Affective brain patterns as multivariate neural correlates of cardiovascular disease risk

Abstract 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.

[1]  Jonathan H. Chung,et al.  Chronic Stress-Related Neural Activity Associates With Subclinical Cardiovascular Disease in Psoriasis: A Prospective Cohort Study. , 2020, JACC. Cardiovascular imaging.

[2]  Gael Varoquaux,et al.  Establishment of Best Practices for Evidence for Prediction: A Review. , 2019, JAMA psychiatry.

[3]  Ahmad R. Hariri,et al.  What is the Test-Retest Reliability of Common Task-fMRI Measures? New Empirical Evidence and a Meta-Analysis , 2019, Biological Psychiatry.

[4]  Dustin Scheinost,et al.  Ten simple rules for predictive modeling of individual differences in neuroimaging , 2019, NeuroImage.

[5]  Z. Fayad,et al.  Amygdalar activity predicts future incident diabetes independently of adiposity , 2019, Psychoneuroendocrinology.

[6]  T. Wager,et al.  Functional neuroanatomy of peripheral inflammatory physiology: A meta-analysis of human neuroimaging studies , 2018, Neuroscience & Biobehavioral Reviews.

[7]  G. Miller,et al.  Selected Psychological Comorbidities in Coronary Heart Disease: Challenges and Grand Opportunities , 2018, The American psychologist.

[8]  J. Jennings,et al.  Host in the Machine: A Neurobiological Perspective on Psychological Stress and Cardiovascular Disease , 2018, The American psychologist.

[9]  Z. Fayad,et al.  Amygdalar Metabolic Activity Independently Associates With Progression of Visceral Adiposity , 2018, The Journal of clinical endocrinology and metabolism.

[10]  A. Meyer-Lindenberg,et al.  No association between cardiometabolic risk and neural reactivity to acute psychosocial stress , 2018, NeuroImage: Clinical.

[11]  Peter J. Gianaros,et al.  Neural Mechanisms Linking Emotion with Cardiovascular Disease , 2018, Current Cardiology Reports.

[12]  Colin L. Sauder,et al.  Robust is not necessarily reliable: From within-subjects fMRI contrasts to between-subjects comparisons , 2018, NeuroImage.

[13]  Aarti Singh,et al.  A Brain Phenotype for Stressor‐Evoked Blood Pressure Reactivity , 2017, Journal of the American Heart Association.

[14]  Jonathan P. Roiser,et al.  Unreliability of putative fMRI biomarkers during emotional face processing , 2017, NeuroImage.

[15]  A. Zonderman,et al.  Distributions of Subclinical Cardiovascular Disease in a Socioeconomically and Racially Diverse Sample , 2017, Stroke.

[16]  P. Gianaros,et al.  Cardiovascular and autonomic reactivity to psychological stress: Neurophysiological substrates and links to cardiovascular disease , 2017, Autonomic Neuroscience.

[17]  Ahmed Tawakol,et al.  Relation between resting amygdalar activity and cardiovascular events: a longitudinal and cohort study , 2017, The Lancet.

[18]  Luke J. Chang,et al.  Building better biomarkers: brain models in translational neuroimaging , 2017, Nature Neuroscience.

[19]  M. Szklo,et al.  Carotid Intima‐Media Thickness Score, Positive Coronary Artery Calcium Score, and Incident Coronary Heart Disease: The Multi‐Ethnic Study of Atherosclerosis , 2017, Journal of the American Heart Association.

[20]  H. Øygarden.  Carotid Intima‐Media Thickness and Prediction of Cardiovascular Disease , 2017, Journal of the American Heart Association.

[21]  Luke J. Chang,et al.  Multivariate Brain Prediction of Heart Rate and Skin Conductance Responses to Social Threat , 2016, The Journal of Neuroscience.

[22]  J. Boissoneault,et al.  What reliability can and cannot tell us about pain report and pain neuroimaging. , 2016, Pain.

[23]  V. Hachinski,et al.  Human cerebral circuitry related to cardiac control: A neuroimaging meta‐analysis , 2016, Annals of neurology.

[24]  D. Cechetto,et al.  The Insular Cortex and the Regulation of Cardiac Function. , 2016, Comprehensive Physiology.

[25]  Daniel A. Hackman,et al.  Community Socioeconomic Disadvantage in Midlife Relates to Cortical Morphology via Neuroendocrine and Cardiometabolic Pathways , 2015, Cerebral cortex.

[26]  Tor D Wager,et al.  Brain-Body Pathways Linking Psychological Stress and Physical Health , 2015, Current directions in psychological science.

[27]  Luke J. Chang,et al.  A Sensitive and Specific Neural Signature for Picture-Induced Negative Affect , 2015, PLoS biology.

[28]  Daniel S. Margulies,et al.  NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain , 2014, bioRxiv.

