title: 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.
authors: INRIA, MISTIS, Grenoble, France.
authors: Lotfi
authors: L
authors: Chaari
authors: Florence
authors: F
authors: Forbes
authors: Thomas
authors: T
authors: Vincent
authors: Michel
authors: M
authors: Dojat
authors: Philippe
authors: P
authors: Ciuciu
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D009474:Neurons / Q000473:pathology; D016011:Normal Distribution; D012984:Software; D013997:Time Factors