Towards a network-based operationalization of plasticity for predicting the transition from depression to mental health

Plasticity is the ability to modify brain and behavior and allows the transition from psychopathology to mental wellbeing. High plasticity has been associated with high susceptibility to contextual factors, for example, living conditions, which ultimately drive the plasticity outcome. Here we exploited network analysis to show that plasticity—in this case, the susceptibility to modify the depression score—can be measured by assessing the symptom network connectivity: the weaker the connectivity, the higher the plasticity, resulting in a greater modifcation in mood symptoms. We analyzed the STAR*D dataset and found that baseline connectivity strength was weaker in responder patients than non-responder patients. Moreover, connectivity strength was inversely correlated with improvement in depression score (ρ = –0.88, P = 0.002) and susceptibility to change mood according to context (ρ = 0.78, P = 0.028). This operationalization of plasticity provides a mathematical tool to predict resilience, vulnerability and recovery, and to develop novel approaches for the prevention and treatment of major depressive disorder.