Analysis of multiple sclerosis lesions via spatially varying coefficients

TL;DR

This paper introduces a Bayesian spatial model for analyzing binary multiple sclerosis lesion maps from MRI data, effectively capturing spatial dependence and covariate effects, outperforming traditional methods in modeling and prediction.

Contribution

The paper presents a novel Bayesian spatial model tailored for binary lesion maps, addressing limitations of existing linear models and incorporating spatial dependence and covariates.

Findings

Model accurately captures spatial dependence in lesion data

Outperforms existing methods in predictive accuracy

Reveals associations between lesion location and clinical covariates

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 $T_2$-weighted MRI images from 250 multiple sclerosis patients classified into five clinical subtypes, and demonstrate unique modeling and predictive capabilities over existing methods.