Meta-analysis in human neuroimaging: Computational modeling of large-scale databases

Peter T. Fox, Jack L. Lancaster, Angela R. Laird, Simon B. Eickhoff

Research output: Contribution to journalReview article

83 Scopus citations

Abstract

Spatial normalization-applying standardized coordinates as anatomical addresses within a reference space-was introduced to human neuroimaging research nearly 30 years ago. Over these three decades, an impressive series of methodological advances have adopted, extended, and popularized this standard. Collectively, this work has generated a methodologically coherent literature of unprecedented rigor, size, and scope. Large-scale online databases have compiled these observations and their associated meta-data, stimulating the development of meta-analytic methods to exploit this expanding corpus. Coordinate-based meta-analytic methods have emerged and evolved in rigor and utility. Early methods computed cross-study consensus, in a manner roughly comparable to traditional (nonimaging) meta-analysis. Recent advances now compute coactivation-based connectivity, connectivity-based functional parcellation, and complex network models powered from data sets representing tens of thousands of subjects. Meta-analyses of human neuroimaging data in large-scale databases now stand at the forefront of computational neurobiology. ©

Original languageEnglish (US)
Pages (from-to)409-434
Number of pages26
JournalAnnual Review of Neuroscience
Volume37
DOIs
StatePublished - Jul 2014

Keywords

  • ALE
  • Activation likelihood estimation
  • FMRI
  • Human brain mapping
  • MRI
  • Magnetic resonance imaging

ASJC Scopus subject areas

  • Neuroscience(all)

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