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Neurobehavioural predictors of depression relapse

English title Neurobehavioural predictors of depression relapse
Applicant Huys Quentin
Number 153449
Funding scheme Project funding (Div. I-III)
Research institution Institut für Biomedizinische Technik Universität Zürich und ETH Zürich
Institution of higher education University of Zurich - ZH
Main discipline Neurophysiology and Brain Research
Start/End 01.09.2014 - 31.08.2019
Approved amount 429'000.00
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All Disciplines (2)

Discipline
Neurophysiology and Brain Research
Neurology, Psychiatry

Keywords (9)

Prediction; Neuroimaging; Computational Psychiatry; Cognitive control; Depression; Learned Helplessness; Emotional Regulation; Relapse; Biomarker

Lay Summary (German)

Lead
Neuronale und auf Entscheidungsverhalten basierende Messung des Rückfallrisikos nach Absetzen antidepressiver Medikamente.
Lay summary

Depression ist eine rezidivierende Krankheit. Nach dem Absetzen von Medikamenten sind Rückfälle besonders häufig, treten aber doch nicht bei jedem Patienten auf. Eine längere Einnahme der Medikamente reduziert das Risiko von Rückfällen. Die Entscheidung, ob Antidepressiva abgesetzt werden sollen, ist also wichtig. Weil aber kaum Prädiktoren bekannt sind, die bei dieser Entscheidung helfen können, sind Behandlungsrichtlinien sehr vorsichtig, und empfehlen, die Medikamente lange Zeit nach dem Abklingen der jeweiligen depressiven Episode weiter einzunehmen. Um sowohl Patienten als auch Ärzten bei der Entscheidung zu helfen, wird dieses Projekt versuchen, das Rückfallrisiko individueller Patienten anhand einer Kombination von Messungen zu bestimmen. Eine zweite Frage, die das Projekt untersuchen wird, ist inwieweit Unterschiede in der neuronalen Funktion remittierter Patienten sich von denen von noch nie erkrankten Kontrollprobanden unterscheidet, und welcher Anteil dieses Unterschiedes den Medikamenten zuzuschreiben ist.

Dazu werden Patienten, die auf ein Antidepressivum angesprochen haben und dieses absetzen wollen, zwei mal untersucht - ein mal vor dem Absetzen, und einmal nach dem Absetzen. Eine zweite Gruppe von Patienten wird zwei mal vor dem Absetzen untersucht, und gesunde Kontrollprobanden werden als Vergleichsgruppe einmalig untersucht. Die Untersuchungen beinhalten sowohl ausführliche psychopathologische Untersuchungen, Blutentnahmen, als auch Verhalten und funktionale kernspintomographische Datenerhebungen. Die Teilnehmer werden daraufhin für 6 Monate nachverfolgt um Rückfälle zu messen.

Direct link to Lay Summary Last update: 17.09.2014

Responsible applicant and co-applicants

Employees

Publications

Publication
Advancing Clinical Improvements for Patients Using the Theory-Driven and Data-Driven Branches of Computational Psychiatry.
Huys Quentin J M (2018), Advancing Clinical Improvements for Patients Using the Theory-Driven and Data-Driven Branches of Computational Psychiatry., in JAMA psychiatry, 75, 225-226.
Effort and Reward Evaluation in Remitted Depression: A Preliminary Report on a Possible Predictor of Relapse
Berwian I, Schnuerer I, Wenzel J, Renz D, Stephan K E, Walter H, Huys Q J M (2018), Effort and Reward Evaluation in Remitted Depression: A Preliminary Report on a Possible Predictor of Relapse, in Biological Psychiatry, 83(9), 126-127, Society of Biological Psychiatry, Brentwood, TN, USA 83(9), 126-127.
The impact of traumatic stress on Pavlovian biases.
Ousdal O T, Huys Q J, Milde A M, Craven A R, Ersland L, Endestad T, Melinder A, Hugdahl K, Dolan R J (2018), The impact of traumatic stress on Pavlovian biases., in Psychological medicine, 48, 327-336.
A Formal Valuation Framework for Emotions and Their Control.
Huys Quentin J M, Renz Daniel (2017), A Formal Valuation Framework for Emotions and Their Control., in Biological Psychiatry, 82, 413-420.
Predicting relapse after antidepressant withdrawal - a systematic review.
Berwian I M, Walter H, Seifritz E, Huys Q J M (2017), Predicting relapse after antidepressant withdrawal - a systematic review., in Psychological medicine, 47, 426-437.
Theory-Based Computational Psychiatry.
Maia Tiago V, Huys Quentin J M, Frank Michael J (2017), Theory-Based Computational Psychiatry., in Biological psychiatry, 82, 382-384.
A Roadmap for the Development of Applied Computational Psychiatry.
Paulus Martin P, Huys Quentin J M, Maia Tiago V (2016), A Roadmap for the Development of Applied Computational Psychiatry., in Biological psychiatry: CNNI, 1, 386-392.
Computational psychiatry as a bridge from neuroscience to clinical applications.
Huys Quentin J M., Maia Tiago V., Frank Michael J. (2016), Computational psychiatry as a bridge from neuroscience to clinical applications., in Nat Neurosci, 19(3), 404-413.
Computational Psychiatry: From Mechanistic Insights to the Development of New Treatments.
Huys Quentin J M, Maia Tiago V, Paulus Martin P (2016), Computational Psychiatry: From Mechanistic Insights to the Development of New Treatments., in Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 1, 382-385.
Computational Psychiatry: towards a mathematically informed understanding of mental illness.
Adams Rick A, Huys Quentin J M, Roiser Jonathan P (2016), Computational Psychiatry: towards a mathematically informed understanding of mental illness., in Journal of neurology, neurosurgery, and psychiatry, 87, 53-63.
German Translation and Validation of the {Cognitive Style Questionnaire Short Form (CSQ-SF-D)}.
Huys Quentin J M, Renz Daniel, Petzschner Frederike, Berwian Isabel, Stoppel Christian, Haker Helene (2016), German Translation and Validation of the {Cognitive Style Questionnaire Short Form (CSQ-SF-D)}., in PLoS One, 11, 0149530-0149530.
Model-Free Temporal-Difference Learning and Dopamine in Alcohol Dependence: Examining Concepts From Theory and Animals in Human Imaging
Huys Quentin J.M., Deserno Lorenz, Obermayer Klaus, Schlagenhauf Florian, Heinz Andreas (2016), Model-Free Temporal-Difference Learning and Dopamine in Alcohol Dependence: Examining Concepts From Theory and Animals in Human Imaging, in Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 1(5), 401-410.
The specificity of Pavlovian regulation is associated with recovery from depression.
Huys Q J M., Gölzer M., Friedel E., Heinz A., Cools R., Dayan P., Dolan R. J. (2016), The specificity of Pavlovian regulation is associated with recovery from depression., in Psychol Med, 46(5), 1027-1035.

