Association of the Cervical Canal Area With Disability and Progression in People With Multiple Sclerosis.

Background And Objectives: In multiple sclerosis (MS), brain reserve serves as a protective factor against cognitive impairment. Previous research has suggested a structural counterpart in the spine-spinal cord reserve-seemed to be associated with physical disability. This study aimed to investigate the potential of the cervical canal area (CCaA) as a proxy for spinal cord reserve in a multicentric cohort of people with MS (PwMS).

Trans-Synaptic Degeneration in the Visual Pathway in Patients With Myelin Oligodendrocyte Glycoprotein Antibody-Associated Disease.

Background And Objectives: We aimed to assess the presence of retinal neurodegeneration independent of optic neuritis (ON) in myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) and to investigate the development of trans-synaptic anterograde degeneration in these patients after ON.

Methods: Cross-sectional, retrospective study of 34 adult patients with MOGAD and 23 healthy controls (HC). Clinical, optical coherence tomography (OCT), and MRI data were collected.

Optic chiasm manual and automated measurements in sub-acute optic neuritis with OCT and MRI correlations.

Purpose: The optic chiasm (OC) is a central structure in the visual pathway and can be visualized in conventional MRI, but no consensus regarding its measurement has been defined. We aim to investigate the most reproducible manual approach to OC measurement and to explore associations of OC with optical coherence tomography (OCT) parameters, and automatic brain segmentation (FreeSurfer) in subacute optic neuritis (sON), multiple sclerosis without optic neuritis (MSwoON), and healthy subjects (HS).

Materials And Methods: We reproduced two previously reported methodologies and implemented a new proposed simplified approach, entitled optic chiasm mean area (OCMA).

Global and Regional Deep Learning Models for Multiple Sclerosis Stratification From MRI.

Background: The combination of anatomical MRI and deep learning-based methods such as convolutional neural networks (CNNs) is a promising strategy to build predictive models of multiple sclerosis (MS) prognosis. However, studies assessing the effect of different input strategies on model's performance are lacking.

Purpose: To compare whole-brain input sampling strategies and regional/specific-tissue strategies, which focus on a priori known relevant areas for disability accrual, to stratify MS patients based on their disability level.

Deciphering multiple sclerosis disability with deep learning attention maps on clinical MRI.

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).

Relationship between paramagnetic rim lesions and slowly expanding lesions in multiple sclerosis.

Background: Magnetic resonance imaging (MRI) markers for chronic active lesions in MS include slowly expanding lesions (SELs) and paramagnetic rim lesions (PRLs).

Objectives: To identify the relationship between SELs and PRLs in MS, and their association with disability.

Methods: 61 people with MS (pwMS) followed retrospectively with MRI including baseline susceptibility-weighted imaging, and longitudinal T1 and T2-weighted scans.

Detection of lesions in the optic nerve with magnetic resonance imaging using a 3D convolutional neural network.

Background: Optic neuritis (ON) is one of the first manifestations of multiple sclerosis, a disabling disease with rising prevalence. Detecting optic nerve lesions could be a relevant diagnostic marker in patients with multiple sclerosis.

Objectives: We aim to create an automated, interpretable method for optic nerve lesion detection from MRI scans.

T1/T2-weighted ratio in multiple sclerosis: A longitudinal study with clinical associations.

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.

Quantitative comparison of subcortical and ventricular volumetry derived from MPRAGE and MP2RAGE images using different brain morphometry software.

Objective: In brain volume assessment with MR imaging, it is of interest to know the effects of the pulse sequence and software used, to determine whether they provide equivalent data. The aim of this study was to compare cross-sectional volumes of subcortical and ventricular structures and their repeatability derived from MP2RAGE and MPRAGE images using MorphoBox, and FIRST or ALVIN.

Materials And Methods: MPRAGE and MP2RAGE T1-weighted images were obtained from 24 healthy volunteers.

Assessment of brain volumes obtained from MP-RAGE and MP2RAGE images, quantified using different segmentation methods.

Objective: For clinical purposes and research projects in neurological disease, it is of interest to evaluate the performance and comparability of available sequences and software packages for brain volume assessment to determine whether they provide equivalent results. This study compares cross-sectional brain volume values derived from images obtained with MP-RAGE or MP2RAGE sequences, using SIENA/X, SPM, or MorphoBox.

Materials And Methods: MP-RAGE and MP2RAGE T1-weighted images were obtained from 24 healthy volunteers.

A validation study of manual atrophy measures in patients with Multiple Sclerosis.

Purpose: Manual measures such as corpus callosum index, normalized corpus callosum area, and width of the third ventricle are potential biomarkers for brain atrophy. In this work, we investigate their suitability to assess the neurodegenerative component of multiple sclerosis (MS) by comparing them to volumetric measures and expanded disability status scale (EDSS).

Methods: Fifty-eight patients with a clinically isolated syndrome, 48 MS patients treated with interferon β, and 26 treated with natalizumab underwent a brain MRI at baseline and after 1 year.

Brain regional volume estimations with NeuroQuant and FIRST: a study in patients with a clinically isolated syndrome.

Purpose: Brain volume estimates from magnetic resonance images (MRIs) are of great interest in multiple sclerosis, and several automated tools have been developed for this purpose. The goal of this study was to assess the agreement between two tools, NeuroQuant® (NQ) and FMRIB's Integrated Registration Segmentation Tool (FIRST), for estimating overall and regional brain volume in a cohort of patients with a clinically isolated syndrome (CIS). In addition, white matter lesion volume was estimated with NQ and the Lesion Segmentation Toolbox (LST).