Testing the Neuroscience of Guided Learning in Depression

Virginia Polytechnic Institute and State University

Status: Completed

Conditions: Major Depressive Disorder

Treatments: Behavioral: Guided Learning: Changing statistics; Behavioral: Standard Learning; Behavioral: Guided Learning: Instructed statistics

Study type: Interventional

Funder types: Other; NIH

Identifiers

NCT03203954

MDDLEARNING

R01MH106756 (U.S. NIH Grant/Contract)

Details and patient eligibility

About

Major depression is a prevalent and impairing illness. To better understand the basic science and treatment of depression, the investigators study the behavioral and brain processes associated with learning in depression and how potential disruptions in learning may be repaired. Understanding different methods that change learning may lead to novel treatments that contribute to recovery in people with depression.

Full description

Major depressive disorder ranks among the most significant causes of mortality and disability in the world. Recent data from the investigators and others highlight that impairments in reward and loss learning are central to depression, have distinct neural substrates, and improve with successful treatment. Together, these findings suggest an urgent need to delineate the relationships among neural and behavioral learning impairments and depression. Equally important, these insights suggest new targets for treatment such that manipulating the neural and behavioral substrates of learning may facilitate symptom change in depression.

To address these issues, the investigators use functional neuroimaging and a computational psychiatry framework to i) systematically characterize the neural and behavioral substrates that attend reward- and loss- learning in depression and ii) assess the degree to which learning in depression responds to two behavioral methods that target learning in different ways. The investigators test the broad hypotheses that i) that depression may be characterized by distinct neural and behavioral disruptions of learning, and ii) these disruptions and associated symptoms may be ameliorated through different methods of guiding learning. Recent advances in computational psychiatry provide a mechanism-based framework within which to understand the nature and trajectory of potential learning impairments in depression and suggest new ways that disrupted learning and associated symptoms may be improved in depression

Enrollment: 216 estimated patients

Sex: All

Ages: 18 to 55 years old

Volunteers: Accepts Healthy Volunteers

Inclusion criteria

• males and females of all ethnicities
• meet diagnostic criteria for major depression or non-depressed control (assessed by study staff)
• ages 18-55
• fluent in English
• able to see computer display clearly
• able to provide informed consent
• able to follow verbal or written instructions
• for participants who are referred by a clinician, a letter from that clinician indicating that participation in the study does not constitute an elevated medical or behavioral risk to the participant will be requested.

Exclusion criteria

• current pregnancy or menopause
• claustrophobia
• MRI contraindications
• psychotic or bipolar disorder

Trial design

Primary purpose: Other

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Double Blind

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216 participants in 3 patient groups

No Training

Active Comparator group

Description:

Guided learning, standard learning: Repeat administration of standard behavioral learning task

Treatment:

Behavioral: Standard Learning

Instructed Statistics

Experimental group

Description:

Guided learning, instructed statistics: Repeat administration of behavioral learning task with instructions about task statistics

Treatment:

Behavioral: Guided Learning: Instructed statistics

Not Instructed Statistics

Experimental group

Description:

Guided learning, changing statistics: Repeat administration of behavioral learning task with changing task statistics

Treatment:

Behavioral: Guided Learning: Changing statistics