Assessing the reliability to detect cerebral hypometabolism in probable Alzheimer's disease and amnestic mild cognitive impairment

Xia Wu, Kewei Chen, Li Yao, Napatkamon Ayutyanont, Jessica B S Langbaum, Adam Fleisher, Cole Reschke, Wendy Lee, Xiaofen Liu, Gene E. Alexander, Dan Bandy, Norman L. Foster, Paul M. Thompson, Danielle J Harvey, Michael W. Weiner, Robert A. Koeppe, William J. Jagust, Eric M. Reiman

Research output: Contribution to journalArticle

15 Scopus citations

Abstract

Fluorodeoxyglucose positron emission tomography (FDG-PET) studies report characteristic patterns of cerebral hypometabolism in probable Alzheimer's disease (pAD) and amnestic mild cognitive impairment (aMCI). This study aims to characterize the consistency of regional hypometabolism in pAD and aMCI patients enrolled in the AD neuroimaging initiative (ADNI) using statistical parametric mapping (SPM) and bootstrap resampling, and to compare bootstrap-based reliability index to the commonly used type-I error approach with or without correction for multiple comparisons. Batched SPM5 was run for each of 1000 bootstrap iterations to compare FDG-PET images from 74 pAD and 142 aMCI patients, respectively, to 82 normal controls. Maps of the hypometabolic voxels detected for at least a specific percentage of times over the 1000 runs were examined and compared to an overlap of the hypometabolic maps obtained from 3 randomly partitioned independent sub-datasets. The results from the bootstrap derived reliability of regional hypometabolism in the overall data set were similar to that observed in each of the three non-overlapping sub-sets using family-wise error. Strong but non-linear association was found between the bootstrap-based reliability index and the type-I error. For threshold p= 0.0005, pAD was associated with extensive hypometabolic voxels in the posterior cingulate/precuneus and parietotemporal regions with reliability between 90% and 100%. Bootstrap analysis provides an alternative to the parametric family-wise error approach used to examine consistency of hypometabolic brain voxels in pAD and aMCI patients. These results provide a foundation for the use of bootstrap analysis characterize statistical ROIs or search regions in both cross-sectional and longitudinal FDG-PET studies. This approach offers promise in the early detection and tracking of AD, the evaluation of AD-modifying treatments, and other biologically or clinical important measurements using brain images and voxel-based data analysis techniques.

Original languageEnglish (US)
Pages (from-to)277-285
Number of pages9
JournalJournal of Neuroscience Methods
Volume192
Issue number2
DOIs
StatePublished - 2010

Keywords

  • Alzheimer's disease
  • Bootstrap resampling
  • Family-wise error
  • FDG-PET
  • MCI
  • Reliability
  • Reproducibility of results
  • SPM

ASJC Scopus subject areas

  • Neuroscience(all)

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  • Cite this

    Wu, X., Chen, K., Yao, L., Ayutyanont, N., Langbaum, J. B. S., Fleisher, A., Reschke, C., Lee, W., Liu, X., Alexander, G. E., Bandy, D., Foster, N. L., Thompson, P. M., Harvey, D. J., Weiner, M. W., Koeppe, R. A., Jagust, W. J., & Reiman, E. M. (2010). Assessing the reliability to detect cerebral hypometabolism in probable Alzheimer's disease and amnestic mild cognitive impairment. Journal of Neuroscience Methods, 192(2), 277-285. https://doi.org/10.1016/j.jneumeth.2010.07.030