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

doi:10.1016/j.jneumeth.2010.07.030

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

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

16 Citations (Scopus)

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 language	English
Pages (from-to)	277-285
Number of pages	9
Journal	Journal of Neuroscience Methods
Volume	192
Issue number	2
DOIs	https://doi.org/10.1016/j.jneumeth.2010.07.030
Publication status	Published - 2010
Externally published	Yes

Keywords

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

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10.1016/j.jneumeth.2010.07.030

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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

@article{86e9902d4a4f48ba9c02171fc407d0a1,

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

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.",

keywords = "Alzheimer's disease, Bootstrap resampling, FDG-PET, Family-wise error, MCI, Reliability, Reproducibility of results, SPM",

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

year = "2010",

doi = "10.1016/j.jneumeth.2010.07.030",

language = "English",

volume = "192",

pages = "277--285",

journal = "Journal of Neuroscience Methods",

issn = "0165-0270",

publisher = "Elsevier B.V.",

number = "2",

}

Wu, X, Chen, K, Yao, L, Ayutyanont, N, Langbaum, JBS, Fleisher, A, Reschke, C, Lee, W, Liu, X, Alexander, GE, Bandy, D, Foster, NL, Thompson, PM, Harvey, DJ, Weiner, MW, Koeppe, RA, Jagust, WJ & Reiman, EM 2010, 'Assessing the reliability to detect cerebral hypometabolism in probable Alzheimer's disease and amnestic mild cognitive impairment', Journal of Neuroscience Methods, vol. 192, no. 2, pp. 277-285. https://doi.org/10.1016/j.jneumeth.2010.07.030

TY - JOUR

T1 - Assessing the reliability to detect cerebral hypometabolism in probable Alzheimer's disease and amnestic mild cognitive impairment

AU - Wu, Xia

AU - Chen, Kewei

AU - Yao, Li

AU - Ayutyanont, Napatkamon

AU - Langbaum, Jessica B.S.

AU - Fleisher, Adam

AU - Reschke, Cole

AU - Lee, Wendy

AU - Liu, Xiaofen

AU - Alexander, Gene E.

AU - Bandy, Dan

AU - Foster, Norman L.

AU - Thompson, Paul M.

AU - Harvey, Danielle J.

AU - Weiner, Michael W.

AU - Koeppe, Robert A.

AU - Jagust, William J.

AU - Reiman, Eric M.

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

KW - Alzheimer's disease

KW - Bootstrap resampling

KW - FDG-PET

KW - Family-wise error

KW - MCI

KW - Reliability

KW - Reproducibility of results

KW - SPM

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U2 - 10.1016/j.jneumeth.2010.07.030

DO - 10.1016/j.jneumeth.2010.07.030

M3 - Article

C2 - 20678521

AN - SCOPUS:77956727855

SN - 0165-0270

VL - 192

SP - 277

EP - 285

JO - Journal of Neuroscience Methods

JF - Journal of Neuroscience Methods

IS - 2

ER -

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

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Keywords

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