Posttraumatic stress disorder (PTSD) is a prevalent psychiatric disorder with etiology and symptom expression that can vary greatly among patients. Currently, no objective clinical biomarker exists for assessing clinical severity and treatment response. In order to develop a reliable method of characterizing PTSD, we must understand how the brain changes in response to trauma. We propose a novel approach, combining graph theory analysis and scaled subprofile modeling (SSM) to identify degree centrality and its group-discriminating topographical patterns, respectively. This method has been successful in distinguishing fMRI scans of PTSD patients from trauma-exposed controls, and resulted in a reliable PTSD-related network configuration.