Parsing Heterogeneity in the Brain Connectivity of Depressed and Healthy Adults During Positive Mood

There is well-known heterogeneity in affective mechanisms in depression that may extend to positive affect. We used data-driven parsing of neural connectivity to reveal subgroups present across depressed and healthy individuals during positive processing, informing targets for mechanistic intervention

Multivariate Functional Brain Imaging Signatures of Cardiovascular Reactivity During Psychological Stress

Cardiovascular reactions to psychological stressors are associated with cardiovascular disease (CVD) risk. Human brain imaging studies have identified brain regions and systems implicated in generating and regulating stressor-evoked cardiovascular reactivity, yet the reliability and generalizability of these findings remain unclear. Predictive modeling using multivariate and machine learning approaches has the promise of developing signatures of brain activity that can reliably predict outcomes, yet few studies have applied these approaches toward identifying signatures of stressor-evoked cardiovascular reactivity. Thus, the aims of the present study were (1) to develop novel multivariate signatures of stressor-evoked brain activity that could reliably predict concurrent cardiovascular physiology during stress within individuals, and (2) to evaluate whether previously reported brain signatures of cardiovascular reactivity generalize to new individuals, stressor contexts, and measures of cardiovascular physiology. Participants were 242 midlife adults (118 men and 124 women; age 30 to 51 years; 71% white) without psychiatric, immune, or cardiovascular diagnoses. Participants completed two validated cognitive stressor tasks during functional magnetic resonance imaging (fMRI) and concurrent monitoring of systolic blood pressure (SBP) and heart rate (HR). Multivariate machine learning models combining dimensionality reduction, regularized regression, and cross-validation were used to predict within-participant changes in SBP and HR during stress. Separately, two previously published multivariate signatures were applied to maps of stressor-evoked brain activity to predict SBP and HR. Contrary to hypotheses and prior reports, multivariate patterns of stressor-evoked brain activity did not reliably predict changes in SBP and HR during stress. Notwithstanding their unreliable prediction of SBP and HR, brain activity patterns relating to SBP and HR were comprised of brain regions implicated in psychological stress and physiological control processes. In addition, two previously published multivariate brain signatures of stressor-evoked cardiovascular reactivity were found to modestly predict changes in SBP and HR during stress. These findings extend our understanding of the reliability and stability of fMRI-based signatures reflecting brain processes that may link stressful experiences to CVD risk

Multivariate Brain Activity While Viewing and Reappraising Affective Scenes Does Not Predict the Multiyear Progression of Preclinical Atherosclerosis in Otherwise Healthy Midlife Adults

Cognitive reappraisal is an emotion regulation strategy that is postulated to reduce risk for atherosclerotic cardiovascular disease (CVD), particularly the risk due to negative affect. At present, however, the brain systems and vascular pathways that may link reappraisal to CVD risk remain unclear. This study thus tested whether brain activity evoked by using reappraisal to reduce negative affect would predict the multiyear progression of a vascular marker of preclinical atherosclerosis and CVD risk: carotid artery intima-media thickness (CA-IMT). Participants were 176 otherwise healthy adults (50.6% women; aged 30–51 years) who completed a functional magnetic resonance imaging task involving the reappraisal of unpleasant scenes from the International Affective Picture System. Ultrasonography was used to compute CA-IMT at baseline and a median of 2.78 (interquartile range, 2.67 to 2.98) years later among 146 participants. As expected, reappraisal engaged brain systems implicated in emotion regulation. Reappraisal also reduced self-reported negative affect. On average, CA-IMT progressed over the follow-up period. However, multivariate and cross-validated machine-learning models demonstrated that brain activity during reappraisal failed to predict CA-IMT progression. Contrary to hypotheses, brain activity during cognitive reappraisal to reduce negative affect does not appear to forecast the progression of a vascular marker of CVD risk

Affective Brain Patterns as Multivariate Neural Correlates of Cardiovascular Disease Risk

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