pmid = {39818280},
issn = {1095-9572},
mesh = {Humans ; *Magnetic Resonance Imaging/methods ; Diffusion Tensor Imaging/methods ; *Nerve Net/diagnostic imaging/physiopathology ; *Brain/diagnostic imaging/physiopathology ; *Brain Diseases/diagnostic imaging/physiopathology ; Male ; Female ; *Connectome/methods ; Adult ; },
abstract = {Dynamic brain networks (DBNs) can capture the intricate connections and temporal evolution among brain regions, becoming increasingly crucial in the diagnosis of neurological disorders. However, most existing researches tend to focus on isolated brain network sequence segmented by sliding windows, and they are difficult to effectively uncover the higher-order spatio-temporal topological pattern in DBNs. Meantime, it remains a challenge to utilize the structure connectivity prior in the DBNs analysis. To address these problems, we propose a multi-channel spatio-temporal graph attention contrastive network for DBNs analysis. Specifically, we first construct dynamic brain functional networks from fMRI data with sliding windows, and embed the structural connectivity derived from diffusion tensor imaging (DTI) to the dynamic functional connectivity graph representation to construct multi-modal brain network. Second, we develop a multi-channel spatial attention contrastive network to extract topological features from the brain network within each time window. This network incorporates an intra-window graph contrastive constraint to enhance the discriminative ability of the extracted features. Moreover, temporal dependencies across windows are captured by integrating feature embeddings through a self-attention mechanism, and the inter-window recurrent contrastive constraint is devised to extract higher-order spatio-temporal topological features. Finally, a multi-layer perceptron (MLP) is used to classify the brain networks. Experiments on epilepsy and ADNI datasets show that our method outperforms several state-of-the-art approaches in diagnosing performance, and it provides discriminative graph features for related brain diseases.},
}
@article {pmid39815608,
year = {2025},
author = {Dai, C and Fu, Y and Li, X and Lin, M and Li, Y and Li, X and Huang, K and Zhou, C and Xie, J and Zhao, Q and Hu, S},
title = {Clinical efficacy and safety of vortioxetine as an adjuvant drug for patients with bipolar depression.},
journal = {Journal of Zhejiang University. Science. B},
volume = {26},
number = {1},
pages = {26-38},
pmid = {39815608},
issn = {1862-1783},
support = {OO2020491//the Construction Fund of Key Medical Disciplines of Hangzhou/ ; 2023YFC2506200//the National Key Research and Development Program of China/ ; 2021C03107//the Zhejiang Provincial Key Research and Development Program/ ; JNL-2023001B//the Research Project of Jinan Microecological Biomedicine Shandong Laboratory/ ; 2021R52016//the Leading Talent of Scientific and Technological Innovation&#x2012;"Ten Thousand Talents Program" of Zhejiang Province/ ; 2020R01001//the Innovation Team for Precision Diagnosis and Treatment of Major Brain Diseases/ ; 2022KTZ004//the Chinese Medical Education Association/ ; },
mesh = {Humans ; *Bipolar Disorder/drug therapy ; *Vortioxetine/therapeutic use ; Male ; Female ; Middle Aged ; Adult ; Valproic Acid/therapeutic use/administration &amp; dosage ; Lurasidone Hydrochloride/therapeutic use/administration &amp; dosage ; Prospective Studies ; Treatment Outcome ; Pilot Projects ; Drug Therapy, Combination ; *Sulfides/therapeutic use ; Antidepressive Agents/therapeutic use ; },
abstract = {OBJECTIVES: Whether vortioxetine has a utility as an adjuvant drug in the treatment of bipolar depression remains controversial. This study aimed to validate the efficacy and safety of vortioxetine in bipolar depression.<br><br>METHODS: Patients with bipolar &#x2161; depression were enrolled in this prospective, two-center, randomized, 12-week pilot trial. The main indicator for assessing treatment effectiveness was a Montgomery-Asberg Depression Rating Scale (MADRS) of &#x2265;50%. All eligible patients initially received four weeks of lurasidone monotherapy. Patients who responded well continued to receive this kind of monotherapy. However, no-response patients were randomly assigned to either valproate or vortioxetine treatment for eight weeks. By comprehensively comparing the results of MADRS over a period of 4&#x200d;&#x2012;&#x200d;12 weeks, a systematic analysis was conducted to determine whether vortioxetine could be used as an adjuvant drug for treating bipolar depression.<br><br>RESULTS: Thirty-seven patients responded to lurasidone monotherapy, and 60 patients were randomly assigned to the valproate or vortioxetine group for eight weeks. After two weeks of combined valproate or vortioxetine treatment, the MADRS score in the vortioxetine group was significantly lower than that in the valproate group. There was no difference in the MADRS scores between the two groups at 8 and 12 weeks. The incidence of side effects did not significantly differ between the valproate and vortioxetine groups. Importantly, three patients in the vortioxetine group appeared to switch to mania or hypomania.<br><br>CONCLUSIONS: This study suggested that lurasidone combination with vortioxetine might have potential benefits to bipolar II depression in the early stage, while disease progression should be monitored closely for the risk of switching to mania.},
}
@article {pmid39814210,
year = {2025},
author = {Kyoda, Y and Shibamori, K and Tachikawa, K and Nofuji, S and Saito, Y and Tabata, H and Shindo, T and Hashimoto, K and Kobayashi, K and Tanaka, T and Masumori, N},
title = {The Change of Detrusor Contractility at 5 Years After Transurethral Resection of the Prostate: A Single Center Prospective Observational Study.},
journal = {Urology},
volume = {197},
number = {},
pages = {166-173},
doi = {10.1016/j.urology.2024.12.038},
pmid = {39814210},
issn = {1527-9995},
mesh = {Humans ; Male ; *Transurethral Resection of Prostate/adverse effects ; Prospective Studies ; Aged ; *Urinary Bladder Neck Obstruction/physiopathology/etiology/surgery ; *Urinary Bladder/physiopathology ; Middle Aged ; *Muscle Contraction/physiology ; Time Factors ; *Prostatic Hyperplasia/surgery/complications/physiopathology ; Urodynamics ; Follow-Up Studies ; Aged, 80 and over ; },
abstract = {OBJECTIVE: To prospectively assess the impact of transurethral resection of the prostate (TURP) on detrusor function using pressure flow study (PFS) at 5 years after surgery in a single center prospective non-randomized observational study.<br><br>METHODS: Sixty consecutive male patients were prospectively enrolled and underwent TURP from November 2014 to November 2018. A questionnaire survey, free uroflowmetry, and PFS were performed at baseline, and 6, 24, and 60 months after surgery. We divided the age groups at 70 years and defined the younger group as those younger than 70 years, and the elderly group as those aged 70 years or older. The primary endpoint was the change in the bladder contractility index (BCI).<br><br>RESULTS: Of the 60 patients, 39 completed the protocol. Regardless of age, the bladder outlet obstruction indices at 6, 24, and 60 months after surgery were significantly lower than before surgery (all, P<.01). Although the BCI did not significantly change during 60 months for the entire group of 39 patients, it was significantly decreased at 60 months (85.6) after surgery compared to before surgery (102) in the elderly group (P=.02).<br><br>CONCLUSION: We prospectively evaluated detrusor contractility up to 5 years after TURP. It was significantly reduced in the elderly, in spite of which the relief of bladder outlet obstruction was maintained for 5 years after surgery.},
}
@article {pmid39814172,
year = {2025},
author = {Wei, Q and Fan, W and Li, HF and Wang, PS and Xu, M and Dong, HL and Yu, H and Lyu, J and Luo, WJ and Chen, DF and Ge, W and Wu, ZY},
title = {Biallelic variants in SREBF2 cause autosomal recessive spastic paraplegia.},
journal = {Journal of genetics and genomics = Yi chuan xue bao},
volume = {52},
number = {8},
pages = {1021-1033},
doi = {10.1016/j.jgg.2025.01.004},
pmid = {39814172},
issn = {1673-8527},
mesh = {Humans ; *Spastic Paraplegia, Hereditary/genetics/pathology ; Male ; Animals ; Female ; *Sterol Regulatory Element Binding Protein 2/genetics/metabolism ; *Alleles ; Pedigree ; *Genes, Recessive ; Exome Sequencing ; Cholesterol/metabolism ; Adult ; Mutation ; },
abstract = {Hereditary spastic paraplegias (HSPs) refer to a genetically and clinically heterogeneous group of neurodegenerative disorders characterized by the degeneration of motor neurons. To date, a significant number of patients still have not received a definite genetic diagnosis. Therefore, identifying unreported causative genes continues to be of great importance. Here, we perform whole-exome sequencing in a cohort of Chinese HSP patients. Three homozygous variants (p.L604W, p.S517F, and p.T984A) within the sterol regulatory element-binding factor 2 (SREBF2) gene are identified in one autosomal recessive family and two sporadic patients, respectively. Co-segregation is confirmed by Sanger sequencing in all available members. The three variants are rare in the public or in-house database and are predicted to be damaging. The biological impacts of variants in SREBF2 are examined by functional experiments in patient-derived fibroblasts and Drosophila. We find that the variants upregulate cellular cholesterol due to the overactivation of SREBP2, eventually impairing the autophagosomal and lysosomal functions. The overexpression of the mature form of SREBP2 leads to locomotion defects in Drosophila. Our findings identify SREBF2 as a causative gene for HSP and highlight the impairment of cholesterol as a critical pathway for HSP.},
}
@article {pmid39813939,
year = {2025},
author = {Chen, L and Yin, Z and Gu, X and Zhang, X and Cao, X and Zhang, C and Li, X},
title = {Neurophysiological data augmentation for EEG-fNIRS multimodal features based on a denoising diffusion probabilistic model.},
journal = {Computer methods and programs in biomedicine},
volume = {261},
number = {},
pages = {108594},
doi = {10.1016/j.cmpb.2025.108594},
pmid = {39813939},
issn = {1872-7565},
mesh = {*Electroencephalography/methods ; Humans ; Spectroscopy, Near-Infrared/methods ; *Brain-Computer Interfaces ; *Models, Statistical ; Algorithms ; Signal-To-Noise Ratio ; Signal Processing, Computer-Assisted ; Brain/physiology ; Deep Learning ; Normal Distribution ; },
abstract = {BACKGROUND AND OBJECTIVE: The hybrid brain computer interfaces (BCI) combining electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) have attracted extensive attention for overcoming the decoding limitations of the single-modality BCI. With the deepening application of deep learning approaches in BCI systems, its significant performance improvement has become apparent. However, the scarcity of brain signal data limits the performance of deep learning models.<br><br>METHODS: In this study, we proposed an EEG-fNIRS data augmentation framework based on the combination of denoising diffusion probabilistic model (DDPM) and adding Gaussian noise (EFDA-CDG), for enhancing the performance of hybrid BCI systems. Firstly, we unified the temporal and spatial dimensions of EEG and fNIRS by manually extracting features and spatial mapping interpolation to create EEG-fNIRS joint distribution samples. Then, the DDPM generative model was combined with the traditional method of adding Gaussian noise to provide richer training data for the classifier. Finally, we constructed a classification module that applies EEG feature attention and fNIRS terrain attention to improve classification accuracy.<br><br>RESULTS: In order to evaluate the effectiveness of EFDA-CDG framework, experiments were conducted and fully validated on three publicly available databases and one self-collected database. In the context of a participant-dependent training approach, our method achieves accuracy rates of 82.02% for motor imagery, 91.93% for mental arithmetic, and 90.54% for n-back tasks on public databases. Additionally, our method boasts an accuracy rate of 97.82% for drug addiction discrimination task on the self-collected database.<br><br>CONCLUSIONS: EFDA-CDG framework successfully facilitates data augmentation, thereby enhancing the performance of EEG-fNIRS hybrid BCI systems.},
}
@article {pmid39811472,
year = {2024},
author = {Maibam, PC and Pei, D and Olikkal, P and Vinjamuri, RK and Kakoty, NM},
title = {Enhancing prosthetic hand control: A synergistic multi-channel electroencephalogram.},
journal = {Wearable technologies},
volume = {5},
number = {},
pages = {e18},
pmid = {39811472},
issn = {2631-7176},
abstract = {Electromyogram (EMG) has been a fundamental approach for prosthetic hand control. However it is limited by the functionality of residual muscles and muscle fatigue. Currently, exploring temporal shifts in brain networks and accurately classifying noninvasive electroencephalogram (EEG) for prosthetic hand control remains challenging. In this manuscript, it is hypothesized that the coordinated and synchronized temporal patterns within the brain network, termed as brain synergy, contain valuable information to decode hand movements. 32-channel EEGs were acquired from 10 healthy participants during hand grasp and open. Synergistic spatial distribution pattern and power spectra of brain activity were investigated using independent component analysis of EEG. Out of 32 EEG channels, 15 channels spanning the frontal, central and parietal regions were strategically selected based on the synergy of spatial distribution pattern and power spectrum of independent components. Time-domain and synergistic features were extracted from the selected 15 EEG channels. These features were employed to train a Bayesian optimizer-based support vector machine (SVM). The optimized SVM classifier could achieve an average testing accuracy of 94.39 .84% using synergistic features. The paired t-test showed that synergistic features yielded significantly higher area under curve values (p < .05) compared to time-domain features in classifying hand movements. The output of the classifier was employed for the control of the prosthetic hand. This synergistic approach for analyzing temporal activities in motor control and control of prosthetic hands have potential contributions to future research. It addresses the limitations of EMG-based approaches and emphasizes the effectiveness of synergy-based control for prostheses.},
}
@article {pmid39809040,
year = {2025},
author = {Xu, R and Allison, BZ and Zhao, X and Liang, W and Wang, X and Cichocki, A and Jin, J},
title = {Multi-Scale Pyramid Squeeze Attention Similarity Optimization Classification Neural Network for ERP Detection.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {184},
number = {},
pages = {107124},
doi = {10.1016/j.neunet.2025.107124},
pmid = {39809040},
issn = {1879-2782},
mesh = {Humans ; *Neural Networks, Computer ; *Evoked Potentials/physiology ; Electroencephalography/methods ; *Attention/physiology ; *Brain-Computer Interfaces ; Brain/physiology ; Algorithms ; Deep Learning ; },
abstract = {Event-related potentials (ERPs) can reveal brain activity elicited by external stimuli. Innovative methods to decode ERPs could enhance the accuracy of brain-computer interface (BCI) technology and promote the understanding of cognitive processes. This paper proposes a novel Multi-Scale Pyramid Squeeze Attention Similarity Optimization Classification Neural Network (MS-PSA-SOC) for ERP Detection. The model integrates a multi-scale architecture, self-attention mechanism, and deep metric learning to achieve a more comprehensive, refined, and discriminative feature representation. The MS module aggregates fine-grained local features and global features with a larger receptive field within a multi-scale architecture, effectively capturing the dynamic characteristics of complex oscillatory activities in the brain at different levels of abstraction. This preserves complementary spatiotemporal representation information. The PSA module continues the multi-scale contextual modeling from the previous module and achieves adaptive recalibration of multi-scale features. By employing effective aggregation and selection mechanisms, it highlights key features while suppressing redundant information. The SOC module jointly optimizes similarity metric loss and classification loss, maintaining the feature space distribution while focusing on sample class labels. This optimization of similarity relationships between samples improves the model's generalization ability and robustness. Results from public and self-collected datasets demonstrate that the command recognition accuracy of the MS-PSA-SOC model is at least 3.1% and 2.8% higher than other advanced algorithms, achieving superior performance. Additionally, the method demonstrates a lower standard deviation across both datasets. This study also validated the network parameters based on Shannon's sampling theorem and EEG "microstates" through relevant experiments.},
}
@article {pmid39808940,
year = {2025},
author = {Gu, M and Pei, W and Gao, X and Wang, Y},
title = {Optimizing the proportion of stimulation area in a grid stimulus for user-friendly SSVEP-based BCIs.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/adaa1e},
pmid = {39808940},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Male ; Female ; *Electroencephalography/methods ; Adult ; *Photic Stimulation/methods ; Young Adult ; },
abstract = {Objective.Steady-state visual evoked potentials (SSVEPs) rely on the photic driving response to encode electroencephalogram (EEG) signals stably and efficiently. However, the user experience of the traditional stimulation with high-contrast flickers urgently needs to be improved. In this study, we introduce a novel paradigm of grid stimulation with weak flickering perception, distinguished by a markedly lower proportion of stimulation area in the overall pattern.Approach.In an offline single-target experiment, we investigated the unique characteristics of SSVEPs evoked by varying proportions in grid stimuli within low and medium frequency bands. Based on the analysis of simulation performance across a four-class brain-computer interface (BCI) task and the evaluation of user experience questionnaires, a subset of paradigms that balance performance and comfort were selected for implementation in four-target online BCI systems.Main results.Our results demonstrate that even ultra-low stimulation proportion paradigms can still evoke strong responses within specific frequency bands, effectively enhancing user experience with low and middle frequency stimuli. Notably, proportions of 0.94% and 2.10% within the 3-5 Hz range provide an optimal balance between performance and user experience. For frequencies extending up to 15 Hz, a 2.10% proportion remains ideal. At 20 Hz, slightly higher proportions of 3.75% and 8.43% maintain these benefits.Significance.These findings are crucial for advancing the development of effective and user-friendly SSVEP-based BCI systems.},
}
@article {pmid39808939,
year = {2025},
author = {Prakash, PR and Lei, T and Flint, RD and Hsieh, JK and Fitzgerald, Z and Mugler, E and Templer, J and Goldrick, MA and Tate, MC and Rosenow, J and Glaser, J and Slutzky, MW},
title = {Decoding speech intent from non-frontal cortical areas.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
pmid = {39808939},
issn = {1741-2552},
support = {UL1 TR000150/TR/NCATS NIH HHS/United States ; R01 NS094748/NS/NINDS NIH HHS/United States ; R01 NS112942/NS/NINDS NIH HHS/United States ; RF1 NS125026/NS/NINDS NIH HHS/United States ; UL1 TR001422/TR/NCATS NIH HHS/United States ; R21 NS084069/NS/NINDS NIH HHS/United States ; T32 NS047987/NS/NINDS NIH HHS/United States ; UM1 TR005121/TR/NCATS NIH HHS/United States ; F32 DC015708/DC/NIDCD NIH HHS/United States ; R01 NS099210/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; Male ; Female ; *Speech/physiology ; Adult ; *Temporal Lobe/physiology ; *Brain-Computer Interfaces ; *Intention ; *Parietal Lobe/physiology ; Young Adult ; Electrocorticography/methods ; },
abstract = {Objective. Brain machine interfaces (BMIs) that can restore speech have predominantly focused on decoding speech signals from the speech motor cortices. A few studies have shown some information outside the speech motor cortices, such as in parietal and temporal lobes, that also may be useful for BMIs. The ability to use information from outside the frontal lobe could be useful not only for people with locked-in syndrome, but also to people with frontal lobe damage, which can cause nonfluent aphasia or apraxia of speech. However, temporal and parietal lobes are predominantly involved in perceptive speech processing and comprehension. Therefore, to be able to use signals from these areas in a speech BMI, it is important to ascertain that they are related to production. Here, using intracranial recordings, we sought evidence for whether, when and where neural information related to speech intent could be found in the temporal and parietal corticesApproach. Using intracranial recordings, we examined neural activity across temporal and parietal cortices to identify signals associated with speech intent. We employed causal information to distinguish speech intent from resting states and other language-related processes, such as comprehension and working memory. Neural signals were analyzed for their spatial distribution and temporal dynamics to determine their relevance to speech production.Main results. Causal information enabled us to distinguish speech intent from resting state and other processes involved in language processing or working memory. Information related to speech intent was distributed widely across the temporal and parietal lobes, including superior temporal, medial temporal, angular, and supramarginal gyri.Significance. Loss of communication due to neurological diseases can be devastating. While speech BMIs have made strides in decoding speech from frontal lobe signals, our study reveals that the temporal and parietal cortices contain information about speech production intent that can be causally decoded prior to the onset of voice. This information is distributed across a large network. This information can be used to improve current speech BMIs and potentially expand the patient population for speech BMIs to include people with frontal lobe damage from stroke or traumatic brain injury.},
}
@article {pmid39808931,
year = {2025},
author = {Russo, JS and Shiels, TA and Lin, CS and John, SE and Grayden, DB},
title = {Decoding imagined movement in people with multiple sclerosis for brain-computer interface translation.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/adaa1d},
pmid = {39808931},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; *Multiple Sclerosis/physiopathology/diagnosis/rehabilitation ; *Imagination/physiology ; Male ; Female ; Adult ; Movement/physiology ; Middle Aged ; Electroencephalography/methods ; },
abstract = {Objective.Multiple sclerosis (MS) is a heterogeneous autoimmune-mediated disorder affecting the central nervous system, commonly manifesting as fatigue and progressive limb impairment. This can significantly impact quality of life due to weakness or paralysis in the upper and lower limbs. A brain-computer interface (BCI) aims to restore quality of life through control of an external device, such as a wheelchair. However, the limited BCI research in people with MS has been confined to exploring the P300 response and brain signals associated with attempted movement. The current study aims to expand the MS-BCI literature by highlighting the feasibility of decoding MS imagined movement.Approach.We collected electroencephalography data from eight participants with various symptoms of MS and ten neurotypical control participants. Participants made imagined movements of the hands and feet as directed by a go no-go protocol. Binary regularised linear discriminant analysis was used to classify imagined movement vs. rest and vs. movement at individual time-frequency points. The frequency bands which provided the maximal accuracy, and the associated latency, were compared.Main Results.In all MS participants, the classification algorithm achieved above 70% accuracy in at least one imagined movement vs. rest classification and most movement vs. movement classifications. There was no significant difference between classification of limbs with weakness or paralysis to neurotypical controls. Both the MS and control groups possessed decodable information within the alpha (7-13 Hz) and beta (16-30 Hz) bands at similar latency.Significance.This study is the first to demonstrate the feasibility of decoding imagined movements in people with MS. As an alternative to the P300 response, motor imagery-based control of a BCI may also be combined with existing motor imagery therapy to supplement MS rehabilitation. These promising results merit further long term BCI studies to investigate the effect of MS progression on classification performance.},
}
@article {pmid39808922,
year = {2025},
author = {Dekleva, BM and Collinger, JL},
title = {Using transient, effector-specific neural responses to gate decoding for brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
pmid = {39808922},
issn = {1741-2552},
support = {R01 NS121079/NS/NINDS NIH HHS/United States ; U01 NS108922/NS/NINDS NIH HHS/United States ; U01 NS123125/NS/NINDS NIH HHS/United States ; UH3 NS107714/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Female ; Humans ; Male ; Young Adult ; Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Hand/physiology ; Hand Strength/physiology ; *Motor Cortex/physiology ; Movement/physiology ; Psychomotor Performance/physiology ; },
abstract = {Objective.Real-world implementation of brain-computer interfaces (BCIs) for continuous control of devices should ideally rely on fully asynchronous decoding approaches. That is, the decoding algorithm should continuously update its output by estimating the user's intended actions from real-time neural activity, without the need for any temporal alignment to an external cue. This kind of open-ended temporal flexibility is necessary to achieve naturalistic and intuitive control. However, the relation between cortical activity and behavior is not stationary: neural responses that appear related to a certain aspect of behavior (e.g. grasp force) in one context will exhibit a relationship to something else in another context (e.g. reach speed). This presents a challenge for generalizable decoding, since the applicability of a decoder for a given parameter changes over time.Approach.We developed a method to simplify the problem of continuous decoding that uses transient, end effector-specific neural responses to identify periods of relevant effector engagement. Specifically, we use transient responses in the population response observed at the onset and offset of all hand-related actions to signal the applicability of hand-related feature decoders (e.g. digit movement or force). By using this transient-based gating approach, specific feature decoding models can be simpler (owing to local linearities) and are less sensitive to interference from cross-effector interference such as combined reaching and grasping actions.Main results.The transient-based decoding approach enabled high-quality online decoding of grasp force and individual finger control in multiple behavioral paradigms. The benefits of the gated approach are most evident in tasks that require both hand and arm control, for which standard continuous decoding approaches exhibit high output variability.Significance.The approach proposed here addresses the challenge of decoder generalization across contexts. By limiting decoding to identified periods of effector engagement, this approach can support reliable BCI control in real-world applications.Clinical Trial ID: NCT01894802.},
}
@article {pmid39805390,
year = {2025},
author = {Xu, A and Huang, Y and Wu, B and Zhang, J and Deng, B and Cai, M and Cao, J and Wang, J and Yang, B and Shao, X and He, Q and Ying, M},
title = {Phase separation-based screening identifies arsenic trioxide as the N-Myc-DNA interaction inhibitor for neuroblastoma therapy.},
journal = {Cancer letters},
volume = {612},
number = {},
pages = {217449},
doi = {10.1016/j.canlet.2025.217449},
pmid = {39805390},
issn = {1872-7980},
}
@article {pmid39805258,
year = {2025},
author = {Liu, DH and Kumar, S and Alawieh, H and Racz, FS and Millan, JDR},
title = {Personalized μ-transcranial alternating current stimulation improves online brain-computer interface control.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ada9c0},
pmid = {39805258},
issn = {1741-2552},
abstract = {OBJECTIVE: A motor imagery (MI)-based brain-computer interface (BCI) enables users to engage with external environments by capturing and decoding electroencephalography (EEG) signals associated with the imagined movement of specific limbs. Despite significant advancements in BCI technologies over the past 40 years, a notable challenge remains: many users lack BCI proficiency, unable to produce sufficiently distinct and reliable MI brain patterns, hence leading to low classification rates in their BCIs. The objective of this study is to enhance the online performance of MI-BCIs in a personalized, biomarker-driven approach using transcranial alternating current stimulation (tACS).<br><br>APPROACH: Previous studies have identified that the peak power spectral density (PSD) value in sensorimotor idling rhythms is a neural correlate of participants' upper limb MI-BCI performances. In this active-controlled, single-blind study, we applied 20 minutes of tACS at the participant-specific, peak &#xb5; frequency in resting-state sensorimotor rhythms (SMRs), with the goal of enhancing resting-state &#xb5; SMRs.<br><br>MAIN RESULTS: After tACS, we observed significant improvements in event-related desynchronizations (ERDs) of &#xb5; sensorimotor rhythms (SMRs), and in the performance of an online MI-BCI that decodes left versus right hand commands in healthy participants (N=10) -but not in an active control-stimulation control group (N=10). Lastly, we showed a significant correlation between the resting-state &#xb5; SMRs and &#xb5; ERD, offering a mechanistic interpretation behind the observed changes in online BCI performances.<br><br>SIGNIFICANCE: Our research lays the groundwork for future non-invasive interventions designed to enhance BCI performances, thereby improving the independence and interactions of individuals who rely on these systems.},
}
@article {pmid39803338,
year = {2024},
author = {Kim, KH and Jeong, JH and Ko, MJ and Lee, S and Kwon, WK and Lee, BJ},
title = {Using Artificial Intelligence in the Comprehensive Management of Spinal Cord Injury.},
journal = {Korean journal of neurotrauma},
volume = {20},
number = {4},
pages = {215-224},
pmid = {39803338},
issn = {2234-8999},
abstract = {Spinal cord injury (SCI) frequently results in persistent motor, sensory, or autonomic dysfunction, and the outcomes are largely determined by the location and severity of the injury. Despite significant technological progress, the intricate nature of the spinal cord anatomy and the difficulties associated with neuroregeneration make full recovery from SCI uncommon. This review explores the potential of artificial intelligence (AI), with a particular focus on machine learning, to enhance patient outcomes in SCI management. The application of AI, specifically machine learning, has revolutionized the diagnosis, treatment, prognosis, and rehabilitation of patients with SCI. By leveraging large datasets and identifying complex patterns, AI contributes to improved diagnostic accuracy, optimizes surgical procedures, and enables the personalization of therapeutic interventions. AI-driven prognostic models provide accurate predictions of recovery, facilitating improved planning and resource allocation. Additionally, AI-powered rehabilitation systems, including robotic devices and brain-computer interfaces, increase the effectiveness and accessibility of therapy. However, realizing the full potential of AI in SCI care requires ongoing research, interdisciplinary collaboration, and the development of comprehensive datasets. As AI continues to evolve, it is expected to play an increasingly vital role in enhancing the outcomes of patients with SCI.},
}
@article {pmid39801915,
year = {2025},
author = {Gu, C and Jin, X and Zhu, L and Yi, H and Liu, H and Yang, X and Babiloni, F and Kong, W},
title = {Cross-session SSVEP brainprint recognition using attentive multi-sub-band depth identity embedding learning network.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {15},
pmid = {39801915},
issn = {1871-4080},
abstract = {Brainprint recognition technology, regarded as a promising biometric technology, encounters challenges stemming from the time-varied, low signal-to-noise ratio of brain signals, such as electroencephalogram (EEG). Steady-state visual evoked potentials (SSVEP) exhibit high signal-to-noise ratio and frequency locking, making them a promising paradigm for brainprint recognition. Consequently, the extraction of time-invariant identity information from SSVEP EEG signals is essential. In this paper, we propose an Attentive Multi-sub-band Depth Identity Embedding Learning Network for stable cross-session SSVEP brainprint recognition. To address the issue of low recognition accuracy across sessions, we introduce the Sub-band Attentive Frequency mechanism, which integrates the frequency-domain relevant characteristics of the SSVEP paradigm and focuses on exploring depth-frequency identity embedding information. Also, we employ Attentive Statistic Pooling to enhance the stability of frequency domain feature distributions across sessions. Extensive experimentation and validation were conducted on two multi-session SSVEP benchmark datasets. The experimental results show that our approach outperforms other state-of-art models on 2-second samples across sessions and has the potential to serve as a benchmark in multi-subject biometric recognition systems.},
}
@article {pmid39801913,
year = {2025},
author = {Zhou, Y and Wang, P and Gong, P and Wan, P and Wen, X and Zhang, D},
title = {Cross-subject mental workload recognition using bi-classifier domain adversarial learning.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {16},
pmid = {39801913},
issn = {1871-4080},
abstract = {To deploy Electroencephalogram (EEG) based Mental Workload Recognition (MWR) systems in the real world, it is crucial to develop general models that can be applied across subjects. Previous studies have utilized domain adaptation to mitigate inter-subject discrepancies in EEG data distributions. However, they have focused on reducing global domain discrepancy, while neglecting local workload-categorical domain divergence. This degrades the workload-discriminating ability of subject-invariant features. To deal with this problem, we propose a novel joint category-wise and domain-wise alignment Domain Adaptation (cdaDA) algorithm, using bi-classifier learning and domain discriminative adversarial learning. The bi-classifier learning approach is adopted to address the similarities and differences between categories, helping to align EEG data within the same mental workload categories. Additionally, the domain discriminative adversarial learning technique is adopted to consider global domain information by minimizing global domain discrepancy. By integrating both local category information and global domain information, the cdaDA model performs a coarse-to-fine alignment and achieves promising cross-subject MWR results.},
}
@article {pmid39801910,
year = {2025},
author = {Wang, J and Zhang, L and Chen, S and Xue, H and Du, M and Xu, Y and Liu, S and Ming, D},
title = {Individuals with high autistic traits exhibit altered interhemispheric brain functional connectivity patterns.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {9},
pmid = {39801910},
issn = {1871-4080},
abstract = {Individuals with high autistic traits (AT) encounter challenges in social interaction, similar to autistic persons. Precise screening and focused interventions positively contribute to improving this situation. Functional connectivity analyses can measure information transmission and integration between brain regions, providing neurophysiological insights into these challenges. This study aimed to investigate the patterns of brain networks in high AT individuals to offer theoretical support for screening and intervention decisions. EEG data were collected during a 4-min resting state session with eyes open and closed from 48 participants. Using the Autism Spectrum Quotient (AQ) scale, participants were categorized into the high AT group (HAT, n = 15) and low AT groups (LAT, n = 15). We computed the interhemispheric and intrahemispheric alpha coherence in two groups. The correlation between physiological indices and AQ scores was also examined. Results revealed that HAT exhibited significantly lower alpha coherence in the homologous hemispheres of the occipital cortex compared to LAT during the eyes-closed resting state. Additionally, significant negative correlations were observed between the degree of AT (AQ scores) and the alpha coherence in the occipital cortex, as well as in the right frontal and left occipital regions. The findings indicated that high AT individuals exhibit decreased connectivity in the occipital region, potentially resulting in diminished ability to process social information from visual inputs. Our discovery contributes to a deeper comprehension of the neural underpinnings of social challenges in high AT individuals, providing neurophysiological signatures for screening and intervention strategies for this population.},
}
@article {pmid39798828,
year = {2025},
author = {Wu, L and Jiang, M and Zhao, M and Hu, X and Wang, J and Zhang, K and Jia, K and Ren, F and Gao, F},
title = {Right inferior frontal cortex and preSMA in response inhibition: An investigation based on PTC model.},
journal = {NeuroImage},
volume = {306},
number = {},
pages = {121004},
doi = {10.1016/j.neuroimage.2025.121004},
pmid = {39798828},
issn = {1095-9572},
mesh = {Humans ; Male ; Female ; Magnetic Resonance Imaging ; *Inhibition, Psychological ; Young Adult ; Adult ; *Frontal Lobe/physiology ; Brain Mapping/methods ; *Motor Cortex/physiology ; *Psychomotor Performance/physiology ; *Models, Neurological ; *Executive Function/physiology ; },
abstract = {Response inhibition is an essential component of cognitive function. A large body of literature has used neuroimaging data to uncover the neural architecture that regulates inhibitory control in general and movement cancelation. The presupplementary motor area (preSMA) and the right inferior frontal cortex (rIFC) are the key nodes in the inhibitory control network. However, how these two regions contribute to response inhibition remains controversial. Based on the Pause-then-Cancel Model (PTC), this study employed functional magnetic resonance imaging (fMRI) to investigate the functional specificity of two regions in the stopping process. The Go/No-Go task (GNGT) and the Stop Signal Task (SST) were administered to the same group of participants. We used the GNGT to dissociate the pause process and both the GNGT and the SST to investigate the inhibition mechanism. Imaging data revealed that response inhibition produced by both tasks activated the preSMA and rIFC. Furthermore, an across-participants analysis showed that increased activation in the rIFC was associated with a delay in the go response in the GNGT. In contrast, increased activation in the preSMA was associated with good inhibition efficiency via the striatum in both GNGT and SST. These behavioral and imaging findings support the PTC model of the role of rIFC and preSMA, that the former is involved in a pause process to delay motor responses, whereas the preSMA is involved in the stopping of motor responses.},
}
@article {pmid39796947,
year = {2024},
author = {Chio, N and Quiles-Cucarella, E},
title = {A Bibliometric Review of Brain-Computer Interfaces in Motor Imagery and Steady-State Visually Evoked Potentials for Applications in Rehabilitation and Robotics.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {1},
pages = {},
pmid = {39796947},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; Humans ; *Robotics/methods ; *Evoked Potentials, Visual/physiology ; *Rehabilitation/methods ; Bibliometrics ; *Imagination/physiology ; },
abstract = {In this paper, a bibliometric review is conducted on brain-computer interfaces (BCI) in non-invasive paradigms like motor imagery (MI) and steady-state visually evoked potentials (SSVEP) for applications in rehabilitation and robotics. An exploratory and descriptive approach is used in the analysis. Computational tools such as the biblioshiny application for R-Bibliometrix and VOSViewer are employed to generate data on years, sources, authors, affiliation, country, documents, co-author, co-citation, and co-occurrence. This article allows for the identification of different bibliometric indicators such as the research process, evolution, visibility, volume, influence, impact, and production in the field of brain-computer interfaces for MI and SSVEP paradigms in rehabilitation and robotics applications from 2000 to August 2024.},
}
@article {pmid39796911,
year = {2024},
author = {Özkahraman, A and Ölmez, T and Dokur, Z},
title = {Performance Improvement with Reduced Number of Channels in Motor Imagery BCI System.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {1},
pages = {},
pmid = {39796911},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; Electrooculography/methods ; Signal Processing, Computer-Assisted ; Deep Learning ; Algorithms ; Brain/physiology ; *Imagination/physiology ; },
abstract = {Classifying Motor Imaging (MI) Electroencephalogram (EEG) signals is of vital importance for Brain-Computer Interface (BCI) systems, but challenges remain. A key challenge is to reduce the number of channels to improve flexibility, portability, and computational efficiency, especially in multi-class scenarios where more channels are needed for accurate classification. This study demonstrates that combining Electrooculogram (EOG) channels with a reduced set of EEG channels is more effective than relying on a large number of EEG channels alone. EOG channels provide useful information for MI signal classification, countering the notion that they only introduce eye-related noise. The study uses advanced deep learning techniques, including multiple 1D convolution blocks and depthwise-separable convolutions, to optimize classification accuracy. The findings in this study are tested on two datasets: dataset 1, the BCI Competition IV Dataset IIa (4-class MI), and dataset 2, the Weibo dataset (7-class MI). The performance for dataset 1, utilizing 3 EEG and 3 EOG channels (6 channels total), is of 83% accuracy, while dataset 2, with 3 EEG and 2 EOG channels (5 channels total), achieves an accuracy of 61%, demonstrating the effectiveness of the proposed channel reduction method and deep learning model.},
