Inferring structural connectivity using Ising couplings in models of neuronal networks

Abstract Functional connectivity metrics have been widely used to infer the underlying structural connectivity in neuronal networks. Maximum entropy based Ising models have been suggested to discount the effect of indirect interactions and give good results in inferring the true anatomical connectio...

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Autores principales:	Balasundaram Kadirvelu, Yoshikatsu Hayashi, Slawomir J. Nasuto
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b195e15f002a4a339ee62c6324e0909c
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spelling	oai:doaj.org-article:b195e15f002a4a339ee62c6324e0909c2021-12-02T15:05:14ZInferring structural connectivity using Ising couplings in models of neuronal networks10.1038/s41598-017-05462-22045-2322https://doaj.org/article/b195e15f002a4a339ee62c6324e0909c2017-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-05462-2https://doaj.org/toc/2045-2322Abstract Functional connectivity metrics have been widely used to infer the underlying structural connectivity in neuronal networks. Maximum entropy based Ising models have been suggested to discount the effect of indirect interactions and give good results in inferring the true anatomical connections. However, no benchmarking is currently available to assess the performance of Ising couplings against other functional connectivity metrics in the microscopic scale of neuronal networks through a wide set of network conditions and network structures. In this paper, we study the performance of the Ising model couplings to infer the synaptic connectivity in in silico networks of neurons and compare its performance against partial and cross-correlations for different correlation levels, firing rates, network sizes, network densities, and topologies. Our results show that the relative performance amongst the three functional connectivity metrics depends primarily on the network correlation levels. Ising couplings detected the most structural links at very weak network correlation levels, and partial correlations outperformed Ising couplings and cross-correlations at strong correlation levels. The result was consistent across varying firing rates, network sizes, and topologies. The findings of this paper serve as a guide in choosing the right functional connectivity tool to reconstruct the structural connectivity.Balasundaram KadirveluYoshikatsu HayashiSlawomir J. NasutoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-12 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Balasundaram Kadirvelu Yoshikatsu Hayashi Slawomir J. Nasuto Inferring structural connectivity using Ising couplings in models of neuronal networks
description	Abstract Functional connectivity metrics have been widely used to infer the underlying structural connectivity in neuronal networks. Maximum entropy based Ising models have been suggested to discount the effect of indirect interactions and give good results in inferring the true anatomical connections. However, no benchmarking is currently available to assess the performance of Ising couplings against other functional connectivity metrics in the microscopic scale of neuronal networks through a wide set of network conditions and network structures. In this paper, we study the performance of the Ising model couplings to infer the synaptic connectivity in in silico networks of neurons and compare its performance against partial and cross-correlations for different correlation levels, firing rates, network sizes, network densities, and topologies. Our results show that the relative performance amongst the three functional connectivity metrics depends primarily on the network correlation levels. Ising couplings detected the most structural links at very weak network correlation levels, and partial correlations outperformed Ising couplings and cross-correlations at strong correlation levels. The result was consistent across varying firing rates, network sizes, and topologies. The findings of this paper serve as a guide in choosing the right functional connectivity tool to reconstruct the structural connectivity.
format	article
author	Balasundaram Kadirvelu Yoshikatsu Hayashi Slawomir J. Nasuto
author_facet	Balasundaram Kadirvelu Yoshikatsu Hayashi Slawomir J. Nasuto
author_sort	Balasundaram Kadirvelu
title	Inferring structural connectivity using Ising couplings in models of neuronal networks
title_short	Inferring structural connectivity using Ising couplings in models of neuronal networks
title_full	Inferring structural connectivity using Ising couplings in models of neuronal networks
title_fullStr	Inferring structural connectivity using Ising couplings in models of neuronal networks
title_full_unstemmed	Inferring structural connectivity using Ising couplings in models of neuronal networks
title_sort	inferring structural connectivity using ising couplings in models of neuronal networks
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/b195e15f002a4a339ee62c6324e0909c
work_keys_str_mv	AT balasundaramkadirvelu inferringstructuralconnectivityusingisingcouplingsinmodelsofneuronalnetworks AT yoshikatsuhayashi inferringstructuralconnectivityusingisingcouplingsinmodelsofneuronalnetworks AT slawomirjnasuto inferringstructuralconnectivityusingisingcouplingsinmodelsofneuronalnetworks
_version_	1718388915493142528

Inferring structural connectivity using Ising couplings in models of neuronal networks

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