Modified test statistics by inter-voxel variance shrinkage with an application to f MRI.

Biostatistics
Shu-Chih SuSusan Spear Bassett

Abstract

Functional magnetic resonance imaging (f MRI) 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 f MRI 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 f MRI 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 er...Continue Reading

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Citations

Nov 26, 2015·NeuroImage·Martin A LindquistTor D Wager
Jun 1, 2014·NeuroImage·Haochang ShouCiprian M Crainiceanu
Mar 10, 2015·Wiley Interdisciplinary Reviews. Computational Statistics·Linlin ZhangMarina Vannucci
May 8, 2021·NeuroImage·Guoqing WangMartin A Lindquist

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