Title: Hierarchical Bayesian models for integrating multimodal neuroimaging data
Authors: Fengqing Zhang - Drexel University (United States) [presenting]
Abstract: 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. We aim to examine structural and functional brain changes specific to post-traumatic stress disorder (PTSD), a chronic and disabling anxiety disorder that can develop after a person is exposed to a traumatic event. 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 hierarchical Bayesian models to combine heterogeneous data from multiple modalities and select predictive multimodal imaging signatures of PTSD.