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Predicting Alzheimer's disease progression using deep recurrent neural networks
Nguyen M, He T, An L, Alexander DC, Feng J, Yeo BTT, Alzheimer's Disease Neuroimaging Initiative.. NeuroImage, 2020-08-04
Topic: Amyloid, tau and blood-test diagnosis
Early identification of individuals at risk of developing Alzheimer's disease (AD) dementia is important for developing disease-modifying therapies. In this study, given multimodal AD markers and clinical diagnosis of an individual from one or more timepoints, we seek to predict the clinical diagnosis, cognition and ventricular volume of the individual for every month (indefinitely) into the future. We proposed and applied a minimal recurrent neural network (minimalRNN) model to data from The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) challenge, comprising longitudinal data of 1677 participants (Marinescu et al., 2018) from the Alzheimer's Disease Neuroimaging Initiative (ADNI).
Key findings
- In this study, given multimodal AD markers and clinical diagnosis of an individual from one or more timepoints, we seek to predict the clinical diagnosis, cognition and ventricular volume of the individual for every month (indefinitely) into the future.
- We proposed and applied a minimal recurrent neural network (minimalRNN) model to data from The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) challenge, comprising longitudinal data of 1677 participants (Marinescu et al., 2018) from the Alzheimer's Disease Neuroimaging Initiative (ADNI).
- We compared the performance of the minimalRNN model and four baseline algorithms up to 6 years into the future.
- Most previous work on predicting AD progression ignore the issue of missing data, which is a prevalent issue in longitudinal data.
Why it matters
This study uses a deep recurrent neural network to predict future clinical diagnosis, cognition, and ventricular volume in people with Alzheimer's disease markers. It is a prediction tool and does not test any treatment, cure, or prevention.
Study details
- Study designcross-sectional
- Participants1,677
- Evidence strengthModerate
How this study scored 65/100
Published in a peer-reviewed journal, and it is a snapshot of people at one point in time. Scored zero for effect reported and randomised / blinded because the record does not state it. We have no data on senior author's track record, so that was left out: it earned 62 of the 96 points we could judge it on, which is 65 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (NeuroImage) |
| Journal's citation record | 4 / 4 | NeuroImage: journal h-index 523 (OpenAlex, 2026-10-29) |
| Study design | 4 / 12 | A snapshot of people at one point in time |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 2 / 5 | 2020 — within 10 year(s) of the newest study here |
| Sample size | 3 / 4 | 1,677 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 | 5 / 7 | Cited 213 times (OpenAlex, 2026-10-10) |
| Citations per year | 4 / 5 | 26.6 citations a year over 8 year(s) since 2020 |
| Impact for its field and year | 4 / 6 | Cited 15.2× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 4 other studies in this library |
| Still being cited | 3 / 4 | Cited 118 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | 2 / 3 | Massachusetts General Hospital: institution h-index 2,057 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 4 institution(s) in 3 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 32763427) |
| Free to read | 2 / 2 | Free to read |
| Points | 62 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 62 of the 96 points we could judge it on is 65 out of 100. |
| Total | 65 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1016/j.neuroimage.2020.117203
- PubMed: 32763427
- Original source →
- Free full text: NeuroImage →
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 names 7 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
Nguyen, M., He, T., An, L., Alexander, D. C., Feng, J., Yeo, B. T. T., et al. (2020). Predicting Alzheimer's disease progression using deep recurrent neural networks. NeuroImage. https://doi.org/10.1016/j.neuroimage.2020.117203
Vancouver
Nguyen M, He T, An L, Alexander DC, Feng J, Yeo BTT, et al. Predicting Alzheimer's disease progression using deep recurrent neural networks. NeuroImage. 2020. doi:10.1016/j.neuroimage.2020.117203. PMID: 32763427.
BibTeX
@article{nguyen2020-32763427,
author = {Nguyen, M. and He, T. and An, L. and Alexander, D. C. and Feng, J. and Yeo, B. T. T. and {Alzheimer's Disease Neuroimaging Initiative}},
title = {{Predicting Alzheimer's disease progression using deep recurrent neural networks}},
journal = {NeuroImage},
year = {2020},
doi = {10.1016/j.neuroimage.2020.117203},
pmid = {32763427},
url = {https://doi.org/10.1016/j.neuroimage.2020.117203}
}
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