Computing the social brain connectome across systems and states

Daniel Alcalá-López, Jonathan Smallwood, Elizabeth Jefferies, Frank Van Overwalle, Kai Vogeley, Rogier B. Mars, Bruce I. Turetsky, Angela R. Laird, Peter T. Fox, Simon B. Eickhoff, Danilo Bzdok

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

Social skills probably emerge from the interaction between different neural processing levels. However, social neuroscience is fragmented into highly specialized, rarely cross-referenced topics. The present study attempts a systematic reconciliation by deriving a social brain definition from neural activity meta-analyses on social-cognitive capacities. The social brain was characterized by meta-analytic connectivity modeling evaluating coactivation in task-focused brain states and physiological fluctuations evaluating correlations in task-free brain states. Network clustering proposed a functional segregation into (1) lower sensory, (2) limbic, (3) intermediate, and (4) high associative neural circuits that together mediate various social phenomena. Functional profiling suggested that no brain region or network is exclusively devoted to social processes. Finally, nodes of the putative mirror-neuron system were coherently cross-connected during tasks and more tightly coupled to embodied simulation systems rather than abstract emulation systems. These first steps may help reintegrate the specialized research agendas in the social and affective sciences.

Original languageEnglish (US)
Pages (from-to)2207-2232
Number of pages26
JournalCerebral Cortex
Volume28
Issue number7
DOIs
StatePublished - 2018

Keywords

  • BrainMap database
  • Meta-analytic connectivity modeling
  • Resting-state correlations
  • Social cognition
  • Statistical learning
  • Systems neuroscience

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Cellular and Molecular Neuroscience

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