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A Guide to Parent-Child fNIRS Hyperscanning Data Processing and Analysis
The use of functional near-infrared spectroscopy (fNIRS) hyperscanning during naturalistic interactions in parent–child dyads has substantially advanced our understanding of the neurobiological underpinnings of human social interaction. However, despite the rise of developmental hyperscanning studie...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8231828/ https://www.ncbi.nlm.nih.gov/pubmed/34199222 http://dx.doi.org/10.3390/s21124075 |
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author | Nguyen, Trinh Hoehl, Stefanie Vrtička, Pascal |
author_facet | Nguyen, Trinh Hoehl, Stefanie Vrtička, Pascal |
author_sort | Nguyen, Trinh |
collection | PubMed |
description | The use of functional near-infrared spectroscopy (fNIRS) hyperscanning during naturalistic interactions in parent–child dyads has substantially advanced our understanding of the neurobiological underpinnings of human social interaction. However, despite the rise of developmental hyperscanning studies over the last years, analysis procedures have not yet been standardized and are often individually developed by each research team. This article offers a guide on parent–child fNIRS hyperscanning data analysis in MATLAB and R. We provide an example dataset of 20 dyads assessed during a cooperative versus individual problem-solving task, with brain signal acquired using 16 channels located over bilateral frontal and temporo-parietal areas. We use MATLAB toolboxes Homer2 and SPM for fNIRS to preprocess the acquired brain signal data and suggest a standardized procedure. Next, we calculate interpersonal neural synchrony between dyads using Wavelet Transform Coherence (WTC) and illustrate how to run a random pair analysis to control for spurious correlations in the signal. We then use RStudio to estimate Generalized Linear Mixed Models (GLMM) to account for the bounded distribution of coherence values for interpersonal neural synchrony analyses. With this guide, we hope to offer advice for future parent–child fNIRS hyperscanning investigations and to enhance replicability within the field. |
format | Online Article Text |
id | pubmed-8231828 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-82318282021-06-26 A Guide to Parent-Child fNIRS Hyperscanning Data Processing and Analysis Nguyen, Trinh Hoehl, Stefanie Vrtička, Pascal Sensors (Basel) Article The use of functional near-infrared spectroscopy (fNIRS) hyperscanning during naturalistic interactions in parent–child dyads has substantially advanced our understanding of the neurobiological underpinnings of human social interaction. However, despite the rise of developmental hyperscanning studies over the last years, analysis procedures have not yet been standardized and are often individually developed by each research team. This article offers a guide on parent–child fNIRS hyperscanning data analysis in MATLAB and R. We provide an example dataset of 20 dyads assessed during a cooperative versus individual problem-solving task, with brain signal acquired using 16 channels located over bilateral frontal and temporo-parietal areas. We use MATLAB toolboxes Homer2 and SPM for fNIRS to preprocess the acquired brain signal data and suggest a standardized procedure. Next, we calculate interpersonal neural synchrony between dyads using Wavelet Transform Coherence (WTC) and illustrate how to run a random pair analysis to control for spurious correlations in the signal. We then use RStudio to estimate Generalized Linear Mixed Models (GLMM) to account for the bounded distribution of coherence values for interpersonal neural synchrony analyses. With this guide, we hope to offer advice for future parent–child fNIRS hyperscanning investigations and to enhance replicability within the field. MDPI 2021-06-13 /pmc/articles/PMC8231828/ /pubmed/34199222 http://dx.doi.org/10.3390/s21124075 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Nguyen, Trinh Hoehl, Stefanie Vrtička, Pascal A Guide to Parent-Child fNIRS Hyperscanning Data Processing and Analysis |
title | A Guide to Parent-Child fNIRS Hyperscanning Data Processing and Analysis |
title_full | A Guide to Parent-Child fNIRS Hyperscanning Data Processing and Analysis |
title_fullStr | A Guide to Parent-Child fNIRS Hyperscanning Data Processing and Analysis |
title_full_unstemmed | A Guide to Parent-Child fNIRS Hyperscanning Data Processing and Analysis |
title_short | A Guide to Parent-Child fNIRS Hyperscanning Data Processing and Analysis |
title_sort | guide to parent-child fnirs hyperscanning data processing and analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8231828/ https://www.ncbi.nlm.nih.gov/pubmed/34199222 http://dx.doi.org/10.3390/s21124075 |
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