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 T2-weighted MRI images from 250 multiple sclerosis patients classified into five clinical subtypes, and demonstrate unique modeling and predictive capabilities over existing methods.
Highlights
Multiple sclerosis (MS) is an autoimmune disease of the central nervous system characterized by neuronal demyelination that results in brain and spinal cord lesions
In this paper we present a Bayesian spatial model that respects the binary nature of the data and exploits the spatial structure of MS lesion maps without use of an arbitrary smoothing parameter
The method is suitable to model any patterns of lesions, including T2 lesions, which show a variety of sizes and shapes, T1 “black-hole” lesions and any other types of lesions from which a binary image marking the location of the lesions can be derived
Summary
Multiple sclerosis (MS) is an autoimmune disease of the central nervous system characterized by neuronal demyelination that results in brain and spinal cord lesions. We note that our model is not dependent on the method of lesion identification and will work with any type of atlas-registered binary image data exhibiting spatial dependence.
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