TY  - JOUR
AU  - Eickhoff, S. B.
AU  - Laird, A.R.
AU  - Grefkes, C.
AU  - Wang, L.E.
AU  - Zilles, K.
AU  - Fox, P.T.
TI  - Coordinate-Based Activaton Likelihood Estimation Meta-Analysis of Neuroimaging Data: A Random-Effects Approach Based on Empirical Estimates of Spatial Uncertainty
JO  - Human brain mapping
VL  - 30
SN  - 1065-9471
CY  - New York, NY
PB  - Wiley-Liss
M1  - PreJuSER-5635
SP  - 2907 - 2926
PY  - 2009
N1  - National Institute of Biomedical Imaging and Bioengineering, National Institute of Neurological Disorders and Stroke, National Institute of Mental Health.
AB  - 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.
KW  - Adult
KW  - Algorithms
KW  - Brain: anatomy & histology
KW  - Brain: physiology
KW  - Brain: radionuclide imaging
KW  - Brain Mapping: methods
KW  - Computational Biology: methods
KW  - Computer Simulation
KW  - Data Interpretation, Statistical
KW  - Female
KW  - Humans
KW  - Image Processing, Computer-Assisted: methods
KW  - Magnetic Resonance Imaging: methods
KW  - Male
KW  - Meta-Analysis as Topic
KW  - Middle Aged
KW  - Models, Neurological
KW  - Positron-Emission Tomography: methods
KW  - Probability
KW  - Psychomotor Performance: physiology
KW  - Uncertainty
KW  - J (WoSType)
LB  - PUB:(DE-HGF)16
C6  - pmid:19172646
C2  - pmc:PMC2872071
UR  - <Go to ISI:>//WOS:000269510200018
DO  - DOI:10.1002/hbm.20718
UR  - https://juser.fz-juelich.de/record/5635
ER  -