Advanced MRI Measures of Myelin and Axon Volume Identify Repair in Multiple Sclerosis.
Journal
Annals of neurology
ISSN: 1531-8249
Titre abrégé: Ann Neurol
Pays: United States
ID NLM: 7707449
Informations de publication
Date de publication:
10 Oct 2024
10 Oct 2024
Historique:
revised:
10
08
2024
received:
17
01
2024
accepted:
04
09
2024
medline:
11
10
2024
pubmed:
11
10
2024
entrez:
11
10
2024
Statut:
aheadofprint
Résumé
Pathological studies suggest that multiple sclerosis (MS) lesions endure multiple waves of damage and repair; however, the dynamics and characteristics of these processes are poorly understood in patients living with MS. We studied 128 MS patients (75 relapsing-remitting, 53 progressive) and 72 healthy controls who underwent advanced magnetic resonance imaging and clinical examination at baseline and 2 years later. Magnetization transfer saturation and multi-shell diffusion imaging were used to quantify longitudinal changes in myelin and axon volumes within MS lesions. Lesions were grouped into 4 classes (repair, damage, mixed repair damage, and stable). The frequency of each class was correlated to clinical measures, demographic characteristics, and levels of serum neurofilament light chain (sNfL). Stable lesions were the most frequent (n = 2,276; 44%), followed by lesions with patterns of "repair" (n = 1,352; 26.2%) and damage (n = 1,214; 23.5%). The frequency of "repair" lesion was negatively associated with disability (β = -0.04; p < 0.001) and sNfL (β = -0.16; p < 0.001) at follow-up. The frequency of the "damage" class was higher in progressive than relapsing-remitting patients (p < 0.05) and was related to disability (baseline: β = -0.078; follow-up: β = -0.076; p < 0.001) and age (baseline: β = -0.078; p < 0.001). Stable lesions were more frequent in relapsing-remitting than in progressive patients (p < 0.05), and in younger patients versus older (β = -0.07; p < 0.001) at baseline. Further, "mixed" lesions were most frequent in older patients (β = 0.004; p < 0.001) at baseline. These findings show that repair and damage processes within MS lesions occur across the entire disease spectrum and that their frequency correlates with patients disability, age, disease duration, and extent of neuroaxonal damage. ANN NEUROL 2024.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Stiftung zur Förderung der gastroenterologischen
Organisme : allgemeinen klinischen Forschung
Organisme : Swiss National Science Foundation (SNSF)
ID : grant PP00P3_176984
Organisme : Horizon 2020 Eurostar program
ID : grant E!113682
Organisme : Biogen
Organisme : Novartis
Informations de copyright
© 2024 The Author(s). Annals of Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.
Références
Oh J, Bar‐Or A. Emerging therapies to target CNS pathophysiology in multiple sclerosis. Nat Rev Neurol 2022;18:466–475. https://doi.org/10.1038/s41582-022-00675-0.
Oh J, Ontaneda D, Azevedo C, et al. Imaging outcome measures of neuroprotection and repair in MS: A consensus statement from NAIMS. Neurology 2019;92:519–533. https://doi.org/10.1212/WNL.0000000000007099.
Kuhlmann T, Moccia M, Coetzee T, et al. Multiple sclerosis progression: time for a new mechanism‐driven framework. Lancet Neurol 2023;22:78–88. https://doi.org/10.1016/S1474-4422(22)00289-7.
Reich DS, Lucchinetti CF, Calabresi PA. Multiple Sclerosis. N Engl J Med 2018;378:169–180. https://doi.org/10.1056/NEJMRA1401483/SUPPL_FILE/NEJMRA1401483_DISCLOSURES.PDF.
Ferguson B, Matyszak MK, Esiri MM, Perry VH. Axonal damage in acute multiple sclerosis lesions. Brain 1997;120:393–399. https://doi.org/10.1093/BRAIN/120.3.393.
Lucchinetti C, Brück W, Parisi J, et al. Heterogeneity of multiple sclerosis lesions: Implications for the pathogenesis of demyelination. Ann Neurol 2000;47:707–717. https://doi.org/10.1002/1531-8249(200006)47:6<707::AID-ANA3>3.0.CO;2-Q.
Barnett MH, Prineas JW. Relapsing and remitting multiple sclerosis: Pathology of the newly forming lesion. Ann Neurol 2004;55:458–468. https://doi.org/10.1002/ANA.20016.
