Unsupervised Pattern Analysis to Differentiate Multiple Sclerosis Phenotypes Using Principal Component Analysis on Various MRI Sequences.

: Multiple sclerosis (MS) has two main phenotypes: relapse-remitting MS (RRMS) and progressive MS (PMS), distinguished by disability profiles and treatment response. Differentiating them using conventional MRI is challenging. : This study explores the use of scaled subprofile modelling using principal component analysis (SSM/PCA) on MRI data to distinguish between MS phenotypes.

Innate immune cells and myelin profile in multiple sclerosis: a multi-tracer PET/MR study.

Purpose: Neuropathological studies have demonstrated distinct profiles of microglia activation and myelin injury among different multiple sclerosis (MS) phenotypes and disability stages. PET imaging using specific tracers may uncover the in vivo molecular pathology and broaden the understanding of the disease heterogeneity.

Methods: We used the 18-kDa translocator protein (TSPO) tracer (R)-[C]PK11195 and [C]PIB PET images acquired in a hybrid PET/MR 3 T system to characterize, respectively, the profile of innate immune cells and myelin content in 47 patients with MS compared to 18 healthy controls (HC).

The effect of lesion filling on brain network analysis in multiple sclerosis using structural magnetic resonance imaging.

Background: Graph theoretical network analysis with structural magnetic resonance imaging (MRI) of multiple sclerosis (MS) patients can be used to assess subtle changes in brain networks. However, the presence of multiple focal brain lesions might impair the accuracy of automatic tissue segmentation methods, and hamper the performance of graph theoretical network analysis. Applying "lesion filling" by substituting the voxel intensities of a lesion with the voxel intensities of nearby voxels, thus creating an image devoid of lesions, might improve segmentation and graph theoretical network analysis.

Drug-related demyelinating syndromes: understanding risk factors, pathophysiological mechanisms and magnetic resonance imaging findings.

Some drugs and medications can precipitate immune system deregulations, which might be confused with recurrent demyelinating diseases, such as multiple sclerosis (MS) and neuromyelitis optica spectrum disorders (NMO), exacerbations of an existing disease, neoplastic lesions or other conditions. In this narrative review we describe some of the most relevant drugs and medications associated with iatrogenic demyelination. The anthelminthic agent levamisole is a frequent cocaine adulterant and can precipitate an exacerbated immune response attacking the central nervous system (CNS).

Gray matter networks and cognitive impairment in multiple sclerosis.

Background: Coordinated patterns of gray matter morphology can be represented as networks, and network disruptions may explain cognitive dysfunction related to multiple sclerosis (MS).

Objective: To investigate whether single-subject gray matter network properties are related to impaired cognition in MS.

Methods: We studied 148 MS patients (99 female) and 33 healthy controls (HC, 21 female).

Anti-MOG (Myelin Oligodendrocyte Glycoprotein)-Positive Severe Optic Neuritis with Optic Disc Ischaemia and Macular Star.

A 44-year-old man presented with severe right visual loss. The right fundus examination showed marked optic disc oedema associated with partial macular star. Serological blood tests for infectious agents were all negative.

Cognitive and Functional Impairment in Stroke Survivors with Basilar Artery Occlusive Disease.

Background: Despite growing literature on posterior vascular disease, specific information about the cognitive and functional profiles of patients with basilar artery occlusion disease (BAOD) is scarce. The aims of this study were (1) to compare the cognitive statuses of BAOD survivors versus healthy controls and (2) to correlate the functional capacity outcomes with the cognitive profiles of BAOD patients.

Methods: Comprehensive cognitive and functional assessments were carried out in 28 patients with BAOD and 27 age- and education level-matched healthy controls.