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.

项目摘要/摘要 该建议对FOA作出回应（RFA-NS-18-030）呼吁1）“新颖的方法来理解中性 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）“经验和分析方法来了解如何 行为状态是神经元，电路和网络相互作用的新兴特性。” 控制控制的皮层电路，自然行为对于理解大脑功能至关重要。我们 旨在了解下丘脑延伸的不同电路节点之间的动态相互作用 杏仁核决定（“头”）网络控制先天的社会行为决策，例如在激进和 生殖行为。我们提出了一种结合微观镜检查的综合方法 具有自动化的基于机器学习的基因鉴定的神经元亚群的成像（MEI） 自由移动小鼠中社会行为的分类，以及神经元活动的功能扰动 体内。我们广泛的长期目标是了解互连之间的分布活动 头网络中的结构控制着竞争目标定向之间的时刻决策 对于动物和人类生存至关重要的行为。该提议的核心目的是 了解信息如何在社交互动过程中流过该网络，并被解码以控制 决定从事生殖与积极的社会行为。了解“上游”中的活动 节点控制“下游”节点中的神经表示，我们将实施一种新颖的方法 对前者的可逆化学抑制作用，并同时对神经元种群活性的同时成像 后者。这种方法的理由是对系统的理解需要表征效果 行为和电路级表型的功能操作。为了实现我们的目标，我们将 首先是在扩展的多个节点中的行为和特定性别身份的神经编码 杏仁核，使用单位微观镜面成像和计算分析方法（AIM 1）； 确定此类节点活性的扰动如何影响下丘脑淋巴结中的表示形式 （目标2）;研究内和核间相互作用在确定活动平衡中的作用 在侵略性和繁殖下丘脑淋巴结之间（AIM 3）；确定这种平衡如何 由下游中脑结构解码，以确定表达的社会行为类型（AIM 4）。这 贡献很重要，因为它代表了一种系统级别的方法来理解皮层如何 网络控制行为决策。贡献是创新的，因为它整合了对 神经元人口活动，自然主义社会行为的定量测量和功能 特定神经元亚群中活性的扰动，以深入了解分布式神经元电路 控制生存行为，在与导致人类精神疾病的不适有关的情况下。