•We detail EEG psychometric reliability profiles for testing individual differences.•We review internal consistency of power, ERP, nonlinear, connectivity measures.•We review test-retest reliability ...of power, ERP, nonlinear, connectivity measures.•We show how denoising, data quality measures improve individual difference studies.•We provide actionable recommendations to improve EEG individual difference analyses.
Electroencephalographic (EEG) methods have great potential to serve both basic and clinical science approaches to understand individual differences in human neural function. Importantly, the psychometric properties of EEG data, such as internal consistency and test-retest reliability, constrain their ability to differentiate individuals successfully. Rapid and recent technological and computational advancements in EEG research make it timely to revisit the topic of psychometric reliability in the context of individual difference analyses. Moreover, pediatric and clinical samples provide some of the most salient and urgent opportunities to apply individual difference approaches, but the changes these populations experience over time also provide unique challenges from a psychometric perspective. Here we take a developmental neuroscience perspective to consider progress and new opportunities for parsing the reliability and stability of individual differences in EEG measurements across the lifespan. We first conceptually map the different profiles of measurement reliability expected for different types of individual difference analyses over the lifespan. Next, we summarize and evaluate the state of the field's empirical knowledge and need for testing measurement reliability, both internal consistency and test-retest reliability, across EEG measures of power, event-related potentials, nonlinearity, and functional connectivity across ages. Finally, we highlight how standardized pre-processing software for EEG denoising and empirical metrics of individual data quality may be used to further improve EEG-based individual differences research moving forward. We also include recommendations and resources throughout that individual researchers can implement to improve the utility and reproducibility of individual differences analyses with EEG across the lifespan.
Lower-density Electroencephalography (EEG) recordings (from 1 to approximately 32 electrodes) are widely-used in research and clinical practice and enable scalable brain function measurement across a ...variety of settings and populations. Though a number of automated pipelines have recently been proposed to standardize and optimize EEG pre-processing for high-density systems with state-of-the-art methods, few solutions have emerged that are compatible with lower-density systems. However, lower-density data often include long recording times and/or large sample sizes that would benefit from similar standardization and automation with contemporary methods. To address this need, we propose the HAPPE In Low Electrode Electroencephalography (HAPPILEE) pipeline as a standardized, automated pipeline optimized for EEG recordings with lower density channel layouts of any size. HAPPILEE processes task-free (e.g., resting-state) and task-related EEG (including event-related potential data by interfacing with the HAPPE+ER pipeline), from raw files through a series of processing steps including filtering, line noise reduction, bad channel detection, artifact correction from continuous data, segmentation, and bad segment rejection that have all been optimized for lower density data. HAPPILEE also includes post-processing reports of data and pipeline quality metrics to facilitate the evaluation and reporting of data quality and processing-related changes to the data in a standardized manner. Here the HAPPILEE steps and their optimization with both recorded and simulated EEG data are described. HAPPILEE's performance is then compared relative to other artifact correction and rejection strategies. The HAPPILEE pipeline is freely available as part of HAPPE 2.0 software under the terms of the GNU General Public License at: https://github.com/PINE-Lab/HAPPE.
Event-Related Potential (ERP) designs are a common method for interrogating neurocognitive function with electroencephalography (EEG). However, the traditional method of preprocessing ERP data is ...manual-editing – a subjective, time-consuming processes. A number of automated pipelines have recently been created to address the need for standardization, automation, and quantification of EEG data pre-processing; however, few are optimized for ERP analyses (especially in developmental or clinical populations). We propose and validate the HAPPE plus Event-Related (HAPPE+ER) software, a standardized and automated pre-processing pipeline optimized for ERP analyses across the lifespan. HAPPE+ER processes event-related potential data from raw files through preprocessing and generation of event-related potentials for statistical analyses. HAPPE+ER also includes post-processing reports of both data quality and pipeline quality metrics to facilitate the evaluation and reporting of data processing in a standardized manner. Finally, HAPPE+ER includes post-processing scripts to facilitate validating HAPPE+ER performance and/or comparing to performance of other preprocessing pipelines in users’ own data via simulated ERPs. We describe multiple approaches with simulated and real ERP data to optimize pipeline performance and compare to other methods and pipelines. HAPPE+ER software is freely available under the terms of GNU General Public License at https://www.gnu.org/licenses/#GPL
Although the amygdala’s role in shaping social behavior is especially important during early post-natal development, very little is known of amygdala functional development before childhood. To ...address this gap, this study uses resting-state fMRI to examine early amygdalar functional network development in a cross-sectional sample of 80 children from 3-months to 5-years of age. Whole brain functional connectivity with the amygdala, and its laterobasal and superficial sub-regions, were largely similar to those seen in older children and adults. Functional distinctions between sub-region networks were already established. These patterns suggest many amygdala functional circuits are intact from infancy, especially those that are part of motor, visual, auditory and subcortical networks. Developmental changes in connectivity were observed between the laterobasal nucleus and bilateral ventral temporal and motor cortex as well as between the superficial nuclei and medial thalamus, occipital cortex and a different region of motor cortex. These results show amygdala-subcortical and sensory-cortex connectivity begins refinement prior to childhood, though connectivity changes with associative and frontal cortical areas, seen after early childhood, were not evident in this age range. These findings represent early steps in understanding amygdala network dynamics across infancy through early childhood, an important period of emotional and cognitive development.
