Multisensory BCI promotes motor recovery via high-order network-mediated interhemispheric integration in chronic stroke

作者：Rongrong Lu, Zhen Pang, Tianhao Gao, Zhijie He, Yiqian Hu, Jie Zhuang, Qin Zhang, Zhengrun Gao · 发表于：BMC Medicine · 年份：2025 · DOI：10.1186/s12916-025-04214-8 · 被引用次数：14 · 研究领域：EEG and Brain-Computer Interfaces、Stroke Rehabilitation and Recovery、Transcranial Magnetic Stimulation Studies

BACKGROUND: Chronic stroke patients often experience persistent motor impairments, and current rehabilitation therapies rarely achieve substantial functional recovery. Sensory feedback during movement plays a pivotal role in driving neuroplasticity. This study introduces a novel multi-modal sensory feedback brain-computer interface (Multi-FDBK-BCI) system that integrates proprioceptive, tactile, and visual stimuli into motor imagery-based training. We aimed to explore the potential therapeutic efficacy and elucidate its neural mechanisms underlying motor recovery. METHODS: Thirty-nine chronic stroke patients were randomized to either the Multi-FDBK-BCI group (n = 20) or the conventional motor imagery therapy group (n = 19). Motor recovery was assessed using the Fugl-Meyer Assessment (primary outcome), Motor Status Scale, Action Research Arm Test, and surface electromyography. Functional MRI was used to examine brain activation patterns during upper limb tasks, while Granger causality analysis and machine learning evaluated inter-regional connectivity changes and their predictive value for recovery. RESULTS: Multi-FDBK-BCI training led to significantly greater motor recovery compared to conventional therapy. Functional MRI revealed enhanced activation of high-order transmodal networks-including the default mode, dorsal/ventral attention, and frontoparietal networks-during paralyzed limb movement, with activation strength positively correlated with motor improvement. Granger ca...