Discriminative Power of EEG-Based Biomarkers in Major Depressive Disorder: A Systematic Review
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BiomarkersCognitive scienceDepressive subtypesEarly detectionElectroencephalogra-phy (EEG)EEG measuresMajor depressive disorder (MDD).
Greco, C., Matarazzo, O., Cordasco, G., Vinciarelli, A., Callejas, Z., & Esposito, A. (2021). Discriminative power of EEG-based biomarkers in major depressive disorder: A systematic review. IEEE Access. [ 10.1109/ACCESS.2021.3103047]
SponsorshipProject AutoNomous DiscoveRy Of depressIve Disorder Signs (ANDROIDS) through the Program VAnviteLli pEr la RicErca (V:ALERE) 2019 Universita della Campania "Luigi Vanvitelli'' D.R.906del4/10/2019; EU H2020 Research and Innovation Program 769872- 823907; Ministry of Education, Universities and Research (MIUR) D.D.1735
Currently, the diagnosis of major depressive disorder (MDD) and its subtypes is mainly based on subjective assessments and self-reported measures. However, objective criteria as Electroencephalography (EEG) features would be helpful in detecting depressive states at early stages to prevent the worsening of the symptoms. Scientific community has widely investigated the effectiveness of EEG-based measures to discriminate between depressed and healthy subjects, with the aim to better understand the mechanisms behind the disorder and find biomarkers useful for diagnosis. This work offers a comprehensive review of the extant literature concerning the EEG-based biomarkers for MDD and its subtypes, and identify possible future directions for this line of research. Scopus, PubMed and Web of Science databases were researched following PRISMA's guidelines. The initial papers' screening was based on titles and abstracts; then full texts of the identified articles were examined, and a synthesis of findings was developed using tables and thematic analysis. After screening 1871 articles, 76 studies were identified as relevant and included in the systematic review. Reviewed markers include EEG frequency bands power, EEG asymmetry, ERP components, non-linear and functional connectivity measures. Results were discussed in relations to the different EEG measures assessed in the studies. Findings confirmed the effectiveness of those measures in discriminating between healthy and depressed subjects. However, the review highlights that the causal link between EEG measures and depressive subtypes needs to be further investigated and points out that some methodological issues need to be solved to enhance future research in this field.