Neural correlates of weighted reward prediction error during reinforcement learning classify response to cognitive behavioral therapy in depression

Filippo Queirazza (Lead / Corresponding author), Elsa Fouragnan, Douglas Steele, Jonathan Cavanagh, Marios G. Philiastides

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
47 Downloads (Pure)

Abstract

While cognitive behavioral therapy (CBT) is an effective treatment for major depressive disorder, only up to 45% of depressed patients will respond to it. At present, there is no clinically viable neuroimaging predictor of CBT response. Notably, the lack of a mechanistic understanding of treatment response has hindered identification of predictive biomarkers. To obtain mechanistically meaningful fMRI predictors of CBT response, we capitalize on pretreatment neural activity encoding a weighted reward prediction error (RPE), which is implicated in the acquisition and processing of feedback information during probabilistic learning. Using a conventional mass-univariate fMRI analysis, we demonstrate that, at the group level, responders exhibit greater pretreatment neural activity encoding a weighted RPE in the right striatum and right amygdala. Crucially, using multivariate methods, we show that this activity offers significant out-of-sample classification of treatment response. Our findings support the feasibility and validity of neurocomputational approaches to treatment prediction in psychiatry.

Original languageEnglish
Article numbereaav4962
Pages (from-to)1-15
Number of pages15
JournalScience Advances
Volume5
Issue number7
DOIs
Publication statusPublished - 31 Jul 2019

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