Machine learning-derived Alzheimer's disease dimensions: neuroanatomical, cognitive, clinical, functional, and genetic risk heterogeneity signatures
Le, H. N.; Ishrat, S.; Yaghooti, B.; Murad, T.; Sapkota, R.; Thakuri, D. S.; Wong, D. F.; Cruchaga et al.. medRxiv (preprint), 2026-09-07
Topic: Amyloid, tau and blood-test diagnosis
Alzheimers disease (AD) is characterized by substantial clinical and biological heterogeneity, with diverse neuroanatomical, cognitive, clinical, functional, and genetic risk profiles that are poorly captured by current diagnostic frameworks. Identifying reproducible disease dimensions is critical for understanding divergent pathophysiological mechanisms and designing future stratified therapeutic interventions. Herein, we applied a semi-supervised machine learning clustering framework and robust reproducibility validation strategies to structural MRI data from 1,757 participants, comprising 1,108 cognitively normal controls (CN) and 649 patients with mild cognitive impairment (MCI) or dementia due to AD. We identified two MRI-based neuroanatomical dimensions of AD: Dimension 1 (n = 393; prevalence [~] 61%) and Dimension 2 (n = 256; prevalence [~] 39%). Dimension 1 exhibited widespread cortical and subcortical atrophy, including involvement of the hippocampus, amygdala, parahippocampal, temporal, frontal, and occipital regions, while Dimension 2 showed relatively preserved brain patterns. The two dimensions were not statistically different in demographic characteristics, including age, sex, race, and education (all p > 0.05). Compared to Dimension 2, Dimension 1 had more severe cognitive and clinical impairment, greater functional impairment, elevated APOE {varepsilon}4 carrier burden, and higher polygenic risk for AD (p < 0.05). Analysis of individual-level summary neuroanatomical signature expression scores, quantifying each subjects continuous position along two disease dimensions, revealed that Dimension 1 signature expression correlated strongly with cognitive impairment, clinical severity, functional impairment, and genetic risk, while Dimensional 2 signature expression showed relatively weaker associations. These results reveal that AD encompasses at least two biologically distinct dimensions identifiable from structural MRI profiles and their distinct associations with cognitive, clinical, functional, and genetic profiles. These findings may aid in AD patient stratification for designing targeted therapeutic approaches in the future.
Key findings
- Compared to Dimension 2, Dimension 1 had more severe cognitive and clinical impairment, greater functional impairment, elevated APOE {varepsilon}4 carrier burden, and higher polygenic risk for AD (p < 0.05).
- Analysis of individual-level summary neuroanatomical signature expression scores, quantifying each subjects continuous position along two disease dimensions, revealed that Dimension 1 signature expression correlated strongly with cognitive impairment, clinical severity, functional impairment, and ge
- These results reveal that AD encompasses at least two biologically distinct dimensions identifiable from structural MRI profiles and their distinct associations with cognitive, clinical, functional, and genetic profiles.
- These findings may aid in AD patient stratification for designing targeted therapeutic approaches in the future.
Why it matters
This study identified two MRI-based neuroanatomical dimensions of Alzheimer's disease, one with widespread atrophy and more severe cognitive, clinical and functional impairment and higher genetic risk, and one with relatively preserved brain patterns. It describes disease heterogeneity and does not test any treatment, cure or prevention.
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Study details
- Participants393
- Evidence strengthLow
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 27 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for study design, 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 25 of the 94 points we could judge it on, which is 27 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (medRxiv (preprint)) |
| Journal's citation record | 0 / 4 | Not a journal article, so the journal's record does not count |
| 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 | 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 | 2 / 4 | 393 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 | 1 / 4 | Last-listed author Ganesh B. Chand: h-index 17 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | Washington University in St. Louis: institution h-index 1,962 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 4 institution(s) in 1 country |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 2 / 2 | Free to read |
| Points | 25 / 94 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 25 of the 94 points we could judge it on is 27 out of 100. |
| Total | 27 / 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
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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
Le, H. N., Ishrat, S., Yaghooti, B., Murad, T., Sapkota, R., Thakuri, D. S., et al. (2026). Machine learning-derived Alzheimer's disease dimensions: neuroanatomical, cognitive, clinical, functional, and genetic risk heterogeneity signatures. medRxiv (preprint). https://doi.org/10.64898/2026.09.02.26361668
Vancouver
Le HN, Ishrat S, Yaghooti B, Murad T, Sapkota R, Thakuri DS, et al. Machine learning-derived Alzheimer's disease dimensions: neuroanatomical, cognitive, clinical, functional, and genetic risk heterogeneity signatures. medRxiv (preprint). 2026. doi:10.64898/2026.09.02.26361668.
BibTeX
@article{le2026-bf822a70,
author = {Le, H. N. and Ishrat, S. and Yaghooti, B. and Murad, T. and Sapkota, R. and Thakuri, D. S. and Wong, D. F. and Cruchaga and others},
title = {{Machine learning-derived Alzheimer's disease dimensions: neuroanatomical, cognitive, clinical, functional, and genetic risk heterogeneity signatures}},
journal = {medRxiv (preprint)},
year = {2026},
doi = {10.64898/2026.09.02.26361668},
url = {https://doi.org/10.64898/2026.09.02.26361668}
}
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here. Showing 12 of 16.