NCT05383248

Computational Cognitive Training To Boost Reward Responsiveness In Anhedonic Patients

• Status: Completed
• Phase: N/A
• Study type: Interventional
• Enrollment: 50 (actual)
• Sponsor: University of California, San Diego · Academic / Other
• Sex: All
• Age: 18 Years – 65 Years
• Healthy volunteers: Not accepted

Summary

Anhedonia, i.e., reduced positive mood and decreased sensitivity to rewards, is observed in many psychiatric illnesses, particularly depression and anxiety disorders. Untreated anhedonia predicts worse clinical outcomes and poorer response to treatment, yet cognitive behavioral treatment approaches to target anhedonia are fraught with poor patient compliance in real-life settings. The proposed study aims to address this gap by 1) testing the usefulness of a non-invasive, computationally informed, cognitive training in boosting reward sensitivity and reducing anhedonia in depressed and anxious patients, and 2) delineating the neurocomputational mechanisms of change associated with such intervention. In other words, can we train the brain to obtain rewards and boost positive mood among depressed and anxious individuals? This project will help to develop a computational training protocol aimed at reducing anhedonia and improving existing interventions for psychiatric conditions characterized by reward processing deficits. Long-term goals include expanding this framework to a broader range of appetitive and social stimuli to develop precise cognitive training tools to treat anhedonia.

Conditions

• Anhedonia
• Depression

Interventions

Type	Name	Description
BEHAVIORAL	High Reward - High Variance	Computerized reward learning task designed to increase reward responsiveness
BEHAVIORAL	High Reward - Low Variance	Computerized reward learning task designed to have minimal effect on reward responsiveness

Timeline

• Start date: 2022-09-14
• Primary completion: 2023-11-16
• Completion: 2023-11-16
• First posted: 2022-05-20
• Last updated: 2023-11-29

Locations

1 site across 1 country: United States