Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland; Department of Computer Science and Engineering, University of Oulu, Oulu, Finland.
Magn Reson Imaging. 2013 Oct;31(8):1338-48. doi: 10.1016/j.mri.2013.06.002. Epub 2013 Jul 8.
Subject-level resting-state fMRI (RS-fMRI) spatial independent component analysis (sICA) may provide new ways to analyze the data when performed in the sliding time window. However, whether principal component analysis (PCA) and voxel-wise variance normalization (VN) are applicable pre-processing procedures in the sliding-window context, as they are for regular sICA, has not been addressed so far. Also model order selection requires further studies concerning sliding-window sICA. In this paper we have addressed these concerns. First, we compared PCA-retained subspaces concerning overlapping parts of consecutive temporal windows to answer whether in-window PCA and VN can confound comparisons between sICA analyses in consecutive windows. Second, we compared the PCA subspaces between windowed and full data to assess expected comparability between windowed and full-data sICA results. Third, temporal evolution of dimensionality estimates in RS-fMRI data sets was monitored to identify potential challenges in model order selection in a sliding-window sICA context. Our results illustrate that in-window VN can be safely used, in-window PCA is applicable with most window widths and that comparisons between windowed and full data should not be performed from a subspace similarity point of view. In addition, our studies on dimensionality estimates demonstrated that there are sustained, periodic and very case-specific changes in signal-to-noise ratio within RS-fMRI data sets. Consequently, dimensionality estimation is needed for well-founded model order determination in the sliding-window case. The observed periodic changes correspond to a frequency band of ≤0.1 Hz, which is commonly associated with brain activity in RS-fMRI and become on average most pronounced at window widths of 80 and 60 time points (144 and 108 s, respectively). Wider windows provided only slightly better comparability between consecutive windows, and 60 time point or shorter windows also provided the best comparability with full-data results. Further studies are needed to determine the cause for dimensionality variations.
基于体素的静息态功能磁共振成像 (RS-fMRI) 空间独立成分分析 (sICA) 可提供新的方法来分析滑动时间窗口中的数据。然而，主成分分析 (PCA) 和体素方差归一化 (VN) 是否适用于滑动窗口中的预处理过程，就像它们适用于常规 sICA 一样，到目前为止还没有得到解决。此外，模型阶数选择也需要进一步研究滑动窗口 sICA。在本文中，我们解决了这些问题。首先，我们比较了重叠部分连续时间窗口的 PCA 保留子空间，以回答在窗口内 PCA 和 VN 是否会混淆连续窗口中 sICA 分析之间的比较。其次，我们比较了窗口化和全数据之间的 PCA 子空间，以评估窗口化和全数据 sICA 结果之间的预期可比性。第三，监测 RS-fMRI 数据集的时间演变，以确定在滑动窗口 sICA 环境中模型阶数选择的潜在挑战。我们的结果表明，在窗口内 VN 可以安全使用，在大多数窗口宽度内，在窗口内 PCA 是适用的，并且不应从子空间相似性的角度对窗口化和全数据进行比较。此外，我们对维度估计的研究表明，RS-fMRI 数据集中的信噪比存在持续、周期性和非常特定于案例的变化。因此，在滑动窗口情况下，需要进行维度估计以进行合理的模型阶数确定。观察到的周期性变化对应于≤0.1 Hz 的频率带，这通常与 RS-fMRI 中的大脑活动相关，并且在窗口宽度为 80 和 60 个时间点（分别为 144 和 108 秒）时平均最为明显。更宽的窗口仅提供了相邻窗口之间更好的可比性，而 60 个时间点或更短的窗口也提供了与全数据结果的最佳可比性。需要进一步研究来确定维度变化的原因。
