An Integrative Model for Assessing Multimodal Neuroimaging Signatures of Post-Traumatic Stress Disorder

• Activity Number: 150 - Recent Advances for Modeling Neuroimaging Data
• Type: Topic Contributed
• Date/Time: Monday, July 31, 2017 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Imaging
• Author(s): Fengqing Zhang* and Xin Niu
• Companies: Drexel University and Drexel University
• Keywords: Multimodal Neuroimaging ; PTSD ; Data Mining ; Classification ; Feature Selection ; High Dimensional Data Analysis

Abstract

Post-traumatic stress disorder (PTSD) is a chronic and disabling anxiety disorder that can develop after a person is exposed to a traumatic event. Human neuroimaging provides exciting opportunities to examine structural and functional brain changes specific to PTSD. The use of multimodal neuroimaging is a promising and recent approach to study complex brain disorders by utilizing complementary physical and physiological sensitivities. At the same time, however, the advent of multimodal neuroimaging has brought the need to analyze and integrate neuroimaging data with advanced statistical methods that can make full usage of their informational complexity. Using data from the Philadelphia Neurodevelopmental Cohort (PNC) study, we identify three distinct groups, people with trauma exposure and no PTSD symptoms, people with trauma exposure and long-lasting PTSD symptoms as well as healthy controls. A large number of imaging features from different modalities including MRI, DTI, and resting-state fMRI are derived. We then develop an integrative probabilistic model to combine heterogeneous data from multiple modalities and select predictive multimodal imaging signatures of PTSD.