Variational solution to the joint detection estimation of brain activity in fMRI

Lotfi Chaari 1 Florence Forbes 1 Thomas Vincent 2 Michel Dojat 3, * Philippe Ciuciu 2
* Corresponding author
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
2 LNAO - Laboratoire de Neuroimagerie Assistée par Ordinateur
3 ANTE-INSERM U836, équipe 5 - Neuro-imagerie fonctionnelle et métabolique
GIN - Grenoble Institut des Neurosciences
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.
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Submitted on : Tuesday, October 25, 2011 - 11:07:37 AM
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Lotfi Chaari, Florence Forbes, Thomas Vincent, Michel Dojat, Philippe Ciuciu. Variational solution to the joint detection estimation of brain activity in fMRI. MICCAI 2011 - 14th International Conference on Medical Image Computing and Computer-Assisted Intervention, Sep 2011, Toronto, Canada. pp.260-268, ⟨10.1007/978-3-642-23629-7_32⟩. ⟨inserm-00635384⟩

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