Predicting disability progression and cognitive worsening in multiple sclerosis using patterns of grey matter volumes.


Journal

Journal of neurology, neurosurgery, and psychiatry
ISSN: 1468-330X
Titre abrégé: J Neurol Neurosurg Psychiatry
Pays: England
ID NLM: 2985191R

Informations de publication

Date de publication:
09 2021
Historique:
received: 12 11 2020
revised: 18 03 2021
accepted: 20 03 2021
pubmed: 22 4 2021
medline: 6 1 2022
entrez: 21 4 2021
Statut: ppublish

Résumé

In multiple sclerosis (MS), MRI measures at the whole brain or regional level are only modestly associated with disability, while network-based measures are emerging as promising prognostic markers. We sought to demonstrate whether data-driven patterns of covarying regional grey matter (GM) volumes predict future disability in secondary progressive MS (SPMS). We used cross-sectional structural MRI, and baseline and longitudinal data of Expanded Disability Status Scale, Nine-Hole Peg Test (9HPT) and Symbol Digit Modalities Test (SDMT), from a clinical trial in 988 people with SPMS. We processed T1-weighted scans to obtain GM probability maps and applied spatial independent component analysis (ICA). We repeated ICA on 400 healthy controls. We used survival models to determine whether baseline patterns of covarying GM volume measures predict cognitive and motor worsening. We identified 15 patterns of regionally covarying GM features. Compared with whole brain GM, deep GM and lesion volumes, some ICA components correlated more closely with clinical outcomes. A mainly basal ganglia component had the highest correlations at baseline with the SDMT and was associated with cognitive worsening (HR=1.29, 95% CI 1.09 to 1.52, p<0.005). Two ICA components were associated with 9HPT worsening (HR=1.30, 95% CI 1.06 to 1.60, p<0.01 and HR=1.21, 95% CI 1.01 to 1.45, p<0.05). ICA measures could better predict SDMT and 9HPT worsening (C-index=0.69-0.71) compared with models including only whole and regional MRI measures (C-index=0.65-0.69, p value for all comparison <0.05). The disability progression was better predicted by some of the covarying GM regions patterns, than by single regional or whole-brain measures. ICA, which may represent structural brain networks, can be applied to clinical trials and may play a role in stratifying participants who have the most potential to show a treatment effect.

Identifiants

pubmed: 33879535
pii: jnnp-2020-325610
doi: 10.1136/jnnp-2020-325610
pmc: PMC8372398
doi:

Types de publication

Clinical Trial, Phase III Journal Article Randomized Controlled Trial Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

995-1006

Subventions

Organisme : Medical Research Council
ID : MR/S026088/1
Pays : United Kingdom
Organisme : Department of Health
ID : RP-2017-08-ST2-004
Pays : United Kingdom

Informations de copyright

© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Déclaration de conflit d'intérêts

Competing interests: None declared.

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Auteurs

Elisa Colato (E)

NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK elisa.colato.18@ucl.ac.uk.

Jonathan Stutters (J)

NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.

Carmen Tur (C)

NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.

Sridar Narayanan (S)

McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.

Douglas L Arnold (DL)

McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.

Claudia A M Gandini Wheeler-Kingshott (CAM)

NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.
Department of Brain & Behavioural Sciences, University of Pavia, Pavia, Italy.
Brain Connectivity Centre, IRCCS Mondino Foundation, Pavia, Italy.

Frederik Barkhof (F)

NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.
Centre for Medical Image Computing (CMIC), Department of Computer Science, University College London, London, UK.
Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, NL.

Olga Ciccarelli (O)

NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.
Institute for Health Research (NIHR), University College London Hospitals (UCLH) Biomedical Research Centre (BRC), London, UK.

Declan T Chard (DT)

NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.
Institute for Health Research (NIHR), University College London Hospitals (UCLH) Biomedical Research Centre (BRC), London, UK.

Arman Eshaghi (A)

NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.
Centre for Medical Image Computing (CMIC), Department of Computer Science, University College London, London, UK.

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