Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, 12 Queen Square, London, UK.
Neuroimage. 2010 Jan 1;49(1):217-24. doi: 10.1016/j.neuroimage.2009.08.051. Epub 2009 Sep 2.
This technical note describes the construction of posterior probability maps (PPMs) for Bayesian model selection (BMS) at the group level. This technique allows neuroimagers to make inferences about regionally specific effects using imaging data from a group of subjects. These effects are characterised using Bayesian model comparisons that are analogous to the F-tests used in statistical parametric mapping, with the advantage that the models to be compared do not need to be nested. Additionally, an arbitrary number of models can be compared together. This note describes the integration of the Bayesian mapping approach with a random effects analysis model for BMS using group data. We illustrate the method using fMRI data from a group of subjects performing a target detection task.
本技术说明描述了如何在群组水平上构建贝叶斯模型选择（BMS）的后验概率图（PPM）。该技术允许神经成像研究人员使用来自一组受试者的成像数据对局部特定效应进行推断。这些效应是通过类似于统计参数映射中使用的 F 检验的贝叶斯模型比较来描述的，其优点是不需要嵌套要比较的模型。此外，还可以同时比较任意数量的模型。本说明描述了使用群组数据将贝叶斯映射方法与 BMS 的随机效应分析模型集成。我们使用执行目标检测任务的一组受试者的 fMRI 数据来说明该方法。
