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。 对脊髓回路的研究非常适合回答这些问题：感觉神经元和神经元之间的直接联系。 输入和电机输出提供了一个精致的实验温顺，已被利用，因为谢林顿的 本体感受反射通路的开创性工作1.事实上，自那时以来， 理解本体感受器（即，肌肉感觉神经元）形成运动活动。皮肤触觉感受器 (i.e.,皮肤感觉神经元）编码感觉形态，如振动，压痕，和滑动，也是 这对我们适应环境变化的方式至关重要。然而，脊髓整合 对塑造运动活动的触觉通路仍然知之甚少。解决关键概念问题， 和技术挑战，我们已经建立了一个广泛的小鼠遗传工具箱，以可视化，量化和 操纵特定的脊髓回路此外，我们还将这些强大的遗传工具与电机相结合， 涉及高速摄像机、计算机视觉和机器学习的分析，以量化体感 以前所未有的敏感度。结合这些技术，我们确定了一种新的触摸特定 前运动网络对感觉运动功能很重要。我们的总体假设是，这个网络代表了一个 整合触摸信息的关键节点，以影响肌肉群的特定模式， 运动过程中的纠正运动和自然行为过程中的运动“转换”。我们审问这个 一个新的网络，以解决基本问题，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形成运动。在目标1和2中，我们将联合收割机遗传方法结合起来， 高分辨率突触分析，切片电生理学和体内肌肉记录来验证假设 这个网络整合了多模态的感觉信息，以协调特定的肌肉， 皮肤输入AIM 3结合了关节和肌肉活动记录来测试这个网络 塑造皮肤反应以促进运动期间的矫正运动。我们将这些行为 研究通过实施计算机视觉和机器学习，将自然行为解析为子 第二个动作，以测试这一假设，触摸特定的前运动网络造型如何微运动 拼凑成复杂的运动行为 .通过理解运动组织的最终路径 (i.e.,我们的研究将导致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，本研究为新的方法奠定了关键基础 来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.