A Novel Unified SPM-ICA-PCA Method for Detecting Epileptic Activities in Resting-State fMRI
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.
KeywordsfMRI Data Independent Component Analysis Statistical Parameter Mapping Bold Signal Blind Source Separation
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