Neural Circuit Mechanisms of Social Homeostasis in Individuals and Supraorganismal Social Groups

点击翻译按钮获取中文摘要

Intricate social hierarchies that go through both dynamic and stable phases exist in species ranging from humans to mice to insects and have been richly described in psychology and ecology, but virtually nothing is known about the neural circuit mechanisms that govern the remarkable coordination of large groups of animals. Systems neuroscience has exploded with a number of novel technologies, yet the culture of this field has been largely reductionist – focusing on animals living in isolation or with a single-digit number of cage mates performing highly-controlled tasks. Although there is abundant ongoing research in the domain of social reward (motivation to engage in social behavior for hedonic value that social interaction provides), there is no ongoing research (to my knowledge) examining the neural representation of a negative valence need state (a loneliness-like state), the social homeostatic set-point, or how this is related to social rank. Indeed, this unexplored face of social behavior may have greater relevance to mental health and the burden on society. This proposal is completely different from any previous work done by myself or any investigator because we will do the following: 1) present a model for social homeostasis where social rank dictates the set-point for quality/quantity of social contact; 2) bridge behavioral ecology and systems neuroscience by using complex, naturalistic vivariums of large groups of mice in combination with nascent neural recording technology and expertise across a wide range of functional circuit dissection techniques; 3) simultaneously record across many brains using wireless recording devices to determine how composite dominance hierarchy is represented and determine whether meta-brain patterns for group social homeostasis (in both stable and dynamic phases) exist and observe how they change during dominance hierarchical reorganizations; and 4) identify site(s) and circuit(s) that represent social rank by applying machine learning approaches to decode ensembles that accurately predict the animal’s social rank, and use this information to move towards a mathematical model for social homeostasis on a supraorganismal group level.