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| Application of fMRI Activation Area Adaptive Extraction Based on Temporal SelfCorrelation Method of ICA |
Institute of Biomedical and Electromagnetic Engineering, Shenyang University of Technology, Shenyang 110870, China
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Abstract The traditional ICA based activation zone detection is to analyze the correlation between the separated independent component and the reference signal. However, in practical problems, since the differences among the hemodynamic responses of the cerebral regions, the standard reference signal is often not available. Aiming at such problems, in this paper, the method of temporal selfcorrelation (TSC) combined with infomaxICA was proposed. This method processed fMRI data point by point with 5adjacent voxels based ICA, then detected the correlation between each time series period with temporal selfcorrelation algorithm and selected the maximum autocorrelation coefficient as the signal value of the voxel. After that we conversed correlation coefficient distribution to Z distribution which obey N (0, 1) by Ztransform, extracted the active regions with significant difference (a=0.05) in the statistical parametric mapping. The algorithm was applied to deal with simulation data and 12 set of real fMRI data of fist movement with both hands. Results: The method can accurately extract the active region of the simulation data. For real data processing, results of this method have a high similarity with GLM method in the spatial domain (0.465 3±0.136 8 vs 0.490 5±0.134 1) and better than GLM method in the temporal domain (0.636 4±0.011 1 vs 0.369 2±0.010 9). These results have statistical significant. Experimental results showed that, this method has good capacities of detection of functional brain activation areas and spatial orientation.
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［1］杨福生, 洪波. 独立分量分析的原理与应用［M］. 北京：清华大学出版社, 2006: 184-195.
［2］Jutten C, Harault J. Blind separation of sources, part I:an adaptive algorithm based on neuromimetic architecture［J］. Signal Processing, 1991, 24(1):1-10.
［3］潘丽丽, 史振威, 唐焕文,等. fMRI 信号盲分离的一种独立成分分析算法［J］. 大连理工大学学报, 2005, 45(4): 407-611.
［4］Esposito F, Formisano E, Seifritz E, et al. Spatial independent component analysis of functional MRI timeseries: to what extent do results depend on the algorithm used［J］. Human Brain Mapping, 2002, 16: 146-157.
［5］韩彤, 崔世民, 刘梅丽, 等. 利用fMRI和双手交替运动模式研究脑肿瘤所致的运动功能重组［J］. 中国影像技术, 2005, 9: 1324-1328.
［6］McKeown MJ, Makeig S, Brown GG, et al. Analysis of fMRI data by blind separation into independent spatial components ［J］. Human Brain Mapping, 1998, 6: 160-188.
［7］Li Yu, Liu Jingsen. Mechanism and Improvement of Direct Anonymous Attestation Scheme ［J］. Journal of Henan University, 2007, 37(2): 195-197.
［8］Gossl C, Fahrmeir L. Bayesian Modeling of the Hemodynamic Response Function in BOLD fMRI ［J］. Neuro Image, 2001, 14: 140-148.
［9］吴义根, 李可. SPM软件包数据处理原理简介——第一部分:基本数学原理［J］. 中国医学影像技术, 2004,11:1768-1772.
［10］McKeown MJ. Whole brain function MR imaging activation from a fingertapping task examined with independent component analysis ［J］. American J Neuroradiol, 2000, 21:1629-1635.
［11］Calhoun VD, Adali T, Pearlson GD, et al. A method for making group inference from functional MRI data using independent component analysis ［J］.Hum Brain Map, 2001, 14:140-151.
［12］Calhoun VD, Adali T, Pearlson GD, et al. Spatial and temporal independent component analysis of function MRI data containing a pair of taskrelaed waveforms［J］. Hum Brain Map, 2001, 13:43-53.
［13］王世杰，王立功，罗立民. 自适应阈值化功能磁共振统计参数映射图［J］. 中国图像学报, 2003, 10: 1196-2000. |
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