To reduce complexity and enhance computation efficiency, we introduce a voxel-wise With the Expectation-Maximization (EM) algorithm which will be introduced in the following Number of Gaussian densities required for approximating to the true underlying density function Such aĬomplexity can cause longer iteration or failure of convergence, and necessitates a much larger A part of suchĬomplexity is due to heterogeneous mean and variance at each voxel across subjects. Irregular and complicated by presence of multi-modes and/or highly skewed tails. We note that a distribution from pooling of all the voxels in a large scaled ROI can be highly Mean of the FA measurements in a ROI become smaller in older ages?” Variance of the FA measurements in a ROI become smaller or larger in older ages?” and “Does ROI changes by each risk factor level and enables researchers to answer such questions as “Does Of note, the proposed method examines how FA distribution from each Structured in two steps: (1) subject-level density estimation and (2) group-level or specific risklevel density function. White matter tracts, where the proposed method successfully detected any departure of the FAĭistribution from the normal state by disease: p10000) and approaching to the size of the whole brain. The proposed method was demonstrated with a simulated data set and real FA data sets from two Risk-level density function estimation across subjects (Step 2). Our method consists of two steps: subject-level (Step 1) and group-level or a specific We propose a method that aims to facilitate simple and clear characterization of underlyingĭistribution. Intensity in individual voxels, however, fails to account for change in distribution of image Measured by fractional anisotropy (FA) between cases and controls or regression analysis forĪssociating mean intensity with putative risk factors. Which are often assessed using voxel-by-voxel t-tests for comparing mean image intensities Magnetic resonance imaging reveals macro- and microstructural correlates of neurodegeneration, Phone 71 Fax focused on mean change fail to detect deviation in higher order moments byĪ two-step Gaussian mixture model approach was proposed to meet such a limitation inĪging-related FA change was found in mean, variance, skewness, and kurtosis. Lipton, The Gruss Magnetic Resonance Research Center, Radiology, theĪlbert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461, USA Namhee Kim, The Gruss Magnetic Resonance Research Center, Radiology, the AlbertĮinstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461, USA Phoneħ1 Fax 71 L. The Dominick P Purpura Department of Neuroscience, the Albert Einstein College ofĭepartment of Radiology, The Montefiore Medical Center, Bronx, NY, USA The Gruss Magnetic Resonance Research Center, Radiology, the Albert Einstein College ofĭepartment of Epidemiology and Population Health, the Albert Einstein College of Medicine,ĭepartment of Physiology and Biophysics, the Albert Einstein College of Medicine, Bronx,ĭepartment of Psychiatry and Behavioral Sciences, the Albert Einstein College of Medicine, Namhee Kim1, Moonseong Heo2, Roman Fleysher1, Craig A. Two step Gaussian mixture model approach to characterize white matter disease based Please note that during the production processĮrrors may be discovered which could affect the content, and all legal disclaimers that The manuscript will undergo copyediting, typesetting, and review of the resulting proofīefore it is published in its final form.
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This is a PDF file of an unedited manuscript that has been accepted for publication.Īs a service to our customers we are providing this early version of the manuscript. White matter disease based on distributional changes.Journal of Neuroscience Methods Please cite this article as: Kim Namhee, Heo Moonseong, Fleysher Roman, BranchĬraig A, Lipton Michael L.Two step Gaussian mixture model approach to characterize Title: Two step Gaussian mixture model approach toĬharacterize white matter disease based on distributionalĪuthor: Namhee Kim Moonseong Heo Roman Fleysher