权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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.

项目总结