Neuromechanical Determinants of Muscle Activity in Human Postural Control

描述(申请人提供)：失去平衡导致跌倒是老年人受伤和意外死亡的主要原因。这一建议阐明了一个创新的量化框架，用于研究和理解人体平衡控制肌肉激活模式的时间和空间特征。该提案的目的是开发和验证一个定量模型来预测姿势控制过程中的时空肌肉激活模式。利用工程工具，我们将结合新的实验方法和计算机模拟来了解神经系统是如何执行姿势反馈控制的。我们的框架将证明，复杂的，高维的肌肉协调模式的姿势控制可以解释只有几个参数相关的任务水平变量。提高我们量化和预测肌肉协调模式的能力对于实现我们的长期目标至关重要，即使用成对的实验测量和工程模型来预测神经运动损伤和介入治疗的功能后果。在特定的目标1中，我们将确定肌肉协同效应和任务变量，这些变量控制着站立姿势多方向扰动时肌肉活动的空间组织。我们的方法是使用最优化从实验数据中提取肌肉协同效应，并将每个肌肉协同效应的激活与任务相关变量的产生关联起来。我们将使用腿部的肌肉骨骼模型来确定产生肌肉激活模式和实验测量的任务变量所需的协同结构。在特定的目标2中，我们将确定在矢状扰动到站立姿势期间，控制肌肉活动时间组织的任务级反馈环。我们的方法是通过实验表征质心运动和时间肌肉激活模式之间的反馈关系，然后使用一个简单的倒立摆模型来测试反馈回路在产生合适的时间肌肉激活模式和质心运动学方面的可行性。我们的姿势控制模型将使我们能够预测运动模式变化导致的运动功能障碍。因此，我们的结果将使我们能够开发针对平衡和运动障碍的定量诊断工具，并促进为运动康复设计有效的介入疗法、神经假体和神经修复策略。

DESCRIPTION (provided by applicant): Loss of balance leading to falls is a primary cause of injury and accidental death in older adults. This proposal articulates an innovative and quantitative framework for both investigating and understanding temporal and spatial features of muscle activation patterns for human balance control. The objective of the proposal is to develop and validate a quantitative model to predict spatiotemporal muscle activation patterns during postural control. Using engineering tools, we will integrate novel experimental methods and computer simulations to understand how feedback control of posture is executed by the nervous system. Our framework will demonstrate that complex, high-dimensional muscle coordination patterns for postural control can be explained by just a few parameters related to task-level variables. Advancing our ability to quantify and predict muscle coordination patterns is critical to achieving our long-term goal of using paired experimental measures and engineering models to predict the functional consequences of neuromotor impairments and interventional therapies. In Specific Aim 1, we will identify muscle synergies and task variables governing spatial organization of muscle activity during multidirectional perturbations to standing posture. Our approach is to extract muscle synergies from experimental data using optimization and correlate the activation of each muscle synergy to the production of a task-related variable. We will use a musculoskeletal model of the leg to determine the structure of synergies required to produce the muscle activation patterns and task-variables measured experimentally. In Specific Aim 2, we will identify the task- level feedback loops governing temporal organization of muscle activity during sagittal perturbations to standing posture. Our approach is to characterize the feedback relationships between center of mass motion and temporal muscle activation patterns experimentally, and then use a simple inverted pendulum model to test the feasibility of the feedback loops in generating appropriate temporal muscle activation patterns and center of mass kinematics. Our models of postural control will allow us to predict motor dysfunction resulting from changes in motor patterns. Our results will therefore allow us to develop quantitative diagnostic tools for balance and movement disorders and facilitate the design of effective interventional therapies, neural prostheses, and neural repair strategies for motor rehabilitation.