Bayesian second-level analysis of functional magnetic resonance images
Bayesian second-level analysis of functional magnetic resonance images
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DOI：
10.1016/s1053-8119(03)00443-9
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发表时间：
2003-10-01
期刊：
影响因子：
5.7
通讯作者：
Lohmann, G
中科院分区：
文献类型：
--
作者：
Neumann, J;Lohmann, G
提出了一种新的基于贝叶斯统计的功能磁共振数据二级分析方法。我们的方法不需要在第一级分析中计算代价高昂的贝叶斯模型。相反，单个对象的建模是通过通常应用的通用线性模型来实现的。在单个受试者的参数估计的基础上，我们计算后验概率图和受试者群体中感兴趣的影响大小的后验概率图。将该方法与传统的基于t统计量的分析方法进行了比较，结果表明新方法对异常值具有更强的鲁棒性。此外，我们的方法克服了无效假设显著性检验的一些严重问题，例如需要纠正多次比较，并促进了难以用经典推理来表述的推断。(C)2003 Elsevier Inc.保留所有权利。
We propose a new method for the second-level analysis of functional MRI data based on Bayesian statistics. Our method does not require a computationally costly Bayesian model on the first level of analysis. Rather, modeling for single subjects is realized by means of the commonly applied General Linear Model. On the basis of the resulting parameter estimates for single subjects we calculate posterior probability maps and maps of the effect size for effects of interest in groups of subjects. A comparison of this method with the conventional analysis based on t statistics shows that the new approach is more robust against outliers. Moreover, our method overcomes some of the severe problems of null hypothesis significance tests such as the need to correct for multiple comparisons and facilitates inferences which are hard to formulate in terms of classical inferences. (C) 2003 Elsevier Inc. All rights reserved.