Patterns of inflammation, microstructural alterations, and sodium accumulation define multiple sclerosis subtypes after 15 years from onset

Conventional MRI is routinely used for the characterization of pathological changes in multiple sclerosis (MS), but due to its lack of specificity is unable to provide accurate prognoses, explain disease heterogeneity and reconcile the gap between observed clinical symptoms and radiological evidence...

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Published in:	Frontiers in neuroinformatics Vol. 17; p. 1060511
Main Authors:	Ricciardi, Antonio, Grussu, Francesco, Kanber, Baris, Prados, Ferran, Yiannakas, Marios C., Solanky, Bhavana S., Riemer, Frank, Golay, Xavier, Brownlee, Wallace, Ciccarelli, Olga, Alexander, Daniel C., Gandini Wheeler-Kingshott, Claudia A. M.
Format:	Journal Article
Language:	English
Published:	Switzerland Frontiers Research Foundation 23.03.2023 Frontiers Media S.A
Subjects:	Age Atrophy Biomarkers Brain research Classification Clinical medicine diffusion Inflammation Machine learning Magnetic resonance imaging MRI multi-modal Multiple sclerosis Neuroscience Pathophysiology Phenotypes quantitative Sodium sodium diffusion MRI multiple sclerosis quantitative random forest machine learning multi-modal
ISSN:	1662-5196, 1662-5196
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Abstract	Conventional MRI is routinely used for the characterization of pathological changes in multiple sclerosis (MS), but due to its lack of specificity is unable to provide accurate prognoses, explain disease heterogeneity and reconcile the gap between observed clinical symptoms and radiological evidence. Quantitative MRI provides measures of physiological abnormalities, otherwise invisible to conventional MRI, that correlate with MS severity. Analyzing quantitative MRI measures through machine learning techniques has been shown to improve the understanding of the underlying disease by better delineating its alteration patterns. In this retrospective study, a cohort of healthy controls (HC) and MS patients with different subtypes, followed up 15 years from clinically isolated syndrome (CIS), was analyzed to produce a multi-modal set of quantitative MRI features encompassing relaxometry, microstructure, sodium ion concentration, and tissue volumetry. Random forest classifiers were used to train a model able to discriminate between HC, CIS, relapsing remitting (RR) and secondary progressive (SP) MS patients based on these features and, for each classification task, to identify the relative contribution of each MRI-derived tissue property to the classification task itself. Average classification accuracy scores of 99 and 95% were obtained when discriminating HC and CIS vs. SP, respectively; 82 and 83% for HC and CIS vs. RR; 76% for RR vs. SP, and 79% for HC vs. CIS. Different patterns of alterations were observed for each classification task, offering key insights in the understanding of MS phenotypes pathophysiology: atrophy and relaxometry emerged particularly in the classification of HC and CIS vs. MS, relaxometry within lesions in RR vs. SP, sodium ion concentration in HC vs. CIS, and microstructural alterations were involved across all tasks.
