{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T12:16:01Z","timestamp":1780316161085,"version":"3.54.1"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T00:00:00Z","timestamp":1722211200000},"content-version":"vor","delay-in-days":210,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institute for Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000765","name":"University College London","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000765","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007155","name":"Medical Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100007155","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000381","name":"Multiple Sclerosis Society","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000381","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000272","name":"National Institute for Health Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000272","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012317","name":"University College London Hospitals Biomedical Research Centre","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012317","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,7,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>In multiple sclerosis clinical trials, MRI outcome measures are typically extracted at a whole-brain level, but pathology is not homogeneous across the brain and so whole-brain measures may overlook regional treatment effects. Data-driven methods, such as independent component analysis, have shown promise in identifying regional disease effects but can only be computed at a group level and cannot be applied prospectively. The aim of this work was to develop a technique to extract longitudinal independent component analysis network-based measures of co-varying grey matter volumes, derived from T1-weighted volumetric MRI, in individual study participants, and assess their association with disability progression and treatment effects in clinical trials. We used longitudinal MRI and clinical data from 5089 participants (22 045 visits) with multiple sclerosis from eight clinical trials. We included people with relapsing\u2013remitting, primary and secondary progressive multiple sclerosis. We used data from five negative clinical trials (2764 participants, 13 222 visits) to extract the independent component analysis-based measures. We then trained and cross-validated a least absolute shrinkage and selection operator regression model (which can be applied prospectively to previously unseen data) to predict the independent component analysis measures from the same regional MRI volume measures and applied it to data from three positive clinical trials (2325 participants, 8823 visits). We used nested mixed-effect models to determine how networks differ across multiple sclerosis phenotypes are associated with disability progression and to test sensitivity to treatment effects. We found 17 consistent patterns of co-varying regional volumes. In the training cohort, volume loss was faster in four networks in people with secondary progressive compared with relapsing\u2013remitting multiple sclerosis and three networks with primary progressive multiple sclerosis. Volume changes were faster in secondary compared with primary progressive multiple sclerosis in four networks. In the combined positive trials cohort, eight independent component analysis networks and whole-brain grey matter volume measures showed treatment effects, and the magnitude of treatment\u2013placebo differences in the network-based measures was consistently greater than with whole-brain grey matter volume measures. Longitudinal network-based analysis of grey matter volume changes is feasible using clinical trial data, showing differences cross-sectionally and longitudinally between multiple sclerosis phenotypes, associated with disability progression, and treatment effects. Future work is required to understand the pathological mechanisms underlying these regional changes.<\/jats:p>","DOI":"10.1093\/braincomms\/fcae234","type":"journal-article","created":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T23:26:49Z","timestamp":1722295609000},"source":"Crossref","is-referenced-by-count":3,"title":["Longitudinal network-based brain grey matter MRI measures are clinically relevant and sensitive to treatment effects in multiple sclerosis"],"prefix":"10.1093","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4101-7058","authenticated-orcid":false,"given":"Elisa","family":"Colato","sequence":"first","affiliation":[{"name":"Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London (UCL) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London , London, WC1N 3BG , UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan","family":"Stutters","sequence":"additional","affiliation":[{"name":"Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London (UCL) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London , London, WC1N 3BG , UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sridar","family":"Narayanan","sequence":"additional","affiliation":[{"name":"McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University , Montreal, Quebec, H3A 2B4 , Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Douglas L","family":"Arnold","sequence":"additional","affiliation":[{"name":"McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University , Montreal, Quebec, H3A 2B4 , Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7286-6901","authenticated-orcid":false,"given":"Jeremy","family":"Chataway","sequence":"additional","affiliation":[{"name":"Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London (UCL) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London , London, WC1N 3BG , UK"},{"name":"National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre (BRC) , London, W1T 7DN , UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4832-1300","authenticated-orcid":false,"given":"Claudia A M","family":"Gandini Wheeler-Kingshott","sequence":"additional","affiliation":[{"name":"Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London (UCL) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London , London, WC1N 3BG , UK"},{"name":"Brain Connectivity Centre, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Mondino Foundation , Pavia, 27100 , Italy"},{"name":"Department of Brain and Behavioural Sciences, University of Pavia , Pavia, 27100 , Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frederik","family":"Barkhof","sequence":"additional","affiliation":[{"name":"Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London (UCL) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London , London, WC1N 3BG , UK"},{"name":"National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre (BRC) , London, W1T 7DN , UK"},{"name":"Department of Radiology and Nuclear Medicine, Vrije Universiteit (VU) Medical Centre , Amsterdam, 1081 HZ , The Netherlands"},{"name":"Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College London , London, WC1V 6LJ , UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olga","family":"Ciccarelli","sequence":"additional","affiliation":[{"name":"Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London (UCL) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London , London, WC1N 3BG , UK"},{"name":"National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre (BRC) , London, W1T 7DN , UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arman","family":"Eshaghi","sequence":"additional","affiliation":[{"name":"Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London (UCL) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London , London, WC1N 3BG , UK"},{"name":"Centre for Medical Image Computing (CMIC), Department of Computer Science, University College London , London, WC1V 6LJ , UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3076-2682","authenticated-orcid":false,"given":"Declan T","family":"Chard","sequence":"additional","affiliation":[{"name":"Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London (UCL) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London , London, WC1N 3BG , UK"},{"name":"National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre (BRC) , London, W1T 7DN , 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