Using ALE, the identified literature coordinates were modeled with a three-dimensional Gaussian distribution, and their convergence across experiments was quantitatively assessed. Rather than using a pre-specified FWHM as in the original ALE approach, an algorithm was employed to model the spatial uncertainty of each focus using an estimation of the inter-subject and inter-laboratory variability typically observed in neuroimaging experiments. This algorithm limits the meta-analysis to an anatomically constrained space specified by a grey matter mask, and includes a method that calculates the above-chance clustering between experiments (i.e., random-effects analysis), rather than between foci (i.e., fixed-effects analysis) (Eickhoff et al., 2009b (link)).
ALE was performed in Talairach space using GingerALE 2.0. Coordinates originally published in MNI space were converted to Talairach space using the Lancaster (icbm2tal) transformation (Lancaster et al., 2007 (link)). The resultant ALE map was thresholded at a false discovery rate (FDR) corrected threshold of P < 0.005 (Laird et al., 2005a (link)), a conservative threshold that was selected to determine only the most strongly concordant regions. Images were viewed in Mango (multi-image analysis GUI), developed at the Research Imaging Center in San Antonio (http://ric.uthscsa.edu/mango/ ).
ALE was performed in Talairach space using GingerALE 2.0. Coordinates originally published in MNI space were converted to Talairach space using the Lancaster (icbm2tal) transformation (Lancaster et al., 2007 (link)). The resultant ALE map was thresholded at a false discovery rate (FDR) corrected threshold of P < 0.005 (Laird et al., 2005a (link)), a conservative threshold that was selected to determine only the most strongly concordant regions. Images were viewed in Mango (multi-image analysis GUI), developed at the Research Imaging Center in San Antonio (
