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Prediction and classification of Alzheimer disease based on quantification of MRI deformation
Long X, Chen L, Jiang C, Zhang L, Alzheimer’s Disease Neuroimaging Initiative.. PloS one, 2017-03-06
Topic: Amyloid, tau and blood-test diagnosis
Detecting early morphological changes in the brain and making early diagnosis are important for Alzheimer's disease (AD). High resolution magnetic resonance imaging can be used to help diagnosis and prediction of the disease. In this paper, we proposed a machine learning method to discriminate patients with AD or mild cognitive impairment (MCI) from healthy elderly and to predict the AD conversion in MCI patients by computing and analyzing the regional morphological differences of brain between groups. Distance between each pair of subjects was quantified from a symmetric diffeomorphic registration, followed by an embedding algorithm and a learning approach for classification. The proposed method obtained accuracy of 96.5% in differentiating mild AD from healthy elderly with the whole-brain gray matter or temporal lobe as region of interest (ROI), 91.74% in differentiating progressive MCI from healthy elderly and 88.99% in classifying progressive MCI versus stable MCI with amygdala or hippocampus as ROI. This deformation-based method has made full use of the pair-wise macroscopic shape difference between groups and consequently increased the power for discrimination.
Key findings
- The proposed method obtained accuracy of 96.5% in differentiating mild AD from healthy elderly with the whole-brain gray matter or temporal lobe as region of interest (ROI), 91.74% in differentiating progressive MCI from healthy elderly and 88.99% in classifying progressive MCI versus stable MCI with amygdala or hippocampus as ROI.
Why it matters
This study used a machine learning method based on MRI brain shape differences to distinguish people with Alzheimer's disease or mild cognitive impairment from healthy older adults and to predict which mild cognitive impairment patients would progress. It is a diagnostic and prediction tool and does not test any treatment, cure, or prevention.
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Study details
- Effect size96.5%; 91.74%; 88.99%
- Evidence strengthLow
How this study scored 53/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, 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 51 of the 96 points we could judge it on, which is 53 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 | 2 / 5 | 2017 — within 10 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 | 2 / 3 | Effect reported as a number, without an interval or test |
| 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 | 5 / 7 | Cited 176 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 16.0 citations a year over 11 year(s) since 2017 |
| Impact for its field and year | 4 / 6 | Cited 11.6× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 3 / 6 | Cited by 10 other studies in this library |
| Still being cited | 2 / 4 | Cited 43 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 | 2 / 3 | Chinese Academy of Sciences: institution h-index 1,801 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 4 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 28264071) |
| Free to read | 2 / 2 | Free to read |
| Points | 51 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 51 of the 96 points we could judge it on is 53 out of 100. |
| Total | 53 / 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).
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
Long, X., Chen, L., Jiang, C., Zhang, L., & Alzheimer’s Disease Neuroimaging Initiative. (2017). Prediction and classification of Alzheimer disease based on quantification of MRI deformation. PloS one. https://doi.org/10.1371/journal.pone.0173372
Vancouver
Long X, Chen L, Jiang C, Zhang L, Alzheimer’s Disease Neuroimaging Initiative. Prediction and classification of Alzheimer disease based on quantification of MRI deformation. PloS one. 2017. doi:10.1371/journal.pone.0173372. PMID: 28264071.
BibTeX
@article{long2017-28264071,
author = {Long, X. and Chen, L. and Jiang, C. and Zhang, L. and {Alzheimer’s Disease Neuroimaging Initiative}},
title = {{Prediction and classification of Alzheimer disease based on quantification of MRI deformation}},
journal = {PloS one},
year = {2017},
doi = {10.1371/journal.pone.0173372},
pmid = {28264071},
url = {https://doi.org/10.1371/journal.pone.0173372}
}
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