MULTIVARIATE VOXELWISE ANALYSIS OF MULTIMODALITY IMAGING

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This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. This is an added subproject to Imaging Core, Project 1 Brain image analyses, particularly in the study of neurodegenerative diseases, usually concentrate on a single imaging modality, e.g. structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), perfusion MRI. Different imaging modalities provide complementary, but not necessarily independent, information about the brain. The goal of this project is to develop statistical methodology for analyzing several imaging modalities simultaneously in order to increase the statistical power of finding localized characteristics of disease, as well as revealing relationships between the modalities and between different locations in the brain. For this purpose, we assume that the imaging data is given as a set of co-registered scalar images from a number of subjects and corresponding to various imaging modalities. These images may be, among others, volume expansion/contraction obtained from TBM applied to sMRI, blood flow measurements obtained from perfusion MRI, and scalar summaries such as fractional anisotropy obtained from DTI. Specific Aim 1: Develop a multivariate statistical methodology for testing the effect of disease status on multimodality imaging simultaneously at each voxel. This includes: + Comparison of univariate and multivariate regression approaches + Software implementation, performance evaluation via simulations, application to the study of Alzheimer's disease (AD). Specific Aim 2: Develop a multivariate statistical methodology for testing the effect of disease status on multimodality imaging simultaneously at different voxels. This includes: + Comparison of cross-correlation analysis and canonical correlation analysis + Software implementation, performance evaluation via simulations, application to the study of Alzheimer's disease (AD).