Computational model of obsessive-compulsive disorder: examination of etiologic hypothesis and treatment strategies

Research has shown that obsessive compulsive disorder (OCD) is related to structural and functional abnormalities in the brain, and several authors have organized these findings into theories of OCD neuropsychiatric dysfunction. In this paper, these theories were used to develop a neural network model of OCD. OCD symptoms were hypothesized to result from a hyperactive orbitofrontal-striato-thalamic-orbitofrontal neural loop. The network was constructed and trained with a backpropagation algorithm, and it was then used to assess etiologic theories of OCD (e.g., basal ganglia dysfunction, inadequate dopaminergic inhibitory influence on basal ganglia and excessive input from the limbic system). The network was also observed in analogues of the treatment of OCD with serotonergic medications and behavior therapy. Results show that a) the network behaved both normally and abnormally, depending on what combinations of perceptual, motivational, and neurochemical inputs were presented to it; b) several etiologic mechanisms produced changes in the networks' behaviors similar to patients' subjective experiences of OCD symptoms; and c) different treatment strategies, both those modeled as pharmacologic and behavioral therapies, produced reductions in simulated OCD symptoms.