Home > Publications database > Coordinate-Based Activaton Likelihood Estimation Meta-Analysis of Neuroimaging Data: A Random-Effects Approach Based on Empirical Estimates of Spatial Uncertainty > print

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024 7 _ |2 pmid
|a pmid:19172646
024 7 _ |2 pmc
|a pmc:PMC2872071
024 7 _ |2 DOI
|a 10.1002/hbm.20718
024 7 _ |2 WOS
|a WOS:000269510200018
024 7 _ |a altmetric:18349361
|2 altmetric
037 _ _ |a PreJuSER-5635
041 _ _ |a eng
082 _ _ |a 610
084 _ _ |2 WoS
|a Neurosciences
084 _ _ |2 WoS
|a Neuroimaging
084 _ _ |2 WoS
|a Radiology, Nuclear Medicine & Medical Imaging
100 1 _ |0 P:(DE-Juel1)131678
|a Eickhoff, S. B.
|b 0
|u FZJ
245 _ _ |a Coordinate-Based Activaton Likelihood Estimation Meta-Analysis of Neuroimaging Data: A Random-Effects Approach Based on Empirical Estimates of Spatial Uncertainty
260 _ _ |a New York, NY
|b Wiley-Liss
|c 2009
300 _ _ |a 2907 - 2926
336 7 _ |0 PUB:(DE-HGF)16
|2 PUB:(DE-HGF)
|a Journal Article
336 7 _ |2 DataCite
|a Output Types/Journal article
336 7 _ |0 0
|2 EndNote
|a Journal Article
336 7 _ |2 BibTeX
|a ARTICLE
336 7 _ |2 ORCID
|a JOURNAL_ARTICLE
336 7 _ |2 DRIVER
|a article
440 _ 0 |0 2398
|a Human Brain Mapping
|v 30
|x 1065-9471
500 _ _ |a National Institute of Biomedical Imaging and Bioengineering, National Institute of Neurological Disorders and Stroke, National Institute of Mental Health.
520 _ _ |a 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.
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588 _ _ |a Dataset connected to Web of Science, Pubmed
650 _ 2 |2 MeSH
|a Adult
650 _ 2 |2 MeSH
|a Algorithms
650 _ 2 |2 MeSH
|a Brain: anatomy & histology
650 _ 2 |2 MeSH
|a Brain: physiology
650 _ 2 |2 MeSH
|a Brain: radionuclide imaging
650 _ 2 |2 MeSH
|a Brain Mapping: methods
650 _ 2 |2 MeSH
|a Computational Biology: methods
650 _ 2 |2 MeSH
|a Computer Simulation
650 _ 2 |2 MeSH
|a Data Interpretation, Statistical
650 _ 2 |2 MeSH
|a Female
650 _ 2 |2 MeSH
|a Humans
650 _ 2 |2 MeSH
|a Image Processing, Computer-Assisted: methods
650 _ 2 |2 MeSH
|a Magnetic Resonance Imaging: methods
650 _ 2 |2 MeSH
|a Male
650 _ 2 |2 MeSH
|a Meta-Analysis as Topic
650 _ 2 |2 MeSH
|a Middle Aged
650 _ 2 |2 MeSH
|a Models, Neurological
650 _ 2 |2 MeSH
|a Positron-Emission Tomography: methods
650 _ 2 |2 MeSH
|a Probability
650 _ 2 |2 MeSH
|a Psychomotor Performance: physiology
650 _ 2 |2 MeSH
|a Uncertainty
650 _ 7 |2 WoSType
|a J
653 2 0 |2 Author
|a fMRI
653 2 0 |2 Author
|a PET
653 2 0 |2 Author
|a permutation
653 2 0 |2 Author
|a between-subject variability
653 2 0 |2 Author
|a variance
653 2 0 |2 Author
|a random-effects
700 1 _ |0 P:(DE-HGF)0
|a Laird, A.R.
|b 1
700 1 _ |0 P:(DE-Juel1)VDB500
|a Grefkes, C.
|b 2
|u FZJ
700 1 _ |0 P:(DE-Juel1)VDB75806
|a Wang, L.E.
|b 3
|u FZJ
700 1 _ |0 P:(DE-Juel1)131714
|a Zilles, K.
|b 4
|u FZJ
700 1 _ |0 P:(DE-Juel1)VDB78079
|a Fox, P.T.
|b 5
|u FZJ
773 _ _ |0 PERI:(DE-600)1492703-2
|a 10.1002/hbm.20718
|g Vol. 30, p. 2907 - 2926
|p 2907 - 2926
|q 30<2907 - 2926
|t Human brain mapping
|v 30
|x 1065-9471
|y 2009
856 7 _ |2 Pubmed Central
|u http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2872071
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