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

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BACKGROUND: Overgeneralised self-blaming emotions, such as self-disgust, are core symptoms of major depressive disorder (MDD) 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 like "creating a distance from oneself") and an overgeneralised perception of control are characteristic of MDD, even after remission of symptoms. Here, we probed the potential of this cognitive signature, and its combination with previously employed 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: 52 medication-free remitted MDD patients, 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 fronto-limbic a priori regions of interest, as well as established clinical and standard psychological predictors. Prediction models used elastic-net regularised logistic regression with nested 10-fold cross-validation.

RESULTS: Cross-validated discrimination was highly promising (AuC≥0.86), and positive predictive values over 80% were achieved when including fMRI in multi-modal models, but only up to 71% (AuC≤.74) when solely relying on cognitive and clinical measures.

CONCLUSIONS: This shows the high potential of multi-modal 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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