Symptom networks of therapy-seeking individuals with comorbid psychosis and PTSD
Abstract Background Post-traumatic sequelae are theorized to contribute to and maintain psychotic symptoms. The relative contribution of core posttraumatic stress disorder (PTSD) symptoms and those of complex PTSD are understudied. Network analysis was used to examine interactions between psychosis, core PTSD, and complex post-traumatic symptoms. Based on previous evidence, it was predicted that trauma-related beliefs would have the greatest predictability. It was also hypothesized that the association between voices and trauma-related beliefs would be greater than the association between voices and re-experiencing. Methods Baseline data from the Study of Trauma and Recovery (STAR), a randomized controlled therapy trial of people with schizophrenia-spectrum disorder who experience distressing delusions and/or hallucinations and comorbid PTSD (N = 305). A network was estimated using a Bayesian Gaussian graphical model, comprised of psychosis symptoms (delusions and voices), posttraumatic stress symptoms (re-experiencing, avoidance, and sense of threat), complex posttraumatic stress symptoms (affective dysregulation, negative self-concept, disturbed relationships), and trauma-related beliefs (negative cognitions about the self and world, self-blame). A direct acyclic graph was plotted to estimate the potential directionality of associations between symptoms. Results The hypotheses were not supported. All symptoms had similar predictability scores. There was no evidence for conditionally dependent relations between re-experiencing and voices nor between trauma-related beliefs and voices. However, self-blame was associated with delusions, and affective dysregulation was found to predict voices. Conclusions Affective dysregulation and self-blame may maintain voices and delusions, respectively. Heterogeneity in trauma-related psychosis could explain inconsistent findings; future research should identify symptom presentation subgroups to improve network reliability.