Jbabdi S, Woolrich M W, Behrens T E J
Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, John Radcliffe Hospital, Oxford, Oxford, UK.
Neuroimage. 2009 Jan 15;44(2):373-84. doi: 10.1016/j.neuroimage.2008.08.044. Epub 2008 Sep 19.
We propose a hierarchical infinite mixture model approach to address two issues in connectivity-based parcellations: (i) choosing the number of clusters, and (ii) combining data from different subjects. In a Bayesian setting, we model voxel-wise anatomical connectivity profiles as an infinite mixture of multivariate Gaussian distributions, with a Dirichlet process prior on the cluster parameters. This type of prior allows us to conveniently model the number of clusters and estimate its posterior distribution directly from the data. An important benefit of using Bayesian modelling is the extension to multiple subjects clustering via a hierarchical mixture of Dirichlet processes. Data from different subjects are used to infer on class parameters and the number of classes at individual and group level. Such a method accounts for inter-subject variability, while still benefiting from combining different subjects data to yield more robust estimates of the individual clusterings.
我们提出一种分层无限混合模型方法，以解决基于连通性的脑区划分中的两个问题：（i）选择聚类数量，以及（ii）合并来自不同受试者的数据。在贝叶斯框架下，我们将体素层面的解剖连通性概况建模为多元高斯分布的无限混合，并对聚类参数采用狄利克雷过程先验。这种先验使我们能够方便地对聚类数量进行建模，并直接从数据中估计其后验分布。使用贝叶斯建模的一个重要好处是通过狄利克雷过程的分层混合扩展到多受试者聚类。来自不同受试者的数据用于推断个体和群体层面的类别参数及类别数量。这种方法考虑了受试者间的变异性，同时仍受益于合并不同受试者的数据，以对个体聚类产生更稳健的估计。
