Detection of consistently task-related activations in fMRI data with hybrid independent component analysis
Detection of consistently task-related activations in fMRI data with hybrid independent component analysis
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DOI：
10.1006/nimg.1999.0518
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发表时间：
2000-01-01
期刊：
影响因子：
5.7
通讯作者：
McKeown, MJ
中科院分区：
文献类型：
--
作者：
McKeown, MJ
尽管fMRI信号的许多成分很难明确地指定，但通常通过测试每个体素与特定假设波形的时间过程来分析fMRI数据。相比之下，纯粹的数据驱动技术，通过关注数据的内在结构，缺乏直接的手段来测试研究者感兴趣的假设。在这两个极端之间，混合方法可以发挥作用，它使用强大的数据驱动技术来充分描述数据，但也使用一些先验假设来指导分析。在这里，我们描述了这样一种混合技术，HYBICA，它使用的fMRI数据的初始特征，从独立成分分析，并允许实验者顺序联合收割机假设的任务相关的组件，使一个可以优雅地从一个完全的数据派生的方法导航到一个完全假设驱动的方法。我们描述了两个人工和两个真实的数据集的测试结果的方法。使用基于诊断预测平方和统计量的度量来选择最佳数量的空间独立分量以在标准回归框架中进行联合收割机组合和利用。所提出的指标提供了一种客观的方法，以确定是否更数据驱动或更假设驱动的方法是适当的，这取决于假设的参考函数和数据中的功能之间的不匹配程度。HYBICA提供了一种稳健的方式，将数据导出的独立分量联合收割机组合成数据导出的激活波形和适当的混淆，从而可以执行标准统计分析。(C)北京大学出版社.
fMRI data are commonly analyzed by testing the time course from each voxel against specific hypothesized waveforms, despite the fact that many components of fMRI signals are difficult to specify explicitly. In contrast, purely data-driven techniques, by focusing on the intrinsic structure of the data, lack a direct means to test hypotheses of interest to the examiner. Between these two extremes, there is a role for hybrid methods that use powerful data-driven techniques to fully characterize the data, but also use some a priori hypotheses to guide the analysis. Here we describe such a hybrid technique, HYBICA, which uses the initial characterization of the fMRI data from Independent Component Analysis and allows the experimenter to sequentially combine assumed task-related components so that one can gracefully navigate from a fully data-derived approach to a fully hypothesis-driven approach. We describe the results of testing the method with two artificial and two real data sees. A metric based on the diagnostic Predicted Sum of Squares statistic was used to select the best number of spatially independent components to combine and utilize in a standard regressional framework. The proposed metric provided an objective method to determine whether a more data-driven or a more hypothesis-driven approach was appropriate, depending on the degree of mismatch between the hypothesized reference function and the features in the data. HYBICA provides a robust way to combine the data-derived independent components into a data-derived activation waveform and suitable confounds so that standard statistical analysis can be performed. (C) 2000 Academic Press.