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The application of convolutional neural networks (CNNs) to MRI data has emerged as a promising approach to achieving unprecedented levels of accuracy when predicting the course of neurological conditions, including multiple sclerosis, by means of extracting image features not detectable through conventional methods. Additionally, the study of CNN-derived attention maps, which indicate the most relevant anatomical features for CNN-based decisions, has the potential to uncover key disease mechanisms leading to disability accumulation.From a cohort of patients prospectively followed up after a first demyelinating attack, we selected those with T1-weighted and T2-FLAIR brain MRI sequences available for image analysis and a clinical assessment performed within the following six months (N = 319). Patients were divided into two groups according to expanded disability status scale (EDSS) score: ≥3.0 and

Purpose: Several studies have indicated the relevance of spinal cord grey matter atrophy for Multiple Sclerosis (MS) related disability. This work aimed at evaluating feasibility and clinical relevance of MRI-derived estimations of spinal cord grey matter atrophy in clinical practice. Methods: A convenience sample of MS patients (n=48) and healthy controls (HC, n=11) was scanned using brain sagittal 3D T1w (MPRAGE) and transverse T2w-FLAIR, and transverse single-slice spinal cord 2D heavily T1w (PSIR: phase-sensitive inversion recovery) sequences. An experienced technician with neuroradiological supervision used a semiautomated thresholding method implemented in JIM software on PSIR sequences to obtain cross-sectional area (CSA) of the spinal canal (CSAcanal), whole cord (CSAcord), grey (CSAgm) and white matter (CSAwm) at C2-C3. MPRAGE and T2w-FLAIR sequences were used to obtain brain parenchymal, grey and white matter fractions (BPF, GMF and WMF) using the Statistical Parametric Mapping software. Appropriate statistical tests were used before and after age and sex adjustment. Results: CSAgm and CSAwm could be obtained in all HC, but only 18 patients (37.5%) due to presence of lesions at C2-C3. Significant univariate associations between EDSS and BPF (rho =−0.376, p=0.015), GMF (rho =−0.287, p=0.069), CSAcanal (rho =−0.310, p=0.049), CSAcord (rho =−0.533 p

Background: T1w/T2-w ratio has been proposed as a clinically feasible MRI biomarker to assess tissue integrity in multiple sclerosis. However, no data is available in the earliest stages of the disease and longitudinal studies analysing clinical associations are scarce. Objective: To describe longitudinal changes in T1-w/T2-w in patients with clinically isolated syndrome (CIS) and multiple sclerosis, and to investigate their clinical associations. Methods: T1-w/T2-w images were generated and the mean value obtained in the corresponding lesion, normal-appearing grey (NAGM) and white matter (NAWM) masks. By co-registering baseline to follow-up MRI, evolved lesions were assessed; and by placing the mask of new lesions to the baseline study, the pre-lesional tissue integrity was measured. Results: We included 171 CIS patients and 22 established multiple sclerosis patients. In CIS, evolved lesions showed significant T1-w/T2-w increases compared to baseline (+7.6%, P

This article analyzes the personal leadership resources utilized by a sample of school principals in Catalonia (Spain) during the confinement and post-confinement periods due to the COVID-19 crisis. A questionnaire was designed, validated, and provided to the principals from Primary Education schools to carry out the study. The questionnaire analyzed personal leadership resources used by the principals during the confinement and post-confinement periods, compared to a former ¨normal situation¨. The data analysis results confirmed that the role of the principals was crucial in redirecting the situation and completing the academic course satisfactorily. The principals scored their leadership resources remarkably high in the former normality and maintained proactivity at a similar level during the crisis. However, other resources scored lower during the same period. As a direct result, there was a high degree of adaptation to this situation from the principals. The results indicate that principals do not lead in the same manner in times of crisis as in normal times. Age, experience, and type of school influence the results only in former normal situations but not in times of crisis.

Machine learning classification is an attractive approach to automatically differentiate patients from healthy subjects, and to predict future disease outcomes. A clinically isolated syndrome (CIS) is often the first presentation of multiple sclerosis (MS), but it is difficult at onset to predict who will have a second relapse and hence convert to clinically definite MS. In this study, we thus aimed to distinguish CIS converters from non-converters at onset of a CIS, using recursive feature elimination and weight averaging with support vector machines. We also sought to assess the influence of cohort size and cross-validation methods on the accuracy estimate of the classification.We retrospectively collected 400 patients with CIS from six European MAGNIMS MS centres. Patients underwent brain MRI at onset of a CIS according to local standard-of-care protocols. The diagnosis of clinically definite MS at one-year follow-up was the standard against which the accuracy of the model was tested. For each patient, we derived MRI-based features, such as grey matter probability, white matter lesion load, cortical thickness, and volume of specific cortical and white matter regions. Features with little contribution to the classification model were removed iteratively through an interleaved sample bootstrapping and feature averaging approach. Classification of CIS outcome at one-year follow-up was performed with 2-fold, 5-fold, 10-fold and leave-one-out cross-validation for each centre cohort independently and in all patients together.The estimated classification accuracy across centres ranged from 64.9% to 88.1% using 2-fold cross-validation and from 73% to 92.9% using leave-one-out cross-validation. The classification accuracy estimate was higher in single-centre, smaller data sets than in combinations of data sets, being the lowest when all patients were merged together.Regional MRI features such as WM lesions, grey matter probability in the thalamus and the precuneus or cortical thickness in the cuneus and inferior temporal gyrus predicted the occurrence of a second relapse in patients at onset of a CIS using support vector machines. The increased accuracy estimate of the classification achieved with smaller and single-centre samples may indicate a model bias (overfitting) when data points were limited, but also more homogeneous. We provide an overview of classifier performance from a range of cross-validation schemes to give insight into the variability across schemes. The proposed recursive feature elimination approach with weight averaging can be used both in single- and multi-centre data sets in order to bridge the gap between group-level comparisons and making predictions for individual patients. Keywords: Multiple sclerosis, Machine learning classification, Feature selection