The Mind Research Network, Albuquerque, New Mexico 87106, USA.
Hum Brain Mapp. 2011 Dec;32(12):2075-95. doi: 10.1002/hbm.21170. Epub 2010 Dec 15.
Spatial independent component analysis (ICA) applied to functional magnetic resonance imaging (fMRI) data identifies functionally connected networks by estimating spatially independent patterns from their linearly mixed fMRI signals. Several multi-subject ICA approaches estimating subject-specific time courses (TCs) and spatial maps (SMs) have been developed, however, there has not yet been a full comparison of the implications of their use. Here, we provide extensive comparisons of four multi-subject ICA approaches in combination with data reduction methods for simulated and fMRI task data. For multi-subject ICA, the data first undergo reduction at the subject and group levels using principal component analysis (PCA). Comparisons of subject-specific, spatial concatenation, and group data mean subject-level reduction strategies using PCA and probabilistic PCA (PPCA) show that computationally intensive PPCA is equivalent to PCA, and that subject-specific and group data mean subject-level PCA are preferred because of well-estimated TCs and SMs. Second, aggregate independent components are estimated using either noise-free ICA or probabilistic ICA (PICA). Third, subject-specific SMs and TCs are estimated using back-reconstruction. We compare several direct group ICA (GICA) back-reconstruction approaches (GICA1-GICA3) and an indirect back-reconstruction approach, spatio-temporal regression (STR, or dual regression). Results show the earlier group ICA (GICA1) approximates STR, however STR has contradictory assumptions and may show mixed-component artifacts in estimated SMs. Our evidence-based recommendation is to use GICA3, introduced here, with subject-specific PCA and noise-free ICA, providing the most robust and accurate estimated SMs and TCs in addition to offering an intuitive interpretation.
空间独立成分分析（ICA）应用于功能磁共振成像（fMRI）数据，通过从其线性混合 fMRI 信号中估计空间独立模式，来识别功能连接网络。已经开发了几种多主体 ICA 方法来估计主体特定的时间过程（TC）和空间图（SM），但是，尚未对其使用的含义进行全面比较。在这里，我们结合数据减少方法，对模拟和 fMRI 任务数据进行了四种多主体 ICA 方法的广泛比较。对于多主体 ICA，数据首先在主体和组级别使用主成分分析（PCA）进行减少。使用 PCA 和概率 PCA（PPCA）对主体特定、空间连接和组数据均值主体级别的减少策略进行比较，结果表明计算密集型 PPCA 等效于 PCA，并且主体特定和组数据均值主体级别的 PCA 更受欢迎，因为它们可以很好地估计 TC 和 SM。其次，使用无噪声 ICA 或概率 ICA（PICA）估计聚合独立成分。第三，使用反向重建来估计主体特定的 SM 和 TC。我们比较了几种直接组 ICA（GICA）反向重建方法（GICA1-GICA3）和一种间接反向重建方法，时空回归（STR，或双回归）。结果表明，早期的组 ICA（GICA1）近似于 STR，但是 STR 具有矛盾的假设，并且在估计的 SM 中可能显示混合成分伪影。我们基于证据的建议是使用这里介绍的具有主体特定 PCA 和无噪声 ICA 的 GICA3，除了提供直观的解释外，还可以提供最稳健和准确的估计 SM 和 TC。
