Cargando…
A New Statistical Approach for fNIRS Hyperscanning to Predict Brain Activity of Preschoolers’ Using Teacher’s
Hyperscanning studies using functional Near-Infrared Spectroscopy (fNIRS) have been performed to understand the neural mechanisms underlying human-human interactions. In this study, we propose a novel methodological approach that is developed for fNIRS multi-brain analysis. Our method uses support v...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8137814/ https://www.ncbi.nlm.nih.gov/pubmed/34025373 http://dx.doi.org/10.3389/fnhum.2021.622146 |
_version_ | 1783695677220454400 |
---|---|
author | Barreto, Candida Bruneri, Guilherme de Albuquerque Brockington, Guilherme Ayaz, Hasan Sato, Joao Ricardo |
author_facet | Barreto, Candida Bruneri, Guilherme de Albuquerque Brockington, Guilherme Ayaz, Hasan Sato, Joao Ricardo |
author_sort | Barreto, Candida |
collection | PubMed |
description | Hyperscanning studies using functional Near-Infrared Spectroscopy (fNIRS) have been performed to understand the neural mechanisms underlying human-human interactions. In this study, we propose a novel methodological approach that is developed for fNIRS multi-brain analysis. Our method uses support vector regression (SVR) to predict one brain activity time series using another as the predictor. We applied the proposed methodology to explore the teacher-student interaction, which plays a critical role in the formal learning process. In an illustrative application, we collected fNIRS data of the teacher and preschoolers’ dyads performing an interaction task. The teacher explained to the child how to add two numbers in the context of a game. The Prefrontal cortex and temporal-parietal junction of both teacher and student were recorded. A multivariate regression model was built for each channel in each dyad, with the student’s signal as the response variable and the teacher’s ones as the predictors. We compared the predictions of SVR with the conventional ordinary least square (OLS) predictor. The results predicted by the SVR model were statistically significantly correlated with the actual test data at least one channel-pair for all dyads. Overall, 29/90 channel-pairs across the five dyads (18 channels 5 dyads = 90 channel-pairs) presented significant signal predictions withthe SVR approach. The conventional OLS resulted in only 4 out of 90 valid predictions. These results demonstrated that the SVR could be used to perform channel-wise predictions across individuals, and the teachers’ cortical activity can be used to predict the student brain hemodynamic response. |
format | Online Article Text |
id | pubmed-8137814 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-81378142021-05-22 A New Statistical Approach for fNIRS Hyperscanning to Predict Brain Activity of Preschoolers’ Using Teacher’s Barreto, Candida Bruneri, Guilherme de Albuquerque Brockington, Guilherme Ayaz, Hasan Sato, Joao Ricardo Front Hum Neurosci Human Neuroscience Hyperscanning studies using functional Near-Infrared Spectroscopy (fNIRS) have been performed to understand the neural mechanisms underlying human-human interactions. In this study, we propose a novel methodological approach that is developed for fNIRS multi-brain analysis. Our method uses support vector regression (SVR) to predict one brain activity time series using another as the predictor. We applied the proposed methodology to explore the teacher-student interaction, which plays a critical role in the formal learning process. In an illustrative application, we collected fNIRS data of the teacher and preschoolers’ dyads performing an interaction task. The teacher explained to the child how to add two numbers in the context of a game. The Prefrontal cortex and temporal-parietal junction of both teacher and student were recorded. A multivariate regression model was built for each channel in each dyad, with the student’s signal as the response variable and the teacher’s ones as the predictors. We compared the predictions of SVR with the conventional ordinary least square (OLS) predictor. The results predicted by the SVR model were statistically significantly correlated with the actual test data at least one channel-pair for all dyads. Overall, 29/90 channel-pairs across the five dyads (18 channels 5 dyads = 90 channel-pairs) presented significant signal predictions withthe SVR approach. The conventional OLS resulted in only 4 out of 90 valid predictions. These results demonstrated that the SVR could be used to perform channel-wise predictions across individuals, and the teachers’ cortical activity can be used to predict the student brain hemodynamic response. Frontiers Media S.A. 2021-05-07 /pmc/articles/PMC8137814/ /pubmed/34025373 http://dx.doi.org/10.3389/fnhum.2021.622146 Text en Copyright © 2021 Barreto, Bruneri, Brockington, Ayaz 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 | Human Neuroscience Barreto, Candida Bruneri, Guilherme de Albuquerque Brockington, Guilherme Ayaz, Hasan Sato, Joao Ricardo A New Statistical Approach for fNIRS Hyperscanning to Predict Brain Activity of Preschoolers’ Using Teacher’s |
title | A New Statistical Approach for fNIRS Hyperscanning to Predict Brain Activity of Preschoolers’ Using Teacher’s |
title_full | A New Statistical Approach for fNIRS Hyperscanning to Predict Brain Activity of Preschoolers’ Using Teacher’s |
title_fullStr | A New Statistical Approach for fNIRS Hyperscanning to Predict Brain Activity of Preschoolers’ Using Teacher’s |
title_full_unstemmed | A New Statistical Approach for fNIRS Hyperscanning to Predict Brain Activity of Preschoolers’ Using Teacher’s |
title_short | A New Statistical Approach for fNIRS Hyperscanning to Predict Brain Activity of Preschoolers’ Using Teacher’s |
title_sort | new statistical approach for fnirs hyperscanning to predict brain activity of preschoolers’ using teacher’s |
topic | Human Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8137814/ https://www.ncbi.nlm.nih.gov/pubmed/34025373 http://dx.doi.org/10.3389/fnhum.2021.622146 |
work_keys_str_mv | AT barretocandida anewstatisticalapproachforfnirshyperscanningtopredictbrainactivityofpreschoolersusingteachers AT bruneriguilhermedealbuquerque anewstatisticalapproachforfnirshyperscanningtopredictbrainactivityofpreschoolersusingteachers AT brockingtonguilherme anewstatisticalapproachforfnirshyperscanningtopredictbrainactivityofpreschoolersusingteachers AT ayazhasan anewstatisticalapproachforfnirshyperscanningtopredictbrainactivityofpreschoolersusingteachers AT satojoaoricardo anewstatisticalapproachforfnirshyperscanningtopredictbrainactivityofpreschoolersusingteachers AT barretocandida newstatisticalapproachforfnirshyperscanningtopredictbrainactivityofpreschoolersusingteachers AT bruneriguilhermedealbuquerque newstatisticalapproachforfnirshyperscanningtopredictbrainactivityofpreschoolersusingteachers AT brockingtonguilherme newstatisticalapproachforfnirshyperscanningtopredictbrainactivityofpreschoolersusingteachers AT ayazhasan newstatisticalapproachforfnirshyperscanningtopredictbrainactivityofpreschoolersusingteachers AT satojoaoricardo newstatisticalapproachforfnirshyperscanningtopredictbrainactivityofpreschoolersusingteachers |