Confounds in multivariate pattern analysis: Theory and rule representation case study
Confounds in multivariate pattern analysis: Theory and rule representation case study
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DOI：
10.1016/j.neuroimage.2013.03.039
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发表时间：
2013-08-15
期刊：
影响因子：
5.7
通讯作者：
Cohen, Jonathan D.
中科院分区：
文献类型：
--
作者：
Todd, Michael T.;Nystrom, Leigh E.;Cohen, Jonathan D.
多变量模式分析（MVPA）是功能磁共振成像（fMRI）方法中的一个相对较新的创新。MVPA被越来越广泛地使用，因为它显然比经典的一般线性模型分析（GLMA）更有效地检测响应模式或表示，分布在一个很好的空间尺度。然而，我们证明，广泛使用的方法MVPA可以系统地承认某些混淆，适当地消除了GLMA。因此，混淆，而不是分布式表示可以解释一些情况下，MVPA产生积极的结果，但GLMA没有。问题在于，MVPA中的常见做法是对单个受试者的汇总统计量进行组检验，这些统计量会丢弃潜在效应的符号或方向，而GLMA组检验则直接对单个受试者效应本身进行。我们描述了这种常见的MVPA做法如何破坏标准的实验设计逻辑，旨在控制在组水平的某些类型的混淆，如任务时间和个体差异。此外，我们注意到，在许多情况下使用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与新的功能磁共振成像数据在域的规则表示，或灵活的刺激-反应映射，这已经看到了最近的MVPA出版物。在我们的新数据集中，与最近的报告一样，标准MVPA似乎揭示了前额叶皮层区域的规则表征，而GLMA产生空结果。然而，控制一个变量，这是混淆与规则在个人受试者的水平，但不是组的水平（反应时间差异的规则）消除MVPA的结果。这就提出了一个问题，最近报道的结果是否真实地反映了规则表示，或者更确切地说，反应时间，难度或其他不感兴趣的变量等混淆的影响。(C)2013 Elsevier Inc. All rights reserved.
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. (C) 2013 Elsevier Inc. All rights reserved.