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

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.

Brain structural covariance network differences in adults with alcohol dependence and heavy‐drinking adolescents

“…Poor segregation and higher integration have previously been related to other psychiatric and neurological conditions [44], including dependence on alcohol and other substances [16–18]. Typically, segregation peaks by late adolescence and young adulthood, probably reflecting functional specialization among cortical regions [44,45]. Therefore, we speculate that our results suggest a protracted cortical maturation in the alcohol‐drinking groups [13,42,46].…”

Development of thalamus mediates paternal age effect on offspring reading: A preliminary investigation

“…Third, the complementary analyses implied DAN as the candidate functional system associated with PatAGE. It should be noted that the brain atlases and public datasets implemented in Neurosynth are primarily from research on adults, while children have specific characteristics regarding brain organization (Tooley et al, 2021;Vijayakumar et al, 2021). We tempted to address this issue by adopting the functional network template for children in analyzing diffusion imaging data available in a subgroup of the current samples and found that the PatAGE-cluster more strongly connected with DAN than VAN.…”

“…Given that the functional community structure in children is to some extent different from that in adults and the highest uncertainty was found in attention networks (Tooley, Bassett, & Mackey, 2021;Vijayakumar et al, 2021), in the final step, we examined the structural connectivity pattern in a sub-group of our own samples where diffusion imaging data were available. Moreover, we adopted a pediatric intrinsic functional network template that was extracted from 670 children aged 9-11 years (Tooley et al, 2021) with the same approach as in Yeo et al (2011).…”

Structural Covariance of the Ipsilesional Primary Motor Cortex in Subcortical Stroke Patients with Motor Deficits

“…Therefore, the analysis of structural covariance may provide complementary information to fMRI and diffusion tensor imaging (DTI) networks and help to further clarify the specific mechanisms that underlie neurological and psychiatric disorders. Thus far, structural covariance analysis has successfully been used to explore changes in the anatomical coupling of healthy subjects and patients with various psychiatric and neurological disorders, such as schizophrenia, major depressive disorder, Parkinson's disease, and Alzheimer's disease [ 14 , 16 – 22 ].…”