A polygenic-informed approach to a predictive EEG signature empowers antidepressant treatment prediction: A proof-of-concept study

Hannah Meijs*, Amourie Prentice, Bochao D. Lin, Bieke De Wilde, Jan Van Hecke, Peter Niemegeers, Kristel van Eijk, Jurjen J. Luykx, Martijn Arns

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

15 Downloads (Pure)


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, 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.

Original languageEnglish
Pages (from-to)49-60
Number of pages12
JournalEuropean Neuropsychopharmacology
Publication statusPublished - Sept 2022


  • antidepressant
  • EEG
  • MDD
  • Prediction
  • PRS


Dive into the research topics of 'A polygenic-informed approach to a predictive EEG signature empowers antidepressant treatment prediction: A proof-of-concept study'. Together they form a unique fingerprint.

Cite this