A correlation-matrix-based hierarchical clustering method for functional connectivity analysis.

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.