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Sparse learning and stability selection for predicting MCI to AD conversion using baseline ADNI data
Ye J, Farnum M, Yang E, Verbeeck R, Lobanov V, Raghavan N, Novak G, DiBernardo A et al.. BMC neurology, 2012-06-25
Topic: Risk, prevention and care in population studies
Background Patients with Mild Cognitive Impairment (MCI) are at high risk of progression to Alzheimer's dementia. Identifying MCI individuals with high likelihood of conversion to dementia and the associated biosignatures has recently received increasing attention in AD research. Different biosignatures for AD (neuroimaging, demographic, genetic and cognitive measures) may contain complementary information for diagnosis and prognosis of AD.
Key findings
- Identifying MCI individuals with high likelihood of conversion to dementia and the associated biosignatures has recently received increasing attention in AD research.
- Different biosignatures for AD (neuroimaging, demographic, genetic and cognitive measures) may contain complementary information for diagnosis and prognosis of AD.
- Methods We have conducted a comprehensive study using a large number of samples from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to test the power of integrating various baseline data for predicting the conversion from MCI to probable AD and identifying a small subset of biosignatures for the prediction and assess the relative importance of different modalities in predicting MCI to AD conversion.
- We have employed sparse logistic regression with stability selection for the integration and selection of potential predictors.
Why it matters
This work uses sparse learning and stability selection on baseline ADNI data to predict which people with mild cognitive impairment will convert to Alzheimer's dementia. It is a prediction method and does not test any treatment, cure, or prevention.
Study details
- Evidence strengthModerate
How this study scored 44/100
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 (BMC neurology) |
| Journal's citation record | 1 / 4 | BMC Neurology: journal h-index 122 (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 | 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 | 1 / 5 | 2012 — 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 | 5 / 7 | Cited 145 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 9.1 citations a year over 16 year(s) since 2012 |
| Impact for its field and year | 3 / 6 | Cited 7.4× 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 | 1 / 4 | Cited 12 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Vaibhav A. Narayan: h-index 35 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Arizona State University: institution h-index 987 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 22731740) |
| Free to read | 2 / 2 | Free to read |
| Total | 44 / 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. 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
Ye, J., Farnum, M., Yang, E., Verbeeck, R., Lobanov, V., Raghavan, N., et al. (2012). Sparse learning and stability selection for predicting MCI to AD conversion using baseline ADNI data. BMC neurology. https://doi.org/10.1186/1471-2377-12-46
Vancouver
Ye J, Farnum M, Yang E, Verbeeck R, Lobanov V, Raghavan N, et al. Sparse learning and stability selection for predicting MCI to AD conversion using baseline ADNI data. BMC neurology. 2012. doi:10.1186/1471-2377-12-46. PMID: 22731740.
BibTeX
@article{ye2012-22731740,
author = {Ye, J. and Farnum, M. and Yang, E. and Verbeeck, R. and Lobanov, V. and Raghavan, N. and Novak, G. and DiBernardo, A. and others},
title = {{Sparse learning and stability selection for predicting MCI to AD conversion using baseline ADNI data}},
journal = {BMC neurology},
year = {2012},
doi = {10.1186/1471-2377-12-46},
pmid = {22731740},
url = {https://doi.org/10.1186/1471-2377-12-46}
}
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