Functional connectivity between non-motor and motor networks predicts motor recovery changes after stroke.

ABSTRACT: Stroke impairs limb motor function, which affects patients' quality of life and imposes economic burdens. Early prediction of motor recovery is essential for guiding treatment and rehabilitation. While the corticospinal tract is a known biomarker, the role of non-motor brain regions remains under explored. Fifty-five stroke patients with unilateral subcortical lesions and 49 healthy controls underwent resting-state functional MRI scans at 1 week, 4 weeks, and 12 weeks after stroke. Focusing on two motor and 15 non-motor networks defined by the Schaefer atlas, machine learning models were used to predict changes in motor function measured by the Fugl-Meyer assessment using functional connectivity (FC) data. The network-based statistic (NBS) method was used to identify significant FC differe

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC12638762 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature