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Cited by 162

The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.

Characterizing Alzheimer's disease using a hypometabolic convergence index

Open access
Chen, Kewei, Ayutyanont, Napatkamon, Langbaum, Jessica B.S., Fleisher, Adam S., Reschke, Cole, Lee, Wendy, Liu, Xiaofen, Bandy, Dan, Alexander, Gene E., Thompson, Paul M., Shaw, Leslie, Trojanowski, John Q., Jack, Clifford R., Landau, Susan M., Foster, Norman L., Harvey, Danielle J., Weiner, Michael W., Koeppe, Robert A., Jagust, William J., Reiman, Eric M.·2011·NeuroImage
DOI 10.1016/j.neuroimage.2011.01.049
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Abstract

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 demonstrate 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 corresponds 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 18 months, 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 HCI’s 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, have 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.

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Metadata

Authors
Chen, K., Ayutyanont, N., Langbaum, J. B. S., Fleisher, A. S., Reschke, C., Lee, W., Liu, X., Bandy, D., Alexander, G. E., Thompson, P. M., Shaw, L., Trojanowski, J. Q., Jack, C. R., Landau, S. M., Foster, N. L., Harvey, D. J., Weiner, M. W., Koeppe, R. A., Jagust, W. J., & Reiman, E. M.
Venue
NeuroImage
Publisher
Elsevier BV
Published
2011-05
Volume
56
Issue
1
Pages
52-60
ISSN
1053-8119 (ISSN-L)
DOI
10.1016/j.neuroimage.2011.01.049
OpenAlex
W1990181857
Cited by
162
References
17
FWCI
10.05
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Characterizing Alzheimer's disease using a hypometabolic convergence index. https://app.cochat.ai/papers/10.1016/j.neuroimage.2011.01.049
The paper
Chen, K., Ayutyanont, N., Langbaum, J. B. S., Fleisher, A. S., Reschke, C., Lee, W., Liu, X., Bandy, D., Alexander, G. E., Thompson, P. M., Shaw, L., Trojanowski, J. Q., Jack, C. R., Landau, S. M., Foster, N. L., Harvey, D. J., Weiner, M. W., Koeppe, R. A., Jagust, W. J., & Reiman, E. M. (2011). Characterizing Alzheimer's disease using a hypometabolic convergence index. NeuroImage. https://doi.org/10.1016/j.neuroimage.2011.01.049

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.