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：
10670769
负责人：
Muhannad Bakir
金额：
$ 37.45万
依托单位：
EMORY UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-06-15 至 2025-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10670769
关键词：
Address Animal Model Behavior Behavior Control Clinical Complex Complex Analysis Computational Technique Computer software Cutaneous Data Data Collection Data Set Development Devices Electrical Engineering Electrodes Electromyography 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 Property Rehabilitation therapy Research Research Personnel Signal Transduction Skin Songbirds Sorting Surface Surgical sutures System Systems Analysis Techniques Technology Tissues Training Work animal data complement C2a computerized data processing computing resources data acquisition data exchange design dynamic system fabrication flexibility human subject improved innovation invention motor behavior motor control new technology novel open source prototype respiratory scale up skills sobriety technology validation 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.

运动神经科学中的一个关键问题是了解如何激活肌肉以产生正常或病理运动行为。但是，我们对单个运动单元的理解（肌肉纤维由于单个电动机神经元的激活）模式它们的精确控制行为的峰值由于当前肌电图（EMG）方法的局限性，其中包括插入肌肉中的细线和，最近，皮肤EMG阵列用于记录单个运动单元，尤其是在人类受试者中。但是，电线和皮肤阵列都面临着关键的限制。值得注意的是，他们无法记录由于电线插入引起的组织损伤和/或深度肌肉，介导细胞运动控制的肌肉和/或通过表面阵列无法监测深肌的信号。此外，表面阵列有限在人类中使用紧密控制的（例如等距力）任务之外，不适合长期使用自然行为或康复背景下的行为，在动物模型中表现不佳。最后，电极通常仅提供批量多元记录（而不是单单元尖峰火车）。在这个跨学科电生理学家（Sober博士）和电气工程师（Bakir博士）之间的建议，我们建议填补通过创建新一代的微型，高频道计数EMG阵列来记录大量记录，这一差距自然行为期间，许多肌肉的平行单单元数据。