Infant EEG Research Articles

Adaptive threshold algorithm for detecting EEG-interburst intervals in extremely preterm neonates.

Objective. This study provides an adaptive threshold algorithm for burst detection in electroencephalograms (EEG) of preterm infantes and evaluates its performance using clinical real-world EEG data.Approach. We developed an adaptive threshold algorithm for burst detection in EEG recordings from preterm infants. To assess its applicability in the real-world, we tested the algorithm on a dataset of 30 clinical EEG recordings which were not preselected for good quality, to ensure a real-world scenario.Main results. Interrater agreement was substantial at a kappa of 0.73 (0.68-0.79 inter-quantile range). The performance of the algorithm showed a similar agreement with one clinical expert of 0.73 (0.67-0.76) and a sensitivity and specificity of 0.90 (0.82-0.94) and 0.95 (0.93-0.97), respectively.Significance. The adaptive threshold algorithm demonstrated robust performance in detecting burst patterns in clinical EEG data from preterm infants, highlighting its practical utility. The fine-tuned algorithm achieved similar performance to human raters. The algorithm proves to be a valuable tool for automated burst detection in the EEG of preterm infants.

A machine learning artefact detection method for single-channel infant event-related potential studies

Objective. Automated detection of artefact in stimulus-evoked electroencephalographic (EEG) data recorded in neonates will improve the reproducibility and speed of analysis in clinical research compared with manual identification of artefact. Some studies use very short, single-channel epochs of EEG data with little recorded EEG per infant—for example because the clinical vulnerability of the infants limits access for recording. Current artefact-detection methods that perform well on adult data and resting-state and multi-channel data in infants are not suitable for this application. The aim of this study was to create and test an automated method of detecting artefact in single-channel 1500 ms epochs of infant EEG. Approach. A total of 410 epochs of EEG were used, collected from 160 infants of 28–43 weeks postmenstrual age. This dataset—which was balanced to include epochs of background activity and responses to visual, auditory, tactile and noxious stimuli—was presented to seven independent raters, who independently labelled the epochs according to whether or not they were able to visually identify artefacts. The data was split into a training set (340 epochs) and an independent test set (70 epochs). A random forest model was trained to identify epochs as either artefact or not artefact. Main results. This model performs well, achieving a balanced accuracy of 0.81, which is as good as manual review of data. Accuracy was not significantly related to the infant age or type of stimulus. Significance. This method provides an objective tool for automated artefact rejection for short epoch, single-channel EEG in neonates and could increase the utility of EEG in neonates in both the clinical and research setting.

Amplitude-Integrated EEG in Infants at Risk of Hypoxic-Ischemic Encephalopathy: A Feasibility Study in Road and Air Transport in Western Australia.

Infants at risk of HIE require early identification and initiation of therapeutic hypothermia (TH). Earlier treatment with TH is associated with better outcomes. aEEG is frequently used when making the decision whether to commence TH. As this is often limited to tertiary centers, TH may be delayed if the infant requires transport to a center that provides it. We aimed to provide a method for the application of amplitude-integrated electroencephalogram (aEEG) and to determine the feasibility of acquiring clinically meaningful information during transport. All infants ≥35 weeks, at risk of HIE at referral, were eligible for inclusion. Scalp electrodes were placed in the C3-C4; P3-P4 position on the infant's scalp and connected to the aEEG amplifier. The aEEG amplifier was, in turn, connected to a clinical tablet computer with EEG software to collect and analyze aEEG information. Recordings were reviewed by the chief principal investigator and two independent reviewers (blinded) for background trace and artifact. Predefined criteria for data quality were set to movement artifacts and software impedance notifications. Surveys were completed by healthcare staff and parents for acceptability and ease of use.

