van Kesteren Erik-Jan, Kievit Rogier A
Utrecht University, Department of Methodology and Statistics, Utrecht, the Netherlands.
University of Cambridge, MRC Cognition and Brain Sciences Unit, Cambridge, UK.
Netw Neurosci. 2021 Feb 1;5(1):1-27. doi: 10.1162/netn_a_00162. eCollection 2021.
Dimension reduction is widely used and often necessary to make network analyses and their interpretation tractable by reducing high-dimensional data to a small number of underlying variables. Techniques such as exploratory factor analysis (EFA) are used by neuroscientists to reduce measurements from a large number of brain regions to a tractable number of factors. However, dimension reduction often ignores relevant a priori knowledge about the structure of the data. For example, it is well established that the brain is highly symmetric. In this paper, we (a) show the adverse consequences of ignoring a priori structure in factor analysis, (b) propose a technique to accommodate structure in EFA by using structured residuals (EFAST), and (c) apply this technique to three large and varied brain-imaging network datasets, demonstrating the superior fit and interpretability of our approach. We provide an R software package to enable researchers to apply EFAST to other suitable datasets.
降维被广泛应用，并且通常对于通过将高维数据减少到少量潜在变量来使网络分析及其解释变得易于处理是必要的。诸如探索性因子分析（EFA）之类的技术被神经科学家用于将来自大量脑区的测量值减少到易于处理的因子数量。然而，降维通常会忽略关于数据结构的相关先验知识。例如，众所周知大脑具有高度对称性。在本文中，我们（a）展示了在因子分析中忽略先验结构的不利后果，（b）提出了一种通过使用结构化残差（EFAST）来在探索性因子分析中纳入结构的技术，并且（c）将此技术应用于三个大型且多样的脑成像网络数据集，证明了我们方法的优越拟合度和可解释性。我们提供了一个R软件包，以使研究人员能够将EFAST应用于其他合适的数据集。
