Disruptions to brain networks, measured using structural (sMRI), diffusion (dMRI), or functional (fMRI) MRI, have been shown in people with multiple sclerosis (PwMS), highlighting the relevance of regions in the core of the connectome but yielding mixed results depending on the studied connectivity domain. Using a multilayer network approach, we integrated these three modalities to portray an enriched representation of the brain's core-periphery organization and explore its alterations in PwMS. In this retrospective cross-sectional study, we selected PwMS and healthy controls with complete multimodal brain MRI acquisitions from 13 European centers within the MAGNIMS network. Physical disability and cognition were assessed with the Expanded Disability Status Scale (EDSS) and the symbol digit modalities test (SDMT), respectively. SMRI, dMRI, and resting-state fMRI data were parcellated into 100 cortical and 14 subcortical regions to obtain networks of morphological covariance, structural connectivity, and functional connectivity. Connectivity matrices were merged in a multiplex, from which regional coreness-the probability of a node being part of the multiplex core-and coreness disruption index (κ)-the global weakening of the core-periphery structure-were computed. The associations of κ with disease status (PwMS vs. healthy controls), clinical phenotype, level of physical disability (EDSS ≥ 4 vs. EDSS < 4), and cognitive impairment (SDMT z-score < -1.5) were tested within a linear model framework. Using random forest permutation feature importance, we assessed the relative contribution of κ in the multiplex and single-layer domains, in addition to conventional MRI measures (brain and lesion volumes), in predicting disease status, physical disability, and cognitive impairment. We studied 1048 PwMS (695F, mean ± SD age: 43.3 ± 11.4 years) and 436 healthy controls (250F, mean ± SD age: 38.3 ± 11.8 years). PwMS showed significant disruption of the multiplex core-periphery organization (κ = -0.14, Hedges' g = 0.49, p < 0.001), correlating with clinical phenotype (F = 3.90, p = 0.009), EDSS (Hedges' g = 0.18, p = 0.01), and SDMT (Hedges' g = 0.30, p < 0.001). Multiplex κ was the only connectomic measure adding to conventional MRI in predicting disease status and cognitive impairment, while physical disability also depended on single-layer contributions. In conclusion, we show that multilayer networks represent a biologically and clinically meaningful framework to model multimodal MRI data, with disruption of the core-periphery structure emerging as a potential connectomic biomarker for disease severity and cognitive impairment in PwMS.