Meta-Analysis in Human Neuroimaging: Computational Modeling of Large-Scale Databases

Abstract

Spatial normalization—applying standardized coordinates as anatomical addresses within a reference space—was introduced to human neuroimaging research nearly 30 years ago. Over these three decades, an impressive series of methodological advances have adopted, extended, and popularized this standard. Collectively, this work has generated a methodologically coherent literature of unprecedented rigor, size, and scope. Large-scale online databases have compiled these observations and their associated meta-data, stimulating the development of meta-analytic methods to exploit this expanding corpus. Coordinate-based meta-analytic methods have emerged and evolved in rigor and utility. Early methods computed cross-study consensus, in a manner roughly comparable to traditional (nonimaging) meta-analysis. Recent advances now compute coactivation-based connectivity, connectivity-based functional parcellation, and complex network models powered from data sets representing tens of thousands of subjects. Meta-analyses of human neuroimaging data in large-scale databases now stand at the forefront of computational neurobiology.

Resting brain activity in disorders of consciousness

“…[8][9][10] A detailed account of the ALE approach has been published elsewhere. 8,11 In brief, coordinates in 3-dimensional stereotactic space (e.g., Talairach or Montreal Neurological Institute [MNI]) are pooled from different studies and spatially normalized to a single template. ALE model coordinates using a gaussian kernel, with a full width at half maximum value corresponding to the sample size of the study, to accommodate for spatial uncertainty associated with the reported coordinates.…”

Behavior, sensitivity, and power of activation likelihood estimation characterized by massive empirical simulation

“…The first pool contained 15 hand-coded datasets (Bzdok et al, 2012; Cieslik et al, 2015; Hardwick et al, 2013; Kohn et al, 2014; Rottschy et al, 2012). The second pool consisted of 105 datasets that were automatically extracted from the BrainMap database (www.brainmap.org; (Fox et al, 2014; Laird et al, 2011a; Laird et al, 2009)). This was achieved by combinations of the paradigm class and behavioral domain classifications (Laird et al, 2011a) that yielded between 30 and 200 experiments.…”

“…Here we would like to reiterate, that the latter may not be easily equated with “false positives” in the statistical sense. Rather, these effects represent spurious convergence due to the non-homogeneous likelihood of activating any particular voxel in the brain in neuroimaging (Fox et al, 2014; Laird et al, 2011b; Langner et al, 2014). In the context of a specific ALE meta-analysis, however, such incidental convergence due to the fact that a region may be frequently activated would often be broadly equivalent to a false positive relative to the subject under investigation.…”

Predicting personality from network-based resting-state functional connectivity

“…To solve the problem of a more objective definition of relevant nodes in a given functional network, quantitative meta-analyses of task-based neuroimaging studies aggregate the findings of many individual task-activation studies into a core network representing those locations that are reliably recruited by engaging in a given kind of mental process (cf. Fox et al 2014). The investigation of RSFC in meta-analytically defined networks representing specific social, affective, executive, or memory functions, therefore, provides a viable approach to capturing the complex intrinsic neural architecture underlying personality (Adelstein et al 2011; Sampaio et al 2014).…”