1 McGill University, Montréal, QC, Canada.
2 University of Montréal, Montréal, QC, Canada.
Neurorehabil Neural Repair. 2019 Jan;33(1):70-81. doi: 10.1177/1545968318818902. Epub 2018 Dec 29.
Passive robot-generated arm movements in conjunction with proprioceptive decision making and feedback modulate functional connectivity (FC) in sensory motor networks and improve sensorimotor adaptation in normal individuals. This proof-of-principle study investigates whether these effects can be observed in stroke patients.
A total of 10 chronic stroke patients with a range of stable motor and sensory deficits (Fugl-Meyer Arm score [FMA] 0-65, Nottingham Sensory Assessment [NSA] 10-40) underwent resting-state functional magnetic resonance imaging before and after a single session of robot-controlled proprioceptive training with feedback. Changes in FC were identified in each patient using independent component analysis as well as a seed region-based approach. FC changes were related to impairment and changes in task performance were assessed.
A single training session improved average arm reaching accuracy in 6 and proprioception in 8 patients. Two networks showing training-associated FC change were identified. Network C1 was present in all patients and network C2 only in patients with FM scores >7. Relatively larger C1 volume in the ipsilesional hemisphere was associated with less impairment ( r = 0.83 for NSA, r = 0.73 for FMA). This association was driven by specific regions in the contralesional hemisphere and their functional connections (supramarginal gyrus with FM scores r = 0.82, S1 with NSA scores r = 0.70, and cerebellum with NSA score r = -0.82).
A single session of robot-controlled proprioceptive training with feedback improved movement accuracy and induced FC changes in sensory motor networks of chronic stroke patients. FC changes are related to functional impairment and comprise bilateral sensory and motor network nodes.
被动机器人生成的手臂运动与本体感受决策和反馈相结合，可以调节感觉运动网络中的功能连接（FC），并改善正常个体的感觉运动适应。这项原理验证研究调查了这些效果是否可以在中风患者中观察到。
共有 10 名慢性中风患者，其运动和感觉障碍程度不一（Fugl-Meyer 上肢评分 [FMA] 0-65，诺丁汉感觉评估 [NSA] 10-40），在接受单次机器人控制的本体感受训练和反馈后，进行了静息态功能磁共振成像。使用独立成分分析和种子区域方法，在每个患者中确定了 FC 的变化。FC 变化与损伤有关，并评估了任务表现的变化。
单次训练 session 可提高 6 名患者的平均手臂伸展准确性和 8 名患者的本体感觉准确性。确定了两个与训练相关的 FC 变化网络。网络 C1 存在于所有患者中，网络 C2 仅存在于 FMA 评分 >7 的患者中。患侧半球相对较大的 C1 体积与较小的损伤程度相关（NSA 与 FMA 的 r = 0.83，FMA 的 r = 0.73）。这种相关性是由对侧半球的特定区域及其功能连接驱动的（缘上回与 FMA 评分的 r = 0.82，S1 与 NSA 评分的 r = 0.70，小脑与 NSA 评分的 r = -0.82）。
单次机器人控制的本体感受训练和反馈可改善慢性中风患者的运动准确性，并诱导感觉运动网络中的 FC 变化。FC 变化与功能障碍有关，包括双侧感觉和运动网络节点。
