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Hum Brain Mapp. 2008 Feb;29(2):177-92.

Model-based clustering of meta-analytic functional imaging data.

Author information

1
Max-Planck-Institute for Human Cognitive and Brain Sciences, Stephanstrasse 1a, Leipzig, Germany. neumann@cbs.mpg.de

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.

PMID:
17390315
PMCID:
PMC2885605
DOI:
10.1002/hbm.20380
[Indexed for MEDLINE]
Free PMC Article

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