title: Probabilistic modeling of single-trial fMRI data.
abstract: This paper describes a probabilistic framework for modeling single-trial functional magnetic resonance (fMR) images based on a parametric model for the hemodynamic response and Markov random field (MRF) image models. The model is fitted to image data by maximizing a lower bound on the log likelihood. The result is an approximate maximum a posteriori estimate of the joint distribution over the model parameters and pixel labels. Examples show how this technique can used to segment two-dimensional (2-D) fMR images, or parts thereof, into regions with different characteristics of their hemodynamic response.
authors.affiliation: Max-Planck Institute of Cognitive Neuroscience, Leipzig, Germany. svensen@cns.mpg.de
authors.forename: M
authors.initials: M
authors.lastname: Svensén
authors.forename: F
authors.initials: F
authors.lastname: Kruggel
authors.forename: D Y
authors.initials: DY
authors.lastname: von Cramon
mesh_terms: D001921:Brain / Q000098:blood supply* / Q000473:pathology; D003198:Computer Simulation; D006439:Hemodynamics / Q000502:physiology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016013:Likelihood Functions; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D008390:Markov Chains; D015233:Models, Statistical; D019047:Phantoms, Imaging