Six actions to improve detection of critical features for neuroimaging coordinate-based meta-analysis preparation.

Coordinate-based meta-analysis (CBMA) is a research strategy widely used in the field of human brain imaging. Although dedicated tools as BrainMap or Neurosynth had been developed in past years, some of the crucial steps necessary to identify and compose the dataset are still user-based, resulting in a not standardized approach to literature search, as well as in time-consuming and prone to errors procedures. In particular, this concern involves the assessment of voxel-wise whole brain analyses in contrast to ROI-based ones, and the identification of available lists of peaks of effect (i.e., x,y,z coordinates of the foci). Here, we propose six simple actions that can be undertaken by any researcher and by the publishing system, allowing to limit the risk of erroneous decisions on the inclusion of experimental data in the meta-analytic dataset. This straightforward and useful strategy would reduce possible bias in CBMA, therefore allowing to obtain more reliable results.