A general probabilistic model for group independent component analysis and its estimation methods.
A general probabilistic model for group independent component analysis and its estimation methods.
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DOI：
10.1111/j.1541-0420.2011.01601.x
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发表时间：
2011-12
期刊：
影响因子：
1.9
通讯作者：
Guo Y
中科院分区：
文献类型：
--
作者：
Guo Y
独立成分分析（伊卡）已成为分析功能磁共振成像（fMRI）研究数据的重要工具。伊卡已成功应用于单一主题的fMRI数据。然而，伊卡的扩展到神经影像学研究中的组推断，是具有挑战性的，由于一个预先指定的组设计矩阵的不可用性和受试者之间的不确定性变化的fMRI数据。我们提出了一个通用的概率伊卡（PICA）模型，可以容纳不同的组结构的多学科时空过程。该模型的一个优点是，它可以灵活地模拟各种类型的组结构在不同的底层神经源信号和不同的实验条件下的fMRI研究。最大似然法被用来估计这个一般的组伊卡模型。我们提出了两种EM算法来获得ML估计。第一种方法是一个精确的EM算法，它提供了一个精确的E-步骤和一个显式的非迭代M-步骤。第二种方法是变分逼近EM算法，其计算效率比精确EM更高。在仿真研究中，我们首先比较了所提出的一般组PICA模型和现有的概率组伊卡方法的性能。然后，我们比较了两个建议的EM算法，并显示变分近似EM达到相当的准确性，以显着减少计算时间的确切EM。fMRI数据的例子来说明所提出的方法的应用。
Independent component analysis (ICA) has become an important tool for analyzing data from functional magnetic resonance imaging (fMRI) studies. ICA has been successfully applied to single-subject fMRI data. The extension of ICA to group inferences in neuroimaging studies, however, is challenging due to the unavailability of a pre-specified group design matrix and the uncertainty in between-subjects variability in fMRI data. We present a general probabilistic ICA (PICA) model that can accommodate varying group structures of multi-subject spatio-temporal processes. An advantage of the proposed model is that it can flexibly model various types of group structures in different underlying neural source signals and under different experimental conditions in fMRI studies. A maximum likelihood method is used for estimating this general group ICA model. We propose two EM algorithms to obtain the ML estimates. The first method is an exact EM algorithm which provides an exact E-step and an explicit noniterative M-step. The second method is an variational approximation EM algorithm which is computationally more efficient than the exact EM. In simulation studies, we first compare the performance of the proposed general group PICA model and the existing probabilistic group ICA approach. We then compare the two proposed EM algorithms and show the variational approximation EM achieves comparable accuracy to the exact EM with significantly less computation time. An fMRI data example is used to illustrate application of the proposed methods.
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影响因子：
6
作者：
Hyvarinen, A
通讯作者：
Hyvarinen, A
影响因子：
5.7
作者：
Beckmann, CF;Smith, SM
通讯作者：
Smith, SM
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Svensén, M;Kruggel, F;Benali, H
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作者：
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