Maximizing the potential of EEG as a developmental neuroscience tool

Generalizability of developmental EEG: Demographic reporting, representation, and sample size

Electroencephalography (EEG) is one of the main neuroscientific measures used with infants and children to identify potential biomarkers of cognitive and social developmental processes. Given the implications of developmental EEG research within policy, clinical, and educational domains, it is important to ensure that reported results are generalizable and reproducible. In this review, to provide an initial assessment of previous and current practices regarding participant recruitment (sample size and representation) and demographic reporting, we carried out a systematic review of six notable journals for publishing pediatric EEG studies between 2011 and 2023. We identified 700 articles reporting on pediatric EEG. We found that most studies did not provide complete reporting of basic demographic information (e.g., race, ethnicity, socioeconomic status, geographical location). This trend persisted across years of publication, suggesting continued underreporting. However, the reporting of demographic information differed between journals, suggesting solutions for improving reporting practices. Our review also indicated that samples were of modest sample size (Median = 51) and consisted of mostly White participants (78 %) from North America and Western Europe (85 %). Our discussion emphasizes the need for larger, more diverse samples and greater transparency in developmental EEG studies, while providing recommendations to address barriers to representation and reproducibility.

EEG time-frequency dynamics of early cognitive control development

Cognitive control is crucial for goal-directed behavior, and essential for other aspects of cognitive and socioemotional development. This review examines when and how the neural dynamics of cognitive control emerge and develop, focusing on electroencephalography measures used to study cognitive control in infants and children. We argue that time-frequency analyses are uniquely able to capture two distinct components of cognitive control: 1) the detection that control is needed, and 2) the instantiation of control. Starting in infancy and increasing across childhood and adolescence, studies suggest the signal strength and consistency of midfrontal theta and delta oscillations are involved in processes that detect the need for control. For control instantiation, there is evidence that theta band connectivity between midfrontal and lateral-frontal cortices is present from early childhood. There is also evidence for the involvement of midfrontal theta power in the instantiation of control in infancy. We further review emerging evidence that indicates individual differences in midfrontal theta are not only proximally related to behavior, but also sensitive to variations in early experience and risk for psychopathology, providing a neural mechanism linking early adversity to future psychopathology. We discuss needed future steps, including novel paradigms, computational models, and aperiodic/periodic modeling of EEG.

Heterogeneity in pediatric resting EEG data processing and analysis: A state of the field

Developmental, resting electroencephalography (EEG) is gaining rapid popularity with implementation in large-scale studies as well as a recent WHO report naming resting EEG as a gold standard measure of brain health. With an increased interest in resting EEG as a potential biomarker for neurocognition, it is paramount that resting EEG findings are reliable and reproducible. One of the major threats to replicability and reproducibility stems from variations in preprocessing and analysis. One of the primary challenges facing the field of developmental EEG is that it can be challenging to acquire data from infants and children, which commonly makes data cleaning and analysis difficult and unstandardized. The goal of the present manuscript is to take a state of the field of the methods experts in resting EEG report they would use to clean and analyze a hypothetical data set. Here we report on the responses of 66 self-identified experts in developmental psychophysiology, none of which submitted identical preregistrations. As expected, there were areas of more and less consensus, but ultimately, we believe our findings highlight opportunities for core methodological work and field-level efforts to establish consensus.

Altered Social Processing as a Potential Mechanism of BPD Risk in Girls with ADHD: A Call for Multi-Method Developmental Research

Childhood attention-deficit/hyperactivity disorder (ADHD) symptoms prospectively predict the development of borderline personality disorder (BPD) symptoms in adolescence and adulthood; adult women with BPD, in particular, often retrospectively report childhood ADHD symptoms. However, little is known about specific developmental pathways and mechanisms that contribute to this sequential comorbidity. Herein we outline a call for multi-method developmental research examining altered social processing as a potential mechanism underlying risk for BPD in girls with ADHD. We review relevant developmental psychopathology theory, describe recent empirical work, and outline steps for future work with the goal of promoting continued research focused on reducing the personal and societal burden associated with ADHD and BPD.

Advancing the reporting of pediatric EEG data: Tools for estimating reliability, effect size, and data quality metrics

EEG studies play a crucial role in enhancing our understanding of brain development across the lifespan. The increasing clinical and policy implications of EEG research underscore the importance of utilizing reliable EEG measures and increasing the reproducibility of EEG studies. However, important data characteristics like reliability, effect sizes, and data quality metrics are often underreported in pediatric EEG studies. This gap in reporting could stem from the lack of accessible computational tools for quantifying these metrics for EEG data. To help address the lack of reporting, we developed a toolbox that facilitates the estimation of internal consistency reliability, effect size, and standardized measurement error with user-friendly software that facilitates both computing and interpreting these measures. In addition, our tool provides subsampled reliability and effect size in increasing numbers of trials. These estimates offer insights into the number of trials needed for detecting significant effects and reliable measures, informing the minimum number of trial thresholds for the inclusion of participants in individual difference analyses and the optimal trial number for future study designs. Importantly, our toolbox is integrated into commonly used preprocessing pipelines to increase the estimation and reporting of data quality metrics in developmental neuroscience.

