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: 10685483
• 负责人: David J Anderson
• 金额: $ 58.76万
• 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/10685483
• 关键词: Address; Aggressive behavior; Algorithms; Amygdaloid structure; Animals; Behavior; Behavior Control; Behavioral; Biological Models; Brain; Cell Nucleus; Cells; Code; Computing Methodologies; Data; Decision Making; Encapsulated; Endoscopy; Equilibrium; Female; Functional Imaging; Genetic; Goals; Human; Hypothalamic structure; Image; Individual; Knowledge; Logic; Machine Learning; Maps; Measurement; Measures; Medial; Mental disorders; Midbrain structure; Modeling; Motivation; Mus; Neurons; Organism; Output; Partner in relationship; Phase; Phenotype; Population; Population Dynamics; Preoptic Areas; Property; Public Health; Reproduction; Reproductive Behavior; Research; Resolution; Rewards; Role; Shapes; Site; Social Behavior; Social Controls; Social Interaction; Structure; System; Testing; Video Recording; Work; behavioral response; cell type; cellular targeting; experimental study; imaging study; in vivo; innovation; insight; machine learning algorithm; machine learning classification; male; midbrain central gray substance; neural; neural circuit; neuroimaging; neuroregulation; novel; novel strategies; optogenetics; predictive modeling; programs; 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.

项目总结/文摘