Circuit basis of social behavior decision-making in a subcortical network

项目总结/摘要 该提案响应了FOA（RFA-NS-18-030）的要求：1）“了解神经系统的新方法” 与明确定义的社会行为相关的电路; 2）有助于协调的分布式电路 动机状态和奖励行为;“3）”经验和分析方法，以了解如何 行为状态是神经元、电路和网络相互作用的涌现特性。”研究 控制保守的自然行为的皮层下回路对于理解大脑功能至关重要。我们 目的是了解下丘脑-扩展神经元中不同回路节点之间的动态相互作用 杏仁核决策（“HEAD”）网络控制先天的社会行为决策，例如，在侵略性和 生殖行为我们提出了一个综合的方法来解决这个问题，结合显微内窥镜 利用自动化的、基于机器学习的神经元亚群的遗传识别成像（MEI） 自由活动小鼠的社会行为分类，以及神经元活动的功能扰动 in vivo.我们广泛的、长期的目标是了解互联网络中的分布式活动是如何 HEAD网络中的结构控制着竞争目标导向之间的即时决策， 这些行为对动物和人类的生存至关重要。这项建议的主要目的是 了解信息如何在社交互动中通过这个网络流动，并被解码以控制社交网络。 决定从事生殖与侵略性的社会行为。为了理解“上游”中的活动 节点控制“下游”节点中的神经表征，我们将实现一种新的方法， 前者的可逆化学发生抑制与神经元群体活动的同时成像， 后者这种方法的基本原理是，要理解系统，就需要描述影响的特征 对行为和电路水平表型的功能操纵。为了达到我们的目标，我们会 首先描述神经编码的行为和同种性别身份的多个节点的扩展 杏仁核，使用单部位显微内窥镜成像和计算分析方法（目标1）; 确定这些节点活动的扰动如何影响下丘脑节点的表征 (Aim 2）;研究核内和核间相互作用在确定活动平衡中的作用 之间的侵略性和生殖促进下丘脑节点（目标3）;确定如何平衡， 由下游的中脑结构解码，以确定要表达的社会行为类型（目标4）。这 贡献是重要的，因为它代表了一个系统级的方法来理解如何皮层下 网络控制行为决策。贡献是创新的，因为它整合了分析 神经元群体活动，定量测量自然主义的社会行为和功能 在特定的神经元亚群的活动扰动，以深入了解如何分布的神经回路 控制生存行为，在一个环境中，是相关的适应不良导致人类精神疾病。

Project Summary/Abstract This proposal responds to an FOA (RFA-NS-18-030) calling for 1) “novel approaches to understand neural circuitry associated with well-defined social behaviors;” 2) Distributed circuits that contribute to the coordination of motivational states and reward behavior;” 3) “Empirical and analytical approaches to understand how behavioral states are emergent properties of the interaction of neurons, circuits and networks.” The study of subcortical circuits that control conserved, naturalistic behaviors is crucial to understanding brain function. We aim to understand how dynamic interactions between different circuit nodes in the Hypothalamic-Extended Amygdala Decision (“HEAD”) network control innate social behavior decisions, e.g., between aggressive and reproductive behaviors. We propose an integrated approach to this problem that combines microendoscopic imaging (MEI) of genetically identified neuronal subpopulations with automated, machine learning-based classification of social behavior in freely moving mice, together with functional perturbations of neuronal activity in vivo. Our broad, long-term objective is to understand how distributed activity among interconnected structures in the HEAD network controls moment-to-moment decisions between competing goal-directed behaviors that are crucial for the survival of animals and humans. The central objective of this proposal is to understand how information flows through this network during social interactions, and is decoded to control the decision to engage in reproductive vs. aggressive social behaviors. To understand how activity in “upstream” nodes controls neural representations in “downstream” nodes, we will implement a novel approach combining reversible chemogenetic inhibition of the former with concurrent imaging of neuronal population activity in the latter. The rationale for this approach is that an understanding of the system requires characterizing the effects of functional manipulations on both behavioral and circuit-level phenotypes. To achieve our objective, we will first characterize the neural coding of behavior and conspecific sex identity in multiple nodes of the extended amygdala, using single-site microendoscopic imaging and computational analytic approaches (Aim 1); determine how perturbations in the activity of such nodes influence representations in hypothalamic nodes (Aim 2); investigate the roles of intra- and inter-nuclear interactions in determining the balance of activity between aggression and reproduction-promoting hypothalamic nodes (Aim 3); determine how this balance is decoded by downstream mid-brain structures to determine the type of social behavior to express (Aim 4). This contribution is significant because it represents a systems-level approach to understanding how a subcortical network controls behavioral decision-making. The contribution is innovative because it integrates analysis of neuronal population activity, quantitative measurement of naturalistic social behavior and functional perturbations of activity in specific neuronal subpopulations to gain insight into how distributed neural circuits control survival behaviors, in a context that is relevant to maladaptations causing human psychiatric disorders.