RELAX-Jr: An Automated Pre-Processing Pipeline for Developmental EEG Recordings

Automated EEG pre-processing pipelines provide several key advantages over traditional manual data cleaning approaches; primarily, they are less time-intensive and remove potential experimenter error/bias. Automated pipelines also require fewer technical expertise as they remove the need for manual artefact identification. We recently developed the fully automated Reduction of Electroencephalographic Artefacts (RELAX) pipeline and demonstrated its performance in cleaning EEG data recorded from adult populations. Here, we introduce the RELAX-Jr pipeline, which was adapted from RELAX and designed specifically for pre-processing of data collected from children. RELAX-Jr implements multi-channel Wiener filtering (MWF) and/or wavelet-enhanced independent component analysis (wICA) combined with the adjusted-ADJUST automated independent component classification algorithm to identify and reduce all artefacts using algorithms adapted to optimally identify artefacts in EEG recordings taken from children. Using a dataset of resting-state EEG recordings (N = 136) from children spanning early-to-middle childhood (4-12 years), we assessed the cleaning performance of RELAX-Jr using a range of metrics including signal-to-error ratio, artefact-to-residue ratio, ability to reduce blink and muscle contamination, and differences in estimates of alpha power between eyes-open and eyes-closed recordings. We also compared the performance of RELAX-Jr against four publicly available automated cleaning pipelines. We demonstrate that RELAX-Jr provides strong cleaning performance across a range of metrics, supporting its use as an effective and fully automated cleaning pipeline for neurodevelopmental EEG data.

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.

Theta activity and cognitive functioning: Integrating evidence from resting-state and task-related developmental electroencephalography (EEG) research

The theta band is one of the most prominent frequency bands in the electroencephalography (EEG) power spectrum and presents an interesting paradox: while elevated theta power during resting state is linked to lower cognitive abilities in children and adolescents, increased theta power during cognitive tasks is associated with higher cognitive performance. Why does theta power, measured during resting state versus cognitive tasks, show differential correlations with cognitive functioning? This review provides an integrated account of the functional correlates of theta across different contexts. We first present evidence that higher theta power during resting state is correlated with lower executive functioning, attentional abilities, language skills, and IQ. Next, we review research showing that theta power increases during memory, attention, and cognitive control, and that higher theta power during these processes is correlated with better performance. Finally, we discuss potential explanations for the differential correlations between resting/task-related theta and cognitive functioning, and offer suggestions for future research in this area.

Getting closer to social interactions using electroencephalography in developmental cognitive neuroscience

The field of developmental cognitive neuroscience is advancing rapidly, with large-scale, population-wide, longitudinal studies emerging as a key means of unraveling the complexity of the developing brain and cognitive processes in children. While numerous neuroscientific techniques like functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), magnetoencephalography (MEG), and transcranial magnetic stimulation (TMS) have proved advantageous in such investigations, this perspective proposes a renewed focus on electroencephalography (EEG), leveraging underexplored possibilities of EEG. In addition to its temporal precision, low costs, and ease of application, EEG distinguishes itself with its ability to capture neural activity linked to social interactions in increasingly ecologically valid settings. Specifically, EEG can be measured during social interactions in the lab, hyperscanning can be used to study brain activity in two (or more) people simultaneously, and mobile EEG can be used to measure brain activity in real-life settings. This perspective paper summarizes research in these three areas, making a persuasive argument for the renewed inclusion of EEG into the toolkit of developmental cognitive and social neuroscientists.

Information-Theoretical Analysis of the Cycle of Creation of Knowledge and Meaning in Brains under Multiple Cognitive Modalities

It is of great interest to develop advanced sensory technologies allowing non-invasive monitoring of neural correlates of cognitive processing in people performing everyday tasks. A lot of progress has been reported in recent years in this research area using scalp EEG arrays, but the high level of noise in the electrode signals poses a lot of challenges. This study presents results of detailed statistical analysis of experimental data on the cycle of creation of knowledge and meaning in human brains under multiple cognitive modalities. We measure brain dynamics using a HydroCel Geodesic Sensor Net, 128-electrode dense-array electroencephalography (EEG). We compute a pragmatic information (PI) index derived from analytic amplitude and phase, by Hilbert transforming the EEG signals of 20 participants in six modalities, which combine various audiovisual stimuli, leading to different mental states, including relaxed and cognitively engaged conditions. We derive several relevant measures to classify different brain states based on the PI indices. We demonstrate significant differences between engaged brain states that require sensory information processing to create meaning and knowledge for intentional action, and relaxed-meditative brain states with less demand on psychophysiological resources. We also point out that different kinds of meanings may lead to different brain dynamics and behavioral responses.

Neurophysiological mechanisms of cognition in the developing brain: Insights from intracranial EEG studies

The quest to understand how the development of the brain supports the development of complex cognitive functions is fueled by advances in cognitive neuroscience methods. Intracranial EEG (iEEG) recorded directly from the developing human brain provides unprecedented spatial and temporal resolution for mapping the neurophysiological mechanisms supporting cognitive development. In this paper, we focus on episodic memory, the ability to remember detailed information about past experiences, which improves from childhood into adulthood. We review memory effects based on broadband spectral power and emphasize the importance of isolating narrowband oscillations from broadband activity to determine mechanisms of neural coordination within and between brain regions. We then review evidence of developmental variability in neural oscillations and present emerging evidence linking the development of neural oscillations to the development of memory. We conclude by proposing that the development of oscillations increases the precision of neural coordination and may be an essential factor underlying memory development. More broadly, we demonstrate how recording neural activity directly from the developing brain holds immense potential to advance our understanding of cognitive development.