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52/100

✓ Peer-reviewed alzhub usefulness score 52 out of 100. Lewy body dementia

Topic: Amyloid, tau and blood-test diagnosis

Abstract Alzheimer’s disease (AD) and frontotemporal dementia (FTD) are two major causes of dementia, with overlapping clinical and pathophysiological characteristics. We investigated whether resting-state EEG functional connectivity could distinguish AD, FTD, and healthy controls (HC) using a stacked ensemble learning approach. A publicly available dataset (openneuro.org/datasets/ds004504) was used to extract multiple connectivity metrics across frequency bands. Base classifiers were trained using the Fisher’s Geodesic Minimum Distance to Mean (FgMDM) approach with Euclidean or Riemannian distance metrics, and a meta-classifier was trained on their cross-validated predictions. Using leave-one-subject-out cross-validation, the stacked model achieved ROC-AUC values of 81.80% and 71.36% for distinguishing AD and FTD from HC, respectively. This was lower than the best single-base classifier performance, where several connectivity metrics achieved ROC-AUC values of 85% or higher for distinguishing AD and FTD. AD-FTD discrimination proved most challenging (ROC-AUC of 65.10%), suggesting overlapping network disruptions that limit separability. Classification performance was highest in the alpha band when distinguishing AD and FTD from HC, whereas the highest performance for distinguishing AD from FTD was observed in the delta band. Overall, these findings highlight the potential of EEG connectivity features for dementia classification and emphasize the importance of careful feature engineering and low-complexity models in the case of small-sample datasets.

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

APA 7th

Mlinarič, T., Van Den Kerchove, A., Barinaga, Z. I., & Van Hulle, M. M. (2026). EEG-based classification of alzheimer’s disease and frontotemporal dementia using functional connectivity. Scientific Reports. https://doi.org/10.1038/s41598-026-35316-9

Vancouver

Mlinarič T, Van Den Kerchove A, Barinaga ZI, Van Hulle MM. EEG-based classification of alzheimer’s disease and frontotemporal dementia using functional connectivity. Scientific Reports. 2026. doi:10.1038/s41598-026-35316-9. PMID: 41513739.

BibTeX

@article{mlinaric2026-41513739,
  author  = {Mlinarič, Tjaša and Van Den Kerchove, Arne and Barinaga, Zoe I. and Van Hulle, Marc M.},
  title   = {{EEG-based classification of alzheimer’s disease and frontotemporal dementia using functional connectivity}},
  journal = {Scientific Reports},
  year    = {2026},
  doi     = {10.1038/s41598-026-35316-9},
  pmid    = {41513739},
  url     = {https://doi.org/10.1038/s41598-026-35316-9}
}

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Key findings

Overall, these findings highlight the potential of EEG connectivity features for dementia classification and emphasize the importance of careful feature engineering and low-complexity models in the case of small-sample datasets.

Why it matters

EEG connectivity features showed potential for classifying Alzheimer's disease and frontotemporal dementia, but AD-FTD discrimination proved most challenging with overlapping network disruptions limiting separability. The findings emphasize the importance of careful feature engineering and low-complexity models for small-sample datasets.

Study details

Effect size81.80%; 71.36%; 65.10%
Evidence strengthLow

How this study scored 52/100

Published in a peer-reviewed journal. Scored zero for study design, sample size, 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	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	5 / 5	2026 — within 1 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	2 / 3	Effect reported as a number, without an interval or test
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	3 / 7	Cited 15 times (OpenAlex, 2026-10-10)
Citations per year	2 / 5	7.5 citations a year over 2 year(s) since 2026
Impact for its field and year	6 / 6	Cited 70.7× the average for papers of its field and year (OpenAlex FWCI)
Cited by studies here	0 / 6	No other study in this library cites it
Still being cited	2 / 4	Cited 15 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	2 / 4	Last-listed author Marc M. Van Hulle: h-index 43 (OpenAlex, whole career)
Institution's track record	3 / 3	Centre National de la Recherche Scientifique: institution h-index 2,511 (OpenAlex)
Multi-centre collaboration	2 / 3	OpenAlex lists 4 institution(s) in 2 countries
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 41513739)
Free to read	2 / 2	Free to read
Total	52 / 100	—

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