Hagio Shota, Kouzaki Motoki
Japan Society for the Promotion of Science, Tokyo, Japan.
Exp Brain Res. 2015 Jun;233(6):1811-23. doi: 10.1007/s00221-015-4253-5. Epub 2015 Mar 21.
It has long been assumed that the human central nervous system uses flexible combinations of several muscle synergies to effortlessly and efficiently control redundant movements. However, whether muscle synergies exist in the neural circuit remains controversial, and it is critical to examine the association between the recruitment pattern of synergies and motor output. In this study, we examined the relationship between the activation of muscle synergies and endpoint force fluctuations in the presence of signal-dependent noise. Subjects performed multi-directional isometric force generations around the right ankle on the sagittal plane. We then extracted muscle synergies from measured electromyogram (EMG) data using nonnegative matrix factorization. As a result, the sum of the activation of muscle synergies was correlated with the endpoint force variability from the desired directions. Furthermore, we determined that the activation trace of each synergy reflected the endpoint force fluctuations using cross-correlation analysis. Therefore, these results suggest that muscle synergies statistically calculated from EMG data should be related to the motor output.
长期以来,人们一直认为人类中枢神经系统利用几种肌肉协同作用的灵活组合来轻松、高效地控制冗余运动。然而,肌肉协同作用是否存在于神经回路中仍存在争议,研究协同作用的募集模式与运动输出之间的关联至关重要。在本研究中,我们研究了在存在信号相关噪声的情况下肌肉协同作用的激活与端点力波动之间的关系。受试者在矢状面上围绕右踝关节进行多方向等长力生成。然后,我们使用非负矩阵分解从测量的肌电图(EMG)数据中提取肌肉协同作用。结果,肌肉协同作用激活的总和与端点力相对于期望方向的变异性相关。此外,我们通过互相关分析确定每个协同作用的激活轨迹反映了端点力波动。因此,这些结果表明,从EMG数据中统计计算出的肌肉协同作用应与运动输出相关。