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

项目摘要/摘要 该建议响应了旨在开发和研究小说的NIMH通知NOT-MH-18-036， 通过计算定义的行为分析，以及应用理论和数学模型更好地捕获 复杂的自然主义行为的丰富性。具体地说，我们的目标是开发新的计算工具 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 在控制这些行为表达的大脑区域。许多人的社会行为受到影响 精神障碍，如自闭症、精神分裂症和抑郁症。我们建议建立一个跨学科的， 填补社会行为研究障碍的两个主要空白的协作方法：1)缺乏 对自由活动的动物的自然主义社会行为的定量和高分辨率描述，以及2) 很难将皮层下深层区域记录的神经活动与控制此类行为的活动联系起来，例如 下丘脑和延伸的杏仁核，动物的行为或行为控制的模型。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，并将这些应用于一种新的分析 研究社会行为决策。这项提议的中心目标是扩大我们的鼠标 行动识别系统(MARS)创建了一个平台，允许对受监督和 非监督行为分类器，与同时获取的神经记录或 成像数据，并且可以灵活地适应额外的行为分析。这种方法的基本原理是 社会行为的细粒度量化及其与神经记录的相关性对于 大脑皮质下区域行为控制的形成和测试理论。在自动跟踪和“摆姿势”的同时 DeepLabCut等估计软件使追踪动物身体位置变得更加可行， 从姿势数据中识别社会行为不是一个简单的问题，需要单独的计算 一种考虑到多个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产生式模型拓宽火星可以检测到的社会行为的调色板 (目标1)，开发将这些行为与神经活动联系起来的方法(目标2)，并将MARS扩展到其他 研究社会决策的神经关联的方法。这一贡献意义重大，因为它将创造 这一资源将改变我们研究控制社会的微尺度和中尺度皮层下回路的能力 行为。这项贡献是创新的，因为它结合了电路神经科学和计算机的专业知识 视觉/机器学习为理解神经活动和行为之间的联系创造了新的工具，在 与理解人类精神障碍背后的神经回路功能障碍相关的背景。

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.