From Social Networks to Neural Networks: Investigating the Neural Basis of Real-Life Social Relationships

从社交网络到神经网络:研究现实生活中社会关系的神经基础

基本信息

项目摘要

PROJECT SUMMARY Social relationships develop between individuals with a social network. Difficulties in mediating social relationships with other individuals is strongly associated with severe mental disorders ranging from depression, chronic stress, autism and other. Thus, understanding the neural underpinning of social relationships is paramount. To gain insight that would inform of real-life behavior, I propose to study the nervous system under real-life conditions in which social interactions in humans and animals typically occur. In particular, I focus on the fact that social interactions typically involve multiple participants, employ the usage of a flexible repertoire of communication signals, and occur between individuals of varying social bonds and personality traits. Furthermore, social relationships evolve over prolonged periods of time in a dynamic fashion. In this proposal we focus on the anterior cingulate cortex (ACC). We do so because activity in this area has previously been strongly associated with social behaviors across a wide range of mammalian species, including humans. However, much less is known about the neural computations in the ACC with respect to social relationships, especially during real-life and multi-dimensional social conditions. To do so, we use the Egyptian fruit bat, a highly social, long-lived mammal that is accustom to group living and where individuals engage in relationships that extend over many months/years. We further develop advanced behavioral measurements that allow us to monitor the social interactions of individuals within our colonies continuously and characterize their social relationships between group members. To study the neural circuits that underlie social relationships we develop wireless neurophysiological tools that enable monitoring neural activity from entire colonies of bats simultaneously at cellular and millisecond resolutions (electrophysiology) and over prolonged periods of time (calcium imaging). This novel approach allows us to consider the true complexity of real-life social interactions and consider the social bonds between the individuals, the dynamic structure of the social relationships as well as the individual variability in personality traits. Specifically, we aim to achieve the following aims: (1) We start by describing the basic neural dynamics in the ACC during semi-natural, dyadic, social interactions and communication. (2) We next describe the ACC neural dynamics during interaction occurring within real-life, stable, social networks while considering the relationships between individuals (3) We describe the evolution of ACC neural dynamic in parallel to the dynamical changes that occur in real-life social networks. (4) We use optogenetics tools to disrupt neural activity in the ACC during group social interactions in order to assess its causal role in real-life social relationships with other individuals. Combined, these experiments will provide a detailed description of ACC neural computations underlying the mediation of social relationships within a social network. In doing so, we aim for these results to provide important insight that could be used in clinical future application in patients.
项目总结

项目成果

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Michael Moshe Yartsev其他文献

Michael Moshe Yartsev的其他文献

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{{ truncateString('Michael Moshe Yartsev', 18)}}的其他基金

From Social Networks to Neural Networks: Investigating the Neural Basis of Real-Life Social Relationships
从社交网络到神经网络:研究现实生活中社会关系的神经基础
  • 批准号:
    10402781
  • 财政年份:
    2021
  • 资助金额:
    $ 36.88万
  • 项目类别:
The Emergence, Persistence and Plasticity of Neural Codes for Self-Selected Goal-Directed Navigation
自选目标导向导航神经编码的出现、持久性和可塑性
  • 批准号:
    10053126
  • 财政年份:
    2020
  • 资助金额:
    $ 36.88万
  • 项目类别:
The Emergence Persistence and Plasticity of Neural Codes for Self-Selected Goal-Directed Navigation
自选目标导向导航神经编码的出现持久性和可塑性
  • 批准号:
    10700767
  • 财政年份:
    2020
  • 资助金额:
    $ 36.88万
  • 项目类别:

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