Touching on locomotion: an anatomical and functional analysis of spinal cord circuits that shape the way we move

项目概要/摘要 神经科学生物医学研究的一个核心挑战是定义行为背后的神经回路。 脊髓回路的研究非常适合回答以下问题：感觉和感觉之间的直接联系 输入和电机输出提供了精致的实验易处理性，自谢林顿以来一直得到利用 本体感觉反射通路的开创性工作1。事实上，自那时以来，已经取得了巨大进展 了解本体感受器（即肌肉感觉神经元）如何塑造运动活动。皮肤中的触觉感受器 （即皮肤感觉神经元）编码振动、压痕和滑动等感觉模式，也是 对于调整我们的行走方式以适应环境的变化至关重要。然而，脊髓整合 塑造运动活动的触觉通路仍然知之甚少。解决关键概念 和该领域的技术挑战，我们建立了一个广泛的小鼠遗传工具箱来可视化、量化和 操纵特定于触摸的脊髓回路。此外，我们将这些强大的遗传工具与运动结合起来 涉及高速摄像机、计算机视觉和机器学习的测定，以量化体感 行为具有前所未有的敏感性。结合这些技术，我们发现了一种新颖的触摸特定 运动前网络对于感觉运动功能很重要。我们的总体假设是该网络代表 整合触摸信息以影响肌肉群特定模式的关键节点，从而促进两者 自然行为期间运动和运动“切换”过程中的纠正动作。我们审问这个 新颖的网络来解决基本问题，其答案将使人们更深入地了解如何 触觉通路在脊髓中汇聚以塑造运动。在目标 1 和 2 中，我们结合了遗传方法， 高分辨率突触分析、切片电生理学和体内肌肉记录来检验假设 该网络集成了多模式感觉信息来协调特定肌肉以响应 皮肤输入。目标 3 结合关节和肌肉活动记录来测试该网络的假设 塑造皮肤反应，以促进运动过程中的纠正运动。我们扩展这些行为 通过实施计算机视觉和机器学习来将自然行为解析为子行为的研究 第二个动作来测试触摸特异性前运动网络如何塑造微动作的假设 被拼凑成复杂的运动行为 。通过了解运动组织的最终路径 （即脊髓）我们的研究将带来旨在改善人们生活质量的新疗法 患有多种神经系统疾病。因此，这项研究为新方法奠定了重要基础 调节脊髓回路以改善运动功能。

Project Summary/Abstract A central challenge in neuroscience biomedical research is to define the neural circuits that underlie behavior. Investigations of spinal cord circuits are ideally suited to answer these questions: the direct link between sensory input and motor output affords an exquisite experimental tractability that has been leveraged since Sherrington’s pioneering work on the proprioceptive reflex pathway1. Indeed, great progress has been made since then in understanding how proprioceptors (i.e., muscle sensory neurons) shape motor activity. Touch receptors in skin (i.e., cutaneous sensory neurons) encoding sensory modalities like vibration, indentation, and slip, are also critical for adapting the way we walk in response to changes in our environment. However, spinal cord integration of touch pathways that sculpt motor activity remains profoundly poorly understood. To address key conceptual and technical challenges in this field, we have built an extensive mouse genetic toolbox to visualize, quantify and manipulate touch-specific spinal cord circuits. In addition, we merge these powerful genetic tools with motor assays involving high-speed cameras, computer vision, and machine learning to quantify somatosensory behavior with unprecedented sensitivity. Combining these technologies, we identified a novel touch-specific premotor network important for sensorimotor function. Our overall hypothesis is that this network represents a critical node for integrating touch information to influence specific patterns of muscle groups that facilitate both corrective movements during locomotion and motor ‘switching’ during naturalistic behaviors. We interrogate this novel network to address fundamental questions whose answers will enable a deeper understanding of how touch pathways converge in the spinal cord to shape movement. In Aims 1 and 2 we combine genetic approaches, high-resolution synaptic analysis, slice electrophysiology and in-vivo muscle recordings to test the hypothesis that this network integrates multimodal sensory information to coordinate specific muscles in response to cutaneous input. Aim 3 combines joint and muscle activity recordings to test the hypothesis that this network shapes cutaneous responses to facilitate corrective movements during locomotion. We extend these behavioral studies by implementing computer vision and machine learning to parse out naturalistic behaviors into sub- second movements to test the hypothesis that touch-specific premotor networks sculpt how micro-movements are pieced together into complex motor behaviors . By understanding the final path for movement organization (i.e., the spinal cord) our research will lead to new therapies aimed at improving the quality of life of people suffering from a variety of neurological disorders. Thus, this research lays the critical foundation for novel ways to modulate spinal circuits for improving motor function.