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Exponential random graph-based eXplainable Artificial Intelligence for Alzheimer disease
Nicola Amoroso, Ester Pantaleo, Marianna La Rocca, Loredana Bellantuono, Saverio Pascazio, Sabina Tangaro, Alfonso Monaco, R. Bellotti. Physical review. E, 2026-01-06
Topic: Amyloid, tau and blood-test diagnosis
The use of statistical physics models to investigate real-world networks and reveal their underlying dynamics has shown promising results and acquired increasing attention. Here, we show how exponential random-graph (ERG) models can be suitably adopted to characterize how Alzheimer's disease (AD) affects brain connectivity. Magnetic-resonance imaging (MRI) of the brain was used to define a brain connectivity network whose nodes are the different brain regions, and the links indicate the pairwise structural relationships. Based on T1-weighted MRI brain scans of 126 normal controls (NC) and 92 AD patients, ERGs were able to outline both "global" and "local" disease patterns. Our findings demonstrate that ERGs accurately highlight how AD affects brain connectivity reaching an overall classification accuracy of 0.82±0.08. Besides, ERGs outline which regions of the brain are the most affected by the disease, thus proving to be a formidable instrument also to investigate the disease pathological mechanisms; more importantly, as these effects are evaluated at patient level, they can be exploited to design innovative diagnosis support systems or to provide a novel explainable framework for decision support systems. Finally, thanks to its generality, the approach proposed in this study paves the way for further applications and investigations inquiring into the use of ERGs for other diseases and different data sources or the use of alternative models.
Key findings
- The use of statistical physics models to investigate real-world networks and reveal their underlying dynamics has shown promising results and acquired increasing attention.
- Our findings demonstrate that ERGs accurately highlight how AD affects brain connectivity reaching an overall classification accuracy of 0.82±0.08.
Why it matters
Using T1-weighted MRI scans from 126 normal controls and 92 Alzheimer's patients, this study applied exponential random graph models to brain connectivity networks and reached an overall classification accuracy of 0.82±0.08. The authors say the approach could support diagnosis and help investigate disease mechanisms, but it is a diagnostic and explanatory tool, not a treatment, cure or prevention.
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Study details
- Evidence strengthLow
How this study scored 32/100
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 (Physical review. E) |
| Journal's citation record | 1 / 4 | Physical review. E: journal h-index 118 (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 | 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 | 0 / 7 | Cited 0 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.0 citations a year over 2 year(s) since 2026 |
| Impact for its field and year | 0 / 6 | Cited 0.0× 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 | 0 / 4 | Cited 0 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author R. Bellotti: h-index 68 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Istituto Nazionale di Fisica Nucleare, Sezione di Bari: institution h-index 643 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 41715836) |
| Free to read | 2 / 2 | Free to read |
| Total | 32 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
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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 8 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
Amoroso, N., Pantaleo, E., La Rocca, M., Bellantuono, L., Pascazio, S., Tangaro, S., et al. (2026). Exponential random graph-based eXplainable Artificial Intelligence for Alzheimer disease. Physical review. E. https://doi.org/10.1103/dkn2-9b2t
Vancouver
Amoroso N, Pantaleo E, La Rocca M, Bellantuono L, Pascazio S, Tangaro S, et al. Exponential random graph-based eXplainable Artificial Intelligence for Alzheimer disease. Physical review. E. 2026. doi:10.1103/dkn2-9b2t. PMID: 41715836.
BibTeX
@article{amoroso2026-41715836,
author = {Amoroso, Nicola and Pantaleo, Ester and La Rocca, Marianna and Bellantuono, Loredana and Pascazio, Saverio and Tangaro, Sabina and Monaco, Alfonso and Bellotti, R.},
title = {{Exponential random graph-based eXplainable Artificial Intelligence for Alzheimer disease}},
journal = {Physical review. E},
year = {2026},
doi = {10.1103/dkn2-9b2t},
pmid = {41715836},
url = {https://doi.org/10.1103/dkn2-9b2t}
}
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here.