Dissociative experiences, traditionally studied in relation to trauma and PTSD, may be important phenomena across many different psychological conditions, including as a contributory causal factor for psychotic experiences. In this study, the aim was to explore, using network approaches, how dissociative experiences taking the form of a Felt Sense of Anomaly (FSA) relate to both common mental health conditions and psychotic experiences. 6941 individuals from the general population completed online assessments of FSA-dissociation, post-traumatic stress symptoms (PTSS), anxiety, depression, insomnia, worry, distress tolerance, hallucinations, grandiosity, paranoia, and cognitive disorganisation. An undirected partial correlation network analysis was used to explore the network structure, then Bayesian inference with Directed Acyclic Graphs (DAGs) was used to identify potential directions of relationships between dissociation and mental health symptoms. Dissociation was found to be highly connected in both network models. Both networks found direct relationships between dissociation and hallucinations, grandiosity, paranoia, cognitive disorganisation, anxiety, depression, and PTSS. In the DAGs analysis, the direction of influence between dissociation and hallucinations, PTSS, anxiety and depression was unclear, however it was found to be probable that dissociation influences paranoia (97.66% of sampled DAGs found the direction dissociation to paranoia, versus 2.34% finding the reverse direction), cognitive disorganization (99.74% vs. 0.26%), and grandiosity (93.49% vs. 6.51%). Further, dissociation was found to be a probable influence of insomnia and distress tolerance via indirect pathways. In summary, dissociation is connected to many mental health disorders, and may influence a number of presentations, particularly psychotic experiences. The importance of dissociation in mental health may therefore currently be under-recognised.
Journal of Psychiatric Research
dissociation, psychosis, network analysis, psychopathology, Bayesian inference, directed acyclic graphs