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Characterizing Alzheimer's disease using a hypometabolic convergence index
Chen K, Ayutyanont N, Langbaum JB, Fleisher AS, Reschke C, Lee W, Liu X, Bandy D et al.. NeuroImage, 2011-01-27
Topic: Amyloid, tau and blood-test diagnosis
This article introduces a hypometabolic convergence index (HCI) for the assessment of Alzheimer's disease (AD); compares it to other biological, cognitive and clinical measures; and demonstrates its promise to predict clinical decline in mild cognitive impairment (MCI) patients using data from the AD Neuroimaging Initiative (ADNI). The HCI is intended to reflect in a single measurement the extent to which the pattern and magnitude of cerebral hypometabolism in an individual's fluorodeoxyglucose positron emission tomography (FDG-PET) image correspond to that in probable AD patients, and is generated using a fully automated voxel-based image-analysis algorithm. HCIs, magnetic resonance imaging (MRI) hippocampal volume measurements, cerebrospinal fluid (CSF) assays, memory test scores, and clinical ratings were compared in 47 probable AD patients, 21 MCI patients who converted to probable AD within the next 18months, 76 MCI patients who did not, and 47 normal controls (NCs) in terms of their ability to characterize clinical disease severity and predict conversion rates from MCI to probable AD. HCIs were significantly different in the probable AD, MCI converter, MCI stable and NC groups (p=9e-17) and correlated with clinical disease severity. Using retrospectively characterized threshold criteria, MCI patients with either higher HCIs or smaller hippocampal volumes had the highest hazard ratios (HRs) for 18-month progression to probable AD (7.38 and 6.34, respectively), and those with both had an even higher HR (36.72). In conclusion, the HCI, alone or in combination with certain other biomarker measurements, has the potential to help characterize AD and predict subsequent rates of clinical decline. More generally, our conversion index strategy could be applied to a range of imaging modalities and voxel-based image-analysis algorithms.
Key findings
- HCIs were significantly different in the probable AD, MCI converter, MCI stable and NC groups (p=9e-17) and correlated with clinical disease severity.
- In conclusion, the HCI, alone or in combination with certain other biomarker measurements, has the potential to help characterize AD and predict subsequent rates of clinical decline.
Why it matters
A hypometabolic convergence index derived from FDG-PET differed significantly across probable Alzheimer's, mild cognitive impairment converter, mild cognitive impairment stable and normal groups and correlated with disease severity. Alone or with other biomarkers, it has potential to help characterize Alzheimer's disease and predict clinical decline, but it is not 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 | 5 / 7 | Cited 163 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 9.6 citations a year over 17 year(s) since 2011 |
| Impact for its field and year | 4 / 6 | Cited 10.1× 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 16 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 4 / 4 | Last-listed author Eric M. Reiman: h-index 151 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | University of Michigan: institution h-index 2,240 (OpenAlex) |
| Multi-centre collaboration | 3 / 3 | OpenAlex lists 14 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 21276856) |
| 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.2011.01.049
- PubMed: 21276856
- 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. 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
Chen, K., Ayutyanont, N., Langbaum, J. B., Fleisher, A. S., Reschke, C., Lee, W., et al. (2011). Characterizing Alzheimer's disease using a hypometabolic convergence index. NeuroImage. https://doi.org/10.1016/j.neuroimage.2011.01.049
Vancouver
Chen K, Ayutyanont N, Langbaum JB, Fleisher AS, Reschke C, Lee W, et al. Characterizing Alzheimer's disease using a hypometabolic convergence index. NeuroImage. 2011. doi:10.1016/j.neuroimage.2011.01.049. PMID: 21276856.
BibTeX
@article{chen2011-21276856,
author = {Chen, K. and Ayutyanont, N. and Langbaum, J. B. and Fleisher, A. S. and Reschke, C. and Lee, W. and Liu, X. and Bandy, D. and others},
title = {{Characterizing Alzheimer's disease using a hypometabolic convergence index}},
journal = {NeuroImage},
year = {2011},
doi = {10.1016/j.neuroimage.2011.01.049},
pmid = {21276856},
url = {https://doi.org/10.1016/j.neuroimage.2011.01.049}
}
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