Altered Connectome Topology in Newborns at Risk for Cognitive Developmental Delay: A Cross-Etiologic Study

Abstract

The human brain connectome is characterized by the duality of highly modular structure and efficient integration, supporting information processing. Newborns with congenital heart disease (CHD), prematurity, or spina bifida aperta (SBA) constitute a population at risk for altered brain development and developmental delay (DD). We hypothesize that, independent of etiology, alterations of connectomic organization reflect neural circuitry impairments in cognitive DD. Our study aim is to address this knowledge gap by using a multi-etiologic neonatal dataset to reveal potential commonalities and distinctions in the structural brain connectome and their associations with DD. We used diffusion tensor imaging of 187 newborns (42 controls, 51 with CHD, 51 with prematurity, and 43 with SBA). Structural weighted connectomes were constructed using constrained spherical deconvolution-based probabilistic tractography and the Edinburgh Neonatal Atlas. Assessment of brain network topology encompassed the analysis of global graph features, network-based statistics, and low-dimensional representation of global and local graph features. The Cognitive Composite Score of the Bayley scales of Infant and Toddler Development 3rd edition was used as outcome measure at corrected 2 years for the preterm born individuals and SBA patients, and at 1 year for the healthy controls and CHD. We detected differences in the connectomic structure of newborns across the four groups after visualizing the connectomes in a two-dimensional space defined by network integration and segregation. Further, analysis of covariance analyses revealed differences in global efficiency (p < 0.0001), modularity (p < 0.0001), mean rich club coefficient (p = 0.017), and small-worldness (p = 0.016) between groups after adjustment for postmenstrual age at scan and gestational age at birth. Moreover, small-worldness was significantly associated with poorer cognitive outcome, specifically in the CHD cohort (r = -0.41, p = 0.005). Our cross-etiologic study identified divergent structural brain connectome profiles linked to deviations from optimal network integration and segregation in newborns at risk for DD. Small-worldness emerges as a key feature, associating with early cognitive outcomes, especially within the CHD cohort, emphasizing small-worldness' crucial role in shaping neurodevelopmental trajectories. Neonatal connectomic alterations associated with DD may serve as a marker identifying newborns at-risk for DD and provide early therapeutic interventions. Trial Registration: ClinicalTrials.gov identifier: NCT00313946.

FIGURE 1

Connectome pipeline. Overview of the image processing steps in the connectome pipeline which takes T2‐weighted super‐resolution and dMRI images as input and outputs a structural connectome. ANTs, DIPY, FSL, and MRtrix are neuroimaging software. ACT = anatomically constrained tractography, CSD = constrained spherical deconvolution, dMRI = diffusion MRI, ENA33 = Edinburgh neonatal atlas, ODF = orientation distribution functions, T2 SR = T2‐weighted super‐resolution.

FIGURE 2

Adjusted means after post hoc analysis of z‐normalized global efficiency, z‐normalized modularity, z‐normalized rich club coefficients, and small‐worldness in different groups. Pairwise comparison between groups using the emmeans test with Bonferroni correction showing the adjusted, estimated means. The colors depict the four different groups and the * flag the level of significance of the adjusted p‐values. CHD = congenital heart disease, PB = premature birth, SBA = spina bifida aperta.

FIGURE 3

Two low‐dimensional representations of the connectomes with respect to the four groups. In the integration‐segregation morphospace on the left side, the x‐ and y‐axis show the z‐normalized graph feature scores. In the t‐SNE embedding on the right side, the initial positions were determined using the first three principal components for dimensionality reduction (Perplexity = 20; Standardize = True; Algorithm = exact) on the z‐normalized local graph feature. CHD = congenital heart disease, IVH = intraventricular hemorrhage, MCAS = middle cerebral artery stroke, PB = premature birth, PCAS = posterior cerebral artery stroke, SBA = spina bifida aperta.

FIGURE 4

Average z‐normalized local efficiency of core and periphery nodes across etiologies after outlier removal. CHD = congenital heart disease, IVH = intraventricular hemorrhage, MCAS = middle cerebral artery stroke, PB = premature birth, PCAS = posterior cerebral artery stroke, SBA = spina bifida aperta.

FIGURE 5

Scatterplots showing the linear regression analysis between graph features and neurodevelopmental outcome over all subjects and for each etiology separately. GA = gestational age. r = partial correlation coefficient. IVH = intraventricular hemorrhage, MCAS = middle cerebral artery stroke, PCAS = posterior cerebral artery stroke.

FIGURE 6

Two‐dimensional connectomic landscape with respect to the neurodevelopmental outcome. In the integration‐segregation morphospace on the left side, the x‐ and y‐axis show the z‐normalized graph feature scores. In the t‐SNE embedding on the right side, the initial positions were determined using the first three principal components for dimensionality reduction (Perplexity = 20; Standardize = True; Algorithm = exact) on the z‐normalized local graph features). Low and high correspond to the dichotomized ND outcome scores from the BCCS. IVH = intraventricular hemorrhage, MCAS = middle cerebral artery stroke, PCAS = posterior cerebral artery stroke.