Biology subjects

Eglen, S. J.

Publications and source records attributed to Eglen, S. J..

5 recordsLinked to original sources

bioRxiv2025

Mecp2 deficiency impairs microscale cortical network topology and dynamics in a Rett syndrome mouse model

Rett syndrome is a debilitating neurodevelopmental disorder with cerebral processing impairments caused by MECP2 loss-of-function mutations. Mecp2-deficient mouse models reveal disruptions of microscale cortical circuits. Yet how cellular-scale information processing is altered in Mecp2-deficient microscale functional networks is unknown. We investigated the development of functional connectivity, network topology, and dynamics in microelectrode array (MEA) recordings of primary cortical cultures from Mecp2-deficient and wild-type mice. Mecp2-deficient cortical networks developed more slowly and showed decreased functional connectivity compared to wild-type, leading to smaller network size, density, and strength of connectivity. Altered network topological features in Mecp2-deficient microscale circuits predicted decreased efficiency and information-sharing capacity. This reveals developmental deficits in microscale functional networks, which may in turn underlie the cortical decline and severe cognitive disability in Rett syndrome. These findings also offer circuit-level targets and an in-vitro approach for evaluating new therapeutic products for restoring microscale network function.

neuroscience↗

bioRxiv2024

MEA-NAP compares microscale functional connectivity, topology, and network dynamics in organoid or monolayer neuronal cultures

Microelectrode array (MEA) recordings are commonly used to compare firing and burst rates in neuronal cultures. MEA recordings can also reveal microscale functional connectivity, topology, and network dynamics--patterns seen in brain networks across spatial scales. Network topology is frequently characterized in neuroimaging with graph theoretical metrics. However, few computational tools exist for analyzing microscale functional brain networks from MEA recordings. Here, we present a MATLAB MEA network analysis pipeline (MEA-NAP) for raw voltage time-series acquired from single- or multi-well MEAs. Applications to 3D human cerebral organoids or 2D human-derived or murine cultures reveal differences in network development, including topology, node cartography, and dimensionality. MEA-NAP incorporates multi-unit template-based spike detection, probabilistic thresholding for determining significant functional connections, and normalization techniques for comparing networks. MEA-NAP can identify network-level effects of pharmacologic perturbation and/or disease-causing mutations and, thus, can provide a translational platform for revealing mechanistic insights and screening new therapeutic approaches.

neuroscience↗

bioRxiv2022

Homophilic wiring principles underpin neuronal network topology in vitro

Economic efficiency has been a popular explanation for how networks self-organize within the developing nervous system. However, the precise nature of the economic negotiations governing this putative organizational principle remains unclear. Here, we address this question further by combining large-scale electrophysiological recordings, to characterize the functional connectivity of developing neuronal networks in vitro, with a generative modeling approach capable of simulating network formation. We find that the best fitting model uses a homophilic generative wiring principle in which neurons form connections to other neurons which are spatially proximal and have similar connectivity patterns to themselves. Homophilic generative models outperform more canonical models in which neurons wire depending upon their spatial proximity either alone or in combination with the extent of their local connectivity. This homophily-based mechanism for neuronal network emergence accounts for a wide range of observations that are described, but not sufficiently explained, by traditional analyses of network topology. Using rodent and human monolayer and organoid cultures, we show that homophilic generative mechanisms can accurately recapitulate the topology of emerging cellular functional connectivity, representing an important wiring principle and determining factor of neuronal network formation in vitro.

neuroscience↗

bioRxiv2018

meaRtools: an R package for the Comprehensive Analysis of Neuronal Networks Recorded on Multi-Electrode Arrays

Here we present an open-source R package meaRtools that provides a platform for analyzing neuronal networks recorded on Microelectrode Arrays (MEAs). Cultured neuronal networks monitored with MEAs are now being widely used to characterize in vitro models of neurological disorders and to evaluate pharmaceutical compounds. meaRtools provides core algorithms for MEA spike train analysis, feature extraction, statistical analysis and plotting of multiple MEA recordings with multiple genotypes and treatments. meaRtools functionality covers novel solutions for spike train analysis, including algorithms to assess electrode cross-correlation using the spike train tiling coefficient (STTC), mutual information, synchronized bursts and entropy within cultured wells. Also integrated is a solution to account for bursts variability originating from mixed-cell neuronal cultures. The package provides a statistical platform built specifically for MEA data that can combine multiple MEA recordings and compare extracted features between different genetic models or treatments. We demonstrate the utilization of meaRtools to successfully identify epilepsy-like phenotypes in neuronal networks from Celf4 knockout mice. The package is freely available under the GPL license (GPL>=3) and is updated frequently on the CRAN web-server repository. The package, along with full documentation can be downloaded from: https://cran.r-project.org/web/packages/meaRtools/.\n\nAuthor summaryCultured neuronal networks are widely used to study and characterize neuronal network activity. Among the many uses of neuronal cultures are the capabilities to evaluate neurotoxicity and the effects of pharmacological compounds on cellular physiology. Multi-well microelectrode arrays (MEAs) can collect high-throughput data from multiple neuronal cultures simultaneously, and thereby make possible hypotheses-driven inquiries into neurobiology and neuropharmacology. The analysis of MEA-derived information presents many computational challenges. High frequency data recorded simultaneously from hundreds of electrodes can be difficult to handle. The need to compare network activity across various drug treatments or genotypes recorded on the same plate from experiments lasting several weeks presents another challenge. These challenges inspired us to develop meaRtools; an MEA data analysis package that contains new methods to characterize network activity patterns, which are illustrated here using examples from a genetic mouse model of epilepsy. Among the highlights of meaRtools are novel algorithms designed to characterize neuronal activity dynamics and network properties such as bursting and synchronization, options to combine multiple recordings and use a robust statistical framework to draw appropriate statistical inferences, and finally data visualizations and plots. In summary, meaRtools provides a platform for the analyses of singular and longitudinal MEA experiments.

neuroscience↗

bioRxiv2017

A mechanistic model of the spatial interaction between blue cones and blue cone bipolar cells in macaque retina

The spatial positions of blue cones (BC) and blue cone bipolars (BCBP) are positively correlated in macaque retina (Kouyama & Marshak, 1997): BCs, located in the outer nuclear layer (ONL), and BCBPs, located in the inner nuclear layer (INL), tend to be located close to each other in the lateral dimension. They also form separate homotypic mosaics and, finally, most BCBPs contact only a single BC pedicle. We present a mechanistic model of the BC-BCBP interaction to account for the development of these two mosaics and their connections. It assumes that BCs are immobile and that BCBPs can migrate within the INL due to a compromise between a dendritic string force and an intrinsic INL friction force. Model parameters were selected to optimise the fit with observed fields (Kouyama & Marshak, 1997). Simulated density recovery profiles (DRPs) closely mimic the observed DRPs. In particular, for each of the five retinal fields studied, the simulated DRP for the interaction between BCs and BCBPs has a peak at distances less than around 20 m and a small dip at distances up to the maximal lateral dendritic length ([~] 44 m), matching the profiles of the real data. We conclude that our mechanistic model is a candidate for the unknown mechanism that drives the observed interaction between BCs and BCBPs in macaque retina.

neuroscience↗