Advancing the Network Theory of Mental Disorders: A Computational Model of Panic Disorder

The network theory of psychopathology posits that mental disorders are complex systems of mutually reinforcing symptoms. This overarching framework has proven highly generative but does not specify precisely how any specific mental disorder operates as such a system. We address this gap in the literature by developing a network theory of Panic Disorder and formalizing that theory as a computational model. We first review prior psychological theory and research on Panic Disorder in order to identify its core components as well as the plausible causal relations among those components. We then construct and evaluate a computational model of Panic Disorder as a non-linear dynamical system. We show that this model can explain a great deal, including individual differences in the propensity to experience panic attacks, key phenomenological characteristics of those attacks, the onset of Panic Disorder, and the efficacy of cognitive behavioral therapy. We also show that the model identifies significant gaps in our understanding of Panic Disorder and propose a theory-driven research agenda for Panic Disorder that follows from our evaluation of the model. We conclude by discussing the implications of the model for how we understand and investigate mental disorders as complex systems.