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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Vydané v:	Frontiers in neuroinformatics Ročník 17; s. 1060511
Hlavní autori:	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.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland Frontiers Research Foundation 23.03.2023 Frontiers Media S.A
Predmet:	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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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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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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