Activation likelihood estimation meta-analysis revisited

Abstract

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.

Evaluating methods of correcting for multiple comparisons implemented in SPM12 in social neuroscience fMRI studies: an example from moral psychology

“…We evaluated the four correction methods by comparing findings from our analyses of fMRI data to those from a meta-analysis of fMRI studies on moral cognition and emotion (Han, 2017). GingerALE software (version 2.3.6), which implements the activation likelihood estimation (ALE) method (Eickhoff et al, 2009;Eickhoff, Bzdok, Laird, Kurth, & Fox, 2012;Laird, Lancaster, & Fox, 2005), was employed in the meta-analysis. The meta-analysis examined a previously collected set of activation foci that were found by previous neuroimaging studies that compared neural correlates between moral and non-moral task conditions (for details see Han, 2017).…”

A meta‐analysis of functional magnetic resonance imaging studies of eye movements and visual word reading

“…The meta‐analysis was performed using the ALE algorithm (Eickhoff et al., 2009, 2012; Turkeltaub et al., 2012) found in the GingerALE2.3 software (http://brainmap.org/ale/; RRID:SCR_014921). In the ALE approach, spatial probability distributions for the foci were modeled at the center of three‐dimensional Gaussian functions and the Gaussian distributions were aggregated across the entire set of experiments to generate a map of consistencies among studies that estimated the likelihood of activation for each voxel—the ALE statistic (Eickhoff et al., 2009).…”

“…Previous neuroimaging studies have shown that frontoparietal attentional regions play a critical role in eye movements (Corbetta & Shulman, 2002; Simon, Mangin, Cohen, Le Bihan, & Dehaene, 2002), consistent with their function for spatial representation and spatial updating (Merriam, Genovese, & Colby, 2003; Pertzov, Avidan, & Zohary, 2011; Silver & Kastner, 2009). In a recent meta‐analysis, Jamadar, Fielding, and Egan (2013) have used the activation likelihood estimation method (ALE; Turkeltaub et al., 2012; Eickhoff et al., 2009; Eickhoff, Bzdok, Laird, Kurth, & Fox, 2012) to compare the neural networks of prosaccades and antisaccades. At the cortical level, they found that the network of prosaccades includes the primary visual cortex, extrastriate cortex, parietal eye field (PEF, in the posterior parietal cortex), frontal eye field (FEF, in the superior part of the prefrontal gyrus), and supplementary eye field (SEF, in the medial frontal gyrus [MedFG]).…”

Coordinate based meta-analysis does not show grey matter atrophy in narcolepsy

“…Subjects with narcolepsy The data were subjected to a fourth analysis using the most recent type 1 error control scheme [15] implemented into GingerALE (version 2.3.6). An uncorrected p-value threshold of p<0001 was combined with a cluster-level FWE rate threshold of 005, and 1000 iterations were performed; these are the recommended settings.…”

“…These results were subsequently demonstrated to be due to an implementation issue with the software [11], and re-analysis using a corrected version (GingerALE 2.3.3) detected no consistent significant regional GM loss [12]. This reanalyses employed the false discovery rate (FDR) method of controlling the type 1 error [13], which is no longer the recommended option in GingerALE [14] and has been superseded [15]. This has prompted a second re-evaluation of the narcolepsy data by Zhong and colleagues [16] employing the signed differential mapping (SDM) CBMA algorithm [17,18].…”