Identification of nonlinear fMRI models using Auxiliary Particle Filter and kernel smoothing method.
Hemodynamic models have a high potential in application to understanding the functional differences of the brain. However, full system identification with respect to model fitting to actual functional magnetic resonance imaging (fMRI) data is practically difficult and is still an active area of research. We present a simulation based Bayesian approach for nonlinear model based analysis of the fMRI data. The idea is to do a joint state and parameter estimation within a general filtering framework. One advantage of using Bayesian methods is that they provide a complete description of the posterior distribution, not just a single point estimate. We use an Auxiliary Particle Filter adjoined with a kernel smoothing approach to address this joint estimation problem.
Centre for Intelligent Systems research, Deakin University, Australia. ith@ deakin.edu.au
Imali T
IT
Hettiarachchi
Shady
S
Mohamed
Saeid
S
Nahavandi
D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D017711:Nonlinear Dynamics; D010101:Oxygen Consumption / Q000502:physiology*