Author ORCID Identifier

0000-0001-5095-7685

Date of Award

12-14-2022

Degree Type

Thesis

Degree Name

Master of Arts (MA)

Department

Psychology

First Advisor

Tricia Z. King

Second Advisor

Negar Fani

Third Advisor

Jessica Turner

Abstract

Posttraumatic stress disorder (PTSD) impacts millions of Americans annually. Altered white matter microstructure may be a potential diagnostic biomarker for PTSD. White matter microstructural differences in persons with PTSD have been studied using machine learning, a method uniquely suited for biological datasets. This study examined the utility of white matter tracts in classifying persons with and without PTSD and predicting PTSD symptom cluster severity amongst trauma-exposed Black American women. Fractional anisotropy of 53 white matter tracts served as input features. Current PTSD presence was estimated using the Clinician-Administered PTSD Scale. Symptom cluster scores were calculated using the PTSD Symptom Scales. Only the random forest model demonstrated above-chance accuracy (58.88%) when classifying persons with and without PTSD. Regression models for symptom scores failed to show positive R-values. Results show a minimal signal for white matter microstructure and suggest a restricted set of white matter tracts is relevant to PTSD presence.

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