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Review

An update on the role of magnetic resonance imaging in predicting and monitoring multiple sclerosis progression

ORCID Icon, ORCID Icon & ORCID Icon
Pages 201-216 | Received 01 Nov 2023, Accepted 08 Jan 2024, Published online: 18 Jan 2024

References

  • Filippi M, Bar-Or A, Piehl F, et al. Multiple sclerosis. Nat Rev Dis Primers. 2018 Nov 08;4(1):43. doi: 10.1038/s41572-018-0041-4
  • Compston A, Coles A. Multiple sclerosis. Lancet. 2002 Apr 6;359(9313):1221–31.
  • Mackenzie IS, Morant SV, Bloomfield GA, et al. Incidence and prevalence of multiple sclerosis in the UK 1990–2010: a descriptive study in the general practice research database. J Neurol Neurosurg Psychiatry. 2014;85(1):76–84. doi: 10.1136/jnnp-2013-305450
  • Society M. MS in the UK 2023. [cited 2023 Nov 9]. Available from: https://www.mssociety.org.uk/what-we-do/our-work/our-evidence/ms-in-the-uk
  • Confavreux C, Vukusic S, Moreau T, et al. Relapses and progression of disability in multiple sclerosis. N Engl J Med. 2000;343(20):1430–1438. doi: 10.1056/NEJM200011163432001
  • Lassmann H, van Horssen J, Mahad D. Progressive multiple sclerosis: pathology and pathogenesis. Nat Rev Neurol. 2012 Nov 5;8(11):647–56.
  • Cree BAC, Arnold DL, Chataway J, et al. Secondary progressive multiple sclerosis: new insights. Neurology. 2021 Aug 24;97(8):378–388. doi: 10.1212/WNL.0000000000012323
  • Antonio S, Anneke N, Martin D, et al. Onset of secondary progressive phase and long-term evolution of multiple sclerosis. J Neurol Neurosurg Psychiatr. 2014;85(1):67.
  • Ziemssen T, Bhan V, Chataway J, et al. Secondary progressive multiple sclerosis: a review of clinical characteristics, definition, prognostic tools, and disease-modifying therapies. Neurol(r) Neuroimmunol Neuroinflammation. 2023 Jan;10(1). doi: 10.1212/NXI.0000000000200064
  • Tur C, Carbonell-Mirabent P, Cobo-Calvo Á, et al. Association of early progression independent of relapse activity with long-term disability after a first demyelinating event in multiple sclerosis. JAMA Neurol. 2023;80(2):151–160. doi: 10.1001/jamaneurol.2022.4655
  • Thompson AJ, Banwell BL, Barkhof F, et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 2018;17(2):162–173. doi: 10.1016/S1474-4422(17)30470-2
  • Brown JW, Chard DT. The role of MRI in the evaluation of secondary progressive multiple sclerosis. Expert Rev Neurother. 2016;16(2):157–71. doi: 10.1586/14737175.2016.1134323
  • Combes AJE, Clarke MA, O’Grady KP, et al. Advanced spinal cord MRI in multiple sclerosis: current techniques and future directions. NeuroImage Clin. 2022;36:103244. doi: 10.1016/j.nicl.2022.103244
  • Correale J, Gaitán MI, Ysrraelit MC, et al. Progressive multiple sclerosis: from pathogenic mechanisms to treatment. Brain. 2017 Mar 1;140(3):527–546. doi: 10.1093/brain/aww258
  • Lassmann H. Pathogenic mechanisms associated with different clinical courses of multiple sclerosis. Front Immunol. 2019;9:3116. doi: 10.3389/fimmu.2018.03116
  • Lublin FD, Coetzee T, Cohen JA, et al. The 2013 clinical course descriptors for multiple sclerosis. Neurology. 2020;94(24):1088–1092. doi: 10.1212/WNL.0000000000009636
  • Lublin FD, Häring DA, Ganjgahi H, et al. How patients with multiple sclerosis acquire disability. Brain. 2022 Sep 14;145(9):3147–3161. doi: 10.1093/brain/awac016
  • van Munster CE, Uitdehaag BM. Outcome measures in clinical trials for multiple sclerosis. CNS Drugs. 2017 Mar;31(3):217–236. doi: 10.1007/s40263-017-0412-5
  • Cadavid D, Cohen JA, Freedman MS, et al. The EDSS-Plus, an improved endpoint for disability progression in secondary progressive multiple sclerosis. Mult Scler J. 2017;23(1):94–105. doi: 10.1177/1352458516638941
  • Kappos L, Wolinsky JS, Giovannoni G, et al. Contribution of relapse-independent progression vs relapse-associated worsening to overall confirmed disability accumulation in typical relapsing multiple sclerosis in a pooled analysis of 2 randomized clinical trials. JAMA Neurol. 2020 Sep 1;77(9):1132–1140. doi: 10.1001/jamaneurol.2020.1568
  • Sorensen PS, Sellebjerg F, Hartung H-P, et al. The apparently milder course of multiple sclerosis: changes in the diagnostic criteria, therapy and natural history. Brain. 2020;143(9):2637–2652. doi: 10.1093/brain/awaa145
  • Chung KK, Altmann D, Barkhof F, et al. A 30-year clinical and magnetic resonance imaging observational study of multiple sclerosis and clinically isolated syndromes. Ann Neurol. 2020 Jan;87(1):63–74.
  • Haider L, Chung K, Birch G, et al. Linear brain atrophy measures in multiple sclerosis and clinically isolated syndromes: a 30-year follow-up. J Neurol Neurosurg Psychiatry. 2021 Mar 30;92(8):839–846. doi: 10.1136/jnnp-2020-325421
  • Confavreux C. Early clinical predictors and progression of irreversible disability in multiple sclerosis: an amnesic process. Brain. 2003 Apr;126(4):770–782. doi: 10.1093/brain/awg081
  • Tremlett H, Yousefi M, Devonshire V, et al. Impact of multiple sclerosis relapses on progression diminishes with time. Neurology. 2009 Nov 17;73(20):1616–1623. doi: 10.1212/WNL.0b013e3181c1e44f
  • Barnett M, Barnett Y, Reddel S. MRI and laboratory monitoring of disease-modifying therapy efficacy and risks. Curr Opin Neurol. 2022;35(3):278–285. doi: 10.1097/WCO.0000000000001067
  • Kalincik T, Diouf I, Sharmin S, et al. Effect of disease-modifying therapy on disability in relapsing-remitting multiple sclerosis over 15 years. Neurology. 2021 Feb 2;96(5):e783–e797. doi: 10.1212/WNL.0000000000011242
  • Brown JWL, Coles A, Horakova D, et al. Association of initial disease-modifying therapy with later conversion to secondary progressive multiple sclerosis. JAMA. 2019;321(2):175–187. doi: 10.1001/jama.2018.20588
  • Iaffaldano P, Lucisano G, Butzkueven H, et al. Early treatment delays long-term disability accrual in RRMS: results from the BMSD network. Mult Scler. 2021 Sep;27(10):1543–1555.
  • Kavaliunas A, Manouchehrinia A, Stawiarz L, et al. Importance of early treatment initiation in the clinical course of multiple sclerosis. Mult Scler. 2017 Aug;23(9):1233–1240.
  • Selmaj K, Cree BAC, Barnett M, et al. Multiple sclerosis: time for early treatment with high-efficacy drugs. J Neurol. 2024 Oct 18;271(1):105–115. doi: 10.1007/s00415-023-11969-8
  • Mahad DH, Trapp BD, Lassmann H. Pathological mechanisms in progressive multiple sclerosis. Lancet Neurol. 2015 Feb;14(2):183–93. doi: 10.1016/S1474-4422(14)70256-X
  • Trapp BD, Peterson J, Ransohoff RM, et al. Axonal transection in the lesions of multiple sclerosis. N Engl J Med. 1998 Jan 29;338(5):278–85. doi: 10.1056/NEJM199801293380502
  • Kornek B, Storch MK, Weissert R, et al. Multiple sclerosis and chronic autoimmune encephalomyelitis: a comparative quantitative study of axonal injury in active, inactive, and remyelinated lesions. Am J Pathol. 2000 Jul;157(1):267–726.
