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Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's disease
Plant C, Teipel SJ, Oswald A, Böhm C, Meindl T, Mourao-Miranda J, Bokde AW, Hampel H et al.. NeuroImage, 2009-12-02
Topic: Amyloid, tau and blood-test diagnosis
Subjects with mild cognitive impairment (MCI) have an increased risk to develop Alzheimer's disease (AD). Voxel-based MRI studies have demonstrated that widely distributed cortical and subcortical brain areas show atrophic changes in MCI, preceding the onset of AD-type dementia. Here we developed a novel data mining framework in combination with three different classifiers including support vector machine (SVM), Bayes statistics, and voting feature intervals (VFI) to derive a quantitative index of pattern matching for the prediction of the conversion from MCI to AD. MRI was collected in 32 AD patients, 24 MCI subjects and 18 healthy controls (HC). Nine out of 24 MCI subjects converted to AD after an average follow-up interval of 2.5 years. Using feature selection algorithms, brain regions showing the highest accuracy for the discrimination between AD and HC were identified, reaching a classification accuracy of up to 92%. The extracted AD clusters were used as a search region to extract those brain areas that are predictive of conversion to AD within MCI subjects. The most predictive brain areas included the anterior cingulate gyrus and orbitofrontal cortex. The best prediction accuracy, which was cross-validated via train-and-test, was 75% for the prediction of the conversion from MCI to AD. The present results suggest that novel multivariate methods of pattern matching reach a clinically relevant accuracy for the a priori prediction of the progression from MCI to AD.
Key findings
- Subjects with mild cognitive impairment (MCI) have an increased risk to develop Alzheimer's disease (AD).
- Voxel-based MRI studies have demonstrated that widely distributed cortical and subcortical brain areas show atrophic changes in MCI, preceding the onset of AD-type dementia.
- The present results suggest that novel multivariate methods of pattern matching reach a clinically relevant accuracy for the a priori prediction of the progression from MCI to AD.
Why it matters
In this small study, a computer method using MRI patterns predicted which people with mild cognitive impairment would convert to Alzheimer's disease with 75% accuracy, with the most predictive areas being the anterior cingulate gyrus and orbitofrontal cortex. This is a prediction method only and does not test any treatment, cure, or prevention.
Study details
- Evidence strengthLow
How this study scored 52/100
Published in a peer-reviewed journal. Scored zero for study design, 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 (NeuroImage) |
| Journal's citation record | 4 / 4 | NeuroImage: journal h-index 523 (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 | 1 / 5 | 2010 — 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 | 6 / 7 | Cited 344 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 19.1 citations a year over 18 year(s) since 2010 |
| Impact for its field and year | 4 / 6 | Cited 9.8× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 3 / 6 | Cited by 17 other studies in this library |
| Still being cited | 2 / 4 | Cited 38 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Michael Ewers: h-index 74 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | University College London: institution h-index 2,029 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 8 institution(s) in 3 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 19961938) |
| Free to read | 2 / 2 | Free to read |
| Total | 52 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1016/j.neuroimage.2009.11.046
- PubMed: 19961938
- 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'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
Plant, C., Teipel, S. J., Oswald, A., Böhm, C., Meindl, T., Mourao-Miranda, J., et al. (2010). Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's disease. NeuroImage. https://doi.org/10.1016/j.neuroimage.2009.11.046
Vancouver
Plant C, Teipel SJ, Oswald A, Böhm C, Meindl T, Mourao-Miranda J, et al. Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's disease. NeuroImage. 2010. doi:10.1016/j.neuroimage.2009.11.046. PMID: 19961938.
BibTeX
@article{plant2010-19961938,
author = {Plant, C. and Teipel, S. J. and Oswald, A. and Böhm, C. and Meindl, T. and Mourao-Miranda, J. and Bokde, A. W. and Hampel, H. and others},
title = {{Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's disease}},
journal = {NeuroImage},
year = {2010},
doi = {10.1016/j.neuroimage.2009.11.046},
pmid = {19961938},
url = {https://doi.org/10.1016/j.neuroimage.2009.11.046}
}
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