Estimation of the hemodynamic response in event-related functional MRI: Bayesian networks as a framework for efficient Bayesian modeling and inference. Export

@article{citeulike:839724, abstract = {A convenient way to analyze blood-oxygen-level-dependent functional magnetic resonance imaging data consists of modeling the whole brain as a stationary, linear system characterized by its transfer function: the hemodynamic response function (HRF). HRF estimation, though of the greatest interest, is still under investigation, for the problem is ill-conditioned. In this paper, we recall the most general Bayesian model for HRF estimation and show how it can beneficially be translated in terms of Bayesian graphical models, leading to 1) a clear and efficient representation of all structural and functional relationships entailed by the model, and 2) a straightforward numerical scheme to approximate the joint posterior distribution, allowing for estimation of the HRF, as well as all other model parameters. We finally apply this novel technique on both simulations and real data.}, address = {INSERM U494, CHU Piti\'{e}-Salp\^{e}tri\`{e}re, 91 boulevard de l'H\^{o}pital, 75634 Paris Cedex 13, France.}, author = {Marrelec, G. and Ciuciu, P. and P\'{e}l\'{e}grini-Issac, M. and Benali, H.}, citeulike-article-id = {839724}, citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/15338730}, citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=15338730}, issn = {0278-0062}, journal = {IEEE Trans Med Imaging}, keywords = {bayesian, fmri, hrf}, month = {August}, number = {8}, pages = {959--967}, posted-at = {2006-09-12 01:41:13}, priority = {2}, title = {Estimation of the hemodynamic response in event-related functional MRI: Bayesian networks as a framework for efficient Bayesian modeling and inference.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/15338730}, volume = {23}, year = {2004} }

TY - JOUR ID - citeulike:839724 L3 - citeulike-article-id:839724 N2 - A convenient way to analyze blood-oxygen-level-dependent functional magnetic resonance imaging data consists of modeling the whole brain as a stationary, linear system characterized by its transfer function: the hemodynamic response function (HRF). HRF estimation, though of the greatest interest, is still under investigation, for the problem is ill-conditioned. In this paper, we recall the most general Bayesian model for HRF estimation and show how it can beneficially be translated in terms of Bayesian graphical models, leading to 1) a clear and efficient representation of all structural and functional relationships entailed by the model, and 2) a straightforward numerical scheme to approximate the joint posterior distribution, allowing for estimation of the HRF, as well as all other model parameters. We finally apply this novel technique on both simulations and real data. IS - 8 JF - IEEE Trans Med Imaging SN - 0278-0062 AD - INSERM U494, CHU Pitié-Salpêtrière, 91 boulevard de l'Hôpital, 75634 Paris Cedex 13, France. EP - 967 TI - Estimation of the hemodynamic response in event-related functional MRI: Bayesian networks as a framework for efficient Bayesian modeling and inference. VL - 23 SP - 959 KW - bayesian KW - fmri KW - hrf AU - Marrelec, G AU - Ciuciu, P AU - Pélégrini-Issac, M AU - Benali, H PY - 2004/08// UR - http://view.ncbi.nlm.nih.gov/pubmed/15338730 ER -