Human brain functional networks are critical in understanding intrinsic functional organization and systems. However, functional brain parcellation is affected by noise, resulting in artificial small patches and decreased functional homogeneity within certain networks. Using resting-state fMRI, we proposed a novel data-driven regularized-Ncut (RNcut) method by integrating a smoothing term and a small patches removal term to conventional Ncut for parcellating functional networks. The proposed method could delineate parcellated functional networks with higher functional homogeneity and better spatial contiguity with less noisy patches. A broad range of brain network applications and analyses could benefit from the proposed
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