Biology subjects

Moreni, G.

Publications and source records attributed to Moreni, G..

6 recordsLinked to original sources

bioRxiv2025

Functional connectivity drifts during sleep as a marker of fluctuations in the level of consciousness

During the wake-sleep cycle, consciousness waxes and wanes, and this is thought to be reflected in varying levels of integration between brain areas. Recent studies challenged the notion that consciousness is homogeneously present or absent in a brain state, as exemplified by conscious reports found in otherwise unconscious Non-REM sleep. We tested if functional connectivity between neurons varies within brain states in a way compatible with a fluctuating level of consciousness. We examined directed functional connectivity between neurons across the wake-sleep cycle in rats, at a scale of a few seconds. We analyzed patterns of functional connectivity to determine if Non-REM sleep contains epochs in which inter-areal integration is comparable to that observed in wakefulness and REM sleep, and vice versa. This study will potentially reveal if circuit-level connectivity patterns are observed during sleep stages, in line with the presence of an alternation between levels of consciousness not only between but also within brain states.

neuroscience↗

bioRxiv2024

Efficient laminar-distributed interactions and orientation selectivity in the mouse V1 cortical column

The emergence of orientation selectivity in the visual cortex is a well-known phenomenon in neuroscience, but the details of such emergence and the role of different cortical layers and cell types, particularly in rodents which lack a topographical organization of orientation-selectivity (OS) properties, are less clear. To tackle this question, we use an existing biologically detailed model of the mouse V1 cortical column, which is constrained by existing connectivity data across cortical layers and between pyramidal, PV, SST and VIP cell types. Using this model as a basis, we implemented activity-dependent structural plasticity induced by stimulation with orientated drifting gratings, leading to a good match of tuning properties of pyramidal cells with experimentally observed OS laminar distribution, their evoked firing rate and tuning width. We then employed a mean-field model to uncover the role of co-tuned subnetworks in laminar signal propagation and explain the effects of intra- and inter-laminar coupling distributions. Our plasticity-induced modified model and mean-field model were able to explain both the excitatory enhancement through co-tuned subnetworks and inter-laminar disynaptic inhibition. Overall, our work highlights the importance of the clustering of neural selectivity features for effective excitatory transmission in cortical circuits.

neuroscience↗

bioRxiv2024

Cell type specific firing patterns in a V1 cortical column model depend on feedforward and feedback activity

Stimulation of specific cell groups under different network regimes (e.g., spontaneous activity or sensory-evoked activity) can provide insights into the neural dynamics of cortical columns. While these protocols are challenging to perform experimentally, modelling can serve as a powerful tool for such explorations. Using detailed electrophysiological and anatomical data from mouse V1, we built a novel spiking network model of a cortical column, which incorporates pyramidal cells and three distinct interneuron types (PV, SST, and VIP cells, specified per lamina), as well as the dynamic and voltage-dependent properties of AMPA, GABA, and NMDA receptors. We first demonstrate that thalamocortical feedforward (FF) and feedback (FB) stimuli arriving in the column have opposite effects, leading to net columnar excitation and inhibition respectively and revealing translaminar gain control via full-column inhibition by layer 6. We then perturb one cell group (i.e. a cell type in a specific layer) at a time and observe the effects on other cell groups under distinct network states: spontaneous, feedforward-driven, feedback-driven, and a combination of feedforward and feedback. Our findings reveal that when a given group is perturbed, the columnar response varies significantly based on its state, with strong sensory feedforward input decreasing columnar sensitivity to all perturbations and feedback input serving as modulator of intra columnar interactions. Given that activity changes within specific neuronal populations are difficult to predict a priori in experiments, our model may constitute a useful tool to predict outcomes of perturbations and assist in experimental design. Author SummaryIn this study, we explored how stimulating specific groups of neurons under different conditions (spontaneous or stimulus-evoked states) can help us understand the dynamics of visual cortical columns in mouse cortex. We developed a data-constrained model of a cortical column that includes several cell types (excitatory neurons as well as multiple inhibitory neuron types) and synaptic receptors. Our findings reveal that signals coming from higher brain areas (feedback input) and sensory input (feedforward input) have different impacts on the column: sensory inputs generally increase activity within the column, while feedback tends to decrease it. We then considered input targeting one cell type at a time to see how it affects the internal dynamics. Our results show that neural responses to selected input vary depending on whether the cortical column is in a resting state or being stimulated by feedforward input or feedback input. This model is a useful tool for predicting the outcomes of these cell interactions, which can help in planning real-world experiments and understanding how our brain processes information.

neuroscience↗

bioRxiv2023

Assessment of The Broad-Spectrum Host Targeting Antiviral Efficacy of Halofuginone Hydrobromide in Human Airway, Intestinal and Brain Organoid Models.