  • Frischer JM, Weigand SD, Guo Y, et al. Clinical and pathological insights into the dynamic nature of the white matter multiple sclerosis plaque. Ann Neurol. 2015 Nov;78(5):710–721.
  • Luchetti S, Fransen NL, van Eden CG, et al. Progressive multiple sclerosis patients show substantial lesion activity that correlates with clinical disease severity and sex: a retrospective autopsy cohort analysis. Acta Neuropathol. 2018 Apr;135(4):511–528.
  • Frischer JM, Bramow S, Dal-Bianco A, et al. The relation between inflammation and neurodegeneration in multiple sclerosis brains. Brain. 2009 May;132(5):1175–1189.
  • Chard DT, Brex PA, Ciccarelli O, et al. The longitudinal relation between brain lesion load and atrophy in multiple sclerosis: a 14 year follow up study. J Neurol Neurosurg Psychiatry. 2003 Nov;74(11):1551–1554.
  • Lassmann H. Cortical lesions in multiple sclerosis: inflammation versus neurodegeneration. Brain. 2012 Oct;135(10):2904–2905. doi: 10.1093/brain/aws260
  • Calabrese M, De Stefano N, Atzori M, et al. Detection of cortical inflammatory lesions by double inversion recovery magnetic resonance imaging in patients with multiple sclerosis. Arch Neurol. 2007;64(10):1416–1422. doi: 10.1001/archneur.64.10.1416
  • Sethi V, Yousry TA, Muhlert N, et al. Improved detection of cortical MS lesions with phase-sensitive inversion recovery MRI. J Neurol Neurosurg Psychiatry. 2012 Sep;83(9):877–82.
  • Bø L, Vedeler CA, Nyland HI, et al. Subpial demyelination in the cerebral cortex of multiple sclerosis patients. J Neuropathol Exp Neurol. 2003 Jul;62(7):723–732.
  • Lassmann H. Axonal and neuronal pathology in multiple sclerosis: what have we learnt from animal models. Exp Neurol. 2010 Sep;225(1):2–8. doi: 10.1016/j.expneurol.2009.10.009
  • Magliozzi R, Howell OW, Reeves C, et al. A gradient of neuronal loss and meningeal inflammation in multiple sclerosis. Ann Neurol. 2010 Oct;68(4):477–493.
  • Filippi M, Brück W, Chard D, et al. Association between pathological and MRI findings in multiple sclerosis. Lancet Neurol. 2019 Feb;18(2):198–210.
  • Calabrese M, Rocca MA, Atzori M, et al. A 3-year magnetic resonance imaging study of cortical lesions in relapse-onset multiple sclerosis. Ann Neurol. 2010 Mar;67(3):376–383.
  • Chard DT, Miller DH. What lies beneath grey matter atrophy in multiple sclerosis? Brain. 2016 Jan;139(1):7–10. doi: 10.1093/brain/awv354
  • Haider L, Prados F, Chung K, et al. Cortical involvement determines impairment 30 years after a clinically isolated syndrome. Brain. 2021 Jun 22;144(5):1384–1395. doi: 10.1093/brain/awab033
  • Yong HYF, Yong VW. Mechanism-based criteria to improve therapeutic outcomes in progressive multiple sclerosis. Nat Rev Neurol. 2022 Jan;18(1):40–55. doi: 10.1038/s41582-021-00581-x
  • Prineas JW, Kwon EE, Cho E-S, et al. Immunopathology of secondary-progressive multiple sclerosis. Ann Neurol. 2001 Nov 01;50(5):646–657. doi: 10.1002/ana.1255
  • van Horssen J, Singh S, van der Pol S, et al. Clusters of activated microglia in normal-appearing white matter show signs of innate immune activation. J Neuroinflammation. 2012 Jul 02;9(1):156. doi: 10.1186/1742-2094-9-156
  • Howell OW, Reeves CA, Nicholas R, et al. Meningeal inflammation is widespread and linked to cortical pathology in multiple sclerosis. Brain. 2011 Sep;134(9):2755–2771.
  • Pardini M, Brown JWL, Magliozzi R, et al. Surface-in pathology in multiple sclerosis: a new view on pathogenesis? Brain. 2021;144(6):1646–1654. doi: 10.1093/brain/awab025
  • Hochmeister S, Grundtner R, Bauer J, et al. Dysferlin is a new marker for leaky brain blood vessels in multiple sclerosis. J Neuropathol Exp Neurol. 2006 Sep;65(9):855–865.
  • Magliozzi R, Howell O, Vora A, et al. Meningeal B-cell follicles in secondary progressive multiple sclerosis associate with early onset of disease and severe cortical pathology. Brain. 2006;130(4):1089–1104. doi: 10.1093/brain/awm038
  • Magliozzi R, Columba-Cabezas S, Serafini B, et al. Intracerebral expression of CXCL13 and BAFF is accompanied by formation of lymphoid follicle-like structures in the meninges of mice with relapsing experimental autoimmune encephalomyelitis. J Neuroimmunol. 2004 Mar 01;148(1–2):11–23. doi: 10.1016/j.jneuroim.2003.10.056
  • Kuerten S, Schickel A, Kerkloh C, et al. Tertiary lymphoid organ development coincides with determinant spreading of the myelin-specific T cell response. Acta Neuropathol. 2012 Dec 01;124(6):861–873. doi: 10.1007/s00401-012-1023-3
  • Petzold A. Intrathecal oligoclonal IgG synthesis in multiple sclerosis. J Neuroimmunol. 2013 Sep 15;262(1–2):1–10.
  • Absinta M, Vuolo L, Rao A, et al. Gadolinium-based MRI characterization of leptomeningeal inflammation in multiple sclerosis. Neurology. 2015 Jul 7;85(1):18–28. doi: 10.1212/WNL.0000000000001587
  • Lucchinetti CF, Popescu BF, Bunyan RF, et al. Inflammatory cortical demyelination in early multiple sclerosis. N Engl J Med. 2011 Dec 8;365(23):2188–2197. doi: 10.1056/NEJMoa1100648
  • Serafini B, Rosicarelli B, Magliozzi R, et al. Detection of ectopic B-cell follicles with germinal centers in the meninges of patients with secondary progressive multiple sclerosis. Brain Pathol. 2004 Apr;14(2):164–74.
  • Choi SR, Howell OW, Carassiti D, et al. Meningeal inflammation plays a role in the pathology of primary progressive multiple sclerosis. Brain. 2012 Oct;135(10):2925–2937.
  • Jessen NA, Munk AS, Lundgaard I, et al. The Glymphatic System: a beginner’s guide. Neurochem Res. 2015 Dec;40(12):2583–2599.
  • Taoka T, Masutani Y, Kawai H, et al. Evaluation of glymphatic system activity with the diffusion MR technique: diffusion tensor image analysis along the perivascular space (DTI-ALPS) in Alzheimer’s disease cases. Jpn J Radiol. 2017 Apr;35(4):172–178.
  • McKnight CD, Trujillo P, Lopez AM, et al. Diffusion along perivascular spaces reveals evidence supportive of glymphatic function impairment in parkinson disease. Parkinsonism Relat Disord. 2021 Aug;89:98–104.
