Abstract
Recently, sparse representation has received a great deal of attention in voxel selection and decoding analysis of functional magnetic resonance imaging (fMRI) data. Due to contamination of large noise in fMRI data, the conventional sparse representation methods may not get stable results. Moreover, the selected activated brain regions may lose clustering effects and are less biologically interpretable. In order to overcome the above mentioned problems, we exploit the error-tolerant formulation of sparse representation and introduce multi-dimensional derivative constraints (smoothness constraints) in optimization. Two new methods are proposed in this paper. One is robust voxel selection with multi-dimensional constraint (RVSMDC). With the error-tolerant formulation and smoothness constraints on regression coefficients, RVSMDC is robust against noise/error and achieves clustering effects. To directly decode neural activities from fMRI data, we also proposed robust sparse decoding with multi-dimensional constraints (RSDMDC), which minimize the regression error of fMRI data to the task function with sparsity and smoothness constraints on regression coefficients. Numerical results validate the effectiveness of the two proposed methods.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grants 61105121, 61175114, 91120305, the Natural Science Foundation of Guangdong under Grants S2012020010945, the Fundamental Research Funds for the Central Universities, SCUT under Grants 2012ZG0008, 2013ZZ0040, the National High-tech R&D Program of China (863 Program) under Grant 2012AA011601, and High Level Talent Project of Guangdong Province, China.
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Yu, Z., Feng, B., Gu, Z. et al. Voxel selection and neural decoding of fMRI data based on robust sparse programming with multi-dimensional derivative constraints. Multidim Syst Sign Process 26, 225–241 (2015). https://doi.org/10.1007/s11045-013-0254-3
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DOI: https://doi.org/10.1007/s11045-013-0254-3