}
@article {pmid39793200,
year = {2025},
author = {Peters, B and Celik, B and Gaines, D and Galvin-McLaughlin, D and Imbiriba, T and Kinsella, M and Klee, D and Lawhead, M and Memmott, T and Smedemark-Margulies, N and Wiedrick, J and Erdogmus, D and Oken, B and Vertanen, K and Fried-Oken, M},
title = {RSVP keyboard with inquiry preview: mixed performance and user experience with an adaptive, multimodal typing interface combining EEG and switch input.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
pmid = {39793200},
issn = {1741-2552},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; },
mesh = {Humans ; *Electroencephalography/methods/instrumentation ; *Brain-Computer Interfaces ; Male ; Female ; Adult ; Middle Aged ; *Communication Devices for People with Disabilities ; Young Adult ; *Computer Peripherals ; *Psychomotor Performance/physiology ; Equipment Design ; Aged ; },
abstract = {Objective.The RSVP Keyboard is a non-implantable, event-related potential-based brain-computer interface (BCI) system designed to support communication access for people with severe speech and physical impairments. Here we introduce inquiry preview (IP), a new RSVP Keyboard interface incorporating switch input for users with some voluntary motor function, and describe its effects on typing performance and other outcomes.Approach.Four individuals with disabilities participated in the collaborative design of possible switch input applications for the RSVP Keyboard, leading to the development of IP and a method of fusing switch input with language model and electroencephalography (EEG) evidence for typing. Twenty-four participants without disabilities and one potential end user with incomplete locked-in syndrome took part in two experiments investigating the effects of IP and two modes of switch input on typing accuracy and speed during a copy-spelling task.Main results.For participants without disabilities, IP and switch input tended to worsen typing performance compared to the standard RSVP Keyboard condition, with more consistent effects across participants for speed than for accuracy. However, there was considerable variability, with some participants demonstrating improved typing performance and better user experience (UX) with IP and switch input. Typing performance for the potential end user was comparable to that of participants without disabilities. He typed most quickly and accurately with IP and switch input and gave favorable UX ratings to those conditions, but preferred standard RSVP Keyboard.Significance.IP is a novel multimodal interface for the RSVP Keyboard BCI, incorporating switch input as an additional control signal. Typing performance and UX and preference varied widely across participants, reinforcing the need for flexible, customizable BCI systems that can adapt to individual users. ClinicalTrials.gov Identifier: NCT04468919.},
}
@article {pmid39793091,
year = {2025},
author = {Zhang, F and Pu, Y and Kong, XZ},
title = {Parallel vector memories or single memory updating?.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {122},
number = {1},
pages = {e2422788121},
pmid = {39793091},
issn = {1091-6490},
support = {32171031//MOST | National Natural Science Foundation of China (NSFC)/ ; 32400882//MOST | National Natural Science Foundation of China (NSFC)/ ; 2021ZD0200409//STI 2030-Major Projects/ ; },
}
@article {pmid39793054,
year = {2025},
author = {Chivukula, S and Aflalo, T and Zhang, C and Rosario, ER and Bari, A and Pouratian, N and Andersen, RA},
title = {Population encoding of observed and actual somatosensations in the human posterior parietal cortex.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {122},
number = {1},
pages = {e2316012121},
pmid = {39793054},
issn = {1091-6490},
support = {P50 MH094258/MH/NIMH NIH HHS/United States ; UG1 EY032039/EY/NEI NIH HHS/United States ; P50MH094258//CIT | Caltech Conte Center for Social Decision Making (Caltech Conte Center for Neuroscience)/ ; R01EY015545//HHS | NIH | National Eye Institute (NEI)/ ; R01 EY015545/EY/NEI NIH HHS/United States ; },
mesh = {Female ; Middle Aged ; Neurons/physiology ; *Parietal Lobe/physiology ; *Sensation/physiology ; Task Performance and Analysis ; Touch/physiology ; Humans ; },
abstract = {Cognition relies on transforming sensory inputs into a generalizable understanding of the world. Mirror neurons have been proposed to underlie this process, mapping visual representations of others' actions and sensations onto neurons that mediate our own, providing a conduit for understanding. However, this theory has limitations. Here, we hypothesize that mirror-like responses represent one facet of a broader framework in which our brains engage internal models for cognition. We recorded populations of single neurons in the human posterior parietal cortex (PPC) of a brain-machine interface clinical trial participant implanted with a microelectrode array while she either experienced actual touch, or observed diverse tactile stimuli applied to other individuals. Two body locations were tested, on each of the participant and other individuals. Some neurons exhibited mirror-like properties, consistent with earlier literature. However, they were fragile, breaking with increased task complexity. Population responses were better characterized by generalizable and compositional basic-level features encoded within neural subspaces. These features enable the population to respond to diverse actual and observed touch stimuli and are recruited similarly for similar forms of touch. Mirror-like neurons belong within these subspaces, contributing more globally to compositionality and generalizability. We speculate that at a population-level, human PPC manifests an internal model for touch, and that cognition unfolds in the high-level human cortex by versatility in its representational building blocks. In a broad sense, we speculate that the population features we demonstrate support a broad mechanism by which the high-level human cortex enables understanding.},
}
@article {pmid39789264,
year = {2025},
author = {Lin, N and Wang, S and Li, Y and Wang, B and Shi, S and He, Y and Zhang, W and Yu, Y and Zhang, Y and Zhang, X and Wong, K and Wang, S and Chen, X and Jiang, H and Zhang, X and Lin, P and Xu, X and Qi, X and Wang, Z and Shang, D and Liu, Q and Liu, M},
title = {Resistive memory-based zero-shot liquid state machine for multimodal event data learning.},
journal = {Nature computational science},
volume = {5},
number = {1},
pages = {37-47},
pmid = {39789264},
issn = {2662-8457},
support = {62422004//National Natural Science Foundation of China (National Science Foundation of China)/ ; Z210006//Natural Science Foundation of Beijing Municipality (Beijing Natural Science Foundation)/ ; },
abstract = {The human brain is a complex spiking neural network (SNN) capable of learning multimodal signals in a zero-shot manner by generalizing existing knowledge. Remarkably, it maintains minimal power consumption through event-based signal propagation. However, replicating the human brain in neuromorphic hardware presents both hardware and software challenges. Hardware limitations, such as the slowdown of Moore's law and Von Neumann bottleneck, hinder the efficiency of digital computers. In addition, SNNs are characterized by their software training complexities. Here, to this end, we propose a hardware-software co-design on a 40 nm 256 kB in-memory computing macro that physically integrates a fixed and random liquid state machine SNN encoder with trainable artificial neural network projections. We showcase the zero-shot learning of multimodal events on the N-MNIST and N-TIDIGITS datasets, including visual and audio data association, as well as neural and visual data alignment for brain-machine interfaces. Our co-design achieves classification accuracy comparable to fully optimized software models, resulting in a 152.83- and 393.07-fold reduction in training costs compared with state-of-the-art spiking recurrent neural network-based contrastive learning and prototypical networks, and a 23.34- and 160-fold improvement in energy efficiency compared with cutting-edge digital hardware, respectively. These proof-of-principle prototypes demonstrate zero-shot multimodal events learning capability for emerging efficient and compact neuromorphic hardware.},
}
@article {pmid39788124,
year = {2025},
author = {An, D and You, Y and Ma, Q and Xu, Z and Liu, Z and Liao, R and Chen, H and Wang, Y and Wang, Y and Dai, H and Li, H and Jiang, L and Chen, Z and Hu, W},
title = {Deficiency of histamine H2 receptors in parvalbumin-positive neurons leads to hyperactivity, impulsivity, and impaired attention.},
journal = {Neuron},
volume = {113},
number = {4},
pages = {572-589.e6},
doi = {10.1016/j.neuron.2024.12.002},
pmid = {39788124},
issn = {1097-4199},
mesh = {Animals ; *Attention Deficit Disorder with Hyperactivity/metabolism/genetics ; *Parvalbumins/metabolism ; Mice ; *Neurons/metabolism ; *Impulsive Behavior/physiology ; *Receptors, Histamine H2/deficiency/genetics/metabolism ; Male ; *Attention/physiology ; Humans ; Substantia Nigra/metabolism ; Mice, Inbred C57BL ; Mice, Knockout ; *Hyperkinesis/metabolism ; Dopaminergic Neurons/metabolism ; },
abstract = {Attention deficit hyperactivity disorder (ADHD), affecting 4% of the population, is characterized by inattention, hyperactivity, and impulsivity; however, its neurophysiological mechanisms remain unclear. Here, we discovered that deficiency of histamine H2 receptor (H2R) in parvalbumin-positive neurons in substantia nigra pars recticulata (PV[SNr]) attenuates PV[+] neuronal activity and induces hyperactivity, impulsivity, and inattention in mice. Moreover, decreased H2R expression was observed in PV[SNr] in patients with ADHD symptoms and dopamine-transporter-deficient mice, whose behavioral phenotypes were alleviated by H2R agonist treatment. Dysfunction of PV[SNr] efferents to the substantia nigra pars compacta dopaminergic neurons and superior colliculus differently contributes to H2R-deficiency-induced behavioral disorders. Collectively, our results demonstrate that H2R deficiency in PV[+] neurons contributes to hyperactivity, impulsivity, and inattention by dampening PV[SNr] activity and involving different efferents in mice. It may enhance understanding of the molecular and circuit-level basis of ADHD and afford new potential therapeutic targets for ADHD-like psychiatric diseases.},
}
@article {pmid39787896,
year = {2025},
author = {Li, L and Menendez-Lustri, DM and Hartzler, A and Pogharian, A and Zaorski, B and Chen, A and Palen, J and Traylor, B and Quill, E and Pawlowski, CL and Bruckman, MA and Gupta, AS and Capadona, JR and Shoffstall, AJ},
title = {Systemically administered platelet-inspired nanoparticles to reduce inflammation surrounding intracortical microelectrodes.},
journal = {Biomaterials},
volume = {317},
number = {},
pages = {123082},
doi = {10.1016/j.biomaterials.2025.123082},
pmid = {39787896},
issn = {1878-5905},
support = {I01 RX003420/RX/RRD VA/United States ; },
mesh = {Animals ; Rats, Sprague-Dawley ; *Blood Platelets/metabolism/chemistry ; Male ; Microelectrodes/adverse effects ; *Nanoparticles/chemistry/administration &amp; dosage ; Rats ; *Inflammation/prevention &amp; control/etiology ; Blood-Brain Barrier/metabolism ; *Electrodes, Implanted/adverse effects ; },
abstract = {Intracortical microelectrodes (IMEs) are essential for neural signal acquisition in neuroscience and brain-machine interface (BMI) systems, aiding patients with neurological disorders, paralysis, and amputations. However, IMEs often fail to maintain robust signal quality over time, partly due to neuroinflammation caused by vascular damage during insertion. Platelet-inspired nanoparticles (PIN), which possess injury-targeting functions, mimic the adhesion and aggregation of active platelets through conjugated collagen-binding peptides (CBP), von Willebrand Factor-binding peptides (VBP), and fibrinogen-mimetic peptides (FMP). Systemically administered PINs can potentially enhance hemostasis and promote the resealing of IME insertion-induced leaky blood-brain barrier (BBB), thereby attenuating the influx of blood-derived proteins into the brain parenchyma that trigger neuroinflammation. This study explores the potential of PINs to mitigate neuroinflammation at implant sites. Male Sprague Dawley rats underwent craniotomies and IME implantations, followed by a single dose of Cy5 labeled PINs (2 mg/kg). Rats were sacrificed at intervals from 0 to 4 days post-implantation (DPI) for biodistribution analysis using an in vivo live imaging system (IVIS) and immunohistochemistry (IHC) to assess neuroinflammation, BBB permeability, and active platelet distribution. Another cohort of rats received weekly PINs, trehalose buffer (TH, diluent control), or control nanoparticles (CP, PEG-coated liposomes) for 4 weeks, with similar endpoint analyses. Results indicated that PIN concentrations were significantly elevated near IME interfaces acutely (0-4 DPI) and after 4 weeks of repeated dosing. At 3 DPI, peak intensities of active platelets (CD62P), activated microglia/macrophages (CD68), and PINs were observed. Immunoglobulin G (IgG) was upregulated during the first 24 h near implant sites but declined thereafter. At 4 weeks, the PINs group exhibited higher intensities of active platelets and PINs, and reduced CD68 and IgG levels compared to controls. PINs effectively targeted the IME-tissue interface, alongside endogenous activated platelets, resulting in reduced neuroinflammatory and BBB-leakage markers compared to the diluent-only-infused control group. Repeated dosing of PINs presents a promising approach for enhancing the quality of neural recordings in future studies.},
}
@article {pmid39787745,
year = {2025},
author = {Elsohaby, I and Kostoulas, P and Fayez, M and Elmoslemany, A and Alkafafy, ME and Bahhary, AM and Alzahrani, R and Morsi, AEKM and Arango-Sabogal, JC},
title = {Bayesian estimation of diagnostic accuracy of fecal smears, fecal PCR and serum ELISA for detecting Mycobacterium avium subsp. paratuberculosis infections in four domestic ruminant species in Saudi Arabia.},
journal = {Veterinary microbiology},
volume = {301},
number = {},
pages = {110377},
doi = {10.1016/j.vetmic.2025.110377},
pmid = {39787745},
issn = {1873-2542},
mesh = {Animals ; *Paratuberculosis/diagnosis/microbiology/epidemiology ; Saudi Arabia/epidemiology ; *Mycobacterium avium subsp. paratuberculosis/isolation &amp; purification/genetics ; Enzyme-Linked Immunosorbent Assay/veterinary ; *Feces/microbiology ; Bayes Theorem ; *Polymerase Chain Reaction/veterinary ; Goats/microbiology ; Sheep ; Cattle ; Camelus/microbiology ; Sensitivity and Specificity ; Cross-Sectional Studies ; Goat Diseases/diagnosis/microbiology ; Sheep Diseases/diagnosis/microbiology ; *Ruminants/microbiology ; Antibodies, Bacterial/blood ; },
abstract = {Paratuberculosis, a chronic wasting disease affecting domestic and wild ruminants worldwide, is caused by Mycobacterium avium subsp. paratuberculosis (MAP). Various diagnostic tests exist for detecting MAP infection; however, none of them possess perfect accuracy to be qualified as a reference standard test, particularly due to their notably low sensitivity. Therefore, we used Bayesian latent class models (BLCMs) to estimate diagnostic accuracy of fecal smears (FS), fecal PCR and serum ELISA for detecting MAP infections in sheep, goats, cattle, and camels older than 2 years in Saudi Arabia. Data from a cross-sectional study conducted in the Eastern Province of Saudi Arabia on 31 different farms with a history of MAP infection were analyzed. Fecal and blood samples from all animals older than 2 years in each farm were collected, resulting in a total of 220 sheep, 123 goats, 66 cattle, and 240 camels sampled. FS and IS900-PCR were performed on fecal samples to detect acid-fast bacilli and MAP DNA, respectively. The IDEXX ELISA kit was used to detect MAP antibodies in serum samples. For each ruminant species population, a BLCM was fitted to obtain posterior estimates [medians and 95 % Bayesian credible intervals (95 % BCI)] for sensitivity (Se) and specificity (Sp) of the three tests. We assumed FS and PCR to be conditionally dependent on the true animal MAP status. Prior distributions for test accuracy were used if available. FS had the highest Se among all tests and across all species with median values around 80 % in sheep, goats and camels, and near 50 % in cattle. Median Sp estimates of ELISA and PCR were higher than 90 % for all species. FS yielded the lowest Sp of the study when applied in camels, sheep, and goats. Using the prevalence observed in this study, median positive predictive value (PPV) was higher for PCR and ELISA than FS for camels, sheep, and goats. In cattle, PPV of all tests was similar with median estimates > 95 %. In camels, sheep, and goats, median negative predicative value (NPV) of all tests were > 60 %. The lowest median NPV for all tests were observed in cattle (< 30 %). Our results suggest that ELISA is a suitable option to identify MAP infected animals in farms with previous history of MAP in the Eastern region of Saudi Arabia.},
}
@article {pmid39787440,
year = {2024},
author = {Alkawadri, CI and Yan, Q and Kocuglu Kinal, AG and Spencer, DD and Alkawadri, R},
title = {Comparison of EEG Signal Characteristics of Subdural and Depth Electrodes.},
journal = {Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society},
volume = {42},
number = {5},
pages = {391-399},
doi = {10.1097/WNP.0000000000001139},
pmid = {39787440},
issn = {1537-1603},
mesh = {Humans ; Male ; Female ; Adult ; *Subdural Space ; *Electrodes, Implanted ; *Electroencephalography/methods/instrumentation ; *Drug Resistant Epilepsy/physiopathology/surgery ; Middle Aged ; Young Adult ; *Electrocorticography/methods/instrumentation ; Adolescent ; *Brain/physiopathology ; },
abstract = {OBJECTIVES: Our study aimed to compare signal characteristics of subdural electrodes (SDE) and depth stereo EEG placed within a 5-mm vicinity in patients with drug-resistant epilepsy. We report how electrode design and placement collectively affect signal content from a shared source between these electrode types.<br><br>METHODS: In subjects undergoing invasive intracranial EEG evaluation at a surgical epilepsy center from 2012 to 2018, stereo EEG and SDE electrode contacts placed within a 5-mm vicinity were identified. Of these, 24 contacts (12 pairs) met our criteria for signal-to-noise ratio and data availability for final analysis. We used Welch method to analyze the correlation of power spectral densities of EEG segments, root mean square of 1-second windows, and fast-Fourier transform to calculate coherence across conventional frequency bands.<br><br>RESULTS: We observed a median distance of 3.7 mm between the electrode contact pairs. Time-aware analysis highlighted the coherence's strength primarily in the high-gamma band, where the median (r) was 0.889. In addition, the median power ratios between the SDE and stereo EEG signal was 1.99. This ratio decreased from high-gamma to infra-low frequencies, with medians of 2.07 and 0.97, respectively. The power spectral densities for the stereo EEG and SDE electrodes demonstrated a strong correlation, with a median correlation coefficient (r) of 0.99 and an interquartile range from 0.915 to 0.996.<br><br>CONCLUSIONS: Signals captured by standard subdural and depth (intracranial EEG) electrodes within a 5-mm radius exhibit band-specific coherence and are not identical. The association was most pronounced in the high-gamma band, with coherence decreasing with lower frequencies. Our findings underscore the combined effects of electrode size, design, placement, preferred bandwidth, and the nature of the activity source on signal recording. Particularly, SDE employed herein may offer advantages for high-frequency signals, but the impact of electrode size on recordings necessitates careful consideration in context-specific situations.<br><br>SIGNIFICANCE: The findings relate to surgical epilepsy care and may inform the design of brain-computer interface.},
}
@article {pmid39781056,
year = {2025},
author = {Cai, Z and Li, P and Cheng, L and Yuan, D and Li, M and Li, H},
title = {A high performance heterogeneous hardware architecture for brain computer interface.},
journal = {Biomedical engineering letters},
volume = {15},
number = {1},
pages = {217-227},
pmid = {39781056},
issn = {2093-985X},
abstract = {Brain-computer interface (BCI) has been widely used in human-computer interaction. The introduction of artificial intelligence has further improved the performance of BCI system. In recent years, the development of BCI has gradually shifted from personal computers to embedded devices, which boasts lower power consumption and smaller size, but at the cost of limited device resources and computing speed, thus can hardly improve the support of complex algorithms. This paper proposes a heterogeneous BCI architecture based on ARM + FPGA, enabling real-time processing of electroencephalogram (EEG) signals. Adopting data quantization, layer fusion and data augmentation to optimize the compact neural network model EEGNet, and design dedicated hardware engines to accelerate the network. Experimental results show that the system achieves 93.3% classification accuracy for steady-state visual evoked potential signals, with a time delay of 0.2 ms per trail, and a power consumption of approximately (1.91 W). That is 31.5 times faster acceleration is realized at the cost of only 0.7% lower accuracy compared with the conventional processor. The results show that the BCI architecture proposed in this study has strong practicability and high research significance.},
}
@article {pmid39779796,
year = {2025},
author = {Shelishiyah, R and Thiyam, DB and Margaret, MJ and Banu, NMM},
title = {A hybrid CNN model for classification of motor tasks obtained from hybrid BCI system.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {1360},
pmid = {39779796},
issn = {2045-2322},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Neural Networks, Computer ; Deep Learning ; Adult ; Male ; Movement/physiology ; },
abstract = {The Hybrid-Brain Computer Interface (BCI) has shown improved performance, especially in classifying multi-class data. Two non-invasive BCI modules are combined to achieve an improved classification which are Electroencephalogram (EEG) and functional Near Infra-red Spectroscopy (fNIRS). Classifying contralateral and ipsilateral motor movements is found challenging among the other mental activity signals. The current work focuses on the performance of deep learning methods like - Convolutional Neural Networks (CNN) and Bidirectional Long-Short term memory (Bi-LSTM) in classifying a four-class motor execution of Right Hand, Left Hand, Right Arm and Left Arm taken from the CORE dataset. The model performance was evaluated using metrics such as Accuracy, F1 - score, Precision, Recall, AUC and ROC curve. The CNN and Hybrid CNN models have resulted in 98.3% and 99% accuracy respectively.},
}
@article {pmid39776784,
year = {2024},
author = {Ahmadi, S and Desain, P and Thielen, J},
title = {A Bayesian dynamic stopping method for evoked response brain-computer interfacing.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1437965},
pmid = {39776784},
issn = {1662-5161},
abstract = {INTRODUCTION: As brain-computer interfacing (BCI) systems transition fromassistive technology to more diverse applications, their speed, reliability, and user experience become increasingly important. Dynamic stopping methods enhance BCI system speed by deciding at any moment whether to output a result or wait for more information. Such approach leverages trial variance, allowing good trials to be detected earlier, thereby speeding up the process without significantly compromising accuracy. Existing dynamic stopping algorithms typically optimize measures such as symbols per minute (SPM) and information transfer rate (ITR). However, these metrics may not accurately reflect system performance for specific applications or user types. Moreover, many methods depend on arbitrary thresholds or parameters that require extensive training data.<br><br>METHODS: We propose a model-based approach that takes advantage of the analytical knowledge that we have about the underlying classification model. By using a risk minimization approach, our model allows precise control over the types of errors and the balance between precision and speed. This adaptability makes it ideal for customizing BCI systems to meet the diverse needs of various applications.<br><br>RESULTS AND DISCUSSION: We validate our proposed method on a publicly available dataset, comparing it with established static and dynamic stopping methods. Our results demonstrate that our approach offers a broad range of accuracy-speed trade-offs and achieves higher precision than baseline stopping methods.},
}
@article {pmid39775950,
year = {2025},
author = {Zeng, X and Kang, T and Huang, W and Jin, T},
title = {How Should the BCI and BOOI Index be Correctly Applied in Patients With Low-Compliance Bladder?.},
journal = {Neurourology and urodynamics},
volume = {44},
number = {6},
pages = {1382-1384},
doi = {10.1002/nau.25663},
pmid = {39775950},
issn = {1520-6777},
}
@article {pmid39771903,
year = {2024},
author = {Alzahrani, S and Banjar, H and Mirza, R},
title = {Systematic Review of EEG-Based Imagined Speech Classification Methods.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {24},
pages = {},
pmid = {39771903},
issn = {1424-8220},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Speech/physiology ; Machine Learning ; Signal Processing, Computer-Assisted ; Imagination/physiology ; Neural Networks, Computer ; Brain/physiology ; },
abstract = {This systematic review examines EEG-based imagined speech classification, emphasizing directional words essential for development in the brain-computer interface (BCI). This study employed a structured methodology to analyze approaches using public datasets, ensuring systematic evaluation and validation of results. This review highlights the feature extraction techniques that are pivotal to classification performance. These include deep learning, adaptive optimization, and frequency-specific decomposition, which enhance accuracy and robustness. Classification methods were explored by comparing traditional machine learning with deep learning and emphasizing the role of brain lateralization in imagined speech for effective recognition and classification. This study discusses the challenges of generalizability and scalability in imagined speech recognition, focusing on subject-independent approaches and multiclass scalability. Performance benchmarking across various datasets and methodologies revealed varied classification accuracies, reflecting the complexity and variability of EEG signals. This review concludes that challenges remain despite progress, particularly in classifying directional words. Future research directions include improved signal processing techniques, advanced neural network architectures, and more personalized, adaptive BCI systems. This review is critical for future efforts to develop practical communication tools for individuals with speech and motor impairments using EEG-based BCIs.},
}
@article {pmid39771862,
year = {2024},
author = {Khabti, J and AlAhmadi, S and Soudani, A},
title = {Enhancing Deep-Learning Classification for Remote Motor Imagery Rehabilitation Using Multi-Subject Transfer Learning in IoT Environment.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {24},
pages = {},
pmid = {39771862},
issn = {1424-8220},
mesh = {Humans ; *Brain-Computer Interfaces ; *Deep Learning ; *Electroencephalography/methods ; Internet of Things ; Signal Processing, Computer-Assisted ; },
abstract = {One of the most promising applications for electroencephalogram (EEG)-based brain-computer interfaces (BCIs) is motor rehabilitation through motor imagery (MI) tasks. However, current MI training requires physical attendance, while remote MI training can be applied anywhere, facilitating flexible rehabilitation. Providing remote MI training raises challenges to ensuring an accurate recognition of MI tasks by healthcare providers, in addition to managing computation and communication costs. The MI tasks are recognized through EEG signal processing and classification, which can drain sensor energy due to the complexity of the data and the presence of redundant information, often influenced by subject-dependent factors. To address these challenges, we propose in this paper a multi-subject transfer-learning approach for an efficient MI training framework in remote rehabilitation within an IoT environment. For efficient implementation, we propose an IoT architecture that includes cloud/edge computing as a solution to enhance the system's efficiency and reduce the use of network resources. Furthermore, deep-learning classification with and without channel selection is applied in the cloud, while multi-subject transfer-learning classification is utilized at the edge node. Various transfer-learning strategies, including different epochs, freezing layers, and data divisions, were employed to improve accuracy and efficiency. To validate this framework, we used the BCI IV 2a dataset, focusing on subjects 7, 8, and 9 as targets. The results demonstrated that our approach significantly enhanced the average accuracy in both multi-subject and single-subject transfer-learning classification. In three-subject transfer-learning classification, the FCNNA model achieved up to 79.77% accuracy without channel selection and 76.90% with channel selection. For two-subject and single-subject transfer learning, the application of transfer learning improved the average accuracy by up to 6.55% and 12.19%, respectively, compared to classification without transfer learning. This framework offers a promising solution for remote MI rehabilitation, providing both accurate task recognition and efficient resource usage.},
}
@article {pmid39771843,
year = {2024},
author = {Mikhaylov, D and Saeed, M and Husain Alhosani, M and F Al Wahedi, Y},
title = {Comparison of EEG Signal Spectral Characteristics Obtained with Consumer- and Research-Grade Devices.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {24},
pages = {},
pmid = {39771843},
issn = {1424-8220},
mesh = {Humans ; *Electroencephalography/methods/instrumentation ; *Signal Processing, Computer-Assisted ; Adult ; Male ; Female ; Brain-Computer Interfaces ; Brain/physiology ; Wearable Electronic Devices ; Electrodes ; Young Adult ; },
abstract = {Electroencephalography (EEG) has emerged as a pivotal tool in both research and clinical practice due to its non-invasive nature, cost-effectiveness, and ability to provide real-time monitoring of brain activity. Wearable EEG technology opens new avenues for consumer applications, such as mental health monitoring, neurofeedback training, and brain-computer interfaces. However, there is still much to verify and re-examine regarding the functionality of these devices and the quality of the signal they capture, particularly as the field evolves rapidly. In this study, we recorded the resting-state brain activity of healthy volunteers via three consumer-grade EEG devices, namely PSBD Headband Pro, PSBD Headphones Lite, and Muse S Gen 2, and compared the spectral characteristics of the signal obtained with that recorded via the research-grade Brain Product amplifier (BP) with the mirroring montages. The results showed that all devices exhibited higher mean power in the low-frequency bands, which are characteristic of dry-electrode technology. PSBD Headband proved to match BP most precisely among the other examined devices. PSBD Headphones displayed a moderate correspondence with BP and signal quality issues in the central group of electrodes. Muse demonstrated the poorest signal quality, with extremely low alignment with BP. Overall, this study underscores the importance of considering device-specific design constraints and emphasizes the need for further validation to ensure the reliability and accuracy of wearable EEG devices.},
}
@article {pmid39771785,
year = {2024},
author = {Novičić, M and Djordjević, O and Miler-Jerković, V and Konstantinović, L and Savić, AM},
title = {Improving the Performance of Electrotactile Brain-Computer Interface Using Machine Learning Methods on Multi-Channel Features of Somatosensory Event-Related Potentials.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {24},
pages = {},
pmid = {39771785},
issn = {1424-8220},
support = {6066223//Science Fund of the Republic of Serbia/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Evoked Potentials, Somatosensory/physiology ; Male ; *Machine Learning ; Adult ; Female ; Algorithms ; Touch/physiology ; Young Adult ; Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {Traditional tactile brain-computer interfaces (BCIs), particularly those based on steady-state somatosensory-evoked potentials, face challenges such as lower accuracy, reduced bit rates, and the need for spatially distant stimulation points. In contrast, using transient electrical stimuli offers a promising alternative for generating tactile BCI control signals: somatosensory event-related potentials (sERPs). This study aimed to optimize the performance of a novel electrotactile BCI by employing advanced feature extraction and machine learning techniques on sERP signals for the classification of users' selective tactile attention. The experimental protocol involved ten healthy subjects performing a tactile attention task, with EEG signals recorded from five EEG channels over the sensory-motor cortex. We employed sequential forward selection (SFS) of features from temporal sERP waveforms of all EEG channels. We systematically tested classification performance using machine learning algorithms, including logistic regression, k-nearest neighbors, support vector machines, random forests, and artificial neural networks. We explored the effects of the number of stimuli required to obtain sERP features for classification and their influence on accuracy and information transfer rate. Our approach indicated significant improvements in classification accuracy compared to previous studies. We demonstrated that the number of stimuli for sERP generation can be reduced while increasing the information transfer rate without a statistically significant decrease in classification accuracy. In the case of the support vector machine classifier, we achieved a mean accuracy over 90% for 10 electrical stimuli, while for 6 stimuli, the accuracy decreased by less than 7%, and the information transfer rate increased by 60%. This research advances methods for tactile BCI control based on event-related potentials. This work is significant since tactile stimulation is an understudied modality for BCI control, and electrically induced sERPs are the least studied control signals in reactive BCIs. Exploring and optimizing the parameters of sERP elicitation, as well as feature extraction and classification methods, is crucial for addressing the accuracy versus speed trade-off in various assistive BCI applications where the tactile modality may have added value.},
}
@article {pmid39771721,
year = {2024},
author = {Víg, L and Zátonyi, A and Csernyus, B and Horváth, &#xc1;C and Bojtár, M and Kele, P and Madarász, M and Rózsa, B and Fürjes, P and Hermann, P and Hakkel, O and Péter, L and Fekete, Z},
title = {Optically Controlled Drug Delivery Through Microscale Brain-Machine Interfaces Using Integrated Upconverting Nanoparticles.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {24},
pages = {},
pmid = {39771721},
issn = {1424-8220},
support = {TKP2021-EGA-42//National Research, Development and Innovation Office/ ; TKP2021-EGA-04//National Research, Development and Innovation Office/ ; VKE-2018-00032//National Research, Development and Innovation Office/ ; KFI-2018-00097//National Research, Development and Innovation Office/ ; 2020-2.1.1-ED-2022-00208//National Research, Development and Innovation Office/ ; (NAP2022I-8/2022//Hungarian Academy of Sciences/ ; Bolyai Janos Scholarship//Hungarian Academy of Sciences/ ; NKFIH FK 134403//National Research, Development and Innovation Office/ ; },
mesh = {*Nanoparticles/chemistry ; *Brain-Computer Interfaces ; *Drug Delivery Systems/methods/instrumentation ; Animals ; Brain/physiology ; Rats ; },
abstract = {The aim of this work is to incorporate lanthanide-cored upconversion nanoparticles (UCNP) into the surface of microengineered biomedical implants to create a spatially controlled and optically releasable model drug delivery device in an integrated fashion. Our approach enables silicone-based microelectrocorticography (ECoG) implants holding platinum/iridium recording sites to serve as a stable host of UCNPs. Nanoparticles excitable in the near-infrared (lower energy) regime and emitting visible (higher energy) light are utilized in a study. With the upconverted higher energy photons, we demonstrate the induction of photochemical (cleaving) reactions that enable the local release of specific dyes as a model system near the implant. The modified ECoG electrodes can be implanted in brain tissue to act as an uncaging system that releases small amounts of substance while simultaneously measuring the evoked neural response upon light activation. In this paper, several technological challenges like the surface modification of UCNPs, the immobilization of particles on the implantable platform, and measuring the stability of integrated UCNPs in in vitro and in vivo conditions are addressed in detail. Besides the chemical, mechanical, and optical characterization of the ready-to-use devices, the effect of nanoparticles on the original electrophysiological function is also evaluated. The results confirm that silicone-based brain-machine interfaces can be efficiently complemented with UCNPs to facilitate local model drug release.},
}
@article {pmid39771689,
year = {2024},
author = {Fang, F and Gao, T and Wu, J},
title = {Humanity Test-EEG Data Mediated Artificial Intelligence Multi-Person Interactive System.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {24},
pages = {},
pmid = {39771689},
issn = {1424-8220},
mesh = {Humans ; *Electroencephalography/methods ; *Artificial Intelligence ; *Emotions/physiology ; Male ; Female ; Adult ; Young Adult ; },
abstract = {Artificial intelligence (AI) systems are widely applied in various industries and everyday life, particularly in fields such as virtual assistants, healthcare, and education. However, this paper highlights that existing research has often overlooked the philosophical and media aspects. To address this, we developed an interactive system called "Human Nature Test". In this context, "human nature" refers to emotion and consciousness, while "test" involves a critical analysis of AI technology and an exploration of the differences between humanity and technicality. Additionally, through experimental research and literature analysis, we found that the integration of electroencephalogram (EEG) data with AI systems is becoming a significant trend. The experiment involved 20 participants, with two conditions: C1 (using EEG data) and C2 (without EEG data). The results indicated a significant increase in immersion under the C1 condition, along with a more positive emotional experience. We summarized three design directions: enhancing immersion, creating emotional experiences, and expressing philosophical concepts. Based on these findings, there is potential for further developing EEG data as a medium to enrich interactive experiences, offering new insights into the fusion of technology and human emotion.},
}
@article {pmid39771656,
year = {2024},
author = {Khalil, AEK and Perez-Diaz, JA and Cantoral-Ceballos, JA and Antelis, JM},
title = {Unlocking Security for Comprehensive Electroencephalogram-Based User Authentication Systems.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {24},
pages = {},
pmid = {39771656},
issn = {1424-8220},
support = {N/A//Tecnol&#xf3;gico de Monterrey/ ; },
mesh = {*Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; Computer Security ; Signal Processing, Computer-Assisted ; Algorithms ; Brain-Computer Interfaces ; Brain/physiology ; },
abstract = {With recent significant advancements in artificial intelligence, the necessity for more reliable recognition systems has rapidly increased to safeguard individual assets. The use of brain signals for authentication has gained substantial interest within the scientific community over the past decade. Most previous efforts have focused on identifying distinctive information within electroencephalogram (EEG) recordings. In this study, an EEG-based user authentication scheme is presented, employing a multi-layer perceptron feedforward neural network (MLP FFNN). The scheme utilizes P300 potentials derived from EEG signals, focusing on the user's intent to select specific characters. This approach involves two phases: user identification and user authentication. Both phases utilize EEG recordings of brain signals, data preprocessing, a database to store and manage these recordings for efficient retrieval and organization, and feature extraction using mutual information (MI) from selected EEG data segments, specifically targeting power spectral density (PSD) across five frequency bands. The user identification phase employs multi-class classifiers to predict the identity of a user from a set of enrolled users. The user authentication phase associates the predicted user identities with user labels using probability assessments, verifying the claimed identity as either genuine or an impostor. This scheme combines EEG data segments with user mapping, confidence calculations, and claimed user verification for robust authentication. It also accommodates new users by transforming EEG data into feature vectors without the need for retraining. The model extracts selected features to identify users and to classify the input based on these features to authenticate the user. The experiments show that the proposed scheme can achieve 97% accuracy in EEG-based user identification and authentication.},
}
@article {pmid39768099,
year = {2024},
author = {Mahmoud, TSM and Munawar, A and Nawaz, MZ and Chen, Y},
title = {Enhancing Multispectral Breast Imaging Quality Through Frame Accumulation and Hybrid GA-CPSO Registration.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {11},
number = {12},
pages = {},
pmid = {39768099},
issn = {2306-5354},
