IEEE Transactions on Neural Systems and Rehabilitation Engineering最新文献

{"title":"A Global-Local Dynamic Directed Graph Neural Network for Parkinson’s Disease Detection","authors":"Xiaotian Wang;Guanhai Zhou;Zhifu Zhao;Xiaoyi Zhang;Fu Li;Fei Qi","doi":"10.1109/TNSRE.2025.3614430","DOIUrl":"10.1109/TNSRE.2025.3614430","url":null,"abstract":"Graph Neural Networks (GNNs) for gait analysis utilizing Vertical Ground Reaction Force (VGRF) signals have demonstrated significant potential in Parkinson’s Disease (PD) diagnosis and rehabilitation fields. However, existing GNN-based methods in this area normally model the VGRF signals as static topological structures, and ignore the dynamic variations of the VGRF graph structures during walking. To address this issue, a Global-Local Dynamic Directed Graph Neural Network (GL<inline-formula> <tex-math>$text {D}^{{2}}$ </tex-math></inline-formula>-GNN) is proposed to represent the dynamic spatio-temporal features of VGRF signals. The core component of the proposed model is the DyDGNN block, which is composed of a Dynamic Graph Learning (DGL) unit, a Dynamic Directed Graph Network (DyDGN) unit, and a Temporal Convolutional Network (TCN) unit. First, the DGL unit is proposed to learn dynamic topological relationships of VGRF signals. Based on learned graph structures, the DyDGN unit is constructed to extract the spatial patterns and capture topological dynamic features from VGRF signals. Subsequently, local temporal patterns of VGRF signals are extracted by the TCN unit. The proposed method is evaluated through k-fold cross-validation and cross-dataset validation on three datasets Ga, Ju and Si. Compared with existing methods, such as RFdGAD, Transformer, and AST-DGNN, GL<inline-formula> <tex-math>$text {D}^{{2}}$ </tex-math></inline-formula>-GNN demonstrates superior performance in the validation experiments. Notably, our method achieves an average improvement of 4.45% in accuracy, 2.93% in F1 score, and 2.88% in geometric mean across cross-dataset validation. Extensive experiments have demonstrated that GL<inline-formula> <tex-math>$text {D}^{{2}}$ </tex-math></inline-formula>-GNN exhibits both the representational ability for complex gait patterns and the generalization ability across various datasets by capturing dynamics of VGRF topological structures and spatio-temporal features from VGRF signals. For future work, we plan to combine our method with multi-modal methods and integrate our framework into a complete gait analysis system.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"3947-3957"},"PeriodicalIF":5.2,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11180065","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145148886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic Stability Analysis of Progressive Supranuclear Palsy Affected Gait Using Lyapunov Floquet Theory","authors":"Sandesh G. Bhat;Farwa Ali;Cecilia A. Hogen;Asghar Rezaei;Keith A. Josephs;Jennifer L. Whitwell;Kenton R. Kaufman","doi":"10.1109/TNSRE.2025.3614555","DOIUrl":"10.1109/TNSRE.2025.3614555","url":null,"abstract":"Progressive supranuclear palsy (PSP) is a neurodegenerative disease with severe gait and balance deficits. There are no effective ways to assess dynamic balance during walking in PSP. The Lyapunov Floquet (LF) theory has been utilized to study dynamic balance in healthy and pathologic gait but has not been applied to PSP affected gait. In the current study, the medio-lateral motion of the center of mass during gait for 40 patients with PSP (PSP group) and 33 healthy older adults (Control group) were studied. Metrics from LF theory, such as the maximal Floquet multiplier (FM), maximal long-term Lyapunov Exponent (LE<inline-formula> <tex-math>${}_{text {L}}text {)}$ </tex-math></inline-formula>, and maximal short-term Lyapunov Exponent (LE<inline-formula> <tex-math>${}_{text {S}}text {)}$ </tex-math></inline-formula> were used to study walking stability. Although all the gait dynamics for all the participants were stable and non-chaotic, the PSP group was observed to be closer to an unstable system and more susceptible to perturbations (<inline-formula> <tex-math>$vert $ </tex-math></inline-formula>FM<inline-formula> <tex-math>$vert $ </tex-math></inline-formula> closer to 1 and LE_L closer to 0) than the Control group (p < 0.001). The control group’s stability deteriorated, and the gait system became more susceptible to perturbations with age. Such a trend was not observed in the PSP group. The risk of falls increased with increase in cadence in the PSP group (p < 0.001). These findings demonstrate the potential of LF theory measures to evaluate dynamic stability in patients with PSP and the need for future research using quantitative measures.