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Multivariate statistical mapping of spectroscopic imaging data
Author(s) -
Young Karl,
Govind Varan,
Sharma Khema,
Studholme Colin,
Maudsley Andrew A.,
Schuff Norbert
Publication year - 2010
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.22190
Subject(s) - univariate , multivariate statistics , magnetic resonance imaging , voxel , statistical parametric mapping , multivariate analysis , functional magnetic resonance imaging , neuroimaging , magnetic resonance spectroscopic imaging , computer science , nuclear magnetic resonance , pattern recognition (psychology) , artificial intelligence , medicine , neuroscience , psychology , physics , machine learning , radiology
For magnetic resonance spectroscopic imaging studies of the brain, it is important to measure the distribution of metabolites in a regionally unbiased way; that is, without restrictions to a priori defined regions of interest. Since magnetic resonance spectroscopic imaging provides measures of multiple metabolites simultaneously at each voxel, there is furthermore great interest in utilizing the multidimensional nature of magnetic resonance spectroscopic imaging for gains in statistical power. Voxelwise multivariate statistical mapping is expected to address both of these issues, but it has not been previously employed for spectroscopic imaging (SI) studies of brain. The aims of this study were to (1) develop and validate multivariate voxel‐based statistical mapping for magnetic resonance spectroscopic imaging and (2) demonstrate that multivariate tests can be more powerful than univariate tests in identifying patterns of altered brain metabolism. Specifically, we compared multivariate to univariate tests in identifying known regional patterns in simulated data and regional patterns of metabolite alterations due to amyotrophic lateral sclerosis, a devastating brain disease of the motor neurons. Magn Reson Med, 2010. © 2009 Wiley‐Liss, Inc.