Modified test statistics by inter-voxel variance shrinkage with an application to fMRI
Modified test statistics by inter-voxel variance shrinkage with an application to fMRI
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DOI：
10.1093/biostatistics/kxn028
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发表时间：
2009-04-01
期刊：
影响因子：
2.1
通讯作者：
Bassett, Susan Spear
中科院分区：
文献类型：
--
作者：
Su, Shu-Chih;Caffo, Brian;Bassett, Susan Spear
Functional magnetic resonance imaging (fMRI) is a noninvasive technique which is commonly used to quantify changes in blood oxygenation and flow coupled to neuronal activation. One of the primary goals of fMRI studies is to identify localized brain regions where neuronal activation levels vary between groups. Single voxel t-tests have been commonly used to determine whether activation related to the protocol differs across groups. Due to the generally limited number of subjects within each study, accurate estimation of variance at each voxel is difficult. Thus, combining information across voxels is desirable in order to improve efficiency. Here, we construct a hierarchical model and apply an empirical Bayesian framework for the analysis of group fMRI data, employing techniques used in high-throughput genomic studies. The key idea is to shrink residual variances by combining information across voxels and subsequently to construct an improved test statistic. This hierarchical model results in a shrinkage of voxel-wise residual sample variances toward a common value. The shrunken estimator for voxel-specific variance components on the group analyses outperforms the classical residual error estimator in terms of mean-squared error. Moreover, the shrunken test statistic decreases false-positive rates when testing differences in brain contrast maps across a wide range of simulation studies. This methodology was also applied to experimental data regarding a cognitive activation task.
