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Abstract #3921

Abnormal brain network configuration associated with posttraumatic stress disorder: a novel combined approach using graph theory and scaled subprofile modeling

Natalie Wright1,2, Ronak Patel3, Candice Monson4, Todd Girard4, and Ji Hyun Ko1,2

1Department of Human Anatomy and Cell Science, University of Manitoba, Winnipeg, MB, Canada, 2Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Winnipeg, MB, Canada, 3Department of Psychology, University of Manitoba, Winnipeg, MB, Canada, 4Department of Psychology, Ryerson University, Toronto, ON, Canada

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.

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