Reproducibility of electroencephalography alpha band biomarkers for diagnosis of major depressive disorder

Major depressive disorder (MDD) and other psychiatric diseases can greatly benefit from objective decision support in diagnosis and therapy. Machine learning approaches based on electroencephalography (EEG) have the potential to serve as low-cost decision support systems. Despite the successful demonstration of this approach, contradictory findings regarding the diagnostic value of those biomarkers hamper their deployment in a clinical setting. Therefore, the reproducibility and robustness of these biomarkers needs to be established first. We employ a multiverse analysis to systematically investigate variations in five data processing steps, which may be one source of contradictory findings. These steps are normalization, time-series segment length, biomarker from the alpha band, aggregation, and classification algorithm. For replicability of our results, we utilize two publicly available EEG data sets with eyes-closed resting-state data containing 16/19 MDD patients and 14/14 healthy control subjects. The diagnostic classifiers range from chance level up to 85%, dependent on dataset and combination of processing steps. We find a large influence of choice of processing steps and their combinations. However, only the biomarker has an overall significant effect on both datasets. We find one biomarker candidate that has shown a robust and reproducible high performance for MDD diagnostic support, the relative centroid frequency. Overall, the replicability of our findings with the two datasets is rather inconsistent. This study is a showcase for the advantages of employing a multiverse approach in EEG data analysis and advocates for larger, well-curated data sets to further neuroscience research that can be translated to clinical practice.