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Original research
Networks of microstructural damage predict disability in multiple sclerosis
  1. Elisa Colato1,
  2. Ferran Prados1,2,3,4,
  3. Jonathan Stutters1,
  4. Alessia Bianchi1,
  5. Sridar Narayanan5,
  6. Douglas L Arnold5,
  7. Claudia Wheeler-Kingshott1,6,7,
  8. Frederik Barkhof1,3,8,9,
  9. Olga Ciccarelli1,9,
  10. Declan T Chard1,9,
  11. Arman Eshaghi1,3
  1. 1Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
  2. 2Centre for Medical Image Computing (CMIC), Department of Computer Science, University College London, London, UK
  3. 3Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College London, London, UK
  4. 4e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
  5. 5McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
  6. 6Brain Connectivity Centre, IRCCS Mondino Foundation, Pavia, Italy
  7. 7Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
  8. 8Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, location Vrije Universiteit, Amsterdam, Netherlands
  9. 9Institute for Health Research (NIHR), University College London Hospitals (UCLH) Biomedical Research Centre (BRC), London, UK
  1. Correspondence to Ms Elisa Colato, Neuroinflammation, University College London, London, UK; elisa.colato.18{at}ucl.ac.uk

Abstract

Background Network-based measures are emerging MRI markers in multiple sclerosis (MS). We aimed to identify networks of white (WM) and grey matter (GM) damage that predict disability progression and cognitive worsening using data-driven methods.

Methods We analysed data from 1836 participants with different MS phenotypes (843 in a discovery cohort and 842 in a replication cohort). We calculated standardised T1-weighted/T2-weighted (sT1w/T2w) ratio maps in brain GM and WM, and applied spatial independent component analysis to identify networks of covarying microstructural damage. Clinical outcomes were Expanded Disability Status Scale worsening confirmed at 24 weeks (24-week confirmed disability progression (CDP)) and time to cognitive worsening assessed by the Symbol Digit Modalities Test (SDMT). We used Cox proportional hazard models to calculate predictive value of network measures.

Results We identified 8 WM and 7 GM sT1w/T2w networks (of regional covariation in sT1w/T2w measures) in both cohorts. Network loading represents the degree of covariation in regional T1/T2 ratio within a given network. The loading factor in the anterior corona radiata and temporo-parieto-frontal components were associated with higher risks of developing CDP both in the discovery (HR=0.85, p<0.05 and HR=0.83, p<0.05, respectively) and replication cohorts (HR=0.84, p<0.05 and HR=0.80, p<0.005, respectively). The decreasing or increasing loading factor in the arcuate fasciculus, corpus callosum, deep GM, cortico-cerebellar patterns and lesion load were associated with a higher risk of developing SDMT worsening both in the discovery (HR=0.82, p<0.01; HR=0.87, p<0.05; HR=0.75, p<0.001; HR=0.86, p<0.05 and HR=1.27, p<0.0001) and replication cohorts (HR=0.82, p<0.005; HR=0.73, p<0.0001; HR=0.80, p<0.005; HR=0.85, p<0.01 and HR=1.26, p<0.0001).

Conclusions GM and WM networks of microstructural changes predict disability and cognitive worsening in MS. Our approach may be used to identify patients at greater risk of disability worsening and stratify cohorts in treatment trials.

  • neural networks
  • multiple sclerosis
  • image analysis
  • brain mapping

Data availability statement

Data presented in this manuscript are controlled by various pharmaceutical companies. Therefore, data cannot be shared by investigators but can be requested directly from the pharmaceutical companies sponsoring each clinical trial. Data presented in this manuscript are controlled by various pharmaceutical companies. Therefore, data cannot be shared by investigators but can be requested directly from the pharmaceutical companies sponsoring each clinical trial. The computer code to obtain standardised T1w/T2w ratio maps in native space is available at: https://github.com/co-el/Estimate-standardized-T1-T2-maps.

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Data availability statement

Data presented in this manuscript are controlled by various pharmaceutical companies. Therefore, data cannot be shared by investigators but can be requested directly from the pharmaceutical companies sponsoring each clinical trial. Data presented in this manuscript are controlled by various pharmaceutical companies. Therefore, data cannot be shared by investigators but can be requested directly from the pharmaceutical companies sponsoring each clinical trial. The computer code to obtain standardised T1w/T2w ratio maps in native space is available at: https://github.com/co-el/Estimate-standardized-T1-T2-maps.

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Footnotes

  • Twitter @elisacolato, @alessia_bia, @es_arman

  • Contributors EC, DTC and AE contributed to study concept and design. SN, DLA, FB, OC, DTC and AE contributed to data acquisition. EC, FP, JS and AE contributed to data analysis. EC, AB, FB, OC, DTC and AE contributed to results interpretation. EC, FP, JS, SN, DLA, CW, FB, OC, DTC and AE contributed to drafting of the manuscript and figures. EC and AE are the guarantors of the study.

  • Funding HCP funding was provided by the National Institute of Dental and Craniofacial Research (NIDCR), the National Institute of Mental Health (NIMH) and the National Institute of Neurological Disorders and Stroke (NINDS). HCP data are disseminated by the Laboratory of NeuroImaging at the University of Southern California.

  • Competing interests The authors have no competing interests with respect to this research. The full disclosure statement is as follows: DLA reports consulting fees from Albert Charitable Trust, Alexion Pharma, Biogen, Celgene, Frequency Therapautics, Genentech, Med-Ex Learning, Merck, Novartis, Population Council, Receptos, Roche and Sanofi-Aventis, grants from Biogen, Immunotec and Novartis and an equity interest in NeuroRx. FB has received compensation for consulting services and/or speaking activities from Bayer Schering Pharma, Biogen Idec, Merck Serono, Novartis, Genzyme, Synthon BV, Roche, Teva, Jansen Research and IXICO and is supported by the NIHR Biomedical Research Centre at UCLH. OC has received research grants from the MS Society of Great Britain & Northern Ireland, National Institute for Health Research (NIHR) University College London Hospitals Biomedical Research Centre, EUH2020, Spinal Cord Research Foundation and Rosetrees Trust. She serves as a consultant for Novartis, Teva and Roche and has received an honorarium from the American Academy of Neurology as Associate Editor of Neurology and serves on the Editorial Board of Multiple Sclerosis Journal. DC is a consultant for Hoffmann-La Roche. In the last 3 years, he has been a consultant for Biogen, has received research funding from Hoffmann-La Roche, the International Progressive MS Alliance, the MS Society, the Medical Research Council and the National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre and a speaker’s honorarium from Novartis. He co-supervises a clinical fellowship at the National Hospital for Neurology and Neurosurgery, London, which is supported by Merck. AE has received speaker’s honoraria from Biogen and At The Limits educational programme. AE has received research grants from Medical Research Council, UK Research and Innovation’s Innovate UK, Biogen, UCL Innovation and Enterprise, Roche and Merck. AE serves on the Editorial Board of Neurology. He has received travel support from the National Multiple Sclerosis Society and honorarium from the Journal of Neurology, Neurosurgery and Psychiatry for Editorial Commentaries. AE and FB have equity stake in Queen Square Analytics. FP was funded by a Guarantors of Brain non-clinical Postdoctoral Fellowship. EC and JS have nothing to disclose.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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