Patients recovering from COVID-19 who presented with anosmia during their acute episode have behavioral, functional, and structural brain alterations.

Patients recovering from COVID-19 commonly exhibit cognitive and brain alterations, yet the specific neuropathological mechanisms and risk factors underlying these alterations remain elusive. Given the significant global incidence of COVID-19, identifying factors that can distinguish individuals at risk of developing brain alterations is crucial for prioritizing follow-up care. Here, we report findings from a sample of patients consisting of 73 adults with a mild to moderate SARS-CoV-2 infection without signs of respiratory failure and 27 with infections attributed to other agents and no history of COVID-19.

Virtual tissue microstructure reconstruction across species using generative deep learning.

Analyzing tissue microstructure is essential for understanding complex biological systems in different species. Tissue functions largely depend on their intrinsic tissue architecture. Therefore, studying the three-dimensional (3D) microstructure of tissues, such as the liver, is particularly fascinating due to its conserved essential roles in metabolic processes and detoxification.

Comparison of baseline correction algorithms for in vivo H-MRS.

Proton MRS is used clinically to collect localized, quantitative metabolic data from living tissues. However, the presence of baselines in the spectra complicates accurate MRS data quantification. The occurrence of baselines is not specific to short-echo-time MRS data.

PhyberSIM: a tool for the generation of ground truth to evaluate brain fiber clustering algorithms.

Diffusion Magnetic Resonance Imaging tractography is a non-invasive technique that produces a collection of streamlines representing the main white matter bundle trajectories. Methods, such as fiber clustering algorithms, are important in computational neuroscience and have been the basis of several white matter analysis methods and studies. Nevertheless, these clustering methods face the challenge of the absence of ground truth of white matter fibers, making their evaluation difficult.

A brief tablet-based intervention benefits linguistic and communicative abilities in toddlers and preschoolers.

Young children's linguistic and communicative abilities are foundational for their academic achievement and overall well-being. We present the positive outcomes of a brief tablet-based intervention aimed at teaching toddlers and preschoolers new word-object and letter-sound associations. We conducted two experiments, one involving toddlers ( ~ 24 months old, n = 101) and the other with preschoolers ( ~ 42 months old, n = 152).

Short fiber bundle filtering and test-retest reproducibility of the Superficial White Matter.

In recent years, there has been a growing interest in studying the Superficial White Matter (SWM). The SWM consists of short association fibers connecting near giry of the cortex, with a complex organization due to their close relationship with the cortical folding patterns. Therefore, their segmentation from dMRI tractography datasets requires dedicated methodologies to identify the main fiber bundle shape and deal with spurious fibers.

Phybers: a package for brain tractography analysis.

We present a Python library (Phybers) for analyzing brain tractography data. Tractography datasets contain streamlines (also called fibers) composed of 3D points representing the main white matter pathways. Several algorithms have been proposed to analyze this data, including clustering, segmentation, and visualization methods.

Whole genomes from bacteria collected at diagnostic units around the world 2020.

The Two Weeks in the World research project has resulted in a dataset of 3087 clinically relevant bacterial genomes with pertaining metadata, collected from 59 diagnostic units in 35 countries around the world during 2020. A relational database is available with metadata and summary data from selected bioinformatic analysis, such as species prediction and identification of acquired resistance genes.

Capillary Refill Time and Serum Lactate as Predictors of Mortality and Postoperative Extracorporeal Membrane Oxygenation Requirement in Congenital Heart Surgery.

Multiple tissue perfusion markers are described to guide therapy in critically ill pediatric patients undergoing congenital heart surgery. Given the advantages of capillary refill time, our goal is to determine its predictive capacity for mortality and postoperative extracorporeal oxygenation requirements in congenital heart surgery and compare it to serum lactate. We conducted a prospective cohort observational study in a single high-complexity university hospital.

"My Body, My Rhythm, My Voice": a community dance pilot intervention engaging breast cancer survivors in physical activity in a middle-income country.

Background: Interventions to promote physical activity among women breast cancer survivors (BCS) in low- to middle-income countries are limited. We assessed the acceptability and preliminary effectiveness of a theory-driven, group-based dance intervention for BCS delivered in Bogotá, Colombia.

Methods: We conducted a quasi-experimental study employing a mixed-methods approach to assess the 8-week, 3 times/week group dance intervention.

Brain volumetric correlates of remotely versus in-person administered symbol digit modalities test in multiple sclerosis.

Background: Prior studies in multiple sclerosis (MS) support reliability of telehealth-delivered cognitive batteries, although, to date, none have reported relationships of cognitive test performance to neural correlates across administration modalities. In this study we aimed to compare brain-behavior relationships, using the Symbol Digit Modalities Test (SDMT), the most reliable and sensitive cognitive measure in MS, measured from patients seen via telehealth versus in-person.

