Biology subjects

Marques, T.

Publications and source records attributed to Marques, T..

9 recordsLinked to original sources

bioRxiv2024

Small non-coding RNA content in plasma-derived extracellular vesicles distinguish ataxic SCA3 mutation carriers from pre-ataxic and control subjects

Spinocerebellar ataxia type 3 (SCA3), a neurodegenerative disorder caused by a CAG expansion in the ATXN3 gene, is the most common spinocerebellar ataxia subtype worldwide. Currently, there is no therapy to stop or prevent disease progression. Promising therapeutic strategies are emerging, but their translation into clinical practice requires sensitive and reliable biomarkers. Blood circulating extracellular vesicles constitute a promising source of biomarkers with potential to track alterations of the central nervous system due to their ability to cross the blood brain barrier. Here, we perform sequencing analysis of small RNAs from plasma-derived extracellular vesicles from SCA3 mutation carriers (10 pre-ataxic and 10 ataxic) and 12 control subjects to identify potential RNA biomarker candidates for this disease. Data showed that plasma-derived extracellular vesicles from ataxic SCA3 mutation carriers are enriched in mitochondrial, nuclear, and nucleolar RNA biotypes compared to pre-ataxic and control subjects. Moreover, ataxic mutation carriers could be discriminated from control and pre-ataxic subjects based on the miRNAs or piRNAs content, but not tRNA. Furthermore, we identified a subset of differentially expressed miRNAs and piRNAs that clearly differentiate ataxic mutation carriers from pre-ataxic and control subjects. These findings open new avenues for further investigation on the role of these RNAs in the pathogenesis of SCA3 and their potential as biomarkers for this disease.

neuroscience↗

bioRxiv2022

Altered infective competence of the human gut microbiome in COVID-19

ObjectivesInfections with SARS-CoV-2 have a pronounced impact on the gastrointestinal tract and its resident microbiome. Clear differences between severe cases of infection and healthy individuals have been reported, including the loss of commensal taxa. We aimed to understand if microbiome alterations including functional shifts are unique to severe cases or a common effect of COVID-19. DesignWe used high-resolution systematic multi-omic analyses to profile the gut microbiome in asymptomatic-to-moderate COVID-19 individuals compared to a control group. ResultsWe found a striking increase in the overall abundance and expression of both virulence factors and antimicrobial resistance genes in COVID-19. Importantly, these genes are encoded and expressed by commensal taxa from families such as Acidaminococcaceae and Erysipelatoclostridiaceae, which we found to be enriched in COVID-19 positive individuals. We also found an enrichment in the expression of a betaherpesvirus and rotavirus C genes in COVID-19 positive individuals compared to healthy controls. ConclusionOur analyses identified an altered and increased infective competence of the gut microbiome in COVID-19 patients.

microbiology↗

bioRxiv2022

Visual experience instructs the organization of cortical feedback inputs to primary visual cortex.

Cortical feedback (FB) projections are thought to modulate lower-order activity depending on learned expectations. However, whether FB inputs become bound to specific lower-order neurons depending on experience is unknown. We measured the effects of dark rearing and manipulations of experienced visual statistics on the retinotopic specificity of projections from the lateromedial (LM) visual area to layer 1 of the mouse primary visual cortex (V1). LM inputs were, on average, retinotopically matched with V1 neurons irrespective of visual experience. While the orientation tuning of LM axons determined the retinotopic position of the V1 neurons they innervated, this organization was absent in dark-reared mice. Restricting visual experience to a narrow range of orientations revealed that visual experience exerts an instructive role in the retinotopic organization of LM inputs in V1. Our observations support theories of hierarchical computation proposing that inputs from higher-order neurons to lower-order ones reflect learned hierarchical associations.

neuroscience↗

bioRxiv2021

Multi-scale hierarchical neural network models that bridge from single neurons in the primate primary visual cortex to object recognition behavior

