14 challenges and their solutions for conducting social neuroscience and longitudinal EEG research with infants

Abstract

The use of electroencephalography (EEG) to study infant brain development is a growing trend. In addition to classical longitudinal designs that study the development of the neural, cognitive and behavioural function, new areas of EEG application are emerging, such as novel social neuroscience paradigms using dual infant-adult EEG recordings. However, most of the experimental designs, analysis methods, as well as EEG hardware were originally developed for single-person adult research. When applied to the study of infant development, adult-based solutions often pose unique problems that may go unrecognised. Here, we identify 14 challenges that infant EEG researchers may encounter when designing new experiments, collecting data, and conducting data analysis. Challenges related to the experimental design are: (1) small sample size and data attrition, and (2) varying arousal in younger infants. Challenges related to data acquisition are: (3) determining the optimal location for reference and ground electrodes, (4) control of impedance when testing with the high-density sponge electrode nets, (5) poor fit of standard EEG caps to the varying infant head shapes, and (6) ensuring a high degree of temporal synchronisation between amplifiers and recording devices during dual-EEG acquisition. Challenges related to the analysis of longitudinal and social neuroscience datasets are: (7) developmental changes in head anatomy, (8) prevalence and diversity of infant myogenic artefacts, (9) a lack of stereotypical topography of eye movements needed for the ICA-based data cleaning, (10) and relatively high inter-individual variability of EEG responses in younger cohorts. Additional challenges for the analysis of dual EEG data are: (11) developmental shifts in canonical EEG rhythms and difficulties in differentiating true inter-personal synchrony from spurious synchrony due to (12) common intrinsic properties of the signal and (13) shared external perturbation. Finally, (14) there is a lack of test-retest reliability studies of infant EEG. We describe each of these challenges and suggest possible solutions. While we focus specifically on the social neuroscience and longitudinal research, many of the issues we raise are relevant for all fields of infant EEG research.

Longitudinal changes in aperiodic and periodic activity in electrophysiological recordings in the first seven months of life

“…These components were then projected out (mean and standard deviation of number of rejected components: 1.15±1.13). In general, because the separation between noise and signal ICA components in infant EEG data is less distinct than in adults (Noreika et al, 2020), we rejected components conservatively. The data was re-referenced to a common average reference.…”

Limiting data loss in infant EEG: putting hunches to the test

“…With the advance of neuroimaging techniques, the last decades saw an additional increase in studies relying on infant EEG ( Azhari et al, 2020 ). However, measuring EEG in infants is not a straightforward task, and comes with its own challenges, often resulting in high attrition rates ( Noreika et al, 2020 ; Stets et al, 2012 ). Fortunately, there is literature focusing on explaining and improving the methodology (cf.…”

Towards a Personalized Multi-Domain Digital Neurophenotyping Model for the Detection and Treatment of Mood Trajectories

“…Furthermore, in the fast-advancing field of social neuroscience, dual-EEG (concurrent neural recording from two interacting individuals) is increasingly being used in naturalistic settings to track dynamic changes in interpersonal neural coupling, even between infants and adult caregivers [ 39 , 40 , 41 ]. Although there are unique technical challenges associated with the collection and interpretation of naturalistic dual-EEG data, particularly with infant participants [ 42 ], the mother–infant interpersonal neural network has been found to be exquisitely sensitive to changes in maternal emotional state [ 43 ] or parenting stress [ 44 ], and also predicts the likelihood of infant social learning from their parent [ 41 ]. Dual-EEG studies with adults have demonstrated that interpersonal neural coupling may also index empathy, differentiate the emotional tone of the conversation [ 45 ], and signify the degree of cooperation between interacting members of a dyad [ 46 , 47 ].…”