Improving Treatments for Panic Disorder using Computational Modeling

Panic disorder is a highly prevalent mental health condition that significantly impacts patients' quality of life. However, current treatments are not universally effective and have shown limited improvement since their introduction decades ago. This lack of progress is due in part to our incomplete understanding of the system underlying panic disorder and how different treatments intervene on it. Existing theories suggest that this system comprises multiple components that interact in non-linear ways over different time scales. Because of the counter-intuitive behavior of such systems, verbal theorizing can only provide limited understanding about them. To address this issue, we extended an existing computational model of panic disorder with a typical Cognitive Behavioral Therapy (CBT) treatment. Simulating treatment outcomes allows us to study how different treatment components interact with each other. Based on this analysis, we develop a new CBT treatment and demonstrate that its simulated outcomes are superior. Next, we introduce inter-individual variation in key parts of the model, and study which treatment work best for which type of patients, which leads us to personalized treatment plans. We close by discussing how computational models can advance treatment research and may lead to the development of better and more personalized treatments.