Does the structure of dynamic symptom networks depend on baseline psychopathology in students?

Network theory conceptualizes mental disorders as systems of dynamic interactions among symptoms and other variables, and proposes that people with psychopathology have distinct networks as compared to healthy people. However, this idea is rarely investigated, and networks are mostly estimated on cross-sectional data. Importantly, as network theory is specified on the within-person level, it is necessary to estimate networks based on intensive time-series data. This study estimated contemporaneous and temporal transdiagnostic networks on time-series data of participants with different levels of psychopathology. 192 university students completed an Ecological Momentary Assessment (EMA) protocol. A newly developed bootstrap method was used to compare the multi-level Vector Autoregressive (mlVAR) effects between groups. Network connectivity did not differ between groups. Only a few edges differed significantly between groups, with small effect sizes. These results suggest that networks of groups of people with different levels of psychopathology might not differ. Explanations and implications for these results, such as the impact of focusing on heterogeneous groups instead of homogeneous groups or individuals, the relevance of node levels, and methodological and analytical decisions, are discussed.