The Annals of Applied Statistics

Analysis of multiple sclerosis lesions via spatially varying coefficients

Tian Ge, Nicole Müller-Lenke, Kerstin Bendfeldt, Thomas E. Nichols, and Timothy D. Johnson

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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.

Article information

Ann. Appl. Stat., Volume 8, Number 2 (2014), 1095-1118.

First available in Project Euclid: 1 July 2014

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Image analysis multiple sclerosis magnetic resonance imaging lesion probability map Markov random fields conditional autoregressive model spatially varying coefficients


Ge, Tian; Müller-Lenke, Nicole; Bendfeldt, Kerstin; Nichols, Thomas E.; Johnson, Timothy D. Analysis of multiple sclerosis lesions via spatially varying coefficients. Ann. Appl. Stat. 8 (2014), no. 2, 1095--1118. doi:10.1214/14-AOAS718.

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Supplemental materials

  • Supplementary material: Supplement to “Analysis of multiple sclerosis lesions via spatially varying coefficients”. This supplement contains full details of the Gibbs sampler, leave-one-out cross-validation and the naïve Bayesian classifier. It also contains supplementary figures.