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

Authors

Filippo Queirazza, University of Glasgow
Elsa Fouragnan, School of Psychology
J. Douglas Steele, University of Dundee
Jonathan Cavanagh, University of Glasgow
Marios G. Philiastides, University of Glasgow

ORCID

• Elsa Fouragnan: 0000-0003-1485-0332

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.

DOI

10.1126/sciadv.aav4962

Publication Date

2019-01-01

Publication Title

Science advances

Volume

5

Issue

7

Embargo Period

2019-08-28

First Page

4962

Last Page

4962

Recommended Citation

Queirazza, F., Fouragnan, E., Steele, J., Cavanagh, J., & Philiastides, M. (2019) 'Neural correlates of weighted reward prediction error during reinforcement learning classify response to cognitive behavioral therapy in depression', Science advances, 5(7), pp. 4962-4962. Available at: 10.1126/sciadv.aav4962