Department of Psychology and Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA.
Neuroimage. 2013 Aug 15;77:157-65. doi: 10.1016/j.neuroimage.2013.03.039. Epub 2013 Apr 2.
Multivariate pattern analysis (MVPA) is a relatively recent innovation in functional magnetic resonance imaging (fMRI) methods. MVPA is increasingly widely used, as it is apparently more effective than classical general linear model analysis (GLMA) for detecting response patterns or representations that are distributed at a fine spatial scale. However, we demonstrate that widely used approaches to MVPA can systematically admit certain confounds that are appropriately eliminated by GLMA. Thus confounds rather than distributed representations may explain some cases in which MVPA produced positive results but GLMA did not. The issue is that it is common practice in MVPA to conduct group tests on single-subject summary statistics that discard the sign or direction of underlying effects, whereas GLMA group tests are conducted directly on single-subject effects themselves. We describe how this common MVPA practice undermines standard experiment design logic that is intended to control at the group level for certain types of confounds, such as time on task and individual differences. Furthermore, we note that a simple application of linear regression can restore experimental control when using MVPA in many situations. Finally, we present a case study with novel fMRI data in the domain of rule representations, or flexible stimulus-response mappings, which has seen several recent MVPA publications. In our new dataset, as with recent reports, standard MVPA appears to reveal rule representations in prefrontal cortex regions, whereas GLMA produces null results. However, controlling for a variable that is confounded with rule at the individual-subject level but not the group level (reaction time differences across rules) eliminates the MVPA results. This raises the question of whether recently reported results truly reflect rule representations, or rather the effects of confounds such as reaction time, difficulty, or other variables of no interest.
多元模式分析（MVPA）是功能磁共振成像（fMRI）方法中的一项相对较新的创新。MVPA 应用越来越广泛，因为它显然比经典的一般线性模型分析（GLMA）更有效地检测分布在精细空间尺度上的反应模式或表示。然而，我们证明了广泛使用的 MVPA 方法可以系统地接受某些混淆因素，而 GLMA 可以适当消除这些混淆因素。因此，可能是分布的表示而不是混淆因素解释了某些情况下 MVPA 产生阳性结果而 GLMA 没有产生阳性结果的原因。问题在于，在 MVPA 中，常见的做法是对丢弃潜在效应的符号或方向的单个被试汇总统计数据进行组测试，而 GLMA 组测试则直接对单个被试效应本身进行测试。我们描述了这种常见的 MVPA 做法如何破坏了旨在控制特定类型混淆因素（例如任务时间和个体差异）的标准实验设计逻辑，而这些混淆因素在组水平上是可以控制的。此外，我们注意到，在许多情况下，当使用 MVPA 时，线性回归的简单应用可以恢复实验控制。最后，我们提出了一个案例研究，涉及规则表示或灵活的刺激-反应映射领域的新 fMRI 数据，该研究领域最近有几项 MVPA 出版物。在我们的新数据集和最近的报告中，标准的 MVPA 似乎揭示了前额叶皮层区域的规则表示，而 GLMA 则产生了零结果。然而，控制个体水平上与规则混淆但不是组水平上混淆的变量（规则之间的反应时间差异）消除了 MVPA 的结果。这就提出了一个问题，即最近报告的结果是否真的反映了规则表示，还是仅仅反映了反应时间、难度或其他无兴趣变量等混淆因素的影响。
