Brain activation detection by neighborhood one-class SVM

Brain activation detection is an important problem in fMRI data analysis. In this paper, we propose a data-driven activation detection method called neighborhood one-class SVM (NOC-SVM). Based on the probability distribution assumption of the one-class SVM algorithm and the neighborhood consistency hypothesis, NOC-SVM identifies a voxel as either an activated or non-activated voxel by a weighted distance between its near neighbors and a hyperplane in a high-dimensional kernel space. The proposed NOC-SVM are evaluated by using both synthetic and real datasets. On two synthetic datasets with different SNRs, NOC-SVM performs better than K-means and fuzzy K-means clustering and is comparable to POM. On a real fMRI dataset, NOC-SVM can discover activated regions similar to K-means and fuzzy K-means. These results show that the proposed algorithm is an effective activation detection method for fMRI data analysis. Furthermore, it is stabler than K-means and fuzzy K-means clustering.