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"3958-3964"},"PeriodicalIF":5.2,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11180067","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145148910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EEG Microstate Imbalance in Anti-LGI1 Encephalitis: A Correlation With Inflammation and Cognitive Dysfunction","authors":"Xue Yang;Xiaotang Wang;Si Chen;Xiaxin Yang;Xiuhe Zhao","doi":"10.1109/TNSRE.2025.3614263","DOIUrl":"10.1109/TNSRE.2025.3614263","url":null,"abstract":"This study investigates changes in electroencephalogram (EEG) microstates in patients with anti-leucine-rich glioma-inactivated protein 1 (anti-LGI1) encephalitis and explores their correlation with inflammatory markers and cognitive function. Thirty patients with LGI1 encephalitis were compared to a control group of thirty healthy individuals, analyzing demographics, blood/cerebrospinal fluid (CSF) tests, and cognitive assessments. Each participant underwent at least 10 minutes of resting-state EEG recording. Microstate analysis and functional connectivity were performed using MATLAB’s EEGLAB toolbox, while standardized low-resolution brain electromagnetic tomography (sLORETA) was employed for microstate source reconstruction. Patients with anti-LGI1 encephalitis exhibited prolonged durations of microstates A, B, and C, reduced occurrence of microstate D, and increased transitions from C to A, with no significant changes in coverage. Microstate A demonstrated decreased activity in the anterior cingulate and lingual gyri. Functional connectivity analysis revealed enhanced slow-wave connectivity and diminished fast-wave connectivity in the frontal-parietal lobes. Correlation analyses showed that microstate A positively correlated with inflammatory indices such as the neutrophil-to-lymphocyte ratio (NLR), systemic inflammation index (SII), and systemic inflammatory response index (SIRI). In contrast, microstate D correlated with the derived neutrophil-to-lymphocyte ratio (dNLR). Microstates B and C inversely correlated with NLR and SII. Furthermore, the duration and coverage of microstate A, along with the transition probability from A to D, inversely correlated with Mini-Mental State Examination (MMSE) scores. Similarly, the AD/BC ratio in occurrence, duration, and coverage also negatively correlated with MMSE scores. These findings revealed alterations in EEG microstates in patients with LGI1 encephalitis, highlighting an imbalance in the AD/BC ratio associated with inflammatory processes and cognitive impairments.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"3937-3946"},"PeriodicalIF":5.2,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11180128","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145148896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Versatile Neural Activation Predictor With Axon Structure Tailoring Capability Enabling Personalized Neuromodulation Computation","authors":"Hongda Li;Shunjing Wang;Xuesong Luo;Changqing Jiang;Boyang Zhang","doi":"10.1109/TNSRE.2025.3614215","DOIUrl":"10.1109/TNSRE.2025.3614215","url":null,"abstract":"Neuromodulation therapies are evolving to be more and more intelligent and personalized, driving the need for more precise and efficient stimulation strategies. Biophysically detailed computational models integrated with anatomically accurate neural structures could offer critical insights into neural activation patterns under various stimulation conditions, which are essential to optimize the treatment. However, solving these models containing a large number of nerve fibers is computationally intensive, especially when the neural targets comprise heterogenous axons, e.g., with varying geometries. Also, current methods lack generalizability across various neuromodulation scenarios, limiting the scalability and clinical utility of such models. In this study, we present a convolutional neural network (CNN)-based framework as a universal, rapid, and accurate alternative to conventional case-by-case brutal force computation methods. Our approach achieves a mean absolute error (MAE) of <inline-formula> <tex-math>$boldsymbol {textbf {6}.textbf {91}times textbf {10}^{-textbf {3}}}$ </tex-math></inline-formula> mV and over 95% prediction accuracy under diverse extracellular stimulation scenarios, facilitating personalized simulations and tailored neuromodulation treatments.