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Forty-four participants underwent resting-state EEG before and after intervention, with directed connectivity estimated via source-level PDC, graph theory and laterality index, followed by machine learning using frequency-specific features. Results showed connectivity increases after training, altered laterality, and frequency-specific EEG features enabling 93.18% classification accuracy, supporting EEG biomarkers for precision 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Foundation  \nRutgers University  \nStroke, Rehabilitation, Exoskeleton, Gait, EEG, Rest State Connectivity, Asymmetry, Machine Learning  \n December 9th, 2025  \n [https://doi.org/10.21203/rs.3.rs-7411628/v1](https://doi.org/10.21203/rs.3.rs-7411628/v1)  \n 􀁱 􀅏 This work is licensed under a Creative Commons Attribution 4.0 International License.  \nRead Full License  \n No competing interests reported.  \nStroke-induced motor impairments are linked to disrupted cortical connectivity and remain a major challenge in neurorehabilitation. The purpose of this investigation was to evaluate brain network reorganization and its relationship to motor recovery following a 10-week gait rehabilitation program (3 days/week for a total of 30 sessions) .  \n Forty-four participants (22 healthy adults HC, 22 individuals diagnosed with stroke) were enrolled, individuals in the stroke group were randomized into Exoskeleton-Assisted Rehabilitation (ER=9) or Standard of Care (SOC=13) groups. Resting-state EEG was recorded pre-and post-intervention. Directed functional connectivity was estimated from localized sources using Partial Directed Coherence (PDC), followed by graph theory analysis, laterality index (LI) computation, and machine learning classi«cation using frequency-speci«c EEG features.  \n At baseline, individuals in the stroke group demonstrated reduced connectivity relative to HC group in the affected supplementary motor area (SMA, p\u003C0 .005) and unaffected ventral premotor cortex (vPM, p\u003C0 .01), with compensatory increases in the affected insula (INS) and dorsalA6 . Post-intervention, connectivity increased from the unaffected primary motor cortex (M1) and affected postcentral gyrus (PoG, p\u003C0 .01), with dorsalA6 showing enhanced node strength (p\u003C0 .01) . LI analysis indicated reduced contralesional compensation in cingulate gyrus (CG) and middle temporal gyrus (MTG), and reengagement of the affected hemisphere (PoG, dorsalA6) . DorsalA6 connectivity correlated with FuglMeyer score improvements (r=0 . 64, p=0 .001) . Machine learning achieved 93. 18% accuracy and 94.83% Area Under ROC Curve to differentiate between the groups with features from the alpha and gamma being the most discriminative.  \n Source-level EEG connectivity and frequency-speci«c features are sensitive biomarkers of neuroplasticity. The 10-week program enhanced motor network integration and hemispheric balance, re¬ected in connectivity and clinical outcomes. EEG-based machine learning tools offer scalable solutions for precision assessment of neurorehabilitation.  \nStroke is a leading cause of long-term disability and mortality worldwide and often leads to motor and sensory impairments due to disrupted cerebral blood ¬ow, resulting in functional limitations such ashemiparesis, impaired balance, and abnormal gait [1] . Despite standard rehabilitation, 20–30% of individuals post stroke continue to experience severe mobility challenges, making gait restoration a key therapeutic goal [2],[3],[4] .  \nRehabilitation efforts now emphasize intensive, repetitive, and patient-centered approaches, which are more effective at promoting neuroplasticity than traditional therapies [8, 9] . Exoskeleton-Assisted Rehabilitation (ER) has emerged as a promising strategy for improving gait and motor function in stroke survivors. ER facilitates sagittal plane movement patterns through assist-as-needed gait rehabilitation,  \npotentially enhancing motor learning and reducing maladaptive compensation. Compared with body weight-supported treadmill training, such as the G-EO System [ 5] and Lokomat [ 6], robotic exoskeletonscan allow individuals to ability to control step initiation, promote symmetry, improve midline balance and trunk control, as well as integrate the variability of visuospatial ¬ow and vestibular stimulation. Additionally, when combined with or compared to Standard of Care (SOC) rehabilitation","cbCainIIQZrbDuZr","https://ap.wps.com/l/cbCainIIQZrbDuZr","pdf",1548060,33,"English","# Stroke rehabilitation and neural connectivity background\n## Rehabilitation strategy and robotic exoskeleton-assisted training\n## Why EEG and directed connectivity biomarkers\n# Study design and analysis pipeline\n## Participants and intervention groups\n## Resting-state EEG and source-level PDC estimation\n## Graph theory, laterality index, and machine learning classification\n# Findings and implications","[{\"question\":\"What was the goal of the 10-week rehabilitation investigation?\",\"answer\":\"To evaluate brain network reorganization and how it relates to motor recovery after a 10-week gait rehabilitation program.\"},{\"question\":\"How were directed functional connectivity and laterality assessed?\",\"answer\":\"Resting-state EEG was recorded before and after intervention, directed functional connectivity was estimated using source-level Partial Directed Coherence (PDC), and graph theory with a laterality index (LI) was computed.\"},{\"question\":\"What did the machine learning model achieve in distinguishing groups?\",\"answer\":\"It achieved 93.18% accuracy and an ROC AUC of 94.83%, with discriminative features mainly from alpha and gamma frequency bands.\"}]","Stroke-Induced Motor Impairments: Frequency-Specific Neural Reorganization Revealed by Source-Level Resting-State EEG Connectivity - Machine Learning Biomarkers | PDF",1790741034,83]