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Classification of Alzheimer disease, mild cognitive impairment, and normal cognitive status with large-scale network analysis based on resting-state functional MR imaging
Chen G, Ward BD, Xie C, Li W, Wu Z, Jones JL, Franczak M, Antuono P et al.. Radiology, 2011-01-19
Topic: Amyloid, tau and blood-test diagnosis
Purpose To use large-scale network (LSN) analysis to classify subjects with Alzheimer disease (AD), those with amnestic mild cognitive impairment (aMCI), and cognitively normal (CN) subjects. Materials and methods The study was conducted with institutional review board approval and was in compliance with HIPAA regulations. Written informed consent was obtained from each participant. Resting-state functional magnetic resonance (MR) imaging was used to acquire the voxelwise time series in 55 subjects with clinically diagnosed AD (n = 20), aMCI (n =15), and normal cognitive function (n = 20). The brains were divided into 116 regions of interest (ROIs). The Pearson product moment correlation coefficients of pairwise ROIs were used to classify these subjects. Error estimation of the classifications was performed with the leave-one-out cross-validation method. Linear regression analysis was performed to analyze the relationship between changes in network connectivity strengths and behavioral scores. Results The area under the receiver operating characteristic curve (AUC) yielded 87% classification power, 85% sensitivity, and 80% specificity between the AD group and the non-AD group (subjects with aMCI and CN subjects) in the first-step classification. For differentiation between subjects with aMCI and CN subjects, AUC was 95%; sensitivity, 93%; and specificity, 90%. The decreased network indexes were significantly correlated with the Mini-Mental State Examination score in all tested subjects. Similarly, changes in network indexes significantly correlated with Rey Auditory Verbal Leaning Test delayed recall scores in subjects with aMCI and CN subjects. Conclusion LSN analysis revealed that interconnectivity patterns of brain regions can be used to classify subjects with AD, those with aMCI, and CN subjects. In addition, the altered connectivity networks were significantly correlated with the results of cognitive tests.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Chen, G., Ward, B. D., Xie, C., Li, W., Wu, Z., Jones, J. L., et al. (2011). Classification of Alzheimer disease, mild cognitive impairment, and normal cognitive status with large-scale network analysis based on resting-state functional MR imaging. Radiology. https://doi.org/10.1148/radiol.10100734
Vancouver
Chen G, Ward BD, Xie C, Li W, Wu Z, Jones JL, et al. Classification of Alzheimer disease, mild cognitive impairment, and normal cognitive status with large-scale network analysis based on resting-state functional MR imaging. Radiology. 2011. doi:10.1148/radiol.10100734. PMID: 21248238.
BibTeX
@article{chen2011-21248238,
author = {Chen, G. and Ward, B. D. and Xie, C. and Li, W. and Wu, Z. and Jones, J. L. and Franczak, M. and Antuono, P. and others},
title = {{Classification of Alzheimer disease, mild cognitive impairment, and normal cognitive status with large-scale network analysis based on resting-state functional MR imaging}},
journal = {Radiology},
year = {2011},
doi = {10.1148/radiol.10100734},
pmid = {21248238},
url = {https://doi.org/10.1148/radiol.10100734}
}
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Key findings
- Results The area under the receiver operating characteristic curve (AUC) yielded 87% classification power, 85% sensitivity, and 80% specificity between the AD group and the non-AD group (subjects with aMCI and CN subjects) in the first-step classification.
- The decreased network indexes were significantly correlated with the Mini-Mental State Examination score in all tested subjects.
- Similarly, changes in network indexes significantly correlated with Rey Auditory Verbal Leaning Test delayed recall scores in subjects with aMCI and CN subjects.
- Conclusion LSN analysis revealed that interconnectivity patterns of brain regions can be used to classify subjects with AD, those with aMCI, and CN subjects.
- In addition, the altered connectivity networks were significantly correlated with the results of cognitive tests.
Why it matters
This small study used resting-state functional MRI and network analysis to distinguish people with Alzheimer disease, amnestic mild cognitive impairment, and normal cognition with fairly high accuracy. It concerns classification and diagnosis only, not treating, curing, or preventing the disease.
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Study details
- Participants20
- Evidence strengthLow
How this study scored 47/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomized / 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 45 of the 96 points we could judge it on, which is 47 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Radiology) |
| Journal's citation record | 3 / 4 | Radiology: journal h-index 431 (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 | 1 / 4 | 20 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) |
| Randomized / blinded | 0 / 2 | No randomization or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 5 / 7 | Cited 276 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 16.2 citations a year over 17 year(s) since 2011 |
| Impact for its field and year | 4 / 6 | Cited 9.8× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 4 other studies in this library |
| Still being cited | 2 / 4 | Cited 20 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | — / 4 | OpenAlex's profile for last-listed author Shi-Jiang Li holds only 6 work(s), too few to judge a track record — left out of this score |
| Institution's track record | 1 / 3 | Medical College of Wisconsin: institution h-index 747 (OpenAlex) |
| Multicenter 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 21248238) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 45 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 45 of the 96 points we could judge it on is 47 out of 100. |
| Total | 47 / 100 | — |
What each of these 20 components measures, and what this score is not →
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