  • Carotenuto A, Cacciaguerra L, Pagani E, et al. Glymphatic system impairment in multiple sclerosis: relation with brain damage and disability. Brain. 2022 Aug 27;145(8):2785–2795. doi: 10.1093/brain/awab454
  • Zierfuss B, Wang Z, Jackson AN, et al. Iron in multiple sclerosis - neuropathology, immunology, and real-world considerations. Mult Scler Relat Disord. 2023 Aug 9;78:104934.
  • Hametner S, Wimmer I, Haider L, et al. Iron and neurodegeneration in the multiple sclerosis brain. Ann Neurol. 2013 Dec 01;74(6):848–861. doi: 10.1002/ana.23974
  • Haider L, Simeonidou C, Steinberger G, et al. Multiple sclerosis deep grey matter: the relation between demyelination, neurodegeneration, inflammation and iron. J Neurol Neurosurg Psychiatry. 2014;85(12):1386–1395. doi: 10.1136/jnnp-2014-307712
  • Filippi M, Rocca MA, Ciccarelli O, et al. MRI criteria for the diagnosis of multiple sclerosis: MAGNIMS consensus guidelines. Lancet Neurol. 2016;15(3):292–303. doi: 10.1016/S1474-4422(15)00393-2
  • Paty DW, Oger JJ, Kastrukoff LF, et al. MRI in the diagnosis of MS: a prospective study with comparison of clinical evaluation, evoked potentials, oligoclonal banding, and CT. Neurology. 1988 Feb;38(2):180–185.
  • Fazekas F, Offenbacher H, Fuchs S, et al. Criteria for an increased specificity of MRI interpretation in elderly subjects with suspected multiple sclerosis. Neurology. 1988 Dec;38(12):1822–1825.
  • Barkhof F, Filippi M, Miller DH, et al. Comparison of MRI criteria at first presentation to predict conversion to clinically definite multiple sclerosis. Brain. 1997 Nov;120(11):2059–2069.
  • Silver N, Lai M, Symms M, et al. Serial gadolinium-enhanced and magnetization transfer imaging to investigate the relationship between the duration of blood-brain barrier disruption and extent of demyelination in new multiple sclerosis lesions. J Neurol. 1999 Aug;246(8):728–730.
  • Giorgio A, Stromillo ML, Bartolozzi ML, et al. Relevance of hypointense brain MRI lesions for long-term worsening of clinical disability in relapsing multiple sclerosis. Mult Scler. 2014 Feb;20(2):214–219.
  • Tintoré M, Rovira A, Río J, et al. Baseline MRI predicts future attacks and disability in clinically isolated syndromes. Neurology. 2006 Sep 26;67(6):968–972. doi: 10.1212/01.wnl.0000237354.10144.ec
  • Fisniku LK, Brex PA, Altmann DR, et al. Disability and T2 MRI lesions: a 20-year follow-up of patients with relapse onset of multiple sclerosis. Brain. 2008 Mar;131(3):808–817.
  • Minneboo A, Barkhof F, Polman CH, et al. Infratentorial lesions predict long-term disability in patients with initial findings suggestive of multiple sclerosis. Arch Neurol. 2004;61(2):217–221. doi: 10.1001/archneur.61.2.217
  • Swanton JK, Fernando KT, Dalton CM, et al. Early MRI in optic neuritis: the risk for disability. Neurology. 2009 Feb 10;72(6):542–550. doi: 10.1212/01.wnl.0000341935.41852.82
  • Chard D, Trip SA. Resolving the clinico-radiological paradox in multiple sclerosis. F1000Res. 2017;6:1828. doi: 10.12688/f1000research.11932.1
  • Clarke MA, Pareto D, Pessini-Ferreira L, et al. Value of 3T susceptibility-weighted imaging in the diagnosis of multiple sclerosis. AJNR Am J Neuroradiol. 2020 Jun;41(6):1001–1008.
  • Ng Kee Kwong KC, Mollison D, Meijboom R, et al. The prevalence of paramagnetic rim lesions in multiple sclerosis: a systematic review and meta-analysis. PLoS One. 2021;16(9):e0256845. doi: 10.1371/journal.pone.0256845
  • Dal-Bianco A, Grabner G, Kronnerwetter C, et al. Slow expansion of multiple sclerosis iron rim lesions: pathology and 7 T magnetic resonance imaging. Acta Neuropathol. 2017 Jan;133(1):25–42.
  • Absinta M, Sati P, Schindler M, et al. Persistent 7-tesla phase rim predicts poor outcome in new multiple sclerosis patient lesions. J Clin Invest. 2016 Jul 1;126(7):2597–2609. doi: 10.1172/JCI86198
  • Kutzelnigg A, Lucchinetti CF, Stadelmann C, et al. Cortical demyelination and diffuse white matter injury in multiple sclerosis. Brain. 2005;128(11):2705–2712. doi: 10.1093/brain/awh641
  • Elliott C, Belachew S, Wolinsky JS, et al. Chronic white matter lesion activity predicts clinical progression in primary progressive multiple sclerosis. Brain. 2019;142(9):2787–2799. doi: 10.1093/brain/awz212
  • Hametner S, Dal Bianco A, Trattnig S, et al. Iron related changes in MS lesions and their validity to characterize MS lesion types and dynamics with Ultra-high field magnetic resonance imaging. Brain Pathol. 2018 Sep;28(5):743–749.
  • Mehta V, Pei W, Yang G, et al. Iron is a sensitive biomarker for inflammation in multiple sclerosis lesions. PLoS One. 2013;8(3):e57573. doi: 10.1371/journal.pone.0057573
  • Absinta M, Sati P, Masuzzo F, et al. Association of chronic active multiple sclerosis lesions with disability in vivo. JAMA Neurol. 2019 Dec 1;76(12):1474–1483. doi: 10.1001/jamaneurol.2019.2399
  • Elliott C, Wolinsky JS, Hauser SL, et al. Slowly expanding/evolving lesions as a magnetic resonance imaging marker of chronic active multiple sclerosis lesions. Mult Scler. 2019 Dec;25(14):1915–1925.
  • Preziosa P, Pagani E, Meani A, et al. Slowly expanding lesions predict 9-year multiple sclerosis disease progression. Neurol(r) Neuroimmunol Neuroinflammation. 2022 Mar;9(2). doi: 10.1212/NXI.0000000000001139
  • Klistorner S, Barnett MH, Yiannikas C, et al. Expansion of chronic lesions is linked to disease progression in relapsing-remitting multiple sclerosis patients. Mult Scler. 2021 Sep;27(10):1533–1542.
  • Geurts JJ, Bö L, Pouwels PJ, et al. Cortical lesions in multiple sclerosis: combined postmortem MR imaging and histopathology. AJNR Am J Neuroradiol. 2005 Mar;26(3):572–577.
  • Klaver R, De Vries HE, Schenk GJ, et al. Grey matter damage in multiple sclerosis. Prion. 2013 Jan 01;7(1):66–75. doi: 10.4161/pri.23499
  • Geurts JJG, Pouwels PJW, Uitdehaag BMJ, et al. Intracortical lesions in multiple sclerosis: improved detection with 3D double inversion-recovery MR imaging. Radiology. 2005 Jul 01;236(1):254–260. doi: 10.1148/radiol.2361040450
  • Bouman PM, Steenwijk MD, Pouwels PJW, et al. Histopathology-validated recommendations for cortical lesion imaging in multiple sclerosis. Brain. 2020 Oct 1;143(10):2988–2997. doi: 10.1093/brain/awaa233
  • Seewann A, Kooi EJ, Roosendaal SD, et al. Postmortem verification of MS cortical lesion detection with 3D DIR. Neurology. 2012 Jan 31;78(5):302–308. doi: 10.1212/WNL.0b013e31824528a0
  • Springer E, Dymerska B, Cardoso PL, et al. Comparison of routine brain imaging at 3 T and 7 T. Invest Radiol. 2016 Aug;51(8):469–482.
