title: fMRI data analysis with nonstationary noise models: a Bayesian approach.
abstract: The assumption of noise stationarity in the functional magnetic resonance imaging (fMRI) data analysis may lead to the loss of crucial dynamic features of the data and thus result in inaccurate activation detection. In this paper, a Bayesian approach is proposed to analyze the fMRI data with two nonstationary noise models (the time-varying variance noise model and the fractional noise model). The covariance matrices of the time-varying variance noise and the fractional noise after wavelet transform are diagonal matrices. This property is investigated under the Bayesian framework. The Bayesian estimator not only gives an accurate estimate of the weights in general linear model, but also provides posterior probability of activation in a voxel and, hence, avoids the limitations (i.e., using only hypothesis testing) in the classical methods. The performance of the proposed Bayesian methods (under the assumption of different noise models) are compared with the ordinary least squares (OLS) and the weighted least squares (WLS) methods. Results from the simulation studies validate the superiority of the proposed approach to the OLS and WLS methods considering the complex noise structures in the fMRI data.
authors.affiliation: Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore. g0305766@nus.edu.sg
authors.forename: Huaien
authors.initials: H
authors.lastname: Luo
authors.forename: Sadasivan
authors.initials: S
authors.lastname: Puthusserypady
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D015233:Models, Statistical; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013269:Stochastic Processes