Estimating the number of independent components for functional magnetic resonance Imaging data
Estimating the number of independent components for functional magnetic resonance Imaging data
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DOI：
10.1002/hbm.20359
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发表时间：
2007-11-01
影响因子：
4.8
通讯作者：
Calhoun, Vince D.
中科院分区：
文献类型：
--
作者：
Li, Yi-Ou;Adali, Tuelay;Calhoun, Vince D.
多元分析方法，如独立成分分析（伊卡）已被应用于功能磁共振成像（fMRI）数据的分析，以研究大脑功能。由于fMRI数据的高维数和高噪声水平，阶数选择，即，信息分量的数量的估计对于减少这种方法中的过拟合/欠拟合是至关重要的。在空间和时间域中的fMRI数据样本之间的依赖性限制了用于顺序选择的信息理论标准（ITC）的实际公式的有用性，因为它们是基于独立同分布（i.i.d.）数据样本。为了解决这个问题，我们提出了一个子采样方案，以获得一组有效的i.i.d.样本，并将ITC公式应用于有效i.i.d.用于订单选择的样品组。我们应用所提出的方法对模拟数据，并表明它显着提高了从依赖数据的顺序选择的准确性。我们还执行顺序选择从视觉任务的fMRI数据，并表明，该方法消除了过度估计的大脑源的数量，由于固有的平滑性和平滑的预处理的fMRI数据。我们使用软件包ICASSO（亨贝格et al. [2004]：Neuroimage 22：1214-1222）来分析不同阶次的独立分量（IC）估计，并表明当伊卡以高估的阶次执行时，IC估计的稳定性降低，并且任务相关脑激活的估计显示出退化。
Multivariate analysis methods such as independent component analysis (ICA) have been applied to the analysis of functional magnetic resonance imaging (fMRI) data to study brain function. Because of the high dimensionality and high noise level of the fMRI data, order selection, i.e., estimation of the number of informative components, is critical to reduce over/ underfitting in such methods. Dependence among fMRI data samples in the spatial and temporal domain limits the usefulness of the practical formulations of information-theoretic criteria (ITC) for order selection, since they are based on likelihood of independent and identically distributed (i.i.d.) data samples. To address this issue, we propose a subsampling scheme to obtain a set of effectively i.i.d. samples from the dependent data samples and apply the ITC formulas to the effectively i.i.d. sample set for order selection. We apply the proposed method on the simulated data and show that it significantly improves the accuracy of order selection from dependent data. We also perform order selection on fMRI data from a visuomotor task and show that the proposed method alleviates the over-estimation on the number of brain sources due to the intrinsic smoothness and the smooth preprocessing of fMRI data. We use the software package ICASSO (Himberg et al. [2004]: Neuroimage 22:1214-1222) to analyze the independent component (IC) estimates at different orders and show that, when ICA is performed at overestimated orders, the stability of the IC estimates decreases and the estimation of task related brain activations show degradation.