Metz I, Weigand SD, Popescu BFG, et al. Pathologic heterogeneity persists in early active multiple sclerosis lesions. Ann Neurol 2014;75:728–738. https://doi.org/10.1002/ANA.24163.
Granziera C, Wuerfel J, Barkhof F, et al. Quantitative magnetic resonance imaging towards clinical application in multiple sclerosis. Brain 2021;144:1296–1311. https://doi.org/10.1093/brain/awab029.
Kitzler HH, Wahl H, Kuntke P, et al. Exploring in vivo lesion myelination dynamics: Longitudinal Myelin Water Imaging in early Multiple Sclerosis. NeuroImage Clin 2022;36:103192. https://doi.org/10.1016/J.NICL.2022.103192.
Laule C, Moore GRW. Myelin water imaging to detect demyelination and remyelination and its validation in pathology. Brain Pathol 2018;28:750–764. https://doi.org/10.1111/bpa.12645.
Laule C, Vavasour IM, Whittall KP, et al. Evolution of focal and diffuse magnetisation transfer abnormalities in multiple sclerosis. J Neurol 2003;250:924–931. https://doi.org/10.1007/s00415-003-1115-z.
Laule C, Vavasour IM, Kolind SH, et al. Magnetic resonance imaging of myelin. Neurother J Am Soc Exp Neurother 2007;4:460–484. https://doi.org/10.1016/j.nurt.2007.05.004.
Caverzasi E, Papinutto N, Cordano C, et al. MWF of the corpus callosum is a robust measure of remyelination: Results from the ReBUILD trial. Proc Natl Acad Sci 2023;120:e2217635120. https://doi.org/10.1073/pnas.2217635120.
Vavasour IM, Huijskens SC, Li DK, et al. Global loss of myelin water over 5 years in multiple sclerosis normal‐appearing white matter. Mult Scler 2018;24:1557–1568. https://doi.org/10.1177/1352458517723717.
Kolb H, Absinta M, Beck ES, et al. 7T MRI differentiates remyelinated from demyelinated multiple sclerosis lesions. Ann Neurol 2021;90:612–626. https://doi.org/10.1002/ANA.26194.
Vavasour IM, Chang KL, Combes AJE, et al. Water content changes in new multiple sclerosis lesions have a minimal effect on the determination of myelin water fraction values. J Neuroimaging 2021;31:1119–1125. https://doi.org/10.1111/jon.12908.
Wang CT, Barnett M, Barnett Y. Imaging the multiple sclerosis lesion: insights into pathogenesis, progression and repair. Curr Opin Neurol 2019;32:338–345. https://doi.org/10.1097/WCO.0000000000000698.
York EN, Meijboom R, Thrippleton MJ, et al. Longitudinal microstructural MRI markers of demyelination and neurodegeneration in early relapsing‐remitting multiple sclerosis: Magnetisation transfer, water diffusion and g‐ratio. NeuroImage Clin 2022;36:103228. https://doi.org/10.1016/J.NICL.2022.103228.
Rahmanzadeh R, Galbusera R, Lu PJ, et al. A New Advanced MRI Biomarker for Remyelinated Lesions in Multiple Sclerosis. Ann Neurol 2022;92:486–502. https://doi.org/10.1002/ANA.26441.
Tagge IJ, Leppert IR, Fetco D, et al. Permanent tissue damage in multiple sclerosis lesions is associated with reduced pre‐lesion myelin and axon volume fractions. Mult Scler. 2022;28:2027–2037. https://doi.org/10.1177/13524585221110585.
Gloor M, Andelova M, Gaetano L, et al. Longitudinal analysis of new multiple sclerosis lesions with magnetization transfer and diffusion tensor imaging. Eur Radiol 2023;34:1680–1691. https://doi.org/10.1007/s00330-023-10173-6.
Bonnier G, Maréchal B, Fartaria MJ, et al. The combined quantification and interpretation of multiple quantitative magnetic resonance imaging metrics enlightens longitudinal changes compatible with brain repair in relapsing‐remitting multiple sclerosis patients. Front Neurol 2017;8:506. https://doi.org/10.3389/fneur.2017.00506.
Parry A, Clare S, Jenkinson M, et al. MRI brain T1 relaxation time changes in MS patients increase over time in both the white matter and the cortex. J Neuroimaging 2003;13:234–239.
Altokhis AI, Hibbert AM, Allen CM, et al. Longitudinal clinical study of patients with iron rim lesions in multiple sclerosis. Mult Scler 2022;28:2202–2211. https://doi.org/10.1177/13524585221114750.
