A Spatial GLMM and the Estimation of Spatially Varying Coefficients with Application to Multiple Sclerosis MRI Data

• Activity Number: 65
• Type: Topic Contributed
• Date/Time: Sunday, August 4, 2013 : 4:00 PM to 5:50 PM
• Sponsor: SSC
• Author(s): Timothy Johnson*+ and Thomas Nichols and Tian Ge
• Companies: Univ of Michigan and University of Warwick and University of Warwick
• Keywords: Multiple Sclerosis ; Spatially Vary Coefficients ; Bayesian Modeling ; Multivariate CAR

Abstract

Magnetic resonance imaging (MRI) is playing an increasing role in the scientic 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. 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 T2 lesion maps from 250 multiple sclerosis patients sub-classified into five clinical subtypes, and demonstrate unique modeling and predictive capabilities over existing methods.