Cortical Surface-Based Construction of Individual Structural Network with Application to Early Brain Development Study

Abstract

Analysis of anatomical covariance for cortex morphology in individual subjects plays an important role in the study of human brains. However, the approaches for constructing individual structural networks have not been well developed yet. Existing methods based on patch-wise image intensity similarity suffer from several major drawbacks, i.e., 1) violation of cortical topological properties, 2) sensitivity to intensity heterogeneity, and 3) influence by patch size heterogeneity. To overcome these limitations, this paper presents a novel cortical surface-based method for constructing individual structural networks. Specifically, our method first maps the cortical surfaces onto a standard spherical surface atlas and then uniformly samples vertices on the spherical surface as the nodes of the networks. The similarity between any two nodes is computed based on the biologically-meaningful cortical attributes (e.g., cortical thickness) in the spherical neighborhood of their sampled vertices. The connection between any two nodes is established only if the similarity is larger than a user-specified threshold. Through leveraging spherical cortical surface patches, our method generates biologically-meaningful individual networks that are comparable across ages and subjects. The proposed method has been applied to construct cortical-thickness networks for 73 healthy infants, with each infant having two MRI scans at 0 and 1 year of age. The constructed networks during the two ages were compared using various network metrics, such as degree, clustering coefficient, shortest path length, small world property, global efficiency, and local efficiency. Experimental results demonstrate that our method can effectively construct individual structural networks and reveal meaningful patterns in early brain development.

MRI-based structural covariance network in early human brain development

“…Currently, research on SCN-based early brain development patterns of children across the first 5 years of life is still in a nascent stage. Studies have found that in full-term neonates, SCN already exhibits the small-world topology and ordered modular organization (Fan et al, 2011;Meng et al, 2015). During 0-2 years old, the global efficiency, local efficiency, clustering coefficient, and modularity index of SCN constructed based on CT increases with age, and the shortest path length decreases with age (Fan et al, 2011;Meng et al, 2015), which suggest that both network segregation and integration enhances with age.…”

“…Studies have found that in full-term neonates, SCN already exhibits the small-world topology and ordered modular organization (Fan et al, 2011;Meng et al, 2015). During 0-2 years old, the global efficiency, local efficiency, clustering coefficient, and modularity index of SCN constructed based on CT increases with age, and the shortest path length decreases with age (Fan et al, 2011;Meng et al, 2015), which suggest that both network segregation and integration enhances with age. However, a study on infants aged 0-2 years old found that in SCNs constructed based on CT, the global efficiency increases while the local efficiency decreases with age; in SCNs constructed based on curvedness, the global efficiency decreases while the local efficiency increases with age (Nie et al, 2014).…”

“…at the population level (He et al, 2007;Alexander-Bloch et al, 2013;Gilmore et al, 2018), which has been widely applied in adult and adolescent populations. In recent years, some studies have proposed SCNs constructed at the individual level (Kong et al, 2015;Meng et al, 2015;Li et al, 2017Li et al, , 2021Seidlitz et al, 2018;Yu et al, 2018;Sebenius et al, 2023), which can capture individual difference information. However, current studies of early brain development based on cortical morphology mainly focus on statistical analysis of morphological features, while exploration of the SCN is relatively limited, especially at the individual level.…”