The role of diversity in complex ICA algorithms for fMRI analysis.
The role of diversity in complex ICA algorithms for fMRI analysis.
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DOI：
10.1016/j.jneumeth.2016.03.012
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发表时间：
2016-05-01
影响因子：
3
通讯作者：
Adalı T
中科院分区：
文献类型：
--
作者：
Du W;Levin-Schwartz Y;Fu GS;Ma S;Calhoun VD;Adalı T
数据驱动的方法，如独立成分分析（伊卡），用于分析功能性磁共振成像数据（fMRI）的广泛使用，使神经功能的更深入的理解。然而，大多数流行的伊卡算法的fMRI分析几个简化的假设，从而忽略了统计信息的来源，多样性的类型，并限制其性能。我们建议使用复杂的熵率界最小化（CERBM）的实际功能磁共振成像数据在其本地，复杂，域的分析。虽然CERBM通过利用复杂fMRI数据固有的三种多样性（非圆性、非高斯性和样本间依赖性）实现了增强的性能，但CERBM产生的结果比简单的方法更易变。这促使开发基于最小生成树（MST）的稳定性分析，以减轻CERBM的可变性。为了验证我们的方法，我们比较了CERBM的性能与流行的CInfomax以及复杂的熵界最小化（CEBM）。我们表明，通过利用CERBM和基于MST的稳定性分析，我们能够始终如一地产生在物理上有意义的区域中具有更多激活体素的组件，并且可以比使用简单模型生成的组件更准确地对精神分裂症患者进行分类。我们的研究结果表明，使用伊卡算法的优势，可以利用所有固有类型的多样性的fMRI数据的分析时，再加上适当的稳定性分析。
The widespread use of data-driven methods, such as independent component analysis (ICA), for the analysis of functional magnetic resonance imaging data (fMRI) has enabled deeper understanding of neural function. However, most popular ICA algorithms for fMRI analysis make several simplifying assumptions, thus ignoring sources of statistical information, types of diversity, and limiting their performance. We propose the use of complex entropy rate bound minimization (CERBM) for the analysis of actual fMRI data in its native, complex, domain. Though CERBM achieves enhanced performance through the exploitation of the three types of diversity inherent to complex fMRI data: noncircularity, non-Gaussianity, and sample-to-sample dependence, CERBM produces results that are more variable than simpler methods. This motivates the development of a minimum spanning tree (MST)-based stability analysis that mitigates the variability of CERBM. In order to validate our method, we compare the performance of CERBM with the popular CInfomax as well as complex entropy bound minimization (CEBM). We show that by leveraging CERBM and the MST-based stability analysis, we are able to consistently produce components that have a greater number of activated voxels in physically meaningful regions and can more accurately classify patients with schizophrenia than components generated using simpler models. Our results demonstrate the advantages of using ICA algorithms that can exploit all inherent types of diversity for the analysis of fMRI data when coupled with appropriate stability analyses.