title: Multimodal MRI-Based Classification of Trauma Survivors with and without Post-Traumatic Stress Disorder.
abstract: Post-traumatic stress disorder (PTSD) is a debilitating psychiatric disorder. It can be difficult to discern the symptoms of PTSD and obtain an accurate diagnosis. Different magnetic resonance imaging (MRI) modalities focus on different aspects, which may provide complementary information for PTSD discrimination. However, none of the published studies assessed the diagnostic potential of multimodal MRI in identifying individuals with and without PTSD. In the current study, we investigated whether the complementary information conveyed by multimodal MRI scans could be combined to improve PTSD classification performance. Structural and resting-state functional MRI (rs-fMRI) scans were conducted on 17 PTSD patients, 20 trauma-exposed controls without PTSD (TEC) and 20 non-traumatized healthy controls (HC). Gray matter volume (GMV), amplitude of low-frequency fluctuations (ALFF), and regional homogeneity were extracted as classification features, and in order to integrate the information of structural and functional MRI data, the extracted features were combined by a multi-kernel combination strategy. Then a support vector machine (SVM) classifier was trained to distinguish the subjects at individual level. The performance of the classifier was evaluated using the leave-one-out cross-validation (LOOCV) method. In the pairwise comparison of PTSD, TEC, and HC groups, classification accuracies obtained by the proposed approach were 2.70, 2.50, and 2.71% higher than the best single feature way, with the accuracies of 89.19, 90.00, and 67.57% for PTSD vs. HC, TEC vs. HC, and PTSD vs. TEC respectively. The proposed approach could improve PTSD identification at individual level. Additionally, it provides preliminary support to develop the multimodal MRI method as a clinical diagnostic aid.
authors.affiliation: Department of Medical Information Engineering, School of Electrical Engineering and Information, Sichuan University Chengdu, China.
authors.forename: Qiongmin
authors.initials: Q
authors.lastname: Zhang
authors.affiliation: Monash Medical Imaging, Monash University Clayton, VIC, Australia.
authors.forename: Qizhu
authors.initials: Q
authors.lastname: Wu
authors.affiliation: Mental Health Center, West China Hospital of Sichuan University Chengdu, China.
authors.forename: Hongru
authors.initials: H
authors.lastname: Zhu
authors.affiliation: Department of Medical Information Engineering, School of Electrical Engineering and Information, Sichuan University Chengdu, China.
authors.forename: Ling
authors.initials: L
authors.lastname: He
authors.affiliation: Department of Medical Information Engineering, School of Electrical Engineering and Information, Sichuan University Chengdu, China.
authors.forename: Hua
authors.initials: H
authors.lastname: Huang
authors.affiliation: Department of Medical Information Engineering, School of Electrical Engineering and Information, Sichuan University Chengdu, China.
authors.forename: Junran
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Mental Health Center, West China Hospital of Sichuan University Chengdu, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Zhang
keywords: amplitude of low-frequency fluctuations; gray matter volume; multi-kernel based support vector machine; post-traumatic stress disorder; regional homogeneity; resting-state functional MRI; structural MRI