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

项目概要/摘要 该提案响应了NIMH通知NOT-MH-18-036，该通知旨在开发和研究新的， 计算定义的行为分析，并在应用理论和数学建模，以更好地捕捉 丰富的复杂自然行为具体来说，我们的目标是开发新的计算工具， 分析自由活动小鼠的社会行为，并将这些识别的行为与神经回路活动相关联 控制这些行为的大脑区域。许多人的社会行为受到影响 精神疾病，如自闭症、精神分裂症和抑郁症。我们提出了一个跨学科的， 合作的方法，以填补两个主要的差距，目前的障碍，社会行为的研究：1）缺乏 对自由移动动物的自然主义社会行为进行定量和高分辨率描述，以及2） 难以将皮层下深层区域记录的神经活动与这些行为联系起来，例如 下丘脑和延伸杏仁核，动物的行动或行为控制模型。我们的目标 是创建一个计算行为分析平台， 社会行为、神经活动同步大规模记录或成像，并将这些应用于新的测定 来研究社会行为决策。这项提案的核心目标是扩大我们的鼠标 行动识别系统（MARS）创建一个平台，允许监督和 无监督的行为分类器，与同时获得的神经记录或 成像数据，并且其可以灵活地适应于附加的行为测定。这种方法的基本原理 对社会行为及其与神经记录的相关性进行细粒度的量化， 形成并检验大脑皮层下区域的行为控制理论。当自动跟踪和“姿势” 估计软件，如DeepLabCut，使跟踪动物的身体位置更加可行， 从姿势数据识别社会行为是一个重要的问题，需要单独的计算 这种方法考虑了多个动物随时间的相对运动。为了实现我们的目标， 我们将扩大MARS可以使用机器学习和生成模型检测的社会行为的范围 (Aim 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。这一贡献意义重大，因为它将创造 一个资源，将改变我们的能力，研究微观和中尺度皮层下电路控制社会 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，以创造新的工具来理解神经活动和行为之间的联系， 这是相关的理解神经回路的功能障碍，人类精神疾病的基础。

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.