Prenatal family income, but not parental education, is associated with resting brain activity in 1-month-old infants

Childhood socioeconomic disadvantage is associated with disparities in development and health, possibly through adaptations in children’s brain function. However, it is not clear how early in development such neural adaptations might emerge. This study examined whether prenatal family socioeconomic status, operationalized as family income and average years of parental education, prospectively predicts individual differences in infant resting electroencephalography (EEG; theta, alpha, beta, and gamma power) at approximately 1 month of age (N = 160). Infants of mothers reporting lower family income showed more lower-frequency (theta) and less higher-frequency (beta and gamma) power. These associations held when adjusting for other prenatal and postnatal experiences, as well as infant demographic and health-related factors. In contrast, parental education was not significantly associated with infant EEG power in any frequency band. These data suggest that lower prenatal family income is associated with developmental differences in brain function that are detectable within the first month of life.

Microstate Analysis of Continuous Infant EEG: Tutorial and Reliability

Microstate analysis of resting-state EEG is a unique data-driven method for identifying patterns of scalp potential topographies, or microstates, that reflect stable but transient periods of synchronized neural activity evolving dynamically over time. During infancy – a critical period of rapid brain development and plasticity – microstate analysis offers a unique opportunity for characterizing the spatial and temporal dynamics of brain activity. However, whether measurements derived from this approach (e.g., temporal properties, transition probabilities, neural sources) show strong psychometric properties (i.e., reliability) during infancy is unknown and key information for advancing our understanding of how microstates are shaped by early life experiences and whether they relate to individual differences in infant abilities. A lack of methodological resources for performing microstate analysis of infant EEG has further hindered adoption of this cutting-edge approach by infant researchers. As a result, in the current study, we systematically addressed these knowledge gaps and report that most microstate-based measurements of brain organization and functioning except for transition probabilities were stable with four minutes of video-watching resting-state data and highly internally consistent with just one minute. In addition to these results, we provide a step-by-step tutorial, accompanying website, and open-access data for performing microstate analysis using a free, user-friendly software called Cartool. Taken together, the current study supports the reliability and feasibility of using EEG microstate analysis to study infant brain development and increases the accessibility of this approach for the field of developmental neuroscience.

Coregistration of EEG and eye-tracking in infants and developing populations.

Infants cannot be instructed where to look; therefore, infant researchers rely on observation of their participant's gaze to make inferences about their cognitive processes. They therefore started studying infant attention in the real world from early on. Developmental researchers were early adopters of methods combining observations of gaze and behaviour with electroencephalography (EEG) to study attention and other cognitive functions. However, the direct combination of eye-tracking methods and EEG to test infants is still rare, as it includes specific challenges. The current article reviews the development of co-registration research in infancy. It points out specific challenges of co-registration in infant research and suggests ways to overcome them. It ends with recommendations for implementing the co-registration of EEG and eye-tracking in infant research to maximise the benefits of the two measures and their combination and to orient on Open Science principles while doing so. In summary, this work shows that the co-registration of EEG and eye-tracking in infant research can be beneficial to studying natural and real-world behaviour despite its challenges.

EEG-based spatiotemporal dynamics of fast ripple networks and hubs in infantile epileptic spasms.

Infantile epileptic spasms (IS) are epileptic seizures that are associated with increased risk for developmental impairments, adult epilepsies, and mortality. Here, we investigated coherence-based network dynamics in scalp EEG of infants with IS to identify frequency-dependent networks associated with spasms. We hypothesized that there is a network of increased fast ripple connectivity during the electrographic onset of clinical spasms, which is distinct from controls. We retrospectively analyzed peri-ictal and interictal EEG recordings of 14 IS patients. The data was compared with 9 age-matched controls. Wavelet phase coherence (WPC) was computed between 0.2 and 400 Hz. Frequency- and time-dependent brain networks were constructed using this coherence as the strength of connection between two EEG channels, based on graph theory principles. Connectivity was evaluated through global efficiency (GE) and channel-based closeness centrality (CC), over frequency and time. GE in the fast ripple band (251-400 Hz) was significantly greater following the onset of spasms in all patients (P < 0.05). Fast ripple networks during the first 10s from spasm onset show enhanced anteroposterior gradient in connectivity (posterior > central > anterior, Kruskal-Wallis P < 0.001), with maximum CC over the centroparietal channels in 10/14 patients. Additionally, this anteroposterior gradient in CC connectivity is observed during spasms but not during the interictal awake or asleep states of infants with IS. In controls, anteroposterior gradient in fast ripple CC was noted during arousals and wakefulness but not during sleep. There was also a simultaneous decrease in GE in the 5-8 Hz range after the onset of spasms (P < 0.05), of unclear biological significance. We identified an anteroposterior gradient in the CC connectivity of fast ripple hubs during spasms. This anteroposterior gradient observed during spasms is similar to the anteroposterior gradient in the CC connectivity observed in wakefulness or arousals in controls, suggesting that this state change is related to arousal networks.

