Correlations and Multiple Comparisons in Functional Imaging: A Statistical Perspective (Commentary on Vul et al., 2009)
Correlations and Multiple Comparisons in Functional Imaging: A Statistical Perspective (Commentary on Vul et al., 2009)
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DOI：
10.1111/j.1745-6924.2009.01130.x
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发表时间：
2009-05-01
影响因子：
12.6
通讯作者：
Gelman, Andrew
中科院分区：
文献类型：
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作者：
Lindquist, Martin A.;Gelman, Andrew
Vul、Harris、Winkielman 和 Pashler（2009 年，本期）在他们的文章中声称，fMRI 研究中报告的相关性通常被夸大，因为研究人员倾向于只报告最高的相关性或只报告那些超过某个阈值的相关性。他们的文章在短时间内引发了关于大多数功能性神经影像学研究核心的关键统计问题的激烈辩论。这场辩论提供了一个讨论神经影像学核心统计问题的有用机会，并最终为该领域的发展和前进提供了机会。这篇评论从根本上统计的角度来探讨这场争论。我们首先总结了正在讨论的几个关键点，然后从统计的角度对这些问题进行了我们自己的评论。我们通过思考是否是时候超越导致如此多混乱的相关性和多重比较框架来结束我们的讨论，而是将所有相关的研究问题表示为一个连贯的多级模型中的参数。
Vul, Harris, Winkielman, and Pashler (2009, this issue) claim in their article that the correlations reported in fMRI studies are commonly overstated because researchers tend to report only the highest correlations or only those correlations that exceed some threshold. Their article has in a short time given rise to a spirited debate about key statistical issues at the heart of most functional neuroimaging studies. The debate provides a useful opportunity to discuss core statistical issues in neuroimaging and ultimately provides a chance for the field to grow and move forward. This commentary approaches the debate from a fundamentally statistical perspective. We begin by summarizing several of the key points under discussion, followed by our own commentary on these issues from a statistical point of view. We conclude our discussion by contemplating whether it may be time to move beyond the correlation and multiple comparisons framework that is causing so much confusion and instead represent all relevant research questions as parameters in one coherent multilevel model.