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.

Exploring the Association Between EEG Microstates During Resting-State and Error-Related Activity in Young Children

The error-related negativity (ERN) is a negative deflection in the electroencephalography (EEG) waveform at frontal-central scalp sites that occurs after error commission. The relationship between the ERN and broader patterns of brain activity measured across the entire scalp that support error processing during early childhood is unclear. We examined the relationship between the ERN and EEG microstates - whole-brain patterns of dynamically evolving scalp potential topographies that reflect periods of synchronized neural activity - during both a go/no-go task and resting-state in 90, 4-8-year-old children. The mean amplitude of the ERN was quantified during the -64 to 108 millisecond (ms) period of time relative to error commission, which was determined by data-driven microstate segmentation of error-related activity. We found that greater magnitude of the ERN associated with greater global explained variance (GEV; i.e., the percentage of total variance in the data explained by a given microstate) of an error-related microstate observed during the same -64 to 108 ms period (i.e., error-related microstate 3), and to greater anxiety risk as measured by parent-reported behavioral inhibition. During resting-state, six data-driven microstates were identified. Both greater magnitude of the ERN and greater GEV values of error-related microstate 3 associated with greater GEV values of resting-state microstate 4, which showed a frontal-central scalp topography. Source localization results revealed overlap between the underlying neural generators of error-related microstate 3 and resting-state microstate 4 and canonical brain networks (e.g., ventral attention) known to support the higher-order cognitive processes involved in error processing. Taken together, our results clarify how individual differences in error-related and intrinsic brain activity are related and enhance our understanding of developing brain network function and organization supporting error processing during early childhood.

Exploring the association between EEG microstates during resting-state and error-related activity in young children

The error-related negativity (ERN) is a negative deflection in the electroencephalography (EEG) waveform at frontal-central scalp sites that occurs after error commission. The relationship between the ERN and broader patterns of brain activity measured across the entire scalp that support error processing during early childhood is unclear. We examined the relationship between the ERN and EEG microstates - whole-brain patterns of dynamically evolving scalp potential topographies that reflect periods of synchronized neural activity - during both a go/no-go task and resting-state in 90, 4-8-year-old children. The mean amplitude of the ERN was quantified during the - 64 to 108 millisecond (ms) period of time relative to error commission, which was determined by data-driven microstate segmentation of error-related activity. We found that greater magnitude of the ERN associated with greater global explained variance (GEV; i.e., the percentage of total variance in the data explained by a given microstate) of an error-related microstate observed during the same - 64 to 108 ms period (i.e., error-related microstate 3), and to greater parent-report-measured anxiety risk. During resting-state, six data-driven microstates were identified. Both greater magnitude of the ERN and greater GEV values of error-related microstate 3 associated with greater GEV values of resting-state microstate 4, which showed a frontal-central scalp topography. Source localization results revealed overlap between the underlying neural generators of error-related microstate 3 and resting-state microstate 4 and canonical brain networks (e.g., ventral attention) known to support the higher-order cognitive processes involved in error processing. Taken together, our results clarify how individual differences in error-related and intrinsic brain activity are related and enhance our understanding of developing brain network function and organization supporting error processing during early childhood.

Toxicant exposure and the developing brain: A systematic review of the structural and functional MRI literature

Youth worldwide are regularly exposed to pollutants and chemicals (i.e., toxicants) that may interfere with healthy brain development, and a surge in MRI research has begun to characterize the neurobiological consequences of these exposures. Here, a systematic review following PRISMA guidelines was conducted on developmental MRI studies of toxicants with known or suspected neurobiological impact. Associations were reviewed for 9 toxicant classes, including metals, air pollution, and flame retardants. Of 1264 identified studies, 46 met inclusion criteria. Qualitative synthesis revealed that most studies: (1) investigated air pollutants or metals, (2) assessed exposures prenatally, (3) assessed the brain in late middle childhood, (4) took place in North America or Western Europe, (5) drew samples from existing cohort studies, and (6) have been published since 2017. Given substantial heterogeneity in MRI measures, toxicant measures, and age groups assessed, more research is needed on all toxicants reviewed here. Future studies should also include larger samples, employ personal exposure monitoring, study independent samples in diverse world regions, and assess toxicant mixtures.

