Search

Many patients with multiple sclerosis (MS) experience information processing speed (IPS) deficits, and the Symbol Digit Modalities Test (SDMT) has been recommended as a valid screening test. Magnetic resonance imaging (MRI) has markedly improved the understanding of the mechanisms associated with cognitive deficits in MS. However, which structural MRI markers are the most closely related to cognitive performance is still unclear. We used the multicenter 3T-MRI data set of the Italian Neuroimaging Network Initiative to extract multimodal data (i.e., demographic, clinical, neuropsychological, and structural MRIs) of 540 MS patients. We aimed to assess, through machine learning techniques, the contribution of brain MRI structural volumes in the prediction of IPS deficits when combined with demographic and clinical features. We trained and tested the eXtreme Gradient Boosting (XGBoost) model following a rigorous validation scheme to obtain reliable generalization performance. We carried out a classification and a regression task based on SDMT scores feeding each model with different combinations of features. For the classification task, the model trained with thalamus, cortical gray matter, hippocampus, and lesions volumes achieved an area under the receiver operating characteristic curve of 0.74. For the regression task, the model trained with cortical gray matter and thalamus volumes, EDSS, nucleus accumbens, lesions, and putamen volumes, and age reached a mean absolute error of 0.95. In conclusion, our results confirmed that damage to cortical gray matter and relevant deep and archaic gray matter structures, such as the thalamus and hippocampus, is among the most relevant predictors of cognitive performance in MS.

Background: For multiple sclerosis (MS) phenotypes classification, the presence of "disease activity" can be defined by clinical relapses and/or by magnetic resonance imaging (MRI) through gadolinium-enhancing (Gd+) lesions or new/enlarged T2 lesions. Recent MRI and pathology findings have demonstrated Gd deposition in the brain, suggesting to avoid Gd administration when dispensable. In this scenario, we aimed to evaluate the contribution of post-contrast MRIs to the definition of "active" MS phenotype. Methods: We retrospectively selected 84 "active" relapsing-remitting MS (RRMS) patients according to Lublin 2013, calculating both the number of Gd+ lesions not detectable as new/unequivocally enlarged on T2 images and the proportion of patients who would be still correctly classified as "active" without Gd administration. Results: 13 out of 164 (7.9%) Gd+ lesions did not correspond to a new/enlarged T2 lesion. Gd administration did not modify the classification of MS as "active" in 83 out of 84 subjects (98.8%). Conclusion: The contribution of Gd+ lesions to the correct classification of RRMS patients as "active" is marginal, thus limiting the need of routine Gd administration for this scope. Further studies are warranted to support these conclusions.

ObjectiveAssessing the risk of clinical and radiological reactivation during pregnancy and post partum in women with multiple sclerosis (MS) treated with natalizumab (NTZ) throughout pregnancy (LONG_EXP) compared with women interrupting treatment before (NO_EXP) and within >−30 days and ≤90 days from conception (SHORT_EXP), and describing newborns’ outcomes.MethodsMaternal clinical and radiological outcomes and obstetric and fetal outcomes were retrospectively collected and compared among groups (NO_EXP, SHORT_EXP, LONG_EXP). Predictors of clinical and radiological reactivation were investigated through univariable and multivariable analysis.Results170 eligible pregnancies from 163 women referring to 29 Italian MS centres were included. Annualised relapse rate (ARR) was significantly lower in LONG_EXP (n=66, 0.02 (0.001–0.09)) compared with NO_EXP (n=31, 0.43 (0.21–0.75), p=0.002) and SHORT_EXP (n=73, 0.46 (0.30–0.66), p=0.0004) during pregnancy, and in LONG_EXP (0.12 (0.05–0.24)) compared with SHORT_EXP (0.30 (0.17–0.50), p=0.008) during post partum. Gadolinium-enhancing (Gd+) lesions were less frequent in LONG_EXP (n=6/50, 2.00%) compared with NO_EXP (n=9/21, 42.86%) and SHORT_EXP after delivery (n=17/49, 34.69%, p=0.010).Delaying NTZ resumption after delivery significantly increased the risk of relapses (OR=1.29 (95% CI 1.07 to 1.57), p=0.009) and Gd+ lesions (OR=1.49 (95% CI 1.17 to 1.89, p=0.001). Newborns’ weight, length, head circumference and gestational age did not differ among groups after adjusting for confounders. Anaemia was tracked in 4/69 LONG_EXP newborns. Congenital anomaly rate was within the expected range for the untreated MS population.ConclusionsOur findings indicate that in women with MS treated with NTZ before conception, continuation of NTZ throughout pregnancy and its early resumption after delivery mitigate the risk of clinical and radiological reactivation. This approach has no major impact on newborns’ outcomes.

