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Evaluation of the Performance of Information Theory-Based Methods and Cross-Correlation to Estimate the Functional Connectivity in Cortical Networks

Functional connectivity of in vitro neuronal networks was estimated by applying different statistical algorithms on data collected by Micro-Electrode Arrays (MEAs). First we tested these “connectivity methods” on neuronal network models at an increasing level of complexity and evaluated the performa...

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Detalles Bibliográficos
Autores principales: Garofalo, Matteo, Nieus, Thierry, Massobrio, Paolo, Martinoia, Sergio
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2715865/
https://www.ncbi.nlm.nih.gov/pubmed/19652720
http://dx.doi.org/10.1371/journal.pone.0006482
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author Garofalo, Matteo
Nieus, Thierry
Massobrio, Paolo
Martinoia, Sergio
author_facet Garofalo, Matteo
Nieus, Thierry
Massobrio, Paolo
Martinoia, Sergio
author_sort Garofalo, Matteo
collection PubMed
description Functional connectivity of in vitro neuronal networks was estimated by applying different statistical algorithms on data collected by Micro-Electrode Arrays (MEAs). First we tested these “connectivity methods” on neuronal network models at an increasing level of complexity and evaluated the performance in terms of ROC (Receiver Operating Characteristic) and PPC (Positive Precision Curve), a new defined complementary method specifically developed for functional links identification. Then, the algorithms better estimated the actual connectivity of the network models, were used to extract functional connectivity from cultured cortical networks coupled to MEAs. Among the proposed approaches, Transfer Entropy and Joint-Entropy showed the best results suggesting those methods as good candidates to extract functional links in actual neuronal networks from multi-site recordings.
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spelling pubmed-27158652009-08-04 Evaluation of the Performance of Information Theory-Based Methods and Cross-Correlation to Estimate the Functional Connectivity in Cortical Networks Garofalo, Matteo Nieus, Thierry Massobrio, Paolo Martinoia, Sergio PLoS One Research Article Functional connectivity of in vitro neuronal networks was estimated by applying different statistical algorithms on data collected by Micro-Electrode Arrays (MEAs). First we tested these “connectivity methods” on neuronal network models at an increasing level of complexity and evaluated the performance in terms of ROC (Receiver Operating Characteristic) and PPC (Positive Precision Curve), a new defined complementary method specifically developed for functional links identification. Then, the algorithms better estimated the actual connectivity of the network models, were used to extract functional connectivity from cultured cortical networks coupled to MEAs. Among the proposed approaches, Transfer Entropy and Joint-Entropy showed the best results suggesting those methods as good candidates to extract functional links in actual neuronal networks from multi-site recordings. Public Library of Science 2009-08-04 /pmc/articles/PMC2715865/ /pubmed/19652720 http://dx.doi.org/10.1371/journal.pone.0006482 Text en Garofalo et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Garofalo, Matteo
Nieus, Thierry
Massobrio, Paolo
Martinoia, Sergio
Evaluation of the Performance of Information Theory-Based Methods and Cross-Correlation to Estimate the Functional Connectivity in Cortical Networks
title Evaluation of the Performance of Information Theory-Based Methods and Cross-Correlation to Estimate the Functional Connectivity in Cortical Networks
title_full Evaluation of the Performance of Information Theory-Based Methods and Cross-Correlation to Estimate the Functional Connectivity in Cortical Networks
title_fullStr Evaluation of the Performance of Information Theory-Based Methods and Cross-Correlation to Estimate the Functional Connectivity in Cortical Networks
title_full_unstemmed Evaluation of the Performance of Information Theory-Based Methods and Cross-Correlation to Estimate the Functional Connectivity in Cortical Networks
title_short Evaluation of the Performance of Information Theory-Based Methods and Cross-Correlation to Estimate the Functional Connectivity in Cortical Networks
title_sort evaluation of the performance of information theory-based methods and cross-correlation to estimate the functional connectivity in cortical networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2715865/
https://www.ncbi.nlm.nih.gov/pubmed/19652720
http://dx.doi.org/10.1371/journal.pone.0006482
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