# Intracranial cortical network connectivity underlying complexity changes during anesthetic emergence

> **NIH NIH K99** · STANFORD UNIVERSITY · 2020 · $96,746

## Abstract

PROJECT SUMMARY
Each year 23 million people in the United States require anesthesia for surgery; however, it is up to the
anesthesiologist’s expertise monitoring anesthetic depth to ensure patients are adequately anesthetized. Lack
of appropriate monitoring results in patients receiving either too little anesthesia (which causes poor
psychological outcomes and post-traumatic stress disorder), or too much anesthesia (which causes poor
cognitive outcomes such as perioperative neurocognitive disorders). Though direct measures of brain activity
from noninvasive scalp electrodes using electroencephalography (EEG) improve intraoperative depth of
anesthesia monitoring, much of this work takes place in healthy human volunteers receiving easy-to-monitor
anesthetic agents, and thus is not universally applicable.
EEG complexity measures (derived from nonlinear dynamics) yield superior prediction of anesthetic depth in
traditionally hard-to-monitor surgical patients, as well as in patients receiving hard-to-monitor anesthetic
agents. A knowledge gap in the field is the extent to which these measures capture loss of information flow in
the brain, which is a critical network feature underlying conscious experience. In order to establish a link
between complexity measures and the underlying cortical dynamics, activity from scalp EEG as well as
intracranial EEG (iEEG) needs to be capture simultaneously.
In this proposal, both scalp EEG and iEEG signals will be recorded from epileptic patients exposed to
anesthesia who are undergoing iEEG implantation for clinical purposes. The Aims in this grant will support the
testing and validation of sophisticated new measures for anesthetic depth monitoring. Specifically the goals of
this proposal are to: (1) validate whether the EEG complexity changes occur in iEEG signals during emergence
from anesthesia (and to map the topology of complexity changes), (2) identify the cortical connectivity and
efficiency dynamics that underlie complexity changes (using standard functional connectivity tools applied to
iEEG signals) and (3) translate and optimize the clinical utility of these measures using scalp EEG in a different
patient population (geriatric patients at risk for perioperative neurocognitive disorders). Collectively, the results
will provide the necessary steps to build a new generation of sophisticated, easily-implemented, and accurate
EEG monitoring for anesthetic depth. A better understanding of the brain dynamics during anesthesia
administration will ultimately help physicians better monitor patient anesthetic depth to reduce poor outcomes.
This career developmental award will add clinical and translational hands-on data collection and training for Dr.
Sarah Eagleman. Additionally, Dr. Eagleman will gain experience working with a new electrophysiology
modality (multichannel iEEG) as well as learn the computational tools to analyze datasets with multiple
channels to prepare her for her transition to an independent sc...

## Key facts

- **NIH application ID:** 10105168
- **Project number:** 1K99GM140215-01
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Sarah Eagleman
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $96,746
- **Award type:** 1
- **Project period:** 2020-09-21 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10105168

## Citation

> US National Institutes of Health, RePORTER application 10105168, Intracranial cortical network connectivity underlying complexity changes during anesthetic emergence (1K99GM140215-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10105168. Licensed CC0.

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