The development of structural covariance networks during the transition from childhood to adolescence

Nandita Vijayakumar,Gareth Ball,Lisa Mundy,Marc L. Seal,Sarah Whittle,Tim Silk

doi:10.1038/s41598-021-88918-w

Abstract

Structural covariance conceptualizes how morphologic properties of brain regions are related to one another (across individuals). It can provide unique information to cortical structure (e.g., thickness) about the development of functionally meaningful networks. The current study investigated how structural covariance networks develop during the transition from childhood to adolescence, a period characterized by marked structural re-organization. Participants (N = 192; scans = 366) completed MRI assessments between 8.5 and 14.5 years of age. A sliding window approach was used to create “age-bins”, and structural covariance networks (based on cortical thickness) were created for each bin. Next, generalized additive models were used to characterize trajectories of age-related changes in network properties. Results revealed nonlinear trajectories with “peaks” in mean correlation and global density that are suggestive of a period of convergence in anatomical properties across the cortex during early adolescence, prior to regional specialization. “Hub” regions in sensorimotor cortices were present by late childhood, but the extent and strength of association cortices as “hubs” increased into mid-adolescence. Moreover, these regional changes were found to be related to rates of thinning across the cortex. In the context of neurocognitive networks, the frontoparietal, default mode, and attention systems exhibited age-related increases in within-network and between-network covariance. These regional and modular developmental patterns are consistent with continued refinement of socioemotional and other complex executive functions that are supported by higher-order cognitive networks during early adolescence.

Highlights

Structural covariance conceptualizes how morphologic properties of brain regions are related to one another
Structural covariance is a multivariate analysis technique that conceptualizes how morphological properties of different brain regions relate to each other at the group-level. Properties such as cortical thickness are measured for each brain region in a group of subjects, and correlations between these regional estimates are calculated for each pair of regions across the group
Studies of early childhood to late adolescence have found patterns of increasing integration and decreasing segregation of networks until late childhood, followed by inverse trajectories or plateaus during adolescence[19,20,21]

Summary

Introduction

Structural covariance conceptualizes how morphologic properties of brain regions are related to one another (across individuals). Studies of early childhood to late adolescence have found patterns of increasing integration (i.e., capacity to facilitate the combination of information from distributed brain regions) and decreasing segregation (i.e., capacity to facilitate specialized processing within groups of regions) of networks until late childhood, followed by inverse trajectories or plateaus during adolescence[19,20,21] Those focusing on later adolescence and young adulthood have shown that the strength of overall cortical correlations decreases between 14 and 20 years of age before p lateauing[8], which is hypothesized to reflect inter-individual variability in the timing of maturation of different brain regions. Extending such analyses to focus on the transition from childhood to adolescence may provide novel insight into prominent models of neurodevelopment that purport a mismatch between neurocognitive systems that begins during this transition period e.g. 23,24

Methods

Results

Discussion

Conclusion