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

项目摘要所有的运动指令都流经脊髓和脑干中的运动神经元。AS 对于整个中枢神经回路的输入，这些命令包括三个主要组成部分：两种类型 包括电离性输入(兴奋和抑制)和一组G蛋白偶联输入(神经调节)。缺乏 对这些组件如何产生产出的理解构成了 神经运动控制的基础。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肉纤维的动作电位是1：1，形成运动 动作电位可以相对容易地记录在肌肉中的单位。使用这些马达的潜力 用于理解运动指令的单位射击模式长期以来一直受到重视。我们的目标是最大限度地 通过开发基于超级计算机的反向工程发动机单元点火模式的技术来实现这一潜力 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。允许同时记录许多电机单元的最新进展已经 使我们能够识别机动部队发射模式中独特的非线性行为。我们的现实主义发展 运动神经元的模型表明，这些非线性是由输入之间复杂的相互作用引起的 组件。我们计划使用这些模型作为逆向工程(RE)方法的核心，以估计 这三种成分来自于人体运动单位的非线性发射模式。我们的前提是实施 我们在阿贡国家实验室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射击模式。这些输入组织准确地 然后，重新创建一组测量到的发射模式将被认为是解决方案空间的一部分 特定的马达输出。这一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，然后反向工程将揭示巨大的解决方案空间，这些空间提供 对运动指令缺乏洞察力。总体而言，力和肌电等马达输出都会受到这个问题的影响。我们的 然而，概念是，通过运动单位记录，在单个神经元水平上测量电机输出，允许 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反求 发动机单元点火模式分析的工程技术。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。3)将RE部署到 研究每块肌肉特有的抑制-神经调节相互作用是否相对 固定的，或者不间断地适应不同的运动任务。基于超级计算机的发展 分析技术为测量激发模式的技术的出现提供了理想的补充 大量的机动单位。我们新的逆向工程方法有可能改变我们的 理解人类运动指令的突触组织。

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.