Literature DB >> 29913282

Structural network maturation of the preterm human brain.

Tengda Zhao1, Virendra Mishra2, Tina Jeon3, Minhui Ouyang3, Qinmu Peng3, Lina Chalak4, Jessica Lee Wisnowski5, Roy Heyne4, Nancy Rollins6, Ni Shu7, Hao Huang8.   

Abstract

During the 3rd trimester, large-scale neural circuits are formed in the human brain, resulting in a highly efficient and segregated connectome at birth. Despite recent findings identifying important preterm human brain network properties such as rich-club organization, how the structural network develops differentially across brain regions and among different types of connections in this period is not yet known. Here, using high resolution diffusion MRI of 77 preterm-born and full-term neonates scanned at 31.9-41.7 postmenstrual weeks (PMW), we constructed structural connectivity matrices and performed graph-theory-based analyses. Faster increases of nodal efficiency were mainly located at the brain hubs distributed in primary sensorimotor regions, superior-middle frontal, and precuneus regions during 31.9-41.7PMW. Higher rates of edge strength increases were found in the rich-club and within-module connections, compared to other connections. The edge strength of short-range connections increased faster than that of long-range connections. Nodal efficiencies of the hubs predicted individual postmenstrual ages more accurately than those of non-hubs. Collectively, these findings revealed more rapid efficiency increases of the hub and rich-club connections as well as higher developmental rates of edge strength in short-range and within-module connections. These jointly underlie network segregation and differentiated emergence of brain functions.
Copyright © 2018 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Baby connectome; Brain network; Differentiated maturation; Diffusion MRI; Segregation; Structural connectivity

Mesh:

Year:  2018        PMID: 29913282      PMCID: PMC6289855          DOI: 10.1016/j.neuroimage.2018.06.047

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  94 in total

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