TY - JOUR
T1 - Linking functional and structural brain images with multivariate network analyses
T2 - A novel application of the partial least square method
AU - Chen, Kewei
AU - Reiman, Eric M.
AU - Huan, Zhongdan
AU - Caselli, Richard J.
AU - Bandy, Daniel
AU - Ayutyanont, Napatkamon
AU - Alexander, Gene E.
N1 - Funding Information:
Earlier versions of this work were presented at the World Congress on Medical Physics and Biomedical Engineering, Aug, 2003, Australia, the Nuclear Medicine Annual meeting, June, 2004, and the 9th International Conference on Alzheimer's Disease and Related Disorders, 2004. This work was supported by the NIMH (MH57899), NIA (P30AG19610, AG025526, MH57899), the Alzheimer's Association (IIRG-98-068, IIRG-98-078, and IIRG-02-3784), the Mayo Clinic and Banner Alzheimer's Foundations, and the Arizona Department of Health Services. The authors thank Wendy Lee, Sandy Goodwin, Les Mullen, Pat Raso, Debbie Intorcia, Anita Prouty, Cole Reschke, Xiaofen Liu, Justin Venditti, Dr. Lawrence Mayer and Dr. Richard Gerkin for their support.
PY - 2009/8/15
Y1 - 2009/8/15
N2 - In this article, we introduce a multimodal multivariate network analysis to characterize the linkage between the patterns of information from the same individual's complementary brain images, and illustrate its potential by showing its ability to distinguish older from younger adults with greater power than several previously established methods. Our proposed method uses measurements from every brain voxel in each person's complementary co-registered images and uses the partial least square (PLS) algorithm to form a combined latent variable that maximizes the covariance among all of the combined variables. It represents a new way to calculate the singular value decomposition from the high-dimensional covariance matrix in a computationally feasible way. Analyzing fluorodeoxyglucose positron emission tomography (PET) and volumetric magnetic resonance imaging (MRI) images, this method distinguished 14 older adults from 15 younger adults (p = 4e- 12) with no overlap between groups, no need to correct for multiple comparisons, and greater power than the univariate Statistical Parametric Mapping (SPM), multimodal SPM or multivariate PLS analysis of either imaging modality alone. This technique has the potential to link patterns of information among any number of complementary images from an individual, to use other kinds of complementary complex datasets besides brain images, and to characterize individual state- or trait-dependent brain patterns in a more powerful way.
AB - In this article, we introduce a multimodal multivariate network analysis to characterize the linkage between the patterns of information from the same individual's complementary brain images, and illustrate its potential by showing its ability to distinguish older from younger adults with greater power than several previously established methods. Our proposed method uses measurements from every brain voxel in each person's complementary co-registered images and uses the partial least square (PLS) algorithm to form a combined latent variable that maximizes the covariance among all of the combined variables. It represents a new way to calculate the singular value decomposition from the high-dimensional covariance matrix in a computationally feasible way. Analyzing fluorodeoxyglucose positron emission tomography (PET) and volumetric magnetic resonance imaging (MRI) images, this method distinguished 14 older adults from 15 younger adults (p = 4e- 12) with no overlap between groups, no need to correct for multiple comparisons, and greater power than the univariate Statistical Parametric Mapping (SPM), multimodal SPM or multivariate PLS analysis of either imaging modality alone. This technique has the potential to link patterns of information among any number of complementary images from an individual, to use other kinds of complementary complex datasets besides brain images, and to characterize individual state- or trait-dependent brain patterns in a more powerful way.
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U2 - 10.1016/j.neuroimage.2009.04.053
DO - 10.1016/j.neuroimage.2009.04.053
M3 - Article
C2 - 19393744
AN - SCOPUS:67349174191
SN - 1053-8119
VL - 47
SP - 602
EP - 610
JO - NeuroImage
JF - NeuroImage
IS - 2
ER -