Affective brain patterns as multivariate neural correlates of cardiovascular disease risk.

Journal Article (Journal Article)

This study tested whether brain activity patterns evoked by affective stimuli relate to individual differences in an indicator of pre-clinical atherosclerosis: carotid artery intima-media thickness (CA-IMT). Adults (aged 30-54 years) completed functional magnetic resonance imaging (fMRI) tasks that involved viewing three sets of affective stimuli. Two sets included facial expressions of emotion, and one set included neutral and unpleasant images from the International Affective Picture System (IAPS). Cross-validated, multivariate and machine learning models showed that individual differences in CA-IMT were partially predicted by brain activity patterns evoked by unpleasant IAPS images, even after accounting for age, sex and known cardiovascular disease risk factors. CA-IMT was also predicted by brain activity patterns evoked by angry and fearful faces from one of the two stimulus sets of facial expressions, but this predictive association did not persist after accounting for known cardiovascular risk factors. The reliability (internal consistency) of brain activity patterns evoked by affective stimuli may have constrained their prediction of CA-IMT. Distributed brain activity patterns could comprise affective neural correlates of pre-clinical atherosclerosis; however, the interpretation of such correlates may depend on their psychometric properties, as well as the influence of other cardiovascular risk factors and specific affective cues.

Full Text

Duke Authors

Cited Authors

  • Gianaros, PJ; Kraynak, TE; Kuan, DC-H; Gross, JJ; McRae, K; Hariri, AR; Manuck, SB; Rasero, J; Verstynen, TD

Published Date

  • November 2020

Published In

Volume / Issue

  • 15 / 10

Start / End Page

  • 1034 - 1045

PubMed ID

  • 32301993

Pubmed Central ID

  • PMC7657455

Electronic International Standard Serial Number (EISSN)

  • 1749-5024

International Standard Serial Number (ISSN)

  • 1749-5016

Digital Object Identifier (DOI)

  • 10.1093/scan/nsaa050

Language

  • eng