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Open Access Review

Meta-analytic methods for neuroimaging data explained

Joaquim Radua12* and David Mataix-Cols1

Author affiliations

1 Institute of Psychiatry, King's College London, De Crespigny Park, London, UK

2 Research Unit, FIDMAG-CIBERSAM, Sant Boi de Llobregat, Barcelona, Spain

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Citation and License

Biology of Mood & Anxiety Disorders 2012, 2:6  doi:10.1186/2045-5380-2-6

Published: 8 March 2012

Abstract

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

Keywords:
activation likelihood estimation; effect-size signed differential mapping; functional magnetic resonance imaging; kernel density analysis; meta-analysis; magnetic resonance imaging; multilevel kernel density analysis; parametric voxel-based meta-analysis; region of interest; signed differential mapping; voxel-based morphometry