A Guide to Parent-Child fNIRS Hyperscanning Data Processing and Analysis.

Trinh Nguyen,Pascal Vrtička,Stefanie Hoehl

doi:10.3390/s21124075

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.

Highlights

Children begin to understand themselves and their social surroundings by engaging in embodied social interactions with others
The correlational evidence was recently extended by work using multi-brain stimulation to increase interpersonal neural synchrony and subsequent behavioral coordination [15]. These findings provide a causal framework for the social effects of interpersonal neural synchrony
Wavelet Transform Coherence (WTC) is entered as the response variable

Summary

Introduction

Children begin to understand themselves and their social surroundings by engaging in embodied social interactions with others As this process is interpersonal by definition, it should ideally be studied by simultaneously obtaining data from all interaction partners. Such a second-person social neuroscience approach has only gained momentum in developmental research in recent years [1]. Methodological challenges made it difficult to obtain neural measures from two (or more) individuals at the same time during naturalistic, live interactions [2]. We are able to investigate the neurobiological underpinnings of socio-cognitive and affective processes underlying these early social interactions and, thereby, deepening our understanding of child development from a second-person social neuroscience perspective

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