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Assessing reliability of resting-state EEG metrics in school-age children using a naturalistic paradigm

Poster Session F - Tuesday, April 1, 2025, 8:00 – 10:00 am EDT, Back Bay Ballroom/Republic Ballroom

Madhumita Nambiar¹, Devin Kearns², Fumiko Hoeft¹, Silvia Siu-Yin Clement-Lam¹; ¹University of Connecticut, ²North Caroline State University

Resting-state EEG (rsEEG) provides valuable insights into neural dynamics, with power spectral densities showing potential for identifying atypical neurophysiological patterns in neurodevelopmental disorders. However, the reliability of these metrics, particularly in children, remains underexplored. This study leverages longitudinal rsEEG recordings across four time points over a month in 29 school-age children (7–10 years) attending summer research camps. EEG data were recorded using a Brain Vision 32-Channel ActiChamp system while children watched a naturalistic video (Inscapes). These repeated measures provide a more robust evaluation of the reliability and variability of rsEEG metrics, specifically delta (0.5–3.5 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (12–30 Hz), and gamma (>30 Hz) frequency bands. Currently, data preprocessing is underway using EEGLAB in Matlab. Steps include 1-Hz high-pass filter, noisy data removal, interpolation, average referencing, independent component analysis for artifact rejection, and segmentation into 2-second epochs. Power spectral analysis using Fast Fourier Transform will compute absolute and relative power for frontal, central, parietal, and occipital regions . Test-retest reliability will be assessed through Intraclass Correlation Coefficients for each frequency band and electrode cluster across the four time points. Growth curve modeling will examine individual and group-level trajectories of EEG power over time. Discussions will focus on the reliability and variability of power spectral densities across frequency bands and scalp regions. Additionally, we will discuss how naturalistic paradigms and frequent recordings contribute to methodological advancements in pediatric EEG research, guiding the application of rsEEG metrics in developmental studies.

Topic Area: METHODS: Electrophysiology