Bayesian estimation of dynamical systems: an application to fMRI.
This paper presents a method for estimating the conditional or posterior distribution of the parameters of deterministic dynamical systems. The procedure conforms to an EM implementation of a Gauss-Newton search for the maximum of the conditional or posterior density. The inclusion of priors in the estimation procedure ensures robust and rapid convergence and the resulting conditional densities enable Bayesian inference about the model parameters. The method is demonstrated using an input-state-output model of the hemodynamic coupling between experimentally designed causes or factors in fMRI studies and the ensuing BOLD response. This example represents a generalization of current fMRI analysis models that accommodates nonlinearities and in which the parameters have an explicit physical interpretation. Second, the approach extends classical inference, based on the likelihood of the data given a null hypothesis about the parameters, to more plausible inferences about the parameters of the model given the data. This inference provides for confidence intervals based on the conditional density.
Friston
K J
KJ
The Wellcome Department of Cognitive Neurology, Institute of Neurology, Queen Square, London, United Kingdom WC1N 3BG.
D001499:Bayes Theorem; D001921:Brain / Q000502:physiology*; D002560:Cerebrovascular Circulation; D006439:Hemodynamics; D006801:Humans; D016013:Likelihood Functions; D008279:Magnetic Resonance Imaging; D008955:Models, Cardiovascular; D008959:Models, Neurological; D017711:Nonlinear Dynamics; D010100:Oxygen / Q000097:blood; D011336:Probability; D013223:Statistics as Topic / Q000379:methods
D010100:Oxygen