The number of neuroimaging studies has grown exponentially in recent years and their results are not always\r\nconsistent. Meta-analyses are helpful to summarize this vast literature and also offer insights that are not apparent\r\nfrom the individual studies. In this review, we describe the main methods used for meta-analyzing neuroimaging\r\ndata, with special emphasis on their relative advantages and disadvantages. We describe and discuss metaanalytical\r\nmethods for global brain volumes, methods based on regions of interest, label-based reviews, voxelbased\r\nmeta-analytic methods and online databases. Regions of interest-based methods allow for optimal statistical\r\nanalyses but are affected by a limited and potentially biased inclusion of brain regions, whilst voxel-based methods\r\nbenefit from a more exhaustive and unbiased inclusion of studies but are statistically more limited. There are also\r\nrelevant differences between the different available voxel-based meta-analytic methods, and the field is rapidly\r\nevolving to develop more accurate and robust methods. We suggest that in any meta-analysis of neuroimaging\r\ndata, authors should aim to: only include studies exploring the whole brain; ensure that the same threshold\r\nthroughout the whole brain is used within each included study; and explore the robustness of the findings via\r\ncomplementary analyses to minimize the risk of false positives.
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