科研之友
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摘要
Currently, all the data processing strategies for functional magnetic resonance imaging (fMRI) utilize temporal informationpaying little attention to or totally ignoring frequency information. In this paper, a new method is proposed to detect the functional activation regions in the brain by using the frequency information of fMRI time series. The main idea is that the frequency entropy information (FEI) difference of fMRI data between task and control states is specified as brain activation index. The validity of the proposed FEI approach is confirmed by analyzing the result of the simulated synthesized data. Additionally, the comparison of receiver operating characteristic (ROC) curves acquired respectively from the proposed scheme, the statistical parametric mapping (SPM), and the Support Vector Machine (SVM) methods of fMRI data analysis indicate an obvious superiority of the former. In vivo fMRI studies of subjects with event-related experiment reveal that FEI method can enable the effective detection of brain functional activation.
