The standard ALE algorithm is illustrated in Figure 1. In ALE, users organize the activation foci in their dataset based on their source in the literature. Typically, foci are organized by the experiment that reported them. Gaussian widths are calculated based on empirical quantification of the uncertainty inherent in spatial normalization, and the relationship between sample size and inter-subject localization uncertainty [Eickhoff et al., 2009 (link)]. An individual map of activation likelihood, called a Modeled Activation map (MA map), is then calculated for each experiment by taking the voxelwise union of the Gaussians for all of the foci derived from that experiment. The ALE map is then calculated as the voxelwise union of the MA maps from a dataset. Null distributions account for the increased likelihood of identifying activation foci in gray matter, and a random-effects significance test uses the null hypothesis that neuroimaging experiments produce patterns of activation that are spatially independent from one another [Eickhoff et al., 2009 (link)]. Analyses were performed using Ginger ALE 2.0 (brainmap.org), and also implemented in C++ for further calculations on MA values. Standard ALE and modified ALE analyses were conducted using two different critical thresholds (FDR of 0.05 and 0.01), and a minimum cluster size of 100 mm3.