Object category classification of fMRI data using support vector machine combined with deactivation voxel selection

Abstract

Support Vector Machine (SVM) is an accurate pattern recognition method which has been widely used in functional MRI (fMRI) data classification. Voxel selection is a very important part in classification. In general, voxel selection is based on brain regions associated with activation caused by different experiment conditions or stimulations. However, negative blood oxygenation level-dependent responses (deactivation) which have also been found in humans or animals contribute to the classification of different cognitive tasks. Different from traditional studies which focused merely on the activation voxel selection methods, our aim is to investigate the deactivation voxel selection methods in the classification of fMRI data using SVM. In this study, three different voxel selection methods (deactivation, activation, the combination of deactivation and activation) are applied to decide which voxel is included in SVM classifier with linear kernel in classifying 4-category objects on fMRI data. The average accuracies of deactivation classification were 73.36%(house vs. face), 60.34%(house vs. car), 60.94%(house vs. cat), 71.43%(face vs. car), 63.17%(face vs. cat) and 61.61%(car vs. cat). The classification results of deactivation were significantly above the chance level which implies the deactivation is informative. The accuracies of combination of activation and deactivation method were close to that of activation method, and it was even better for some representative subjects. These results suggest deactivation provides useful information in the object category classification on fMRI data and the method of voxel selection based on both activation and deactivation will be a significant method in classification in the future.