Modeling the symptom connections between posttraumatic stress disorder and comorbid severe mental illness

Although comorbidity between PTSD and severe mental illness (SMI; schizophrenia, bipolar disorder) is common, the underlying connections between disorders remain unclear. Network theory, a data-analytic technique for modeling complex relationships between several variables, examines relationships between individual symptoms of distinct disorders. This study used network analysis to explore symptom-level connections between PTSD and SMI to clarify pathways outlined in the interactive model between the disorders.

Participants were 126 Veterans with SMI (75.4 % male; 50.8 % Black, 42.9 % White; M_age = 46.4 years, SD = 12.6) recruited from a PTSD specialty clinic in the southeastern United States. PTSD symptoms were assessed using the PTSD Checklist (PCL), and psychiatric symptoms using the Brief Psychiatric Rating Scale - Expanded (BPRS-E). Network models incorporated PCL symptom clusters and both the four- and five-factor models of the BPRS-E. Data were analyzed in R using RStudio, open-source software for data analysis and visualization.

Hyperarousal symptoms were strongly connected to both reexperiencing and avoidance, which were not directly linked. In the full networks, hyperarousal was closely associated with activation symptoms; avoidance was linked to negative symptoms; and activation and negative symptoms were inversely related. Hyperarousal had the highest expected influence on psychiatric symptoms, followed by avoidance.

These results highlight hyperarousal as a central connecting factor between PTSD and SMI. Avoidance also emerged as a key bridge, particularly with negative symptoms. Findings underscore the clinical relevance of targeting hyperarousal and avoidance in interventions for comorbid PTSD and SMI and demonstrate the value of network analysis in understanding complex symptom interactions.