Citation

de Aguiar Neto FS, Rosa JLG. Neurosci. Biobehav. Rev. 2019; 105: 83-93.

Affiliation

Institute of Mathematical and Computer Sciences, University of São Paulo, Av. Trabalhador São Carlense, 400, Centro, São Carlos, SP 13566-590, Brazil. Electronic address: joaoluis@icmc.usp.br.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.neubiorev.2019.07.021

PMID

31400570

Abstract

Depression is a serious neurological disorder characterized by strong loss of interest, possibly leading to suicide. According to the World Health Organization, more than 300 million people worldwide suffer from this disorder, being the leading cause of disability. The advancements in electroencephalography (EEG) make it a powerful tool for non-invasive studies on neurological disorders including depression. Scientific community has used EEG to better understand the mechanisms behind the disorder and find biomarkers, which are characteristics that can be precisely measured in order to identify or diagnose a disorder. This work presents a systematic mapping of recent studies ranging from 2014 to the end of 2018 which use non-invasive EEG to detect depression biomarkers. Our research has analyzed more than 250 articles and we discuss the findings and promising biomarkers of 42 studies, finding that the depressed brain appear to have a more random network structure, also finding promising features for diagnostic, such as, gamma band and signal complexity; among others which may detect specific depression-related symptoms such as suicidal ideation.

Copyright © 2019 Elsevier Ltd. All rights reserved.

Language: en

Keywords

Biomarkers; Depression; Diagnosis; Non-invasive EEG; Review