Abstract #5548

Regularized-Ncut: Robust functional parcellation of brain networks

Qinmu Peng^1,2, Ouyang Minhui^1,2, Jiaojian Wang^1,2, Qinlin Yu^1,2, Chenying Zhao³, Slinger Michelle¹, Hongming Li², Yong Fan², Bo Hong⁴, and Hao Huang^1,2

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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