Robust data driven model order estimation for independent component analysis of FMRI data with low contrast to noise.
Robust data driven model order estimation for independent component analysis of FMRI data with low contrast to noise.
复制标题
DOI：
10.1371/journal.pone.0094943
复制
发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Avison MJ
中科院分区：
文献类型：
--
作者：
Majeed W;Avison MJ
独立成分分析（伊卡）已成功地用于分析功能磁共振成像（fMRI）数据的任务相关以及静息状态的研究。虽然它有希望成为一个无偏的数据驱动的分析技术，一些选择之前必须进行伊卡，选择的方法来确定独立分量（nIC）的数量是其中之一。nIC的选择已被证明会影响伊卡地图，并且已在常用的伊卡分析包（例如MELODIC和GIFT）中提出并实现了各种方法（主要依赖于信息理论标准）。然而，对于nIC选择的最佳方法还没有达成共识，许多研究使用任意选择的nIC值。在感兴趣的信号仅占总方差的一小部分，即非常低的对比度噪声比（CNR）和/或非常局灶性反应的情况下，准确可靠地确定真实nIC尤其重要。在这项研究中，我们评估了不同的模型阶数选择标准的性能，并证明了基于主成分的自举稳定性选择的模型阶数会产生更可靠和准确的模型阶数估计。然后，我们证明了这种完全数据驱动的方法来检测弱和局灶性刺激驱动的反应在真实的数据的实用性。最后，我们比较了不同的多运行伊卡方法使用伪真实数据的性能。
Independent component analysis (ICA) has been successfully utilized for analysis of functional MRI (fMRI) data for task related as well as resting state studies. Although it holds the promise of becoming an unbiased data-driven analysis technique, a few choices have to be made prior to performing ICA, selection of a method for determining the number of independent components (nIC) being one of them. Choice of nIC has been shown to influence the ICA maps, and various approaches (mostly relying on information theoretic criteria) have been proposed and implemented in commonly used ICA analysis packages, such as MELODIC and GIFT. However, there has been no consensus on the optimal method for nIC selection, and many studies utilize arbitrarily chosen values for nIC. Accurate and reliable determination of true nIC is especially important in the setting where the signals of interest contribute only a small fraction of the total variance, i.e. very low contrast-to-noise ratio (CNR), and/or very focal response. In this study, we evaluate the performance of different model order selection criteria and demonstrate that the model order selected based upon bootstrap stability of principal components yields more reliable and accurate estimates of model order. We then demonstrate the utility of this fully data-driven approach to detect weak and focal stimulus-driven responses in real data. Finally, we compare the performance of different multi-run ICA approaches using pseudo-real data.
登录
查看更多内容
影响因子：
5.7
作者：
Congdon, Eliza;Mumford, Jeanette A.;Cohen, Jessica R.;Galvan, Adriana;Aron, Adam R.;Xue, Gui;Miller, Eric;Poldrack, Russell A.
通讯作者：
Poldrack, Russell A.
DOI：
10.1098/rstb.2005.1634
发表时间：
2005-05-29
影响因子：
6.3
作者：
Beckmann, CF;DeLuca, M;Smith, SM
通讯作者：
Smith, SM
影响因子：
7.8
作者：
Hyvärinen, A;Oja, E
通讯作者：
Oja, E
影响因子：
4.8
作者：
Abou-Elseoud, Ahmed;Starck, Tuomo;Kiviniemi, Vesa
通讯作者：
Kiviniemi, Vesa
影响因子：
5.7
作者：
Svensén, M;Kruggel, F;Benali, H
通讯作者：
Benali, H