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
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2012.2225636

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.