Multiscale models of proprioceptive encoding to reveal mechanisms of impaired sensorimotor control

本体感觉编码的多尺度模型揭示感觉运动控制受损的机制

• 批准号: 10612452
• 负责人: Timothy C Cope
• 金额: $ 58.24万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-16 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10612452
• 关键词: Address; Affect; Biological; Biomechanics; Biophysics; Cerebral Palsy; Clinical; Clinical assessments; Computer Models; Consensus; Coupling; Data; Decerebration procedure; Diagnosis; Diagnostic; Disease; Dissociation; Dyskinetic syndrome; Dystonia; Electromyography; Funding; Goals; Health; Human; Hyperreflexia; Impairment; Individual; Joints; Lead; Left; Leg; Limb structure; Measures; Mechanics; Methods; Modeling; Motion; Motor; Motor output; Movement; Movement Disorders; Muscle; Muscle Fibers; Muscle Hypertonia; Muscle Spindles; Muscle Tension; Nervous System Physiology; Neurologic; Neuromechanics; Neurosciences; Organ; Parkinson Disease; Parkinsonian Disorders; Physics; Preparation; Property; Rattus; Research; Resistance; Robotics; Role; Sensory; Severities; Signal Transduction; Spinal; Spinal cord injury; Stretching; Stroke; Symptoms; System; Tendon structure; Testing; Tissues; Validation; Vertebral column; Work; biological systems; biomechanical model; biophysical model; clinically relevant; in silico; in vivo; insight; instrument; kinematics; motor impairment; multi-scale modeling; nervous system disorder; neural; neuromechanism; neuromuscular; novel; predictive signature; robotic system; sensory input; simulation; spasticity; treatment strategy; virtual; virtual reality

PROJECT SUMMARY Our long-term goal is to identify neural mechanisms and the functional roles of sensorimotor signals in health and disease as needed to guide mechanistically targeted diagnoses, assessments, and treatments for neurological movement disorders. Here we address the scientific barriers to understanding and treating a broad class of movement disorder symptoms recently defined as joint hyper-resistance, which encompass spasticity in stroke, spinal cord injury, or cerebral palsy; parkinsonian rigidity, and hypertonia. The objective of this collaborative, interdisciplinary proposal is to identify neural mechanisms of hyper-resistance and dissociate their relative roles in abnormal movement. We will focus on the neural mechanisms underlying two clinically- defined neural contributions to hyper-resistance: non-velocity dependent involuntary background activation and velocity-dependent stretch hyper-reflexia. We hypothesize that increased spinal excitability in many neurological disorders causes involuntary background activation and velocity-dependent stretch hyper-reflexia via three dissociable neural mechanisms: 1) alpha-drive to extrafusal muscle fibers increasing background muscle tension, 2) gamma-drive to specialized intrafusal muscle fibers in muscle spindles sensory organs, increasing their sensitivity to muscle stretch, and 3) sensorimotor gain of the spinal transformation of monosynaptic sensory input into motor output. Our proposed tests of this hypothesis will advance understanding of the important, yet still unresolved relative contributions made by these neural mechanisms to hyper-resistance. Based on our neuromechanical and multiscale modeling advances in the prior funding period, in Aim 1 we will develop a multiscale in silico neuromuscular circuit model to predict how independent changes in alpha- drive, gamma-drive, and sensorimotor gain differentially affect clinically-relevant movements such as the tendon tap and pendulum test. In Aim 2, we will characterize the relative increases in alpha-drive, gamma-drive, and sensorimotor gain across clinically-relevant spinal excitability levels in a living biological neuromuscular circuit in vivo using a decerebrate rat preparation. In Aim 3 we will identify clinically-relevant movement abnormalities across spinal excitability levels in a novel biohybrid robotic system coupling the living neuromuscular circuit (in vivo) to a virtual biomechanical limb (in silico). A robotic controller will enforce the physics of dynamically changing inertial and gravitational forces, allowing movement to emerge from the causal interaction between the in vivo neuromuscular circuit and the virtual limb. Through the close coordination of these Aims, we will establish a computational and experimental framework to address clinical barriers (1) to determine how changes in neural mechanisms and the inertial properties of the limb could correct movement abnormalities, (2) to provide insight into how these mechanisms could be identified through different clinical assessment scenarios, and (3) to compare the relative effects of different treatment targets. The proposed work will likely impact both clinically-relevant human sensorimotor research and basic sensorimotor neuroscience.

项目摘要 我们的长期目标是确定感觉运动信号在健康中的神经机制和功能作用 和疾病所需的疾病，以指导机械定位的诊断，评估和治疗方法 神经运动障碍。在这里，我们解决了理解和治疗的科学障碍 广泛的运动障碍症状最近被定义为联合超级抵抗力，其中包括 中风，脊髓损伤或脑瘫的痉挛；帕金森氏症僵硬和高血压。目的 该协作的跨学科建议是确定过度抗性的神经机制并分离 它们在异常运动中的相对作用。我们将专注于两个临床上的神经机制 定义的神经对超阻力的贡献：非速度依赖性的非自愿背景激活和 速度依赖性拉伸超反射。我们假设许多人在许多 神经系统疾病会导致非自愿背景激活和速度依赖性拉伸超反射症 通过三种可解散的神经机制：1）α驱动到肌外肌肉纤维增加背景 肌肉张力，2）γ-驱动肌肉中专门的肌内肌肉纤维，旋转感觉器官 增加对肌肉伸展的敏感性，3） 单突触的感觉输入到电动机输出中。我们提出的对该假设的测试将提高理解 这些神经机制对过度抗性做出的重要但仍未解决的相对贡献。 基于我们在以前的资金期间的神经力学和多尺度建模的进步，在AIM 1中，我们将 在计算机神经肌肉电路模型中开发多尺度，以预测α-独立变化 驱动，伽马驱动和感觉运动的增益会差异地影响与临床相关的运动，例如肌腱 点击和摆测试。在AIM 2中，我们将表征Alpha-Drive，Gamma-Drove的相对增加， 在生物生物学中，临床上与临床相关的脊柱兴奋性水平的感觉运动增益 使用杂交大鼠制剂在体内神经肌肉回路。在AIM 3中，我们将确定与临床相关的 新型生物混合机器人系统耦合中脊柱兴奋性水平跨脊柱兴奋性水平的运动异常 活的神经肌肉电路（体内）到虚拟生物力学肢体（在计算机中）。机器人控制器将执行 动态改变惯性和引力的物理学，使运动从 体内神经肌肉电路与虚拟肢体之间的因果相互作用。通过紧密的协调 在这些目标中，我们将建立一个计算和实验框架，以解决临床障碍（1） 确定神经机制的变化和肢体的惯性特性如何纠正运动 异常，（2）提供有关如何通过不同临床识别这些机制的见解 评估方案和（3）比较不同治疗靶标的相对效应。拟议的工作 可能会影响临床上与人类感觉运动研究和基本感觉运动神经科学。