Resting-state network complexity and magnitude changes in neonates with severe hypoxic ischemic encephalopathy

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Abstract

Resting-state functional magnetic resonance imaging has revealed disrupted brain network connectivity in adults and teenagers with cerebral palsy. However, the specific brain networks implicated in neonatal cases remain poorly understood. In this study, we recruited 14 term-born infants with mild hypoxic ischemic encephalopathy and 14 term-born infants with severe hypoxic ischemic encephalopathy from Changzhou Children’s Hospital, China. Resting-state functional magnetic resonance imaging data showed efficient small-world organization in whole-brain networks in both the mild and severe hypoxic ischemic encephalopathy groups. However, compared with the mild hypoxic ischemic encephalopathy group, the severe hypoxic ischemic encephalopathy group exhibited decreased local efficiency and a low clustering coefficient. The distribution of hub regions in the functional networks had fewer nodes in the severe hypoxic ischemic encephalopathy group compared with the mild hypoxic ischemic encephalopathy group. Moreover, nodal efficiency was reduced in the left rolandic operculum, left supramarginal gyrus, bilateral superior temporal gyrus, and right middle temporal gyrus. These results suggest that the topological structure of the resting state functional network in children with severe hypoxic ischemic encephalopathy is clearly distinct from that in children with mild hypoxic ischemic encephalopathy, and may be associated with impaired language, motion, and cognition. These data indicate that it may be possible to make early predictions regarding brain development in children with severe hypoxic ischemic encephalopathy, enabling early interventions targeting brain function. This study was approved by the Regional Ethics Review Boards of the Changzhou Children’s Hospital (approval No. 2013-001) on January 31, 2013. Informed consent was obtained from the family members of the children. The trial was registered with the Chinese Clinical Trial Registry (registration number: ChiCTR1800016409) and the protocol version is 1.0.

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Infant Brain Atlases from Neonates to 1- and 2-Year-Olds

Feng Shi, Pew-Thian Yap, Guorong Wu … (2011)

Background Studies for infants are usually hindered by the insufficient image contrast, especially for neonates. Prior knowledge, in the form of atlas, can provide additional guidance for the data processing such as spatial normalization, label propagation, and tissue segmentation. Although it is highly desired, there is currently no such infant atlas which caters for all these applications. The reason may be largely due to the dramatic early brain development, image processing difficulties, and the need of a large sample size. Methodology To this end, after several years of subject recruitment and data acquisition, we have collected a unique longitudinal dataset, involving 95 normal infants (56 males and 39 females) with MRI scanned at 3 ages, i.e., neonate, 1-year-old, and 2-year-old. State-of-the-art MR image segmentation and registration techniques were employed, to construct which include the templates (grayscale average images), tissue probability maps (TPMs), and brain parcellation maps (i.e., meaningful anatomical regions of interest) for each age group. In addition, the longitudinal correspondences between age-specific atlases were also obtained. Experiments of typical infant applications validated that the proposed atlas outperformed other atlases and is hence very useful for infant-related studies. Conclusions We expect that the proposed infant 0–1–2 brain atlases would be significantly conducive to structural and functional studies of the infant brains. These atlases are publicly available in our website, http://bric.unc.edu/ideagroup/free-softwares/.

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Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation.

Pierrick Coupé, José Manjón, Vladimir Fonov … (2011)

Quantitative magnetic resonance analysis often requires accurate, robust, and reliable automatic extraction of anatomical structures. Recently, template-warping methods incorporating a label fusion strategy have demonstrated high accuracy in segmenting cerebral structures. In this study, we propose a novel patch-based method using expert manual segmentations as priors to achieve this task. Inspired by recent work in image denoising, the proposed nonlocal patch-based label fusion produces accurate and robust segmentation. Validation with two different datasets is presented. In our experiments, the hippocampi of 80 healthy subjects and the lateral ventricles of 80 patients with Alzheimer's disease were segmented. The influence on segmentation accuracy of different parameters such as patch size and number of training subjects was also studied. A comparison with an appearance-based method and a template-based method was also carried out. The highest median kappa index values obtained with the proposed method were 0.884 for hippocampus segmentation and 0.959 for lateral ventricle segmentation. Crown Copyright © 2010. Published by Elsevier Inc. All rights reserved.

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The functional architecture of the infant brain as revealed by resting-state fMRI.

Peter Fransson, Ulrika Ådén, Mats Blennow … (2011)

The functional network topology of the adult human brain has recently begun to be noninvasively mapped using resting-state functional connectivity magnetic resonance imaging and described using mathematical tools originating from graph theory. Previous studies have revealed the existence of disproportionally connected brain regions, so called cortical hubs, which act as information convergence zones and supposedly capture key aspects of how the brain's architecture supports human behavior and how it is affected by disease. In this study, we present results showing that cortical hubs and their associated cortical networks are largely confined to primary sensory and motor brain regions in the infant brain. Our findings in infants stand in stark contrast to the situation found in adults where the majority of cortical hubs and hub-related networks are located in heteromodal association cortex. Our findings suggest that the functional network architecture in infants is linked to support tasks that are of a perception-action nature.

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Journal

Journal ID (nlm-ta): Neural Regen Res

Journal ID (iso-abbrev): Neural Regen Res

Journal ID (publisher-id): NRR

Title: Neural Regeneration Research

Publisher: Medknow Publications & Media Pvt Ltd (India )

ISSN (Print): 1673-5374

ISSN (Electronic): 1876-7958

Publication date (Print): April 2019

Volume: 14

Issue: 4

Pages: 642-648

Affiliations

[1 ]Department of Neonatology, Changzhou Children's Hospital, Changzhou, Jiangsu Province, China

[2 ]Graduate Student, Nantong University, Nantong, Jiangsu Province, China

[3 ]Department of Children's Health Research Center, Changzhou Children's Hospital, Changzhou, Jiangsu Province, China

[4 ]Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China

[5 ]Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Author notes

[* ] Correspondence to: Li-Chi Zhang, lichizhang@ 123456sjtu.edu.cn .

Author contributions: Definition of intellectual content of this topic and paper review: LCZ; literature search and study design: HXL; paper preparation and editing: ABZ; data acquisition: HXL, ABZ, MY and WJT; data analysis: HXL, QFZ and LCZ; statistical analysis: MY and GWH. All authors approved the final version of the paper.

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Li-Chi Zhang http://orcid.org/0000-0003-4396-4566

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Publisher ID: NRR-14-642

DOI: 10.4103/1673-5374.247468

PMC ID: 6352595

PubMed ID: 30632504

SO-VID: e792b112-d55c-459f-b3b9-cae6b281c956

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Resting-state network complexity and magnitude changes in neonates with severe hypoxic ischemic encephalopathy

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Most cited references 30

Infant Brain Atlases from Neonates to 1- and 2-Year-Olds

Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation.

The functional architecture of the infant brain as revealed by resting-state fMRI.

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