title: Fast joint detection-estimation of evoked brain activity in event-related FMRI using a variational approach.
abstract: In standard within-subject analyses of event-related functional magnetic resonance imaging (fMRI) data, two steps are usually performed separately: detection of brain activity and estimation of the hemodynamic response. Because these two steps are inherently linked, we adopt the so-called region-based joint detection-estimation (JDE) framework that addresses this joint issue using a multivariate inference for detection and estimation. JDE is built by making use of a regional bilinear generative model of the BOLD response and constraining the parameter estimation by physiological priors using temporal and spatial information in a Markovian model. In contrast to previous works that use Markov Chain Monte Carlo (MCMC) techniques to sample the resulting intractable posterior distribution, we recast the JDE into a missing data framework and derive a variational expectation-maximization (VEM) algorithm for its inference. A variational approximation is used to approximate the Markovian model in the unsupervised spatially adaptive JDE inference, which allows automatic fine-tuning of spatial regularization parameters. It provides a new algorithm that exhibits interesting properties in terms of estimation error and computational cost compared to the previously used MCMC-based approach. Experiments on artificial and real data show that VEM-JDE is robust to model misspecification and provides computational gain while maintaining good performance in terms of activation detection and hemodynamic shape recovery.
authors.affiliation: Inria Grenoble Rhône-Alpes, 38334 Saint Ismier Cedex, France. lotfi.chaari@inria.fr
authors.forename: Lotfi
authors.initials: L
authors.lastname: Chaari
authors.forename: Thomas
authors.initials: T
authors.lastname: Vincent
authors.forename: Florence
authors.initials: F
authors.lastname: Forbes
authors.forename: Michel
authors.initials: M
authors.lastname: Dojat
authors.forename: Philippe
authors.initials: P
authors.lastname: Ciuciu
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000098:blood supply / Q000502:physiology; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D016208:Databases, Factual; D006439:Hemodynamics; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008390:Markov Chains; D012815:Signal Processing, Computer-Assisted