AbstractList	Conventional MRI is routinely used for the characterization of pathological changes in multiple sclerosis (MS), but due to its lack of specificity is unable to provide accurate prognoses, explain disease heterogeneity and reconcile the gap between observed clinical symptoms and radiological evidence. Quantitative MRI provides measures of physiological abnormalities, otherwise invisible to conventional MRI, that correlate with MS severity. Analyzing quantitative MRI measures through machine learning techniques has been shown to improve the understanding of the underlying disease by better delineating its alteration patterns. In this retrospective study, a cohort of healthy controls (HC) and MS patients with different subtypes, followed up 15 years from clinically isolated syndrome (CIS), was analyzed to produce a multi-modal set of quantitative MRI features encompassing relaxometry, microstructure, sodium ion concentration, and tissue volumetry. Random forest classifiers were used to train a model able to discriminate between HC, CIS, relapsing remitting (RR) and secondary progressive (SP) MS patients based on these features and, for each classification task, to identify the relative contribution of each MRI-derived tissue property to the classification task itself. Average classification accuracy scores of 99 and 95% were obtained when discriminating HC and CIS vs. SP, respectively; 82 and 83% for HC and CIS vs. RR; 76% for RR vs. SP, and 79% for HC vs. CIS. Different patterns of alterations were observed for each classification task, offering key insights in the understanding of MS phenotypes pathophysiology: atrophy and relaxometry emerged particularly in the classification of HC and CIS vs. MS, relaxometry within lesions in RR vs. SP, sodium ion concentration in HC vs. CIS, and microstructural alterations were involved across all tasks. IntroductionConventional MRI is routinely used for the characterization of pathological changes in multiple sclerosis (MS), but due to its lack of specificity is unable to provide accurate prognoses, explain disease heterogeneity and reconcile the gap between observed clinical symptoms and radiological evidence. Quantitative MRI provides measures of physiological abnormalities, otherwise invisible to conventional MRI, that correlate with MS severity. Analyzing quantitative MRI measures through machine learning techniques has been shown to improve the understanding of the underlying disease by better delineating its alteration patterns.MethodsIn this retrospective study, a cohort of healthy controls (HC) and MS patients with different subtypes, followed up 15 years from clinically isolated syndrome (CIS), was analyzed to produce a multi-modal set of quantitative MRI features encompassing relaxometry, microstructure, sodium ion concentration, and tissue volumetry. Random forest classifiers were used to train a model able to discriminate between HC, CIS, relapsing remitting (RR) and secondary progressive (SP) MS patients based on these features and, for each classification task, to identify the relative contribution of each MRI-derived tissue property to the classification task itself.Results and discussionAverage classification accuracy scores of 99 and 95% were obtained when discriminating HC and CIS vs. SP, respectively; 82 and 83% for HC and CIS vs. RR; 76% for RR vs. SP, and 79% for HC vs. CIS. Different patterns of alterations were observed for each classification task, offering key insights in the understanding of MS phenotypes pathophysiology: atrophy and relaxometry emerged particularly in the classification of HC and CIS vs. MS, relaxometry within lesions in RR vs. SP, sodium ion concentration in HC vs. CIS, and microstructural alterations were involved across all tasks. Conventional MRI is routinely used for the characterization of pathological changes in multiple sclerosis (MS), but due to its lack of specificity is unable to provide accurate prognoses, explain disease heterogeneity and reconcile the gap between observed clinical symptoms and radiological evidence. Quantitative MRI provides measures of physiological abnormalities, otherwise invisible to conventional MRI, that correlate with MS severity. Analyzing quantitative MRI measures through machine learning techniques has been shown to improve the understanding of the underlying disease by better delineating its alteration patterns.IntroductionConventional MRI is routinely used for the characterization of pathological changes in multiple sclerosis (MS), but due to its lack of specificity is unable to provide accurate prognoses, explain disease heterogeneity and reconcile the gap between observed clinical symptoms and radiological