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不同外骨骼机器人下肢训练方式对卒中神经网络重塑的直接影像证据
机器人圈· 2025-10-30 11:07
Core Insights - The article discusses a high-quality randomized controlled study published in the Archives of Physical Medicine and Rehabilitation, which reveals significant differences in neural remodeling pathways between passive and assistive training modes for stroke patients using lower-limb exoskeleton robots [2][4][7] Research Background - Stroke-related lower limb motor impairments often result in decreased cortical drive on the affected side and compensatory movement strategies. Traditional physical training improves performance but does not precisely regulate the recovery direction of brain networks. The emergence of exoskeletons enhances patient engagement in movement, yet there is uncertainty regarding the effectiveness of passive versus assistive gait training [3][4] Research Overview - The study included over 50 subacute stroke patients who underwent two weeks of exoskeleton gait training, with dynamic brain function monitoring using functional near-infrared spectroscopy (fNIRS). Passive training was found to directly promote synchronization in the affected motor-related networks, aligning cortical interactions with healthy gait strategies, which correlated with improvements in motor function [4][5] Key Findings - In patients with severe functional impairments, assistive training led to increased activation in contralateral brain regions associated with compensatory strategies. While this activation supports task execution, it does not foster beneficial coupling with core motor networks, indicating that assistive training may provide short-term benefits but hinder long-term recovery of the affected networks [5][7] Research Significance - The study provides critical theoretical and practical insights, demonstrating that passive training has greater potential for restoring the affected cortical areas, while excessive early implementation of assistive training may lead to reliance on compensatory pathways. This finding emphasizes the importance of evaluating training effectiveness based on neural recovery rather than solely on observable movement success [7][8]