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: 10436158
• 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/10436158
• 关键词: Address; Affect; Biological; Biomechanics; Biophysics; Cerebral Palsy; Cerebrum; Clinical; Clinical assessments; Computer Models; Consensus; Coupling; Data; Diagnosis; Diagnostic; Disease; Dyskinetic syndrome; Dystonia; Electromyography; Funding; Goals; Gravitation; 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; Validation; Work; base; biological systems; biomechanical model; biophysical model; clinically relevant; in silico; in vivo; insight; instrument; kinematics; motor impairment; multi-scale modeling; nervous system disorder; neuromechanism; neuromuscular; novel; predictive signature; relating to nervous system; 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神经机制和感觉运动信号在健康中的功能作用 和疾病，以指导机械有针对性的诊断，评估和治疗， 神经运动障碍在这里，我们解决了理解和治疗的科学障碍， 最近被定义为关节过度抵抗的广泛的运动障碍症状，包括 中风、脊髓损伤或脑性麻痹中的痉挛;帕金森氏强直和肌张力亢进。的目标 这个合作的跨学科的建议是确定超抵抗和解离的神经机制， 它们在异常运动中的相对作用。我们将集中在两个临床上潜在的神经机制- 定义的神经对超阻力的贡献：非速度依赖性不自主背景激活， 速度依赖性牵张反射亢进我们假设，在许多情况下， 神经系统疾病引起不自主的背景激活和速度依赖性牵张反射亢进 通过三种可分离的神经机制：1）α驱动梭外肌纤维增加背景 肌张力，2）肌梭感觉器官中的特化梭内肌纤维的γ驱动， 增加他们对肌肉拉伸的敏感性，和3）脊髓转换的感觉运动增益， 将单突触感觉输入转化为运动输出。我们对这一假设的测试将促进对 这些神经机制对超抵抗力的重要但尚未解决的相对贡献。 基于我们在前一个资助期内的神经力学和多尺度建模进展，在目标1中，我们将 开发一个多尺度的计算机神经肌肉回路模型，以预测α- 驱动、伽马驱动和感觉运动增益对临床相关运动的影响不同， 敲击和摆锤试验。在目标2中，我们将描述阿尔法驱动，伽马驱动， 和感觉运动增益在临床相关的脊髓兴奋性水平在一个活的生物 使用去大脑大鼠制备的体内神经肌肉回路。在目标3中，我们将确定临床相关的 一种新型生物混合机器人系统耦合中脊髓兴奋性水平的运动异常 活的神经肌肉回路（在体内）到虚拟生物力学肢体（在计算机上）。一个机器人控制器将强制执行 动态变化的惯性力和引力的物理学，允许运动从 体内神经肌肉回路和虚拟肢体之间的因果相互作用。通过密切协调， 在这些目标中，我们将建立一个计算和实验框架来解决临床障碍（1）， 确定神经机制和肢体惯性特性的变化如何纠正运动 异常，（2）提供如何通过不同的临床 评估方案，以及（3）比较不同治疗目标的相对效果。拟议工作 可能会影响临床相关的人类感觉运动研究和基础感觉运动神经科学。