Fast Joint Detection-Estimation of Evoked Brain Activity in Event-Related fMRI Using a Variational Approach

• Published in: IEEE transactions on medical imaging Vol. 32; no. 5; pp. 821 - 837
• Main Authors: Chaari, L., Vincent, T., Forbes, F., Dojat, M., Ciuciu, P.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2013; Institute of Electrical and Electronics Engineers
• Subjects: Algorithms; Approximation methods; Bayes Theorem; Bayesian methods; Brain - blood supply; Brain - physiology; Brain Mapping - methods; Computational modeling; Computer Science; Computer Simulation; Data models; Databases, Factual; Engineering Sciences; Estimation; Expectation-maximization (EM) algorithm; functional magnetic resonance imaging (fMRI); Hemodynamics; Hidden Markov models; Humans; joint detection-estimation; Joints; Life Sciences; Magnetic Resonance Imaging - methods; Markov Chains; Markov random field; Medical Imaging; Neurons and Cognition; Signal and Image Processing; Signal Processing, Computer-Assisted; variational approximation; brain imaging; functional MRI; fMRI; Biomedical signal detection-estimation; Variational approximation; Markov random field; Joint Detection-Estimation; Mean-field; EM algorithm; VEM

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-bas...