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@ARTICLE{Eickhoff:19632,
author = {Eickhoff, S.B. and Bzdok, D. and Laird, A.R. and Kurth, F.
and Fox, P.T.},
title = {{A}ctivation likelihood estimation meta-analyis revisited},
journal = {NeuroImage},
volume = {59},
issn = {1053-8119},
address = {Orlando, Fla.},
publisher = {Academic Press},
reportid = {PreJuSER-19632},
pages = {2349- 2361},
year = {2012},
note = {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).},
abstract = {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.},
keywords = {Algorithms / Brain: anatomy $\&$ histology / Cluster
Analysis / Data Interpretation, Statistical / False Positive
Reactions / Humans / Image Processing, Computer-Assisted:
methods / Image Processing, Computer-Assisted: statistics
$\&$ numerical data / Likelihood Functions / Magnetic
Resonance Imaging: methods / Magnetic Resonance Imaging:
statistics $\&$ numerical data / Meta-Analysis as Topic /
Positron-Emission Tomography: methods / Positron-Emission
Tomography: statistics $\&$ numerical data / Signal
Processing, Computer-Assisted / J (WoSType)},
cin = {INM-2 / INM-1},
ddc = {610},
cid = {I:(DE-Juel1)INM-2-20090406 / I:(DE-Juel1)INM-1-20090406},
pnm = {Funktion und Dysfunktion des Nervensystems (FUEK409) /
89574 - Theory, modelling and simulation (POF2-89574)},
pid = {G:(DE-Juel1)FUEK409 / G:(DE-HGF)POF2-89574},
shelfmark = {Neurosciences / Neuroimaging / Radiology, Nuclear Medicine
$\&$ Medical Imaging},
typ = {PUB:(DE-HGF)16},
pubmed = {pmid:21963913},
pmc = {pmc:PMC3254820},
UT = {WOS:000299494000037},
doi = {10.1016/j.neuroimage.2011.09.017},
url = {https://juser.fz-juelich.de/record/19632},
}