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 language | English (US) |
|---|---|
| Pages (from-to) | 156-164 |
| Number of pages | 9 |
| Journal | Journal of Neuroscience Methods |
| Volume | 270 |
| DOIs | |
| State | Published - Sep 1 2016 |
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Keywords
- Aging effects
- Density function estimation
- Diffusion tensor imaging
- Fractional anisotropy
- Gaussian mixture model
ASJC Scopus subject areas
- Neuroscience(all)
Cite this
Two step Gaussian mixture model approach to characterize white matter disease based on distributional changes. / Kim, Namhee; Heo, Moonseong; Fleysher, Roman; Branch, Craig A.; Lipton, Michael L.
In: Journal of Neuroscience Methods, Vol. 270, 01.09.2016, p. 156-164.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Two step Gaussian mixture model approach to characterize white matter disease based on distributional changes
AU - Kim, Namhee
AU - Heo, Moonseong
AU - Fleysher, Roman
AU - Branch, Craig A.
AU - Lipton, Michael L.
PY - 2016/9/1
Y1 - 2016/9/1
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
KW - Aging effects
KW - Density function estimation
KW - Diffusion tensor imaging
KW - Fractional anisotropy
KW - Gaussian mixture model
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UR - http://www.scopus.com/inward/citedby.url?scp=84977109171&partnerID=8YFLogxK
U2 - 10.1016/j.jneumeth.2016.04.024
DO - 10.1016/j.jneumeth.2016.04.024
M3 - Article
C2 - 27139737
AN - SCOPUS:84977109171
VL - 270
SP - 156
EP - 164
JO - Journal of Neuroscience Methods
JF - Journal of Neuroscience Methods
SN - 0165-0270
ER -