Utility of icobrain for brain volumetry in multiple sclerosis clinical practice.

Background: Few studies on multiple sclerosis (MS) have explored the variability of percentage brain volume change (PBVC) measurements obtained from different clinical MRIs. In a retrospective multicentre cohort study, we quantified the variability of annualised PBVC in clinical MRIs.

Methods: Clinical MRIs of relapse-onset MS patients were assessed by icobrain.

Disentangling Neurodegeneration From Aging in Multiple Sclerosis Using Deep Learning: The Brain-Predicted Disease Duration Gap.

Background And Objectives: Disentangling brain aging from disease-related neurodegeneration in patients with multiple sclerosis (PwMS) is increasingly topical. The brain-age paradigm offers a window into this problem but may miss disease-specific effects. In this study, we investigated whether a disease-specific model might complement the brain-age gap (BAG) by capturing aspects unique to MS.

Lipid and brain volumetric measures in multiple sclerosis patients: findings from a large observational study.

Objectives: This study aimed to investigate relationships between cholesterol profile, brain volumetric MRI, and clinical measures in a large observational cohort of multiple sclerosis (MS) patients.

Materials And Methods: We included 1.505 patients with 4.

Lipid measures are associated with cognitive functioning in multiple sclerosis patients.

Background: An association between lipid measures and cognitive decline in patients with multiple sclerosis (MS) has been suggested.

Objectives: This study aimed to investigate relationships between lipid profile and cognitive performance in a large observational cohort of MS patients.

Materials And Methods: We included 211 patients with 316 available pairs of lipid and cognitive measures performed over follow-up.

Microstructural characterization of multiple sclerosis lesion phenotypes using multiparametric longitudinal analysis.

Background And Objectives: In multiple sclerosis (MS), slowly expanding lesions were shown to be associated with worse disability and prognosis. Their timely detection from cross-sectional data at early disease stages could be clinically relevant to inform treatment planning. Here, we propose to use multiparametric, quantitative MRI to allow a better cross-sectional characterization of lesions with different longitudinal phenotypes.