Xiao Liu1,2, Xiao-Hong Zhu1, Yi Zhang1, Peihua Qiu2, Wei Chen1
1CMRR, Radiology, University of Minnesota, Minneapolis, MN, United States; 2Statistics, University of Minnesota, Minneapolis, MN, United States
In this study, we introduce a novel correlation-matrix-based clustering method for extracting correlation patterns in spontaneous BOLD fluctuations and for identifying multiple resting-state networks. This method has merits beyond commonly-used seed-based correlation mapping and spatial independent component analysis (ICA): no priori information required, easy interpretation of outcomes, easy for the group level analysis, and effective in identifying multiple resting networks with clear and robust patterns at one time. It could be a powerful tool for investigating resting-state brain networks detected by spontaneous BOLD fluctuations.