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Machine Learning-based Individual Assessment of Cortical Atrophy Pattern in Alzheimer's Disease Spectrum: Development of the Classifier and Longitudinal Evaluation
Lee JS, Kim C, Shin JH, Cho H, Shin DS, Kim N, Kim HJ, Kim Y et al.. Scientific reports, 2018-03-07
Topic: Amyloid, tau and blood-test diagnosis
To develop a new method for measuring Alzheimer's disease (AD)-specific similarity of cortical atrophy patterns at the individual-level, we employed an individual-level machine learning algorithm. A total of 869 cognitively normal (CN) individuals and 473 patients with probable AD dementia who underwent high-resolution 3T brain MRI were included. We propose a machine learning-based method for measuring the similarity of an individual subject's cortical atrophy pattern with that of a representative AD patient cohort. In addition, we validated this similarity measure in two longitudinal cohorts consisting of 79 patients with amnestic-mild cognitive impairment (aMCI) and 27 patients with probable AD dementia. Surface-based morphometry classifier for discriminating AD from CN showed sensitivity and specificity values of 87.1% and 93.3%, respectively. In the longitudinal validation study, aMCI-converts had higher atrophy similarity at both baseline (p < 0.001) and first year visits (p < 0.001) relative to non-converters. Similarly, AD patients with faster decline had higher atrophy similarity than slower decliners at baseline (p = 0.042), first year (p = 0.028), and third year visits (p = 0.027). The AD-specific atrophy similarity measure is a novel approach for the prediction of dementia risk and for the evaluation of AD trajectories on an individual subject level.
Key findings
- In addition, we validated this similarity measure in two longitudinal cohorts consisting of 79 patients with amnestic-mild cognitive impairment (aMCI) and 27 patients with probable AD dementia.
- Surface-based morphometry classifier for discriminating AD from CN showed sensitivity and specificity values of 87.1% and 93.3%, respectively.
- In the longitudinal validation study, aMCI-converts had higher atrophy similarity at both baseline (p < 0.001) and first year visits (p < 0.001) relative to non-converters.
- Similarly, AD patients with faster decline had higher atrophy similarity than slower decliners at baseline (p = 0.042), first year (p = 0.028), and third year visits (p = 0.027).
- The AD-specific atrophy similarity measure is a novel approach for the prediction of dementia risk and for the evaluation of AD trajectories on an individual subject level.
Why it matters
This study developed a machine learning method to measure how similar an individual's cortical atrophy pattern is to that of a representative Alzheimer's disease cohort. The measure predicted dementia risk and disease trajectory, but it is a diagnostic and predictive tool, not a treatment.
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Study details
- Study designlongitudinal study
- Participants473
- Effect size87.1%; 93.3%
- Evidence strengthModerate
How this study scored 62/100
Published in a peer-reviewed journal, and it is a longitudinal study following the same people over time. Scored zero for randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Scientific reports) |
| Journal's citation record | 3 / 4 | Scientific Reports: journal h-index 463 (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 | 2 / 5 | 2018 — within 10 year(s) of the newest study here |
| Sample size | 2 / 4 | 473 participants |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| 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 | 4 / 7 | Cited 56 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 5.6 citations a year over 10 year(s) since 2018 |
| Impact for its field and year | 3 / 6 | Cited 3.8× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 3 other studies in this library |
| Still being cited | 1 / 4 | Cited 12 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Joon‐Kyung Seong: h-index 34 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | University of California, Berkeley: institution h-index 2,012 (OpenAlex) |
| Multi-centre collaboration | 3 / 3 | OpenAlex lists 11 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 29515131) |
| Free to read | 2 / 2 | Free to read |
| Total | 62 / 100 | — |
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Read the original
- DOI: 10.1038/s41598-018-22277-x
- PubMed: 29515131
- Original source →
- Free full text: Scientific Reports →
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
Lee, J. S., Kim, C., Shin, J. H., Cho, H., Shin, D. S., Kim, N., et al. (2018). Machine Learning-based Individual Assessment of Cortical Atrophy Pattern in Alzheimer's Disease Spectrum: Development of the Classifier and Longitudinal Evaluation. Scientific reports. https://doi.org/10.1038/s41598-018-22277-x
Vancouver
Lee JS, Kim C, Shin JH, Cho H, Shin DS, Kim N, et al. Machine Learning-based Individual Assessment of Cortical Atrophy Pattern in Alzheimer's Disease Spectrum: Development of the Classifier and Longitudinal Evaluation. Scientific reports. 2018. doi:10.1038/s41598-018-22277-x. PMID: 29515131.
BibTeX
@article{lee2018-29515131,
author = {Lee, J. S. and Kim, C. and Shin, J. H. and Cho, H. and Shin, D. S. and Kim, N. and Kim, H. J. and Kim, Y. and others},
title = {{Machine Learning-based Individual Assessment of Cortical Atrophy Pattern in Alzheimer's Disease Spectrum: Development of the Classifier and Longitudinal Evaluation}},
journal = {Scientific reports},
year = {2018},
doi = {10.1038/s41598-018-22277-x},
pmid = {29515131},
url = {https://doi.org/10.1038/s41598-018-22277-x}
}
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