Meta‐analysis of neuroimaging data

Abstract

As the number of neuroimaging studies that investigate psychological phenomena grows, it becomes increasingly difficult to integrate the knowledge that has accrued across studies. Metaanalyses are designed to serve this purpose, as they allow the synthesis of findings not only across studies but also across laboratories and task variants. Meta-analyses are uniquely suited to answer questions about whether brain regions or networks are consistently associated with particular psychological domains, including broad categories such as working memory or more specific categories such as conditioned fear. Meta-analysis can also address questions of specificity, which pertains to whether activation of regions or networks is unique to a particular psychological domain, or is a feature of multiple types of tasks. This review discusses several techniques that have been used to test consistency and specificity in published neuroimaging data, including the kernel density analysis (KDA), activation likelihood estimate (ALE), and the recently developed multilevel kernel density analysis (MKDA). We discuss these techniques in light of current and future directions in the field.Studies that use neuroimaging methods such as positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) allow us to investigate the function of the human brain, in both healthy and disordered populations. In recent years, the number of such investigations increased dramatically across a range of psychological domains. Indeed, some areas of investigation such as cognitive control and emotion have been the subject of many such studies, representing an ever-increasing body of knowledge that complements findings from animal and human lesion studies, electrophysiology, transcranial magnetic stimulation, and other methods. However, activation of a brain region in a single fMRI or PET study cannot usually provide conclusive evidence until it is replicated across laboratories, task variants, and scanning procedures. Meta-analyses of neuroimaging data can therefore serve a crucial function of integrating over research findings to evaluate the consistency of activation in any particular domain. In this way, meta-analyses can determine which regions are consistently activated across a large group of studies that address a single psychological function. A second crucial function served by meta-analysis is the evaluation of functional specificity of activations in a region to a particular type of psychological process. Metaanalysis can determine whether a particular brain region that is consistently activated by a single psychological domain (e.g., cognitive control) is unique to that domain, or whether it is shared by a broader set of cognitive processes (e.g., working memory). WHY USE META-ANALYSIS? Evaluating consistencyIn order to understand and evaluate hundreds of neuroimaging studies published in a single domain (such as emotion), we need to know which regions are most consistently activated.

Functional neuroanatomy of meditation: A review and meta-analysis of 78 functional neuroimaging investigations

“…We therefore echo other neuroscientists in arguing that quantitative meta-analysis of neuroimaging data is a potent (indeed necessary) tool for better understanding the neural basis of mental processes (Wager et al, 2007, Salimi-Khorshidi et al, 2009, Kober and Wager, 2010, Yarkoni et al, 2010, Hupé, 2015. More specifically, meta-analysis can provide: (i) a less-biased overview of an evidence base than a simple narrative review or qualitative survey of prior work (Schmidt, 1992); (ii) specific peaks of meta-analytic activation, rather than just broad regional results; (iii) statistically significant activation overlaps across studies, instead of merely indicating broad 'replications'; and (iv) a way to mitigate the lack of statistical power caused by small samples found in many neuroimaging studies -a problem especially prevalent in the study of meditation practitioners, where experienced individuals are difficult to recruit.…”

Acute neural effects of selective serotonin reuptake inhibitors versus noradrenaline reuptake inhibitors on emotion processing: Implications for differential treatment efficacy

“…Additionally, we were also interested in examining the differential effects of task on activation and whether there were any specific effects relating to the class of antidepressant (SSRI vs. NRI). In order to address these objectives, we utilised MKDA to conduct our meta-analysis (Kober and Wager, 2010;Wager et al, 2009). MKDA is a quantitative, coordinatebased approach used in several recently published studies (e.g., Denny et al, 2012;Etkin and Wager, 2007;Kober et al, 2008).…”

“…MKDA is a quantitative, coordinatebased approach used in several recently published studies (e.g., Denny et al, 2012;Etkin and Wager, 2007;Kober et al, 2008). The MKDA statistic reflects the number of nominally independent contrast maps (i.e., statistical parametric maps from individual studies) that activate in the vicinity (e.g., within 10 mm) of each voxel in the brain; the null hypothesis is that the activation "blobs" from individual contrast maps are randomly distributed (Kober and Wager, 2010;Wager et al, 2009). Thus, a significant result indicates that more contrast maps activate near a specific voxel than expected by chance (Kober and Wager, 2010;Wager et al, 2009).…”

“…The MKDA statistic reflects the number of nominally independent contrast maps (i.e., statistical parametric maps from individual studies) that activate in the vicinity (e.g., within 10 mm) of each voxel in the brain; the null hypothesis is that the activation "blobs" from individual contrast maps are randomly distributed (Kober and Wager, 2010;Wager et al, 2009). Thus, a significant result indicates that more contrast maps activate near a specific voxel than expected by chance (Kober and Wager, 2010;Wager et al, 2009). While there are many meta-analytic methods available in neuroimaging research (Radua and Mataix-Cols, 2012), we selected the MKDA method for a number of reasons.…”

“…The MKDA also allows the weighting of studies by sample size and effect (fixed vs. random), meaning those studies with a randomeffects model and a larger number of participants are given more weight than those with a smaller sample size or fixed-effects model. A third advantage is the summary statistic's clear-cut interpretability: it is simply the proportion of contrasts activating within 10 mm of a given voxel (Kober and Wager, 2010;Wager et al, 2009). Analyses were performed in MATLAB version 2011b, using the MKDA tool package (Wager, 2008; http://wagerlab.colorado.edu/ files/tools/meta-analysis.html).…”