d'Avella Andrea
Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, Messina, Italy.
Laboratory of Neuromotor Physiology, Santa Lucia Foundation, Rome, Italy.
Adv Exp Med Biol. 2016;957:3-19. doi: 10.1007/978-3-319-47313-0_1.
How the central nervous system (CNS) overcomes the complexity of multi-joint and multi-muscle control and how it acquires or adapts motor skills are fundamental and open questions in neuroscience. A modular architecture may simplify control by embedding features of both the dynamic behavior of the musculoskeletal system and of the task into a small number of modules and by directly mapping task goals into module combination parameters. Several studies of the electromyographic (EMG) activity recorded from many muscles during the performance of different tasks have shown that motor commands are generated by the combination of a small number of muscle synergies, coordinated recruitment of groups of muscles with specific amplitude balances or activation waveforms, thus supporting a modular organization of motor control. Modularity may also help understanding motor learning. In a modular architecture, acquisition of a new motor skill or adaptation of an existing skill after a perturbation may occur at the level of modules or at the level of module combinations. As learning or adapting an existing skill through recombination of modules is likely faster than learning or adapting a skill by acquiring new modules, compatibility with the modules predicts learning difficulty. A recent study in which human subjects used myoelectric control to move a mass in a virtual environment has tested this prediction. By altering the mapping between recorded muscle activity and simulated force applied on the mass, as in a complex surgical rearrangement of the tendons, it has been possible to show that it is easier to adapt to a perturbation that is compatible with the muscle synergies used to generate hand force than to a similar but incompatible perturbation. This result provides direct support for a modular organization of motor control and motor learning.
中枢神经系统（CNS）如何克服多关节和多肌肉控制的复杂性，以及它如何获取或适应运动技能，是神经科学中基本且尚未解决的问题。模块化架构可以通过将肌肉骨骼系统的动态行为和任务的特征嵌入少量模块，并通过将任务目标直接映射到模块组合参数来简化控制。几项关于在执行不同任务期间从许多肌肉记录的肌电图（EMG）活动的研究表明，运动指令是由少量肌肉协同作用的组合产生的，即通过特定幅度平衡或激活波形对肌肉群进行协调募集，从而支持运动控制的模块化组织。模块化也可能有助于理解运动学习。在模块化架构中，新运动技能的获取或在受到干扰后对现有技能的适应可能发生在模块层面或模块组合层面。由于通过模块重组来学习或适应现有技能可能比通过获取新模块来学习或适应技能更快，因此与模块的兼容性预测了学习难度。最近一项关于人类受试者在虚拟环境中使用肌电控制来移动重物的研究对这一预测进行了测试。通过改变记录的肌肉活动与施加在重物上的模拟力之间的映射关系，就像在复杂的肌腱手术重排中一样，已经有可能表明，与用于产生手部力量的肌肉协同作用兼容的干扰比类似但不兼容的干扰更容易适应。这一结果为运动控制和运动学习的模块化组织提供了直接支持。
