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High-density surface EMG decomposition allows for recording of motorunit discharge from proximal and distal flexion synergy muscles simultaneouslyin individuals with stroke

机译:高密度表面肌电分解可记录电机同时从近端和远端屈曲协同肌肉放电在中风患者中

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摘要

Analysis of motor unit discharge can provide insight into the neural control of movement in healthy and pathological states, but it is typically completed in one muscle at a time. For some research investigations, it would be advantageous to study motor unit discharge from multiple muscles simultaneously. One such example is investigation of the flexion synergy, an abnormal muscle co-activation pattern in post-stroke individuals in which activation of shoulder abductors is involuntarily coupled with that of elbow and finger flexors. However, limitations in available technology have hindered the ability to efficiently extract motor unit discharge from multiple muscles simultaneously. In this study, we propose the use of high-density surface EMG decomposition from proximal and distal flexion synergy muscles (deltoid, biceps, wrist/finger flexors) in combination with an isometric joint torque recording device in individuals with chronic stroke. This innovative approach provides the ability to efficiently analyze both motor units and joint torques that have been simultaneously recorded from the shoulder, elbow, and fingers. In preliminary experiments, 3 stroke and 5 control participants generated shoulder abduction, elbow flexion, and finger flexion torques at 10, 20, 30 and 40% of maximumtorque. Motor unit spike trains could be extracted from all muscles at eachtorque level. Mean motor unit firing rates were significantly lower in thestroke group than in the control group for all three muscles. Within the strokegroup, wrist/finger flexor motor units had the lowest coefficient of variation.Additionally, modulation of mean firing rates across torque levels wassignificantly impaired in all three paretic muscles. The implications of thesefindings and overall impact of this approach are discussed.
机译:运动单位放电的分析可以深入了解健康和病理状态下运动的神经控制,但通常一次完成一次肌肉运动。对于某些研究调查,同时研究多个肌肉的运动单位放电将是有利的。一个这样的例子是对屈曲协同作用的研究,屈曲协同作用是中风后个体中异常的肌肉共激活模式,其中肩外展肌的激活不自觉地与肘关节和手指屈肌的激活相结合。然而,可用技术的局限性阻碍了同时从多块肌肉中有效提取运动单位放电的能力。在这项研究中,我们建议在患有中风的慢性卒中患者中,结合近端和远端屈曲协同肌肉(三角肌,二头肌,腕部/手指屈肌)使用高密度表面肌电图分解结合等距关节扭矩记录装置。这种创新的方法提供了有效地分析从肩膀,肘部和手指同时记录的运动单位和关节扭矩的能力。在初步实验中,有3名中风和5名对照组参与者产生了最大外展力的10%,20%,30%和40%的肩外展,肘部屈曲和手指屈曲扭矩扭矩。可以从每个位置的所有肌肉中提取出运动单位的峰值训练扭矩水平。在中风组的全部三块肌肉均比对照组多。中风之内组中,腕/指屈肌运动单元的变异系数最低。此外,整个扭矩水平上的平均点火速率调制为在所有三个平行肌中均明显受损。这些的含义讨论了该方法的发现和总体影响。

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