scholarly journals Directed effective connectivity and synaptic weights of in vitro neuronal cultures revealed from high-density multielectrode array recordings

2020 ◽  
Author(s):  
Chumin Sun ◽  
K.C. Lin ◽  
Yu-Ting Huang ◽  
Emily S.C. Ching ◽  
Pik-Yin Lai ◽  
...  
Keyword(s):  
Neuronal Networks ◽  
Spatial Scales ◽  
Effective Connectivity ◽  
Small World ◽  
Synaptic Strength ◽  
Multielectrode Array ◽  
Neuronal Cultures ◽  
Cross Covariance ◽  
Spatially Extended

AbstractStudying connectivity of neuronal cultures can provide insights for understanding brain networks but it is challenging to reveal neuronal connectivity from measurements. We apply a novel method that uses a theoretical relation between the time-lagged cross-covariance and the equal-time cross-covariance to reveal directed effective connectivity and synaptic weights of cortical neuron cultures at different days in vitro from multielectrode array recordings. Using a stochastic leaky-integrate-and-fire model, we show that the simulated spiking activity of the reconstructed networks can well capture the measured network bursts. The neuronal networks are found to be highly nonrandom with an over-representation of bidirectionally connections as compared to a random network of the same connection probability, with the fraction of inhibitory nodes comparable to the measured fractions of inhibitory neurons in various cortical regions in monkey, and have small-world topology with basic network measures comparable to those of the nematode C. elegans chemical synaptic network. Our analyses further reveal that (i) the excitatory and inhibitory incoming degrees have bimodal distributions the excitatory and inhibitory incoming degrees have bimodal distributions, which are that distributions that have been indicated to be optimal against both random failures and attacks in undirected networks; (ii) the distribution of the physical length of excitatory incoming links has two peaks indicating that excitatory signal is transmitted at two spatial scales, one localized to nearest nodes and the other spatially extended to nodes millimeters away, and the shortest links are mostly excitatory towards excitatory nodes and have larger synaptic weights on average; (iii) the average incoming and outgoing synaptic strength is non-Gaussian with long tails and, in particular, the distribution of outgoing synaptic strength of excitatory nodes with excitatory incoming synaptic strength is lognormal, similar to the measured excitatory postsynaptic potential in rat cortex.Author summaryTo understand how the brain processes signal and carries out its function, it is useful to know the connectivity of the underlying neuronal circuits. For large-scale neuronal networks, it is difficult to measure connectivity directly using electron microscopy techniques and methods that can estimate connectivity from electrophysiological recordings are thus highly desirable. Existing methods focus mainly on estimating functional connectivity, which is defined by statistical dependencies between neuronal activities but the relevant direct casual interactions are captured by effective connectivity. Here we apply a novel covariance-relation based method to estimate the directed effective connectivity and synaptic weights of cortical neuron cultures from recordings of multielectrode array of over 4000 electrodes taken at different days in vitro. The neuronal networks are found to be nonrandom, small-world, excitation/inhibition balanced as measured in monkey cortex, and with feeder hubs. Our analyses further suggest some form of specialisation of nodes in receiving excitatory and inhibitory signals and the transmission of excitatory signals at two spatial scales, one localized to nearest nodes and the other spatially extended to nodes millimeters away, and reveal that the distributions of the average incoming and outgoing synaptic strength are skewed with long tails.

scholarly journals Super-selective reconstruction of causal and direct connectivity with application to in-vitro iPSC neuronal networks

2020 ◽  
Author(s):  
Francesca Puppo ◽  
Deborah Pré ◽  
Anne Bang ◽  
Gabriel A. Silva
Keyword(s):  
Neuronal Network ◽  
Neural Development ◽  
Drug Testing ◽  
Neuronal Networks ◽  
Effective Connectivity ◽  
Functional Characterization ◽  
Neuronal Cultures ◽  
Causal Connection ◽  
Model Free

AbstractDespite advancements in the development of cell-based in-vitro neuronal network models, the lack of appropriate computational tools limits their analyses. Methods aimed at deciphering the effective connections between neurons from extracellular spike recordings would increase utility of in-vitro local neural circuits, especially for studies of human neural development and disease based on induced pluripotent stem cells (hiPSC). Current techniques allow statistical inference of functional couplings in the network but are fundamentally unable to correctly identify indirect and apparent connections between neurons, generating redundant maps with limited ability to model the causal dynamics of the network. In this paper, we describe a novel mathematically rigorous, model-free method to map effective - direct and causal - connectivity of neuronal networks from multi-electrode array data. The inference algorithm uses a combination of statistical and deterministic indicators which, first, enables identification of all existing functional links in the network and then, reconstructs the directed and causal connection diagram via a super-selective rule enabling highly accurate classification of direct, indirect and apparent links. Our method can be generally applied to the functional characterization of any in-vitro neuronal networks. Here, we show that, given its accuracy, it can offer important insights into the functional development of in-vitro iPSC-derived neuronal cultures by reconstructing their effective connectivity, thus facilitating future efforts to generate predictive models for neurological disorders, drug testing and neuronal network modeling.

