Modulatory effect of Gracilaria gracilis on European seabass gut microbiota community and its functionality.

Seaweeds are an important source of nutrients and bioactive compounds and have a high potential as health boosters in aquaculture. This study evaluated the effect of dietary inclusion of Gracilaria gracilis biomass or its extract on the European seabass (Dicentrarchus labrax) gut microbial community. Juvenile fish were fed a commercial-like diet with 2.5% or 5% seaweed biomass or 0.35% seaweed extract for 47 days. The gut microbiome was assessed by 16S rRNA amplicon sequencing, and its diversity was not altered by the seaweed supplementation. However, a reduction in Proteobacteria abundance was observed. Random forest analysis highlighted the genera Photobacterium, Staphylococcus, Acinetobacter, Micrococcus and Sphingomonas, and their abundances were reduced when fish were fed diets with algae. SparCC correlation network analysis suggested several mutualistic and other antagonistic relationships that could be related to the predicted altered functions. These pathways were mainly related to the metabolism and biosynthesis of protective compounds such as ectoine and were upregulated in fish fed diets supplemented with algae. This study shows the beneficial potential of Gracilaria as a functional ingredient through the modulation of the complex microbial network towards fish health improvement.

Potential use of macroalgae Gracilaria gracilis in diets for European seabass (Dicentrarchus labrax): Health benefits from a sustainable source.

Seaweeds still possess a large undisclosed potential, mainly due to their constituent's richness, which may have several uses for society. In aquaculture, they may play a role as an ecological sustainable aquafeed supplement to increase overall health and fight pathogenic outbreaks. This study aimed to evaluate the general health modulation that the inclusion of Gracilaria gracilis could accomplish in the diet of Dicentrarchus labrax. Dried algae at 2.5% and 5% and algal extract at 0.35% inclusion levels were supplemented to seabass diet to evaluate possible growth, haematological, immunological, antioxidant, metabolic, and intestinal morphological modulations. The supplementations did not impact growth or feed utilization, and barely affected the haematological profile and some metabolic parameters. Nevertheless, it caused a marked outcome on lysozyme, some oxidative stress biomarkers, and intestine morphology, suggesting beneficial consequences from the algal inclusion. Dried algae powder, with a 2.5% inclusion, boosted immune response, with higher plasmatic lysozyme and intestinal acid goblet cells and protected against oxidative damages by improved enzymatic and non-enzymatic responses. Thus, we provide evidence that dietary seaweed application may be a path towards a more sustainable aquaculture industry.

Training the social brain: Clinical and neural effects of an 8-week real-time functional magnetic resonance imaging neurofeedback Phase IIa Clinical Trial in Autism.

LAY ABSTRACT: Neurofeedback is an emerging therapeutic approach in neuropsychiatric disorders. Its potential application in autism spectrum disorder remains to be tested. Here, we demonstrate the feasibility of real-time functional magnetic resonance imaging volitional neurofeedback in targeting social brain regions in autism spectrum disorder. In this clinical trial, autism spectrum disorder patients were enrolled in a program with five training sessions of neurofeedback. Participants were able to control their own brain activity in this social brain region, with positive clinical and neural effects. Larger, controlled, and blinded clinical studies will be required to confirm the benefits.

How much of the BOLD-fMRI signal can be approximated from simultaneous EEG data: relevance for the transfer and dissemination of neurofeedback interventions.

