Cited by 1,924Functional Brain Connectivity StudiesMeta-analysis and systematic reviewsAdvanced Neuroimaging Techniques and Applications

Coordinate‐based activation likelihood estimation meta‐analysis of neuroimaging data: A random‐effects approach based on empirical estimates of spatial uncertainty

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Eickhoff, Simon B., Laird, Angela R., Grefkes, Christian, Wang, Ling E., Zilles, Karl, Fox, Peter T.·2009·Human Brain Mapping

Abstract

A widely used technique for coordinate‐based meta‐analyses of neuroimaging data is activation likelihood estimation (ALE). ALE assesses the overlap between foci based on modeling them as probability distributions centered at the respective coordinates. In this Human Brain Project/Neuroinformatics research, the authors present a revised ALE algorithm addressing drawbacks associated with former implementations. The first change pertains to the size of the probability distributions, which had to be specified by the used. To provide a more principled solution, the authors analyzed fMRI data of 21 subjects, each normalized into MNI space using nine different approaches. This analysis provided quantitative estimates of between‐subject and between‐template variability for 16 functionally defined regions, which were then used to explicitly model the spatial uncertainty associated with each reported coordinate. Secondly, instead of testing for an above‐chance clustering between foci, the revised algorithm assesses above‐chance clustering between experiments. The spatial relationship between foci in a given experiment is now assumed to be fixed and ALE results are assessed against a null‐distribution of random spatial association between experiments. Critically, this modification entails a change from fixed‐ to random‐effects inference in ALE analysis allowing generalization of the results to the entire population of studies analyzed. By comparative analysis of real and simulated data, the authors showed that the revised ALE‐algorithm overcomes conceptual problems of former meta‐analyses and increases the specificity of the ensuing results without loosing the sensitivity of the original approach. It may thus provide a methodologically improved tool for coordinate‐based meta‐analyses on functional imaging data. Hum Brain Mapp 2009. © 2009 Wiley‐Liss, Inc.

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  • Increased grey matter volume in the anterior cingulate cortex of meditators accounts for the improvement of specific abilities such as self-regulation, self-control, focused problem-solving, and adaptive behavioral responses under changing conditions.

    “It could be that the increased grey matter volume in the anterior cingulate cortex of meditators accounts for the improvement of specific abilities such as self-regulation, self-control, focused problem-solving, and adaptive behavioral responses under changing conditions [60]”
  • Meditation activates brain areas involved in processing self-relevant information, self-regulation, focused problem-solving, adaptive behavior, interoception, reorienting attention, and the experiential enactive self.

    “Results indicate that meditation leads to activation in brain areas involved in processing self-relevant information, self-regulation, focused problem-solving, adaptive behavior, and interoception.”
  • The resulting ALE scores were tested against the earlier calculated true ALE scores and cut off at a cluster-level-corrected threshold of p < 0.05.

    “The resulting ALE scores were tested against the earlier calculated ‘true’ ALE scores and cut off at a cluster-level-corrected threshold of p < 0.05.”
  • A random-effects inference was invoked, focusing on above-chance convergence across different experiments.

    “a random-effects inference was invoked, focusing on the above-chance convergence across different experiments”

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  1. Timm NH 2002 Applied Multivariate Analysis Springer New York

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Authors
Eickhoff, S. B., Laird, A. R., Grefkes, C., Wang, L. E., Zilles, K., & Fox, P. T.
Affiliations
  • Forschungszentrum Jülich (DE)
  • The University of Texas at San Antonio Health Science Center (US)
Venue
Human Brain Mapping
Publisher
Wiley
Published
2009-01-26
Volume
30
Issue
9
Pages
2907-2926
ISSN
  • 1065-9471 (ISSN-L)
  • 1097-0193
OpenAlex
W2164025570
Cited by
1,924
References
92
Citations by year
2026: 91 · 2025: 136 · 2024: 104 · 2023: 125 · 2022: 171 · 2021: 150 · 2020: 127 · 2019: 134 · 2018: 100 · 2017: 105 · 2016: 113 · 2015: 125 · 2014: 119 · 2013: 107 · 2012: 101
FWCI
34.52
Type
journal article
Language
en
Open access
Locations
  • Human Brain Mapping (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • Max Planck Institute for Plasma Physics (submittedVersion)
  • RWTH Publications (RWTH Aachen) (submittedVersion)
  • MPG.PuRe (Max Planck Society) (submittedVersion)
Funders
  • Deutsche Forschungsgemeinschaft
  • National Institute of Mental Health (R01MH074457, R56MH074457)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
Keywords
activation likelihood estimation, coordinate-based meta-analysis, neuroimaging meta-analysis, spatial uncertainty, functional MRI, between-subject variability, null distribution, functional brain regions
MeSH terms
Adult, Algorithms, Brain, Brain Mapping, Computer Simulation, Data Interpretation, Statistical, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Models, Neurological, Probability, Psychomotor Performance, Meta-Analysis as Topic, Computational Biology, Uncertainty, Positron-Emission Tomography

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