The Individual- and Population-level Mechanistic Implications of Statistical Networks of Symptoms

Mental health issues, particularly depression, arise from complex symptom interactions that traditional psychiatric models often fail to capture. We address this issue by introducing a continuous-time mechanistic network model of depressive symptoms, inspired by empirical findings. The model captures bistability between healthy and depressed states, illustrates transitions and tipping points, and shows post-shock persistence. It also allows the study of resilience by simulating responses to external shocks. Importantly, we demonstrate how the same underlying dynamics can give rise to different statistical networks depending on the timing of observation, thereby showing how statistical networks estimated from simulated observations can differ in density across healthy, depressed, and shock phases, even when the underlying mechanistic couplings remain unchanged. Using synthetic data across resilience levels, we compare model-derived networks to those estimated from a large-scale dataset ($n$ = 23,283, HELIUS study), showing strong agreement and revealing that in-degree in the mechanistic network maps onto statistical centrality. Our aim is not to propose a definitive model of depression. Rather, the model provides both a plausible representation of symptom dynamics and an illustrative framework for linking mechanistic processes to statistical symptom networks, thereby clarifying how dynamic mechanisms can underpin diverse empirical findings across individuals and populations.