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WIREs Cogn Sci
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Meta‐analysis of neuroimaging data

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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. 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. Copyright © 2010 John Wiley & Sons, Ltd. This article is categorized under: Neuroscience > Cognition

Schematic representation of the procedures for multilevel kernel density analysis (MKDA). (a) Peak coordinates in three of the 437 CMs included in a recent emotion meta‐analysis.6 (b) Peak coordinates in each map were separately convolved with a 10‐mm kernel, generating contrast indicator maps (CIMs) of values 0 or (1 shown in black). (c) The weighted average of the CIMs (weights based on sample size and analysis type) is thresholded by the maximum proportion of activated comparison maps expected under the null hypothesis [shown in (d)] to produce significant results. (e) Significant results: yellow voxels are familywise error rate (FWER) corrected at P < 0.05. Other colored regions are FWER corrected for spatial extent at P < 0.05 with primary alpha levels of 0.001 (orange) and 0.01(pink).

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Assessing specificity using the multilevel kernel density analysis (MKDA). Differences between positive and negative valence using 240 CIMs from 95 studies of emotional experience are shown. Positive experience was associated with relatively greater activation in rostral‐dorsal anterior cingulate cortex (rdACC), ventromedial prefrontal cortex (vmPFC), hypothalamus (Hy), ventral striatum (vStr), basal forebrain (BF), and the ventral tegmental area (VTA). Conversely, negative experience was associated with greater activation in amygdala (Amy), anterior insula (aIns), hippocampus (Hipp), periaqueductal gray (PAG), and more posterior portions of ventral striatum (vStr).

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