fMRI time series analysis based on stationary wavelet and spectrum analysis

作     者：ZHI Lianhe~ 1,2,3 , ZHAO Xia~4, SHAN Baoci~1**, PENG Silong~5, YAN Qiang~1, YUAN Xiuli~1 and TANG Xiaowei~ 1,6 (1. Key Laboratory of Nuclear Analysis Techniques, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China 2. Graduate School of Chinese Academy of Sciences, Beijing 100049, China 3. Department of Physics, Zhoukou Normal University, Zhoukou 466100, China 4. Research Imaging Center, University of Texas Health Science Center, San Antonio, Texas 78229, USA 5. National ASIC Design and Engineering Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100049, China 6. Department of Physics, Zhejiang University, Hangzhou 310027, China) 

作者机构：Key Laboratory of Nuclear Analysis Techniques Institute of High Energy Physics Chinese Academy of Sciences Beijing 100049 China Graduate School of Chinese Academy of Sciences Beijing 100049 China Department of Physics Zhoukou Normal University Zhoukou 466100 China Research Imaging Center University of Texas Health Science Center San Antonio TX 78229 United States National ASIC Design and Engineering Center Institute of Automation Chinese Academy of Sciences Beijing 100049 China Department of Physics Zhejiang University Hangzhou 310027 China 

出 版 物：《Progress in Natural Science:Materials International》 (自然科学进展·国际材料(英文))

年 卷 期：2006年第16卷第11期

页      面：1171-1176页

核心收录：

学科分类：08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0827[工学-核科学与技术] 082701[工学-核能科学与工程] 0702[理学-物理学] 

基　　金：Supported by National Natural Science Foundation of China ( Grant No . 30570508) and National Basic Research Program of China(2006CB705700) 

主　　题：fMRI stationary wavelet transform spectrum analysis data analysis. 

摘      要：The low signal to noise ratio (SNR) of functional MRI (fMRI) prefers more sensitive data analysis methods. Based on stationary wavelet transform and spectrum analysis, a new method with high detective sensitivity was developed for analyzing fMRI time series, which does not require any prior assumption of the characteristics of noises. In the proposed method, every component of fMRI time series in the different time-frequency scales of stationary wavelet transform was discerned by the spectrum analysis, then the components from noises were removed using the stationary wavelet transform, finally the components of real brain activation were detected by cross-correlation analysis. The results obtained from both simulated and in vivo visual experiments illustrated that the proposed method has much higher sensitivity than the traditional cross-correlation method.