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Conference papers
Variational solution to the joint detection estimation of brain activity in fMRI
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.
Cited literature [14 references]
https://www.hal.inserm.fr/inserm-00635384
Contributor : Michel Dojat <>
Submitted on : Tuesday, October 25, 2011 - 11:07:37 AM
Last modification on : Thursday, July 9, 2020 - 9:44:34 AM
Document(s) archivé(s) le : Monday, December 5, 2016 - 12:57:34 AM
Contributor : Michel Dojat <>
Submitted on : Tuesday, October 25, 2011 - 11:07:37 AM
Last modification on : Thursday, July 9, 2020 - 9:44:34 AM
Document(s) archivé(s) le : Monday, December 5, 2016 - 12:57:34 AM
