Markerless Tracking of 3D Posture to Reveal the Sensory Origins of Body Schema

3D 姿势无标记跟踪揭示身体图式的感官起源

• 批准号: 10410450
• 负责人: Kyle Scott Severson
• 金额: $ 6.98万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10410450
• 关键词: 3-Dimensional; Address; Affect; Amputation; Anatomy; Animals; Area; Awareness; Behavior; Behavioral; Body part; Brain; Clinical; Code; Cognitive; Collaborations; Communities; Computer Analysis; Computer Vision Systems; Data Analyses; Deafferentation procedure; Disease; Educational workshop; Electrophysiology (science); Elements; Engineering; Environment; Excision; Experimental Models; Faculty; Feedback; Goals; Head; Health; Human; Individual; Intervention; Knowledge; Label; Learning; Limb Prosthesis; Limb structure; Locomotion; Machine Learning; Maintenance; Medical; Mentorship; Methodology; Methods; Modeling; Monitor; Motor; Movement; Mus; Neurobiology; Neurons; Operative Surgical Procedures; Parietal Lobe; Positioning Attribute; Posture; Process; Proxy; Reporting; Research; Research Training; Self Perception; Sensory; Somatosensory Cortex; Specialist; Speed; Stroke; System; Tactile; Talents; Technical Expertise; Techniques; Technology; Testing; Touch sensation; Training; Universities; Update; Vertebral column; base; brain machine interface; career; computational neuroscience; convolutional neural network; deep learning; design; experimental study; meetings; motor control; neural circuit; neural correlate; neuromechanism; novel; novel strategies; optogenetics; professor; relating to nervous system; research to practice; sensory input; skills; social cognition; somatosensory; stroke recovery; symposium; tenure track; tool

The goal of this proposed research is to reveal the sensory origins underlying the body schema representation. Body schema is the brain's internal model of the body's spatial configuration. This internal representation is critical for sensorimotor processing, movement control, and self-awareness, and is continuously updated during movement. Body schema representations are disrupted when somatosensory input is lost. The first step toward discover the neural correlates of body schema is to uncover neural mechanisms that generate body posture representation. We hypothesize that sensory inputs from primary somatosensory cortex (S1) and secondary somatosensory cortex (S2) to the posterior parietal cortex (PPC) are transformed to construct a body posture representation. To delineate the mechanisms underlying the neural coding of body posture, this project will utilize large- scale monitoring, apply interventional tools, develop new data analysis tools, and integrate new approaches. Our approach is to perform large-scale electrophysiological recording and novel markerless tracking of 3D posture in freely moving mice. To track posture, the first aim is to adapt a markerless tracking pipeline comprised of a deep 3D convolutional neural network to process high-speed videography of mouse behavior from multiple cameras. The second aim is to perform large-scale recording of neurons in S1, S2, and PPC and use advanced computational approaches to determine which postural features best explain the activity of neurons in these cortical areas. Finally, the third aim is to use optogenetic and projection-specific manipulations to address the causal impact of proprioceptive inputs from S1 and S2 on coding of posture in PPC. This research promises to uncover how sensory inputs are involved in generating the body schema representation and guiding behavior. Extensive training will be required to carry out this project and achieve my goal of earning a tenure-track professor position. The rigorous methodological and intellectual environment in Dr. Fan Wang’s lab and the Duke Neurobiology community will advance my conceptual knowledge and technical skills. I will implement deep learning techniques through training and collaboration with specialists. I will learn new techniques by attending Neuropixel and computational neuroscience courses. Finally, I will develop my professional skills by frequent attendance of seminars, workshops, and meeting with a postdoctoral mentorship committee. The proposed project will be conducted in the Department of Neurobiology at the Duke University Medical Campus. This interdisciplinary community at Duke will bolster the research and training included in this application through frequent interaction with talented and collaborative faculty, organization of seminars and symposia, numerous opportunities to practice research talks and receive valuable feedback, formation of a personalized postdoctoral mentorship committee, extensive career and professional training, and invaluable support from the postdoctoral association.

本研究的目的是揭示身体图式背后的感觉起源 表示.身体图式是大脑对身体空间结构的内部模型。这种内部 表征对于感觉运动处理、运动控制和自我意识至关重要， 在运动过程中不断更新。当体感输入时，身体图式表征会被破坏 已经消失了发现身体图式的神经关联的第一步是揭示神经机制， 生成身体姿势表示。我们假设来自初级躯体感觉皮层的感觉输入 (S1)和次级躯体感觉皮层（S2）到后顶叶皮层（PPC）被转化为 构建身体姿势表示。 为了阐明身体姿势的神经编码机制，本项目将利用大型- 扩大监测规模，应用干预工具，开发新的数据分析工具，并整合新的方法。我们 一种方法是进行大规模的电生理记录和新的无标记跟踪的3D姿态 在自由移动的老鼠中。为了跟踪姿势，第一个目标是适配由以下各项组成的无标记跟踪流水线： 深度3D卷积神经网络处理多个鼠标行为的高速视频 相机第二个目标是在S1，S2和PPC中进行大规模的神经元记录，并使用先进的 计算方法来确定哪些姿势特征最能解释这些神经元的活动， 皮质区最后，第三个目的是使用光遗传学和投射特异性操作来解决基因表达的问题。 来自S1和S2的本体感受输入对PPC中姿势编码的因果影响。这项研究承诺， 揭示感官输入如何参与产生身体图式表征和指导行为。 广泛的培训将需要进行这一项目，并实现我的目标，赚取终身教职的轨道 教授职位。王凡博士的实验室和杜克大学严格的方法论和知识环境 神经生物学社区将提高我的概念知识和技术技能。我将深入实施 通过培训和与专家合作学习技术。我将通过参加 神经像素和计算神经科学课程。最后，我将通过频繁的工作来提高我的专业技能。 参加研讨会，讲习班，并与博士后导师委员会会议。 拟议的项目将在杜克大学医学院神经生物学系进行 校园杜克的这个跨学科社区将加强本课程中的研究和培训。 通过与有才华的和协作的教师，组织研讨会， 研讨会，无数的机会，实践研究会谈，并获得宝贵的反馈，形成一个 个性化的博士后导师委员会，广泛的职业和专业培训， 博士后协会的支持。

项目成果

Geometric deep learning enables 3D kinematic profiling across species and environments.

• DOI: 10.1038/s41592-021-01106-6
• 发表时间: 2021-05
• 期刊: Nature methods
• 影响因子: 48
• 作者: Dunn TW;Marshall JD;Severson KS;Aldarondo DE;Hildebrand DGC;Chettih SN;Wang WL;Gellis AJ;Carlson DE;Aronov D;Freiwald WA;Wang F;Ölveczky BP
• 通讯作者: Ölveczky BP