An individual-based model for predicting the prevalence of depression

On the criteria for diagnosing depression in bereaved individuals: a self-organizing map approach

Bereavement exclusion (BE) is a criterion for excluding the diagnosis of major depressive disorder (MDD). Simplistically, this criterion states that an individual who reports MDD symptoms should not be diagnosed as suffering from this mental illness, if such an individual is grieving a sorrowful loss. BE was introduced in 1980 to avoid confusing MDD with normal grief, because several cognitive and physical symptoms of grief and depression can look similar. However, in 2013, BE was removed from the MDD diagnosis guidelines. Here, this controversial topic is computationally investigated. A virtual population is generated according to the Brazilian data of death rate and MDD prevalence and its five kinds of individuals are clustered by using a Kohonen's self-organizing map (SOM). In addition, by examining the current guidelines for diagnosing MDD from an analytical perspective, a slight modification is proposed. With this modification, an adequate clustering is achieved by the SOM neural network. Therefore, for mathematical consistency, unbalanced scores should be assigned to the items composing the MDD diagnostic criteria. With the proposed criteria, the co-occurrence of normal grief and MDD can also be satisfactorily clustered.

Monoamine neurotransmitters and mood swings: a dynamical systems approach

Serotonin, dopamine and norepinephrine are monoamine neurotransmitters that modulate our mood state. Hence, imbalances in the levels of these neurotransmitters have been linked to the incidence of several psychiatric disorders. Here, a mathematical model written in terms of ordinary differential equations is proposed to represent the interaction of these three neurotransmitters. It is analytically and numerically shown that this model can experience a Hopf bifurcation. Thus, by varying a parameter value, the neurotransmitter levels can change from a steady state to an oscillatory behavior, which may be at least a partial explanation of the mood swings observed in depressed people.

A Game Theory-Based Model for Predicting Depression due to Frustration in Competitive Environments

A computational model based on game theory is here proposed to forecast the prevalence of depression caused by frustration in a competitive environment. This model comprises a spatially structured game, in which the individuals are socially connected. This game, which is equivalent to the well-known prisoner's dilemma, represents the payoffs that can be received by the individuals in the labor market. These individuals may or may not have invested in a formal academic education. It is assumed that an individual becomes depressed when the difference between the average payoff earned by the neighbors in this game and the personal payoff surpasses a critical number, which can be distinct for men and women. Thus, the transition to depression depends on two thresholds, whose values are tuned for the model accurately predicting the percentage of individuals that become depressed due to a frustrating payoff. Here, this tuning is performed by using data of young adults living in the United Kingdom in 2014-2016.