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， 行为几十年来，研究人员通过记录运动区的尖峰活动来解决这个问题 并试图了解皮层锋电位序列如何控制运动输出，其特征在于： 行为本身（例如，手轨迹）或通过标准EMG方法（其提供批量、多单元 测量肌肉活动水平）。神经记录硬件的最新进展现在允许尖峰序列 大量的神经元同时记录下来。此外，新的计算方法， 分析大量同时记录的神经活动数据集， 皮层网络编码行为。然而，目前没有记录大尖峰的方法 在行为过程中聚集在肌肉细胞中，阻碍了我们对神经系统如何最终 协调运动。这种未满足的需求阻碍了我们对大脑功能的理解，无论是从 理解神经系统正常功能和设计临床应用的角度 可以利用肌电图活动来使病人控制假肢。这项建议建立在最近的 发明（由PI Sober的小组）的一种新型的柔性电极阵列，位于肌肉表面， 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，开发和验证用于记录 鸣禽发声器官和小鼠前肢的整个肌肉群。为此，我们将 通过按数量级增加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， 灵活阵列内的电路，以允许数据多路复用，以及创建和分发软件工具， 处理和分析结果数据集。