abstract = {Multispectral transmission imaging has emerged as a promising technique for imaging breast tissue with high resolution. However, the method encounters challenges such as low grayscale, noisy transmission images with weak signals, primarily due to the strong absorption and scattering of light in breast tissue. A common approach to improve the signal-to-noise ratio (SNR) and overall image quality is frame accumulation. However, factors such as camera jitter and respiratory motion during image acquisition can cause frame misalignment, degrading the quality of the accumulated image. To address these issues, this study proposes a novel image registration method. A hybrid approach combining a genetic algorithm (GA) and a constriction factor-based particle swarm optimization (CPSO), referred to as GA-CPSO, is applied for image registration before frame accumulation. The efficiency of this hybrid method is enhanced by incorporating a squared constriction factor (SCF), which speeds up the registration process and improves convergence towards optimal solutions. The GA identifies potential solutions, which are then refined by CPSO to expedite convergence. This methodology was validated on the sequence of breast frames taken at 600 nm, 620 nm, 670 nm, and 760 nm wavelength of light and proved the enhancement of accuracy by various mathematical assessments. It demonstrated high accuracy (99.93%) and reduced registration time. As a result, the GA-CPSO approach significantly improves the effectiveness of frame accumulation and enhances overall image quality. This study explored the groundwork for precise multispectral transmission image segmentation and classification.},
}
@article {pmid39768034,
year = {2024},
author = {Geravanchizadeh, M and Shaygan Asl, A and Danishvar, S},
title = {Selective Auditory Attention Detection Using Combined Transformer and Convolutional Graph Neural Networks.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {11},
number = {12},
pages = {},
pmid = {39768034},
issn = {2306-5354},
abstract = {Attention is one of many human cognitive functions that are essential in everyday life. Given our limited processing capacity, attention helps us focus only on what matters. Focusing attention on one speaker in an environment with many speakers is a critical ability of the human auditory system. This paper proposes a new end-to-end method based on the combined transformer and graph convolutional neural network (TraGCNN) that can effectively detect auditory attention from electroencephalograms (EEGs). This approach eliminates the need for manual feature extraction, which is often time-consuming and subjective. Here, the first EEG signals are converted to graphs. We then extract attention information from these graphs using spatial and temporal approaches. Finally, our models are trained with these data. Our model can detect auditory attention in both the spatial and temporal domains. Here, the EEG input is first processed by transformer layers to obtain a sequential representation of EEG based on attention onsets. Then, a family of graph convolutional layers is used to find the most active electrodes using the spatial position of electrodes. Finally, the corresponding EEG features of active electrodes are fed into the graph attention layers to detect auditory attention. The Fuglsang 2020 dataset is used in the experiments to train and test the proposed and baseline systems. The new TraGCNN approach, as compared with state-of-the-art attention classification methods from the literature, yields the highest performance in terms of accuracy (80.12%) as a classification metric. Additionally, the proposed model results in higher performance than our previously graph-based model for different lengths of EEG segments. The new TraGCNN approach is advantageous because attenuation detection is achieved from EEG signals of subjects without requiring speech stimuli, as is the case with conventional auditory attention detection methods. Furthermore, examining the proposed model for different lengths of EEG segments shows that the model is faster than our previous graph-based detection method in terms of computational complexity. The findings of this study have important implications for the understanding and assessment of auditory attention, which is crucial for many applications, such as brain-computer interface (BCI) systems, speech separation, and neuro-steered hearing aid development.},
}
@article {pmid39766488,
year = {2024},
author = {Ma, Y and Huang, Z and Yang, Y and Zhang, S and Dong, Q and Wang, R and Hu, L},
title = {Emotion Recognition Model of EEG Signals Based on Double Attention Mechanism.},
journal = {Brain sciences},
volume = {14},
number = {12},
pages = {},
pmid = {39766488},
issn = {2076-3425},
abstract = {BACKGROUND: Emotions play a crucial role in people's lives, profoundly affecting their cognition, decision-making, and interpersonal communication. Emotion recognition based on brain signals has become a significant challenge in the fields of affective computing and human-computer interaction.<br><br>METHODS: Addressing the issue of inaccurate feature extraction and low accuracy of existing deep learning models in emotion recognition, this paper proposes a multi-channel automatic classification model for emotion EEG signals named DACB, which is based on dual attention mechanisms, convolutional neural networks, and bidirectional long short-term memory networks. DACB extracts features in both temporal and spatial dimensions, incorporating not only convolutional neural networks but also SE attention mechanism modules for learning the importance of different channel features, thereby enhancing the network's performance. DACB also introduces dot product attention mechanisms to learn the importance of spatial and temporal features, effectively improving the model's accuracy.<br><br>RESULTS: The accuracy of this method in single-shot validation tests on the SEED-IV and DREAMER (Valence-Arousal-Dominance three-classification) datasets is 99.96% and 87.52%, 90.06%, and 89.05%, respectively. In 10-fold cross-validation tests, the accuracy is 99.73% and 84.26%, 85.40%, and 85.02%, outperforming other models.<br><br>CONCLUSIONS: This demonstrates that the DACB model achieves high accuracy in emotion classification tasks, demonstrating outstanding performance and generalization ability and providing new directions for future research in EEG signal recognition.},
}
@article {pmid39766471,
year = {2024},
author = {Adolf, A and Köllőd, CM and Márton, G and Fadel, W and Ulbert, I},
title = {The Effect of Processing Techniques on the Classification Accuracy of Brain-Computer Interface Systems.},
journal = {Brain sciences},
volume = {14},
number = {12},
pages = {},
pmid = {39766471},
issn = {2076-3425},
support = {KDP-2021-12, 1022428/001//National Research, Development and Innovation Office/ ; FK146115//National Research, Development and Innovation Office/ ; NAP2022-I-2/2022//Hungarian Academy of Sciences/ ; RRF-2.3.1-21-2022-00015//National Research, Development and Innovation Office/ ; },
abstract = {Background/Objectives: Accurately classifying Electroencephalography (EEG) signals is essential for the effective operation of Brain-Computer Interfaces (BCI), which is needed for reliable neurorehabilitation applications. However, many factors in the processing pipeline can influence classification performance. The objective of this study is to assess the effects of different processing steps on classification accuracy in EEG-based BCI systems. Methods: This study explores the impact of various processing techniques and stages, including the FASTER algorithm for artifact rejection (AR), frequency filtering, transfer learning, and cropped training. The Physionet dataset, consisting of four motor imagery classes, was used as input due to its relatively large number of subjects. The raw EEG was tested with EEGNet and Shallow ConvNet. To examine the impact of adding a spatial dimension to the input data, we also used the Multi-branch Conv3D Net and developed two new models, Conv2D Net and Conv3D Net. Results: Our analysis showed that classification accuracy can be affected by many factors at every stage. Applying the AR method, for instance, can either enhance or degrade classification performance, depending on the subject and the specific network architecture. Transfer learning was effective in improving the performance of all networks for both raw and artifact-rejected data. However, the improvement in classification accuracy for artifact-rejected data was less pronounced compared to unfiltered data, resulting in reduced precision. For instance, the best classifier achieved 46.1% accuracy on unfiltered data, which increased to 63.5% with transfer learning. In the filtered case, accuracy rose from 45.5% to only 55.9% when transfer learning was applied. An unexpected outcome regarding frequency filtering was observed: networks demonstrated better classification performance when focusing on lower-frequency components. Higher frequency ranges were more discriminative for EEGNet and Shallow ConvNet, but only when cropped training was applied. Conclusions: The findings of this study highlight the complex interaction between processing techniques and neural network performance, emphasizing the necessity for customized processing approaches tailored to specific subjects and network architectures.},
}
@article {pmid39766454,
year = {2024},
author = {Beck, S and Liberman, Y and Dubljević, V},
title = {Media Representation of the Ethical Issues Pertaining to Brain-Computer Interface (BCI) Technology.},
journal = {Brain sciences},
volume = {14},
number = {12},
pages = {},
pmid = {39766454},
issn = {2076-3425},
abstract = {BACKGROUND/OBJECTIVES: Brain-computer interfaces (BCIs) are a rapidly developing technology that captures and transmits brain signals to external sources, allowing the user control of devices such as prosthetics. BCI technology offers the potential to restore physical capabilities in the body and change how we interact and communicate with computers and each other. While BCI technology has existed for decades, recent developments have caused the technology to generate a host of ethical issues and discussions in both academic and public circles. Given that media representation has the potential to shape public perception and policy, it is necessary to evaluate the space that these issues take in public discourse.<br><br>METHODS: We conducted a rapid review of media articles in English discussing ethical issues of BCI technology from 2013 to 2024 as indexed by LexisNexis. Our searches yielded 675 articles, with a final sample containing 182 articles. We assessed the themes of the articles and coded them based on the ethical issues discussed, ethical frameworks, recommendations, tone, and application of technology.<br><br>RESULTS: Our results showed a marked rise in interest in media articles over time, signaling an increased focus on this topic. The majority of articles adopted a balanced or neutral tone when discussing BCIs and focused on ethical issues regarding privacy, autonomy, and regulation.<br><br>CONCLUSIONS: Current discussion of ethical issues reflects growing news coverage of companies such as Neuralink, and reveals a mounting distrust of BCI technology. The growing recognition of ethical considerations in BCI highlights the importance of ethical discourse in shaping the future of the field.},
}
@article {pmid39760490,
year = {2025},
author = {Zhai, H and Li, P and Wang, H and Wang, X},
title = {DMSO-promoted α-bromination of α-aryl ketones for the construction of 2-aryl-2-bromo-cycloketones.},
journal = {Organic &amp; biomolecular chemistry},
volume = {23},
number = {7},
pages = {1627-1632},
doi = {10.1039/d4ob01937g},
pmid = {39760490},
issn = {1477-0539},
abstract = {A DMSO-promoted practical one-step α-bromination reaction of α-aryl ketones with NBS has been developed for the construction of 2-aryl-2-bromo-cycloketones. The desired regioselective α-bromination products were isolated in moderate to good yields, with a maximum tested scale of 15 mmol. Notably, ketamine derivatives could be smoothly synthesized in two steps.},
}
@article {pmid39760422,
year = {2025},
author = {Zhu, L and Wang, Y and Huang, A and Tan, X and Zhang, J},
title = {A multi-branch, multi-scale, and multi-view CNN with lightweight temporal attention mechanism for EEG-based motor imagery decoding.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-15},
doi = {10.1080/10255842.2024.2448576},
pmid = {39760422},
issn = {1476-8259},
abstract = {Convolutional neural networks (CNNs) have been widely utilized for decoding motor imagery (MI) from electroencephalogram (EEG) signals. However, extracting discriminative spatial-temporal-spectral features from low signal-to-noise ratio EEG signals remains challenging. This paper proposes MBMSNet , a multi-branch, multi-scale, and multi-view CNN with a lightweight temporal attention mechanism for EEG-Based MI decoding. Specifically, MBMSNet first extracts multi-view representations from raw EEG signals, followed by independent branches to capture spatial, spectral, temporal-spatial, and temporal-spectral features. Each branch includes a domain-specific convolutional layer, a variance layer, and a temporal attention layer. Finally, the features derived from each branch are concatenated with weights and classified through a fully connected layer. Experiments demonstrate MBMSNet outperforms state-of-the-art models, achieving accuracies of 84.60% on BCI Competition IV 2a, 87.80% on 2b, and 74.58% on OpenBMI, showcasing its potential for robust BCI applications.},
}
@article {pmid39759760,
year = {2024},
author = {Cisotto, G and Zancanaro, A and Zoppis, IF and Manzoni, SL},
title = {hvEEGNet: a novel deep learning model for high-fidelity EEG reconstruction.},
journal = {Frontiers in neuroinformatics},
volume = {18},
number = {},
pages = {1459970},
pmid = {39759760},
issn = {1662-5196},
abstract = {INTRODUCTION: Modeling multi-channel electroencephalographic (EEG) time-series is a challenging tasks, even for the most recent deep learning approaches. Particularly, in this work, we targeted our efforts to the high-fidelity reconstruction of this type of data, as this is of key relevance for several applications such as classification, anomaly detection, automatic labeling, and brain-computer interfaces.<br><br>METHODS: We analyzed the most recent works finding that high-fidelity reconstruction is seriously challenged by the complex dynamics of the EEG signals and the large inter-subject variability. So far, previous works provided good results in either high-fidelity reconstruction of single-channel signals, or poor-quality reconstruction of multi-channel datasets. Therefore, in this paper, we present a novel deep learning model, called hvEEGNet, designed as a hierarchical variational autoencoder and trained with a new loss function. We tested it on the benchmark Dataset 2a (including 22-channel EEG data from 9 subjects).<br><br>RESULTS: We show that it is able to reconstruct all EEG channels with high-fidelity, fastly (in a few tens of epochs), and with high consistency across different subjects. We also investigated the relationship between reconstruction fidelity and the training duration and, using hvEEGNet as an anomaly detector, we spotted some data in the benchmark dataset that are corrupted and never highlighted before.<br><br>DISCUSSION: Thus, hvEEGNet could be very useful in several applications where automatic labeling of large EEG dataset is needed and time-consuming. At the same time, this work opens new fundamental research questions about (1) the effectiveness of deep learning models training (for EEG data) and (2) the need for a systematic characterization of the input EEG data to ensure robust modeling.},
}
@article {pmid39759080,
year = {2024},
author = {Zhao, X and Xu, S and Geng, K and Zhou, T and Xu, T and Wang, Z and Feng, S and Hu, H},
title = {MP: A steady-state visual evoked potential dataset based on multiple paradigms.},
journal = {iScience},
volume = {27},
number = {11},
pages = {111030},
pmid = {39759080},
issn = {2589-0042},
abstract = {In the field of steady-state visual evoked potential (SSVEP), stimulus paradigms are regularly arranged or mimic the style of a keyboard with the same size. However, stimulation paradigms have important effects on the performance of SSVEP systems, which correlate with the electroencephalogram (EEG) signal amplitude and recognition accuracy. This paper provides MP dataset that was acquired using a 12-target BCI speller. MP dataset contains 9-channel EEG signals from the occipital region of 24 subjects under 5 stimulation paradigms with different stimulus sizes and arrangements. Stimuli were encoded using joint frequency and phase modulation (JFPM) method. Subjects completed an offline prompted spelling task using a speller under 5 paradigms. Each experiment contains 8 blocks, and each block contains 12 trials. Designers can use this dataset to test the performance of algorithms considering "stimulus size" and "stimulus arrangement". EEG data showed SSVEP features through amplitude-frequency analysis. FBCCA and TRCA confirmed its suitability.},
}
@article {pmid39758181,
year = {2025},
author = {Ammar, A and Salem, A and Simak, ML and Horst, F and Schöllhorn, WI},
title = {Acute effects of motor learning models on technical efficiency in strength-coordination exercises: a comparative analysis of Olympic snatch biomechanics in beginners.},
journal = {Biology of sport},
volume = {42},
number = {1},
pages = {151-161},
pmid = {39758181},
issn = {0860-021X},
abstract = {Despite the development of various motor learning models over many decades, the question of which model is most effective under which conditions to optimize the acquisition of skills remains a heated and recurring debate. This is particularly important in connection with learning sports movements with a high strength component. This study aims to examine the acute effects of various motor learning models on technical efficiency and force production during the Olympic snatch movement. In a within-subject design, sixteen highly active male participants (mean age: 23.13 ± 2.09 years), who were absolute beginners regarding the learning task, engaged in randomized snatch learning bouts, consisting of 36 trials across different learning models: differential learning (DL), contextual interference (serial, sCI; and blocked, bCI), and repetitive learning (RL). Kinematic and kinetic data were collected from three snatch trials executed following each learning bout. Discrete data from the most commonly monitored biomechanical parameters in Olympic weightlifting were analyzed using inferential statistics to identify differences between learning models. The statistical analysis revealed no significant differences between the learning models across all tested parameters, with p-values ranging from 0.236 to 0.99. However, it was observed that only the bouts with an exercise sequence following the DL model resulted in an average antero-posterior displacement of the barbell that matched the optimal displacement. This was characterized by a mean positive displacement towards the lifter during the pulling phases, a negative displacement away from the lifter in the turnover phase, and a return to positive displacement in the catch phase. These findings indicate the limited acute impact of the exercise sequences based on the three motor learning models on Olympic snatch technical efficiency in beginners, yet they hint at a possible slight advantage for the DL model. Coaches might therefore consider incorporating the DL model to potentially enhance technical efficiency, especially during the early stages of skill acquisition. Future research, involving even bigger amounts of exercise noise, longer learning periods, or a greater number of total learning trials and sessions, is essential to verify the potential advantages of the DL model for weightlifting technical efficiency.},
}
@article {pmid39758128,
year = {2025},
author = {Wang, B and Zhang, Y and Li, H and Dou, H and Guo, Y and Deng, Y},
title = {Biologically inspired heterogeneous learning for accurate, efficient and low-latency neural network.},
journal = {National science review},
volume = {12},
number = {1},
pages = {nwae301},
pmid = {39758128},
issn = {2053-714X},
abstract = {The pursuit of artificial neural networks that mirror the accuracy, efficiency and low latency of biological neural networks remains a cornerstone of artificial intelligence (AI) research. Here, we incorporated recent neuroscientific findings of self-inhibiting autapse and neuron heterogeneity for innovating a spiking neural network (SNN) with enhanced learning and memorizing capacities. A bi-level programming paradigm was formulated to respectively learn neuron-level biophysical variables and network-level synapse weights for nested heterogeneous learning. We successfully demonstrated that our biologically inspired neuron model could reproduce neural statistics at both individual and group levels, contributing to the effective decoding of brain-computer interface data. Furthermore, the heterogeneous SNN showed higher accuracy (1%-10% improvement), superior efficiency (maximal 17.83-fold reduction in energy) and lower latency (maximal 5-fold improvement) in performing several AI tasks. For the first time, we benchmarked SNN for conducting cell type identification from scRNA-seq data. The proposed model correctly identified very rare cell types associated with severe brain diseases where typical SNNs failed.},
}
@article {pmid39757218,
year = {2025},
author = {Kim, MS and Park, H and Kwon, I and An, KO and Kim, H and Park, G and Hyung, W and Im, CH and Shin, JH},
title = {Efficacy of brain-computer interface training with motor imagery-contingent feedback in improving upper limb function and neuroplasticity among persons with chronic stroke: a double-blinded, parallel-group, randomized controlled trial.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {1},
pmid = {39757218},
issn = {1743-0003},
support = {NRCTR-IN20001//Translational Research Program for Rehabilitation Robots, National Rehabilitation Center, Ministry of Health and Welfare, Republic of Korea/ ; },
mesh = {Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; *Brain-Computer Interfaces ; Chronic Disease ; Double-Blind Method ; Electroencephalography ; *Imagery, Psychotherapy/methods ; Imagination/physiology ; *Neuronal Plasticity/physiology ; *Recovery of Function/physiology ; *Stroke/physiopathology ; Stroke Rehabilitation/methods ; Treatment Outcome ; *Upper Extremity/physiopathology ; *Feedback ; },
abstract = {BACKGROUND: Brain-computer interface (BCI) technology can enhance neural plasticity and motor recovery in persons with stroke. However, the effects of BCI training with motor imagery (MI)-contingent feedback versus MI-independent feedback remain unclear. This study aimed to investigate whether the contingent connection between MI-induced brain activity and feedback influences functional and neural plasticity outcomes. We hypothesized that BCI training, with MI-contingent feedback, would result in greater improvements in upper limb function and neural plasticity compared to BCI training, with MI-independent feedback.<br><br>METHODS: This randomized controlled trial included persons with chronic stroke who underwent BCI training involving functional electrical stimulation feedback on the affected wrist extensor. Primary outcomes included the Medical Research Council (MRC) scale score for muscle strength in the wrist extensor (MRC-WE) and active range of motion in wrist extension (AROM-WE). Resting-state electroencephalogram recordings were used to assess neural plasticity.<br><br>RESULTS: Compared to the MI-independent feedback BCI group, the MI-contingent feedback BCI group showed significantly greater improvements in MRC-WE scores (mean difference&#x2009;=&#x2009;0.52, 95% CI&#x2009;=&#x2009;0.03-1.00, p&#x2009;=&#x2009;0.036) and demonstrated increased AROM-WE at 4 weeks post-intervention (p&#x2009;=&#x2009;0.019). Enhanced functional connectivity in the affected hemisphere was observed in the MI-contingent feedback BCI group, correlating with MRC-WE and Fugl-Meyer assessment-distal scores. Improvements were also observed in the unaffected hemisphere's functional connectivity.<br><br>CONCLUSIONS: BCI training with MI-contingent feedback is more effective than MI-independent feedback in improving AROM-WE, MRC, and neural plasticity in individuals with chronic stroke. BCI technology could be a valuable addition to conventional rehabilitation for stroke survivors, enhancing recovery outcomes.<br><br>TRIAL REGISTRATION: CRIS (KCT0009013).},
}
@article {pmid39755291,
year = {2025},
author = {Vogeley, AO and Livinski, AA and Dabaghi Varnosfaderani, S and Javaheripour, N and Jamalabadi, H and Kotoula, V and Henter, ID and Hejazi, NS and Price, RB and Yavi, M and Walter, M and Zarate, CA and Kheirkhah, M},
title = {Temporal dynamics of affective scene processing in the healthy adult human brain.},
journal = {Neuroscience and biobehavioral reviews},
volume = {169},
number = {},
pages = {106003},
pmid = {39755291},
issn = {1873-7528},
support = {ZIA MH002857/ImNIH/Intramural NIH HHS/United States ; ZIA MH002927/ImNIH/Intramural NIH HHS/United States ; },
mesh = {Humans ; *Emotions/physiology ; Magnetoencephalography ; Adult ; *Brain/physiology ; Electroencephalography ; *Affect/physiology ; },
abstract = {Understanding how the brain distinguishes emotional from neutral scenes is crucial for advancing brain-computer interfaces, enabling real-time emotion detection for faster, more effective responses, and improving treatments for emotional disorders like depression and anxiety. However, inconsistent research findings have arisen from differences in study settings, such as variations in the time windows, brain regions, and emotion categories examined across studies. This review sought to compile the existing literature on the timing at which the adult brain differentiates basic affective from neutral scenes in less than one second, as previous studies have consistently shown that the brain can begin recognizing emotions within just a few milliseconds. The review includes studies that used electroencephalography (EEG) or magnetoencephalography (MEG) in healthy adults to examine brain responses to emotional versus neutral images within one second. Articles of interest were limited to the English language but not to any publication year. Excluded studies involved only patients (of any diagnosis), participants under age 18 (since emotional processing can differ between adults and younger individuals), non-passive tasks, low temporal resolution techniques, time intervals over one second, and animals. Of the 3045 screened articles, 19 met these criteria. Despite the variations between studies, the earliest onset for heightened brain responses to basic affective scenes compared to neutral ones was most commonly observed within the 250-300 ms time window. To the best of our knowledge, this review is the first to synthesize data on the timing of brain differentiation between emotional and neutral scenes in healthy adults.},
}
@article {pmid39755229,
year = {2025},
author = {Ma, X and Xue, S and Ma, H and Saeed, S and Zhang, Y and Meng, Y and Chen, H and Yu, H and Wang, H and Hu, S and Cai, M},
title = {Esketamine alleviates LPS-induced depression-like behavior by activating Nrf2-mediated anti-inflammatory response in adolescent mice.},
journal = {Neuroscience},
volume = {567},
number = {},
pages = {294-307},
doi = {10.1016/j.neuroscience.2024.12.062},
pmid = {39755229},
issn = {1873-7544},
mesh = {Animals ; *Ketamine/pharmacology/analogs &amp; derivatives ; *NF-E2-Related Factor 2/metabolism ; Male ; Lipopolysaccharides ; *Depression/drug therapy/metabolism/chemically induced ; Mice ; Mice, Inbred C57BL ; *Antidepressive Agents/pharmacology ; Hippocampus/drug effects/metabolism ; Cytokines/metabolism ; Inflammation/metabolism/drug therapy ; *Anti-Inflammatory Agents/pharmacology ; },
abstract = {BACKGROUND: The mechanisms underlying esketamine's therapeutic effects remain elusive. The study aimed to explore the impact of single esketamine treatment on LPS-induced adolescent depressive-like behaviors and the role of Nrf2 regulated neuroinflammatory response in esketamine-produced rapid antidepressant efficacy.<br><br>METHODS: Adolescent male C57BL/6J mice were randomly assigned to three groups: control, LPS, and LPS&#xa0;+&#xa0;esketamine (15&#xa0;mg/kg, i.p.). Depressive-like behaviors were evaluated via the OFT, NFST, and TST. Protein expression of Nrf2 and inflammatory cytokines, including TNF-&#x3b1;, IL-1&#x3b2;, and iNOS in the hippocampus and mPFC, were measured by western blot. Moreover, the Nrf2 inhibitor, ML385, was also applied in the current study. The depressive-like behaviors and the protein expression of Nrf2, TNF-&#x3b1;, IL-1&#x3b2;, and iNOS in mPFC and hippocampus were also measured. Additionally, the plasma's pro-inflammatory cytokines and anti-inflammatory cytokines were assessed using ELISA methods with or without ML385.<br><br>RESULTS: A single administration of esketamine treatment alleviated the LPS-induced depressive-like behaviors. Esketamine increased the expression of Nrf2 and reduced the expression of the inflammatory cytokines, including TNF-&#x3b1;, IL-1&#x3b2;, and iNOS, in the mPFC and hippocampus. Notably, pharmacological inhibition of Nrf2 via ML385 administration abrogated the antidepressive-like behaviors and anti-inflammatory effects induced by esketamine. In the periphery, esketamine mitigated the LPS-induced elevation of pro-inflammatory cytokines, and the reduction of anti-inflammatory cytokines, and this effect was reversed by Nrf2 inhibition.<br><br>CONCLUSION: Esketamine treatment exerts rapid antidepressant effects and attenuates neuroinflammation in LPS-induced adolescent depressive-like behaviors, potentially through the activation of Nrf2-mediated anti-inflammatory signaling.},
}
@article {pmid39755222,
year = {2025},
author = {Huang, J and Wei, S and Gao, Z and Jiang, S and Wang, M and Sun, L and Ding, W and Zhang, D},
title = {Local structural-functional coupling with counterfactual explanations for epilepsy prediction.},
journal = {NeuroImage},
volume = {306},
number = {},
pages = {120978},
doi = {10.1016/j.neuroimage.2024.120978},
pmid = {39755222},
issn = {1095-9572},
mesh = {Humans ; Diffusion Tensor Imaging/methods ; Magnetic Resonance Imaging/methods ; *Epilepsy/physiopathology/diagnostic imaging/pathology ; *Brain/physiopathology/diagnostic imaging/pathology ; *Nerve Net/physiopathology/diagnostic imaging ; Male ; Female ; *White Matter/diagnostic imaging/physiopathology/pathology ; Adult ; *Connectome/methods ; },
abstract = {The structural-functional brain connections coupling (SC-FC coupling) describes the relationship between white matter structural connections (SC) and the corresponding functional activation or functional connections (FC). It has been widely used to identify brain disorders. However, the existing research on SC-FC coupling focuses on global and regional scales, and few studies have investigated the impact of brain disorders on this relationship from the perspective of multi-brain region cooperation (i.e., local scale). Here, we propose the local SC-FC coupling pattern for brain disorders prediction. Compared with previous methods, the proposed patterns quantify the relationship between SC and FC in terms of subgraphs rather than whole connections or single brain regions. Specifically, we first construct structural and functional connections using diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) data, subsequently organizing them into a multimodal brain network. Then, we extract subgraphs from these multimodal brain networks and select them based on their frequencies to generate local SC-FC coupling patterns. Finally, we employ these patterns to identify brain disorders while refining abnormal patterns to generate counterfactual explanations. Results on a real epilepsy dataset suggest that the proposed method not only outperforms existing methods in accuracy but also provides insights into the local SC-FC coupling pattern and their changes in brain disorders. Code available at https://github.com/UAIBC-Brain/Local-SC-FC-coupling-pattern.},
}
@article {pmid39755127,
year = {2025},
author = {Chu, T and Si, X and Song, X and Che, K and Dong, F and Guo, Y and Chen, D and Yao, W and Zhao, F and Xie, H and Shi, Y and Ma, H and Ming, D and Mao, N},
title = {Understanding structural-functional connectivity coupling in patients with major depressive disorder: A white matter perspective.},
journal = {Journal of affective disorders},
volume = {373},
number = {},
pages = {219-226},
doi = {10.1016/j.jad.2024.12.082},
pmid = {39755127},
issn = {1573-2517},
mesh = {Humans ; *Depressive Disorder, Major/physiopathology/pathology/diagnostic imaging/psychology ; *White Matter/physiopathology/diagnostic imaging/pathology ; Female ; Male ; Adult ; Middle Aged ; Suicidal Ideation ; Magnetic Resonance Imaging ; Suicide, Attempted/psychology ; Neural Pathways/physiopathology/pathology ; Pyramidal Tracts/physiopathology/pathology ; *Brain/physiopathology/pathology ; Diffusion Tensor Imaging ; },
abstract = {PURPOSE: To elucidate the structural-functional connectivity (SC-FC) coupling in white matter (WM) tracts in patients with major depressive disorder (MDD).<br><br>METHODS: A total of 178 individuals diagnosed with MDD and 173 healthy controls (HCs) were recruited for this study. The Euclidean distance was calculated to assess SC-FC coupling. The primary analyses focused on investigating alterations in SC-FC coupling in WM tracts of individuals with MDD. Additionally, we explored the association between coupling and clinical symptoms. Secondary analyses examined differences among three subgroups of MDD: those with suicidal ideation (SI), those with a history of suicidal attempts (SA), and those non-suicidal (NS).<br><br>RESULTS: The study revealed increased SC-FC coupling mainly in the middle cerebellar peduncle and bilateral corticospinal tract (PFDR&#xa0;<&#xa0;0.05) in patients with MDD compared with HCs. Additionally, right cerebral peduncle coupling strength exhibited a significant positive correlation with Hamilton Anxiety Scale scores (r&#xa0;=&#xa0;0.269, PFDR&#xa0;=&#xa0;0.041), while right cingulum (hippocampus) coupling strength showed a significant negative correlation with Nurses' Global Assessment of Suicide Risk scores (r&#xa0;=&#xa0;-0.159, PFDR&#xa0;=&#xa0;0.036). An increase in left anterior limb of internal capsule (PBonferroni&#xa0;<&#xa0;0.01) and left corticospinal tract (PBonferroni&#xa0;<&#xa0;0.05) coupling has been observed in MDD with SI. Additionally, a decrease in right posterior limb of internal capsule coupling has been found in MDD with SA (PBonferroni&#xa0;<&#xa0;0.05).<br><br>CONCLUSIONS: This study emphasizes the variations in SC-FC coupling in WM tracts in individuals with MDD and its subgroups, highlighting the crucial role of WM networks in the pathophysiology of MDD.},
}
@article {pmid39754303,
year = {2025},
author = {Qiu, C and Zhang, D and Wang, M and Mei, X and Chen, W and Yu, H and Yin, W and Peng, G and Hu, S},
title = {Peripheral Single-Cell Immune Characteristics Contribute to the Diagnosis of Alzheimer's Disease and Dementia With Lewy Bodies.},
journal = {CNS neuroscience &amp; therapeutics},
volume = {31},
number = {1},
pages = {e70204},
pmid = {39754303},
issn = {1755-5949},
support = {2022YFC3602604//National Key Research and Development Program of China/ ; 2023YFC2506200//National Key Research and Development Program of China/ ; 2021R52016//Leading Talent of Scientific and Technological Innovation Ten Thousand Talents Program of Zhejiang Province/ ; 2020R01001//Innovation team for precision diagnosis and treatment of major brain diseases/ ; JNL 2023001B//Research Project of Jinan Microecological Biomedicine Shandong Laboratory/ ; 2021C03107//Zhejiang Provincial Key Research and Development Program/ ; 2022KTZ004//Chinese Medical Education Association/ ; 2022 F28//NINGBO Medical Health Leading Academic Discipline Project/ ; },
mesh = {Humans ; *Alzheimer Disease/immunology/diagnosis ; *Lewy Body Disease/immunology/diagnosis ; Aged ; Female ; Male ; *Leukocytes, Mononuclear/immunology/metabolism ; Aged, 80 and over ; Single-Cell Analysis/methods ; Middle Aged ; Biomarkers/blood ; T-Lymphocytes/immunology ; },
abstract = {OBJECTIVE: Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) are common neurodegenerative diseases with distinct but overlapping pathogenic mechanisms. The clinical similarities between these diseases often result in high misdiagnosis rates, leading to serious consequences. Peripheral blood mononuclear cells (PBMCs) are easy to collect and can accurately reflect the immune characteristics of both DLB and AD.<br><br>METHODS: We utilized time-of-flight mass cytometry (CyTOF) with single-cell resolution to quantitatively analyze peripheral PBMCs, identifying 1228 immune characteristics. Based on the top-selected immune features, we constructed immunological elastic net (iEN) models.<br><br>RESULTS: These models demonstrated high diagnostic efficacy in distinguishing diseased samples from healthy donors as well as distinguishing AD and DLB cases. The selected features reveal that the primary peripheral immune characteristic of AD is a decrease in total T cells, while DLB is characterized by low expression of I-kappa-B-alpha (IKB&#x3b1;) in the classical monocyte subset.<br><br>CONCLUSIONS: These findings suggest that peripheral immune characteristics could serve as potential biomarkers, facilitating the diagnosis of neurodegenerative diseases.},
}
@article {pmid39753090,
year = {2025},
author = {Pang, Y and Zhao, S and Zhang, Z and Xu, J and Gao, L and Zhang, R and Li, Z and Lu, F and Chen, H and Wu, H and Chen, M and Chen, K and Wang, J},
title = {Individual structural covariance connectome reveals aberrant brain developmental trajectories associated with childhood maltreatment.},
journal = {Journal of psychiatric research},
volume = {181},
number = {},
pages = {709-715},
doi = {10.1016/j.jpsychires.2024.12.032},
pmid = {39753090},
issn = {1879-1379},
mesh = {Humans ; Male ; Female ; *Connectome ; Adult ; Adolescent ; Young Adult ; *Brain/growth &amp; development/diagnostic imaging/pathology ; Magnetic Resonance Imaging ; *Child Abuse/psychology ; Child ; Age Factors ; },
abstract = {BACKGROUND: The long-term impact of childhood maltreatment (CM) on an individual's physical and mental health is suggested to be mediated by altered neurodevelopment. However, the exact neurobiological consequences of CM remain unclear.<br><br>METHODS: The present study aimed to investigate the relationship between CM and brain age based on structural magnetic resonance imaging data from a sample of 214 adults. The participants were divided into CM and non_CM groups according to Childhood Trauma Questionnaire. For each participant, brain connectome age was estimated from a large-scale structural covariance network through relevance vector regression. Brain predicted age difference (brain_PAD) was then calculated for each participant.<br><br>RESULTS: The brain connectome age matched well with chronological age in young adults (age range: 18-25 years) and adults (age range: 26-44 years) without CM, but not in individuals with CM. Compared with non_CM group, CM group was characterized by higher brain_PAD scores in young adults, whereas lower brain_PAD scores in adults. The finding revealed that brain development in individuals with CM seems to be accelerated in younger adults but retardation with increasing age. Moreover, individuals who suffered child abuse (but not neglect) showed higher brain_PAD scores than non_CM group, suggesting the different influence of abuse and neglect on neurodevelopment. Finally, the brain_PAD was positively correlated with attentional impulsivity in CM group.<br><br>CONCLUSIONS: CM affects different stages of adult brain development differently, and abuse and neglect have different influenced patterns, which may provide new evidence for the impact of CM on structural brain development.},
}
@article {pmid39752412,
year = {2025},
author = {Ding, C and Kim Geok, S and Sun, H and Roslan, S and Cao, S and Zhao, Y},
title = {Does music counteract mental fatigue? A systematic review.},
journal = {PloS one},
volume = {20},
number = {1},
pages = {e0316252},
pmid = {39752412},
issn = {1932-6203},
mesh = {*Mental Fatigue/prevention &amp; control ; Humans ; *Music/psychology ; *Cognition/physiology ; Music Therapy/methods ; },
abstract = {INTRODUCTION: Mental fatigue, a psychobiological state induced by prolonged and sustained cognitive tasks, impairs both cognitive and physical performance. Several studies have investigated strategies to counteract mental fatigue. However, potential health risks and contextual restrictions often limit these strategies, which hinder their practical application. Due to its noninvasive and portable nature, music has been proposed as a promising strategy to counteract mental fatigue. However, the effects of music on performance decrements vary with different music styles. Synthesizing studies that systematically report music style and its impact on counteracting performance decrements is crucial for theoretical and practical applications.<br><br>OBJECTIVES: This review aims to provide a comprehensive systematic analysis of different music styles in counteracting mental fatigue and their effects on performance decrements induced by mental fatigue. Additionally, the mechanisms by which music counteracts mental fatigue will be discussed.<br><br>METHODS: A comprehensive search was conducted across five databases-Web of Science, PubMed, SCOPUS, SPORTDiscus via EBSCOhost, and the Psychological and Behavioral Sciences Collection via EBSCOhost-up to November 18, 2023. The selected studies focused solely on music interventions, with outcomes including subjective feelings of mental fatigue, physiological markers, and both cognitive and behavioral performance.<br><br>RESULTS: Nine studies met the predetermined criteria for inclusion in this review. The types of music interventions that counteract mental fatigue include relaxing, exciting, and personal preference music, all of which were associated with decreased subjective feelings of mental fatigue and changes in objective physiological markers. Cognitive performance, particularly in inhibition and working memory tasks impaired by mental fatigue, was countered by both relaxing and exciting music. Exciting music was found to decrease reaction time more effectively than relaxing music in working memory tasks. The physiological marker of steady-state visually evoked potential-based brain-computer interface (SSVEP-BCI) amplitude increased, confirming that exciting music counteracts mental fatigue more effectively than relaxing music. Behavioral performance in tasks such as arm-pointing, the Yo-Yo intermittent test, and the 5 km time-trial, which were impaired by mental fatigue, were counteracted by personal preference music.<br><br>CONCLUSION: Relaxing music, exciting music, and personal preference music effectively counteract mental fatigue by reducing feelings of fatigue and mitigating performance decrements. Individuals engaged in mentally demanding tasks can effectively counteract concurrent or subsequent cognitive performance decrements by simultaneously listening to relaxing or exciting music without lyrics or by using music during recovery from mental fatigue. Exciting music is more effective than relaxing music in counteracting mental fatigue. Personal preference music is effective in counteracting behavioral performance decrements in motor control and endurance tasks. Mentally fatigued individuals could apply personal preference music to counteract subsequent motor control performance decrements or simultaneously listen to it to counteract endurance performance decrements. Future studies should specify and examine the effects of different music genres, tempos, and intensities in counteracting mental fatigue. Additionally, the role of music in counteracting mental fatigue in contexts such as work productivity, traffic accident risk, and sports requires further investigation, along with the underlying mechanisms.},
