Biology subjects

Whitehorn, M.

Publications and source records attributed to Whitehorn, M..

3 recordsLinked to original sources

bioRxiv2022

Gaze onsets during naturalistic infant-caregiver interaction associate with 'sender' but not 'receiver' neural responses, and do not lead to changes in inter-brain synchrony.

Temporal coordination during infant-caregiver social interaction is thought to be crucial for supporting early language acquisition and cognitive development. Despite a growing prevalence of theories suggesting that increased inter-brain synchrony associates with many key aspects of social interactions such as mutual gaze, little is known about how this arises during development. Here, we investigated the role of mutual gaze onsets as a potential driver of inter-brain synchrony. We extracted dual EEG activity around naturally occurring gaze onsets during infant-caregiver social interactions in N=55 dyads (mean age 12 months). We differentiated between two types of gaze onset, depending on each partners role. Sender gaze onsets were defined at a time when either the adult or the infant made a gaze shift towards their partner at a time when their partner was either already looking at them (mutual) or not looking at them (non-mutual). Receiver gaze onsets were defined at a time when their partner made a gaze shift towards them at a time when either the adult or the infant was already looking at their partner (mutual) or not (non-mutual). Contrary to our hypothesis we found that, during a naturalistic interaction, both mutual and non-mutual gaze onsets were associated with changes in the sender, but not the receivers brain activity and were not associated with increases in inter-brain synchrony above baseline. Further, we found that mutual, compared to non-mutual gaze onsets were not associated with increased inter brain synchrony. Overall, our results suggest that the effects of mutual gaze are strongest at the intra-brain level, in the sender but not the receiver of the mutual gaze.

neuroscience↗

bioRxiv2021

Using dual EEG to analyse event-locked changes in child-adult neural connectivity

Current approaches typically measure the connectivity between interacting physiological systems as a time-invariant property. This approach obscures crucial information about how connectivity between interacting systems is established and maintained. Here, we describe methods, and present computational algorithms, that will allow researchers to address this deficit. We focus on how two different approaches to measuring connectivity, namely concurrent (e.g., power correlations, phase locking) and sequential (e.g., Granger causality), can be applied to three aspects of the brain signal, namely amplitude, power, and phase. We guide the reader through worked examples using mainly simulated data on how to leverage these methods to measure changes in interbrain connectivity between adults and children/infants relative to events identified within continuous EEG data during a free-flowing naturalistic interaction. For each, we aim to provide a detailed explanation of the interpretation of the analysis and how they can be usefully used when studying early social interactions.

neuroscience↗

bioRxiv2021

Automatic classification of ICA components from infant EEG using MARA

Automated systems for identifying and removing non-neural ICA components are growing in popularity among adult EEG researchers. Infant EEG data differs in many ways from adult EEG data, but there exists almost no specific system for automated classification of source components from paediatric populations. Here, we adapt one of the most popular systems for adult ICA component classification for use with infant EEG data. Our adapted classifier significantly outperformed the original adult classifier on samples of naturalistic free play EEG data recorded from 10 to 12-month-old infants, achieving agreement rates with the manual classification of over 75% across two validation studies (n=44, n=25). Additionally, we examined both classifiers ability to remove stereotyped ocular artifact from a basic visual processing ERP dataset, compared to manual ICA data cleaning. Here the new classifier performed on level with expert manual cleaning and was again significantly better than the adult classifier at removing artifact whilst retaining a greater amount of genuine neural signal, operationalised through comparing ERP activations in time and space. Our new system (iMARA) offers developmental EEG researchers a flexible tool for automatic identification and removal of artifactual ICA components.

neuroscience↗