Stanberry Larissa, Murua Alejandro, Cordes Dietmar
Department of Statistics, University of Washington, Seattle, Washington 98195-4322, USA.
Hum Brain Mapp. 2008 Apr;29(4):422-40. doi: 10.1002/hbm.20397.
An unsupervised stochastic clustering method based on the ferromagnetic Potts spin model is introduced as a powerful tool to determine functionally connected regions. The method provides an intuitively simple approach to clustering and makes no assumptions of the number of clusters in the data or their underlying distribution. The performance of the method and its dependence on the intrinsic parameters (size of the neighborhood, form of the interaction term, etc.) is investigated on the simulated data and real fMRI data acquired during a conventional periodic finger tapping task. The merits of incorporating Euclidean information into the connectivity analysis are discussed. The ability of the Potts model clustering to uncover the hidden structure in the complex data is demonstrated through its application to the resting-state data to determine functional connectivity networks of the anterior and posterior cingulate cortices for the group of nine healthy male subjects.
介绍了一种基于铁磁Potts自旋模型的无监督随机聚类方法，作为确定功能连接区域的有力工具。该方法为聚类提供了一种直观简单的方法，并且不对数据中的聚类数量或其潜在分布做任何假设。在模拟数据和传统周期性手指轻敲任务期间采集的真实功能磁共振成像（fMRI）数据上，研究了该方法的性能及其对内在参数（邻域大小、相互作用项形式等）的依赖性。讨论了将欧几里得信息纳入连通性分析的优点。通过将Potts模型聚类应用于静息态数据，以确定九名健康男性受试者组的前扣带回和后扣带回皮质的功能连接网络，证明了Potts模型聚类揭示复杂数据中隐藏结构的能力。
