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EEG microstate complexity for aiding early diagnosis of Alzheimer’s disease

Tait, Luke, Tamagnini, Francesco, Stothart, George, Barvas, Edoardo, Monaldini, Chiara, Frusciante, Roberto, Volpini, Micro, Guttmann, Susanna, Coulthard, Elizabeth, Brown, Jon T., Kazanina, Nina and Goodfellow, Marc 2020. EEG microstate complexity for aiding early diagnosis of Alzheimer’s disease. Scientific Reports 10 , 17627. 10.1038/s41598-020-74790-7

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Abstract

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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Psychology
Cardiff University Brain Research Imaging Centre (CUBRIC)
Additional Information: This article is licensed under a Creative Commons Attribution 4.0 International License
Publisher: Nature Publishing Group
ISSN: 2045-2322
Date of First Compliant Deposit: 20 October 2020
Date of Acceptance: 5 October 2020
Last Modified: 10 Nov 2020 15:01
URI: http://orca.cf.ac.uk/id/eprint/135757

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