Merge branch 'mkda' of github.com:neurostuff/compose-docs into mkda

blog/2024-1-2-new-year.md

@@ -18,13 +18,13 @@ We’ve also continued to introduce new features and improve the user experience
 
 **Large-scale association tests**
 
-A key feature that set aside Neurosynth were large-scale “association tests” (previously referred to as “reverse inference” test) 
+A key feature that set aside Neurosynth were large-scale “association tests” (previously referred to as “reverse inference” tests).
 
 Whereas a typical meta-analysis tells you about the consistency of activation in a set of studies, an association test tells you whether activation in a region occurs *more consistently for studies in your set of studies versus a large reference sample of studies*. 
 
 Neurosynth leverages large-scale neuroimaging datasets as a reference to which you can compare the specific studies in given meta-analysis. 
 
-That's important, because this effectively allows you to control for base rate differences between regions. Certain regions, such as the insula or lateral PFC for instance, play a very broad role in cognition, and hence tend to be consistently activated for many different terms. Thus, if you see insula activity in your meta-analysis, you can erroneously conclude that the insula is involved in the process of your study. A large-scale association test lets you determine if the activity you observe in a region occurs to a greater extent in your meta-analysis than in other studies, making it possible to make more confident claims that a given region is involved in a particular process, and isn't involved in just about every task.
+That's important, because this effectively allows you to control for base rate differences between regions. Certain regions, such as the insula or lateral PFC for instance, play a very broad role in cognition, and hence tend to be consistently activated for many different terms. Thus, if you see insula activity in your meta-analysis, you might erroneously conclude that the insula is involved in the process of your study. A large-scale association test lets you determine if the activity you observe in a region occurs to a greater extent in your meta-analysis than in other studies, making it possible to make more confident claims that a given region is involved in a particular process, and isn't involved in just about every task.
 
 Previously these association tests were available for the automatically generated maps on neurosynth.org. **Now you can perform large-scale association tests for your custom meta-analyses in Neurosynth Compose.**
 

docs/tutorial/advanced/mkda_association.md

@@ -83,7 +83,7 @@ The uniformity test map can be interpreted in roughly the same way as most stand
 
 - **Association test map**: z-scores from a two-way ANOVA testing for the presence of a non-zero association between term use and voxel activation.
 
-The association test maps tell you whether activation in a region occurs more consistently for studies in your meta-analysis m than for other studies in the reference dataset. In other words, a large positive z-score implies that studies in a meta-analysis are more likely to report XXX activation than studies whose abstracts don't include the word 'emotion'. 
+The association test maps tell you whether activation in a region **XXX** occurs more consistently for studies in your meta-analytic sample **m** than for other studies in the reference dataset. In other words, a large positive z-score implies that studies in a meta-analysis are more likely to report **XXX** activation than studies whose abstracts don't include the word 'emotion'. 
 
 Note that association maps *do not* tell you what the probability of a given psychological concept or task is. High Z-scores do not imply that a certain region or voxel is *selective* for a given concept or task. Instead, it just means there is evidence that there is at least a non-zero difference between reference studies, and studies in the meta-analysis.