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Title: A novel method for extracting hierarchical functional subnetworks based on a multi-subject spectral clustering approach.
Austin Authors: Liang, Xiaoyun;Yeh, Chun-Hung;Connelly, Alan;Calamante, Fernando
Affiliation: Australian Catholic University, 95359, Mary Mackillop Institute for Health Research, Melbourne, Victoria, Australia
Florey Institute of Neuroscience and Mental Health, Imaging Division, Melbourne, Victoria, Australia
The Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia
The University of Melbourne, The Florey Department of Neuroscience and Mental Health, Melbourne, Victoria, Australia
The University of Sydney, Sydney Imaging, and School of Aeronautical, Mechanical and Mechatronic Engineering, Sydney, New South Wales, Australia
Issue Date: Jun-2019 2019-04-23
Publication information: Brain connectivity 2019; 9(5): 399-414
Abstract: 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 MRI 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 multi-subject 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, i.e. 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 size 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 are 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.
DOI: 10.1089/brain.2019.0668
ORCID: 0000-0002-7550-3142
PubMed URL: 30880430
Type: Journal Article
Subjects: Brain connectivity
Brain networks
Resting-state functional connectivity Magnetic Resonance Imaging (R-fMRI)
Resting-state networks
Appears in Collections:Journal articles

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