Classification of PTSD and Non-PTSD Using Cortical Structural Measures in Machine Learning Analyses—Preliminary Study of ENIGMA-Psychiatric Genomics Consortium PTSD Workgroup

Published

Conference Paper

© 2020, Springer Nature Switzerland AG. Classification and prediction of posttraumatic stress disorder (PTSD) based on brain imaging measures is important because it could aid in PTSD diagnosis and clinical management of PTSD. The goal of the present study was to test the effectiveness of using cortical morphological measures (i.e. volume, thickness, and surface area) to classify PTSD cases and controls on 3571 individuals from the ENIGMA-Psychiatric Genomics Consortium PTSD Workgroup, the largest PTSD neuroimaging dataset to date. We constructed 6 feature sets from different demographic variables (age and sex) and cortical morphological measures and used four machine learning algorithms for classification: logistic regression, random forest, support vector machine, and multi-layer perceptron. We found that classifiers trained using only cortical morphological measures (any one of volume, thickness, or surface area) performed better than classifiers trained using only demographic variables. Among all 6 feature sets, combining demographic variables and all three cortical morphological measures yielded the best prediction accuracy, with area under the receiver operating characteristic curve (ROC AUC) scores ranging from 0.615 for logistic regression to 0.648 for random forest. These findings suggest that using cortical morphological measures only has modest prediction power for PTSD classification. Future studies that wish to produce clinically and practically significant findings should consider using whole brain morphological measures, as well as incorporating other neuroimaging modalities and relevant clinical and behavioral symptoms.

Full Text

Duke Authors

Cited Authors

  • O’Leary, B; Shih, CH; Chen, T; Xie, H; Cotton, AS; Xu, KS; Morey, R; Wang, X

Published Date

  • January 1, 2020

Published In

Volume / Issue

  • 12241 LNAI /

Start / End Page

  • 118 - 127

Electronic International Standard Serial Number (EISSN)

  • 1611-3349

International Standard Serial Number (ISSN)

  • 0302-9743

International Standard Book Number 13 (ISBN-13)

  • 9783030592769

Digital Object Identifier (DOI)

  • 10.1007/978-3-030-59277-6_11

Citation Source

  • Scopus