Model-based clustering of meta-analytic functional imaging data

Hum Brain Mapp. 2008 Feb;29(2):177-92. doi: 10.1002/hbm.20380.


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.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Brain / physiology*
  • Brain Mapping*
  • Cluster Analysis
  • Humans
  • Magnetic Resonance Imaging
  • Meta-Analysis as Topic
  • Models, Theoretical