1 The Florey Institute of Neuroscience and Mental Health, Heidelberg, Australia.
2 Mary Mackillop Institute for Health Research, Australian Catholic University, Melbourne, Australia.
Brain Connect. 2019 Jun;9(5):399-414. doi: 10.1089/brain.2019.0668. Epub 2019 Apr 23.
Brain network modularity analysis has attracted increasing interest due to its capability in measuring the level of integration and segregation across subnetworks. Most studies have focused on extracting modules at a single level, although brain network modules are known to be organized in a hierarchical manner. A few techniques have been developed to extract hierarchical modularity in human functional brain networks using resting-state functional magnetic resonance imaging (fMRI) data; however, the focus of those methods is binary networks produced by applying arbitrary thresholds of correlation coefficients to the connectivity matrices. In this study, we propose a new multisubject spectral clustering technique, called group-level network hierarchical clustering (GNetHiClus), to extract the hierarchical structure of the functional network based on full weighted connectivity information. The most reliable results of hierarchical clustering are then estimated using a bootstrap aggregation algorithm. Specifically, we employ a voting-based ensemble method, that is, majority voting; random subsamples with replacement are created for clustering brain regions, which are further aggregated to select the most reliable clustering results. The proposed method is evaluated over a range of group sample sizes, based on resting-state fMRI data from the Human Connectome Project. Our results show that GNetHiClus can extract relatively consistent hierarchical network structures across a range of sample sizes investigated. In addition, the results demonstrate that GNetHiClus can hierarchically cluster brain functional networks into specialized subnetworks from upper-to-lower level, including the high-level cognitive and the low-level perceptual networks. Conversely, from lower-to-upper level, information processed by specialized lower level subnetworks is integrated into upper level for achieving optimal efficiency for brain functional communications. Importantly, these findings are consistent with the concept of network segregation and integration, suggesting that the proposed technique can be helpful to promote the understanding of brain network from a hierarchical point of view.
脑网络模块性分析因其能够测量子网间的整合和分离程度而受到越来越多的关注。大多数研究都集中在提取单一层次的模块，尽管已知脑网络模块是按层次组织的。已经开发了一些技术来使用静息态功能磁共振成像 (fMRI) 数据提取人类功能脑网络的层次模块性；然而，这些方法的重点是通过将相关系数的任意阈值应用于连接矩阵来生成二进制网络。在这项研究中，我们提出了一种新的多主体谱聚类技术，称为组水平网络层次聚类 (GNetHiClus)，用于根据全加权连接信息提取功能网络的层次结构。然后使用引导聚合算法估计层次聚类的最可靠结果。具体来说，我们采用基于投票的集成方法，即多数投票；为聚类脑区创建带替换的随机子样本，并进一步聚合以选择最可靠的聚类结果。该方法基于人类连接组计划的静息态 fMRI 数据，在一系列组样本大小上进行了评估。我们的结果表明，GNetHiClus 可以在研究的一系列样本大小范围内提取相对一致的层次网络结构。此外，结果表明，GNetHiClus 可以将脑功能网络层次聚类为从高级认知到低级感知的专门子网，包括高级认知和低级感知网络。相反，从低级到高级，专门的低级子网处理的信息被整合到高级子网中，以实现脑功能通信的最佳效率。重要的是，这些发现与网络分离和整合的概念一致，表明该技术有助于从层次角度促进对脑网络的理解。
