Reverse Engineering Motor Unit Discharge Patterns

逆向工程电机单元放电模式

• 批准号: 9319331
• 负责人: Charles Heckman
• 金额: $ 32.85万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-15 至 2019-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9319331
• 关键词: Action Potentials; Address; Affect; Animals; Behavior; Brain Stem; Cells; Computer Simulation; Data Set; Development; Disease; Effectiveness; Electrodes; Engineering; Foundations; Goals; Human; Individual; Isometric Exercise; Joints; Measurement; Measures; Methods; Modeling; Motor; Motor Neurons; Motor output; Movement; Muscle; Muscle Fibers; Output; Pathologic; Pattern; Play; Population; Preparation; Property; Recreation; Research; Role; Shapes; Signal Transduction; Spinal Cord; Structure; Surface; Synapses; Techniques; Testing; Time; Torque; base; design; human subject; improved; motor control; neuroregulation; public health relevance; simulation; success; synaptic inhibition; tool; voltage clamp

DESCRIPTION (provided by applicant): All movements are controlled by the graded activation of different muscles. Muscle activation is controlled by the activation of motoneurons in the brainstem and spinal cord. Each motoneuron drives the muscle fibers it innervates in a one-to-one fashion. Muscle fiber action potentials are relatively easy to record in human subjects and it has long been appreciated that the firing patterns of motor units provide a unique window on the properties of motoneurons and their inputs. The goal of this proposal is to deduce the pattern of inputs to motoneurons based on an analysis of their output. The success of this reverse engineering approach depends upon two factors: (1) the existence of accurate models of the input-output properties of a population of motoneurons, and (2) the ability to extract the maximum amount of information from motoneuron output, based upon recording the activity of multiple, simultaneously-active motor units. We have recently developed a set of motoneuron models that can replicate several key features of the input-output properties of motoneurons with different intrinsic excitability. When these models are driven with different patterns of excitatory and inhibitory inputs they reproduce a number of the features of the discharge of human motor units during voluntary contractions. Previously, recording the behavior of multiple units required combining recordings taken across multiple sessions and subjects. New developments in recording and analysis techniques now make it possible to record from 10 or more motor units in each trial. This provides a rich data set that can be used to constrain the set of input patterns that give rise to a given pattern of output. The overall hypothesis of this proposal is that the firing patterns of a population of motoneurons are determined by the patterns of their synaptic inputs and the level of neuromodulatory drive, making it possible to deduce input patterns from the firing patterns of multiple motor units. We will test this hypothesi by comparing our estimates of input based on the reverse engineering approach with direct voltage-clamp measurements of the inputs in the same experimental preparation. There are three specific aims (1) to improve techniques for recording the simultaneous activity of multiple motor units, (2) to validate our reverse engineering approach by comparing synaptic input patterns that are predicted from recordings of motor unit discharge to those that are directly recorded from motoneurons, and (3) to determine the role of synaptic inhibition in shaping motor unit discharge patterns, and the ability of our reverse engineering approach to detect different patterns of inhibition. Once successfully developed in our animal preparation, the reverse engineering methods can be adapted for use in human subjects, allowing maximal use of the rich information available in motor unit firing patterns for understanding the structure of motor commands in humans in both normal and pathological states.

描述(申请人提供)：所有动作都由不同肌肉的分级激活控制。肌肉的激活是由脑干和脊髓中运动神经元的激活控制的。每个运动神经元以一对一的方式驱动它所支配的肌肉纤维。肌肉纤维动作电位在受试者中相对容易记录，长期以来人们一直认为运动单位的放电模式为了解运动神经元及其输入的特性提供了一个独特的窗口。这项提议的目标是根据对运动神经元输出的分析来推断运动神经元的输入模式。这种逆向工程方法的成功取决于两个因素：(1)运动神经元种群的输入-输出特性的准确模型的存在；(2)基于记录多个同时活动的运动单位的活动，从运动神经元输出中提取最大信息量的能力。我们最近开发了一套运动神经元模型，可以复制具有不同内在兴奋性的运动神经元输入输出特性的几个关键特征。当这些模型由不同的兴奋性和抑制性输入模式驱动时，它们重现了人类自主收缩期间运动单位放电的一些特征。以前，记录多个单元的行为需要组合跨多个会话和主题进行的记录。记录和分析技术的新发展现在使得在每次试验中从10个或更多的马达单元进行记录成为可能。这提供了可用于约束集合的丰富数据集 产生给定输出模式的输入模式。这一建议的总体假设是，一组运动神经元的放电模式由它们的突触输入模式和神经调制驱动的水平决定，因此可以从多个运动神经元的放电模式中推断出输入模式。我们将通过将基于反向工程方法的输入估计与相同实验准备中的输入的直接电压钳位测量进行比较来验证这一假设。有三个具体的目标(1)改进记录多个运动单位同时活动的技术，(2)通过比较从运动单位放电记录预测的突触输入模式和从运动神经元直接记录的突触输入模式来验证我们的反向工程方法，以及(3)确定突触抑制在塑造运动单位放电模式中的作用，以及我们的反向工程方法检测不同抑制模式的能力。一旦在我们的动物准备中成功开发，反向工程方法就可以适用于人类受试者，允许最大限度地利用运动单位放电模式中可用的丰富信息，以了解人类在正常和病理状态下的运动命令结构。