Multi-source feature learning for joint analysis of incomplete multiple heterogeneous neuroimaging data
Yuan L, Wang Y, Thompson PM, Narayan VA, Ye J, Alzheimer's Disease Neuroimaging Initiative.. NeuroImage, 2012-03-29
Topic: Risk, prevention and care in population studies
Analysis of incomplete data is a big challenge when integrating large-scale brain imaging datasets from different imaging modalities. In the Alzheimer's Disease Neuroimaging Initiative (ADNI), for example, over half of the subjects lack cerebrospinal fluid (CSF) measurements; an independent half of the subjects do not have fluorodeoxyglucose positron emission tomography (FDG-PET) scans; many lack proteomics measurements. Traditionally, subjects with missing measures are discarded, resulting in a severe loss of available information. In this paper, we address this problem by proposing an incomplete Multi-Source Feature (iMSF) learning method where all the samples (with at least one available data source) can be used. To illustrate the proposed approach, we classify patients from the ADNI study into groups with Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal controls, based on the multi-modality data. At baseline, ADNI's 780 participants (172AD, 397 MCI, 211 NC), have at least one of four data types: magnetic resonance imaging (MRI), FDG-PET, CSF and proteomics. These data are used to test our algorithm. Depending on the problem being solved, we divide our samples according to the availability of data sources, and we learn shared sets of features with state-of-the-art sparse learning methods. To build a practical and robust system, we construct a classifier ensemble by combining our method with four other methods for missing value estimation. Comprehensive experiments with various parameters show that our proposed iMSF method and the ensemble model yield stable and promising results.
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APA 7th
Yuan, L., Wang, Y., Thompson, P. M., Narayan, V. A., Ye, J., & Alzheimer's Disease Neuroimaging Initiative. (2012). Multi-source feature learning for joint analysis of incomplete multiple heterogeneous neuroimaging data. NeuroImage. https://doi.org/10.1016/j.neuroimage.2012.03.059
Vancouver
Yuan L, Wang Y, Thompson PM, Narayan VA, Ye J, Alzheimer's Disease Neuroimaging Initiative. Multi-source feature learning for joint analysis of incomplete multiple heterogeneous neuroimaging data. NeuroImage. 2012. doi:10.1016/j.neuroimage.2012.03.059. PMID: 22498655.
BibTeX
@article{yuan2012-22498655,
author = {Yuan, L. and Wang, Y. and Thompson, P. M. and Narayan, V. A. and Ye, J. and {Alzheimer's Disease Neuroimaging Initiative}},
title = {{Multi-source feature learning for joint analysis of incomplete multiple heterogeneous neuroimaging data}},
journal = {NeuroImage},
year = {2012},
doi = {10.1016/j.neuroimage.2012.03.059},
pmid = {22498655},
url = {https://doi.org/10.1016/j.neuroimage.2012.03.059}
}
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Key findings
- Comprehensive experiments with various parameters show that our proposed iMSF method and the ensemble model yield stable and promising results.
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Study details
- Participants780
- Evidence strengthLow
How this study scored 45/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 26 of the 58 points we could judge it on, which is 45 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (NeuroImage) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| 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 | 2012 — within 20 year(s) of the newest study here |
| Sample size | 2 / 4 | 780 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| 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 | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 22498655) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 26 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 26 of the 58 points we could judge it on is 45 out of 100. |
| Total | 45 / 100 | — |
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