The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Predicting Cognitive Decline in Subjects at Risk for Alzheimer Disease by Using Combined Cerebrospinal Fluid, MR Imaging, and PET Biomarkers
Open accessAbstract
PURPOSE: To assess the extent to which multiple Alzheimer disease (AD) biomarkers improve the ability to predict future decline in subjects with mild cognitive impairment (MCI) compared with predictions based on clinical parameters alone. MATERIALS AND METHODS: All protocols were approved by the institutional review board at each site, and written informed consent was obtained from all subjects. The study was HIPAA compliant. Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline magnetic resonance (MR) imaging and fluorine 18 fluorodeoxyglucose (FDG) positron emission tomography (PET) studies for 97 subjects with MCI were used. MR imaging-derived gray matter probability maps and FDG PET images were analyzed by using independent component analysis, an unbiased data-driven method to extract independent sources of information from whole-brain data. The loading parameters for all MR imaging and FDG components, along with cerebrospinal fluid (CSF) proteins, were entered into logistic regression models (dependent variable: conversion to AD within 4 years). Eight models were considered, including all combinations of MR imaging, PET, and CSF markers with the covariates (age, education, apolipoprotein E genotype, Alzheimer's Disease Assessment Scale-Cognitive subscale score). RESULTS: Combining MR imaging, FDG PET, and CSF data with routine clinical tests significantly increased the accuracy of predicting conversion to AD compared with clinical testing alone. The misclassification rate decreased from 41.3% to 28.4% (P < .00001). FDG PET contributed more information to routine tests (P < .00001) than CSF (P = .32) or MR imaging (P = .08). CONCLUSION: Imaging and CSF biomarkers can improve prediction of conversion from MCI to AD compared with baseline clinical testing. FDG PET appears to add the greatest prognostic information.
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Self-citationComparing predictors of conversion and decline in mild cognitive impairment
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Hippocampal formation glucose metabolism and volume losses in MCI and AD
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Neuroimaging and Early Diagnosis of Alzheimer Disease: A Look to the Future
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“The components highlight regions that have been implicated in the development of AD, including the medial temporal lobes at MR imaging and the temporoparietal lobes and posterior cingulate region at FDG PET (2,13,14).”
BackgroundInfluentialSelf-citationA meta-analysis of diffusion tensor imaging in mild cognitive impairment and Alzheimer's disease
Sexton, C. E., Kalu, U. G., Filippini, N., Mackay, C. E., & Ebmeier, K. P. (2011) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.05.019 · Cited by 410
Cited by
Mild cognitive impairment: a concept in evolution
Petersen, R. C., Caracciolo, B., Brayne, C., Gauthier, S., Jelic, V., & Fratiglioni, L. (2014) · Journal of Internal Medicine · doi:10.1111/joim.12190 · Cited by 1,694
Multimodal deep learning models for early detection of Alzheimer’s disease stage
Venugopalan, J., Tong, L., Hassanzadeh, H. R., & Wang, M. D. (2021) · Scientific Reports · doi:10.1038/s41598-020-74399-w · Cited by 732
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Self-citationLombardi, G., Crescioli, G., Cavedo, E., Lucenteforte, E., Casazza, G., Bellatorre, A.-G., Lista, C., Costantino, G., Frisoni, G., Virgili, G., et al. (2020) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd009628.pub2 · Cited by 214
Metadata
- Authors
- Shaffer, J. L., Petrella, J. R., Sheldon, F. C., Choudhury, K. R., Calhoun, V. D., Coleman, R. E., Doraiswamy, P. M., & Initiative, F. T. A. D. N.
- Venue
- Radiology
- Published
- 2013-02
- Volume
- 266
- Issue
- 2
- Pages
- 583-591
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- OpenAlex
- W1967737804
- Cited by
- 255
- References
- 10
- FWCI
- 15.37
- Type
- journal article
- Language
- en
- Open access
- green
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