Sleep and age shape EEG brain signals during wakefulness

Clinicians use electroencephalography (EEG) to assess brain activity in epilepsy and sleep pathologies, and this powerful tool has shown promise for other conditions. Emerging evidence suggests that brain development, age, and the time of day affect EEG signals measured during sleep. In a new eNeuro paper, researchers at the University Children's Hospital of Zurich explored how differences in brain development, age, and sleep affect measures of EEG signals recorded from awake individuals.

Elaborates lead author Sophia Snipes, "EEG studies have typically relied on summary measures when comparing patients or experimental conditions, but we picked apart the EEG signal with more detail to better understand the meaning behind the differences we were observing." 

First, supporting previous findings, the researchers found that four measures of EEG signals from 163 awake people aged 3–25 were differentially affected by sleep history and age. One measure showed an interaction between sleep history and age that could potentially reflect how children experience more brain changes during learning and memory compared to adults-a new finding, according to the researchers. Another measure displayed a surprising developmental shift, with opposite results in children and adults after a night of sleep. These findings ultimately demonstrate that brain signals during wakefulness depend on prior sleep, and that this effect differs between children and adults. 

Because ADHD patients have developmental differences to neurotypical people and because previous work identified brain activity differences in EEG recordings taken from sleeping ADHD patients, the researchers further assessed their specific measures from the data of 58 awake ADHD children. No differences in the measures were observed based on ADHD diagnosis alone, suggesting to the researchers that sleep quality, more than symptoms of ADHD itself, may explain previously observed EEG data variability, though more work is needed. 

Expanding on how this work may improve research and clinical use of EEG, says Snipes, "[Researchers and clinicians] have used this tool for a while, but more elaborate forms of data analysis can improve interpretations of EEG recordings. Even though we know certain variables change the EEG signal, we can't assume what these changes mean if we don't know what parts of the signal are changing."