Biology subjects

Reva, M.

Publications and source records attributed to Reva, M..

8 recordsLinked to original sources

bioRxiv2026

A Detailed Model for Understanding the Human Neocortex

The neocortex underlies cognitive abilities that set humans apart from other species. Although Ramon y Cajal initiated its study in the 19th century, much about its fundamental properties remain poorly understood. Biologically detail modeling, has been shown to serve as a tool to understand the modeled system better. By comparing computational models for different species we can highlight functional differences between them, find their anatomical or physiological basis and thus improve our understanding of cortical function. In this study we built a detailed model of a human cortical microcircuit following an established workflow. We compared the human data and results against a previously published reconstruction of rat cortical circuitry. To parametrize the human model, we gathered new original data on human morphological reconstructions, axonal bouton densities, single cell and synaptic recordings. We combined them with data available in the literature and open-sourced databases. We also developed various strategies to overcome the missing data, such as generalizing or adapting data from rodents. The resulting model consists of seven columnar units with similar characteristics. Each column has a radius of 476 {micro}m, a height of 2622 {micro}m, a volume of 1.86 mm3, a total cell density of 24,186 cells/mm3, on the order of 35,000 cells, around 12 million connections and approximately 47 million synapses. Comparing the rat and the human model showed that the human cortex is less dense in terms of cell bodies than the rodent cortex. Human cells have more complex branching, but lower bouton densities than rodent cells. However, the number of connections between cell types is similar.

neuroscience↗

bioRxiv2025

Distinct modes of dopamine modulation on striatopallidal synaptic transmission

Dopamine (DA) affects voluntary movement by modulating basal ganglia function. In the classical model, DA depletion leads to overactivity of the indirect pathway and excessively inhibits the thalamus, resulting in hypokinesia. The contribution of DA on striatopallidal synapses, an initial hub in the indirect pathway connecting the striatum to the external globus pallidus (GPe), remains poorly understood because of the sparse DA innervation. Here, we combine optogenetic projection targeting, whole cell patch clamp recordings in acute brain slices from mice, and computational modeling to overcome this limitation. We show that DA activates D2R receptors (D2Rs) and D4 receptors (D4Rs) differentially in distinct GPe subregions. In a pinwheel-like fashion, dorsolateral and ventromedial GPe expresses high levels of D2Rs, which exert presynaptic inhibition, while in dorsomedial and ventrolateral GPe D4Rs cause postsynaptic inhibition. DA depletion by 6-OHDA (6-hydroxydopamine) reverses the region- specific effect of DA, shifting it in the opposite direction and contributing to hypokinesia. These findings reveal the mechanism by which the different modality information conveyed spatially through the indirect pathway is differentially modulated by DA at striatopallidal synapses.

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bioRxiv2024

Higher-Order Interactions in Neuronal Function: From Genes to Ionic Currents in Biophysical Models

Neuronal firing patterns are the consequence of precise variations in neuronal membrane potential, which are themselves shaped by multiple ionic currents. In this study, we use biophysical models, statistical methods, and information theory to explore the interaction between these ionic currents and neuron electrophysiological phenotype. We created numerous electrical models with diverse firing patterns. By analyzing these models, we identified intricate relationships between model parameters and electrical features. Our findings show that neuronal activity is often influenced by multiple biophysical model parameters, in a non-additive (i.e. synergistic) fashion. When comparing this with single-cell RNAseq data, we found a contrasting structure: gene expression profiles were dominated by redundancy, reflecting differences in regulatory constraints and sampling diversity. This research sheds light on the complex links between biophysical parameters and neuronal phenotypes.

neuroscience↗

bioRxiv2023

Modeling and Simulation of Neocortical Micro- and Mesocircuitry. Part II: Physiology and Experimentation

Cortical dynamics underlie many cognitive processes and emerge from complex multi-scale interactions, which are challenging to study in vivo. Large-scale, biophysically detailed models offer a tool which can complement laboratory approaches. We present a model comprising eight somatosensory cortex subregions, 4.2 million morphological and electrically-detailed neurons, and 13.2 billion local and mid-range synapses. In silico tools enabled reproduction and extension of complex laboratory experiments under a single parameterization, providing strong validation. The model reproduced millisecond-precise stimulus-responses, stimulus-encoding under targeted optogenetic activation, and selective propagation of stimulus-evoked activity to downstream areas. The models direct correspondence with biology generated predictions about how multiscale organization shapes activity; for example, how cortical activity is shaped by high-dimensional connectivity motifs in local and mid-range connectivity, and spatial targeting rules by inhibitory subpopulations. The latter was facilitated using a rewired connectome which included specific targeting rules observed for different inhibitory neuron types in electron microscopy. The model also predicted the role of inhibitory interneuron types and different layers in stimulus encoding. Simulation tools and a large subvolume of the model are made available to enable further community-driven improvement, validation and investigation.

