Obsession, compulsion and learning in obsessive-compulsive disorder: A multilevel computational model

Obsessive-compulsive disorder (OCD) is characterized symptomatically by obsessive thoughts and compulsive behaviors to separate its behavioral features. Despite extensive research efforts into the pathogenesis and neural substrates in OCD, the interactions and causal associations between the two behaviors remain unclear. To theoretically fill this gap in mechanisms, we combine a cortico-basal ganglia-thalamic model of motor loops and a limbic system model based on classical reward prediction errors to model the complex dynamics between different loops in OCD. Based on the widely accepted hypothesis that dopamine bursts and reinforcement learning influence synaptic plasticity, we explore the mechanisms behind the progression of OCD related to stress triggers, overtraining, and striatal lesions, in terms of behavioral flexibility as a measurement. The obtained results indicate that reinforcement learning affects synaptic gain, which in turn influences loop stability and contributes to imbalance between loops. At the same time, it pulls the network into an over-stable state, similar to compulsive actions. Learning serves as the bridge between the limbic system (obsession) and the motor system (compulsion). Both systems work synergistically to facilitate the etiological transmission and progression of OCD. Our model provides plausible theoretical explanations for the behavioral features of OCD and may offer new approaches for its treatment.