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Frequency-Resolved Connectivity Graphs for EEG-Based Classification of Alzheimer and Frontotemporal Dementia
Radwan M, Lind PG, Yazidi A.. bioRxiv, 2026-10-22
Topic: Brain imaging, connectivity and assessment
A bstract In this study, we build EEG based graph model that uses frequency-resolved connectivity features, with Coherence, phase locking value (PLV) and phase lag index (PLI) in five frequency bands as multi-dimensional edge features. The model is used to classify Alzheimer’s disease (AD) and frontotemporal dementia (FTD) against healthy controls (CN). This paper extends our earlier work on FTD [1] with AD vs CN as the main new task, a feature ablation for it, and a joint analysis of both tasks. With leave-one-subject-out (LOSO) validation, the model reaches an accuracy of 81.13% for AD vs CN and 76.06% for FTD vs CN, which is competitive with the baselines reported with the dataset with far fewer parameters (159 and 282 thousand versus 170.5 million). Under the random cross-validation setting used for the DICE-net model, our model has a comparable accuracy for FTD and a lower accuracy for AD. Node and edge saliency maps highlight occipital and temporal electrodes, the Alpha band for node features and the Theta band for edge features, which is consistent with earlier EEG studies. In an ablation on the AD task, using all 15 edge features performed better than using a single connectivity measure or edge features averaged over the bands.
Key findings
- With leave-one-subject-out (LOSO) validation, the model reaches an accuracy of 81.13% for AD vs CN and 76.06% for FTD vs CN, which is competitive with the baselines reported with the dataset with far fewer parameters (159 and 282 thousand versus 170.5 million).
Study details
- Effect size81.13%; 76.06%
- Evidence strengthLow
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 24 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for study design, sample size, studied in people and randomised / blinded because the record does not state it. We have no data on times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 15 of the 62 points we could judge it on, which is 24 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (bioRxiv) |
| Journal's citation record | 0 / 4 | Not a journal article, so the journal's record does not count |
| 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 | 4 / 10 | Preprint — not yet peer-reviewed. Hosted on a preprint server. |
| 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 | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 15 / 62 | Scored on 12 of 20 parts. We have no data for the rest, so they are left out: 15 of the 62 points we could judge it on is 24 out of 100. |
| Total | 24 / 100 | Capped to 16 because this is not a peer-reviewed article. |
What each of these 20 components measures, and what this score is not →
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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
Radwan, M., Lind, P. G., & Yazidi, A. (2026). Frequency-Resolved Connectivity Graphs for EEG-Based Classification of Alzheimer and Frontotemporal Dementia. bioRxiv. https://doi.org/10.64898/2026.10.16.759776
Vancouver
Radwan M, Lind PG, Yazidi A. Frequency-Resolved Connectivity Graphs for EEG-Based Classification of Alzheimer and Frontotemporal Dementia. bioRxiv. 2026. doi:10.64898/2026.10.16.759776.
BibTeX
@article{radwan2026-18f8f9a3,
author = {Radwan, M. and Lind, P. G. and Yazidi, A.},
title = {{Frequency-Resolved Connectivity Graphs for EEG-Based Classification of Alzheimer and Frontotemporal Dementia}},
journal = {bioRxiv},
year = {2026},
doi = {10.64898/2026.10.16.759776},
url = {https://doi.org/10.64898/2026.10.16.759776}
}