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Patterns of inflammation, microstructural alterations, and sodium accumulation define multiple sclerosis subtypes after 15 years from onset
INTRODUCTION: 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 radiolo...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10076673/ https://www.ncbi.nlm.nih.gov/pubmed/37035717 http://dx.doi.org/10.3389/fninf.2023.1060511 |
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author | 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. |
author_facet | 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. |
author_sort | Ricciardi, Antonio |
collection | PubMed |
description | INTRODUCTION: 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. METHODS: 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. RESULTS AND DISCUSSION: 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. |
format | Online Article Text |
id | pubmed-10076673 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-100766732023-04-07 Patterns of inflammation, microstructural alterations, and sodium accumulation define multiple sclerosis subtypes after 15 years from onset 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. Front Neuroinform Neuroscience INTRODUCTION: 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. METHODS: 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. RESULTS AND DISCUSSION: 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. Frontiers Media S.A. 2023-03-23 /pmc/articles/PMC10076673/ /pubmed/37035717 http://dx.doi.org/10.3389/fninf.2023.1060511 Text en Copyright © 2023 Ricciardi, Grussu, Kanber, Prados, Yiannakas, Solanky, Riemer, Golay, Brownlee, Ciccarelli, Alexander and Gandini Wheeler-Kingshott. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience 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. Patterns of inflammation, microstructural alterations, and sodium accumulation define multiple sclerosis subtypes after 15 years from onset |
title | Patterns of inflammation, microstructural alterations, and sodium accumulation define multiple sclerosis subtypes after 15 years from onset |
title_full | Patterns of inflammation, microstructural alterations, and sodium accumulation define multiple sclerosis subtypes after 15 years from onset |
title_fullStr | Patterns of inflammation, microstructural alterations, and sodium accumulation define multiple sclerosis subtypes after 15 years from onset |
title_full_unstemmed | Patterns of inflammation, microstructural alterations, and sodium accumulation define multiple sclerosis subtypes after 15 years from onset |
title_short | Patterns of inflammation, microstructural alterations, and sodium accumulation define multiple sclerosis subtypes after 15 years from onset |
title_sort | patterns of inflammation, microstructural alterations, and sodium accumulation define multiple sclerosis subtypes after 15 years from onset |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10076673/ https://www.ncbi.nlm.nih.gov/pubmed/37035717 http://dx.doi.org/10.3389/fninf.2023.1060511 |
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