Primate visual object recognition relies on the representations in cortical areas at the top of the ventral stream that are computed by a complex, hierarchical network of neural populations. While recent work has created reasonably accurate image-computable hierarchical neural network models of those neural stages, those models do not yet bridge between the properties of individual neurons and the overall emergent behavior of the ventral stream. One reason we cannot yet do this is that individual artificial neurons in multi-stage models have not been shown to be functionally similar to individual biological neurons. Here, we took an important first step by building and evaluating hundreds of hierarchical neural network models in how well their artificial single neurons approximate macaque primary visual cortical (V1) neurons. We found that single neurons in certain models are surprisingly similar to their biological counterparts and that the distributions of single neuron properties, such as those related to orientation and spatial frequency tuning, approximately match those in macaque V1. Critically, we observed that hierarchical models with V1 stages that better match macaque V1 at the single neuron level are also more aligned with human object recognition behavior. Finally, we show that an optimized classical neuroscientific model of V1 is more functionally similar to primate V1 than all of the tested multi-stage models, suggesting room for further model improvements with tangible payoffs in closer alignment to human behavior. These results provide the first multi-stage, multi-scale models that allow our field to ask precisely how the specific properties of individual V1 neurons relate to recognition behavior. HighlightsO_LIImage-computable hierarchical neural network models can be naturally extended to create hierarchical "brain models" that allow direct comparison with biological neural networks at multiple scales - from single neurons, to population of neurons, to behavior. C_LIO_LISingle neurons in some of these hierarchical brain models are functionally similar to single neurons in macaque primate visual cortex (V1) C_LIO_LISome hierarchical brain models have processing stages in which the entire distribution of artificial neuron properties closely matches the biological distributions of those same properties in macaque V1 C_LIO_LIHierarchical brain models whose V1 processing stages better match the macaque V1 stage also tend to be more aligned with human object recognition behavior at their output stage C_LI

neuroscience↗

bioRxiv2020

No evidence for prolactin's involvement in the post-ejaculatory refractory period

In many species, ejaculation is followed by a state of decreased sexual motivation, the post-ejaculatory refractory period. Several lines of evidence have suggested prolactin, a pituitary hormone released around the time of ejaculation in humans and other animals, to be a decisive player in the establishment of the refractory period. However, data supporting this hypothesis is controversial. We took advantage of two different strains of house mouse, a wild derived and a classical laboratory strain, that differ substantially in their sexual behavior, to investigate prolactins involvement in sexual motivation and the refractory period. First, we show that there is prolactin release during sexual behavior in male mice. Second, using a pharmacological approach, we show that acute manipulations of prolactin levels, either mimicking the natural release during sexual behavior or inhibiting its occurrence, do not affect sexual motivation or shorten the refractory period, respectively. Therefore, we show compelling evidence refuting the idea that prolactin released during copulation is involved in the establishment of the refractory period, a long-standing hypothesis in the field of behavioral endocrinology.

animal behavior and cognition↗

bioRxiv2020

Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations

Current state-of-the-art object recognition models are largely based on convolutional neural network (CNN) architectures, which are loosely inspired by the primate visual system. However, these CNNs can be fooled by imperceptibly small, explicitly crafted perturbations, and struggle to recognize objects in corrupted images that are easily recognized by humans. Here, by making comparisons with primate neural data, we first observed that CNN models with a neural hidden layer that better matches primate primary visual cortex (V1) are also more robust to adversarial attacks. Inspired by this observation, we developed VOneNets, a new class of hybrid CNN vision models. Each VOneNet contains a fixed weight neural network front-end that simulates primate V1, called the VOneBlock, followed by a neural network back-end adapted from current CNN vision models. The VOneBlock is based on a classical neuroscientific model of V1: the linear-nonlinear-Poisson model, consisting of a biologically-constrained Gabor filter bank, simple and complex cell nonlinearities, and a V1 neuronal stochasticity generator. After training, VOneNets retain high ImageNet performance, but each is substantially more robust, outperforming the base CNNs and state-of-the-art methods by 18% and 3%, respectively, on a conglomerate benchmark of perturbations comprised of white box adversarial attacks and common image corruptions. Finally, we show that all components of the VOneBlock work in synergy to improve robustness. While current CNN architectures are arguably brain-inspired, the results presented here demonstrate that more precisely mimicking just one stage of the primate visual system leads to new gains in ImageNet-level computer vision applications.

neuroscience↗

bioRxiv2020

Wiring Up Vision: Minimizing Supervised Synaptic Updates Needed to Produce a Primate Ventral Stream

