A system for long-term high-resolution 3D tracking of movement kinematics in freely behaving animals

用于对自由行为动物的运动学进行长期高分辨率 3D 跟踪的系统

• 批准号: 10317118
• 负责人: Bence P Olveczky
• 金额: $ 39.77万
• 依托单位: HARVARD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10317118
• 关键词: 3-Dimensional; Address; Adopted; Anatomy; Animal Behavior; Animal Model; Animals; Behavior; Behavioral; Benchmarking; Biological Models; Biomedical Research; Brain; Callithrix; Cephalometry; Communities; Complex; Data; Data Set; Deer Mouse; Disease; Ensure; Environment; Gold; Grant; Hand; Head; Human; Image; Image Analysis; Individual; Intelligence; Label; Learning; Learning Disorders; Lighting; Limb structure; Logic; Longitudinal Studies; Machine Learning; Measurement; Measures; Mental disorders; Methods; Modeling; Monitor; Monkeys; Motion; Movement; Mus; Nervous System Physiology; Nervous System control; Neurologic Deficit; Output; Patients; Performance; Positioning Attribute; Posture; Process; Rattus; Research; Research Personnel; Resolution; Rodent; System; Techniques; Technology; Testing; Time; Training; Work; appendage; base; computer science; convolutional neural network; cost; deep neural network; design; expectation; experimental study; flexibility; improved; innovation; kinematics; motor control; neural network; neuromechanism; new technology; novel diagnostics; novel therapeutic intervention; programs; relating to nervous system; scale up; skeletal; spatiotemporal

PROJECT SUMMARY The aim of this proposal is to deliver an innovative and easy-to-use experimental platform for measuring and quantifying naturalistic behaviors of mammalian animal models used for biomedical research, including rodents and monkeys, across a range of spatial and temporal scales. This will require developing a method for tracking movements freely behaving animals with far higher spatiotemporal resolution and more kinematic detail than currently possible. To overcome the limitations of current technologies, a new solution is proposed that synergistically combines two methods - marker based motion capture and a video- based machine learning approach. First, using marker-based motion capture, the gold standard for 3D tracking in humans, the position of experimental subjects' head, trunk, and limbs will be tracked in 3D with submillimeter precision. An innovative marker design, placement strategy, and post-processing pipeline will ensure an unprecedentedly detailed description of rodent behavior over a large range of timescales. To make the system more efficient, robust, affordable and better suited for high-throughput longitudinal studies, the unprecedentedly rich and large 3D datasets generated by the motion capture experiments will be leveraged to train a deep neural network to predict pose and appendage positions from a set of 1-6 normal video cameras. To best capitalize on the large training datasets, the latest advances in convolutional neural networks for image analysis will be incorporated. Together, these advances will promote generalization of the high-resolution 3D tracking system to a variety of animals and environments, thus establishing a cheap, flexible, and easy-to use kinematic tracking method that can easily be scaled up and adopted by other labs. The large ground-truth datasets will allow the system to be benchmarked and compared against state-of-the art technologies in quantitative and rigorous ways. Preliminary studies have been very positive and suggest large improvements over current methods both when it comes to the range of behaviors that can be tracked and the precision with which they can be measured. Importantly, all new technology will be readily shared with the scientific community, thereby leveraging from this single grant the potential for numerous investigators to dramatically improve the efficiency of their research programs requiring rigorous quantitative descriptions of animal behavior.

项目概要 该提案的目的是提供一个创新且易于使用的实验平台来测量 并量化用于生物医学研究的哺乳动物模型的自然行为， 包括啮齿动物和猴子，跨越一系列空间和时间尺度。这将需要开发 一种以更高的时空分辨率追踪自由行为动物的运动的方法 比目前更多的运动细节。为了克服现有技术的局限性，一种新的 提出的解决方案协同结合了两种方法——基于标记的运动捕捉和视频- 基于机器学习的方法。首先，使用基于标记的动作捕捉，这是 3D 的黄金标准 在人体跟踪中，实验对象的头部、躯干和四肢的位置将通过 3D 方式进行跟踪 亚毫米精度。创新的标记设计、放置策略和后处理流程 将确保对大范围时间尺度上的啮齿动物行为进行前所未有的详细描述。到 使系统更加高效、稳健、价格实惠并且更适合高通量纵向 研究表明，动作捕捉实验生成的前所未有的丰富且庞大的 3D 数据集将 用于训练深度神经网络，从一组 1-6 个正常值中预测姿势和附肢位置 摄像机。为了最好地利用大型训练数据集，卷积神经网络的最新进展 将合并图像分析网络。这些进步共同将促进 高分辨率 3D 跟踪系统对各种动物和环境进行跟踪，从而建立了一种廉价、 灵活且易于使用的运动跟踪方法，可以轻松扩展并被其他实验室采用。 大型真实数据集将允许对系统进行基准测试并与最新状态进行比较 以定量和严格的方式研究艺术技术。初步研究非常积极并表明 在可跟踪的行为范围方面，比当前方法有很大改进 以及它们的测量精度。重要的是，所有新技术都将很容易共享 与科学界合作，从而利用这一单项资助的潜力 研究人员大幅提高其研究项目的效率，需要严格的 动物行为的定量描述。