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Applications of graph theory to the analysis of fNIRS data in hyperscanning paradigms
Hyperscanning is a promising tool for investigating the neurobiological underpinning of social interactions and affective bonds. Recently, graph theory measures, such as modularity, have been proposed for estimating the global synchronization between brains. This paper proposes the bootstrap modular...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9521601/ https://www.ncbi.nlm.nih.gov/pubmed/36185711 http://dx.doi.org/10.3389/fncom.2022.975743 |
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author | Oku, Amanda Yumi Ambriola Barreto, Candida Bruneri, Guilherme Brockington, Guilherme Fujita, Andre Sato, João Ricardo |
author_facet | Oku, Amanda Yumi Ambriola Barreto, Candida Bruneri, Guilherme Brockington, Guilherme Fujita, Andre Sato, João Ricardo |
author_sort | Oku, Amanda Yumi Ambriola |
collection | PubMed |
description | Hyperscanning is a promising tool for investigating the neurobiological underpinning of social interactions and affective bonds. Recently, graph theory measures, such as modularity, have been proposed for estimating the global synchronization between brains. This paper proposes the bootstrap modularity test as a way of determining whether a pair of brains is coactivated. This test is illustrated as a screening tool in an application to fNIRS data collected from the prefrontal cortex and temporoparietal junction of five dyads composed of a teacher and a preschooler while performing an interaction task. In this application, graph hub centrality measures identify that the dyad's synchronization is critically explained by the relation between teacher's language and number processing and the child's phonological processing. The analysis of these metrics may provide further insights into the neurobiological underpinnings of interaction, such as in educational contexts. |
format | Online Article Text |
id | pubmed-9521601 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95216012022-09-30 Applications of graph theory to the analysis of fNIRS data in hyperscanning paradigms Oku, Amanda Yumi Ambriola Barreto, Candida Bruneri, Guilherme Brockington, Guilherme Fujita, Andre Sato, João Ricardo Front Comput Neurosci Neuroscience Hyperscanning is a promising tool for investigating the neurobiological underpinning of social interactions and affective bonds. Recently, graph theory measures, such as modularity, have been proposed for estimating the global synchronization between brains. This paper proposes the bootstrap modularity test as a way of determining whether a pair of brains is coactivated. This test is illustrated as a screening tool in an application to fNIRS data collected from the prefrontal cortex and temporoparietal junction of five dyads composed of a teacher and a preschooler while performing an interaction task. In this application, graph hub centrality measures identify that the dyad's synchronization is critically explained by the relation between teacher's language and number processing and the child's phonological processing. The analysis of these metrics may provide further insights into the neurobiological underpinnings of interaction, such as in educational contexts. Frontiers Media S.A. 2022-09-14 /pmc/articles/PMC9521601/ /pubmed/36185711 http://dx.doi.org/10.3389/fncom.2022.975743 Text en Copyright © 2022 Oku, Barreto, Bruneri, Brockington, Fujita and Sato. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Oku, Amanda Yumi Ambriola Barreto, Candida Bruneri, Guilherme Brockington, Guilherme Fujita, Andre Sato, João Ricardo Applications of graph theory to the analysis of fNIRS data in hyperscanning paradigms |
title | Applications of graph theory to the analysis of fNIRS data in hyperscanning paradigms |
title_full | Applications of graph theory to the analysis of fNIRS data in hyperscanning paradigms |
title_fullStr | Applications of graph theory to the analysis of fNIRS data in hyperscanning paradigms |
title_full_unstemmed | Applications of graph theory to the analysis of fNIRS data in hyperscanning paradigms |
title_short | Applications of graph theory to the analysis of fNIRS data in hyperscanning paradigms |
title_sort | applications of graph theory to the analysis of fnirs data in hyperscanning paradigms |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9521601/ https://www.ncbi.nlm.nih.gov/pubmed/36185711 http://dx.doi.org/10.3389/fncom.2022.975743 |
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