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EEG microstate complexity for aiding early diagnosis of Alzheimer's disease
Tait L, Tamagnini F, Stothart G, Barvas E, Monaldini C, Frusciante R, Volpini M, Guttmann S et al.. Scientific reports, 2020-10-19
Topic: Amyloid, tau and blood-test diagnosis
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.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Tait, L., Tamagnini, F., Stothart, G., Barvas, E., Monaldini, C., Frusciante, R., et al. (2020). EEG microstate complexity for aiding early diagnosis of Alzheimer's disease. Scientific reports. https://doi.org/10.1038/s41598-020-74790-7
Vancouver
Tait L, Tamagnini F, Stothart G, Barvas E, Monaldini C, Frusciante R, et al. EEG microstate complexity for aiding early diagnosis of Alzheimer's disease. Scientific reports. 2020. doi:10.1038/s41598-020-74790-7. PMID: 33077823.
BibTeX
@article{tait2020-33077823,
author = {Tait, L. and Tamagnini, F. and Stothart, G. and Barvas, E. and Monaldini, C. and Frusciante, R. and Volpini, M. and Guttmann, S. and others},
title = {{EEG microstate complexity for aiding early diagnosis of Alzheimer's disease}},
journal = {Scientific reports},
year = {2020},
doi = {10.1038/s41598-020-74790-7},
pmid = {33077823},
url = {https://doi.org/10.1038/s41598-020-74790-7}
}
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Key findings
- 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.
Why it matters
EEG microstate complexity was altered in Alzheimer's disease, with slower and less complex microstate transitioning, and combined with a spectral measure it classified Alzheimer's with sensitivity and specificity above 80%. It could predict progression from mild cognitive impairment to Alzheimer's in a small preliminary cohort of 11 participants, so this is early work.
Study details
- Evidence strengthModerate
How this study scored 51/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Scientific reports) |
| Journal's citation record | 3 / 4 | Scientific Reports: journal h-index 463 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 2 / 5 | 2020 — within 10 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 5 / 7 | Cited 166 times (OpenAlex, 2026-10-10) |
| Citations per year | 4 / 5 | 20.8 citations a year over 8 year(s) since 2020 |
| Impact for its field and year | 4 / 6 | Cited 9.0× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 2 other studies in this library |
| Still being cited | 3 / 4 | Cited 93 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Marc Goodfellow: h-index 27 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | University of Bristol: institution h-index 1,260 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 6 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 33077823) |
| Free to read | 2 / 2 | Free to read |
| Total | 51 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1038/s41598-020-74790-7
- PubMed: 33077823
- Original source →
- Free full text: Scientific Reports →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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