Tor Wager has developed his own meta-analysis software (Multilevel Kernel Density Analysis), a set of Matlab scripts and functions freely available from his website. In addition, he has a nifty collection of meta-analysis data and image files that can be downloaded as well. The documentation for MKDA (PDF) notes some problems with other meta-analysis procedures:Prior meta-analyses have divided the brain into voxels and plotted peak coordinates. Then count how many peaks within each voxel (the observed frequency count). Compare this to the number expected by chance if peaks were distributed randomly throughout the brain (the expected frequency count). Have to establish threshold (using Monte Carlo method).Problems with this method: This is a fixed effect procedure which ignores the fact that points are not independent of one another (as they are nested within contrasts within studies). An important consequence is that any single study that has a large number of peaks (due to differences in reporting, voxel size, thresholding) can overly influence the analyses.MKDA offers a number of different options for dealing with these issues (Wager et al., 2009).In his talk, Wager discussed four major uses of meta-analysis:
1. Formulating a priori hypotheses
2. Testing existing theories
3. Developing new theories
4. Making inferences about psychological states
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