Danna-Dos-Santos Alessander, Shapkova Elena Yu, Shapkova Alexandra L, Degani Adriana M, Latash Mark L
Department of Kinesiology, The Pennsylvania State University, Rec. Hall-267, University Park, PA 16802, USA.
Exp Brain Res. 2009 Mar;193(4):565-79. doi: 10.1007/s00221-008-1659-3. Epub 2008 Dec 6.
We studied the organization of leg and trunk muscles into groups (M-modes) and co-variation of M-mode involvement (M-mode synergies) during whole-body tasks associated with large variations of the moment of force about the vertical body axis. Our major questions were: (1) can muscle activation patterns during such tasks be described with a few M-modes common across tasks and subjects? (2) do these modes form the basis for synergies stabilizing M(z) time pattern? (3) will this organization differ between an explicit body-rotation task and a task associated with locomotor-like alternating arm movements? Healthy subjects stood barefoot on the force platform and performed two motor tasks while paced by the metronome at 0.7, 1.0, and 1.4 Hz: cyclic rotation of the upper body about the vertical body axis (body-rotation task), and alternating rhythmic arm movements imitating those during running or quick walking (arm-movement task). Principal component analysis was used to identify three M-modes within the space of integrated indices of muscle activity. The M-mode vectors showed clustering neither across subjects nor across frequencies. Variance in the M-mode space across sway cycles was partitioned into two components, one that did not affect the average value of M(z) shift ("good variance") and the other that did. An index was computed reflecting the relative amount of the "good variance"; positive values of this index have been interpreted as reflecting a multi-M-mode synergy stabilizing the M(z) trajectory. On average, the index was positive for both tasks and across all frequencies studied. However, the magnitude of the index was smaller for the intermediate frequency (1 Hz). The results show that the organization of muscles into groups during relatively complex whole-body tasks can differ significantly across both task variations and subjects. Nevertheless, the central nervous system seems to be able to build M(z) stabilizing synergies based on different sets of M-modes, within the approach accepted in this study. The drop in the synergy index at the frequency of 1 Hz, which was close to the preferred movement frequency, may be interpreted as corroborating the neural origin of the M-mode co-variation.
我们研究了在与围绕身体垂直轴的力矩大幅变化相关的全身任务中,腿部和躯干肌肉如何分组(M模式)以及M模式参与的共同变化(M模式协同作用)。我们的主要问题是:(1)在这类任务中,肌肉激活模式能否用一些跨任务和受试者通用的M模式来描述?(2)这些模式是否构成稳定M(z)时间模式的协同作用的基础?(3)在明确的身体旋转任务和与类似运动的交替手臂运动相关的任务之间,这种组织会有所不同吗?健康受试者赤脚站在力平台上,在节拍器以0.7、1.0和1.4Hz的频率节拍下执行两项运动任务:上半身围绕身体垂直轴的循环旋转(身体旋转任务),以及模仿跑步或快走时的交替有节奏手臂运动(手臂运动任务)。主成分分析用于在肌肉活动综合指标空间内识别三种M模式。M模式向量在受试者之间和频率之间均未显示聚类。M模式空间中跨摇摆周期的方差被分为两个分量,一个不影响M(z)偏移的平均值(“良好方差”),另一个则影响。计算了一个反映“良好方差”相对量的指标;该指标的正值被解释为反映了稳定M(z)轨迹的多M模式协同作用。平均而言,该指标在两项任务以及所有研究频率下均为正值。然而,该指标在中频(1Hz)时的幅度较小。结果表明,在相对复杂的全身任务中,肌肉分组的组织在任务变化和受试者之间可能存在显著差异。尽管如此,在本研究采用的方法范围内,中枢神经系统似乎能够基于不同的M模式集构建稳定M(z)的协同作用。在接近首选运动频率的1Hz频率下协同指标的下降,可能被解释为证实了M模式共同变化的神经起源。