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

AbstractBackgroundOvergeneralised 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.Methods52 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.ResultsCross-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.ConclusionsThis 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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Joint Analysis of Psychiatric Disorders Increases Accuracy of Risk Prediction for Schizophrenia, Bipolar Disorder, and Major Depressive Disorder

The American Journal of Human Genetics ◽

10.1016/j.ajhg.2014.12.006 ◽

2015 ◽

Vol 96 (2) ◽

pp. 283-294 ◽

Cited By ~ 139

Author(s):

Robert Maier ◽

Gerhard Moser ◽

Guo-Bo Chen ◽

Stephan Ripke ◽

William Coryell ◽

...

Keyword(s):

Bipolar Disorder ◽

Major Depressive Disorder ◽

Depressive Disorder ◽

Psychiatric Disorders ◽

Risk Prediction ◽

Joint Analysis ◽

Major Depressive

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Treatment response classes in major depressive disorder identified by model-based clustering and validated by clinical prediction models

Translational Psychiatry ◽

10.1038/s41398-019-0524-4 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 3

Author(s):

Riya Paul ◽

Till. F. M. Andlauer ◽

Darina Czamara ◽

David Hoehn ◽

Susanne Lucae ◽

...

Keyword(s):

Major Depressive Disorder ◽

Depressive Disorder ◽

Treatment Response ◽

Prediction Models ◽

Clinical Prediction ◽

Clinical Prediction Models ◽

Major Depressive ◽

Model Based Clustering ◽

Model Based

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The role of blame-related action tendencies in the vulnerability to major depressive disorder

10.1101/2020.11.15.20232058 ◽

2020 ◽

Author(s):

Suqian Duan ◽

Andrew Lawrence ◽

Lucia Valmaggia ◽

Jorge Moll ◽

Roland Zahn

Keyword(s):

Major Depressive Disorder ◽

Depressive Disorder ◽

Major Depressive ◽

Self Blame ◽

Cognitive Markers ◽

Healthy Control ◽

History Of ◽

General Scientific ◽

Action Tendencies

AbstractBackgroundPersisting self-blaming emotional biases were previously associated with vulnerability to major depressive disorder (MDD). More specifically self-contempt/disgust biases distinguished remitted MDD, compared with never-depressed control participants. The contribution of action tendencies to MDD vulnerability and their relationship with blame-related emotions to prepare for subsequent behaviour is elusive. Here, we investigated whether maladaptive action tendencies such as “creating a distance from oneself” and “hiding” are associated with MDD vulnerability, as well as with self-disgust/contempt and shame respectively.Methods76 participants with medication-free remitted MDD and 44 healthy control (HC) participants without a personal or family history of MDD completed the value-related moral sentiment task, which measured their blame-related emotions during hypothetical social interactions and a novel task to assess their blame-related action tendencies.ResultsAs predicted, the MDD group exhibited a higher proneness to feeling like hiding and creating a distance from themselves compared with the HC group. Interestingly, apologising for one’s wrongdoing, was associated with all self-blaming emotions including shame, guilt, self-contempt/disgust and self-indignation, but was more common in HC. In contrast, apologising and perceiving to be in control of one’s friend’s wrongdoings were more common in MDD. Although shame was indeed associated with hiding, this was also true of guilt. Self-disgust/contempt was associated with attacking rather than creating a distance from oneself.ConclusionsMDD vulnerability was associated with specific maladaptive action tendencies which were not clearly predicted by the type of emotion, thus unveiling novel cognitive markers and neurocognitive treatment targets.General Scientific summaryThis study confirmed the hypothesis that specific maladaptive action tendencies related to self-blame, such as feeling like hiding and feeling like creating a distance from oneself, were distinctive of people with major depressive disorder, even on remission of symptoms. These action tendencies were not clearly predicted by the type of emotion experienced, showing the importance of assessing them directly. This calls for novel psychological and neurocognitive treatments specifically aiming at maladaptive action tendencies which have so far not been directly addressed in standard assessments and treatments.

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Personalise antidepressant treatment for unipolar depression combining individual choices, risks and big data (PETRUSHKA): rationale and protocol

Evidence-Based Mental Health ◽

10.1136/ebmental-2019-300118 ◽

2019 ◽

Vol 23 (2) ◽

pp. 52-56 ◽

Cited By ~ 6

Author(s):

Anneka Tomlinson ◽

Toshi A Furukawa ◽

Orestis Efthimiou ◽

Georgia Salanti ◽

Franco De Crescenzo ◽

...

Keyword(s):

Major Depressive Disorder ◽

Depressive Disorder ◽

Real World ◽

Prediction Models ◽

Antidepressant Treatment ◽

Clinical Settings ◽

Major Depressive ◽

Treatment As Usual ◽

Support Tool ◽

Treatment Indications

IntroductionMatching treatment to specific patients is too often a matter of trial and error, while treatment efficacy should be optimised by limiting risks and costs and by incorporating patients’ preferences. Factors influencing an individual’s drug response in major depressive disorder may include a number of clinical variables (such as previous treatments, severity of illness, concomitant anxiety etc) as well demographics (for instance, age, weight, social support and family history). Our project, funded by the National Institute of Health Research, is aimed at developing and subsequently testing a precision medicine approach to the pharmacological treatment of major depressive disorder in adults, which can be used in everyday clinical settings.Methods and analysisWe will jointly synthesise data from patients with major depressive disorder, obtained from diverse datasets, including randomised trials as well as observational, real-world studies. We will summarise the highest quality and most up-to-date scientific evidence about comparative effectiveness and tolerability (adverse effects) of antidepressants for major depressive disorder, develop and externally validate prediction models to produce stratified treatment recommendations. Results from this analysis will subsequently inform a web-based platform and build a decision support tool combining the stratified recommendations with clinicians and patients’ preferences, to adapt the tool, increase its’ reliability and tailor treatment indications to the individual-patient level. We will then test whether use of the tool relative to treatment as usual in real-world clinical settings leads to enhanced treatment adherence and response, is acceptable to clinicians and patients, and is economically viable in the UK National Health Service.DiscussionThis is a clinically oriented study, coordinated by an international team of experts, with important implications for patients treated in real-world setting. This project will form a test-case that, if effective, will be extended to non-pharmacological treatments (either face-to-face or internet-delivered), to other populations and disorders in psychiatry (for instance, children and adolescents, or schizophrenia and treatment-resistant depression) and to other fields of medicine.

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