摘要
精神分裂症与一些认知障碍如信息处理、工作记忆等联系紧密,研究工作是记忆任务中精神分裂症患者与正常人的多通道脑电在各个阶段、各个频段存在哪些显著性差异,可为精神分裂症的诊断提供依据。使用相位锁值(Phase Locking Value,PLV)来量化任意两个电极通道之间的相位同步性,构建脑功能网络的关联矩阵,计算不同稀疏度下脑网络的全局属性以及局部属性曲线下面积,在同一阶段、同一频段下对精神分裂症患者和正常人得到的属性值进行非参数检验,找出差异显著的属性及节点,将对应值作为特征训练SVM分类器,进而将精神分裂症患者和正常人分类。属性分析结果表明,工作记忆任务中θ和α频段发挥主要作用的脑区集中在右侧额叶区和枕叶区,γ频段相关的脑区集中在顶叶区;精神分裂症患者额叶右侧区域与枕叶区电极间θ、α波相关性低于正常人,而其顶叶区电极间γ波的相关性高于正常人。
Schizophrenia and cognitive impairments such as information processing,Working Memory(WM)is closelylinked.The aim of this study is to investigate the significant differences in various frequency bands,in each stage of electroencephalographs(EEGs)between schizophrenia patients and normal controls during visual WM task,providing the basisfor diagnosis of schizophrenia.This paper quantizes phase synchronism between two pairs of electrodes by phase lockingvalue,and constructs an incidence matrix.The research computes global properties and the area under the curve of localproperties in different sparsity of networks and uses non-parametric test to find the significance properties and nodes asfeatures.In addition,this experiment inputs these features into a support vector machine to distinguish patients and controls.The results show that the right frontal and occipital region play a major role inθandαbands and theγ-band-relatedbrain regions are concentrated in the parietal lobe region during WM task;and in the corresponding region patient’sθandαwave correlation between electrodes are lower than normal,butγwave correlation is higher than normal.
作者
孙丽婷
阴桂梅
谭淑平
赵艳丽
张进国
李东
李海芳
SUN Liting;YIN Guimei;TAN Shuping(Department of Computer Science, College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030600, China;Department of Computer Science and Technology, Taiyuan Normal University, Jinzhong, Shanxi 030619, China;北京回龙观医院精神医学研究中心,北京100096)
出处
《计算机工程与应用》
CSCD
北大核心
2017年第12期25-30,共6页
Computer Engineering and Applications
基金
国家自然科学基金(No.61472270)
关键词
工作记忆
精神分裂症
EEG功能网络属性
特征频段
特征脑区
working memory
schizophrenia
EEG functional network properties
feature frequency band
feature brain region
