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

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 re...

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Published in	Human brain mapping Vol. 30; no. 9; pp. 2907 - 2926
Main Authors	Eickhoff, Simon B., Laird, Angela R., Grefkes, Christian, Wang, Ling E., Zilles, Karl, Fox, Peter T.
Format	Journal Article
Language	English
Published	Hoboken Wiley Subscription Services, Inc., A Wiley Company 15.09.2009 Wiley-Liss
Subjects	Adult Algorithms between-subject variability Biological and medical sciences Brain - anatomy & histology Brain - diagnostic imaging Brain - physiology Brain Mapping - methods Computational Biology - methods Computer Simulation Data Interpretation, Statistical Female fMRI Humans Image Processing, Computer-Assisted - methods Investigative techniques, diagnostic techniques (general aspects) Magnetic Resonance Imaging - methods Male Medical sciences Meta-Analysis as Topic Middle Aged Models, Neurological Nervous system Nervous system involvement in other diseases. Miscellaneous Neurology permutation PET Positron-Emission Tomography - methods Probability Psychomotor Performance - physiology Radiodiagnosis. Nmr imagery. Nmr spectrometry random-effects Uncertainty variance Human Data analysis Uncertainty Nervous system diseases Radiodiagnosis Estimation Variability random-effects Nuclear magnetic resonance imaging Variance fMRI Random effect Surgical approach between-subject variability Positron emission tomography Activation analysis PET Emission tomography permutation
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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.
AbstractList	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. 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. 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. 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.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.
Author	Wang, Ling E. Zilles, Karl Grefkes, Christian Eickhoff, Simon B. Laird, Angela R. Fox, Peter T.
AuthorAffiliation	1 Institut for Neuroscience and Biophysics—Medicine (INB 3), Research Center Jülich, Jülich, Germany 6 C. & O. Vogt Institute for Brain Research, Heinrich‐Heine‐University, Düsseldorf, Germany 4 Department of Neurology, University Hospital Cologne, Max Planck Institute for Neurological Research, Cologne, Germany 2 JARA—Translational Brain Medicine, Jülich, Germany 3 Research Imaging Center, University of Texas Health Science Center at San Antonio, San Antonio, Texas 5 Brain Imaging Center West (BICW), Jülich, Germany 7 International Consortium for Human Brain Mapping (ICBM), Jülich, Germany
AuthorAffiliation_xml	– name: 1 Institut for Neuroscience and Biophysics—Medicine (INB 3), Research Center Jülich, Jülich, Germany – name: 2 JARA—Translational Brain Medicine, Jülich, Germany – name: 3 Research Imaging Center, University of Texas Health Science Center at San Antonio, San Antonio, Texas – name: 6 C. & O. Vogt Institute for Brain Research, Heinrich‐Heine‐University, Düsseldorf, Germany – name: 5 Brain Imaging Center West (BICW), Jülich, Germany – name: 4 Department of Neurology, University Hospital Cologne, Max Planck Institute for Neurological Research, Cologne, Germany – name: 7 International Consortium for Human Brain Mapping (ICBM), Jülich, Germany
Author_xml	– sequence: 1 givenname: Simon B. surname: Eickhoff fullname: Eickhoff, Simon B. email: s.eickhoff@fz-juelich.de organization: Institut for Neuroscience and Biophysics-Medicine (INB 3), Research Center Jülich, Jülich, Germany – sequence: 2 givenname: Angela R. surname: Laird fullname: Laird, Angela R. organization: Research Imaging Center, University of Texas Health Science Center at San Antonio, San Antonio, Texas – sequence: 3 givenname: Christian surname: Grefkes fullname: Grefkes, Christian organization: Institut for Neuroscience and Biophysics-Medicine (INB 3), Research Center Jülich, Jülich, Germany – sequence: 4 givenname: Ling E. surname: Wang fullname: Wang, Ling E. organization: Institut for Neuroscience and Biophysics-Medicine (INB 3), Research Center Jülich, Jülich, Germany – sequence: 5 givenname: Karl surname: Zilles fullname: Zilles, Karl organization: Institut for Neuroscience and Biophysics-Medicine (INB 3), Research Center Jülich, Jülich, Germany – sequence: 6 givenname: Peter T. surname: Fox fullname: Fox, Peter T. organization: Research Imaging Center, University of Texas Health Science Center at San Antonio, San Antonio, Texas
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Snippet	A widely used technique for coordinate‐based meta‐analyses of neuroimaging data is activation likelihood estimation (ALE). ALE assesses the overlap between... A widely used technique for coordinate-based meta-analyses of neuroimaging data is activation likelihood estimation (ALE). ALE assesses the overlap between...
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SubjectTerms	Adult Algorithms between-subject variability Biological and medical sciences Brain - anatomy & histology Brain - diagnostic imaging Brain - physiology Brain Mapping - methods Computational Biology - methods Computer Simulation Data Interpretation, Statistical Female fMRI Humans Image Processing, Computer-Assisted - methods Investigative techniques, diagnostic techniques (general aspects) Magnetic Resonance Imaging - methods Male Medical sciences Meta-Analysis as Topic Middle Aged Models, Neurological Nervous system Nervous system involvement in other diseases. Miscellaneous Neurology permutation PET Positron-Emission Tomography - methods Probability Psychomotor Performance - physiology Radiodiagnosis. Nmr imagery. Nmr spectrometry random-effects Uncertainty variance
Title	Coordinate-based activation likelihood estimation meta-analysis of neuroimaging data: A random-effects approach based on empirical estimates of spatial uncertainty
URI	https://api.istex.fr/ark:/67375/WNG-R5V21TTD-F/fulltext.pdf https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fhbm.20718 https://www.ncbi.nlm.nih.gov/pubmed/19172646 https://www.proquest.com/docview/67616284 https://www.proquest.com/docview/745936878 https://www.proquest.com/docview/754550808 https://pubmed.ncbi.nlm.nih.gov/PMC2872071
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