Brain tau PET-based identification and characterization of subpopulations in patients with Alzheimer’s disease using deep learning-derived saliency maps
Yanxiao Li, Xiuying Wang, Qi Ge, Manuel B. Graeber, Shaozhen Yan, Jian Li, Shuyu Li, Wenjian Gu et al.. EJNMMI Physics, 2025-06-09
Topic: Amyloid, tau and blood-test diagnosis
Alzheimer’s disease (AD) is a heterogeneous neurodegenerative disorder in which tau neurofibrillary tangles are a pathological hallmark closely associated with cognitive dysfunction and neurodegeneration. In this study, we used brain tau data to investigate AD heterogeneity by identifying and characterizing the subpopulations among patients. We included 615 cognitively normal and 159 AD brain 18 F-flortaucipr PET scans, along with T1-weighted MRI from the Alzheimer Disease Neuroimaging Initiative database. A three dimensional-convolutional neural network model was employed for AD detection using standardized uptake value ratio (SUVR) images. The model-derived saliency maps were generated and employed as informative image features for clustering AD participants. Among the identified subpopulations, statistical analysis of demographics, neuropsychological measures, and SUVR were compared. Correlations between neuropsychological measures and regional SUVRs were assessed. A generalized linear model was utilized to investigate the sex and APOE ε4 interaction effect on regional SUVRs. Two distinct subpopulations of AD patients were revealed, denoted as S Hi and S Lo . Compared to the S Lo group, the S Hi group exhibited a significantly higher global tau burden in the brain, but both groups showed similar cognition distribution levels. In the S Hi group, the associations between the neuropsychological measurements and regional tau deposition were weakened. Moreover, a significant interaction effect of sex and APOE ε4 on tau deposition was observed in the S Lo group, but no such effect was found in the S Hi group. Our results suggest that tau tangles, as shown by SUVR, continue to accumulate even when cognitive function plateaus in AD patients, highlighting the advantages of PET in later disease stages. The differing relationships between cognition and tau deposition, and between gender, APOE4, and tau deposition, provide potential for subtype-specific treatments. Targeting gender-specific and genetic factors influencing tau deposition, as well as interventions aimed at tau’s impact on cognition, may be effective.
Key findings
- Alzheimer’s disease (AD) is a heterogeneous neurodegenerative disorder in which tau neurofibrillary tangles are a pathological hallmark closely associated with cognitive dysfunction and neurodegeneration.
- Two distinct subpopulations of AD patients were revealed, denoted as S Hi and S Lo .
- Compared to the S Lo group, the S Hi group exhibited a significantly higher global tau burden in the brain, but both groups showed similar cognition distribution levels.
- Moreover, a significant interaction effect of sex and APOE ε4 on tau deposition was observed in the S Lo group, but no such effect was found in the S Hi group.
- Our results suggest that tau tangles, as shown by SUVR, continue to accumulate even when cognitive function plateaus in AD patients, highlighting the advantages of PET in later disease stages.
Why it matters
Using tau PET scans and deep learning, this study identified two subpopulations of Alzheimer's patients, one with higher global tau burden but similar cognition to the other group. This describes disease heterogeneity and does not test or propose a treatment or cure.
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Study details
- Participants615
- Evidence strengthLow
How this study scored 44/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (EJNMMI Physics) |
| Journal's citation record | 0 / 4 | EJNMMI Physics: journal h-index 55 (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 | 2 / 4 | 615 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 | 1 / 7 | Cited 3 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 1.0 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 2 / 6 | Cited 1.9× 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 | 1 / 4 | Cited 3 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Yun Zhou: h-index 50 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | Washington University in St. Louis: institution h-index 1,962 (OpenAlex) |
| Multi-centre collaboration | 3 / 3 | OpenAlex lists 10 institution(s) in 3 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40488912) |
| Free to read | 2 / 2 | Free to read |
| Total | 44 / 100 | — |
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Read the original
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APA 7th
Li, Y., Wang, X., Ge, Q., Graeber, M. B., Yan, S., Li, J., et al. (2025). Brain tau PET-based identification and characterization of subpopulations in patients with Alzheimer’s disease using deep learning-derived saliency maps. EJNMMI Physics. https://doi.org/10.1186/s40658-025-00761-4
Vancouver
Li Y, Wang X, Ge Q, Graeber MB, Yan S, Li J, et al. Brain tau PET-based identification and characterization of subpopulations in patients with Alzheimer’s disease using deep learning-derived saliency maps. EJNMMI Physics. 2025. doi:10.1186/s40658-025-00761-4. PMID: 40488912.
BibTeX
@article{li2025-40488912,
author = {Li, Yanxiao and Wang, Xiuying and Ge, Qi and Graeber, Manuel B. and Yan, Shaozhen and Li, Jian and Li, Shuyu and Gu, Wenjian and others},
title = {{Brain tau PET-based identification and characterization of subpopulations in patients with Alzheimer’s disease using deep learning-derived saliency maps}},
journal = {EJNMMI Physics},
year = {2025},
doi = {10.1186/s40658-025-00761-4},
pmid = {40488912},
url = {https://doi.org/10.1186/s40658-025-00761-4}
}
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