Anhedonia, Depression
Conditions
Brief 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.
Interventions
BEHAVIORALHigh Reward - High Variance
Computerized reward learning task designed to increase reward responsiveness
BEHAVIORALHigh Reward - Low Variance
Computerized reward learning task designed to have minimal effect on reward responsiveness
Sponsors
University of California, San Diego
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
TREATMENT
Masking
DOUBLE (Subject, Outcomes Assessor)
Eligibility
Sex/Gender
ALL
Age
18 Years to 65 Years
Healthy volunteers
No
Inclusion criteria
* Age 18-65 (all races, both males and females will be included) * Current diagnosis of Major Depressive Disorder (MDD) or Generalized Anxiety Disorder (GAD) * A score \< 7 on the Snaith-Hamilton Pleasure Scale (SHAPS) or \< 25 on the Positive Affect subscale from the Positive And Negative Affect Scale (PANAS) * Sufficient proficiency in English language to understand and complete informed consent, interviews, questionnaires, and all other study procedures. * Having normal or corrected vision * Having normal or corrected hearing
Exclusion criteria
* Endorsement of past or current psychotic or bipolar disorder. * Endorsement of recent severe alcohol or substance use disorder (past 3 months). * Inability to discontinue for 48 hours prior to scan any medications incompatible with measuring reliable/interpretable neural activity (e.g., e.g., benzodiazepines/beta-blockers, atypical anti-depressants, anxiolytics and sedative hypnotics, antipsychotics, and opioid analgesics) * Being unsafe to undergo functional magnetic resonance imaging (fMRI), i.e., being pregnant, presence of non-removable metal in body, medical conditions that are incompatible with safe MRI (e.g., claustrophobia, inability to lie still on one's back for approximately 60 minutes; prior neurosurgery) * Clinical conditions assessed by the interviewer that necessitate more imminent clinical care (e.g., active suicidal ideation): These criteria are in place so participants with these other, more several symptoms can be referred for appropriate mental health services.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Reward Maximization | Change between Pre-Training and Post-Training Assessments (about 20 min) | Individually fitted computational parameter of reward maximization strategy during the reward learning task |
| Positive Affect | Change between Pre-Training and Post-Training Assessments (about 20 min) | State positive affect measured with the Positive and Negative Affect Schedule (PANAS) |
| Neural Tracking of Reward Expectation | Change between Pre-Training and Post-Training Assessments (about 20 min) | Neural activity, i.e., blood oxygen level dependent (BOLD) response, in the striatum and medial prefrontal cortex correlating with participants' trial-level model-based reward expectations and associated prediction errors during the Reward Learning Task |
Countries
United States
Outcome results
None listed