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

个体和超有机体社会群体社会稳态的神经回路机制

• 批准号: 10015204
• 负责人: Kay Maxine Tye
• 金额: $ 134.68万
• 依托单位: SALK INSTITUTE FOR BIOLOGICAL STUDIES
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-30 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10015204
• 关键词: Animals; Behavioral; Brain; Complex; Devices; Digit structure; Dissection; Ecology; Face; Homeostasis; Human; Individual; Insecta; Knowledge; Loneliness; Machine Learning; Mental Health; Modeling; Modernization; Motivation; Mus; Negative Valence; Neurosciences; Partner in relationship; Pattern; Phase; Psychology; Research; Research Personnel; Rewards; Signal Transduction; Site; Social Behavior; Social Dominance; Social Hierarchy; Social Interaction; Social Values; Societies; System; Techniques; Technology; Translating; Wireless Technology; Work; expectation; experience; hedonic; mathematical model; neural circuit; new technology; relating to nervous system; social; social group; virtual

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.

物种中存在复杂的社会等级制度，经历动态和稳定阶段 从人类到老鼠再到昆虫，在心理学和心理学领域都有丰富的描述。 生态学，但实际上对控制生物的神经回路机制一无所知 大群体动物的系统神经科学的显着协调性已经爆炸式增长。 许多新技术，但这一领域的文化在很大程度上是还原论的—— 重点关注单独生活的动物或笼内同伴进行个位数表演的动物 尽管社会领域正在进行大量的研究。 奖励（为了享乐价值而参与社会行为的动机，社交互动 提供），（据我所知）没有正在进行的研究检查神经表征 负价需求状态（类似孤独的状态），社会稳态设定点，或 事实上，这种未经探索的社会行为可能与社会等级有什么关系。 与心理健康和社会负担具有更大的相关性。 该提案与我自己或任何研究者之前所做的任何工作完全不同 因为我们将做以下事情：1）提出一个社会稳态模型，其中社会等级 规定社会接触的质量/数量的设定点；2) 桥接行为生态学和 系统神经科学，通过使用大群小鼠的复杂、自然的饲养室 与新兴的神经记录技术和广泛的专业知识相结合 功能电路解剖技术；3）使用多个大脑同时记录 无线记录设备以确定如何表示复合优势层次结构 并确定群体社会稳态的元脑模式（稳定和稳定） 动态阶段）存在并观察它们在统治等级期间如何变化 重组；以及 4) 通过应用来识别代表社会等级的站点和电路 机器学习方法可以解码准确预测动物社交的集合 排名，并使用这些信息来建立社会稳态的数学模型 超有机体群体水平。