Kim Seyoung, Smyth Padhraic, Stern Hal
Bren School of Information and Computer Sciences University of California, Irvine, CA 92697-3425, USA.
Med Image Comput Comput Assist Interv. 2006;9(Pt 2):217-24. doi: 10.1007/11866763_27.
Traditional techniques for statistical fMRI analysis are often based on thresholding of individual voxel values or averaging voxel values over a region of interest. In this paper we present a mixture-based response-surface technique for extracting and characterizing spatial clusters of activation patterns from fMRI data. Each mixture component models a local cluster of activated voxels with a parametric surface function. A novel aspect of our approach is the use of Bayesian nonparametric methods to automatically select the number of activation clusters in an image. We describe an MCMC sampling method to estimate both parameters for shape features and the number of local activations at the same time, and illustrate the application of the algorithm to a number of different fMRI brain images.
传统的功能磁共振成像（fMRI）统计分析技术通常基于对单个体素值进行阈值处理或在感兴趣区域内对体素值求平均。在本文中，我们提出了一种基于混合的响应曲面技术，用于从fMRI数据中提取和表征激活模式的空间簇。每个混合成分用一个参数化曲面函数对激活体素的局部簇进行建模。我们方法的一个新颖之处在于使用贝叶斯非参数方法来自动选择图像中激活簇的数量。我们描述了一种马尔可夫链蒙特卡罗（MCMC）采样方法，用于同时估计形状特征的参数和局部激活的数量，并举例说明了该算法在一些不同的fMRI脑图像上的应用。