}
@article {pmid39752045,
year = {2025},
author = {Xin, Q and Hu, H},
title = {To Attack or Not: A Neural Circuit Coding Sexually Dimorphic Aggression.},
journal = {Neuroscience bulletin},
volume = {41},
number = {4},
pages = {728-730},
pmid = {39752045},
issn = {1995-8218},
}
@article {pmid39748261,
year = {2025},
author = {Wu, JY and Zhang, JY and Xia, WQ and Kang, YN and Liao, RY and Chen, YL and Li, XM and Wen, Y and Meng, FX and Xu, LL and Wen, SH and Liu, HF and Li, YQ and Gu, JR and Lv, Q and Ren, Y},
title = {Predicting autoimmune thyroiditis in primary Sjogren's syndrome patients using a random forest classifier: a retrospective study.},
journal = {Arthritis research &amp; therapy},
volume = {27},
number = {1},
pages = {1},
pmid = {39748261},
issn = {1478-6362},
support = {No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; },
mesh = {Humans ; Retrospective Studies ; *Sjogren's Syndrome/immunology/diagnosis ; Female ; Male ; Middle Aged ; *Thyroiditis, Autoimmune/immunology/diagnosis ; Adult ; *Autoantibodies/immunology/blood ; *Machine Learning ; Algorithms ; Aged ; Random Forest ; },
abstract = {BACKGROUND: Primary Sjogren's syndrome (pSS) and autoimmune thyroiditis (AIT) share overlapping genetic and immunological profiles. This retrospective study evaluates the efficacy of machine learning algorithms, with a focus on the Random Forest Classifier, to predict the presence of thyroid-specific autoantibodies (TPOAb and TgAb) in pSS patients.<br><br>METHODS: A total of 96 patients with pSS were included in the retrospective study. All participants underwent a complete clinical and laboratory evaluation. All participants underwent thyroid function tests, including TPOAb and TgAb, and were accordingly divided into positive and negative thyroid autoantibody groups. Four machine learning algorithms were then used to analyze the risk factors affecting patients with pSS with positive and negative for thyroid autoantibodies.<br><br>RESULTS: The results indicated that the Random Forest Classifier algorithm (AUC&#x2009;=&#x2009;0.755) outperformed the other three machine learning algorithms. The random forest classifier indicated Age, IgG, C4 and dry mouth were the main factors influencing the prediction of positive thyroid autoantibodies in pSS patients. It is feasible to predict AIT in pSS using machine learning algorithms.<br><br>CONCLUSIONS: Analyzing clinical and laboratory data from 96 pSS patients, the Random Forest model demonstrated superior performance (AUC&#x2009;=&#x2009;0.755), identifying age, IgG levels, complement component 4 (C4), and absence of dry mouth as primary predictors. This approach offers a promising tool for early identification and management of AIT in pSS patients.<br><br>TRIAL REGISTRATION: This retrospective study was approved and monitored by the Ethics Committee of The Third Affiliated Hospital of Sun Yat-sen University (No.II2023-254-02).},
}
@article {pmid39748063,
year = {2025},
author = {Wei, Q and Li, C and Wang, Y and Gao, X},
title = {Enhancing the performance of SSVEP-based BCIs by combining task-related component analysis and deep neural network.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {365},
pmid = {39748063},
issn = {2045-2322},
support = {62066028//National Natural Science Foundation of China/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Neural Networks, Computer ; *Algorithms ; *Electroencephalography/methods ; Deep Learning ; Signal Processing, Computer-Assisted ; },
abstract = {Steady-State Visually Evoked Potential (SSVEP) signals can be decoded by either a traditional machine learning algorithm or a deep learning network. Combining the two methods is expected to enhance the performance of an SSVEP-based brain-computer interface (BCI) by exploiting their advantages. However, an efficient strategy for integrating the two methods has not yet been established. To address this issue, we propose a classification framework named eTRCA + sbCNN that combines an ensemble task-related component analysis (eTRCA) algorithm and a sub-band convolutional neural network (sbCNN) for recognizing the frequency of SSVEP signals. The two models are first trained separately, then their classification score vectors are added together, and finally the frequency corresponding to the maximal summed score is decided as the frequency of SSVEP signals. The proposed framework can effectively exploit the complementarity between the two kinds of feature signals and significantly improve the classification performance of SSVEP-based BCIs. The performance of the proposed method is validated on two SSVEP BCI datasets and compared with that of eTRCA, sbCNN and other state-of-the-art models. Experimental results indicate that the proposed method significantly outperform the compared algorithms, and thus helps to promote the practical application of SSVEP- BCI systems.},
}
@article {pmid39747930,
year = {2025},
author = {Anderson, L and De Ridder, D and Glue, P and Mani, R and van Sleeuwen, C and Smith, M and Adhia, DB},
title = {A safety and feasibility randomized placebo controlled trial exploring electroencephalographic effective connectivity neurofeedback treatment for fibromyalgia.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {209},
pmid = {39747930},
issn = {2045-2322},
mesh = {Humans ; *Fibromyalgia/therapy/physiopathology ; *Neurofeedback/methods ; Female ; Middle Aged ; *Electroencephalography ; Adult ; *Feasibility Studies ; Male ; Treatment Outcome ; Gyrus Cinguli/physiopathology/diagnostic imaging ; Chronic Pain/therapy/physiopathology ; },
abstract = {Fibromyalgia is a chronic pain condition contributing to significant disability worldwide. Neuroimaging studies identify abnormal effective connectivity between cortical areas responsible for descending pain modulation (pregenual anterior cingulate cortex, pgACC) and sensory components of pain experience (primary somatosensory cortex, S1). Neurofeedback, a brain-computer interface technique, can normalise dysfunctional brain activity, thereby improving pain and function. This study evaluates the safety, feasibility, and acceptability of a novel electroencephalography-based neurofeedback training, targeting effective alpha-band connectivity from the pgACC to S1 and exploring its effect on pain and function. Participants with fibromyalgia (N = 30; 15 = active, 15 = placebo) received 12 sessions of neurofeedback. Feasibility and outcome measures of pain (Brief Pain Inventory) and function (Revised Fibromyalgia Impact Questionnaire) were collected at baseline and immediately, ten-days, and one-month post-intervention. Descriptive statistics demonstrate effective connectivity neurofeedback training is feasible (recruitment rate: 6 participants per-month, mean adherence: 80.5%, dropout rate: 20%), safe (no adverse events) and highly acceptable (average 8.0/10) treatment approach for fibromyalgia. Active and placebo groups were comparable in their decrease in pain and functional impact. Future fully powered clinical trial is warranted to test the efficacy of the effective connectivity neurofeedback training in people with fibromyalgia with versus without chronic fatigue.},
}
@article {pmid39745941,
year = {2025},
author = {Stavisky, SD},
title = {Restoring Speech Using Brain-Computer Interfaces.},
journal = {Annual review of biomedical engineering},
volume = {27},
number = {1},
pages = {29-54},
doi = {10.1146/annurev-bioeng-110122-012818},
pmid = {39745941},
issn = {1545-4274},
mesh = {*Brain-Computer Interfaces ; Humans ; *Speech/physiology ; Electroencephalography/methods ; Communication Devices for People with Disabilities ; Brain/physiology ; },
abstract = {People who have lost the ability to speak due to neurological injuries would greatly benefit from assistive technology that provides a fast, intuitive, and naturalistic means of communication. This need can be met with brain-computer interfaces (BCIs): medical devices that bypass injured parts of the nervous system and directly transform neural activity into outputs such as text or sound. BCIs for restoring movement and typing have progressed rapidly in recent clinical trials; speech BCIs are the next frontier. This review covers the clinical need for speech BCIs, surveys foundational studies that point to where and how speech can be decoded in the brain, describes recent progress in both discrete and continuous speech decoding and closed-loop speech BCIs, provides metrics for assessing these systems' performance, and highlights key remaining challenges on the road toward clinically useful speech neuroprostheses.},
}
@article {pmid39745924,
year = {2025},
author = {Trevelyan, AJ and Marks, VS and Graham, RT and Denison, T and Jackson, A and Smith, EH},
title = {On brain stimulation in epilepsy.},
journal = {Brain : a journal of neurology},
volume = {148},
number = {3},
pages = {746-752},
pmid = {39745924},
issn = {1460-2156},
mesh = {Humans ; Animals ; *Epilepsy/therapy/physiopathology ; *Deep Brain Stimulation/methods ; Optogenetics/methods ; *Brain/physiopathology ; Vagus Nerve Stimulation/methods ; },
abstract = {Brain stimulation has, for many decades, been considered as a potential solution for the unmet needs of the many people living with drug-resistant epilepsy. Clinically, there are several different approaches in use, including vagus nerve stimulation, deep brain stimulation of the thalamus, and responsive neurostimulation. Across populations of patients, all deliver reductions in seizure load and sudden unexpected death in epilepsy risk, yet do so variably, and the improvements seem incremental rather than transformative. In contrast, within the field of experimental neuroscience, the transformational impact of optogenetic stimulation is evident; by providing a means to control subsets of neurons in isolation, it has revolutionized our ability to dissect out the functional relations within neuronal microcircuits. It is worth asking, therefore, how preclinical optogenetics research could advance clinical practice in epilepsy? Here, we review the state of the clinical field, and the recent progress in preclinical animal research. We report various breakthrough results, including the development of new models of seizure initiation, its use for seizure prediction, and for fast, closed-loop control of pathological brain rhythms, and what these experiments tell us about epileptic pathophysiology. Finally, we consider how these preclinical research advances may be translated into clinical practice.},
}
@article {pmid39745545,
year = {2025},
author = {Romano, V and Manto, M},
title = {How and where Effectively Apply Cerebellum Stimulation: The frequency-dependent Modulation of Cerebellar Output by Transcranial Alternating Current Stimulation.},
journal = {Cerebellum (London, England)},
volume = {24},
number = {1},
pages = {22},
pmid = {39745545},
issn = {1473-4230},
mesh = {Animals ; Humans ; Rats ; *Cerebellum/physiology ; *Transcranial Direct Current Stimulation/methods ; },
abstract = {As brain-machine interfaces (BMI) are growingly used in clinical settings, understanding how to apply brain stimulation is increasingly important. Despite the emergence of optogenetic techniques, ethical and medical concerns suggest that interventions that are safe and non-invasive, such as Transcranial Alternating Current Stimulation (tACS), are more likely to be employed in human in the near future. Consequently, the question of how and where to apply current stimulation is becoming increasingly important for the efficient neuromodulation of both neurological and psychiatric disorders. In this edition of The Cerebellum, Mourra et al. demonstrate how ctACS influences cerebellar output at both single-cell and population levels by stimulating Crus I in rats. As the neuron generating this output serves as a crucial convergence and divergence center in the nervous system, it can be leveraged as a strategic hub to target multiple brain structures and influence various behaviors. Accordingly, the discovery that neurons in this relatively deep brain region can be indirectly entrained through Purkinje neuron activation and optimal frequency around 80 Hz could be highly relevant for future medical interventions. In light of these findings, high-γ-tACS might be more effective in humans compared to the more commonly used low-γ (50 Hz) or θ-tACS (5 Hz). This could enhance the chance of cerebellar tACS being utilized in clinical settings and BMI.},
}
@article {pmid39744695,
year = {2025},
author = {Li, C and Miao, C and Ge, Y and Wu, J and Gao, P and Yin, S and Zhang, P and Yang, H and Tian, B and Chen, W and Chen, XQ},
title = {A molecularly distinct cell type in the midbrain regulates intermale aggression behaviors in mice.},
journal = {Theranostics},
volume = {15},
number = {2},
pages = {707-725},
pmid = {39744695},
issn = {1838-7640},
mesh = {Animals ; *Aggression/physiology ; Mice ; *Periaqueductal Gray/metabolism/physiology ; Male ; *Neurons/metabolism/physiology ; Mice, Inbred C57BL ; Tachykinins/metabolism/genetics ; Behavior, Animal/physiology ; Mesencephalon/metabolism/physiology ; Serotonin/metabolism ; },
abstract = {Rationale: The periaqueductal gray (PAG) is a central hub for the regulation of aggression, whereas the circuitry and molecular mechanisms underlying this regulation remain uncharacterized. In this study, we investigate the role of a distinct cell type, Tachykinin 2-expressing (Tac2[+]) neurons, located in the dorsomedial PAG (dmPAG) and their modulation of aggressive behavior in mice. Methods: We combined activity mapping, in vivo Ca[2+] recording, chemogenetic and pharmacological manipulation, and a viral-based translating ribosome affinity purification (TRAP) profiling using a mouse resident-intruder model. Results: We revealed that dmPAG[Tac2] neurons are selectively activated by fighting behaviors. Chemogenetic activation of these neurons evoked fighting behaviors, while inhibition or genetic ablation of dmPAG[Tac2] neurons attenuated fighting behaviors. TRAP profiling of dmPAG[Tac2] neurons revealed an enrichment of serotonin-associated transcripts in response to fighting behaviors. Finally, we validated these effects by selectively administering pharmacological agents to the dmPAG, reversing the behavioral outcomes induced by chemogenetic manipulation. Conclusions: We identify dmPAG[Tac2] neurons as critical modulators of aggressive behavior in mouse and thus suggest a distinct molecular target for the treatment of exacerbated aggressive behaviors in populations that exhibit high-level of violence.},
}
@article {pmid39743292,
year = {2024},
author = {Huang, T and Guo, X and Huang, X and Yi, C and Cui, Y and Dong, Y},
title = {Input-output specific orchestration of aversive valence in lateral habenula during stress dynamics.},
journal = {Journal of Zhejiang University. Science. B},
volume = {25},
number = {12},
pages = {1055-1065},
pmid = {39743292},
issn = {1862-1783},
support = {2022ZD0211700//the STI2030-Major Projects/ ; 32371057, 31922031, 32071017, 81971309, 32170980, 82201707 and 82200562//the National Natural Science Foundation of China/ ; LDQ24C090001//the Zhejiang Provincial Natural Science Foundation of China/ ; 2023-PT310-01//the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences/ ; JCTD-2022-11//the CAS Youth Interdisciplinary Team/ ; BX20230319//the China Postdoctoral Science Foundation/ ; 2022B1515020012 and 2021A1515110121//the Guangdong Basic and Applied Basic Research Foundation/ ; 2023B1212060018//the Science and Technology Planning Project of Guangdong Province/ ; JCYJ20210324123212035, RCYX20200714114644167, ZDSYS20220606100801003, JCYJ20210324122809025 and JCYJ20230807110315031//the Shenzhen Fundamental Research Program/ ; },
mesh = {*Habenula/physiology ; Animals ; *Stress, Psychological/physiopathology ; Humans ; Neurons/physiology ; Neuronal Plasticity/physiology ; Depression/physiopathology ; Neural Pathways/physiology ; },
abstract = {Stress has been considered as a major risk factor for depressive disorders, triggering depression onset via inducing persistent dysfunctions in specialized brain regions and neural circuits. Among various regions across the brain, the lateral habenula (LHb) serves as a critical hub for processing aversive information during the dynamic process of stress accumulation, thus having been implicated in the pathogenesis of depression. LHb neurons integrate aversive valence conveyed by distinct upstream inputs, many of which selectively innervate the medial part (LHbM) or lateral part (LHbL) of LHb. LHb subregions also separately assign aversive valence via dissociable projections to the downstream targets in the midbrain which provides feedback loops. Despite these strides, the spatiotemporal dynamics of LHb-centric neural circuits remain elusive during the progression of depression-like state under stress. In this review, we attempt to describe a framework in which LHb orchestrates aversive valence via the input-output specific neuronal architecture. Notably, a physiological form of Hebbian plasticity in LHb under multiple stressors has been unveiled to incubate neuronal hyperactivity in an input-specific manner, which causally encodes chronic stress experience and drives depression onset. Collectively, the recent progress and future efforts in elucidating LHb circuits shed light on early interventions and circuit-specific antidepressant therapies.},
}
@article {pmid39743145,
year = {2025},
author = {Hao, X and Ma, M and Meng, F and Liang, H and Liang, C and Liu, X and Zhang, B and Ju, Y and Liu, S and Ming, D},
title = {Diminished attention network activity and heightened salience-default mode transitions in generalized anxiety disorder: Evidence from resting-state EEG microstate analysis.},
journal = {Journal of affective disorders},
volume = {373},
number = {},
pages = {227-236},
doi = {10.1016/j.jad.2024.12.095},
pmid = {39743145},
issn = {1573-2517},
mesh = {Humans ; *Anxiety Disorders/physiopathology/psychology ; Female ; Male ; *Electroencephalography ; Adult ; *Attention/physiology ; *Default Mode Network/physiopathology ; *Brain/physiopathology ; Young Adult ; Nerve Net/physiopathology ; Magnetic Resonance Imaging ; Generalized Anxiety Disorder ; },
abstract = {Generalized anxiety disorder (GAD) is a common anxiety disorder characterized by excessive, uncontrollable worry and physical symptoms such as difficulty concentrating and sleep disturbances. Although functional magnetic resonance imaging (fMRI) studies have reported aberrant network-level activity related to cognition and emotion in GAD, its low temporal resolution restricts its ability to capture the rapid neural activity in mental processes. EEG microstate analysis offers millisecond-resolution for tracking the dynamic changes in brain electrical activity, thereby illuminating the neurophysiological mechanisms underlying the cognitive and emotional dysfunctions in GAD. This study collected 64-channel resting-state EEG data from 28 GAD patients and 28 healthy controls (HC), identifying five microstate classes (A-E) in both groups. Results showed that GAD patients exhibited significantly lower duration (p < 0.01), occurrence (p < 0.05), and coverage (p < 0.01) of microstate class D, potentially reflecting deficits in attention-related networks. Such alterations may contribute to the impairments in attention maintenance and cognitive control. Additionally, GAD patients displayed reduced transition probabilities in A → D, B → D, C → D, and E → D (all corrected p < 0.05), but increased in C → E (corrected p < 0.05) and E → C (corrected p < 0.01). These results highlight a significant reduction in the brain's ability to transition into microstate class D, alongside overactivity in switching between the default mode network and the salience network. Such neurophysiological changes may underlie cognitive control deficits, increased spontaneous rumination, and emotional regulation challenges observed in GAD. Together, these insights provide a new perspective for understanding the neurophysiological and pathological mechanisms underlying GAD.},
}
@article {pmid39742538,
year = {2025},
author = {Cao, L and Zhao, W and Sun, B},
title = {Emotion recognition using multi-scale EEG features through graph convolutional attention network.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {184},
number = {},
pages = {107060},
doi = {10.1016/j.neunet.2024.107060},
pmid = {39742538},
issn = {1879-2782},
mesh = {*Electroencephalography/methods ; *Emotions/physiology ; Humans ; *Neural Networks, Computer ; *Attention/physiology ; Brain-Computer Interfaces ; },
abstract = {Emotion recognition via electroencephalogram (EEG) signals holds significant promise across various domains, including the detection of emotions in patients with consciousness disorders, assisting in the diagnosis of depression, and assessing cognitive load. This process is critically important in the development and research of brain-computer interfaces, where precise and efficient recognition of emotions is paramount. In this work, we introduce a novel approach for emotion recognition employing multi-scale EEG features, denominated as the Dynamic Spatial-Spectral-Temporal Network (DSSTNet). DSSTNet includes three main parts, the first is spatial features extractor, which converts EEG signal into graph structure data, and uses graph convolutional network (GCN) to dynamically optimize the adjacency matrix during the training process to obtain the spatial features between the channels. Next, band attention module is composed of semi-global pooling, localized cross-band interaction and adaptive weighting, which further extracts frequency information. Finally, through the temporal features extractor, the deep temporal information is extracted by stacking several one-dimensional convolutional layers. In addition, in order to improve the performance of emotion recognition and filter valid channels, we add a ℓ2,1-norm regularization term to the loss function to make the adjacency matrix constraint sparse. This makes it easier to preserve emotionally relevant channels and eliminate noise in irrelevant channel. Combined with the channel selection method of graph theory, a small number of optimal channels are selected. We used a self-constructed dataset TJU-EmoEEG and a publicly available SEED dataset to evaluate DSSTNet. The experimental results demonstrate that DSSTNet outperforms current state-of-the-art (SOTA) methods in emotional recognition tasks.},
}
@article {pmid39741784,
year = {2024},
author = {Degirmenci, M and Yuce, YK and Perc, M and Isler, Y},
title = {EEG channel and feature investigation in binary and multiple motor imagery task predictions.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1525139},
pmid = {39741784},
issn = {1662-5161},
abstract = {INTRODUCTION: Motor Imagery (MI) Electroencephalography (EEG) signals are non-stationary and dynamic physiological signals which have low signal-to-noise ratio. Hence, it is difficult to achieve high classification accuracy. Although various machine learning methods have already proven useful to that effect, the use of many features and ineffective EEG channels often leads to a complex structure of classifier algorithms. State-of-the-art studies were interested in improving classification performance with complex feature extraction and classification methods by neglecting detailed EEG channel and feature investigation in predicting MI tasks from EEGs. Here, we investigate the effects of the statistically significant feature selection method on four different feature domains (time-domain, frequency-domain, time-frequency domain, and non-linear domain) and their two different combinations to reduce the number of features and classify MI-EEG features by comparing low-dimensional matrices with well-known machine learning algorithms.<br><br>METHODS: Our main goal is not to find the best classifier performance but to perform feature and channel investigation in MI task classification. Therefore, the detailed investigation of the effect of EEG channels and features is implemented using a statistically significant feature distribution on 22 EEG channels for each feature set separately. We used the BCI Competition IV Dataset IIa and 288 samples per person. A total of 1,364 MI-EEG features were analyzed in this study. We tested nine distinct classifiers: Decision tree, Discriminant analysis, Logistic regression, Naive Bayes, Support vector machine, k-Nearest neighbor, Ensemble learning, Neural networks, and Kernel approximation.<br><br>RESULTS: Among all feature sets considered, classifications performed with non-linear and combined feature sets resulted in a maximum accuracy of 63.04% and 47.36% for binary and multiple MI task predictions, respectively. The ensemble learning classifier achieved the maximum accuracy in almost all feature sets for binary and multiple MI task classifications.<br><br>DISCUSSION: Our research thus shows that the statistically significant feature-based feature selection method significantly improves the classification performance with fewer features in almost all feature sets, enabling detailed and effective EEG channel and feature investigation.},
}
@article {pmid39741250,
year = {2024},
author = {Liang, S and Li, L and Zu, W and Feng, W and Hang, W},
title = {Adaptive deep feature representation learning for cross-subject EEG decoding.},
journal = {BMC bioinformatics},
volume = {25},
number = {1},
pages = {393},
pmid = {39741250},
issn = {1471-2105},
support = {61902197//National Natural Science Foundation of China/ ; KYCX23_1073//Postgraduate Research &amp; Practice Innovation Program of Jiangsu Province/ ; 23KJB520012//Natural Science Research of Jiangsu Higher Education Institutions of China/ ; },
mesh = {*Electroencephalography/methods ; Humans ; Deep Learning ; Algorithms ; Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; },
abstract = {BACKGROUND: The collection of substantial amounts of electroencephalogram (EEG) data is typically time-consuming and labor-intensive, which adversely impacts the development of decoding models with strong generalizability, particularly when the available data is limited. Utilizing sufficient EEG data from other subjects to aid in modeling the target subject presents a potential solution, commonly referred to as domain adaptation. Most current domain adaptation techniques for EEG decoding primarily focus on learning shared feature representations through domain alignment strategies. Since the domain shift cannot be completely removed, target EEG samples located near the edge of clusters are also susceptible to misclassification.<br><br>METHODS: We propose a novel adaptive deep feature representation (ADFR) framework to improve the cross-subject EEG classification performance through learning transferable EEG feature representations. Specifically, we first minimize the distribution discrepancy between the source and target domains by employing maximum mean discrepancy (MMD) regularization, which aids in learning the shared feature representations. We then utilize the instance-based discriminative feature learning (IDFL) regularization to make the learned feature representations more discriminative. Finally, the entropy minimization (EM) regularization is further integrated to adjust the classifier to pass through the low-density region between clusters. The synergistic learning between above regularizations during the training process enhances EEG decoding performance across subjects.<br><br>RESULTS: The effectiveness of the ADFR framework was evaluated on two public motor imagery (MI)-based EEG datasets: BCI Competition III dataset 4a and BCI Competition IV dataset 2a. In terms of average accuracy, ADFR achieved improvements of 3.0% and 2.1%, respectively, over the state-of-the-art methods on these datasets.<br><br>CONCLUSIONS: The promising results highlight the effectiveness of the ADFR algorithm for EEG decoding and show its potential for practical applications.},
}
@article {pmid39738346,
year = {2024},
author = {Le, TT and Luong, DAQ and Joo, H and Kim, D and Woo, J},
title = {Differences in spatiotemporal dynamics for processing specific semantic categories: An EEG study.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {31918},
pmid = {39738346},
issn = {2045-2322},
support = {NRF-2020R1A2C2003319//National Research Foundation of Korea/ ; },
mesh = {Humans ; *Electroencephalography ; *Semantics ; Male ; Female ; Adult ; *Brain Mapping ; Brain/physiology ; Young Adult ; Comprehension/physiology ; },
abstract = {Semantic processing is an essential mechanism in human language comprehension and has profound implications for speech brain-computer interface technologies. Despite recent advances in brain imaging techniques and data analysis algorithms, the mechanisms underlying human brain semantic representations remain a topic of debate, specifically whether this occurs through the activation of selectively separated cortical regions or via a network of distributed and overlapping regions. This study investigates spatiotemporal neural representation during the perception of semantic words related to faces, numbers, and animals using electroencephalography. Source-level analysis focuses on contrasting neural responses to different semantic categories. Critical intervals used in the source contrast analysis are defined using the peak duration of global field power. Effective connectivity, determined through a causality analysis of brain regions activated for semantic processing, is explored. The findings reveal the necessity of a distributed network of regions for processing specific semantic categories and provide evidence suggesting the existence of a neural substrate for semantic representations.},
}
@article {pmid39738221,
year = {2024},
author = {Wolde, HF and Clements, ACA and Gilmour, B and Alene, KA},
title = {Spatial co-distribution of tuberculosis prevalence and low BCG vaccination coverage in Ethiopia.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {31561},
pmid = {39738221},
issn = {2045-2322},
support = {APP1196549//Australian National Health and Medical Research Council/ ; },
mesh = {Humans ; Ethiopia/epidemiology ; *BCG Vaccine/administration &amp; dosage ; Prevalence ; *Vaccination Coverage/statistics &amp; numerical data ; Female ; *Tuberculosis/epidemiology/prevention &amp; control ; Male ; *Bayes Theorem ; Infant ; Child, Preschool ; Adult ; Adolescent ; Vaccination/statistics &amp; numerical data ; Young Adult ; Child ; Spatial Analysis ; },
abstract = {While bacille-calmette-guerin (BCG) vaccination is one of the recommended strategies for preventing tuberculosis (TB), its coverage is low in several countries, including Ethiopia. This study investigated the spatial co-distribution and drivers of TB prevalence and low BCG coverage in Ethiopia. This ecological study was conducted using data from a national TB prevalence survey and the Ethiopian demographic and health survey (EDHS) to map the spatial co-distribution of BCG vaccination coverage and TB prevalence. A Bayesian geostatistical model was built to identify the drivers for the spatial distribution of TB prevalence and low BCG vaccination coverage. BCG vaccination coverage was defined as the number of children who received the vaccine divided by the total number of children born within five years preceding the EDHS surveys. Parameter estimation was done using binary logistic regression. Prediction maps for the co-distribution of high TB prevalence and low BCG vaccination coverage were created by overlying spatial prediction surfaces of the two outcomes. Posterior means and a 95% Bayesian credible interval (CrI) were used to summarize the parameters of the model. The national prevalence was 0.40% (95% confidence interval (CI) 0.34%, 0.47%) for TB and 47% (95% CI 46%, 48%) for vaccination coverage. Substantial spatial variation in TB prevalence and low BCG coverage was observed at a regional and local level, particularly in border areas of the country, including the Somali, Afar, and Oromia regions. Approximately 58% of the pixels (i.e., geographical area or spatial units) with high TB prevalence exhibited low BCG coverage in the same location. While travel time to cities (Mean = 0.28, 95% BCI: 0.15, 0.41) and distance to health facilities (Mean = 0.43, 95% CI 0.22, 0.63), were positively associated, population density (Mean = -0.04, 95% BCI -0.05, -0.02) was negatively associated, with the proportion of unvaccinated children for BCG indicating areas near health facilities and cities have better BCG coverage. However, there were no significant predictors for TB prevalence. Substantial spatial co-distribution between high TB prevalence and low BCG coverage was observed in some parts of the country, indicating that there are areas where the TB burden is not being adequately managed through the provision of vaccines in Ethiopia. Scaling up BCG vaccination coverage and TB diagnosis and treatment through improving access to health services in border regions such as Somalia and Afar would be important to reduce the prevalence of TB in Ethiopia.},
}
@article {pmid39738213,
year = {2024},
author = {Liu, Y and Huang, S and Xu, W and Wang, Z and Ming, D},
title = {An fMRI study on the generalization of motor learning after brain actuated supernumerary robot training.},
journal = {NPJ science of learning},
volume = {9},
number = {1},
pages = {80},
pmid = {39738213},
issn = {2056-7936},
support = {62273251//National Natural Science Foundation of China (National Science Foundation of China)/ ; 81925020//National Natural Science Foundation of China (National Science Foundation of China)/ ; 21JCYBJC00520//Natural Science Foundation of Tianjin City (Natural Science Foundation of Tianjin)/ ; MSV202418//State Key Laboratory of Mechanical System and Vibration/ ; },
abstract = {Generalization is central to motor learning. However, few studies are on the learning generalization of BCI-actuated supernumerary robotic finger (BCI-SRF) for human-machine interaction training, and no studies have explored its longitudinal neuroplasticity mechanisms. Here, 20 healthy right-handed participants were recruited and randomly assigned to BCI-SRF group or inborn finger group (Finger) for 4-week training and measured by novel SRF-finger opposition sequences and multimodal MRI. After training, the BCI-SRF group showed 350% times compared to the Finger group in the improvement of sequence opposition accuracy before and after training, and accompanied by significant functional connectivity increases in the sensorimotor region and prefrontal cortex, as well as in the intra- and inter-hemisphere of the sensorimotor network. Moreover, Granger Causality Analysis identified causal effect main transfer within the sensorimotor cortex-cerebellar-thalamus loop and frontal-parietal loop. The findings suggest that BCI-SRF training enhances motor sequence learning ability by influencing the functional reorganization of sensorimotor network.},
}
@article {pmid39735964,
year = {2024},
author = {Luo, TJ and Li, J and Li, R and Zhang, X and Wu, SR and Peng, H},
title = {Motion Cognitive Decoding of Cross-Subject Motor Imagery Guided on Different Visual Stimulus Materials.},
journal = {Journal of integrative neuroscience},
volume = {23},
number = {12},
pages = {218},
doi = {10.31083/j.jin2312218},
pmid = {39735964},
issn = {0219-6352},
support = {24JCXK01YB//Planning Project of Philosophy and Social Science of Zhejiang Province/ ; 24JCXK02YB//Planning Project of Philosophy and Social Science of Zhejiang Province/ ; 62106049//National Natural Science Foundation of China/ ; 61662025//National Natural Science Foundation of China/ ; 61871289//National Natural Science Foundation of China/ ; 62007016//National Natural Science Foundation of China/ ; 2022J01655//Natural Science Foundation of Fujian Province of China/ ; },
mesh = {Humans ; *Imagination/physiology ; *Electroencephalography/methods ; Adult ; Young Adult ; Male ; *Brain-Computer Interfaces ; Female ; Motor Activity/physiology ; Psychomotor Performance/physiology ; Motion Perception/physiology ; },
abstract = {BACKGROUND: Motor imagery (MI) plays an important role in brain-computer interfaces, especially in evoking event-related desynchronization and synchronization (ERD/S) rhythms in electroencephalogram (EEG) signals. However, the procedure for performing a MI task for a single subject is subjective, making it difficult to determine the actual situation of an individual's MI task and resulting in significant individual EEG response variations during motion cognitive decoding.<br><br>METHODS: To explore this issue, we designed three visual stimuli (arrow, human, and robot), each of which was used to present three MI tasks (left arm, right arm, and feet), and evaluated differences in brain response in terms of ERD/S rhythms. To compare subject-specific variations of different visual stimuli, a novel cross-subject MI-EEG classification method was proposed for the three visual stimuli. The proposed method employed a covariance matrix centroid alignment for preprocessing of EEG samples, followed by a model agnostic meta-learning method for cross-subject MI-EEG classification.<br><br>RESULTS AND CONCLUSION: The experimental results showed that robot stimulus materials were better than arrow or human stimulus materials, with an optimal cross-subject motion cognitive decoding accuracy of 79.04%. Moreover, the proposed method produced robust classification of cross-subject MI-EEG signal decoding, showing superior results to conventional methods on collected EEG signals.},
}
@article {pmid39733553,
year = {2025},
author = {Jain, A and Kumar, L},
title = {ESI-GAL: EEG source imaging-based trajectory estimation for grasp and lift task.},
journal = {Computers in biology and medicine},
volume = {186},
number = {},
pages = {109608},
doi = {10.1016/j.compbiomed.2024.109608},
pmid = {39733553},
issn = {1879-0534},
mesh = {Humans ; *Electroencephalography/methods ; *Hand Strength/physiology ; *Brain-Computer Interfaces ; Male ; Biomechanical Phenomena ; Adult ; Female ; Deep Learning ; *Signal Processing, Computer-Assisted ; Hand/physiology ; *Brain/physiology ; },
abstract = {BACKGROUND: Electroencephalogram (EEG) signals-based motor kinematics prediction (MKP) has been an active area of research to develop Brain-computer interface (BCI) systems such as exosuits, prostheses, and rehabilitation devices. However, EEG source imaging (ESI) based kinematics prediction is sparsely explored in the literature.<br><br>METHOD: In this study, pre-movement EEG features are utilized to predict three-dimensional (3D) hand kinematics for the grasp-and-lift motor task. A public dataset, WAY-EEG-GAL, is utilized for MKP analysis. In particular, sensor-domain (EEG data) and source-domain (ESI data) based features from the frontoparietal region are explored for MKP. Deep learning-based models are explored to achieve efficient kinematics decoding. Various time-lagged and window sizes are analyzed for hand kinematics prediction. Subsequently, intra-subject and inter-subject MKP analysis is performed to investigate the subject-specific and subject-independent motor-learning capabilities of the neural decoders. The Pearson correlation coefficient (PCC) is used as the performance metric for kinematics trajectory decoding.<br><br>RESULTS: The rEEGNet neural decoder achieved the best performance with sensor-domain and source-domain features with the time lag and window size of 100ms and 450ms, respectively. The highest mean PCC values of 0.790, 0.795, and 0.637 are achieved using sensor-domain features, while 0.769, 0.777, and 0.647 are achieved using source-domain features in x, y, and z-directions, respectively.<br><br>CONCLUSION: This study explores the feasibility of trajectory prediction using EEG sensor-domain and source-domain features for the grasp-and-lift task. Furthermore, inter-subject trajectory estimation is performed using the proposed deep learning decoder with EEG source domain features.},
}
@article {pmid39733023,
year = {2024},
author = {Cheng, Z and Bu, X and Wang, Q and Yang, T and Tu, J},
title = {EEG-based emotion recognition using multi-scale dynamic CNN and gated transformer.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {31319},
pmid = {39733023},
issn = {2045-2322},
support = {2020CB-34//the Science and Technology Plan Project of Jingzhou City/ ; 2022BCE009//Key Plan of Science and Technology Department of Hubei Province/ ; },
mesh = {*Electroencephalography/methods ; *Emotions/physiology ; Humans ; *Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Algorithms ; },
abstract = {Emotions play a crucial role in human thoughts, cognitive processes, and decision-making. EEG has become a widely utilized tool in emotion recognition due to its high temporal resolution, real-time monitoring capabilities, portability, and cost-effectiveness. In this paper, we propose a novel end-to-end emotion recognition method from EEG signals, called MSDCGTNet, which is based on the Multi-Scale Dynamic 1D CNN and the Gated Transformer. First, the Multi-Scale Dynamic CNN is used to extract complex spatial and spectral features from raw EEG signals, which not only avoids information loss but also reduces computational costs associated with the time-frequency conversion of signals. Then, the Gated Transformer Encoder is utilized to capture global dependencies of EEG signals. This encoder focuses on specific regions of the input sequence while reducing computational resources through parallel processing with the improved multi-head self-attention mechanisms. Third, the Temporal Convolution Network is used to extract temporal features from the EEG signals. Finally, the extracted abstract features are fed into a classification module for emotion recognition. The proposed method was evaluated on three publicly available datasets: DEAP, SEED, and SEED_IV. Experimental results demonstrate the high accuracy and efficiency of the proposed method for emotion recognition. This approach proves to be robust and suitable for various practical applications. By addressing challenges posed by existing methods, the proposed method provides a valuable and effective solution for the field of Brain-Computer Interface (BCI).},
}
@article {pmid39732819,
year = {2024},
author = {Jin, L and Hu, J and Li, Y and Zhu, Y and He, X and Bai, R and Wang, L},
title = {Altered neurovascular coupling and structure-function coupling in Moyamoya disease affect postoperative collateral formation.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {31324},
pmid = {39732819},
issn = {2045-2322},
support = {81870910//National Natural Science Foundation of China/ ; 2022C03133//Key Research and Development Program of Zhejiang Province/ ; },