After training on large datasets, certain deep neural networks are surprisingly good models of the neural mechanisms of adult primate visual object recognition. Nevertheless, these models are poor models of the development of the visual system because they posit millions of sequential, precisely coordinated synaptic updates, each based on a labeled image. While ongoing research is pursuing the use of unsupervised proxies for labels, we here explore a complementary strategy of reducing the required number of supervised synaptic updates to produce an adult-like ventral visual stream (as judged by the match to V1, V2, V4, IT, and behavior). Such models might require less precise machinery and energy expenditure to coordinate these updates and would thus move us closer to viable neuroscientific hypotheses about how the visual system wires itself up. Relative to the current leading model of the adult ventral stream, we here demonstrate that the total number of supervised weight updates can be substantially reduced using three complementary strategies: First, we find that only 2% of supervised updates (epochs and images) are needed to achieve ~80% of the match to adult ventral stream. Second, by improving the random distribution of synaptic connectivity, we find that 54% of the brain match can already be achieved "at birth" (i.e. no training at all). Third, we find that, by training only ~5% of model synapses, we can still achieve nearly 80% of the match to the ventral stream. When these three strategies are applied in combination, we find that these new models achieve ~80% of a fully trained models match to the brain, while using two orders of magnitude fewer supervised synaptic updates. These results reflect first steps in modeling not just primate adult visual processing during inference, but also how the ventral visual stream might be "wired up" by evolution (a models "birth" state) and by developmental learning (a models updates based on visual experience).

neuroscience↗

bioRxiv2019

Large-scale hyperparameter search for predicting human brain responses in the Algonauts challenge

Image features computed by specific convolutional artificial neural networks (ANNs) can be used to make state-of-the-art predictions of primate ventral stream responses to visual stimuli.\n\nHowever, in addition to selecting the specific ANN and layer that is used, the modeler makes other choices in preprocessing the stimulus image and generating brain predictions from ANN features. The effect of these choices on brain predictivity is currently underexplored.\n\nHere, we directly evaluated many of these choices by performing a grid search over network architectures, layers, image preprocessing strategies, feature pooling mechanisms, and the use of dimensionality reduction. Our goal was to identify model configurations that produce responses to visual stimuli that are most similar to the human neural representations, as measured by human fMRI and MEG responses. In total, we evaluated more than 140,338 model configurations. We found that specific configurations of CORnet-S best predicted fMRI responses in early visual cortex, and CORnet-R and SqueezeNet models best predicted fMRI responses in inferior temporal cortex. We found specific configurations of VGG-16 and CORnet-S models that best predicted the MEG responses.\n\nWe also observed that downsizing input images to ~50-75% of the input tensor size lead to better performing models compared to no downsizing (the default choice in most brain models for vision). Taken together, we present evidence that brain predictivity is sensitive not only to which ANN architecture and layer is used, but choices in image preprocessing and feature postprocessing, and these choices should be further explored.

neuroscience↗

bioRxiv2019

Links between central CB1-receptor availability and peripheral endocannabinoids in patients with first episode psychosis

There is an established, albeit poorly-understood link between psychosis and metabolic abnormalities such as altered glucose metabolism and dyslipidemia, which often precede the initiation of antipsychotic treatment. It is known that obesity-associated metabolic disorders are promoted by peripheral activation of the endocannabinoid system (ECS). Our recent data suggest that ECS dysregulation may also play a role in psychosis. With the aim of characterizing the involvement of the central and peripheral ECSs and their mutual associations, here we performed a combined neuroimaging and metabolomic study in patients with first-episode psychosis (FEP) and healthy controls (HC). Regional brain cannabinoid receptor type 1 (CB1R) availability was quantified in two, independent samples of patients with FEP (n=20 and n=8) and HC (n=20 and n=10), by applying 3D positron emission tomography (PET), using two radiotracers, [11C]MePPEP and [18F]FMPEP-d2. Ten endogenous endocannabinoids or related metabolites were quantified in serum, drawn from these individuals during the same imaging session. Circulating levels of arachidonic acid and oleyl ethanolamide were reduced in FEP individuals, but not in those who were predominantly medication-free. In HC, there was an inverse association between levels of circulating arachidonoyl glycerol, anandamide, oleyl ethanolamide and palmitoyl ethanolamide, and CB1R availability in the posterior cingulate cortex. This phenomenon was, however, not observed in FEP patients. Our data thus provide evidence of cross-talk and dysregulation between peripheral endocannabinoids and central CB1R availability in FEP.

neuroscience↗