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Title: A Sparse Representation-Based Algorithm for Pattern Localization in Brain Imaging Data Analysis
Author: Li, YQ ; Long, JY ; He, L ; Lu, HD ; Gu, ZH ; Sun, P
Source: PLOS ONE
Issued Date: 2012
Volume: 7, Issue:12, Pages:-e50332
Keyword: SUPPORT VECTOR MACHINES ; MONKEY STRIATE CORTEX ; HUMAN EXTRASTRIATE CORTEX ; FUNCTIONAL ARCHITECTURE ; RECEPTIVE FIELDS ; VISUAL-CORTEX ; FMRI DATA ; FACES ; CLASSIFICATION ; ORIENTATION
Subject: Science & Technology - Other Topics
Corresponding Author: Li, YQ (reprint author), S China Univ Technol, Ctr Brain Comp Interfaces & Brain Informat Proc, Guangzhou, Guangdong, Peoples R China.,auyqli@scut.edu.cn
English Abstract: Considering the two-class classification problem in brain imaging data analysis, we propose a sparse representation-based multi-variate pattern analysis (MVPA) algorithm to localize brain activation patterns corresponding to different stimulus classes/brain states respectively. Feature selection can be modeled as a sparse representation (or sparse regression) problem. Such technique has been successfully applied to voxel selection in fMRI data analysis. However, single selection based on sparse representation or other methods is prone to obtain a subset of the most informative features rather than all. Herein, our proposed algorithm recursively eliminates informative features selected by a sparse regression method until the decoding accuracy based on the remaining features drops to a threshold close to chance level. In this way, the resultant feature set including all the identified features is expected to involve all the informative features for discrimination. According to the signs of the sparse regression weights, these selected features are separated into two sets corresponding to two stimulus classes/brain states. Next, in order to remove irrelevant/noisy features in the two selected feature sets, we perform a nonparametric permutation test at the individual subject level or the group level. In data analysis, we verified our algorithm with a toy data set and an intrinsic signal optical imaging data set. The results show that our algorithm has accurately localized two class-related patterns. As an application example, we used our algorithm on a functional magnetic resonance imaging (fMRI) data set. Two sets of informative voxels, corresponding to two semantic categories (i.e., "old people'' and "young people''), respectively, are obtained in the human brain. Citation: Li Y, Long J, He L, Lu H, Gu Z, et al. (2012) A Sparse Representation-Based Algorithm for Pattern Localization in Brain Imaging Data Analysis. PLoS ONE 7(12): e50332. doi:10.1371/journal.pone.0050332
Indexed Type: SCI
Language: 英语
Content Type: 期刊论文
URI: http://ir.sibs.ac.cn/handle/331001/2474
Appears in Collections:神经所(总)_期刊论文

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Recommended Citation:
Li, YQ; Long, JY; He, L; Lu, HD; Gu, ZH; Sun, P.A Sparse Representation-Based Algorithm for Pattern Localization in Brain Imaging Data Analysis,PLOS ONE,2012,7(12):-e50332
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