Abstract
Mental disorders like major depressive disorder can be modeled as complex dynamical systems. In this study we investigate the dynamic behavior 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 numerically illustrating 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.
Highlights
Major depressive disorder (MDD) is not that uncommon: around 350 million people around the globe suffer from MDD (World Health Organization, 2012)
We looked at the mean scores of the Depression and Anxiety Stress Scale (DASS; Lovibond and Lovibond, 1995a,b), the Quick Inventory of Depressive Symptomatology (QIDS; Rush et al, 2003, 2006), and the Positive Affect Negative Affect Scale (PANAS; Peeters et al, 1996; Raes et al, 2009)
The present study combined dynamical systems theory and network theory to assess the expectancy for a transition, a sudden jump between two stable mood states, using a mean field model
Summary
Major depressive disorder (MDD) is not that uncommon: around 350 million people around the globe suffer from MDD (World Health Organization, 2012). Since this sequence of states only depends on the proportion of active nodes at the previous time point, we obtain what is called a Markov chain and we can estimate the parameters by means of maximum likelihood estimation in a straight forward manner Using this dynamical system allows us to determine whether for a person it is possible that a transition may occur or not. Proportion of active emotions and thereby experience an episode of depression For this individual we know (from external evidence) that a depressive episode had taken place after the time series that we used to determine the state of the person (see Wichers et al, 2016; Kossakowski et al, 2017). We apply our method to two datasets to show how the method works in different contexts
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
