Resting State Brain Network Modeling Based On Functional Magnetic Resonance Imaging
In this paper, the functional magnetic resonance imaging (fMRI) technique and complex network method were used to study the brain functional network of normal subjects. We used the partial least squares (PLS) regression modeling method to construct the normal human brain function network. The global statistical properties of the brain network revealed the brain functional network had small-world effect. Through the evaluation of centrality indices, the gyri callosus, the supramarginal gyrus,gyri frontalis superior and the gyrus angularis were the key areas of the brain functional network in resting state. The result showed that compared with the Pearson correlation analysis method, the PLS algorithm was better to construct the brain network model. It is not only expressed in the brain network threshold is generally high, the small world attribute is more obvious, but also the key brain regions that were inferred are more accurate and more consistent with physiological results.
Complex network Partial least squares Pearson correlation Functional magnetic resonance imaging
Ming Ke Zhijing Li Zhao Cao Xiaoping Yang
College of Computer and Communication, Lanzhou University of Technology, Lanzhou, Gansu, 730050, Chi Department of Imaging Diagnosis, Lanzhou General Hospital of Lanzhou Military Command, Lanzhou, Gans
国际会议
重庆
英文
389-392
2015-12-18（万方平台首次上网日期，不代表论文的发表时间）