TITLE:
Dynamic Spatial Discrimination Maps of Discriminative Activation between Different Tasks Based on Support Vector Machines
AUTHORS:
Guangxin Huang, Huafu Chen, Feng Yin
KEYWORDS:
Functional Magnetic Resonance Imaging, Principal Component Analysis, Support Vector Machine, Pattern Recognition Methods, Maximum-Margin Hyperplane
JOURNAL NAME:
Applied Mathematics,
Vol.2 No.1,
January
30,
2011
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.