Python implementations of the activation likelihood estimation (ALE)[1][2][3] and specific co-activation likelihood estimation (SCALE)[4] algorithms for coordinate-based meta-analyses of fMRI research. The implementations in this repository are useful for researchers interested in studying the actual algorithms used for CBMAs[5][6], performing large-scale meta-analytic parcellation[7][8], identifying patterns in metadata across the literature[9][10], or other similar analyses. Some of these analyses require access to a large database of meta-analytic data, such as the BrainMap database. If you are interested in performing coactivation-based parcellation (CBP) or meta-analytic coactivation modeling (MACM) analyses using the BrainMap database, you will need to contact the BrainMap Development Team and request a large-scale data dump as part of a collaborative use agreement. More information is available here.
For anyone interested in performing standard content-based meta-analyses, please use the official implementation of ALE, GingerALE (http://brainmap.org/ale/). The versions available here are unofficial implementations, and may include errors. Please use them at your own risk.