Prediction; Neuroimaging; Computational Psychiatry; Cognitive control; Depression; Learned Helplessness; Emotional Regulation; Relapse; Biomarker
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.
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.
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.
Huys Quentin J M, Renz Daniel (2017), A Formal Valuation Framework for Emotions and Their Control., in
Biological Psychiatry, 82, 413-420.
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.
Maia Tiago V, Huys Quentin J M, Frank Michael J (2017), Theory-Based Computational Psychiatry., in
Biological psychiatry, 82, 382-384.
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.
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.
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.
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.
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.
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.
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.
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.