ANALYSIS OF MULTIPLE SCLEROSIS LESIONS VIA SPATIALLY VARYING COEFFICIENTS

• Published in: The annals of applied statistics Vol. 8; no. 2; p. 1095
• Main Authors: Ge, Tian, Müller-Lenke, Nicole, Bendfeldt, Kerstin, Nichols, Thomas E, Johnson, Timothy D
• Format: Journal Article
• Language: English
• Published: United States 2014
• Subjects: Lesion probability map; Image analysis; Multiple sclerosis; Conditional autoregressive model; Magnetic resonance imaging; Spatially varying coefficients; Markov random fields
• ISSN: 1932-6157
• DOI: 10.1214/14-AOAS718

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 -weighted MRI images from 250 multiple sclerosis patients classified into five clinical subtypes, and demonstrate unique modeling and predictive capabilities over existing methods.