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

• 批准号: 10303478
• 负责人: Muhannad Bakir
• 金额: $ 3.1万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10303478
• 关键词: Area; Behavior; Brain; Clinical; Collection; Computing Methodologies; Data; Data Set; Electrodes; Forelimb; Hand; Individual; Measurement; Measures; Methods; Morphology; Motor; Motor Neurons; Motor output; Movement; Mus; Muscle; Muscle Cells; Muscle Fibers; Nervous System Physiology; Nervous system structure; Neurons; Neurosciences; Organ; Patients; Rehabilitation therapy; Research; Research Personnel; Software Tools; Songbirds; Surface; Techniques; Technology; Training; clinical application; design; flexibility; improved; insight; invention; large datasets; motor control; new technology; novel; prosthesis control; relating to nervous system; sobriety

A crucial problem in neuroscience is to understand how the brain coordinates muscle activity to produce behavior. For decades, researchers have approached this problem by recording spiking activity in motor areas and attempting to understand how cortical spike trains control motor output, which is characterized either by the behavior itself (e.g. hand trajectories) or by standard EMG methods (which provide a bulk, multiunit measure of muscle activity levels). Recent advances in both neural recording hardware now allow spike trains from a large number of neurons to be recorded simultaneously. Furthermore, novel computational methods for analyzing large datasets of concurrently-recorded neural activity have led to dramatic new insights into how cortical networks encode behavior. However, there are currently no methods for recording large spiking ensembles in muscle cells during behavior, hindering our understanding of how the nervous system ultimately coordinates movement. This unmet need hinders our understanding of brain function, both from the perspective of comprehending the normal function of the nervous system and in designing clinical applications that might use EMG activity to enable patients to control prostheses. This proposal builds on the recent invention (by PI Sober's group) of a novel class of flexible electrode array that sits on the muscle surface and provides single-unit recordings of individual motor units, the collection of muscle fibers innervated by a single motor neuron. We will build on these preliminary results to develop and validate technologies for recording the entire ensemble of muscles in both the songbird vocal organ and the mouse forelimb. To do so we will advance the current design by scaling it up the channel count by orders of magnitude, developing species- specific array morphologies that can record units from all relevant muscles simultaneously, embedding active circuitry within the flexible array to allow data multiplexing, and creating and distributing software tools for processing and analyzing the resulting datasets.

神经科学中的一个关键问题是了解大脑如何协调肌肉活动来产生 行为。几十年来，研究人员一直通过记录运动区的尖峰活动来解决这个问题 并试图了解皮质棘波训练是如何控制电机输出的，这一特点是 行为本身(例如，手部轨迹)或通过标准的肌电方法(其提供大量、多个单元 衡量肌肉活动水平)。这两种神经记录硬件的最新进展现在都允许尖峰训练 从大量的神经元中同时记录下来。此外，还提出了新的计算方法 对同时记录的神经活动的大数据集进行分析，导致了对如何 大脑皮层网络编码行为。然而，目前还没有记录大尖峰的方法 在行为过程中肌肉细胞中的集合，阻碍了我们对神经系统最终是如何 协调移动。这种未得到满足的需求阻碍了我们对大脑功能的理解，从 理解神经系统的正常功能和设计临床应用的观点 这可能会使用肌电活动来使患者能够控制假肢。这项建议是建立在最近的 (由Pi Sober的团队)发明了一种新型的柔性电极阵列，它位于肌肉表面并 提供单个运动单位的单个单位记录，由单个运动单位支配的肌肉纤维的集合 运动神经元。我们将在这些初步结果的基础上开发和验证记录 鸣禽发声器官和老鼠前肢的全部肌肉。要做到这一点，我们将 通过按数量级扩大通道数量来推进当前的设计，开发物种- 特定的阵列形态，可以同时记录所有相关肌肉的单位，嵌入活动 灵活阵列内的电路，允许数据多路传输，以及创建和分发软件工具 处理和分析结果数据集。