A novel unified SPM-ICA-PCA method for detecting epileptic activities in resting-state fMRI

Authors:
Qiyi Song;Feng Yin;Huafu Chen;Yi Zhang;Qiaoli Hu;Dezhong Yao
Affiliations:
Center of Neuroinformatics, School of Applied Mathmatics, University of Electronic Science and Technology of China, Chengdu, PR China;Department of Mathematics, Sichuan University of Science and Engineering, Zigong, PR China;Center of Neuroinformatics, School of Applied Mathmatics, University of Electronic Science and Technology of China, Chengdu, PR China;Center of Neuroinformatics, School of Applied Mathmatics, University of Electronic Science and Technology of China, Chengdu, PR China;Center of Neuroinformatics, School of Applied Mathmatics, University of Electronic Science and Technology of China, Chengdu, PR China;Center of Neuroinformatics, School of Applied Mathmatics, University of Electronic Science and Technology of China, Chengdu, PR China
Venue:
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
Year:
2006

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Abstract

In this paper, it is reported that the method and primary application of a novel noninvasive technique, resting functional magnetic resonance imaging (fMRI) with unified statistical parameter mapping (SPM) independent component analysis (ICA), and principal component analysis( PCA), for localizing interictal epileptic activities of glioma foci. SPM is based on the general linear model (GLM). ICA combined PCA was firstly applied to fMRI datasets to disclose independent components, which is specified as the equivalent stimulus response patterns in the design matrix of a GLM. Then, parameters were estimated and regionally-specific statistical inferences were made about activations in the usual way. The validity is tested by simulation experiment. Finally, the fMRI data of two glioma patients is analyzed, whose results are consisting with the clinical estimate.