Robust smoothness estimation in statistical parametric maps using standardized residuals from the general linear model
Robust smoothness estimation in statistical parametric maps using standardized residuals from the general linear model
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DOI：
10.1006/nimg.1999.0508
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发表时间：
1999-12-01
期刊：
影响因子：
5.7
通讯作者：
Worsley, KJ
中科院分区：
文献类型：
--
作者：
Kiebel, SJ;Poline, JB;Worsley, KJ
使用基于体素的方法评估功能成像中的显著激活通常依赖于从高斯随机场理论导出的结果。这些结果解决了多重比较问题，并假设数据的空间相关性或平滑性是已知的或可以估计的。最终结果（即，与局部最大值、聚类或聚类集相关的P值）严重依赖于该评估，该评估应尽可能准确和可靠。在统计参数映射（SPM）（SPM 94，SPM 95）的一些早期实现中，平滑度是在高斯化t场（Gt-f）上评估的，这些t场通常不含生理信号。这种技术有两个局限性。首先，估计是不稳定的（估计器的方差远非可忽略），其次，Gt-f中的生理信号将使估计有偏差。在本文中，我们描述了一种估计方法，克服了这些缺点。新的方法涉及估计的平滑度的标准化的残差字段近似的平滑度相关联的t-字段的分量字段。知道这些分量场的平滑度是重要的，因为它允许计算除了t场或Gt-f之外的统计场的校正P值（例如，F-map），并避免由于偏离零假设而产生的偏差。我们验证了模拟数据的方法，并证明它使用功能性MRI研究的数据。(C)北京：科学出版社.
The assessment of significant activations in functional imaging using voxel-based methods often relies on results derived from the theory of Gaussian random fields. These results solve the multiple comparison problem and assume that the spatial correlation or smoothness of the data is known or can be estimated. End results (i.e., P values associated with local maxima, clusters, or sets of clusters) critically depend on this assessment, which should be as exact and as reliable as possible. In some earlier implementations of statistical parametric mapping (SPM) (SPM94, SPM95) the smoothness was assessed on Gaussianized t-fields (Gt-f) that are not generally free of physiological signal. This technique has two limitations. First, the estimation is not stable (the variance of the estimator being far from negligible) and, second, physiological signal in the Gt-f will bias the estimation. In this paper, we describe an estimation method that overcomes these drawbacks. The new approach involves estimating the smoothness of standardized residual fields which approximates the smoothness of the component fields of the associated t-field. Knowing the smoothness of these component fields is important because it allows one to compute corrected P values for statistical fields other than the t-field or the Gt-f (e.g., the F-map) and eschews bias due to deviation from the null hypothesis. We validate the method on simulated data and demonstrate it using data from a functional MRI study. (C) 1999 Academic Press.