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Predictive markers for AD in a multi-modality framework: an analysis of MCI progression in the ADNI population
Hinrichs C, Singh V, Xu G, Johnson SC, Alzheimers Disease Neuroimaging Initiative.. NeuroImage, 2010-12-10
Topic: Risk, prevention and care in population studies
Alzheimer's Disease (AD) and other neurodegenerative diseases affect over 20 million people worldwide, and this number is projected to significantly increase in the coming decades. Proposed imaging-based markers have shown steadily improving levels of sensitivity/specificity in classifying individual subjects as AD or normal. Several of these efforts have utilized statistical machine learning techniques, using brain images as input, as means of deriving such AD-related markers. A common characteristic of this line of research is a focus on either (1) using a single imaging modality for classification, or (2) incorporating several modalities, but reporting separate results for each. One strategy to improve on the success of these methods is to leverage all available imaging modalities together in a single automated learning framework. The rationale is that some subjects may show signs of pathology in one modality but not in another-by combining all available images a clearer view of the progression of disease pathology will emerge. Our method is based on the Multi-Kernel Learning (MKL) framework, which allows the inclusion of an arbitrary number of views of the data in a maximum margin, kernel learning framework. The principal innovation behind MKL is that it learns an optimal combination of kernel (similarity) matrices while simultaneously training a classifier. In classification experiments MKL outperformed an SVM trained on all available features by 3%-4%. We are especially interested in whether such markers are capable of identifying early signs of the disease. To address this question, we have examined whether our multi-modal disease marker (MMDM) can predict conversion from Mild Cognitive Impairment (MCI) to AD. Our experiments reveal that this measure shows significant group differences between MCI subjects who progressed to AD, and those who remained stable for 3 years. These differences were most significant in MMDMs based on imaging data. We also discuss the relationship between our MMDM and an individual's conversion from MCI to AD.
Key findings
- Alzheimer's Disease (AD) and other neurodegenerative diseases affect over 20 million people worldwide, and this number is projected to significantly increase in the coming decades.
- A common characteristic of this line of research is a focus on either (1) using a single imaging modality for classification, or (2) incorporating several modalities, but reporting separate results for each.
- Our experiments reveal that this measure shows significant group differences between MCI subjects who progressed to AD, and those who remained stable for 3 years.
- These differences were most significant in MMDMs based on imaging data.
Why it matters
A machine learning method that combines multiple types of brain imaging showed differences between people with mild cognitive impairment who progressed and those who did not. This approach may improve prediction of progression, but it is a technical analysis and does not provide a treatment.
Study details
- Evidence strengthLow
How this study scored 51/100
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 | 2011 — 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 407 times (OpenAlex, 2026-10-10) |
| Citations per year | 4 / 5 | 23.9 citations a year over 17 year(s) since 2011 |
| Impact for its field and year | 4 / 6 | Cited 13.8× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 3 / 6 | Cited by 20 other studies in this library |
| Still being cited | 2 / 4 | Cited 37 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 4 / 4 | Last-listed author Sterling C. Johnson: h-index 112 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | University of Wisconsin–Madison: institution h-index 1,518 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 21146621) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 51 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1016/j.neuroimage.2010.10.081
- PubMed: 21146621
- Original source →
- Free full text: PubMed Central →
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
Hinrichs, C., Singh, V., Xu, G., Johnson, S. C., & Alzheimers Disease Neuroimaging Initiative. (2011). Predictive markers for AD in a multi-modality framework: an analysis of MCI progression in the ADNI population. NeuroImage. https://doi.org/10.1016/j.neuroimage.2010.10.081
Vancouver
Hinrichs C, Singh V, Xu G, Johnson SC, Alzheimers Disease Neuroimaging Initiative. Predictive markers for AD in a multi-modality framework: an analysis of MCI progression in the ADNI population. NeuroImage. 2011. doi:10.1016/j.neuroimage.2010.10.081. PMID: 21146621.
BibTeX
@article{hinrichs2011-21146621,
author = {Hinrichs, C. and Singh, V. and Xu, G. and Johnson, S. C. and {Alzheimers Disease Neuroimaging Initiative}},
title = {{Predictive markers for AD in a multi-modality framework: an analysis of MCI progression in the ADNI population}},
journal = {NeuroImage},
year = {2011},
doi = {10.1016/j.neuroimage.2010.10.081},
pmid = {21146621},
url = {https://doi.org/10.1016/j.neuroimage.2010.10.081}
}
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