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Biomarker Investigation Using Multiple Brain Measures from MRI Through Explainable Artificial Intelligence in Alzheimer’s Disease Classification
Davide Coluzzi, Valentina Bordin, Massimo Walter Rivolta, Igor Fortel, Liang Zhan, Alex Leow, Giuseppe Baselli. Bioengineering, 2025-01-17
Topic: Amyloid, tau and blood-test diagnosis
As the leading cause of dementia worldwide, Alzheimer’s Disease (AD) has prompted significant interest in developing Deep Learning (DL) approaches for its classification. However, it currently remains unclear whether these models rely on established biological indicators. This work compares a novel DL model using structural connectivity (namely, BC-GCN-SE adapted from functional connectivity tasks) with an established model using structural magnetic resonance imaging (MRI) scans (namely, ResNet18). Unlike most studies primarily focusing on performance, our work places explainability at the forefront. Specifically, we define a novel Explainable Artificial Intelligence (XAI) metric, based on gradient-weighted class activation mapping. Its aim is quantitatively measuring how effectively these models fare against established AD biomarkers in their decision-making. The XAI assessment was conducted across 132 brain parcels. Results were compared to AD-relevant regions to measure adherence to domain knowledge. Then, differences in explainability patterns between the two models were assessed to explore the insights offered by each piece of data (i.e., MRI vs. connectivity). Classification performance was satisfactory in terms of both the median true positive (ResNet18: 0.817, BC-GCN-SE: 0.703) and true negative rates (ResNet18: 0.816; BC-GCN-SE: 0.738). Statistical tests (p < 0.05) and ranking of the 15% most relevant parcels revealed the involvement of target areas: the medial temporal lobe for ResNet18 and the default mode network for BC-GCN-SE. Additionally, our findings suggest that different imaging modalities provide complementary information to DL models. This lays the foundation for bioengineering advancements in developing more comprehensive and trustworthy DL models, potentially enhancing their applicability as diagnostic support tools for neurodegenerative diseases.
Key findings
- As the leading cause of dementia worldwide, Alzheimer’s Disease (AD) has prompted significant interest in developing Deep Learning (DL) approaches for its classification.
- Results were compared to AD-relevant regions to measure adherence to domain knowledge.
- Statistical tests (p < 0.05) and ranking of the 15% most relevant parcels revealed the involvement of target areas: the medial temporal lobe for ResNet18 and the default mode network for BC-GCN-SE.
- Additionally, our findings suggest that different imaging modalities provide complementary information to DL models.
Why it matters
Two deep learning models classified Alzheimer's disease from MRI data with satisfactory performance, and an explainability analysis showed they relied on brain regions relevant to Alzheimer's disease. This is a classification study and does not test treatment, cure, or prevention.
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Study details
- Evidence strengthLow
How this study scored 43/100
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 (Bioengineering) |
| Journal's citation record | 1 / 4 | Bioengineering: journal h-index 100 (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 | 3 / 7 | Cited 19 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 6.3 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 4 / 6 | Cited 12.1× 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 19 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Giuseppe Baselli: h-index 46 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | University of Pittsburgh: institution h-index 1,470 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 4 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 39851356) |
| Free to read | 2 / 2 | Free to read |
| Total | 43 / 100 | — |
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Read the original
- DOI: 10.3390/bioengineering12010082
- PubMed: 39851356
- Original source →
- Free full text: Bioengineering →
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. The record names 7 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.
APA 7th
Coluzzi, D., Bordin, V., Rivolta, M. W., Fortel, I., Zhan, L., Leow, A., et al. (2025). Biomarker Investigation Using Multiple Brain Measures from MRI Through Explainable Artificial Intelligence in Alzheimer’s Disease Classification. Bioengineering. https://doi.org/10.3390/bioengineering12010082
Vancouver
Coluzzi D, Bordin V, Rivolta MW, Fortel I, Zhan L, Leow A, et al. Biomarker Investigation Using Multiple Brain Measures from MRI Through Explainable Artificial Intelligence in Alzheimer’s Disease Classification. Bioengineering. 2025. doi:10.3390/bioengineering12010082. PMID: 39851356.
BibTeX
@article{coluzzi2025-39851356,
author = {Coluzzi, Davide and Bordin, Valentina and Rivolta, Massimo Walter and Fortel, Igor and Zhan, Liang and Leow, Alex and Baselli, Giuseppe},
title = {{Biomarker Investigation Using Multiple Brain Measures from MRI Through Explainable Artificial Intelligence in Alzheimer’s Disease Classification}},
journal = {Bioengineering},
year = {2025},
doi = {10.3390/bioengineering12010082},
pmid = {39851356},
url = {https://doi.org/10.3390/bioengineering12010082}
}
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here.