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Graph-Based Inter-subject Classification of Local fMRI Patterns

  • Sylvain Takerkart
  • Guillaume Auzias
  • Bertrand Thirion
  • Daniele Schön
  • Liva Ralaivola
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7588)

Abstract

Classification of medical images in multi-subjects settings is a difficult challenge due to the variability that exists between individuals. Here we introduce a new graph-based framework designed to deal with inter-subject functional variability present in fMRI data. A graphical model is constructed to encode the functional, geometric and structural properties of local activation patterns. We then design a specific graph kernel, allowing to conduct SVM classification in graph space. Experiments conducted in an inter-subject classification task of patterns recorded in the auditory cortex show that it is the only approach to perform above chance level, among a wide range of tested methods.

Keywords

fMRI classification graphs kernels inter-subject variability 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Sylvain Takerkart
    • 1
    • 2
  • Guillaume Auzias
    • 3
    • 1
  • Bertrand Thirion
    • 4
  • Daniele Schön
    • 5
  • Liva Ralaivola
    • 2
  1. 1.CNRS, INT (UMR 7289)MarseilleFrance
  2. 2.LIF (UMR 7189)Aix-Marseille UniversityMarseilleFrance
  3. 3.CNRS, LSIS (UMR 7296)MarseilleFrance
  4. 4.INRIA-Saclay-Ile-de-France, Parietal TeamPalaiseauFrance
  5. 5.CNRS, INS (UMR 1106)MarseilleFrance

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