Sing to me, baby: Infants show neural tracking and rhythmic movements to live and dynamic maternal singing

Infant-directed singing has unique acoustic characteristics that may allow even very young infants to respond to the rhythms carried through the caregiver’s voice. The goal of this study was to examine neural and movement responses to live and dynamic maternal singing in 7-month-old infants and their relation to linguistic development. In total, 60 mother-infant dyads were observed during two singing conditions (playsong and lullaby). In Study 1 (n = 30), we measured infant EEG and used an encoding approach utilizing ridge regressions to measure neural tracking. In Study 2 (n =40), we coded infant rhythmic movements. In both studies, we assessed children’s vocabulary when they were 20 months old. In Study 1, we found above-threshold neural tracking of maternal singing, with superior tracking of lullabies than playsongs. We also found that the acoustic features of infant-directed singing modulated tracking. In Study 2, infants showed more rhythmic movement to playsongs than lullabies. Importantly, neural coordination (Study 1) and rhythmic movement (Study 2) to playsongs were positively related to infants’ expressive vocabulary at 20 months. These results highlight the importance of infants’ brain and movement coordination to their caregiver’s musical presentations, potentially as a function of musical variability.

Behavioral and neural measures of infant responsivity increase with maternal multisensory input in non-irritable infants.

Parents often use sensory stimulation during early-life interactions with infants. These interactions, including gazing, rocking, or singing, scaffold child development. Previous studies have examined infant neural processing during highly controlled sensory stimulus presentation paradigms. In this study, we investigated infant behavioral and neural responsiveness during a mother-child social interaction during which the mother provided infant stimulation with a progressive increase in the number of sensory modalities. We prospectively collected and analyzed video-coded behavioral interactions and electroencephalogram (EEG) frontal asymmetry (FAS) from infants (n=60) at 2-4 months born at ≥34weeks gestation. As the number of sensory modalities progressively increased during the interaction, infant behaviors of emotional connection in facial expressiveness, sensitivity to mother, and vocal communication increased significantly. Conversely, infant FAS for the entire cohort did not change significantly. However, when we accounted for infant irritability, both video-coded behaviors and EEG FAS markers of infant responsiveness increased across the interaction in the non-irritable infants. The non-irritable infants (49%) demonstrated positive FAS, indicating readiness to engage with, rather than to withdraw from, multisensory but not unisensory interactions with their mothers. These results suggest that multisensory input from mothers is associated with greater infant neural approach state and highlight the importance of infant behavioral state during neural measures of infant responsiveness.

A biomarker discovery framework for childhood anxiety.

