IEEE Trans Biomed Eng. 2019 Jan;66(1):289-299. doi: 10.1109/TBME.2018.2831186. Epub 2018 May 17.
In this work, we conduct comprehensive comparisons between four variants of independent component analysis (ICA) methods and three variants of sparse dictionary learning (SDL) methods, both at the subject-level, by using synthesized fMRI data with ground-truth. Our results showed that ICA methods perform very well and slightly better than SDL methods when functional networks' spatial overlaps are minor, but ICA methods have difficulty in differentiating functional networks with moderate or significant spatial overlaps. In contrast, the SDL algorithms perform consistently well no matter how functional networks spatially overlap, and importantly, SDL methods are significantly better than ICA methods when spatial overlaps between networks are moderate or severe. This work offers empirical better understanding of ICA and SDL algorithms in inferring functional networks from fMRI data and provides new guidelines and caveats when constructing and interpreting functional networks in the era of fMRI-based connectomics.
在这项工作中，我们使用具有真实数据的合成 fMRI 数据，在主体水平上对独立成分分析 (ICA) 方法的四种变体和稀疏字典学习 (SDL) 方法的三种变体进行了全面比较。结果表明，当功能网络的空间重叠较小时，ICA 方法的表现非常好，略优于 SDL 方法，但 ICA 方法难以区分具有中等或显著空间重叠的功能网络。相比之下，SDL 算法无论功能网络的空间重叠程度如何，表现都非常一致，并且重要的是，当网络之间的空间重叠处于中等或严重程度时，SDL 方法明显优于 ICA 方法。这项工作为从 fMRI 数据中推断功能网络时 ICA 和 SDL 算法提供了更好的经验理解，并为基于 fMRI 的连接组学时代构建和解释功能网络提供了新的指导方针和注意事项。
