Developmental Stages and Sex Differences of White Matter and Behavioral Development through Adolescence: A Longitudinal Diffusion Tensor Imaging (DTI) Study

By Daniel J. Simmonds, Michael N. Hallquist, Miya R. Asato, Beatriz Luna. NeuroImage, 2013

424 citations recorded by the source corpus.

White matter (WM) continues to mature through adolescence in parallel with gains in cognitive ability. To date, developmental changes in human WM microstructure have been inferred using analyses of cross-sectional or two time-point follow-up studies, limiting our understanding of individual developmental trajectories. The aims of the present longitudinal study were to characterize the timing of WM growth and investigate how sex and behavior are associated with different developmental trajectories. We utilized diffusion tensor imaging (DTI) in 128 individuals ages 8-28, who received annual scans for up to 5 years and completed motor and cognitive tasks. Flexible nonlinear growth curves indicated a hierarchical pattern of WM development. By late childhood, posterior cortical-subcortical connections were similar to adults. During adolescence, WM microstructure reached adult levels, including frontocortical, frontosubcortical and cerebellar connections. Later to mature in adulthood were major corticolimbic association tracts and connections at terminal gray matter sites in cortical and basal ganglia regions. These patterns may reflect adolescent maturation of frontal connectivity supporting cognitive abilities, particularly the protracted refinement of corticolimbic connectivity underlying cognition-emotion interactions. Sex and behavior also played a large role. Males showed continuous WM growth from childhood through early adulthood, whereas females mainly showed growth during mid-adolescence. Further, earlier WM growth in adolescence was associated with faster and more efficient responding and better inhibitory control whereas later growth in adulthood was associated with poorer performance, suggesting that the timing of WM growth is important for cognitive development.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/b4a8fb727f28b3c709ff626f1eefbad6cc19f742

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Citations in the corpus

The Development of Brain White Matter Microstructure (2018, background): “Several DTI studies report earlier development in females, as evidenced by microstructural parameter changes that occur at younger ages in girls than boys, and/or continued changes of parameters during adolescence in males, with slower and/or absent changes in females (Asato et al., 2010; Clayden et”
Adolescence as a neurobiological critical period for the development of higher-order cognition (2018, neutral): “…higher order cognition—undergo continued structural and functional maturation during adolescence, including gross morphology, connectivity, and task-induced activation (Giedd, 2004; Gogtay et al., 2004; Luna et al., 2010, 2015; Marek et al., 2015; Simmonds et al., 2014; Sowell et al., 2004).”
Neurodevelopment of the association cortices: Patterns, mechanisms, and implications for psychopathology (2021, neutral): “…emotional and executive functions, such as the uncinate fasciculi and the cingulum bundles, exhibit their largest increases in FA and decreases in MD from adolescence until approximately the mid-thirties (Kochunov et al., 2012; Lebel and Beaulieu, 2011; Simmonds et al., 2014; Slater et al., 2019).”
Altered oscillatory activity pattern during reconfiguration processes of perception–action representations in adolescents with AD(H)D (2025, unknown): “As far as we know, no appropriate template exists that can accommodate children and adolescents of various ages while accounting for the brain ’ s ongoing development (Simmonds et al., 2014).”
Aperiodic EEG and 7T MRSI evidence for maturation of E/I balance supporting the development of working memory through adolescence (2024, methodology): “Consistent with previous work (Wood, 2017; Simmonds et al., 2014; Calabro et al., 2020), 10000 simulated GAM fits and their derivatives (generated at age intervals of 0.1 years) were computed from a multivariate normal distribution; vector means and covariance of which corresponded to the fitted…”