A Real-Time Neurophysiologic Stress Test for the Aging Brain: Novel Perioperative and ICU Applications of EEG in Older Surgical Patients

Abstract

As of 2022, individuals age 65 and older represent approximately 10% of the global population [1], and older adults make up more than one third of anesthesia and surgical cases in developed countries [2, 3]. With approximately > 234 million major surgical procedures performed annually worldwide [4], this suggests that > 70 million surgeries are performed on older adults across the globe each year. The most common postoperative complications seen in these older surgical patients are perioperative neurocognitive disorders including postoperative delirium, which are associated with an increased risk for mortality [5], greater economic burden [6, 7], and greater risk for developing long-term cognitive decline [8] such as Alzheimer's disease and/or related dementias (ADRD). Thus, anesthesia, surgery, and postoperative hospitalization have been viewed as a biological "stress test" for the aging brain, in which postoperative delirium indicates a failed stress test and consequent risk for later cognitive decline (see Fig. 3). Further, it has been hypothesized that interventions that prevent postoperative delirium might reduce the risk of long-term cognitive decline. Recent advances suggest that rather than waiting for the development of postoperative delirium to indicate whether a patient "passed" or "failed" this stress test, the status of the brain can be monitored in real-time via electroencephalography (EEG) in the perioperative period. Beyond the traditional intraoperative use of EEG monitoring for anesthetic titration, perioperative EEG may be a viable tool for identifying waveforms indicative of reduced brain integrity and potential risk for postoperative delirium and long-term cognitive decline. In principle, research incorporating routine perioperative EEG monitoring may provide insight into neuronal patterns of dysfunction associated with risk of postoperative delirium, long-term cognitive decline, or even specific types of aging-related neurodegenerative disease pathology. This research would accelerate our understanding of which waveforms or neuronal patterns necessitate diagnostic workup and intervention in the perioperative period, which could potentially reduce postoperative delirium and/or dementia risk. Thus, here we present recommendations for the use of perioperative EEG as a "predictor" of delirium and perioperative cognitive decline in older surgical patients.

Keywords: Alzheimer’s disease; Anesthesia; Cognitive impairment; Delirium; Dementia; Neurophysiology.

Fig. 1

A tentative physiological model of resting state EEG oscillations in the normal and Alzheimer’s disease brain, in the awake state and in the anesthetized state. The red arrows indicate anteriorization of the EEG rhythms due to GABAergic anesthetic agents such as propofol or volatile anesthetics. In the normal brain, dominant EEG rhythms are observed at alpha frequencies (8–12 Hz), denoting the background, spontaneous synchronization ~ 10 Hz of neural networks that regulate global arousal and consciousness states. These networks span neural populations of the cerebral cortex, thalamus, basal forebrain, and brainstem, including glutamatergic, cholinergic, dopaminergic, and serotoninergic parts of the reticular ascending systems. The “slowing” of rsEEG rhythms depicted in the Alzheimer’s brain would mainly reflect a thalamocortical “disconnection.” Adapted from Babiloni et al., 2021 (courtesy of the Publisher) [19]

Fig. 2

An overview of electroencephalographic (EEG) methodology to investigate cortical rhythm changes related to altered vigilance in older adults at risk of postoperative delirium and/or mild cognitive impairment due to age-related neurodegenerative diseases. Top left: top down view of 19 scalp electrodes from the International 10–20 System (Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, and O2). This setup has been used for many rsEEG recordings. In this figure, A1 and A2 indicate the position of linked earlobe reference electrodes. Top right: example of resting-state electroencephalographic (rsEEG) activity with artifacts. Bottom left: an example of an EEG power density spectrum computed at an occipital electrode (i.e., O1) in a healthy control person. Note the EEG power density peak at 10 Hz is typically called “individual alpha frequency peak (IAFp),” which can be used to distinguish low- and high-frequency alpha sub-bands within individuals. Bottom right: an example of cortical source solutions from rsEEG alpha rhythms computed by the exact low-resolution brain electromagnetic tomography (eLORETA) freeware. The eLORETA source solutions can be averaged within cortical lobes of interest and compared among older adults with intact cognitive status (including those with preclinical Alzheimer’s disease (AD) pathology) and individuals with AD and related dementias

Fig. 3

Similarities between the use of ECG in cardiac exercise stress tests, and the potential use of EEG as a real-time readout of patient responses to the stress test of anesthesia/surgery