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Muti-band Morlet mutual information functional connectivity for classifying Alzheimer's disease and frontotemporal dementia with a deep learning technique
Mingkan Shen, Peng Paul Wen, Bo Song, Yan Li. Computers in Biology and Medicine, 2025-09-05
Topic: Amyloid, tau and blood-test diagnosis
Accurate differentiation of Alzheimer's disease (AD), frontotemporal dementia (FTD), and healthy control (HC) is critical for early diagnosis and intervention of brain disorders. This study introduces a deep learning framework that leverages electroencephalography (EEG)-derived multiband functional connectivity (FC) features. Multiband Morlet wavelet mutual information (MMMIFC) was utilized to generate high-resolution FC matrices across 1-20 Hz, which were subsequently processed by a 3D convolutional neural network (3D-CNN) based on a modified VGG architecture. The proposed model achieved classification accuracies of 90.77 % for AD vs HC and 90.38 % for FTD vs HC, with sensitivity and specificity of 88.89 % and 93.10 % for AD vs HC, and 86.96 % and 93.10 % for FTD vs HC, respectively. Beyond classification performance, the analysis identified distinct EEG-based biomarkers within the default mode network. In AD analysis, global efficiency, diffusion efficiency, and clustering coefficient were consistently reduced in the delta and theta bands, reflecting disrupted low-frequency network integration and the theta band presents the most prominent group differences between AD and HC. In FTD analysis, graph theory metrics were also reduced in the delta and theta bands, with the delta band showing the most pronounced group differences compared to HC.
Key findings
- In FTD analysis, graph theory metrics were also reduced in the delta and theta bands, with the delta band showing the most pronounced group differences compared to HC.
Why it matters
A deep learning framework using EEG functional connectivity achieved about 90% accuracy in distinguishing Alzheimer's disease and frontotemporal dementia from healthy controls. This is a diagnostic classification study and does not provide a treatment, cure, or prevention.
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Study details
- Evidence strengthLow
How this study scored 38/100
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. We have no data on senior author's track record, so that was left out: it earned 36 of the 96 points we could judge it on, which is 38 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Computers in Biology and Medicine) |
| Journal's citation record | 2 / 4 | Computers in Biology and Medicine: journal h-index 189 (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 | 1 / 7 | Cited 3 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 1.0 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 2 / 6 | Cited 1.7× 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 | 1 / 4 | Cited 3 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | 2 / 3 | The University of Queensland: institution h-index 1,516 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40902468) |
| Free to read | 2 / 2 | Free to read |
| Points | 36 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 36 of the 96 points we could judge it on is 38 out of 100. |
| Total | 38 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1016/j.compbiomed.2025.111041
- PubMed: 40902468
- Original source →
- Free full text: Computers in Biology and Medicine →
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
Shen, M., Wen, P. P., Song, B., & Li, Y. (2025). Muti-band Morlet mutual information functional connectivity for classifying Alzheimer's disease and frontotemporal dementia with a deep learning technique. Computers in Biology and Medicine. https://doi.org/10.1016/j.compbiomed.2025.111041
Vancouver
Shen M, Wen PP, Song B, Li Y. Muti-band Morlet mutual information functional connectivity for classifying Alzheimer's disease and frontotemporal dementia with a deep learning technique. Computers in Biology and Medicine. 2025. doi:10.1016/j.compbiomed.2025.111041. PMID: 40902468.
BibTeX
@article{shen2025-40902468,
author = {Shen, Mingkan and Wen, Peng Paul and Song, Bo and Li, Yan},
title = {{Muti-band Morlet mutual information functional connectivity for classifying Alzheimer's disease and frontotemporal dementia with a deep learning technique}},
journal = {Computers in Biology and Medicine},
year = {2025},
doi = {10.1016/j.compbiomed.2025.111041},
pmid = {40902468},
url = {https://doi.org/10.1016/j.compbiomed.2025.111041}
}
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