Imaging Institute, Cleveland Clinic, 9500 Euclid Ave-U15, Cleveland, OH 44195, USA.
J Neurosci Methods. 2010 Aug 30;191(2):263-76. doi: 10.1016/j.jneumeth.2010.06.024. Epub 2010 Jun 30.
The impact of physiologic noise on spatial ICA analyses of resting state BOLD-weighted MRI data is investigated. Using FastICA and Infomax ICA, two common ICA algorithms, we apply a group spatial ICA method across multiple subjects. We compare the spatial maps from five commonly identified functional networks and show that physiologic noise correction techniques introduce significant changes in the spatial ICA decomposition of all five networks, greater than the changes introduced by either algorithmic indeterminacy (re-running ICA) or the changes introduced by decreasing the decomposition dimensionality due to physiologic noise removal. In addition, we demonstrate that the sources associated with these components have significant temporal correlation to parallel measures of cardiac and respiratory rates, and these are reduced after correction. We conclude that ICA decomposition is significantly affected by physiologic noise and the ICA process alone is not sufficient to separate physiologic noise effects in the brain. It is recommended that physiologic noise correction be applied to timeseries data prior to ICA decomposition.
研究了生理噪声对静息状态血氧水平依赖功能磁共振成像数据的空间独立成分分析（ICA）的影响。我们使用两种常见的 ICA 算法，即 FastICA 和 Infomax ICA，在多个被试中应用了组空间 ICA 方法。我们比较了从五个常见功能网络中识别出的空间图谱，并表明生理噪声校正技术在所有五个网络的空间 ICA 分解中引入了显著变化，大于算法不确定性（重新运行 ICA）或由于生理噪声去除导致分解维度减少所引入的变化。此外，我们证明了与这些成分相关的源与心脏和呼吸率的平行测量有显著的时间相关性，而这些相关性在校正后会降低。我们得出结论，ICA 分解受到生理噪声的显著影响，仅 ICA 过程不足以分离大脑中的生理噪声影响。建议在 ICA 分解之前对时间序列数据应用生理噪声校正。
