Control of fast-reaching movements by muscle synergy combinations
Control of fast-reaching movements by muscle synergy combinations
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DOI：
10.1523/jneurosci.0830-06.2006
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发表时间：
2006-07-26
影响因子：
5.3
通讯作者：
Lacquaniti, Francesco
中科院分区：
文献类型：
--
作者：
d'Avella, Andrea;Portone, Alessandro;Lacquaniti, Francesco
中枢神经系统如何选择适当的肌肉模式来实现行为目标是一个悬而未决的问题。为了深入了解这一过程，我们描述了快速伸展运动的肌肉模式的时空组织。我们记录了多达19个肩部和手臂肌肉的肌电活动，在2个垂直平面的中心位置和8个外围目标之间的点对点运动。我们使用优化算法来识别一组随时间变化的肌肉协同作用，即，具有特定时变轮廓的肌肉群的协调激活。对于每一个9个主题，我们提取了四个或五个协同作用的组合，在幅度缩放和时间转移后，每个协同作用独立于每个运动条件，解释了73-82%的数据变化。然后，我们测试了这些协同作用是否可以重建肌肉模式的点对点运动与不同的负载或前臂姿势和逆转和通过点的运动。我们发现，重建精度仍然很高，这表明在这些条件下的泛化。最后，协同幅度系数根据余弦函数进行定向调谐，该余弦函数具有优选方向，该优选方向显示出比个体肌肉的优选方向更小的可变性，随着负荷、姿势和终点的变化。因此，复杂的时空特性的肌肉模式达到了少量的组件的组合，这表明参与肌肉模式的生成机制利用这种低维度，以简化控制。
How the CNS selects the appropriate muscle patterns to achieve a behavioral goal is an open question. To gain insight into this process, we characterized the spatiotemporal organization of the muscle patterns for fast-reaching movements. We recorded electromyographic activity from up to 19 shoulder and arm muscles during point-to-point movements between a central location and 8 peripheral targets in each of 2 vertical planes. We used an optimization algorithm to identify a set of time-varying muscle synergies, i.e., the coordinated activations of groups of muscles with specific time-varying profiles. For each one of nine subjects, we extracted four or five synergies whose combinations, after scaling in amplitude and shifting in time each synergy independently for each movement condition, explained 73-82% of the data variation. We then tested whether these synergies could reconstruct the muscle patterns for point-to-point movements with different loads or forearm postures and for reversal and via-point movements. We found that reconstruction accuracy remained high, indicating generalization across these conditions. Finally, the synergy amplitude coefficients were directionally tuned according to a cosine function with a preferred direction that showed a smaller variability with changes of load, posture, and endpoint than the preferred direction of individual muscles. Thus the complex spatiotemporal characteristics of the muscles patterns for reaching were captured by the combinations of a small number of components, suggesting that the mechanisms involved in the generation of the muscle patterns exploit this low dimensionality to simplify control.