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：项目概要/摘要 绒猴正在成为神经科学研究的重要模式物种， 开发新技术，如CRISPR，允许有针对性的基因修饰， 这个物种。这些发展将使灵长类动物研究能够利用强大的 以前主要限于啮齿动物的遗传工具，包括光遗传学，遗传学， 活性报告基因，以及涉及脑功能的内源性基因的靶向突变， 人类疾病绒猴非常适合这种方法，因为它们体型小，繁殖快 与大多数灵长类动物相比。它们通常以家庭为单位居住，并展示各种各样的 包括复杂的声乐曲目在内的社会行为。因此，绒猴代表了 这是一个很有前途的系统，用于研究灵长类动物模型的社会行为和其他认知功能， 它们还在建模影响认知功能的脑部疾病方面大有可为， 很难在其他物种如啮齿类动物中进行研究。为了充分利用这些新兴的 动物模型，有必要开发新的方法来分析他们的行为，包括 标准化行为任务无法完美捕捉的自然主义社会互动。我们 因此，计划开发一个系统，用于自动分析家庭中的绒猴行为 笼子该系统将由一个集成的传感器阵列组成，包括摄像机、深度传感器、 传感器和安装在衣领上的可穿戴麦克风。由此产生的多模态数据将是 使用计算机视觉、语音处理、机器视觉等方法进行同步和分析 学习和多模态数据分析。具体来说，我们将制定跟踪分析， 概率图形模型，这将允许视频数据与音频集成 记录，以及未来可能探索的其他模式，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 制定方法对来电进行分类，识别个别来电者，跟踪来电者的位置和身份， 每个动物在三个维度上，并对不同的行为进行分类，包括动物之间的相互作用 个体我们预计，我们的系统将是有用的基础和广泛的研究， 翻译神经科学，特别是它将有助于研究行为表型 在人类精神疾病的遗传模型中，以及将行为异常与 他们潜在的遗传和神经原因