Intracranial cortical network connectivity underlying complexity changes during anesthetic emergence

麻醉苏醒期间颅内皮质网络连接的复杂性变化

• 批准号: 10267719
• 负责人: Sarah Eagleman
• 金额: $ 0.55万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-21 至 2021-11-03
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10267719
• 关键词: Anesthesia procedures; Anesthetics; Anterior; Area; Award; Awareness; Brain; Brain region; Clinical; Cognitive; Computer Analysis; Conscious; Data Collection; Data Set; Databases; Delirium; Development; Dimensions; Discrimination; Elderly; Electrodes; Electroencephalography; Electrophysiology (science); Ensure; Entropy; Epilepsy; Exposure to; General Anesthesia; Generations; Goals; Grant; Human Volunteers; Implantation procedure; Implanted Electrodes; Knowledge; Lead; Learning; Legal patent; Link; Maps; Marshal; Measures; Modality; Monitor; Nonlinear Dynamics; Operative Surgical Procedures; Outcome; Patient Monitoring; Patients; Perioperative; Phase; Physicians; Population; Post-Traumatic Stress Disorders; Postdoctoral Fellow; Property; Protocols documentation; Qualifying; Recovery; Risk; Scalp structure; Scientist; Seizures; Signal Transduction; Site; Structure; System; Techniques; Testing; Training; Translating; United States; Validation; Work; behavioral response; career; clinically relevant; cognitive recovery; computerized tools; experience; implantation; improved; indexing; neural network; neurocognitive disorder; patient population; post-traumatic stress; predict clinical outcome; prospective; psychological outcomes; relating to nervous system; skills; tool

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 scientist.

项目总结