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Bipolar disorder is a chronic, recurrent mental illness characterized by extreme episodes of depressed and manic mood, interspersed with less severe but highly variable mood fluctuations. Here we develop a novel mathematical approach for exploring the dynamics of bipolar disorder. We investigate how the dynamics of subjective experience of mood in bipolar disorder can be understood using a relaxation oscillator framework and test the model against mood time series fluctuations from a set of individuals with bipolar disorder. We show that variable mood fluctuations in individuals diagnosed with bipolar disorder can be driven by the coupled effects of deterministic dynamics (captured by relaxation oscillators) and noise. Using a statistical likelihood-based approach we show that, in general, mood dynamics are described by two independent relaxation oscillators with differing levels of endogenous variability amongst individuals. We suggest that this sort of nonlinear approach to bipolar disorder has neurobiological, cognitive and clinical implications for understanding this mental illness through a mechacognitive framework.

Type

Journal article

Journal

Journal of the Royal Society Interface

Publisher

Royal Society, The