Inouye Joshua M, Valero-Cuevas Francisco J
Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America.
Department of Biomedical Engineering, University of Southern California, Los Angeles, California, United States of America.
PLoS Comput Biol. 2016 Feb 11;12(2):e1004737. doi: 10.1371/journal.pcbi.1004737. eCollection 2016 Feb.
Much debate has arisen from research on muscle synergies with respect to both limb impedance control and energy consumption. Studies of limb impedance control in the context of reaching movements and postural tasks have produced divergent findings, and this study explores whether the use of synergies by the central nervous system (CNS) can resolve these findings and also provide insights on mechanisms of energy consumption. In this study, we phrase these debates at the conceptual level of interactions between neural degrees of freedom and tasks constraints. This allows us to examine the ability of experimentally-observed synergies--correlated muscle activations--to control both energy consumption and the stiffness component of limb endpoint impedance. In our nominal 6-muscle planar arm model, muscle synergies and the desired size, shape, and orientation of endpoint stiffness ellipses, are expressed as linear constraints that define the set of feasible muscle activation patterns. Quadratic programming allows us to predict whether and how energy consumption can be minimized throughout the workspace of the limb given those linear constraints. We show that the presence of synergies drastically decreases the ability of the CNS to vary the properties of the endpoint stiffness and can even preclude the ability to minimize energy. Furthermore, the capacity to minimize energy consumption--when available--can be greatly affected by arm posture. Our computational approach helps reconcile divergent findings and conclusions about task-specific regulation of endpoint stiffness and energy consumption in the context of synergies. But more generally, these results provide further evidence that the benefits and disadvantages of muscle synergies go hand-in-hand with the structure of feasible muscle activation patterns afforded by the mechanics of the limb and task constraints. These insights will help design experiments to elucidate the interplay between synergies and the mechanisms of learning, plasticity, versatility and pathology in neuromuscular systems.
关于肌肉协同作用在肢体阻抗控制和能量消耗方面的研究引发了诸多争论。在伸手动作和姿势任务背景下对肢体阻抗控制的研究得出了不同的结果，本研究探讨中枢神经系统（CNS）对协同作用的运用是否能够解决这些结果，同时也为能量消耗机制提供见解。在本研究中，我们在神经自由度与任务约束之间相互作用的概念层面阐述这些争论。这使我们能够检验实验观察到的协同作用——相关肌肉激活——控制能量消耗和肢体端点阻抗刚度分量的能力。在我们的标称6肌肉平面手臂模型中，肌肉协同作用以及端点刚度椭圆所需的大小、形状和方向，被表示为定义可行肌肉激活模式集的线性约束。二次规划使我们能够预测在给定这些线性约束的情况下，在肢体的整个工作空间中能量消耗是否以及如何能够最小化。我们表明，协同作用的存在极大地降低了中枢神经系统改变端点刚度特性的能力，甚至可能排除最小化能量的能力。此外，最小化能量消耗的能力——如果可行的话——会受到手臂姿势的极大影响。我们的计算方法有助于调和在协同作用背景下关于端点刚度和能量消耗的任务特定调节的不同发现和结论。但更普遍地说，这些结果进一步证明，肌肉协同作用的利弊与肢体力学和任务约束所提供的可行肌肉激活模式的结构密切相关。这些见解将有助于设计实验，以阐明协同作用与神经肌肉系统中学习、可塑性、多功能性和病理学机制之间的相互作用。
