Graph-theory based parcellation of functional subunits in the brain from resting-state fMRI data

Resting-state fMRI provides a method to examine the functional network of the brain under spontaneous fluctuations. A number of studies have proposed using resting-state BOLD data to parcellate the brain into functional subunits. In this work, we present two state-of-the-art graph-based partitioning approaches, and investigate their application to the problem of brain network segmentation using resting-state fMRI. The two approaches, the normalized cut (Ncut) and the modularity detection algorithm, are also compared to the Gaussian mixture model (GMM) approach. We show that the Ncut approach performs consistently better than the modularity detection approach, and it also outperforms the GMM approach for in vivo fMRI data. Resting-state fMRI data were acquired from 43 healthy subjects, and the Ncut algorithm was used to parcellate several different cortical regions of interest. The group-wise delineation of the functional subunits based on resting-state fMRI was highly consistent with the parcellation results from two task-based fMRI studies (one with 18 subjects and the other with 20 subjects). The findings suggest that whole-brain parcellation of the cortex using resting-state fMRI is feasible, and that the Ncut algorithm provides the appropriate technique for this task.