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Dependency Network Analysis (D(EP)NA) Reveals Context Related Influence of Brain Network Nodes
Communication between and within brain regions is essential for information processing within functional networks. The current methods to determine the influence of one region on another are either based on temporal resolution, or require a predefined model for the connectivity direction. However th...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4895213/ https://www.ncbi.nlm.nih.gov/pubmed/27271458 http://dx.doi.org/10.1038/srep27444 |
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author | Jacob, Yael Winetraub, Yonatan Raz, Gal Ben-Simon, Eti Okon-Singer, Hadas Rosenberg-Katz, Keren Hendler, Talma Ben-Jacob, Eshel |
author_facet | Jacob, Yael Winetraub, Yonatan Raz, Gal Ben-Simon, Eti Okon-Singer, Hadas Rosenberg-Katz, Keren Hendler, Talma Ben-Jacob, Eshel |
author_sort | Jacob, Yael |
collection | PubMed |
description | Communication between and within brain regions is essential for information processing within functional networks. The current methods to determine the influence of one region on another are either based on temporal resolution, or require a predefined model for the connectivity direction. However these requirements are not always achieved, especially in fMRI studies, which have poor temporal resolution. We thus propose a new graph theory approach that focuses on the correlation influence between selected brain regions, entitled Dependency Network Analysis (D(EP)NA). Partial correlations are used to quantify the level of influence of each node during task performance. As a proof of concept, we conducted the D(EP)NA on simulated datasets and on two empirical motor and working memory fMRI tasks. The simulations revealed that the D(EP)NA correctly captures the network’s hierarchy of influence. Applying D(EP)NA to the functional tasks reveals the dynamics between specific nodes as would be expected from prior knowledge. To conclude, we demonstrate that D(EP)NA can capture the most influencing nodes in the network, as they emerge during specific cognitive processes. This ability opens a new horizon for example in delineating critical nodes for specific clinical interventions. |
format | Online Article Text |
id | pubmed-4895213 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-48952132016-06-10 Dependency Network Analysis (D(EP)NA) Reveals Context Related Influence of Brain Network Nodes Jacob, Yael Winetraub, Yonatan Raz, Gal Ben-Simon, Eti Okon-Singer, Hadas Rosenberg-Katz, Keren Hendler, Talma Ben-Jacob, Eshel Sci Rep Article Communication between and within brain regions is essential for information processing within functional networks. The current methods to determine the influence of one region on another are either based on temporal resolution, or require a predefined model for the connectivity direction. However these requirements are not always achieved, especially in fMRI studies, which have poor temporal resolution. We thus propose a new graph theory approach that focuses on the correlation influence between selected brain regions, entitled Dependency Network Analysis (D(EP)NA). Partial correlations are used to quantify the level of influence of each node during task performance. As a proof of concept, we conducted the D(EP)NA on simulated datasets and on two empirical motor and working memory fMRI tasks. The simulations revealed that the D(EP)NA correctly captures the network’s hierarchy of influence. Applying D(EP)NA to the functional tasks reveals the dynamics between specific nodes as would be expected from prior knowledge. To conclude, we demonstrate that D(EP)NA can capture the most influencing nodes in the network, as they emerge during specific cognitive processes. This ability opens a new horizon for example in delineating critical nodes for specific clinical interventions. Nature Publishing Group 2016-06-07 /pmc/articles/PMC4895213/ /pubmed/27271458 http://dx.doi.org/10.1038/srep27444 Text en Copyright © 2016, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Jacob, Yael Winetraub, Yonatan Raz, Gal Ben-Simon, Eti Okon-Singer, Hadas Rosenberg-Katz, Keren Hendler, Talma Ben-Jacob, Eshel Dependency Network Analysis (D(EP)NA) Reveals Context Related Influence of Brain Network Nodes |
title | Dependency Network Analysis (D(EP)NA) Reveals Context Related Influence of Brain Network Nodes |
title_full | Dependency Network Analysis (D(EP)NA) Reveals Context Related Influence of Brain Network Nodes |
title_fullStr | Dependency Network Analysis (D(EP)NA) Reveals Context Related Influence of Brain Network Nodes |
title_full_unstemmed | Dependency Network Analysis (D(EP)NA) Reveals Context Related Influence of Brain Network Nodes |
title_short | Dependency Network Analysis (D(EP)NA) Reveals Context Related Influence of Brain Network Nodes |
title_sort | dependency network analysis (d(ep)na) reveals context related influence of brain network nodes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4895213/ https://www.ncbi.nlm.nih.gov/pubmed/27271458 http://dx.doi.org/10.1038/srep27444 |
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