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 美国每年有2300万人需要麻醉才能做手术；然而，这取决于 麻醉师的专业技术监测麻醉深度，以确保患者充分麻醉。缺欠 适当的监测结果导致患者接受的麻醉太少(这导致较差 心理结果和创伤后应激障碍)，或麻醉过多(这会导致不良反应 认知结果，如围手术期神经认知障碍)。通过直接测量大脑活动 使用脑电(EEG)从无创性头皮电极改良术中深度 麻醉监测，这项工作的大部分是在健康的人类志愿者身上进行的，接受易于监测的 麻醉剂，因此不是普遍适用的。 脑电复杂性测量(源于非线性动力学)对麻醉深度的预测效果更好 传统上难以监测的外科患者，以及接受难以监测的麻醉剂的患者 探员们。该领域的知识缺口是指这些措施在多大程度上捕获信息流在 大脑，这是潜藏在意识体验之下的关键网络功能。为了建立一条链接 在复杂性测量和潜在的皮质动力学之间，头皮EEG的活动以及 需要同时采集颅内脑电(IEEG)。 在这项提议中，头皮脑电和iEEG信号将被记录来自暴露于 临床上正在接受iEEG植入的麻醉人员。这笔赠款的目标将支持 麻醉深度监测复杂新措施的测试和验证。具体地说， 本研究的目的是：(1)验证iEEG信号在紧急情况下是否发生了脑电复杂性的变化 从麻醉(并绘制复杂性变化的拓扑图)，(2)识别皮质连通性和 作为复杂性变化基础的效率动态(使用应用于 IEEG信号)和(3)使用不同的头皮EEG来翻译和优化这些措施的临床实用性 患者群体(有围手术期神经认知障碍风险的老年患者)。总而言之，结果 将提供必要的步骤来构建新一代复杂、易于实现和准确的 脑电监测麻醉深度。更好地了解麻醉过程中的脑动力学 给药最终将帮助医生更好地监测患者的麻醉深度，以减少不良结果。 这一职业发展奖将增加临床和翻译动手数据收集和培训博士。 莎拉·伊格曼。此外，伊格尔曼博士还将获得从事新电生理学工作的经验 模式(多通道iEEG)以及学习计算工具来分析具有多个 为她过渡到一名独立科学家做准备的渠道。