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Coordinate-Based Pattern-Mining on Functional Neuroimaging Databases

  • Julian Caspers
  • Karl Zilles
  • Simon B. Eickhoff
  • Christoph Beierle
Part of the Communications in Computer and Information Science book series (CCIS, volume 297)

Abstract

Aiming to exploit the rich fundus of available functional neuroimaging data, we present a coordinate-based pattern-mining approach. Gaussian mixture modeling is applied on three-dimensional brain coordinates obtained from neuroimaging databases collecting peak activations seen in functional neuroimaging experiments. We show how the Apriori algorithm used in association analysis can be applied to the obtained results, revealing frequent patterns of the identified coordinate clusters. These patterns can be interpreted as common networks of functionally connected brain regions and hence give deeper insights into the functional organization of the human brain.

Keywords

Brain Region Functional Connectivity Bayesian Information Criterion Gaussian Mixture Modeling Frequent Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Julian Caspers
    • 1
    • 2
    • 3
  • Karl Zilles
    • 1
    • 3
    • 4
  • Simon B. Eickhoff
    • 1
    • 5
  • Christoph Beierle
    • 2
  1. 1.Research Centre JülichInstitute of Neuroscience and Medicine (INM-2)Germany
  2. 2.Department of Computer ScienceFernUniversität in HagenGermany
  3. 3.C. & O. Vogt Institute for Brain ResearchUniversity of DüsseldorfGermany
  4. 4.JARA-BRAIN, Jülich Aachen Research AllianceGermany
  5. 5.Institute of Clinical Neuroscience and Medical PsychologyUniversity Hospital DüsseldorfGermany

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