Development of an Integrated System for Monitoring Home-Cage Behavior in Non-Human Primates

开发用于监测非人类灵长类动物笼内行为的综合系统

• 批准号: 9901577
• 负责人: Robert Desimone
• 金额: $ 52.2万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-08 至 2022-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9901577
• 关键词: 3-Dimensional; Acoustics; Affect; Animal Behavior; Animal Model; Animals; Behavior; Behavior monitoring; Behavioral; Brain; Brain Diseases; Breeding; Callithrix; Clustered Regularly Interspaced Short Palindromic Repeats; Complex; Computer Systems; Computer Vision Systems; Computing Methodologies; Data; Data Analyses; Development; Disease; Environment; Exhibits; Family; Functional disorder; Future; Genes; Genetic; Genetic Models; Goals; Grooming; Head; Home environment; Housing; Human; Individual; Language Development; Location; Machine Learning; Measures; Mental disorders; Methods; Modality; Modeling; Modification; Monitor; Motion; Mutation; Neurosciences Research; Noise; Parents; Pathologic; Pathology; Phenotype; Physiological; Posture; Primates; Reporter; Research; Research Personnel; Rodent; Scheme; Schizophrenia; Sleep; Social Behavior; Social Interaction; Standardization; System; Time; Training; Video Recording; Visual; Wireless Technology; autism spectrum disorder; automated analysis; base; behavioral phenotyping; behavioral study; cognitive function; data streams; feeding; gaze; human disease; human model; limb movement; machine learning method; microphone; motion sensor; multimodal data; multimodality; multiple data sources; neuropsychiatric disorder; new technology; nonhuman primate; offspring; optogenetics; relating to nervous system; sensor; social communication; social deficits; speech processing; tool; translational neuroscience; vibration; vocal learning; vocalization

7: Project Summary/Abstract Marmosets are emerging as an important model species for neuroscience research, driven by the development of new technologies such as CRISPR that allow targeted genetic modifications in this species. These developments will allow primate research to take advantage of powerful genetic tools that were previously restricted largely to rodents, including optogenetics, genetic activity reporters, and targeted mutation of endogenous genes implicated in brain function and human disease. Marmosets are well suited to this approach, being small and fast-breeding compared to most primates. They are typically housed in family groups, and exhibit a variety of social behaviors in captivity including complex vocal repertoires. Marmosets thus represent a promising system for studying social behavior and other cognitive functions in a primate model, and they also hold great promise for modeling brain disorders that affect cognitive functions that are difficult to study in other species such as rodents. To take full advantage of these emerging animal models, it is necessary to develop new methods for analyzing their behavior, including naturalistic social interactions that are imperfectly captured by standardized behavioral tasks. We therefore plan to develop a system for automated analysis of marmoset behaviors in the home cage. The system will consist of an integrated array of sensors including video cameras, depth sensors, and collar-mounted wearable microphones. The resulting multimodal data will be synchronized and analyzed using methods from computer vision, speech processing, machine learning, and multimodal data analysis. Specifically we will formulate the tracking analysis as a probabilistic graphical model, which will allow video data to be integrated with audio recordings, and with other modalities that could be explored in future, including inertial motion sensors, physiological recordings and other contextual data. Based on this approach we will develop methods to classify calls, identify individual callers, track the locations and identities of each animal in three dimensions, and classify different actions, including interactions between individuals. We envisage that our system will be useful for a wide range of studies in basic and translational neuroscience, and in particular it will be useful for studying behavioral phenotypes in genetic models of human psychiatric disorders, and for relating behavioral abnormalities to their underlying genetic and neural causes.

7：项目摘要/摘要 马莫果正在成为神经科学研究的重要模型，由 开发新技术，例如CRISPR，允许有针对性的遗传修饰 这个物种。这些发展将使灵长类动物研究能够利用强大的 以前在很大程度上限制啮齿动物的遗传工具，包括光遗传学，遗传学 活性记者，以及与大脑功能和有关性基因的靶向突变 人类疾病。摩尔果非常适合这种方法，很小而快速繁殖 与大多数灵长类动物相比。他们通常被安置在家庭群体中，并展示了各种各样的 囚禁中的社会行为，包括复杂的声乐曲目。果冻因此代表 在灵长类动物模型中研究社会行为和其他认知功能的有希望的系统， 而且他们还对影响认知功能的脑疾病进行建模巨大的希望 在其他物种（例如啮齿动物）中很难研究。充分利用这些新兴 动物模型，有必要开发新方法来分析其行为，包括 由标准化的行为任务不完美地捕获的自然社会互动。我们 因此，计划开发一个用于自动分析摩尔马斯行为的系统 笼。该系统将由一个集成的传感器组成，包括摄像机，深度 传感器和衣领安装的可穿戴麦克风。由此产生的多模式数据将是 使用计算机视觉，语音处理，机器中的方法进行同步和分析 学习和多模式数据分析。具体而言，我们将表达跟踪分析作为一个 概率图形模型，该模型将允许视频数据与音频集成 录音以及将来可以探索的其他方式，包括惯性运动 传感器，生理记录和其他上下文数据。基于这种方法，我们将 开发对呼叫进行分类，识别单个呼叫者的方法，跟踪的位置和身份 每个动物在三个维度上，并对不同的动作进行分类，包括 个人。我们设想我们的系统将对基本和基础研究的广泛研究有用 翻译神经科学，特别是它将对研究行为表型有用 在人类精神疾病的遗传模型中，并将行为异常与 它们的基本遗传和神经原因。