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

项目摘要/摘要 神经科学生物医学研究中的一个中心挑战是定义构成行为基础的神经回路。 对脊髓回路的研究非常适合回答这些问题：感觉之间的直接联系 输入和马达输出提供了一种自Sherrington以来一直被利用的精致的实验可控性 本体感觉反射通路方面的开创性工作1。事实上，自那时以来，在以下方面取得了很大进展 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)编码感觉形式，如振动、凹陷和滑动，也是 对于适应我们的环境变化而改变我们的走路方式至关重要。然而，脊髓整合 对于塑造运动活动的触摸路径，人们仍然知之甚少。解决关键的概念问题 和这一领域的技术挑战，我们已经建立了一个广泛的小鼠遗传工具箱来可视化、量化和 操控触觉特定的脊髓回路。此外，我们将这些强大的基因工具与马达相结合 利用高速摄像机、计算机视觉和机器学习来量化体感的分析 行为具有前所未有的敏感度。结合这些技术，我们确定了一种新的触摸特定 运动前网络对感觉运动功能很重要。我们的总体假设是，这个网络代表着 整合触摸信息以影响肌肉群的特定模式的关键节点 运动中的矫正动作和自然行为中的马达“切换”。我们审问这件事 解决基本问题的新型网络，其答案将使您能够更深入地了解 触觉路径汇聚在脊髓中，形成运动。在目标1和目标2中，我们结合了遗传方法， 高分辨率突触分析、切片电生理学和活体肌肉记录来验证这一假设 这个网络集成了多模式的感觉信息来协调特定的肌肉 皮肤输入。目标3结合关节和肌肉活动记录来测试这个网络的假设 塑造皮肤反应，以促进运动过程中的矫正运动。我们扩展了这些行为 研究通过实施计算机视觉和机器学习来将自然行为解析为子 第二个动作来测试触摸特定的运动前网络如何塑造微动作的假设 拼凑成复杂的运动行为 。通过了解运动组织的最终路径 我们的研究将带来旨在提高人们生活质量的新疗法 患有各种神经系统疾病。因此，这项研究为新的方式奠定了关键的基础 调节脊髓神经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.