Set Your Goal: Engaging Go/No-Go Active Learning
| Status: | Recruiting |
|---|---|
| Conditions: | Anxiety, Anxiety, Depression, Depression |
| Therapuetic Areas: | Psychiatry / Psychology |
| Healthy: | No |
| Age Range: | 21 - 40 |
| Updated: | 5/30/2018 |
| Start Date: | May 1, 2018 |
| End Date: | March 1, 2019 |
| Contact: | Jackie K Gollan, Ph.D. |
| Email: | j-gollan@northwestern.edu |
| Phone: | 312695-6121 |
Computational Modeling of Reinforcement Learning in Depression
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.
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.
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
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