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

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  • Additional Information
    • Source:
      Publisher: BMJ Publishing Group Country of Publication: England NLM ID: 2985191R Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1468-330X (Electronic) Linking ISSN: 00223050 NLM ISO Abbreviation: J Neurol Neurosurg Psychiatry Subsets: MEDLINE
    • Publication Information:
      Publication: London : BMJ Publishing Group
      Original Publication: London : British Medical Association
    • Subject Terms:
      Brain/*diagnostic imaging ; Cognition/*physiology ; Cognition Disorders/*diagnostic imaging ; Gray Matter/*diagnostic imaging ; Multiple Sclerosis/*diagnostic imaging; Adult ; Cognition Disorders/etiology ; Cognition Disorders/psychology ; Disability Evaluation ; Disease Progression ; Female ; Humans ; Magnetic Resonance Imaging ; Male ; Multiple Sclerosis/complications ; Multiple Sclerosis/psychology ; Neuropsychological Tests
    • Abstract:
      Objective: 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).
      Methods: 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.
      Results: 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).
      Conclusions: 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.
      Competing Interests: Competing interests: None declared.
      (© 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.)
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    • Grant Information:
      MR/S026088/1 United Kingdom MRC_ Medical Research Council; RP-2017-08-ST2-004 United Kingdom DH_ Department of Health
    • Publication Date:
      Date Created: 20210421 Date Completed: 20220105 Latest Revision: 20220105
    • Publication Date:
      20231215
    • Accession Number:
      PMC8372398
    • Accession Number:
      10.1136/jnnp-2020-325610
    • Accession Number:
      33879535