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Abstract #0529

Neonate functional brain networks and distinctive intra-network connectivity

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

Little is known on the network organization in the neonatal brain. Here, we used a novel parcellation method, called regularized-Ncut (RNcut), to parcellate the neonate brain into functional networks with resting-state fMRI. RNcut effectively delineates the neonatal functional networks including the primary sensorimotor and higher-order networks. Based on RNcut parcellation of functional networks, intra-network connectivity was quantified. Distinctive intra-network connectivity was revealed for the first time. We found that the primary sensorimotor network has the highest intra-network connectivity while higher-order fronto-parietal network has lower intra-network connectivity. The distinctive intra-network connectivity pattern underlies heterogeneous emergence of early brain functional systems.

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