Set Your Goal: Engaging Go/No-Go Active Learning

Study Overview
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Brief Summary
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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.

Detailed Description
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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.

Official Title
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Computational Modeling of Reinforcement Learning in Depression

Conditions
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Depression, Anxiety

Intervention / Treatment
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* Behavioral: Go/No-Go Active Learning (GOAL)


Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 21 Years
Maximum Age: 40 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Treatment
Intervention Model: Single Group Assignment
Interventional Model Description: Adapted version of Behavioral Activation psychotherapy designed to optimize decision making and learning.
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Go/No-Go Active Learning (GOAL)<br>Adaptation of Behavioral Activation, focused on reinforcement learning strategies. | Behavioral: Go/No-Go Active Learning (GOAL)<br>* Behavioral Activation psychotherapy adapted to engage go/no-go learning<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Integrated Bayesian Information Criterion (BIC) score based on models using modified Q-learning models with two pairs of action values (go and no-go) for each state. | Models will include a learning rate, a slope of the softmax rule, noise factor, a bias factor to the action-value for 'go', and a Pavlovian factor. | Baseline (Week 0) | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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reinforcement learning, computational models, neuroimaging