Model‐based clustering of meta‐analytic functional imaging data

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Abstract
We present a method for the analysis of meta‐analytic functional imaging data. It is based on Activation Likelihood Estimation (ALE) and subsequent model‐based clustering using Gaussian mixture models, expectation‐maximization (EM) for model fitting, and the Bayesian Information Criterion (BIC) for model selection. Our method facilitates the clustering of activation maxima from previously performed imaging experiments in a hierarchical fashion. Regions with a high concentration of activation coordinates are first identified using ALE. Activation coordinates within these regions are then subjected to model‐based clustering for a more detailed cluster analysis. We demonstrate the usefulness of the method in a meta‐analysis of 26 fMRI studies investigating the well‐known Stroop paradigm. Hum Brain Mapp, 2008.

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