  • Madsen MAJ, Wiggermann V, Bramow S, et al. Imaging cortical multiple sclerosis lesions with ultra-high field MRI. NeuroImage Clin. 2021 Jan 01;32:102847.
  • Kilsdonk ID, Jonkman LE, Klaver R, et al. Increased cortical grey matter lesion detection in multiple sclerosis with 7 T MRI: a post-mortem verification study. Brain. 2016;139(5):1472–1481. doi: 10.1093/brain/aww037
  • Pitt D, Boster A, Pei W, et al. Imaging cortical lesions in multiple sclerosis with ultra-high-field magnetic resonance imaging. Arch Neurol. 2010 Jul;67(7):812–818.
  • Harrison DM, Roy S, Oh J, et al. Association of Cortical Lesion Burden on 7-T magnetic resonance imaging with cognition and disability in multiple sclerosis. JAMA Neurol. 2015 Sep;72(9):1004–1112.
  • Granberg T, Fan Q, Treaba CA, et al. In vivo characterization of cortical and white matter neuroaxonal pathology in early multiple sclerosis. Brain. 2017 Nov 1;140(11):2912–2926. doi: 10.1093/brain/awx247
  • Sethi V, Yousry T, Muhlert N, et al. A longitudinal study of cortical grey matter lesion subtypes in relapse-onset multiple sclerosis. J Neurol Neurosurg Psychiatry. 2016 Jul;87(7):750–753.
  • Treaba CA, Granberg TE, Sormani MP, et al. Longitudinal characterization of Cortical Lesion Development and evolution in multiple sclerosis with 7.0-T MRI. Radiology. 2019 Jun;291(3):740–749.
  • Calabrese M, Rocca MA, Atzori M, et al. A 3-year magnetic resonance imaging study of cortical lesions in relapse-onset multiple sclerosis. Ann Neurol. 2010 Mar 01;67(3):376–383. doi: 10.1002/ana.21906
  • Mike A, Glanz BI, Hildenbrand P, et al. Identification and clinical impact of multiple sclerosis cortical lesions as assessed by routine 3T MR Imaging. Am J Neuroradiol. 2011;32(3):515–521. doi: 10.3174/ajnr.A2340
  • Treaba CA, Herranz E, Barletta VT, et al. The relevance of multiple sclerosis cortical lesions on cortical thinning and their clinical impact as assessed by 7.0-T MRI. J Neurol. 2021 Jul 01;268(7):2473–2481. doi: 10.1007/s00415-021-10400-4
  • Calabrese M, Agosta F, Rinaldi F, et al. Cortical lesions and atrophy associated with cognitive impairment in relapsing-remitting multiple sclerosis. Arch Neurol. 2009 Sep;66(9):1144–1150.
  • Curti E, Graziuso S, Tsantes E, et al. Correlation between cortical lesions and cognitive impairment in multiple sclerosis. Brain Behav. 2018 Jun;8(6):e00955.
  • Treaba CA, Conti A, Klawiter EC, et al. Cortical and phase rim lesions on 7 T MRI as markers of multiple sclerosis disease progression. Brain Commun. 2021;3(3):fcab134. doi: 10.1093/braincomms/fcab134
  • Ziccardi S, Pisani AI, Schiavi GM, et al. Cortical lesions at diagnosis predict long-term cognitive impairment in multiple sclerosis: a 20-year study. Euro J of Neurol. 2023 May 01;30(5):1378–1388. doi: 10.1111/ene.15697
  • Pisani AI, Scalfari A, Crescenzo F, et al. A novel prognostic score to assess the risk of progression in relapsing-remitting multiple sclerosis patients. Eur J Neurol. 2021 Aug;28(8):2503–2512.
  • Peterson JW, Bö L, Mörk S, et al. Transected neurites, apoptotic neurons, and reduced inflammation in cortical multiple sclerosis lesions. Ann Neurol. 2001 Sep;50(3):389–400.
  • Kooi EJ, Strijbis EM, van der Valk P, et al. Heterogeneity of cortical lesions in multiple sclerosis: clinical and pathologic implications. Neurology. 2012 Sep 25;79(13):1369–1376. doi: 10.1212/WNL.0b013e31826c1b1c
  • Pitt D, Boster A, Pei W, et al. Imaging cortical lesions in multiple sclerosis with ultra–high-Field magnetic resonance imaging. Arch Neurol. 2010;67(7):812–818. doi: 10.1001/archneurol.2010.148
  • Castellaro M, Magliozzi R, Palombit A, et al. Heterogeneity of cortical lesion susceptibility mapping in multiple sclerosis. AJNR Am J Neuroradiol. 2017 Jun;38(6):1087–1095.
  • Bodini B, Tonietto M, Airas L, et al. Positron emission tomography in multiple sclerosis — straight to the target. Nat Rev Neurol. 2021 Nov 01;17(11):663–675. doi: 10.1038/s41582-021-00537-1
  • Herranz E, Giannì C, Louapre C, et al. Neuroinflammatory component of gray matter pathology in multiple sclerosis. Ann Neurol. 2016 Nov;80(5):776–790.
  • Fischer MT, Sharma R, Lim JL, et al. NADPH oxidase expression in active multiple sclerosis lesions in relation to oxidative tissue damage and mitochondrial injury. Brain. 2012 Mar;135(3):886–899.
  • Airas L, Yong VW. Microglia in multiple sclerosis - pathogenesis and imaging. Curr Opin Neurol. 2022 Jun 1;35(3):299–306.
  • Datta G, Colasanti A, Rabiner EA, et al. Neuroinflammation and its relationship to changes in brain volume and white matter lesions in multiple sclerosis. Brain. 2017 Nov 1;140(11):2927–2938. doi: 10.1093/brain/awx228
  • Harrison DM, Wang KY, Fiol J, et al. Leptomeningeal enhancement at 7T in multiple sclerosis: frequency, morphology, and relationship to cortical volume. J Neuroimaging. 2017 Sep;27(5):461–468.
  • Makshakov G, Magonov E, Totolyan N, et al. Leptomeningeal contrast enhancement is associated with disability progression and Grey Matter Atrophy in multiple sclerosis. Neurol Res Int. 2017 Oct 02;2017:1–7.
