Laboratory of Neuromotor Physiology, Santa Lucia Foundation, Italy.
J Neurophysiol. 2011 Dec;106(6):2796-812. doi: 10.1152/jn.00675.2010. Epub 2011 Aug 31.
We have recently shown that the muscle patterns for reaching are well described by the combination of a few time-varying muscle synergies supporting the notion of a modular architecture for arm control. Here we investigated whether the muscle patterns for reaching movements involving online corrections are also generated by the combination of the same set of time-varying muscle synergies used for point-to-point movements. We recorded endpoint kinematics and EMGs from up to 16 arm muscles of 5 subjects reaching from a central location to 8 peripheral targets in the frontal plane, from each peripheral target to 1 of the 2 adjacent targets, and from the central location initially to 1 peripheral target and, after a delay of either 50, 150, or 250 ms from the go signal, to 1 of the 2 adjacent targets. Time-varying muscle synergies were extracted from the averaged, phasic, normalized EMGs of point-to-point movements and fit to the patterns of target change movements using an iterative optimization algorithm. In all subjects, three time-varying muscle synergies explained a large fraction of the data variation of point-to-point movements. The superposition and modulation of the same three synergies reconstructed the muscle patterns for target change movements better than the superposition and modulation of the corresponding point-to-point muscle patterns, appropriately aligned. While at the kinematic level the corrective trajectory for reaching during a change in target location can be obtained by the delayed superposition of the trajectory from the initial to the final target, at the muscle level the underlying phasic muscle patterns are captured by the amplitude and timing modulation of the same time-varying muscle synergies recruited for point-to-point movements. These results suggest that a common modular architecture is used for the control of unperturbed arm movement and for its visually guided online corrections.
我们最近表明，手臂运动的肌肉模式可以很好地通过组合几个随时间变化的肌肉协同作用来描述，这支持了手臂控制的模块化架构的概念。在这里，我们研究了涉及在线校正的手臂运动的肌肉模式是否也是由用于点对点运动的相同的随时间变化的肌肉协同作用的组合产生的。我们记录了 5 名受试者的运动末端运动学和肌电图，他们从中心位置到达额面中的 8 个外周目标，从每个外周目标到达 2 个相邻目标中的 1 个，以及从中心位置最初到达 1 个外周目标，然后在发出信号后 50、150 或 250ms 延迟后到达 2 个相邻目标中的 1 个。从点对点运动的平均、相位、归一化肌电图中提取随时间变化的肌肉协同作用，并使用迭代优化算法将其拟合到目标变化运动的模式。在所有受试者中，三个随时间变化的肌肉协同作用解释了点对点运动数据变化的很大一部分。三个协同作用的叠加和调制比相应的点对点肌肉模式的叠加和调制更好地重建了目标变化运动的肌肉模式，这些模式是适当对齐的。虽然在运动学水平上，目标位置变化时的到达校正轨迹可以通过初始轨迹的延迟叠加来获得，但是在肌肉水平上，用于点对点运动的相同随时间变化的肌肉协同作用的幅度和时间调制捕获了潜在的相位肌肉模式。这些结果表明，用于控制未受干扰的手臂运动及其视觉引导的在线校正的通用模块化架构。
