Minimizing Within-Experiment and Within-Group Effects in Activation Likelihood Estimation Meta-Analyses

By Peter Turkeltaub, Simon B. Eickhoff, Angela R. Laird, M. Fox, Martin Wiener, Peter T. Fox. Human Brain Mapping, 2011

1,091 citations recorded by the source corpus.

Activation Likelihood Estimation (ALE) is an objective, quantitative technique for coordinate‐based meta‐analysis (CBMA) of neuroimaging results that has been validated for a variety of uses. Stepwise modifications have improved ALE's theoretical and statistical rigor since its introduction. Here, we evaluate two avenues to further optimize ALE. First, we demonstrate that the maximum contribution of an experiment makes to an ALE map is related to the number of foci it reports and their proximity. We present a modified ALE algorithm that eliminates these within‐experiment effects. However, we show that these effects only account for 2–3% of cumulative ALE values, and removing them has little impact on thresholded ALE maps. Next, we present an alternate organizational approach to datasets that prevents subject groups with multiple experiments in a dataset from influencing ALE values more than others. This modification decreases cumulative ALE values by 7–9%, changes the relative magnitude of some clusters, and reduces cluster extents. Overall, differences between results of the standard approach and these new methods were small. This finding validates previous ALE reports against concerns that they were driven by within‐experiment or within‐group effects. We suggest that the modified ALE algorithm is theoretically advantageous compared with the current algorithm, and that the alternate organization of datasets is the most conservative approach for typical ALE analyses and other CBMA methods. Combining the two modifications minimizes both within‐experiment and within‐group effects, optimizing the degree to which ALE values represent concordance of findings across independent reports. Hum Brain Mapp, 2012. © 2011 Wiley Periodicals, Inc.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/7c91cf8d9c17423f52f7b5af4e3910f4be86625a

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In Cognitive Neuroscience

Citations in the corpus

Neural network of cognitive emotion regulation — An ALE meta-analysis and MACM analysis (2013, methodology): “Modeled activation maps (MA maps) for each study were generated by combining the probabilities of all activation foci for each voxel (Turkeltaub et al., 2012).”
The Autonomic Brain: An Activation Likelihood Estimation Meta-Analysis for Central Processing of Autonomic Function (2013, methodology): “All ALE-related calculations were carried out in GingerALE 2.3 (http://www.brainmap.org/ale/; Research Imaging Center, University of Texas, San Antonio, TX) using the nonadditive ALE algorithm (Turkeltaub et al., 2012).”
Neural substrates of smoking cue reactivity: A meta-analysis of fMRI studies (2011, neutral): “…note that no such caveat is needed for the results of the pooled meta-analysis, which used a well-validated technique (Eickhoff et al., 2009; Farrell et al., 2005; Laird et al., 2005; Liu et al., 2011; Price et al., 2005; Sabatinelli et al., 2011; Turkeltaub et al., 2002a; Turkeltaub et al., 2011).”
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).”
Connectivity profiles reveal the relationship between brain areas for social cognition in human and monkey temporoparietal cortex (2013, methodology): “ALE was performed using the modified algorithm of Turkeltaub and colleagues (48).”
Informatic parcellation of the network involved in the computation of subjective value. (2014, methodology): “Statistical inference for coordinate-based meta-analysis 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).”
Modelling neural correlates of working memory: A coordinate-based meta-analysis (2011, neutral): “As discussed in detail in Turkeltaub et al., 2011 there is an albeit moderate possibility that this may bias findings of convergence across experiments.”
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).”
Where smart brains are different: A quantitative meta-analysis of functional and structural brain imaging studies on intelligence (2015, methodology): “3 (Eickhoff, Bzdok, Laird, Kurth, & Fox, 2012; Eickhoff et al., 2009; Turkeltaub et al., 2012), available from http://www.”
A quantitative meta-analysis and review of motor learning in the human brain (2013, methodology): “Activation Likelihood Estimation (ALE) provides a well established technique for quantitative voxelwise random effects meta-analysis (Eickhoff et al., 2009, 2012; Laird et al., 2005; Turkeltaub et al., 2002, 2012).”
Sustaining attention to simple tasks: a meta-analytic review of the neural mechanisms of vigilant attention. (2013, methodology): “Subsequently, the probabilities of all foci reported of a given experiment were combined for each voxel, yielding a modeled activation map (Turkeltaub et al., 2012).”