[ENH] add blog post on ALE and MKDA

blog/2026-04-14-ale-and-mkda.md

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+---
+title: ALE and MKDA-A Comparison of Two Coordinate-Based Meta-Analysis Methods
+authors: james
+tags: [neurosynth, ALE, MKDA]
+---
+
+# Why Choose MKDA Over ALE for Coordinate-Based Meta-Analysis?
+
+I'm talking about 4 algorithms in :contentReference[oaicite:0]{index=0}:
+- `ALE`
+- `ALESubtraction`
+- `MKDADensity`
+- `MKDAChi2`
+
+## The Problem with ALE
+
+There's nothing wrong with ALE. It is a well-established and widely validated method for coordinate-based meta-analysis.
+
+But during the development of this platform, I have noticed that many users prefer to stick with `ALE` (especially `ALESubtraction`), and that is making some of our AWS instanses sputter.
+
+Specifically, an `ALESubtraction` analysis where the comparison group is the entire NeuroStore database (which we currently allow).
+
+I could just change the interface to disallow that (and we probably will), but I wanted to spill some ink about MKDA and `MKDAChi2` in case you aren't as familiar with them.
+
+## ALE vs MKDA: A Conceptual Comparison
+
+If ALE tells you where activations align, MKDA tells you how consistently studies agree.
+
+The biggest difference between ALE and MKDA is that ALE models the spatial uncertainty of reported coordinates using a Gaussian kernel, while MKDA uses a binary indicator of whether a voxel is within a specified radius of a reported coordinate.
+
+This means that ALE provides a probabilistic map of activation likelihood, while MKDA provides a more direct count (or proportion) of how many studies report a coordinate near each voxel.
+
+## The Advantages of MKDA
+
+This gives several advantages to MKDA:
+
+- The output is more interpretable, as it directly reflects the proportion of studies that report activation near a given voxel, rather than a probability density that can be harder to interpret.
+
+- The contribution of each study to a particular voxel is strictly binary, so a single study cannot dominate the results by reporting multiple nearby coordinates. ALE mitigates this by taking the maximum across overlapping kernels, but the probability mass in that area is still more concentrated than if there were only a single coordinate.
+
+- MKDA is more computationally efficient, as it does not require estimating a probability density function, while still maintaining a high level of sensitivity to consistent activation across studies (equivalent or better than ALE in simulations, forthcoming).
+
+This isn't a hit piece on ALE, which does have its strengths for precise spatial localization. You can check for yourself whether that difference is meaningful in practice.
+
+## Pairwise Comparisons and Subtraction Analyses
+
+Here's the rub. `ALESubtraction` feels more intuitive, because you're taking one group's probability density map and subtracting another's, then thresholding the result to find areas where one group shows greater activation likelihood than the other.
+
+`MKDAChi2` looks less familiar on the surface, but the underlying logic is straightforward.
+
+The main question being tested is whether a voxel shows a higher proportion of studies reporting activation in one group versus another.
+
+For example:
+- 9/10 studies report a coordinate within 6mm of this voxel in group A  
+- 2/10 studies report a coordinate within 6mm of this voxel in group B  
+
+A chi-square test then evaluates whether that difference is statistically significant.
+
+What I just described is the `association` test output (as opposed to `uniformity`, which tests whether the proportion of studies reporting activation at a voxel is higher than expected by chance).
+
+`MKDAChi2` is the algorithm that was used to generate the thousands of maps in neurosynth.org, where studies were split based on whether they contained a given term in the title/abstract.
+
+However, `MKDAChi2` is much more flexible than that—you can compare any two groups of studies (including the entire database vs a subset).
+
+Why would you do this?
+
+I just ran 1200 `MKDAChi2` analyses on simulated data—it took about 2 minutes.
+
+Guess how long it took to run 1200 `ALESubtraction` analyses on the same data?
+
+Over 6 hours.
+
+`MKDAChi2` is over 180× faster, and you still get a statistically rigorous comparison.
+
+More compute does not always mean better results—so if you want to compare two groups of studies, try `MKDAChi2`.
+
+---
+
+# Further Reading
+
+## ALE (Activation Likelihood Estimation)
+
+- Turkeltaub et al. (2002) – Original ALE paper  
+  https://doi.org/10.1006/nimg.2002.1139  
+
+- Eickhoff et al. (2009) – Random-effects ALE formulation  
+  https://doi.org/10.1016/j.neuroimage.2008.09.059  
+
+- Eickhoff et al. (2012) – Updated ALE methods and corrections  
+  https://doi.org/10.1016/j.neuroimage.2011.09.017  
+
+## MKDA (Multilevel Kernel Density Analysis)
+
+- Wager et al. (2007) – Foundational MKDA framework  
+  https://doi.org/10.1093/scan/nsm015  
+
+- Kober et al. (2008) – MKDA applied to emotion studies  
+  https://doi.org/10.1016/j.neuroimage.2008.03.059  
+
+## Comparative Work
+
+- Salimi-Khorshidi et al. (2009) – Comparison of CBMA methods (ALE, MKDA, etc.)  
+  https://doi.org/10.1016/j.neuroimage.2009.06.072