Vandeleene N, Guillemin C, Dauby S, et al. Using quantitative magnetic resonance imaging to track cerebral alterations in multiple sclerosis brain: A longitudinal study. Brain Behav 2023;13:e2923. https://doi.org/10.1002/brb3.2923.
Moccia M, Van De Pavert S, Eshaghi A, et al. Pathologic correlates of the magnetization transfer ratio in multiple sclerosis. Neurology 2020;95:e2965–e2976. https://doi.org/10.1212/WNL.0000000000010909.
Helms G, Dathe H, Kallenberg K, Dechent P. High‐resolution maps of magnetization transfer with inherent correction for RF inhomogeneity and T1 relaxation obtained from 3D FLASH MRI. Magn Reson Med 2008;60:1396–1407. https://doi.org/10.1002/MRM.21732.
Liu C, Li W, Tong KA, et al. Susceptibility‐weighted imaging and quantitative susceptibility mapping in the brain. J Magn Reson Imaging 2015;42:23–41. https://doi.org/10.1002/JMRI.24768.
Zhang H, Schneider T, Wheeler‐Kingshott CA, Alexander DC. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage 2012;61:1000–1016. https://doi.org/10.1016/J.NEUROIMAGE.2012.03.072.
Colgan N, Siow B, O'Callaghan JM, et al. Application of neurite orientation dispersion and density imaging (NODDI) to a tau pathology model of Alzheimer's disease. Neuroimage 2016;125:739–744. https://doi.org/10.1016/J.NEUROIMAGE.2015.10.043.
Stikov N, Campbell JSW, Stroh T, et al. In vivo histology of the myelin g‐ratio with magnetic resonance imaging. Neuroimage 2015;118:397–405. https://doi.org/10.1016/j.neuroimage.2015.05.023.
Lublin FD, Reingold SC, Cohen JA, et al. Defining the clinical course of multiple sclerosis. Neurology 2014;83:278–286. https://doi.org/10.1212/WNL.0000000000000560.
Thompson AJ, Banwell BL, Barkhof F, et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol 2018;17:162–173. https://doi.org/10.1016/S1474-4422(17)30470-2.
Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology 1983;33:1444–1452. https://doi.org/10.1212/WNL.33.11.1444.
Benkert P, Meier S, Schaedelin S, et al. Serum neurofilament light chain for individual prognostication of disease activity in people with multiple sclerosis: a retrospective modelling and validation study. Lancet Neurol 2022;21:246–257. https://doi.org/10.1016/S1474-4422(22)00009-6.
Barro C, Benkert P, Disanto G, et al. Serum neurofilament as a predictor of disease worsening and brain and spinal cord atrophy in multiple sclerosis. Brain 2018;141:2382–2391. https://doi.org/10.1093/brain/awy154.
Disanto G, Barro C, Benkert P, et al. Serum Neurofilament light: A biomarker of neuronal damage in multiple sclerosis. Ann Neurol 2017;81:857–870. https://doi.org/10.1002/ANA.24954.
Helms G, Dathe H, Dechent P. Quantitative FLASH MRI at 3T using a rational approximation of the Ernst equation. Magn Reson Med 2008;59:667–672. https://doi.org/10.1002/MRM.21542.
Helms G, Dechent P. Increased SNR and reduced distortions by averaging multiple gradient echo signals in 3D FLASH imaging of the human brain at 3T. J Magn Reson Imaging 2009;29:198–204. https://doi.org/10.1002/JMRI.21629.
Helms G, Draganski B, Frackowiak R, et al. Improved segmentation of deep brain grey matter structures using magnetization transfer (MT) parameter maps. Neuroimage 2009;47:194–198. https://doi.org/10.1016/J.NEUROIMAGE.2009.03.053.
Ganter C, Settles M, Dregely I, et al. B1+‐mapping with the transient phase of unbalanced steady‐state free precession. Magn Reson Med 2013;70:1515–1523. https://doi.org/10.1002/MRM.24598.
La Rosa F, Abdulkadir A, Fartaria MJ, et al. Multiple sclerosis cortical and WM lesion segmentation at 3T MRI: a deep learning method based on FLAIR and MP2RAGE. NeuroImage Clin 2020;27:102335. https://doi.org/10.1016/J.NICL.2020.102335.
Battaglini M, Jenkinson M, De Stefano N. Evaluating and reducing the impact of white matter lesions on brain volume measurements. Hum Brain Mapp 2012;33:2062–2071. https://doi.org/10.1002/HBM.21344.
