Abstract #0529

Neonate functional brain networks and distinctive intra-network connectivity

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

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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