A Gaussian mixture model approach for estimating and comparing the shapes of distributions of neuroimaging data: Diffusion-measured aging effects in brain white matter

Namhee Kim, Moonseong Heo, Roman Fleysher, Craig A. Branch, Michael L. Lipton

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Neuroimaging signal intensity measures underlying physiology at each voxel unit.The brainwide distribution of signal intensities may be used to assess gross brain abnormality. To compare distributions of brain image data between groups, t-tests are widely applied. This approach, however, only compares group means and fails to consider the shapes of the distributions.We propose a simple approach for estimating both subject- and group-level density functions based on the framework of Gaussian mixture modeling, with mixture probabilities that are testable between groups.We demonstrate this approach by application to the analysis of fractional anisotropy image data for assessment of aging effects in white matter.

Original languageEnglish (US)
Article number32
JournalFrontiers in Public Health
Volume2
Issue numberAPR
DOIs
StatePublished - Apr 14 2014

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Neuroimaging
Anisotropy
Brain
White Matter

Keywords

  • Aging effects
  • Density function estimation
  • Diffusion tensor imaging
  • Fractional anisotropy
  • Gaussian mixture model

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health

Cite this

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