Behavior, sensitivity, and power of activation likelihood estimation characterized by massive empirical simulation.
Behavior, sensitivity, and power of activation likelihood estimation characterized by massive empirical simulation.
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DOI：
10.1016/j.neuroimage.2016.04.072
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发表时间：
2016-08-15
期刊：
影响因子：
5.7
通讯作者：
Eickhoff CR
中科院分区：
文献类型：
--
作者：
Eickhoff SB;Nichols TE;Laird AR;Hoffstaedter F;Amunts K;Fox PT;Bzdok D;Eickhoff CR
鉴于神经成像出版物的数量不断增加，通过定量Meta分析自动提取大脑行为关联的知识已成为一个非常重要且迅速增长的研究领域。在进行基于坐标的神经成像Meta分析的几种方法中，激活似然估计(ALE)已被广泛采用。在本文中，我们解决了与ALE Meta分析相关的两个紧迫问题：i)哪种阈值方法最适合进行统计推断？二)要进行稳健的荟萃分析，需要多少样本量，即试验次数？我们通过使用来自BrainMap数据库的经验参数(即受试者数量、报告的焦点数量、激活焦点的分布)模拟超过12万个荟萃分析数据集，为这些问题提供了定量的答案。这使得能够表征ALE分析的行为，得出神经成像Meta分析的第一次能量估计，从而为未来的ALE研究制定建议。作为第一个结果，我们可以证明，簇级家族误差(FWE)校正是最适合统计推断的方法，而体素级FWE校正是有效的，但更保守。相反，应该避免未经纠正的推断和错误发现率的纠正。作为第二个结果，研究人员的目标应该是将至少20个实验纳入ALE荟萃分析，以获得足够的力量来产生适度的效果。然而，我们想指出的是，这些计算和建议是特定于ALE的，可能不会被推断到(神经成像)荟萃分析的其他方法。
Given the increasing number of neuroimaging publications, the automated knowledge extraction on brain-behavior associations by quantitative meta-analyses has become a highly important and rapidly growing field of research. Among several methods to perform coordinate-based neuroimaging meta-analyses, Activation Likelihood Estimation (ALE) has been widely adopted. In this paper, we addressed two pressing questions related to ALE meta-analysis: i) Which thresholding method is most appropriate to perform statistical inference? ii) Which sample size, i.e., number of experiments, is needed to perform robust meta-analyses? We provided quantitative answers to these questions by simulating more than 120,000 meta-analysis datasets using empirical parameters (i.e., number of subjects, number of reported foci, distribution of activation foci) derived from the BrainMap database. This allowed to characterize the behavior of ALE analyses, to derive first power estimates for neuroimaging meta-analyses, and to thus formulate recommendations for future ALE studies. We could show as a first consequence that cluster-level family-wise error (FWE) correction represents the most appropriate method for statistical inference, while voxel-level FWE correction is valid but more conservative. In contrast, uncorrected inference and false-discovery rate correction should be avoided. As a second consequence, researchers should aim to include at least 20 experiments into an ALE meta-analysis to achieve sufficient power for moderate effects. We would like to note, though, that these calculations and recommendations are specific to ALE and may not be extrapolated to other approaches for (neuroimaging) meta-analysis.