Task-State EEG Reveals Fine Hand Motor Impairment in Acute Ischemic Stroke: A Multidimensional Analysis of Cortical Dynamics.

PurposeTo systematically characterize the cortical dynamics underlying fine hand motor impairment in patients with acute ischemic stroke (AIS) using a multidimensional task-state electroencephalography (EEG) analysis framework.Materials and MethodsFifteen patients with AIS and sixteen age- and sex-matched healthy controls were enrolled. EEG signals were recorded while participants performed three standardized fine motor tasks, including fist clenching, index finger pointing, and thumb-finger opposition. Source localization, time-frequency analysis, and brain network topology analysis were jointly applied to extract multidimensional electrophysiological features. Correlation analyses were further conducted to examine the relationships between EEG-derived metrics and clinical measures, including muscle strength grading, National Institutes of Health Stroke Scale (NIHSS) scores, and Activities of Daily Living (ADL) scores.ResultsCompared with healthy controls, patients with AIS showed abnormal spatiotemporal cortical dynamics during fine hand movements. These abnormalities included delayed movement-related potentials, a shift of alpha- and beta-band event-related desynchronization from contralateral dominance toward more bilateral and diffuse activation, and altered brain network organization characterized by reduced network efficiency and changes in nodal centrality. In addition, EEG-derived features were significantly associated with clinical measures. Specifically, several event-related desynchronization/event-related synchronization (ERD/ERS)-related amplitudes and small-world properties were significantly correlated with muscle strength grading, NIHSS scores, and ADL scores after false discovery rate correction.ConclusionMultidimensional task-state EEG analysis can characterize cortical activation abnormalities and network reorganization associated with fine hand motor impairment in patients with AIS. The identified EEG-derived metrics may serve as objective markers for post-stroke motor function assessment and recovery monitoring, and may help support individualized rehabilitation evaluation and planning.