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.

项目摘要/摘要 阐明我们的大脑如何整合信息以引起适当的行为反应需要机械 深入了解我们的感觉系统如何将潜水员的感觉方式整合在一起并将其转变为 运动动作的神经法规。脊髓电路的研究非常适合探索这些问题： 感觉输入与电机输出（即肌肉收缩）之间的直接联系提供了独家实验 自谢灵顿（Sherrington）在本体感受反射途径上开创性的工作以来，已经利用了障碍。 确实，从那时起，就已经取得了巨大进步，以理解本体感受器如何（即肌肉感觉 神经元）塑造运动活动。皮肤中的触摸受体编码振动，凹痕，凹痕， 滑倒也是适应我们对环境变化的回应方式至关重要的。但是， 雕刻运动活动的触摸途径的脊髓整合仍然深入了解。到 解决该领域的关键概念和技术挑战，我们建立了广泛的鼠标基因工具箱 可视化，量化和操纵触摸特异性的脊髓电路。此外，我们合并了这些强大的 带有运动测定的遗传工具，涉及高速摄像机，计算机视觉和机器学习以量化 体感行为具有前所未有的灵敏度。结合了这些技术，我们确定了一本小说 触摸特异性前运动网络对于感觉运动功能很重要。我们的总体假设是该网络 代表了整合触摸和本体感受信息的关键节点，以影响特定模式 在移动期间既有矫正运动的肌肉群 自然行为。我们询问这个新颖的网络，以解决基本问题的答案 能够了解触摸路径如何汇聚以塑造运动。在目标1和2中，我们结合了 遗传方法，高分辨率合成分析，切片电生理学和体内肌肉记录 测试该网络集成多模式感官信息以影响特定肌肉的假设 对感觉输入的响应。 AIM 3结合了关节和肌肉活动记录，以检验以下假设。 网络形状皮肤反应以促进运动过程中的纠正措施。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解析自然主义行为 进入亚秒运动，以检验以下假设，即触摸特异性的前运动网络雕刻了微观 动作将组合成复杂的运动行为 。通过了解最终运动道路 组织（即脊髓）我们的研究将导致新的疗法，以改善生活质量 患有各种神经系统疾病的人。这是这项研究为小说的关键基础 思考调节脊柱回路以改善运动功能的方式。