  • Zivadinov R, Ramasamy DP, Vaneckova M, et al. Leptomeningeal contrast enhancement is associated with progression of cortical atrophy in MS: a retrospective, pilot, observational longitudinal study. Mult Scler. 2017 Sep 01;23(10):1336–1345. doi: 10.1177/1352458516678083
  • Jonas SN, Izbudak I, Frazier AA, et al. Longitudinal persistence of meningeal enhancement on postcontrast 7T 3D-FLAIR MRI in multiple sclerosis. Am J Neuroradiol. 2018;39(10):1799. doi: 10.3174/ajnr.A5796
  • Hildesheim FE, Ramasamy DP, Bergsland N, et al. Leptomeningeal, dura mater and meningeal vessel wall enhancements in multiple sclerosis. Multi Sclerosis Relat Disord. 2021;47:47. doi: 10.1016/j.msard.2020.102653
  • Zivadinov R, Bergsland N, Carl E, et al. Effect of Teriflunomide and dimethyl fumarate on cortical atrophy and leptomeningeal inflammation in multiple sclerosis: a retrospective, observational, case-control Pilot study. J Clin Med. 2019;8(3):344. doi: 10.3390/jcm8030344
  • Bhargava P, Wicken C, Smith MD, et al. Trial of intrathecal rituximab in progressive multiple sclerosis patients with evidence of leptomeningeal contrast enhancement. Multi Sclerosis Relat Disord. 2019;30:136–140. doi: 10.1016/j.msard.2019.02.013
  • Zivadinov R, Jakimovski D, Ramanathan M, et al. Effect of ocrelizumab on leptomeningeal inflammation and humoral response to Epstein-Barr virus in multiple sclerosis. A pilot study. Multi Sclerosis Relat Disord. 2022;67:67. doi: 10.1016/j.msard.2022.104094
  • Favaretto A, Lazzarotto A, Riccardi A, et al. Enlarged Virchow Robin spaces associate with cognitive decline in multiple sclerosis. PLoS One. 2017;12(10):e0185626. doi: 10.1371/journal.pone.0185626
  • Conforti R, Cirillo M, Sardaro A, et al. Dilated perivascular spaces and fatigue: is there a link? magnetic resonance retrospective 3Tesla study. Neuroradiology. 2016 Sep 01;58(9):859–866. doi: 10.1007/s00234-016-1711-0
  • Wuerfel J, Haertle M, Waiczies H, et al. Perivascular spaces—MRI marker of inflammatory activity in the brain? Brain. 2008;131(9):2332–2340. doi: 10.1093/brain/awn171
  • Kilsdonk ID, Steenwijk MD, Pouwels PJW, et al. Perivascular spaces in MS patients at 7 tesla MRI: a marker of neurodegeneration? Mult Scler. 2015 Feb 01;21(2):155–162. doi: 10.1177/1352458514540358
  • Absinta M, Lassmann H, Trapp BD. Mechanisms underlying progression in multiple sclerosis. Curr Opin Neurol. 2020 Jun;33(3):277–285. doi: 10.1097/WCO.0000000000000818
  • Popescu V, Klaver R, Voorn P, et al. What drives MRI-measured cortical atrophy in multiple sclerosis? Mult Scler. 2015 Sep;21(10):1280–1290.
  • Klaver R, Popescu V, Voorn P, et al. Neuronal and axonal loss in normal-appearing gray matter and subpial lesions in multiple sclerosis. J Neuropathol Exp Neurol. 2015 May;74(5):453–458.
  • Popescu V, Klaver R, Versteeg A, et al. Postmortem validation of MRI cortical volume measurements in MS. Hum Brain Mapp. 2016 Jun;37(6):2223–2233.
  • Sastre-Garriga J, Pareto D, Battaglini M, et al. MAGNIMS consensus recommendations on the use of brain and spinal cord atrophy measures in clinical practice. Nat Rev Neurol. 2020 Mar;16(3):171–182.
  • Cree BAC, Hollenbach JA, Bove R, et al. Silent progression in disease activity-free relapsing multiple sclerosis. Ann Neurol. 2019 May;85(5):653–666.
  • Ghione E, Bergsland N, Dwyer MG, et al. Brain atrophy is associated with disability progression in patients with MS followed in a clinical routine. AJNR Am J Neuroradiol. 2018 Dec;39(12):2237–2242.
  • Ghione E, Bergsland N, Dwyer MG, et al. Disability improvement is associated with less brain atrophy development in multiple sclerosis. AJNR Am J Neuroradiol. 2020 Sep;41(9):1577–1583.
  • Zivadinov R, Bergsland N, Korn JR, et al. Feasibility of brain atrophy measurement in clinical routine without prior standardization of the MRI protocol: results from MS-MRIUS, a longitudinal observational, multicenter real-world outcome study in patients with relapsing-remitting MS. AJNR Am J Neuroradiol. 2018 Feb;39(2):289–295.
  • Eshaghi A, Prados F, Brownlee WJ, et al. Deep gray matter volume loss drives disability worsening in multiple sclerosis. Ann Neurol. 2018 Feb;83(2):210–222. doi: 10.1002/ana.25145
  • Rocca MA, Mesaros S, Pagani E, et al. Thalamic damage and long-term progression of disability in multiple sclerosis. Radiology. 2010 Nov;257(2):463–469.
  • Schoonheim MM, Pinter D, Prouskas SE, et al. Disability in multiple sclerosis is related to thalamic connectivity and cortical network atrophy. Mult Scler. 2022 Jan 01;28(1):61–70. doi: 10.1177/13524585211008743
  • Hänninen K, Viitala M, Paavilainen T, et al. Thalamic atrophy predicts 5-year disability progression in multiple sclerosis. Front Neurol. 2020;11:606. doi: 10.3389/fneur.2020.00606
  • Hänninen K, Viitala M, Paavilainen T, et al. Thalamic atrophy without whole brain atrophy is associated with absence of 2-year NEDA in multiple sclerosis [original research]. Front Neurol. 2019 May 03;10:10.
  • Gaetano L, Häring DA, Radue EW, et al. Fingolimod effect on gray matter, thalamus, and white matter in patients with multiple sclerosis. Neurology. 2018 Apr 10;90(15):e1324–e1332. doi: 10.1212/WNL.0000000000005292
  • Mehndiratta A, Treaba CA, Barletta V, et al. Characterization of thalamic lesions and their correlates in multiple sclerosis by ultra-high-field MRI. Mult Scler. 2021 Apr;27(5):674–683.
  • Steenwijk MD, Geurts JJG, Daams M, et al. Cortical atrophy patterns in multiple sclerosis are non-random and clinically relevant. Brain. 2016;139(1):115–126. doi: 10.1093/brain/awv337
  • Elisa C, Jonathan S, Carmen T, et al. Predicting disability progression and cognitive worsening in multiple sclerosis using patterns of grey matter volumes. J Neurol Neurosurg Psychiatry. 2021;92(9):995. doi: 10.1136/jnnp-2020-325610
  • Bergsland N, Horakova D, Dwyer MG, et al. Gray matter atrophy patterns in multiple sclerosis: a 10-year source-based morphometry study. NeuroImage Clin. 2018 Jan 01;17:444–451.
  • Chard DT, Alahmadi AAS, Audoin B, et al. Mind the gap: from neurons to networks to outcomes in multiple sclerosis. Nat Rev Neurol. 2021 Mar 01;17(3):173–184. doi: 10.1038/s41582-020-00439-8
  • Roosendaal SD, Schoonheim MM, Hulst HE, et al. Resting state networks change in clinically isolated syndrome. Brain. 2010 Jun;133(6):1612–1621.
  • Faivre A, Rico A, Zaaraoui W, et al. Assessing brain connectivity at rest is clinically relevant in early multiple sclerosis. Mult Scler. 2012 Sep;18(9):1251–1258.
  • Liu Y, Wang H, Duan Y, et al. Functional brain network alterations in clinically isolated syndrome and multiple sclerosis: a graph-based connectome Study. Radiology. 2017 Feb;282(2):534–541.
  • Faivre A, Robinet E, Guye M, et al. Depletion of brain functional connectivity enhancement leads to disability progression in multiple sclerosis: a longitudinal resting-state fMRI study. Mult Scler. 2016 Nov;22(13):1695–1708.
  • Tewarie P, Steenwijk MD, Tijms BM, et al. Disruption of structural and functional networks in long-standing multiple sclerosis. Hum Brain Mapp. 2014 Dec;35(12):5946–5961.
  • Rimkus CM, Schoonheim MM, Steenwijk MD, et al. Gray matter networks and cognitive impairment in multiple sclerosis. Mult Scler. 2019 Mar;25(3):382–391.
  • Fleischer V, Koirala N, Droby A, et al. Longitudinal cortical network reorganization in early relapsing–remitting multiple sclerosis. Ther Adv Neurol Disord. 2019 Jan 01;12:1756286419838673.
  • Tur C, Kanber B, Eshaghi A, et al. Clinical relevance of cortical network dynamics in early primary progressive MS. Mult Scler. 2020 Apr 01;26(4):442–456. doi: 10.1177/1352458519831400
  • Schoonheim MM, Broeders TAA, Geurts JJG. The network collapse in multiple sclerosis: an overview of novel concepts to address disease dynamics. NeuroImage Clin. 2022;35:103108. doi: 10.1016/j.nicl.2022.103108
  • Kuceyeski A, Monohan E, Morris E, et al. Baseline biomarkers of connectome disruption and atrophy predict future processing speed in early multiple sclerosis. NeuroImage Clin. 2018 Jan 01;19:417–424.
