Reverse Engineering Motor Unit Discharge Patterns

描述（由申请人提供）：所有动作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。人体肌纤维动作电位相对容易记录，运动单元的放电模式为运动神经元及其输入的特性提供了一个独特的窗口。这一建议的目标是在分析运动神经元输出的基础上，推断运动神经元输入的模式。这种逆向工程方法的成功取决于两个因素：(1)一群运动神经元的输入输出属性的精确模型的存在；(2)基于记录多个同时活动的运动单元的活动，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的输入输出特性的几个关键特征。当这些模型以不同的兴奋性和抑制性输入模式驱动时，它们再现了人类运动单位在自愿收缩期间放电的许多特征。以前，记录多个单元的行为需要将多个会话和主题的记录结合起来。记录和分析技术的新发展现在可以在每次试验中记录10个或更多的运动单元。这提供了一个丰富的数据集，可用于约束该集

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.