Mechanisms of anesthetic-induced unconsciousness

项目摘要 这项提案的长期目标是了解导致意识丧失的机制 (LOC)在全身麻醉下以前在动物模型中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。更精确 意识状态的确定在人类受试者中是可行的，但是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。这项提案将克服 这些局限性通过利用独特的机会，直接记录从人脑。使用 在神经外科患者的皮层电图，我们将研究神经网络调制的一般 麻醉 我们的首要目标是确定皮层网络的活动和连接模式，这些模式跟踪大脑皮层的变化。 在麻醉和睡眠期间的意识内容（即意识）。我们的方法是识别 听觉预测编码的基础网络发生的变化。预测编码最小化 内部生成的结构和经验数据之间的差异，通过持续的交互作用 在感觉和高级皮层区域之间。此模型非常适合此项目，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，这是一种基本功能， 识这个项目的科学前提是，在系统层面上， 由大规模皮层网络提供的信息代表了麻醉诱导的无意识的特征。 为了实现我们的目标，我们将追求三个具体目标。第一个目的是要改善我们的理解 在清醒的行为主体中参与听觉预测编码的皮层网络。这一目标将侧重于 识别在短和长时间尺度上支持预测编码的网络的连通性， 据信，对这些网络的影响是不同的。第二个目标是审查网络的变化 麻醉后的结构这将通过记录诱导期间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检测结果的一般性。目标3 旨在识别麻醉下和睡眠期间睡眠障碍的常见电生理特征。这将 通过测量自然睡眠期间相同受试者的大脑活动来实现。结果将有广泛的 定义和解释精神状态改变患者预后体征的临床适用性（例如， 慢性植物人和最低意识状态）、精神疾病（如谵妄和精神分裂症），以及 开发用于监测麻醉深度的新算法。

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.