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慢性脑卒中幸存者上肢肌肉网络的改变。

Alterations in Muscle Networks in the Upper Extremity of Chronic Stroke Survivors.

出版信息

IEEE Trans Neural Syst Rehabil Eng. 2021;29:1026-1034. doi: 10.1109/TNSRE.2021.3075907. Epub 2021 Jun 9.

Abstract

Muscle networks describe the functional connectivity between muscles quantified through the decomposition of intermuscular coherence (IMC) to identify shared frequencies at which certain muscles are co-modulated by common neural input. Efforts have been devoted to characterizing muscle networks in healthy subjects but stroke-linked alterations to muscle networks remain unexplored. Muscle networks were assessed for eight key upper extremity muscles during isometric force generation in stroke survivors with mild, moderate, and severe impairment and compared against healthy controls to identify stroke-specificalterations in muscle connectivity. Coherence matrices were decomposed using non-negative matrix factorization. The variance accounted for thresholding was then assessed to identify the number of muscle networks. Results showed that the number of muscle networks decreased in stroke survivors compared to age-matched healthy controls (four networks in the healthy control group) as the severity of post-stroke motor impairment increased (three in the mild- and two in the moderate- and severe-strokegroups). Statistically significant reductions of IMC in the synergistic deltoid muscles in the alpha-band in stroke patients versus healthy controls ( p < 0.05) were identified. This study represents the first effort, to the best of our knowledge, to assess stroke-linked alterations in functional intermuscular connectivity using muscle network analysis. The findings revealed a pattern of alterations to muscle networks in stroke survivors compared to healthy controls, as a result of the loss of brain function associated with the stroke. These alterations in muscle networks reflected underlying pathophysiology. These findings can help better understand the motor impairment and motor control in stroke and may advance rehabilitation efforts for stroke by identifying the impaired neuromuscular coordination among multiple muscles in the frequency domain.

摘要

肌肉网络描述了肌肉之间的功能连接,通过对肌肉间相干性(IMC)的分解来量化,以识别某些肌肉被共同神经输入共同调制的共享频率。人们已经致力于描述健康受试者的肌肉网络,但与中风相关的肌肉网络变化仍未被探索。评估了 8 名上肢关键肌肉在中风幸存者的等长力产生过程中的肌肉网络,这些幸存者的损伤程度从轻度、中度到重度不等,并与健康对照组进行比较,以确定肌肉连接中的中风特异性变化。使用非负矩阵分解来分解相干矩阵。然后评估解释方差的阈值,以确定肌肉网络的数量。结果表明,与年龄匹配的健康对照组(健康对照组有 4 个网络)相比,随着中风后运动损伤程度的增加,中风幸存者的肌肉网络数量减少(轻度组有 3 个,中度和重度组各有 2 个)。与健康对照组相比,在中风患者的协同三角肌的 alpha 波段中,IMC 显著降低(p < 0.05)。这是迄今为止首次使用肌肉网络分析来评估中风对功能性肌肉间连接的影响。与健康对照组相比,中风幸存者的肌肉网络存在变化模式,这是与中风相关的大脑功能丧失的结果。这些肌肉网络的变化反映了潜在的病理生理学。这些发现可以帮助更好地理解中风中的运动障碍和运动控制,并通过在频域中识别多个肌肉之间受损的神经肌肉协调,为中风的康复努力提供帮助。

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