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

皮层下网络社会行为决策的电路基础

• 批准号: 10300937
• 负责人: David J Anderson
• 金额: $ 67.54万
• 依托单位: CALIFORNIA INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10300937
• 关键词: Address; Aggressive behavior; Algorithms; Amygdaloid structure; Animals; Behavior; Behavior Control; Behavioral; Biological Models; Brain; Cell Nucleus; Cells; Classification; Code; Computing Methodologies; Data; Decision Making; Encapsulated; Equilibrium; Female; Functional Imaging; Goals; Human; Hypothalamic structure; Image; Individual; Knowledge; Logic; Machine Learning; Maps; Measurement; Measures; Medial; Mental disorders; Midbrain structure; Modeling; Motivation; Mus; Neurons; Nuclear; Organism; Output; Partner in relationship; Phase; Phenotype; Population; Population Dynamics; Preoptic Areas; Property; Public Health; Reproduction; Reproductive Behavior; Research; Resolution; Rewards; Role; Sex Behavior; Shapes; Site; Social Behavior; Social Controls; Social Interaction; Structure; System; Testing; Video Recording; Work; base; behavioral response; cell type; cellular targeting; experimental study; imaging study; in vivo; innovation; insight; machine learning algorithm; male; midbrain central gray substance; neural circuit; neuroregulation; novel; novel strategies; optogenetics; predictive modeling; programs; relating to nervous system; reproductive; sex

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.

项目概要/摘要 该提案响应了 FOA (RFA-NS-18-030) 的要求：1)“理解神经网络的新方法 与明确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）“了解如何进行的经验和分析方法 行为状态是神经元、回路和网络相互作用的自然属性。” 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。 旨在了解下丘脑扩展中不同电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，攻击性和攻击性之间的决策 我们提出了一种结合显微内窥镜的综合方法来解决这个问题。 使用基于机器学习的自动化技术对基因识别的神经元亚群进行成像 (MEI) 自由活动小鼠的社会行为分类以及神经活动的功能扰动 我们广泛的长期目标是了解分布式活动如何相互关联。 HEAD 网络中的结构控制相互竞争的目标导向之间的即时决策 该提案的核心目标是对动物和人类的生存至关重要的行为。 了解信息在社交互动过程中如何流经该网络，并被解码以控制 决定进行生殖行为还是攻击性社会行为 了解“上游”的活动如何。 节点控制“下游”节点中的神经表示，我们将实现一种结合 前者的可逆化学遗传学抑制，同时对神经元群活动进行成像 后者的基本原理是对系统的理解需要描述其影响。 为了实现我们的目标，我们将对行为和电路水平的表型进行功能操作。 首先描述扩展的多个节点中行为和同种性别认同的神经编码 杏仁核，使用单点显微内窥镜成像和计算分析方法（目标 1）； 确定此类节点活动的扰动如何影响下丘脑节点的表征 （目标 2）；研究核内和核间相互作用在确定活性平衡中的作用 攻击性和促进生殖的下丘脑节点之间的平衡（目标 3）； 由下游中脑结构解码以确定要表达的社会行为类型（目标 4）。 贡献是重要的，因为它代表了一种系统级方法来理解皮层下如何 网络控制行为决策的贡献是创新的，因为它集成了分析。 神经群体活动、自然社会行为和功能的定量测量 特定神经亚群活动的扰动，以深入了解分布式神经回路的分布 在与导致人类精神疾病的适应不良相关的背景下控制生存行为。