Task-Related modulations of BOLD low-frequency fluctuations within the default mode Network

Spontaneous low-frequency Blood-Oxygenation Level-Dependent (BOLD) signals acquired during resting state are characterized by spatial patterns of synchronous fluctuations, ultimately leading to the identification of robust brain networks. The resting-state brain networks, including the Default Mode Network (DMN), are demonstrated to persist during sustained task execution, but the exact features of task-related changes of network properties are still not well characterized. In this work we sought to examine in a group of 20 healthy volunteers (age 33 ± 6 years, 8 F/12 M) the relationship between changes of spectral and spatiotemporal features of one prominent resting-state network, namely the DMN, during the continuous execution of a working memory n-back task. We found that task execution impacted on both functional connectivity and amplitude of BOLD fluctuations within large parts of the DMN, but these changes correlated between each other only in a small area of the posterior cingulate. We conclude that combined analysis of multiple parameters related to connectivity, and their changes during the transition from resting state to continuous task execution, can contribute to a better understanding of how brain networks rearrange themselves in response to a task

emerging and potentially emerging viruses in water environments

Among microorganisms, viruses are best fit to become emerging pathogens since they are able to adapt not only by mutation but also through recombination and reassortment and can thus become able to infect new hosts and to adjust to new environments. Enteric viruses are among the commonest and most hazardous waterborne pathogens, causing both sporadic and outbreak-related illness. The main health effect associated with enteric viruses is gastrointestinal illness, but they can also cause respiratory symptoms, conjunctivitis, hepatitis, central nervous system infections, and chronic diseases. Non-enteric viruses, such as respiratory and epitheliotrophic viruses are not considered waterborne, as they are not readily transmitted to water sources from infected individuals. The present review will focus on viral pathogens shown to be transmitted through water. It will also provide an overview of viruses that had not been a concern for waterborne transmission in the past, but that may represent potentially emerging waterborne pathogens due to their occurrence and persistence in water environments

Scale-invariant rearrangement of resting state networks in the human brain under sustained stimulation

Brain activity at rest is characterized by widely distributed and spatially specific patterns of synchronized low-frequency blood-oxygenation level-dependent (BOLD) fluctuations, which correspond to physiologically relevant brain networks. This network behaviour is known to persist also during task execution, yet the details underlying task-associated modulations of within- and between-network connectivity are largely unknown. In this study we exploited a multi-parametric and multi-scale approach to investigate how low-frequency fluctuations adapt to a sustained n-back working memory task. We found that the transition from the resting state to the task state involves a behaviourally relevant and scale-invariant modulation of synchronization patterns within both task-positive and default mode networks. Specifically, decreases of connectivity within networks are accompanied by increases of connectivity between networks. In spite of large and widespread changes of connectivity strength, the overall topology of brain networks is remarkably preserved. We show that these findings are strongly influenced by connectivity at rest, suggesting that the absolute change of connectivity (i.e., disregarding the baseline) may not be the most suitable metric to study dynamic modulations of functional connectivity. Our results indicate that a task can evoke scale-invariant, distributed changes of BOLD fluctuations, further confirming that low frequency BOLD oscillations show a specialized response and are tightly bound to task-evoked activation

Forces during cellular uptake of viruses and nanoparticles at the ventral side

Many intracellular pathogens, such as mammalian reovirus, mimic extracellular matrix motifs to specifically interact with the host membrane. Whether and how cell-matrix interactions influence virus particle uptake is unknown, as it is usually studied from the dorsal side. Here we show that the forces exerted at the ventral side of adherent cells during reovirus uptake exceed the binding strength of biotin-neutravidin anchoring viruses to a biofunctionalized substrate. Analysis of virus dissociation kinetics using the Bell model revealed mean forces higher than 30 pN per virus, preferentially applied in the cell periphery where close matrix contacts form. Utilizing 100 nm-sized nanoparticles decorated with integrin adhesion motifs, we demonstrate that the uptake forces scale with the adhesion energy, while actin/myosin inhibitions strongly reduce the uptake frequency, but not uptake kinetics. We hypothesize that particle adhesion and the push by the substrate provide the main driving forces for uptake

Evaluation of denoising strategies for task-based functional connectivity: Equalizing residual motion artifacts between rest and cognitively demanding tasks

In-scanner head motion represents a major confounding factor in functional connectivity studies and it raises particular concerns when motion correlates with the effect of interest. One such instance regards research focused on functional connectivity modulations induced by sustained cognitively demanding tasks. Indeed, cognitive engagement is generally associated with substantially lower in-scanner movement compared with unconstrained, or minimally constrained, conditions. Consequently, the reliability of condition-dependent changes in functional connectivity relies on effective denoising strategies. In this study, we evaluated the ability of common denoising pipelines to minimize and balance residual motion-related artifacts between resting-state and task conditions. Denoising pipelines—including realignment/tissue-based regression, PCA/ICA-based methods (aCompCor and ICA-AROMA, respectively), global signal regression, and censoring of motion-contaminated volumes—were evaluated according to a set of benchmarks designed to assess either residual artifacts or network identifiability. We found a marked heterogeneity in pipeline performance, with many approaches showing a differential efficacy between rest and task conditions. The most effective approaches included aCompCor, optimized to increase the noise prediction power of the extracted confounding signals, and global signal regression, although both strategies performed poorly in mitigating the spurious distance-dependent association between motion and connectivity. Censoring was the only approach that substantially reduced distance-dependent artifacts, yet this came at the great cost of reduced network identifiability. The implications of these findings for best practice in denoising task-based functional connectivity data, and more generally for resting-state data, are discussed