Activation Likelihood Estimation meta-analysis revisited

By Simon B. Eickhoff, Danilo Bzdok, Angela R. Laird, Florian Kurth, Peter T. Fox. NeuroImage, 2011

1,406 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/4d11b33a8c82e8ef8dacff4e7ce413f65c62d904

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

Citations in the corpus

Neural network of cognitive emotion regulation — An ALE meta-analysis and MACM analysis (2013, methodology): “These ALE scores were then compared to an ALE null distribution (Eickhoff et al., 2012) in which the same number of activation foci was randomly relocated and restricted by a gray matter probability map (Evans et al., 1994).”
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 wandering brain: Meta-analysis of functional neuroimaging studies of mind-wandering and related spontaneous thought processes (2015, methodology): “A small but substantial number of peak foci (n=15) were reported at coordinates that lay outside of the template brain images usedwithin the activation likelihood estimation meta-analysis software, GingerALE.”
The valuation system: A coordinate-based meta-analysis of BOLD fMRI experiments examining neural correlates of subjective value (2013, positive): “Recent work (Eickhoff et al., 2012) indicates that coordinate-based meta-analyses can achieve considerably improved sensitivity by incorporating spatial clustering (cf. Forman et al., 1995).”
A meta-analytic review of multisensory imagery identifies the neural correlates of modality-specific and modality-general imagery (2012, methodology): “baseline activation foci reported across the neuroimaging literature was analyzed using a widely used activation likelihood estimate (ALE) meta-analytic approach (Eickhoff et al., 2012).”
Behavior, Sensitivity, and power of activation likelihood estimation characterized by massive empirical simulation (2016, neutral): “a) Family-wise error correction on the voxel level controls the chance of observing a given Z-value if foci were randomly distributed (Eickhoff et al., 2012).”
Activation likelihood estimation meta-analysis of motor-related neural activity after stroke (2012, methodology): “We applied the revised ALE technique for quantitative coordinatebased meta-analyses of functional neuroimaging results (Eickhoff et al., 2009; Eickhoff et al., 2011; Turkeltaub et al., 2011).”
Informatic parcellation of the network involved in the computation of subjective value. (2014, methodology): “For CBMA, we employed the widely used activation likelihood estimation (ALE) method (Laird et al., 2005; Eickhoff et al., 2012; Fox and Friston, 2012; Turkeltaub et al., 2012).”
Neural Correlates of Susceptibility to Group Opinions in Online Word-of-Mouth Recommendations (2015, methodology): “GingerALE performs an activation likelihood estimation on coordinates in MNI and/or Talairach space (Eickhoff et al. 2009, 2012; Turkeltaub et al. 2012).”
A quantitative meta-analysis and review of motor learning in the human brain (2013, methodology): “All contrast analyses were computed using the most recent version of the (random effects) ALE subtraction analysis (Eickhoff et al., 2012).”
Statistical localization of human olfactory cortex (2013, methodology): “Histogram permutation testing was subsequently used to test against the null hypothesis that activation foci are distributed uniformly across the brain (Eickhoff et al., 2012; Turkeltaub et al., 2012).”