Krouchev Nedialko, Kalaska John F, Drew Trevor
Département de physiologie, Université de Montréal, Centre-ville, Montreal, Quebec, H3C 3J7 Canada.
J Neurophysiol. 2006 Oct;96(4):1991-2010. doi: 10.1152/jn.00241.2006. Epub 2006 Jul 5.
During goal-directed locomotion, descending signals from supraspinal structures act through spinal interneuron pathways to effect modifications of muscle activity that are appropriate to the task requirements. Recent studies using decomposition methods suggest that this control might be facilitated by activating synergies organized at the level of the spinal cord. However, it is difficult to directly relate these mathematically defined synergies to the patterns of electromyographic activity observed in the original recordings. To address this issue, we have used a novel cluster analysis to make a detailed study of the organization of the synergistic patterns of muscle activity observed in the fore- and hindlimb during treadmill locomotion. The results show that the activity of a large number of forelimb muscles (26 bursts of activity from 18 muscles) can be grouped into 11 clusters on the basis of synchronous co-activation. Nine (9/11) of these clusters defined muscle activity during the swing phase of locomotion; these clusters were distributed in a sequential manner and were related to discrete behavioral events. A comparison with the synergies identified by linear decomposition methods showed some striking similarities between the synergies identified by the different methods. In the hindlimb, a simpler organization was observed, and a sequential activation of muscles similar to that observed in the forelimb during swing was less clear. We suggest that this organization of synergistic muscles provides a means by which descending signals could provide the detailed control of different muscle groups that is necessary for the flexible control of multi-articular movements.
在目标导向的运动过程中，来自脊髓上结构的下行信号通过脊髓中间神经元通路起作用，以实现与任务要求相适应的肌肉活动的改变。最近使用分解方法的研究表明，这种控制可能通过激活在脊髓水平组织的协同作用而得到促进。然而，很难将这些数学定义的协同作用直接与原始记录中观察到的肌电图活动模式联系起来。为了解决这个问题，我们使用了一种新颖的聚类分析方法，对跑步机运动过程中前肢和后肢观察到的肌肉活动协同模式的组织进行了详细研究。结果表明，大量前肢肌肉的活动（来自18块肌肉的26次活动爆发）可以根据同步共同激活被分组为11个簇。其中九个（9/11）簇定义了运动摆动阶段的肌肉活动；这些簇以连续的方式分布，并且与离散的行为事件相关。与通过线性分解方法识别的协同作用进行比较，结果显示不同方法识别的协同作用之间存在一些显著的相似之处。在后肢中，观察到一种更简单的组织方式，并且类似于前肢摆动期间观察到的肌肉顺序激活不太明显。我们认为，这种协同肌肉的组织方式提供了一种手段，通过这种手段下行信号可以对不同肌肉群进行详细控制，这对于多关节运动的灵活控制是必要的。
