Wang Yanlu, Msghina Mussie, Li Tie-Qiang
Department of Clinical Science, Intervention, and Technology, Karolinska Institute Stockholm, Sweden.
Department of Clinical Neuroscience, Karolinska University Hospital Huddinge, Sweden.
Front Hum Neurosci. 2016 Mar 8;10:75. doi: 10.3389/fnhum.2016.00075. eCollection 2016.
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.
层次聚类是一种用于对复杂数据进行分类的实用数据驱动方法，已被用于分析静息态功能磁共振成像（fMRI）数据，并在非常大的尺度上推导人类大脑的功能网络，例如整个视觉或感觉运动皮层。在本研究中，我们开发了一种基于体素的全脑层次聚类框架，以对不同细节水平的组平均静息态fMRI数据进行多阶段分析。利用该框架，我们特别详细地分析了体感运动和视觉系统，并构建了相应的子树状图，这些子树状图与先前临床和实验研究中已知的模块化组织一致。该框架为静息态fMRI数据的数据驱动分析提供了一个有用的工具，以深入了解亚单位之间的层次组织和功能调制程度。
