Understanding the frequency dependent alterations in brain-muscle communication after stroke is crucial for advancing targeted neurorehabilitation strategies. In this study, we propose a novel multilayer corticomuscular network (MCMN) model based on functional corticomuscular coupling characteristics. Using multi-channel electrophysiological recordings acquired during a multi-joint motor task, we constructed a super-connectivity matrix by combining phase synchronization and phase-amplitude coupling across frequency bands. We then examined both local (single-layer) and global (multilayer) network properties by comparing nodal metrics between stroke patients and healthy controls in terms of functional connectivity and topological organization. The results revealed that stroke patients exhibited enhanced theta band within-frequency subnetwork relative to controls, but significantly reduced beta and gamma band subnetworks. Cross-frequency subnetworks in patients showed diminished integrative capacity compared to controls, with the exception of proximal muscle nodes in the beta-gamma subnetwork, which displayed pronounced hub properties. At the global level, patients demonstrated contralateral compensatory reorganization, whereas the contralateral hemisphere exhibited impaired cross-layer integration. The MCMN of stroke patients showed reduced algebraic connectivity, reflecting lower network robustness and information transfer efficiency. Finally, we found that node degree of gamma band and multiplex clustering coefficient of ipsilateral exhibited a linear correlation with FMA-UE scores in stroke patients. This multilayer network approach reveals frequency-specific and topological reorganization of corticomuscular interactions following stroke, providing a novel systems level framework for exploring motor network plasticity and informing precision neurorehabilitation.
Hao et al. (2026) studied this question.