Maternal chemosignals enhance infant-adult brain-to-brain synchrony

Abstract

Maternal chemosignals increase infant-adult interbrain synchrony, suggesting their role in social brain maturation.

Brains in Sync: Practical Guideline for Parent–Infant EEG During Natural Interaction

“…In short, a general parent–infant EEG preprocessing steps includes the following steps: (1) visually inspection of the raw data and removal of flat electrodes, (2) re-referencing the data (optional; see discussion on reference electrodes in Section “Parent–Infant EEG Equipment”), (3) filtering the data (e.g., 1–30 Hz bandpass and a 50/60 Hz notch filter), (4) interpolation or removal of spurious electrodes, (5) manual artifact rejection of high motion-contaminated segments according to the video (see for example, Leong et al, 2017 ; Wass et al, 2018 ) or visual assessment of the EEG signal by an experienced researcher, (6) data-driven algorithms for the detection of motion based on independent component analysis (ICA) or wavelet analysis, (7) manual or (semi)automatic artifact rejection to further exclude segments where the amplitude of infants’ or adults’ EEG exceeded a certain voltage (e.g., +100 μV), (8) segmentation into 2 s overlapping [epochs (1 s overlap) or 1 s epochs with 500 ms overlap ( Endevelt-Shapira et al, 2021 )]. Down sampling can help with reducing computational power needed to perform heavier preprocessing steps, such as ICA.…”

“…For example, visual assessment and removal of data significantly improves data quality, but it is time consuming and inter-observer variability might introduce subjective, biased variation in the data. Beside visual assessment of the EEG signal, applying an automatic algorithm to detect noisy segments might be an option for dual-EEG processing as well, such as MNE’s “AutoReject” with Bayesian optimization as the threshold method ( Djalovski et al, 2021 ; Endevelt-Shapira et al, 2021 ). Autoreject is an automatic data-driven algorithm for detection and repair of bad segments, using optimal peak-to-peak rejection thresholds subject-wise ( Jas et al, 2017 ).…”

“…It is recommended to add ICA as an additional preprocessing step, specifically to correct for stereotypical movement artifacts and to limit data loss as a consequence of strict rejection procedures. For example, semi-automatic ICA, such as MNE’s implementation of FastICA and CORRMAP ( Viola et al, 2009 ), seems to work well with oculomotor artifacts in parent–infant EEG ( Endevelt-Shapira et al, 2021 ). The CORRMAP algorithms are based on the idea that stereotypical artifacts are generally similar over large number of participants from the same sample and age (e.g., parents or infants).…”

“…In developmental research, hyperscanning comprises the simultaneous measurement of brain activity in parent/adult and child using different neuroimaging methods, including dual-EEG ( Atzaba-Poria et al, 2017 ; Leong et al, 2017 ; Krzeczkowski et al, 2020 ; Endevelt-Shapira et al, 2021 ; preprint Wass et al, 2018 ; Leong et al, 2019 ; Perone et al, 2020 ; Santamaria et al, 2020 ), dual-fNIRS ( Reindl et al, 2018 ; Azhari et al, 2019 ; Miller et al, 2019 ; Nguyen et al, 2020 , a , b , c ; Piazza et al, 2020 ; Quinones-Camacho et al, 2020 ; Wang et al, 2020 ) and dual-MEG ( Hirata et al, 2014 ; Hasegawa et al, 2016 ) or sequential-MEG ( Levy et al, 2017 , 2021 ). While all these studies clearly demonstrate that social interactions are marked by the coordination of two brains, so called brain-to-brain synchrony , the empirical methods used in these studies show great variability ( Czeszumski et al, 2020 ).…”

“…Here, we will focus on parent–infant EEG as it enables the exploration of synchrony at the level of brain rhythms and importantly because it is a well-adapted infant-friendly method to tackle brain-to-brain synchrony at the speed of fast changing social interaction in a natural setting. Parent–infant EEG studies provided accumulating evidence for the coupling of neural activity and attunement of fast-changing social signals such as attention ( Wass et al, 2018 ), mutual gaze ( Leong et al, 2017 ; Endevelt-Shapira et al, 2021 ), negative behavior ( Atzaba-Poria et al, 2017 ), and emotions ( Krzeczkowski et al, 2020 ; Perone et al, 2020 ; Santamaria et al, 2020 ) between parent and infant. These fascinating initial findings underscore the multitude of possibilities to investigate parent–infant synchrony on a whole new multimodal and interpersonal level (for review see, Markova et al, 2019 ; Wass et al, 2020 ; Turk et al, 2022 ), making it possible to explore new neurobiological aspects of natural interaction and to examine differences in clinical cohorts ( Leong and Schilbach, 2019 ).…”