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Robust automated detection of microstructural white matter degeneration in Alzheimer's disease using machine learning classification of multicenter DTI data
Dyrba M, Ewers M, Wegrzyn M, Kilimann I, Plant C, Oswald A, Meindl T, Pievani M et al.. PloS one, 2013-05-31
Topic: Amyloid, tau and blood-test diagnosis
Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer's disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample.
Key findings
- Accuracies for NB were significantly lower, ranging between 68% and 75%.
- Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers.
- After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation.
- Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample.
Why it matters
Machine learning classification of diffusion tensor imaging data from multiple scanners achieved up to 83% accuracy in distinguishing Alzheimer's disease patients from healthy controls. This supports the use of DTI as a diagnostic biomarker and does not treat, cure, or prevent Alzheimer's disease.
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Study details
- Participants137
- Evidence strengthLow
How this study scored 52/100
Published in a peer-reviewed journal. Scored zero for study design, 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 50 of the 96 points we could judge it on, which is 52 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (PloS one) |
| Journal's citation record | 4 / 4 | PLoS ONE: journal h-index 594 (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 | 2013 — within 20 year(s) of the newest study here |
| Sample size | 2 / 4 | 137 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 113 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 7.5 citations a year over 15 year(s) since 2013 |
| Impact for its field and year | 3 / 6 | Cited 7.3× 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 | 2 / 4 | Cited 15 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 | 1 / 3 | Ludwig-Maximilians-Universität München: institution h-index 1,224 (OpenAlex) |
| Multi-centre collaboration | 3 / 3 | OpenAlex lists 13 institution(s) in 3 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 23741425) |
| Free to read | 2 / 2 | Free to read |
| Points | 50 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 50 of the 96 points we could judge it on is 52 out of 100. |
| Total | 52 / 100 | — |
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Read the original
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
Dyrba, M., Ewers, M., Wegrzyn, M., Kilimann, I., Plant, C., Oswald, A., et al. (2013). Robust automated detection of microstructural white matter degeneration in Alzheimer's disease using machine learning classification of multicenter DTI data. PloS one. https://doi.org/10.1371/journal.pone.0064925
Vancouver
Dyrba M, Ewers M, Wegrzyn M, Kilimann I, Plant C, Oswald A, et al. Robust automated detection of microstructural white matter degeneration in Alzheimer's disease using machine learning classification of multicenter DTI data. PloS one. 2013. doi:10.1371/journal.pone.0064925. PMID: 23741425.
BibTeX
@article{dyrba2013-23741425,
author = {Dyrba, M. and Ewers, M. and Wegrzyn, M. and Kilimann, I. and Plant, C. and Oswald, A. and Meindl, T. and Pievani, M. and others},
title = {{Robust automated detection of microstructural white matter degeneration in Alzheimer's disease using machine learning classification of multicenter DTI data}},
journal = {PloS one},
year = {2013},
doi = {10.1371/journal.pone.0064925},
pmid = {23741425},
url = {https://doi.org/10.1371/journal.pone.0064925}
}
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