Publisher Correction: Finding influential nodes for integration in brain networks using optimal percolation theory

The original version of this Article contained an error in the last sentence of the first paragraph of the Introduction, which incorrectly read ‘Correlation of brain activity is typically measured using functional magnetic resonance imaging (fMRI), and the correlation structure is often referred to...

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Autores principales:	Gino Del Ferraro, Andrea Moreno, Byungjoon Min, Flaviano Morone, Úrsula Pérez-Ramírez, Laura Pérez-Cervera, Lucas C. Parra, Andrei Holodny, Santiago Canals, Hernán A. Makse
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/577ed0517ad1483facb93aa2f254f92a
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spelling	oai:doaj.org-article:577ed0517ad1483facb93aa2f254f92a2021-12-02T14:39:47ZPublisher Correction: Finding influential nodes for integration in brain networks using optimal percolation theory10.1038/s41467-018-05686-42041-1723https://doaj.org/article/577ed0517ad1483facb93aa2f254f92a2018-08-01T00:00:00Zhttps://doi.org/10.1038/s41467-018-05686-4https://doaj.org/toc/2041-1723The original version of this Article contained an error in the last sentence of the first paragraph of the Introduction, which incorrectly read ‘Correlation of brain activity is typically measured using functional magnetic resonance imaging (fMRI), and the correlation structure is often referred to as “fu’. The correct version states ‘referred to as “functional connectivity”2–6’ in place of ‘referred to as “fu’. This has been corrected in both the PDF and HTML versions of the Article.Gino Del FerraroAndrea MorenoByungjoon MinFlaviano MoroneÚrsula Pérez-RamírezLaura Pérez-CerveraLucas C. ParraAndrei HolodnySantiago CanalsHernán A. MakseNature PortfolioarticleScienceQENNature Communications, Vol 9, Iss 1, Pp 1-1 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Gino Del Ferraro Andrea Moreno Byungjoon Min Flaviano Morone Úrsula Pérez-Ramírez Laura Pérez-Cervera Lucas C. Parra Andrei Holodny Santiago Canals Hernán A. Makse Publisher Correction: Finding influential nodes for integration in brain networks using optimal percolation theory
description	The original version of this Article contained an error in the last sentence of the first paragraph of the Introduction, which incorrectly read ‘Correlation of brain activity is typically measured using functional magnetic resonance imaging (fMRI), and the correlation structure is often referred to as “fu’. The correct version states ‘referred to as “functional connectivity”2–6’ in place of ‘referred to as “fu’. This has been corrected in both the PDF and HTML versions of the Article.
format	article
author	Gino Del Ferraro Andrea Moreno Byungjoon Min Flaviano Morone Úrsula Pérez-Ramírez Laura Pérez-Cervera Lucas C. Parra Andrei Holodny Santiago Canals Hernán A. Makse
author_facet	Gino Del Ferraro Andrea Moreno Byungjoon Min Flaviano Morone Úrsula Pérez-Ramírez Laura Pérez-Cervera Lucas C. Parra Andrei Holodny Santiago Canals Hernán A. Makse
author_sort	Gino Del Ferraro
title	Publisher Correction: Finding influential nodes for integration in brain networks using optimal percolation theory
title_short	Publisher Correction: Finding influential nodes for integration in brain networks using optimal percolation theory
title_full	Publisher Correction: Finding influential nodes for integration in brain networks using optimal percolation theory
title_fullStr	Publisher Correction: Finding influential nodes for integration in brain networks using optimal percolation theory
title_full_unstemmed	Publisher Correction: Finding influential nodes for integration in brain networks using optimal percolation theory
title_sort	publisher correction: finding influential nodes for integration in brain networks using optimal percolation theory
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/577ed0517ad1483facb93aa2f254f92a
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Publisher Correction: Finding influential nodes for integration in brain networks using optimal percolation theory

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