脑电图中包含的信息允许表征神经退行性疾病的认知衰退。

IF 1.6 4区 医学 Q3 CLINICAL NEUROLOGY
Sebastian M Keller, Cornelius Reyneke, Ute Gschwandtner, Peter Fuhr
{"title":"脑电图中包含的信息允许表征神经退行性疾病的认知衰退。","authors":"Sebastian M Keller,&nbsp;Cornelius Reyneke,&nbsp;Ute Gschwandtner,&nbsp;Peter Fuhr","doi":"10.1177/15500594221120734","DOIUrl":null,"url":null,"abstract":"<p><p>Over the last few decades, electroencephalography (EEG) has evolved from being a method that purely relies on visual inspection into a quantitative method. Quantitative EEG, or QEEG, enables the assessment of neurological disorders based on spectral features, dynamic characterizations of EEG resting-state activity, brain connectivity analyzes or quantification of EEG signal complexity. The information contained in EEG is multidimensional: Electrodes, positioned at different scalp locations, provide a spatial dimension to the analysis of EEG while time provides a dynamic dimension: This multidimensional property of EEG makes its quantification a challenging task. In this narrative review we present quantitative models focused on different aspects of EEG: While microstate models focus more on the quantification of the dynamic aspects of EEG, spectral methods, connectivity analysis and entropy based models are more concerned with its spatial aspects. Nevertheless, these diverse approaches have provided neurophysiology based biomarkers, especially for monitoring and predicting the course of various neurodegenerative disorders. However, their translation into clinical practice crucially depends on the ability to automate the analysis of EEG in a user-friendly manner, without compromising on the validity of the provided results. Once this has been accomplished, EEG would provide an inexpensive and widely available method for monitoring disease progression, identifying patients at risk of neurodegeneration-especially before the onset of clinical symptoms, and predicting future cognition. For stratification of patients to clinical trials, EEG would allow shortening the trial duration and lowering the number of necessary participants by identifying patients at risk of fast cognitive decline.</p>","PeriodicalId":10682,"journal":{"name":"Clinical EEG and Neuroscience","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Information Contained in EEG Allows Characterization of Cognitive Decline in Neurodegenerative Disorders.\",\"authors\":\"Sebastian M Keller,&nbsp;Cornelius Reyneke,&nbsp;Ute Gschwandtner,&nbsp;Peter Fuhr\",\"doi\":\"10.1177/15500594221120734\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Over the last few decades, electroencephalography (EEG) has evolved from being a method that purely relies on visual inspection into a quantitative method. Quantitative EEG, or QEEG, enables the assessment of neurological disorders based on spectral features, dynamic characterizations of EEG resting-state activity, brain connectivity analyzes or quantification of EEG signal complexity. The information contained in EEG is multidimensional: Electrodes, positioned at different scalp locations, provide a spatial dimension to the analysis of EEG while time provides a dynamic dimension: This multidimensional property of EEG makes its quantification a challenging task. In this narrative review we present quantitative models focused on different aspects of EEG: While microstate models focus more on the quantification of the dynamic aspects of EEG, spectral methods, connectivity analysis and entropy based models are more concerned with its spatial aspects. Nevertheless, these diverse approaches have provided neurophysiology based biomarkers, especially for monitoring and predicting the course of various neurodegenerative disorders. However, their translation into clinical practice crucially depends on the ability to automate the analysis of EEG in a user-friendly manner, without compromising on the validity of the provided results. Once this has been accomplished, EEG would provide an inexpensive and widely available method for monitoring disease progression, identifying patients at risk of neurodegeneration-especially before the onset of clinical symptoms, and predicting future cognition. For stratification of patients to clinical trials, EEG would allow shortening the trial duration and lowering the number of necessary participants by identifying patients at risk of fast cognitive decline.</p>\",\"PeriodicalId\":10682,\"journal\":{\"name\":\"Clinical EEG and Neuroscience\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2023-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical EEG and Neuroscience\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1177/15500594221120734\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical EEG and Neuroscience","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/15500594221120734","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 1

摘要

在过去的几十年里,脑电图(EEG)已经从一种纯粹依靠视觉检查的方法发展成为一种定量方法。定量脑电图(QEEG)能够基于频谱特征、脑电图静息状态活动的动态特征、脑连通性分析或脑电图信号复杂性的量化来评估神经系统疾病。脑电图所包含的信息是多维的:电极位于头皮的不同位置,为脑电图的分析提供了空间维度,而时间提供了动态维度,脑电图的这种多维性使其量化成为一项具有挑战性的任务。在这篇叙述性综述中,我们提出了专注于脑电图不同方面的定量模型:微观状态模型更侧重于脑电图动态方面的量化,而频谱方法、连通性分析和基于熵的模型更关注脑电图的空间方面。尽管如此,这些不同的方法已经提供了基于神经生理学的生物标志物,特别是用于监测和预测各种神经退行性疾病的病程。然而,它们转化为临床实践的关键取决于以用户友好的方式自动分析脑电图的能力,而不影响所提供结果的有效性。一旦这项工作完成,脑电图将提供一种廉价且广泛可用的方法来监测疾病进展,识别有神经退行性疾病风险的患者,特别是在临床症状出现之前,并预测未来的认知能力。为了分层患者进行临床试验,脑电图可以通过识别有快速认知能力下降风险的患者来缩短试验时间和减少必要的参与者人数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Information Contained in EEG Allows Characterization of Cognitive Decline in Neurodegenerative Disorders.

Over the last few decades, electroencephalography (EEG) has evolved from being a method that purely relies on visual inspection into a quantitative method. Quantitative EEG, or QEEG, enables the assessment of neurological disorders based on spectral features, dynamic characterizations of EEG resting-state activity, brain connectivity analyzes or quantification of EEG signal complexity. The information contained in EEG is multidimensional: Electrodes, positioned at different scalp locations, provide a spatial dimension to the analysis of EEG while time provides a dynamic dimension: This multidimensional property of EEG makes its quantification a challenging task. In this narrative review we present quantitative models focused on different aspects of EEG: While microstate models focus more on the quantification of the dynamic aspects of EEG, spectral methods, connectivity analysis and entropy based models are more concerned with its spatial aspects. Nevertheless, these diverse approaches have provided neurophysiology based biomarkers, especially for monitoring and predicting the course of various neurodegenerative disorders. However, their translation into clinical practice crucially depends on the ability to automate the analysis of EEG in a user-friendly manner, without compromising on the validity of the provided results. Once this has been accomplished, EEG would provide an inexpensive and widely available method for monitoring disease progression, identifying patients at risk of neurodegeneration-especially before the onset of clinical symptoms, and predicting future cognition. For stratification of patients to clinical trials, EEG would allow shortening the trial duration and lowering the number of necessary participants by identifying patients at risk of fast cognitive decline.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Clinical EEG and Neuroscience
Clinical EEG and Neuroscience 医学-临床神经学
CiteScore
5.20
自引率
5.00%
发文量
66
审稿时长
>12 weeks
期刊介绍: Clinical EEG and Neuroscience conveys clinically relevant research and development in electroencephalography and neuroscience. Original articles on any aspect of clinical neurophysiology or related work in allied fields are invited for publication.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信