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\u00a0eNeuro\u00a0paper, 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\u00a0awake\u00a0individuals.<\/p>\n
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.”\u00a0<\/p>\n
First, supporting\u00a0previous\u00a0findings, the researchers found that four measures of EEG signals from 163 awake people aged 3\u201325 were differentially affected by sleep history and\u00a0age.\u00a0One 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.\u00a0Another measure displayed a surprising developmental shift, with opposite results in children and adults after a night of sleep.\u00a0These findings\u00a0ultimately demonstrate\u00a0that\u00a0brain\u00a0signals during wakefulness depend on prior sleep, and that this effect differs\u00a0between children and adults.\u00a0<\/p>\n
Because ADHD patients have developmental differences to neurotypical people and because\u00a0previous\u00a0work\u00a0identified\u00a0brain 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\u00a0observed\u00a0based 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.\u00a0<\/p>\n
Expanding on\u00a0how this\u00a0work\u00a0may improve research and clinical use of EEG, says\u00a0Snipes, “[Researchers and clinicians]\u00a0have used this tool for a while, but more elaborate forms of data analysis\u00a0can improve interpretations\u00a0of EEG recordings.\u00a0Even though\u00a0we know\u00a0certain\u00a0variables change the EEG signal,\u00a0we\u00a0can’t\u00a0assume what these changes mean if we\u00a0don’t\u00a0know what parts of the signal are changing.”\u00a0<\/p>\n
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