Controlling the error in FMRI: Hypothesis testing or set estimation?
Controlling the error in FMRI: Hypothesis testing or set estimation?
复制标题
控制 FMRI 中的误差：假设检验还是集合估计？
DOI：
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发表时间：
2008
期刊：
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通讯作者：
R. Nowak
中科院分区：
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作者：
Zachary T. Harmany;R. Willett;Aarti Singh;R. Nowak
本文描述了一种新的方法和相关的理论分析，用于快速准确地从功能MRI数据中提取激活区域。目前使用的大多数fMRI数据分析方法采用假设检验方法，其中将单个体素或体素簇中的BOLD信号与阈值进行比较。为了获得统计上有意义的结果，测试必须限制在非常少的体素/簇上，或者必须将阈值设置得非常高。此外，体素化引入了部分体积效应（PVE），这在活动定位中呈现出持久的错误，没有任何测试程序可以克服。本文放弃了多假设检验方法，提出了一种基于集合估计的新方法。我们的方法旨在控制误差的空间体积，而不是试图控制误差的概率。为此，我们将激活区域视为所考虑的统计参数图（SPM）的水平集。在存在噪声的情况下，水平集的估计被视为一个统计推断问题。我们提出了一个水平集估计器，并表明误差的预期体积与体素的边长成正比。由于pve是不可避免的，并且会产生相同量级的误差，因此这是可实现的最小误差量。实验证明了这种新理论和方法的优越性，以及控制误差量而不是误差概率的统计合理性。
This paper describes a new methodology and associated theoretical analysis for rapid and accurate extraction of activation regions from functional MRI data. Most fMRI data analysis methods in use today adopt a hypothesis testing approach, in which the BOLD signals in individual voxels or clusters of voxels are compared to a threshold. In order to obtain statistically meaningful results, the testing must be limited to very small numbers of voxels/clusters or the threshold must be set extremely high. Furthermore, voxelization introduces partial volume effects (PVE), which present a persistent error in the localization of activity that no testing procedure can overcome. We abandon the multiple hypothesis testing approach in this paper, and instead advocate a new approach based on set estimation. Rather then attempting to control the probability of error, our method aims to control the spatial volume of the error. To do this, we view the activation regions as level sets of the statistical parametric map (SPM) under consideration. The estimation of the level sets, in the presence of noise, is then treated as a statistical inference problem. We propose a level set estimator and show that the expected volume of the error is proportional to the sidelength of a voxel. Since PVEs are unavoidable and produce errors of the same order, this is the smallest error volume achievable. Experiments demonstrate the advantages of this new theory and methodology, and the statistical reasonability of controlling the volume of the error rather than the probability of error.