A feature-selective independent component analysis method for functional MRI.
A feature-selective independent component analysis method for functional MRI.
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DOI：
10.1155/2007/15635
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发表时间：
2007
影响因子：
7.6
通讯作者：
Calhoun VD
中科院分区：
文献类型：
--
作者：
Li YO;Adali T;Calhoun VD
在这项工作中，我们提出了一个简单而有效的计划，将先验知识的利益（SOI）的独立成分分析（伊卡）的来源，并应用该方法来估计脑激活功能磁共振成像（fMRI）数据。我们命名为特征选择伊卡的方法，因为它结合了在伊卡估计的独立分量的样本空间中的功能。特征选择方案是通过在源样本空间中进行滤波操作，然后通过迭代伊卡过程中的最小二乘投影到去混合向量空间上来实现的。我们进行伊卡估计的人工激活叠加到静息状态fMRI数据集显示，功能选择性方案提高了独立分量伊卡估计的注入激活的检测。我们还比较了任务相关的源估计从真正的功能磁共振成像数据的特征选择伊卡算法与伊卡算法，并显示证据表明，特征选择方案有助于提高空间激活模式和时间过程中的源的估计。
In this work, we propose a simple and effective scheme to incorporate prior knowledge about the sources of interest (SOIs) in independent component analysis (ICA) and apply the method to estimate brain activations from functional magnetic resonance imaging (fMRI) data. We name the proposed method as feature-selective ICA since it incorporates the features in the sample space of the independent components during ICA estimation. The feature-selective scheme is achieved through a filtering operation in the source sample space followed by a projection onto the demixing vector space by a least squares projection in an iterative ICA process. We perform ICA estimation of artificial activations superimposed into a resting state fMRI dataset to show that the feature-selective scheme improves the detection of injected activation from the independent component estimated by ICA. We also compare the task-related sources estimated from true fMRI data by a feature-selective ICA algorithm versus an ICA algorithm and show evidence that the feature-selective scheme helps improve the estimation of the sources in both spatial activation patterns and the time courses.