Neumann Jane, Lohmann Gabriele
Max-Planck-Institute of Cognitive Neuroscience, Stephanstrasse 1a, D-04103, Leipzig, Germany.
Neuroimage. 2003 Oct;20(2):1346-55. doi: 10.1016/S1053-8119(03)00443-9.
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.
我们提出了一种基于贝叶斯统计的功能磁共振成像（fMRI）数据二级分析新方法。我们的方法在一级分析中不需要计算成本高昂的贝叶斯模型。相反，单受试者建模通过常用的一般线性模型来实现。基于单受试者得到的参数估计，我们计算后验概率图以及感兴趣效应在受试者组中的效应量图。将该方法与基于t统计的传统分析进行比较表明，新方法对异常值更具鲁棒性。此外，我们的方法克服了零假设显著性检验的一些严重问题，比如需要进行多重比较校正，并且便于进行难以用经典推断表述的推断。
