Coordinate-based ALE meta-analysis of neuroimaging data: A random-effects approach based on empirical estimates of spatial uncertainty

By Simon B. Eickhoff, Angela R. Laird, Christian Grefkes, Ling E. Wang, Karl Zilles, Peter T. Fox. Human Brain Mapping, 2009

1,881 citations recorded by the source corpus.

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. Hum Brain Mapp 2009. © 2009 Wiley‐Liss, Inc.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/9b9a6489172c1f564927c29b243276ad5ec131a8

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Citations in the corpus

Activation likelihood estimation meta-analysis of motor-related neural activity after stroke (2012, methodology): “…meta-analyses such as activation likelihood estimation (ALE) allow identification of consistent neural activity across different PET and fMRI studies, and are powerful tools to overcome the limited generalizability of single experiments (Eickhoff et al., 2009; Turkeltaub et al., 2011).”
Neural Correlates of Alzheimer's Disease and Mild Cognitive Impairment: A Systematic and Quantitative Meta-Analysis involving 1,351 Patients (2009, neutral): “As the accuracy of peak coordinates reported in different imaging studies is limited, for example, by anatomical intersubject variability, data smoothing, or the use of different brain templates (Eickhoff et al., 2009; Turkeltaub et al., 2002), peak coordinates should not be viewed as single points ”
The hubs of the human connectome are generally implicated in the anatomy of brain disorders (2014, methodology): “We used the anatomical likelihood estimation (ALE) method of meta-analysis (Eickhoff et al., 2009) to identify locations of significant grey matter deficits or lesions ‘on average’ over a given set of primary studies with P5 0.05 (FDR corrected) and a cluster size threshold of 200 mm3.”
Modelling neural correlates of working memory: A coordinate-based meta-analysis (2011, positive): “All meta-analyses were performed using the revised activation likelihood estimation (ALE) algorithm for coordinate-based meta-analysis of neuroimaging results (Eickhoff et al., 2009 2011; Laird et al., 2009a, 2009b; Turkeltaub et al., 2002) implemented as in-house MATLAB tools.”
A link between the systems: functional differentiation and integration within the human insula revealed by meta-analysis (2010, methodology): “The null-hypothesis, against which a particular ALE map was tested, represented the case of no convergence between the single experiments of the respective category (Eickhoff et al. 2009) , that is, no spatial association between the different individual experiments.”
Activation Likelihood Estimation meta-analysis revisited (2011, methodology): “Revised approach for computing the null-distribution The analytical null-distributions for inference on both datasets were compared to those derived from the random sampling algorithm described by Eickhoff et al. (2009).”
Meta-analytical definition and functional connectivity of the human vestibular cortex (2012, methodology): “Activation likelihood estimation (ALE) algorithm Meta-analysis was carried out using a revised version (Eickhoff et al., 2009) of the ALE approach (Laird et al., 2005; Turkeltaub et al., 2002) implemented in MATLAB (The MathWorks Inc., Natick, USA).”
The valuation system: A coordinate-based meta-analysis of BOLD fMRI experiments examining neural correlates of subjective value (2013, positive): “However, despite careful efforts to quantify certain sources of uncertainty (Eickhoff et al., 2009), it remains difficult, in our view, to determine confidently the appropriate degree (or form) of weighting.”
The first taste is always with the eyes: A meta-analysis on the neural correlates of processing visual food cues (2011, methodology): “We used the revised version of the ALE approach (Eickhoff et al., 2009) for coordinate-based meta-analysis of neuroimaging results (Turkeltaub et al.”
Social cognition and the cerebellum: A meta-analysis of over 350 fMRI studies (2014, methodology): “We used the Activation Likelihood Estimation (ALE) procedure to present the data of our meta-analyses, as implemented by GingerALE 2.1 (Eickhoff et al., 2009; Laird et al., 2005).”
Naturalizing aesthetics: Brain areas for aesthetic appraisal across sensory modalities (2011, neutral): “The ALE statistic, first described by Turkeltaub et al. (2002), was computed according to the modified procedure of Eickhoff et al. (2009), which is based on creating activation maps for each experiment, and then running a random effects analysis across all experiments, with significance determined…”
Meta-analytic evidence for common and distinct neural networks associated with directly experienced pain and empathy for pain (2011, positive): “These foci were analyzed using the latest version of the GingerALE software (version 2.0.1, http://www. brainmap.org/ale/), which uses random-effects inference and an algorithm taking into account the sample size of each contrast (Eickhoff et al., 2009).”
Bilingual brain training: A neurobiological framework of how bilingual experience improves executive function (2012, methodology): “Stereotactic coordinates that were originally given in the Talairach-Tourneaux system were converted to the MNI system using the non-linear transformation algorithm provided in the GingerALE software (Eickhoff et al., 2009).”
A meta-analysis of neurofunctional imaging studies of emotion and cognition in major depression (2012, neutral): “The size of the modeled threedimensional Gaussian distribution was empirically determined and included a weighting of each study by the number of included subjects (Eickhoff et al., 2009).”
Co-activation patterns distinguish cortical modules, their connectivity and functional differentiation (2011, methodology): “…described co-activation parcellation of the seed region, ALE scores for the MACM analysis of this cluster were compared to a null-distribution that reflects a random spatial association between experiments, but regards the within-experiment distribution of foci as fixed (Eickhoff et al., 2009).”
Brain templates and atlases (2012, methodology): “More recently, this has been used as a simulation tool to generate probabilistic estimates of likely activation foci for any given experimental design using the ALE methodology (Eickhoff et al., 2009, 2012; Laird et al., 2005b, 2009; Turkeltaub et al., 2002).”
The Autonomic Brain: An Activation Likelihood Estimation Meta-Analysis for Central Processing of Autonomic Function (2013, methodology): “…of single-study activation foci as peaks of three-dimensional Gaussian probability densities with subject-based full-width at half-maximum values (Eickhoff et al., 2009); (2) summation of probability densities to produce a statistical map estimating the likelihood of activation at each voxel;…”