Diffusion-based structural connectivity patterns of multiple sclerosis phenotypes

Abstract

Background. We aimed to describe the severity of the changes in brain diffusion-based connectivity as multiple sclerosis (MS) progresses and the microstructural characteristics of these networks that are associated with distinct MS phenotypes. Methods. Clinical information and brain magnetic resonance images were collected from 221 healthy individuals and 823 people with MS at eight MAGNIMS centers. The patients were divided into four clinical phenotypes: clinically isolated syndrome, relapsing-remitting, secondary-progressive, and primary-progressive. Advanced tractography methods were used to obtain connectivity matrices. Then, differences in whole-brain and nodal graph-derived measures, and in the fractional anisotropy of connections between groups were analyzed. Support vector machine algorithms were used to classify groups. Results. Clinically isolated syndrome and relapsing-remitting patients shared similar network changes relative to controls. However, most global and local network properties differed in secondary progressive patients compared with the other groups, with lower fractional anisotropy in most connections. Primary progressive participants had fewer differences in global and local graph measures compared to clinically isolated syndrome and relapsing-remitting patients, and reductions in fractional anisotropy were only evident for a few connections. The accuracy of support vector machine to discriminate patients from healthy controls based on connection was 81%, and ranged between 64% and 74% in distinguishing among the clinical phenotypes. Conclusions. In conclusion, brain connectivity is disrupted in MS and has differential patterns according to the phenotype. Secondary progressive is associated with more widespread changes in connectivity. Additionally, classification tasks can distinguish between MS types, with subcortical connections being the most important factor.

Microscopic fractional anisotropy outperforms multiple sclerosis lesion assessment and clinical outcome associations over standard fractional anisotropy tensor

“…Standard diffusion tensor imaging (DTI) (Basser & Pierpaoli, 1996) has been widely used in clinical practice and neuroimaging research due to its simplicity and sensitivity to WM tissue changes, enabling brain connectivity mapping (Reid et al, 2017). For example, computing the mean fractional anisotropy (FA) of streamlines delineated with tractography is a traditional approach of assessing structural connectivity (Llufriu et al, 2017;Martinez-Heras et al, 2023). However, earlier publications have highlighted the limitations of DTI in representing complex fiber patterns within a voxel (Landman et al, 2010;Wiegell et al, 2000).…”