Longitudinal characterization of EEG power spectra during eyes open and eyes closed conditions in children

Abstract

This study is the first to examine spectrum-wide (1 to 250 Hz) differences in electroencephalogram (EEG) power between eyes open (EO) and eyes closed (EC) resting state conditions in 486 children. The results extend the findings of previous studies by characterizing EEG power differences from 30 to 250 Hz between EO and EC across childhood. Developmental changes in EEG power showed spatial and frequency band differences as a function of age and EO/EC condition.A 64-electrode system was used to record EEG at 4, 5, 7, 9, and 11 years of age. Specific findings were: (1) the alpha peak shifts from 8 Hz at 4 years to 9 Hz at 11 years, (2) EC results in increased EEG power (compared to EO) at lower frequencies but decreased EEG power at higher frequencies for all ages, (3) the EEG power difference between EO and EC changes from positive to negative within a narrow frequency band which shifts toward higher frequencies with age, from 9 to 12 Hz at 4 years to 32 Hz at 11 years, (4) at all ages EC is characterized by an increase in lower frequency EEG power most prominently over posterior regions, (5) at all ages, during EC, decreases in EEG power above 30 Hz are mostly over anterior regions of the scalp. This report demonstrates that the simple challenge of opening and closing the eyes offers the potential to provide quantitative biomarkers of phenotypic variation in brain maturation by employing a brief, minimally invasive protocol throughout childhood.

Development of peak alpha frequency reflects a distinct trajectory of neural maturation in autistic children

“…Recent work (e.g., Isler et al, 2022; Van Noordt & Willoughby, 2021) has sought to characterize developmental shifts in EEG power spectra throughout childhood as a means of understanding typical cortical maturation. Future research should examine PAF and other EEG measures longitudinally to expand understanding of maturation of PAF throughout development in the context of typical development and in neurodevelopmental conditions.…”

“…During the eyes‐open paradigm, participants viewed a gray screen with a crosshair at center. Given that alpha activity is more pronounced during eyes‐closed conditions (Barry et al, 2007; Isler et al, 2022; Murphy & Ongur, 2019), only eyes‐closed resting EEG data were included in the present analyses. At least 26 s of artifact‐free data were available for all participants included in analyses.…”

“…Because observed differences in absolute power may be confounded by frequency shifts (Haegens et al, 2014), peak alpha frequency (PAF), defined as the alpha frequency at which absolute power is maximal, offers a more robust representation of oscillatory alpha activity. Several studies have demonstrated that PAF indexes the development of neural networks (Miskovic et al, 2015; Valdes‐Hernandez et al, 2010) and that PAF reliably increases with age from birth to ~20 years of age (Cellier et al, 2021; Chiang et al, 2011; Freschl et al, 2022; Isler et al, 2022) and subsequently decreases throughout adulthood (Chiang et al, 2011; Hashemi et al, 2016). These developmental shifts in PAF represent oscillatory acceleration during childhood as cortical structures develop and subsequent age‐related oscillatory slowing over the course of adulthood.…”

Resting brain activity in early childhood predicts IQ at 18 years

“…Absolute power (µV²) was computed in theta (baseline: 3–5 Hz; 30 months: 3–5 Hz; 42 months: 3–5 Hz;), alpha (baseline: 6–9 Hz; 30 months: 6–10 Hz; 42 months: 6–10 Hz), beta (baseline:10–18 Hz; 30 months: 11–18 Hz; 42 months: 13–20 Hz), and gamma (baseline: 19–50 Hz; 30 months: 19–50 Hz; 42 months: 21–50 Hz) frequency bands. The frequency ranges were based on prior EEG studies using this sample ( Marshall et al, 2004 , Marshall et al, 2008 ); different frequency ranges were used for different ages because the frequency composition of power spectra changes across development ( Isler et al, 2022 , Marshall et al, 2002 ). To reduce data skewness, a natural log transformation was applied on the EEG power values.…”

Theta activity and cognitive functioning: Integrating evidence from resting-state and task-related developmental electroencephalography (EEG) research

“…Developmental EEG research demonstrates that frequency bands with similar functional and topographical characteristics tend to be slower in younger (vs. older) individuals (Isler et al, 2022;Marshall et al, 2002;Orekhova et al, 2006;Stroganova et al, 1999). To account for this age-related shift, the boundaries of different frequency bands are usually set at lower values for younger children (Saby & Marshall, 2012).…”