Department of Electrical and Computer Engineering, New York University, New York, NY, USA.
Department of Medicine, New York University Langone Health, New York, NY, USA.
Sci Rep. 2022 Jul 29;12(1):13029. doi: 10.1038/s41598-022-16483-x.
Sensory information is critical for motor coordination. However, understanding sensorimotor integration is complicated, especially in individuals with impairment due to injury to the central nervous system. This research presents a novel functional biomarker, based on a nonlinear network graph of muscle connectivity, called InfoMuNet, to quantify the role of sensory information on motor performance. Thirty-two individuals with post-stroke hemiparesis performed a grasp-and-lift task, while their muscle activity from 8 muscles in each arm was measured using surface electromyography. Subjects performed the task with their affected hand before and after sensory exposure to the task performed with the less-affected hand. For the first time, this work shows that InfoMuNet robustly quantifies changes in functional muscle connectivity in the affected hand after exposure to sensory information from the less-affected side. > 90% of the subjects conformed with the improvement resulting from this sensory exposure. InfoMuNet also shows high sensitivity to tactile, kinesthetic, and visual input alterations at the subject level, highlighting its potential use in precision rehabilitation interventions.
感觉信息对于运动协调至关重要。然而，理解感觉运动整合很复杂，特别是对于因中枢神经系统损伤而导致障碍的个体。本研究提出了一种新的功能生物标志物，基于肌肉连接的非线性网络图，称为 InfoMuNet，用于量化感觉信息对运动表现的作用。32 名脑卒中偏瘫患者进行了抓握和提起任务，同时使用表面肌电图测量他们每个手臂的 8 块肌肉的肌肉活动。患者先用患侧手完成任务，然后再用健侧手完成任务以暴露在感觉信息下，之后再用患侧手完成任务。这项工作首次表明，InfoMuNet 可以稳健地量化在暴露于健侧手的感觉信息后患侧手功能肌肉连接的变化。>90%的患者在接受这种感觉暴露后表现出改善。InfoMuNet 在个体水平上也对触觉、运动觉和视觉输入的变化具有高灵敏度，突出了其在精准康复干预中的潜在用途。
