Department of Psychology, University of Minnesota, Minneapolis, MN, 55455, USA; School of Statistics, University of Minnesota, Minneapolis, MN, 55455, USA.
Department of Psychology, University of Minnesota, Minneapolis, MN, 55455, USA.
Neuroimage. 2019 Nov 1;201:116019. doi: 10.1016/j.neuroimage.2019.116019. Epub 2019 Jul 15.
Component models such as PCA and ICA are often used to reduce neuroimaging data into a smaller number of components, which are thought to reflect latent brain networks. When data from multiple subjects are available, the components are typically estimated simultaneously (i.e., for all subjects combined) using either tensor ICA or group ICA. As we demonstrate in this paper, neither of these approaches is ideal if one hopes to find latent brain networks that cross-validate to new samples of data. Specifically, we note that the tensor ICA model is too rigid to capture real-world heterogeneity in the component time courses, whereas the group ICA approach is too flexible to uniquely identify latent brain networks. For multi-subject component analysis, we recommend comparing a hierarchy of simultaneous component analysis (SCA) models. Our proposed model hierarchy includes a flexible variant of the SCA framework (the Parafac2 model), which is able to both (i) model heterogeneity in the component time courses, and (ii) uniquely identify latent brain networks. Furthermore, we propose cross-validation methods to tune the relevant model parameters, which reduces the potential of over-fitting the observed data. Using simulated and real data examples, we demonstrate the benefits of the proposed approach for finding credible components that reveal interpretable individual and group differences in latent brain networks.
成分模型，如 PCA 和 ICA，常用于将神经影像学数据减少到更小的成分数量，这些成分被认为反映了潜在的大脑网络。当有多个主体的数据可用时，通常使用张量 ICA 或组 ICA 同时估计这些成分（即，所有主体的组合）。正如我们在本文中所展示的，如果希望找到与新数据样本交叉验证的潜在大脑网络，那么这两种方法都不理想。具体来说，我们注意到张量 ICA 模型过于僵化，无法捕捉组成时间过程中的真实世界异质性，而组 ICA 方法过于灵活，无法唯一识别潜在的大脑网络。对于多主体成分分析，我们建议比较同时成分分析（SCA）模型的层次结构。我们提出的模型层次结构包括 SCA 框架的灵活变体（Parafac2 模型），该模型能够：（i）对组成时间过程中的异质性进行建模；（ii）唯一识别潜在的大脑网络。此外，我们提出了交叉验证方法来调整相关的模型参数，从而降低了观察数据过度拟合的可能性。使用模拟和真实数据示例，我们展示了该方法在寻找可揭示潜在大脑网络中个体和组差异的可信成分方面的优势。
