Source-Level Resting-State EEG Connectivity Reveals Frequency-Specific Neural Reorganization and Predicts Motor Recovery in Individuals Post Stroke Following Gait Rehabilitation

TL;DR

This study shows that resting-state EEG can track brain changes after stroke rehabilitation and predict motor recovery, offering a new tool for neurorehabilitation.

Contribution

The study introduces source-level EEG connectivity as a novel biomarker for tracking neural reorganization and predicting motor recovery post-stroke.

Findings

Post-stroke individuals showed baseline reduced connectivity in SMA and vPM, with compensatory increases in the insula and dorsalA6.

After rehabilitation, connectivity increased in M1 and PoG, with dorsalA6 showing enhanced node strength and correlating with motor recovery.

Machine learning achieved high accuracy in differentiating groups using alpha and gamma frequency EEG features.

Abstract

Stroke-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).

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 classification using frequency-specific EEG features.

At baseline, individuals in the stroke group demonstrated reduced connectivity relative to HC group in the affected supplementary motor area (SMA, p<0.005) and unaffected ventral premotor cortex (vPM, p<0.01), 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<0.01), with dorsalA6 showing enhanced node strength (p<0.01). LI analysis indicated reduced contralesional compensation in cingulate gyrus (CG) and middle temporal gyrus (MTG), and re-engagement of the affected hemisphere (PoG, dorsalA6). DorsalA6 connectivity correlated with Fugl-Meyer 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.

Source-level EEG connectivity and frequency-specific features are sensitive biomarkers of neuroplasticity. The 10-week program enhanced motor network integration and hemispheric balance, reflected in connectivity and clinical outcomes. EEG-based machine learning tools offer scalable solutions for precision assessment of neurorehabilitation.

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