scholarly journals Self-organization of in vitro neuronal assemblies drives to complex network topology

2021 ◽  
Author(s):  
Priscila Corrêa Antonello ◽  
Thomas F Varley ◽  
John Beggs ◽  
Marimélia Porcionatto ◽  
Olaf Sporns ◽  
...  
Keyword(s):  
Complex Network ◽  
Network Topology ◽  
Effective Connectivity ◽  
Small World ◽  
Self Organization ◽  
Neuronal Cultures ◽  
Highly Active ◽  
Neuronal Interactions ◽  
Effective Network

Activity-dependent self-organization plays an important role in the formation of specific and stereotyped connectivity patterns in neural circuits. By combining neuronal cultures, tools with approaches from network neuroscience and information theory, we can study how complex network topology emerges from local neuronal interactions. We constructed effective connectivity networks using a transfer entropy analysis of electrophysiological signals recorded from rat embryo dissociated hippocampal neuron cultures between 6 and 35 days in vitro to investigate how the neuronal network topology evolves during maturation. The methodology for constructing the networks considered the synapse delay and addressed the influence of firing rate and population bursts as well as spurious effects on the inference of connections. We found that the number of links in the networks grew over the course of development, shifting from a segregated to a more integrated architecture. As part of this progression, three significant aspects of complex network topology emerged. In agreement with previous in silico and in vitro studies, a small-world architecture was detected, largely due to strong clustering among neurons. Additionally, the networks developed in a modular community topology, with most modules comprising nearby neurons. Finally, highly active neurons acquired topological characteristics that made them important nodes to the network and integrators of communities. These findings leverage new insights into how neuronal effective network topology relates to neuronal assembly self-organization mechanisms.

scholarly journals Cell–cell coupling and DNA methylation abnormal phenotypes in the after-hours mice

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Federico Tinarelli ◽  
Elena Ivanova ◽  
Ilaria Colombi ◽  
Erica Barini ◽  
Edoardo Balzani ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Neuronal Networks ◽  
Molecular Mechanisms ◽  
Ex Vivo ◽  
Neuronal Cultures ◽  
Functional Impairments ◽  
After Hours ◽  
The Impact

Abstract Background DNA methylation has emerged as an important epigenetic regulator of brain processes, including circadian rhythms. However, how DNA methylation intervenes between environmental signals, such as light entrainment, and the transcriptional and translational molecular mechanisms of the cellular clock is currently unknown. Here, we studied the after-hours mice, which have a point mutation in the Fbxl3 gene and a lengthened circadian period. Methods In this study, we used a combination of in vivo, ex vivo and in vitro approaches. We measured retinal responses in Afh animals and we have run reduced representation bisulphite sequencing (RRBS), pyrosequencing and gene expression analysis in a variety of brain tissues ex vivo. In vitro, we used primary neuronal cultures combined to micro electrode array (MEA) technology and gene expression. Results We observed functional impairments in mutant neuronal networks, and a reduction in the retinal responses to light-dependent stimuli. We detected abnormalities in the expression of photoreceptive melanopsin (OPN4). Furthermore, we identified alterations in the DNA methylation pathways throughout the retinohypothalamic tract terminals and links between the transcription factor Rev-Erbα and Fbxl3. Conclusions The results of this study, primarily represent a contribution towards an understanding of electrophysiological and molecular phenotypic responses to external stimuli in the Afh model. Moreover, as DNA methylation has recently emerged as a new regulator of neuronal networks with important consequences for circadian behaviour, we discuss the impact of the Afh mutation on the epigenetic landscape of circadian biology.

scholarly journals Versatile live-cell activity analysis platform for characterization of neuronal dynamics at single-cell and network level

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Xinyue Yuan ◽  
Manuel Schröter ◽  
Marie Engelene J. Obien ◽  
Michele Fiscella ◽  
Wei Gong ◽  
...  
Keyword(s):  
Neuronal Networks ◽  
Microelectrode Array ◽  
Cell Activity ◽  
Live Cell ◽  
High Signal ◽  
Neuronal Cultures ◽  
Label Free ◽  
Fluorescent Indicators ◽  
Analysis Platform

