Title |
Studying Sub-Dendrograms of Resting-State Functional Networks with Voxel-Wise Hierarchical Clustering
|
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Published in |
Frontiers in Human Neuroscience, March 2016
|
DOI | 10.3389/fnhum.2016.00075 |
Pubmed ID | |
Authors |
Yanlu Wang, Mussie Msghina, Tie-Qiang Li |
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. |
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