Spatiotemporal dynamics of EEG microstates in four- to eight-year-old children: Age- and sex-related effects

The ultrafast spatiotemporal dynamics of large-scale neural networks can be examined using resting-state electroencephalography (EEG) microstates, representing transient periods of synchronized neural activity that evolve dynamically over time. In adults, four canonical microstates have been shown to explain most topographic variance in resting-state EEG. Their temporal structures are age-, sex- and state-dependent, and are susceptible to pathological brain states. However, no studies have assessed the spatial and temporal properties of EEG microstates exclusively during early childhood, a critical period of rapid brain development. Here we sought to investigate EEG microstates recorded with high-density EEG in a large sample of 103, 4-8-year-old children. Using data-driven k-means cluster analysis, we show that the four canonical microstates reported in adult populations already exist in early childhood. Using multiple linear regressions, we demonstrate that the temporal dynamics of two microstates are associated with age and sex. Source localization suggests that attention- and cognitive control-related networks govern the topographies of the age- and sex-dependent microstates. These novel findings provide unique insights into functional brain development in children captured with EEG microstates.

Associations between sleep problems and domains relevant to daytime functioning and clinical symptomatology in autism: A meta-analysis

Autistic individuals experience significantly higher rates of sleep problems compared to the general population, which negatively impacts various aspects of daytime functioning. The strength of associations across domains of functioning has not yet been summarized across studies. The present meta-analysis examined the strength of associations between sleep problems and various domains of daytime functioning in autistic individuals. Searches were conducted in EMBASE, PubMed, Web of Science, and Google Scholar through May 2020. Inclusion criteria were: an index of sleep disturbance in individuals diagnosed with autism spectrum disorder (ASD); data collected prior to any sleep-related intervention; statistical data indicating relations between sleep problems and outcomes relevant to behavior, cognition, and physical or mental health. Exclusion criteria were: statistics characterizing the relationship between sleep disturbance and outcome variables that partialled out covariates; studies examining correlations between different measures of sleep disturbance. Participants totaled 15,074 from 49 published articles and 51 samples, yielding 209 effect sizes. Sleep problems were significantly associated with more clinical symptomatology and worse daytime functioning. Subgroup analyses demonstrated that sleep problems were most strongly associated with internalizing and externalizing symptoms and executive functioning, followed by core autism symptoms, family factors, and adaptive functioning. Findings highlight the far-reaching consequences of sleep problems on daytime functioning for autistic individuals and support the continued prioritization of sleep as a target for intervention through integrated care models to improve wellbeing. LAY SUMMARY: Autistic individuals experience higher rates of sleep problems, such as difficulty falling asleep and staying asleep, compared to the general population. We quantitatively summarized the literature about how sleep problems are related to different aspects of daytime functioning to identify areas that may be most affected by sleep. Sleep problems were related to all areas assessed, with the strongest associations for mood and anxiety symptoms. We recommend prioritizing sleep health in autistic individuals to improve wellbeing and quality of life.

Atypicalities of Gesture Form and Function in Autistic Adults

While well-represented on clinical measures, co-speech gesture production has never been formally studied in autistic adults. Twenty-one verbally fluent autistic adults and 21 typically developing controls engaged in a controlled conversational task. Group differences were observed in both semantic/pragmatic and motoric features of spontaneously produced co-speech gestures. Autistic adults prioritized different functions of co-speech gesture. Specifically, they used gesture more than controls to facilitate conversational turn-taking, demonstrating a novel nonverbal strategy for regulating conversational dynamics. Autistic adults were more likely to gesture unilaterally than bilaterally, a motoric feature of gesture that was individually associated with autism symptoms. Co-speech gestures may provide a link between nonverbal communication symptoms and known differences in motor performance in autism.