Classification of temporal ICA components for separating global noise from fMRI data: Reply to Power
Classification of temporal ICA components for separating global noise from fMRI data: Reply to Power
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DOI：
10.1016/j.neuroimage.2019.04.046
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发表时间：
2019-08-15
期刊：
影响因子：
5.7
通讯作者：
Smith, Stephen M.
中科院分区：
文献类型：
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作者：
Glasser, Matthew F.;Coalson, Timothy S.;Smith, Stephen M.
我们回应批评我们的时间独立成分分析（伊卡）的方法分离的全球性噪声从全球性的fMRI数据，专注于信号与噪声的几个组件的分类。虽然我们同意Power的一些评论，但我们提供了证据和分析来反驳他的主要批评，并向读者保证时间伊卡仍然是一种强大且有前途的去噪方法。
We respond to a critique of our temporal Independent Components Analysis (ICA) method for separating global noise from global signal in fMRI data that focuses on the signal versus noise classification of several components. While we agree with several of Power's comments, we provide evidence and analysis to rebut his major criticisms and to reassure readers that temporal ICA remains a powerful and promising denoising approach.