evidence. Quantitative MRI provides measures of physiological abnormalities, otherwise invisible to conventional MRI, that correlate with MS severity. Analyzing quantitative MRI measures through machine learning techniques has been shown to improve the understanding of the underlying disease by better delineating its alteration patterns.In this retrospective study, a cohort of healthy controls (HC) and MS patients with different subtypes, followed up 15 years from clinically isolated syndrome (CIS), was analyzed to produce a multi-modal set of quantitative MRI features encompassing relaxometry, microstructure, sodium ion concentration, and tissue volumetry. Random forest classifiers were used to train a model able to discriminate between HC, CIS, relapsing remitting (RR) and secondary progressive (SP) MS patients based on these features and, for each classification task, to identify the relative contribution of each MRI-derived tissue property to the classification task itself.MethodsIn this retrospective study, a cohort of healthy controls (HC) and MS patients with different subtypes, followed up 15 years from clinically isolated syndrome (CIS), was analyzed to produce a multi-modal set of quantitative MRI features encompassing relaxometry, microstructure, sodium ion concentration, and tissue volumetry. Random forest classifiers were used to train a model able to discriminate between HC, CIS, relapsing remitting (RR) and secondary progressive (SP) MS patients based on these features and, for each classification task, to identify the relative contribution of each MRI-derived tissue property to the classification task itself.Average classification accuracy scores of 99 and 95% were obtained when discriminating HC and CIS vs. SP, respectively; 82 and 83% for HC and CIS vs. RR; 76% for RR vs. SP, and 79% for HC vs. CIS. Different patterns of alterations were observed for each classification task, offering key insights in the understanding of MS phenotypes pathophysiology: atrophy and relaxometry emerged particularly in the classification of HC and CIS vs. MS, relaxometry within lesions in RR vs. SP, sodium ion concentration in HC vs. CIS, and microstructural alterations were involved across all tasks.Results and discussionAverage classification accuracy scores of 99 and 95% were obtained when discriminating HC and CIS vs. SP, respectively; 82 and 83% for HC and CIS vs. RR; 76% for RR vs. SP, and 79% for HC vs. CIS. Different patterns of alterations were observed for each classification task, offering key insights in the understanding of MS phenotypes pathophysiology: atrophy and relaxometry emerged particularly in the classification of HC and CIS vs. MS, relaxometry within lesions in RR vs. SP, sodium ion concentration in HC vs. CIS, and microstructural alterations were involved across all tasks. Conventional MRI is routinely used for the characterisation of pathological changes in multiple sclerosis (MS), but due to its lack of specificity is unable to provide accurate prognoses, explain disease heterogeneity and reconcile the gap between observed clinical symptoms and radiological evidence. Quantitative MRI provides measures of physiological abnormalities, otherwise invisible to conventional MRI, that correlate with MS severity. Analysing quantitative MRI measures through machine learning techniques has been shown to improve the understanding of the underlying disease by better delineating its alteration patterns. In this retrospective study, a cohort of healthy controls (HC) and MS patients with different subtypes, followed up 15 years from clinically isolated syndrome (CIS), was analysed to produce a multi-modal set of quantitative MRI features encompassing relaxometry, microstructure, sodium ion concentration and tissue volumetry. Random forest classifiers were used to train a model able to discriminate between HC, CIS, relapsing remitting (RR) and secondary progressive (SP) MS patients based on these features and, for each classification task, to identify the relative contribution of each MRI-derived tissue property to the classification task itself. Average classification accuracy scores of 99% and 95% were obtained when discriminating HC and CIS versus SP, respectively; 82% and 83% for HC and CIS versus RR; 76% for RR versus SP, and 79% for HC versus CIS. Different patterns of alterations were observed for each classification task, offering key insights in the understanding of MS phenotypes pathophysiology: atrophy and relaxometry emerged particularly in the classification of HC and CIS versus MS, relaxometry within lesions in RR versus SP, sodium ion concentration in HC versus CIS, and microstructural alterations were involved across all tasks.
