Identification of excitatory-inhibitory links and network topology in large-scale neuronal assemblies from multi-electrode recordings

Pastore, Vito Paolo; Massobrio, Paolo; Godjoski, Aleksandar; Martinoia, Sergio

doi:10.1371/journal.pcbi.1006381

Cited by 73 publications

(85 citation statements)

References 70 publications

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“…Further work must also be done to extend this approach to the explicit identification of inhibitory inputs and their role. We note that inhibitory connections are implicitly defined in the reconstructed network; however, in contrast to more recent works [64], our analysis has not explicitly distinguished them from excitatory links. We have estimated the connectivity matrix of a sub-network which includes the strongest recurrent links in the neuronal culture, major determinants of spontaneous activity [65] that we are interested in perturbing and analyzing.…”

Section: Discussionmentioning

confidence: 88%

Super-selective reconstruction of causal and direct connectivity with application toin-vitroiPSC neuronal networks

Puppo

PrÐ

Bang

et al. 2020

Preprint

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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 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.

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Section: Discussionmentioning

confidence: 88%

Super-selective reconstruction of causal and direct connectivity with application toin-vitroiPSC neuronal networks

Puppo

PrÐ

Bang

et al. 2020

Preprint

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“…We calculated both the incoming degree k in and the outgoing degree k out for each node, 172 and k in = k out as the neuronal networks are directed. The distributions of the incoming 173 and outgoing degrees are qualitatively the same for all the 8 DIVs and the results for 174 DIV25 are shown in Fig 4. A striking feature of the distribution of the incoming degree 175 is its approximate bimodal feature, which is in contrast to the degree distributions of 176 the chemical synapse network of C. elegans [20] and the scale-free distribution found in 177 the functional connectivity [10]. For undirected networks, there have been studies 178 indicating that the robustness against both random failures and attacks can be 179 optimized by having a bimodal degree distribution [26,27].…”

Section: Distributions Of Incoming and Outgoing Degrees 171mentioning

confidence: 99%

“…This observation of an 147 over-representation of bidirectional connections as compared to a random network in 148 the reconstructed neuronal networks echoes previous reports for local regions of the rat 149 cortex [21][22][23][24] Thus neuronal networks are highly nonrandom. As a comparison, we 150 calculated the number of bidirectional connections in the functional connectivity 151 estimated by using the cross-covariance based methods [10] Table 2. Basic network measures of the reconstructed networks for the 8 DIVs including the connection probability p, the ratio r B of the number of bidirectionally connected pairs to the expected number N (N − 1)p 2 /2 for a random network with p, the fractions f E and f I of excitatory and inhibitory nodes, the fraction f SCC of nodes that form the strongly connected component, the characteristic path length l, the average clustering coefficient CC and the small world index SWI.…”

Section: Basic Network Features 137mentioning

confidence: 99%

“…Bidirectional connected pairs can play a 338 direct role in forming feedback control in neuronal circuits. We estimated the functional 339 connectivity for DIV25, DIV45 and DIV66 by using the cross-covariance based 340 methods [10] and bidirectionally connected pairs are not found. Neuronal networks are 341 also expected to be robust against random failure or targeted attacks.…”

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confidence: 99%

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

Sun

Lin

Huang

et al. 2020

Preprint

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Studying 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 1 know the connectivity of the underlying neuronal circuits. For large-scale neuronal 2 networks, it is difficult to measure connectivity directly using electron microscopy 3 February 3, 2020 1/22 techniques and methods that can estimate connectivity from electrophysiological 4 recordings are thus highly desirable. Existing methods focus mainly on estimating 5 functional connectivity, which is defined by statistical dependencies between neuronal 6 activities but the relevant direct casual interactions are captured by effective 7 connectivity. Here we apply a novel covariance-relation based method to estimate the 8 directed effective connectivity and synaptic weights of cortical neuron cultures from 9recordings of multielectrode array of over 4000 electrodes taken at different days in 10 vitro. The neuronal networks are found to be nonrandom, small-world, 11 excitation/inhibitio...

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“…Alternative approaches to infer connectivity from spike trains, other than those addressed above, have employed models of sparsely and linearly interacting point processes [85], or have been designed in a model-free manner [51,86,87], for example, using CCGs [51,86] similarly to our comparisons. A general challenge in subsampled networks arises from pairwise spike train correlations at small time lags generated by shared connections from unobserved neurons, regardless of whether a direct connection is present.…”

Section: Estimation Of Synaptic Couplingmentioning

confidence: 99%

Inferring and validating mechanistic models of neural microcircuits based on spike-train data

Ladenbauer

McKenzie

English

et al. 2018

Preprint

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The interpretation of neuronal spike-train recordings often relies on abstract statistical models that allow for principled parameter estimation and model-selection but provide only limited insights into underlying microcircuits. On the other hand, mechanistic neuronal models are useful to interpret microcircuit dynamics, but are rarely quantitatively matched to experimental data due to methodological challenges. We present analytical methods to efficiently fit spiking circuit models to single-trial spike trains. Using the maximallikelihood approach, we statistically infer the mean and variance of hidden inputs, neuronal adaptation properties and connectivity for coupled integrate-and-fire neurons. Parameter estimation is found to be very accurate, even for highly sub-sampled networks. We apply our methods to recordings from cortical neurons of awake ferrets to reveal properties of the hidden synaptic inputs, in particular underlying population-level equalization between excitatory and inhibitory inputs. The methods introduced here open the door to a quantitative, mechanistic interpretation of recorded neuronal population activity.

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Identification of excitatory-inhibitory links and network topology in large-scale neuronal assemblies from multi-electrode recordings

Cited by 73 publications

References 70 publications

Super-selective reconstruction of causal and direct connectivity with application toin-vitroiPSC neuronal networks

Super-selective reconstruction of causal and direct connectivity with application toin-vitroiPSC neuronal networks

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

Inferring and validating mechanistic models of neural microcircuits based on spike-train data

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