A Comparison Study of CMBHC and ICA Methods for Human Functional Connectivity Analysis

Xiao Liu¹, Xiao-Hong Zhu¹, Wei Chen¹

In this study, a recently proposed correlation-matrix-based hierarchical clustering (CMBHC) method was tested and evaluated using human resting-state fMRI datasets, and the results were compared with those obtained using the independent component analysis (ICA). It was found that the CMBHC was able not only to extract more coherent patterns than the ICA, but also to retain a number of weak but consistent functional connections (e.g., cortico-subcortical connections) that were largely missed by the ICA. The overall results suggest that the CMBHC could be a powerful tool for analyzing resting-state fMRI data, especially at the single subject level.