Mapping Structural Disconnection and Morphometric Similarity Alterations in Multiple Sclerosis

Abstract

Whilst multiple sclerosis (MS) can be conceptualized as a network disorder, brain network analyses are typically dependent on advanced MRI sequences not commonly acquired in clinical practice. Here, we used conventional MRI to assess cross-sectional and longitudinal modifications of structural disconnection and morphometric similarity networks in people with MS (pwMS), along with their relationship with clinical disability. In this longitudinal monocentric study, 3T structural MRI scans of pwMS and healthy controls (HC) were retrospectively analysed. Physical and cognitive disabilities were assessed with the expanded disability status scale (EDSS) and the symbol digit modalities test (SDMT), respectively. Demyelinating lesions were automatically segmented on 3D-T1w and FLAIR images and, based on normative tractography data, the corresponding masks were used to compute pairwise structural disconnection between atlas-defined brain regions (100 cortical and 14 subcortical). Using the Morphometric Inverse Divergence (MIND) method, we built matrices of morphometric similarity between cortical regions based on FreeSurfer surface reconstruction. Using network-based statistics (NBS) and its prediction-based extension NBS-predict, we tested whether subject-level connectomes were associated with disease status, progression, clinical disability, and long-term confirmed disability progression (CDP), independently from global lesion burden and atrophy. The coupling between structural disconnection and morphometric similarity was assessed at different scales. We studied 461 pwMS (age=37.2±10.6 years, F/M=324/137), corresponding to 1235 visits (mean follow-up time=1.9±2.0 years, range=0.1-13.3 years), and 55 HC (age=42.4±15.7 years; F/M=25/30). Long-term clinical follow-up was available for 285 pwMS (mean follow-up time=12.4±2.8 years), 127 of whom (44.6%) exhibited CDP. At baseline, structural disconnection in pwMS was mostly centered around the thalami and cortical sensory and association hubs, while morphometric similarity was extensively disrupted (pFWE<0.01). EDSS was related to fronto-thalamic disconnection (pFWE<0.01) and disrupted morphometric similarity around the left perisylvian cortex (pFWE=0.02), whilst SDMT was associated with cortico-subcortical disconnection in the left hemisphere (pFWE<0.01). Longitudinally, both structural disconnection and morphometric similarity disruption significantly progressed (pFWE=0.04 and pFWE<0.01), correlating with EDSS increase (rho=0.07, p=0.02 and rho=0.11, p<0.001), whilst baseline disconnection predicted long-term CDP with nearly 60% accuracy (p=0.03). On average, structural disconnection and morphometric similarity were positively associated at both the edge (rho=0.18, p<0.001) and node (rho=0.16, p<0.001) levels. Structural disconnection and morphometric similarity networks, as assessed through conventional MRI, are sensitive to MS-related brain damage and its progression. They explain disease-related clinical disability and predict its long-term evolution independently from global lesion burden and atrophy, potentially adding to established MRI measures as network-based biomarkers of disease severity and progression.