Spencer, Chrystal
(2023)
Title Page
Exploring the Functional Neural Correlates of Perceived Stress: A Machine Learning Approach.
Master's Thesis, University of Pittsburgh.
(Unpublished)
Abstract
Appraisals of perceived psychological stress vary widely between individuals and are associated with chronic disease risk and poor health outcomes. However, the brain systems that may connect perceived stress and physical health outcomes remain unclear. Accordingly, the present study tested whether whole-brain resting-state functional connectivity patterns would predict individual differences in perceived stress. Participants (N = 417; 53% female; aged 30-54) completed the 10-item Perceived Stress Scale and underwent a 5-minute resting-state functional magnetic resonance imaging (fMRI) scan. Functional connectivity (FC) was computed between areas distributed across the brain. Using cross-validated and multivariate machine learning methods, we found that whole-brain resting-state FC patterns failed to predict individual differences in perceived stress, but they successfully predicted age. These results suggest that individual differences in self-reports of perceived stress may not relate reliably to resting-state FC patterns.
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Details
Item Type: |
University of Pittsburgh ETD
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Status: |
Unpublished |
Creators/Authors: |
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ETD Committee: |
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Date: |
21 July 2023 |
Defense Date: |
14 July 2023 |
Approval Date: |
6 September 2023 |
Submission Date: |
24 July 2023 |
Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
Number of Pages: |
44 |
Institution: |
University of Pittsburgh |
Schools and Programs: |
Dietrich School of Arts and Sciences > Psychology |
Degree: |
MS - Master of Science |
Thesis Type: |
Master's Thesis |
Refereed: |
Yes |
Uncontrolled Keywords: |
functional connectivity, perceived stress, penalized regression |
Date Deposited: |
07 Sep 2023 01:40 |
Last Modified: |
07 Sep 2023 01:40 |
URI: |
http://d-scholarship.pitt.edu/id/eprint/45137 |
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