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      A Brain Phenotype for Stressor‐Evoked Blood Pressure Reactivity

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          Abstract

          Background

          Individuals who exhibit large‐magnitude blood pressure ( BP) reactions to acute psychological stressors are at risk for hypertension and premature death by cardiovascular disease. This study tested whether a multivariate pattern of stressor‐evoked brain activity could reliably predict individual differences in BP reactivity, providing novel evidence for a candidate neurophysiological source of stress‐related cardiovascular risk.

          Methods and Results

          Community‐dwelling adults (N=310; 30–51 years; 153 women) underwent functional magnetic resonance imaging with concurrent BP monitoring while completing a standardized battery of stressor tasks. Across individuals, the battery evoked an increase systolic and diastolic BP relative to a nonstressor baseline period (M ∆systolic BP/∆diastolic BP=4.3/1.9 mm Hg [95% confidence interval=3.7–5.0/1.4–2.3 mm Hg]). Using cross‐validation and machine learning approaches, including dimensionality reduction and linear shrinkage models, a multivariate pattern of stressor‐evoked functional magnetic resonance imaging activity was identified in a training subsample (N=206). This multivariate pattern reliably predicted both systolic BP ( r=0.32; P<0.005) and diastolic BP ( r=0.25; P<0.01) reactivity in an independent subsample used for testing and replication (N=104). Brain areas encompassed by the pattern that were strongly predictive included those implicated in psychological stressor processing and cardiovascular responding through autonomic pathways, including the medial prefrontal cortex, anterior cingulate cortex, and insula.

          Conclusions

          A novel multivariate pattern of stressor‐evoked brain activity may comprise a phenotype that partly accounts for individual differences in BP reactivity, a stress‐related cardiovascular risk factor.

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          Most cited references47

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          Measuring emotion: the Self-Assessment Manikin and the Semantic Differential.

          The Self-Assessment Manikin (SAM) is a non-verbal pictorial assessment technique that directly measures the pleasure, arousal, and dominance associated with a person's affective reaction to a wide variety of stimuli. In this experiment, we compare reports of affective experience obtained using SAM, which requires only three simple judgments, to the Semantic Differential scale devised by Mehrabian and Russell (An approach to environmental psychology, 1974) which requires 18 different ratings. Subjective reports were measured to a series of pictures that varied in both affective valence and intensity. Correlations across the two rating methods were high both for reports of experienced pleasure and felt arousal. Differences obtained in the dominance dimension of the two instruments suggest that SAM may better track the personal response to an affective stimulus. SAM is an inexpensive, easy method for quickly assessing reports of affective response in many contexts.
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            Psychological stress and cardiovascular disease.

            There is an enormous amount of literature on psychological stress and cardiovascular disease. This report reviews conceptual issues in defining stress and then explores the ramifications of stress in terms of the effects of acute versus long-term stressors on cardiac functioning. Examples of acute stressor studies are discussed in terms of disasters (earthquakes) and in the context of experimental stress physiology studies, which offer a more detailed perspective on underlying physiology. Studies of chronic stressors are discussed in terms of job stress, marital unhappiness, and burden of caregiving. From all of these studies there are extensive data concerning stressors' contributions to diverse pathophysiological changes including sudden death, myocardial infarction, myocardial ischemia, and wall motion abnormalities, as well as to alterations in cardiac regulation as indexed by changes in sympathetic nervous system activity and hemostasis. Although stressors trigger events, it is less clear that stress "causes" the events. There is nonetheless overwhelming evidence both for the deleterious effects of stress on the heart and for the fact that vulnerability and resilience factors play a role in amplifying or dampening those effects. Numerous approaches are available for stress management that can decrease patients' suffering and enhance their quality of life.
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              Least Angle Regression

              , , (2004)
              The purpose of model selection algorithms such as All Subsets, Forward Selection and Backward Elimination is to choose a linear model on the basis of the same set of data to which the model will be applied. Typically we have available a large collection of possible covariates from which we hope to select a parsimonious set for the efficient prediction of a response variable. Least Angle Regression (LARS), a new model selection algorithm, is a useful and less greedy version of traditional forward selection methods. Three main properties are derived: (1) A simple modification of the LARS algorithm implements the Lasso, an attractive version of ordinary least squares that constrains the sum of the absolute regression coefficients; the LARS modification calculates all possible Lasso estimates for a given problem, using an order of magnitude less computer time than previous methods. (2) A different LARS modification efficiently implements Forward Stagewise linear regression, another promising new model selection method;
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                Author and article information

                Contributors
                gianaros@pitt.edu
                Journal
                J Am Heart Assoc
                J Am Heart Assoc
                10.1002/(ISSN)2047-9980
                JAH3
                ahaoa
                Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease
                John Wiley and Sons Inc. (Hoboken )
                2047-9980
                23 August 2017
                September 2017
                : 6
                : 9 ( doiID: 10.1002/jah3.2017.6.issue-9 )
                : e006053
                Affiliations
                [ 1 ] Department of Psychology University of Pittsburgh Pittsburgh PA
                [ 2 ] Departments of Psychology and Psychiatry University of Pittsburgh Pittsburgh PA
                [ 3 ] Center for the Neural Basis of Cognition University of Pittsburgh and Carnegie Mellon University Pittsburgh PA
                [ 4 ] Department of Psychology Carnegie Mellon University Pittsburgh PA
                [ 5 ] Machine Learning Department Carnegie Mellon University Pittsburgh PA
                [ 6 ] Departments of Psychology and Neuroscience University of Colorado at Boulder CO
                Author notes
                [*] [* ] Correspondence to: Peter J. Gianaros, PhD, Department of Psychology, 3131 Sennott Sq, 210 S Bouquet St, Pittsburgh, PA 15260‐9150. E‐mail: gianaros@ 123456pitt.edu
                Article
                JAH32475
                10.1161/JAHA.117.006053
                5634271
                28835356
                7c471957-1c34-4463-bc7a-f09883f7268d
                © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

                This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                History
                : 10 March 2017
                : 07 July 2017
                Page count
                Figures: 4, Tables: 1, Pages: 15, Words: 11562
                Funding
                Funded by: Swiss National Science Foundation
                Award ID: 33CM30‐124112
                Award ID: 310030‐118353
                Categories
                Original Research
                Original Research
                Imaging
                Custom metadata
                2.0
                jah32475
                September 2017
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.2.0 mode:remove_FC converted:26.09.2017

                Cardiovascular Medicine
                cardiovascular reactivity,functional magnetic resonance imaging,machine learning,mental stress,psychology and behavior,stress,high blood pressure,blood pressure,functional magnetic resonance imaging (fmri),autonomic nervous system,biomarkers

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