The term Multi-level Kernel Density Analysis (MKDA) was coined by Tor Wager, and his own implementation is available at his lab's website for download.

In short, the general idea is to perform the following steps

• literature review and selection of articles that report spatial locations (coordinate tables) of a common neuropsychological function (or set of functions)
• creation of a compound table containing all found coordinates (possibly matching further selection criteria)
• performing the MKDA (or a similar algorithm, such as found in the GingerALE package), which tests the coordinates in the table against a Monte-Carlo random sampling (empirical null)
• interpreting the results of the MKDA (making statistical inferences based on areas where the null hypothesis of interest can be safely rejected)

For many reasons, most neuroimaging experiments (where data is collected and spatial maps are created, allowing functional representations to be located across the brain) and their results as reported in journals (in this context that means tables linking specific spatial locations, i.e. coordinates, to certain functions/phenomena) are not well suited to generate “factual knowledge”:

• without a clear model that underlies and fits the observed spatial representation (networks subserving the experimentally manipulated function), the results do not represent “knowledge” (strong inference) but rather new “hypotheses” (potential explanations based on reverse inference)
• the choice of subjects, stimuli, experimentation design, etc. could have biased the results to make them less informative for the more general population case (false-positive and false-negative identification)
• noise components in the data (on all levels) could have masked important aspects (locations, false-negative identification)

One potential way to overcome these problems is to aggregate coordinates from several (at least ten to 15) studies (or rather contrasts from those studies) and then test whether certain spatial locations are implicated more often in a given function than warranted by chance (Monte-Carlo null distribution via simulating data drawn from, say, a gray matter mask).

However, there are some additional problems that are only partially addressable with meta analyses of any kind, such as:

• the file-drawer problem (null findings are not reported, which for specific locations means that if an assumed cluster is not part of a spatial map, the analysis seems “unpublishable”)
• the researcher-degrees-of-freedom problem (during the collection, processing, and analyses stages, many choices can be/have to be made by the researcher, sometimes heavily influencing the results without apparent reason to reject either possible outcome as false)
• the fact that certain fields (e.g. negative emotion in the context of autobiographic memories) might be dominated by few research labs (potentially leading to an imposed “worldview” of those labs' researchers' beliefs on the results)

And it must be noted that even meta analyses cannot, per se, create “knowledge” (strong inferences) in absence of a model that explains and fits the observed patterns. Still, by summarizing several independent datasets into a single spatial map (e.g. via MKDA), the likelihood of making certain types of mistakes is highly reduced!

Following the introduction, the first step is to look through the literature and select articles you wish to include in the MKDA. Next, you need to create a tabular representation of all coordinates found in the tables (or text) of those articles, such as the following example demonstrates:

MKDA_sample.txt

Study;x;y;z;CoordSys;N;Contrast;
Ochsner_et_al_2008;15;24;21;MNI;21;LookNeg>LookNeu;
Ochsner_et_al_2008;-15;-15;-18;MNI;21;LookNeg>LookNeu;
Ochsner_et_al_2008;15;-18;-15;MNI;21;LookNeg>LookNeu;
Ochsner_et_al_2008;15;33;48;MNI;21;LookNeg>LookNeu;
Lieberman_et_al_2010;36;21;15;T88;16;Negative>Neutral;

If you wish to use this table in Tor Wager's MKDA tool as well, the first row should contain a single line with the number of fields:

MKDA_sample_with_fields.txt

7;;;;;;;
Study;x;y;z;CoordSys;N;Contrast;
Ochsner_et_al_2008;15;24;21;MNI;21;LookNeg>LookNeu;
Ochsner_et_al_2008;-15;-15;-18;MNI;21;LookNeg>LookNeu;
Ochsner_et_al_2008;15;-18;-15;MNI;21;LookNeg>LookNeu;
Ochsner_et_al_2008;15;33;48;MNI;21;LookNeg>LookNeu;
Lieberman_et_al_2010;36;21;15;T88;16;Negative>Neutral;