A mathematical formula of plasticity: Measuring susceptibility to change in mental health and data science

Plasticity is increasingly recognized as a critical concept in psychiatry and mental health because it allows the reorganization of neural circuits and behavior during the transition from psychopathology to wellbeing. Differences in individual plasticity may explain why therapies, such as psychotherapeutic and environmental interventions, are highly effective in some but not in all patients. Here I propose a mathematical formula to assess plasticity – i.e., the susceptibility to change – to identify, at baseline, which individuals or populations are more likely to modify their behavioral outcome according to therapies or contextual factors. The formula is grounded in the network theory of plasticity so that, when representing a system (e.g., a patient's psychopathology) as a weighed network where the nodes are the system features (e.g., symptoms) and the edges are the connections (i.e., correlations) among them, the network connectivity strength is an inverse measure of the plasticity of the system: the weaker the connectivity, the higher the plasticity and the greater the susceptibility to change. The formula is predicted to be generalizable, measuring plasticity at multiple scales, from the single cell to the whole brain, and can be applied to a wide range of research fields, including neuroscience, psychiatry, ecology, sociology, physics, market and finance.