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BackgroundWe 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.MethodsClinical 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.ResultsClinically 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.ConclusionsIn 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.What is already known on this topicMS is a neurodegenerative disease characterized by inflammation and demyelination in the central nervous system, leading to disrupted neural connections and varying clinical phenotypes.Diffusion-based MRI techniques and graph theory can be used to study microstructural changes and brain network alterations in MS patients across different phenotypes.What this study addsThe study highlights distinct patterns of brain connectivity disruptions associated with different MS phenotypes, particularly revealing more widespread changes in connectivity for secondary-progressive MS.It demonstrates the effectiveness of support vector machine algorithms in classifying patients from healthy controls (81% accuracy) and distinguishing among clinical phenotypes (64% to 74% accuracy) based on brain connectivity patterns.The study emphasizes the importance of subcortical connections as a key factor in differentiating MS types, providing valuable insights into the underlying neural mechanisms related to MS phenotypes.How this study might affect research, practice or policyThis study might affect research, practice, or policy by providing a better understanding of the differential patterns of brain connectivity disruptions across MS phenotypes, which can guide the development of more accurate diagnostic and prognostic tools, leading to improved personalized treatment and management strategies for people with multiple sclerosis.

The relationship between brain diffusion microstructural changes and disability in multiple sclerosis (MS) remains poorly understood. We aimed to explore the predictive value of microstructural properties in white (WM) and grey matter (GM), and identify areas associated with mid-term disability in MS patients. We studied 185 patients (71% female; 86% RRMS) with the Expanded Disability Status Scale (EDSS), timed 25-foot walk (T25FW), nine-hole peg test (9HPT), and Symbol Digit Modalities Test (SDMT) at two time-points. We used Lasso regression to analyse the predictive value of baseline WM fractional anisotropy and GM mean diffusivity, and to identify areas related to each outcome at 4.1 years follow-up. Motor performance was associated with WM (T25FW: RMSE = 0.524, R2 = 0.304; 9HPT dominant hand: RMSE = 0.662, R2 = 0.062; 9HPT non-dominant hand: RMSE = 0.649, R2 = 0.139), and SDMT with GM diffusion metrics (RMSE = 0.772, R2 = 0.186). Cingulum, longitudinal fasciculus, optic radiation, forceps minor and frontal aslant were the WM tracts most closely linked to motor dysfunction, and temporal and frontal cortex were relevant for cognition. Regional specificity related to clinical outcomes provide valuable information that can be used to develop more accurate predictive models that could improve therapeutic strategies.