Altered development of structural MRI connectome hubs at near-term age in very and moderately preterm infants

Abstract

Preterm infants may exhibit altered developmental patterns of the brain structural network by endogenous and exogenous stimuli, which are quantifiable through hub and modular network topologies that develop in the third trimester. Although preterm brain networks can compensate for white matter microstructural abnormalities of core connections, less is known about how the network developmental characteristics of preterm infants differ from those of full-term infants. We identified 13 hubs and 4 modules and revealed subtle differences in edgewise connectivity and local network properties between 134 preterm and 76 full-term infants, identifying specific developmental patterns of the brain structural network in preterm infants. The modules of preterm infants showed an imbalanced composition. The edgewise connectivity in preterm infants showed significantly decreased long- and short-range connections and local network properties in the dorsal superior frontal gyrus. In contrast, the fusiform gyrus and several nonhub regions showed significantly increased wiring of short-range connections and local network properties. Our results suggested that decreased local network in the frontal lobe and excessive development in the occipital lobe may contribute to the understanding of brain developmental deviances in preterm infants.

Predicting 2-year neurodevelopmental outcomes in preterm infants using multimodal structural brain magnetic resonance imaging with local connectivity

“…Prior to brain network quantification, a sparsity threshold of 0.25 (i.e., which is the ratio of the number of actual edges to the maximum possible number of edges in a structural network) 85 , was applied to individual networks to remove the weakest connections subject to experimental noise 88 . The specific threshold selection procedure followed that of our previous network study 23 . Global and local network properties were analyzed using the Brain Connectivity Toolbox 89 and GRETNA software ( http://www.nitrc.org/projects/gretna/ ) 90 .…”

“…Graph metrics were used to quantify brain global (global efficiency, E glob ; local efficiency, E loc ; modularity, Q; small-worldness, S; normalized clustering coefficient, C p ; normalized shortest path length, L p ) 89 and local (betweenness centrality, BC; degree centrality, DC; nodal clustering coefficient, NC p ; nodal shortest path length, NL p ; nodal efficiency, L e ; nodal local efficiency, NL e ) 91 , 92 connectivity. Global metrics were computed for 1,000 random networks with conserved number of nodes, number of edges, and degree distribution at predefined sparsity thresholds 23 . Local network metrics were used as indicators of neonatal and children brain development, and employed to elucidate clinical implications 23 , 93 – 96 .…”

“…Global metrics were computed for 1,000 random networks with conserved number of nodes, number of edges, and degree distribution at predefined sparsity thresholds 23 . Local network metrics were used as indicators of neonatal and children brain development, and employed to elucidate clinical implications 23 , 93 – 96 . Details of the described graph-theoretical measures can be found in supplementary text 1 .…”

“…Moreover, depending on GA, the normal scheme of myelination in a typical caudorostral pattern may be disrupted 20 . Graph theory-based WM connectome analysis has been used to quantify the efficiency of information transmission in brain regions altered by preterm labor and distinguish between normal and abnormal brain network characteristics 21 – 23 , demonstrating their relevance to various neurodevelopmental disorders 24 – 26 .…”

Preterm‐birth alters the development of nodal clustering and neural connection pattern in brain structural network at term‐equivalent age

“…Other studies have begun to characterize WM development of premature neonates from a network organization perspective (Ball et al, 2014;Batalle et al, 2017;Brown et al, 2014;Jang et al, 2023;Sa de Almeida et al, 2021;van den Heuvel et al, 2015;Young et al, 2018;Zhao et al, 2019), using graph theory to depict the topological integration and segregation of a network via a series of quantitative measures (Bullmore & Sporns, 2009;Rubinov & Sporns, 2010).…”