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Objectives: Conventional MRI is not sensitive to many pathological processes underpinning multiple sclerosis (MS) ongoing in normal appearing brain tissue (NABT). Quantitative MRI (qMRI) and a multiparameter mapping (MPM) protocol are used to simultaneously quantify magnetization transfer (MT) saturation, transverse relaxation rate R2* (1/T2*) and longitudinal relaxation rate R1 (1/T1), and assess differences in NABT microstructure between MS patients and healthy controls (HC)., Methods: This prospective cross-sectional study involves 36 MS patients (21 females, 15 males; age range 22-63 years; 15 relapsing-remitting MS - RRMS; 21 primary or secondary progressive MS - PMS) and 36 age-matched HC (20 females, 16 males); age range 21-61 years). The qMRI maps are computed and segmented in lesions and 3 normal appearing cerebral tissue classes: normal appearing cortical grey matter (NACGM), normal appearing deep grey matter (NADGM), normal appearing white matter (NAWM). Individual median values are extracted for each tissue class and MR parameter. MANOVAs and stepwise regressions assess differences between patients and HC., Results: MS patients are characterized by a decrease in MT, R2* and R1 within NACGM (p < .0001) and NAWM (p < .0001). In NADGM, MT decreases (p < .0001) but R2* and R1 remain normal. These observations tend to be more pronounced in PMS. Quantitative MRI parameters are independent predictors of clinical status: EDSS is significantly related to R1 in NACGM and R2* in NADGM; the latter also predicts motor score. Cognitive score is best predicted by MT parameter within lesions., Conclusions: Multiparametric data of brain microstructure concord with the literature, predict clinical performance and suggest a diffuse reduction in myelin and/or iron content within NABT of MS patients., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Background and purpose: Data from neuro‐imaging techniques allow us to estimate a brain's age. Brain age is easily interpretable as 'how old the brain looks' and could therefore be an attractive communication tool for brain health in clinical practice. This study aimed to investigate its clinical utility by investigating the relationship between brain age and cognitive performance in multiple sclerosis (MS). Methods: A linear regression model was trained to predict age from brain magnetic resonance imaging volumetric features and sex in a healthy control dataset (HC_train, n = 1673). This model was used to predict brain age in two test sets: HC_test (n = 50) and MS_test (n = 201). Brain‐predicted age difference (BPAD) was calculated as BPAD = brain age minus chronological age. Cognitive performance was assessed by the Symbol Digit Modalities Test (SDMT). Results: Brain age was significantly related to SDMT scores in the MS_test dataset (r = −0.46, p < 0.001) and contributed uniquely to variance in SDMT beyond chronological age, reflected by a significant correlation between BPAD and SDMT (r = −0.24, p < 0.001) and a significant weight (−0.25, p = 0.002) in a multivariate regression equation with age. Conclusions: Brain age is a candidate biomarker for cognitive dysfunction in MS and an easy to grasp metric for brain health. [ABSTRACT FROM AUTHOR]

Background: Functional biomarkers able to identify multiple sclerosis (MS) patients at high risk of fast disability progression are lacking. The aim of this study was to evaluate the ability of multimodal (upper and lower limbs motor, visual, lower limbs somatosensory) evoked potentials (EP) to monitor disease course and identify patients exposed to unfavourable evolution.Methods: One hundred MS patients were assessed with visual, somatosensory and motor EP and rated on the Expanded Disability Status Scale (EDSS) at baseline (T0) and about 6 years later (T1). The Spearman correlation (rS) was used to evaluate the relationship between conventional EP scores and clinical findings. Multiple (logistic) regression analysis estimated the predictive value of baseline electrophysiological data for three clinical outcomes: EDSS, annual EDSS progression, and the risk of EDSS worsening.Results: In contrast to longitudinal correlations, cross-sectional correlations between the different EP scores and EDSS were all significant (0.33 ≤ rS < 0.67, p < 0.001). Baseline global EP score and EDSS were highly significant predictors (p < 0.0001) of EDSS progression 6 years later. The baseline global EP score was found to be an independent predictor of the EDSS annual progression rate (p < 0.001), and of the risk of disability progression over time (p < 0.005). Based on a ROC curve determination, we defined a Global EP Score cut off point (17/30) to identify patients at high risk of disability progression illustrated by a positive predictive value of 70%.Conclusion: This study provides a proof of the concept that electrophysiology could be added to MRI and used as another complementary prognostic tool in MS patients. [ABSTRACT FROM AUTHOR]