Andrew J Lawrence1, Daniel Stahl2, Suqian Duan1, Diede Fennema1, Tanja Jaeckle1, Allan H Young3, Paola Dazzan3, Jorge Moll4, Roland Zahn5. 1. Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom. 2. Department of Biostatistics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom. 3. Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom; National Service for Affective Disorders, South London and Maudsley NHS Foundation Trust, London, United Kingdom. 4. Cognitive and Behavioral Neuroscience Unit, D'Or Institute for Research and Education, Rio de Janeiro, Brazil. 5. Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom; National Service for Affective Disorders, South London and Maudsley NHS Foundation Trust, London, United Kingdom; Cognitive and Behavioral Neuroscience Unit, D'Or Institute for Research and Education, Rio de Janeiro, Brazil. Electronic address: roland.zahn@kcl.ac.uk.
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.
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.
Authors: Peter F Hitchcock; Willoughby B Britton; Kahini P Mehta; Michael J Frank Journal: Cogn Affect Behav Neurosci Date: 2022-09-27 Impact factor: 3.526