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000019632 084__ $$2WoS$$aNeurosciences
000019632 084__ $$2WoS$$aNeuroimaging
000019632 084__ $$2WoS$$aRadiology, Nuclear Medicine & Medical Imaging
000019632 1001_ $$0P:(DE-Juel1)131678$$aEickhoff, S.B.$$b0$$uFZJ
000019632 245__ $$aActivation likelihood estimation meta-analyis revisited
000019632 260__ $$aOrlando, Fla.$$bAcademic Press$$c2012
000019632 300__ $$a2349-  2361
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000019632 440_0 $$04545$$aNeuroImage$$v59$$x1053-8119$$y3
000019632 500__ $$aWe acknowledge funding by the Human Brain Project (R01-MH074457-01A1; PTF, ARL, SBE), the DFG (IRTG 1328; SBE, DB) and the Helmholtz Initiative on Systems-Biology "The Human Brain Model" (SBE).
000019632 520__ $$aA widely used technique for coordinate-based meta-analysis of neuroimaging data is activation likelihood estimation (ALE), which determines the convergence of foci reported from different experiments. ALE analysis involves modelling these foci as probability distributions whose width is based on empirical estimates of the spatial uncertainty due to the between-subject and between-template variability of neuroimaging data. ALE results are assessed against a null-distribution of random spatial association between experiments, resulting in random-effects inference. In the present revision of this algorithm, we address two remaining drawbacks of the previous algorithm. First, the assessment of spatial association between experiments was based on a highly time-consuming permutation test, which nevertheless entailed the danger of underestimating the right tail of the null-distribution. In this report, we outline how this previous approach may be replaced by a faster and more precise analytical method. Second, the previously applied correction procedure, i.e. controlling the false discovery rate (FDR), is supplemented by new approaches for correcting the family-wise error rate and the cluster-level significance. The different alternatives for drawing inference on meta-analytic results are evaluated on an exemplary dataset on face perception as well as discussed with respect to their methodological limitations and advantages. In summary, we thus replaced the previous permutation algorithm with a faster and more rigorous analytical solution for the null-distribution and comprehensively address the issue of multiple-comparison corrections. The proposed revision of the ALE-algorithm should provide an improved tool for conducting coordinate-based meta-analyses on functional imaging data.
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000019632 65320 $$2Author$$afMRI
000019632 65320 $$2Author$$aPET
000019632 65320 $$2Author$$aPermutation
000019632 65320 $$2Author$$aInference
000019632 65320 $$2Author$$aCluster-thresholding
000019632 650_2 $$2MeSH$$aAlgorithms
000019632 650_2 $$2MeSH$$aBrain: anatomy & histology
000019632 650_2 $$2MeSH$$aCluster Analysis
000019632 650_2 $$2MeSH$$aData Interpretation, Statistical
000019632 650_2 $$2MeSH$$aFalse Positive Reactions
000019632 650_2 $$2MeSH$$aHumans
000019632 650_2 $$2MeSH$$aImage Processing, Computer-Assisted: methods
000019632 650_2 $$2MeSH$$aImage Processing, Computer-Assisted: statistics & numerical data
000019632 650_2 $$2MeSH$$aLikelihood Functions
000019632 650_2 $$2MeSH$$aMagnetic Resonance Imaging: methods
000019632 650_2 $$2MeSH$$aMagnetic Resonance Imaging: statistics & numerical data
000019632 650_2 $$2MeSH$$aMeta-Analysis as Topic
000019632 650_2 $$2MeSH$$aPositron-Emission Tomography: methods
000019632 650_2 $$2MeSH$$aPositron-Emission Tomography: statistics & numerical data
000019632 650_2 $$2MeSH$$aSignal Processing, Computer-Assisted
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000019632 7001_ $$0P:(DE-Juel1)136848$$aBzdok, D.$$b1$$uFZJ
000019632 7001_ $$0P:(DE-Juel1)VDB78077$$aLaird, A.R.$$b2$$uFZJ
000019632 7001_ $$0P:(DE-Juel1)VDB67936$$aKurth, F.$$b3$$uFZJ
000019632 7001_ $$0P:(DE-HGF)0$$aFox, P.T.$$b4
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000019632 8567_ $$2Pubmed Central$$uhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC3254820
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