Temporal control of muscle synergies is linked with alpha-band neural drive.

The Journal of Physiology Pub Date : 2021-07-01 Epub Date: 2021-05-31 DOI:10.1113/JP281232
Christopher M Laine, Brian A Cohn, Francisco J Valero-Cuevas
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引用次数: 8

Abstract

Key points: It is theorized that the nervous system controls groups of muscles together as functional units, or 'synergies', resulting in correlated electromyographic (EMG) signals among muscles. However, such correlation does not necessarily imply group-level neural control. Oscillatory synchronization (coherence) among EMG signals implies neural coupling, but it is not clear how this relates to control of muscle synergies. EMG was recorded from seven arm muscles of 10 adult participants rotating an upper limb ergometer, and EMG-EMG coherence, EMG amplitude correlations and their relationship with each other were characterized. A novel method to derive multi-muscle synergies from EMG-EMG coherence is presented and these are compared with classically defined synergies. Coherent alpha-band (8-16 Hz) drive was strongest among muscles whose gross activity levels are well correlated within a given task. The cross-muscle distribution and temporal modulation of coherent alpha-band drive suggests a possible role in the neural coordination/monitoring of synergies.

Abstract: During movement, groups of muscles may be controlled together by the nervous system as an adaptable functional entity, or 'synergy'. The rules governing when (or if) this occurs during voluntary behaviour in humans are not well understood, at least in part because synergies are usually defined by correlated patterns of muscle activity without regard for the underlying structure of their neural control. In this study, we investigated the extent to which comodulation of muscle output (i.e. correlation of electromyographic (EMG) amplitudes) implies that muscles share intermuscular neural input (assessed via EMG-EMG coherence analysis). We first examined this relationship among pairs of upper limb muscles engaged in an arm cycling task. We then applied a novel multidimensional EMG-EMG coherence analysis allowing synergies to be characterized on the basis of shared neural drive. We found that alpha-band coherence (8-16 Hz) is related to the degree to which overall muscle activity levels correlate over time. The extension of this coherence analysis to describe the cross-muscle distribution and temporal modulation of alpha-band drive revealed a close match to the temporal and structural features of traditionally defined muscle synergies. Interestingly, the coherence-derived neural drive was inversely associated with, and preceded, changes in EMG amplitudes by ∼200 ms. Our novel characterization of how alpha-band neural drive is dynamically distributed among muscles is a fundamental step forward in understanding the neural origins and correlates of muscle synergies.

Abstract Image

Abstract Image

肌肉协同作用的时间控制与α带神经驱动有关。
重点:从理论上讲,神经系统将肌肉群作为功能单位或“协同作用”控制在一起,从而导致肌肉之间的相关肌电图(EMG)信号。然而,这种相关性并不一定意味着群体水平的神经控制。肌电图信号之间的振荡同步(相干性)意味着神经耦合,但尚不清楚这与肌肉协同作用的控制有何关系。对10名旋转上肢测功仪的成人受试者的7块手臂肌肉进行肌电图记录,并对肌电图相干性、肌电图幅值相关性及其相互关系进行表征。提出了一种从肌电-肌电相干性中推导多肌肉协同作用的新方法,并将其与经典定义的协同作用进行了比较。在一个给定的任务中,总的活动水平相关的肌肉中,相干α频带(8-16赫兹)驱动最强。相干α带驱动的跨肌肉分布和时间调制表明可能在协同作用的神经协调/监测中起作用。摘要:在运动过程中,神经系统可以作为一个适应性功能实体或“协同”控制肌肉群。在人类的自愿行为中,何时(或是否)发生协同作用的规则尚不清楚,至少部分原因是协同作用通常由肌肉活动的相关模式来定义,而不考虑其神经控制的潜在结构。在这项研究中,我们研究了肌肉输出的调节(即肌电图(EMG)振幅的相关性)意味着肌肉共享肌间神经输入的程度(通过肌电图-肌电图相干性分析进行评估)。我们首先在参与手臂循环任务的上肢肌肉对之间研究了这种关系。然后,我们应用了一种新颖的多维肌电图-肌电图相干性分析,允许在共享神经驱动的基础上表征协同作用。我们发现,α波段相干性(8-16赫兹)与整体肌肉活动水平随时间的相关程度有关。将这种相干性分析扩展到描述跨肌肉分布和α波段驱动的时间调制,揭示了与传统定义的肌肉协同作用的时间和结构特征的密切匹配。有趣的是,相干性衍生的神经驱动与肌电振幅的变化呈负相关,并先于肌电振幅的变化约200 ms。我们对α带神经驱动如何在肌肉中动态分布的新描述是理解神经起源和肌肉协同作用相关关系的重要一步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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