SYSTEMATIC REVIEW article
Front. Neurosci.
Sec. Translational Neuroscience
Volume 19 - 2025 | doi: 10.3389/fnins.2025.1595221
This article is part of the Research TopicTranslational applications of neuroimaging, volume IIView all 3 articles
Beyond the Label "Major Depressive Disorder" -Detailed Characterization of Study Population Matters for EEG-Biomarker Research
Provisionally accepted
Roman Mähler1,2
Alexandra Reichenbach1,2*- 1Center for Machine Learning, Heilbronn University, Heilbronn, Germany
- 2Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Baden-Württemberg, Germany
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Major Depressive Disorder (MDD) is a prevalent, multi-faceted psychiatric disorder influenced by a plethora of physiological and environmental factors. Neuroimaging biomarkers such as diagnosis support systems based on electroencephalography (EEG) recordings have the potential to substantially improve its diagnostic procedure. Research on these biomarkers, however, provides inconsistent findings regarding the robustness of specific markers. One potential source of these contradictions that is frequently neglected may arise from the variability in study populations. This study systematically reviews 66 original studies from the last five years that investigate resting-state EEG-biomarker for MDD detection or diagnosis. The study populations are compared regarding demographic factors, diagnostic procedures and medication, as well as neuropsychological characteristics. Furthermore, we investigate the impact these factors have on the biomarkers, if they were included in the analysis. Finally, we provide further insights into the impact of diagnostic choices and the heterogeneity of a study population based on exploratory analyses in two publicly available data sets. We find indeed a large variability in the study populations with respect to all factors included in the review. Furthermore, these factors are often neglected in analyses even though the studies that include them tend to find effects. In light of the variability in diagnostic procedures and heterogeneity in neuropsychological characteristics of the study populations, we advocate for more differentiated target variables in biomarker research then simply MDD and healthy control. Furthermore, the study populations need to be more extensively described and analyses need to include this information in order to provide comparable findings.
Keywords: Major Depressive Disorder, diagnosis, Label, Electroencephalography, biomarker, Study population
Received: 17 Mar 2025; Accepted: 28 May 2025.
Copyright: © 2025 Mähler and Reichenbach. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Alexandra Reichenbach, Center for Machine Learning, Heilbronn University, Heilbronn, Germany
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.