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Anatomically Informed Convolution Kernels for the Projection of fMRI Data on the Cortical Surface

  • Grégory Operto
  • Rémy Bulot
  • Jean-Luc Anton
  • Olivier Coulon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)

Abstract

We present here a method that aims at producing representations of functional brain data on the cortical surface from functional MRI volumes. Such representations are required for subsequent cortical-based functional analysis. We propose a projection technique based on the definition, around each node of the grey/white matter interface mesh, of convolution kernels whose shape and distribution rely on the geometry of the local anatomy. For one anatomy, a set of convolution kernels is computed that can be used to project any functional data registered with this anatomy. The method is presented together with experiments on synthetic data and real statistical t-maps.

Keywords

fMRI Data Geodesic Distance Cortical Surface Convolution Kernel Functional Volume 
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 2006

Authors and Affiliations

  • Grégory Operto
    • 1
  • Rémy Bulot
    • 1
  • Jean-Luc Anton
    • 2
  • Olivier Coulon
    • 1
  1. 1.Laboratoire LSIS, UMR 6168, CNRSMarseilleFrance
  2. 2.Centre IRMf de MarseilleMarseilleFrance

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