The gradient model of brain organization in decisions involving "empathy for pain".
decision making
default mode network
empathy for pain
task deactivations
Journal
Cerebral cortex (New York, N.Y. : 1991)
ISSN: 1460-2199
Titre abrégé: Cereb Cortex
Pays: United States
ID NLM: 9110718
Informations de publication
Date de publication:
09 05 2023
09 05 2023
Historique:
received:
24
07
2022
revised:
20
09
2022
accepted:
26
09
2022
medline:
16
5
2023
pubmed:
21
12
2022
entrez:
20
12
2022
Statut:
ppublish
Résumé
Influential models of cortical organization propose a close relationship between heteromodal association areas and highly connected hubs in the default mode network. The "gradient model" of cortical organization proposes a close relationship between these areas and highly connected hubs in the default mode network, a set of cortical areas deactivated by demanding tasks. Here, we used a decision-making task and representational similarity analysis with classic "empathy for pain" stimuli to probe the relationship between high-level representations of imminent pain in others and these areas. High-level representations were colocalized with task deactivations or the transitions from activations to deactivations. These loci belonged to 2 groups: those that loaded on the high end of the principal cortical gradient and were associated by meta-analytic decoding with the default mode network, and those that appeared to accompany functional repurposing of somatosensory cortex in the presence of visual stimuli. These findings suggest that task deactivations may set out cortical areas that host high-level representations. We anticipate that an increased understanding of the cortical correlates of high-level representations may improve neurobiological models of social interactions and psychopathology.
Identifiants
pubmed: 36537039
pii: 6936420
doi: 10.1093/cercor/bhac464
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
5839-5850Informations de copyright
© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.