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1000
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Abstract/Summary
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Background: Adolescence is a critical time for brain development. Findings from previous studies have been inconsistent, failing to distinguish the influence of pubertal status and aging on brain maturation. The current study sought to address these inconsistencies, addressing the trajectories of pubertal development and aging by longitudinally tracking structural brain development during adolescence. Methods: Two cohorts of healthy children were recruited (cohort 1: 9-10 years old; cohort 2: 12-13 years old at baseline). MRI data were acquired for gray matter volume and white matter tract measures. To determine whether age, pubertal status, both or their interaction best modelled longitudinal data, we compared four multi-level linear regression models to the null model (general brain growth indexed by total segmented volume) using Bayesian model selection. Results: Data were collected at baseline (n = 116), 12 months (n = 97) and 24 months (n = 84) after baseline. Findings demonstrated that the development of most regional gray matter volume, and white matter tract measures, were best modelled by age. Interestingly, precentral and paracentral regions of the cortex, as well as the accumbens demonstrated significant preference for the pubertal status model. None of the white matter tract measures were better modelled by pubertal status. Limitations: The major limitation of this study is the two-cohort recruitment. Although this allowed a faster coverage of the age span, a complete per person trajectory over 6 years of development (9-15 years) could not be investigated. Conclusions: Comparing the impact of age and pubertal status on regional gray matter volume and white matter tract measures, we found age to best predict longitudinal changes. Further longitudinal studies investigating the differential influence of puberty status and age on brain development in more diverse samples are needed to replicate the present results and address mechanisms underlying norm-variants in brain development.
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1000
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Hinweis
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DeepGreen-ID: 187a9ff68d674ed3a37053e72a719a28 ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
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