Journal
COGNITIVE SYSTEMS RESEARCH
Volume 11, Issue 1, Pages 16-24Publisher
ELSEVIER
DOI: 10.1016/j.cogsys.2008.08.001
Keywords
fMRI Data analysis; Activation detection; Clustering analysis; One-class SVM; Neighborhood consistency
Funding
- National Grand Fundamental Research 973 Program of China [2004CB318103]
- National Science Foundation of China [60575001, 60673015, 60775039]
- China Postdoctoral Science Foundation [20070420016]
- Japanese Ministry of Education, Culture, Sports, Science and Technology [18300053]
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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. (C) 2008 Elsevier B.V. All rights reserved.
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