Multivariate statistical mapping of spectroscopic imaging data

Karl Young, Varan Govind, Khema Sharma, Colin Studholme, Andrew A. Maudsley, Norbert Schuff

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)20-24
Number of pages5
JournalMagnetic Resonance in Medicine
Issue number1
StatePublished - Jan 2010


  • Amytrophic lateral sclerosis
  • Brain metabolism
  • Multivariate analysis
  • Simulation
  • Spectroscopic imaging

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging


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