Computational models in psychiatry: a toy-model to apprehend the dynamics of psychiatric disorders

Aim: Given this research attention on the dynamical aspects of psychiatric disorders in recent years and their clinical significance, this article seeks to provide a computational model capturing the heterogeneous individual evolutions of psychiatric disorders.Methods: We propose a 3+1 dimensional toy-model reproducing the clinical observations encountered in clinical psychiatry. This model is based on differential equations aggregating perceived environmental influence over time, internal and subjective patient specific factors, and their interaction with the apparent rate or intensity of symptoms.Results: Constrained by clinical observation of case formulations, four main psychiatric conditions were modelized: i) a healthy situation, ii) a kind of psychiatric disorder evolving following an outbreak (i.e., schizophrenia spectrum), iii) a kind of psychiatric disorder evolving by kindling and bursts (e.g., bipolar and related disorders); iv) and a kind of psychiatric disorder evolving due to its susceptibility to the environment (e.g., persistent complex bereavement disorder). Moreover, still following the observational constraint of stereotyped case formulations, we simulate the action of treatments on different psychiatric disorders.Conclusion: Dynamical systems allows to understand the interactions of psychiatric disorders with the environmental, descriptive, subjective and biological variables. Although this non-linear dynamical toy-model have limitations (e.g., explanatory scope or discriminant validity), simulations provide at least five main interests for clinical psychiatry, as a visualization of the potential different evolution of psychiatric disorders, sustaining case formulations, information about attracting states, or the possibility of a nosological refinement of psychiatric models (e.g., staging and symptom network models).