Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

Multivariate Functional Brain Imaging Signatures of Cardiovascular Reactivity During Psychological Stress

Kraynak, Thomas E (2024) Multivariate Functional Brain Imaging Signatures of Cardiovascular Reactivity During Psychological Stress. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

Download (2MB) | Preview


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.


Social Networking:
Share |


Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Kraynak, Thomas Etekraynak@pitt.edutek310000-0002-0510-0866
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairGianaros, Petergianaros@pitt.edu0000-0003-2313-5277
Committee MemberMarsland, Annamarsland@pitt.edu0000-0001-8951-7513
Committee MemberCoutanche, Marcmarc.coutanche@pitt.edu0000-0002-2232-3519
Committee MemberVerstynen, Timothytimothyv@andrew.cmu.edu0000-0003-4720-0336
Committee MemberWager, TorTor.D.Wager@Dartmouth.edu0000-0002-1936-5574
Date: 14 February 2024
Date Type: Publication
Defense Date: 30 November 2021
Approval Date: 14 February 2024
Submission Date: 9 December 2021
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 112
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Psychology
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: stress, brain, fMRI, blood pressure, heart rate, machine learning, cardiovascular disease
Date Deposited: 14 Feb 2024 19:04
Last Modified: 14 Feb 2024 19:04


Monthly Views for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item