The extended neural architecture of human attachment: An fMRI coordinate-based meta-analysis of affiliative studies.

Affiliation Friendship Love Social cognition

Journal

Neuroscience and biobehavioral reviews
ISSN: 1873-7528
Titre abrégé: Neurosci Biobehav Rev
Pays: United States
ID NLM: 7806090

Informations de publication

Date de publication:
Apr 2024
Historique:
received: 25 09 2023
revised: 30 01 2024
accepted: 12 02 2024
pubmed: 18 2 2024
medline: 18 2 2024
entrez: 17 2 2024
Statut: ppublish

Résumé

Functional imaging studies and clinical evidence indicate that cortical areas relevant to social cognition are closely integrated with evolutionarily conserved basal forebrain structures and neighboring regions, enabling human attachment and affiliative emotions. The neural circuitry of human affiliation is continually being unraveled as functional magnetic resonance imaging (fMRI) becomes increasingly prevalent, with studies examining human brain responses to various attachment figures. However, previous fMRI meta-analyses on affiliative stimuli have encountered challenges, such as low statistical power and the absence of robustness measures. To address these issues, we conducted an exhaustive coordinate-based meta-analysis of 79 fMRI studies, focusing on personalized affiliative stimuli, including one's infants, family, romantic partners, and friends. We employed complementary coordinate-based analyses (Activation Likelihood Estimation and Signed Differential Mapping) and conducted a robustness analysis of the results. Findings revealed cluster convergence in cortical and subcortical structures related to reward and motivation, salience detection, social bonding, and cognition. Our study thoroughly explores the neural correlates underpinning affiliative responses, effectively overcoming the limitations noted in previous meta-analyses. It provides an extensive view of the neural substrates associated with affiliative stimuli, illuminating the intricate interaction between cortical and subcortical regions. Our findings significantly contribute to understanding the neurobiology of human affiliation, expanding the known human attachment circuitry beyond the traditional basal forebrain regions observed in other mammals to include uniquely human isocortical structures.

Identifiants

pubmed: 38367888
pii: S0149-7634(24)00053-8
doi: 10.1016/j.neubiorev.2024.105584
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

105584

Informations de copyright

Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest None.

Auteurs

Tiago Bortolini (T)

Cognitive Neuroscience and Neuroinformatics Unit, The D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil; IDOR - Pioneer Science Initiative, São Paulo, Brazil. Electronic address: tiago.bortolini@idor.org.

Maria Clara Laport (MC)

Cognitive Neuroscience and Neuroinformatics Unit, The D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil.

Sofia Latgé-Tovar (S)

Institute of Psychiatry, Center for Alzheimer's Disease, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, RJ, Brazil.

Ronald Fischer (R)

Cognitive Neuroscience and Neuroinformatics Unit, The D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil; IDOR - Pioneer Science Initiative, São Paulo, Brazil; School of Psychology, PO Box 600, Victoria University of Wellington, Wellington 6021, New Zealand.

Roland Zahn (R)

Centre for Affective Disorders, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, UK.

Ricardo de Oliveira-Souza (R)

Cognitive Neuroscience and Neuroinformatics Unit, The D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil; The Federal University of the State of Rio de Janeiro, Rio de Janeiro, Brazil.

Jorge Moll (J)

Cognitive Neuroscience and Neuroinformatics Unit, The D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil; IDOR - Pioneer Science Initiative, São Paulo, Brazil.

Classifications MeSH