Age-Related EEG Features of Bursting Activity During Anesthetic-Induced Burst Suppression

Abstract

Electroencephalographic (EEG) Burst Suppression (BSUPP) is a discontinuous pattern characterized by episodes of low voltage disrupted by bursts of cortical synaptic activity. It can occur while delivering high-dose anesthesia. Current research suggests an association between BSUPP and the occurrence of postoperative delirium in the post-anesthesia care unit (PACU) and beyond. We investigated burst micro-architecture to further understand how age influences the neurophysiology of this pharmacologically-induced state. We analyzed a subset of EEG recordings (n = 102) taken from a larger data set previously published. We selected the initial burst that followed a visually identified “silent second,” i.e., at least 1 s of iso-electricity of the EEG during propofol induction. We derived the (normalized) power spectral density [(n)PSD], the alpha band power, the maximum amplitude, the maximum slope of the EEG as well as the permutation entropy (PeEn) for the first 1.5 s of the initial burst of each patient. In the old patients >65 years, we observed significantly lower (p < 0.001) EEG power in the 1–15 Hz range. In general, their EEG contained a significantly higher amount of faster oscillations (>15 Hz). Alpha band power (p < 0.001), EEG amplitude (p = 0.001), and maximum EEG slope (p = 0.045) all significantly decreased with age, whereas PeEn increased (p = 0.008). Hence, we can describe an age-related change in features during EEG burst suppression. Sub-group analysis revealed no change in results based on pre-medication. These EEG changes add knowledge to the impact of age on cortical synaptic activity. In addition to a reduction in EEG amplitude, age-associated burst features can complicate the identification of excessive anesthetic administration in patients under general anesthesia. Knowledge of these neurophysiologic changes may not only improve anesthesia care through improved detection of burst suppression but might also provide insight into changes in neuronal network organization in patients at risk for age-related neurocognitive problems.