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Neurocognitive Predictors of Depression Relapse

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Chair: Dr Rudolf Cardinal

Abstract: The burden of depression is to no small part due to its chronic or recurring nature. As such, the maintenance of any treatment gains is of paramount importance. A key step in this process is the decision to discontinue antidepressant medication. However, at present there are no predictors to indicate who can safely discontinue medication. The AIDA study recruited 123 patients who had remitted on antidepressant medication and were intent on discontinuing their medication. Patients were randomized into two groups. Both groups underwent two extensive assessments involving clinical, behavioural, imaging and biochemical assessments, but one group was tested before and after discontinuing antidepressants, while the other was tested twice before discontinuation. Patients were followed up for 6 months to monitor for relapses. 57 healthy, never-depressed matched controls were recruited. Of 104 patients who completed at least one assessment, 84 completed the study, with 34 relapsing during the follow-up. Amongst standard clinical variables, only treatment by non-specialists was robustly associated with relapse (p=0.005), but did not predict relapse out-of-sample. In contrast, several behavioural (effort-related), psychological (brooding rumination, neuroticism) and imaging (EEG alpha asymmetry and task-related fMRI amygdala activation) variables had predictive power, while resting-state connectivity showed some effects of discontinuation. Overall, relapse after antidepressant discontinuation can be predicted by a number of variables. A combination of these may reach an accuracy sufficient to have clinical relevance.

Biography: Dr Huys is a Clinical Associate Professor in Computational Psychiatry at the Division of Psychiatry and the Max Planck UCL Centre for Computational Psychiatry and Ageing Research at University College London, and an Honorary Consultant Psychiatrist with the Camden and Islington NHS Foundation Trust. He started training at Gonville and Caius College, Cambridge University, followed by a MB/PhD at UCL Medical School and the Gatsby Computational Neuroscience Unit (with Peter Dayan). Dr Huys research interest is in Computational psychiatry, with particular focus on developing computational tools to improve patient outcomes in depression and addictive disorders. For detailed biography of Dr Huys, please visit: https://quentinhuys.com/index.html

This talk is part of the Department of Psychiatry & CPFT Thursday Lunchtime Seminar Series series.

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