Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, Key Laboratory of Neuro-Functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, National Research Center for Rehabilitation Technical Aids, Beijing, China.
Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering and Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China.
PLoS One. 2022 Jun 27;17(6):e0270556. doi: 10.1371/journal.pone.0270556. eCollection 2022.
Independent component analysis (ICA) has been shown to be a powerful blind source separation technique for analyzing functional magnetic resonance imaging (fMRI) data sets. ICA can extract independent spatial maps and their corresponding time courses from fMRI data without a priori specification of time courses. Some popular ICA algorithms such as Infomax or FastICA generate different results after repeated analysis from the same data volume, which is generally acknowledged as a drawback for ICA approaches. The reliability of some ICA algorithms has been explored by methods such as ICASSO and RAICAR (ranking and averaging independent component analysis by reproducibility). However, the exact algorithmic reliability of different ICA algorithms has not been examined and compared with each other. Here, the quality index generated with ICASSO and spatial correlation coefficients were used to examine the reliability of different ICA algorithms. The results demonstrated that Infomax running 10 times with ICASSO could generate consistent independent components from fMRI data sets.
独立成分分析（ICA）已被证明是一种强大的盲源分离技术，可用于分析功能磁共振成像（fMRI）数据集。ICA 可以从 fMRI 数据中提取独立的空间图及其相应的时间过程，而无需事先指定时间过程。一些流行的 ICA 算法，如 Infomax 或 FastICA，在对相同数据量进行重复分析后会产生不同的结果，这通常被认为是 ICA 方法的一个缺点。通过 ICASSO 和 RAICAR（通过可重复性对独立成分分析进行排名和平均）等方法已经探索了一些 ICA 算法的可靠性。然而，不同 ICA 算法的确切算法可靠性尚未相互检查和比较。在这里，使用 ICASSO 生成的质量指数和空间相关系数来检查不同 ICA 算法的可靠性。结果表明，使用 ICASSO 运行 10 次的 Infomax 可以从 fMRI 数据集中生成一致的独立成分。
