title: Voxelwise multivariate analysis of multimodality magnetic resonance imaging.
abstract: Most brain magnetic resonance imaging (MRI) studies concentrate on a single MRI contrast or modality, frequently structural MRI. By performing an integrated analysis of several modalities, such as structural, perfusion-weighted, and diffusion-weighted MRI, new insights may be attained to better understand the underlying processes of brain diseases. We compare two voxelwise approaches: (1) fitting multiple univariate models, one for each outcome and then adjusting for multiple comparisons among the outcomes and (2) fitting a multivariate model. In both cases, adjustment for multiple comparisons is performed over all voxels jointly to account for the search over the brain. The multivariate model is able to account for the multiple comparisons over outcomes without assuming independence because the covariance structure between modalities is estimated. Simulations show that the multivariate approach is more powerful when the outcomes are correlated and, even when the outcomes are independent, the multivariate approach is just as powerful or more powerful when at least two outcomes are dependent on predictors in the model. However, multiple univariate regressions with Bonferroni correction remain a desirable alternative in some circumstances. To illustrate the power of each approach, we analyze a case control study of Alzheimer's disease, in which data from three MRI modalities are available.
authors: Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts; Pritzker School of Medicine, University of Chicago, Chicago, Illinois.
authors: Melissa G
authors: MG
authors: Naylor
authors: Valerie A
authors: VA
authors: Cardenas
authors: Duygu
authors: D
authors: Tosun
authors: Norbert
authors: N
authors: Schuff
authors: Michael
authors: M
authors: Weiner
authors: Armin
authors: A
authors: Schwartzman
mesh_terms: D000368:Aged; D000544:Alzheimer Disease / Q000473:pathology* / Q000503:physiopathology; D016880:Anisotropy; D001921:Brain / Q000033:anatomy & histology / Q000473:pathology / Q000502:physiology / Q000503:physiopathology; D016022:Case-Control Studies; D002560:Cerebrovascular Circulation / Q000502:physiology; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D056324:Diffusion Tensor Imaging / Q000295:instrumentation / Q000379:methods; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000295:instrumentation / Q000379:methods*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D015999:Multivariate Analysis; D011897:Random Allocation; D013113:Spin Labels
chemical_list: D013113:Spin Labels
keywords: Alzheimer's disease; diffusion tensor imaging; multimodality imaging; multiple comparisons; multivariate analysis; perfusion weighted magnetic resonance imaging; structural magnetic resonance imaging