Anxiety is the most common manifestation of psychopathology in youth, negatively affecting academic, social, and adaptive functioning and increasing risk for mental health problems into adulthood. Anxiety disorders are diagnosed only after clinical symptoms emerge, potentially missing opportunities to intervene during critical early prodromal periods. In this study, we used a new empirical approach to extracting nonlinear features of the electroencephalogram (EEG), with the goal of discovering differences in brain electrodynamics that distinguish children with anxiety disorders from healthy children. Additionally, we examined whether this approach could distinguish children with externalizing disorders from healthy children and children with anxiety. We used a novel supervised tensor factorization method to extract latent factors from repeated multifrequency nonlinear EEG measures in a longitudinal sample of children assessed in infancy and at ages 3, 5, and 7 years of age. We first examined the validity of this method by showing that calendar age is highly correlated with latent EEG complexity factors (r = 0.77). We then computed latent factors separately for distinguishing children with anxiety disorders from healthy controls using a 5-fold cross validation scheme and similarly for distinguishing children with externalizing disorders from healthy controls. We found that latent factors derived from EEG recordings at age 7 years were required to distinguish children with an anxiety disorder from healthy controls; recordings from infancy, 3 years, or 5 years alone were insufficient. However, recordings from two (5, 7 years) or three (3, 5, 7 years) recordings gave much better results than 7 year recordings alone. Externalizing disorders could be detected using 3- and 5 years EEG data, also giving better results with two or three recordings than any single snapshot. Further, sex assigned at birth was an important covariate that improved accuracy for both disorder groups, and birthweight as a covariate modestly improved accuracy for externalizing disorders. Recordings from infant EEG did not contribute to the classification accuracy for either anxiety or externalizing disorders. This study suggests that latent factors extracted from EEG recordings in childhood are promising candidate biomarkers for anxiety and for externalizing disorders if chosen at appropriate ages.

Coherence analysis in electroencephalography. Prospects and problems of using coherent analysis in EEG practice (lecture one)

In the lecture, the author analyzes in detail the legitimacy of using the coherence function to analyze the bioelectrical activity of the brain in practical electroencephalography from the standpoint of mathematical and clinical validity. The author touches upon the issues of the informational significance of EEG coherence indicators in the disclosure in the assessment of the functional state of the brain about the mental characteristics of a person. As an evidence base, the author demonstrates examples of the information content of EEG coherence analysis data in terms of predicting the outcome in patients in an unconscious state. The author describes the stages of the formation of intracerebral connections according to the data of coherent analysis of the EEG at the stages of age-related development on the model of infants of age, after term birth and in preterm infants with a favorable course of development, and also the formation of spatio- temporal relations of the EEG of premature infants with periventricular leukomalacia on the 1st — 2 nd years of life.

Peri-ictal EEG in infants with PRRT2-related self-limited infantile epilepsy.

Pathogenic PRRT2 variants cause self-limited (familial) infantile epilepsy (SeLIE), which is responsive to sodium channel blocking antiseizure medications. The interictal EEG is typically normal. We describe a cohort of infants with PRRT2-related SeLIE with striking peri-ictal EEG abnormalities. We included all infants diagnosed with PRRT2-related SeLIE during July 2020 to November 2021 at the Royal Children's Hospital, Melbourne. Clinical features and results of aetiologic investigations were collected from electronic medical records. All EEGs were reviewed independently by two epileptologists. Ten infants presented with focal seizures at a median age of 5 months (range: 3-6 months). Eight had a family history of epilepsy, paroxysmal kinesigenic dyskinesia (PKD) or hemiplegic migraine. Seven of the eight infants with an EEG performed within 24 h of the most recent seizure had epileptiform discharges. Their EEGs showed focal sharp waves, spikes, polyspikes or fast activity independently over the left and right temporo-occipital regions. Conversely, the two infants with last known seizure greater than 24 h prior to their EEG had no epileptiform discharges. Oxcarbazepine was commenced in two infants and was effective. Eight infants were initially treated with levetiracetam, and all were subsequently switched to oxcarbazepine due to ongoing seizures or side effects. Posterior polymorphic focal epileptiform discharges on a peri-ictal EEG recording are a feature of PRRT2-related SeLIE. This finding, particularly in the presence of a family history of infantile epilepsy, PKD or hemiplegic migraine, suggests a diagnosis of PRRT2-related SeLIE and has important treatment implications.

