Bayesian fMRI data analysis with sparse spatial basis function priors
Bayesian fMRI data analysis with sparse spatial basis function priors
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DOI：
10.1016/j.neuroimage.2006.10.005
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发表时间：
2007-02-01
期刊：
影响因子：
5.7
通讯作者：
Penny, William D.
中科院分区：
文献类型：
--
作者：
Flandin, Guillaume;Penny, William D.
在之前的工作中，我们描述了一种用于分析脑功能磁共振成像（fMRI）数据的空间正则化广义线性模型（GLM），其中后验概率图（PPM）用于表征区域特定效应。空间正则化是通过拉普拉斯核矩阵在回归系数上定义的，并体现了诱发反应在空间上连续且局部均匀的先验知识。在本文中，我们建议通过使用稀疏空间基础函数（SSBF）指定空间先验来完善这个贝叶斯框架。这些是通过分层概率模型定义的，该模型在反转时会自动选择适当的基函数子集。该方法包括非线性小波收缩作为特例。与拉普拉斯空间先验相比，SSBF 允许信号平滑度的空间变化，计算效率更高，并且对异方差噪声具有鲁棒性。结果显示在合成数据和事件相关功能磁共振成像实验的数据上。 (c) 2006 Elsevier Inc. 保留所有权利。
In previous work we have described a spatially regularised General Linear Model (GLM) for the analysis of brain functional Magnetic Resonance Imaging (fMRI) data where Posterior Probability Maps (PPMs) are used to characterise regionally specific effects. The spatial regularisation is defined over regression coefficients via a Laplacian kernel matrix and embodies prior knowledge that evoked responses are spatially contiguous and locally homogeneous. In this paper we propose to finesse this Bayesian framework by specifying spatial priors using Sparse Spatial Basis Functions (SSBFs). These are defined via a hierarchical probabilistic model which, when inverted, automatically selects an appropriate subset of basis functions. The method includes non-linear wavelet shrinkage as a special case. As compared to Laplacian spatial priors, SSBFs allow for spatial variations in signal smoothness, are more computationally efficient and are robust to heteroscedastic noise. Results are shown on synthetic data and on data from an event-related fMRI experiment. (c) 2006 Elsevier Inc. All rights reserved.