ArticleEuropean radiology2025
Spatial structural abnormality maps associated with cognitive and physical performance in relapsing-remitting multiple sclerosis.
Article in European radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Cortical lesions in multiple sclerosis: 25 years of progress, remaining challenges and future directions.Nature reviews. Neurology · 2026Review
- Deep gray matter atrophy mediates the associations between glymphatic dysfunction and clinical disability in relapsing-remitting multiple sclerosis: a neuroimaging subgroup study.Frontiers in immunology · 2026Article
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20 authors.
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Abstract
objectivesWe aimed to characterize the brain abnormalities that are associated with the cognitive and physical performance of patients with relapsing-remitting multiple sclerosis (RRMS) using a deep learning algorithm. MATERIALS AND
methodsThree-dimensional (3D) nnU-Net was employed to calculate a novel spatial abnormality map by T1-weighted images and 281 RRMS patients (Dataset-1, male/female = 101/180, median age [range] = 35.0 [17.0, 65.0] years) were categorized into subtypes. Comparison of clinical and MRI features between RRMS subtypes was conducted by Kruskal-Wallis test. Kaplan-Meier analysis was conducted to investigate disability progression in RRMS subtypes. Additional validation using two other RRMS datasets (Dataset-2, n = 33 and Dataset-3, n = 56) was conducted.
resultsFive RRMS subtypes were identified: (1) a Frontal-I subtype showing preserved cognitive performance and mild physical disability, and low risk of disability worsening; (2) a Frontal-II subtype showing low cognitive scores and severe physical disability with significant brain volume loss, and a high propensity for disability worsening; (3) a temporal-cerebellar subtype demonstrating lowest cognitive scores and severest physical disability among all subtypes but remaining relatively stable during follow-up; (4) an occipital subtype demonstrating similar clinical and imaging characteristics as the Frontal-II subtype, except a large number of relapses at baseline and preserved cognitive performance; and (5) a subcortical subtype showing preserved cognitive performance and low physical disability but a similar prognosis as the occipital and Frontal-II subtypes. Additional validation confirmed the above findings.
conclusionSpatial abnormality maps can explain heterogeneity in cognitive and physical performance in RRMS and may contribute to stratified management. KEY POINTS: Question Can a deep learning algorithm characterize the brain abnormalities associated with the cognitive and physical performance of patients with RRMS? Findings Five RRMS subtypes were identified by the algorithm that demonstrated variable cognitive and physical performance. Clinical relevance The spatial abnormality maps derived RRMS subtypes had distinct cognitive and physical performances, which have a potential for individually tailored management.
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