Reinforcement Learning Disruptions in Individuals With Depression and Sensitivity to Symptom Change Following Cognitive Behavioral Therapy.

ABSTRACT:

Importance

Major depressive disorder is prevalent and impairing. Parsing neurocomputational substrates of reinforcement learning in individuals with depression may facilitate a mechanistic understanding of the disorder and suggest new cognitive therapeutic targets.

Objective

To determine associations among computational model-derived reinforcement learning parameters, depression symptoms, and symptom changes after treatment.

Design, setting, and participants

In this mixed cross-sectional-cohort study, individuals performed reward and loss variants of a probabilistic learning task during functional magnetic resonance imaging at baseline and follow-up. A volunteer sample with and without a depression diagnosis was recruited from the community. Participants were assessed

SUBMITTER: Brown VM 

PROVIDER: S-EPMC8319827 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature