Coordinate-based voxel-wise meta-analysis: dividends of spatial normalization. Report of a virtual workshop.

Spatial normalization transforms a brain image from its natural form (“native space”) into a standardized form defined by a reference brain [Fox, 1995a]. The original motivation for introducing this technique was to allow the brain locations of task-induced functional activations to be reported in a “precise and unambiguous” manner, thereby “facilitating direct comparison of experimental results from different laboratories” [Fox et al., 1985]. The prospect of clear communication as a “dividend” from a community commitment to spatial normalization, however, proved largely unconvincing to the still-nascent brain mapping community of the middle 1980s. Improvement in the signal-to-noise ratio of functional brain maps that could be achieved by intersubject image averaging in standardized space [Fox et al., 1988; Friston et al., 1991] proved to be a very salient motivation, leading to widespread adoption of this data analysis standard. We estimate the human functional brain mapping (HFBM) literature reporting brain activations as x-y-z coordinates in standardized space to be no less than 2,500 articles (!10,000 experiments) with !500 new articles (2,000 experiments) published per year (Fig. 1). Fortunately, regardless of the motivation for adoption of this standard, the widespread use of spatial standardization makes the HFBM literature fertile ground for quantitative meta-analysis methods based on spatial concordance [Fox and Lancaster, 1996a,b; Fox et al., 1998]. In reference to the title of this article, voxel-based, function-location meta-analysis can be considered a dividend that the HFBM community is now receiving from its long-term investment in the development and promulgation of community standards for data-analysis and, in particular, spatial normalization. Meta-analysis is defined most generally as the post-hoc combination of results from independently performed studies to estimate better a parameter of interest. The original and by far the most prevalent form of meta-analysis pools studies with nonsignificant effects to test for significance in the collective, using the increase in n to increase statistical power [Pearson, 1904]. Effect-size meta-analyses have come under criticism for a variety of misuses, but are growing steadily in power and acceptance [Fox et al., 1998]. In the HFBM community, fundamentally new forms of meta-analysis are emerging, in which statistically significant effects are pooled and contrasted to estimate better such parameters as the spatial location, spatial distribution, activation likelihood, co-occurrence patterns, and underlying cognitive operations for specific categories of task. In the first published meta-analysis in cognitive neuroimaging, coordinates from three prior reports were tabulated and plotted to guide interpretation of results in a primary (non-meta-analytic) study [Frith et al., 1991]. Shortly thereafter, “stand-alone” HFBM meta-analyses began to appear in the literature [Buckner and Petersen, 1996; Fox, 1995b; Paus, 1996; Picard and Strick, 1996; Tulving et al., 1994]. To date, more than 50 meta-analyses of coordinate-based HFBM studies have appeared in the peer-reviewed literature. Although most of these meta-analyses are semiquantitative and statistically informal, this is changing. The trend toward quantitative, statistically formal HFBM meta-analysis began with Paus [1996], who computed and interpreted means and standard deviations of the x-y-z addresses in a review of studies of the frontal eye fields. Fox et al. [1997, 2001] extended this initiative by correcting raw estimates of spatial location and variance for sample size to create scalable models of location probabilities (functional volumes models; FVM) and suggesting uses of such models *Correspondence to: Peter Fox, University of Texas Health Science Center, 7703 Floyd Curl Drive, San Antonio, TX 78284. E-mail: [email protected] Received for publication 7 February 2005; Accepted 8 February 2005 DOI: 10.1002/hbm.20139 Published online in Wiley InterScience (www.interscience.wiley. com). ! Human Brain Mapping 25:1–5(2005) !