S4. IDENTIFYING HETEROGENEITY IN SYMPTOM NETWORKS IN THE GENERAL POPULATION: A RECURSIVE PARTITIONING APPROACH

Background Network models of psychopathology have gained increasing ground recently. It is suggested that psychopathology arises from the reciprocal associations between symptoms and other psycho-biological factors. Given the heterogeneity in psychopathological phenomena, it seems likely that subgroups with distinct network structures may emerge given different demographic and environmental risk factors. Thus, the identification of heterogeneity in symptom networks associated with specific variables may promote an understanding of the mechanisms that underlie the relation between environmental factors and psychopathology. Methods We took a recursive partitioning approach based on conditional inference trees that iteratively splits the sample of interest based on a predefined set of covariates to detect subgroups with significantly different network structures, resulting in a network tree. We used general population data from the 2000 and 2007 English National Survey of Psychiatric Morbidity, with a combined sample size of n = 15,983 (age range: 16–95 years, 55.9% female), to model networks of psychotic experiences (hallucinations, persecutory ideation) and affective symptoms (worry, mood instability, depression, anxiety, sleep problems). Split variables explored as sources of heterogeneity in networks were sex, age, and exposure to environmental risk factors (cannabis use in past month, lifetime sexual abuse, lifetime experience of bullying). We used a stop-splitting rule based on Bonferroni-adjusted p-values to determine the final tree size (α = .01). Results Environmental factors were the primary sources of heterogeneity in network structures, with exposure to these factors being linked to more densely connected networks. Globally, cannabis use was associated with particularly strong connections between hallucinations and persecutory ideation, depression and persecutory ideation, and depression and mood instability. In those participants with cannabis use and experiences of sexual abuse, the association between depression and persecutory ideation was particularly strong, and further, strong connections were present between the affective symptoms. Similarly, those with exposure to both cannabis and bullying showed stronger associations involving sleep problems than participants exposed to either bullying or cannabis alone. Exposure to either bullying or sexual abuse without concurrent cannabis use was linked to a strongly connected cluster of worry, anxiety, and depression, with only weak associations to other symptoms. Lastly, the sample was split at 60 years of age. The younger group was divided further by age, with participants younger than 26 years showing stronger associations between hallucinations and persecutory ideation and worry and depression than those older than 26 years. In participants older than 60 years, another split was made by gender: women showed a more densely connected network than men. Discussion Findings from this exploratory analysis document substantial heterogeneity in symptom network structures in a large general population sample. Exposure to risk factors is linked to more strongly connected, probably less resilient symptom networks, with evidence for additive vulnerability given the presence of several risk factors. Exposure to sexual abuse or bullying mainly seems to relate to higher connectivity of affective symptoms, while cannabis use links to higher connection of psychotic symptoms with each other, but also with affective symptoms. The analysis also highlights demographic variables as sources of heterogeneity in symptom networks, pointing to specifically relevant symptom interactions in subgroups of age and gender.