This study will test a computational model reinforcement learning in depression and anxiety and test the extent to which the computational model predicts response to an adapted version of behavioral activation psychotherapy. The model will be based on a data from a computer task of reinforcement learning during 3T functional magnetic resonance imaging at baseline.

The dysfunction of reinforcement learning is emerging as a transdiagnostic dimension of mood and anxiety. Computational models of reinforcement learning may expedite our ability to identify predictors of response, thereby improving efficacy rates. We will will, first, examine the neural substrates of reinforcement learning in depression and anxiety, and, second, test a computational model of reinforcement learning as a predictor of response to an adapted version of behavioral activation psychotherapy. Subjects (N=10) will be enrolled in a two week evaluation, followed with a nine week weekly intervention program. Assessments will be conducted at baseline, and during the intervention as the 3-, 6-, 9-week follow-ups. Reinforcement learning will be measured using 3T magnetic resonance imaging during a computer task. All other measures include structured clinical interviews, questionnaires, and computer tasks.

Inclusion Criteria:

• Between the ages of 21 and 40
• Physically healthy
• Right handed
• Normal or corrected to normal vision
• Scores equal or higher of (a) 24 on Inventory of Depressive Symptomatology, Self Report, or (b) 15 on the Generalized Anxiety Disorder Self Report.

Exclusion Criteria:

• Not currently in therapy or taking medications for anxiety or depression
• No contraindications for the magnetic resonance scan (claustrophobic)
• No history of head trauma, seizures, loss of consciousness
• Not taking hormone replacement, not pregnant
• No imminent suicidality
• No report of excessive alcohol or drug use in past three months