Disambiguating coma etiologies by assessing the lability of EEG dynamics

项目摘要 昏迷是一种由于严重脑损伤而导致的无意识状态，其中患者对 外部刺激。由于当前临床试验在识别特定损伤或相关原因方面的局限性， 对于昏迷，为昏迷患者设计治疗策略是持续的临床挑战。 昏迷的一个标志性特征是大脑电活动严重中断。因此，脑电图 (EEG)，测量大脑的电活动模式，通常用于神经病学， 神经外科重症监护室（NNICU），以监测昏迷患者。然而，脑电图用于诊断 昏迷很大程度上限于临床医生以“原始”形式将电活动阅读为监视器上的波形描记。 拟议研究的主要目标是开发和评估新的算法，来自工程 一种理论，将从脑电图中提取昏迷的信息，这些信息在阅读活动时可能不明显 用肉眼观察。因此，这些新方法将使自动EEG为基础的分类， 昏迷病因、损伤严重程度分级和临床结局预测。最终，这些技术可以 潜在地用于帮助为昏迷患者定制临床治疗策略。 在这个项目中，我们将记录脑电图数据，从病人诊断与一系列昏迷病因。这些数据 将被吸收到大脑如何产生电活动的生物数学模型中，即，的 神经动力学在这些模型的支持下，我们将使用一种新的分析类型，称为网络可达性 分析，其表征模型可以产生的不同类型的电活动模式。作为 打个比方，飞行中的飞机看起来相对静止，但飞机的动力学实际上是复杂的 因为它可以在任何时候执行许多不同的机动。我们的分析将描述有多少“演习” 大脑能够做出，从而提供大脑不稳定性的动态定量表征。 我们的假设是不同类型的昏迷会表现出不同的不稳定性。为了验证这个假设， 其临床实用性，我们将应用网络可达性分析，我们将从患者获得的记录， 昏迷通过这种分析，我们将构建定量生物标志物，可以整合到一个新的类型， 专门为昏迷和其他相关疾病量身定制的脑电图监测仪。 因此，该项目的结果将通过促进神经重症监护产生重大和直接的影响。 更精确的定量分析昏迷的神经动力学。更一般地说，这些发展 这些技术可能会为无意识的病理状态下的机制提供新的线索， 以及正常的睡眠和清醒。

Project Summary Coma is a state of unconsciousness due to severe brain injury, in which patients are rendered unresponsive to external stimuli. Due to the limitations of current clinical tests in identifying a specific injury or causes associated with coma, devising treatment strategies for coma patients is a persistent clinical challenge. A signature feature of coma is severe disruption of the brain's electrical activity. Thus, the electroencephalogram (EEG), which measures the brain's electrical activity patterns, is routinely used in the neurology and neurosurgery intensive care unit (NNICU) to monitor patients in coma. However, the utility of EEG for diagnosing coma is largely limited to clinicians reading electrical activity in `raw' form as waveform tracings on a monitor. The primary goal of the proposed research is to develop and evaluate new algorithms, derived from engineering theory that will extract information about coma from the EEG that might not be apparent when reading the activity with the naked eye. Consequently, these new methods will enable the automatic EEG-based classification of coma etiology, gradation of injury severity, and prediction of clinical outcome. Eventually, these techniques could potentially be used to help tailor clinical treatment strategies for patients in coma. In this project, we will record EEG data from patients diagnosed with a range of coma etiologies. These data will be assimilated into a biological mathematical model for how the brain produces electrical activity, i.e., the neural dynamics. Enabled by these models, we will use a new type of analysis, called network reachability analysis, which characterizes the different types of electrical activity patterns that the models can produce. As an analogy, an airplane in flight might seem relatively stationary, but the plane's dynamics are actually complex since it could execute many different maneuvers at any time. Our analysis will describe how many `maneuvers' the brain is capable of making, thus providing a dynamical, quantitative characterization of the brain's lability. Our hypothesis is that different types of coma will exhibit different lability. To test this hypothesis, and to explore its clinical utility, we will apply network reachability analysis to the recordings we will obtain from patients with coma. Through this analysis, we will construct quantitative biomarkers that could be integrated into a new type of EEG monitor tailored for coma and other related disorders. Thus, the outcomes of this project will have significant and immediate impact on neurocritical care by facilitating more precise quantitative analysis of the neural dynamics of coma. More generally, the development of these techniques might shed new light on the mechanisms that underlie pathological states of unconsciousness, as well as normal sleep and wakefulness.