Associations Between Infant Screen Use, Electroencephalography Markers, and Cognitive Outcomes

Research evidence is mounting for the association between infant screen use and negative cognitive outcomes related to attention and executive functions. The nature, timing, and persistence of screen time exposure on neural functions are currently unknown. Electroencephalography (EEG) permits elucidation of the neural correlates associated with cognitive impairments. To examine the associations between infant screen time, EEG markers, and school-age cognitive outcomes using mediation analysis with structural equation modeling. This prospective maternal-child dyad cohort study included participants from the population-based study Growing Up in Singapore Toward Healthy Outcomes (GUSTO). Pregnant mothers were enrolled in their first trimester from June 2009 through December 2010. A subset of children who completed neurodevelopmental visits at ages 12 months and 9 years had EEG performed at age 18 months. Data were reported from 3 time points at ages 12 months, 18 months, and 9 years. Mediation analyses were used to investigate how neural correlates were involved in the paths from infant screen time to the latent construct of attention and executive functioning. Data for this study were collected from November 2010 to March 2020 and were analyzed between October 2021 and May 2022. Parent-reported screen time at age 12 months. Power spectral density from EEG was collected at age 18 months. Child attention and executive functions were measured with teacher-reported questionnaires and objective laboratory-based tasks at age 9 years. In this sample of 437 children, the mean (SD) age at follow-up was 8.84 (0.07) years, and 227 children (51.9%) were male. The mean (SD) amount of daily screen time at age 12 months was 2.01 (1.86) hours. Screen time at age 12 months contributed to multiple 9-year attention and executive functioning measures (η2, 0.03-0.16; Cohen d, 0.35-0.87). A subset of 157 children had EEG performed at age 18 months; EEG relative theta power and theta/beta ratio at the frontocentral and parietal regions showed a graded correlation with 12-month screen use (r = 0.35-0.37). In the structural equation model accounting for household income, frontocentral and parietal theta/beta ratios partially mediated the association between infant screen time and executive functioning at school age (exposure-mediator β, 0.41; 95% CI, 0.22 to 0.59; mediator-outcome β, -0.38; 95% CI, -0.64 to -0.11), forming an indirect path that accounted for 39.4% of the association. In this study, infant screen use was associated with altered cortical EEG activity before age 2 years; the identified EEG markers mediated the association between infant screen time and executive functions. Further efforts are urgently needed to distinguish the direct association of infant screen use compared with family factors that predispose early screen use on executive function impairments.

A Bedside Method for Measuring Effects of a Sedative Drug on Cerebral Function in Newborn Infants.

Data on the cerebral effects of analgesic and sedative drugs are needed for the development of safe and effective treatments during neonatal intensive care. Electroencephalography (EEG) is an objective, but interpreter-dependent method for monitoring cortical activity. Quantitative computerized analyses might reveal EEG changes otherwise not detectable. EEG registrations were retrospectively collected from 21 infants (mean 38.7 gestational weeks; range 27-42) who received dexmedetomidine during neonatal care. The registrations were transformed into computational features and analyzed visually, and with two computational measures quantifying relative and absolute changes in power (range EEG; rEEG) and cortico-cortical synchrony (activation synchrony index; ASI), respectively. The visual assessment did not reveal any drug effects. In rEEG analyses, a negative correlation was found between the baseline and the referential frontal (rho = 0.612, p = 0.006) and parietal (rho = -0.489, p = 0.035) derivations. The change in ASI was negatively correlated to baseline values in the interhemispheric (rho = -0.753; p = 0.001) and frontal comparisons (rho = -0.496; p = 0.038). Cerebral effects of dexmedetomidine as determined by EEG in newborn infants are related to cortical activity prior to DEX administration, indicating that higher brain activity levels (higher rEEG) during baseline links to a more pronounced reduction by DEX. The computational measurements indicate drug effects on both overall cortical activity and cortico-cortical communication. These effects were not evident in visual analysis.

