背景:聚类方法越来越多地使用静息状态功能磁共振成像(rs-fMRI)将大脑区域划分为功能细分。然而,这些方法对(I)采用的精确算法高度敏感,(ii)他们的初始化,和(iii)用于从数据中发现最优聚类数量的度量。
方法:为了解决这些问题,我们开发了一个新的共识聚类证据积累(CC-EAC)框架,它有效地结合了使用rs-fMRI数据分割大脑区域的多种聚类方法。使用广泛的计算机模拟,我们研究了广泛使用的聚类算法的性能,包括K-means,分层,和谱聚类以及它们的组合。我们还检查了确定最佳聚类数量的五个客观标准的准确性和有效性:互信息,信息的变化,修改后的轮廓,兰特指数,和概率兰特指数。
结果:CC-EAC框架结合了基本K均值聚类(KC)和层次聚类(HC),以概率Rand指数作为选择最佳聚类数的标准,从模拟数据集中准确发现正确数量的集群。在rs-fMRI实验数据中,这些方法可靠地检测了辅助电机区域的功能细分,脑岛,顶内沟,角回,和纹状体。
方法:与传统方法不同,CC-EAC可以准确地确定rs-fMRI数据中稳定簇的最佳数量,并且对自由参数的初始化和选择具有鲁棒性。
结论:提出了一种新颖的CC-EAC框架来分割大脑区域,通过有效地结合多种聚类方法,识别rs-fMRI数据中的最优稳定功能簇。
BACKGROUND: Clustering methods are increasingly employed to segment brain regions into functional subdivisions using resting-state functional magnetic resonance imaging (rs-fMRI). However, these methods are highly sensitive to the (i) precise algorithms employed, (ii) their initializations, and (iii) metrics used for uncovering the optimal number of clusters from the data.
METHODS: To address these issues, we develop a novel
consensus clustering evidence accumulation (CC-EAC) framework, which effectively combines multiple clustering methods for segmenting brain regions using rs-fMRI data. Using extensive computer simulations, we examine the performance of widely used clustering algorithms including K-means, hierarchical, and spectral clustering as well as their combinations. We also examine the accuracy and validity of five objective criteria for determining the optimal number of clusters: mutual information, variation of information, modified silhouette, Rand index, and probabilistic Rand index.
RESULTS: A CC-EAC framework with a combination of base K-means clustering (KC) and hierarchical clustering (HC) with probabilistic Rand index as the criterion for choosing the optimal number of clusters, accurately uncovered the correct number of clusters from simulated datasets. In experimental rs-fMRI data, these methods reliably detected functional subdivisions of the supplementary motor area, insula, intraparietal sulcus, angular gyrus, and striatum.
METHODS: Unlike conventional approaches, CC-EAC can accurately determine the optimal number of stable clusters in rs-fMRI data, and is robust to initialization and choice of free parameters.
CONCLUSIONS: A novel CC-EAC framework is proposed for segmenting brain regions, by effectively combining multiple clustering methods and identifying optimal stable functional clusters in rs-fMRI data.