Cargando…

Inferring structural connectivity using Ising couplings in models of neuronal networks

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. Howev...

Descripción completa

Detalles Bibliográficos
Autores principales: Kadirvelu, Balasundaram, Hayashi, Yoshikatsu, Nasuto, Slawomir J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5557813/
https://www.ncbi.nlm.nih.gov/pubmed/28811468
http://dx.doi.org/10.1038/s41598-017-05462-2
_version_ 1783257273303302144
author Kadirvelu, Balasundaram
Hayashi, Yoshikatsu
Nasuto, Slawomir J.
author_facet Kadirvelu, Balasundaram
Hayashi, Yoshikatsu
Nasuto, Slawomir J.
author_sort Kadirvelu, Balasundaram
collection PubMed
description 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 Online
Article
Text
id pubmed-5557813
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-55578132017-08-16 Inferring structural connectivity using Ising couplings in models of neuronal networks Kadirvelu, Balasundaram Hayashi, Yoshikatsu Nasuto, Slawomir J. Sci Rep Article 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. Nature Publishing Group UK 2017-08-15 /pmc/articles/PMC5557813/ /pubmed/28811468 http://dx.doi.org/10.1038/s41598-017-05462-2 Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Kadirvelu, Balasundaram
Hayashi, Yoshikatsu
Nasuto, Slawomir J.
Inferring structural connectivity using Ising couplings in models of neuronal networks
title 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_short Inferring structural connectivity using Ising couplings in models of neuronal networks
title_sort inferring structural connectivity using ising couplings in models of neuronal networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5557813/
https://www.ncbi.nlm.nih.gov/pubmed/28811468
http://dx.doi.org/10.1038/s41598-017-05462-2
work_keys_str_mv AT kadirvelubalasundaram inferringstructuralconnectivityusingisingcouplingsinmodelsofneuronalnetworks
AT hayashiyoshikatsu inferringstructuralconnectivityusingisingcouplingsinmodelsofneuronalnetworks
AT nasutoslawomirj inferringstructuralconnectivityusingisingcouplingsinmodelsofneuronalnetworks