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Characterizing directed functional pathways in the visual system by multivariate nonlinear coherence of fMRI data
A multivariate measure of directed functional connectivity is used with resting-state fMRI data of 40 healthy subjects to identify directed pathways of signal progression in the human visual system. The method utilizes 4-nodes networks of mutual interacted BOLD signals to obtains their temporal hier...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6218499/ https://www.ncbi.nlm.nih.gov/pubmed/30397245 http://dx.doi.org/10.1038/s41598-018-34672-5 |
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author | Goelman, Gadi Dan, Rotem Keadan, Tarek |
author_facet | Goelman, Gadi Dan, Rotem Keadan, Tarek |
author_sort | Goelman, Gadi |
collection | PubMed |
description | A multivariate measure of directed functional connectivity is used with resting-state fMRI data of 40 healthy subjects to identify directed pathways of signal progression in the human visual system. The method utilizes 4-nodes networks of mutual interacted BOLD signals to obtains their temporal hierarchy and functional connectivity. Patterns of signal progression were defined at frequency windows by appealing to a hierarchy based upon phase differences, and their significance was assessed by permutation testing. Assuming consistent phase relationship between neuronal and fMRI signals and unidirectional coupling, we were able to characterize directed pathways in the visual system. The ventral and dorsal systems were found to have different functional organizations. The dorsal system, particularly of the left hemisphere, had numerous feedforward pathways connecting the striate and extrastriate cortices with non-visual regions. The ventral system had fewer pathways primarily of two types: (1) feedback pathways initiated in the fusiform gyrus that were either confined to the striate and the extrastriate cortices or connected to the temporal cortex, (2) feedforward pathways initiated in V2, excluded the striate cortex, and connected to non-visual regions. The multivariate measure demonstrated higher specificity than bivariate (pairwise) measure. The analysis can be applied to other neuroimaging and electrophysiological data. |
format | Online Article Text |
id | pubmed-6218499 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-62184992018-11-07 Characterizing directed functional pathways in the visual system by multivariate nonlinear coherence of fMRI data Goelman, Gadi Dan, Rotem Keadan, Tarek Sci Rep Article A multivariate measure of directed functional connectivity is used with resting-state fMRI data of 40 healthy subjects to identify directed pathways of signal progression in the human visual system. The method utilizes 4-nodes networks of mutual interacted BOLD signals to obtains their temporal hierarchy and functional connectivity. Patterns of signal progression were defined at frequency windows by appealing to a hierarchy based upon phase differences, and their significance was assessed by permutation testing. Assuming consistent phase relationship between neuronal and fMRI signals and unidirectional coupling, we were able to characterize directed pathways in the visual system. The ventral and dorsal systems were found to have different functional organizations. The dorsal system, particularly of the left hemisphere, had numerous feedforward pathways connecting the striate and extrastriate cortices with non-visual regions. The ventral system had fewer pathways primarily of two types: (1) feedback pathways initiated in the fusiform gyrus that were either confined to the striate and the extrastriate cortices or connected to the temporal cortex, (2) feedforward pathways initiated in V2, excluded the striate cortex, and connected to non-visual regions. The multivariate measure demonstrated higher specificity than bivariate (pairwise) measure. The analysis can be applied to other neuroimaging and electrophysiological data. Nature Publishing Group UK 2018-11-05 /pmc/articles/PMC6218499/ /pubmed/30397245 http://dx.doi.org/10.1038/s41598-018-34672-5 Text en © The Author(s) 2018 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 Goelman, Gadi Dan, Rotem Keadan, Tarek Characterizing directed functional pathways in the visual system by multivariate nonlinear coherence of fMRI data |
title | Characterizing directed functional pathways in the visual system by multivariate nonlinear coherence of fMRI data |
title_full | Characterizing directed functional pathways in the visual system by multivariate nonlinear coherence of fMRI data |
title_fullStr | Characterizing directed functional pathways in the visual system by multivariate nonlinear coherence of fMRI data |
title_full_unstemmed | Characterizing directed functional pathways in the visual system by multivariate nonlinear coherence of fMRI data |
title_short | Characterizing directed functional pathways in the visual system by multivariate nonlinear coherence of fMRI data |
title_sort | characterizing directed functional pathways in the visual system by multivariate nonlinear coherence of fmri data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6218499/ https://www.ncbi.nlm.nih.gov/pubmed/30397245 http://dx.doi.org/10.1038/s41598-018-34672-5 |
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