Smoothing and cluster thresholding for cortical surface-based group analysis of fMRI data
Smoothing and cluster thresholding for cortical surface-based group analysis of fMRI data
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DOI：
10.1016/j.neuroimage.2006.07.036
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发表时间：
2006-12-01
期刊：
影响因子：
5.7
通讯作者：
Sereno, Martin I.
中科院分区：
文献类型：
--
作者：
Hagler, Donald J., Jr.;Saygin, Ayse Pinar;Sereno, Martin I.
基于皮质表面的fMRI数据分析已被证明是一种有用的方法，具有优于三维体积分析的几个优点。在3D分析中使用的许多统计方法可以适用于基于表面的分析。在FreeSurfer软件包的框架内，我们实现了一个基于表面的聚类大小排除方法，用于多次比较校正。此外，我们开发了一种新的方法，用于在皮质表面上生成感兴趣的区域，使用簇排除的滑动阈值，然后是簇生长。利用随机场理论估计了多个概率阈值的聚类大小限制，并用蒙特卡罗模拟进行了验证。RFT或簇大小模拟的先决条件是对数据的平滑性进行估计。为了估计组分析统计量的内在平滑性，独立于真实激活，我们对模拟噪声数据集进行了组分析。由于皮质表面网格的平滑通常使用迭代方法实现，而不是直接应用高斯模糊核，因此还需要确定等效高斯模糊核的宽度作为平滑步骤的函数。迭代平滑以前被建模为连续的热扩散，为预测等效核宽度提供了理论基础，但模型的预测没有得到实证检验。我们通过执行基于表面的平滑模拟生成了经验热扩散核宽度函数，并发现期望核宽度与实际核宽度之间存在很大差异。(c) 2006爱思唯尔公司版权所有。
Cortical surface-based analysis of fMRI data has proven to be a useful method with several advantages over 3-dimensional volumetric analyses. Many of the statistical methods used in 3D analyses can be adapted for use with surface-based analyses. Operating within the framework of the FreeSurfer soft-ware package, we have implemented a surface-based version of the cluster size exclusion method used for multiple comparisons correction. Furthermore, we have a developed a new method for generating regions of interest on the cortical surface using a sliding threshold of cluster exclusion followed by cluster growth. Cluster size limits for multiple probability thresholds were estimated using random field theory and validated with Monte Carlo simulation. A prerequisite of RFT or cluster size simulation is an estimate of the smoothness of the data. In order to estimate the intrinsic smoothness of group analysis statistics, independent of true activations, we conducted a group analysis of simulated noise data sets. Because smoothing on a cortical surface mesh is typically implemented using an iterative method, rather than directly applying a Gaussian blurring kernel, it is also necessary to determine the width of the equivalent Gaussian blurring kernel as a function of smoothing steps. Iterative smoothing has previously been modeled as continuous heat diffusion, providing a theoretical basis for predicting the equivalent kernel width, but the predictions of the model were not empirically tested. We generated an empirical heat diffusion kernel width function by performing surface-based smoothing simulations and found a large disparity between the expected and actual kernel widths. (c) 2006 Elsevier Inc. All rights reserved.