  • Rise HH, Brune S, Chien C, et al. Brain disconnectome mapping derived from white matter lesions and serum neurofilament light levels in multiple sclerosis: a longitudinal multicenter study. NeuroImage Clin. 2022 Jan 01;35:103099.
  • Dwyer MG, Bergsland N, Ramasamy DP, et al. Atrophied brain lesion volume: a new imaging biomarker in multiple sclerosis. J Neuroimaging. 2018 Sep;28(5):490–495.
  • Tavazzi E, Bergsland N, Kuhle J, et al. A multimodal approach to assess the validity of atrophied T2-lesion volume as an MRI marker of disease progression in multiple sclerosis. J Neurol. 2020 Mar 01;267(3):802–811. doi: 10.1007/s00415-019-09643-z
  • Genovese AV, Hagemeier J, Bergsland N, et al. Atrophied brain T2 lesion volume at MRI is associated with disability progression and conversion to secondary progressive multiple sclerosis. Radiology. 2019 Nov;293(2):424–433.
  • Zivadinov R, Horakova D, Bergsland N, et al. A serial 10-year follow-up study of atrophied brain lesion volume and disability progression in patients with relapsing-remitting MS. AJNR Am J Neuroradiol. 2019 Mar;40(3):446–452.
  • Allen IV, McKeown SR. A histological, histochemical and biochemical study of the macroscopically normal white matter in multiple sclerosis. J Neurolog Sci. 1979 Mar;41(1):81–91. doi: 10.1016/0022-510X(79)90142-4
  • Granziera C, Wuerfel J, Barkhof F, et al. Quantitative magnetic resonance imaging towards clinical application in multiple sclerosis. Brain. 2021;144(5):1296–1311. doi: 10.1093/brain/awab029
  • Kolind S, Matthews L, Johansen-Berg H, et al. Myelin water imaging reflects clinical variability in multiple sclerosis. Neuroimage. 2012 Mar;60(1):263–270.
  • Bodini B, Khaleeli Z, Cercignani M, et al. Exploring the relationship between white matter and gray matter damage in early primary progressive multiple sclerosis: an in vivo study with TBSS and VBM. Hum Brain Mapp. 2009 Sep;30(9):2852–2861.
  • Schmierer K, Wheeler-Kingshott CA, Boulby PA, et al. Diffusion tensor imaging of post mortem multiple sclerosis brain. Neuroimage. 2007 Apr 1;35(2):467–477. doi: 10.1016/j.neuroimage.2006.12.010
  • Bodini B, Cercignani M, Toosy A, et al. A novel approach with “skeletonised MTR” measures tract-specific microstructural changes in early primary-progressive MS. Hum Brain Mapp. 2014 Feb;35(2):723–733.
  • Harel A, Sperling D, Petracca M, et al. Brain microstructural injury occurs in patients with RRMS despite ‘no evidence of disease activity’. J Neurol Neurosurg Psychiatry. 2018 Sep;89(9):977–982.
  • Eijlers AJC, van Geest Q, Dekker I, et al. Predicting cognitive decline in multiple sclerosis: a 5-year follow-up study. Brain. 2018 Sep 1;141(9):2605–2618. doi: 10.1093/brain/awy202
  • Rocca MA, Sormani MP, Rovaris M, et al. Long-term disability progression in primary progressive multiple sclerosis: a 15-year study. Brain. 2017 Nov 1;140(11):2814–2819. doi: 10.1093/brain/awx250
  • Schneider T, Brownlee W, Zhang H, et al. Sensitivity of multi-shell NODDI to multiple sclerosis white matter changes: a pilot study. Funct Neurol. 2017 Apr;32(2):97–101.
  • Caranova M, Soares JF, Batista S, et al. A systematic review of microstructural abnormalities in multiple sclerosis detected with NODDI and DTI models of diffusion-weighted magnetic resonance imaging. Magn Reson Imaging. 2023 Dec 01;104:61–71.
  • Seyedmirzaei H, Nabizadeh F, Aarabi MH, et al. Neurite orientation dispersion and density imaging in multiple sclerosis: a systematic review. J Magn Reson Imaging. 2023 Oct 01;58(4):1011–1029. doi: 10.1002/jmri.28727
  • Spanò B, Giulietti G, Pisani V, et al. Disruption of neurite morphology parallels MS progression. Neurol(r) Neuroimmunol Neuroinflammation. 2018 Nov;5(6):e502.
  • Johnson D, Ricciardi A, Brownlee W, et al. Comparison of neurite orientation dispersion and density imaging and two-compartment spherical mean technique parameter maps in multiple sclerosis. Front Neurol. 2021;12:662855. doi: 10.3389/fneur.2021.662855
  • Drayer B, Burger P, Hurwitz B, et al. Reduced Signal Intensity on MR images of thalamus and putamen in multiple sclerosis: increased iron content? Am J Neuroradiol. 1987;8(3):413–419.
  • Grimaud J, Millar J, Thorpe JW, et al. Signal intensity on MRI of basal ganglia in multiple sclerosis. J Neurol Neurosurg Psychiatry. 1995;59(3):306–308. doi: 10.1136/jnnp.59.3.306
  • Bakshi R, Dmochowski J, Shaikh ZA, et al. Gray matter T2 hypointensity is related to plaques and atrophy in the brains of multiple sclerosis patients. J Neurol Sci. 2001 Mar 15;185(1):19–26. doi: 10.1016/S0022-510X(01)00477-4
  • Bakshi R, Benedict RHB, Bermel RA, et al. T2 hypointensity in the deep gray matter of patients with multiple sclerosis: a quantitative magnetic resonance imaging study. Arch Neurol. 2002;59(1):62–68. doi: 10.1001/archneur.59.1.62
  • Zhang Y, Zabad RK, Wei X, et al. Deep grey matter `black T2` on 3 tesla magnetic resonance imaging correlates with disability in multiple sclerosis. Mult Scler. 2007 Aug 01;13(7):880–883. doi: 10.1177/1352458507076411
  • Bakshi R, Shaikh ZA, Janardhan V. MRI T2 shortening (‘black T2’) in multiple sclerosis: frequency, location, and clinical correlation. Neuroreport. 2000;11(1):15–21. doi: 10.1097/00001756-200001170-00004
  • Ceccarelli A, Filippi M, Neema M, et al. T2 hypointensity in the deep gray matter of patients with benign multiple sclerosis. Mult Scler. 2009 Jun 01;15(6):678–686. doi: 10.1177/1352458509103611
  • Brass SD, Benedict RH, Weinstock-Guttman B, et al. Cognitive impairment is associated with subcortical magnetic resonance imaging grey matter T2 hypointensity in multiple sclerosis. Mult Scler. 2006 Aug 01;12(4):437–444. doi: 10.1191/135248506ms1301oa
  • Zhang Y, Metz LM, Yong VW, et al. 3T deep gray matter T2 hypointensity correlates with disability over time in stable relapsing–remitting multiple sclerosis: a 3-year pilot study. J Neurolog Sci. 2010 Oct 15;297(1–2):76–81. doi: 10.1016/j.jns.2010.07.014
  • Neema M, Arora A, Healy BC, et al. Deep gray matter involvement on brain MRI scans is associated with clinical progression in multiple sclerosis. J Neuroimaging. 2009 Jan 01;19(1):3–8. doi: 10.1111/j.1552-6569.2008.00296.x
  • Bermel RA, Puli SR, Rudick RA, et al. Prediction of longitudinal brain atrophy in multiple sclerosis by gray matter magnetic resonance imaging T2 hypointensity. Arch Neurol. 2005;62(9):1371–1376. doi: 10.1001/archneur.62.9.1371
  • Hametner S, Endmayr V, Deistung A, et al. The influence of brain iron and myelin on magnetic susceptibility and effective transverse relaxation - a biochemical and histological validation study. Neuroimage. 2018 Oct 1;179:117–133.
