Improved FastICA algorithm in fMRI data analysis using the sparsity property of the sources
文献类型：期刊论文
| 作者 | Ruiyang Ge; Yubao Wang; Jipeng Zhang; Li Yao; Hang Zhang; Zhiying Long |
| 刊名 | Journal of Neuroscience Methods
 |
| 出版日期 | 2016 |
| 英文摘要 | As a blind source separation technique, independent component analysis (ICA) has many applications in functional magnetic resonance imaging (fMRI). Although either temporal or spatial prior information has been introduced into the constrained ICA and semi-blind ICA methods to improve the performance of ICA in fMRI data analysis, certain types of additional prior information, such as the sparsity, has seldom been added to the ICA algorithms as constraints.In this study, we proposed a SparseFastICA method by adding the source sparsity as a constraint to the FastICA algorithm to improve the performance of the widely used FastICA. The source sparsity is estimated through a smoothed ℓ0 norm method. We performed experimental tests on both simulated data and real fMRI data to investigate the feasibility and robustness of SparseFastICA and made a performance comparison between SparseFastICA, FastICA and Infomax ICA.Results of the simulated and real fMRI data demonstrated the feasibility and robustness of SparseFastICA for the source separation in fMRI data.Comparison with existing methods Both the simulated and real fMRI experimental results showed that SparseFastICA has better robustness to noise and better spatial detection power than FastICA. Although the spatial detection power of SparseFastICA and Infomax did not show significant difference, SparseFastICA had faster computation speed than Infomax.SparseFastICA was comparable to the Infomax algorithm with a faster computation speed. More importantly, SparseFastICA outperformed FastICA in robustness and spatial detection power and can be used to identify more accurate brain networks than FastICA algorithm. |
| 收录类别 | SCI |
| 原文出处 | http://www.sciencedirect.com/science/article/pii/S0165027016000625 |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/10493]  |
| 专题 | 深圳先进技术研究院_医工所 |
| 作者单位 | Journal of Neuroscience Methods |
| 推荐引用方式 GB/T 7714 | Ruiyang Ge,Yubao Wang,Jipeng Zhang,et al. Improved FastICA algorithm in fMRI data analysis using the sparsity property of the sources[J]. Journal of Neuroscience Methods,2016. |
| APA | Ruiyang Ge,Yubao Wang,Jipeng Zhang,Li Yao,Hang Zhang,&Zhiying Long.(2016).Improved FastICA algorithm in fMRI data analysis using the sparsity property of the sources.Journal of Neuroscience Methods. |
| MLA | Ruiyang Ge,et al."Improved FastICA algorithm in fMRI data analysis using the sparsity property of the sources".Journal of Neuroscience Methods (2016). |
入库方式： OAI收割
来源：深圳先进技术研究院
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