Short- and long-term changes in joint co-contraction associated with motor learning as revealed from surface EMG
Short- and long-term changes in joint co-contraction associated with motor learning as revealed from surface EMG
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DOI：
10.1152/jn.2002.88.2.991
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发表时间：
2002-08-01
影响因子：
2.5
通讯作者：
Kawato, M
中科院分区：
文献类型：
--
作者：
Osu, R;Franklin, DW;Kawato, M
在运动控制领域，有两个假说一直存在争议：大脑是否获取能产生精确运动指令的内部模型，还是通过利用肌肉骨骼系统的粘弹性来避免这种情况。近期对经过训练的运动中相对较低的刚度的观察支持内部模型的存在。然而，没有研究揭示出与学习相关的粘弹性降低，而这种降低可能意味着内部模型的改进以及两种假设机制之间的协同作用。先前观察到的肌电图（EMG）下降可能有其他解释，比如减少关节扭矩的轨迹改变。为了规避这些复杂情况，我们要求严格的轨迹控制，并且只检查具有相同轨迹和扭矩曲线的成功试验。要求受试者与沿着特定且不寻常轨迹移动的目标同步进行手部运动，肩部和肘部在肩部水平的水平面内。为了评估在学习这种运动过程中的关节粘弹性，我们提出了一个关节周围肌肉共同收缩的指标（IMCJ）。IMCJ被定义为关节周围拮抗肌扭矩绝对值的总和，并根据表面肌电图和关节扭矩之间的线性关系计算得出。等长收缩期间以及运动期间的IMCJ被证实与使用常规方法（即施加机械扰动）估计的关节刚度有良好的相关性。因此，利用估计的肌电图 - 扭矩关系，为每次试验的每个关节计算运动学习过程中的IMCJ。同时，将每次试验的性能误差指定为整个轨迹上每个时间步长目标与手之间距离的均方根。IMCJ和性能误差的时间序列数据被分解为长期成分，这些成分显示随着学习IMCJ降低，而轨迹变化很小，以及IMCJ和性能误差之间的短期相互作用。交叉相关分析和脉冲响应都表明，在连续几次试验中，较高的IMCJ伴随着较差的性能，较低的IMCJ伴随着较好的性能。我们的结果支持这样的假说：当内部模型不准确时，粘弹性贡献更大，而在学习完成后内部模型贡献更大。研究表明，中枢神经系统根据性能误差在短期和长期基础上调节粘弹性，并最终在整个学习过程中在保持稳定性的同时获得平稳且精确的运动。
In the field of motor control, two hypotheses have been controversial: whether the brain acquires internal models that generate accurate motor commands, or whether the brain avoids this by using the viscoelasticity of musculoskeletal system. Recent observations on relatively low stiffness during trained movements support the existence of internal models. However, no study has revealed the decrease in viscoelasticity associated with learning that would imply improvement of internal models as well as synergy between the two hypothetical mechanisms. Previously observed decreases in electromyogram (EMG) might have other explanations, such as trajectory modifications that reduce joint torques. To circumvent such complications, we required strict trajectory control and examined only successful trials having identical trajectory and torque profiles. Subjects were asked to perform a hand movement in unison with a target moving along a specified and unusual trajectory, with shoulder and elbow in the horizontal plane at the shoulder level. To evaluate joint viscoelasticity during the learning of this movement, we proposed an index of muscle co-contraction around the joint (IMCJ). The IMCJ was defined as the summation of the absolute values of antagonistic muscle torques around the joint and computed from the linear relation between surface EMG and joint torque. The IMCJ during isometric contraction, as well as during movements, was confirmed to correlate well with joint stiffness estimated using the conventional method, i.e., applying mechanical perturbations. Accordingly, the IMCJ during the learning of the movement was computed for each joint of each trial using estimated EMG-torque relationship. At the same time, the performance error for each trial was specified as the root mean square of the distance between the target and hand at each time step over the entire trajectory. The time-series data of IMCJ and performance error were decomposed into long-term components that showed decreases in IMCJ in accordance with learning with little change in the trajectory and short-term interactions between the IMCJ and performance error. A cross-correlation analysis and impulse responses both suggested that higher IMCJs follow poor performances, and lower IMCJs follow good performances within a few successive trials. Our results support the hypothesis that viscoelasticity contributes more when internal models are inaccurate, while internal models contribute more after the completion of learning. It is demonstrated that the CNS regulates viscoelasticity on a short- and long-term basis depending on performance error and finally acquires smooth and accurate movements while maintaining stability during the entire learning process.