Hannah's Diversity Supplement grant

项目摘要/摘要 阐明我们的大脑如何整合信息以引发适当的行为反应需要机械论 洞察我们的感觉系统是如何连接的，以整合不同的感觉模式，并将它们转化为 运动动作的神经编码。对脊髓回路的研究非常适合探索这些问题： 感觉输入和运动输出(即肌肉收缩)之间的直接联系提供了一个精致的实验 自谢林顿在本体感觉反射通路上的开创性工作以来一直被利用的可控性。 事实上，从那时起，在理解本体感受器(即肌肉感觉)如何 神经元)塑造运动活动。皮肤中的触摸感受器编码感觉方式，如振动、压痕、 和滑倒，对于我们适应环境变化的走路方式也是至关重要的。然而， 脊髓整合触摸路径来塑造运动活动仍然知之甚少。至 为了解决这一领域的关键概念和技术挑战，我们建立了一个广泛的小鼠遗传工具箱 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 基因工具和运动分析，涉及高速摄像机、计算机视觉和机器学习来量化 具有前所未有的敏感度的体感行为。结合这些技术，我们确定了一种新的 触觉前置运动网络对感觉运动功能非常重要。我们的总体假设是，这个网络 表示集成触摸和本体感觉信息以影响特定模式的关键节点 在运动过程中促进矫正运动的肌肉群，以及在运动过程中促进运动“切换”的肌肉群 自然主义的行为。我们审问这个新颖的网络来解决基本问题，这些问题的答案将 使您能够理解触摸路径如何汇聚为运动形状。在目标1和目标2中，我们结合了 遗传学方法、高分辨率突触分析、切片电生理学和活体肌肉记录 测试该网络整合多模式感觉信息以影响特定肌肉的假设 对感官输入的反应。目标3结合关节和肌肉活动记录来检验这一假设 网络塑造皮肤反应，以促进运动过程中的矫正运动。我们延长了这些 通过实施计算机视觉和机器学习来解析自然行为的行为研究 以测试触摸特定的前驱运动网络如何塑造微小的 动作被拼凑成复杂的运动行为 。通过了解移动的最终路径 组织(即脊髓)我们的研究将导致旨在提高患者生活质量的新疗法 患有各种神经系统疾病的人。因此，这一研究为小说创作奠定了批判性基础 调制脊髓环路改善运动功能的思路。

Project Summary/Abstract Elucidating how our brain integrates information to elicit appropriate behavioral responses requires mechanistic insights into how our sensory systems are wired to integrate diverse sensory modalities and transform them into the neural codes of motor action. Studies of spinal cord circuits are well-suited to exploring these questions: the direct link between sensory input and motor output (i.e., muscle contraction) affords an exquisite experimental tractability that has been leveraged since Sherrington’s pioneering work on the proprioceptive reflex pathway. Indeed, great progress has been made since then in understanding how proprioceptors (i.e., muscle sensory neurons) shape motor activity. Touch receptors in skin 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, the spinal cord integration of touch pathways to 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 and proprioceptive 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 an understanding of how touch pathways converge 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 influence specific muscle responses to sensory 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 of thinking about modulating spinal circuits for improving motor function.