Methods: SDMT was administered to individuals with MS either in-person (N=60, mean age=39.

Superficial white matter bundle atlas based on hierarchical fiber clustering over probabilistic tractography data.

The study of short association fibers is still an incomplete task due to their higher inter-subject variability and the smaller size of this kind of fibers in comparison to known long association bundles. However, their description is essential to understand human brain dysfunction and better characterize the human brain connectome. In this work, we present a multi-subject atlas of short association fibers, which was computed using a superficial white matter bundle identification method based on fiber clustering.

Disentangling the variability of the superficial white matter organization using regional-tractogram-based population stratification.

Each variation of the cortical folding pattern implies a particular rearrangement of the geometry of the fibers of the underlying white matter. While this rearrangement only impacts the ends of the long pathways, it may affect most of the trajectory of the short bundles. Therefore, mapping the short fibers of the human brain using diffusion-based tractography requires a dedicated strategy to overcome the variability of the folding patterns.

Gender Differences in Methamphetamine Use Initiation and Trajectory of Use Among People Who Use Methamphetamine in a Mexico-U.S. Border City.

Introduction: Methamphetamine (meth) is a stimulant increasing in use and its prevalence has not yet been determined on the Mexico-U.S. border.

Enhanced Automatic Segmentation for Superficial White Matter Fiber Bundles for Probabilistic Tractography Datasets.

This paper presents an enhanced algorithm for automatic segmentation of superficial white matter (SWM) bundles from probabilistic dMRI tractography datasets, based on a multi-subject bundle atlas. Previous segmentation methods use the maximum Euclidean distance between corresponding points of the subject fibers and the atlas centroids. However, this scheme might include noisy fibers.

Group-Wise Cortical Surface Parcellation Based on Inter-Subject Fiber Clustering.

We present an automatic algorithm for the group-wise parcellation of the cortical surface. The method is based on the structural connectivity obtained from representative brain fiber clusters, calculated via an inter-subject clustering scheme. Preliminary regions were defined from cluster-cortical mesh intersection points.

Fiber Clustering Acceleration With a Modified Kmeans++ Algorithm Using Data Parallelism.

Fiber clustering methods are typically used in brain research to study the organization of white matter bundles from large diffusion MRI tractography datasets. These methods enable exploratory bundle inspection using visualization and other methods that require identifying brain white matter structures in individuals or a population. Some applications, such as real-time visualization and inter-subject clustering, need fast and high-quality intra-subject clustering algorithms.

Machine learning to investigate superficial white matter integrity in early multiple sclerosis.

Background And Purpose: This study aims todetermine the sensitivity of superficial white matter (SWM) integrity as a metric to distinguish early multiple sclerosis (MS) patients from healthy controls (HC).

Methods: Fractional anisotropy and mean diffusivity (MD) values from SWM bundles across the cortex and major deep white matter (DWM) tracts were extracted from 29 early MS patients and 31 age- and sex-matched HC. Thickness of 68 cortical regions and resting-state functional-connectivity (RSFC) among them were calculated.

A genomic snapshot of Salmonella enterica serovar Typhi in Colombia.

Little is known about the genetic diversity of Salmonella enterica serovar Typhi (S. Typhi) circulating in Latin America. It has been observed that typhoid fever is still endemic in this part of the world; however, a lack of standardized blood culture surveillance across Latin American makes estimating the true disease burden problematic.

Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?

White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation.

ABrainVis: an android brain image visualization tool.

Background: The visualization and analysis of brain data such as white matter diffusion tractography and magnetic resonance imaging (MRI) volumes is commonly used by neuro-specialist and researchers to help the understanding of brain structure, functionality and connectivity. As mobile devices are widely used among users and their technology shows a continuous improvement in performance, different types of applications have been designed to help users in different work areas.

Results: We present, ABrainVis, an Android mobile tool that allows users to visualize different types of brain images, such as white matter diffusion tractographies, represented as fibers in 3D, segmented fiber bundles, MRI 3D images as rendered volumes and slices, and meshes.

Impact of serpentinized peridotite mine waste on the composition and quality of sediments in the Ría de Ortigueira (Galicia, NW Spain).

The Ría de Ortigueira is an environmentally well conserved; however, the sediments show high concentrations of toxic elements. In some zones, the concentrations of Ni (60-1080 mg kg) and Cr (9-567 mg kg) were extremely high, while the concentrations of other toxic elements were within normal ranges. PCA revealed that metal enrichment was due to dumping of waste sludge from a peridotite mine.