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.
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 differential antidepressant treatment response. Using a polygenic-informed electroencephalography (EEG) data-driven, data-reduction approach, we identify a functional brain network in a large cohort of predominantly psychiatric patients (N=1,123), and discover that this network is sex-specifically associated with polygenic liability to psychiatric illness. Subsequently, we demonstrate the utility of this network in predicting response to repetitive transcranial magnetic stimulation (rTMS) and antidepressant medication in two independent datasets (N=196 and N=1,008). A stratification model aimed at stratifying patients to rTMS or sertraline based on only this EEG component yields improved remission rates varying from 22% to 39%. 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.
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