Electroenchephalography (EEG) recordings collected with developmental populations present particular challenges from a data processing perspective. These EEGs have a high degree of artifact ...contamination and often short recording lengths. As both sample sizes and EEG channel densities increase, traditional processing approaches like manual data rejection are becoming unsustainable. Moreover, such subjective approaches preclude standardized metrics of data quality, despite the heightened importance of such measures for EEGs with high rates of initial artifact contamination. There is presently a paucity of automated resources for processing these EEG data and no consistent reporting of data quality measures. To address these challenges, we propose the Harvard Automated Processing Pipeline for EEG (HAPPE) as a standardized, automated pipeline compatible with EEG recordings of variable lengths and artifact contamination levels, including high-artifact and short EEG recordings from young children or those with neurodevelopmental disorders. HAPPE processes event-related and resting-state EEG data from raw files through a series of filtering, artifact rejection, and re-referencing steps to processed EEG suitable for time-frequency-domain analyses. HAPPE also includes a post-processing report of data quality metrics to facilitate the evaluation and reporting of data quality in a standardized manner. Here, we describe each processing step in HAPPE, perform an example analysis with EEG files we have made freely available, and show that HAPPE outperforms seven alternative, widely-used processing approaches. HAPPE removes more artifact than all alternative approaches while simultaneously preserving greater or equivalent amounts of EEG signal in almost all instances. We also provide distributions of HAPPE's data quality metrics in an 867 file dataset as a reference distribution and in support of HAPPE's performance across EEG data with variable artifact contamination and recording lengths. HAPPE software is freely available under the terms of the GNU General Public License at https://github.com/lcnhappe/happe.
Under typical conditions, medial prefrontal cortex (mPFC) connections with the amygdala are immature during childhood and become adult-like during adolescence. Rodent models show that maternal ...deprivation accelerates this development, prompting examination of human amygdala–mPFC phenotypes following maternal deprivation. Previously institutionalized youths, who experienced early maternal deprivation, exhibited atypical amygdala–mPFC connectivity. Specifically, unlike the immature connectivity (positive amygdala–mPFC coupling) of comparison children, children with a history of early adversity evidenced mature connectivity (negative amygdala–mPFC coupling) and thus, resembled the adolescent phenotype. This connectivity pattern was mediated by the hormone cortisol, suggesting that stress-induced modifications of the hypothalamic–pituitary–adrenal axis shape amygdala–mPFC circuitry. Despite being age-atypical, negative amygdala–mPFC coupling conferred some degree of reduced anxiety, although anxiety was still significantly higher in the previously institutionalized group. These findings suggest that accelerated amygdala–mPFC development is an ontogenetic adaptation in response to early adversity.
Highlights ► Depressive behaviors higher in adolescents than children following early-life stress. ► Nucleus accumbens developmental trajectory following early-life stress is aberrant. ► Thus, ...nucleus accumbens is hyporeactive in adolescence following early-life stress. ► Nucleus accumbens hyporeactivity is associated with depressive behaviors.
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.
Functional connections (FC) between the amygdala and cortical and subcortical regions underlie a range of affective and cognitive processes. Despite the central role amygdala networks have in these ...functions, the normative developmental emergence of FC between the amygdala and the rest of the brain is still largely undefined. This study employed amygdala subregion maps and resting-state functional magnetic resonance imaging to characterize the typical development of human amygdala FC from age 4 to 23years old (n=58). Amygdala FC with subcortical and limbic regions was largely stable across this developmental period. However, three cortical regions exhibited age-dependent changes in FC: amygdala FC with the medial prefrontal cortex (mPFC) increased with age, amygdala FC with a region including the insula and superior temporal sulcus decreased with age, and amygdala FC with a region encompassing the parahippocampal gyrus and posterior cingulate also decreased with age. The transition from childhood to adolescence (around age 10years) marked an important change-point in the nature of amygdala–cortical FC. We distinguished unique developmental patterns of coupling for three amygdala subregions and found particularly robust convergence of FC for all subregions with the mPFC. These findings suggest that there are extensive changes in amygdala–cortical functional connectivity that emerge between childhood and adolescence.
•Amygdala resting functional connectivity (FC) was assessed in 4- through 23-year olds.•Most FC with amygdala does not change during this period, resembling adult patterns.•FC with medial PFC, insula/STS, and PCC/hippocampus shows age-dependent changes.•Age-dependent FC transitions occur between childhood and adolescence (10–11years).•Subnuclei analyses confirmed that most robust developmental change is with medial PFC.
PDF
