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49/100

✓ Peer-reviewed alzhub usefulness score 49 out of 100. Mild cognitive impairment

Topic: Amyloid, tau and blood-test diagnosis

Alzheimer's disease (AD) is a devastating disorder of increasing prevalence in modern society. Mild cognitive impairment (MCI) is considered a transitional stage between normal aging and AD; however, not all subjects with MCI progress to AD. Prediction of conversion to AD at an early stage would enable an earlier, and potentially more effective, treatment of AD. Electroencephalography (EEG) biomarkers would provide a non-invasive and relatively cheap screening tool to predict conversion to AD; however, traditional EEG biomarkers have not been considered accurate enough to be useful in clinical practice. Here, we aim to combine the information from multiple EEG biomarkers into a diagnostic classification index in order to improve the accuracy of predicting conversion from MCI to AD within a 2-year period. We followed 86 patients initially diagnosed with MCI for 2 years during which 25 patients converted to AD. We show that multiple EEG biomarkers mainly related to activity in the beta-frequency range (13-30 Hz) can predict conversion from MCI to AD. Importantly, by integrating six EEG biomarkers into a diagnostic index using logistic regression the prediction improved compared with the classification using the individual biomarkers, with a sensitivity of 88% and specificity of 82%, compared with a sensitivity of 64% and specificity of 62% of the best individual biomarker in this index. In order to identify this diagnostic index we developed a data mining approach implemented in the Neurophysiological Biomarker Toolbox (http://www.nbtwiki.net/). We suggest that this approach can be used to identify optimal combinations of biomarkers (integrative biomarkers) also in other modalities. Potentially, these integrative biomarkers could be more sensitive to disease progression and response to therapeutic intervention.

Cite this study APA, Vancouver, BibTeX · download .bib or .ris

APA 7th

Poil, S. S., de Haan, W., van der Flier, W. M., Mansvelder, H. D., Scheltens, P., & Linkenkaer-Hansen, K. (2013). Integrative EEG biomarkers predict progression to Alzheimer's disease at the MCI stage. Frontiers in aging neuroscience. https://doi.org/10.3389/fnagi.2013.00058

Vancouver

Poil SS, de Haan W, van der Flier WM, Mansvelder HD, Scheltens P, Linkenkaer-Hansen K. Integrative EEG biomarkers predict progression to Alzheimer's disease at the MCI stage. Frontiers in aging neuroscience. 2013. doi:10.3389/fnagi.2013.00058. PMID: 24106478.

BibTeX

@article{poil2013-24106478,
  author  = {Poil, S. S. and de Haan, W. and van der Flier, W. M. and Mansvelder, H. D. and Scheltens, P. and Linkenkaer-Hansen, K.},
  title   = {{Integrative EEG biomarkers predict progression to Alzheimer's disease at the MCI stage}},
  journal = {Frontiers in aging neuroscience},
  year    = {2013},
  doi     = {10.3389/fnagi.2013.00058},
  pmid    = {24106478},
  url     = {https://doi.org/10.3389/fnagi.2013.00058}
}

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Key findings

Importantly, by integrating six EEG biomarkers into a diagnostic index using logistic regression the prediction improved compared with the classification using the individual biomarkers, with a sensitivity of 88% and specificity of 82%, compared with a sensitivity of 64% and specificity of 62% of the best individual biomarker in this index.
We suggest that this approach can be used to identify optimal combinations of biomarkers (integrative biomarkers) also in other modalities.

Why it matters

In this study of 86 patients with mild cognitive impairment followed for two years, combining six EEG biomarkers into a diagnostic index predicted conversion to Alzheimer's disease with 88% sensitivity and 82% specificity. This suggests EEG could serve as a non-invasive and relatively cheap screening tool, but it addresses prediction, not treatment or cure.

Study details

Participants86
Evidence strengthLow

How this study scored 49/100

Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (Frontiers in aging neuroscience)
Journal's citation record	2 / 4	Frontiers in Aging Neuroscience: journal h-index 174 (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	1 / 5	Rated “low” in our summary
Peer-review validation	10 / 10	Peer-reviewed. Published as an article in a named peer-reviewed journal.
How recent	1 / 5	2013 — within 20 year(s) of the newest study here
Sample size	1 / 4	86 participants
Effect reported	0 / 3	No effect size reported — scores zero (not stated in the record — scored 0)
Studied in people	3 / 3	Studied in people (participant count stated)
Randomised / blinded	0 / 2	No randomisation or blinding stated — scores zero (not stated in the record — scored 0)
Times cited	5 / 7	Cited 209 times (OpenAlex, 2026-10-10)
Citations per year	3 / 5	13.9 citations a year over 15 year(s) since 2013
Impact for its field and year	3 / 6	Cited 6.2× the average for papers of its field and year (OpenAlex FWCI)
Cited by studies here	2 / 6	Cited by 8 other studies in this library
Still being cited	3 / 4	Cited 52 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	2 / 4	Last-listed author Klaus Linkenkaer‐Hansen: h-index 40 (OpenAlex, whole career)
Institution's track record	1 / 3	Vrije Universiteit Amsterdam: institution h-index 1,014 (OpenAlex)
Multi-centre collaboration	1 / 3	OpenAlex lists 2 institution(s) in 1 country
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 24106478)
Free to read	2 / 2	Free to read
Total	49 / 100	—

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