AbstractChronic imaging of neuronal networks in vitro has provided fundamental insights into mechanisms underlying neuronal function. Current labeling and optical imaging methods, however, cannot be used for continuous and long-term recordings of the dynamics and evolution of neuronal networks, as fluorescent indicators can cause phototoxicity. Here, we introduce a versatile platform for label-free, comprehensive and detailed electrophysiological live-cell imaging of various neurogenic cells and tissues over extended time scales. We report on a dual-mode high-density microelectrode array, which can simultaneously record in (i) full-frame mode with 19,584 recording sites and (ii) high-signal-to-noise mode with 246 channels. We set out to demonstrate the capabilities of this platform with recordings from primary and iPSC-derived neuronal cultures and tissue preparations over several weeks, providing detailed morpho-electrical phenotypic parameters at subcellular, cellular and network level. Moreover, we develop reliable analysis tools, which drastically increase the throughput to infer axonal morphology and conduction speed.

scholarly journals Versatile live-cell activity analysis platform for characterization of neuronal dynamics at single-cell and network level

2020 ◽  
Author(s):  
Xinyue Yuan ◽  
Manuel Schröter ◽  
Marie Engelene J. Obien ◽  
Michele Fiscella ◽  
Wei Gong ◽  
...  
Keyword(s):  
Neuronal Networks ◽  
Microelectrode Array ◽  
Cell Activity ◽  
Live Cell ◽  
High Signal ◽  
Neuronal Cultures ◽  
Label Free ◽  
Analysis Platform

AbstractChronic imaging of neuronal networks in vitro has provided fundamental insights into mechanisms underlying neuronal function. Existing labeling and optical imaging methods, however, cannot be used for continuous and long-term recordings of the dynamics and evolution of neuronal networks, as fluorescence indicators can cause phototoxicity. Here, we introduce a versatile platform for label-free, comprehensive and detailed electrophysiological live-cell imaging of various neurogenic cells and tissues over extended times. We report on a novel dual-mode high-density microelectrode array, which can simultaneously record in i) full-frame mode with 19,584 recording sites and ii) high-signal-to-noise mode with 246 channels. We set out to demonstrate the capabilities of this platform with recordings from primary and iPSC-derived neuronal cultures and tissue preparations over several weeks, providing detailed morpho-electrical phenotypic parameters at subcellular, cellular and network level. Moreover, we developed reliable analysis tools with drastically increased throughput for extracting axonal morphology and conduction parameters.

scholarly journals Super-Selective Reconstruction of Causal and Direct Connectivity With Application to in vitro iPSC Neuronal Networks

2021 ◽  
Vol 15 ◽  
Author(s):  
Francesca Puppo ◽  
Deborah Pré ◽  
Anne G. Bang ◽  
Gabriel A. Silva
Keyword(s):  
Neural Development ◽  
Neuronal Networks ◽  
Functional Characterization ◽  
Electrode Array ◽  
Network Models ◽  
Neuronal Cultures ◽  
Causal Connection ◽  
Model Free ◽  
Rigorous Model

Despite advancements in the development of cell-based in-vitro neuronal network models, the lack of appropriate computational tools limits their analyses. Methods aimed at deciphering the effective connections between neurons from extracellular spike recordings would increase utility of in vitro local neural circuits, especially for studies of human neural development and disease based on induced pluripotent stem cells (hiPSC). Current techniques allow statistical inference of functional couplings in the network but are fundamentally unable to correctly identify indirect and apparent connections between neurons, generating redundant maps with limited ability to model the causal dynamics of the network. In this paper, we describe a novel mathematically rigorous, model-free method to map effective—direct and causal—connectivity of neuronal networks from multi-electrode array data. The inference algorithm uses a combination of statistical and deterministic indicators which, first, enables identification of all existing functional links in the network and then reconstructs the directed and causal connection diagram via a super-selective rule enabling highly accurate classification of direct, indirect, and apparent links. Our method can be generally applied to the functional characterization of any in vitro neuronal networks. Here, we show that, given its accuracy, it can offer important insights into the functional development of in vitro hiPSC-derived neuronal cultures.

scholarly journals In vitro neuronal networks show bidirectional axonal conduction with antidromic action potentials effectively depolarizing the soma

2021 ◽  
Author(s):  
JC Mateus ◽  
CDF Lopes ◽  
M Aroso ◽  
AR Costa ◽  
A Gerós ◽  
...  
Keyword(s):  
Action Potential ◽  
Action Potentials ◽  
Neuronal Networks ◽  
Neuronal Cultures ◽  
Neuronal Function ◽  
Axonal Conduction ◽  
Technological Advances ◽  
Transduction Mechanisms ◽  
Or Gene

