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急性丘脑缺血性卒中患者大脑的脑电图复杂性及功能连接异常

Abnormal EEG Complexity and Functional Connectivity of Brain in Patients with Acute Thalamic Ischemic Stroke.

作者信息

Liu Shuang, Guo Jie, Meng Jiayuan, Wang Zhijun, Yao Yang, Yang Jiajia, Qi Hongzhi, Ming Dong

机构信息

Neural Engineering & Rehabilitation Lab, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China.

Department of Neurology, Tianjin First Center Hospital, Tianjin 300072, China.

出版信息

Comput Math Methods Med. 2016;2016:2582478. doi: 10.1155/2016/2582478. Epub 2016 Jun 14.

Abstract

Ischemic thalamus stroke has become a serious cardiovascular and cerebral disease in recent years. To date the existing researches mostly concentrated on the power spectral density (PSD) in several frequency bands. In this paper, we investigated the nonlinear features of EEG and brain functional connectivity in patients with acute thalamic ischemic stroke and healthy subjects. Electroencephalography (EEG) in resting condition with eyes closed was recorded for 12 stroke patients and 11 healthy subjects as control group. Lempel-Ziv complexity (LZC), Sample Entropy (SampEn), and brain network using partial directed coherence (PDC) were calculated for feature extraction. Results showed that patients had increased mean LZC and SampEn than the controls, which implied the stroke group has higher EEG complexity. For the brain network, the stroke group displayed a trend of weaker cortical connectivity, which suggests a functional impairment of information transmission in cortical connections in stroke patients. These findings suggest that nonlinear analysis and brain network could provide essential information for better understanding the brain dysfunction in the stroke and assisting monitoring or prognostication of stroke evolution.

摘要

近年来,缺血性丘脑卒中已成为一种严重的心脑血管疾病。迄今为止,现有的研究大多集中在几个频段的功率谱密度(PSD)上。在本文中,我们研究了急性丘脑缺血性卒中患者和健康受试者的脑电图(EEG)非线性特征及脑功能连接。对12例卒中患者和11例健康受试者作为对照组,记录闭眼静息状态下的脑电图(EEG)。计算Lempel-Ziv复杂度(LZC)、样本熵(SampEn)以及使用偏相干分析(PDC)构建脑网络进行特征提取。结果显示,患者的平均LZC和SampEn高于对照组,这意味着卒中组的脑电图复杂度更高。对于脑网络,卒中组表现出皮质连接较弱的趋势,这表明卒中患者皮质连接中的信息传递存在功能障碍。这些发现表明,非线性分析和脑网络可为更好地理解卒中时的脑功能障碍以及辅助监测或预测卒中进展提供重要信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9f56/4923597/f38dba638307/CMMM2016-2582478.001.jpg

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