[29]  Mario J. Garcia,et al.  Prevalence, impact, and predictive value of detecting subclinical coronary and carotid atherosclerosis in asymptomatic adults: the BioImage study. , 2015, Journal of the American College of Cardiology.

[30]  Peter Kirsch,et al.  Amygdala habituation: A reliable fMRI phenotype , 2014, NeuroImage.

[31]  T. Verstynen,et al.  Health Neuroscience , 2014, Current directions in psychological science.

[32]  A. Hariri,et al.  An Inflammatory Pathway Links Atherosclerotic Cardiovascular Disease Risk to Neural Activity Evoked by the Cognitive Regulation of Emotion , 2014, Biological Psychiatry.

[33]  John T. Cacioppo,et al.  Social Neuroscience , 2013, Perspectives on psychological science : a journal of the Association for Psychological Science.

[34]  J. Gross,et al.  Affective science and health: the importance of emotion and emotion regulation. , 2013, Health psychology : official journal of the Division of Health Psychology, American Psychological Association.

[35]  M. Lindquist,et al.  An fMRI-based neurologic signature of physical pain. , 2013, The New England journal of medicine.

[36]  N. Eisenberger,et al.  A Social Neuroscience Perspective on Stress and Health. , 2012, Social and personality psychology compass.

[37]  J. Thayer,et al.  eview meta-analysis of heart rate variability and neuroimaging studies : Implications or heart rate variability as a marker of stress and health , 2012 .

[38]  S. Peters,et al.  Improvements in risk stratification for the occurrence of cardiovascular disease by imaging subclinical atherosclerosis: a systematic review , 2011, Heart.

[39]  Jeffrey N. Rouder,et al.  Bayes factor approaches for testing interval null hypotheses. , 2011, Psychological methods.

[40]  Tor D Wager,et al.  Predicting Individual Differences in Placebo Analgesia: Contributions of Brain Activity during Anticipation and Pain Experience , 2011, The Journal of Neuroscience.

[41]  George Forman,et al.  Apples-to-apples in cross-validation studies: pitfalls in classifier performance measurement , 2010, SKDD.

[42]  Christos Davatzikos,et al.  Intima-Media Thickness and Regional Cerebral Blood Flow in Older Adults , 2010, Stroke.

[43]  P. Zhao,et al.  The composite absolute penalties family for grouped and hierarchical variable selection , 2009, 0909.0411.

[44]  Ahmad R. Hariri,et al.  Preclinical Atherosclerosis Covaries with Individual Differences in Reactivity and Functional Connectivity of the Amygdala , 2009, Biological Psychiatry.

[45]  Christopher B. Kendall,et al.  Use of carotid ultrasound to identify subclinical vascular disease and evaluate cardiovascular disease risk: a consensus statement from the American Society of Echocardiography Carotid Intima-Media Thickness Task Force. Endorsed by the Society for Vascular Medicine. , 2008, Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography.

[46]  Jörn Diedrichsen,et al.  Detecting and adjusting for artifacts in fMRI time series data , 2005, NeuroImage.

[47]  R. Dodhia.  A Review of Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences (3rd ed.) , 2005 .

[48]  Jerry Suls,et al.  Anger, anxiety, and depression as risk factors for cardiovascular disease: the problems and implications of overlapping affective dispositions. , 2005, Psychological bulletin.

[49]  R. Tibshirani,et al.  REJOINDER TO "LEAST ANGLE REGRESSION" BY EFRON ET AL. , 2004, math/0406474.

[50]  D. Ruppert.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[51]  Paul J. Laurienti,et al.  An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets , 2003, NeuroImage.

[52]  David S. Krantz,et al.  Effects of psychological and social factors on organic disease: a critical assessment of research on coronary heart disease. , 2002, Annual review of psychology.

[53]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[54]  J. Blumenthal,et al.  Impact of psychological factors on the pathogenesis of cardiovascular disease and implications for therapy. , 1999, Circulation.

[55]  J. Edelstein,et al.  Ultrasonic-pathological comparison of the human arterial wall. Verification of intima-media thickness. , 1993, Arteriosclerosis and thrombosis : a journal of vascular biology.

[56]  P Pignoli,et al.  Intimal plus medial thickness of the arterial wall: a direct measurement with ultrasound imaging. , 1986, Circulation.

[57]  Jacob Cohen,et al.  Applied multiple regression/correlation analysis for the behavioral sciences , 1979 .

[58]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[59]  P. Lang.  International affective picture system (IAPS) : affective ratings of pictures and instruction manual , 2005 .

[60]  J. Franklin,et al.  The elements of statistical learning: data mining, inference and prediction , 2005 .

[61]  Kim Love,et al.  What a Brain , 1997 .

[62]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[63]  P. Ekman.  Pictures of Facial Affect , 1976 .