Li, Yuanqiang, Namburi, Praneeth, Yu, Zhuliang, Guan, Cuntai, Feng, Jianfeng and Gu, Zhenghui. (2009) Voxel selection in fMRI data analysis based on sparse representation. IEEE Transactions on Biomedical Engineering, Vol.56 (No.10). pp. 2439-2451. ISSN 0018-9294

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Abstract

Multivariate pattern analysis approaches toward detection of brain regions from fMRI data have been gaining attention recently. In this study, we introduce an iterative sparse-representation-based algorithm for detection of voxels in functional MRI (fMRI) data with task relevant information. In each iteration of the algorithm, a linear programming problem is solved and a sparse weight vector is subsequently obtained. The final weight vector is the mean of those obtained in all iterations. The characteristics of our algorithm are as follows: 1) the weight vector (output) is sparse; 2) the magnitude of each entry of the weight vector represents the significance of its corresponding variable or feature in a classification or regression problem; and 3) due to the convergence of this algorithm, a stable weight vector is obtained. To demonstrate the validity of our algorithm and illustrate its application, we apply the algorithm to the Pittsburgh Brain Activity Interpretation Competition 2007 functional fMRI dataset for selecting the voxels, which are the most relevant to the tasks of the subjects. Based on this dataset, the aforementioned characteristics of our algorithm are analyzed, and a comparison between our method with the univariate general-linear-model-based statistical parametric mapping is performed. Using our method, a combination of voxels are selected based on the principle of effective/sparse representation of a task. Data analysis results in this paper show that this combination of voxels is suitable for decoding tasks and demonstrate the effectiveness of our method.

Item Type:	Journal Article
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions:	Faculty of Science > Centre for Scientific Computing Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH):	Magnetic resonance imaging -- Research, Volumetric analysis, Mappings (Mathematics), Medical statistics -- Research
Journal or Publication Title:	IEEE Transactions on Biomedical Engineering
Publisher:	IEEE
ISSN:	0018-9294
Date:	October 2009
Volume:	Vol.56
Number:	No.10
Page Range:	pp. 2439-2451
Identification Number:	10.1109/TBME.2009.2025866
Status:	Peer Reviewed
Access rights to Published version:	Open Access
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URI:	http://wrap.warwick.ac.uk/id/eprint/2438

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