Coordinate-Based Activation Likelihood Estimation Meta-Analysis of Neuroimaging Data: A Random-Effects Approach Based on Empirical Estimates of Spatial Uncertainty

被引:1475
作者
Eickhoff, Simon B. [1 ,2 ]
Laird, Angela R. [3 ]
Grefkes, Christian [1 ,4 ]
Wang, Ling E. [1 ]
Zilles, Karl [1 ,2 ,5 ,6 ,7 ]
Fox, Peter T. [3 ,7 ]
机构
[1] Res Ctr Julich, Inst Neurosci & Biophys Med INB 3, Julich, Germany
[2] JARA Translat Brain Med, Julich, Germany
[3] Univ Texas Hlth Sci Ctr San Antonio, Res Imaging Ctr, San Antonio, TX 78229 USA
[4] Univ Hosp Cologne, Dept Neurol, Max Planck Inst Neurol Res, Cologne, Germany
[5] BICW, Julich, Germany
[6] Univ Dusseldorf, C&O Vogt Inst Brain Res, Dusseldorf, Germany
[7] Int Consortium Human Brain Mapping ICBM, Julich, Germany
关键词
fMRI; PET; permutation; between-subject variability; variance; random-effects; CEREBRAL-BLOOD-FLOW; POSITRON-EMISSION-TOMOGRAPHY; SEQUENTIAL FINGER MOVEMENTS; SUPPLEMENTARY MOTOR AREA; HUMAN PARIETAL OPERCULUM; FALSE DISCOVERY RATE; BRAIN ACTIVATION; HAND MOVEMENTS; BASAL GANGLIA; INTERSUBJECT VARIABILITY;
D O I
10.1002/hbm.20718
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
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 generatization 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. Hum Brain Mapp 30:2907-2926, 2009. (C) 2009 Wiley-Liss, Inc.
引用
收藏
页码:2907 / 2926
页数:20
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