Meta-analysis of neuroimaging data.

As the number of neuroimaging studies that investigate psychological phenomena grows, it becomes increasingly difficult to integrate the knowledge that has accrued across studies. Meta-analyses 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.