Diagnostic potential of multimodal neuroimaging in posttraumatic stress disorder.

Abstract

Despite accumulating evidence of physiological abnormalities related to posttraumatic stress disorder (PTSD), the current diagnostic criteria for PTSD still rely on clinical interviews. In this study, we investigated the diagnostic potential of multimodal neuroimaging for identifying posttraumatic symptom trajectory after trauma exposure. Thirty trauma-exposed individuals and 29 trauma-unexposed healthy individuals were followed up over a 5-year period. Three waves of assessments using multimodal neuroimaging, including structural magnetic resonance imaging (MRI) and diffusion-weighted MRI, were performed. Based on previous findings that the structural features of the fear circuitry-related brain regions may dynamically change during recovery from the trauma, we employed a machine learning approach to determine whether local, connectivity, and network features of brain regions of the fear circuitry including the amygdala, orbitofrontal and ventromedial prefrontal cortex (OMPFC), hippocampus, insula, and thalamus could distinguish trauma-exposed individuals from trauma-unexposed individuals at each recovery stage. Significant improvement in PTSD symptoms was observed in 23%, 52%, and 88% of trauma-exposed individuals at 1.43, 2.68, and 3.91 years after the trauma, respectively. The structural features of the amygdala were found as major classifiers for discriminating trauma-exposed individuals from trauma-unexposed individuals at 1.43 years after the trauma, but these features were nearly normalized at later phases when most of the trauma-exposed individuals showed clinical improvement in PTSD symptoms. Additionally, the structural features of the OMPFC showed consistent predictive values throughout the recovery period. In conclusion, the current study provides a promising step forward in the development of a clinically applicable predictive model for diagnosing PTSD and predicting recovery from PTSD.