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Dependence measures are frequently applied in neuroimagining studies as a tool for analysis and classification of fMRI data. The aim of this thesis is to evaluate an algorithm for its use in classifying fMRI data using dependence measures. The focus is on evaluating the algorithm under a few changes, for example without adding voxel-based tests in voxel selection, for future use in classification. Additionally, the thesis aims to compare the performance of two dependence measures, the RV coefficient and its modified version. The classification performance of the algorithm is evaluated on a simulated fMRI data as well as resting-state and task-based fMRI data sets. On simulated fMRI data the algorithm yields an estimated accuracy of 81.41 percent versus 75.00 percent for the classifier using the RV coefficient and the modified RV coefficient, respectively. However, when evaluated on real fMRI data the estimated accuracy is close to, or even lower, than 50 percent. This indicates that the classification performance is not far from what would be expected from a classifier picked at random. It is expected that implementing additional tests to select a subset of voxels, to use in the classification step of the algorithm, may prove helpful. Further, some differences in classification performance of the RV coefficients are found. Based on the observed differences it is not possible to conclude that one measure can be preferred over the other. |
