Oikonomou Vangelis P, Tripoliti Evanthia E, Fotiadis Dimitrios I
Department of Computer Science, University of Ioannina, GR 45 110, Ioannina, Greece.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:4444-8. doi: 10.1109/IEMBS.2009.5334281.
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.
在这项工作中，贝叶斯框架被用于功能性磁共振成像（fMRI）数据的分析。所提出方法的新颖之处在于引入了一个时空模型，用于估计图像和体素中噪声的方差。所提出的方法基于广义线性模型（GLM）的时空版本。为了估计GLM的回归参数以及噪声的方差分量，采用了变分贝叶斯（VB）方法。VB方法的使用产生了一种迭代算法，其中在图像和体素之间，以一种优雅且完全自动化的方式交替进行回归系数的估计和噪声方差分量的估计。将所提出的方法与加权最小二乘法（WLS）方法进行了比较，并在一个实际的fMRI实验中对这两种方法进行了评估。
