Author ORCID Identifier
https://orcid.org/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.
DOI
https://doi.org/10.57709/32366568
Recommended Citation
Haller, Olivia, "Identifying Posttraumatic Stress Disorder and its Symptoms: a Diffusion Tensor Imaging Machine Learning Study." Thesis, Georgia State University, 2022.
doi: https://doi.org/10.57709/32366568
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