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Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers
Zhang D, Shen D, Alzheimer's Disease Neuroimaging Initiative.. PloS one, 2012-03-22
Topic: Risk, prevention and care in population studies
Accurate prediction of clinical changes of mild cognitive impairment (MCI) patients, including both qualitative change (i.e., conversion to Alzheimer's disease (AD)) and quantitative change (i.e., cognitive scores) at future time points, is important for early diagnosis of AD and for monitoring the disease progression. In this paper, we propose to predict future clinical changes of MCI patients by using both baseline and longitudinal multimodality data. To do this, we first develop a longitudinal feature selection method to jointly select brain regions across multiple time points for each modality. Specifically, for each time point, we train a sparse linear regression model by using the imaging data and the corresponding clinical scores, with an extra 'group regularization' to group the weights corresponding to the same brain region across multiple time points together and to allow for selection of brain regions based on the strength of multiple time points jointly. Then, to further reflect the longitudinal changes on the selected brain regions, we extract a set of longitudinal features from the original baseline and longitudinal data. Finally, we combine all features on the selected brain regions, from different modalities, for prediction by using our previously proposed multi-kernel SVM. We validate our method on 88 ADNI MCI subjects, with both MRI and FDG-PET data and the corresponding clinical scores (i.e., MMSE and ADAS-Cog) at 5 different time points. We first predict the clinical scores (MMSE and ADAS-Cog) at 24-month by using the multimodality data at previous time points, and then predict the conversion of MCI to AD by using the multimodality data at time points which are at least 6-month ahead of the conversion. The results on both sets of experiments show that our proposed method can achieve better performance in predicting future clinical changes of MCI patients than the conventional methods.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Zhang, D., Shen, D., & Alzheimer's Disease Neuroimaging Initiative. (2012). Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers. PloS one. https://doi.org/10.1371/journal.pone.0033182
Vancouver
Zhang D, Shen D, Alzheimer's Disease Neuroimaging Initiative. Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers. PloS one. 2012. doi:10.1371/journal.pone.0033182. PMID: 22457741.
BibTeX
@article{zhang2012-22457741,
author = {Zhang, D. and Shen, D. and {Alzheimer's Disease Neuroimaging Initiative}},
title = {{Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers}},
journal = {PloS one},
year = {2012},
doi = {10.1371/journal.pone.0033182},
pmid = {22457741},
url = {https://doi.org/10.1371/journal.pone.0033182}
}
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.
Key findings
- The results on both sets of experiments show that our proposed method can achieve better performance in predicting future clinical changes of MCI patients than the conventional methods.
Why it matters
This study developed a method using imaging and other data over time to predict future cognitive changes and conversion to Alzheimer's disease in people with MCI. It is a prediction tool for early diagnosis and monitoring, not a treatment or prevention.
Study details
- Study designlongitudinal study
- Evidence strengthModerate
How this study scored 58/100
Published in a peer-reviewed journal, and it is a longitudinal study following the same people over time. Scored zero for sample size, effect reported, 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 56 of the 96 points we could judge it on, which is 58 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 | 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 | 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 293 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 18.3 citations a year over 16 year(s) since 2012 |
| Impact for its field and year | 4 / 6 | Cited 15.2× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 3 / 6 | Cited by 28 other studies in this library |
| Still being cited | 2 / 4 | Cited 44 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 | University of California, Los Angeles: institution h-index 1,923 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 22457741) |
| Free to read | 2 / 2 | Free to read |
| Points | 56 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 56 of the 96 points we could judge it on is 58 out of 100. |
| Total | 58 / 100 | — |
What each of these 20 components measures, and what this score is not →
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