Visualization of Group Inference Data in Functional Neuroimaging
Visualization of Group Inference Data in Functional Neuroimaging
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DOI：
10.1007/s12021-008-9042-x
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发表时间：
2009-03-01
期刊：
影响因子：
3
通讯作者：
Glaescher, Jan
中科院分区：
文献类型：
--
作者：
Glaescher, Jan
虽然阈值统计参数图可以在功能神经成像研究中准确地解释效应的位置和空间范围，但它们在表征更复杂的实验效应方面受到一定的限制，例如析因设计中的交互作用。由此产生的绘制基础数据的必要性早已被认识到。统计参数映射(SPM)是一个广泛使用的软件包，用于分析功能神经成像数据，它提供了各种选项来可视化来自一级分析的数据。然而，如今，统计推论的主旨在于第二个层面，因此允许进行总体推论。不幸的是，从二级分析中可视化数据的选择相当稀少。RFXPlot是一个新的工具箱，旨在通过提供一系列全面的选项来从SPM的二级分析中绘制数据，从而缓解这一问题。这些包括平均效应大小(跨受试者)、平均拟合反应和与事件相关的血氧水平依赖(BOLD)时间进程的曲线图。所有数据都从基本的第一级分析中检索，并且体素选择可以被定制为在定义的搜索量内的每个对象中的最大效果。所有绘图配置都可以通过图形用户界面轻松配置，也可以通过脚本进行非交互配置。大量的绘图选项使rfxplot既适合数据探索，也适合为出版物生成高质量的数字。
While thresholded statistical parametric maps can convey an accurate account for the location and spatial extent of an effect in functional neuroimaging studies, their use is somewhat limited for characterizing more complex experimental effects, such as interactions in a factorial design. The resulting necessity for plotting the underlying data has long been recognized. Statistical Parametric Mapping (SPM) is a widely used software package for analyzing functional neuroimaging data that offers a variety of options for visualizing data from first level analyses. However, nowadays, the thrust of the statistical inference lies at the second level thus allowing for population inference. Unfortunately, the options for visualizing data from second level analyses are quite sparse. rfxplot is a new toolbox designed to alleviate this problem by providing a comprehensive array of options for plotting data from within second level analyses in SPM. These include graphs of average effect sizes (across subjects), averaged fitted responses and event-related blood oxygen level-dependent (BOLD) time courses. All data are retrieved from the underlying first level analyses and voxel selection can be tailored to the maximum effect in each subject within a defined search volume. All plot configurations can be easily configured via a graphical user-interface as well as non-interactively via a script. The large variety of plot options renders rfxplot suitable both for data exploration as well as producing high-quality figures for publications.