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

被引:10
|
作者
Wang, Yanlu [1 ]
Msghina, Mussie [2 ]
Li, Tie-Qiang [1 ,3 ]
机构
[1] Karolinska Inst, Dept Clin Sci Intervent & Technol, Stockholm, Sweden
[2] Karolinska Univ Hosp, Dept Clin Neurosci, Huddinge, Sweden
[3] Karolinska Univ Hosp, Dept Med Phys, Huddinge, Sweden
来源
FRONTIERS IN HUMAN NEUROSCIENCE | 2016年 / 10卷
基金
瑞典研究理事会;
关键词
hierarchical clustering; resting-state networks; intra-network connectivity; somatosensory network; visual network; resting-state fMRI; AUDITORY ASSOCIATION CORTEX; HUMAN VISUAL-CORTEX; RETINOTOPIC ORGANIZATION; BRAIN NETWORKS; PARIETAL LOBE; CONNECTIVITY; AREAS; SYSTEMS; MOTOR; RELIABILITY;
D O I
10.3389/fnhum.2016.00075
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
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.
引用
收藏
页数:13
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