Studying sub-dendrograms of resting-state functional networks with voxel-wise hierarchical clustering

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. © 2016 wang,msghina and li.