Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany.
Neuroimage. 2012 Feb 1;59(3):2349-61. doi: 10.1016/j.neuroimage.2011.09.017. Epub 2011 Sep 22.
A widely used technique for coordinate-based meta-analysis of neuroimaging data is activation likelihood estimation (ALE), which determines the convergence of foci reported from different experiments. ALE analysis involves modelling these foci as probability distributions whose width is based on empirical estimates of the spatial uncertainty due to the between-subject and between-template variability of neuroimaging data. ALE results are assessed against a null-distribution of random spatial association between experiments, resulting in random-effects inference. In the present revision of this algorithm, we address two remaining drawbacks of the previous algorithm. First, the assessment of spatial association between experiments was based on a highly time-consuming permutation test, which nevertheless entailed the danger of underestimating the right tail of the null-distribution. In this report, we outline how this previous approach may be replaced by a faster and more precise analytical method. Second, the previously applied correction procedure, i.e. controlling the false discovery rate (FDR), is supplemented by new approaches for correcting the family-wise error rate and the cluster-level significance. The different alternatives for drawing inference on meta-analytic results are evaluated on an exemplary dataset on face perception as well as discussed with respect to their methodological limitations and advantages. In summary, we thus replaced the previous permutation algorithm with a faster and more rigorous analytical solution for the null-distribution and comprehensively address the issue of multiple-comparison corrections. The proposed revision of the ALE-algorithm should provide an improved tool for conducting coordinate-based meta-analyses on functional imaging data.
一种广泛应用于神经影像学数据的基于坐标的荟萃分析技术是激活似然估计（ALE），它确定了来自不同实验的焦点的收敛性。ALE 分析涉及将这些焦点建模为概率分布，其宽度基于神经影像学数据的个体间和模板间变异性的空间不确定性的经验估计。ALE 结果与实验之间随机空间关联的零分布进行评估，从而进行随机效应推断。在本算法的修订版中，我们解决了之前算法中仍然存在的两个缺陷。首先，实验之间的空间关联的评估是基于高度耗时的置换检验，尽管如此，这仍然存在低估零分布右尾的危险。在本报告中，我们概述了如何用更快和更精确的分析方法替代以前的方法。其次，以前应用的校正程序，即控制假发现率（FDR），通过新的方法来校正总体错误率和簇级显著性。在面部感知的示例数据集上评估了对荟萃分析结果进行推断的不同替代方法，并就其方法学局限性和优点进行了讨论。总之，我们用更快和更严格的零分布分析解决方案替代了以前的置换算法，并全面解决了多重比较校正的问题。ALE 算法的这个修订版应该为在功能成像数据上进行基于坐标的荟萃分析提供一个改进的工具。
