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| fMRI time series analysis based on stationary wavelet and spectrum analysis | |
| 作者姓名： | ZHI Lianhe ZHAO Xi SHAN Baoci PENG Silong YAN Qiang YUAN Xiuli TANG Xiaowei |
| 作者单位： | 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 |
| 基金项目： | 国家自然科学基金;国家重点基础研究发展计划(973计划) |
| 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. |
| 关 键 词： | fMRI， stationary wavelet transform， spectrum analysis， data analysis |
fMRI time series analysis based on stationary wavelet and spectrum analysis |
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| ZHI Lianhe,ZHAO Xi,SHAN Baoci,PENG Silong,YAN Qiang,YUAN Xiuli,TANG Xiaowei.fMRI time series analysis based on stationary wavelet and spectrum analysis[J].Progress in Natural Science,2006,16(11):1171-1176. | |
| Authors: | ZHI Lianhe ZHAO Xia SHAN Baoci PENG Silong YAN Qiang YUAN Xiuli TANG Xiaowei |
| Institution: | 1. 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 2. Research Imaging Center, University of Texas Health Science Center, San Antonio, Texas 78229,USA 3. Key Laboratory of Nuclear Analysis Techniques, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049,China 4. National ASIC Design and Engineering Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100049, China 5. Key Laboratory of Nuclear Analysis Techniques, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049,China;Department of Physics, Zhejiang University, Hangzhou 310027, China |
| Abstract: | 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. |
| Keywords: | fMRI stationary wavelet transform spectrum analysis data analysis |
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