Jenkinson M, Beckmann CF, Behrens TEJ, et al. FSL. Neuroimage 2012;62:782–790. https://doi.org/10.1016/J.NEUROIMAGE.2011.09.015.
Ashburner J, Friston KJ. Unified segmentation. Neuroimage 2005;26:839–851. https://doi.org/10.1016/J.NEUROIMAGE.2005.02.018.
Kaunzner UW, Kang Y, Zhang S, et al. Quantitative susceptibility mapping identifies inflammation in a subset of chronic multiple sclerosis lesions. Brain 2019;142:133–145. https://doi.org/10.1093/BRAIN/AWY296.
Tabelow K, Balteau E, Ashburner J, et al. hMRI – A toolbox for quantitative MRI in neuroscience and clinical research. Neuroimage 2019;194:191–210. https://doi.org/10.1016/J.NEUROIMAGE.2019.01.029.
Campbell JSW, Leppert IR, Narayanan S, et al. Promise and pitfalls of g‐ratio estimation with MRI. Neuroimage 2018;182:80–96. https://doi.org/10.1016/J.NEUROIMAGE.2017.08.038.
Mohammadi S, Carey D, Dick F, et al. Whole‐brain in‐vivo measurements of the axonal G‐ratio in a group of 37 healthy volunteers. Front Neurosci 2015;9:1–13. https://doi.org/10.3389/fnins.2015.00441.
Cercignani M, Giulietti G, Dowell NG, et al. Characterizing axonal myelination within the healthy population: a tract‐by‐tract mapping of effects of age and gender on the fiber g‐ratio. Neurobiol Aging 2017;49:109–118. https://doi.org/10.1016/J.NEUROBIOLAGING.2016.09.016.
Mancini M, Giulietti G, Dowell N, et al. Introducing axonal myelination in connectomics: A preliminary analysis of g‐ratio distribution in healthy subjects. Neuroimage 2018;182:351–359. https://doi.org/10.1016/J.NEUROIMAGE.2017.09.018.
Graf von Keyserlingk D, Schramm U. Diameter of axons and thickness of myelin sheaths of the pyramidal tract fibres in the adult human medullary pyramid. Anat Anz 1984;157:97–111. http://europepmc.org/abstract/MED/6507887.
Veraart J, Novikov DS, Christiaens D, et al. Denoising of diffusion MRI using random matrix theory. Neuroimage 2016;142:394–406. https://doi.org/10.1016/J.NEUROIMAGE.2016.08.016.
Andersson JLR, Sotiropoulos SN. An integrated approach to correction for off‐resonance effects and subject movement in diffusion MR imaging. Neuroimage 2016;125:1063–1078. https://doi.org/10.1016/J.NEUROIMAGE.2015.10.019.
Daducci A, Canales‐Rodríguez EJ, Zhang H, et al. Accelerated Microstructure Imaging via Convex Optimization (AMICO) from diffusion MRI data. Neuroimage 2015;105:32–44. https://doi.org/10.1016/J.NEUROIMAGE.2014.10.026.
Yushkevich PA, Pluta J, Wang H, et al. Fast Automatic Segmentation of Hippocampal Subfields and Medial Temporal Lobe Subregions In 3 Tesla and 7 Tesla T2‐Weighted MRI. Alzheimer's Dement 2016;12:P126–P127. https://doi.org/10.1016/j.jalz.2016.06.205.
Barkhof F, Brück W, De Groot CJA, et al. Remyelinated Lesions in Multiple Sclerosis: Magnetic Resonance Image Appearance. Arch Neurol 2003;60:1073–1081. https://doi.org/10.1001/ARCHNEUR.60.8.1073.
Patrikios P, Stadelmann C, Kutzelnigg A, et al. Remyelination is extensive in a subset of multiple sclerosis patients. Brain 2006;129:3165–3172. https://doi.org/10.1093/BRAIN/AWL217.
Irvine KA, Blakemore WF. Remyelination protects axons from demyelination‐associated axon degeneration. Brain 2008;131:1464–1477. https://doi.org/10.1093/brain/awn080.
Schäffner E, Bosch‐Queralt M, Edgar JM, et al. Myelin insulation as a risk factor for axonal degeneration in autoimmune demyelinating disease. Nat Neurosci 2023;26:1218–1228. https://doi.org/10.1038/s41593-023-01366-9.