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"3986-3997"},"PeriodicalIF":5.2,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11180059","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145148876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Soft Exosuit Based on Fabric Muscle to Assist Shoulder Joint Movements in Patients With Neuromuscular Diseases","authors":"Seong Jun Park;Sungbae Jo;Hyung-Ik Shin;Eunsu Lee;Jung Hyun Kim;Hyunmok Jung;Jeong Min Kim;Yae Lim Lee;Sung Eun Hyun;Woo Hyung Lee;Cheol Hoon Park","doi":"10.1109/TNSRE.2025.3613709","DOIUrl":"10.1109/TNSRE.2025.3613709","url":null,"abstract":"Neuromuscular diseases, such as Duchenne muscular dystrophy, can cause severe muscle weakness, compromising the ability to perform activities of daily living (ADLs). This article presents a lightweight, garment-like soft shoulder exosuit (SSE) that assists shoulder elevation using fabric-type artificial muscles woven from thin shape memory alloy springs. The SSE weighs only 440 g, operates silently, and conforms to the body of the wearer for comfortable, long-term daily use. Its seamless, garment-like design enables discreet and comfortable wear in daily life and allows the suit to be folded and stored in the same manner as regular clothing when not in use. A single actuator passively adapts to shoulder motions and generates active lift assistance through Joule heating. To evaluate its effectiveness, experiments were conducted on eight individuals with Duchenne muscular dystrophy. The active range of motion in shoulder abduction significantly improved by 57.45%, and upper limb functional performance increased by 19.37% in Performance of Upper Limb 2.0 and 13.28% in the Box and Block Test. No adverse events were reported during use. These findings demonstrate the potential of the SSE to reduce muscular effort and restore upper limb function during ADLs, supporting its applicability as a safe, wearable assistive solution for individuals with severe shoulder impairment.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"3866-3877"},"PeriodicalIF":5.2,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11177570","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145137374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Bottom-Up Classification of Wheelchair Users Based on Propulsion Kinematics Beyond Injury Level: Exploratory Findings","authors":"Hyunji Kim;San Hong;Woojin Park;Jooeun Ahn","doi":"10.1109/TNSRE.2025.3613726","DOIUrl":"10.1109/TNSRE.2025.3613726","url":null,"abstract":"Wheelchair propulsion is critical for the health and quality of life of wheelchair users. However, previous studies have primarily focused on quantitative comparisons of propulsion patterns among individuals with different injury levels without considering other factors. This study aims to explore distinct wheelchair propulsion strategies among individuals with spinal cord injury using a bottom-up approach based on kinematic patterns, and to investigate how these strategies relate to injury level, physical activity (PA) level, and coordination. Twenty-eight wheelchair users performed propulsion as fast as possible on an ergometer. Joint kinematics were analyzed using principal component analysis (PCA) to cluster participants. Muscle synergy analysis was performed using non-negative matrix factorization of electromyography data from six upper limb muscles. Three distinct clusters were identified, with differences primarily in PA levels (p = 0.02) and wheelchair use experience (p = 0.04) rather than injury level. Cluster 1 (n = 4), characterized by longer wheelchair use experience and higher PA levels, demonstrated higher hand velocity (p = 0.01) and longer push phase percentage (p < 0.01), with distinct joint coordination patterns consistent with relatively efficient propulsion strategies. Clusters 3 (n = 12) with less physical activity levels showed conservative propulsion patterns, and exhibited significantly lower hand velocity. Muscle synergy analysis revealed differences in neuromuscular control, particularly in the timing and coordination during push phase. Our findings suggest that wheelchair propulsion strategies are influenced more by experience and PA level than by the injury level alone. This highlights the importance of promoting PA and systematic propulsion training in rehabilitation programs to enhance wheelchair users’ mobility and independence.