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Predicting Prodromal Alzheimer's Disease in Subjects with Mild Cognitive Impairment Using Machine Learning Classification of Multimodal Multicenter Diffusion-Tensor and Magnetic Resonance Imaging Data
Dyrba M, Barkhof F, Fellgiebel A, Filippi M, Hausner L, Hauenstein K, Kirste T, Teipel SJ et al.. Journal of neuroimaging : official journal of the American Society of Neuroimaging, 2015-01-28
Topic: Amyloid, tau and blood-test diagnosis
Background Alzheimer's disease (AD) patients show early changes in white matter (WM) structural integrity. We studied the use of diffusion tensor imaging (DTI) in assessing WM alterations in the predementia stage of mild cognitive impairment (MCI). Methods We applied a Support Vector Machine (SVM) classifier to DTI and volumetric magnetic resonance imaging data from 35 amyloid-β42 negative MCI subjects (MCI-Aβ42-), 35 positive MCI subjects (MCI-Aβ42+), and 25 healthy controls (HC) retrieved from the European DTI Study on Dementia. The SVM was applied to DTI-derived fractional anisotropy, mean diffusivity (MD), and mode of anisotropy (MO) maps. For comparison, we studied classification based on gray matter (GM) and WM volume. Results We obtained accuracies of up to 68% for MO and 63% for GM volume when it came to distinguishing between MCI-Aβ42- and MCI-Aβ42+. When it came to separating MCI-Aβ42+ from HC we achieved an accuracy of up to 77% for MD and a significantly lower accuracy of 68% for GM volume. The accuracy of multimodal classification was not higher than the accuracy of the best single modality. Conclusions Our results suggest that DTI data provide better prediction accuracy than GM volume in predementia AD.
Key findings
- Results We obtained accuracies of up to 68% for MO and 63% for GM volume when it came to distinguishing between MCI-Aβ42- and MCI-Aβ42+.
- When it came to separating MCI-Aβ42+ from HC we achieved an accuracy of up to 77% for MD and a significantly lower accuracy of 68% for GM volume.
- Conclusions Our results suggest that DTI data provide better prediction accuracy than GM volume in predementia AD.
Why it matters
This study applied a support vector machine to diffusion tensor imaging and volumetric MRI data to distinguish mild cognitive impairment subjects by amyloid-β42 status and from healthy controls, with accuracies up to 77%. It is a prediction study and does not test any treatment, cure or prevention.
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Study details
- Evidence strengthLow
How this study scored 40/100
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. We have no data on senior author's track record, so that was left out: it earned 38 of the 96 points we could judge it on, which is 40 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Journal of neuroimaging : official journal of the American Society of Neuroimaging) |
| Journal's citation record | 1 / 4 | Journal of Neuroimaging: journal h-index 91 (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 | 2015 — 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 97 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 7.5 citations a year over 13 year(s) since 2015 |
| Impact for its field and year | 3 / 6 | Cited 8.0× 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 | 1 / 4 | Cited 11 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 | Heidelberg University: institution h-index 1,344 (OpenAlex) |
| Multi-centre collaboration | 3 / 3 | OpenAlex lists 10 institution(s) in 3 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 25644739) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 38 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 38 of the 96 points we could judge it on is 40 out of 100. |
| Total | 40 / 100 | — |
What each of these 20 components measures, and what this score is not →
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APA 7th
Dyrba, M., Barkhof, F., Fellgiebel, A., Filippi, M., Hausner, L., Hauenstein, K., et al. (2015). Predicting Prodromal Alzheimer's Disease in Subjects with Mild Cognitive Impairment Using Machine Learning Classification of Multimodal Multicenter Diffusion-Tensor and Magnetic Resonance Imaging Data. Journal of neuroimaging : official journal of the American Society of Neuroimaging. https://doi.org/10.1111/jon.12214
Vancouver
Dyrba M, Barkhof F, Fellgiebel A, Filippi M, Hausner L, Hauenstein K, et al. Predicting Prodromal Alzheimer's Disease in Subjects with Mild Cognitive Impairment Using Machine Learning Classification of Multimodal Multicenter Diffusion-Tensor and Magnetic Resonance Imaging Data. Journal of neuroimaging : official journal of the American Society of Neuroimaging. 2015. doi:10.1111/jon.12214. PMID: 25644739.
BibTeX
@article{dyrba2015-25644739,
author = {Dyrba, M. and Barkhof, F. and Fellgiebel, A. and Filippi, M. and Hausner, L. and Hauenstein, K. and Kirste, T. and Teipel, S. J. and others},
title = {{Predicting Prodromal Alzheimer's Disease in Subjects with Mild Cognitive Impairment Using Machine Learning Classification of Multimodal Multicenter Diffusion-Tensor and Magnetic Resonance Imaging Data}},
journal = {Journal of neuroimaging : official journal of the American Society of Neuroimaging},
year = {2015},
doi = {10.1111/jon.12214},
pmid = {25644739},
url = {https://doi.org/10.1111/jon.12214}
}
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