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

Abstract

The ultrafast spatiotemporal dynamics of large-scale neural networks can be examined using resting-state 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 electroencephalography (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.

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

“…Further, using source localization techniques, we found that microstates 3 and 4 were associated with brain networks previously identi ed in prior fMRI research to support higher-order cognitive functions (Bagdasarov et al, 2022). While these results suggest that similar microstates may be identi ed during error processing, and meaningfully related to neural markers of error-related activity, this has not been directly investigated in children.…”

“…More speci cally, Kleinert et al (2022) recently reported that fewer, but longer lasting microstates during rest (which according to the authors indicated more stable mental processing), regardless of microstate class, were associated with increased neural activity in the left insula and inferior frontal gyrus during a go/no-go task and with greater self-reported self-control. While to our knowledge no study has investigated microstates during error processing in young children, one recent study did assess awake resting-state EEG microstates exclusively during early childhood (Bagdasarov et al, 2022). More speci cally, in a cross-sectional sample of 4-8-year-olds, we found sex-effects in the duration (i.e., males > females) of a microstate with an anterior-posterior orientation (i.e., microstate 3), and age by sex interaction effects for all of the temporal properties (i.e., generally, they decreased with increasing age for males but were constant for females) of a microstate with a centrally posterior orientation (i.e., microstate 4; Bagdasarov et al, 2022).…”

“…The current study aimed to investigate whether microstates during eyes-closed resting-state would associate with error-related neural activity (assessed using both microstate and ERP waveform analyses) during a go/no-go task in 90, 4-8-year-old children using high-density EEG. At the group-level, based on our prior work and that of others using a data-driven approach for microstate identi cation (Bagdasarov et al, 2022;Tomescu et al, 2018), we hypothesized that microstate analysis of resting-state EEG would produce 4-7 resting-state microstates. We also hypothesized, based on previous work on the ERN (Boen et al, 2022;Tamnes et al, 2013), that a microstate -which we will refer to as the error-related microstate hereafter -whose maximal activity would be at a fronto-central or central scalp location would de ne the peak of the ERN waveform surrounding error commission; thus, also de ning the electrodes for which the ERN would be maximally negative.…”

Convolutional Spiking Neural Networks for Detecting Anticipatory Brain Potentials Using Electroencephalogram

“…The ASR attempted to correct bad data periods containing artifacts and its maximum acceptable 0.5 second window standard deviation limit was set to a conservative 20 standard deviations. The values used in this step were the standard default values in EEGLAB and were also used as part of a pre-processing scheme in a previous study [54]. c) Epoched by slicing the data according to the markers corresponding to the countdown numbers or the "Stop" command.…”

Dimensions of Depressive Symptoms in Young Children: Factor Analysis of the Preschool Feelings Checklist-Scale

“…Louis (MO) or Duke University (Durham, NC). These studies investigated the behavioral, neural, and environmental correlates of early emerging internalizing symptomatology (i.e., depression and anxiety; Gaffrey et al, 2013;Gaffrey et al, 2018b;Fowler et al, 2021;Bagdasarov et al, 2022). Families were recruited from daycare centers, pediatricians' offices, community events (e.g., festivals, farmers' market), online posts on parenting pages on Facebook, and university-managed recruitment databases.…”