Supercomputer-based Models of Motoneurons for Estimating Their Synaptic Inputs in Humans

基于超级计算机的运动神经元模型，用于估计人类突触输入

• 批准号: 10467557
• 负责人: Charles Heckman
• 金额: $ 66.9万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10467557
• 关键词: Action Potentials; Affect; Behavior; Brain Stem; Complement; Complex; Contact Lenses; Coupled; Databases; Development; Diffuse; Distal; Effectiveness; Electrodes; Emergency Situation; Evaluation; Exhibits; Foundations; GTP-Binding Proteins; Generations; Goals; Human; Joints; Laboratories; Leg; Measures; Methods; Modeling; Motor; Motor Neurons; Motor output; Movement; Muscle; Muscle Fibers; Muscle function; Neurons; Noise; Output; Pattern; Pharmaceutical Preparations; Population; Posture; Primates; Property; Reverse engineering; Rodent; Role; Spinal Cord; Structure; Synapses; System; Techniques; Testing; Torque; Uncertainty; arm; base; electrical property; experimental study; insight; motor control; muscular structure; neural circuit; neuroregulation; novel; relating to nervous system; robotic device; supercomputer

PROJECT SUMMARY All motor commands flow through motoneurons in the spinal cord and brainstem. As for inputs to neural circuits throughout the CNS, these commands comprise three main components: two types of ionotropic input (excitation and inhibition) and a set of G-protein coupled inputs (neuromodulation). Lack of understanding of how these components produce output constitutes a fundamental uncertainty at the foundation of the neural control of movement. Fortunately, motor output in humans can be studied at the level of single neurons. Motoneuron action potentials are 1-to-1 with those of their muscle fibers, forming motor units whose action potentials can be recorded relatively easily in muscles. The potential for using these motor unit firing patterns for understanding motor commands has long been appreciated. Our goal is to maximize this potential by developing supercomputer-based techniques for reverse engineering motor unit firing patterns to identify the amplitudes and patterns of the excitatory, inhibitory and neuromodulatory inputs underlying motor commands in humans. Recent advances that allow simultaneous recording of many motor units have allowed us to identify distinctive nonlinear behaviors in motor unit firing patterns. Our development of realistic models of motoneurons show that these nonlinearities arise from complex interactions between input components. We plan to use these models as the core of a reverse engineering (RE) approach that estimates these three components from nonlinear human motor unit firing patterns. Our premise is that implementation of our models on supercomputers at Argonne National Laboratories will allow systematic exploration of the firing patterns generated by many thousands of input combinations. Those input organizations that accurately recreate a measured set of firing patterns will then be considered to be part of the “solution space” for that particular motor output. The key problem for this analysis is redundancy. If the same motor output can be produced by many input combinations, then reverse engineering will reveal huge solution spaces that provide little insight into motor commands. Overall motor outputs like force and EMG suffer from this problem. Our concept, however, is that measuring motor output at the single neuron level, via motor unit recordings, allows for effective reverse engineering. We have 3 aims: 1) to develop and evaluate supercomputer-based reverse engineering techniques for analysis of motor unit firing patterns. 2) to deploy RE to investigate the mechanisms of muscle-specific differences in populations of motor unit firing patterns. And 3) to deploy RE to investigate whether inhibitory-neuromodulation interactions that are specific for each muscle are relatively fixed, or instead are continuously adapted for different motor tasks. The development of supercomputer-based analysis techniques provides an ideal complement to emergence of techniques to measure firing patterns of large populations of motor units. Our novel reverse engineering method have the potential to transform our understanding of the synaptic organization of motor commands in humans.

项目摘要 所有运动命令都流经脊髓和脑干中的运动神经元。作为 对于整个 CNS 神经回路的输入，这些命令包含三个主要组成部分：两种类型 离子型输入（激发和抑制）和一组 G 蛋白耦合输入（神经调节）。缺乏 了解这些组件如何产生输出构成了基本的不确定性 运动神经控制的基础。幸运的是，人类的运动输出可以在以下水平进行研究 单个神经元。运动神经元动作电位与其肌纤维的动作电位成1比1，形成运动神经元动作电位 其动作电位可以相对容易地在肌肉中记录的单位。使用这些电机的潜力 用于理解运动命令的单元发射模式长期以来一直受到赞赏。我们的目标是最大化 通过开发基于超级计算机的技术来逆向工程运动单元点火模式来实现这一潜力 识别潜在的兴奋性、抑制性和神经调节输入的幅度和模式 人类的运动命令。最近的进展允许同时记录许多运动单位 使我们能够识别运动单位放电模式中独特的非线性行为。我们的发展切合实际 运动神经元模型表明，这些非线性源于输入之间的复杂相互作用 成分。我们计划使用这些模型作为逆向工程（RE）方法的核心，该方法估计 这三个组成部分来自非线性的人体运动单元放电模式。我们的前提是落实 我们在阿贡国家实验室的超级计算机上的模型将允许系统地探索 由数千种输入组合生成的触发模式。那些准确输入的组织 重新创建一组测量的发射模式将被视为该“解决方案空间”的一部分 特定的电机输出。此分析的关键问题是冗余。如果相同的电机输出可以 由许多输入组合产生，然后逆向工程将揭示巨大的解决方案空间，提供 对运动指令知之甚少。整体电机输出（例如力和肌电图）都存在这个问题。我们的 然而，概念是通过运动单元记录测量单个神经元水平的运动输出，允许 进行有效的逆向工程。我们有 3 个目标：1）开发和评估基于超级计算机的逆向 分析运动单位放电模式的工程技术。 2）部署RE来调查 运动单位放电模式群体中肌肉特异性差异的机制。 3) 将 RE 部署到 研究每块肌肉特有的抑制性神经调节相互作用是否相对 固定，或者不断适应不同的运动任务。基于超级计算机的发展 分析技术为测量发射模式的技术的出现提供了理想的补充 大量的运动单位。我们新颖的逆向工程方法有可能改变我们的 了解人类运动命令的突触组织。