Visual category representations in the infant brain

Visual categorization is a human core cognitive capacity1,2 that depends on the development of visual category representations in the infant brain.3,4,5,6,7 However, the exact nature of infant visual category representations and their relationship to the corresponding adult form remains unknown.8 Our results clarify the nature of visual category representations from electroencephalography (EEG) data in 6- to 8-month-old infants and their developmental trajectory toward adult maturity in the key characteristics of temporal dynamics,2,9 representational format,10,11,12 and spectral properties.13,14 Temporal dynamics change from slowly emerging, developing representations in infants to quickly emerging, complex representations in adults. Despite those differences, infants and adults already partly share visual category representations. The format of infants' representations is visual features of low to intermediate complexity, whereas adults' representations also encode high-complexity features. Theta band activity contributes to visual category representations in infants, and these representations are shifted to the alpha/beta band in adults. Together, we reveal the developmental neural basis of visual categorization in humans, show how information transmission channels change in development, and demonstrate the power of advanced multivariate analysis techniques in infant EEG research for theory building in developmental cognitive science.

Microstate analysis in infancy

BackgroundMicrostate analysis is an emerging method for investigating global brain connections using electroencephalography (EEG). Microstates have been colloquially referred to as the “atom of thought,” meaning that from these underlying networks comes coordinated neural processing and cognition. The present study examined microstates at 6-, 8-, and 10-months of age. It was hypothesized that infants would demonstrate distinct microstates comparable to those identified in adults that also parallel resting-state networks using fMRI. An additional exploratory aim was to examine the relationship between microstates and temperament, assessed via parent reports, to further demonstrate microstate analysis as a viable tool for examining the relationship between neural networks, cognitive processes as well as emotional expression embodied in temperament attributes. MethodsThe microstates analysis was performed with infant EEG data when the infant was either 6- (n = 12), 8- (n = 16), or 10-months (n = 6) old. The resting-state task involved watching a 1-minute video segment of Baby Einstein while listening to the accompanying music. Parents completed the IBQ-R to assess infant temperament. ResultsFour microstate topographies were extracted. Microstate 1 had an isolated posterior activation; Microstate 2 had a symmetric occipital to prefrontal orientation; Microstate 3 had a left occipital to right frontal orientation; and Microstate 4 had a right occipital to left frontal orientation. At 10-months old, Microstate 3, thought to reflect auditory/language processing, became activated more often, for longer periods of time, covering significantly more time across the task and was more likely to be transitioned into. This finding is interpreted as consistent with language acquisition and phonological processing that emerges around 10-months. Microstate topographies and parameters were also correlated with differing temperament broadband and narrowband scales on the IBQ-R. ConclusionThree microstates emerged that appear comparable to underlying networks identified in adult and infant microstate literature and fMRI studies. Each of the temperament domains was related to specific microstates and their parameters. These networks also correspond with auditory and visual processing as well as the default mode network found in prior research and can lead to new investigations examining differences across stimulus presentations to further explain how infants begin to recognize, respond to, and engage with the world around them.

Deep-spindle: An automated sleep spindle detection system for analysis of infant sleep spindles

Background:Sleep spindles are an indicator of the development and integrity of the central nervous system in infants. Identifying sleep spindles manually in EEG is time-consuming and typically requires experienced experts. Automated detection of sleep spindles would greatly facilitate this analysis. Deep learning methods have been widely used recently in EEG analysis. Method:We have developed a deep learning-based automated sleep spindle detection system, Deep-spindle, which employs a convolutional neural network (CNN) combined with a bidirectional Long Short-Term Memory (LSTM) network, which could assist in the analysis of infant sleep spindles. Deep-spindle was trained on the EEGs of ex-term infants to estimate the number and duration of sleep spindles. The ex-term EEG on channel F4-C4 was split into training (N=81) and validation (N=30) sets. An additional 30 ex-term EEG and 54 ex-preterm infant EEGs (channel F4-C4 and F3-C3) were used as an independent test set. Result:Deep-spindle detected the number of sleep spindles with 91.9% to 96.5% sensitivity and 95.3% to 96.7% specificity, and estimated sleep spindle duration with a percent error of 13.1% to 19.1% in the independent test set. For each detected spindle event, the user is presented with amplitude, power spectral density and the spectrogram of the corresponding spindle EEG, and the probability of the event being a sleep spindle event, providing the user with insight into why the event is predicted as a sleep spindle to provide confidence in the predictions. Conclusion:The Deep-spindle system can reduce physicians’ workload, demonstrating the potential to assist physicians in the automated analysis of sleep spindles in infants.

