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Ponzi, A.

Publications and source records attributed to Ponzi, A..

3 recordsLinked to original sources

The C. elegans Connectome Toolbox: an open, extensible framework for multimodal connectomic data integration and analysis

The nematode C. elegans is one of the best studied model organisms in biology. Since the initial description of the C. elegans neuronal connectome by White and colleagues, numerous studies have mapped and analyzed the connectome, and released structured datasets of the connectivity, often merging and adding to previous versions. These datasets now serve as foundational resources that underpin studies into the neuronal basis of C. elegans behavior. In spite of their high utility, heterogeneity in the different datasets and their annotation conventions obscures direct comparison where discrepancies between connectomes frequently stem from differences in coverage or nomenclature rather than genuine biological disagreement. Additionally, datasets describing new connectivity modalities - contact-based (the contactome), extrasynaptic (monoaminergic and neuropeptidergic), and functional connectivity - have also recently become available. Thus, there is a need for a unified framework providing standardized access to these diverse datasets while preserving their individual provenance. Here, we present the C. elegans Connectome Toolbox, a comprehensive framework for consolidating, curating and validating historical and newly published multimodal C. elegans connectivity data. Connectome Toolbox includes an accessible online resource (https://openworm.org/ConnectomeToolbox) for interactive data exploration and is accompanied by a Python library for programmatic access. We demonstrate the utility of the toolbox through a series of analyses that illustrate how connectome data can be explored, visualized, and compared across modalities, including integration of the anatomical connectome with a comprehensive neurotransmitter atlas, and comparison with computational models of C. elegans neural circuits.

neuroscience↗

A cascade of attentional and default mode network dynamics following the resolution of perceptual ambiguity

The neural mechanisms underlying spontaneous perceptual switches during bistable perception, particularly the dynamic interplay between large-scale brain networks, remain incompletely understood. Here, using functional magnetic resonance imaging (fMRI) in six participants and a bistable kinetic depth effect (KDE) stimulus, we investigated the spatiotemporal dynamics of brain activity associated with spontaneous perceptual reversals compared to physically induced (replayed) switches. Leveraging a fine-grained functional brain parcellation (MD758) and time-resolved analysis including noise correlations, we identified a distributed network encompassing occipital (MT/MST, LOC), parietal (IPS), and frontal (FEF, IFC, DLPFC) regions showing differential activity during spontaneous versus replayed switches. This network topography qualitatively overlapped a prior review-derived synthesis of perceptual-switching regions. Temporal decomposition of the differential fMRI signal further revealed a characteristic network cascade after the perceptual report: positive dorsal attention (DAN) and visual (VIS) network contrasts peaked approximately 4 s after the report alongside a negative default mode network (DMN) contrast; a slower, more modest ventral attention (VAN)-frontoparietal (FPN) component was observed around 10 s post-report but was weak and is treated as preliminary. The early 4 s profile was recovered across raw-signal, PCA-denoised, and noise-correlation analyses (three related, not fully independent analyses), with the noise-correlation analysis indicating that it is not fully attributable to shared motor-report signals. These findings delineate the temporal sequence of network recruitment following spontaneous perceptual transitions and suggest that interplay between attentional control and default mode networks accompanies the resolution of perceptual ambiguity.

neuroscience↗

Multiscale modelling of neuronal dynamics in hippocampus CA1

The development of biologically realistic models of brain microcircuits and regions is currently a very relevant topic in computational neuroscience. From basic research to clinical applications, there is an increasing demand for accurate models that incorporate local cellular and network specificities, able to capture a broad range of dynamics and functions associated with given brain regions. One of the main challenges of these models is the passage between different scales, going from the microscale (cellular) to the meso (microcircuit) and macroscale (region or whole-brain level), while keeping at the same time a constraint on the demand of computational resources. One novel approach to this problem is the use of mean-field models of neuronal activity to build large-scale simulations. This provides an effective solution to the passage between scales with relatively low computational demands, which is achieved by a drastic reduction in the dimensionality of the system. In this paper we introduce a multiscale modelling framework for the hippocampal CA1, a region of the brain that plays a key role in functions such as learning, memory consolidation and navigation. Our modelling framework goes from the single cell level to the macroscale and makes use of a novel mean-field model of CA1, introduced in this paper, to bridge the gap between the micro and macro scales. To develop the mean-field model we make use of a recently introduced formalism based on a bottom-up approach that is easily applicable to different neuronal models and cell types. We test and validate the model by analyzing the response of the system to the main brain rhythms observed in the hippocampus and comparing our results with the ones of the corresponding spiking network model of CA1. In addition, we show an example of the implementation of our model to study a stimulus propagation at the macro-scale, and we compare the results obtained from our model with the corresponding spiking network model of the whole CA1 area.

neuroscience↗