Baaklini CS, Rawji KS, Duncan GJ, et al. Central nervous system remyelination: roles of glia and innate immune cells. Front Mol Neurosci 2019;12:12. https://www.frontiersin.org/articles/10.3389/fnmol.2019.00225.
Chen JT, Collins DL, Atkins HL, et al. Magnetization transfer ratio evolution with demyelination and remyelination in multiple sclerosis lesions. Ann Neurol 2008;63:254–262. https://doi.org/10.1002/ana.21302.
Schmierer K, Scaravilli F, Altmann DR, et al. Magnetization transfer ratio and myelin in postmortem multiple sclerosis brain. Ann Neurol 2004;56:407–415. https://doi.org/10.1002/ANA.20202.
Martínez‐Heras E, Solana E, Prados F, et al. Characterization of multiple sclerosis lesions with distinct clinical correlates through quantitative diffusion MRI. NeuroImage Clin 2020;28:102411. https://doi.org/10.1016/j.nicl.2020.102411.
Preziosa P, Pagani E, Meani A, et al. NODDI, diffusion tensor microstructural abnormalities and atrophy of brain white matter and gray matter contribute to cognitive impairment in multiple sclerosis. J Neurol 2023;270:810–823. https://doi.org/10.1007/s00415-022-11415-1.
Vargas WS, Monohan E, Pandya S, et al. Measuring longitudinal myelin water fraction in new multiple sclerosis lesions. NeuroImage Clin 2015;9:369–375. https://doi.org/10.1016/j.nicl.2015.09.003.
Toschi N, De Santis S, Granberg T, et al. Evidence for progressive microstructural damage in early multiple sclerosis by multi‐shell diffusion magnetic resonance imaging. Neuroscience 2019;403:27–34. https://doi.org/10.1016/j.neuroscience.2019.01.022.
Smith KJ, McDonald WI. The pathophysiology of multiple sclerosis: The mechanisms underlying the production of symptoms and the natural history of the disease. Philos Trans R Soc B Biol Sci 1999;354:1649–1673. https://doi.org/10.1098/rstb.1999.0510.
Duncan ID, Brower A, Kondo Y, et al. Extensive remyelination of the CNS leads to functional recovery. Proc Natl Acad Sci U S A 2009;106:6832–6836. https://doi.org/10.1073/PNAS.0812500106/SUPPL_FILE/0812500106SI.PDF.
Franklin RJM, Ffrench‐Constant C, Edgar JM, Smith KJ. Neuroprotection and repair in multiple sclerosis. Nat Rev Neurol 2012;8:624–634. https://doi.org/10.1038/nrneurol.2012.200.
Manrique‐Hoyos N, Jürgens T, Grønborg M, et al. Late motor decline after accomplished remyelination: Impact for progressive multiple sclerosis. Ann Neurol 2012;71:227–244. https://doi.org/10.1002/ana.22681.
Kuhlmann T, Ludwin S, Prat A, et al. An updated histological classification system for multiple sclerosis lesions. Acta Neuropathol 2016;133:13–24. https://doi.org/10.1007/S00401-016-1653-Y.
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;78:710–721. https://doi.org/10.1002/ANA.24497.
Galbusera R, Bahn E, Weigel M, et al. Postmortem quantitative MRI disentangles histological lesion types in multiple sclerosis. Brain Pathol 2022;33:e13136. https://doi.org/10.1111/BPA.13136.
Stadelmann C, Wegner C, Brück W. Inflammation, demyelination, and degeneration — Recent insights from MS pathology. Biochim Biophys Acta – Mol Basis Dis 2011;1812:275–282. https://doi.org/10.1016/j.bbadis.2010.07.007.
Lucchinetti CF. In: Zhang J, ed. Multiple Sclerosis Pathology During Early and Late Disease Phases: Pathogenic and Clinical Relevance BT – Immune Regulation and Immunotherapy in Autoimmune Disease. Springer US, 2007:214‐264. https://doi.org/10.1007/978-0-387-36003-4_12.
Bodini B, Veronese M, García‐Lorenzo D, et al. Dynamic Imaging of Individual Remyelination Profiles in Multiple Sclerosis. Ann Neurol 2016;79:726–738. https://doi.org/10.1002/ANA.24620.
Lazzarotto A, Tonietto M, Poirion E, et al. Clinically relevant profiles of myelin content changes in patients with multiple sclerosis: A multimodal and multicompartment imaging study. 2022;28:1881–1890. https://doi.org/10.1177/13524585221096975.
