Combined spatial and non-spatial prior for inference on MRI time-series
Combined spatial and non-spatial prior for inference on MRI time-series
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DOI：
10.1016/j.neuroimage.2008.12.027
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发表时间：
2009-04-15
期刊：
影响因子：
5.7
通讯作者：
Woolrich, Mark W.
中科院分区：
文献类型：
--
作者：
Groves, Adrian R.;Chappell, Michael A.;Woolrich, Mark W.
当对FMRI和其他MRI时间序列数据建模时，基于自适应空间平滑先验的贝叶斯方法是对预平滑数据使用标准广义线性模型（GLM）的令人信服的替代方法。贝叶斯方法的另一个好处是，生物物理先验信息可以以一种有原则的方式纳入；然而，这种对参数的固定非空间先验的要求通常会排除对同一参数使用空间正则化。我们开发了一个基于高斯过程的先验来应用自适应空间正则化，同时仍然确保在每个体素上正确应用固定的生物物理先验。参数化协方差矩阵提供了对方差（对角线元素）和体素间相关性（由于非对角线元素）的单独控制。分析使用证据优化（EO）进行，对一些参数使用变分贝叶斯（VB）更新。该方法也可以应用于非线性正演模型，通过使用以最新参数估计为中心的线性泰勒展开。将该方法应用于具有受限血流动力学响应函数（HRF）形状模型的FMRI中，与单独使用非空间或空间平滑先验相比，仿真结果显示拟合效果更好。我们还使用非线性灌注模型分析了多倒动脉自旋标记数据，以估计脑血流量和丸到达时间。通过结合两种类型的先验信息，这种新的先验在更广泛的情况下表现得比单独的先验都好，并且在两种类型的先验信息相关时提供更好的估计。(C) 2008爱思唯尔公司版权所有。
When modelling FMRI and other MRI time-series data, a Bayesian approach based on adaptive spatial smoothness priors is a compelling alternative to using a standard generalized linear model (GLM) on presmoothed data. Another benefit of the Bayesian approach is that biophysical prior information can be incorporated in a principled manner; however, this requirement for a fixed non-spatial prior on a parameter would normally preclude using spatial regularization on that same parameter. We have developed a Gaussian-process-based prior to apply adaptive spatial regularization while still ensuring that the fixed biophysical prior is correctly applied on each voxel. A parameterized covariance matrix provides separate control over the variance (the diagonal elements) and the between-voxel correlation (due to off-diagonal elements). Analysis proceeds using evidence optimization (EO), with variational Bayes (VB) updates used for some parameters. The method can also be applied to non-linear forward models by using a linear Taylor expansion centred on the latest parameter estimates. Applying the method to FMRI with a constrained haemodynamic response function (HRF) shape model shows improved fits in simulations, compared to using either the non-spatial or spatial-smoothness prior alone. We also analyse multi-inversion arterial spin labelling data using a non-linear perfusion model to estimate cerebral blood flow and bolus arrival time. By combining both types of prior information, this new prior performs consistently well across a wider range of situations than either prior alone, and provides better estimates when both types of prior information are relevant. (C) 2008 Elsevier Inc. All rights reserved.