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Multi-dimensional attention framework for personalized Alzheimer’s disease progression prediction across sporadic and genetic risk cohorts
Song Z, Morgan SE, Zaman S.. medRxiv, 2025-12-17
Topic: Amyloid, tau and blood-test diagnosis
Alzheimer’s disease manifests through heterogeneous progression patterns across diverse populations, yet current predictive models fail to capture this complexity while maintaining clinical interpretability. Here we present a multi-dimensional attention framework that simultaneously captures temporal dynamics and biomarker importance patterns to predict disease progression across three fundamentally different populations: the general late-onset population using the Alzheimer’s Disease Prediction Of Longitudinal Evolution (TADPOLE) dataset (N=1669), cases with Down Syndrome-associated Alzheimer’s disease using the Alzheimer’s Biomarker Consortium - Down Syndrome (ABC-DS) dataset (N=396) and cases with autosomal dominant Alzheimer’s disease using the Dominantly Inherited Alzheimer Network (DIAN) dataset (N=425). Our framework achieved multi-class area under the ROC curve (mAUC) values of 0
Key findings
- Here we present a multi-dimensional attention framework that simultaneously captures temporal dynamics and biomarker importance patterns to predict disease progression across three fundamentally different populations: the general late-onset population using the Alzheimer’s Disease Prediction Of Longitudinal Evolution (TADPOLE) dataset (N=1669), cases with Down Syndrome-associated Alzheimer’s disease using the Alzheimer’s Biomarker Consortium - Down Syndrome (ABC-DS) dataset (N=396) and cases with autosomal dominant Alzheimer’s disease using the Dominantly Inherited Alzheimer Network (DIAN) dataset (N=425).
- Our framework achieved multi-class area under the ROC curve (mAUC) values of 0.793 (TADPOLE), 0.680 (ABC-DS) and 0.902 (DIAN) when trained on each dataset independently to predict individuals’ future diagnostic status (cognitively normal, mild cognitive impairment, or Alzheimer’s disease) based on their longitudinal biomarker history, outperforming conventional approaches.
- The model generates individual-specific attention maps revealing distinct biomarker importance over time.
- Transfer learning from the TADPOLE dataset-which included neuroimaging data-improved prediction performance on the ABC-DS dataset with no neuroimaging data included from 0.680 to 0.771, demonstrating that disease mechanisms transcend both etiological boundaries and data modalities.
Why it matters
This study presents a computer model predicting Alzheimer's disease progression across general, Down syndrome-associated, and autosomal dominant cohorts, with prediction accuracy varying by dataset. It is a prediction tool and does not treat, cure, or prevent the disease.
Study details
- Study designlongitudinal study
- Participants1,669
- Evidence strengthModerate
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 34 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for effect reported and randomised / blinded because the record does not state it. We have no data on impact for its field and year, so that was left out: it earned 32 of the 94 points we could judge it on, which is 34 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (medRxiv) |
| Journal's citation record | 0 / 4 | Not a journal article, so the journal's record does not count |
| Study design | 7 / 12 | A longitudinal study following the same people over time |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 4 / 10 | Preprint — not yet peer-reviewed. Hosted on a preprint server. |
| How recent | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 3 / 4 | 1,669 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| 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 3 year(s) since 2025 |
| 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 | 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 | 2 / 4 | Last-listed author Shahid H. Zaman: h-index 37 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | University of Cambridge: institution h-index 1,973 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 32 / 94 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 32 of the 94 points we could judge it on is 34 out of 100. |
| Total | 34 / 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 →
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
Song, Z., Morgan, S. E., & Zaman, S. (2025). Multi-dimensional attention framework for personalized Alzheimer’s disease progression prediction across sporadic and genetic risk cohorts. medRxiv. https://doi.org/10.64898/2025.12.13.25342199
Vancouver
Song Z, Morgan SE, Zaman S. Multi-dimensional attention framework for personalized Alzheimer’s disease progression prediction across sporadic and genetic risk cohorts. medRxiv. 2025. doi:10.64898/2025.12.13.25342199.
BibTeX
@article{song2025-f89d89bf,
author = {Song, Z. and Morgan, S. E. and Zaman, S.},
title = {{Multi-dimensional attention framework for personalized Alzheimer’s disease progression prediction across sporadic and genetic risk cohorts}},
journal = {medRxiv},
year = {2025},
doi = {10.64898/2025.12.13.25342199},
url = {https://doi.org/10.64898/2025.12.13.25342199}
}
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here.