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.

项目总结所有电动机命令流过脊髓和脑干中的运动神经元。作为 对于整个中枢神经系统中中性电路的输入，这些命令包括三个主要组件：两种类型 电离输入（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产出构成了基本不确定性 运动神经控制的基础。幸运的是，人类的发动机输出可以在水平上进行研究 单神经元。运动神经元的动作电位为1-1，其肌肉纤维的电势形成电动机 可以在肌肉中相对容易记录其动作电位的单位。使用这些电动机的潜力 长期以来，对理解电动机命令的单元射击模式已得到赞赏。我们的目标是最大化 通过开发基于超级计算机的反向工程电动机发射模式的技术 确定兴奋，抑制性和神经调节输入的放大器和模式 运动命令在人类中。允许许多电动机单元简单记录的最近进步具有 允许我们在电机单元射击模式中识别独特的非线性行为。我们的现实发展 运动神经元的模型表明，这些非线性是由输入之间的复杂相互作用引起的 成分。我们计划将这些模型用作估计的反向工程（RE）方法的核心 这三个组件来自非线性人类运动单元射击模式。我们的前提是实施 我们在Argonne National Laboratories的超级计算机模型中，将允许系统探索 成千上万的输入组合产生的触发模式。那些准确的输入组织 然后，重新创建一组测量的射击模式将被视为“解决方案空间”的一部分 特定的电动机输出。该分析的关键问题是冗余。如果相同的电动机输出可以是 由许多输入组合产生的 对电机命令的了解很少。力量和EMG等总体电动机输出遇到了这个问题。我们的 但是，概念是通过电动机记录在单个神经元水平上测量电动机输出允许 用于有效的逆向工程。我们有3个目标：1）开发和评估基于超级计算机的反向 用于分析电动机射击模式的工程技术。 2）部署重新调查 运动单位射击模式种群肌肉特异性差异的机制。 3）部署到 研究针对每种肌肉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电动机任务。基于超级计算机的开发 分析技术为衡量射击模式的技术的出现提供了理想的完成 大量的电动机单元。我们新颖的反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。