Dynamic Spatial Discrimination Maps of Discriminative Activation between Different Tasks Based on Support Vector Machines
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Abstract
As a set of supervised pattern recognition methods, support vector machines (SVMs) have been successfully applied to functional magnetic resonance imaging (fMRI) field, but few studies have focused on visualizing discriminative regions of whole brain between different cognitive tasks dynamically. This paper presents a SVM-based method for visualizing dynamically discriminative activation of whole-brain voxels between two kinds of tasks without any contrast. Our method provides a series of dynamic spatial discrimination maps (DSDMs), representing the temporal evolution of discriminative brain activation during a duty cycle and describing how the discriminating information changes over the duty cycle. The proposed method was applied to investigate discriminative brain functional activations of whole brain voxels dynamically based on a hand-motor task experiment. A set of DSDMs between left hand movement and right hand movement were reached. Our results demonstrated not only where but also when the discriminative activations of whole brain voxels occurred between left hand movement and right hand movement during one duty cycle.
- V. Vapnik, “The Nature of Statistical Learning Theory,” Springer-Verlag, New York, 1995.
- V. Vapnik, “Statistical Learning Theory,” John Wiley and Sons Inc., New York, 1998.
- C. J. C. Burges, “A Tutorial on Support Vector Machines for Pattern Recognition,” Data Mining and Knowledge Discovery, Vol. 2, No. 2, 1998, pp. 121-167. doi:10.1023/A:1009715923555
- K. J. Friston, A. P. Holmes, K. J. Worsley, J. P. Poline, C. D. Frith and R. S. J. Frackowiak, “Statistical Parametric Maps in Functional Imaging: A General Linear Approach,” Human Brain Mapping, Vol. 2, No. 4, 1995, pp. 189-210. doi:10.1002/hbm.460020402
- D. D. Cox and R. L. Savoy, “Functional Magnetic Resonance Imaging (fMRI) “Brain Reading”: Detecting and Classifying Distributed Patterns of fMRI Activity in Human Visual Cortex,” NeuroImage, Vol. 19, No. 2, 2003, pp. 261-270. doi:10.1016/S1053-8119(03)00049-1
- T. M. Mitchell, R. Hutchinson, R. S. Niculescu, F. Pereira and X. Wang, “Learning to Decode Cognitive States from Brain Images,” Machine Learning, Vol. 57, No. 1-2, 2004, pp. 145-175. doi:10.1023/B:MACH.0000035475.85309.1b
- C. Davatzikos, K. Ruparel, Y. Fan, D. G. Shen, M. Acharyya and J. W. Loughead, “Classifying Spatial Patterns of Brain Activity with Machine Learning Methods: Application to Lie Detection,” NeuroImage, Vol. 28, No. 3, 2005, pp. 663-668. doi:10.1016/j.neuroimage.2005.08.009
- S. LaConte, S. Strother, V. Cherkassky, J. Anderson and X. Hu, “Support Vector Machines for Temporal Classi?cation of Block Design fMRI Data,” NeuroImage, Vol. 26, No. 2, 2005, pp. 317-329. doi:10.1016/j.neuroimage.2005.01.048
- J. Mour?o-Miranda, A. L. W. Bokde, C. Born, H. Hampel and S. Stetter, “Classifying Brain States and Determining the Discriminating Activation Patterns: Support Vector Machine on Functional MRI Data,” NeuroImage, Vol. 28, No. 4, 2005, pp. 980-995. doi:10.1016/j.neuroimage.2005.06.070
- J. Mour?o-Miranda, E. Reynaud, F. McGlone, G. Calvert and M. Brammer, “The Impact of Temporal Compression and Space Selection on SVM Analysis of Single-Subject and Multi-Subject fMRI Data,” NeuroImage, Vol. 33, No. 4, 2006, pp. 1055-1065. doi:10.1016/j.neuroimage.2006.08.016
- J. Mour?o-Miranda, K. Friston and M. Brammer, “Dynamic Discrimination Analysis: A Spatial-Temporal SVM,” NeuroImage, Vol. 36, No. 1, 2007, pp. 88-99.
- Z. Wang, A. R. Childress, J. Wang and J. A. Detre, “Support Vectormachine Learning-Based fMRI Data Group Analysis,” NeuroImage, Vol. 36, No. 4, 2007, pp. 1139-1151. doi:10.1016/j.neuroimage.2007.03.072