Coordinate-based meta-analysis

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Coordinate-based meta-analysis
Abbreviations: CBMA

Coordinate-based voxel-wise meta-analysis
Activation likelihood estimation

Category: Coordinate-based meta-analysis


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Ontologies: MeSH NeuroLex Wikidata Wikipedia
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Coordinate-based meta-analysis (CBMA) analyzes the distribution of coordinates from brain activation studies (Talairach coordinates). This is usually done with kernel density estimation. It is sometimes—more narrowly—refered to as Functional Volumes Modeling (FVM),[1] Activations Likelihood Estimation (ALE), Kernel Density Analysis (KDA), Multi-level Kernel Density Analysis (MKDA) or coordinate-based voxel-wise meta-analysis (CVM).[2] The different approaches have been compared with image-based meta-analysis.[3]

The method is implemented in the Brede Toolbox and in the Sleuth program associated with the BrainMap database. A surface-oriented method is implemented in VAMCA.


[edit] Papers

[edit] Review

  1. Evaluating the consistency and specificity of neuroimaging data using meta-analysis

[edit] Methodological papers

  1. A Bayesian hierarchical spatial point process model for multi-type neuroimaging meta-analysis
  2. A parametric approach to voxel-based meta-analysis
  3. Coordinate-based activation likelihood estimation meta-analysis of neuroimaging data: a random-effects approach based on empirical estimates of spatial uncertainty
  4. Evaluating the consistency and specificity of neuroimaging data using meta-analysis
  5. Functional coactivation map of the human brain
  6. Functional volumes modeling using kernel density estimation
  7. Heterogeneity of coordinate-based meta-analyses of neuroimaging data: an example from studies in OCD
  8. Mass meta-analysis in Talairach space
  9. Meta analysis of functional neuroimaging data via Bayesian spatial point processes
  10. Minimizing within-experiment and within-group effects in activation likelihood estimation meta-analyses
  11. Mining for associations between text and brain activation in a functional neuroimaging database
  12. Modeling of activation data in the BrainMap(TM): detection of outliers
  13. Pooling fMRI data: meta-analysis, mega-analysis and multi-center studies
  14. Spatial Bayesian latent factor regression modeling of coordinate-based meta-analysis data
  15. Testing for difference between two groups of functional neuroimaging experiments
  16. Visualizing data mining results with the Brede tools

[edit] Meta-analyses

  1. A meta-analytic study of changes in brain activation in depression
  2. Anatomy of bipolar disorder and schizophrenia: a meta-analysis
  3. Different brain structures related to self- and external-agency attribution: a brief review and meta-analysis
  4. Gray matter alterations in obsessive-compulsive disorder: an anatomic likelihood estimation meta-analysis
  5. Left inferior frontal gyrus is critical for response inhibition
  6. Meta-analysis of gray matter anomalies in schizophrenia: application of anatomic likelihood estimation and network analysis
  7. Meta-analysis of 41 functional neuroimaging studies of executive function in schizophrenia
  8. N-back working memory paradigm: a meta-analysis of normative functional neuroimaging studies
  9. Neural representation of abstract and concrete concepts: a meta-analysis of neuroimaging studies
  10. Neuroimaging studies of mental rotation: a meta-analysis and review
  11. Provocation of obsessive-compulsive symptoms: a quantitative voxel-based meta-analysis of functional neuroimaging studies
  12. The social evaluation of faces: a meta-analysis of functional neuroimaging studies
  13. Where is the semantic system? a critical review and meta-analysis of 120 functional neuroimaging studies

[edit] Connectivity

  1. Identifying functional co-activation patterns in neuroimaging studies via poisson graphical models
  2. Metaanalytic connectivity modeling: Delineating the functional connectivity of the human amygdala

[edit] Reference

  1. Finn Årup Nielsen, Lars Kai Hansen (2000 january). Functional Volumes Modeling using Kernel Density Estimation.
  2. Peter T. Fox, Angela R. Laird, Jack L. Lancaster (2005). "Coordinate-based voxel-wise meta-analysis: dividends of spatial normalization. report of a virtual workshop". Human Brain Mapping 25: 1-5. doi: 10.1002/hbm.20139.
  3. Gholamreza Salimi-Khorshidi, Stephen M. Smith, John R. Keltner, Tor D. Wager, Thomas E. Nichols (2009). "Meta-analysis of neuroimaging data: a comparison of image-based and coordinate-based pooling of studies". NeuroImage 45: 810-823. doi: 10.1016/j.neuroimage.2008.12.039.
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