Ortega-Auriol Pablo A, Besier Thor F, Byblow Winston D, McMorland Angus J C
Movement Neuroscience Laboratory, Department of Exercise Sciences and Centre for Brain Research, University of Auckland, Auckland, New Zealand.
Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.
Front Hum Neurosci. 2018 Jun 21;12:217. doi: 10.3389/fnhum.2018.00217. eCollection 2018.
The development of fatigue elicits multiple adaptations from the neuromuscular system. Muscle synergies are common patterns of neuromuscular activation that have been proposed as the building blocks of human movement. We wanted to identify possible adaptations of muscle synergies to the development of fatigue in the upper limb. Recent studies have reported that synergy structure remains invariant during the development of fatigue, but these studies did not examine isolated synergies. We propose a novel approach to characterise synergy adaptations to fatigue by taking advantage of the spatial tuning of synergies. This approach allows improved identification of changes to individual synergies that might otherwise be confounded by changing contributions of overlapping synergies. To analyse upper limb synergies, we applied non-negative matrix factorization to 14 EMG signals from muscles of 11 participants performing isometric contractions. A preliminary multidirectional task was used to identify synergy directional tuning. A subsequent fatiguing task was designed to fatigue the participants in their synergies' preferred directions. Both tasks provided virtual reality feedback of the applied force direction and magnitude, and were performed at 40% of each participant's maximal voluntary force. Five epochs were analysed throughout the fatiguing task to identify progressive changes of EMG amplitude, median frequency, synergy structure, and activation coefficients. Three to four synergies were sufficient to account for the variability contained in the original data. Synergy structure was conserved with fatigue, but interestingly synergy activation coefficients decreased on average by 24.5% with fatigue development. EMG amplitude did not change systematically with fatigue, whereas EMG median frequency consistently decreased across all muscles. These results support the notion of a neuromuscular modular organisation as the building blocks of human movement, with adaptations to synergy recruitment occurring with fatigue. When synergy tuning properties are considered, the reduction of activation of muscle synergies may be a reliable marker to identify fatigue.
疲劳的发展会引发神经肌肉系统的多种适应性变化。肌肉协同作用是神经肌肉激活的常见模式，被认为是人类运动的基本组成部分。我们想要确定肌肉协同作用对上肢体疲劳发展的可能适应性变化。最近的研究报告称，在疲劳发展过程中协同结构保持不变，但这些研究并未考察孤立的协同作用。我们提出一种新颖的方法，通过利用协同作用的空间调谐来表征协同作用对疲劳的适应性变化。这种方法能够更好地识别个体协同作用的变化，否则这些变化可能会因重叠协同作用贡献的改变而混淆。为了分析上肢体协同作用，我们将非负矩阵分解应用于11名参与者在进行等长收缩时14块肌肉的肌电图信号。一个初步的多方向任务用于识别协同作用的方向调谐。随后设计了一个疲劳任务，使参与者在其协同作用的偏好方向上产生疲劳。两个任务都提供了施加力的方向和大小的虚拟现实反馈，并且在每个参与者最大自主力的40%下进行。在整个疲劳任务过程中分析了五个时间段，以识别肌电图幅度、中位频率、协同结构和激活系数的渐进变化。三到四个协同作用足以解释原始数据中包含的变异性。协同结构在疲劳过程中保持不变，但有趣的是，随着疲劳的发展，协同作用激活系数平均下降了24.5%。肌电图幅度并未随疲劳而系统性变化，而所有肌肉的肌电图中位频率持续下降。这些结果支持了神经肌肉模块化组织作为人类运动基本组成部分的观点，并且随着疲劳会出现协同作用募集的适应性变化。当考虑协同作用调谐特性时，肌肉协同作用激活的降低可能是识别疲劳的可靠标志。