Automated detection and removal of flat line segments and large amplitude fluctuations in neonatal electroencephalography.

BackgroundArtefact removal in neonatal electroencephalography (EEG) by visual inspection generally depends on the expertise of the operator, is time consuming and is not a consistent pre-processing step to the pipeline for the automated EEG analysis. Therefore, there is the need for the automated detection and removal of artefacts in neonatal EEG, especially of distinct and predominant artefacts such as flat line segments (mainly caused by instrumental error where contact between electrodes and head box is lost) and large amplitude fluctuations (related to neonatal movements).MethodA threshold-based algorithm for the automated detection and removal of flat line segments and large amplitude fluctuations in neonatal EEG of infants at term-equivalent age is developed. The algorithm applies thresholds to the absolute second difference, absolute amplitude, absolute first difference and the ratio between the frequency content above 50 Hz and the frequency content across all frequencies.ResultsThe algorithm reaches a median accuracy of 0.91, a median hit rate of 0.91 and a median false discovery rate of 0.37. Also, a significant improvement (≈10%) in the performance of a four-stage sleep classifier is observed after artefact removal with the proposed algorithm as compared to before its application.SignificanceAn automated artefact removal method contributes to the pipeline of automated EEG analysis. The proposed algorithm has shown to have good performance and to be effective in neonatal EEG applications.

The effects of betamethasone on the amplitude integrated EEG of infants born at 34- or 35-weeks gestation.

Assess if maternal betamethasone administration at 34-35 weeks accelerated neonatal amplitude integrated EEG (aEEG) maturation. Nested, observational cohort in 7 centers participating in the Antenatal Late Preterm Steroid randomized trial. Up to 2 aEEGs were obtained in neonates born from 340-356 weeks gestation before 72 h (aEEG 1) and at 5-7 days (aEEG 2) if hospitalized. Personnel and aEEG central readers were masked to the intervention. The primary outcome was maturation reflected by cycle frequency; secondary outcomes were border voltage, span, and discontinuity. 58 neonates were enrolled (betamethasone, 28, placebo, 30). On aEEG 1, cycle frequency did not differ, but betamethasone exposed infants had a greater lower border voltage and a broader span. On aEEG 2, both groups displayed increases in lower border voltage. Betamethasone associated changes in lower border voltage support accelerated electrical activity. Further investigation is needed to understand the broader span.

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

Parent–infant EEG is a novel hyperscanning paradigm to measure social interaction simultaneously in the brains of parents and infants. The number of studies using parent–infant dual-EEG as a theoretical framework to measure brain-to-brain synchrony during interaction is rapidly growing, while the methodology for measuring synchrony is not yet uniform. While adult dual-EEG methodology is quickly improving, open databases, tutorials, and methodological validations for dual-EEG with infants are largely missing. In this practical guide, we provide a step-by-step manual on how to implement and run parent–infant EEG paradigms in a neurodevelopmental laboratory in naturalistic settings (e.g., free interactions). Next, we highlight insights on the variety of choices that can be made during (pre)processing dual-EEG data, including recommendations on interpersonal neural coupling metrics and interpretations of the results. Moreover, we provide an exemplar dataset of two mother–infant dyads during free interactions (“free play”) that may serve as practice material. Instead of providing a critical note, we would like to move the field of parent–infant EEG forward and be transparent about the challenges that come along with the exciting opportunity to study the development of our social brain within the naturalistic context of dual-EEG.