TY - JOUR
T1 - Evaluating the consistency and specificity of neuroimaging data using meta-analysis.
AU - Wager, Tor D.
AU - Lindquist, Martin A.
AU - Nichols, Thomas E.
AU - Kober, Hedy
AU - Van Snellenberg, Jared X.
N1 - Funding Information:
This research and the preparation of this manuscript were supported in part by National Science Foundation grant (SES631637) and National Institute of Mental Health grant (R01MH076136) to Tor D. Wager. We would like to thank Lisa Feldman Barrett for helpful discussions on multi-level aspects of meta-analysis, and Lisa Feldman Barret, Eliza Bliss-Moreau, John Jonides, Kristen Lindquist, Derek Nee, and Edward Smith, for their contributions to the meta-analysis datasets presented here.
PY - 2009/3
Y1 - 2009/3
N2 - Making sense of a neuroimaging literature that is growing in scope and complexity will require increasingly sophisticated tools for synthesizing findings across studies. Meta-analysis of neuroimaging studies fills a unique niche in this process: It can be used to evaluate the consistency of findings across different laboratories and task variants, and it can be used to evaluate the specificity of findings in brain regions or networks to particular task types. This review discusses examples, implementation, and considerations when choosing meta-analytic techniques. It focuses on the multilevel kernel density analysis (MKDA) framework, which has been used in recent studies to evaluate consistency and specificity of regional activation, identify distributed functional networks from patterns of co-activation, and test hypotheses about functional cortical-subcortical pathways in healthy individuals and patients with mental disorders. Several tests of consistency and specificity are described.
AB - Making sense of a neuroimaging literature that is growing in scope and complexity will require increasingly sophisticated tools for synthesizing findings across studies. Meta-analysis of neuroimaging studies fills a unique niche in this process: It can be used to evaluate the consistency of findings across different laboratories and task variants, and it can be used to evaluate the specificity of findings in brain regions or networks to particular task types. This review discusses examples, implementation, and considerations when choosing meta-analytic techniques. It focuses on the multilevel kernel density analysis (MKDA) framework, which has been used in recent studies to evaluate consistency and specificity of regional activation, identify distributed functional networks from patterns of co-activation, and test hypotheses about functional cortical-subcortical pathways in healthy individuals and patients with mental disorders. Several tests of consistency and specificity are described.
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U2 - 10.1016/j.neuroimage.2008.10.061
DO - 10.1016/j.neuroimage.2008.10.061
M3 - Review article
C2 - 19063980
AN - SCOPUS:65549132536
SN - 1053-8119
VL - 45
SP - S210-221
JO - NeuroImage
JF - NeuroImage
IS - 1 Suppl
ER -