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以产生 行为。数十年来，研究人员通过记录运动区域的尖峰活动来解决这个问题 并试图了解皮质尖峰如何控制电动机输出，这是由 行为本身（例如手动轨迹）或通过标准EMG方法（提供批量多动物） 肌肉活动水平的度量）。现在两种神经记录硬件的最新进展现在都允许尖峰火车 从同时记录的大量神经元中。此外，新颖的计算方法 分析同时录制的神经活动的大型数据集已导致人们对如何的新见解 皮质网络编码行为。但是，目前尚无记录大尖峰的方法 行为过程中肌肉细胞中的合奏，阻碍了我们对神经系统最终如何最终的理解 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 理解神经系统正常功能和设计临床应用的观点 这可能会使用EMG活性使患者控制假体。该提议建立在最近的 一类新型柔性电极阵列的发明（Pi Sober的组）位于肌肉表面和 提供单个电动机单元的单一单元录音，该肌肉纤维的收集由一个 运动神经元。我们将基于这些初步结果，以开发和验证用于记录的技术 鸣禽声音器官和鼠标前肢中的整个肌肉合奏。为此，我们会 通过通过数量级来扩​​大通道数来提高当前设计，发展物种 - 特定的阵列形态可以同时记录所有相关肌肉的单位，并嵌入活跃 灵活数组中的电路以允许数据多路复用，并为 处理和分析结果数据集。