Population-Wide Model-Free Quantification of Blood-Brain-Barrier Dynamics in Multiple Sclerosis

Abstract

The processes by which new white matter lesions in multiple sclerosis (MS) the rate at which contrast agents pass from the plasma to MS lesions. In this paper, we develop a model-free framework for the analysis of these data that provides biologically meaningful quantification of the blood-brain barrier opening in new MS lesions. To accomplish this, we use functional principal components analysis to study directions of variation in the voxel-level time series of intensities both within and across subjects.The analysis reveals and allows quantification of typical spatiotemporal enhancement patterns in acute MS lesions, providing measures of magnitude, rate, shape (ring-like vs. nodular), and dynamics (centrifugal vs. centripetal). Across 10 subjects with relapsing-remitting and primary progressive MS, we found subjects to have between 0 and 12 gadolinium-enhancing lesions, the majority of which enhanced centripetally. We quantified the spatiotemporal behavior within each of these lesion using novel measures.Further application of these techniques will determine the extent to which these lesion metrics can predict or track response to therapy or long-term prognosis in this disorder.

Longitudinal high-dimensional principal components analysis with application to diffusion tensor imaging of multiple sclerosis

“…Successful modeling approaches involving wavelets and splines and adaptive kernels have been reported in the literature (Mohamed and Davatzikos, 2004; Morris and Carroll, 2006; Guo, 2002; Morris et al, 2011; Zhu et al, 2011; Rodriguez et al, 2009; Bigelow and Dunson, 2009; Reiss et al, 2005; Reiss and Ogden, 2008, 2010; Li et al, 2011; Hua et al, 2012; Yuan et al, 2014). A different direction of research has focused on principal component decompositions (Di et al, 2008; Crainiceanu et al, 2009; Aston et al, 2010; Staicu et al, 2010; Greven et al, 2010; Di et al, 2010; Zipunnikov et al, 2011b; Crainiceanu et al, 2011), which led to several applications to imaging data (Shinohara et al, 2011; Goldsmith et al, 2011; Zipunnikov et al, 2011a). However, the high dimensionality of new data sets, the inherent complexity of sampling designs and data collection, and the diversity of new technological measurements raise multiple challenges that are currently unaddressed.…”