Regularized-Ncut: Robust and homogeneous functional parcellation of neonate and adult brain networks
作者：
Highlights：
• RNcut generates robust functional parcellation on noisy dataset.
• RNcut yields smooth boundaries of parcellated networks.
• High functional homogeneity has been achieved for each parcellated network.
• RNcut can be successfully applied to both adult and neonate populations.
• Distinctive neonate intra-network connectivity was found compared to that of adults.
摘要
•RNcut generates robust functional parcellation on noisy dataset.•RNcut yields smooth boundaries of parcellated networks.•High functional homogeneity has been achieved for each parcellated network.•RNcut can be successfully applied to both adult and neonate populations.•Distinctive neonate intra-network connectivity was found compared to that of adults.
论文关键词：Regularized-Ncut,Functional parcellation,Low SNR,Homogeneity,Neonate,Intra-Network connectivity
论文评审过程：Received 17 September 2019, Revised 17 March 2020, Accepted 2 May 2020, Available online 12 May 2020, Version of Record 19 May 2020.
论文官网地址：https://doi.org/10.1016/j.artmed.2020.101872