mesh = {Humans ; *Moyamoya Disease/physiopathology/surgery/diagnostic imaging ; Female ; Male ; Adult ; *Cerebrovascular Circulation/physiology ; *Neurovascular Coupling/physiology ; *Magnetic Resonance Imaging ; Diffusion Tensor Imaging ; Middle Aged ; Collateral Circulation/physiology ; Case-Control Studies ; Brain/physiopathology/diagnostic imaging/pathology ; Young Adult ; },
abstract = {Chronic ischemia in moyamoya disease (MMD) impaired white matter microstructure and neural functional network. However, the coupling between cerebral blood flow (CBF) and functional connectivity and the association between structural and functional network are largely unknown. 38 MMD patients and 20 sex/age-matched healthy controls (HC) were included for T1-weighted imaging, arterial spin labeling imaging, resting-state functional MRI and diffusion tensor imaging. All patients had preoperative and postoperative digital subtraction angiography. Upon constructing the structural connectivity (SC) and functional connectivity (FC) networks, the SC-FC coupling was calculated. After obtaining the graph theoretical parameters, neurovascular coupling represented the spatial correlation between node degree centrality (DC) of functional networks and CBF. The CBF-DC coupling and SC-FC coupling were compared between MMD and HC groups. We further analyzed the correlation between coupling indexes and cognitive scores, as well as postoperative collateral formation. Compared with HC, CBF-DC coupling was decreased in MMD (p = 0.021), especially in the parietal lobe (p = 0.047). SC-FC coupling in MMD decreased in frontal, occipital, and subcortical regions. Cognitive scores were correlated with the CBF-DC coupling in frontal lobes (r = 0.394, p = 0.029) and SC-FC coupling (r = 0.397, p = 0.027). The CBF-DC coupling of patients with good postoperative collateral formation was higher (p = 0.041). Overall, neurovascular decoupling and structure-functional decoupling at the cortical level may be the underlying neuropathological mechanisms of MMD.},
}
@article {pmid39731856,
year = {2025},
author = {Guo, X and Feng, Y and Ji, X and Jia, N and Maimaiti, A and Lai, J and Wang, Z and Yang, S and Hu, S},
title = {Shared genetic architecture and bidirectional clinical risks within the psycho-metabolic nexus.},
journal = {EBioMedicine},
volume = {111},
number = {},
pages = {105530},
pmid = {39731856},
issn = {2352-3964},
mesh = {Humans ; Genome-Wide Association Study ; *Genetic Predisposition to Disease ; Polymorphism, Single Nucleotide ; *Mental Disorders/genetics/metabolism ; Multifactorial Inheritance ; *Metabolic Diseases/genetics ; Genetic Pleiotropy ; Risk Factors ; },
abstract = {BACKGROUND: Increasing evidence suggests a complex interplay between psychiatric disorders and metabolic dysregulations. However, most research has been limited to specific disorder pairs, leaving a significant gap in our understanding of the broader psycho-metabolic nexus.<br><br>METHODS: This study leveraged large-scale cohort data and genome-wide association study (GWAS) summary statistics, covering 8 common psychiatric disorders and 43 metabolic traits. We introduced a comprehensive analytical strategy to identify shared genetic bases sequentially, from key genetic correlation regions to local pleiotropy and pleiotropic genes. Finally, we developed polygenic risk score (PRS) models to translate these findings into clinical applications.<br><br>FINDINGS: We identified significant bidirectional clinical risks between psychiatric disorders and metabolic dysregulations among 310,848 participants from the UK Biobank. Genetic correlation analysis confirmed 104 robust trait pairs, revealing 1088 key genomic regions, including critical hotspots such as chr3: 47588462-50387742. Cross-trait meta-analysis uncovered 388 pleiotropic single nucleotide variants (SNVs) and 126 shared causal variants. Among variants, 45 novel SNVs were associated with psychiatric disorders and 75 novel SNVs were associated with metabolic traits, shedding light on new targets to unravel the mechanism of comorbidity. Notably, RBM6, a gene involved in alternative splicing and cellular stress response regulation, emerged as a key pleiotropic gene. When psychiatric and metabolic genetic information were integrated, PRS models demonstrated enhanced predictive power.<br><br>INTERPRETATION: The study highlights the intertwined genetic and clinical relationships between psychiatric disorders and metabolic dysregulations, emphasising the need for integrated approaches in diagnosis and treatment.<br><br>FUNDING: The National Key Research and Development Program of China (2023YFC2506200, SHH). The National Natural Science Foundation of China (82273741, SY).},
}
@article {pmid39730087,
year = {2024},
author = {Kong, S and Zhang, J and Wang, L and Li, W and Guo, H and Weng, Q and He, Q and Lou, H and Ding, L and Yang, B},
title = {Mechanisms of low MHC I expression and strategies for targeting MHC I with small molecules in cancer immunotherapy.},
journal = {Cancer letters},
volume = {611},
number = {},
pages = {217432},
doi = {10.1016/j.canlet.2024.217432},
pmid = {39730087},
issn = {1872-7980},
abstract = {Major histocompatibility complex (MHC) class I load antigens and present them on the cell surface, which transduces the tumor-associated antigens to CD8[+] T cells, activating the acquired immune system. However, many tumors downregulate MHC I expression to evade immune surveillance. The low expression of MHC I not only reduce recognition by- and cytotoxicity of CD8[+] T cells, but also seriously weakens the anti-tumor effect of immunotherapy by restoring CD8[+] T cells, such as immune checkpoint inhibitors (ICIs). Accumulated evidence suggested that restoring MHC I expression is an effective strategy for enhancing tumor immunotherapy. This review focuses on mechanisms underlying MHC I downregulation include gene deletion and mutation, transcriptional inhibition, reduced mRNA stability, increased protein degradation, and disruption of endocytic trafficking. We also provide a comprehensive review of small molecules that restore or upregulate MHC I expression, as well as clinical trials involving the combination of ICIs and these small molecule drugs.},
}
@article {pmid39729483,
year = {2025},
author = {Gao, S and Sun, Y and Wu, F and Jiang, J and Peng, T and Zhang, R and Ling, C and Han, Y and Xu, Q and Zou, L and Liao, Y and Liang, C and Zhang, D and Qi, S and Tang, J and Xu, X},
title = {Effects on Multimodal Connectivity Patterns in Female Schizophrenia During 8 Weeks of Antipsychotic Treatment.},
journal = {Schizophrenia bulletin},
volume = {51},
number = {3},
pages = {829-840},
pmid = {39729483},
issn = {1745-1701},
support = {82172061//National Natural Science Foundation of China/ ; BE2022677//Key Research and Development Plan in Jiangsu/ ; WSN-166//16th Batch of Six Talent Peak Projects in Jiangsu/ ; YKK23138//Nanjing Health Technology Development Project/ ; 23-25-289//Training and Management of Young Talents in Nanjing Brain Hospital/ ; 2016YFC1306900//National Key R&amp;D Program of China/ ; },
mesh = {Humans ; Female ; *Schizophrenia/drug therapy/physiopathology/diagnostic imaging ; *Antipsychotic Agents/pharmacology/administration &amp; dosage ; Adult ; Magnetic Resonance Imaging ; *Connectome ; *Nerve Net/diagnostic imaging/physiopathology/drug effects ; Young Adult ; *Brain/diagnostic imaging/physiopathology/drug effects ; *Outcome Assessment, Health Care ; },
abstract = {BACKGROUND AND HYPOTHESIS: Respective abnormal structural connectivity (SC) and functional connectivity (FC) have been reported in individuals with schizophrenia. However, transmodal associations between SC and FC following antipsychotic treatment, especially in female schizophrenia, remain unclear. We hypothesized that increased SC-FC coupling may be found in female schizophrenia, and could be normalized after antipsychotic treatment.<br><br>STUDY DESIGN: Sixty-four female drug-na&#xef;ve patients with first-diagnosed schizophrenia treated with antipsychotic drugs for 8 weeks, and 55 female healthy controls (HCs) were enrolled. Magnetic resonance imaging (MRI) data were collected from HCs at baseline and from patients at baseline and after treatment. SC and FC were analyzed by network-based statistics, calculating nonzero SC-FC coupling of the whole brain and altered connectivity following treatment. Finally, an Elastic-net logistic regression analysis was employed to establish a predictive model for evaluating the clinical efficacy treatment.<br><br>STUDY RESULTS: At baseline, female schizophrenia patients exhibited abnormal SC in cortico-cortical, frontal-limbic, frontal-striatal, limbic-striatal, and limbic-cerebellar connectivity compared to HCs, while FC showed no abnormalities. Following treatment, cortico-cortical, frontal-limbic, frontal-striatal, limbic-striatal, temporal-cerebellar, and limbic-cerebellar connectivity were altered in both SC and FC. Additionally, SC-FC coupling of altered connectivity was higher in patients at baseline than in HC, trending toward normalization after treatment. Furthermore, identified FC or/and SC predicted changes in psychopathological symptoms and cognitive impairment among female schizophrenia following treatment.<br><br>CONCLUSIONS: SC-FC coupling may be a potential predictive biomarker of treatment response. Cortico-cortical, frontal-limbic, frontal-striatal, limbic-striatal, temporal-cerebellar, and limbic-cerebellar could represent major targets for antipsychotic drugs in female schizophrenia.},
}
@article {pmid39727765,
year = {2024},
author = {Yousefipour, B and Rajabpour, V and Abdoljabbari, H and Sheykhivand, S and Danishvar, S},
title = {An Ensemble Deep Learning Approach for EEG-Based Emotion Recognition Using Multi-Class CSP.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {9},
number = {12},
pages = {},
pmid = {39727765},
issn = {2313-7673},
abstract = {In recent years, significant advancements have been made in the field of brain-computer interfaces (BCIs), particularly in the area of emotion recognition using EEG signals. The majority of earlier research in this field has missed the spatial-temporal characteristics of EEG signals, which are critical for accurate emotion recognition. In this study, a novel approach is presented for classifying emotions into three categories, positive, negative, and neutral, using a custom-collected dataset. The dataset used in this study was specifically collected for this purpose from 16 participants, comprising EEG recordings corresponding to the three emotional states induced by musical stimuli. A multi-class Common Spatial Pattern (MCCSP) technique was employed for the processing stage of the EEG signals. These processed signals were then fed into an ensemble model comprising three autoencoders with Convolutional Neural Network (CNN) layers. A classification accuracy of 99.44 ± 0.39% for the three emotional classes was achieved by the proposed method. This performance surpasses previous studies, demonstrating the effectiveness of the approach. The high accuracy indicates that the method could be a promising candidate for future BCI applications, providing a reliable means of emotion detection.},
}
@article {pmid39727293,
year = {2025},
author = {Gefen, N and Mazer, B and Krasovsky, T and Weiss, PL},
title = {Novel rehabilitation technologies in pediatric rehabilitation: knowledge towards translation.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {20},
number = {5},
pages = {1209-1218},
doi = {10.1080/17483107.2024.2445017},
pmid = {39727293},
issn = {1748-3115},
mesh = {Humans ; Child ; *Self-Help Devices ; *Children with Disabilities/rehabilitation ; *Translational Research, Biomedical ; Telerehabilitation ; Brain-Computer Interfaces ; Robotics ; },
abstract = {PURPOSE: Knowledge translation (KT) refers to the process of applying the most promising research outcomes into practice to ensure that new discoveries and innovations improve healthcare accessibility, effectiveness, and accountability. The objective of this perspective paper is to discuss and illustrate via examples how the KT process can be implemented in an era of rapid advancement in rehabilitation technologies that have the potential to significantly impact pediatric healthcare.<br><br>METHODS: Using Graham et&#xa0;al.'s (2006) Knowledge-to-Action cycle, which includes the knowledge creation funnel and the action cycle, we illustrate its application in implementing novel technologies into clinical practice and informing healthcare policy changes. We explore three successful applications of technology research: powered mobility, head support systems, and telerehabilitation. Additionally, we examine less clinically mature technologies such as brain-computer interfaces and robotic assistive devices, which are hindered by cost, robustness, and ease-of-use issues.<br><br>CONCLUSIONS: The paper concludes by discussing how technology acceptance and usage in clinical settings are influenced by various barriers and facilitators at different stakeholder levels, including clients, families, clinicians, management, researchers, developers, and society. Recommendations include focusing on early and ongoing design partnerships, transitioning from research to real-life implementation, and identifying optimal timing for clinical adoption of new technologies.},
}
@article {pmid39726882,
year = {2024},
author = {Afrah, R and Amini, Z and Kafieh, R},
title = {An Unsupervised Feature Extraction Method based on CLSTM-AE for Accurate P300 Classification in Brain-Computer Interface Systems.},
journal = {Journal of biomedical physics &amp; engineering},
volume = {14},
number = {6},
pages = {579-592},
pmid = {39726882},
issn = {2251-7200},
abstract = {BACKGROUND: The P300 signal, an endogenous component of event-related potentials, is extracted from an electroencephalography signal and employed in Brain-computer Interface (BCI) devices.<br><br>OBJECTIVE: The current study aimed to address challenges in extracting useful features from P300 components and detecting P300 through a hybrid unsupervised manner based on Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM).<br><br>MATERIAL AND METHODS: In this cross-sectional study, CNN as a useful method for the P300 classification task emphasizes spatial characteristics of data. However, CNN and LSTM networks are combined to modify the classification system by extracting both spatial and temporal features. Then, the CNN-LSTM network was trained in an unsupervised learning method based on an autoencoder to improve Signal-to-noise Ratio (SNR) by extracting main components from latent space. To deal with imbalanced data, an Adaptive Synthetic Sampling Approach (ADASYN) is used and augmented without any duplication.<br><br>RESULTS: The trained model, tested on the BCI competition III dataset, including two normal subjects, with an accuracy of 95% and 94% for subjects A and B in P300 detection, respectively.<br><br>CONCLUSION: CNN-LSTM, was embedded into an autoencoder and introduced to simultaneously extract spatial and temporal features and manage the computational complexity of the method. Further, ADASYN as an augmentation method was proposed to deal with the imbalanced nature of data, which not only maintained feature space as before but also preserved anatomical features of P300. High-quality results highlight the suitable efficiency of the proposed method.},
}
@article {pmid39726001,
year = {2024},
author = {Sun, Q and Merino, EC and Yang, L and Van Hulle, MM},
title = {Unraveling EEG correlates of unimanual finger movements: insights from non-repetitive flexion and extension tasks.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {21},
number = {1},
pages = {228},
pmid = {39726001},
issn = {1743-0003},
support = {202206050022//China Scholarship Council/ ; 101118964//Horizon Europe's Marie Sklodowska-Curie Action/ ; 857375//Horizon 2020 research and innovation programme/ ; C24/18/098//special research fund of the KU Leuven/ ; G0A4118N, G0A4321N, G0C1522N//Belgian Fund for Scientific Research - Flanders/ ; AKUL 043//Hercules Foundation/ ; },
mesh = {Humans ; *Fingers/physiology ; Male ; *Electroencephalography/methods ; Female ; Adult ; *Movement/physiology ; Young Adult ; Brain-Computer Interfaces ; },
abstract = {BACKGROUND: The loss of finger control in individuals with neuromuscular disorders significantly impacts their quality of life. Electroencephalography (EEG)-based brain-computer interfaces that actuate neuroprostheses directly via decoded motor intentions can help restore lost finger mobility. However, the extent to which finger movements exhibit distinct and decodable EEG correlates remains unresolved. This study aims to investigate the EEG correlates of unimanual, non-repetitive finger flexion and extension.<br><br>METHODS: Sixteen healthy, right-handed participants completed multiple sessions of right-hand finger movement experiments. These included five individual (Thumb, Index, Middle, Ring, and Pinky) and four coordinated (Pinch, Point, ThumbsUp, and Fist) finger flexions and extensions, along with a rest condition (None). High-density EEG and finger trajectories were simultaneously recorded and analyzed. We examined low-frequency (0.3-3&#xa0;Hz) time series and movement-related cortical potentials (MRCPs), and event-related desynchronization/synchronization (ERD/S) in the alpha- (8-13&#xa0;Hz) and beta (13-30&#xa0;Hz) bands. A clustering approach based on Riemannian distances was used to chart similarities between the broadband EEG responses (0.3-70&#xa0;Hz) to the different finger scenarios. The contribution of different state-of-the-art features was identified across sub-bands, from low-frequency to low gamma (30-70&#xa0;Hz), and an ensemble approach was used to pairwise classify single-trial finger movements and rest.<br><br>RESULTS: A significant decrease in EEG amplitude in the low-frequency time series was observed in the contralateral frontal-central regions during finger flexion and extension. Distinct MRCP patterns were found in the pre-, ongoing-, and post-movement stages. Additionally, strong ERD was detected in the contralateral central brain regions in both alpha and beta bands during finger flexion and extension, with the beta band showing a stronger rebound (ERS) post-movement. Within the finger movement repertoire, the Thumb was most distinctive, followed by the Fist. Decoding results indicated that low-frequency time-domain amplitude better differentiates finger movements, while alpha and beta band power and Riemannian features better detect movement versus rest. Combining these features yielded over 80% finger movement detection accuracy, while pairwise classification accuracy exceeded 60% for the Thumb versus the other fingers.<br><br>CONCLUSION: Our findings confirm that non-repetitive finger movements, whether individual or coordinated, can be precisely detected from EEG. However, differentiating between specific movements is challenging due to highly overlapping neural correlates in time, spectral, and spatial domains. Nonetheless, certain finger movements, such as those involving the Thumb, exhibit distinct EEG responses, making them prime candidates for dexterous finger neuroprostheses.},
}
@article {pmid39725763,
year = {2025},
author = {Fu, R and Niu, S and Feng, X and Shi, Y and Jia, C and Zhao, J and Wen, G},
title = {Performance investigation of MVMD-MSI algorithm in frequency recognition for SSVEP-based brain-computer interface and its application in robotic arm control.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {63},
number = {5},
pages = {1367-1381},
pmid = {39725763},
issn = {1741-0444},
support = {62073282//the National Natural Science Foundation of China/ ; F2022203092//Natural Science Foundation of Hebei Province/ ; F2020203070//Natural Science Foundation of Hebei Province/ ; 206Z0301G//the Central Guidance on Local Science and Technology Development Fund of Hebei Province/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Algorithms ; *Evoked Potentials, Visual/physiology ; Electroencephalography/methods ; *Robotics/methods ; Signal Processing, Computer-Assisted ; Male ; Adult ; *Arm/physiology ; Young Adult ; Female ; },
abstract = {This study focuses on improving the performance of steady-state visual evoked potential (SSVEP) in brain-computer interfaces (BCIs) for robotic control systems. The challenge lies in effectively reducing the impact of artifacts on raw data to enhance the performance both in quality and reliability. The proposed MVMD-MSI algorithm combines the advantages of multivariate variational mode decomposition (MVMD) and multivariate synchronization index (MSI). Compared to widely used algorithms, the novelty of this method is its capability of decomposing nonlinear and non-stationary EEG signals into intrinsic mode functions (IMF) across different frequency bands with the best center frequency and bandwidth. Therefore, SSVEP decoding performance can be improved by this method, and the effectiveness of MVMD-MSI is evaluated by the robot with 6 degrees-of-freedom. Offline experiments were conducted to optimize the algorithm's parameters, resulting in significant improvements. Additionally, the algorithm showed good performance even with fewer channels and shorter data lengths. In online experiments, the algorithm achieved an average accuracy of 98.31% at 1.8 s, confirming its feasibility and effectiveness for real-time SSVEP BCI-based robotic arm applications. The MVMD-MSI algorithm, as proposed, represents a significant advancement in SSVEP analysis for robotic control systems. It enhances decoding performance and shows promise for practical application in this field.},
}
@article {pmid39723643,
year = {2025},
author = {Tao, T and Liu, S and He, M and Zhao, M and Chen, C and Peng, J and Wang, Y and Cai, J and Xiong, J and Lai, C and Gu, W and Ying, M and Mao, J and Li, L and Jia, X and Wu, X and Peng, W and Zhang, X and Li, Y and Li, T and Wang, J and Shu, Q},
title = {Synchronous bilateral Wilms tumors are prone to develop independently and respond differently to preoperative chemotherapy.},
journal = {International journal of cancer},
volume = {156},
number = {9},
pages = {1746-1755},
doi = {10.1002/ijc.35297},
pmid = {39723643},
issn = {1097-0215},
support = {32270853//National Natural Science Foundation of China/ ; U20A20137//National Natural Science Foundation of China/ ; LHDMZ23H160005//Joint Funds of the Zhejiang Provincial Natural Science Foundation of China/ ; 2024C03181//"Pioneer" and "Leading Goose" R&amp;D Program of Zhejiang Province/ ; },
mesh = {Humans ; *Wilms Tumor/genetics/drug therapy/pathology/surgery ; *Kidney Neoplasms/genetics/drug therapy/pathology/surgery ; Male ; Female ; Infant ; Mutation ; DNA Copy Number Variations ; Child, Preschool ; Exome Sequencing ; WT1 Proteins/genetics ; *Neoplasms, Multiple Primary/genetics/drug therapy/pathology ; beta Catenin/genetics ; Loss of Heterozygosity ; Transcriptome ; *Antineoplastic Combined Chemotherapy Protocols/therapeutic use ; Genetic Predisposition to Disease ; },
abstract = {Wilms tumor (WT) is the most common kidney cancer in infants and young children. The determination of the clonality of bilateral WTs is critical to the treatment, because lineage-independent and metastatic tumors may require different treatment strategies. Here we found synchronous bilateral WT (n = 24 tumors from 12 patients) responded differently to preoperative chemotherapy. Transcriptome, whole-exome and whole-genome analysis (n = 12 tumors from 6 patients) demonstrated that each side of bilateral WT was clonally independent in terms of somatic driver mutations, copy number variations and transcriptomic profile. Molecular timing analysis revealed distinct timing and patterns of chromosomal evolution and mutational processes between the two sides of WT. Mutations in WT1, CTNNB1 and copy-neutral loss of heterozygosity of 11p15.5 provide possible genetic predisposition for the early initiation of bilateral WT. Our results provide comprehensive evidence and new insights regarding the separate initiation and early embryonic development of bilateral WT, which may benefit clinical practices in treating metastatic or refractory bilateral WT.},
}
@article {pmid39720868,
year = {2024},
author = {Downey, JE and Schone, HR and Foldes, ST and Greenspon, C and Liu, F and Verbaarschot, C and Biro, D and Satzer, D and Moon, CH and Coffman, BA and Youssofzadeh, V and Fields, D and Hobbs, TG and Okorokova, E and Tyler-Kabara, EC and Warnke, PC and Gonzalez-Martinez, J and Hatsopoulos, NG and Bensmaia, SJ and Boninger, ML and Gaunt, RA and Collinger, JL},
title = {A Roadmap for Implanting Electrode Arrays to Evoke Tactile Sensations Through Intracortical Stimulation.},
journal = {Human brain mapping},
volume = {45},
number = {18},
pages = {e70118},
pmid = {39720868},
issn = {1097-0193},
support = {UH3 NS107714/NS/NINDS NIH HHS/United States ; N66001-10-C-4056//Defense Advanced Research Projects Agency/ ; UH3 NS107714/NH/NIH HHS/United States ; },
mesh = {Humans ; Male ; Adult ; *Spinal Cord Injuries/physiopathology ; *Electrodes, Implanted ; Female ; *Somatosensory Cortex/physiology/diagnostic imaging ; *Brain-Computer Interfaces ; Middle Aged ; Touch Perception/physiology ; Electric Stimulation/methods ; Brain Mapping/methods ; Hand/physiology ; Magnetic Resonance Imaging ; },
abstract = {Intracortical microstimulation (ICMS) is a method for restoring sensation to people with paralysis as part of a bidirectional brain-computer interface (BCI) to restore upper limb function. Evoking tactile sensations of the hand through ICMS requires precise targeting of implanted electrodes. Here we describe the presurgical imaging procedures used to generate functional maps of the hand area of the somatosensory cortex and subsequent planning that guided the implantation of intracortical microelectrode arrays. In five participants with cervical spinal cord injury, across two study locations, this procedure successfully enabled ICMS-evoked sensations localized to at least the first four digits of the hand. The imaging and planning procedures developed through this clinical trial provide a roadmap for other BCI studies to ensure the successful placement of stimulation electrodes.},
}
@article {pmid39720668,
year = {2024},
author = {Li, X and Chu, Y and Wu, X},
title = {3D convolutional neural network based on spatial-spectral feature pictures learning for decoding motor imagery EEG signal.},
journal = {Frontiers in neurorobotics},
volume = {18},
number = {},
pages = {1485640},
pmid = {39720668},
issn = {1662-5218},
abstract = {Non-invasive brain-computer interfaces (BCI) hold great promise in the field of neurorehabilitation. They are easy to use and do not require surgery, particularly in the area of motor imagery electroencephalography (EEG). However, motor imagery EEG signals often have a low signal-to-noise ratio and limited spatial and temporal resolution. Traditional deep neural networks typically only focus on the spatial and temporal features of EEG, resulting in relatively low decoding and accuracy rates for motor imagery tasks. To address these challenges, this paper proposes a 3D Convolutional Neural Network (P-3DCNN) decoding method that jointly learns spatial-frequency feature maps from the frequency and spatial domains of the EEG signals. First, the Welch method is used to calculate the frequency band power spectrum of the EEG, and a 2D matrix representing the spatial topology distribution of the electrodes is constructed. These spatial-frequency representations are then generated through cubic interpolation of the temporal EEG data. Next, the paper designs a 3DCNN network with 1D and 2D convolutional layers in series to optimize the convolutional kernel parameters and effectively learn the spatial-frequency features of the EEG. Batch normalization and dropout are also applied to improve the training speed and classification performance of the network. Finally, through experiments, the proposed method is compared to various classic machine learning and deep learning techniques. The results show an average decoding accuracy rate of 86.69%, surpassing other advanced networks. This demonstrates the effectiveness of our approach in decoding motor imagery EEG and offers valuable insights for the development of BCI.},
}
@article {pmid39720230,
year = {2024},
author = {Hu, F and He, K and Qian, M and Liu, X and Qiao, Z and Zhang, L and Xiong, J},
title = {STAFNet: an adaptive multi-feature learning network via spatiotemporal fusion for EEG-based emotion recognition.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1519970},
pmid = {39720230},
issn = {1662-4548},
abstract = {INTRODUCTION: Emotion recognition using electroencephalography (EEG) is a key aspect of brain-computer interface research. Achieving precision requires effectively extracting and integrating both spatial and temporal features. However, many studies focus on a single dimension, neglecting the interplay and complementarity of multi-feature information, and the importance of fully integrating spatial and temporal dynamics to enhance performance.<br><br>METHODS: We propose the Spatiotemporal Adaptive Fusion Network (STAFNet), a novel framework combining adaptive graph convolution and temporal transformers to enhance the accuracy and robustness of EEG-based emotion recognition. The model includes an adaptive graph convolutional module to capture brain connectivity patterns through spatial dynamic evolution and a multi-structured transformer fusion module to integrate latent correlations between spatial and temporal features for emotion classification.<br><br>RESULTS: Extensive experiments were conducted on the SEED and SEED-IV datasets to evaluate the performance of STAFNet. The model achieved accuracies of 97.89% and 93.64%, respectively, outperforming state-of-the-art methods. Interpretability analyses, including confusion matrices and t-SNE visualizations, were employed to examine the influence of different emotions on the model's recognition performance. Furthermore, an investigation of varying GCN layer depths demonstrated that STAFNet effectively mitigates the over-smoothing issue in deeper GCN architectures.<br><br>DISCUSSION: In summary, the findings validate the effectiveness of STAFNet in EEG-based emotion recognition. The results emphasize the critical role of spatiotemporal feature extraction and introduce an innovative framework for feature fusion, advancing the state of the art in emotion recognition.},
}
@article {pmid39719191,
year = {2025},
author = {Rudroff, T},
title = {Decoding thoughts, encoding ethics: A narrative review of the BCI-AI revolution.},
journal = {Brain research},
volume = {1850},
number = {},
pages = {149423},
doi = {10.1016/j.brainres.2024.149423},
pmid = {39719191},
issn = {1872-6240},
mesh = {*Brain-Computer Interfaces/ethics ; Humans ; *Artificial Intelligence/ethics ; Electroencephalography/methods ; Brain/physiology ; },
abstract = {OBJECTIVES: This narrative review aims to analyze mechanisms underlying Brain-Computer Interface (BCI) and Artificial Intelligence (AI) integration, evaluate recent advances in signal acquisition and processing techniques, and assess AI-enhanced neural decoding strategies. The review identifies critical research gaps and examines emerging solutions across multiple domains of BCI-AI integration.<br><br>METHODS: A narrative review was conducted using major biomedical and scientific databases including PubMed, Web of Science, IEEE Xplore, and Scopus (2014-2024). Literature was analyzed to identify key developments in BCI-AI integration, with particular emphasis on recent advances (2019-2024). The review process involved thematic analysis of selected publications focusing on practical applications, technical innovations, and emerging challenges.<br><br>RESULTS: Recent advances demonstrate significant improvements in BCI-AI systems: 1) High-density electrode arrays achieve spatial resolution up to 5&#xa0;mm, with stable recordings over 15&#xa0;months; 2) Deep learning decoders show 40&#xa0;% improvement in information transfer rates compared to traditional methods; 3) Adaptive algorithms maintain >90&#xa0;% success rates in motor control tasks over 200-day periods without recalibration; 4) Novel closed-loop optimization frameworks reduce user training time by 55&#xa0;% while improving accuracy. Latest developments in flexible neural interfaces and self-supervised learning approaches show promise in addressing long-term stability and cross-user generalization challenges.<br><br>CONCLUSIONS: BCI-AI integration shows remarkable progress in improving signal quality, decoding accuracy, and user adaptation. While challenges remain in long-term stability and user training, advances in adaptive algorithms and feedback mechanisms demonstrate the technology's growing viability for clinical applications. Recent innovations in electrode technology, AI architectures, and closed-loop systems, combined with emerging standardization frameworks, suggest accelerating progress toward widespread therapeutic use and human augmentation applications.},
}
@article {pmid39719121,
year = {2025},
author = {Li, K and Chen, P and Chen, Q and Li, X},
title = {A hybrid network using transformer with modified locally linear embedding and sliding window convolution for EEG decoding.},
journal = {Journal of neural engineering},
volume = {21},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ada30b},
pmid = {39719121},
issn = {1741-2552},
mesh = {*Electroencephalography/methods ; *Brain-Computer Interfaces ; Humans ; *Neural Networks, Computer ; Imagination/physiology ; Algorithms ; Linear Models ; Signal Processing, Computer-Assisted ; },
abstract = {Objective. Brain-computer interface(BCI) is leveraged by artificial intelligence in EEG signal decoding, which makes it possible to become a new means of human-machine interaction. However, the performance of current EEG decoding methods is still insufficient for clinical applications because of inadequate EEG information extraction and limited computational resources in hospitals. This paper introduces a hybrid network that employs a transformer with modified locally linear embedding and sliding window convolution for EEG decoding.Approach. This network separately extracts channel and temporal features from EEG signals, subsequently fusing these features using a cross-attention mechanism. Simultaneously, manifold learning is employed to lower the computational burden of the model by mapping the high-dimensional EEG data to a low-dimensional space by its dimension reduction function.Main results. The proposed model achieves accuracy rates of 84.44%, 94.96%, and 82.79% on the BCI Competition IV dataset 2a, high gamma dataset, and a self-constructed motor imagery (MI) dataset from the left and right hand fist-clenching tests respectively. The results indicate our model outperforms the baseline models by EEG-channel transformer with dimension-reduced EEG data and window attention with sliding window convolution. Additionally, to enhance the interpretability of the model, features preceding the temporal feature extraction network were visualized. This visualization promotes the understanding of how the model prefers task-related channels.Significance. The transformer-based method makes the MI-EEG decoding more practical for further clinical applications.},
}
@article {pmid39718409,
year = {2024},
author = {Ma, X and Chen, W and Pei, Z and Zhang, J},
title = {An attention-based motor imagery brain-computer interface system for lower limb exoskeletons.},
journal = {The Review of scientific instruments},
volume = {95},
number = {12},
pages = {},
doi = {10.1063/5.0243337},
pmid = {39718409},
issn = {1089-7623},
mesh = {*Brain-Computer Interfaces ; Humans ; *Lower Extremity/physiology ; *Exoskeleton Device ; *Electroencephalography/instrumentation ; Attention/physiology ; Neural Networks, Computer ; Adult ; Male ; Imagination/physiology ; },
abstract = {Lower-limb exoskeletons have become increasingly popular in rehabilitation to help patients with disabilities regain mobility and independence. Brain-computer interface (BCI) offers a natural control method for these exoskeletons, allowing users to operate them through their electroencephalogram (EEG) signals. However, the limited EEG decoding performance of the BCI system restricts its application for lower limb exoskeletons. To address this challenge, we propose an attention-based motor imagery BCI system for lower limb exoskeletons. The decoding module of the proposed BCI system combines the convolutional neural network (CNN) with a lightweight attention module. The CNN aims to extract meaningful features from EEG signals, while the lightweight attention module aims to capture global dependencies among these features. The experiments are divided into offline and online experiments. The offline experiment is conducted to evaluate the effectiveness of different decoding methods, while the online experiment is conducted on a customized lower limb exoskeleton to evaluate the proposed BCI system. Eight subjects are recruited for the experiments. The experimental results demonstrate the great classification performance of the decoding method and validate the feasibility of the proposed BCI system. Our approach establishes a promising BCI system for the lower limb exoskeleton and is expected to achieve a more effective and user-friendly rehabilitation process.},
}
@article {pmid39718390,
year = {2025},
author = {Gordon, CR and Perez, CF},
title = {Review of Functional Cranioplasty and Implantable Neurotechnology.},
journal = {The Journal of craniofacial surgery},
volume = {36},
number = {2},
pages = {387-393},
doi = {10.1097/SCS.0000000000011028},
pmid = {39718390},
issn = {1536-3732},
mesh = {Humans ; *Skull/surgery ; *Prostheses and Implants ; *Plastic Surgery Procedures/methods/instrumentation ; Prosthesis Design ; *Craniotomy/methods ; },
abstract = {Cranioplasty for secondary reconstruction of cranial defects has historically focused on simply replacing the missing cranial bone to restore cerebral protection and fluid dynamics, but recent innovations have led to the development of customized cranial implants that address both bone and soft tissue deficits while avoiding postoperative complications such as temporal hollowing. In addition, customized cranial implants have incorporated implantable neurotechnology like ventriculostomy shunts, intracranial pressure monitoring devices, and medicine delivery systems within low-profile designs to convert previously "basic" implants into "smart" implants for added functionality. These "smart" implants aim to reduce complications and improve patient outcomes by leveraging the cranial space to house advanced technologies, providing benefits such as real-time biosensing, and treatment of chronic neurological conditions. This review outlines the progression of cranioplasty from basic bone replacement to functional implants with embedded neurotechnologies, highlighting the multidisciplinary approaches that enhance surgical outcomes and patient quality of life.},
}
@article {pmid39715900,
year = {2025},
author = {Robinson, JT and Norman, SL and Angle, MR and Constandinou, TG and Denison, T and Donoghue, JP and Field, RM and Forsland, A and Kouider, S and Millán, JDR and Michaels, JA and Orsborn, AL and Pandarinath, C and Pruszynski, JA and Rozell, CJ and Shah, NP and Shanechi, MM and Shoaran, M and Sheth, SA and Stavisky, SD and Trautmann, E and Vachicouras, N and Xie, C},
title = {An application-based taxonomy for brain-computer interfaces.},
journal = {Nature biomedical engineering},
volume = {9},
number = {6},
pages = {789-791},
pmid = {39715900},
issn = {2157-846X},
}
@article {pmid39712143,
year = {2024},
author = {Bai, G and Jin, J and Xu, R and Wang, X and Cichocki, A},
title = {A novel dual-step transfer framework based on domain selection and feature alignment for motor imagery decoding.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {6},
pages = {3549-3563},
pmid = {39712143},
issn = {1871-4080},
abstract = {In brain-computer interfaces (BCIs) based on motor imagery (MI), reducing calibration time is gradually becoming an urgent issue in practical applications. Recently, transfer learning (TL) has demonstrated its effectiveness in reducing calibration time in MI-BCI. However, the different data distribution of subjects greatly affects the application effect of TL in MI-BCI. Therefore, this paper combines data alignment, source domain selection, and feature alignment into the MI-TL. We propose a novel dual-step transfer framework based on source domain selection and feature alignment. First, the source and target domains are aligned using a pre-calibration strategy (PS), and then a sequential reverse selection method is proposed to match the optimal source domain for each target domain with the designed dual model selection strategy. We use filter bank regularization common space pattern (FBRCSP) to obtain more features and introduce manifold embedded distribution alignment (MEDA) to correct the prediction results of the support vector machine (SVM). The experimental results on two competition public datasets (BCI competition IV Dataset 1 and Dataset 2a) and our dataset show that the average classification accuracy of the proposed framework is higher than the baseline method (no domain selection and no feature alignment), which reaches 84.12%, 79.91%, and 78.45%, respectively. And the computational cost is reduced by half compared with the baseline method.},
}
@article {pmid39712134,
year = {2024},
author = {Zhang, J and Shen, C and Chen, W and Ma, X and Liang, Z and Zhang, Y},
title = {Decoding of movement-related cortical potentials at different speeds.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {6},
pages = {3859-3872},
pmid = {39712134},
issn = {1871-4080},
abstract = {The decoding of electroencephalogram (EEG) signals, especially motion-related cortical potentials (MRCP), is vital for the early detection of motor intent before movement execution. To enhance the decoding accuracy of MRCP and promote the application of early motion intention in active rehabilitation training, we propose a method for decoding MRCP signals. Specifically, an experimental paradigm is designed for the efficient capture of MRCP signals. Moreover, a feature extraction method based on differentiation is proposed to effectively characterize action variability. Six subjects were recruited to validate the effectiveness of the decoding method. Experiments such as fixed-window classification, sliding-window detection, and asynchronous analysis demonstrate that the method can detect motion intention 316 milliseconds before action execution and is capable of continuously detecting both rapid and slow movements.},
}
@article {pmid39712133,
year = {2024},
author = {Wu, C and Wang, Y and Qiu, S and He, H},
title = {A bimodal deep learning network based on CNN for fine motor imagery.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {6},
pages = {3791-3804},
pmid = {39712133},
issn = {1871-4080},