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"3878-3887"},"PeriodicalIF":5.2,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11177588","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145137279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Muscle Synergy-Based Iterative Learning Control for Upper Limb Functional Electrical Stimulation in Stroke Rehabilitation","authors":"Yanhong Liu;Yaowei Li;Zan Zhang;Benyan Huo;Long Cheng;Anqin Dong;Gen Li","doi":"10.1109/TNSRE.2025.3613998","DOIUrl":"10.1109/TNSRE.2025.3613998","url":null,"abstract":"Functional Electrical Stimulation (FES) is widely used in the postoperative rehabilitation of stroke patients. Multi-channel FES enables alternating stimulation of multiple muscle groups, effectively delaying muscle fatigue and facilitating precise control of complex upper limb movements. However, high-dimensional control of multiple muscles introduces additional challenges, particularly in coordinating antagonistic muscles and achieving efficient control. This study proposes a novel FES control framework that integrates muscle synergy theory, Long Short-Term Memory (LSTM) networks, and Iterative Learning Control (ILC). In this framework, the LSTM network predicts synergy activation coefficients from joint kinematics (angle and angular velocity), while the ILC algorithm iteratively updates electrical stimulation intensities based on the tracking error from previous iterations. This combination reduces the dimensionality of muscle control and improves the balance of muscle group activation, aligning better with natural motor control strategies. Experiments conducted on eight healthy subjects demonstrated that the proposed synergy-based ILC method significantly reduced joint angle tracking errors (measured by RMSE) over 10 stimulation iterations, compared to reference trajectories derived from voluntary motion. Specifically, in the combined elbow-wrist drinking task, the wrist RMSE decreased from 13.10° to 4.19°, and the elbow RMSE decreased from 45.07° to 5.53°. The coefficient of determination (<inline-formula> <tex-math>${R}^{{2}}$ </tex-math></inline-formula>), reflecting the goodness of fit between predicted and reference trajectories, exceeded 0.96, indicating high tracking accuracy and stability. Preliminary experiments on three stroke patients further support the adaptability and clinical potential of the proposed method.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"3922-3936"},"PeriodicalIF":5.2,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11177617","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145137354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Skeleton-Based Continuous Scale Parkinsonian Gait Score Estimation Using Omni-Dimensional Self-Attention Convolution Networks","authors":"Haoyu Tian;Jun Ma;Yipeng Zhang;Lizhou Fan;Wenjing Jiang;Xin Ma;Yibin Li","doi":"10.1109/TNSRE.2025.3613967","DOIUrl":"10.1109/TNSRE.2025.3613967","url":null,"abstract":"Gait abnormalities constitute a primary motor symptom of Parkinson’s disease (PD). Clinically, the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) is widely recognized as the standard to evaluate gait impairment in PD. Recent skeleton-based methods have sought to estimate MDS-UPDRS gait scores, but most approaches treat score prediction as a coarse classification task, limiting their ability to capture subtle, progressive gait changes over time. These methods also often neglect clinical prior knowledge and fail to model localized gait features, leading to unsatisfactory performance. In addition, a continuous scale evaluation of gait impairment could result in a better formulation and adjustment of the treatment plan. In this paper, we introduce a novel framework for providing continuous-scale gait impairment estimation from the discrete annotation of MDS-UPDRS using skeleton data. First, we convert non-Euclidean skeleton information into two Euclidean spatiotemporal feature maps, ensuring a rigid spatial-temporal structure around the central joint. Next, we employ an omni-dimensional attention convolutional network to extract local spatiotemporal gait features within these normalized feature maps. We then integrate the features from both maps using an adaptive channel feature fusion module, capturing comprehensive gait information. Finally, we propose a numerical score prediction strategy that leverages MDS-UPDRS scores as anchors to predict gait impairment on a continuous scale without requiring continuous-scale annotations from clinicians. The effectiveness of the proposed approach is validated using a substantial clinical PD gait skeleton dataset.