M24. NETWORK STRUCTURE OF PSYCHOPATHOLOGY SYMPTOMS IN A COMMUNITY SAMPLE OF YOUTH

Abstract

BackgroundThe field of network psychometrics has developed into a promising alternative to the common cause theory and depicts mental health disorders as arising from the interactions between individual symptoms. Currently, major depressive disorder and post-traumatic stress disorder have been the two main disorders studied using network models. In this study, we aimed to examine the network structure of psychopathology symptoms in a community youth sample to detect the most influential symptoms. We also identify influential bridge symptoms which may lead to comorbidities.MethodsThe sample (n = 2875) was taken from the Philadelphia Neurodevelopmental Cohort and comprised of youth between the ages of 11–21. 112 variables corresponding to 17 psychopathology symptom groups were used to build the network model. We estimated the network structure using a mixed graphical model. Edges were estimated using a pairwise weighted adjacency matrix with EBIC regularization at a default gamma level of 0.25. The relative influence of each node was determined using predictability and centrality measurements including node strength, closeness, and betweenness. A network was similarly created to detect the most influential bridge symptoms using community clusters.ResultsThe network generated from 17 psychopathology symptom domains (comprising ADD, agoraphobia, conduct disorder, depression, generalized anxiety disorder, mania, OCD, ODD, panic disorder, phobia, psychosis, PTSD, general probes, separation anxiety, psychosis prodromal symptoms, social anxiety and suicide) had several distinct cluster regions and two independent psychosis prodromal symptom nodes. No negative associations were observed in the network. The strongest edge regression coefficient (1.593) was detected between a general screening probe asking whether the subject had received previous treatment and a psychosis variable related to hallucination.An OCD item eliciting subject’s fear over accidentally doing something bad had the greatest average centrality measurement (2.317) followed closely by a conduct disorder item eliciting if the subject had ever threatened someone (2.254). Two depression items - irritability (2.228) and depressive mood (1.825) had the largest average bridge centrality values.History of inpatient treatment (0.997), fear of traveling in a car (0.989) and compulsive checking (0.989) had the largest predictability values, suggesting they could potentially be effective intervention targets.DiscussionOCD and conduct disorder symptoms had the largest centrality values and are influential symptoms that could potentially be used to more effectively screen youth for mental health disorders. Depression symptoms had the largest bridge centrality values and should be targeted to prevent comorbidity of associated symptoms. Understanding psychopathology symptom networks could potentially lead to greater insights for prevention and individualizing treatments.