title: Model-based clustering of meta-analytic functional imaging data.
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.
authors: Max-Planck-Institute for Human Cognitive and Brain Sciences, Stephanstrasse 1a, Leipzig, Germany. neumann@cbs.mpg.de
authors: Jane
authors: J
authors: Neumann
authors: D Yves
authors: DY
authors: von  Cramon
authors: Gabriele
authors: G
authors: Lohmann
mesh_terms: D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D016000:Cluster Analysis; D006801:Humans; D008279:Magnetic Resonance Imaging; D015201:Meta-Analysis as Topic; D008962:Models, Theoretical