Bodini B, Tonietto M, Airas L, Stankoff B. Positron emission tomography in multiple sclerosis — straight to the target. Nat Rev Neurol 2021;17:663–675. https://doi.org/10.1038/s41582-021-00537-1.
Ricigliano VAG, Tonietto M, Hamzaoui M, et al. Spontaneous remyelination in lesions protects the integrity of surrounding tissues over time in multiple sclerosis. Eur J Neurol 2022;29:1719–1729. https://doi.org/10.1111/ene.15285.
Stankoff B, Poirion E, Tonietto M, Bodini B. Exploring the heterogeneity of MS lesions using positron emission tomography: a reappraisal of their contribution to disability. Brain Pathol 2018;28:723–734. https://doi.org/10.1111/bpa.12641.
Lassmann H. Multiple sclerosis pathology. Cold Spring Harb Perspect Med 2018;8:a028936. https://doi.org/10.1101/CSHPERSPECT.A028936.
Lassmann H. Recent neuropathological findings in MS ‐ Implications for diagnosis and therapy. J Neurol Suppl 2004;251:iv2–iv5. https://doi.org/10.1007/S00415-004-1402-3/METRICS.
Briggs FBS, Yu JC, Davis MF, et al. Multiple sclerosis risk factors contribute to onset heterogeneity. Mult Scler Relat Disord 2019;28:11–16. https://doi.org/10.1016/J.MSARD.2018.12.007.
Ebers GC, Kukay K, Bulman DE, et al. A full genome search in multiple sclerosis. Nat Genet 1996;13:472–476. https://doi.org/10.1038/ng0896-472.
Haines JL, Ter‐Minassian M, Bazyk A, et al. A complete genomic screen for multiple sclerosis underscores a role for the major histocompatability complex. Nat Genet 1996;13:469–471. https://doi.org/10.1038/ng0896-469.
Harroud A, Stridh P, McCauley JL, et al. Locus for severity implicates CNS resilience in progression of multiple sclerosis. Nature 2023;619:323–331. https://doi.org/10.1038/s41586-023-06250-x.
van den Bosch A, Fransen N, Mason M, et al. Neurofilament Light Chain Levels in Multiple Sclerosis Correlate With Lesions Containing Foamy Macrophages and With Acute Axonal Damage. Neurol Neuroimmunol Neuroinflamm 2024;9:e1154. https://doi.org/10.1212/NXI.0000000000001154.
Yik JT, Becquart P, Gill J, et al. Serum neurofilament light chain correlates with myelin and axonal magnetic resonance imaging markers in multiple sclerosis. Mult Scler Relat Disord 2022;57:57. https://doi.org/10.1016/j.msard.2021.103366.
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;135:511–528. https://doi.org/10.1007/S00401-018-1818-Y/FIGURES/9.
Lassmann H, Van Horssen J, Mahad D. Progressive multiple sclerosis: pathology and pathogenesis. Nat Rev Neurol 2012;8:647–656. https://doi.org/10.1038/nrneurol.2012.168.
Kuhle J, Nourbakhsh B, Grant D, et al. Serum neurofilament is associated with progression of brain atrophy and disability in early MS. Neurology 2017;88:826–831. https://doi.org/10.1212/WNL.0000000000003653.
Papadopoulos D, Magliozzi R, Mitsikostas DD, et al. Aging, cellular senescence, and progressive multiple sclerosis. Front Cell Neurosci 2020;14:546670. https://doi.org/10.3389/FNCEL.2020.00178/BIBTEX.
Mahad DH, Trapp BD, Lassmann H. Pathological mechanisms in progressive multiple sclerosis. Lancet Neurol 2015;14:183–193. https://doi.org/10.1016/S1474-4422(14)70256-X.
Lassmann H. The pathologic substrate of magnetic resonance alterations in multiple sclerosis. Neuroimaging Clin N Am 2008;18:563–576, ix. https://doi.org/10.1016/j.nic.2008.06.005.
Ellen O, Ye S, Nheu D, et al. The heterogeneous multiple sclerosis lesion: how can we assess and modify a degenerating lesion? Int J Mol Sci 2023;24:11112. https://doi.org/10.3390/ijms241311112.
Rahmanzadeh R, Lu PJ, Barakovic M, et al. Myelin and axon pathology in multiple sclerosis assessed by myelin water and multi‐shell diffusion imaging. Brain 2021;144:1684–1696. https://doi.org/10.1093/BRAIN/AWAB088.