title: Studying Sub-Dendrograms of Resting-State Functional Networks with Voxel-Wise Hierarchical Clustering.
abstract: Hierarchical clustering is a useful data-driven approach to classify complex data and has been used to analyze resting-state functional magnetic resonance imaging (fMRI) data and derive functional networks of the human brain at very large scale, such as the entire visual or sensory-motor cortex. In this study, we developed a voxel-wise, whole-brain hierarchical clustering framework to perform multi-stage analysis of group-averaged resting-state fMRI data in different levels of detail. With the framework we analyzed particularly the somatosensory motor and visual systems in fine details and constructed the corresponding sub-dendrograms, which corroborate consistently with the known modular organizations from previous clinical and experimental studies. The framework provides a useful tool for data-driven analysis of resting-state fMRI data to gain insight into the hierarchical organization and degree of functional modulation among the sub-units.
authors.affiliation: Department of Clinical Science, Intervention, and Technology, Karolinska Institute Stockholm, Sweden.
authors.forename: Yanlu
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Department of Clinical Neuroscience, Karolinska University Hospital Huddinge, Sweden.
authors.forename: Mussie
authors.initials: M
authors.lastname: Msghina
authors.affiliation: Department of Clinical Science, Intervention, and Technology, Karolinska InstituteStockholm, Sweden; Department of Medical Physics, Karolinska University HospitalHuddinge, Sweden.
authors.forename: Tie-Qiang
authors.initials: TQ
authors.lastname: Li
keywords: hierarchical clustering; intra-network connectivity; resting-state fMRI; resting-state networks; somatosensory network; visual network