  • Al-Radaideh AM, Wharton SJ, Lim S-Y, et al. Increased iron accumulation occurs in the earliest stages of demyelinating disease: an ultra-high field susceptibility mapping study in clinically isolated syndrome. Mult Scler. 2013 Jun 01;19(7):896–903. doi: 10.1177/1352458512465135
  • Elkady AM, Cobzas D, Sun H, et al. Progressive iron accumulation across multiple sclerosis phenotypes revealed by sparse classification of deep gray matter. J Magn Reson Imaging. 2017 Nov 01;46(5):1464–1473. doi: 10.1002/jmri.25682
  • Zivadinov R, Tavazzi E, Bergsland N, et al. Brain iron at quantitative MRI is associated with disability in multiple sclerosis. Radiology. 2018 Nov 01;289(2):487–496. doi: 10.1148/radiol.2018180136
  • Schweser F, Raffaini Duarte Martins AL, Hagemeier J, et al. Mapping of thalamic magnetic susceptibility in multiple sclerosis indicates decreasing iron with disease duration: a proposed mechanistic relationship between inflammation and oligodendrocyte vitality. Neuroimage. 2018 Feb 15;167:438–452.
  • Paling D, Tozer D, Wheeler-Kingshott C, et al. Reduced R2’ in multiple sclerosis normal appearing white matter and lesions may reflect decreased myelin and iron content. J Neurol Neurosurg Psychiatry. 2012 Aug;83(8):785–792.
  • Chen W, Zhang Y, Mu K, et al. Quantifying the susceptibility variation of normal-appearing white matter in multiple sclerosis by quantitative susceptibility mapping. AJR Am J Roentgenol. 2017 Oct;209(4):889–894.
  • Möller HE, Bossoni L, Connor JR, et al. Iron, myelin, and the brain: neuroimaging meets neurobiology. Trends Neurosci. 2019 Jun;42(6):384–401.
  • Lazari A, Lipp I. Can MRI measure myelin? Systematic review, qualitative assessment, and meta-analysis of studies validating microstructural imaging with myelin histology. Neuroimage. 2021 Apr 15;230:117744. doi: 10.1016/j.neuroimage.2021.117744
  • van der Weijden CWJ, García DV, Borra RJH, et al. Myelin quantification with MRI: a systematic review of accuracy and reproducibility. Neuroimage. 2021 Feb 01;226:117561.
  • Amann M, Papadopoulou A, Andelova M, et al. Magnetization transfer ratio in lesions rather than normal-appearing brain relates to disability in patients with multiple sclerosis. J Neurol. 2015 Aug 01;262(8):1909–1917. doi: 10.1007/s00415-015-7793-5
  • Santos AC, Narayanan S, de Stefano N, et al. Magnetization transfer can predict clinical evolution in patients with multiple sclerosis. J Neurol. 2002 Jun;249(6):662–8.
  • Yaldizli Ö, Pardini M, Sethi V, et al. Characteristics of lesional and extra-lesional cortical grey matter in relapsing-remitting and secondary progressive multiple sclerosis: a magnetisation transfer and diffusion tensor imaging study. Mult Scler. 2016 Feb;22(2):150–159.
  • Filippi M, Preziosa P, Copetti M, et al. Gray matter damage predicts the accumulation of disability 13 years later in MS. Neurology. 2013 Nov 12;81(20):1759–1767. doi: 10.1212/01.wnl.0000435551.90824.d0
  • Hayton T, Furby J, Smith KJ, et al. Grey matter magnetization transfer ratio independently correlates with neurological deficit in secondary progressive multiple sclerosis. J Neurol. 2009 Mar;256(3):427–435.
  • Rovaris M, Judica E, Sastre-Garriga J, et al. Large-scale, multicentre, quantitative MRI study of brain and cord damage in primary progressive multiple sclerosis. Mult Scler. 2008 May;14(4):455–464.
  • Agosta F, Rovaris M, Pagani E, et al. Magnetization transfer MRI metrics predict the accumulation of disability 8 years later in patients with multiple sclerosis. Brain. 2006;129(10):2620–2627. doi: 10.1093/brain/awl208
  • Khaleeli Z, Altmann DR, Cercignani M, et al. Magnetization transfer ratio in gray matter: a potential surrogate marker for progression in early primary progressive multiple sclerosis. Arch Neurol. 2008 Nov;65(11):1454–1459.
  • Varma G, Duhamel G, de Bazelaire C, et al. Magnetization transfer from inhomogeneously broadened lines: a potential marker for myelin. Magn Reson Med. 2015 Feb 01;73(2):614–622. doi: 10.1002/mrm.25174
  • Van Obberghen E, McHinda S, le Troter A, et al. Evaluation of the sensitivity of inhomogeneous magnetization transfer (ihMT) MRI for multiple sclerosis. AJNR Am J Neuroradiol. 2018 Apr;39(4):634–641.
  • Zhang L, Wen B, Chen T, et al. A comparison study of inhomogeneous magnetization transfer (ihMT) and magnetization transfer (MT) in multiple sclerosis based on whole brain acquisition at 3.0 T. Magn Reson Imaging. 2020 Jul 01;70:43–49.
  • Kitzler HH, Su J, Zeineh M, et al. Deficient MWF mapping in multiple sclerosis using 3D whole-brain multi-component relaxation MRI. Neuroimage. 2012 Feb 1;59(3):2670–2677. doi: 10.1016/j.neuroimage.2011.08.052
  • Abel S, Vavasour I, Lee LE, et al. Myelin damage in normal appearing white matter contributes to impaired cognitive processing speed in multiple sclerosis. J Neuroimaging. 2020 Mar 01;30(2):205–211. doi: 10.1111/jon.12679
  • Laule C, Vavasour IM, Zhao Y, et al. Two-year study of cervical cord volume and myelin water in primary progressive multiple sclerosis. Mult Scler. 2010 Jun;16(6):670–677.
  • Vavasour IM, Huijskens SC, Li DKB, et al. Global loss of myelin water over 5 years in multiple sclerosis normal-appearing white matter. Mult Scler. 2018 Oct 01;24(12):1557–1568. doi: 10.1177/1352458517723717
  • Matthews L, Kolind S, Brazier A, et al. Imaging surrogates of disease activity in Neuromyelitis Optica allow distinction from multiple sclerosis. PloS One. 2015;10(9):e0137715. doi: 10.1371/journal.pone.0137715
  • Liu Z, Pardini M, Yaldizli Ö, et al. Magnetization transfer ratio measures in normal-appearing white matter show periventricular gradient abnormalities in multiple sclerosis. Brain. 2015;138(5):1239–1246. doi: 10.1093/brain/awv065
  • Brown JWL, Pardini M, Brownlee WJ, et al. An abnormal periventricular magnetization transfer ratio gradient occurs early in multiple sclerosis. Brain. 2017;140(2):387–398. doi: 10.1093/brain/aww296
  • Matteo P, Carole HS, Ferran P, et al. Relationship of grey and white matter abnormalities with distance from the surface of the brain in multiple sclerosis. J Neurol Neurosurg Psychiatry. 2016;87(11):1212. doi: 10.1136/jnnp-2016-313979
  • Samson RS, Cardoso MJ, Muhlert N, et al. Investigation of outer cortical magnetisation transfer ratio abnormalities in multiple sclerosis clinical subgroups. Mult Scler. 2014 Sep 01;20(10):1322–1330. doi: 10.1177/1352458514522537
  • Vaneckova M, Piredda GF, Andelova M, et al. Periventricular gradient of T1 tissue alterations in multiple sclerosis. NeuroImage Clin. 2022 Jan 01;34:103009.
