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Neurocognitive measures of self-blame and risk prediction models of recurrence in major depressive disorder

Research output: Contribution to journalArticlepeer-review

Original languageEnglish
Pages (from-to)256-264
Number of pages9
JournalBiological Psychiatry: Cognitive Neuroscience and Neuroimaging
Volume7
Issue number3
Early online date4 Mar 2022
DOIs
E-pub ahead of print4 Mar 2022
PublishedMar 2022

Bibliographical note

Funding Information: This work was supported by a Medical Research Council Clinician Scientist Fellowship (Grant No. G0902304 [to RZ]), the LABS-D'Or Hospital Network (Rio de Janeiro, Brazil) (to JM), and a Medical Research Council Doctoral Training Partnership (Grant No. 2064430 [to DF]). RZ and DS were partly funded and AJL was fully funded by the National Institute for Health Research Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London and by NARSAD Independent Investigator Grant No. 24715 from the Brain & Behavior Research Foundation. The views expressed are those of the authors and not necessarily those of the National Health Service, the National Institute for Health Research, or the Department of Health. Funding Information: This work was supported by a Medical Research Council Clinician Scientist Fellowship (Grant No. G0902304 [to RZ]), the LABS-D'Or Hospital Network (Rio de Janeiro, Brazil) (to JM), and a Medical Research Council Doctoral Training Partnership (Grant No. 2064430 [to DF]). RZ and DS were partly funded and AJL was fully funded by the National Institute for Health Research Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London and by NARSAD Independent Investigator Grant No. 24715 from the Brain & Behavior Research Foundation. The views expressed are those of the authors and not necessarily those of the National Health Service, the National Institute for Health Research, or the Department of Health. We are grateful to Dr. Karen Lythe for collecting the primary data. RZ has collaborations with e-health companies Depsee Ltd, EMIS PLC, and Alloc Modulo Ltd. All other authors report no biomedical financial interests or potential conflicts of interest. Publisher Copyright: © 2021 Society of Biological Psychiatry

King's Authors

Abstract

Background: Overgeneralized self-blaming emotions, such as self-disgust, are core symptoms of major depressive disorder and prompt specific actions (i.e., action tendencies), which are more functionally relevant than the emotions themselves. We have recently shown, using a novel cognitive task, that when feeling self-blaming emotions, maladaptive action tendencies (feeling like hiding and feeling like creating a distance from oneself) and an overgeneralized perception of control are characteristic of major depressive disorder, even after remission of symptoms. Here, we probed the potential of this cognitive signature, and its combination with previously employed functional magnetic resonance imaging (fMRI) measures, to predict individual recurrence risk. For this purpose, we developed a user-friendly hybrid machine/statistical learning tool, which we make freely available. Methods: A total of 52 medication-free patients with remitted major depressive disorder, who had completed the action tendencies task and our self-blame fMRI task at baseline, were followed up clinically over 14 months to determine recurrence. Prospective prediction models included baseline maladaptive self-blame–related action tendencies and anterior temporal fMRI connectivity patterns across a set of frontolimbic a priori regions of interest, as well as including established clinical and standard psychological predictors. Prediction models used elastic net regularized logistic regression with nested 10-fold cross-validation. Results: Cross-validated discrimination was highly promising (area under the receiver-operating characteristic curve ≥ 0.86), and positive predictive values over 80% were achieved when including fMRI in multimodal models, but only up to 71% (area under the receiver-operating characteristic curve ≤ 0.74) when solely relying on cognitive and clinical measures. Conclusions: This study shows the high potential of multimodal signatures of self-blaming biases to predict recurrence risk at an individual level and calls for external validation in an independent sample.

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