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Abstract #5548

Regularized-Ncut: Robust functional parcellation of brain networks

Qinmu Peng1,2, Ouyang Minhui1,2, Jiaojian Wang1,2, Qinlin Yu1,2, Chenying Zhao3, Slinger Michelle1, Hongming Li2, Yong Fan2, Bo Hong4, and Hao Huang1,2

1Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, United States, 2Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States, 3Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, United States, 4Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China

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 RNcut.

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