  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.
