A bayesian spatio - temporal approach for the analysis of FMRI data with non - stationary noise.
In this work, the bayesian framework is used for the analysis of fMRI data. The novelty of the proposed approach is the introduction of a spatio - temporal model used to estimate the variance of the noise across the images and the voxels. The proposed approach is based on a spatio - temporal version of Generalized Linear Model (GLM). To estimate the regression parameters of the GLM as well as the variance components of the noise, the Variational Bayesian (VB) Methodology is employed. The use of VB methodology results in an iterative algorithm, where the estimation of the regression coefficients and the estimation of variance components of the noise, across images and across voxels, are alternated in an elegant and fully automated way. The proposed approach is compared with the Weighted Least Squares (WLS) approach and both methods are evaluated on a real fMRI experiment.
Oikonomou
Vangelis P
VP
Department of Computer Science, University of Ioannina, GR 45 110, Ioannina, Greece. voikonom@cs.uoi.gr
Tripoliti
Evanthia E
EE
Fotiadis
Dimitrios I
DI
D000465:Algorithms; D001499:Bayes Theorem; D001931:Brain Mapping / Q000379:methods; D016000:Cluster Analysis; D006801:Humans; D016018:Least-Squares Analysis; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008962:Models, Theoretical; D012044:Regression Analysis