Model-based clustering of meta-analytic functional imaging data
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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.
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The activity of the mPFC reported in response conflict studies overlaps entirely with the mean peak activity observed in meta-analyses on response conflict by Barch et al. (2001) and Neumann et al. (2008).
“The activity of the mPFC reported in these studies (see Fig. 2D) overlaps entirely with the mean peak activity observed in an extensive meta-analysis on response conflict by Barch et al. (2001; Talairach coordinates 3, 19, 35) and by Neumann et al. (2008).”
From Social cognition and the brain: A meta-analysis (2009) · cites this paper as Neumann et al. (2008)
Meta-analyses of Stroop and working memory tasks implicate bilateral DLPFC, bilateral IFG, dACC, inferior parietal lobule, and precuneus.
“Meta-analyses of both the Stroop task (14,15) and WM tasks (16,17) have implicated bilateral dorsolateral prefrontal cortex (DLPFC), bilateral inferior frontal gyrus (IFG), dorsal anterior cingulate cortex (dACC), inferior parietal lobule, and precuneus.”
From Attentional Control Activation Relates to Working Memory in Attention-Deficit/Hyperactivity Disorder (2010) · cites this paper as [15]
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References 80 · Cited by 57
Research works printed but not linked: 6
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- Authors
- Neumann, J., Cramon, D. Y. V., & Lohmann, G.
- Affiliations
- Max Planck Institute for Human Cognitive and Brain Sciences (DE)
- Corresponding author
- Neumann, Jane
- Venue
- Human Brain Mapping
- Publisher
- Wiley
- Published
- 2007-03-27
- Volume
- 29
- Issue
- 2
- Pages
- 177-192
- ISSN
- 1065-9471 (ISSN-L)
- 1097-0193
- PMC
- PMC2885605
- OpenAlex
- W2166829699
- Cited by
- 57
- References
- 80
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2022: 1 · 2021: 2 · 2019: 1 · 2018: 2 · 2017: 1 · 2016: 5 · 2015: 2 · 2014: 9 · 2013: 2 · 2012: 8
- FWCI
- 2.67
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Human Brain Mapping (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Max Planck Institute for Plasma Physics (submittedVersion)
- Max Planck Digital Library (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- National Institute of Mental Health (R01MH074457, R01 MH74457)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
- Keywords
- model-based clustering, Gaussian mixture model, activation likelihood estimation, Stroop paradigm, hierarchical clustering, expectation-maximization algorithm, Bayesian information criterion
- MeSH terms
- Brain, Brain Mapping, Humans, Magnetic Resonance Imaging, Models, Theoretical, Meta-Analysis as Topic, Cluster Analysis
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