title: A two-step super-Gaussian independent component analysis approach for fMRI data.
abstract: Independent component analysis (ICA) has been widely applied to functional magnetic resonance imaging (fMRI) data analysis. Although ICA assumes that the sources underlying data are statistically independent, it usually ignores sources' additional properties, such as sparsity. In this study, we propose a two-step super-GaussianICA (2SGICA) method that incorporates the sparse prior of the sources into the ICA model. 2SGICA uses the super-Gaussian ICA (SGICA) algorithm that is based on a simplified Lewicki-Sejnowski's model to obtain the initial source estimate in the first step. Using a kernel estimator technique, the source density is acquired and fitted to the Laplacian function based on the initial source estimates. The fitted Laplacian prior is used for each source at the second SGICA step. Moreover, the automatic target generation process for initial value generation is used in 2SGICA to guarantee the stability of the algorithm. An adaptive step size selection criterion is also implemented in the proposed algorithm. We performed experimental tests on both simulated data and real fMRI data to investigate the feasibility and robustness of 2SGICA and made a performance comparison between InfomaxICA, FastICA, mean field ICA (MFICA) with Laplacian prior, sparse online dictionary learning (ODL), SGICA and 2SGICA. Both simulated and real fMRI experiments showed that the 2SGICA was most robust to noises, and had the best spatial detection power and the time course estimation among the six methods.
authors.affiliation: 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.forename: Ruiyang
authors.initials: R
authors.lastname: Ge
authors.affiliation: 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.forename: Li
authors.initials: L
authors.lastname: Yao
authors.affiliation: Paul C. Lauterbur Research Centers for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
authors.forename: Hang
authors.initials: H
authors.lastname: Zhang
authors.affiliation: 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.forename: Zhiying
authors.initials: Z
authors.lastname: Long
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009043:Motor Activity; D017711:Nonlinear Dynamics; D016011:Normal Distribution; D012815:Signal Processing, Computer-Assisted; D013223:Statistics as Topic
keywords: ATGP; ICA; Sparsity; Super-Gaussian; fMRI