Oikonomou Vangelis P, Tripoliti Evanthia E, Fotiadis Dimitrios I
Department of Computer Science, University of Ioannina, Ioannina GR 45110, Greece.
IEEE Trans Inf Technol Biomed. 2010 May;14(3):664-74. doi: 10.1109/TITB.2009.2039712. Epub 2010 Jan 29.
In this paper, the Bayesian framework is used for the analysis of functional MRI (fMRI) data. Two algorithms are proposed to deal with the nonstationarity of the noise. The first algorithm is based on the temporal analysis of the data, while the second algorithm is based on the spatiotemporal analysis. Both algorithms estimate the variance of the noise across the images and the voxels. The first algorithm is based on the generalized linear model (GLM), while the second algorithm is based on a spatiotemporal version of it. In the GLM, an extended design matrix is used to deal with the presence of the drift in the fMRI time series. 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 the 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 voxels, are interchanged in an elegant and fully automated way. The performance of the proposed algorithms (under the assumption of different noise models) is compared with the weighted least-squares (WLSs) method. Results using simulated and real data indicate the superiority of the proposed approach compared to the WLS method, thus taking into account the complex noise structure of the fMRI time series.
在本文中，贝叶斯框架被用于功能磁共振成像（fMRI）数据的分析。提出了两种算法来处理噪声的非平稳性。第一种算法基于数据的时间分析，而第二种算法基于时空分析。两种算法都估计图像和体素上噪声的方差。第一种算法基于广义线性模型（GLM），而第二种算法基于其时空版本。在GLM中，使用扩展设计矩阵来处理fMRI时间序列中的漂移。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，其中回归系数的估计以及图像和体素上噪声的方差分量的估计以一种优雅且完全自动化的方式交替进行。将所提出算法（在不同噪声模型假设下）的性能与加权最小二乘法（WLSs）进行比较。使用模拟数据和真实数据的结果表明，与WLS方法相比，所提出的方法具有优越性，从而考虑到了fMRI时间序列复杂的噪声结构。
