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Evaluating the consistency and specificity of neuroimaging data using meta-analysis
Open accessAbstract
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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“For working memory, the forward inference map revealed the most consistent associations in dorsolateral prefrontal cortex (DLPFC), anterior insula and dorsal medial frontal cortex (MFC), replicating previous findings15,20.”
From Large-scale automated synthesis of human functional neuroimaging data. (2011) · cites this paper as [15]
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“True reverse inference requires knowledge of which brain regions and networks are selectively, and not just consistently, associated with particular cognitive states15,17.”
From Large-scale automated synthesis of human functional neuroimaging data. (2011) · cites this paper as [15]
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“Comparison of automated meta-analyses of three broad psychological terms (‘working memory’, ‘emotion’, and ‘pain’) with previously published meta- or mega-analytic maps14-16 revealed a marked convergence between approaches both qualitatively (Fig. 2) and quantitatively (Supplementary Fig. 5).”
From Large-scale automated synthesis of human functional neuroimaging data. (2011) · cites this paper as [15]
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Metadata
- Authors
- Wager, T. D., Lindquist, M. A., Nichols, T. E., Kober, H., & Van Snellenberg, J. X.
- Affiliations
- Columbia University (US)
- GlaxoSmithKline (United Kingdom) (GB)
- Corresponding author
- Wager, Tor D.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2009-03
- Volume
- 45
- Issue
- 1
- Pages
- S210-S221
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC3318962
- OpenAlex
- W2141058981
- Cited by
- 242
- References
- 80
- Citations by year
- 2026: 10 · 2025: 8 · 2024: 12 · 2023: 12 · 2022: 16 · 2021: 16 · 2020: 16 · 2019: 16 · 2018: 18 · 2017: 17 · 2016: 12 · 2015: 13 · 2014: 11 · 2013: 12 · 2012: 18
- FWCI
- 0.89
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Mental Health (R01MH076136)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- regional brain activation, functional brain networks, CO activation, mental illness
- MeSH terms
- Brain, Humans, Magnetic Resonance Imaging, Sensitivity and Specificity, Meta-Analysis as Topic, Positron-Emission Tomography
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