Differential White Matter Maturation from Birth to 8 Years of Age

Abstract

Comprehensive delineation of white matter (WM) microstructural maturation from birth to childhood is critical for understanding spatiotemporally differential circuit formation. Without a relatively large sample of datasets and coverage of critical developmental periods of both infancy and early childhood, differential maturational charts across WM tracts cannot be delineated. With diffusion tensor imaging (DTI) of 118 typically developing (TD) children aged 0–8 years and 31 children with autistic spectrum disorder (ASD) aged 2–7 years, the microstructure of every major WM tract and tract group was measured with DTI metrics to delineate differential WM maturation. The exponential model of microstructural maturation of all WM was identified. The WM developmental curves were separated into fast, intermediate, and slow phases in 0–8 years with distinctive time period of each phase across the tracts. Shorter periods of the fast and intermediate phases in certain tracts, such as the commissural tracts, indicated faster earlier development. With TD WM maturational curves as the reference, higher residual variance of WM microstructure was found in children with ASD. The presented comprehensive and differential charts of TD WM microstructural maturation of all major tracts and tract groups in 0–8 years provide reference standards for biomarker detection of neuropsychiatric disorders.

Signatures of brain plasticity supporting language recovery after perinatal arterial ischemic stroke

“…These results converge with previous ones pointing to a more graded maturation of the dorsal whitematter tracts supporting language processing, particularly when compared to the maturation of the ventral pathway connecting the superior temporal, angular gyrus and inferior frontal gyri through the inferior fronto-occipital, inferior longitudinal and uncinate fasciculi (Brauer et al, 2013;Dubois et al, 2015;Bajada, Lambon-Ralph & Cloutman, 2015). Recent DTI results point to a similar developmental pattern for white-matter pathways connecting temporo-parietal and frontal regions via the Superior Longitudinal Fasciculus (SLF, Yu et al, 2019;Ouyang et al, 2019). Therefore, the changes observed in the long segment of the AF, as well as those in the anterior segment of the AF (connecting frontal and inferior-parietal regions) might be crucial for early language acquisition.…”

“…By contrast, stroke occurring during the perinatal period over the left hemisphere generally does not induce post-stroke aphasia thus suggesting that brain plasticity can support functional recovery (Bates et al, 2001;Stiles et al, 2005). Interestingly, the speech network is still developing at the time of the insult, as recent neuroimaging studies, conducted in healthy infants, have revealed different maturational time course of grey and white matter components (see Dehaene-Lambertz, 2017;Yu et al, 2019;Ouyang et al, 2019). In this context, the study of children with perinatal arterial ischemic stroke (PAIS) provides a unique opportunity to show the signatures of brain reorganization and plasticity mechanisms occurring before language functions are acquired.…”

Diffusion-MRI-based regional cortical microstructure at birth for predicting neurodevelopmental outcomes of 2-year-olds

“…Individual differences in brain WM microstructural architectures ( Scholz et al, 2009 ; Yu et al, 2020 ), behavior, and functions ( Braga and Buckner, 2017 ; Xu et al, 2019 ) have been well recognized. Individual variability in brain structures and associated individual variability in future behaviors can be harnessed for robust prediction at the single-subject level ( Kanai and Rees, 2011 ; Rosenberg et al, 2018 ), a step further than group classification.…”

Diffusion-MRI-based regional cortical microstructure at birth for predicting neurodevelopmental outcomes of 2-year-olds

“…Individual differences in brain white matter microstructural architectures (Scholz et al, 2009;Yu et al, 2019), behavior, and functions (Braga and Buckner, 2017;Xu et al, 2019) have been well recognized. Individual variability in brain structures and associated individual variability in future behaviors can be harnessed for robust prediction at the single-subject level (Kanai and Rees, 2011;Rosenberg et al, 2018), a step further than group classification.…”