Modeling the Dynamics of Disease States in Depression
Major depressive disorder (MDD) affects around 20% of some point during the lifetime of an individual. For many years the underlying causes of depression have evolved, although this understanding is incomplete and has left many aspects of depression as a subject of discussion and research. Depression is commonly associated with severe and persistent symptoms leading to significant social role impairment, increased medical co-morbidity and mortality. Depression can affect anyone regardless of age, ethnic background, socio-economic status or gender. The heterogeneity of depression implies that multiple neural substrates and mechanisms contribute to its heterogeneity. To counter the effects of depression, a variety of causes have been proposed reflecting on an individual’s psychological, psychosocial, hereditary, evolutionary, and biological factors.
In the model, empirical occurrence and recurrence rates are used to draw a distribution of depressive episodes in the population, whereby the numbers of depressive episodes during the individual’s lifetime are reported. Although epidemiological studies are quite informative, the occurrence rate (OR) in the model is a fraction of the total population that suffers from at least one depressive episode during their lifetime. Therefore, the occurrence rate (OR) will equal the probability of having one or more depressive episodes. To study the time-to-remission and time-to-response in the model, the system is usually initialized in a negative state while the effects of different treatments are illustrated by simulations of the two-sub-populations model whereby depression is the target variable.
Major depressive disorder being clinically and etiologically heterogeneous, this model is abstract and combines several major mechanisms that have significant influence on the dynamics of MDD. Therefore, the model makes it possible to account for the conditions under which the occurrence and recurrence of depressive episodes occur, as well as the effects of antidepressant treatments and cognitive behavioral therapy within the same dynamical systems model through changing a small subset of parameters. Based on the model parameters, it can be concluded that patients suffering from depression can be divided into two sub-populations: a high-risk sub-population that has a high risk of developing chronic depression and a low-risk sub-population, in which patients develop depression stochastically with low probability. As such, the success of antidepressant treatment is stochastic, resulting in different times-to-remission in otherwise identical patients. Although the specific details of this model may be subject to empirical evaluations, the approach shows potential power of computationally modeling depression and the need for different type of quantitative data for proper understanding of depression.
You have just read a summary of the research paper: Demic, Selver, and Sen Cheng. “Modeling the Dynamics of Disease States in Depression.” PloS one 9.10 (2014): e110358. Full text pdf available on pubmed central.