Recent advances in the longitudinal segmentation of multiple sclerosis lesions on magnetic resonance imaging: a review.

Multiple sclerosis (MS) is a chronic autoimmune disease characterized by demyelinating lesions that are often visible on magnetic resonance imaging (MRI). Segmentation of these lesions can provide imaging biomarkers of disease burden that can help monitor disease progression and the imaging response to treatment. Manual delineation of MRI lesions is tedious and prone to subjective bias, while automated lesion segmentation methods offer objectivity and speed, the latter being particularly important when analysing large datasets. Lesion segmentation can be broadly categorised into two groups: cross-sectional methods, which use imaging data acquired at a single time-point to characterise MRI lesions; and longitudinal methods, which use imaging data from the same subject acquired at two or more different time-points to characterise lesions over time. The main objective of longitudinal segmentation approaches is to more accurately detect the presence of new MS lesions and the growth or remission of existing lesions, which may be effective biomarkers of disease progression and treatment response. This paper reviews articles on longitudinal MS lesion segmentation methods published over the past 10 years. These are divided into traditional machine learning methods and deep learning techniques. PubMed articles using longitudinal information and comparing fully automatic two time point segmentations in any step of the process were selected. Nineteen articles were reviewed. There is an increasing number of deep learning techniques for longitudinal MS lesion segmentation that are promising to help better understand disease progression.

Applying multilayer analysis to morphological, structural, and functional brain networks to identify relevant dysfunction patterns.

In recent years, research on network analysis applied to MRI data has advanced significantly. However, the majority of the studies are limited to single networks obtained from resting-state fMRI, diffusion MRI, or gray matter probability maps derived from T1 images. Although a limited number of previous studies have combined two of these networks, none have introduced a framework to combine morphological, structural, and functional brain connectivity networks. The aim of this study was to combine the morphological, structural, and functional information, thus defining a new multilayer network perspective. This has proved advantageous when jointly analyzing multiple types of relational data from the same objects simultaneously using graph- mining techniques. The main contribution of this research is the design, development, and validation of a framework that merges these three layers of information into one multilayer network that links and relates the integrity of white matter connections with gray matter probability maps and resting-state fMRI. To validate our framework, several metrics from graph theory are expanded and adapted to our specific domain characteristics. This proof of concept was applied to a cohort of people with multiple sclerosis, and results show that several brain regions with a synchronized connectivity deterioration could be identified.

A novel mutation I215V in the PRNP gene associated with Creutzfeldt-Jakob and Alzheimer's diseases in three patients with divergent clinical phenotypes.

Genetic human prion diseases are autosomal dominant disorders associated with different mutations in the PRNP gene that are manifested as distinct clinical phenotypes. Here, we report a new pathogenic missense mutation (c.[643A>G], p.[I215V]) in the PRNP gene associated with three pathologically confirmed cases: two of Creutzfeldt-Jakob disease (CJD) and one of Alzheimer's disease (AD) in two different families from the same geographical region in Spain. This mutation has not been found in any of more than 2,000 control cases studied. It represents a conservative amino acid change, and the same change is observed in the PRNP gene from other species. The two CJD cases were homozygous at codon 129 (M/M), but showed divergent clinical phenotypes with onset at ages 55 and 77 years and illness durations of 15 and 6 months, respectively. The postmortem neuropathological analysis of these cases showed homogeneous features compatible with CJD. Interestingly, the AD case (a brother of one of the CJD cases) was heterozygous at codon 129 (M/V). No familiar history was documented for any of the cases, suggesting a de novo mutation, or a partial, age-dependent penetration of the mutation, perhaps related to codon 129 status. This new mutation extends the list of known pathogenic mutations responsible for genetic CJD, reinforces the clinical heterogeneity of the disease, and advocates for the inclusion of PRNP gene examination in the diagnostic workup of patients with poorly classifiable dementia, even in the absence of family history.