Literature DB >> 31400570

Depression biomarkers using non-invasive EEG: A review.

Fernando Soares de Aguiar Neto1, João Luís Garcia Rosa2.   

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.

Entities:  

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

Mesh:

Substances:

Year:  2019        PMID: 31400570     DOI: 10.1016/j.neubiorev.2019.07.021

Source DB:  PubMed          Journal:  Neurosci Biobehav Rev        ISSN: 0149-7634            Impact factor:   8.989


  23 in total

1.  Effectiveness of behavioral activation for depression treatment in medical students: Study protocol for a quasi-experimental design.

Authors:  Alejandro Domínguez Rodríguez; Gustavo Iván Martinez-Maqueda; Paulina Arenas Landgrave; Sofía Cristina Martínez Luna; Flor Rocío Ramírez-Martínez; Jasshel Teresa Salinas Saldivar
Journal:  SAGE Open Med       Date:  2020-07-27

2.  Efficacy and Safety of tDCS and tACS in Treatment of Major Depressive Disorder: A Randomized, Double-Blind, Factorial Placebo-Controlled Study Design.

Authors:  Yuxin Huang; Linjie Shen; Jia Huang; Xianrong Xu; Yong Wang; Hua Jin
Journal:  Neuropsychiatr Dis Treat       Date:  2021-05-12       Impact factor: 2.570

Review 3.  Asymmetry in the Central Nervous System: A Clinical Neuroscience Perspective.

Authors:  Annakarina Mundorf; Jutta Peterburs; Sebastian Ocklenburg
Journal:  Front Syst Neurosci       Date:  2021-12-14

4.  Spontaneous transient states of fronto-temporal and default-mode networks altered by suicide attempt in major depressive disorder.

Authors:  Siqi Zhang; Vladimir Litvak; Shui Tian; Zhongpeng Dai; Hao Tang; Xinyi Wang; Zhijian Yao; Qing Lu
Journal:  Eur Arch Psychiatry Clin Neurosci       Date:  2022-01-28       Impact factor: 5.760

Review 5.  Neuroimaging Biomarkers of New-Onset Psychiatric Disorders Following Traumatic Brain Injury.

Authors:  Andrew R Mayer; Davin K Quinn
Journal:  Biol Psychiatry       Date:  2021-06-12       Impact factor: 13.382

6.  A Deep Learning Approach for Mild Depression Recognition Based on Functional Connectivity Using Electroencephalography.

Authors:  Xiaowei Li; Rong La; Ying Wang; Bin Hu; Xuemin Zhang
Journal:  Front Neurosci       Date:  2020-04-01       Impact factor: 4.677

Review 7.  Pathogenetical and Neurophysiological Features of Patients with Autism Spectrum Disorder: Phenomena and Diagnoses.

Authors:  Yunho Jin; Jeonghyun Choi; Seunghoon Lee; Jong Won Kim; Yonggeun Hong
Journal:  J Clin Med       Date:  2019-10-02       Impact factor: 4.241

8.  The Changes of qEEG Approximate Entropy during Test of Variables of Attention as a Predictor of Major Depressive Disorder.

Authors:  Shao-Tsu Chen; Li-Chi Ku; Shaw-Ji Chen; Tsu-Wang Shen
Journal:  Brain Sci       Date:  2020-11-07

9.  Deep-Asymmetry: Asymmetry Matrix Image for Deep Learning Method in Pre-Screening Depression.

Authors:  Min Kang; Hyunjin Kwon; Jin-Hyeok Park; Seokhwan Kang; Youngho Lee
Journal:  Sensors (Basel)       Date:  2020-11-15       Impact factor: 3.576

10.  Resting-state EEG datasets of adolescents with mild, minimal, and moderate depression.

Authors:  Saravut Rachamanee; Peera Wongupparaj
Journal:  BMC Res Notes       Date:  2021-07-02
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