Objective: To investigate the role of cerebellar sub-regions on motor and cognitive performance in multiple sclerosis (MS) patients. Methods: Whole and sub-regional cerebellar volumes, brain volumes, T2 hyperintense lesion volumes (LV), and motor performance scores were obtained from 95 relapse-onset MS patients and 32 healthy controls (HC). MS patients also underwent an evaluation of working memory and processing speed functions. Cerebellar anterior and posterior lobes were segmented using the Spatially Unbiased Infratentorial Toolbox (SUIT) from Statistical Parametric Mapping (SPM12). Multivariate linear regression models assessed the relationship between magnetic resonance imaging (MRI) measures and motor/cognitive scores. Results: Compared to HC, only secondary progressive multiple sclerosis (SPMS) patients had lower cerebellar volumes (total and posterior cerebellum). In MS patients, lower anterior cerebellar volume and brain T2 LV predicted worse motor performance, whereas lower posterior cerebellar volume and brain T2 LV predicted poor cognitive performance. Global measures of brain volume and infratentorial T2 LV were not selected by the final multivariate models. Conclusion: Cerebellar volumetric abnormalities are likely to play an important contribution to explain motor and cognitive performance in MS patients. Consistently with functional mapping studies, cerebellar posterior–inferior volume accounted for variance in cognitive measures, whereas anterior cerebellar volume accounted for variance in motor performance, supporting the assessment of cerebellar damage at sub-regional level.

The Italian Neuroimaging Network Initiative (INNI) is an expanding repository of brain MRI data from multiple sclerosis (MS) patients recruited at four Italian MRI research sites. We describe the raw data quality of resting-state functional MRI (RS-fMRI) time-series in INNI and the inter-site variability in functional connectivity (FC) features after unified automated data preprocessing. MRI datasets from 489 MS patients and 246 healthy control (HC) subjects were retrieved from the INNI database. Raw data quality metrics included temporal signal-to-noise ratio (tSNR), spatial smoothness (FWHM), framewise displacement (FD), and differential variation in signals (DVARS). Automated preprocessing integrated white-matter lesion segmentation (SAMSEG) into a standard fMRI pipeline (fMRIPrep). FC features were calculated on pre-processed data and harmonized between sites (Combat) prior to assessing general MS-related alterations. Across centers (both groups), median tSNR and FWHM ranged from 47 to 84 and from 2.0 to 2.5, and median FD and DVARS ranged from 0.08 to 0.24 and from 1.06 to 1.22. After preprocessing, only global FC-related features were significantly correlated with FD or DVARS. Across large-scale networks, age/sex/FD-adjusted and harmonized FC features exhibited both inter-site and site-specific inter-group effects. Significant general reductions were obtained for somatomotor and limbic networks in MS patients (vs. HC). The implemented procedures provide technical information on raw data quality and outcome of fully automated preprocessing that might serve as reference in future RS-fMRI studies within INNI. The unified pipeline introduced little bias across sites and appears suitable for multisite FC analyses on harmonized network estimates.

Objective: The objective of this paper is to identify clinical or magnetic resonance imaging (MRI) predictors of long-term clinical progression in a large cohort of multiple sclerosis (MS) patients. Methods: A total of 241 relapsing–remitting (RR) MS patients were included in a nine-year follow-up (FU) study. The reference MRIs were acquired at baseline (BL) as part of a multicenter, cross-sectional, clinical-MRI study. Volumetric MRI metrics were measured by a fully automated, operator-independent, multi-parametric segmentation method. Clinical progression was evaluated as defined by: conversion from RR to secondary progressive (SP) disease course; progression of Expanded Disability Status Scale (EDSS); achievement and time to reach EDSS 4. Results: We concluded that conversion from RR to SP (OR 0.79; CI 0.7–0.9), progression of EDSS (OR 0.85; CI 0.77–0.93), achievement of EDSS 4 (OR 0.8; CI 0.7–0.9), and time to reach EDSS 4 (HR 0.88; CI 0.82–0.94) were all predicted by BL gray matter (GM) volume and, except for progression of EDSS, by BL EDSS (respectively: (OR 2.88; CI 1.9–4.36), (OR 2.7; CI 1.7–4.2), (HR 3.86; CI 1.94–7.70)). Conclusions: BL GM volume and EDSS are the best long-term predictors of disease progression in RRMS patients with a relatively long and mild disease.