Motivated by the need to model joint dependence between regions of interest in functional neuroconnectivity for efficient inference, we propose a new sampling-based Bayesian clustering approach for covariance structures of high-dimensional Gaussian outcomes. The key technique is based on a Dirichlet process that clusters covariance sub-matrices into independent groups of outcomes, thereby naturally inducing sparsity in the whole brain connectivity matrix. A new split-merge algorithm is employed to improve the mixing of the Markov chain sampling that is shown empirically to recover both uniform and Dirichlet partitions with high accuracy. We investigate the empirical performance of the proposed method through extensive simulations. Finally, the proposed approach is used to group regions of interest into functionally independent groups in the Autism Brain Imaging Data Exchange participants with autism spectrum disorder and attention-deficit/hyperactivity disorder.

翻译：受高效推断需求驱动，针对功能性神经连接中感兴趣区域间的联合依赖关系建模问题，我们提出一种基于采样的贝叶斯聚类新方法，用于高维高斯结果的协方差结构分析。其核心技术基于狄利克雷过程，通过将协方差子矩阵聚类成独立的结果组，自然诱导全脑连接矩阵的稀疏性。为改进马尔可夫链采样的混合性能，采用了新的分裂-合并算法，该算法在经验上能够高精度恢复均匀划分和狄利克雷划分。通过大量模拟研究验证了所提方法的实证性能。最后，将该方法应用于自闭症脑成像数据交换库中自闭症谱系障碍及注意力缺陷/多动障碍被试群体的感兴趣区域分组，以识别功能独立的脑区集群。