Marchini Jonathan, Presanis Anne
Department of Statistics, University of Oxford, Oxford OX1 3TG, UK.
Neuroimage. 2004 Jul;22(3):1203-13. doi: 10.1016/j.neuroimage.2004.03.030.
Approaches for the analysis of statistical parametric maps (SPMs) can be crudely grouped into three main categories in which different philosophies are applied to delineate activated regions. These being type I error control thresholding, false discovery rate (FDR) control thresholding and posterior probability thresholding. To better understand the properties of these main approaches, we carried out a simulation study to compare the approaches as they would be used on real data sets. Using default settings, we find that posterior probability thresholding is the most powerful approach, and type I error control thresholding provides the lowest levels of type I error. False discovery rate control thresholding performs in between the other approaches for both these criteria, although for some parameter settings this approach can approximate the performance of posterior probability thresholding. Based on these results, we discuss the relative merits of the three approaches in an attempt to decide upon an optimal approach. We conclude that viewing the problem of delineating areas of activation as a classification problem provides a highly interpretable framework for comparing the methods. Within this framework, we highlight the role of the loss function, which explicitly penalizes the types of errors that may occur in a given analysis.
统计参数映射（SPM）的分析方法大致可分为三大类，其中应用了不同的理念来描绘激活区域。它们分别是I型错误控制阈值法、错误发现率（FDR）控制阈值法和后验概率阈值法。为了更好地理解这些主要方法的特性，我们进行了一项模拟研究，以比较这些方法在实际数据集上的应用情况。使用默认设置，我们发现后验概率阈值法是最强大的方法，而I型错误控制阈值法提供了最低水平的I型错误。在这两个标准上，错误发现率控制阈值法的表现介于其他方法之间，尽管对于某些参数设置，这种方法可以接近后验概率阈值法的性能。基于这些结果，我们讨论了这三种方法的相对优点，试图确定一种最佳方法。我们得出结论，将描绘激活区域的问题视为分类问题，为比较这些方法提供了一个高度可解释的框架。在此框架内，我们强调了损失函数的作用，它明确惩罚了给定分析中可能出现的错误类型。
