Studying Sub-Dendrograms of Resting-State Functional Networks with Voxel-Wise Hierarchical Clustering

摘要: Hierarchical clustering is a useful data-driven approach to classify complex data and has been used analyze resting-state functional magnetic resonance imaging (fMRI) derive networks of the human brain at very large scale, such as entire visual or sensory-motor cortex. In this study, we developed voxel-wise, whole-brain hierarchical framework perform multi-stage analysis group-averaged fMRI in different levels detail. With analyzed particularly somatosensory motor systems fine details constructed corresponding sub-dendrograms, which corroborate consistently with known modular organizations from previous clinical experimental studies. The provides tool for gain insight into organization degree modulation among sub-units.