Home › Research › Digital health tools
How early can we predict Alzheimer's disease using computational anatomy?
Adaszewski S, Dukart J, Kherif F, Frackowiak R, Draganski B, Alzheimer's Disease Neuroimaging Initiative.. Neurobiology of aging, 2013-07-26
Topic: Amyloid, tau and blood-test diagnosis
Computational anatomy with magnetic resonance imaging (MRI) is well established as a noninvasive biomarker of Alzheimer's disease (AD); however, there is less certainty about its dependency on the staging of AD. We use classical group analyses and automated machine learning classification of standard structural MRI scans to investigate AD diagnostic accuracy from the preclinical phase to clinical dementia. Longitudinal data from the Alzheimer's Disease Neuroimaging Initiative were stratified into 4 groups according to the clinical status-(1) AD patients; (2) mild cognitive impairment (MCI) converters; (3) MCI nonconverters; and (4) healthy controls-and submitted to a support vector machine. The obtained classifier was significantly above the chance level (62%) for detecting AD already 4 years before conversion from MCI. Voxel-based univariate tests confirmed the plausibility of our findings detecting a distributed network of hippocampal-temporoparietal atrophy in AD patients. We also identified a subgroup of control subjects with brain structure and cognitive changes highly similar to those observed in AD. Our results indicate that computational anatomy can detect AD substantially earlier than suggested by current models. The demonstrated differential spatial pattern of atrophy between correctly and incorrectly classified AD patients challenges the assumption of a uniform pathophysiological process underlying clinically identified AD.
Key findings
- The obtained classifier was significantly above the chance level (62%) for detecting AD already 4 years before conversion from MCI.
- Voxel-based univariate tests confirmed the plausibility of our findings detecting a distributed network of hippocampal-temporoparietal atrophy in AD patients.
- Our results indicate that computational anatomy can detect AD substantially earlier than suggested by current models.
- The demonstrated differential spatial pattern of atrophy between correctly and incorrectly classified AD patients challenges the assumption of a uniform pathophysiological process underlying clinically identified AD.
Why it matters
Using existing MRI scans from the Alzheimer's Disease Neuroimaging Initiative, a machine-learning classifier picked out Alzheimer's-type brain changes in people with mild cognitive impairment about 4 years before they developed dementia. Its performance was "significantly above the chance level (62%)", which is a research signal, not a reliable test for an individual. The authors also found that the pattern of brain shrinkage differed between Alzheimer's patients the computer classified correctly and those it missed, which "challenges the assumption of a uniform pathophysiological process" behind clinically diagnosed Alzheimer's. The study tested no treatment.
New to this subject? Start with our plain-English guide: Digital health →
Study details
- Study designlongitudinal study
- Evidence strengthModerate
How this study scored 52/100
Published in a peer-reviewed journal, and it is a longitudinal study following the same people over time. Scored zero for sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Neurobiology of aging) |
| Journal's citation record | 3 / 4 | Neurobiology of Aging: journal h-index 263 (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 | 1 / 5 | 2013 — within 20 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 4 / 7 | Cited 99 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 6.6 citations a year over 15 year(s) since 2013 |
| Impact for its field and year | 3 / 6 | Cited 5.3× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 7 other studies in this library |
| Still being cited | 2 / 4 | Cited 16 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Bogdan Draganski: h-index 62 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | University of Lausanne: institution h-index 994 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 4 institution(s) in 3 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 23890839) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 52 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
May need a subscription OpenAlex found no free copy of the full text (checked 6 Feb 1020), so the publisher may charge for it. The PubMed abstract is free.
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. Check the citation against the original before you use it.
APA 7th
Adaszewski, S., Dukart, J., Kherif, F., Frackowiak, R., Draganski, B., & Alzheimer's Disease Neuroimaging Initiative. (2013). How early can we predict Alzheimer's disease using computational anatomy? Neurobiology of aging. https://doi.org/10.1016/j.neurobiolaging.2013.06.015
Vancouver
Adaszewski S, Dukart J, Kherif F, Frackowiak R, Draganski B, Alzheimer's Disease Neuroimaging Initiative. How early can we predict Alzheimer's disease using computational anatomy? Neurobiology of aging. 2013. doi:10.1016/j.neurobiolaging.2013.06.015. PMID: 23890839.
BibTeX
@article{adaszewski2013-23890839,
author = {Adaszewski, S. and Dukart, J. and Kherif, F. and Frackowiak, R. and Draganski, B. and {Alzheimer's Disease Neuroimaging Initiative}},
title = {{How early can we predict Alzheimer's disease using computational anatomy?}},
journal = {Neurobiology of aging},
year = {2013},
doi = {10.1016/j.neurobiolaging.2013.06.015},
pmid = {23890839},
url = {https://doi.org/10.1016/j.neurobiolaging.2013.06.015}
}
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here. Showing 12 of 24.
Later studies that cite this one
7 studies in the alzhub library list this one in their references.
Related studies in Digital health tools
Also in Digital health tools
178 studies in the library are filed under Digital health tools with no topic tag they share with another; these are this study's neighbours in a fixed tour of them. The Digital health tools hub.