  • Mainero C, Benner T, Radding A, et al. In vivo imaging of cortical pathology in multiple sclerosis using ultra-high field MRI. Neurology. 2009;73(12):941. doi: 10.1212/WNL.0b013e3181b64bf7
  • Pardini M, Gualco L, Bommarito G, et al. CSF oligoclonal bands and normal appearing white matter periventricular damage in patients with clinically isolated syndrome suggestive of MS. Multi Sclerosis Relat Disord. 2019 Jun 01;31:93–96.
  • Mainero C, Louapre C, Govindarajan ST, et al. A gradient in cortical pathology in multiple sclerosis by in vivo quantitative 7 T imaging. Brain. 2015;138(4):932–945. doi: 10.1093/brain/awv011
  • Brown JWL, Chowdhury A, Kanber B, et al. Magnetisation transfer ratio abnormalities in primary and secondary progressive multiple sclerosis. Mult Scler. 2020 May;26(6):679–687.
  • Kearney H, Yiannakas MC, Samson RS, et al. Investigation of magnetization transfer ratio-derived pial and subpial abnormalities in the multiple sclerosis spinal cord. Brain. 2014 Sep;137(9):2456–2468.
  • Brown JWL, Prados Carrasco F, Eshaghi A, et al. Periventricular magnetisation transfer ratio abnormalities in multiple sclerosis improve after alemtuzumab. Mult Scler. 2020 Aug 01;26(9):1093–1101. doi: 10.1177/1352458519852093
  • Weber CE, Nagel K, Ebert A, et al. Diffusely appearing white matter in multiple sclerosis: insights from sodium (23Na) MRI. Multi Sclerosis Relat Disord. 2021 Apr 01;49:102752.
  • Eisele P, Konstandin S, Griebe M, et al. Heterogeneity of acute multiple sclerosis lesions on sodium (23Na) MRI. Mult Scler. 2016 Jul 01;22(8):1040–1047. doi: 10.1177/1352458515609430
  • Eisele P, Konstandin S, Szabo K, et al. Temporal evolution of acute multiple sclerosis lesions on serial sodium (23Na) MRI. Mult Scler Relat Disord. 2019 Apr 01;29:48–54.
  • Huhn K, Mennecke A, Linz P, et al. 23Na MRI reveals persistent sodium accumulation in tumefactive MS lesions. J Neurolog Sci. 2017 Aug 15;379:163–166.
  • Inglese M, Madelin G, Oesingmann N, et al. Brain tissue sodium concentration in multiple sclerosis: a sodium imaging study at 3 tesla. Brain. 2010;133(3):847–857. doi: 10.1093/brain/awp334
  • Zaaraoui W, Konstandin S, Audoin B, et al. Distribution of brain sodium accumulation correlates with disability in multiple sclerosis: a cross-sectional 23Na MR imaging study. Radiology. 2012 Sep 01;264(3):859–867. doi: 10.1148/radiol.12112680
  • Paling D, Solanky BS, Riemer F, et al. Sodium accumulation is associated with disability and a progressive course in multiple sclerosis. Brain. 2013;136(7):2305–2317. doi: 10.1093/brain/awt149
  • Maarouf A, Audoin B, Konstandin S, et al. Topography of brain sodium accumulation in progressive multiple sclerosis. Magn Reson Mater Phy. 2014 Feb 01;27(1):53–62. doi: 10.1007/s10334-013-0396-1
  • Vargas WS, Monohan E, Pandya S, et al. Measuring longitudinal myelin water fraction in new multiple sclerosis lesions. NeuroImage Clin. 2015 Jan 01;9:369–375.
  • Vavasour IM, Laule C, Li DK, et al. Longitudinal changes in myelin water fraction in two MS patients with active disease. J Neurol Sci. 2009 Jan 15;276(1–2):49–53. doi: 10.1016/j.jns.2008.08.022
  • van Waesberghe JH, van Walderveen MA, Castelijns JA, et al. Patterns of lesion development in multiple sclerosis: longitudinal observations with T1-weighted spin-echo and magnetization transfer MR. AJNR Am J Neuroradiol. 1998 Apr;19(4):675–683.
  • Chen JT, Collins DL, Atkins HL, et al. Magnetization transfer ratio evolution with demyelination and remyelination in multiple sclerosis lesions. Ann Neurol. 2008 Feb;63(2):254–262.
  • Bodini B, Veronese M, García-Lorenzo D, et al. Dynamic imaging of individual remyelination profiles in multiple sclerosis. Ann Neurol. 2016 May;79(5):726–738. doi: 10.1002/ana.24620
  • Klistorner A, Barnett M. Remyelination trials: are we expecting the unexpected? Neurol Neuroimmunol Neuroinflammation. 2021 Nov;8(6). doi: 10.1212/NXI.0000000000001066
  • Klotz L, Antel J, Kuhlmann T. Inflammation in multiple sclerosis: consequences for remyelination and disease progression. Nat Rev Neurol. 2023 May 01;19(5):305–320.
  • Cunniffe N, Coles A. Promoting remyelination in multiple sclerosis. J Neurol. 2021 Jan;268(1):30–44. doi: 10.1007/s00415-019-09421-x
  • Schwartzbach CJ, Grove RA, Brown R, et al. Lesion remyelinating activity of GSK239512 versus placebo in patients with relapsing-remitting multiple sclerosis: a randomised, single-blind, phase II study. J Neurol. 2017 Feb;264(2):304–315.
  • Vázquez-Marrufo M, Sarrias-Arrabal E, García-Torres M, et al. A systematic review of the application of machine-learning algorithms in multiple sclerosis. Neurologia (Engl Ed). 2021 Feb 3. doi: 10.1016/j.nrl.2020.10.017
  • McKinley R, Wepfer R, Grunder L, et al. Automatic detection of lesion load change in multiple sclerosis using convolutional neural networks with segmentation confidence. NeuroImage Clin. 2020 Jan 01;25:102104.
  • McKinley R, Wepfer R, Aschwanden F, et al. Simultaneous lesion and brain segmentation in multiple sclerosis using deep neural networks. Sci Rep. 2021 Jan 13;11(1):1087. doi: 10.1038/s41598-020-79925-4
  • Caba B, Cafaro A, Lombard A, et al. Single-timepoint low-dimensional characterization and classification of acute versus chronic multiple sclerosis lesions using machine learning. Neuroimage. 2023 Jan;265:119787.
  • Conti A, Treaba CA, Mehndiratta A, et al. An interpretable machine learning model to predict cortical atrophy in multiple sclerosis. Brain Sci. 2023 Jan 24;13(2):198. doi: 10.3390/brainsci13020198
  • Bouman PM, Noteboom S, Nobrega Santos FA, et al. Multicenter evaluation of AI-generated DIR and PSIR for cortical and juxtacortical multiple sclerosis lesion detection. Radiology. 2023 Apr;307(2):e221425.
  • Barnett M, Wang D, Beadnall H, et al. A real-world clinical validation for AI-based MRI monitoring in multiple sclerosis. NPJ Digit Med. 2023 Oct 19;6(1):196. doi:10.1038/s41746-023-00940-6
  • Branco D, Martino B, Esposito A, et al. Machine learning techniques for prediction of multiple sclerosis progression. Soft Comput. 2022 Nov 01;26(22):12041–12055. doi: 10.1007/s00500-022-07503-z
  • Eshaghi A, Young AL, Wijeratne PA, et al. Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data. Nat Commun. 2021 Apr 06;12(1):2078. doi: 10.1038/s41467-021-22265-2