Sacks, D. D. | bioRxiv Science

bioRxiv2024

Longitudinal trajectories of aperiodic EEG activity in early to middle childhood

BackgroundEmerging evidence suggests that aperiodic EEG activity may follow a nonlinear growth trajectory in childhood. However, existing studies are limited by small assessment windows and cross-sectional samples that are unable to fully capture these patterns. The current study aimed to characterize the developmental trajectories of aperiodic activity longitudinally from infancy to middle childhood. We examined potential trajectory differences by sex and brain region. We further investigated whether aperiodic activity is associated with maternal anxiety symptoms, and whether these associations vary because of differential development trajectories. MethodsA community sample of children and their parents (N=391) enrolled in a longitudinal study of emotion processing were assessed at infancy, and at ages 3 years, 5 years, and 7 years. Analyses included individual growth curve and mixed effect models. Developmental trajectories of the aperiodic slope and offset were investigated across whole brain, frontal, central, temporal, and posterior regions. Associations of whole brain slope and offset with maternal anxiety symptoms were also examined. ResultsDevelopmental trajectories for both slope and offset were generally characterized by a relative increase in early childhood and a subsequent decrease or stabilization by age 7, with variation by brain region. Females showed relatively steeper slopes at some ages, and males showed relatively greater offset at certain ages. Maternal anxiety was negatively associated with slope at 3 years and positively associated with slope at 7 years. ConclusionsThe longitudinal developmental trajectory of aperiodic slope in early childhood is nonlinear and shows variation by sex and brain region. The magnitude and direction of associations with maternal anxiety varied by age, corresponding with changes in trajectories. Developmental stage should be considered when interpreting findings related to aperiodic activity in childhood.

neuroscience↗

bioRxiv2021

EEG-based clusters differentiate psychological distress, sleep quality and cognitive function in adolescents

1IntroductionTo better understand the relationships between brain activity, cognitive function and mental health risk in adolescence there is value in identifying data-driven subgroups based on measurements of brain activity and function, and then comparing cognition and mental health symptoms between such subgroups. MethodsHere we implement a multi-stage analysis pipeline to identify data-driven clusters of 12-year-olds (M = 12.64, SD = 0.32) based on frequency characteristics calculated from resting state, eyes-closed electroencephalography (EEG) recordings. EEG data was collected from 59 individuals as part of their baseline assessment in the Longitudinal Adolescent Brain Study (LABS) being undertaken in Queensland, Australia. Applying multiple unsupervised clustering algorithms to these EEG features, we identified well-separated subgroups of individuals. To study patterns of difference in cognitive function and mental health symptoms between core clusters, we applied Bayesian regression models to probabilistically identify differences in these measures between clusters. ResultsWe identified 5 core clusters which were associated with distinct subtypes of resting state EEG frequency content. EEG features that were influential in differentiating clusters included Individual Alpha Frequency, relative power in 4 Hz bands up to 16 Hz, and 95% Spectral Edge Frequency. Bayesian models demonstrated substantial differences in psychological distress, sleep quality and cognitive function between these clusters. By examining associations between neurophysiology and health measures across clusters, we have identified preliminary risk and protective profiles linked to EEG characteristics. ConclusionIn this work we have developed a flexible and scaleable pipeline to identify subgroups of individuals in early adolescence on the basis of resting state EEG activity. These findings provide new clues about neurophysiological subgroups of adolescents in the general population, and associated patterns of health and cognition that are not observed at the whole group level. This approach offers potential utility in clinical risk prediction for mental and cognitive health outcomes throughout adolescent development.

neuroscience↗