Motor cortical signaling of impedance during manipulation

操纵过程中运动皮层阻抗信号

基本信息

批准号：
10362744
负责人：
ANDREW B. SCHWARTZ
金额：
$ 54.14万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-03-01 至 2025-02-28
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10362744
关键词：
3-Dimensional Acceleration Algorithms Behavior Collaborations Complex Data Dimensions Electromyography Environment Feedback Financial compensation Hand Human Isometric Exercise Joints Knowledge Limb structure Measurement Measures Mechanics Modeling Monitor Monkeys Motion Motor Motor Activity Motor Cortex Movement Muscle Neurons Paralysed Pattern Population Primates Reaction Reflex action Rehabilitation therapy Research Research Personnel Resistance Robot Sampling Series Signal Transduction Statistical Models Structure System Testing Time To specify Torque Variant Virtual Tool Viscosity Work arm base cognitive process control theory design electric impedance insight interest kinematics mathematical model motor control multidisciplinary neural correlate neural model neural prosthesis neurophysiology neuroregulation nonhuman primate novel object motion operation prosthesis wearer relating to nervous system response robot control skeletal stretch reflex theories tool two-dimensional virtual

项目摘要

Project Summary A large body of research has led to statistical models showing how movement velocity is encoded in the motor cortex. However, forces also need to be controlled in harmony with motion when interacting with objects and research has rarely focused on how the motor system coordinates both together. The simultaneous variation of force and motion is incorporated in the definition of impedance. Our current neural models do not describe impedance encoding, which limits our understanding of object interaction, an important aspect of human behavior. The proposed research will develop new models of motor cortical impedance encoding during object interaction. Using these new models to decode ongoing impedance signaling, we will substantiate an advanced theory of impedance control used by the motor system to produce accurate object displacement in response to the forces applied by the hand. This research bridges the expertise of Dr. Schwartz in neurophysiology and of Dr. Hogan in robot control. Monkey subjects will perform tasks with real and virtual tools that naturally encourage the use of impedance control. We will record the activity of motor cortical neurons during these tasks and develop new mathematical models to describe the relation between neural activity and force, motion and impedance. Results from electromyography recordings, joint angle measurements and torque calculations, together with the neural models, will be used to better understand how impedance is regulated at the level of muscles and joints. Contributions of stretch reflexes to impedance will be studied and compared to the predictive impedance signaling decoded from motor cortex. This work promises to extend our understanding of the neural control principles governing the way we use our arms and hands to interact with our surroundings. These principles can be used to build new theories of the cognitive processes used to predict and effect changes in the world around us. At the same time, elucidation of the neural and mechanical details of forceful interaction will lead to new rehabilitative and neural prosthetic approaches to paralysis.

项目概要大量研究已经建立了统计模型，显示运动速度是如何在电机中编码的皮质。然而，在与物体相互作用时，力也需要与运动相协调地控制。研究很少关注运动系统如何协调两者。同时变化力和运动的关系被纳入阻抗的定义中。我们当前的神经模型没有描述阻抗编码，限制了我们对物体交互的理解，物体交互是人类的一个重要方面行为。所提出的研究将开发物体过程中运动皮层阻抗编码的新模型相互作用。使用这些新模型来解码正在进行的阻抗信号，我们将证实电机系统使用先进的阻抗控制理论来产生精确的物体位移对手施加的力的响应。这项研究将施瓦茨博士在以下方面的专业知识联系起来：神经生理学和霍根博士在机器人控制方面的研究。猴子受试者将执行真实和虚拟的任务自然鼓励使用阻抗控制的工具。我们将记录运动皮质的活动神经元在这些任务期间并开发新的数学模型来描述神经元之间的关系活动和力、运动和阻抗。肌电图记录结果，关节角度测量和扭矩计算以及神经模型将用于更好地理解如何阻抗在肌肉和关节层面进行调节。牵张反射对阻抗的贡献为研究并与从运动皮层解码的预测阻抗信号进行比较。这项工作承诺扩展我们对控制我们使用手臂和手的方式的神经控制原理的理解与我们周围的环境互动。这些原理可用于建立认知过程的新理论用于预测和影响我们周围世界的变化。同时，阐明了神经和强力相互作用的机械细节将带来新的康复和神经修复方法麻痹。