Generalized mutual information tests applied to fMRI analysis

To analyze functional magnetic resonance imaging (fMRI) data sets, the physiologically induced signals have to be separated from noise or from artifacts as a result of involuntary patient motion or MRI detection techniques. In the past years various methods of analysis of fMRI data were proposed, however one of the unsolved problems is related to dependence of the results with the threshold applied within each analysis. An alternative fMRI time series analysis method based on information theory and Tsallis entropy is proposed to decrease this dependency. The method applies the generalized mutual information (GMI) concept. Within the fMRI framework the GMI between the hemodynamic response function of a given voxel and the paradigm slope is evaluated as a measure of the amount of information that the paradigm could be encoded by a given brain region or voxel. This value represented the activation of the voxels. However, the analysis relies heavily on the Tsallis q parameter, allowing for a broad spectrum of results. As a test of applicability of the method real fMRI data were obtained in a simple motor paradigm and subsequently analyzed with GMI for several values of a q as well as with classical methods (cross-correlation and Studentʹs t-test). The simulated data were used to compare quantitatively the presented method with the classical ones by means of the construction of receiver operating characteristic (ROC) curve. The results showed the applicability of the GMI method as well as the range of optimum q-values