Table of Contents

Multi-level Kernel Density Analysis (MKDA)

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

Motivation

For various reasons, many 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, by themselves, not well suited to generate “factual knowledge”:

One possible way to overcome these problems (to some extent at least) is to aggregate coordinates from several (as a rule of thumb at least ten to 15, with most published meta analyses drawing from at least 40) studies (or rather contrasts from those studies) and then test whether certain spatial locations are implicated more often in the examined brain 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:

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!

Practical outline

The following steps, in detail, have to be performed to run an MKDA in NeuroElf:

Requirements

Creation of database

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 in the first column:

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;

This first step can be performed in a variety of programs with Microsoft Excel being very suitable for this task. Usually it would seem most appropriate to first setup the columns (field names), followed by copying and pasting the coordinates into the table and setting all desired columns to their appropriate values. Eventually, the table must be available as a text-based (ASCII) file with a row of field names at the top followed by the actual data, one coordinate per row.

Importing the database into NeuroElf

In case you wish to perform this step on the command line (which might be particularly helpful if an error occurs to pinpoint the problem), you can use the following syntax:

importplp.m
plp = importplpfrommkdadb('MKDA_sample.txt');

This will create a PLP object containing the coordinates as well as all other columns in a numeric representation. Each non-numeric string will be converted to a unique number such that, for instance, each unique study label will be stored by its numeric index into the Labels property of the PLP object.

To then save the PLP object, please use the following syntax:

saveplp.m
plp.SaveAs('MKDA_sample.plp');
% or simply plp.SaveAs;

Alternatively, you can use the MKDA dialog to import the database.

Running the analysis

For the actual procedure of running the MKDA, please refer to the MKDA UI article.

Algorithm description

The general algorithm works as follows: