COMPARING FUNCTIONAL (PET) IMAGES - THE ASSESSMENT OF SIGNIFICANT CHANGE
COMPARING FUNCTIONAL (PET) IMAGES - THE ASSESSMENT OF SIGNIFICANT CHANGE
复制标题
DOI：
10.1038/jcbfm.1991.122
复制
发表时间：
1991-07-01
影响因子：
6.3
通讯作者：
FRACKOWIAK, RSJ
中科院分区：
文献类型：
--
作者：
FRISTON, KJ;FRITH, CD;FRACKOWIAK, RSJ
统计参数图（SPM）是定位局部脑活动差异的潜在有力方法。 由于在评估这些地图的重要性方面存在不确定性，这一潜力受到限制。 在本报告中，我们描述了一种可能部分解决此问题的方法。 使用SPM作为变化意义的图像和使用它们来识别显着变化的焦点之间的区别。 在第一种情况下，SPM可以作为一个单一的数学对象，其综合意义无选择地报告。 或者，SPM构成了大脑上的大量重复测量。 为了拒绝零假设，即在特定位置没有发生变化，必须进行阈值调整，以说明所进行的大量比较。 这种调整取决于SPM的平滑度。 平滑度可以凭经验确定，并用于计算识别显著病灶所需的阈值。 该方法将SPM建模为平稳随机过程。 的理论和应用程序说明使用统一的幻影图像和数据从四个正常受试者的言语流畅性激活研究。
Statistical parametric maps (SPMs) are potentially powerful ways of localizing differences in regional cerebral activity. This potential is limited by uncertainties in assessing the significance of these maps. In this report, we describe an approach that may partially resolve this issue. A distinction is made between using SPMs as images of change significance and using them to identify foci of significant change. In the first case, the SPM can be reported nonselectively as a single mathematical object with its omnibus significance. Alternatively, the SPM constitutes a large number of repeated measures over the brain. To reject the null hypothesis, that no change has occurred at a specific location, a threshold adjustment must be made that accounts for the large number of comparisons made. This adjustment is shown to depend on the SPM's smoothness. Smoothness can be determined empirically and be used to calculate a threshold required to identify significant foci. The approach models the SPM as a stationary stochastic process. The theory and applications are illustrated using uniform phantom images and data from a verbal fluency activation study of four normal subjects.