abstract = {Motor imagery (MI) is an important brain-computer interface (BCI) paradigm. The traditional MI paradigm (imagining different limbs) limits the intuitive control of the outer devices, while fine MI paradigm (imagining different joint movements from the same limb) can control the mechanical arm without cognitive disconnection. However, the decoding performance of fine MI limits its application. Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) are widely used in BCI systems because of their portability and easy operation. In this study, a fine MI paradigm including four classes (hand, wrist, shoulder and rest) was designed, and the data of EEG-fNIRS bimodal brain activity was collected from 12 subjects. Event-related desynchronization (ERD) from EEG signals shows a contralateral dominant phenomenon, and there is difference between the ERD of the four classes. For fNIRS signal in the time dimension, the time periods with significant difference can be observed in the activation patterns of four MI tasks. Spatially, the signal peak based brain topographic map also shows difference of these four MI tasks. The EEG signal and fNIRS signal of these four classes are distinguishable. In this study, a bimodal fusion network is proposed to improve the fine MI tasks decoding performance. The features of these two modalities are extracted separately by two feature extractors based on convolutional neural networks (CNN). The recognition performance was significantly improved by the bimodal method proposed in this study, compared with the performance of the single-modal network. The proposed method outperformed all comparison methods, and achieved a four-class accuracy of 58.96%. This paper demonstrates the feasibility of EEG and fNIRS bimodal BCI systems for fine MI, and shows the effectiveness of the proposed bimodal fusion method. This research is supposed to support fine MI-based BCI systems with theories and techniques.},
}
@article {pmid39712131,
year = {2024},
author = {Meng, M and Xu, B and Ma, Y and Gao, Y and Luo, Z},
title = {STGAT-CS: spatio-temporal-graph attention network based channel selection for MI-based BCI.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {6},
pages = {3663-3678},
pmid = {39712131},
issn = {1871-4080},
abstract = {Brain-computer interface (BCI) based on the motor imagery paradigm typically utilizes multi-channel electroencephalogram (EEG) to ensure accurate capture of physiological phenomena. However, excessive channels often contain redundant information and noise, which can significantly degrade BCI performance. Although there have been numerous studies on EEG channel selection, most of them require manual feature extraction, and the extracted features are difficult to fully represent the effective information of EEG signals. In this paper, we propose a spatio-temporal-graph attention network for channel selection (STGAT-CS) of EEG signals. We consider the EEG channels and their inter-channel connectivity as a graph and treat the channel selection problem as a node classification problem on the graph. We leverage the multi-head attention mechanism of graph attention network to dynamically capture topological relationships between nodes and update node features accordingly. Additionally, we introduce one-dimensional convolution to automatically extract temporal features from each channel in the original EEG signal, thereby obtaining more comprehensive spatiotemporal characteristics. In the classification tasks of the BCI Competition III Dataset IVa and BCI Competition IV Dataset I, STGAT-CS achieved average accuracies of 91.5% and 85.4% respectively, demonstrating the effectiveness of the proposed method.},
}
@article {pmid39712122,
year = {2024},
author = {Li, M and Li, J and Zheng, X and Ge, J and Xu, G},
title = {MSHANet: a multi-scale residual network with hybrid attention for motor imagery EEG decoding.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {6},
pages = {3463-3476},
pmid = {39712122},
issn = {1871-4080},
abstract = {EEG decoding plays a crucial role in the development of motor imagery brain-computer interface. Deep learning has great potential to automatically extract EEG features for end-to-end decoding. Currently, the deep learning is faced with the chanllenge of decoding from a large amount of time-variant EEG to retain a stable peroformance with different sessions. This study proposes a multi-scale residual network with hybrid attention (MSHANet) to decode four motor imagery classes. The MSHANet combines a multi-head attention and squeeze-and-excitation attention to hybridly focus on important information of the EEG features; and applies a multi-scale residual block to extracts rich EEG features, sharing part of the block parameters to extract common features. Compared with seven state-of-the-art methods, the MSHANet exhits the best accuracy on BCI Competition IV 2a with an accuracy of 83.18% for session- specific task and 80.09% for cross-session task. Thus, the proposed MSHANet decodes the time-varying EEG robustly and can save the time cost of MI-BCI, which is beneficial for long-term use.},
}
@article {pmid39712121,
year = {2024},
author = {Rahman, N and Khan, DM and Masroor, K and Arshad, M and Rafiq, A and Fahim, SM},
title = {Advances in brain-computer interface for decoding speech imagery from EEG signals: a systematic review.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {6},
pages = {3565-3583},
pmid = {39712121},
issn = {1871-4080},
abstract = {Numerous individuals encounter challenges in verbal communication due to various factors, including physical disabilities, neurological disorders, and strokes. In response to this pressing need, technology has actively pursued solutions to bridge the communication gap, recognizing the inherent difficulties faced in verbal communication, particularly in contexts where traditional methods may be inadequate. Electroencephalogram (EEG) has emerged as a primary non-invasive method for measuring brain activity, offering valuable insights from a cognitive neurodevelopmental perspective. It forms the basis for Brain-Computer Interfaces (BCIs) that provide a communication channel for individuals with neurological impairments, thereby empowering them to express themselves effectively. EEG-based BCIs, especially those adapted to decode imagined speech from EEG signals, represent a significant advancement in enabling individuals with speech disabilities to communicate through text or synthesized speech. By utilizing cognitive neurodevelopmental insights, researchers have been able to develop innovative approaches for interpreting EEG signals and translating them into meaningful communication outputs. To aid researchers in effectively addressing this complex challenge, this review article synthesizes key findings from state-of-the-art significant studies. It investigates into the methodologies employed by various researchers, including preprocessing techniques, feature extraction methods, and classification algorithms utilizing Deep Learning and Machine Learning approaches and their integration. Furthermore, the review outlines the potential avenues for future research, with the goal of advancing the practical implementation of EEG-based BCI systems for decoding imagined speech from a cognitive neurodevelopmental perspective.},
}
@article {pmid39712116,
year = {2024},
author = {Tang, C and Gao, T and Wang, G and Chen, B},
title = {Coherence-based channel selection and Riemannian geometry features for magnetoencephalography decoding.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {6},
pages = {3535-3548},
pmid = {39712116},
issn = {1871-4080},
abstract = {Magnetoencephalography (MEG) records the extremely weak magnetic fields on the surface of the scalp through highly sensitive sensors. Multi-channel MEG data provide higher spatial and temporal resolution when measuring brain activities, and can be applied for brain-computer interfaces as well. However, a large number of channels leads to high computational complexity and can potentially impact decoding accuracy. To improve the accuracy of MEG decoding, this paper proposes a new coherence-based channel selection method that effectively identifies task-relevant channels, reducing the presence of noisy and redundant information. Riemannian geometry is then used to extract effective features from selected channels of MEG data. Finally, MEG decoding is achieved by training a support vector machine classifier with the Radial Basis Function kernel. Experiments were conducted on two public MEG datasets to validate the effectiveness of the proposed method. The results from Dataset 1 show that Riemannian geometry achieves higher classification accuracy (compared to common spatial patterns and power spectral density) in the single-subject visual decoding task. Moreover, coherence-based channel selection significantly (P = 0.0002) outperforms the use of all channels. Moving on to Dataset 2, the results reveal that coherence-based channel selection is also significantly (P <0.0001) superior to all channels and channels around C3 and C4 in cross-session mental imagery decoding tasks. Additionally, the proposed method outperforms state-of-the-art methods in motor imagery tasks.},
}
@article {pmid39712104,
year = {2024},
author = {Yu, H and Hu, Z and Zhao, Q and Liu, J},
title = {Deep source transfer learning for the estimation of internal brain dynamics using scalp EEG.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {6},
pages = {3507-3520},
pmid = {39712104},
issn = {1871-4080},
abstract = {Electroencephalography (EEG) provides high temporal resolution neural data for brain-computer interfacing via noninvasive electrophysiological recording. Estimating the internal brain activity by means of source imaging techniques can further improve the spatial resolution of EEG and enhance the reliability of neural decoding and brain-computer interaction. In this work, we propose a novel EEG data-driven source imaging scheme for precise and efficient estimation of macroscale spatiotemporal brain dynamics across thalamus and cortical regions with deep learning methods. A deep source imaging framework with a convolutional-recurrent neural network is designed to estimate the internal brain dynamics from high-density EEG recordings. Moreover, a brain model including 210 cortical regions and 16 thalamic nuclei is established based on human brain connectome to provide synthetic training data, which manifests intrinsic characteristics of underlying brain dynamics in spontaneous, stimulation-evoked, and pathological states. Transfer learning algorithm is further applied to the trained network to reduce the dynamical differences between synthetic and realistic EEG. Extensive experiments exhibit that the proposed deep-learning method can accurately estimate the spatial and temporal activity of brain sources and achieves superior performance compared to the state-of-the-art approaches. Moreover, the EEG data-driven source imaging framework is effective in the location of seizure onset zone in epilepsy and reconstruction of dynamical thalamocortical interactions during sensory processing of acupuncture stimulation, implying its applicability in brain-computer interfacing for neuroscience research and clinical applications.},
}
@article {pmid39712096,
year = {2024},
author = {Zhang, Z and Chen, Y and Zhao, X and Fan, W and Peng, D and Li, T and Zhao, L and Fu, Y},
title = {A review of ethical considerations for the medical applications of brain-computer interfaces.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {6},
pages = {3603-3614},
pmid = {39712096},
issn = {1871-4080},
abstract = {The development and potential applications of brain-computer interfaces (BCIs) are directly related to the human brain and may have adverse effects on the users' physical and mental health. Ethical issues, particularly those associated with BCIs, including both non-medical and medical applications, have captured societal attention. This article initially reviews the application of three ethical frameworks in BCI technology: consequentialism, deontology, and virtue ethics. Subsequently, it introduces the ethical standards under consideration within the medical objective framework for BCI medical applications. Finally, the paper discusses and forecasts the ethical standards for BCI medical applications. The paper emphasizes the necessity to differentiate between the ethical issues of implantable and non-implantable BCIs, to approach the research on BCI-based "controlling the brain" with caution, and to establish standardized operational procedures and efficacy evaluation methods for BCI medical applications. This paper aims to provide ideas for the establishment of ethical standards in BCI medical applications.},
}
@article {pmid39712090,
year = {2024},
author = {Huang, Y and Huan, Y and Zou, Z and Wang, Y and Gao, X and Zheng, L},
title = {Data-driven natural computational psychophysiology in class.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {6},
pages = {3477-3489},
pmid = {39712090},
issn = {1871-4080},
abstract = {Objective. The assessment of mental fatigue (MF) and attention span in educational and healthcare settings frequently relies on subjective scales or methods such as induced-task interruption tools. However, these approaches are deficient in real-time evaluation and dynamic definitions. To address this gap, this paper proposes a Continuous Quantitative Scale (CQS) that allows for the natural and real-time measurement of MF based on group-synchronized electroencephalogram (EEG) data. Approach. In this study, computational psychophysiology was used to measure MF scores during a realistic class. Our methodology continuously monitored participants' psychological states without interrupting their regular routines, providing an objective evaluation. By analyzing multi-subject brain-computer interface (mBCI) data with a collaborative computing approach, the group-synchronized data were obtained from 10 healthy participants to assess MF levels. Each participant wore an EEG headset for only 10 min of preparation before performing a sustained task for 80 min. Main results. Our findings indicate that a lecture duration of 18.9 min is most effective, while a duration of 43.1 min leads to heightened MF levels. By focusing on the group-level simultaneous data analysis, the effects of individual variability were mitigated and the efficiency of cognitive computing was improved. From the perspective of a neurocomputational measure, these results confirm previous research. Significance. The proposed CQS provides a reliable, objective, memory- and emotion-free approach to the assessment of MF and attention span. These findings have significant implications not only for education, but also for the study of group cognitive mechanisms and for improving the quality of mental healthcare.},
}
@article {pmid39711742,
year = {2024},
author = {Li, J and Nan, Z and Qi, G and Cai, J and Zhao, X and Li, X and Liu, S and Wang, Y and Wu, Y and Miao, X and Yu, G},
title = {Assessing severity of pediatric pneumonia using multimodal transformers with multi-task learning.},
journal = {Digital health},
volume = {10},
number = {},
pages = {20552076241305168},
pmid = {39711742},
issn = {2055-2076},
abstract = {OBJECTIVE: While current multimodal approaches in the diagnosis and severity assessment of pneumonia demonstrate remarkable performance, they frequently overlook the issue of modality absence-a common challenge in clinical practice. Thus, we present the robust multimodal transformer (RMT) model, crafted to bridge this gap. The RMT model aims to enhance diagnosis and severity assessment accuracy in situations with incomplete data, thereby ensuring it meets the complex needs of real-world clinical settings.<br><br>METHOD: The RMT model leverages multimodal data, integrating X-ray images and clinical text data through a sophisticated AI-driven framework. It employs a Transformer-based architecture, enhanced by multi-task learning and mask attention mechanism. This approach aims to optimize the model's performance across different modalities, particularly under conditions of modality absence.<br><br>RESULTS: The RMT model demonstrates superior performance over traditional diagnostic methods and baseline models in accuracy, precision, sensitivity, and specificity. In tests involving various scenarios, including single-modal and multimodal tasks, the model shows remarkable robustness in handling incomplete data. Its effectiveness is further validated through extensive comparative analysis and ablation studies.<br><br>CONCLUSION: The RMT model represents a substantial advancement in pediatric pneumonia severity assessment. It successfully harnesses multimodal data and advanced AI techniques to improve assessment precision. While the RMT model sets a new precedent in AI applications in medical diagnostics, the development of a comprehensive pediatric pneumonia dataset marks a pivotal contribution, providing a robust foundation for future research.},
}
@article {pmid39711704,
year = {2025},
author = {Deng, G and Niu, M and Rao, S and Luo, Y and Zhang, J and Xie, J and Yu, Z and Liu, W and Zhang, J and Zhao, S and Pan, G and Li, X and Deng, W and Guo, W and Zhang, Y and Li, T and Jiang, H},
title = {A Unified Flexible Large Polysomnography Model for Sleep Staging and Mental Disorder Diagnosis.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
pmid = {39711704},
abstract = {Sleep disorders affect billions worldwide, yet clinical polysomnography (PSG) analysis remains hindered by labor-intensive manual scoring and limited generalizability of automated sleep staging tools across heterogeneous protocols. We present LPSGM, a large-scale PSG model designed to address two critical challenges in sleep medicine: cross-center generalization and adaptable diagnosis of neuropsychiatric disorders. Trained on 220,500 hours of multi-center PSG data (24,000 full-night recordings from 16 public datasets), LPSGM integrates domain-adaptive pre-training, flexible channel configurations, and a unified architecture to mitigate variability in equipment, montages, and populations during sleep staging while enabling downstream fine-tuning for mental disorder detection. In prospective validation, LPSGM achieves expert-level consensus in sleep staging (κ = 0.845 ± 0.066 vs. inter-expert κ = 0.850 ± 0.102) and matches the performance of fully supervised models on two independent private cohorts. When fine-tuned, it attains 88.01% accuracy in narcolepsy detection and 100% accuracy in identifying major depressive disorder (MDD), highlighting shared physiological biomarkers between sleep architecture and neuropsychiatric symptoms. By bridging automated sleep staging with real-world clinical deployment, LPSGM establishes a scalable, data-efficient framework for integrated sleep and mental health diagnostics. The code and pre-trained model are publicly available at https://github.com/Deng-GuiFeng/LPSGM to advance reproducibility and translational research in sleep medicine.},
}
@article {pmid39705724,
year = {2025},
author = {de Melo, GC and Castellano, G and Forner-Cordero, A},
title = {Identification and analysis of reference-independent movement event-related desynchronization.},
journal = {Biomedical physics &amp; engineering express},
volume = {11},
number = {2},
pages = {},
doi = {10.1088/2057-1976/ada1dc},
pmid = {39705724},
issn = {2057-1976},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Movement ; Male ; Female ; Adult ; Electrodes ; Signal Processing, Computer-Assisted ; Algorithms ; Cortical Synchronization/physiology ; Young Adult ; Brain/physiology ; Motor Cortex/physiology ; },
abstract = {Characterization of the electroencephalography (EEG) signals related to motor activity, such as alpha- and beta-band motor event-related desynchronizations (ERDs), is essential for Brain Computer Interface (BCI) development. Determining the best electrode combination to detect the ERD is crucial for the success of the BCI. Considering that the EEG signals are bipolar, this involves the choice of the main and reference electrodes. So far, no strategy to guarantee signals free of the activity from the reference electrode has achieved consensus among the scientific community. Therefore, mapping the ERD in terms of the spatial distribution of the main and reference electrodes can provide additional perspectives for the BCI field. The goal of this work is to identify subject-specific channels where ERD is temporally coupled to the initiation of an upper-limb motor task. We defined a criterion to determine the presence of the ERD linked to the movement onset and searched, separately for each subject, for the single channel with the most prominent ERD. The search was conducted over all available channels composed by a pair of electrodes, and the selected signals were analyzed according to their temporal and spatial characteristics. We found that alpha- and beta-band ERD temporarily linked to movement onset can be detected in atypical channels (pairs of electrodes) across the scalp. The selected channels were different across subjects. Four ERD temporal patterns were observed in terms of the initiation instant of the ERD. These patterns revealed that the M1 cortex seems to be related to later ERDs. Moreover, they were also associated to different cortical processes related to the motor task. To the best of our knowledge, this is the first time these findings are reported. Aiming at BCI development, further experiments with more subjects and with motor-imagery tasks are desirable for more robustness and applicability of these findings.},
}
@article {pmid39704693,
year = {2025},
author = {Svejgaard, B and Modrau, B and Hernández-Gloria, JJ and Wested, CL and Dosen, S and Stevenson, AJT and Mrachacz-Kersting, N},
title = {Associative brain-computer interface training increases wrist extensor corticospinal excitability in patients with subacute stroke.},
journal = {Journal of neurophysiology},
volume = {133},
number = {1},
pages = {333-341},
doi = {10.1152/jn.00452.2024},
pmid = {39704693},
issn = {1522-1598},
support = {2021-0008//Sundhedsinnovationspuljen, Region Nordjylland/ ; 229643//Melsen Fonden/ ; 21.584//Grosserer L. F. Foghts Fond (Grosserer LF Foghts Fund)/ ; 7683//Jascha Fonden (Jascha Foundation)/ ; },
mesh = {Humans ; Male ; *Brain-Computer Interfaces ; Female ; Middle Aged ; *Stroke Rehabilitation/methods ; *Pyramidal Tracts/physiopathology ; *Stroke/physiopathology/therapy ; *Evoked Potentials, Motor/physiology ; *Wrist/physiopathology/innervation ; Aged ; *Motor Cortex/physiopathology ; Transcranial Magnetic Stimulation ; Electroencephalography ; Adult ; Muscle, Skeletal/physiopathology ; },
abstract = {In a recently developed associative rehabilitative brain-computer interface (BCI) system, electroencephalography (EEG) is used to identify the most active phase of the motor cortex during attempted movement and deliver precisely timed peripheral stimulation during training. This approach has been demonstrated to facilitate corticospinal excitability and functional recovery in patients with lower limb weakness following stroke. The current study expands those findings by investigating changes in corticospinal excitability following the associative BCI intervention in patients with post stroke with upper limb weakness. In a randomized controlled trial, 24 patients with subacute stroke, subdivided into an intervention group and a "sham" control group, performed 30 wrist extensions. The intervention comprised 30 pairings of single peripheral nerve stimulation at the motor threshold, timed so that the generated afferent volley arrived at the motor cortex during the peak negativity of the movement-related cortical potential (MRCP), which was identified with EEG. The sham group underwent the same intervention, though the intensity of the nerve stimulation was below the perception threshold. Immediately after training, patients in the associative group exhibited significantly larger amplitudes of muscular-evoked potentials, compared with pretraining measurements in response to transcranial magnetic stimulation. These changes persisted for at least 30 min and were not observed in the sham group. We demonstrate that motor-evoked potential amplitudes increased significantly following paired associative BCI training targeting upper limb muscles in patients with subacute stroke, which is in line with results from lower limb studies.NEW &amp; NOTEWORTHY We have demonstrated that a single training session with an associative brain-computer interface increased corticospinal excitability in patients suffering from upper limb weakness following stroke. This is the first time such an effect is described in the upper limb, which paves the way for effect augmentation of existing upper limb rehabilitation protocols.},
}
@article {pmid39703669,
year = {2024},
author = {Cao, Y and Gao, S and Yu, H and Zhao, Z and Zang, D and Wang, C},
title = {A motor imagery classification model based on hybrid brain-computer interface and multitask learning of electroencephalographic and electromyographic deep features.},
journal = {Frontiers in physiology},
volume = {15},
number = {},
pages = {1487809},
pmid = {39703669},
issn = {1664-042X},
abstract = {OBJECTIVE: Extracting deep features from participants' bioelectric signals and constructing models are key research directions in motor imagery (MI) classification tasks. In this study, we constructed a multimodal multitask hybrid brain-computer interface net (2M-hBCINet) based on deep features of electroencephalogram (EEG) and electromyography (EMG) to effectively accomplish motor imagery classification tasks.<br><br>METHODS: The model first used a variational autoencoder (VAE) network for unsupervised learning of EEG and EMG signals to extract their deep features, and subsequently applied the channel attention mechanism (CAM) to select these deep features and highlight the advantageous features and minimize the disadvantageous ones. Moreover, in this study, multitask learning (MTL) was applied to train the 2M-hBCINet model, incorporating the primary task that is the MI classification task, and auxiliary tasks including EEG reconstruction task, EMG reconstruction task, and a feature metric learning task, each with distinct loss functions to enhance the performance of each task. Finally, we designed module ablation experiments, multitask learning comparison experiments, multi-frequency band comparison experiments, and muscle fatigue experiments. Using leave-one-out cross-validation(LOOCV), the accuracy and effectiveness of each module of the 2M-hBCINet model were validated using the self-made MI-EEMG dataset and the public datasets WAY-EEG-GAL and ESEMIT.<br><br>RESULTS: The results indicated that compared to comparative models, the 2M-hBCINet model demonstrated good performance and achieved the best results across different frequency bands and under muscle fatigue conditions.<br><br>CONCLUSION: The 2M-hBCINet model constructed based on EMG and EEG data innovatively in this study demonstrated excellent performance and strong generalization in the MI classification task. As an excellent end-to-end model, 2M-hBCINet can be generalized to be used in EEG-related fields such as anomaly detection and emotion analysis.},
}
@article {pmid39703101,
year = {2024},
author = {Zhou, H and Wang, M and Xu, T and Zhang, X and Zhao, X and Tang, L and Zhao, P and Wang, D and Lai, J and Wang, F and Zhang, S and Hu, S},
title = {Cognitive Remediation in Patients With Bipolar Disorder: A Randomized Trial by Sequential tDCS and Navigated rTMS Targeting the Primary Visual Cortex.},
journal = {CNS neuroscience &amp; therapeutics},
volume = {30},
number = {12},
pages = {e70179},
pmid = {39703101},
issn = {1755-5949},
support = {82201675//National Natural Science Foundation of China/ ; 2023ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 226-2022-00002//Fundamental Research Funds for the Central Universities/ ; 226-2022-00193//Fundamental Research Funds for the Central Universities/ ; 2021C03107//Key Research and Development Program of Zhejiang Province/ ; 2023YFC2506200//National Key Research and Development Program of China/ ; },
mesh = {Humans ; Male ; Female ; *Transcranial Direct Current Stimulation/methods ; *Bipolar Disorder/therapy/psychology ; *Transcranial Magnetic Stimulation/methods ; Adult ; Middle Aged ; *Cognitive Remediation/methods ; *Visual Cortex ; Young Adult ; Treatment Outcome ; },
abstract = {BACKGROUND: Non-invasive brain stimulation (NIBS), such as transcranial direct current stimulation (tDCS) and repetitive transcranial magnetic stimulation (rTMS), has emerged as a promising alternative in the precise treatment of clinical symptoms, such as the cognitive impairment of bipolar disorder (BD). Optimizing the neurocognitive effects by combining tDCS and rTMS to strengthen the clinical outcome is a challenging research issue.<br><br>OBJECTIVE: In this randomized, controlled trial, we first combined tDCS and neuronavigated rTMS targeting the V1 region to explore the efficacy on neurocognitive function in BD patients with depressive episodes.<br><br>METHODS: Eligible individuals (n&#x2009;=&#x2009;105) were assigned into three groups, Group A (active tDCS-active rTMS), Group B (sham tDCS-active rTMS), and Group C (active tDCS-sham rTMS). All participants received 3-week treatment in which every participant received 15 sessions of stimulation through the study, 5 sessions every week, with tDCS treatment followed by neuronavigated rTMS every session. We evaluated the cognitive, emotional, and safety outcomes at week-0 (w0, baseline), week-3 (w3, immediately post-treatment), and week-8 (w8, follow-up period). The THINC-integrated tool (THINC-it), 17-item Hamilton Depression Rating Scale, and Young Mania Rating Scale were applied for evaluating the cognitive function and emotional state, respectively. Data were analyzed by repeated measure ANOVA and paired t-test.<br><br>RESULTS: Eventually, 32 patients in Group A, 27 in Group B, and 23 in Group C completed the entire treatment. Compared to Groups B and C, Group A showed greater improvement in Symbol Check items (Time and Accuracy) at W3 and Symbol Check Accuracy at W8 (p&#x2009;<&#x2009;0.01). The W0-W3 analysis indicated a significant improvement in depressive symptoms in both Group A and Group B (p&#x2009;<&#x2009;0.01). Additionally, neuroimaging data revealed increased activity in the calcarine sulcus in Group A, suggesting potential neuroplastic changes in the visual cortex following the electromagnetic stimulation.<br><br>CONCLUSIONS: These findings provide preliminary evidence that the combination of navigated rTMS with tDCS targeting V1 region may serve as a potential treatment strategy for improving cognitive impairment and depressive symptoms in BD patients.<br><br>TRIAL REGISTRATION: Clinical Trial Registry number: NCT05596461.},
}
@article {pmid39702315,
year = {2024},
author = {Capecci, M and Gandolfi, M and Straudi, S and Calabrò, RS and Baldini, N and Pepa, L and Andrenelli, E and Smania, N and Ceravolo, MG and Morone, G and Bonaiuti, D and , },
title = {Advancing public health through technological rehabilitation: insights from a national clinician survey.},
journal = {BMC health services research},
volume = {24},
number = {1},
pages = {1626},
pmid = {39702315},
issn = {1472-6963},
mesh = {Humans ; Cross-Sectional Studies ; Italy ; Surveys and Questionnaires ; Male ; Female ; *Public Health ; Physical and Rehabilitation Medicine ; Middle Aged ; Adult ; Rehabilitation ; },
abstract = {INTRODUCTION: In the evolving healthcare landscape, technology has emerged as a key component in enhancing system efficiency and offering new avenues for patient rehabilitation. Despite its growing importance, detailed information on technology's specific use, types, and applications in clinical rehabilitation settings, particularly within the Italian framework, remains unclear. This study aimed to explore the use of technology and its needs by Physical Medicine and Rehabilitation medical doctors in Italy.<br><br>METHODS: We conducted a cross-sectional online survey aimed at 186 Italian clinicians affiliated with the Italian Society of Physical and Rehabilitation Medicine (SIMFER). The online questionnaire consists of 71 structured questions designed to collect demographic and geographical data of the respondents, as well as detailed insights into the prevalence and range of technologies they use, together with their specific applications in clinical settings."<br><br>RESULTS: A broad range of technologies, predominantly commercial medical devices, has been documented. These technologies are employed for various conditions, including common neurological diseases, musculoskeletal disorders, dementia, and rheumatologic issues. The application of these technologies indicates a broadening scope beyond enhancing sensorimotor functions, addressing both physical and social aspects of patient care.<br><br>DISCUSSION: In recent years, there's been a notable surge in using technology for rehabilitation across various disorders. The upcoming challenge is to update health policies to integrate these technologies better, aiming to extend their benefits to a wider range of disabling conditions, marking a progressive shift in public health and rehabilitation practices.},
}
@article {pmid39700898,
year = {2025},
author = {Zhang, Y and Gao, S and Liang, C and Bustillo, J and Kochunov, P and Turner, JA and Calhoun, VD and Wu, L and Fu, Z and Jiang, R and Zhang, D and Jiang, J and Wu, F and Peng, T and Xu, X and Qi, S},
title = {Consistent frontal-limbic-occipital connections in distinguishing treatment-resistant and non-treatment-resistant schizophrenia.},
journal = {NeuroImage. Clinical},
volume = {45},
number = {},
pages = {103726},
pmid = {39700898},
issn = {2213-1582},
support = {R01 NS114628/NS/NINDS NIH HHS/United States ; R01 MH116948/MH/NIMH NIH HHS/United States ; R01 EB015611/EB/NIBIB NIH HHS/United States ; RF1 NS114628/NS/NINDS NIH HHS/United States ; RF1 MH123163/MH/NIMH NIH HHS/United States ; S10 OD023696/OD/NIH HHS/United States ; },
mesh = {Humans ; Male ; Female ; Adult ; *Schizophrenia/diagnostic imaging/physiopathology ; Magnetic Resonance Imaging/methods ; *Schizophrenia, Treatment-Resistant/diagnostic imaging/physiopathology ; *Occipital Lobe/diagnostic imaging/physiopathology ; *Frontal Lobe/physiopathology/diagnostic imaging ; Young Adult ; Middle Aged ; Neural Pathways/physiopathology/diagnostic imaging ; *Limbic System/diagnostic imaging/physiopathology ; *Limbic Lobe/diagnostic imaging/physiopathology ; *Nerve Net/diagnostic imaging/physiopathology ; },
abstract = {BACKGROUND AND HYPOTHESIS: Treatment-resistant schizophrenia (TR-SZ) and non-treatment-resistant schizophrenia (NTR-SZ) lack specific biomarkers to distinguish from each other. This investigation aims to identify consistent dysfunctional brain connections with different atlases, multiple feature selection strategies, and several classifiers in distinguishing TR-SZ and NTR-SZ.<br><br>STUDY DESIGN: 55 TR-SZs, 239 NTR-SZs, and 87 healthy controls (HCs) were recruited from the Affiliated Brain Hospital of Nanjing Medical University. Resting-state functional connection (FC) matrices were constructed from automated anatomical labeling (AAL), Yeo-Networks (YEO) and Brainnetome (BNA) atlases. Two feature selection methods (Select From Model and Recursive Feature Elimination) and four classifiers (Adaptive Boost, Bernoulli Na&#xef;ve Bayes, Gradient Boosting and Random Forest) were combined to identify the consistent FCs in distinguishing TR-SZ and HC, NTR-SZ and HC, TR-SZ and NTR-SZ.<br><br>STUDY RESULTS: The whole brain FCs, except the temporal-occipital FC, were consistent in distinguishing SZ and HC. Abnormal frontal-limbic, frontal-parietal and occipital-temporal FCs were consistent in distinguishing TR-SZ and NTR-SZ, that were further correlated with disease progression, symptoms and medication dosage. Moreover, the frontal-limbic and frontal-parietal FCs were highly consistent for the diagnosis of SZ (TR-SZ vs. HC, NTR-SZ vs. HC and TR-SZ vs. NTR-SZ). The BNA atlas achieved the highest classification accuracy (>90&#xa0;%) comparing with AAL and YEO in the most diagnostic tasks.<br><br>CONCLUSIONS: These results indicate that the frontal-limbic and the frontal-parietal FCs are the robust neural pathways in the diagnosis of SZ, whereas the frontal-limbic, frontal-parietal and occipital-temporal FCs may be informative in recognizing those TR-SZ in the clinical practice.},
}
@article {pmid39700269,
year = {2024},
author = {Li, W and Li, J and Li, J and Wei, C and Laviv, T and Dong, M and Lin, J and Calubag, M and Colgan, LA and Jin, K and Zhou, B and Shen, Y and Li, H and Cui, Y and Gao, Z and Li, T and Hu, H and Yasuda, R and Ma, H},
title = {Boosting neuronal activity-driven mitochondrial DNA transcription improves cognition in aged mice.},
journal = {Science (New York, N.Y.)},
volume = {386},
number = {6728},
pages = {eadp6547},
doi = {10.1126/science.adp6547},
pmid = {39700269},
issn = {1095-9203},
support = {R01 MH080047/MH/NIMH NIH HHS/United States ; R35 NS116804/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Male ; Mice ; *Aging/genetics ; *Brain/metabolism/physiology ; Cell Nucleus/metabolism ; *Cognition ; Cognitive Dysfunction/genetics ; *DNA, Mitochondrial/genetics/metabolism ; Mice, Inbred C57BL ; Mitochondria/metabolism ; *Neurons/metabolism/physiology ; *Transcription, Genetic ; },
abstract = {Deciphering the complex interplay between neuronal activity and mitochondrial function is pivotal in understanding brain aging, a multifaceted process marked by declines in synaptic function and mitochondrial performance. Here, we identified an age-dependent coupling between neuronal and synaptic excitation and mitochondrial DNA transcription (E-TCmito), which operates differently compared to classic excitation-transcription coupling in the nucleus (E-TCnuc). We demonstrated that E-TCmito repurposes molecules traditionally associated with E-TCnuc to regulate mitochondrial DNA expression in areas closely linked to synaptic activation. The effectiveness of E-TCmito weakens with age, contributing to age-related neurological deficits in mice. Boosting brain E-TCmito in aged animals ameliorated these impairments, offering a potential target to counteract age-related cognitive decline.},
}
@article {pmid39698084,
year = {2024},
author = {Morozova, M and Yakovlev, L and Syrov, N and Lebedev, M and Kaplan, A},
title = {Tactile imagery affects cortical responses to vibrotactile stimulation of the fingertip.},
journal = {Heliyon},
volume = {10},
number = {23},
pages = {e40807},
pmid = {39698084},
issn = {2405-8440},
abstract = {Mental imagery is a crucial cognitive process, yet its underlying neural mechanisms remain less understood compared to perception. Furthermore, within the realm of mental imagery, the somatosensory domain is particularly underexplored compared to other sensory modalities. This study aims to investigate the influence of tactile imagery (TI) on cortical somatosensory processing. We explored the cortical manifestations of TI by recording EEG activity in healthy human subjects. We investigated event-related somatosensory oscillatory dynamics during TI compared to actual tactile stimulation, as well as somatosensory evoked potentials (SEPs) in response to short vibrational stimuli, examining their amplitude-temporal characteristics and spatial distribution across the scalp. EEG activity exhibited significant changes during TI compared to the no-imagery baseline. TI caused event-related desynchronization (ERD) of the contralateral μ-rhythm, with a notable correlation between ERD during imagery and real stimulation across subjects. TI also modulated several SEP components in sensorimotor and frontal areas, showing increases in the contralateral P100 and P300, contra- and ipsilateral P300, frontal P200, and parietal P600 components. The results clearly indicate that TI affects cortical processing of somatosensory stimuli, impacting EEG responses in various cortical areas. The assessment of SEPs in EEG could serve as a versatile marker of tactile imagery in practical applications. We propose incorporating TI in imagery-based brain-computer interfaces (BCIs) to enhance sensorimotor restoration and sensory substitution. This approach underscores the importance of somatosensory mental imagery in cognitive neuroscience and its potential applications in neurorehabilitation and assistive technologies.},
}
@article {pmid39697780,
year = {2024},
author = {Wan, X and Xing, S and Zhang, Y and Duan, D and Liu, T and Li, D and Yu, H and Wen, D},
title = {Combining motion performance with EEG for diagnosis of mild cognitive impairment: a new perspective.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1476730},
pmid = {39697780},
issn = {1662-4548},
}
@article {pmid39697779,
year = {2024},
author = {Cui, S and Lee, D and Wen, D},
title = {Toward brain-inspired foundation model for EEG signal processing: our opinion.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1507654},
pmid = {39697779},
issn = {1662-4548},
}
@article {pmid39696695,
year = {2024},
author = {Xue, YY and Zhang, ZS and Lin, RR and Huang, HF and Zhu, KQ and Chen, DF and Wu, ZY and Tao, QQ},
title = {CD2AP deficiency aggravates Alzheimer's disease phenotypes and pathology through p38 MAPK activation.},
journal = {Translational neurodegeneration},
volume = {13},
number = {1},
pages = {64},
pmid = {39696695},
issn = {2047-9158},
support = {81970998//National Natural Science Foundation of China/ ; 2024SSYS0018//Key Research and Development Program of Zhejiang Province/ ; 2021ZD0201103//Science Innovation 2030-Brain Science and Brain-Inspired Intelligence Technology Major Projects/ ; 2021ZD0201803//Science Innovation 2030-Brain Science and Brain-Inspired Intelligence Technology Major Projects/ ; },
mesh = {Animals ; *Alzheimer Disease/genetics/pathology/metabolism ; *p38 Mitogen-Activated Protein Kinases/genetics/metabolism ; Mice ; *Adaptor Proteins, Signal Transducing/genetics/deficiency ; Mice, Transgenic ; Phenotype ; Disease Models, Animal ; Humans ; Mice, Knockout ; Neurons/pathology/metabolism ; Cytoskeletal Proteins ; },
abstract = {BACKGROUND: Alzheimer's disease (AD) is the most common form of neurodegenerative disorder, which is characterized by a decline in cognitive abilities. Genome-wide association and clinicopathological studies have demonstrated that the CD2-associated protein (CD2AP) gene is one of the most important genetic risk factors for AD. However, the precise mechanisms by which CD2AP is linked to AD pathogenesis remain unclear.<br><br>METHODS: The spatiotemporal expression pattern of CD2AP was determined. Then, we generated and characterized an APP/PS1 mouse model with neuron-specific Cd2ap deletion, using immunoblotting, immunofluorescence, enzyme-linked immunosorbent assay, electrophysiology and behavioral tests. Additionally, we established a stable CD2AP-knockdown SH-SY5Y cell line to further elucidate the specific molecular mechanisms by which CD2AP contributes to AD pathogenesis. Finally, the APP/PS1 mice with neuron-specific Cd2ap deletion were treated with an inhibitor targeting the pathway identified above to further validate our findings.<br><br>RESULTS: CD2AP is widely expressed in various regions of the mouse brain, with predominant expression in neurons and vascular endothelial cells. In APP/PS1 mice, neuronal knockout of Cd2ap significantly aggravated tau pathology, synaptic impairments and cognitive deficits. Mechanistically, the knockout of Cd2ap activated p38 mitogen-activated protein kinase (MAPK) signaling, which contributed to increased tau phosphorylation, synaptic injury, neuronal apoptosis and cognitive impairment. Furthermore, the phenotypes of neuronal Cd2ap knockout were ameliorated by a p38 MAPK inhibitor.<br><br>CONCLUSION: Our study presents the first in vivo evidence that CD2AP deficiency exacerbates the phenotypes and pathology of AD through the p38 MAPK pathway, identifying CD2AP/p38 MAPK as promising therapeutic targets for AD.},
