Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland.
Neuroimage. 2011 May 15;56(2):554-69. doi: 10.1016/j.neuroimage.2010.04.268. Epub 2010 May 6.
Spatial independent components analysis (sICA) has become a widely applied data-driven method for fMRI data, especially for resting-state studies. These sICA approaches are often based on iterative estimation algorithms and there are concerns about accuracy due to noise. Repeatability measures such as ICASSO, RAICAR and ARABICA have been introduced as remedies but information on their effects on estimates is limited. The contribution of this study was to provide more of such information and test if the repeatability analyses are necessary. We compared FastICA-based ordinary and repeatability approaches concerning mixing vector estimates. Comparisons included original FastICA, FSL4 Melodic FastICA and original and modified ICASSO. The effects of bootstrapping and convergence threshold were evaluated. The results show that there is only moderate improvement due to repeatability measures and only in the bootstrapping case. Bootstrapping attenuated power from time courses of resting-state network related ICs at frequencies higher than 0.1 Hz and made subsets of low frequency oscillations more emphasized IC-wise. The convergence threshold did not have a significant role concerning the accuracy of estimates. The performance results suggest that repeatability measures or strict converge criteria might not be needed in sICA analyses of fMRI data. Consequently, the results in existing sICA fMRI literature are probably valid in this sense. A decreased accuracy of original bootstrapping ICASSO was observed and corrected by using centrotype mixing estimates but the results warrant for thorough evaluations of data-driven methods in general. Also, given the fMRI-specific considerations, further development of sICA methods is strongly encouraged.
空间独立成分分析（sICA）已成为一种广泛应用的数据驱动方法，尤其适用于静息态研究。这些 sICA 方法通常基于迭代估计算法，由于噪声，准确性令人担忧。ICASSO、RAICAR 和 ARABICA 等可重复性测量方法已被引入作为补救措施，但关于它们对估计的影响的信息有限。本研究的贡献在于提供更多此类信息，并测试可重复性分析是否必要。我们比较了基于 FastICA 的普通和重复性方法，比较了混合向量估计。比较包括原始 FastICA、FSL4 Melodic FastICA 以及原始和修改后的 ICASSO。评估了引导和收敛阈值的效果。结果表明，由于重复性措施，只有适度的改进，并且仅在引导情况下。引导降低了与静息态网络相关的 IC 高于 0.1 Hz 的频率的时间序列的功率，并使低频振荡的子集在 IC 方面更加突出。收敛阈值对估计的准确性没有显著作用。性能结果表明，在 fMRI 数据的 sICA 分析中，可能不需要重复性措施或严格的收敛标准。因此，从这个意义上说，现有 sICA fMRI 文献中的结果可能是有效的。观察到原始引导 ICASSO 的准确性降低，并通过使用中心型混合估计进行了校正，但结果需要对数据驱动方法进行全面评估。此外，鉴于 fMRI 的特殊考虑，强烈鼓励进一步开发 sICA 方法。
