Network-based assessment of plasticity predicts vulnerability to depressive symptomatology in healthy individuals

Early identification of individuals at elevated risk for depressive symptoms is essential for prevention. Plasticity, defined as the capacity to transition across mental states, may serve as an individual prognostic marker of mental health trajectories. Indeed, plasticity should be understood from a for-better-or-for-worse perspective, as it enables changes not only toward improvement, such as recovery, but also toward worsening, determining vulnerability. Recently, a network theory of plasticity has been introduced, proposing a computational, network-based approach to quantify plasticity and thus the individual's capacity for change, by estimating connectivity strength among psychological features. Using such an approach, we analyzed ecological momentary assessment (EMA) and symptomatology data collected in 146 healthy people from the Multi-Mood study to test whether baseline plasticity predicts vulnerability to depressive symptomatology. Baseline plasticity was operationalized as the inverse of connectivity strength within EMA items, so that high plasticity corresponds to weak connectivity. We found that individual connectivity strength at baseline was negatively associated with maximal absolute subsequent change in depressive symptoms (ρ = -0.54, P < .001). Additionally, individuals with strong connectivity reached depressive-symptom threshold later (χ² = 41, P < .0001) and showed a markedly reduced risk (HR = 0.20, P < .001) compared with those with weak connectivity. These findings support the network-based assessment of plasticity as a prognostic marker for early detection and prevention strategies within precision psychiatry.