避免 fMRI 数据分析中的非独立性：排除一个被试。
Avoiding non-independence in fMRI data analysis: leave one subject out.
机构信息
Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA.
出版信息
Neuroimage. 2010 Apr 1;50(2):572-6. doi: 10.1016/j.neuroimage.2009.10.092. Epub 2009 Dec 16.
Abstract
Concerns regarding certain fMRI data analysis practices have recently evoked lively debate. The principal concern regards the issue of non-independence, in which an initial statistical test is followed by further non-independent statistical tests. In this report, we propose a simple, practical solution to reduce bias in secondary tests due to non-independence using a leave-one-subject-out (LOSO) approach. We provide examples of this method, show how it reduces effect size inflation, and suggest that it can serve as a functional localizer when within-subject methods are impractical.
摘要
最近，人们对某些 fMRI 数据分析实践的关注引发了激烈的争论。主要关注点在于非独立性问题，即初始统计检验后接着进行进一步的非独立统计检验。在本报告中，我们提出了一种简单实用的解决方案，即使用离开一个受试者（LOSO）方法来减少由于非独立性导致的二次检验中的偏差。我们提供了这种方法的示例，展示了它如何减少效应大小膨胀，并建议当使用基于个体的方法不切实际时，它可以作为一种功能定位器。
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