Classifying spatial patterns of fMRI activity for object category based on information mapping

Abstract

Multi-voxel pattern analysis (MVPA) has been widely used in the object category classification of functional magnetic resonance imaging (fMRI) data. Feature selection is an essential operation in pattern classification. Searchlight, based on information mapping, is one method of feature selection. In contrast with traditional methods based on activation, searchlight has more sensitivity and then provides higher statistical power. In this study, we applied two different feature selection methods, searchlight and activation, combined with linear support vector machine (SVM) classifier, to investigate the classification effect in classifying 4-category objects on fMRI data. We found that the average classification accuracies of searchlight were 0.8095 (house vs. face), 0.7240 (house vs. car), 0.7247 (house vs. cat), 0.6980 (face vs. car), 0.5982 (face vs. cat) and 0.6860 (car vs. cat). For house vs. car, the average classification accuracy based on searchlight was better than that based on activation (0.7240 vs. 0.7143). Specially, searchlight method performed better than activation for some subjects. The results showed that object category classification of fMRI data based on information mapping were significantly reliable. Our findings suggest that information mapping can be applied in pattern classification in future work.