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: 10789100
• 负责人: Charles Heckman
• 金额: $ 5.64万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2026-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10789100
• 关键词: Action Potentials; Behavior; Brain Stem; Complement; Complex; Coupled; Development; Foundations; GTP-Binding Proteins; Goals; Human; Laboratories; Measures; Methods; Modeling; Motor; Motor Neurons; Motor output; Movement; Muscle; Muscle Fibers; Neurons; Output; Pattern; Population; Reverse engineering; Spinal Cord; Synapses; Techniques; Uncertainty; insight; neural circuit; neuroregulation; novel; 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.

所有的运动指令都流经脊髓和脑干中的运动神经元。作为 对于整个中枢神经系统的神经回路的输入，这些指令包括三个主要部分：两种类型 离子型输入（兴奋和抑制）和一组G蛋白耦合输入（神经调节）。缺乏 对这些组成部分如何产生产出的理解构成了一个基本的不确定性， 运动神经控制的基础。幸运的是，人类的运动输出可以在 单个神经元。运动神经元的动作电位与其肌纤维的动作电位是1：1，形成运动神经元。 动作电位可以相对容易地记录在肌肉中的单位。使用这些电动机的潜力 用于理解运动指令的单元激发模式长期以来一直受到重视。我们的目标是 通过开发基于超级计算机的技术来逆向工程运动单元的放电模式， 以确定兴奋性、抑制性和神经调节性输入的幅度和模式， 人类的运动指令允许同时记录许多运动单位的最新进展已经 使我们能够识别运动单位放电模式中独特的非线性行为。我们的发展现实 运动神经元的模型表明，这些非线性来自输入之间的复杂相互作用， 件.我们计划使用这些模型作为逆向工程（RE）方法的核心， 这三个分量来自非线性的人体运动单元放电模式。我们的前提是， 我们的模型在阿贡国家实验室的超级计算机上运行，将允许系统地探索 由成千上万的输入组合产生的放电模式。这些输入组织准确地 然后，重新创建一组测量的发射模式将被认为是“解决方案空间”的一部分， 特别是电机输出。这种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， 对运动指令知之甚少整个电机输出，如力和EMG，都存在这个问题。我们 然而，概念是，通过运动单位记录在单个神经元水平测量运动输出， 进行有效的逆向工程。我们有三个目标：1）开发和评估基于超级计算机的逆向工程。 用于分析运动单位放电模式的工程技术。2)部署RE调查 运动单位放电模式群体中肌肉特异性差异的机制。以及3）部署可再生能源， 研究是否特定于每种肌肉的神经-神经调节相互作用相对 固定的，或者相反地，连续地适应于不同的运动任务。基于超级计算机的发展 分析技术提供了一个理想的补充技术的出现，以测量发射模式的 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 了解人类运动指令的突触组织。