Mason C R, Gomez J E, Ebner T J
Department of Neuroscience and Graduate Program in Neuroscience, University of Minnesota, Minneapolis, Minnesota 55455, USA.
J Neurophysiol. 2001 Dec;86(6):2896-910. doi: 10.1152/jn.2001.86.6.2896.
An emerging viewpoint is that the CNS uses synergies to simplify the control of the hand. Previous work has shown that static hand postures for mimed grasps can be described by a few principal components in which the higher order components explained only a small fraction of the variance yet provided meaningful information. Extending that earlier work, this study addressed whether the entire act of grasp can be described by a small number of postural synergies and whether these synergies are similar for different grasps. Five right-handed adults performed five types of reach-to-grasps including power grasp, power grasp with a lift, precision grasp, and mimed power grasp and mimed precision grasp of 16 different objects. The object shapes were cones, cylinders, and spindles, systematically varied in size to produce a large range of finger joint angle combinations. Three-dimensional reconstructions of 21 positions on the hand and wrist throughout the reach-to-grasp were obtained using a four-camera video system. Singular value decomposition on the temporal sequence of the marker positions was used to identify the common patterns ("eigenpostures") across the 16 objects for each task and their weightings as a function of time. The first eigenposture explained an average of 97.3 +/- 0.89% (mean +/- SD) of the variance of the hand shape, and the second another 1.9 +/- 0.85%. The first eigenposture was characterized by an open hand configuration that opens and closes during reach. The second eigenposture contributed to the control of the thumb and long fingers, particularly in the opening of the hand during the reach and the closing in preparation for object grasp. The eigenpostures and their temporal evolutions were similar across subjects and grasps. The higher order eigenpostures, although explaining only small amounts of the variance, contributed to the movements of the fingers and thumb. These findings suggest that much of reach-to-grasp is effected using a base posture with refinements in finger and thumb positions added in time to yield unique hand shapes.
一种新出现的观点认为,中枢神经系统利用协同作用来简化对手部的控制。先前的研究表明,模拟抓握的静态手部姿势可以用几个主成分来描述,其中高阶成分仅解释了一小部分方差,但却提供了有意义的信息。扩展早期的这项研究,本研究探讨了抓握的整个动作是否可以用少量的姿势协同作用来描述,以及这些协同作用对于不同的抓握是否相似。五名右利手成年人进行了五种类型的伸手抓握动作,包括强力抓握、带提起动作的强力抓握、精确抓握,以及对16种不同物体的模拟强力抓握和模拟精确抓握。物体形状为圆锥体、圆柱体和纺锤体,尺寸系统变化以产生大范围的手指关节角度组合。在整个伸手抓握过程中,使用四摄像头视频系统获取手部和腕部21个位置的三维重建图像。对标记位置的时间序列进行奇异值分解,以识别每个任务中16个物体的共同模式(“特征姿势”)及其作为时间函数的权重。第一个特征姿势平均解释了手部形状方差的97.3±0.89%(平均值±标准差),第二个特征姿势又解释了1.9±0.85%。第一个特征姿势的特点是在伸手过程中张开和闭合的张开手配置。第二个特征姿势有助于控制拇指和长手指,特别是在伸手过程中手的张开以及为抓握物体做准备时的闭合。特征姿势及其时间演变在不同受试者和抓握动作之间是相似的。高阶特征姿势虽然只解释了少量的方差,但对手指和拇指的运动有贡献。这些发现表明,大部分伸手抓握动作是通过一种基本姿势来实现的,随着时间的推移,手指和拇指位置会进行微调,以产生独特的手部形状。