This paper revisits the Activation Likelihood Estimation (ALE) meta-analysis method, a widely used technique for coordinate-based meta-analysis of neuroimaging data. The authors address two key drawbacks of the previous ALE algorithm: the time-consuming permutation test for assessing spatial association between experiments and the use of the False Discovery Rate (FDR) for multiple-comparison correction. They propose an analytical solution using histogram integration to compute the null-distribution more efficiently and accurately. Additionally, they introduce new approaches for correcting the Family-Wise Error (FWE) and cluster-level significance. The revised ALE algorithm is evaluated on a dataset on face perception, demonstrating improved performance in terms of computational efficiency, statistical precision, and sensitivity compared to the previous version. The results show that the new algorithm provides a more robust and comprehensive framework for coordinate-based meta-analyses in neuroimaging research.This paper revisits the Activation Likelihood Estimation (ALE) meta-analysis method, a widely used technique for coordinate-based meta-analysis of neuroimaging data. The authors address two key drawbacks of the previous ALE algorithm: the time-consuming permutation test for assessing spatial association between experiments and the use of the False Discovery Rate (FDR) for multiple-comparison correction. They propose an analytical solution using histogram integration to compute the null-distribution more efficiently and accurately. Additionally, they introduce new approaches for correcting the Family-Wise Error (FWE) and cluster-level significance. The revised ALE algorithm is evaluated on a dataset on face perception, demonstrating improved performance in terms of computational efficiency, statistical precision, and sensitivity compared to the previous version. The results show that the new algorithm provides a more robust and comprehensive framework for coordinate-based meta-analyses in neuroimaging research.