Biology subjects

Abreu, R.

Publications and source records attributed to Abreu, R..

3 recordsLinked to original sources

bioRxiv2021

Optimizing EEG source reconstruction with concurrent fMRI-derived spatial priors

Reconstructing EEG sources involves a complex pipeline, with the inverse problem being the most challenging. Multiple inversion algorithms are being continuously developed, aiming to tackle the non-uniqueness of this problem, which has been shown to be partially circumvented by also including fMRI-derived spatial priors in the inverse models. However, only task activation maps and resting-state networks (RSNs) have been explored so far, overlooking the recent, but already accepted, notion that brain networks exhibit dynamic functional connectivity (dFC) fluctuations. Moreover, there is no consensus regarding the inversion algorithm of choice, nor a systematic comparison between different sets of spatial priors. Using simultaneous EEG-fMRI data, here we compared four different inversion algorithms (MN, LORETA, EBB and MSP) under a Bayesian framework, each with three different sets of priors consisting of: 1) those specific to the algorithm (S1); 2) S1 plus fMRI task activation maps and RSNs (S2); and 3) S2 plus network modules of task-related dFC states estimated from the dFC fluctuations (S3). The quality of the reconstructed EEG sources was quantified in terms of model-based metrics, namely the free-energy and variance explained of the inversion models, and the overlap/proportion of brain regions known to be involved in the visual perception tasks that the participants were submitted to, and RSN templates, with/within EEG source components. Model-based metrics suggested that model parsimony is preferred, with the combination MSP+S1 exhibiting the best performance. However, optimal overlap/proportion values were found using EBB+S2 or MSP+S3, respectively, indicating that fMRI spatial priors, including dFC state modules, are crucial for the EEG source components to reflect neuronal activity of interest. Our results pave the way towards a more informative selection of the optimal EEG source reconstruction approach, which may be crucial in future studies.

neuroscience↗

bioRxiv2020

Interferon-gamma promotes iron export in human macrophages to limit intracellular bacterial replication

Salmonellosis and listeriosis together accounted for more than one third of foodborne illnesses in the United States and almost half the hospitalizations for gastrointestinal diseases in 2018 while tuberculosis afflicted over 10 million people worldwide causing almost 2 million deaths. Regardless of the intrinsic virulence differences among Listeria monocytogenes, Salmonella enterica and Mycobacterium tuberculosis, these intracellular pathogens share the ability to survive and persist inside the macrophage and other cells and thrive in iron rich environments. Interferon-gamma (IFN-{gamma}) is a central cytokine in host defense against intracellular pathogens and has been shown to promote iron export in macrophages. We hypothesize that IFN-{gamma} decreases iron availability to intracellular pathogens consequently limiting replication in these cells. In this study, we show that IFN-{gamma} regulates the expression of iron-related proteins hepcidin, ferroportin, and ferritin to induce iron export from macrophages. Listeria monocytogenes, S. enterica, and M. tuberculosis infections significantly induce iron sequestration in human macrophages. In contrast, IFN-{gamma} significantly reduces hepcidin secretion in S. enterica and M. tuberculosis infected macrophages. Similarly, IFN-{gamma}-activated macrophages express higher ferroportin levels than untreated controls even after infection with L. monocytogenes bacilli; bacterial infection greatly down-regulates ferroportin expression. Collectively, IFN-{gamma} significantly inhibits pathogen-associated intracellular iron sequestration in macrophages and consequently retards the growth of intracellular bacterial pathogens by decreasing iron availability.

cell biology↗

bioRxiv2020

EEG and fMRI connectomes are reliably related: a simultaneous EEG-fMRI study from 1.5T to 7T

Both electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are non-invasive methods that show complementary aspects of human brain activity. Despite measuring different proxies of brain activity, both the measured blood-oxygenation (fMRI) and neurophysiological recordings (EEG) are indirectly coupled. The electrophysiological and BOLD signal can map the underlying functional connectivity structure at the whole brain scale at different timescales. Previous work demonstrated a moderate but significant correlation between resting-state functional connectivity of both modalities, however there is a wide range of technical setups to measure simultaneous EEG-fMRI and the reliability of those measures between different setups remains unknown. This is true notably with respect to different magnetic field strengths (low and high field) and different spatial sampling of EEG (medium to high-density electrode coverage). Here, we investigated the reproducibility of the bimodal EEG-fMRI functional connectome in the most comprehensive resting-state simultaneous EEG-fMRI dataset compiled to date including a total of 72 subjects from four different imaging centers. Data was acquired from 1.5T, 3T and 7T scanners with simultaneously recorded EEG using 64 or 256 electrodes. We demonstrate that the whole-brain monomodal connectivity reproducibly correlates across different datasets and that a moderate crossmodal correlation between EEG and fMRI connectivity of r{approx}0.3 can be reproducibly extracted in low- and high-field scanners. The crossmodal correlation was strongest in the EEG-{beta} frequency band but exists across all frequency bands. Both homotopic and within intrinsic connectivity network (ICN) connections contributed the most to the crossmodal relationship. This study confirms, using a considerably diverse range of recording setups, that simultaneous EEG-fMRI offers a consistent estimate of multimodal functional connectomes in healthy subjects that are dominantly linked through a functional core of ICNs across spanning across the different timescales measured by EEG and fMRI. This opens new avenues for estimating the dynamics of brain function and provides a better understanding of interactions between EEG and fMRI measures. This observed level of reproducibility also defines a baseline for the study of alterations of this coupling in pathological conditions and their role as potential clinical markers.

neuroscience↗