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

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

Sacramental and spiritual use of hallucinogenic drugs

Arguably, the religious use of hallucinogenic drugs stems from a human search of metaphysical insight rather than from a direct need for cognitive, emotional, social, physical, or sexual improvement. Therefore, the sacramental and spiritual intake of hallucinogenic drugs goes so far beyond other biopsychosocial functions that it deserves its own category in the drug instrumentalization list

Toward the Understanding of Topographical and Spectral Signatures of Infant Movement Artifacts in Naturalistic EEG

Electroencephalography (EEG) is perhaps the most widely used brain-imaging technique for pediatric populations. However, EEG signals are prone to distortion by motion. Compared to adults, infants’ motion is both more frequent and less stereotypical yet motion effects on the infant EEG signal are largely undocumented. Here, we present a systematic assessment of naturalistic motion effects on the infant EEG signal. EEG recordings were performed with 14 infants (12 analyzed) who passively watched movies whilst spontaneously producing periods of bodily movement and rest. Each infant produced an average of 38.3 s (SD = 14.7 s) of rest and 18.8 s (SD = 17.9 s) of single motion segments for the final analysis. Five types of infant motions were analyzed: Jaw movements, and Limb movements of the Hand, Arm, Foot, and Leg. Significant movement-related distortions of the EEG signal were detected using cluster-based permutation analysis. This analysis revealed that, relative to resting state, infants’ Jaw and Arm movements produced significant increases in beta (∼15 Hz) power, particularly over peripheral sites. Jaw movements produced more anteriorly located effects than Arm movements, which were most pronounced over posterior parietal and occipital sites. The cluster analysis also revealed trends toward decreased power in the theta and alpha bands observed over central topographies for all motion types. However, given the very limited quantity of infant data in this study, caution is recommended in interpreting these findings before subsequent replications are conducted. Nonetheless, this work is an important first step to inform future development of methods for addressing EEG motion-related artifacts. This work also supports wider use of naturalistic paradigms in social and developmental neuroscience

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 EEG researchers of adult populations. 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

Measuring the temporal dynamics of inter-personal neural entrainment in continuous child-adult EEG hyperscanning data.

Current approaches to analysing EEG hyperscanning data in the developmental literature typically consider interpersonal entrainment between interacting physiological systems as a time-invariant property. This approach obscures crucial information about how entrainment between interacting systems is established and maintained over time. Here, we describe methods, and present computational algorithms, that will allow researchers to address this gap in the literature. We focus on how two different approaches to measuring entrainment, namely concurrent (e.g., power correlations, phase locking) and sequential (e.g., Granger causality) measures, can be applied to three aspects of the brain signal: amplitude, power, and phase. We guide the reader through worked examples using simulated data on how to leverage these methods to measure changes in interbrain entrainment. For each, we aim to provide a detailed explanation of the interpretation and application of these analyses when studying neural entrainment during early social interactions

Emotional valence modulates the topology of the parent-infant inter-brain network

Emotional communication between parents and children is crucial during early life, yet little is known about its neural underpinnings. Here, we adopt a dual connectivity approach to assess how positive and negative emotions modulate the interpersonal neural network between infants and their mothers during naturalistic interaction. Fifteen mothers were asked to model positive and negative emotions toward pairs of objects during social interaction with their infants (mean age 10.3 months) whilst the neural activity of both mothers and infants was concurrently measured using dual electroencephalogram (EEG). Intra-brain and inter-brain network connectivity in the 6–9 Hz range (i.e. infant Alpha band) during maternal expression of positive and negative emotions was computed using directed (partial directed coherence, PDC) and non-directed (phase-locking value, PLV) connectivity metrics. Graph theoretical measures were used to quantify differences in network topology as a function of emotional valence. We found that inter-brain network indices (Density, Strength and Divisibility) consistently revealed strong effects of emotional valence on the parent-child neural network. Parents and children showed stronger integration of their neural processes during maternal demonstrations of positive than negative emotions. Further, directed inter-brain metrics (PDC) indicated that mother to infant directional influences were stronger during the expression of positive than negative emotional states. These results suggest that the parent-infant inter-brain network is modulated by the emotional quality and tone of dyadic social interactions, and that inter-brain graph metrics may be successfully applied to examine these changes in parent-infant inter-brain network topology

