TY  - JOUR
AU  - Eickhoff, S.B.
AU  - Bzdok, D.
AU  - Laird, A.R.
AU  - Kurth, F.
AU  - Fox, P.T.
TI  - Activation likelihood estimation meta-analyis revisited
JO  - NeuroImage
VL  - 59
SN  - 1053-8119
CY  - Orlando, Fla.
PB  - Academic Press
M1  - PreJuSER-19632
SP  - 2349-  2361
PY  - 2012
N1  - We 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).
AB  - A 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.
KW  - Algorithms
KW  - Brain: anatomy & histology
KW  - Cluster Analysis
KW  - Data Interpretation, Statistical
KW  - False Positive Reactions
KW  - Humans
KW  - Image Processing, Computer-Assisted: methods
KW  - Image Processing, Computer-Assisted: statistics & numerical data
KW  - Likelihood Functions
KW  - Magnetic Resonance Imaging: methods
KW  - Magnetic Resonance Imaging: statistics & numerical data
KW  - Meta-Analysis as Topic
KW  - Positron-Emission Tomography: methods
KW  - Positron-Emission Tomography: statistics & numerical data
KW  - Signal Processing, Computer-Assisted
KW  - J (WoSType)
LB  - PUB:(DE-HGF)16
C6  - pmid:21963913
C2  - pmc:PMC3254820
UR  - <Go to ISI:>//WOS:000299494000037
DO  - DOI:10.1016/j.neuroimage.2011.09.017
UR  - https://juser.fz-juelich.de/record/19632
ER  -