Author	Ricciardi, Antonio Brownlee, Wallace Ciccarelli, Olga Grussu, Francesco Golay, Xavier Solanky, Bhavana S. Yiannakas, Marios C. Kanber, Baris Riemer, Frank Gandini Wheeler-Kingshott, Claudia A. M. Alexander, Daniel C. Prados, Ferran
AuthorAffiliation	8 Department of Brain and Behavioural Sciences, University of Pavia , Pavia , Italy 1 NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London , London , United Kingdom 5 Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital , Bergen , Norway 7 NIHR UCLH Biomedical Research Centre , London , United Kingdom 3 Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London , London , United Kingdom 4 eHealth Center, Universitat Oberta de Catalunya , Barcelona , Spain 2 Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus , Barcelona , Spain 6 Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London , London , United Kingdom 9 Brain Connectivity Research Center
AuthorAffiliation_xml	– name: 9 Brain Connectivity Research Center, IRCCS Mondino Foundation , Pavia , Italy – name: 1 NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London , London , United Kingdom – name: 5 Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital , Bergen , Norway – name: 3 Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London , London , United Kingdom – name: 4 eHealth Center, Universitat Oberta de Catalunya , Barcelona , Spain – name: 7 NIHR UCLH Biomedical Research Centre , London , United Kingdom – name: 2 Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus , Barcelona , Spain – name: 6 Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London , London , United Kingdom – name: 8 Department of Brain and Behavioural Sciences, University of Pavia , Pavia , Italy
Author_xml	– sequence: 1 givenname: Antonio surname: Ricciardi fullname: Ricciardi, Antonio – sequence: 2 givenname: Francesco surname: Grussu fullname: Grussu, Francesco – sequence: 3 givenname: Baris surname: Kanber fullname: Kanber, Baris – sequence: 4 givenname: Ferran surname: Prados fullname: Prados, Ferran – sequence: 5 givenname: Marios C. surname: Yiannakas fullname: Yiannakas, Marios C. – sequence: 6 givenname: Bhavana S. surname: Solanky fullname: Solanky, Bhavana S. – sequence: 7 givenname: Frank surname: Riemer fullname: Riemer, Frank – sequence: 8 givenname: Xavier surname: Golay fullname: Golay, Xavier – sequence: 9 givenname: Wallace surname: Brownlee fullname: Brownlee, Wallace – sequence: 10 givenname: Olga surname: Ciccarelli fullname: Ciccarelli, Olga – sequence: 11 givenname: Daniel C. surname: Alexander fullname: Alexander, Daniel C. – sequence: 12 givenname: Claudia A. M. surname: Gandini Wheeler-Kingshott fullname: Gandini Wheeler-Kingshott, Claudia A. M.
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Cites_doi	10.1093/brain/awv386 10.3389/fncel.2019.00021 10.3389/fnins.2018.00810 10.1212/WNL.0000000000000560 10.1212/WNL.0000000000003395 10.1093/brain/awy088 10.12688/f1000research.11932.1 10.1212/WNL.56.3.304 10.1016/j.neuroimage.2021.118371 10.1212/WNL.0000000000003511 10.1016/j.neuroimage.2008.10.026 10.1177/1352458519845105 10.1016/j.jneumeth.2016.08.002 10.1038/nm.3390 10.1007/978-3-030-31635-8_202 10.1073/pnas.0931278100 10.1111/bpa.12642 10.1109/TMI.2015.2418298 10.1016/j.neuroimage.2016.06.002 10.1111/j.1552-6569.2007.00131.x 10.1093/brain/awt149 10.1007/s10334-013-0395-2 10.1177/1352458519885107 10.1177/135245859900500415 10.1136/jnnp-2018-319634 10.1007/s10334-013-0396-1 10.1038/s41582-020-0314-x 10.1016/j.nicl.2014.11.021 10.1148/radiol.2522081399 10.1093/brain/awp334 10.1097/00019052-200206000-00003 10.1016/j.neuroimage.2015.10.019 10.1002/1531-8249(200006)47:6<707::AID-ANA3>3.0.CO;2-Q 10.1016/j.neuroimage.2016.06.053 10.1016/S1474-4422(17)30470-2 10.1016/S1474-4422(18)30451-4 10.1016/j.neuroimage.2020.116884