Spectrally and temporally resolved estimation of neural signal diversity

Quantifying the complexity of neural activity has provided fundamental insights into cognition, consciousness, and clinical conditions. However, the most widely used approach to estimate the complexity of neural dynamics, Lempel-Ziv complexity (LZ), has fundamental limitations that substantially restrict its domain of applicability. In this article we leverage the information-theoretic foundations of LZ to overcome these limitations by introducing a complexity estimator based on state-space models — which we dub Complexity via State-space Entropy Rate (CSER). While having a performance equivalent to LZ in discriminating states of consciousness, CSER boasts two crucial advantages: 1) CSER offers a principled decomposition into spectral components, which allows us to rigorously investigate the relationship between complexity and spectral power; and 2) CSER provides a temporal resolution two orders of magnitude better than LZ, which allows complexity analyses of e.g. event-locked neural signals. As a proof of principle, we use MEG, EEG and ECoG datasets of humans and monkeys to show that CSER identifies the gamma band as the main driver of complexity changes across states of consciousness; and reveals early entropy increases that precede the standard ERP in an auditory mismatch negativity paradigm by approximately 20ms. Overall, by overcoming the main limitations of LZ and substantially extending its range of applicability, CSER opens the door to novel investigations on the fine-grained spectral and temporal structure of the signal complexity associated with cognitive processes and conscious states

The Existence of a Hypnotic State Revealed by Eye Movements

Hypnosis has had a long and controversial history in psychology, psychiatry and neurology, but the basic nature of hypnotic phenomena still remains unclear. Different theoretical approaches disagree as to whether or not hypnosis may involve an altered mental state. So far, a hypnotic state has never been convincingly demonstrated, if the criteria for the state are that it involves some objectively measurable and replicable behavioural or physiological phenomena that cannot be faked or simulated by non-hypnotized control subjects. We present a detailed case study of a highly hypnotizable subject who reliably shows a range of changes in both automatic and volitional eye movements when given a hypnotic induction. These changes correspond well with the phenomenon referred to as the “trance stare” in the hypnosis literature. Our results show that this ‘trance stare’ is associated with large and objective changes in the optokinetic reflex, the pupillary reflex and programming a saccade to a single target. Control subjects could not imitate these changes voluntarily. For the majority of people, hypnotic induction brings about states resembling normal focused attention or mental imagery. Our data nevertheless highlight that in some cases hypnosis may involve a special state, which qualitatively differs from the normal state of consciousness

COVIDiSTRESS Global Survey dataset on psychological and behavioural consequences of the COVID-19 outbreak

This N = 173,426 social science dataset was collected through the collaborative COVIDiSTRESS Global Survey – an open science effort to improve understanding of the human experiences of the 2020 COVID-19 pandemic between 30th March and 30th May, 2020. The dataset allows a cross-cultural study of psychological and behavioural responses to the Coronavirus pandemic and associated government measures like cancellation of public functions and stay at home orders implemented in many countries. The dataset contains demographic background variables as well as measures of Asian Disease Problem, perceived stress (PSS-10), availability of social provisions (SPS-10), trust in various authorities, trust in governmental measures to contain the virus (OECD trust), personality traits (BFF-15), information behaviours, agreement with the level of government intervention, and compliance with preventive measures, along with a rich pool of exploratory variables and written experiences. A global consortium from 39 countries and regions worked together to build and translate a survey with variables of shared interests, and recruited participants in 47 languages and dialects. Raw plus cleaned data and dynamic visualizations are available