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June 2014 Analysis of multiple sclerosis lesions via spatially varying coefficients
Tian Ge, Nicole Müller-Lenke, Kerstin Bendfeldt, Thomas E. Nichols, Timothy D. Johnson
Ann. Appl. Stat. 8(2): 1095-1118 (June 2014). DOI: 10.1214/14-AOAS718

Abstract

Magnetic resonance imaging (MRI) plays a vital role in the scientific investigation and clinical management of multiple sclerosis. Analyses of binary multiple sclerosis lesion maps are typically “mass univariate” and conducted with standard linear models that are ill suited to the binary nature of the data and ignore the spatial dependence between nearby voxels (volume elements). Smoothing the lesion maps does not entirely eliminate the non-Gaussian nature of the data and requires an arbitrary choice of the smoothing parameter. Here we present a Bayesian spatial model to accurately model binary lesion maps and to determine if there is spatial dependence between lesion location and subject specific covariates such as MS subtype, age, gender, disease duration and disease severity measures. We apply our model to binary lesion maps derived from $T_{2}$-weighted MRI images from 250 multiple sclerosis patients classified into five clinical subtypes, and demonstrate unique modeling and predictive capabilities over existing methods.

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Tian Ge. Nicole Müller-Lenke. Kerstin Bendfeldt. Thomas E. Nichols. Timothy D. Johnson. "Analysis of multiple sclerosis lesions via spatially varying coefficients." Ann. Appl. Stat. 8 (2) 1095 - 1118, June 2014. https://doi.org/10.1214/14-AOAS718

Information

Published: June 2014
First available in Project Euclid: 1 July 2014

zbMATH: 06333789
MathSciNet: MR3262547
Digital Object Identifier: 10.1214/14-AOAS718

Keywords: conditional autoregressive model , image analysis , lesion probability map , magnetic resonance imaging , Markov random fields , multiple sclerosis , spatially varying coefficients

Rights: Copyright © 2014 Institute of Mathematical Statistics

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Vol.8 • No. 2 • June 2014
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