}
@article {pmid39695740,
year = {2024},
author = {Lowers, V and Kirby, R and Young, B and Harris, RV},
title = {Scoping review of fidelity strategies used in behaviour change trials delivered in primary dental care settings.},
journal = {Trials},
volume = {25},
number = {1},
pages = {824},
pmid = {39695740},
issn = {1745-6215},
mesh = {Humans ; *Randomized Controlled Trials as Topic/standards/methods ; *Primary Health Care/standards ; *Dental Care/standards ; Health Behavior ; Research Design/standards ; Behavior Therapy/methods/standards ; Oral Health/standards ; Health Knowledge, Attitudes, Practice ; },
abstract = {BACKGROUND: Primary dental care settings are strategically important locations where randomised controlled trials (RCTs) of behaviour change interventions (BCIs) can be tested to tackle oral diseases. Findings have so far produced equivocal results. Improving treatment fidelity is posed as a mechanism to improve scientific rigour, consistency and implementation of BCIs. The National Institutes of Health Behaviour Change Consortium (NIH BCC) developed a tool to assess and evaluate treatment fidelity in health behaviour change interventions, which has yet to be applied to the primary dental care BCI literature.<br><br>METHOD: We conducted a scoping review of RCTs delivered in primary dental care by dental team members (in real-world settings) between 1980 and 2023. Eligible studies were coded using the NIH BCC checklist to determine the presence of reported fidelity strategies across domains: design, training, delivery, receipt and enactment.<br><br>RESULTS: We included 34 eligible articles, reporting 21 RCTs. Fidelity reporting variations were found both between and within NIH BCC domains: strategy reporting ranged from 9.5 to 85.7% in design, 9.5 to 57.1% in training, 0 to 66.7% in delivery, 14.3 to 36.8% in receipt and 13.3 to 33.3% in enactment. The most reported domain was design (M&#x2009;=&#x2009;0.45), and the least reported domain was delivery (M&#x2009;=&#x2009;0.21). Only one study reported over 50% of the recommended strategies in every domain.<br><br>CONCLUSIONS: This review revealed inconsistencies in fidelity reporting with no evidence that fidelity guidelines or frameworks were being used within primary dental care trials. This has highlighted issues with interpretability, reliability and reproducibility of research findings. Recommendations are proposed to assist primary dental care trialists with embedding fidelity strategies into future research.},
}
@article {pmid39694240,
year = {2025},
author = {Fonseca, M and Kurban, D and Roy, JP and Santschi, DE and Molgat, E and Yang, DA and Dufour, S},
title = {Usefulness of differential somatic cell count for udder health monitoring: Identifying referential values for differential somatic cell count in healthy quarters and quarters with subclinical mastitis.},
journal = {Journal of dairy science},
volume = {108},
number = {4},
pages = {3917-3928},
doi = {10.3168/jds.2024-25403},
pmid = {39694240},
issn = {1525-3198},
mesh = {Animals ; Cattle ; Female ; *Mastitis, Bovine/diagnosis ; *Milk/cytology ; Cell Count/veterinary ; *Mammary Glands, Animal ; Dairying ; },
abstract = {Mastitis, an inflammation of the udder primarily caused by an IMI, is one of the most common diseases in dairy cattle. Somatic cell count has been widely used as an indicator of udder inflammation, assisting in the detection of subclinical mastitis. More recently, differential SCC (DSCC), which represents the combined proportion of lymphocytes and polymorphonuclear leukocytes, has become available for routine dairy milk screening, although it was not yet widely studied. Therefore, the objective of this study was to assess and compare the usefulness of quarter-level SCS or DSCC to predict the probability of subclinical mastitis. Additionally, our goals included estimating the sensitivity (Se) and specificity (Sp) of SCS and DSCC across all potential cut-off values. The current study was an observational study conducted on commercial dairy farms. Five dairy herds were selected using a convenience sampling. A Gaussian finite mixture model (GFMM) was applied to investigate the latent quarter subclinical mastitis status with either measurement, SCS or DSCC. Posterior values for SCS and DSCC obtained from the GFMM were used for predictive estimation of the parameters. The estimated SCS distribution for healthy quarters had a mean (SD) of 1.4 (1.3), and, for quarters with subclinical mastitis, it was 4.5 (2.4). For DSCC, the estimated mean was 55.6% (15.2) for healthy quarters, whereas it was 80.4% (6.4) for quarters with subclinical mastitis. The most discriminant cut-off for SCS, as indicated by the Youden index, was 3.0, corresponding to exactly 100,000 cells/mL. At this threshold, the Se and Sp of SCS were 0.73 (95% Bayesian credible interval [BCI]: 0.70-0.77) and 0.90 (95% BCI: 0.89-0.91), respectively. The most discriminant cut-off point for DSCC was 70.0%, with corresponding Se and Sp values of 0.95 (0.93, 0.96) and 0.83 (0.81, 0.85), respectively. For the SCS analysis, we obtained predictive probabilities of subclinical mastitis approaching 0 and 100%, with only a narrow range of SCS results yielding intermediate probabilities. On the other hand, predictive probabilities ranging from 0 to 90% were obtained for DSCC analysis, with a large range of DSCC results presenting intermediate probabilities. Thus, SCS seemed to surpass DSCC for predicting subclinical mastitis. These findings provided a foundation for future studies to further explore and validate the efficacy of GFMM for diagnostic tests yielding quantitative results.},
}
@article {pmid39693762,
year = {2025},
author = {Oxley, TJ and Deo, DR and Cernera, S and Sawyer, A and Putrino, D and Ramsey, NF and Fry, A},
title = {The 'Brussels 4': essential requirements for implantable brain-computer interface user autonomy.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ada0e6},
pmid = {39693762},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces/trends ; *Electrodes, Implanted ; *Personal Autonomy ; *Prostheses and Implants/trends ; *User-Computer Interface ; },
abstract = {Objective. Implantable brain-computer interfaces (iBCIs) hold great promise for individuals with severe paralysis and are advancing toward commercialization. The features required for successful clinical translation and patient adoption of iBCIs may be under recognized within traditional academic iBCI research and deserve further consideration.Approach. Here we consider potentially critical factors to achieve iBCI user autonomy, reflecting the authors' perspectives on discussions during various sessions and workshops across the 10th International BCI Society Meeting, Brussels, 2023.Main results. Four key considerations were identified: (1) immediate use, (2) easy to use, (3) continuous use, and (4) stable system use.Significance. Addressing these considerations may enable successful clinical translation of iBCIs.},
}
@article {pmid39693735,
year = {2024},
author = {Kim, J and Cho, YS and Kim, SP},
title = {Task-relevant stimulus design improves P300-based brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ada0e3},
pmid = {39693735},
issn = {1741-2552},
abstract = {OBJECTIVE: In the pursuit of refining P300-based brain-computer interfaces (BCIs), our research aims to propose a novel stimulus design focused on selective attention and task relevance to address the challenges of P300-based BCIs, including the necessity of repetitive stimulus presentations, accuracy improvement, user variability, and calibration demands.<br><br>APPROACH: In the oddball task for P300-based BCIs, we develop a stimulus design involving task-relevant dynamic stimuli implemented as finger-tapping to enhance the elicitation and consistency of event-related potentials (ERPs). We further improve the performance of P300-based BCIs by optimizing ERP feature extraction and classification in offline analyses.<br><br>MAIN RESULTS: With the proposed stimulus design, online P300-based BCIs in 37 healthy participants achieve an accuracy of 91.2% and an information transfer rate (ITR) of 28.37 bits/min with two stimulus repetitions. With optimized computational modeling in BCIs, our offline analyses reveal the possibility of single-trial execution, showcasing an accuracy of 91.7% and an ITR of 59.92 bits/min. Furthermore, our exploration into the feasibility of across-subject zero-calibration BCIs through offline analyses, where a BCI built on a dataset of 36 participants is directly applied to a left-out participant with no calibration, yields an accuracy of 94.23% and the ITR of 31.56 bits/min with two stimulus repetitions and the accuracy of 87.75% and the ITR of 52.61 bits/min with single-trial execution. When using the finger-tapping stimulus, the variability in performance among participants is the lowest, and a greater increase in performance is observed especially for those showing lower performance using the conventional color-changing stimulus. Signficance. Using a novel task-relevant dynamic stimulus design, this study achieves one of the highest levels of P300-based BCI performance to date. This underscores the importance of coupling stimulus paradigms with computational methods for improving P300-based BCIs.},
}
@article {pmid39693734,
year = {2024},
author = {Srisrisawang, N and Müller-Putz, GR},
title = {Simultaneous encoding of speed, distance, and direction in discrete reaching: an EEG study.},
journal = {Journal of neural engineering},
volume = {21},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ada0ea},
pmid = {39693734},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; Male ; Female ; *Hand/physiology ; *Movement/physiology ; Young Adult ; *Psychomotor Performance/physiology ; Adult ; Biomechanical Phenomena/physiology ; },
abstract = {Objective.The complicated processes of carrying out a hand reach are still far from fully understood. In order to further the understanding of the kinematics of hand movement, the simultaneous representation of speed, distance, and direction in the brain is explored.Approach.We utilized electroencephalography (EEG) signals and hand position recorded during a four-direction center-out reaching task with either quick or slow speed, near and far distance. Linear models were employed in two modes: decoding and encoding. First, to test the discriminability of speed, distance, and direction. Second, to find the contribution of the cortical sources via the source localization. Additionally, we compared the decoding accuracy when using features obtained from EEG signals and source-localized EEG signals based on the results from the encoding model.Main results.Speed, distance, and direction can be classified better than chance. The accuracy of the speed was also higher than the distance, indicating a stronger representation of the speed than the distance. The speed and distance showed similar significant sources in the central regions related to the movement initiation, while the direction indicated significant sources in the parieto-occipital regions related to the movement preparation. The combination of the features from EEG and source localized signals improved the classification.Significance.Directional and non-directional information are represented in two separate networks. The quick movement resulted in improvement in the direction classification. Our results enhance our understanding of hand movement in the brain and help us make informed decisions when designing an improved paradigm in the future.},
}
@article {pmid39693678,
year = {2025},
author = {Shen, J and Wang, K and Gao, W and Liu, JK and Xu, Q and Pan, G and Chen, X and Tang, H},
title = {Temporal spiking generative adversarial networks for heading direction decoding.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {184},
number = {},
pages = {106975},
doi = {10.1016/j.neunet.2024.106975},
pmid = {39693678},
issn = {1879-2782},
mesh = {*Neural Networks, Computer ; Animals ; *Neurons/physiology ; *Action Potentials/physiology ; *Parietal Lobe/physiology ; Models, Neurological ; Macaca mulatta ; Generative Adversarial Networks ; },
abstract = {The spike-based neuronal responses within the ventral intraparietal area (VIP) exhibit intricate spatial and temporal dynamics in the posterior parietal cortex, presenting decoding challenges such as limited data availability at the biological population level. The practical difficulty in collecting VIP neuronal response data hinders the application of sophisticated decoding models. To address this challenge, we propose a unified spike-based decoding framework leveraging spiking neural networks (SNNs) for both generative and decoding purposes, for their energy efficiency and suitability for neural decoding tasks. We propose the Temporal Spiking Generative Adversarial Networks (T-SGAN), a model based on a spiking transformer, to generate synthetic time-series data reflecting the neuronal response of VIP neurons. T-SGAN incorporates temporal segmentation to reduce the temporal dimension length, while spatial self-attention facilitates the extraction of associated information among VIP neurons. This is followed by recurrent SNNs decoder equipped with an attention mechanism, designed to capture the intricate spatial and temporal dynamics for heading direction decoding. Experimental evaluations conducted on biological datasets from monkeys showcase the effectiveness of the proposed framework. Results indicate that T-SGAN successfully generates realistic synthetic data, leading to a significant improvement of up to 1.75% in decoding accuracy for recurrent SNNs. Furthermore, the SNN-based decoding framework capitalizes on the low power consumption advantages, offering substantial benefits for neuronal response decoding applications.},
}
@article {pmid39692757,
year = {2025},
author = {van Oosterhout, K and Chilundo, A and Branco, MP and Aarnoutse, EJ and Timmermans, M and Fattori, M and Ramsey, NF and Cantatore, E},
title = {Brain-Computer Interfaces Using Flexible Electronics: An a-IGZO Front-End for Active ECoG Electrodes.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {12},
number = {6},
pages = {e2408576},
pmid = {39692757},
issn = {2198-3844},
support = {//Dutch Research Council (NWO)/ ; 17608//Stichting voor de Technische Wetenschappen/ ; 19072//Stichting voor de Technische Wetenschappen/ ; UEBIT//EWUU Alliance/ ; },
abstract = {Brain-computer interfaces (BCIs) are evolving toward higher electrode count and fully implantable solutions, which require extremely low power densities (<15mW cm[-2]). To achieve this target, and allow for a large and scalable number of channels, flexible electronics can be used as a multiplexing interface. This work introduces an active analog front-end fabricated with amorphous Indium-Gallium-Zinx-Oxide (a-IGZO) Thin-Film Transistors (TFTs) on foil capable of active matrix multiplexing. The circuit achieves only 70nV per sqrt(Hz) input referred noise, consuming 46µW, or 3.5mW cm[-2]. It demonstrates for the first time in literature a flexible front-end with a noise efficiency factor comparable with Silicon solutions (NEF = 9.8), which is more than 10X lower compared to previously reported flexible front-ends. These results have been achieved using a modified bootstrap-load amplifier. The front end is tested by playing through it recordings obtained from a conventional BCI system. A gesture classification based on the flexible front-end outputs achieves 94% accuracy. Using a flexible active front end can improve the state-of-the-art in high channel count BCI systems by lowering the multiplexer noise and enabling larger areas of the brain to be monitored while reducing power density. Therefore, this work enables a new generation of high channel-count active BCI electrode grids.},
}
@article {pmid39692430,
year = {2024},
author = {Nie, J and Huang, T and Sun, Y and Peng, Z and Dong, W and Chen, J and Zheng, D and Guo, F and Shi, W and Ling, Y and Zhao, W and Yang, H and Shui, T and Yan, X},
title = {Influence of the Enterovirus 71 Vaccine and the COVID-19 Pandemic on Hand, Foot, and Mouth Disease in China Based on Counterfactual Models: Observational Study.},
journal = {JMIR public health and surveillance},
volume = {10},
number = {},
pages = {e63146},
pmid = {39692430},
issn = {2369-2960},
mesh = {*Hand, Foot and Mouth Disease/epidemiology/prevention &amp; control ; Humans ; China/epidemiology ; *COVID-19/epidemiology/prevention &amp; control ; Infant ; Child, Preschool ; Incidence ; *Enterovirus A, Human ; *Viral Vaccines/administration &amp; dosage ; Male ; Child ; Female ; Adolescent ; Pandemics/prevention &amp; control ; },
abstract = {BACKGROUND: Hand, foot, and mouth disease (HFMD) is a highly contagious viral illness. Understanding the long-term trends of HFMD incidence and its epidemic characteristics under the circumstances of the enterovirus 71 (EV71) vaccination program and the outbreak of COVID-19 is crucial for effective disease surveillance and control.<br><br>OBJECTIVE: We aim to give an overview of the trends of HFMD over the past decades and evaluate the impact of the EV71 vaccination program and the COVID-19 pandemic on the epidemic trends of HFMD.<br><br>METHODS: Using official surveillance data from the Yunnan Province, China, we described long-term incidence trends and severity rates of HFMD as well as the variation of enterovirus proportions among cases. We conducted the autoregressive integrated moving average (ARIMA) of time series analyses to predict monthly incidences based on given subsets. The difference between the actual incidences and their counterfactual predictions was compared using absolute percentage errors (APEs) for periods after the EV71 vaccination program and the COVID-19 pandemic, respectively.<br><br>RESULTS: The annual incidence of HFMD fluctuated between 25.62 cases per 100,000 people in 2008 and 221.52 cases per 100,000 people in 2018. The incidence for men ranged from 30 to 250 cases per 100,000 people from 2008 to 2021, which was constantly higher than that for women. The annual incidence for children aged 1 to 2 years old ranged from 54.54 to 630.06 cases per 100,000 people, which was persistently higher than that for other age groups. For monthly incidences, semiannual peaks were observed for each year. All actual monthly incidences of 2014 to 2015 fell within the predicted 95% CI by the ARIMA(1,0,1)(1,1,0)[12] model. The average APE was 19% for a 2-year prediction. After the EV71 vaccination program, the actual monthly incidence of HFMD was consistently lower than the counterfactual predictions by ARIMA(1,0,1)(1,1,0)[12], with negative APEs ranging from -11% to -229% from January 2017 to April 2018. In the meantime, the proportion of EV71 among the enteroviruses causing HFMD decreased significantly, and the proportion was highly correlated (r=0.73, P=.004) with the severity rate. After the onset of the COVID-19 pandemic in 2020, the actual monthly incidence of HFMD consistently maintained a lower magnitude compared to the counterfactual predictions-ARIMA(1,0,1)(0,1,0)[12]-from February to September 2020, with considerable negative APEs (ranging from -31% to -2248%).<br><br>CONCLUSIONS: EV71 vaccination alleviated severe HFMD cases and altered epidemiological trends. The HFMD may also benefit from nonpharmaceutical interventions during outbreaks such as the COVID-19 pandemic. Further development of a multivalent virus vaccine is crucial for effectively controlling HFMD outbreaks. Policymakers should implement nonpharmaceutical interventions and emphasize personal hygiene for routine prevention when appropriate.},
}
@article {pmid39691819,
year = {2024},
author = {Vieira, R and Moreno, P and Vourvopoulos, A},
title = {EEG-based action anticipation in human-robot interaction: a comparative pilot study.},
journal = {Frontiers in neurorobotics},
volume = {18},
number = {},
pages = {1491721},
pmid = {39691819},
issn = {1662-5218},
abstract = {As robots become integral to various sectors, improving human-robot collaboration is crucial, particularly in anticipating human actions to enhance safety and efficiency. Electroencephalographic (EEG) signals offer a promising solution, as they can detect brain activity preceding movement by over a second, enabling predictive capabilities in robots. This study explores how EEG can be used for action anticipation in human-robot interaction (HRI), leveraging its high temporal resolution and modern deep learning techniques. We evaluated multiple Deep Learning classification models on a motor imagery (MI) dataset, achieving up to 80.90% accuracy. These results were further validated in a pilot experiment, where actions were accurately predicted several hundred milliseconds before execution. This research demonstrates the potential of combining EEG with deep learning to enhance real-time collaborative tasks, paving the way for safer and more efficient human-robot interactions.},
}
@article {pmid39691581,
year = {2024},
author = {Eken, A and Yüce, M and Yükselen, G and Erdoğan, SB},
title = {Explainable fNIRS-based pain decoding under pharmacological conditions via deep transfer learning approach.},
journal = {Neurophotonics},
volume = {11},
number = {4},
pages = {045015},
pmid = {39691581},
issn = {2329-423X},
abstract = {SIGNIFICANCE: Assessment of pain and its clinical diagnosis rely on subjective methods which become even more complicated under analgesic drug administrations.<br><br>AIM: We aim to propose a deep learning (DL)-based transfer learning (TL) methodology for objective classification of functional near-infrared spectroscopy (fNIRS)-derived cortical oxygenated hemoglobin responses to painful and non-painful stimuli presented under different timings post-analgesic and placebo drug administration.<br><br>APPROACH: A publicly available fNIRS dataset obtained during painful/non-painful stimuli was used. Separate fNIRS scans were taken under the same protocol before drug (morphine and placebo) administration and at three different timings (30, 60, and 90&#xa0;min) post-administration. Data from pre-drug fNIRS scans were utilized for constructing a base DL model. Knowledge generated from the pre-drug model was transferred to six distinct post-drug conditions by following a TL approach. The DeepSHAP method was utilized to unveil the contribution weights of nine regions of interest for each of the pre-drug and post-drug decoding models.<br><br>RESULTS: Accuracy, sensitivity, specificity, and area under curve (AUC) metrics of the pre-drug model were above 90%, whereas each of the post-drug models demonstrated a performance above 90% for the same metrics. Post-placebo models had higher decoding accuracy than post-morphine models. Knowledge obtained from a pre-drug base model could be successfully utilized to build pain decoding models for six distinct brain states that were scanned at three different timings after either analgesic or placebo drug administration. The contribution of different cortical regions to classification performance varied across the post-drug models.<br><br>CONCLUSIONS: The proposed DL-based TL methodology may remove the necessity to build DL models for data collected at clinical or daily life conditions for which obtaining training data is not practical or building a new decoding model will have a computational cost. Unveiling the explanation power of different cortical regions may aid the design of more computationally efficient fNIRS-based brain-computer interface (BCI) system designs that target other application areas.},
}
@article {pmid39688131,
year = {2025},
author = {Wang, S and Song, X and Xu, J and Wang, J and Yu, L},
title = {Flexible silicon for high-performance photovoltaics, photodetectors and bio-interfaced electronics.},
journal = {Materials horizons},
volume = {12},
number = {4},
pages = {1106-1132},
doi = {10.1039/d4mh01466a},
pmid = {39688131},
issn = {2051-6355},
abstract = {Silicon (Si) is currently the most mature and reliable semiconductor material in the industry, playing a pivotal role in the development of modern microelectronics, renewable energy, and bio-electronic technologies. In recent years, widespread research attention has been devoted to the development of advanced flexible electronics, photovoltaics, and bio-interfaced sensors/detectors, boosting their emerging applications in distributed energy sources, healthcare, environmental monitoring, and brain-computer interfaces (BCIs). Despite the rigid and brittle nature of Si, a series of new fabrication technologies and integration strategies have been developed to enable a wide range of c-Si-based high-performance flexible photovoltaics and electronics, which were previously only achievable with intrinsically soft organic and polymer semiconductors. More interestingly, programmable geometric engineering of crystalline silicon (c-Si) units and logic circuits has been explored to enable the fabrication of various highly flexible nanoprobes for intracellular sensing and the deployment of soft BCI matrices to record and understand brain neural activities for the development of advanced neuroprosthetics. This review will systematically examine the latest progress in the fabrication of Si-based flexible solar cells, photodetectors, and biological probing interfaces over the past decade, identifying key design principles, mechanisms, and technological milestones achieved through novel geometry, morphology, and composition control. These advancements, when combined, will not only promote the practical applications of sustainable energy and wearable electronics but also spur new breakthroughs in emerging human-machine interfaces (HMIs) and artificial intelligence applications, which hold significant implications for understanding neural activities, implementing more efficient artificial Intelligence (AI) algorithms, and developing new therapies or treatments. Finally, we will summarize and provide an outlook on the current challenges and future opportunities of Si-based electronics, flexible optoelectronics, and bio-sensing.},
}
@article {pmid39687714,
year = {2024},
author = {Hofmann, MJ and Chang, YN and Brouwer, H and Zock, M},
title = {Editorial: Neurocomputational models of language processing.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1524366},
doi = {10.3389/fnhum.2024.1524366},
pmid = {39687714},
issn = {1662-5161},
}
@article {pmid39687306,
year = {2024},
author = {Kancaoğlu, M and Kuntalp, M},
title = {Low-cost, mobile EEG hardware for SSVEP applications.},
journal = {HardwareX},
volume = {19},
number = {},
pages = {e00567},
pmid = {39687306},
issn = {2468-0672},
abstract = {The global shortage of integrated circuits due to the COVID-19 pandemic has made it challenging to build biopotential acquisition devices like electroencephalography (EEG) hardware. To address this issue, a new hardware system using common ICs has been designed, which is cost-effective, precise, and easily accessible from global distributors. The hardware system comprises 8-channel inputs EEG hardware with a mobile headset capable of acquiring 5-30Hz EEG signals. First two channels of the design is enabled for steady-state visual evoked potential (SSVEP) operations, and the remaining channels can be powered up as needed. A small 3D-printable enclosure is also designed for the hardware board, which is attached to protective glasses to be used as a head-mounted device. The board includes an additional green LED, 4 pulse width modulation (PWM) outputs for general-purpose input/output (GPIO), 2 buttons for input, and exposed programming pins and digital-to-analog converter (DAC) output from the microcontroller unit (MCU). The proposed hardware system is expected to enable students and young researchers to experiment with EEG signals, especially SSVEP, before investing in professional equipment with the availability of programming codes.},
}
@article {pmid39687114,
year = {2024},
author = {Ma, J and Rui, Z and Zou, Y and Qin, Z and Zhao, Z and Zhang, Y and Mao, Z and Bai, H and Zhang, J},
title = {Neurosurgical and BCI approaches to visual rehabilitation in occipital lobe tumor patients.},
journal = {Heliyon},
volume = {10},
number = {23},
pages = {e39072},
pmid = {39687114},
issn = {2405-8440},
abstract = {This study investigates the effects of occipital lobe tumors on visual processing and the role of brain-computer interface (BCI) technologies in post-surgical visual rehabilitation. Through a combination of pre-surgical functional magnetic resonance imaging (fMRI) and Diffusion Tensor Imaging (DTI), intra-operative direct cortical stimulation (DCS) and Electrocorticography (ECoG), and post-surgical BCI interventions, we provide insight into the complex dynamics between occipital lobe tumors and visual function. Our results highlight a discrepancy between clinical assessments of visual field damage and the patient's reported visual experiences, suggesting a residual functional capacity within the damaged occipital regions. Additionally, the absence of expected visual phenomena during surgery and the promising outcomes from BCI-driven rehabilitation underscore the complexity of visual processing and the potential of technology-enhanced rehabilitation strategies. This work emphasizes the need for an interdisciplinary approach in developing effective treatments for visual impairments related to brain tumors, illustrating the significant implications for neurosurgical practices and the advancement of rehabilitation sciences.},
}
@article {pmid39686818,
year = {2025},
author = {Lv, Q and Li, Q and Cao, P and Wei, C and Li, Y and Wang, Z and Wang, L},
title = {Designing Silk Biomaterials toward Better Future Healthcare: The Development and Application of Silk-Based Implantable Electronic Devices in Clinical Diagnosis and Therapy.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {37},
number = {8},
pages = {e2411946},
doi = {10.1002/adma.202411946},
pmid = {39686818},
issn = {1521-4095},
support = {2022YFC2408100//National Key Research and Development Program of China/ ; 82372511//National Natural Science Foundation of China/ ; 82302585//National Natural Science Foundation of China/ ; 82072167//National Natural Science Foundation of China/ ; 82272277//National Natural Science Foundation of China/ ; 82173315//National Natural Science Foundation of China/ ; 81974382//National Natural Science Foundation of China/ ; [2022] No.11//Hubei Province Science and Technology Innovation Team Project/ ; 2022BCA013//the Major Scientific and Technological Innovation Projects of Hubei Province/ ; 2021CFB416//Natural Science Foundation of Hubei Province/ ; 2024AFB652//Natural Science Foundation of Hubei Province/ ; 2022CFB736//Natural Science Foundation of Hubei Province/ ; },
mesh = {*Biocompatible Materials/chemistry ; *Silk/chemistry ; Humans ; Animals ; *Prostheses and Implants ; Brain-Computer Interfaces ; },
abstract = {Implantable medical electronic devices (IMEDs) have attracted great attention and shown versatility for solving clinical problems ranging from real-time monitoring of physiological/ pathological states to electrical stimulation therapy and from monitoring brain cell activity to deep brain stimulation. The ongoing challenge is to select appropriate materials in target device configuration for biomedical applications. Currently, silk-based biomaterials have been developed for the design of diagnostic and therapeutic electronic devices due to their excellent properties and abundant active sites in the structure. Herein, the aim is to summarize the structural characteristics, physicochemical properties, and bioactivities of natural silk biomaterials as well as their derived materials, with a particular focus on the silk-based implantable biomedical electronic devices, such as implantable devices for invasive brain-computer interfaces, neural recording, and in vivo electrostimulation. In addition, future opportunities and challenges are also envisioned, hoping to spark the interests of researchers in interdisciplinary fields such as biomaterials, clinical medicine, and electronics.},
}
@article {pmid39686393,
year = {2024},
author = {Zhuang, W and Zhang, Y and Wang, Y and He, K},
title = {3D-BCLAM: A Lightweight Neurodynamic Model for Assessing Student Learning Effectiveness.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {23},
pages = {},
pmid = {39686393},
issn = {1424-8220},
mesh = {Humans ; *Neural Networks, Computer ; Learning/physiology ; Students ; Emotions/physiology ; Deep Learning ; Machine Learning ; },
abstract = {Evaluating students' learning effectiveness is of great importance for gaining a deeper understanding of the learning process, accurately diagnosing learning barriers, and developing effective teaching strategies. Emotion, as a key factor influencing learning outcomes, provides a novel perspective for identifying cognitive states and emotional experiences. However, traditional evaluation methods suffer from one sidedness in feature extraction and high complexity in model construction, often making it difficult to fully explore the deep value of emotional data. To address this challenge, we have innovatively proposed a lightweight neurodynamic model: 3D-BCLAM. This model cleverly integrates Bidirectional Convolutional Long Short-Term Memory (BCL) and dynamic attention mechanism, in order to efficiently capture emotional dynamic changes in time series with extremely low computational cost. 3D-BCLAM can achieve a comprehensive evaluation of students' learning outcomes, covering not only the cognitive level but also delving into the emotional dimension for detailed analysis. Under testing on public datasets, 3D-BCLAM has demonstrated outstanding performance, significantly outperforming traditional machine learning and deep learning models based on Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). This achievement not only validates the effectiveness of the 3D-BCLAM model, but also provides strong support for promoting the innovation of student learning effectiveness assessment.},
}
@article {pmid39686227,
year = {2024},
author = {Megalingam, RK and Sankardas, KS and Manoharan, SK},
title = {An Empirical Model-Based Algorithm for Removing Motion-Caused Artifacts in Motor Imagery EEG Data for Classification Using an Optimized CNN Model.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {23},
pages = {},
pmid = {39686227},
issn = {1424-8220},
mesh = {*Electroencephalography/methods ; Humans ; *Artifacts ; *Algorithms ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Motion ; Signal Processing, Computer-Assisted ; Wheelchairs ; Imagination/physiology ; },
abstract = {Electroencephalography (EEG) is a non-invasive technique with high temporal resolution and cost-effective, portable, and easy-to-use features. Motor imagery EEG (MI-EEG) data classification is one of the key applications within brain-computer interface (BCI) systems, utilizing EEG signals from motor imagery tasks. BCI is very useful for people with severe mobility issues like quadriplegics, spinal cord injury patients, stroke patients, etc., giving them the freedom to a certain extent to perform activities without the need for a caretaker, like driving a wheelchair. However, motion artifacts can significantly affect the quality of EEG recordings. The conventional EEG enhancement algorithms are effective in removing ocular and muscle artifacts for a stationary subject but not as effective when the subject is in motion, e.g., a wheelchair user. In this research study, we propose an empirical error model-based artifact removal approach for the cross-subject classification of motor imagery (MI) EEG data using a modified CNN-based deep learning algorithm, designed to assist wheelchair users with severe mobility issues. The classification method applies to real tasks with measured EEG data, focusing on accurately interpreting motor imagery signals for practical application. The empirical error model evolved from the inertial sensor-based acceleration data of the subject in motion, the weight of the wheelchair, the weight of the subject, and the surface friction of the terrain under the wheelchair. Three different wheelchairs and five different terrains, including road, brick, concrete, carpet, and marble, are used for artifact data recording. After evaluating and benchmarking the proposed CNN and empirical model, the classification accuracy achieved is 94.04% for distinguishing between four specific classes: left, right, front, and back. This accuracy demonstrates the model's effectiveness compared to other state-of-the-art techniques. The comparative results show that the proposed approach is a potentially effective way to raise the decoding efficiency of motor imagery BCI.},
}
@article {pmid39686148,
year = {2024},
author = {Li, LL and Cao, GZ and Zhang, YP and Li, WC and Cui, F},
title = {MACNet: A Multidimensional Attention-Based Convolutional Neural Network for Lower-Limb Motor Imagery Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {23},
pages = {},
pmid = {39686148},
issn = {1424-8220},
support = {U1813212//the State Key Program of National Natural Science Foundation of China/ ; 52277061//National Natural Science Foundation of China/ ; JCYJ20220818095804009 and JSGG20200701095406010//Shenzhen Science and Technology Program/ ; 20220809200041001//Shenzhen Science and Technology Program/ ; },
mesh = {Humans ; *Neural Networks, Computer ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Lower Extremity/physiology ; Imagination/physiology ; Attention/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; Brain/physiology ; },
abstract = {Decoding lower-limb motor imagery (MI) is highly important in brain-computer interfaces (BCIs) and rehabilitation engineering. However, it is challenging to classify lower-limb MI from electroencephalogram (EEG) signals, because lower-limb motions (LLMs) including MI are excessively close to physiological representations in the human brain and generate low-quality EEG signals. To address this challenge, this paper proposes a multidimensional attention-based convolutional neural network (CNN), termed MACNet, which is specifically designed for lower-limb MI classification. MACNet integrates a temporal refining module and an attention-enhanced convolutional module by leveraging the local and global feature representation abilities of CNNs and attention mechanisms. The temporal refining module adaptively investigates critical information from each electrode channel to refine EEG signals along the temporal dimension. The attention-enhanced convolutional module extracts temporal and spatial features while refining the feature maps across the channel and spatial dimensions. Owing to the scarcity of public datasets available for lower-limb MI, a specified lower-limb MI dataset involving four routine LLMs is built, consisting of 10 subjects over 20 sessions. Comparison experiments and ablation studies are conducted on this dataset and a public BCI Competition IV 2a EEG dataset. The experimental results show that MACNet achieves state-of-the-art performance and outperforms alternative models for the subject-specific mode. Visualization analysis reveals the excellent feature learning capabilities of MACNet and the potential relationship between lower-limb MI and brain activity. The effectiveness and generalizability of MACNet are verified.},
}
@article {pmid39685825,
year = {2024},
author = {Villiger, AS and Hoehn, D and Ruggeri, G and Vaineau, C and Nirgianakis, K and Imboden, S and Kuhn, A and Mueller, MD},
title = {Lower Urinary Tract Dysfunction Among Patients Undergoing Surgery for Deep Infiltrating Endometriosis: A Prospective Cohort Study.},
journal = {Journal of clinical medicine},
volume = {13},
number = {23},
pages = {},
pmid = {39685825},
issn = {2077-0383},
abstract = {Background/Objectives: Postsurgical lower urinary tract dysfunction (LUTD) is a common problem following deep infiltrating endometriosis (DIE) resection. The condition may be caused either by surgically induced damage to the bladder innervation or by pre-existing endometriosis-associated nerve damage. The aim of this study is to evaluate the efficacy of preoperative and postoperative multichannel urodynamic testing (UD) in identifying pre-existing or surgically induced LUTD among patients with DIE. Methods: Women with suspected DIE and planned surgical resection of DIE at the Department of Obstetrics and Gynecology at the University Hospital of Bern from September 2015 to October 2022 were invited to participate in this prospective cohort study. UD was performed before and 6 weeks after surgery. The primary outcome was the maximum flow rate (uroflow), an indicator of LUTD. Secondary outcomes were further urodynamic observations of cystometry and pressure flow studies, lower urinary tract symptoms (LUTS) as assessed by the International Prostate Symptom Score (IPSS), and pain as assessed by the visual analog scale (VAS). Results: A total of 51 patients requiring surgery for DIE were enrolled in this study. All patients underwent surgical excision of the DIE. The cohort demonstrated a uroflow of 22.1 mL/s prior to surgery, which decreased postoperatively to 21.5 mL/s (p = 0.56, 95%CI -1.5-2.71). The mean bladder contractility index (BCI) exhibited a notable decline from 130.4 preoperatively to 116.6 postoperatively (p = 0.046, 95%CI 0.23-27.27). Significant improvements were observed in the prevalence of dysmenorrhea, abdominal pain, dyspareunia, and dyschezia following surgical intervention (p = <0.001). The IPSS score was within the lower moderate range both pre- and postoperatively (mean 8.37 vs. 8.51, p = 0.893, 95%CI -2.35-2.05). Subgroup analysis identified previous endometriosis surgery as a significant preoperative risk factor for elevated post-void residual (43.6 mL, p = 0.026, 95%CI 13.89-73.37). The postoperative post-void residual increased among participants with DIE on the rectum to 54.39 mL (p = 0.078, 95%CI 24.06-84.71). Participants who underwent hysterectomy exhibited a significantly decreased uroflow (16.4 mL/s, p = 0.014, 95%CI 12-20) and BCI (75.1, p = 0.036, 95%CI 34.9-115.38). Conclusions: Nerve-respecting laparoscopy for DIE may alter bladder function. UD is not advisable before surgery, but the measurement may detect patients with LUTD.},
}
@article {pmid39681925,
year = {2024},
author = {Garrott, K and Ogilvie, D and Panter, J and Petticrew, M and Sowden, A and Jones, CP and Foubister, C and Lawlor, ER and Ikeda, E and Patterson, R and van Tulleken, D and Armstrong-Moore, R and Vethanayakam, G and Bo, L and White, M and Adams, J},
