A longitudinal analysis of puberty‐related cortical development

Vijayakumar, Nandita; Youssef, George; Allen, Nicholas B.; Anderson, Vicki; Efron, Daryl; Hazell, Philip; Mundy, Lisa; Nicholson, Jan M.; Patton, George C.; Seal, Marc L.; Simmons, Julian G; Whittle, Sarah; Silk, Tim

doi:10.1016/j.neuroimage.2020.117684

Cited by 40 publications

(50 citation statements)

References 55 publications

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“…The MRI scanner was also upgraded between waves in one of the cohorts, but we additionally modelled this upgrade as a covariate in our analyses. Moreover, comparison on age-matched participants pre-and post-scanner upgrade failed to identify significant differences 36 . We also note that stringent quality control procedures were undertaken to minimize the influence of head motion, but future studies with estimates of head motion for T1-weighted images are needed to confirm findings while statistically controlling for motion confounds.…”

Section: Limitationsmentioning

confidence: 94%

“…Specifically, 59 participants (28 males) provided a single observation, 92 (46 males) provided two observations, and 41 (22 males) provided three observations. For further breakdown of these numbers by cohort, and distributions of age and sex at each wave, refer to Vijayakumar et al 36 ).…”

Section: Methodsmentioning

confidence: 99%

“…The final wave of NICAP was collected after an upgrade to a MAGNETOM Prisma scanner, which has been accounted for in statistical modelling. Refer to the Vijayakumar et al 36 for further detail of an investigation into potential cortical differences related to scanner upgrade, which found that only one region (the right insula) exhibited significant differences when comparing pre-and post-upgrade estimates in a sample of age-and sex-matched participants (N = 22).…”

Section: Methodsmentioning

confidence: 99%

“…This resulted in the exclusion of a total of 37 scans from 34 participants (i.e., 3 participants had 2 scans removed). No manual edits were made to the remaining (included) data (further details of the quality control procedure have been reported previously 36 ) . Mean cortical thickness estimates from 360 regions of the Human Connectome Pipeline's multimodal parcellation atlas (HCP-MMP1) 41 were extracted 42 and used in subsequent analyses.…”

Section: Methodsmentioning

confidence: 99%

“…Associations with cortical thinning. Rates of thinning were calculated for each region using linear mixed models using the "lmer" package in R. We modelled linear trajectories based on prior work examining cortical development in this dataset 36 : Y = Intercept + d i + β 1 (sex) + β 2 (cohort) + β 3 (scanner) + β 4 (age) + e i . Models were conducted within each i th subject, with a random intercept (d i ) to account for the repeated observations per subject.…”

Section: Development Of Structural Covariance Networkmentioning

confidence: 99%

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

Vijayakumar

Ball

Seal

et al. 2021

Sci Rep

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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.

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Section: Limitationsmentioning

confidence: 94%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Development Of Structural Covariance Networkmentioning

confidence: 99%

See 3 more Smart Citations

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

Vijayakumar

Ball

Seal

et al. 2021

Sci Rep

Self Cite

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Navigating Pubertal Goldilocks: The Optimal Pace for Hierarchical Brain Organization

Szakács,

Mutlu,

Balestrieri

et al. 2024

Advanced Science

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Adolescence is a timed process with an onset, tempo, and duration. Nevertheless, the temporal dimension, especially the pace of maturation, remains an insufficiently studied aspect of developmental progression. The primary objective is to estimate the precise influence of pubertal maturational tempo on the configuration of associative brain regions. To this end, the connection between maturational stages and the level of hierarchical organization of large‐scale brain networks in 12‐13‐year‐old females is analyzed. Skeletal maturity is used as a proxy for pubertal progress. The degree of maturity is defined by the difference between bone age and chronological age. To assess the level of hierarchical organization in the brain, the temporal dynamic of closed eye resting state high‐density electroencephalography (EEG) in the alpha frequency range is analyzed. Different levels of hierarchical order are captured by the measured asymmetry in the directionality of information flow between different regions. The calculated EEG‐based entropy production of participant groups is then compared with accelerated, average, and decelerated maturity. Results indicate that an average maturational trajectory optimally aligns with cerebral hierarchical order, and both accelerated and decelerated timelines result in diminished cortical organization. This suggests that a “Goldilocks rule” of brain development is favoring a particular maturational tempo.

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Adolescent brain development in girls with Turner syndrome

Wun

Foland‐Ross

et al. 2023

Human Brain Mapping

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Turner syndrome (TS) is a common sex chromosome aneuploidy in females associated with various physical, cognitive, and socio‐emotional phenotypes. However, few studies have examined TS‐associated alterations in the development of cortical gray matter volume and the two components that comprise this measure—surface area and thickness. Moreover, the longitudinal direct (i.e., genetic) and indirect (i.e., hormonal) effects of X‐monosomy on the brain are unclear. Brain structure was assessed in 61 girls with TS (11.3 ± 2.8 years) and 55 typically developing girls (10.8 ± 2.3 years) for up to 4 timepoints. Surface‐based analyses of cortical gray matter volume, thickness, and surface area were conducted to examine the direct effects of X‐monosomy present before pubertal onset and indirect hormonal effects of estrogen deficiency/X‐monosomy emerging after pubertal onset. Longitudinal analyses revealed that, whereas typically developing girls exhibited normative declines in gray matter structure during adolescence, this pattern was reduced or inverted in TS. Further, girls with TS demonstrated smaller total surface area and larger average cortical thickness overall. Regionally, the TS group exhibited decreased volume and surface area in the pericalcarine, postcentral, and parietal regions relative to typically developing girls, as well as larger volume in the caudate, amygdala, and temporal lobe regions and increased thickness in parietal and temporal regions. Surface area alterations were predominant by age 8, while maturational differences in thickness emerged by age 10 or later. Taken together, these results suggest the involvement of both direct and indirect effects of X‐chromosome haploinsufficiency on brain development in TS.

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A longitudinal analysis of puberty‐related cortical development

Cited by 40 publications

References 55 publications

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

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

Navigating Pubertal Goldilocks: The Optimal Pace for Hierarchical Brain Organization

Adolescent brain development in girls with Turner syndrome

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