Mumford Jeanette A, Poline Jean-Baptiste, Poldrack Russell A
Center for Investigating Healthy Minds at the Waisman Center, University of Wisconsin-Madison, Madison, WI, USA.
Helen Wills Neuroscience Institute, Brain Imaging Center, University of California, Berkeley, CA, USA.
PLoS One. 2015 Apr 28;10(4):e0126255. doi: 10.1371/journal.pone.0126255. eCollection 2015.
The occurrence of collinearity in fMRI-based GLMs (general linear models) may reduce power or produce unreliable parameter estimates. It is commonly believed that orthogonalizing collinear regressors in the model will solve this problem, and some software packages apply automatic orthogonalization. However, the effects of orthogonalization on the interpretation of the resulting parameter estimates is widely unappreciated or misunderstood. Here we discuss the nature and causes of collinearity in fMRI models, with a focus on the appropriate uses of orthogonalization. Special attention is given to how the two popular fMRI data analysis software packages, SPM and FSL, handle orthogonalization, and pitfalls that may be encountered in their usage. Strategies are discussed for reducing collinearity in fMRI designs and addressing their effects when they occur.
基于功能磁共振成像（fMRI）的一般线性模型（GLMs）中出现的共线性可能会降低统计功效或产生不可靠的参数估计。人们普遍认为，在模型中对共线回归变量进行正交化将解决这个问题，并且一些软件包会应用自动正交化。然而，正交化对所得参数估计解释的影响在很大程度上未得到重视或被误解。在这里，我们讨论fMRI模型中共线性的本质和原因，重点关注正交化的适当用途。特别关注两种流行的fMRI数据分析软件包SPM和FSL如何处理正交化以及在使用它们时可能遇到的陷阱。还讨论了在fMRI设计中减少共线性以及在共线性出现时应对其影响的策略。
