This thesis aimed to identify transdiagnostic markers from electroencephalography (EEG) data, for predicting treatment response in MDD and ADHD. Hereby, we leveraged large and heterogeneous datasets to capture a broad range of EEG features. Utilizing a data-driven data-reduction method at source level activity, various independent EEG-derived functional brain networks were extracted. Polygenic association analysis was employed to select biologically feasible networks, potentially predictive of treatment outcomes. The results of the proof-of-concept study and follow-up study revealed respectively a slow wave network and posterior alpha network related to age, with sex-specific and medication-specific treatment predictive capabilities for MDD, demonstrating the stratification potential of this innovative approach.
About the author(s)
Hannah Meijs
