Large-scale recording of spike train ensembles from muscle fibers during skilled behavior in mice and songbirds

在小鼠和鸣禽的熟练行为过程中大规模记录肌肉纤维的尖峰训练组

基本信息

批准号：
10228429
负责人：
Muhannad Bakir
金额：
$ 4.6万
依托单位：
EMORY UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-10 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10228429
关键词：
Address Animal Model Behavior Behavior Control Clinical Complex Complex Analysis Computational Technique Computer software Cutaneous Data Data Collection Data Set Development Electrical Engineering Electrodes Engineering Face Forelimb Generations Grant Human Individual Investigation Isometric Exercise Letters Measurement Mechanics Mediating Methods Monitor Morphology Motor Motor Neurons Mus Muscle Muscle Fibers Nervous System Physiology Neurosciences Organ Pathologic Pattern Physicians Population Analysis Property Rehabilitation device Rehabilitation therapy Research Research Personnel Signal Transduction Skin Songbirds Sorting - Cell Movement Surface Surgical sutures System Systems Analysis Techniques Technology Tissues Training Work animal data base complement C2a computerized data processing computing resources data acquisition data exchange density design dynamic system flexibility human subject improved innovation invention motor behavior motor control new technology novel open source prototype respiratory scale up tool

项目摘要

A crucial problem in motor neuroscience is to understand how muscles are activated to produce normal or pathological motor behavior. However, our understanding of how individual motor units (the muscle fibers activated by a single motor neuron) pattern their spiking to precisely control behavior is poor due to the limitations of current electromyographic (EMG) methods, which include fine wires inserted into muscles and, more recently, cutaneous EMG arrays used to record individual motor units, particularly in human subjects. However, both wire-based and cutaneous arrays face crucial limitations. Notably, they cannot record the very small and/or deep muscles that mediate fine motor control, due to tissue damage induced by wire insertion and by surface array's inability to monitor signals from deeper muscles. Furthermore, surface arrays have limited use outside of tightly-controlled (e.g. isometric force) tasks in humans, are not appropriate for long-term use in natural behaviors or in rehabilitative contexts, and perform poorly in animal models. Finally, wire electrodes typically provide only bulk multiunit recording (rather than single-unit spike trains). In this cross-disciplinary proposal between an electrophysiologist (Dr. Sober) and an electrical engineer (Dr. Bakir), we propose to fill this gap by creating a new generation of micro-scale, high channel-count EMG arrays to record massively parallel single-unit data across many muscles during natural behaviors.

运动神经科学的一个关键问题是了解肌肉如何被激活以产生正常或病理性运动行为。然而，我们对单个运动单位（肌纤维）如何理解由单个运动神经元激活）模式，它们的尖峰精确控制行为的能力很差，因为当前肌电图 (EMG) 方法的局限性，包括插入肌肉的细线，最近，皮肤肌电图阵列用于记录单个运动单位，特别是人类受试者。然而，基于线的阵列和皮肤阵列都面临着严重的局限性。值得注意的是，他们无法记录最由于导线插入和引起的组织损伤，介导精细运动控制的小和/或深层肌肉由于表面阵列无法监测来自深层肌肉的信号。此外，表面阵列具有有限的在人类严格控制的（例如等长力）任务之外使用，不适合长期使用自然行为或康复环境中，并且在动物模型中表现不佳。最后，电极丝通常仅提供批量多单元记录（而不是单单元尖峰序列）。在这个跨学科电生理学家（Sober 博士）和电气工程师（Bakir 博士）之间的提案，我们建议填写通过创建新一代微型、高通道数 EMG 阵列来进行大规模记录来弥补这一差距在自然行为期间跨许多肌肉并行单个单元数据。