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Copyright	Copyright © 2023 Ricciardi, Grussu, Kanber, Prados, Yiannakas, Solanky, Riemer, Golay, Brownlee, Ciccarelli, Alexander and Gandini Wheeler-Kingshott. 2023. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Copyright © 2023 Ricciardi, Grussu, Kanber, Prados, Yiannakas, Solanky, Riemer, Golay, Brownlee, Ciccarelli, Alexander and Gandini Wheeler-Kingshott. 2023 Ricciardi, Grussu, Kanber, Prados, Yiannakas, Solanky, Riemer, Golay, Brownlee, Ciccarelli, Alexander and Gandini Wheeler-Kingshott
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Keywords	sodium diffusion MRI multiple sclerosis quantitative random forest machine learning multi-modal
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References	Savini (B34) 2019; 13 Armstrong (B3) 2003; 100 Prados (B29); 139 Wottschel (B40) 2015; 7 Sastre-Garriga (B33) 2020; 16 Collorone (B9) 2020; 26 Kaden (B18) 2016; 139 Roosendaal (B32) 2009; 44 Maarouf (B22) 2017; 88 Lublin (B19) 2014; 83 Filippi (B12) 2019; 18 Eshaghi (B11) 2016; 87 Thompson (B37) 2018; 17 Gandini Wheeler-Kingshott (B14) 2018; 12 Neema (B25) 2007; 17 Petracca (B28) 2016; 139 Eshaghi (B10) 2018; 141 Paling (B26) 2013; 136 VanRossum (B39) 2010 Soares (B36) 2020 Maarouf (B21) 2014; 27 Barkhof (B4) 1999; 5 Aquino (B2) 2009; 252 Filippi (B13) 2001; 56 Lucchinetti (B20) 2000; 47 Grussu (B15) 2020; 217 Shin (B35) 2021; 240 Riemer (B31) 2014; 27 Tur (B38) 2020; 26 Andersson (B1) 2016; 125 Grussu (B16) 2016; 273 Magliozzi (B23) 2018; 28 Cardoso (B7) 2015; 34 Chard (B8) 2017; 6 Inglese (B17) 2010; 133 Pedregosa (B27) 2011; 12 Brownlee (B6) 2019; 90 Mezer (B24) 2013; 19 Barkhof (B5) 2002; 15 Prados (B30)
References_xml	– volume: 139 start-page: 795 year: 2016 ident: B28 article-title: Brain intra-and extracellular sodium concentration in multiple sclerosis: a 7 t MRI study publication-title: Brain doi: 10.1093/brain/awv386 – volume: 13 start-page: 21 year: 2019 ident: B34 article-title: Default mode network structural integrity and cerebellar connectivity predict information processing speed deficit in multiple sclerosis publication-title: Front. Cell. Neurosci. doi: 10.3389/fncel.2019.00021 – volume: 12 start-page: 810 year: 2018 ident: B14 article-title: Challenges and perspectives of quantitative functional sodium imaging (fNaI) publication-title: Front. Neurosci. doi: 10.3389/fnins.2018.00810 – volume: 83 start-page: 278 year: 2014 ident: B19 article-title: Defining the clinical course of multiple sclerosis: the 2013 revisions publication-title: Neurology doi: 10.1212/WNL.0000000000000560 – volume: 12 start-page: 2825 year: 2011 ident: B27 article-title: Scikit-learn: machine learning in Python publication-title: J. Mach. Learn. Res. – volume: 87 start-page: 2463 year: 2016 ident: B11 article-title: Gray matter MRI differentiates neuromyelitis optica from multiple sclerosis using random forest publication-title: Neurology doi: 10.1212/WNL.0000000000003395 – volume: 141 start-page: 1665 year: 2018 ident: B10 article-title: Progression of regional grey matter atrophy in multiple sclerosis publication-title: Brain doi: 10.1093/brain/awy088 – volume: 6 start-page: 1828 year: 2017 ident: B8 article-title: Resolving the clinico-radiological paradox in multiple sclerosis publication-title: F1000Res. doi: 10.12688/f1000research.11932.1 – volume: 56 start-page: 304 year: 2001 ident: B13 article-title: Diffusion tensor magnetic resonance imaging in multiple sclerosis publication-title: Neurology doi: 10.1212/WNL.56.3.304 – volume: 240 start-page: 118371 year: 2021 ident: B35 article-title: χ-separation: magnetic susceptibility source separation toward iron and myelin mapping in the brain publication-title: Neuroimage doi: 10.1016/j.neuroimage.2021.118371 – volume-title: ISMRM ident: B30 article-title: