Longitudinal Subject Pairing in Cross-Sectional Neonatal Data Reveals Asynchronous Structural and Functional Brain Maturation

The asynchronous development of structural and functional brain networks in early childhood remains largely unexamined, primarily due to the scarcity of longitudinal neuroimaging data. Resolving this temporal dimension is critical, as it promises to reshape our understanding of structural-functional (S-F) coupling, revealing not only whether brain architecture supports function, but also when and over what timescale its influence emerges. However, while rapid neonatal brain maturation and logistical constraints continue to hinder longitudinal data collection, large-scale cross-sectional multimodal datasets are currently available to bridge this gap. Here, we propose a longitudinal subject-pairing framework that reconstructs developmental trajectories from cross-sectional data. It pairs the infants with a predefined age gap while maximizing their similarity in both structural and functional features, thereby approximating the longitudinal trajectory of functional changes in relation to structural maturation. As a case study, we applied this framework to the perisylvian region in a subset of 505 neonates from the multimodal dHCP brain dataset. The myelination index was derived as a structural feature from MRI, and the fractional amplitude of low-frequency fluctuations (fALFF) was derived as a functional feature from resting-state functional MRI. A conventional cross-sectional analysis revealed a moderate S-F correlation magnitude (r = 0.34). In contrast, the proposed framework demonstrated a significant increase in S-F coupling to r = 0.46 when the structural maturation precedes functional maturation by approximately five days. These findings provide novel evidence of a functional maturation lag relative to structural brain development in neonates. Beyond elucidating S-F relationships in the early developing brain, this work establishes a framework for future longitudinal studies and advances in brain modeling across developmental trajectories, aging, and disease prediction.