Regularized-Ncut: Robust and homogeneous functional parcellation of neonate and adult brain networks.
Brain network parcellation based on resting-state functional MRI (rs-fMRI) is affected by noise, resulting in spurious small patches and decreased functional homogeneity within each network. Obtaining robust and homogeneous parcellation of neonate brain is more difficult, because neonate rs-fMRI is...
| Published in: | Artificial Intelligence in Medicine Vol. 106 |
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| Main Authors: | , , , , , , , , , |
| Format: | Journal Article |
| Published: |
Elsevier B.V.
Jun2020
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=144224068&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 144224068 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09333657 3HY jtl: Artificial Intelligence in Medicine issn: 09333657 maglogo: N pubinfo: dt: Jun2020 vid: 106 pid: 1004 pub: Elsevier B.V. artinfo: ui: 144224068 144224068 NLM32593397 10.1016/j.artmed.2020.101872 NLM32593397 144224068 ppct: 1 formats: tig: atl: Regularized-Ncut: Robust and homogeneous functional parcellation of neonate and adult brain networks. aug: au: Peng, Qinmu Ouyang, Minhui Wang, Jiaojian Yu, Qinlin Zhao, Chenying Slinger, Michelle Li, Hongming Fan, Yong Hong, Bo Huang, Hao affil: Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA sug: subj: Brain Brain Mapping Relaxation Noise Infant, Newborn Adult Magnetic Resonance Imaging Clinical Assessment Tools Scales Questionnaires Infant, Newborn: birth-1 month Adult: 19-44 years ab: Brain network parcellation based on resting-state functional MRI (rs-fMRI) is affected by noise, resulting in spurious small patches and decreased functional homogeneity within each network. Obtaining robust and homogeneous parcellation of neonate brain is more difficult, because neonate rs-fMRI is associated with relatively higher level of noise and no prior knowledge from a functional neonate atlas is available as spatial constraints. To meet these challenges, we developed a novel data-driven Regularized Normalized-cut (RNcut) method. RNcut is formulated by adding two regularization terms, a smoothing term using Markov random fields and a small-patch removal term, to conventional normalized-cut (Ncut) method. The RNcut and competing methods were tested with simulated datasets with known ground truth and then applied to both adult and neonate rs-fMRI datasets. Based on the parcellated networks generated by RNcut, intra-network connectivity was quantified. The test results from simulated datasets demonstrated that the RNcut method is more robust (p < 0.01) to noise and can delineate parcellated functional networks with significantly better (p < 0.01) spatial contiguity and significantly higher (p < 0.01) functional homogeneity than competing methods. Application of RNcut to neonate and adult rs-fMRI dataset revealed distinctive functional brain organization of neonate brains from that of adult brains. Collectively, we developed a novel data-driven RNcut method by integrating conventional Ncut with two regularization terms, generating robust and homogeneous functional parcellation without imposing spatial constraints. A broad range of brain network applications and analyses, especially neonate and infant brain parcellation with noisy and large sample of datasets, can potentially benefit from this RNcut method. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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