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  A guide to parent-child fNIRS hyperscanning data processing and analysis

Nguyen, T., Hoehl, S., & Vrticka, P. (2021). A guide to parent-child fNIRS hyperscanning data processing and analysis. Sensors, 21(12): 4075. doi:10.3390/s21124075.

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 Creators:
Nguyen, Trinh1, Author
Hoehl, Stefanie1, 2, Author           
Vrticka, Pascal3, 4, Author           
Affiliations:
1Department of Developmental and Educational Psychology, Faculty of Psychology, University Vienna, Austria, ou_persistent22              
2Max Planck Research Group Early Social Cognition, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_2355694              
3Research Group Social Stress and Family Health, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_3025667              
4Department of Psychology, University of Essex, United Kingdom, ou_persistent22              

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Free keywords: fNIRS; Hyperscanning; Synchrony
 Abstract: 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.

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Language(s): eng - English
 Dates: 2021-06-13
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.3390/s21124075
PMID: 34199222
PMC: PMC8231828
 Degree: -

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Title: Sensors
Source Genre: Journal
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Publ. Info: MDPI
Pages: - Volume / Issue: 21 (12) Sequence Number: 4075 Start / End Page: - Identifier: ISSN: 1424-8220
CoNE: https://pure.mpg.de/cone/journals/resource/1424-8220