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: 10226273
• 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/10226273
• 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; Psychological Transfer; 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; 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.

项目摘要/摘要 该提案对NIMH通知响应，而不是MH-18-036，旨在开发和研究新颖， 计算定义的行为分析，并应用理论和数学建模来更好地捕获 复杂，自然的行为的丰富性。具体而言，我们旨在开发新颖的计算工具 分析自由移动小鼠的社会行为，并将这些行为与神经电路活动联系起来 在控制这些行为表达的大脑区域中。社会行为受到许多人的影响 自闭症，精神分裂症和抑郁症等精神疾病。我们提出了一个跨学科， 填补两个主要空白的协作方法，这些空白是对社会行为研究障碍的障碍：1）缺乏 自由动物中自然主义社会行为的定量和高分辨率描述，2） 在控制此类行为的深层皮质下区域中记录的神经元活动的困难，例如 下丘脑并扩展了杏仁核，以动物的作用或行为控制模型。我们的目标 是创建一个计算行为分析平台，以整合自然主义的自动测量 社交行为，同步的大规模记录或神经活动的成像，并将其应用于新颖的测定法 调查社会行为决策。该提议的核心目的是扩展我们的鼠标 行动识别系统（MARS）创建一个平台，可轻松培训监督和 无监督的行为分类器，与简单获得的神经记录或 成像数据，并且可以柔韧地适应其他行为分析。这种方法的理由 是否需要细颗粒的社会行为及其与神经记录的相关性 通过皮层脑区域进行行为控制的理论和测试理论。而自动跟踪和“姿势” 诸如deeplabcut之类的估计软件使跟踪动物的身体位置更可行， 从姿势数据识别社交行为是一个非平凡的问题，需要单独的计算 考虑到随着时间的流逝，多种动物的相对运动的方法。为了实现我们的目标， 我们将扩大火星可以使用机器学习和通用模型检测的社会行为调色板 （目标1），开发将这些行为与神经活动联系起来的方法（AIM 2），并将火星扩展到附加 研究社会决策的神经相关性的测定。这种贡献很重要，因为它将创造 一种将改变我们研究微地皮质级的能力的资源 行为。该贡献具有创新性，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，以了解神经活动与行为之间的联系 与理解人类精神病基础的神经回路功能障碍有关的背景。