Home › Research › Digital health tools
Brain properties predict proximity to symptom onset in sporadic Alzheimer's disease
Vogel JW, Vachon-Presseau E, Pichet Binette A, Tam A, Orban P, La Joie R, Savard M, Picard C et al.. Brain : a journal of neurology, 2018-06-01
Topic: Amyloid, tau and blood-test diagnosis
Within the training set, individuals closer to or beyond their parent's symptom onset demonstrated reduced grey matter volume and altered functional connectivity, specifically in regions known to be vulnerable in Alzheimer's disease. This feature set alone significantly predicted years to estimated symptom onset in the unseen testing data. This model, using only neuroimaging features, significantly outperformed a similar model instead trained with cognitive, genetic, imaging and demographic features used in a traditional clinical setting. Adjusting for participant age did not impact any of the results. These findings demonstrate that years to estimated symptom onset or similar measures can be predicted from brain features and may help estimate presymptomatic disease progression in at-risk individuals.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Vogel, J. W., Vachon-Presseau, E., Pichet Binette, A., Tam, A., Orban, P., La Joie, R., et al. (2018). Brain properties predict proximity to symptom onset in sporadic Alzheimer's disease. Brain : a journal of neurology. https://doi.org/10.1093/brain/awy093
Vancouver
Vogel JW, Vachon-Presseau E, Pichet Binette A, Tam A, Orban P, La Joie R, et al. Brain properties predict proximity to symptom onset in sporadic Alzheimer's disease. Brain : a journal of neurology. 2018. doi:10.1093/brain/awy093. PMID: 29688388.
BibTeX
@article{vogel2018-29688388,
author = {Vogel, J. W. and Vachon-Presseau, E. and Pichet Binette, A. and Tam, A. and Orban, P. and La Joie, R. and Savard, M. and Picard, C. and others},
title = {{Brain properties predict proximity to symptom onset in sporadic Alzheimer's disease}},
journal = {Brain : a journal of neurology},
year = {2018},
doi = {10.1093/brain/awy093},
pmid = {29688388},
url = {https://doi.org/10.1093/brain/awy093}
}
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.
Key findings
- Within the training set, individuals closer to or beyond their parent's symptom onset demonstrated reduced grey matter volume and altered functional connectivity, specifically in regions known to be vulnerable in Alzheimer's disease.
- This feature set alone significantly predicted years to estimated symptom onset in the unseen testing data.
- This model, using only neuroimaging features, significantly outperformed a similar model instead trained with cognitive, genetic, imaging and demographic features used in a traditional clinical setting.
- Adjusting for participant age did not impact any of the results.
- These findings demonstrate that years to estimated symptom onset or similar measures can be predicted from brain features and may help estimate presymptomatic disease progression in at-risk individuals.
Why it matters
This study found that neuroimaging features alone predicted years to estimated symptom onset in people at risk for sporadic Alzheimer's disease, outperforming a model using clinical features. It is about estimating presymptomatic progression and does not report any treatment, cure, or prevention.
New to this subject? Start with our plain-English guide: Digital health →
Study details
- Study designlongitudinal study
- Participants138
- Evidence strengthModerate
How this study scored 61/100
Published in a leading specialty journal, and it is a longitudinal study following the same people over time. Scored zero for effect reported and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 9 / 10 | Tier 2 — leading specialty journal (Brain : a journal of neurology) |
| Journal's citation record | 4 / 4 | Brain: journal h-index 503 (OpenAlex, 2026-10-29) |
| 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 | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 2 / 5 | 2018 — within 10 year(s) of the newest study here |
| Sample size | 2 / 4 | 138 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 | 4 / 7 | Cited 56 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 5.6 citations a year over 10 year(s) since 2018 |
| Impact for its field and year | 3 / 6 | Cited 6.1× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 2 other studies in this library |
| Still being cited | 1 / 4 | Cited 12 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Sylvia Villeneuve: h-index 48 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | University of California, San Francisco: institution h-index 1,915 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 8 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 29688388) |
| Free to read | 2 / 2 | Free to read |
| Total | 61 / 100 | — |
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).
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here. Showing 12 of 17.
Later studies that cite this one
2 studies in the alzhub library list this one in their references.
Related studies in Digital health tools
Also tagged “classification”
835 studies in the library carry the topic tag “classification”; these are this study's neighbours in a fixed, alphabetical-by-hash tour of them, not the closest in content. All 835 tagged studies.