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). By incorporating the idea of neighborhood consistency into one-class SVM, the method classifies a voxel as an activated or non-activated voxel by its neighbor weighted distance to a hyperplane in a high-dimensional kernel space. On two synthetic datasets under different SNRs, the proposed method almost has lower error rate than K-means clustering and fuzzy K-means clustering. On a real fMRI dataset, all the three algorithms can detect similar activated regions. Furthermore, the NOC-SVM is more stable than random algorithms, such as K-means clustering and fuzzy K-means clustering. These results show that the proposed NOC-SVM is a new effective method for activation detections in fMRI data.