项目名称: 麻醉中神经血管耦合估计与分析
项目编号: No.61304247
项目类型: 青年科学基金项目
立项/批准年度: 2014
项目学科: 自动化技术、计算机技术
项目作者: 梁振虎
作者单位: 燕山大学
项目金额: 24万元
中文摘要: 最新研究表明,集成脑电和近红外成像技术获取的神经血管耦合信息为研究麻醉药物效应及意识机理提供了新的思路。研究可靠的脑电和近红外信息耦合估计和分析方法,发展有效的神经血管耦合指数,是药物效应和意识机理评估的关键问题。本项目首先基于大鼠实验和临床获取的脑电及近红外数据,采用排序互信息和相位-幅度耦合方法计算神经血管耦合强度,并基于广义线性模型描述两种信息的局部因果关系;引入进化图和空间独立分量分析方法评估脑区间两者的耦合强度和方向;其次结合一阶相变理论和药物代谢建立麻醉中的神经血管耦合模型,采用贝叶斯反演优化模型参数,使其产生替代脑电与血流动力学数据用于算法性能分析;最后针对不同药物浓度效应,以群搜索的人工神经网络优化效应指数,并采用预测概率评价指数性能。课题的开展将为研究麻醉下的神经血管耦合提供新的计算工具,有助于深入理解麻醉药物作用机理和量化效应,为研究麻醉意识消失机制提供新的理论基础。
中文关键词: 麻醉机制;脑电信号;近红外光谱成像;神经群模型;神经血管耦合
英文摘要: Recent studies show that the analysis combining the electroencephalogram (EEG) and near infrared reflectance spectroscopy (NIRS) could reveal the neurovascular coupling of the brain. This provides a new perspective for studying the mechanisms of consciousness and the effect of anesthetic. The key issues in interpreting the mechanism of anesthesia is to develop a reliable neurovascular coupling function by studying the correlation of EEG and NIRS signals in both temporal and spatial domains. In this proposal, the concurrent anesthetic EEG/NIRS data are acquired from the rat experiments and clinical surgeries. Then, the following methods are used to analyze the data: The permutation mutual information and phase-amplitude coupling methods are employed to calculate the coupling strength of EEG and hemodynamic response; The generalized linear model is adopted to evaluate the causality of local hemodynamic and EEG in the spatial domain; The evolution map approach and spatial independent component analysis (ICA) are used to map the brain network and functional connectivity for EEG and NIRS data respectively. Next, a neurovascular coupling model modified by a first-order phase transition with the anesthetic effect is proposed to describe the transient dynamics of the induced unconsciousness, whose parameters are optimiz
英文关键词: anesthesia mechanism;Electroencephalogram;Near infrared reflectance spectroscopy;neural mass model;Neurovascular coupling