neuroscience↗

bioRxiv2023

Controlling morpho-electrophysiological variability of neurons with detailed biophysical models

Variability is a universal feature among biological units such as neuronal cells as they enable a robust encoding of a high volume of information in neuronal circuits and prevent hyper synchronizations such as epileptic seizures. While most computational studies on electrophysiological variability in neuronal circuits were done with simplified neuron models, we instead focus on the variability of detailed biophysical models of neurons. With measures of experimental variability, we leverage a Markov chain Monte Carlo method to generate populations of electrical models able to reproduce the variability from sets of experimental recordings. By matching input resistances of soma and axon initial segments with the one of dendrites, we produce a compatible set of morphologies and electrical models that faithfully represent a given morpho-electrical type. We demonstrate our approach on layer 5 pyramidal cells with continuous adapting firing type and show that morphological variability is insufficient to reproduce electrical variability. Overall, this approach provides a strong statistical basis to create detailed models of neurons with controlled variability.

neuroscience↗

bioRxiv2022

A universal workflow for creation, validation and generalization of detailed neuronal models

Detailed single neuron modeling is widely used to study neuronal functions. While cellular and functional diversity across the mammalian cortex is vast, most of the available computational tools are dedicated to the reproduction of a small set of specific features characteristic of a single neuron. Here, we present a generalized automated workflow for the creation of robust electrical models and illustrate its performance by building cell models for the rat somatosensory cortex (SSCx). Each model is based on a 3D morphological reconstruction and a set of ionic mechanisms specific to the cell type. We use an evolutionary algorithm to optimize passive and active ionic parameters to match the electrophysiological features extracted from whole-cell patch-clamp recordings. To shed light on which parameters are constrained by experimental data and which could be degenerate, we perform a parameter sensitivity analysis. We also validate the optimized models against additional experimental stimuli and assess their generalizability on a population of morphologies with the same morphological type. With this workflow, we generate SSCx neuronal models producing the variability of neuronal responses. Due to its versatility, our workflow can be used to build robust biophysical models of any neuronal type.

neuroscience↗

bioRxiv2022

Using the glial mitochondrial machinery to prevent multilevel dysfunctions observed in a mouse model of Alzheimer s disease

Alzheimers disease (AD) is becoming increasingly prevalent worldwide. It represents one of the greatest medical challenge as no pharmacologic treatments are available to prevent disease progression. Astrocytes play crucial functions within neuronal circuits by providing metabolic and functional support, regulating interstitial solute composition, and modulating synaptic transmission. In addition to these physiological functions, growing evidence points to an essential role of astrocytes in neurodegenerative diseases like AD. Early stage AD is associated with hypometabolism and oxidative stress. Contrary to neurons that are vulnerable to oxydative stress, astrocytes are particularly resistant to mitochondrial dysfunction and are therefore more resilient cells. In our study, we leveraged astrocytic mitochondrial uncoupling and examined neuronal function in the 3xTg AD mouse model. We overexpressed the mitochondrial uncoupling protein 4 (UCP4), which has been shown to improve neuronal survival in vitro. We found that this treatment efficiently prevented alterations of hippocampal metabolite levels observed in AD mice, along with hippocampal atrophy and reduction of basal dendrite arborization of subicular neurons. This approach also averted aberrant neuronal excitability observed in AD subicular neurons and preserved episodic-like memory in AD mice assessed in a spatial recognition task. These findings show that targeting astrocytes and their mitochondria is an effective strategy to prevent the decline of neurons facing AD-related stress at the early stages of the disease.

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bioRxiv2018

Distinct nanoscale calcium channel and synaptic vesicle topographies contribute to the diversity of synaptic function

The nanoscale topographical arrangement of voltage-gated calcium channels (VGCC) and synaptic vesicles (SVs) determines synaptic strength and plasticity, but whether distinct spatial distributions underpin diversity of synaptic function is unknown. We performed single bouton Ca2+ imaging, Ca2+ chelator competition, immunogold electron microscopic (EM) localization of VGCCs and the active zone (AZ) protein Munc13-1, at two cerebellar synapses. Unexpectedly, we found that weak synapses exhibited 3-fold more VGCCs than strong synapses, while the coupling distance was 5-fold longer. Reaction-diffusion modelling could explain both functional and structural data with two strikingly different nanotopographical motifs: strong synapses are composed of SVs that are tightly coupled ([~]10 nm) to VGCC clusters, whereas at weak synapses VGCCs were excluded from the vicinity ([~]50 nm) of docked vesicles. The distinct VGCC-SV topographical motifs also confer differential sensitivity to neuromodulation. Thus VGCC-SV arrangements are not canonical across CNS synapses and their diversity could underlie functional heterogeneity.

neuroscience↗