Hannah's Diversity Supplement grant

汉娜的多元化补助金

• 批准号: 10788997
• 负责人: Victoria Eugenia Guadalupe Abraira
• 金额: $ 4.01万
• 依托单位: RUTGERS, THE STATE UNIV OF N.J.
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-30 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10788997
• 关键词: Address; Affect; Afferent Neurons; Behavior; Behavioral Assay; Biological Assay; Brain; Code; Complex; Computer Vision Systems; Coupled; Cutaneous; Data; Disease; Electrophysiology (science); Environment; Extensor; Flexor; Foundations; Genetic; Grant; Hindlimb; Individual; Injury; Interneurons; Joints; Lateral; Length; Limb structure; Link; Locomotion; Machine Learning; Maps; Mechanics; Modality; Motor; Motor Activity; Motor Neurons; Motor Pathways; Motor output; Movement; Muscle; Muscle Contraction; Neurons; Organ; Output; Parvalbumins; Pathway interactions; Pattern; Persons; Positioning Attribute; Property; Proprioception; Proprioceptor; Quality of life; Reflex action; Research; Resolution; Sensorimotor functions; Sensory; Sensory Process; Shapes; Skin; Slice; Speed; Spinal; Spinal Cord; Spinal cord posterior horn; Structure; Synapses; Technology; Testing; Thinking; Touch sensation; Ventral Horn of the Spinal Cord; Vertebral column; Visualization; Walking; Work; behavioral response; behavioral study; electrical property; genetic approach; improved; in vivo; insight; interdisciplinary approach; motor behavior; motor function improvement; mouse genetics; multimodality; nervous system disorder; neural; novel; novel strategies; novel therapeutics; programs; receptor; response; sensory input; sensory stimulus; sensory system; somatosensory; tool; vibration

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.

项目总结/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的 深入了解我们的感觉系统是如何连接到不同的感觉形式，并将它们转化为 运动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之间的直接联系（即，肌肉收缩）提供了一个精致的实验 自谢林顿在本体感受反射通路上的开创性工作以来，这种易处理性一直被利用。 事实上，从那时起，在理解本体感受器（即，肌感觉 神经元）形成运动活动。皮肤中的触觉感受器编码感觉形式，如振动，压痕， 和滑动，也是我们适应环境变化的关键。但 脊髓整合的触摸途径造型运动活动仍然深刻了解甚少。到 为了解决该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 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 基因工具，包括高速摄像机、计算机视觉和机器学习等运动检测， 具有前所未有的敏感性的躯体感觉行为。结合这些技术，我们发现了一种新的 触觉特异性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 代表了一个关键节点，用于整合触摸和本体感受信息，以影响特定的模式， 肌肉群，促进运动过程中的纠正运动和运动过程中的运动“切换”， 自然主义行为。我们询问这个新颖的网络来解决基本问题，这些问题的答案将 使我们能够理解触摸路径如何汇聚到形状运动。在目标1和2中，我们将联合收割机 遗传学方法、高分辨率突触分析、切片电生理学和体内肌肉记录， 测试这一假设，即该网络整合了多模态感觉信息，以影响特定的肌肉 对感官输入的反应Aim 3结合了关节和肌肉活动记录来测试这一假设， 网络塑造皮肤反应，以促进运动期间的矫正运动。我们扩展了这些 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 亚秒级的动作，以测试这一假设，触摸特定的前运动神经网络如何塑造微观， 运动被拼凑成复杂的运动行为 .通过了解运动的最终路径 组织（即，脊髓）我们的研究将导致新的治疗方法，旨在改善生活质量的 患有各种神经系统疾病的人。因此，本研究奠定了小说的批评基础 思考如何调节脊髓回路以改善运动功能。