The goal of this study is to advance PTSD research by using novel multimodal magnetic resonance imaging (MRI) features including structural MRI, resting state functional MRI (rs-fMRI) and diffusion tensor imaging (DTI), with machine learning methods, in classifying patients with PTSD from trauma-exposed healthy controls (TEHC) individuals using large-scale dataset from ENIGMA PTSD Working Group, and to examine its utility in predicting diagnosis, functional impairment, and symptom severity. This study will lay the groundwork to further utilize multimodal fMRI and machine-learning algorithms to quantify both functional and structural large-scale networks in PTSD, and offer new insights into the identification of potential biomarkers for the clinical diagnosis of PTSD.