摘要
利用脑电图数据建立了大脑功能性网络.分析了该网络的复杂网络统计特征,发现它的聚类系数远大于相应随机网络,明显具有小世界网络的特征,其度分布也接近于无标度网络.进一步验证了大脑功能性网络的复杂网络特性,发现患者的各项复杂网络特征指数与正常人相比有明显不同.定义了大脑神经网络信息熵及神经网络标准信息熵的概念,发现脑病患者的大脑神经网络信息熵明显小于正常人.从一个全新的角度量度了大脑的复杂网络特征,并提示了临床脑病诊疗的判断依据.
Electroencephalogram data is used to extract functional networks connecting correlated human brain sites. Analysis of the resulting network shows statistical characteristic of complex network ： the clustering coefficient is orders of magnitude larger than those of equivalent random networks, which is typical of small-world network and the distribution of degree is close to the scale- free network. All these properties reflect important functional information about brain states. To the alcoholic, the characteristic index of their brain is obviously different from the control group. Brain neural network information entropy and brain neural network normal information entropy are also defined to measure the complex network characteristic. A criterion in diagnosis and therapy of clinical encephalopathy is given. Calculation results illustrate that the brain neural network information entropy of alcoholic is quite distinct from the control.
出处
《物理学报》
SCIE
EI
CAS
CSCD
北大核心
2007年第12期7330-7338,共9页
Acta Physica Sinica
基金
国家自然科学基金(批准号:10572086)
高等学校博士学科点专项科研基金(批准号:20050248031)资助的课题.~~
关键词
脑电图
大脑功能性网络
复杂网络统计特征
信息熵
electroencephalogram, functional brain network, statistical characteristic of complex network, information entropy
