Early life brain network connectivity antecedents of executive function in children born preterm

Abstract

Preterm birth is associated with an increased risk of executive function (EF) deficits, yet the underlying neural mechanisms remain unclear. We combine diffusion MRI, resting-state functional MRI, and graph theory analyses to examine how structural (SC) and functional connectivity (FC) at term-equivalent age (TEA) influence EF outcomes at 3 years corrected age in children born at or below 32 weeks' gestation. Here we show shorter average path length (a measure of efficient structural communication) in the insula is linked to better EF performance, implying that more direct structural pathways in this region facilitate critical cognitive processes. Additionally, higher betweenness centrality (a node-level metric of information flow) in parietal and superior temporal regions is associated with improved EF, reflecting these areas' prominent integrative roles in the whole-brain functional network. Importantly, our multimodal analyses reveal that regional structural efficiency helps shape functional organization, indicating a specific interplay between white-matter architecture and emergent functional hubs at TEA. These findings extend current knowledge by demonstrating how earlier disruptions in SC can alter subsequent FC patterns that support EF. By focusing on precise node-level metrics rather than broad within-network effects, our results clarify the contribution that SC has in guiding functional relationships essential for EF.

Fig. 1. Canonical correlation analysis of structural and functional connectivity.

Scatter plots represent the associations between transformed SC and FC scores for the two significant canonical covariate pairs. a First significant canonical pair demonstrates the association between SC and FC scores with an R² = 0.245 (FDR corrected p < 0.001). b Second significant canonical pair show a similar association with an R² = 0.143 (FDR corrected p < 0.001). Each pair shows the relationship between structural connectivity (SC) and functional connectivity (FC) network components. The marker size and opacity within each plot are adjusted to the connectivity scores’ strength and p-values.

Fig. 2. Non-negative Matrix Factorization.

In this schematic, X is the original data matrix comprising rows for subjects (N) and columns for brain regions (82). By factorizing X into two non-negative matrices, W and H, NMF identifies modes of inter-subject covariation in the data. The matrix W contains subject loadings, indicating how strongly each subject expresses a given mode; the histogram above W shows example loadings for one subject across multiple modes. The matrix H details the contributions of each brain region to these modes; the histogram above H illustrates a row of H, enumerating the regions and their respective weights in one mode. Separate NMF models were applied to structural connectivity (SC, N = 358) and functional connectivity (FC, N = 363).

Fig. 3. Canonical associations between structural and functional connectivity.

This multimodal connectivity circos plot for the canonical covariate pairs shows the intricate associations between FC and SC variables across two unique canonical covariate pairs 1 (a) and 2 (b). The SC variables are depicted along the radial axis, serving as the basis for the inner tracks. Correspondingly, the FC variables are shown as segments along the circular edge. The chords connecting FC and SC variables indicate their mutual influence, quantified by the weighted canonical correlation, and demonstrate the integrated nature of structural and functional attributes in each canonical variate pair. The thickness of each chord is proportional to the combined canonical weights of the connecting variables, serving as a visual proxy for the magnitude of their interaction.