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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...

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Autores principales: Oku, Amanda Yumi Ambriola, Barreto, Candida, Bruneri, Guilherme, Brockington, Guilherme, Fujita, Andre, Sato, João Ricardo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
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.
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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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