A two-step super-Gaussian independent component analysis approach for fMRI data
文献类型：期刊论文
| 作者 | Ruiyang Ge; Li Yao; Hang Zhang; Zhiying Long |
| 刊名 | NEUROIMAGE
 |
| 出版日期 | 2015 |
| 英文摘要 | ndependent 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-Gaussian ICA (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 akernel 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. |
| 收录类别 | SCI |
| 原文出处 | http://www.sciencedirect.com/science/article/pii/S1053811915005042 |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/7171]  |
| 专题 | 深圳先进技术研究院_医工所 |
| 作者单位 | NEUROIMAGE |
| 推荐引用方式 GB/T 7714 | Ruiyang Ge,Li Yao,Hang Zhang,et al. A two-step super-Gaussian independent component analysis approach for fMRI data[J]. NEUROIMAGE,2015. |
| APA | Ruiyang Ge,Li Yao,Hang Zhang,&Zhiying Long.(2015).A two-step super-Gaussian independent component analysis approach for fMRI data.NEUROIMAGE. |
| MLA | Ruiyang Ge,et al."A two-step super-Gaussian independent component analysis approach for fMRI data".NEUROIMAGE (2015). |
入库方式： OAI收割
来源：深圳先进技术研究院
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