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EEG microstate complexity for aiding early diagnosis of Alzheimer’s disease
The dynamics of the resting brain exhibit transitions between a small number of discrete networks, each remaining stable for tens to hundreds of milliseconds. These functional microstates are thought to be the building blocks of spontaneous consciousness. The electroencephalogram (EEG) is a useful t...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7572485/ https://www.ncbi.nlm.nih.gov/pubmed/33077823 http://dx.doi.org/10.1038/s41598-020-74790-7 |
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author | Tait, Luke Tamagnini, Francesco Stothart, George Barvas, Edoardo Monaldini, Chiara Frusciante, Roberto Volpini, Mirco Guttmann, Susanna Coulthard, Elizabeth Brown, Jon T. Kazanina, Nina Goodfellow, Marc |
author_facet | Tait, Luke Tamagnini, Francesco Stothart, George Barvas, Edoardo Monaldini, Chiara Frusciante, Roberto Volpini, Mirco Guttmann, Susanna Coulthard, Elizabeth Brown, Jon T. Kazanina, Nina Goodfellow, Marc |
author_sort | Tait, Luke |
collection | PubMed |
description | The dynamics of the resting brain exhibit transitions between a small number of discrete networks, each remaining stable for tens to hundreds of milliseconds. These functional microstates are thought to be the building blocks of spontaneous consciousness. The electroencephalogram (EEG) is a useful tool for imaging microstates, and EEG microstate analysis can potentially give insight into altered brain dynamics underpinning cognitive impairment in disorders such as Alzheimer’s disease (AD). Since EEG is non-invasive and relatively inexpensive, EEG microstates have the potential to be useful clinical tools for aiding early diagnosis of AD. In this study, EEG was collected from two independent cohorts of probable AD and cognitively healthy control participants, and a cohort of mild cognitive impairment (MCI) patients with four-year clinical follow-up. The microstate associated with the frontoparietal working-memory/attention network was altered in AD due to parietal inactivation. Using a novel measure of complexity, we found microstate transitioning was slower and less complex in AD. When combined with a spectral EEG measure, microstate complexity could classify AD with sensitivity and specificity > 80%, which was tested on an independent cohort, and could predict progression from MCI to AD in a small preliminary test cohort of 11 participants. EEG microstates therefore have potential to be a non-invasive functional biomarker of AD. |
format | Online Article Text |
id | pubmed-7572485 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-75724852020-10-21 EEG microstate complexity for aiding early diagnosis of Alzheimer’s disease Tait, Luke Tamagnini, Francesco Stothart, George Barvas, Edoardo Monaldini, Chiara Frusciante, Roberto Volpini, Mirco Guttmann, Susanna Coulthard, Elizabeth Brown, Jon T. Kazanina, Nina Goodfellow, Marc Sci Rep Article The dynamics of the resting brain exhibit transitions between a small number of discrete networks, each remaining stable for tens to hundreds of milliseconds. These functional microstates are thought to be the building blocks of spontaneous consciousness. The electroencephalogram (EEG) is a useful tool for imaging microstates, and EEG microstate analysis can potentially give insight into altered brain dynamics underpinning cognitive impairment in disorders such as Alzheimer’s disease (AD). Since EEG is non-invasive and relatively inexpensive, EEG microstates have the potential to be useful clinical tools for aiding early diagnosis of AD. In this study, EEG was collected from two independent cohorts of probable AD and cognitively healthy control participants, and a cohort of mild cognitive impairment (MCI) patients with four-year clinical follow-up. The microstate associated with the frontoparietal working-memory/attention network was altered in AD due to parietal inactivation. Using a novel measure of complexity, we found microstate transitioning was slower and less complex in AD. When combined with a spectral EEG measure, microstate complexity could classify AD with sensitivity and specificity > 80%, which was tested on an independent cohort, and could predict progression from MCI to AD in a small preliminary test cohort of 11 participants. EEG microstates therefore have potential to be a non-invasive functional biomarker of AD. Nature Publishing Group UK 2020-10-19 /pmc/articles/PMC7572485/ /pubmed/33077823 http://dx.doi.org/10.1038/s41598-020-74790-7 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Tait, Luke Tamagnini, Francesco Stothart, George Barvas, Edoardo Monaldini, Chiara Frusciante, Roberto Volpini, Mirco Guttmann, Susanna Coulthard, Elizabeth Brown, Jon T. Kazanina, Nina Goodfellow, Marc EEG microstate complexity for aiding early diagnosis of Alzheimer’s disease |
title | EEG microstate complexity for aiding early diagnosis of Alzheimer’s disease |
title_full | EEG microstate complexity for aiding early diagnosis of Alzheimer’s disease |
title_fullStr | EEG microstate complexity for aiding early diagnosis of Alzheimer’s disease |
title_full_unstemmed | EEG microstate complexity for aiding early diagnosis of Alzheimer’s disease |
title_short | EEG microstate complexity for aiding early diagnosis of Alzheimer’s disease |
title_sort | eeg microstate complexity for aiding early diagnosis of alzheimer’s disease |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7572485/ https://www.ncbi.nlm.nih.gov/pubmed/33077823 http://dx.doi.org/10.1038/s41598-020-74790-7 |
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