Halofuginone hydrobromide has shown potent antiviral efficacy against a variety of viruses such as SARS-CoV-2, dengue, or chikungunya virus, and has, therefore, been hypothesized to have broad-spectrum antiviral activity. In this paper, we tested this broad-spectrum antiviral activity of Halofuginone hydrobomide against viruses from different families (Picornaviridae, Herpesviridae, Orthomyxoviridae, Coronaviridae, and Flaviviridae). To this end, we used relevant human models of the airway and intestinal epithelium and regionalised neural organoids. Halofuginone hydrobomide showed antiviral activity against SARS-CoV-2 in the airway epithelium with no toxicity at equivalent concentrations used in human clinical trials but not against any of the other tested viruses. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=199 HEIGHT=200 SRC="FIGDIR/small/565121v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@10d562eorg.highwire.dtl.DTLVardef@94f903org.highwire.dtl.DTLVardef@683b6forg.highwire.dtl.DTLVardef@11620ba_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIHalofuginone hydrobromide was identified as a possible broad-spectrum host targeting antiviral drug. C_LIO_LIHuman organoid models offer a physiologically relevant and clinically translatable model for antiviral research. C_LIO_LIHalofuginone hydrobromide shows antiviral efficacy against SARS-CoV-2, but not against EV-A71, PeV-A1, IAV, RV-A16, HCMV or ZIKV in relevant organoid models. C_LIO_LIThe efficacy of Halofuginone hydrobromide is concentration dependent as well as on proline content of the host receptor(s) or host factors for the specific virus in question. C_LI

microbiology↗

bioRxiv2023

Synaptic plasticity is required for oscillations in a V1 cortical column model with multiple interneuron types

Neural rhythms are ubiquitous in cortical recordings, but it is unclear whether they emerge due to the basic structure of cortical microcircuits, or depend on function. Using detailed electrophysiological and anatomical data of mouse V1, we explored this question by building a spiking network model of a cortical column incorporating pyramidal cells, PV, SST and VIP inhibitory interneurons, and dynamics for AMPA, GABA and NMDA receptors. The resulting model matched in vivo cell-type-specific firing rates for spontaneous and stimulus-evoked conditions in mice, although rhythmic activity was absent. Upon introduction of long-term synaptic plasticity in the form of an STDP rule, broad-band (15-60 Hz) oscillations emerged, with feedforward/feedback input streams enhancing/suppressing the oscillatory drive, respectively. These plasticity-triggered rhythms relied on all cell types, and specific experience-dependent connectivity patterns were required to generate oscillations. Our results suggest that neural rhythms are not necessarily intrinsic properties of cortical circuits, but rather they may arise from structural changes elicited by learning-related mechanisms.

neuroscience↗

bioRxiv2021

A hands-on tutorial on network and topological neuroscience

The brain is an extraordinarily complex system that facilitates the efficient integration of information from different regions to execute its functions. With the recent advances in technology, researchers can now collect enormous amounts of data from the brain using neuroimaging at different scales and from numerous modalities. With that comes the need for sophisticated tools for analysis. The field of network neuroscience has been trying to tackle these challenges, and graph theory has been one of its essential branches through the investigation of brain networks. Recently, topological data analysis has gained more attention as an alternative framework by providing a set of metrics that go beyond pair-wise connections and offer improved robustness against noise. In this hands-on tutorial, our goal is to provide the computational tools to explore neuroimaging data using these frameworks and to facilitate their accessibility, data visualisation, and comprehension for newcomers to the field. We will start by giving a concise (and by no means complete) overview of the field to introduce the two frameworks, and then explain how to compute both well-established and newer metrics on resting-state functional magnetic resonance imaging. We use an open-source language (Python) and provide an accompanying publicly available Jupyter Notebook that uses data from the 1000 Functional Connectomes Project. Moreover, we would like to highlight one part of our notebook that is solely dedicated to realistic visualisation of high order interactions in brain networks. This pipeline provides three-dimensional (3-D) plots of pair-wise and higher-order interactions projected in a brain atlas, a new feature tailor-made for network neuroscience.

neuroscience↗