Collaboration

Group / person Country
Types of collaboration
Prof. Peter Dayan Germany (Europe)
- in-depth/constructive exchanges on approaches, methods or results
- Publication
Prof. Erich Seifritz, University Hospital of Psychiatry Zurich Switzerland (Europe)
- in-depth/constructive exchanges on approaches, methods or results
- Publication
Prof. Klaas Stephan, Translational Neuroimaging Unit (TNU), ETH Zurich and University of Zurich Switzerland (Europe)
- in-depth/constructive exchanges on approaches, methods or results
- Publication
- Research Infrastructure
Prof. Ray Dolan Great Britain and Northern Ireland (Europe)
- in-depth/constructive exchanges on approaches, methods or results
- Publication
- Research Infrastructure
- Exchange of personnel
Prof Henrik Walter Germany (Europe)
- in-depth/constructive exchanges on approaches, methods or results
- Publication

Scientific events

Active participation

Title Type of contribution Title of article or contribution Date Place Persons involved
Society of Biological Psychiatry - annual meeting Individual talk Effort and reward evaluation in remitted depression: a preliminary report on a possible predictor of relapse 18.05.2018 New York, United States of America Walter Henrik; Berwian Isabel; Huys Quentin; Schnürer Inga;


Communication with the public

Communication Title Media Place Year
Media relations: print media, online media Abhängig? Die Pharma interessiert's nicht German-speaking Switzerland 2018
Media relations: print media, online media Niemand mag mich UZH Magazin German-speaking Switzerland 2018
Media relations: print media, online media Den Tiefpunkt überwinden – ohne erneut zu fallen NZZ German-speaking Switzerland 2017
Media relations: radio, television Wann sollen Antidepressiva abgesetzt werden? nau.ch German-speaking Switzerland 2017
Media relations: print media, online media Mit Mathematik gegen schwere Depressionen Die Welt International 2016
Media relations: print media, online media Mit Mathematik gegen Depression Tagesanzeiger German-speaking Switzerland 2015

Awards

Title Year
Georg-Friedrich-Goetz Preis der Universität Zürich 2016

Abstract

We aim to a) identify neuroimaging predictors of a high risk of Major Depressive Disorder (MDD) relapse after antidepressant medication (ADM) discontinuation; and b) examine the effect of medication withdrawal on the remitted depressed state. This is part of an endeavour to use behavioural and neurobiological measures to stratify existing clinical psychopathological entities with respect to treatment outcomes. Current pharmacological depression treatment options lead to eventual remission in up to 70% of patients Rush et al. (2006). Because the risk of relapse after discontinuation is high (30-60% in 6 months; Geddes et al. 2003), guidelines recommend treatment continuation for various periods. However, physicians then face a similar problem again: (i) patients discontinue psychotropic medication independently at very high rates (Lee and Lee, 2011), particularly after achieving remission; and (ii) these recommendations do not take individual variability into account. Markers for safe ADM discontinuation would help identify at-risk patients in whom continuation or further therapy could be recommended on stronger, individually valid, grounds. By providing an objective end-point to treatment this may enhance concordance with treatment. Furthermore, although the neurobiology of affective function after remission has been examined previously, the contribution of ADM remains poorly understood and characterised. Based on a power analysis, we propose a 6-month follow-up study of patients who have been in remission for a minimum of 6 months and intend to discontinue their ADMs independently of this study. We will test the ability of three neuroimaging biomarkers in predicting early relapse. First, subjects will undergo an emotion regulation paradigm, in which they are instructed to experience or regulate their response to emotional images from the IAPS dataset. The experiencing and the regulation parts of the task will be both separately and jointly assessed to predict relapse. Several versions of the former have established validity in predicting response to treatment. The latter has been suggested to be one important characteristic of depression vulnerability. Second, subjects will undergo an established planning task that measures the impact of aversive outcomes on planning. This will be slightly modified to additionally quantify helplessness. All subjects will undergo scanning twice, and will be divided into two groups of equal size. In group 1W2, scan 1 will occur just prior to medication withdrawal, and scan 2 between 5-20 ADM half-lives after withdrawal. In group 12W, both scans will occur before withdrawal: scan 1 approx 5-20 ADM half-lives before, and scan 2 just prior to withdrawal. We will use scan 1 as the main predictor for relapse. We will use the interaction between groups and scans to examine the effect of medication withdrawal. In a subsidiary analysis, we will also use changes between scan 1 and 2 in group 1W2 to predict relapse. In all cases, we will test for incremental predictive power above and beyond clinically available measurements.
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