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Disambiguating coma etiologies by assessing the lability of EEG dynamics

通过评估脑电图动态的不稳定性来消除昏迷病因

基本信息

批准号：
9321999
负责人：
ShiNung Ching
金额：
$ 19.06万
依托单位：
WASHINGTON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-08-01 至 2019-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9321999
关键词：
Acute Algorithms Arousal Behavior Behavioral Assay Biological Biological Assay Biological Markers Biophysics Brain Brain Death Brain Injuries Caring Classification Clinical Clinical Research Clinical Treatment Coma Complex Data Detection Development Diagnosis Diagnostic Diffuse Diffuse Brain Injury Disease Economics Electroencephalogram Engineering Etiology Exhibits Eye Frequencies Goals Hospitals Individual Injury Intensive Care Intensive Care Units Lead Light Measures Methodology Methods Minimally Conscious States Modeling Monitor Neurologic Neurology Neurons Outcome Output Pathologic Pathology Patient Monitoring Patient-Focused Outcomes Patients Pattern Positioning Attribute Prospective Studies Reading Recovery Research Residual state Resolution Retrospective Studies Seizures Severities Signal Transduction Sleep Stimulus Systems Theory Techniques Technology Testing Time Time Series Analysis Treatment outcome Unconscious State Variant Wakefulness base behavioral impairment behavioral outcome brain electrical activity cohort design diagnostic biomarker disability dynamic system follower of religion Jewish imaging study improved innovation insight interest mathematical model neural circuit neuronal circuitry neurosurgery novel novel diagnostics outcome forecast point of care predict clinical outcome predictive marker prognostic prospective public health relevance relating to nervous system signal processing technique development theories tool treatment strategy

项目摘要

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.

项目摘要昏迷是由于严重的脑损伤而导致的一种无意识状态，在这种状态下，患者对外部刺激。由于目前临床测试在确定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一个持久的临床挑战。昏迷的一个显著特征是大脑的电活动严重中断。因此，脑电波脑电(EEG)测量大脑的电活动模式，通常用于神经学和神经外科重症监护病房(NNICU)，用于监测昏迷患者。然而，脑电在诊断中的作用昏迷在很大程度上仅限于临床医生将电活动以“原始”形式读取为监视器上的波形轨迹。拟议研究的主要目标是开发和评估源自工程的新算法。从脑电中提取昏迷信息的理论，这些信息在阅读活动时可能并不明显用肉眼看。因此，这些新方法将使基于脑电的自动分类成为可能昏迷病因学、损伤严重程度分级和临床结果预测。最终，这些技术可以可能被用于帮助昏迷患者量身定做临床治疗策略。在这个项目中，我们将记录被诊断为一系列昏迷原因的患者的脑电数据。这些数据将被同化为一个关于大脑如何产生电活动的生物数学模型，即神经动力学。在这些模型的支持下，我们将使用一种新类型的分析，称为网络可达性分析，它描述了模型可以产生的不同类型的电活动模式。AS 打个比方，飞行中的飞机可能看起来相对静止，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动作。我们的分析将描述有多少次‘演习’ 大脑能够制造，因此提供了大脑不稳定的动态、定量的特征。我们的假设是，不同类型的昏迷会表现出不同的稳定性。来检验这一假设，并探索它的临床用途，我们将应用网络可达性分析，我们将从患者那里获得的录音昏迷。通过这种分析，我们将构建可以整合成一种新类型的定量生物标记物为昏迷和其他相关疾病量身定做的脑电监护仪。因此，该项目的成果将对神经危重护理产生重大和直接的影响更准确地定量分析昏迷的神经动力学。更广泛地说，这些技术的发展技术可能会对潜意识的病理状态背后的机制提供新的线索，如以及正常的睡眠和清醒。