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：项目总结/摘要 狨猴正在成为神经科学研究的重要模式物种，受到 开发诸如 CRISPR 等新技术，可以实现有针对性的基因修饰 这个物种。这些进展将使灵长类动物研究能够利用强大的优势 以前主要仅限于啮齿类动物的遗传工具，包括光遗传学、遗传学 活动记者，以及与大脑功能有关的内源基因的靶向突变 人类疾病。狨猴非常适合这种方法，因为它们体型小且繁殖速度快 与大多数灵长类动物相比。他们通常以家庭形式居住，并展示各种 圈养的社会行为，包括复杂的声音曲目。因此，狨猴代表了 用于研究灵长类动物模型中的社会行为和其他认知功能的有前途的系统， 它们还有望对影响认知功能的大脑疾病进行建模 很难在其他物种（例如啮齿动物）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它们的行为，包括 标准化行为任务无法完美捕捉自然主义的社交互动。我们 因此计划开发一个系统来自动分析家中狨猴的行为 笼。该系统将由集成的传感器阵列组成，包括摄像机、深度 传感器和项圈式可穿戴麦克风。生成的多模态数据将是 使用计算机视觉、语音处理、机器学习等方法进行同步和分析 学习和多模式数据分析。具体来说，我们将跟踪分析表述为 概率图形模型，允许视频数据与音频集成 记录，以及未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、跟踪呼叫者的位置和身份的方法 每个动物的三个维度，并对不同的行为进行分类，包括之间的相互作用 个人。我们设想我们的系统将可用于基础和基础领域的广泛研究。 转化神经科学，特别是对于研究行为表型很有用 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。