Affective brain patterns as multivariate neural correlates of cardiovascular disease risk

Abstract

<jats:title>Abstract</jats:title> <jats:p>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.</jats:p>

Integrating across neuroimaging modalities boosts prediction accuracy of cognitive ability

“…In this regard, specific task-evoked fMRI measurements that directly assess the neural reactivity during cognitive evaluation [27][28][29] could help raise the overall predictive power. For example, we recently showed that brain activation patterns during affective information processing tasks predict an important portion of individual differences of cardiovascular disease risk factors, a finding that we could not have reached had not we used the appropriate and specific task fMRI experiment [30]. Additionally, increasing both spatial resolution to better capture features of structural-functional variation [31] and temporal resolution for a more accurate decoding of the underlying brain dynamics [32] could be valuable and complementary sources of cognitive performance correlation.…”

Health neuroscience 2.0: integration with social, cognitive and affective neuroscience

“… El Kissi et al , 2015 ; Trumpff et al , 2019 ) and genetics ( Flint and Munafò, 2013 ). Contributions in the current special issue similarly reflect the massive shift toward more sophisticated computational methods in order to test some of the original claims and revisit findings based on small samples from early health neuroscience ( Gianaros et al , 2020 ) and fill in gaps in longstanding models of health ( Berkman, 2018 ; Cosme et al , 2020 ). To the extent that innovative solutions to ‘big data’ can be refined at the level of the brain, these advances will also be helpful to understanding biomarkers of health risk.…”

“…Illustrative examples from the current special issue that capitalize on this perspective include those that use state-of-the-science analytical approaches (e.g. machine learning to identify multivariate patterns of neural activity) to predict a host of the most pressing health issues facing society today: cardiovascular disease ( Gianaros et al , 2020 ), obesity ( Stice et al , 2019 ; Cosme et al , 2020 ; Donofry et al , 2020 ; Verstynen et al , 2020 ) and physical pain ( Reddan et al , 2020 ). Similar approaches help us understand links between brain patterns of activity to emotional content and systemic inflammation, a key biological mediator linking psychological experience and health ( Alvarez et al , 2020 ), or to health messages and population-level sharing of the information ( Dore et al ., in press ).…”

“…Prediction of health outcomes from neural activity, however, has also proven difficult (e.g. Gianaros et al , 2020 ; Cosme et al , 2020 ), providing room for a number of future directions. At the most fundamental level, what health-relevant physiological responses are best predicted by brain activity?…”

“…As a specific example from the current special issue on health neuroscience, decades of research from human neuroimaging uses standardized sets of emotional images in order to elicit activity in affect-related brain regions (medial prefrontal cortex, anterior cingulate cortex, anterior insula, amygdala, etc.). More than half of the empirical studies in the current special issue use some version of ‘emotional’ static images ( Inagaki and Meyer, 2019 ; Stice et al , 2019 ; Alvarez et al , 2020 ; Cosme et al , 2020 ; Gianaros et al , 2020 ; Leschak et al , 2020 ). Standardized images have undoubtedly produced valuable information.…”