A polygenic-informed approach to a predictive EEG signature empowers antidepressant treatment prediction: A proof-of-concept study
Meijs, Hannah; Prentice, Amourie; Lin, Bochao D.; De Wilde, Bieke; Van Hecke, Jan; Niemegeers, Peter; van Eijk, Kristel; Luykx, Jurjen J.; Arns, Martijn
(2022) European Neuropsychopharmacology, volume 62, pp. 49 - 60
(Article)
Abstract
The treatment of major depressive disorder (MDD) is hampered by low chances of treatment response in each treatment step, which is partly due to a lack of firmly established outcome-predictive biomarkers. Here, we hypothesize that polygenic-informed EEG signatures may help predict antidepressant treatment response. Using a polygenic-informed electroencephalography (EEG) data-driven,
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data-reduction approach, we identify a brain network in a large cohort (N=1,123), and discover it is sex-specifically (male patients, N=617) associated with polygenic risk score (PRS) of antidepressant response. Subsequently, we demonstrate in three independent datasets the utility of the network in predicting response to antidepressant medication (male, N=232) as well as repetitive transcranial magnetic stimulation (rTMS) and concurrent psychotherapy (male, N=95). This network significantly improves a treatment response prediction model with age and baseline severity data (area under the curve, AUC=0.623 for medicaton; AUC=0.719 for rTMS). A predictive model for MDD patients, aimed at increasing the likelihood of being a responder to antidepressants or rTMS and concurrent psychotherapy based on only this network, yields a positive predictive value (PPV) of 69% for medication and 77% for rTMS. Finally, blinded out-of-sample validation of the network as predictor for psychotherapy response in another independent dataset (male, N=50) results in a within-subsample response rate of 50% (improvement of 56%). Overall, the findings provide a first proof-of-concept of a combined genetic and neurophysiological approach in the search for clinically-relevant biomarkers in psychiatric disorders, and should encourage researchers to incorporate genetic information, such as PRS, in their search for clinically relevant neuroimaging biomarkers.
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Keywords: antidepressant, EEG, LORETA, MDD, Prediction, PRS, Pharmacology, Neurology, Clinical Neurology, Psychiatry and Mental health, Biological Psychiatry, Pharmacology (medical)
ISSN: 0924-977X
Publisher: Elsevier
Note: Funding Information: Funding for this study was provided by a personal UMC Utrecht Brain Center Rudolf Magnus Young Talent Fellowship (H150) to JL. We acknowledge Noralie Krepel, Vera Kruiver, Rosalinde van Ruth, Marleen Stam, Myrthe van Eerdt, Dagmar Timmers for collecting the data used in the rTMS study and we thank Edwin van Dellen and Guido van Wingen for earlier feedback on the manuscript and for advising on and critical appraisal of the methods and results. We acknowledge the iSPOT-D Investigators Group, the contributions of iSPOT-D principal investigators at each site and the central management team (Claire Day, Donna Palmer & Evian Gordon). The data that support the findings of this study are available from the corresponding author, MA, via https://brainclinics.com/resources/ or on reasonable request. Funding Information: Funding for this study was provided by a personal UMC Utrecht Brain Center Rudolf Magnus Young Talent Fellowship (H150) to JL. Publisher Copyright: © 2022 The Authors
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