A Parcellation Based Nonparametric Algorithm for Independent Component Analysis with Application to fMRI Data.
A Parcellation Based Nonparametric Algorithm for Independent Component Analysis with Application to fMRI Data.
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DOI：
10.3389/fnins.2016.00015
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发表时间：
2016
影响因子：
4.3
通讯作者：
Caffo B
中科院分区：
文献类型：
--
作者：
Li S;Chen S;Yue C;Caffo B
独立组件分析（ICA）是一种用于分离已混合在一起的信号的广泛使用的技术。在此手稿中，我们使用密度估计和最大似然提出了一种新型的ICA算法，其中信号的密度是通过基于p-Spline的基于P-Spline的直方图平滑估算的，并且使用优化算法同时估算混合矩阵。该算法非常简单，易于实现，并且对源信号的基础分布视而不见。为了放松密度函数中相同分布的假设，提出了修改的算法，以允许在不同区域上具有不同的密度函数。在不同的仿真设置中评估了所提出的算法的性能。为了进行说明，该算法应用于大量静止状态fMRI数据集的研究调查。结果表明，该算法成功地恢复了已建立的大脑网络。
Independent Component analysis (ICA) is a widely used technique for separating signals that have been mixed together. In this manuscript, we propose a novel ICA algorithm using density estimation and maximum likelihood, where the densities of the signals are estimated via p-spline based histogram smoothing and the mixing matrix is simultaneously estimated using an optimization algorithm. The algorithm is exceedingly simple, easy to implement and blind to the underlying distributions of the source signals. To relax the identically distributed assumption in the density function, a modified algorithm is proposed to allow for different density functions on different regions. The performance of the proposed algorithm is evaluated in different simulation settings. For illustration, the algorithm is applied to a research investigation with a large collection of resting state fMRI datasets. The results show that the algorithm successfully recovers the established brain networks.