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使用非侵入性 EEG 的抑郁症生物标志物:综述。

Depression biomarkers using non-invasive EEG: A review.

机构信息

Institute of Mathematical and Computer Sciences, University of São Paulo, Av. Trabalhador São Carlense, 400, Centro, São Carlos, SP 13566-590, Brazil.

出版信息

Neurosci Biobehav Rev. 2019 Oct;105:83-93. doi: 10.1016/j.neubiorev.2019.07.021. Epub 2019 Aug 7.

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.

摘要

抑郁症是一种严重的神经障碍,其特征是强烈的兴趣丧失,可能导致自杀。根据世界卫生组织的数据,全球有超过 3 亿人患有这种疾病,是导致残疾的主要原因。脑电图 (EEG) 的进步使其成为研究神经障碍(包括抑郁症)的一种强大的非侵入性工具。科学界已经使用 EEG 来更好地了解该疾病背后的机制,并寻找生物标志物,这些特征可以进行精确测量,以识别或诊断疾病。这项工作对 2014 年至 2018 年底期间使用非侵入性 EEG 来检测抑郁症生物标志物的最近研究进行了系统的映射。我们的研究分析了 250 多篇文章,并讨论了 42 项研究的发现和有前途的生物标志物,研究表明,抑郁大脑的网络结构似乎更随机,也发现了一些有前途的特征,可用于诊断,例如伽马波段和信号复杂度;以及其他可能检测到特定的与抑郁相关的症状,如自杀意念。

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