Model-free reconstruction of excitatory neuronal connectivity from calcium imaging signals.

A systematic assessment of global neural network connectivity through direct electrophysiological assays has remained technically infeasible, even in simpler systems like dissociated neuronal cultures. We introduce an improved algorithmic approach based on Transfer Entropy to reconstruct structural...

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Autores principales:	Olav Stetter, Demian Battaglia, Jordi Soriano, Theo Geisel
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/e00ffbc39d3341f9a9790ff7dd697b7c
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spelling	oai:doaj.org-article:e00ffbc39d3341f9a9790ff7dd697b7c2021-11-18T05:51:03ZModel-free reconstruction of excitatory neuronal connectivity from calcium imaging signals.1553-734X1553-735810.1371/journal.pcbi.1002653https://doaj.org/article/e00ffbc39d3341f9a9790ff7dd697b7c2012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22927808/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358A systematic assessment of global neural network connectivity through direct electrophysiological assays has remained technically infeasible, even in simpler systems like dissociated neuronal cultures. We introduce an improved algorithmic approach based on Transfer Entropy to reconstruct structural connectivity from network activity monitored through calcium imaging. We focus in this study on the inference of excitatory synaptic links. Based on information theory, our method requires no prior assumptions on the statistics of neuronal firing and neuronal connections. The performance of our algorithm is benchmarked on surrogate time series of calcium fluorescence generated by the simulated dynamics of a network with known ground-truth topology. We find that the functional network topology revealed by Transfer Entropy depends qualitatively on the time-dependent dynamic state of the network (bursting or non-bursting). Thus by conditioning with respect to the global mean activity, we improve the performance of our method. This allows us to focus the analysis to specific dynamical regimes of the network in which the inferred functional connectivity is shaped by monosynaptic excitatory connections, rather than by collective synchrony. Our method can discriminate between actual causal influences between neurons and spurious non-causal correlations due to light scattering artifacts, which inherently affect the quality of fluorescence imaging. Compared to other reconstruction strategies such as cross-correlation or Granger Causality methods, our method based on improved Transfer Entropy is remarkably more accurate. In particular, it provides a good estimation of the excitatory network clustering coefficient, allowing for discrimination between weakly and strongly clustered topologies. Finally, we demonstrate the applicability of our method to analyses of real recordings of in vitro disinhibited cortical cultures where we suggest that excitatory connections are characterized by an elevated level of clustering compared to a random graph (although not extreme) and can be markedly non-local.Olav StetterDemian BattagliaJordi SorianoTheo GeiselPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 8, Iss 8, p e1002653 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Olav Stetter Demian Battaglia Jordi Soriano Theo Geisel Model-free reconstruction of excitatory neuronal connectivity from calcium imaging signals.
description	A systematic assessment of global neural network connectivity through direct electrophysiological assays has remained technically infeasible, even in simpler systems like dissociated neuronal cultures. We introduce an improved algorithmic approach based on Transfer Entropy to reconstruct structural connectivity from network activity monitored through calcium imaging. We focus in this study on the inference of excitatory synaptic links. Based on information theory, our method requires no prior assumptions on the statistics of neuronal firing and neuronal connections. The performance of our algorithm is benchmarked on surrogate time series of calcium fluorescence generated by the simulated dynamics of a network with known ground-truth topology. We find that the functional network topology revealed by Transfer Entropy depends qualitatively on the time-dependent dynamic state of the network (bursting or non-bursting). Thus by conditioning with respect to the global mean activity, we improve the performance of our method. This allows us to focus the analysis to specific dynamical regimes of the network in which the inferred functional connectivity is shaped by monosynaptic excitatory connections, rather than by collective synchrony. Our method can discriminate between actual causal influences between neurons and spurious non-causal correlations due to light scattering artifacts, which inherently affect the quality of fluorescence imaging. Compared to other reconstruction strategies such as cross-correlation or Granger Causality methods, our method based on improved Transfer Entropy is remarkably more accurate. In particular, it provides a good estimation of the excitatory network clustering coefficient, allowing for discrimination between weakly and strongly clustered topologies. Finally, we demonstrate the applicability of our method to analyses of real recordings of in vitro disinhibited cortical cultures where we suggest that excitatory connections are characterized by an elevated level of clustering compared to a random graph (although not extreme) and can be markedly non-local.
format	article
author	Olav Stetter Demian Battaglia Jordi Soriano Theo Geisel
author_facet	Olav Stetter Demian Battaglia Jordi Soriano Theo Geisel
author_sort	Olav Stetter
title	Model-free reconstruction of excitatory neuronal connectivity from calcium imaging signals.
title_short	Model-free reconstruction of excitatory neuronal connectivity from calcium imaging signals.
title_full	Model-free reconstruction of excitatory neuronal connectivity from calcium imaging signals.
title_fullStr	Model-free reconstruction of excitatory neuronal connectivity from calcium imaging signals.
title_full_unstemmed	Model-free reconstruction of excitatory neuronal connectivity from calcium imaging signals.
title_sort	model-free reconstruction of excitatory neuronal connectivity from calcium imaging signals.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/e00ffbc39d3341f9a9790ff7dd697b7c
work_keys_str_mv	AT olavstetter modelfreereconstructionofexcitatoryneuronalconnectivityfromcalciumimagingsignals AT demianbattaglia modelfreereconstructionofexcitatoryneuronalconnectivityfromcalciumimagingsignals AT jordisoriano modelfreereconstructionofexcitatoryneuronalconnectivityfromcalciumimagingsignals AT theogeisel modelfreereconstructionofexcitatoryneuronalconnectivityfromcalciumimagingsignals
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Model-free reconstruction of excitatory neuronal connectivity from calcium imaging signals.

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