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首页> 外文会议>Annual International Conference of the IEEE Engineering in Medicine and Biology Society >Altered Muscle Networks in Post-Stroke Survivors
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Altered Muscle Networks in Post-Stroke Survivors

机译：脑卒中后幸存者的肌肉网络发生改变

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Muscle networks represent a series of interactions among muscles in the central nervous system’s effort to reduce the redundancy of the musculoskeletal system in motor-control. How this occurs has only been investigated recently in healthy subjects with a novel technique exploring the functional connectivity between muscles through intermuscular coherence (IMC), yet the potential value of this method in characterizing the alteration of muscular networks after stroke remains unknown. In this study, muscle networks were assessed in post-stroke survivors and healthy controls to identify possible alterations in the neural oscillatory drive to muscles after stroke. Surface electromyography (sEMG) was collected from eight key upper extremity muscles to non-invasively determine the common neural input to the spinal motor neurons innervating muscle fibers. Coherence was computed between all possible muscle pairs and further decomposed by non-negative matrix factorization (NMF) to identify the common spectral patterns of coherence underlying the muscle networks. Results suggested that the number of identified muscle networks during dynamic force generation decreased after stroke. The findings in this study could provide a new prospective for understanding the motor control recovery during post-stroke rehabilitation.
机译：肌肉网络代表着中枢神经系统中肌肉之间的一系列相互作用，这些运动旨在减少肌肉骨骼系统在运动控制中的冗余度。最近如何在健康受试者中研究这种情况的发生，该新技术通过肌间连贯性（IMC）探索了肌肉之间的功能连接性，但是该方法在表征卒中后肌肉网络变化方面的潜在价值仍然未知。在这项研究中，对中风后幸存者和健康对照者的肌肉网络进行了评估，以确定中风后肌肉对神经的振荡驱动可能发生的变化。从八个关键的上肢肌肉收集表面肌电图（sEMG），以非侵入方式确定神经肌肉支配的脊髓运动神经元的常见神经输入。计算所有可能的肌肉对之间的连贯性，然后通过非负矩阵分解（NMF）进行分解，以识别肌肉网络基础上连贯性的常见频谱图。结果表明，中风后动态力产生过程中已识别的肌肉网络数量减少。这项研究的发现可以为理解卒中后康复过程中运动控制的恢复提供新的前景。

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