ABSTRACTRecent technological advances are revealing the complex physiology of the axon and challenging long-standing assumptions. Namely, while most action potential (AP) initiation occurs at the axon initial segment in central nervous system neurons, initiation in distal parts of the axon has been shown to occur in both physiological and pathological conditions. However, such ectopic action potential (EAP) activity has not been reported yet in studies using in vitro neuronal networks and its functional role, if exists, is still not clear. Here, we report the spontaneous occurrence of EAPs and effective antidromic conduction in hippocampal neuronal cultures. We also observe a significant fraction of bidirection axonal conduction in dorsal root ganglia neuronal cultures. We set out to investigate and characterize this antidromic propagation via a combination of microelectrode arrays, microfluidics, advanced data analysis and in silico studies. We show that EAPs and antidromic conduction can occur spontaneously, and also after distal axotomy or physiological changes in the axon biochemical environment. Importantly, EAPs may carry information (as orthodromic action potentials do) and can have a functional impact on the neuron, as they consistently depolarize the soma. Plasticity or gene transduction mechanisms triggered by soma depolarization can, therefore, be also affected by these antidromic action potentials/EAPs. Finally, we show that this bidirectional axonal conduction is asymmetrical, with antidromic conduction being slower than orthodromic. Via computational modeling, we show that the experimental difference can be explained by axonal morphology. Altogether, these findings have important implications for the study of neuronal function in vitro, reshaping completely our understanding on how information flows in neuronal cultures.

scholarly journals meaRtools: an R Package for the Analysis of Neuronal Networks Recorded on Microelectrode Arrays

2018 ◽  
Author(s):  
Sahar Gelfman ◽  
Quanli Wang ◽  
Yi-Fan Lu ◽  
Diana Hall ◽  
Christopher D. Bostick ◽  
...  
Keyword(s):  
Spike Train ◽  
Neuronal Networks ◽  
Microelectrode Arrays ◽  
R Package ◽  
Network Activity ◽  
Neuronal Cultures ◽  
Mixed Cell ◽  
Spike Train Analysis ◽  
Neuronal Network Activity

AbstractHere 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/.Author 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.

scholarly journals Dynamical Consequences of Fast-Rising, Slow-Decaying Synapses in Neuronal Networks

2003 ◽  
Vol 15 (11) ◽  
pp. 2483-2522 ◽  
Author(s):  
Bard Ermentrout
Keyword(s):  
Coupled Oscillators ◽  
Neuronal Networks ◽  
Synaptic Strength ◽  
Slow Waves ◽  
Recurrent Networks ◽  
Weakly Coupled Oscillators ◽  
Localized Structures ◽  
Mathematical Properties ◽  
Weakly Coupled ◽  
Spatially Extended

Synapses that rise quickly but have long persistence are shown to have certain computational advantages. They have some unique mathematical properties as well and in some instances can make neurons behave as if they are weakly coupled oscillators. This property allows us to determine their synchronization properties. Furthermore, slowly decaying synapses allow recurrent networks to maintain excitation in the absence of inputs, whereas faster decaying synapses do not. There is an interaction between the synaptic strength and the persistence that allows recurrent networks to fire at low rates if the synapses are sufficiently slow. Waves and localized structures are constructed in spatially extended networks with slowly decaying synapses.

scholarly journals Functional strengthening through synaptic scaling upon connectivity disruption in neuronal cultures

2020 ◽  
Vol 4 (4) ◽  
pp. 1160-1180
Author(s):  
Estefanía Estévez-Priego ◽  
Sara Teller ◽  
Clara Granell ◽  
Alex Arenas ◽  
Jordi Soriano
Keyword(s):  
Effective Connectivity ◽  
Neuronal Cultures ◽  
Second Phase ◽  
Synaptic Scaling ◽  
Network Information ◽  
Activity Dependent ◽  
Processing Network ◽  
Dependent Mechanism

An elusive phenomenon in network neuroscience is the extent of neuronal activity remodeling upon damage. Here, we investigate the action of gradual synaptic blockade on the effective connectivity in cortical networks in vitro. We use two neuronal cultures configurations—one formed by about 130 neuronal aggregates and another one formed by about 600 individual neurons—and monitor their spontaneous activity upon progressive weakening of excitatory connectivity. We report that the effective connectivity in all cultures exhibits a first phase of transient strengthening followed by a second phase of steady deterioration. We quantify these phases by measuring GEFF, the global efficiency in processing network information. We term hyperefficiency the sudden strengthening of GEFF upon network deterioration, which increases by 20–50% depending on culture type. Relying on numerical simulations we reveal the role of synaptic scaling, an activity–dependent mechanism for synaptic plasticity, in counteracting the perturbative action, neatly reproducing the observed hyperefficiency. Our results demonstrate the importance of synaptic scaling as resilience mechanism.

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