Multimodal, integrated analysis of neural activity and naturalistic social behavior in freely moving mice

自由活动小鼠的神经活动和自然社会行为的多模态综合分析

• 批准号: 10415149
• 负责人: David J Anderson
• 金额: $ 41.63万
• 依托单位: CALIFORNIA 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: 2020-08-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10415149
• 关键词: Address; Adoption; Adult; Affect; Amygdaloid structure; Animal Behavior; Animal Model; Animals; Area; Behavior; Behavior Control; Behavioral; Behavioral Assay; Behavioral Model; Benchmarking; Biological Assay; Brain; Brain region; Calcium; Cell Nucleus; Classification; Collection; Communities; Complex; Computer Vision Systems; Computer software; Computing Methodologies; Control Animal; Data; Data Set; Decision Making; Development; Environment; Future; Goals; Grain; Home; Hour; Human; Hypothalamic structure; Image; Joints; Link; Machine Learning; Manuals; Measurement; Measures; Mental Depression; Mental disorders; Methods; Modeling; Motivation; Movement; Mus; Mutation; National Institute of Mental Health; Neuronal Dysfunction; Neurosciences; Performance; Population Dynamics; Public Health; Reciprocal Social Interaction; Reproducibility; Research; Resolution; Resources; Role; Schizophrenia; Social Behavior; Social Identification; Social Interaction; Structure; Supervision; System; Techniques; Testing; Time; Training; Triad Acrylic Resin; Variant; Work; autism spectrum disorder; base; behavioral study; body position; collaborative approach; computerized tools; design; experience; experimental study; flexibility; improved; innovation; insight; interest; machine learning algorithm; mathematical model; multimodality; neural circuit; neural correlate; neuromechanism; neuroregulation; novel; relating to nervous system; social; social defeat; social structure; temporal measurement; theories; tool; transfer learning; user-friendly

Project Summary/Abstract This proposal responds to an NIMH notice NOT-MH-18-036 aimed at the development and study of novel, computationally defined behavioral assays, and at applying theory and mathematical modeling to better capture the richness of complex, naturalistic behaviors. Specifically, we aim to develop novel computational tools for analyzing social behaviors in freely moving mice, and relating those identified behaviors to neural circuit activity in brain regions that govern the expression of those behaviors. Social behavior is affected in many human psychiatric disorders, such as autism, schizophrenia, and depression. We propose an interdisciplinary, collaborative approach to fill two major gaps that present a barrier to studies of social behavior: 1) the lack of quantitative and high-resolution descriptions of naturalistic social behaviors in freely moving animals, and 2) the difficulty of relating neural activity recorded in deep subcortical regions that govern such behaviors, such as the hypothalamus and extended amygdala, to animals' actions or to models of behavioral control. Our objective is to create a computational behavior analysis platform that integrates automated measurement of naturalistic social behavior, synchronous large-scale recording or imaging of neural activity, and apply these to a novel assay to investigate social behavioral decision-making. The central objective of this proposal is to extend our Mouse Action Recognition System (MARS) to create a platform that allows facile training of supervised and unsupervised behavior classifiers, quantitative correlation with simultaneously acquired neural recording or imaging data, and which can be flexibly adapted to additional behavior assays. The rationale for this approach is that fine-grained quantification of social behavior, and its correlation with neural recordings, is necessary to form and test theories of behavioral control by subcortical brain regions. While automated tracking and “pose” estimation software such as DeepLabCut have made tracking of animals' body positions more feasible, the identification of social behaviors from pose data is a non-trivial problem, requiring a separate computational approach that takes into account the relative movements of multiple animals over time. To achieve our objective, we will broaden the palette of social behaviors MARS can detect using machine learning and generative models (Aim 1), develop methods to relate those behaviors to neural activity (Aim 2), and extend MARS to additional assays to study neural correlates of social decision-making. This contribution is significant because it will create a resource that will transform our ability to study micro- and meso-scale subcortical circuits controlling social behavior. The contribution is innovative because it combines expertise from circuit neuroscience and computer vision/machine learning to create new tools for understanding the link between neural activity and behavior, in a context that is relevant to understanding dysfunctions of neural circuits that underlie human psychiatric disorders.

项目概要/摘要 该提案是对 NIMH 通知 NOT-MH-18-036 的回应，该通知旨在开发和研究新颖的、 计算定义的行为分析，并应用理论和数学模型来更好地捕获 具体来说，我们的目标是开发新颖的计算工具。 分析自由活动的小鼠的社会行为，并将这些识别的行为与神经回路活动联系起来 许多人的大脑中控制这些行为表达的区域都会受到社会行为的影响。 我们建议采用跨学科的方法来治疗精神疾病，例如自闭症、精神分裂症和抑郁症。 协作方法填补了社会行为研究的两个主要空白：1）缺乏 对自由活动的动物的自然社会行为进行定量和高分辨率的描述，以及 2) 将控制此类行为的深层皮层下区域记录的神经活动联系起来很困难，例如 下丘脑和扩展杏仁核，动物行为或行为控制模型我们的目标。 是创建一个计算行为分析平台，集成自然主义的自动测量 社会行为、神经活动的同步大规模记录或成像，并将其应用于新的测定中 调查社会行为决策 该提案的中心目标是扩展我们的鼠标。 动作识别系统（MARS）创建一个平台，允许对受监督和 无监督行为分类器，与同时获取的神经记录的定量相关性或 成像数据，并且可以灵活地适应其他行为分析这种方法的基本原理。 社会行为的细粒度量化及其与神经记录的相关性对于 在自动跟踪和“姿势”的同时形成并测试皮层下大脑区域的行为控制理论。 DeepLabCut 等估计软件使跟踪动物的身体位置变得更加可行， 从姿势数据识别社会行为是一个不平凡的问题，需要单独的计算 考虑多种动物随时间的相对运动的方法为了实现我们的目标， 我们将使用机器学习和生成模型扩大 MARS 可以检测的社会行为范围 （目标 1），开发将这些行为与神经活动联系起来的方法（目标 2），并将 MARS 扩展到其他 研究社会决策的神经相关性的分析方法具有重要意义，因为它将创造。 一种资源将改变我们研究控制社会的微观和中观皮层下回路的能力 该行为是创新的，因为它结合了电路神经科学和计算机的专业知识。 视觉/机器学习创建新工具来理解神经活动和行为之间的联系 与理解人类精神疾病背后的神经回路功能障碍相关的背景。