UCLA

Little is known about the individual differences in sociobehavioral tendencies that uniquely characterize individuals occupying social network positions (e.g., eigenvector centrality), which are associated with a disproportionate amount of influence, popularity, and leverage. Furthermore, although it has been well-established that people closer together in their social network often share similarities in demographic attributes, much less is known about the types of inter-individual similarities shared by friends that run deeper than such “surface-level” characteristics. This dissertation integrates tools from social network analysis, neuroimaging, and machine learning to address these gaps in the literature and advance our understanding of how the brain shapes and is shaped by real-world social networks.