Can Polygenic-Informed EEG Biomarkers Predict Differential Antidepressant Treatment Response? an EEG Stratification Marker for Rtms and Sertraline

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 biomarkers may help predict differential antidepressant treatment response. Using a polygenic-informed electroencephalography (EEG) data-driven, data-reduction approach, we identify a functional brain network that is sex-specifically associated with polygenic risk for MDD in psychiatric patients (N=1,123). Subsequently, we demonstrate the utility of this network in predicting response to transcranial magnetic stimulation (TMS) and antidepressant medication in two independent datasets (N=196 and N=1,008). A simulation aimed at stratifying patients to TMS, sertraline or escitalopram/venlafaxine based on only this EEG component yields up to >30% improved remission rates. Overall, our findings highlight the power and utility of a combined polygenic and neurophysiological approach in the search for clinically-relevant biomarkers in psychiatric disorders.