Noise, nonlinear dynamics and the timecourse of affective disorders

Affective disorders, such as depression or mania, tend to be recurrent and progressive. Typically, disease patterns evolve from isolated episodes to rhythmic and finally accelerated “chaotic” mood patterns during the longitudinal course. Concepts from dynamical systems have been considered to explain this progression. However, most natural systems are not only nonlinear but also affected by noise. For this reason it seems important to incorporate cooperative stochastic–dynamic effects into current conceptional models for the course and neurobiology of such disorders. We use a computational perspective and describe behaviors of a simple mathematical model which result from interactions between random and deterministic dynamics. In particular, we focus on a scenario for illness progression that relies on noise enhancement of feedback instabilities. We suggest that noise amplification of subclinical neurobiological vulnerabilities could represent a relevant disease mechanism.