Zeng Weiming, Qiu Anqi, Chodkowski BettyAnn, Pekar James J
F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD 21205, USA.
Neuroimage. 2009 Jul 15;46(4):1041-54. doi: 10.1016/j.neuroimage.2009.02.048. Epub 2009 Mar 12.
Independent component analysis (ICA) decomposes fMRI data into spatially independent maps and their corresponding time courses. However, distinguishing the neurobiologically and biophysically reasonable components from those representing noise and artifacts is not trivial. We present a simple method for the ranking of independent components, by assessing the resemblance between components estimated from all the data, and components estimated from only the odd- (or even-) numbered time points. We show that the meaningful independent components of fMRI data resemble independent components estimated from downsampled data, and thus tend to be highly ranked by the method.
独立成分分析（ICA）将功能磁共振成像（fMRI）数据分解为空间上独立的图谱及其相应的时间历程。然而，要区分神经生物学和生物物理学上合理的成分与那些代表噪声和伪影的成分并非易事。我们提出了一种简单的方法来对独立成分进行排序，即通过评估从所有数据估计出的成分与仅从奇数（或偶数）时间点估计出的成分之间的相似性。我们表明，fMRI数据中有意义的独立成分类似于从下采样数据估计出的独立成分，因此往往会被该方法高度排序。
