Regularized-Ncut: Robust and homogeneous functional parcellation of neonate and adult brain networks
| Autor: | Bo Hong, Hongming Li, Qinmu Peng, Hao Huang, Michelle Slinger, Jiaojian Wang, Qinlin Yu, Yong Fan, Chenying Zhao, Minhui Ouyang |
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| Rok vydání: | 2019 |
| Předmět: |
Adult
Computer science Rest Medicine (miscellaneous) Article 03 medical and health sciences 0302 clinical medicine Artificial Intelligence Humans Spurious relationship 030304 developmental biology Brain network 0303 health sciences Ground truth Brain Mapping Random field Markov chain business.industry Infant Newborn Brain Pattern recognition Magnetic Resonance Imaging Homogeneous Artificial intelligence business Noise 030217 neurology & neurosurgery Smoothing |
| Zdroj: | Artif Intell Med |
| ISSN: | 1873-2860 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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