Two step Gaussian mixture model approach to characterize white matter disease based on distributional changes

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

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Background Magnetic resonance imaging reveals macro- and microstructural correlates of neurodegeneration, which are often assessed using voxel-by-voxel t-tests for comparing mean image intensities measured by fractional anisotropy (FA) between cases and controls or regression analysis for associating mean intensity with putative risk factors. This analytic strategy focusing on mean intensity in individual voxels, however, fails to account for change in distribution of image intensities due to disease. New method We propose a method that aims to facilitate simple and clear characterization of underlying distribution. Our method consists of two steps: subject-level (Step 1) and group-level or a specific risk-level density function estimation across subjects (Step 2). Results The proposed method was demonstrated with a simulated data set and real FA data sets from two white matter tracts, where the proposed method successfully detected any departure of the FA distribution from the normal state by disease: p 

Original languageEnglish (US)
Pages (from-to)156-164
Number of pages9
JournalJournal of Neuroscience Methods
Volume270
DOIs
StatePublished - Sep 1 2016

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Leukoencephalopathies
Anisotropy
Normal Distribution
Regression Analysis
Magnetic Resonance Imaging

Keywords

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

ASJC Scopus subject areas

  • Neuroscience(all)

Cite this

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title = "Two step Gaussian mixture model approach to characterize white matter disease based on distributional changes",
abstract = "Background Magnetic resonance imaging reveals macro- and microstructural correlates of neurodegeneration, which are often assessed using voxel-by-voxel t-tests for comparing mean image intensities measured by fractional anisotropy (FA) between cases and controls or regression analysis for associating mean intensity with putative risk factors. This analytic strategy focusing on mean intensity in individual voxels, however, fails to account for change in distribution of image intensities due to disease. New method We propose a method that aims to facilitate simple and clear characterization of underlying distribution. Our method consists of two steps: subject-level (Step 1) and group-level or a specific risk-level density function estimation across subjects (Step 2). Results The proposed method was demonstrated with a simulated data set and real FA data sets from two white matter tracts, where the proposed method successfully detected any departure of the FA distribution from the normal state by disease: p ",
keywords = "Aging effects, Density function estimation, Diffusion tensor imaging, Fractional anisotropy, Gaussian mixture model",
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AU - Kim, Namhee

AU - Heo, Moonseong

AU - Fleysher, Roman

AU - Branch, Craig A.

AU - Lipton, Michael L.

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N2 - Background Magnetic resonance imaging reveals macro- and microstructural correlates of neurodegeneration, which are often assessed using voxel-by-voxel t-tests for comparing mean image intensities measured by fractional anisotropy (FA) between cases and controls or regression analysis for associating mean intensity with putative risk factors. This analytic strategy focusing on mean intensity in individual voxels, however, fails to account for change in distribution of image intensities due to disease. New method We propose a method that aims to facilitate simple and clear characterization of underlying distribution. Our method consists of two steps: subject-level (Step 1) and group-level or a specific risk-level density function estimation across subjects (Step 2). Results The proposed method was demonstrated with a simulated data set and real FA data sets from two white matter tracts, where the proposed method successfully detected any departure of the FA distribution from the normal state by disease: p 

AB - Background Magnetic resonance imaging reveals macro- and microstructural correlates of neurodegeneration, which are often assessed using voxel-by-voxel t-tests for comparing mean image intensities measured by fractional anisotropy (FA) between cases and controls or regression analysis for associating mean intensity with putative risk factors. This analytic strategy focusing on mean intensity in individual voxels, however, fails to account for change in distribution of image intensities due to disease. New method We propose a method that aims to facilitate simple and clear characterization of underlying distribution. Our method consists of two steps: subject-level (Step 1) and group-level or a specific risk-level density function estimation across subjects (Step 2). Results The proposed method was demonstrated with a simulated data set and real FA data sets from two white matter tracts, where the proposed method successfully detected any departure of the FA distribution from the normal state by disease: p 

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