Goldsmith Jeff, Huang Lei, Crainiceanu Ciprian M
Department of Biostatistics, Columbia University School of Public Health.
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health.
J Comput Graph Stat. 2014 Jan 1;23(1):46-64. doi: 10.1080/10618600.2012.743437.
We develop scalar-on-image regression models when images are registered multidimensional manifolds. We propose a fast and scalable Bayes inferential procedure to estimate the image coefficient. The central idea is the combination of an Ising prior distribution, which controls a latent binary indicator map, and an intrinsic Gaussian Markov random field, which controls the smoothness of the nonzero coefficients. The model is fit using a single-site Gibbs sampler, which allows fitting within minutes for hundreds of subjects with predictor images containing thousands of locations. The code is simple and is provided in less than one page in the Appendix. We apply this method to a neuroimaging study where cognitive outcomes are regressed on measures of white matter microstructure at every voxel of the corpus callosum for hundreds of subjects.
当图像被配准到多维流形时，我们开发了图像上标量回归模型。我们提出了一种快速且可扩展的贝叶斯推理程序来估计图像系数。核心思想是将控制潜在二元指示图的伊辛先验分布与控制非零系数平滑度的内在高斯马尔可夫随机场相结合。该模型使用单站点吉布斯采样器进行拟合，对于数百名受试者，在包含数千个位置的预测图像的情况下，几分钟内即可完成拟合。代码很简单，附录中不到一页纸就给出了。我们将此方法应用于一项神经影像学研究，其中对数百名受试者胼胝体每个体素处的白质微观结构测量值进行认知结果回归分析。
