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Graph Spectral Analysis Using Electroencephalography in Alzheimer Disease and Frontotemporal Dementia Patients
María Paula Bonomini, Eduardo Ghiglioni, Noelia B. Rios. International Journal of Neural Systems, 2025-06-12
Topic: Amyloid, tau and blood-test diagnosis
Graph theory has proven to be useful in studying brain dysfunction in Alzheimer’s disease using MagnetoEncephaloGraphy (MEG) and fMRI signals. However, it has not yet been tested enough with reduced sets of electrodes, as in the 10–20 EEG. In this paper, we applied techniques from the Graph Spectral Analysis (GSA) derived from EEG signals of patients with Alzheimer, Frontotemporal Dementia and control subjects. A collection of global GSA metrics were computed, accounting for general properties of the adjacency or Laplacian matrices. Also, regional GSA metrics were calculated, disentangling centrality measures in five cortical regions (frontal, central, parietal, temporal and occipital). These two sort of measures were then utilized in a binary AD/controls classification problem to test their utility in AD diagnosis and identify most valuable parameters. The Theta band appeared as the most connected and synchronizable rhythm for all three groups. Also, it was the rhythm with most preserved connections among temporal electrodes, exhibiting the shortest average distances among [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text]. In addition, Theta emerged as the rhythm with the highest classification performances based on regional parameters according to a [Formula: see text] cross-validation scheme (mean [Formula: see text], mean [Formula: see text] and mean F1-[Formula: see text]). In general, regional parameters produced better classification performances for most of the rhythms, encouraging further investigation into GSA parameters with refined spatial and functional specificity.
Key findings
- These two sort of measures were then utilized in a binary AD/controls classification problem to test their utility in AD diagnosis and identify most valuable parameters.
- The Theta band appeared as the most connected and synchronizable rhythm for all three groups.
- Also, it was the rhythm with most preserved connections among temporal electrodes, exhibiting the shortest average distances among [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text].
- In addition, Theta emerged as the rhythm with the highest classification performances based on regional parameters according to a [Formula: see text] cross-validation scheme (mean [Formula: see text], mean [Formula: see text] and mean F1-[Formula: see text]).
- In general, regional parameters produced better classification performances for most of the rhythms, encouraging further investigation into GSA parameters with refined spatial and functional specificity.
Why it matters
This study applied graph spectral analysis to EEG signals and found that the Theta band was the most connected and synchronizable rhythm and had the highest classification performance for Alzheimer's disease diagnosis. It suggests a potential diagnostic utility but no treatment or cure.
Study details
- Evidence strengthLow
How this study scored 35/100
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 (International Journal of Neural Systems) |
| Journal's citation record | 1 / 4 | International Journal of Neural Systems: journal h-index 95 (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 | 4 / 5 | 2025 — within 3 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 | 2 / 7 | Cited 6 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 2.0 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 3 / 6 | Cited 3.4× 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 | 1 / 4 | Cited 6 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 0 / 4 | Last-listed author Noelia B. Rios: h-index 4 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Universidad Nacional de La Plata: institution h-index 398 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40624757) |
| Free to read | 2 / 2 | Free to read |
| Total | 35 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Bonomini, M. P., Ghiglioni, E., & Rios, N. B. (2025). Graph Spectral Analysis Using Electroencephalography in Alzheimer Disease and Frontotemporal Dementia Patients. International Journal of Neural Systems. https://doi.org/10.1142/s0129065725500480
Vancouver
Bonomini MP, Ghiglioni E, Rios NB. Graph Spectral Analysis Using Electroencephalography in Alzheimer Disease and Frontotemporal Dementia Patients. International Journal of Neural Systems. 2025. doi:10.1142/s0129065725500480. PMID: 40624757.
BibTeX
@article{bonomini2025-40624757,
author = {Bonomini, María Paula and Ghiglioni, Eduardo and Rios, Noelia B.},
title = {{Graph Spectral Analysis Using Electroencephalography in Alzheimer Disease and Frontotemporal Dementia Patients}},
journal = {International Journal of Neural Systems},
year = {2025},
doi = {10.1142/s0129065725500480},
pmid = {40624757},
url = {https://doi.org/10.1142/s0129065725500480}
}
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