Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA.
J Neurosci Methods. 2012 Oct 15;211(1):94-102. doi: 10.1016/j.jneumeth.2012.08.016. Epub 2012 Aug 23.
In this study, a correlation matrix based hierarchical clustering (CMBHC) method is introduced to extract multiple correlation patterns from resting-state functional magnetic resonance imaging (fMRI) data. It was applied to spontaneous fMRI signals acquired from anesthetized rats, and the results were then compared with those obtained using independent component analysis (ICA), one of the most popular multivariate analysis method for analyzing spontaneous fMRI signals. It was demonstrated that the CMBHC has a higher sensitivity than the ICA, particularly on a single run data, for identifying correlation structures with relatively weak connections, for instance, the thalamocortical connections. Compared to the seed-based correlation analysis, the CMBHC does not require a priori information and thus can avoid potential biases caused by seed selection, and multiple patterns can be extracted at one time. In contrast to other multivariate methods, the CMBHC is based on spatiotemporal correlations of fMRI signals and its analysis outcomes are easy to interpret as the strength of functional connectivity. Moreover, its sensitivity of detecting patterns remains relatively high even for a single dataset. In conclusion, the CMBHC method could be a useful tool for investigating resting-state brain connectivity and function.
在这项研究中，我们介绍了一种基于相关矩阵的层次聚类（CMBHC）方法，用于从静息态功能磁共振成像（fMRI）数据中提取多种相关模式。我们将该方法应用于麻醉大鼠的自发 fMRI 信号，并将结果与独立成分分析（ICA）进行了比较，后者是分析自发 fMRI 信号的最流行的多元分析方法之一。结果表明，CMBHC 比 ICA 具有更高的灵敏度，特别是在单个运行数据中，能够识别具有相对较弱连接的相关结构，例如丘脑皮质连接。与基于种子的相关分析相比，CMBHC 不需要先验信息，因此可以避免由于种子选择而导致的潜在偏差，并且可以一次提取多个模式。与其他多元方法相比，CMBHC 基于 fMRI 信号的时空相关性，其分析结果易于解释为功能连接的强度。此外，即使对于单个数据集，其检测模式的灵敏度仍然相对较高。总之，CMBHC 方法可能是研究静息态大脑连接和功能的有用工具。
