Large-scale online assessment uncovers a distinct Multiple Sclerosis subtype with selective cognitive impairment

Annalaura Lerede (), Alexandra Moura, Valentina Giunchiglia, Elisa Carta, William Trender, Katherine Tuite-Dalton, James Witts, Elaine Craig, Sarah Knowles, Jeff Rodgers, Eleonora Cocco, Peter J. Hellyer, Rod Middleton, Richard Nicholas () and Adam Hampshire
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Annalaura Lerede: King’s College London
Alexandra Moura: Imperial College London
Valentina Giunchiglia: King’s College London
Elisa Carta: University of Cagliari
William Trender: Imperial College London
Katherine Tuite-Dalton: Swansea University Medical School
James Witts: Swansea University Medical School
Elaine Craig: Swansea University Medical School
Sarah Knowles: Swansea University Medical School
Jeff Rodgers: Swansea University Medical School
Eleonora Cocco: University of Cagliari
Peter J. Hellyer: King’s College London
Rod Middleton: Swansea University Medical School
Richard Nicholas: Imperial College London
Adam Hampshire: King’s College London

Nature Communications, 2025, vol. 16, issue 1, 1-15

Abstract: Abstract Cognitive impairments in Multiple Sclerosis (MS) are prevalent and disabling yet often unaddressed. Here, we optimised automated online assessment technology for people with MS and used it to characterise their cognitive deficits in greater detail and at a larger population scale than previously possible. The study involved 4526 UK MS Register members over three stages. Stage 1 evaluated 22 online cognitive tasks and established their feasibility. Based on MS discriminability a 12-task battery was selected. Stage 2 validated the resulting battery at scale, while Stage 3 compared it to a standard neuropsychological assessment. Clustering analysis identified a prevalent MS subtype exhibiting significant cognitive deficits with minimal motor impairment. Disability in this group is currently unrecognised and untreated. These findings underscore the importance of cognitive assessment in MS, the feasibility of integrating online tools into patient registries, and the potential of such large-scale data to derive insights into symptom heterogeneity.

Date: 2025
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DOI: 10.1038/s41467-025-62156-4

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