title: Improved sparse decomposition based on a smoothed L0 norm using a Laplacian kernel to select features from fMRI data.
abstract: BACKGROUND
  Feature selection plays an important role in improving the classification accuracy of multivariate classification techniques in the context of fMRI-based decoding due to the "few samples and large features" nature of functional magnetic resonance imaging (fMRI) data. Recently, several sparse representation methods have been applied to the voxel selection of fMRI data. Despite the low computational efficiency of the sparse representation methods, they still displayed promise for applications that select features from fMRI data.
  
  
  METHODS
  In this study, we proposed the Laplacian smoothed L0 norm (LSL0) approach for feature selection of fMRI data. Based on the fast sparse decomposition using smoothed L0 norm (SL0) (Mohimani, 2007), the LSL0 method used the Laplacian function to approximate the L0 norm of sources.
  
  
  RESULTS
  Results of the simulated and real fMRI data demonstrated the feasibility and robustness of LSL0 for the sparse source estimation and feature selection.
  
  
  METHODS
  Simulated results indicated that LSL0 produced more accurate source estimation than SL0 at high noise levels. The classification accuracy using voxels that were selected by LSL0 was higher than that by SL0 in both simulated and real fMRI experiment. Moreover, both LSL0 and SL0 showed higher classification accuracy and required less time than ICA and t-test for the fMRI decoding.
  
  
  CONCLUSIONS
  LSL0 outperformed SL0 in sparse source estimation at high noise level and in feature selection. Moreover, LSL0 and SL0 showed better performance than ICA and t-test for feature selection.
authors: State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China; College of Information Science and Technology, Beijing Normal University, Beijing 100875, China.
authors: Chuncheng
authors: C
authors: Zhang
authors: School of Education and Psychology, Jinan University, Shandong 250022, China.
authors: Sutao
authors: S
authors: Song
authors: Department of Psychology, Renmin University of China, Beijing 100872, China.
authors: Xiaotong
authors: X
authors: Wen
authors: State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China; College of Information Science and Technology, Beijing Normal University, Beijing 100875, China.
authors: Li
authors: L
authors: Yao
authors: State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China. Electronic address: friskying@163.com.
authors: Zhiying
authors: Z
authors: Long
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D001921:Brain / Q000098:blood supply*; D001931:Brain Mapping; D003198:Computer Simulation; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D008959:Models, Neurological; D010363:Pattern Recognition, Automated; D010364:Pattern Recognition, Visual / Q000502:physiology; D010775:Photic Stimulation; D055815:Young Adult
keywords: Decoding; Feature selection; Sparse representation; fMRI