This study reports spectrum-wide (2-250 Hz) differences in EEG power between eyes open (EO) and eyes closed (EC) brain states in a cross-sectional study of children at 4, 5, 7, 9, and 11 years of age. The principal results are that: 1) the alpha band spectral peak increases from 8 Hz at 4 years of age to 9 Hz at 11 years of age; 2) eye closure results in increased power at lower frequencies but decreased at higher frequencies; 3) the change in sign for the difference between EO and EC conditions occurs in a narrow band of ‘transitional’ frequencies; 4) the transitional frequencies change across childhood, from a center frequency of 9 Hz at 4 years of age to a center frequency of 32 Hz at 11 years of age; 5) at 4 and 5 years of age, eye closure increases lower frequency power most prominently over posterior regions; 5) reduced power at higher frequencies with eye closure is most prominent over anterior regions.
The earliest normative studies of EEG power in response to eye closure focused solely on the alpha band (Chapman, Shelburne, & Bragdon, 1970). Subsequent work with adults explored frequency bands outside of alpha, such as the beta band (14 to 30 Hz) (Barry et al., 2007; Glass & Kwiatkowski, 1970). Those studies found increased beta power in response to eye closure. Barry et. al. (Barry et al., 2009) studied children aged 8 to 12 and found a similar increase in beta power with eye closure. Our findings for the older children are consistent with these adult findings however, we found that at earlier ages beta power decreases with eye closure. Recently, Johnstone et. al. (Johnstone et. al, 2020) reported a developmental increase in frontal alpha power in children from 7 to 12 years old, but no developmental change in beta power. However, akin to Barry et. al., they defined the beta band as 12.5 to 25 Hz. In contrast to both the Barry and Johnstone studies, we examined 3 Hz wide bands across the same beta range and found mixed effects dependent upon both age and frequency (Supplemental Figure 2).
The results we have presented used spectra averaged over three 30-second epochs in each eye condition. We investigated whether we would obtain the same, or very similar results, if we only used one epoch in each condition. Therefore, we repeated all analyses using just the first epoch in each condition. Indeed, we found that, in general, using only one epoch in each condition replicates our initial results (see Supplemental Figures 3 through 6). This suggests that the amount of data needed to observe the described eye closure effect is minimal. This characteristic may be important to some deployments of the eye-closure paradigm.
To our knowledge, this is the first study in children to test for EEG differences induced by eye closure in the gamma band and higher frequencies (above 30 Hz) using scalp EEG. Decreased power in response to eye closure occurred over the entire range of high frequencies from 30 to 200 Hz. Interestingly, one recent study of eye closure used EEG data from adult patients with intracranial electrodes implanted for seizure localization (Geller et al., 2014). Convergent with our results, they reported that eye closure resulted in widespread increased power at lower frequencies but decreased power over a broad frequency range above 30 Hz. Further, the higher frequency effects were limited to occipital regions and two focal frontal areas.
Barry et al (Barry et al., 2007) interpreted their findings as follows: widespread increases in power at lower frequencies with eye closure reflects a decrease in global arousal, while more localized higher frequency power reflects cortical activation and visual processing. This view has some support in the results reported by Geller 2014 (Geller et al., 2014) discussed previously. Furthermore, a recent study of eye closure using fMRI network analyses showed that networks active during EC had higher global efficiency, while networks active during EO had higher local clustering (Xu et al., 2014). Additional studies have demonstrated associations between increased alpha power during the EC condition and increased autism traits in typically developing adults (Carter Leno et al., 2018) suggesting a potential relationship between global arousal and behavioral rigidity.
One limitation of our study, especially regarding the transition frequencies and spatial location of differences between EO and EC, is that these current results were cross-sectional. It would be important to track individuals’ trajectories over time to validate the hypothesis of variation in transition frequencies with age are related to neurobehavioral functions. Further studies that would link these differences to neurodevelopmental measures are expected to offer a likely biomarker of neurodevelopmental risk. A second limitation of our very high frequency results is their origin in scalp EEG, which is predominately susceptible to muscle artifact at those frequencies. However, muscle artifact has a flat spectrum (Nunez, 2006), and the lack of any clear break in the log-log slopes of our power spectra argue against that explanation.
Assessments of different brain states, e.g. sleep states or evoked response states, provide good markers of brain maturation and capacities. However, these types of assessments either require long periods of data acquisition or equipment that allows precise linkage of stimuli presentation and brain activity making these approaches difficult for large cohort studies. We are now initiating studies in which this challenge is being used for remote, in home assessment.
This current report demonstrates that the simple challenge of opening and closing the eyes can provide important information about the maturation of brain states and can be done with a very brief, minimally demanding protocol. In conclusion, the present work demonstrates EO/EC elicits changes in EEG spectra not confined to lower frequencies and which change as function of age during childhood.