ArticleNeurology2025
Conventional MRI-Based Structural Disconnection and Morphometric Similarity Networks and Their Clinical Correlates in Multiple Sclerosis.
Article in Neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Morphometric Inverse Divergence Networks Combined with HYDRA Identify Parkinson's Disease Subtypes with Distinct Transcriptomic and Serum Biomarker Profiles.International journal of molecular sciences · 2026Article
- Depression as a Disease of White Matter Network Disruption: Learning From Multiple Sclerosis.Biological psychiatry · 2026Review
- Brain Age Estimation on T2-FLAIR Scans for Application to Multiple Sclerosis.Human brain mapping · 2026Article
- Assessing neurobiology of lifelong premature ejaculation through brain MRI structural similarity gradient.European radiology experimental · 2025Article
- Cortical morphometric gradients reveal molecular and cognitive underpinnings of bipolar disorder.Psychological medicine · 2025Article
- Estimating Brain Similarity Networks With Diffusion MRI.Human brain mapping · 2025Article
- Individual-level cortical morphological network analysis in idiopathic normal pressure hydrocephalus: diagnostic and prognostic insights.Fluids and barriers of the CNS · 2025Article
- Structural MRI of brain similarity networks.Nature reviews. Neuroscience · 2025Review
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18 authors.
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Abstract
BACKGROUND AND
objectivesAlthough multiple sclerosis (MS) can be conceptualized as a network disorder, brain network analyses typically require advanced MRI sequences not commonly acquired in clinical practice. Using conventional MRI, we assessed cross-sectional and longitudinal structural disconnection and morphometric similarity networks in people with MS (pwMS), along with their relationship with clinical disability.
methodsIn this longitudinal monocentric study, 3T structural MRI of pwMS and healthy controls (HC) was retrospectively analyzed. 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, and the corresponding masks were used to assess pairwise structural disconnection between atlas-defined brain regions based on normative tractography data. Using the Morphometric Inverse Divergence method, we computed morphometric similarity between cortical regions based on FreeSurfer surface reconstruction. Using network-based statistics (NBS) and its 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.
resultsWe studied 461 pwMS (age = 37.2 ± 10.6 years, F/M = 324/137), corresponding to 1,235 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 ( DISCUSSION: 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.
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