Mechanisms of anesthetic-induced unconsciousness

麻醉引起的意识丧失的机制

• 批准号: 9750291
• 负责人: Matthew I Banks
• 金额: $ 49.55万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-02-15 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9750291
• 关键词: Acoustic Stimulation; Algorithms; Anesthesia procedures; Anesthetics; Animal Model; Animals; Area; Attention; Auditory; Auditory area; Awareness; Brain; Chronic; Code; Conscious; Data; Data Analyses; Delirium; Detection; Development; Electric Stimulation; Electrocorticogram; Electroencephalography; Electrophysiology (science); Epilepsy; Feedback; Functional Magnetic Resonance Imaging; General Anesthesia; General anesthetic drugs; Goals; Grant; Human; Intractable Epilepsy; Knowledge; Measures; Mediating; Medical; Mental disorders; Minimally Conscious States; Modeling; Molecular Target; Monitor; Operative Surgical Procedures; Parietal Lobe; Patients; Pattern; Play; Prefrontal Cortex; Process; Rest; Role; Schizophrenia; Sensory; Signal Transduction; Sleep; Sleep Stages; Stimulus; Structure; System; Temporal Lobe; Testing; Time; Translating; Translations; Unconscious State; Update; awake; base; biomarker development; clinical application; design; deviant; expectation; human subject; insight; mental state; neural circuit; neural network; non rapid eye movement; non-invasive monitor; novel; prognostic; relating to nervous system; remediation; research clinical testing; response; side effect; sound; temporal measurement

PROJECT SUMMARY The long-term objective of this proposal is to understand the mechanisms responsible for loss of consciousness (LOC) under general anesthesia. Previous invasive studies in animal models have identified candidate mechanisms, but translating those findings to human consciousness remains approximate. More precise determinations of states of consciousness are feasible in human subjects, but non-invasive measures of brain activity (fMRI, EEG, MEG) can only indirectly assess the underlying neural circuitry. This proposal will overcome these limitations by taking advantage of the unique opportunity to directly record from the human brain. Using electrocorticography in neurosurgical patients, we will investigate neural networks modulated by general anesthesia. Our overarching goal is to identify patterns of activity and connectivity in cortical networks that track changes in contents of consciousness (i.e. awareness) under anesthesia and during sleep. Our approach is to identify changes in the networks underlying auditory predictive coding that occur upon LOC. Predictive coding minimizes the differences between internally generated constructs and empirical data, subserved by ongoing interaction between sensory and higher-order cortical regions. This model is ideal for this project because it engages the crucial interplay between predictions of the world and sensory observations of the world, a fundamental function of consciousness. The scientific premise of this project is that at a systems level, disruption of predictive coding subserved by large-scale cortical networks represents a signature of anesthetic-induced unconsciousness. To accomplish our goals, we will pursue three specific aims. The first aim seeks to refine our understanding of the cortical networks involved in auditory predictive coding in awake behaving subjects. This aim will focus on identifying the connectivity of the networks subserving predictive coding over short and long time scales, as effects of LOC on these networks are believed to be distinct. The second aim examines changes in network structure upon anesthesia LOC. This will be achieved by recording brain activity from subjects during induction of general anesthesia. The generality of the findings will be tested using two different anesthetic agents. Aim 3 seeks to identify common electrophysiological signatures for LOC under anesthesia and during sleep. This will be achieved by measuring brain activity in the same subjects during natural sleep. The results will have broad clinical applicability to defining and interpreting prognostic signs in patients with altered mental status (e.g. chronic vegetative and minimally conscious states), mental illness (e.g. delirium and schizophrenia) and development of novel algorithms for use in monitoring depth of anesthesia.

项目概要 该提案的长期目标是了解导致意识丧失的机制 (LOC) 在全身麻醉下进行。先前对动物模型的侵入性研究已经确定了候选者 机制，但将这些发现转化为人类意识仍然是近似的。更精准 意识状态的测定对于人类受试者是可行的，但对大脑的非侵入性测量 活动（fMRI、EEG、MEG）只能间接评估潜在的神经回路。该提案将克服 这些限制是通过利用独特的机会直接从人脑记录下来的。使用 在神经外科患者的皮层电图检查中，我们将研究由通用调节的神经网络 麻醉。 我们的首要目标是识别皮层网络中的活动和连接模式，以跟踪 麻醉下和睡眠期间的意识内容（即意识）。我们的方法是识别 LOC 时发生的听觉预测编码网络的变化。预测编码最小化 内部生成的构造和经验数据之间的差异，通过持续的交互而受到影响 感觉和高阶皮质区域之间。该模型非常适合该项目，因为它涉及 对世界的预测和对世界的感官观察之间至关重要的相互作用，这是一个基本功能 的意识。该项目的科学前提是，在系统层面上，预测编码的破坏 受到大规模皮质网络的支持代表了麻醉引起的无意识的特征。 为了实现我们的目标，我们将追求三个具体目标。第一个目标旨在加深我们对 参与清醒行为受试者听觉预测编码的皮质网络。这一目标将集中于 识别在短期和长期范围内支持预测编码的网络的连接性，如 LOC 对这些网络的影响被认为是不同的。第二个目标检查网络的变化 麻醉LOC时的结构。这将通过在诱导期间记录受试者的大脑活动来实现 全身麻醉。将使用两种不同的麻醉剂来测试研究结果的普遍性。目标 3 旨在确定麻醉下和睡眠期间 LOC 的常见电生理特征。这将 通过测量同一受试者在自然睡眠期间的大脑活动来实现。结果将具有广泛 定义和解释精神状态改变患者的预后体征的临床适用性（例如，精神状态改变） 慢性植物人和最低意识状态）、精神疾病（例如谵妄和精神分裂症）和 开发用于监测麻醉深度的新算法。