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Multi-dimensional attention framework for personalised Alzheimer's disease progression prediction across sporadic and genetic risk cohorts
Song Z, Song S, Clare IC, Head E, Hom CL, Brickman AM, Hartley S, Lao PJ et al.. Research Square, 2026-08-03
Topic: Amyloid, tau and blood-test diagnosis
Abstract Alzheimer's disease progresses heterogeneously across diverse cohorts, yet current predictive models fail to capture this complexity while remaining clinically interpretable. Here we present a multi-dimensional attention framework that simultaneously captures both temporal dynamics and biomarker importance 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). Trained on each dataset independently, our framework achieved multi-class Area Under the Receiver O
Key findings
- Here we present a multi-dimensional attention framework that simultaneously captures both temporal dynamics and biomarker importance 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).
- Trained on each dataset independently, our framework achieved multi-class Area Under the Receiver Operating Characteristic Curve (mAUC) values of 0.793 (TADPOLE), 0.680 (ABC-DS), and 0.902 (DIAN) when predicting individuals' future diagnostic status (cognitively normal/stable, mild cognitive impairment, or Alzheimer's disease) from their longitudinal biomarker history, outperforming conventional approaches.
- The model generates individual-specific attention maps revealing distinct biomarker importance over time.
- Transfer learning from TADPOLE—which included neuroimaging data—improved prediction performance on the imaging-free ABC-DS dataset 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 that predicts 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 37 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 and senior author's track record, so those were left out: it earned 33 of the 90 points we could judge it on, which is 37 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (Research Square) |
| 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 | 5 / 5 | 2026 — within 1 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 2 year(s) since 2026 |
| 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 | — / 4 | OpenAlex's profile for last-listed author USA MO Dominantly Inherited Alzheimer Network St. Louis holds only 1 work(s), too few to judge a track record — left out of this score |
| Institution's track record | 2 / 3 | Massachusetts General Hospital: institution h-index 2,057 (OpenAlex) |
| Multi-centre collaboration | 3 / 3 | OpenAlex lists 13 institution(s) in 2 countries |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 33 / 90 | Scored on 18 of 20 parts. We have no data for the rest, so they are left out: 33 of the 90 points we could judge it on is 37 out of 100. |
| Total | 37 / 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. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Song, Z., Song, S., Clare, I. C., Head, E., Hom, C. L., Brickman, A. M., et al. (2026). Multi-dimensional attention framework for personalised Alzheimer's disease progression prediction across sporadic and genetic risk cohorts. Research Square. https://doi.org/10.21203/rs.3.rs-10309496/v1
Vancouver
Song Z, Song S, Clare IC, Head E, Hom CL, Brickman AM, et al. Multi-dimensional attention framework for personalised Alzheimer's disease progression prediction across sporadic and genetic risk cohorts. Research Square. 2026. doi:10.21203/rs.3.rs-10309496/v1.
BibTeX
@article{song2026-c369392a,
author = {Song, Z. and Song, S. and Clare, I. C. and Head, E. and Hom, C. L. and Brickman, A. M. and Hartley, S. and Lao, P. J. and others},
title = {{Multi-dimensional attention framework for personalised Alzheimer's disease progression prediction across sporadic and genetic risk cohorts}},
journal = {Research Square},
year = {2026},
doi = {10.21203/rs.3.rs-10309496/v1},
url = {https://doi.org/10.21203/rs.3.rs-10309496/v1}
}
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