diff --git a/docs/tutorial/advanced/mkda_association.md b/docs/tutorial/advanced/mkda_association.md index fa5f9c6..e6b30ba 100644 --- a/docs/tutorial/advanced/mkda_association.md +++ b/docs/tutorial/advanced/mkda_association.md @@ -117,7 +117,7 @@ However, certain regions which we know to have low specificity, such as the insu This example demonstrates how `MKDA Chi-Squared` association analysis can help determine the specificity activity and tasks in a meta-analysis, even for high-quality manual meta-analyses. -## Footnotes & Caveats +## Footnotes & Limitations **What happened to the "forward inference" and "reverse inference" maps?** @@ -125,6 +125,10 @@ We renamed the pre-generated forward and reverse inference maps; they're now ref Although the method we used hasn't changed (`MKDA Chi-Squared`), the latter names more accurately capture what these maps actually mean. It was a mistake on our part to have used the forward and reverse inference labels; those labels should properly be reserved for posterior probability maps generated via a Bayesian estimation analysis, rather than for z-scores resulting from a frequentist inferential test of association. Probability maps are more difficult to interpret and use correctly, as they depend on the *prior* assumed by the researcher. Since setting an appropriate prior is highly non-trivial, these maps are disabled by default. +** Using MKDA Chi Squared on manual meta-analyses ** + +In this tutorial, we applied `MKDA Chi-Squared` to a manual meta-analysis. However, this is not a perfect comparison, as there are differences between the reference sample (Neurosynth), the high-quality manual annotations given as input. Studies in large-scale meta-analytic databases are automatically populated, meaning there are potential sampling biases. Most notably, studies in Neurosynth include all reported coordinates, not only "target" analyses/contrasts. Thus, it is possible that low-level task > no task contrasts are over-represented in this reference sample. + ## References & Further Reading