Automatic sodium maps reconstruction using patchmatch algorithm for phantom detection – volume: 88 start-page: 289 year: 2017 ident: B22 article-title: Increased total sodium concentration in gray matter better explains cognition than atrophy in MS publication-title: Neurology doi: 10.1212/WNL.0000000000003511 – volume: 44 start-page: 1397 year: 2009 ident: B32 article-title: Regional DTI differences in multiple sclerosis patients publication-title: Neuroimage doi: 10.1016/j.neuroimage.2008.10.026 – volume: 26 start-page: 774 year: 2020 ident: B38 article-title: A multi-shell multi-tissue diffusion study of brain connectivity in early multiple sclerosis publication-title: Multiple Scler. J. doi: 10.1177/1352458519845105 – volume: 273 start-page: 20 year: 2016 ident: B16 article-title: A framework for optimal whole-sample histological quantification of neurite orientation dispersion in the human spinal cord publication-title: J. Neurosci. Methods doi: 10.1016/j.jneumeth.2016.08.002 – volume: 19 start-page: 1667 year: 2013 ident: B24 article-title: Quantifying the local tissue volume and composition in individual brains with magnetic resonance imaging publication-title: Nat. Med. doi: 10.1038/nm.3390 – start-page: 1644 volume-title: XV Mediterranean Conference on Medical and Biological Engineering and Computing–MEDICON 2019: Proceedings of MEDICON 2019 year: 2020 ident: B36 article-title: Investigating whole-brain MRI markers in multiple sclerosis–emerging dimensions in morphometric space doi: 10.1007/978-3-030-31635-8_202 – volume: 100 start-page: 6257 year: 2003 ident: B3 article-title: The Na/K pump, Cl ion, and osmotic stabilization of cells publication-title: Proc. Natl. Acad. Sci. U.S.A. doi: 10.1073/pnas.0931278100 – volume: 28 start-page: 735 year: 2018 ident: B23 article-title: MRI of cortical lesions and its use in studying their role in MS pathogenesis and disease course publication-title: Brain Pathol. doi: 10.1111/bpa.12642 – volume: 34 start-page: 1976 year: 2015 ident: B7 article-title: Geodesic information flows: spatially-variant graphs and their application to segmentation and fusion publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2015.2418298 – volume: 139 start-page: 346 year: 2016 ident: B18 article-title: Multi-compartment microscopic diffusion imaging publication-title: Neuroimage doi: 10.1016/j.neuroimage.2016.06.002 – volume: 17 start-page: 16S year: 2007 ident: B25 article-title: T1-and T2-based MRI measures of diffuse gray matter and white matter damage in patients with multiple sclerosis publication-title: J. Neuroimaging doi: 10.1111/j.1552-6569.2007.00131.x – volume: 136 start-page: 2305 year: 2013 ident: B26 article-title: Sodium accumulation is associated with disability and a progressive course in multiple sclerosis publication-title: Brain doi: 10.1093/brain/awt149 – volume: 27 start-page: 35 year: 2014 ident: B31 article-title: Sodium (23Na) ultra-short echo time imaging in the human brain using a 3D-cones trajectory publication-title: Magn. Reson. Mater. Phys. Biol. Med. doi: 10.1007/s10334-013-0395-2 – volume: 26 start-page: 1647 year: 2020 ident: B9 article-title: Reduced neurite density in the brain and cervical spinal cord in relapsing–remitting multiple sclerosis: a Noddi study publication-title: Multiple Scler. J. doi: 10.1177/1352458519885107 – volume: 5 start-page: 283 year: 1999 ident: B4 article-title: MRI in multiple sclerosis: correlation with expanded disability status scale (EDSS) publication-title: Multiple Scler. J. doi: 10.1177/135245859900500415 – year: 2010 ident: B39 publication-title: The Python Language Reference – volume: 90 start-page: 755 year: 2019 ident: B6 article-title: Cortical grey matter sodium accumulation is associated with disability and secondary progressive disease course in