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; Adam Hampshire

doi:10.1038/s41467-025-62156-4

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

Nat Commun. 2025 Sep 3;16(1):6938. doi: 10.1038/s41467-025-62156-4.

Authors

Annalaura Lerede^{1

2}, Alexandra Moura³, Valentina Giunchiglia^{4

3

5}, Elisa Carta⁶, William Trender³, Katherine Tuite-Dalton⁷, James Witts⁷, Elaine Craig⁷, Sarah Knowles⁷, Jeff Rodgers⁷, Eleonora Cocco⁸, Peter J Hellyer⁴, Rod Middleton⁷, Richard Nicholas^#^{9

10}, Adam Hampshire^#^{4

3}

Affiliations

¹ Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK. annalaura.lerede@kcl.ac.uk.
² Department of Brain Sciences, Imperial College London, London, UK. annalaura.lerede@kcl.ac.uk.
³ Department of Brain Sciences, Imperial College London, London, UK.
⁴ Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
⁵ Department of Biomedical Informatics, Harvard University, Boston, MA, USA.
⁶ Multiple Sclerosis Centre, ASL Cagliari, Department of Biomedical Sciences, University of Cagliari, Cagliari, Italy.
⁷ Population Data Science, Swansea University Medical School, Swansea, UK.
⁸ Multiple Sclerosis Centre, ASL Cagliari, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy.
⁹ Department of Brain Sciences, Imperial College London, London, UK. r.nicholas@imperial.ac.uk.
¹⁰ Population Data Science, Swansea University Medical School, Swansea, UK. r.nicholas@imperial.ac.uk.

^# Contributed equally.

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.

MeSH terms

Adult
Aged
Cluster Analysis
Cognitive Dysfunction* / diagnosis
Cognitive Dysfunction* / physiopathology
Female
Humans
Male
Middle Aged
Multiple Sclerosis* / classification
Multiple Sclerosis* / complications
Multiple Sclerosis* / diagnosis
Multiple Sclerosis* / physiopathology
Multiple Sclerosis* / psychology
Neuropsychological Tests
Registries
United Kingdom

Grants and funding

Funded postdoctoral position/King's College London | Institute of Psychiatry, Psychology and Neuroscience, King's College London (Institute of Psychiatry, Psychology & Neuroscience, King's College London)