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Variational Solution to the Joint Detection Estimation of Brain Activity in fMRI

  • Lotfi Chaari
  • Florence Forbes
  • Thomas Vincent
  • Michel Dojat
  • Philippe Ciuciu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6892)

Abstract

We address the issue of jointly detecting brain activity and estimating underlying brain hemodynamics from functional MRI data. We adopt the so-called Joint Detection Estimation (JDE) framework that takes spatial dependencies between voxels into account. We recast the JDE into a missing data framework and derive a Variational Expectation-Maximization (VEM) algorithm for its inference. It follows a new algorithm that has interesting advantages over the previously used intensive simulation methods (Markov Chain Monte Carlo, MCMC): tests on artificial data show that the VEM-JDE is more robust to model mis-specification while additional tests on real data confirm that it achieves similar performance in much less computation time.

Keywords

Markov Chain Monte Carlo Hemodynamic Response Function fMRI Time Series Canonical Hemodynamic Response Function Markov Chain Monte Carlo Scheme 
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 2011

Authors and Affiliations

  • Lotfi Chaari
    • 1
    • 2
  • Florence Forbes
    • 1
    • 2
  • Thomas Vincent
    • 3
  • Michel Dojat
    • 2
    • 4
  • Philippe Ciuciu
    • 3
  1. 1.INRIA, MISTISGrenobleFrance
  2. 2.Grenoble University, LJKGrenobleFrance
  3. 3.CEA/DSV/I2BM/Neurospin, LNAOFrance
  4. 4.INSERM, U836, GINGrenobleFrance

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