Journal
SCIENCE ADVANCES
Volume 5, Issue 7, Pages -Publisher
AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/sciadv.aav4962
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Funding
- Chief Scientist Office grant [PN09CP214]
- Dr Mortimer and Theresa Sackler Foundation
- Biotechnology and Biological Sciences Research Council (BBSRC) [BB/J015393/2]
- Economic and Social Research Council (ESRC) [ES/L012995/1]
- MRC
- MRF
- BBSRC [BB/J015393/2] Funding Source: UKRI
- ESRC [ES/L012995/1] Funding Source: UKRI
- MRC [MR/J000914/1] Funding Source: UKRI
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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.
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