title = {Development and application of the Demands for Population Health Interventions (Depth) framework for categorising the agentic demands of population health interventions.},
journal = {BMC global and public health},
volume = {2},
number = {1},
pages = {13},
pmid = {39681925},
issn = {2731-913X},
support = {Project 05 - G109750//NIHR Public Health Policy Research Unit/ ; Project 05 - G109750//NIHR Public Health Policy Research Unit/ ; Project 05 - G109750//NIHR Public Health Policy Research Unit/ ; Project 05 - G109750//NIHR Public Health Policy Research Unit/ ; Project 05 - G109750//NIHR Public Health Policy Research Unit/ ; Project 05 - G109750//NIHR Public Health Policy Research Unit/ ; Project 05 - G109750//NIHR Public Health Policy Research Unit/ ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; MC_UU_12015/6/MRC_/Medical Research Council/United Kingdom ; MC_UU_12015/6/MRC_/Medical Research Council/United Kingdom ; MC_UU_12015/6/MRC_/Medical Research Council/United Kingdom ; MC_UU_12015/6/MRC_/Medical Research Council/United Kingdom ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; },
abstract = {BACKGROUND: The 'agentic demand' of population health interventions (PHIs) refers to the capacity, resources and freedom to act that interventions demand of their recipients to benefit, which have a socio-economical pattern. Highly agentic interventions, e.g. information campaigns, rely on recipients noticing and responding to the intervention and thus might affect intervention effectiveness and equity. The absence of an adequate framework to classify agentic demands limits the fields' ability to systematically explore these associations.<br><br>METHODS: We systematically developed the Demands for Population Health Interventions (Depth) framework using an iterative approach: (1) developing the Depth framework by systematically identifying examples of PHIs aiming to promote healthier diets and physical activity, coding of intervention actors and actions and synthesising the data to develop the framework; (2) testing the Depth framework in online workshops with academic and policy experts and a quantitative reliability assessment. We applied the final framework in a proof-of-concept review, extracting studies from three existing equity-focused systematic reviews on framework category, overall effectiveness and differential socioeconomic effects and visualised the findings in harvest plots.<br><br>RESULTS: The Depth framework identifies three constructs influencing agentic demand: exposure - initial contact with intervention (two levels), mechanism of action - how the intervention enables or discourages behaviour (five levels) and engagement - recipient response (two levels). When combined, these constructs form a matrix of 20 possible classifications. In the proof-of-concept review, we classified all components of 31 interventions according to the Depth framework. Intervention components were concentrated in a small number of Depth classifications; Depth classification appeared to be related to intervention equity but not effectiveness.<br><br>CONCLUSIONS: This framework holds potential for future research, policy and practice, facilitating the design, selection and evaluation of interventions and evidence synthesis.},
}
@article {pmid39679398,
year = {2024},
author = {Wang, S and Zhang, W and Fu, P and Zhong, Y and Piatkevich, KD and Zhang, D and Lee, HJ},
title = {Structural diversity of Alzheimer-related protein aggregations revealed using photothermal ratio-metric micro-spectroscopy.},
journal = {Biomedical optics express},
volume = {15},
number = {12},
pages = {6768-6782},
pmid = {39679398},
issn = {2156-7085},
abstract = {The crucial link between pathological protein aggregations and lipids in Alzheimer's disease pathogenesis is increasingly recognized, yet its spatial dynamics remain challenging for labeling-based microscopy. Here, we demonstrate photothermal ratio-metric infrared spectro-microscopy (PRISM) to investigate the in situ structural and molecular compositions of pathological features in brain tissues at submicron resolution. By identifying the vibrational spectroscopic signatures of protein secondary structures and lipids, PRISM tracks the structural dynamics of pathological proteins, including amyloid and hyperphosphorylated Tau (pTau). Amyloid-associated lipid features in major brain regions were observed, notably the enrichment of lipid-dissociated plaques in the hippocampus. Spectroscopic profiling of pTau revealed significant heterogeneity in phosphorylation levels and a distinct lipid-pTau relationship that contrasts with the anticipated lipid-plaque correlation. Beyond in vitro studies, our findings provide direct visualization evidence of aggregate-lipid interactions across the brain, offering new insights into mechanistic and therapeutic research of neurodegenerative diseases.},
}
@article {pmid39678727,
year = {2024},
author = {Pan, H and Song, W and Li, L and Qin, X},
title = {The design and implementation of multi-character classification scheme based on EEG signals of visual imagery.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {5},
pages = {2299-2309},
pmid = {39678727},
issn = {1871-4080},
abstract = {In visual-imagery-based brain-computer interface (VI-BCI), there are problems of singleness of imagination task and insufficient description of feature information, which seriously hinder the development and application of VI-BCI technology in the field of restoring communication. In this paper, we design and optimize a multi-character classification scheme based on electroencephalogram (EEG) signals of visual imagery (VI), which is used to classify 29 characters including 26 lowercase English letters and three punctuation marks. Firstly, a new paradigm of randomly presenting characters and including preparation stage is designed to acquire EEG signals and construct a multi-character dataset, which can eliminate the influence between VI tasks. Secondly, tensor data is obtained by the Morlet wavelet transform, and a feature extraction algorithm based on tensor-uncorrelated multilinear principal component analysis is used to extract high-quality features. Finally, three classifiers, namely support vector machine, K-nearest neighbor, and extreme learning machine, are employed for classifying multi-character, and the results are compared. The experimental results demonstrate that, the proposed scheme effectively extracts character features with minimal redundancy, weak correlation, and strong representation capability, and successfully achieves an average classification accuracy 97.59% for 29 characters, surpassing existing research in terms of both accuracy and quantity of classification. The present study designs a new paradigm for acquiring EEG signals of VI, and combines the Morlet wavelet transform and UMPCA algorithm to extract the character features, enabling multi-character classification in various classifiers. This research paves a novel pathway for establishing direct brain-to-world communication.},
}
@article {pmid39678535,
year = {2024},
author = {Wang, Y and Gong, L and Zhao, Y and Yu, Y and Liu, H and Yang, X},
title = {Dynamic graph attention network based on multi-scale frequency domain features for motion imagery decoding in hemiplegic patients.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1493264},
pmid = {39678535},
issn = {1662-4548},
abstract = {Brain-computer interfaces (BCIs) establish a direct communication pathway between the brain and external devices and have been widely applied in upper limb rehabilitation for hemiplegic patients. However, significant individual variability in motor imagery electroencephalogram (MI-EEG) signals leads to poor generalization performance of MI-based BCI decoding methods to new patients. This paper proposes a Multi-scale Frequency domain Feature-based Dynamic graph Attention Network (MFF-DANet) for upper limb MI decoding in hemiplegic patients. MFF-DANet employs convolutional kernels of various scales to extract feature information across multiple frequency bands, followed by a channel attention-based average pooling operation to retain the most critical frequency domain features. Additionally, MFF-DANet integrates a graph attention convolutional network to capture spatial topological features across different electrode channels, utilizing electrode positions as prior knowledge to construct and update the graph adjacency matrix. We validated the performance of MFF-DANet on the public PhysioNet dataset, achieving optimal decoding accuracies of 61.6% for within-subject case and 52.7% for cross-subject case. t-Distributed Stochastic Neighbor Embedding (t-SNE) visualization of the features demonstrates the effectiveness of each designed module, and visualization of the adjacency matrix indicates that the extracted spatial topological features have physiological interpretability.},
}
@article {pmid39678531,
year = {2024},
author = {Wang, N and Tu, WJ},
title = {Editorial: Brain-computer interfaces in neurological disorders: expanding horizons for diagnosis, treatment, and rehabilitation.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1526723},
pmid = {39678531},
issn = {1662-4548},
}
@article {pmid39677402,
year = {2024},
author = {Ninenko, I and Medvedeva, A and Efimova, VL and Kleeva, DF and Morozova, M and Lebedev, MA},
title = {Olfactory neurofeedback: current state and possibilities for further development.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1419552},
pmid = {39677402},
issn = {1662-5161},
abstract = {This perspective considers the novel concept of olfactory neurofeedback (O-NFB) within the framework of brain-computer interfaces (BCIs), where olfactory stimuli are integrated in various BCI control loops. In particular, electroencephalography (EEG)-based O-NFB systems are capable of incorporating different components of complex olfactory processing - from simple discrimination tasks to using olfactory stimuli for rehabilitation of neurological disorders. In our own work, EEG theta and alpha rhythms were probed as control variables for O-NFB. Additionaly, we developed an olfactory-based instructed-delay task. We suggest that the unique functions of olfaction offer numerous medical and consumer applications where O-NFB is combined with sensory inputs of other modalities within a BCI framework to engage brain plasticity. We discuss the ways O-NFB could be implemented, including the integration of different types of olfactory displays in the experiment set-up and EEG features to be utilized. We emphasize the importance of synchronizing O-NFB with respiratory rhythms, which are known to influence EEG patterns and cognitive processing. Overall, we expect that O-NFB systems will contribute to both practical applications in the clinical world and the basic neuroscience of olfaction.},
}
@article {pmid39674592,
year = {2025},
author = {Barker, N and Parker, H},
title = {Hybrid performances in sport: Cybathlon spectatorship for critically imagining technologies for disability futures.},
journal = {Medical humanities},
volume = {50},
number = {4},
pages = {657-669},
pmid = {39674592},
issn = {1473-4265},
mesh = {Humans ; *Disabled Persons ; *Sports ; *Self-Help Devices ; Brain-Computer Interfaces ; Robotics ; Anthropology, Cultural ; Politics ; },
abstract = {Disabled bodies have been historically marginalised in sporting arenas and spectacles. Assistive technologies have been increasingly featuring in, and changing, sporting landscapes. In some ways recent shifts have made disability more present and visible across many (para) sporting cultures, and yet sport continues to operate on a tiered system that assumes a normative able body. This paper responds to this moment by offering imaginaries of future hybrid performances that critically engage with the politics and possibilities of novel technologies in sporting arenas and their wider impact on disability futures. These were generated from a collaborative ethnography that centred on becoming spectators of the Cybathlon Games. The Cybathlon Games began in 2016 as a global event where people with disabilities compete with technologies such as Brain-Computer Interfaces or robotic Prosthesis. Our imaginings are presented as three speculative fragments in the form of pages ripped from a comic book series, The In/Visibles These fragments and critical reflections are grounded on themes generated through watching the Games together. The purpose of this paper is not to offer predictions or even visions of desirable futures. Rather we present future technologised sporting bodies and spectacles with a view to extend critical posthuman discussions to these arenas. Through this we highlight: (1) The arbitrariness of where to draw the between un/natural performances; (2) The absurdities of unrestricted and open use of performance technologies when hybrid forms and functions are judged through current sporting-humanist values; and (3) The need to stay alert to socioeconomic and political drivers of sporting and disability futures. We offer these three zones of friction to guide further research when navigating the complex and shifting relations between sport, technology and the (dis)abled body now and into the future.},
}
@article {pmid39656892,
year = {2025},
author = {Guo, D and Yao, B and Shao, WW and Zuo, JC and Chang, ZH and Shi, JX and Hu, N and Bao, SQ and Chen, MM and Fan, X and Li, XH},
title = {The Critical Role of YAP/BMP/ID1 Axis on Simulated Microgravity-Induced Neural Tube Defects in Human Brain Organoids.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {12},
number = {5},
pages = {e2410188},
pmid = {39656892},
issn = {2198-3844},
support = {2021YFF1200800//National Key Research and Development Program of China/ ; 82171861//National Natural Science Foundation of China/ ; 81971782//National Natural Science Foundation of China/ ; 82101448//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Organoids/metabolism ; *Brain/metabolism ; *Neural Tube Defects/metabolism/genetics/etiology ; *Bone Morphogenetic Proteins/metabolism/genetics ; *Weightlessness Simulation/adverse effects ; Signal Transduction ; YAP-Signaling Proteins/metabolism ; Transcription Factors/metabolism ; *Adaptor Proteins, Signal Transducing/metabolism/genetics ; Neural Stem Cells/metabolism ; Neural Tube/metabolism ; },
abstract = {Integrated biochemical and biophysical signals regulate embryonic development. Correct neural tube formation is critical for the development of central nervous system. However, the role of microgravity in neurodevelopment and its underlying molecular mechanisms remain unclear. In this study, the effects of stimulated microgravity (SMG) on the development of human brain organoids are investigated. SMG impairs N-cadherin-based adherens junction formation, leading to neural tube defects associated with dysregulated self-renewal capacity and neuroepithelial disorganization in human brain organoids. Bulk gene expression analyses reveal that SMG alters Hippo and BMP signaling in brain organoids. The neuropathological deficits in SMG-treated organoids can be rescued by regulating YAP/BMP/ID1 axis. Furthermore, sing-cell RNA sequencing data show that SMG results in perturbations in the number and function of neural stem and progenitor cell subpopulations. One of these subpopulations senses SMG cues and transmits BMP signals to the subpopulation responsible for tube morphogenesis, ultimately affecting the proliferating cell population. Finally, SMG intervention leads to persistent neurologic damage even after returning to normal gravity conditions. Collectively, this study reveals molecular and cellular abnormalities associated with SMG during human brain development, providing opportunities for countermeasures to maintain normal neurodevelopment in space.},
}
@article {pmid39672531,
year = {2025},
author = {Li, K and Qian, L and Zhang, C and Zhang, J and Xue, C and Zhang, Y and Deng, W},
title = {The entorhinal cortex and cognitive impairment in schizophrenia: A comprehensive review.},
journal = {Progress in neuro-psychopharmacology &amp; biological psychiatry},
volume = {136},
number = {},
pages = {111218},
doi = {10.1016/j.pnpbp.2024.111218},
pmid = {39672531},
issn = {1878-4216},
mesh = {Humans ; *Schizophrenia/pathology/complications/physiopathology/diagnostic imaging ; *Entorhinal Cortex/physiopathology/pathology/diagnostic imaging ; *Cognitive Dysfunction/physiopathology/pathology/etiology/diagnostic imaging ; Animals ; },
abstract = {Schizophrenia, a severe mental illness characterized by cognitive impairment and olfactory dysfunction, remains an enigma with its pathological mechanism yet to be fully elucidated. The entorhinal cortex, a pivotal structure involved in numerous neural loop circuits related to olfaction, cognition, and emotion, has garnered significant attention due to its structural and functional abnormalities, which have been implicated in the pathogenesis of schizophrenia. This review focuses on the abnormal structural and functional changes in the entorhinal cortex in schizophrenia patients, as evidenced by neuroimaging, cellular biology, and genetic studies. These changes are posited to play a crucial role in the pathogenesis of cognitive impairment in schizophrenia. Furthermore, this review explores the various intervention strategies targeting the entorhinal cortex in current treatment modalities and proposes potential directions for future research endeavors, thereby providing a novel perspective on unraveling the complexity of neural mechanisms underlying schizophrenia and developing innovative therapeutic approaches for schizophrenia.},
}
@article {pmid39672015,
year = {2025},
author = {Hameed, I and Khan, DM and Ahmed, SM and Aftab, SS and Fazal, H},
title = {Enhancing motor imagery EEG signal decoding through machine learning: A systematic review of recent progress.},
journal = {Computers in biology and medicine},
volume = {185},
number = {},
pages = {109534},
doi = {10.1016/j.compbiomed.2024.109534},
pmid = {39672015},
issn = {1879-0534},
mesh = {Humans ; *Electroencephalography ; *Brain-Computer Interfaces ; *Machine Learning ; *Signal Processing, Computer-Assisted ; *Imagination/physiology ; Deep Learning ; },
abstract = {This systematic literature review explores the intersection of neuroscience and deep learning in the context of decoding motor imagery Electroencephalogram (EEG) signals to enhance the quality of life for individuals with motor disabilities. Currently, the most used non-invasive method for measuring brain activity is the EEG, due to its high temporal resolution, user-friendliness, and safety. A Brain Computer Interface (BCI) framework can be made using these signals which can provide a new communication channel to people that are suffering from motor disabilities or other neurological disorders. However, implementing EEG-based BCI systems in real-world scenarios for motor imagery recognition presents challenges, primarily due to the inherent variability among individuals and low signal-to-noise ratio (SNR) of EEG signals. To assist researchers in navigating this complex problem, a comprehensive review article is presented, summarizing the key findings from relevant studies since 2017. This review primarily focuses on the datasets, preprocessing methods, feature extraction techniques, and deep learning models employed by various researchers. This review aims to contribute valuable insights and serve as a resource for researchers, practitioners, and enthusiasts interested in the combination of neuroscience and deep learning, ultimately hoping to contribute to advancements that bridge the gap between the human mind and machine interfaces.},
}
@article {pmid39671976,
year = {2025},
author = {Brooks, KA and Kolousek, A and Holman, EK and Evans, SS and Govil, N and Alfonso, KP},
title = {MED-EL Bonebridge implantation in pediatric patients age 11 Years and younger: Is it safe and effective?.},
journal = {International journal of pediatric otorhinolaryngology},
volume = {188},
number = {},
pages = {112198},
doi = {10.1016/j.ijporl.2024.112198},
pmid = {39671976},
issn = {1872-8464},
mesh = {Humans ; Child ; Retrospective Studies ; Female ; Male ; Adolescent ; Treatment Outcome ; Age Factors ; Bone Conduction/physiology ; Child, Preschool ; *Hearing Loss, Conductive/surgery ; Speech Perception ; Off-Label Use ; },
abstract = {OBJECTIVE: To present our experience with off-label MED-EL Bonebridge implantation in pediatric patients younger than 12 years of age and compare outcomes to pediatric patients 12 years and older.<br><br>METHODS: Pediatric patients who underwent Bonebridge implantation were included in a retrospective cohort study and were categorized by off-label use (<12 years) and &#x2265;12 years at time of bone conduction implantation (BCI). Hearing outcomes were collected after implant activation, which was typically 4-8 weeks post-implantation. Mann-Whitney U tests were performed to assess for differences between audiometric outcomes. Significance was set at p&#xa0;<&#xa0;0.05.<br><br>RESULTS: Twenty patients (25 implants)&#xa0;<&#xa0;12 years of age and 17 patients (23 implants) &#x2265;12 years of age underwent BCI. Pre-BCI speech recognition threshold (SRT) was better for the older patient group (median 50&#xa0;dB) than the younger patient group (median 60&#xa0;dB). Post-BCI SRT, however, was significantly lower in the younger patient group (median 22.5&#xa0;dB) as compared to the older patient group (median 35&#xa0;dB), (p&#xa0;<&#xa0;0.001, Z&#xa0;=&#xa0;3.1). The two groups performed similarly on age-appropriate wordlists presented at 50&#xa0;dB HL in aided conditions (p&#xa0;>&#xa0;0.05, -1<Z&#xa0;<&#xa0;1). Postoperative complication rate was low for both age groups (9&#xa0;% vs 8&#xa0;%) and operative time was similar for both groups of patients (p=0.96, Z=0.52).<br><br>CONCLUSION: Pediatric patients younger than 12 years showed similar or better audiometric benefit from off-label Bonebridge implantation when compared to older patients. Pediatric patients younger than 12 years can be considered Bonebridge implant candidates if clinically indicated; Bonebridge implantation in this age group appears safe and technically feasible.},
}
@article {pmid39671798,
year = {2024},
author = {Daly, I and Williams, N and Nasuto, SJ},
title = {TMS-evoked potential propagation reflects effective brain connectivity.},
journal = {Journal of neural engineering},
volume = {21},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ad9ee0},
pmid = {39671798},
issn = {1741-2552},
mesh = {Humans ; *Transcranial Magnetic Stimulation/methods ; Male ; Female ; Adult ; *Electroencephalography/methods ; *Brain/physiology ; Young Adult ; Nerve Net/physiology ; Evoked Potentials/physiology ; Feedback, Sensory/physiology ; Connectome/methods ; Evoked Potentials, Motor/physiology ; Psychomotor Performance/physiology ; },
abstract = {Objective.Cognition is achieved through communication between brain regions. Consequently, there is considerable interest in measuring effective connectivity. A promising effective connectivity metric is transcranial magnetic stimulation (TMS) evoked potentials (TEPs), an inflection in amplitude of the electroencephalogram recorded from one brain region as a result of TMS applied to another region. However, the TEP is confounded by multiple factors and there is a need for further investigation of the TEP as a measure of effective connectivity and to compare it to existing statistical measures of effective connectivity.Approach.To this end, we used a pre-existing experimental dataset to compare TEPs between a motor control task with and without visual feedback. We then used the results to compare our TEP-based measures of effective connectivity to established statistical measures of effective connectivity provided by multivariate auto-regressive modelling.Main results.Our results reveal significantly more negative TEPs when feedback is not presented from 40 ms to 100 ms post-TMS over frontal and central channels. We also see significantly more positive later TEPs from 280-400 ms on the contra-lateral hemisphere motor and parietal channels when no feedback is presented. These results suggest differences in effective connectivity are induced by visual feedback of movement. We further find that the variation in one of these early TEPs (the N40) is reliably related to directed coherence.Significance.Taken together, these results indicate components of the TEPs serve as a measure of effective connectivity. Furthermore, our results also support the idea that effective connectivity is a dynamic process and, importantly, support the further use of TEPs in delineating region-to-region maps of changes in effective connectivity as a result of motor control feedback.},
}
@article {pmid39671787,
year = {2025},
author = {H Liu, D and Hsieh, JC and Alawieh, H and Kumar, S and Iwane, F and Pyatnitskiy, I and Ahmad, ZJ and Wang, H and Millán, JDR},
title = {Novel AIRTrode-based wearable electrode supports long-term, online brain-computer interface operations.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ad9edf},
pmid = {39671787},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods/instrumentation ; Male ; *Electrodes ; *Wearable Electronic Devices ; Adult ; Female ; Young Adult ; Online Systems ; },
abstract = {Objective.Non-invasive electroencephalograms (EEG)-based brain-computer interfaces (BCIs) play a crucial role in a diverse range of applications, including motor rehabilitation, assistive and communication technologies, holding potential promise to benefit users across various clinical spectrums. Effective integration of these applications into daily life requires systems that provide stable and reliable BCI control for extended periods. Our prior research introduced the AIRTrode, a self-adhesive (A), injectable (I), and room-temperature (RT) spontaneously-crosslinked hydrogel electrode (AIRTrode). The AIRTrode has shown lower skin-contact impedance and greater stability than dry electrodes and, unlike wet gel electrodes, does not dry out after just a few hours, enhancing its suitability for long-term application. This study aims to demonstrate the efficacy of AIRTrodes in facilitating reliable, stable and long-term online EEG-based BCI operations.Approach.In this study, four healthy participants utilized AIRTrodes in two BCI control tasks-continuous and discrete-across two sessions separated by six hours. Throughout this duration, the AIRTrodes remained attached to the participants' heads. In the continuous task, participants controlled the BCI through decoding of upper-limb motor imagery (MI). In the discrete task, the control was based on decoding of error-related potentials (ErrPs).Main Results.Using AIRTrodes, participants demonstrated consistently reliable online BCI performance across both sessions and tasks. The physiological signals captured during MI and ErrPs tasks were valid and remained stable over sessions. Lastly, both the BCI performances and physiological signals captured were comparable with those from freshly applied, research-grade wet gel electrodes, the latter requiring inconvenient re-application at the start of the second session.Significance.AIRTrodes show great potential promise for integrating non-invasive BCIs into everyday settings due to their ability to support consistent BCI performances over extended periods. This technology could significantly enhance the usability of BCIs in real-world applications, facilitating continuous, all-day functionality that was previously challenging with existing electrode technologies.},
}
@article {pmid39671281,
year = {2024},
author = {Ghodrati, MT and Aghababaei, S and Mirfathollahi, A and Shalchyan, V and Zarrindast, MR and Daliri, MR},
title = {Protocol for state-based decoding of hand movement parameters using neural signals.},
journal = {STAR protocols},
volume = {5},
number = {4},
pages = {103503},
pmid = {39671281},
issn = {2666-1667},
mesh = {Humans ; *Hand/physiology ; *Movement/physiology ; Biomechanical Phenomena/physiology ; Brain-Computer Interfaces ; Somatosensory Cortex/physiology ; },
abstract = {We present a protocol for decoding kinematic and kinetic parameters from the primary somatosensory cortex during active and passive hand movements in a center-out reaching task using state-based and conventional decoders. We describe steps for preparing data and using the state-based model to classify movement directions into states via feature extraction and predict parameters with regression models (partial least squares and multilinear regression) trained per state. This state-based approach outperforms conventional methods, enhancing accuracy for brain-computer interface applications. For complete details on the use and execution of this protocol, please refer to Mirfathollahi et al.[1].},
}
@article {pmid39670475,
year = {2024},
author = {Xie, S and He, C},
title = {An empirical study on native Mandarin-speaking children's metonymy comprehension development.},
journal = {Journal of child language},
volume = {},
number = {},
pages = {1-28},
doi = {10.1017/S0305000924000539},
pmid = {39670475},
issn = {1469-7602},
abstract = {This study investigates Mandarin-speaking children's (age 3-7) comprehension development of novel and conventional metonymy, combining online and offline methods. Both online and offline data show significantly better performances from the oldest group (6-to-7-year-old) and a delayed acquisition of conventional metonymy compared with novel metonymy. However, part of offline data shows no significant difference between adjacent age groups, while the eye-tracking data show a chronological development from age 3-7. Furthermore, in offline tasks, the three-year-old group features a high choice randomness and the four-to-five-year-olds show the longest reaction time. Therefore, we argue that, not only age but also metonymy type can influence metonymy acquisition, and that a lack of socio-cultural experience can be a source of acquisition difficulty for children under six. Methodologically speaking, we believe that online methods should not be considered superior to offline ones as they investigate different aspects of implicit and explicit language comprehension.},
}
@article {pmid39669979,
year = {2024},
author = {Nakamura, D and Kaji, S and Kanai, R and Hayashi, R},
title = {Unsupervised method for representation transfer from one brain to another.},
journal = {Frontiers in neuroinformatics},
volume = {18},
number = {},
pages = {1470845},
pmid = {39669979},
issn = {1662-5196},
abstract = {Although the anatomical arrangement of brain regions and the functional structures within them are similar across individuals, the representation of neural information, such as recorded brain activity, varies among individuals owing to various factors. Therefore, appropriate conversion and translation of brain information is essential when decoding neural information using a model trained using another person's data or to achieving brain-to-brain communication. We propose a brain representation transfer method that involves transforming a data representation obtained from one person's brain into that obtained from another person's brain, without relying on corresponding label information between the transferred datasets. We defined the requirements to enable such brain representation transfer and developed an algorithm that distills the assumption of common similarity structure across the brain datasets into a rotational and reflectional transformation across low-dimensional hyperspheres using encoders for non-linear dimensional reduction. We first validated our proposed method using data from artificial neural networks as substitute neural activity and examining various experimental factors. We then evaluated the applicability of our method to real brain activity using functional magnetic resonance imaging response data acquired from human participants. The results of these validation experiments showed that our method successfully performed representation transfer and achieved transformations in some cases that were similar to those obtained when using corresponding label information. Additionally, we reconstructed images from individuals' data without training personalized decoders by performing brain representation transfer. The results suggest that our unsupervised transfer method is useful for the reapplication of existing models personalized to specific participants and datasets to decode brain information from other individuals. Our findings also serve as a proof of concept for the methodology, enabling the exchange of the latent properties of neural information representing individuals' sensations.},
}
@article {pmid39669288,
year = {2024},
author = {Nejatbakhsh, A and Fumarola, F and Esteki, S and Toyoizumi, T and Kiani, R and Mazzucato, L},
title = {Predicting the effect of micro-stimulation on macaque prefrontal activity based on spontaneous circuit dynamics.},
journal = {Physical review research},
volume = {5},
number = {4},
pages = {},
pmid = {39669288},
issn = {2643-1564},
support = {R01 DA055439/DA/NIDA NIH HHS/United States ; R01 MH109180/MH/NIMH NIH HHS/United States ; R01 MH127375/MH/NIMH NIH HHS/United States ; R01 NS118461/NS/NINDS NIH HHS/United States ; },
abstract = {A crucial challenge in targeted manipulation of neural activity is to identify perturbation sites whose stimulation exerts significant effects downstream with high efficacy, a procedure currently achieved by labor-intensive and potentially harmful trial and error. Can one predict the effects of electrical stimulation on neural activity based on the circuit dynamics during spontaneous periods? Here we show that the effects of single-site micro-stimulation on ensemble activity in an alert monkey's prefrontal cortex can be predicted solely based on the ensemble's spontaneous activity. We first inferred the ensemble's causal flow based on the directed functional interactions inferred during spontaneous periods using convergent cross-mapping and showed that it uncovers a causal hierarchy between the recording electrodes. We find that causal flow inferred at rest successfully predicts the spatiotemporal effects of micro-stimulation. We validate the computational features underlying causal flow using ground truth data from recurrent neural network models, showing that it is robust to noise and common inputs. A detailed comparison between convergent-cross mapping and alternative methods based on information theory reveals the advantages of the former method in predicting perturbation effects. Our results elucidate the causal interactions within neural ensembles and will facilitate the design of intervention protocols and targeted circuit manipulations suitable for brain-machine interfaces.},
}
@article {pmid39667504,
year = {2025},
author = {Cao, HL and Wei, W and Meng, YJ and Tao, YJ and Yang, X and Li, T and Guo, WJ},
title = {Association of altered cortical gyrification and working memory in male early abstinent alcohol-dependent individuals.},
journal = {Brain research bulletin},
volume = {220},
number = {},
pages = {111166},
doi = {10.1016/j.brainresbull.2024.111166},
pmid = {39667504},
issn = {1873-2747},
mesh = {Humans ; Male ; *Memory, Short-Term/physiology ; *Alcoholism/physiopathology/diagnostic imaging/psychology/pathology ; Magnetic Resonance Imaging/methods ; Adult ; Middle Aged ; *Cerebral Cortex/diagnostic imaging/pathology/physiopathology ; *Alcohol Abstinence/psychology ; Neuropsychological Tests ; },
abstract = {BACKGROUND: Alcohol dependence (AD) is an addictive disorder with multifaceted neurobiological features. Recent research on the pathophysiological mechanisms of AD has emphasized the important role of dysconnectivity. Cortical gyrification is known to be a reliable marker of neural connectivity. This study aimed to explore cortical gyrification using the local gyrification index (LGI) between alcohol-dependent patients and controls.<br><br>METHODS: Magnetic resonance images were collected from 60 early abstinent patients with AD (5-12 days after stopping alcohol consumption) and 59 controls and preprocessed using FreeSurfer, followed by surface-based morphometry (SBM) analysis to compare the LGI between the two groups. Cognitive performance was assessed using the Spatial Working Memory (SWM) test in the Cambridge Neuropsychological Test Automated Battery (CANTAB). The relationship between LGI, cognitive performance, and clinical variables was also explored in the patient group.<br><br>RESULTS: Compared with controls, patients with AD exhibited significantly decreased LGI in several regions, including the postcentral gyrus, precentral gyrus, middle frontal, superior temporal, middle temporal, insula, superior parietal, and inferior parietal cortex. AD patients did worse than controls in several SWM measures. Furthermore, decreased LGI in the left postcentral was negatively correlated with working memory performance after multiple comparison corrections in the patient group.<br><br>CONCLUSION: Alcohol-dependent individuals exhibit abnormal patterns of cortical gyrification, which may be underlying neurobiological markers of AD. Our findings further indicate that working memory deficits may be related to abnormalities in cortical gyrification in alcohol addiction.},
}
@article {pmid39667216,
year = {2025},
author = {Sun, P and De Winne, J and Zhang, M and Devos, P and Botteldooren, D},
title = {Delayed knowledge transfer: Cross-modal knowledge transfer from delayed stimulus to EEG for continuous attention detection based on spike-represented EEG signals.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {183},
number = {},
pages = {107003},
doi = {10.1016/j.neunet.2024.107003},
pmid = {39667216},
issn = {1879-2782},
mesh = {*Electroencephalography/methods ; Humans ; *Attention/physiology ; *Brain-Computer Interfaces ; Photic Stimulation/methods ; Acoustic Stimulation/methods ; *Brain/physiology ; *Neurons/physiology ; Auditory Perception/physiology ; *Action Potentials/physiology ; *Transfer, Psychology/physiology ; },
abstract = {Decoding visual and auditory stimuli from brain activities, such as electroencephalography (EEG), offers promising advancements for enhancing machine-to-human interaction. However, effectively representing EEG signals remains a significant challenge. In this paper, we introduce a novel Delayed Knowledge Transfer (DKT) framework that employs spiking neurons for attention detection, using our experimental EEG dataset. This framework extracts patterns from audiovisual stimuli to model brain responses in EEG signals, while accounting for inherent response delays. By aligning audiovisual features with EEG signals through a shared embedding space, our approach improves the performance of brain-computer interface (BCI) systems. We also present WithMeAttention, a multimodal dataset designed to facilitate research in continuously distinguishing between target and distractor responses. Our methodology demonstrates a 3% improvement in accuracy on the WithMeAttention dataset compared to a baseline model that decodes EEG signals from scratch. This significant performance increase highlights the effectiveness of our approach Comprehensive analysis across four distinct conditions shows that rhythmic enhancement of visual information can optimize multi-sensory information processing. Notably, the two conditions featuring rhythmic target presentation - with and without accompanying beeps - achieved significantly superior performance compared to other scenarios. Furthermore, the delay distribution observed under different conditions indicates that our delay layer effectively emulates the neural processing delays in response to stimuli.},
}
@article {pmid39667215,
year = {2025},
author = {Lan, Y and Wang, Y and Zhang, Y and Zhu, H},
title = {Low-power and lightweight spiking transformer for EEG-based auditory attention detection.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {183},
number = {},
pages = {106977},
doi = {10.1016/j.neunet.2024.106977},
pmid = {39667215},
issn = {1879-2782},
mesh = {*Electroencephalography/methods/instrumentation ; *Attention/physiology ; Humans ; *Neural Networks, Computer ; *Auditory Perception/physiology ; Acoustic Stimulation/methods ; Neurons/physiology ; Signal Processing, Computer-Assisted ; *Action Potentials/physiology ; },
abstract = {EEG signal analysis can be used to study brain activity and the function and structure of neural networks, helping to understand neural mechanisms such as cognition, emotion, and behavior. EEG-based auditory attention detection is using EEG signals to determine an individual's level of attention to specific auditory stimuli. In this technique, researchers record and analyze a subject's electrical activity to infer whether an individual is paying attention to a specific auditory stimulus. The model deployed in edge devices will be greatly convenient for subjects to use. However, most of the existing EEG-based auditory attention detection models use traditional neural network models, and their high computing load makes deployment on edge devices challenging. We present a pioneering approach in the form of a binarized spiking Transformer for EEG-based auditory attention detection, which is characterized by high accuracy, low power consumption, and lightweight design, making it highly suitable for deployment on edge devices. In terms of low power consumption, the network is constructed using spiking neurons, which emit sparse and binary spike sequences, which can effectively reduce computing power consumption. In terms of lightweight, we use a post-training quantization strategy to quantize the full-precision network weights into binary weights, which greatly reduces the model size. In addition, the structure of the Transformer ensures that the model can learn effective information and ensure its high performance. We verify the model through mainstream datasets, and experimental results show that our model performance can exceed the existing state-of-the-art models, and the model size can be reduced by more than 21 times compared with the original full-precision network counterpart.},
}
@article {pmid39665789,
year = {2026},
author = {Tang, C and Wang, P and Li, Z and Zhong, S and Yang, L and Li, G},
title = {Neural functional rehabilitation: Exploring neuromuscular reconstruction technology advancements and challenges.},
journal = {Neural regeneration research},
volume = {21},
number = {1},
pages = {173-186},
pmid = {39665789},
issn = {1673-5374},
abstract = {Neural machine interface technology is a pioneering approach that aims to address the complex challenges of neurological dysfunctions and disabilities resulting from conditions such as congenital disorders, traumatic injuries, and neurological diseases. Neural machine interface technology establishes direct connections with the brain or peripheral nervous system to restore impaired motor, sensory, and cognitive functions, significantly improving patients' quality of life. This review analyzes the chronological development and integration of various neural machine interface technologies, includin