Brain activation detection by neighborhood one-class SVM

Authors:
Jian Yang;Ning Zhong;Peipeng Liang;Jue Wang;Yiyu Yao;Shengfu Lu
Affiliations:
International WIC Institute, Beijing University of Technology, Beijing 100124, China and The Key Laboratory of Complex Systems and Intelligence Science, Institution of Automation, Chinese Academy ...;International WIC Institute, Beijing University of Technology, Beijing 100124, China and Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi-City 371-0816, Japan;International WIC Institute, Beijing University of Technology, Beijing 100124, China;The Key Laboratory of Complex Systems and Intelligence Science, Institution of Automation, Chinese Academy of Sciences, Beijing 100190, China;International WIC Institute, Beijing University of Technology, Beijing 100124, China and Department of Computer Science, University of Regina, Regina, Canada S4S 0A2;International WIC Institute, Beijing University of Technology, Beijing 100124, China
Venue:
Cognitive Systems Research
Year:
2010

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Abstract

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.