OBJECTIVE: fMRI-based neurofeedback (NF) interventions represent the method of choice for the neuromodulation of localized brain areas. Although we have already validated an fMRI-NF protocol targeting the facial expressions processing network (FEPN), its dissemination is hampered by the economical and logistical constraints of fMRI-NF interventions, which may be however surpassed by transferring it to EEG setups, due to their low cost and portability. One of the major challenges of this procedure is then to reconstruct the BOLD-fMRI signal measured at the FEPN using only EEG signals. Because these types of approaches have been poorly explored so far, here we systematically investigated the extent at which the BOLD-fMRI signal recorded from the FEPN during a fMRI-NF protocol could be reconstructed from the simultaneously recorded EEG signal. APPROACH: Several features from both scalp and source spaces (the latter estimated using continuous EEG source imaging) were extracted and used as predictors in a regression problem using random forests. Furthermore, three different approaches to deal with the hemodynamic delay of the BOLD signal were tested. The resulting models were compared with the only approach already proposed in the literature that uses spectral features and considers different time delays. MAIN RESULTS: The combination of linear and non-linear features (particularly the largest Lyapunov exponent and entropy measures) projected into the source space, spatially filtered by independent component analysis (ICA) and convolved with multiple HRF functions peaking at different latencies, increases significantly the reconstruction accuracy (defined as the correlation between the measured and approximated BOLD signal) from 20% (direct comparison with the method used in the current literature) to 56%. SIGNIFICANCE: With this pipeline, a more accurate reconstruction of the BOLD signal can be obtained, which will positively impact the transfer of fMRI-based neurofeedback interventions to EEG setups, and more importantly, their dissemination and efficacy in modulating the activity of the desired brain areas.

Targeting dynamic facial processing mechanisms in superior temporal sulcus using a novel fMRI neurofeedback target.

The superior temporal sulcus (STS) encompasses a complex set of regions involved in a wide range of cognitive functions. To understand its functional properties, neuromodulation approaches such brain stimulation or neurofeedback can be used. We investigated whether the posterior STS (pSTS), a core region in the face perception and imagery network, could be specifically identified based on the presence of dynamic facial expressions (and not just on simple motion or static face signals), and probed with neurofeedback. Recognition of facial expressions is critically impaired in autism spectrum disorder, making this region a relevant target for future clinical neurofeedback studies. We used a stringent localizer approach based on the contrast of dynamic facial expressions against static neutral faces plus moving dots. The target region had to be specifically responsive to dynamic facial expressions instead of mere motion and/or the presence of a static face. The localizer was successful in selecting this region across subjects. Neurofeedback was then performed, using this region as a target, with two novel feedback rules (mean or derivative-based, using visual or auditory interfaces). Our results provide evidence that a facial expression-selective cluster in pSTS can be identified and may represent a suitable target for neurofeedback approaches, aiming at social and emotional cognition. These findings highlight the presence of a highly selective region in STS encoding dynamic aspects of facial expressions. Future studies should elucidate its role as a mechanistic target for neurofeedback strategies in clinical disorders of social cognition such as autism.

A Novel Biomarker of Compensatory Recruitment of Face Emotional Imagery Networks in Autism Spectrum Disorder.

Imagery of facial expressions in Autism Spectrum Disorder (ASD) is likely impaired but has been very difficult to capture at a neurophysiological level. We developed an approach that allowed to directly link observation of emotional expressions and imagery in ASD, and to derive biomarkers that are able to classify abnormal imagery in ASD. To provide a handle between perception and action imagery cycles it is important to use visual stimuli exploring the dynamical nature of emotion representation. We conducted a case-control study providing a link between both visualization and mental imagery of dynamic facial expressions and investigated source responses to pure face-expression contrasts. We were able to replicate the same highly group discriminative neural signatures during action observation (dynamical face expressions) and imagery, in the precuneus. Larger activation in regions involved in imagery for the ASD group suggests that this effect is compensatory. We conducted a machine learning procedure to automatically identify these group differences, based on the EEG activity during mental imagery of facial expressions. We compared two classifiers and achieved an accuracy of 81% using 15 features (both linear and non-linear) of the signal from theta, high-beta and gamma bands extracted from right-parietal locations (matching the precuneus region), further confirming the findings regarding standard statistical analysis. This robust classification of signals resulting from imagery of dynamical expressions in ASD is surprising because it far and significantly exceeds the good classification already achieved with observation of neutral face expressions (74%). This novel neural correlate of emotional imagery in autism could potentially serve as a clinical interventional target for studies designed to improve facial expression recognition, or at least as an intervention biomarker.