Ex-utero third trimester developmental changes in functional brain network organization in infants born very and extremely preterm

• Published in: Frontiers in neuroscience 2023-09, Vol.17, p.1214080-1214080
• Main Authors: Cook, Kevin M, De Asis-Cruz, Josepheen, Basu, Sudeepta K, Andescavage, Nickie, Murnick, Jonathan, Spoehr, Emma, du Plessis, Adré J, Limperopoulos, Catherine
• Format: Article
• Language: English
• Subjects: Age; Brain architecture; brain development; Brain mapping; brain network; Efficiency; Fetuses; Functional magnetic resonance imaging; Functional morphology; Gestational age; graph theory; Infants; Neonates; Neuroscience; Newborn babies; Pregnancy; Premature babies; prematurity; Regression analysis; resting state fMRI; stress
• ISSN: 1662-4548; 1662-453X; 1662-453X
• DOI: 10.3389/fnins.2023.1214080

The latter half of gestation is a period of rapid brain development, including the formation of fundamental functional brain network architecture. Unlike fetuses, infants born very and extremely preterm undergo these critical maturational changes in the extrauterine environment, with growing evidence suggesting this may result in altered brain networks. To date, however, the development of functional brain architecture has been unexplored. From a prospective cohort of preterm infants, graph parameters were calculated for fMRI scans acquired prior to reaching term equivalent age. Eight graph properties were calculated, Clustering Coefficient (C), Characteristic Path Length (L), Modularity (Q), Local Efficiency (LE), Global Efficiency (GE), Normalized Clustering (λ), Normalized Path Length (γ), and Small-Worldness (σ). Properties were first compared to values generated from random and lattice networks and cost efficiency was evaluated. Subsequently, linear mixed effect models were used to assess relationship with postmenstrual age and infant sex. A total of 111 fMRI scans were acquired from 85 preterm infants born at a mean GA 28.93 ± 2.8. Infants displayed robust small world properties as well as both locally and globally efficient networks. Regression models found that GE increased while L, Q, λ, γ, and σ decreased with increasing postmenstrual age following multiple comparison correction (r range 0.143-0.401,