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"3845-3855"},"PeriodicalIF":5.2,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11177571","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145137328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling and Analysis of Myoelectric Networks Based on Lower Limb Motor Synergies","authors":"Lingling Chen;Yanglong Wang;Xulong Lu;Junjie Geng;Tengyang Feng","doi":"10.1109/TNSRE.2025.3613408","DOIUrl":"10.1109/TNSRE.2025.3613408","url":null,"abstract":"The synergy between muscles is a prerequisite for the human body to complete various movements, and it undergoes dynamic changes during the movement process, which cannot be characterized using traditional muscle synergy extraction methods. Therefore, it is necessary to establish an analytical framework that covers the entire dynamic process to decode inter-muscular interaction information completely. By extracting the time-domain characteristic values of EMG, a complex network model is established to analyze the EMG dynamics of lower limb from the nodes and edges of network. On the one hand, the nodes are community-detected with the goal of maximum modularity, and the EMG network is hard-divided into several discrete communities. On the other hand, the edges are matrix-decomposed to obtain different subgraphs over time. The surface EMG of 18 subjects were collected during passive and active training with rehabilitation robot. The experimental results show that the muscle synergy is most substantial during the flexion and extension phases of lower limb movement, with muscle groups mainly composed of the rectus femoris, lateral thigh muscles, and calf gastrocnemius. From the results of subgraph decomposition, it can be concluded that the synergistic effects between different regions of the legs are similar, and the synergistic ability between thigh muscles is significantly higher than that of calf muscles. The dynamic network modeling framework provides a new idea for muscle synergy analysis, which can not only analyze the community clustering of muscle groups from a macro perspective, but also quantify the degree of synergy between muscle regions.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"3910-3921"},"PeriodicalIF":5.2,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11176879","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145130813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beta-Band Corticomuscular Coherence: A Novel Biomarker of Functional Corticospinal Tract Integrity and Motor Recovery After Stroke","authors":"Jingyao Sun;Qilu Zhang;Di Ma;Tianyu Jia;Shijie Jia;Xiaoxue Zhai;Ruimou Xie;Ping-Ju Lin;Zhibin Li;Yu Pan;Linhong Ji;Chong Li","doi":"10.1109/TNSRE.2025.3613417","DOIUrl":"10.1109/TNSRE.2025.3613417","url":null,"abstract":"Upper-limb motor impairment following stroke predominantly results from the damage to corticospinal tract (CST) integrity. Current clinical assessments of CST integrity face significant limitations, including high costs, specialized equipment, and inability to guide state-dependent closed-loop rehabilitation therapies. Corticomuscular coherence (CMC), which measures the functional coupling between sensorimotor cortical rhythms and muscular activity, represents a potentially accessible, and clinically feasible alternative for evaluating CST damage in stroke patients. However, it remains unclear whether CMC is a reliable biomarker of CST integrity and poststroke motor recovery. To address this issue, we measured electroencephalography (EEG), electromyography (EMG) and motor-evoked potential (MEP) status from subacute patients during grip and finger extension tasks performed with both affected and unaffected hands. Using a multivariate analysis approach, we identified abnormal modulations of CMC and event-related desynchronization (ERD), characterized by frequency-specific disruptions and distinctive spatial distributions. Crucially, our results also demonstrated that CMC reflects neurophysiological mechanisms distinct from cortical activation. Further analysis revealed significant CMC differences between patient groups stratified by MEP status, and confirmed the predictive value of CMC features for assessing functional CST integrity. Additionally, there existed significant associations between beta-band CMC and clinical motor assessments. These findings highlight the potential utility of CMC as a valuable tool for assessing functional CST integrity and motor recovery after stroke.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"3856-3865"},"PeriodicalIF":5.2,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11176878","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145130790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}