Background

Independent component analysis (ICA) has been widely used for blind source separation in the field of medical imaging. However, despite of previous substantial efforts, the stability of ICA components remains a critical issue which has not been adequately addressed, despite numerous previous efforts. Most critical is the inconsistency of some of the extracted components when ICA is run with different model orders (MOs).

New Method

In this study, a novel method of determining the consistency of component analysis (CoCA) is proposed to evaluate the consistency of extracted components with different model orders. In the method, “consistent components” (CCs) are defined as those which can be extracted repeatably over a range of model orders.

Result

The efficacy of the method was evaluated with simulation data and fMRI datasets. With our method, the simulation result showed a clear difference of consistency between ground truths and noise.

Comparison with existing methods

The information criteria were implemented to provide suggestions for the optimal model order, where some of the ICs were revealed inconsistent in our proposed method.

Conclusions

This method provided an objective protocol for choosing CCs of an ICA decomposition of a data matrix, independent of model order. This is especially useful with high model orders, where noise or other disturbances could possibly lead to an instability of the components.

中文翻译：

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FMRI 独立成分分析的一致性

背景

独立分量分析（ICA）已被广泛用于医学成像领域的盲源分离。然而，尽管之前做出了大量努力，但 ICA 组件的稳定性仍然是一个关键问题，尽管之前进行了多次努力，但仍未得到充分解决。最关键的是，当 ICA 以不同的模型阶数 (MO) 运行时，某些提取的组件会不一致。

新方法

在这项研究中，提出了一种确定组件分析（CoCA）一致性的新方法，以评估具有不同模型阶数的提取组件的一致性。在该方法中，“一致分量”(CC) 被定义为可以在一系列模型阶数上重复提取的分量。

结果

该方法的有效性通过模拟数据和 fMRI 数据集进行评估。使用我们的方法，模拟结果显示了地面实况和噪声之间的一致性的明显差异。

与现有方法的比较

实施信息标准以提供最佳模型顺序的建议，其中一些 IC 在我们提出的方法中显示不一致。

结论

该方法提供了一种客观协议，用于选择数据矩阵的 ICA 分解的 CC，与模型顺序无关。这对于高模型阶数特别有用，其中噪声或其他干扰可能会导致组件不稳定。