Meta-analysis of neuroimaging data

Citation: Kober, H., & Wager, T. D. (2010). Meta-analyses of neuroimaging data. Wiley Interdisciplinary Reviews: Cognitive Science.

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As the number of neuroimaging studies that investigate psychological phenomena
grows, it becomes increasingly dif?cult to integrate the knowledge that has accrued
across studies. Meta-analyses are designed to serve this purpose, as they allow the
synthesis of ?ndings 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 speci?c
categories such as conditioned fear. Meta-analysis can also address questions of
speci?city, 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
speci?city 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 ?eld.