Imagine that your brain is a bustling city, and multiple sclerosis (MS) is causing chaos within its borders. Conventional MRI can capture some of the damage done by MS, but it falls short when it comes to understanding the full extent of the disease and predicting outcomes. That’s where quantitative MRI comes in. By analyzing the hidden physiological abnormalities using machine learning techniques, scientists have made significant progress in unraveling the mysteries of MS. In this study, researchers examined MRI data from MS patients and healthy controls over a span of 15 years to create a comprehensive set of quantitative MRI features. They found distinct patterns of inflammation, microstructural alterations, and sodium accumulation that define different subtypes of MS. These findings shed light on the underlying pathophysiology of MS and offer important insights into the progression of the disease. If you’re curious to learn more about how quantitative MRI is revolutionizing our understanding of MS, dive into the fascinating research behind it!

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.

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