Applying a Dynamical Systems Model and Network Theory to Major Depressive Disorder

TL;DR

This paper introduces a mean field dynamical systems model to analyze mood state transitions in individuals with major depressive disorder, demonstrating its potential to predict mood shifts in clinical settings.

Contribution

The study develops a novel mean field model for complex mood dynamics and validates it with empirical data, aiding in predicting transitions between mood states.

Findings

Majority of clinical patients showed expectancy for mood transition.

Model successfully applied to both clinical and general population samples.

Potential tool for clinical assessment of depression dynamics.

Abstract

Mental disorders like major depressive disorder can be seen as complex dynamical systems. In this study we investigate the dynamic behaviour of individuals to see whether or not we can expect a transition to another mood state. We introduce a mean field model to a binomial process, where we reduce a dynamic multidimensional system (stochastic cellular automaton) to a one-dimensional system to analyse the dynamics. Using maximum likelihood estimation, we can estimate the parameter of interest which, in combination with a bifurcation diagram, reflects the expectancy that someone has to transition to another mood state. After validating the proposed method with simulated data, we apply this method to two empirical examples, where we show its use in a clinical sample consisting of patients diagnosed with major depressive disorder, and a general population sample. Results showed that the majority of the clinical sample was categorized as having an expectancy for a transition, while the majority of the general population sample did not have this expectancy. We conclude that the mean field model has great potential in assessing the expectancy for a transition between mood states. With some extensions it could, in the future, aid clinical therapists in the treatment of depressed patients.