relapse-onset multiple sclerosis publication-title: J. Neurol. Neurosurg. Psychiatry doi: 10.1136/jnnp-2018-319634 – volume: 27 start-page: 53 year: 2014 ident: B21 article-title: Topography of brain sodium accumulation in progressive multiple sclerosis publication-title: Magn. Reson. Mater. Phys. Biol. Med. doi: 10.1007/s10334-013-0396-1 – volume: 16 start-page: 171 year: 2020 ident: B33 article-title: Magnims consensus recommendations on the use of brain and spinal cord atrophy measures in clinical practice publication-title: Nat. Rev. Neurol. doi: 10.1038/s41582-020-0314-x – volume: 7 start-page: 281 year: 2015 ident: B40 article-title: Predicting outcome in clinically isolated syndrome using machine learning publication-title: Neuroimage Clin. doi: 10.1016/j.nicl.2014.11.021 – volume: 252 start-page: 165 year: 2009 ident: B2 article-title: Age-related iron deposition in the basal ganglia: quantitative analysis in healthy subjects publication-title: Radiology doi: 10.1148/radiol.2522081399 – volume: 133 start-page: 847 year: 2010 ident: B17 article-title: Brain tissue sodium concentration in multiple sclerosis: a sodium imaging study at 3 Tesla publication-title: Brain doi: 10.1093/brain/awp334 – volume: 15 start-page: 239 year: 2002 ident: B5 article-title: The clinico-radiological paradox in multiple sclerosis revisited publication-title: Curr. Opin. Neurol. doi: 10.1097/00019052-200206000-00003 – volume: 125 start-page: 1063 year: 2016 ident: B1 article-title: An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging publication-title: Neuroimage doi: 10.1016/j.neuroimage.2015.10.019 – volume: 47 start-page: 707 year: 2000 ident: B20 article-title: Heterogeneity of multiple sclerosis lesions: implications for the pathogenesis of demyelination publication-title: Ann. Neurol. doi: 10.1002/1531-8249(200006)47:6<707::AID-ANA3>3.0.CO;2-Q – volume: 139 start-page: 376 ident: B29 article-title: A multi-time-point modality-agnostic patch-based method for lesion filling in multiple sclerosis publication-title: Neuroimage doi: 10.1016/j.neuroimage.2016.06.053 – volume: 17 start-page: 162 year: 2018 ident: B37 article-title: Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria publication-title: Lancet Neurol. doi: 10.1016/S1474-4422(17)30470-2 – volume: 18 start-page: 198 year: 2019 ident: B12 article-title: Association between pathological and MRI findings in multiple sclerosis publication-title: Lancet Neurol. doi: 10.1016/S1474-4422(18)30451-4 – volume: 217 start-page: 116884 year: 2020 ident: B15 article-title: Multi-parametric quantitative in vivo spinal cord MRI with unified signal readout and image denoising publication-title: Neuroimage doi: 10.1016/j.neuroimage.2020.116884
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Snippet	Conventional MRI is routinely used for the characterization of pathological changes in multiple sclerosis (MS), but due to its lack of specificity is unable to... Conventional MRI is routinely used for the characterisation of pathological changes in multiple sclerosis (MS), but due to its lack of specificity is unable to... IntroductionConventional MRI is routinely used for the characterization of pathological changes in multiple sclerosis (MS), but due to its lack of specificity...
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SubjectTerms	Age Atrophy Biomarkers Brain research Classification Clinical medicine diffusion Inflammation Machine learning Magnetic resonance imaging MRI multi-modal Multiple sclerosis Neuroscience Pathophysiology Phenotypes quantitative Sodium
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Title	Patterns of inflammation, microstructural alterations, and sodium accumulation define multiple sclerosis subtypes after 15 years from onset
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