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Abstract：
A long standing hypothesis in the neuroscience community is that the central nervous system (CNS) generates the muscle activities to accomplish movements by combining a relatively small number of stereotyped patterns of muscle activations, often referred to as \"muscle synergies.\" Different definitions of synergies have been given in the literature. The most well-known are those of synchronous, time-varying and temporal muscle synergies. Each one of them is based on a different mathematical model used to factor some EMG array recordings collected during the execution of variety of motor tasks into a well-determined spatial, temporal or spatio-temporal organization. This plurality of definitions and their separate application to complex tasks have so far complicated the comparison and interpretation of the results obtained across studies, and it has always remained unclear why and when one synergistic decomposition should be preferred to another one. By using well-understood motor tasks such as elbow flexions and extensions, we aimed in this study to clarify better what are the motor features characterized by each kind of decomposition and to assess whether, when and why one of them should be preferred to the others. We found that three temporal synergies, each one of them accounting for specific temporal phases of the movements could account for the majority of the data variation. Similar performances could be achieved by two synchronous synergies, encoding the agonist-antagonist nature of the two muscles considered, and by two time-varying muscle synergies, encoding each one a task-related feature of the elbow movements, specifically their direction. Our findings support the notion that each EMG decomposition provides a set of well-interpretable muscle synergies, identifying reduction of dimensionality in different aspects of the movements. Taken together, our findings suggest that all decompositions are not equivalent and may imply different neurophysiological substrates to be implemented.
摘要：
神经科学界的一个长期存在的假设是，中枢神经系统（CNS）通过结合相对少量的刻板的肌肉激活模式来产生肌肉活动以完成运动，通常被称为“肌肉协同作用”。“文献中对协同作用给出了不同的定义。最著名的是同步的，时变和时间肌肉协同作用。它们中的每一个都基于不同的数学模型，用于将在执行各种运动任务期间收集的一些EMG阵列记录分解为确定的空间，时间或时空组织。到目前为止，这种多种定义及其对复杂任务的单独应用使跨研究获得的结果的比较和解释变得复杂。它一直不清楚为什么和何时一个协同分解应该优先于另一个。通过使用众所周知的运动任务，如肘部弯曲和伸展，在这项研究中，我们的目的是更好地阐明每种分解所表征的运动特征是什么，并评估是否，什么时候以及为什么他们中的一个应该比其他人更受欢迎。我们发现三个时间协同作用，它们中的每一个都考虑了运动的特定时间阶段，可以解释大部分的数据变化。类似的性能可以通过两个同步协同实现，编码所考虑的两种肌肉的激动剂-拮抗剂性质，通过两种时变的肌肉协同作用，对每个肘部运动的任务相关特征进行编码，特别是他们的方向。我们的发现支持这样的观点，即每个EMG分解都提供了一组可解释的肌肉协同作用，识别运动不同方面的降维。一起来看,我们的研究结果表明,所有的分解都不是等价的,可能意味着要实施不同的神经生理学基础.
