Functional magnetic resonance imaging (fMRI) is an in-vivo non-invasive technique for measuring brain activity with excellent spatial and good temporal resolution. Without performing explicit tasks, resting-state fMRI (rfMRI) is widely used to map the functional connectivity network (FCN), which refers to a large-scale network of interdependent or functionally connected brain regions and it could be detected by using different algorithms (Zuo and Xing, 2014). Seed-based correlation is deemed as one of the most widely used method to identify FCN. This approach needs to extract a representative time series by averaging time series of all voxels within a small region of interest (ROI). While the selection of the ROI appears simple and straightforward, it becomes very challenging to determine an ROI for mapping representative activities of FCN including multiple distant areas
