Chan Sherwin S, Moran Daniel W
Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63108, USA.
J Neural Eng. 2006 Dec;3(4):327-37. doi: 10.1088/1741-2560/3/4/010. Epub 2006 Nov 21.
Three-dimensional reaching by non-human primates is an important behavioral paradigm for investigating representations existing in motor control areas of the brain. Most studies to date have correlated neural activity to a few of the many arm motion parameters including: global hand position or velocity, joint angles, joint angular velocities, joint torques or muscle activations. So far, no single study has been able to incorporate all these parameters in a meaningful way that would allow separation of these often highly correlated variables. This paper introduces a three-dimensional, seven degree-of-freedom computational musculoskeletal model of the macaque arm that translates the coordinates of eight tracking markers placed on the arm into joint angles, joint torques, musculotendon lengths and finally into an optimized prediction of muscle forces. This paper uses this model to illustrate how the classic center-out reaching task used by many researchers over the last 20 years is not optimal in separating out intrinsic, extrinsic, kinematic and kinetic variables. However, by using the musculoskeletal model to design and test novel behavioral movement tasks, a priori, it is possible to disassociate the myriad of movement parameters in motor neurophysiological reaching studies.
非人灵长类动物的三维伸手动作是研究大脑运动控制区域中存在的表征的重要行为范式。迄今为止,大多数研究已将神经活动与众多手臂运动参数中的少数几个相关联,这些参数包括:手部整体位置或速度、关节角度、关节角速度、关节扭矩或肌肉激活。到目前为止,尚无一项研究能够以有意义的方式纳入所有这些参数,从而实现对这些通常高度相关变量的分离。本文介绍了一种猕猴手臂的三维、七自由度计算肌肉骨骼模型,该模型将放置在手臂上的八个跟踪标记的坐标转换为关节角度、关节扭矩、肌肉肌腱长度,并最终转化为对肌肉力量的优化预测。本文使用该模型来说明过去20年中许多研究人员使用的经典中心向外伸手任务在分离内在、外在、运动学和动力学变量方面并非最优。然而,通过使用肌肉骨骼模型来设计和测试新的行为运动任务,先验地,有可能在运动神经生理学伸手研究中分离出无数的运动参数。