Poline J B, Worsley K J, Evans A C, Friston K J
Wellcome Department of Cognitive Neurology, Institute of Neurology, London, United Kingdom.
Neuroimage. 1997 Feb;5(2):83-96. doi: 10.1006/nimg.1996.0248.
Within the framework of statistical mapping, there are up to now only two tests used to assess the regional significance in functional images. One is based on the magnitude of the foci and tends to detect high intensity signals, while the second is based on the spatial extent of regions defined by a simple thresholding of the statistical map, a test that is more sensitive to extended signals. The aim of this paper is to combine the two tests into a single test that is more sensitive to a wider range of signals. This combined test is based on an analytical approximation of the distribution of these two parameters (size and height) and is applied in the context of statistical maps. The risk of error in noise-only 2D or 3D volumes is assessed under a wide range of experimental conditions obtained by varying both the resolution of the map and the threshold at which clusters are defined. In addition, we have investigated this new test on simulated signals, and applied it to an experimental PET dataset. The experimental risk of error is close to the predicted one, and the overall sensitivity increases when analyzing a volume containing different types of signals.
在统计映射的框架内,到目前为止只有两种测试用于评估功能图像中的区域显著性。一种基于焦点的大小,倾向于检测高强度信号,而另一种基于通过对统计图进行简单阈值化定义的区域的空间范围,这种测试对扩展信号更敏感。本文的目的是将这两种测试合并为一种对更广泛信号范围更敏感的单一测试。这种组合测试基于这两个参数(大小和高度)分布的解析近似,并应用于统计图的背景下。通过改变地图分辨率和定义聚类的阈值获得的广泛实验条件下,评估了仅含噪声的二维或三维体积中的错误风险。此外,我们在模拟信号上研究了这种新测试,并将其应用于一个实验性PET数据集。实验错误风险接近预测值,并且在分析包含不同类型信号的体积时,整体灵敏度会提高。