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.

[1]  R Baumgartner,et al.  Fuzzy clustering of gradient‐echo functional MRI in the human visual cortex. Part I: Reproducibility , 1997, Journal of magnetic resonance imaging : JMRI.

[2]  Chandan Srivastava,et al.  Support Vector Data Description , 2011 .

[3]  Yiyu Yao,et al.  Peculiarity Oriented Multidatabase Mining , 2003, IEEE Trans. Knowl. Data Eng..

[4]  S Makeig,et al.  Analysis of fMRI data by blind separation into independent spatial components , 1998, Human brain mapping.

[5]  H. Mizuhara,et al.  Peculiarity oriented fMRI brain data analysis for studying human multi-perception mechanism , 2004, Cognitive Systems Research.

[6]  X Hu,et al.  Wavelet transform‐based Wiener filtering of event‐related fMRI data , 2000, Magnetic resonance in medicine.

[7]  L. K. Hansen,et al.  On Clustering fMRI Time Series , 1999, NeuroImage.

[8]  Daniel S. Yeung,et al.  Ellipsoidal support vector clustering for functional MRI analysis , 2007, Pattern Recognit..

[9]  Yiyu Yao,et al.  Peculiarity Oriented Multi-database Mining , 1999, PKDD.

[10]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[11]  H. Knutsson,et al.  Detection of neural activity in functional MRI using canonical correlation analysis , 2001, Magnetic resonance in medicine.

[12]  Karl J. Friston,et al.  Statistical parametric maps in functional imaging: A general linear approach , 1994 .

[13]  Lin Chen,et al.  A new method for fMRI data processing: Neighborhood independent component correlation algorithm and its preliminary application , 2002, Science in China Series F Information Sciences.

[14]  Bernhard Schölkopf,et al.  Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.

[15]  Jian Yang,et al.  Brain Activation Detection by Neighborhood One-Class SVM , 2007 .

[16]  R Baumgartner,et al.  Fuzzy clustering of gradient‐echo functional MRI in the human visual cortex. Part II: Quantification , 1997, Journal of magnetic resonance imaging : JMRI.

[17]  Chung-Chih Lin,et al.  Model Free Functional MRI Analysis Using Kohonen Clustering Neural Network , 1999, IEEE Trans. Medical Imaging.

[18]  Hava T. Siegelmann,et al.  Support Vector Clustering , 2002, J. Mach. Learn. Res..

[19]  Yang Lei,et al.  Recent advances in the data analysis method of functional magnetic resonance imaging and its applications in neuroimaging , 2006 .

[20]  Karl J. Friston,et al.  Statistical parametric mapping , 2013 .

[21]  Bertrand Thirion,et al.  fMRI data analysis : statistics, information and dynamics , 2003 .