DOI: 10.25338/b8b077
title: Network evolution of regional brain volumes in young children reflects neurocognitive scores and mother's education

description: The maturation of regional brain volumes from birth to preadolescence is a
 critical developmental process that underlies emerging brain structural
 connectivity and function. Regulated by genes and environment, the
 coordinated growth of different brain regions plays an important role in
 cognitive development. Current knowledge about structural network
 evolution is limited, partly due to the sparse and irregular nature of
 most longitudinal neuroimaging data. In particular, it is unknown how
 factors such as mother’s education or sex of the child impact the
 structural network evolution. To address this issue, we propose a method
 to construct evolving structural networks and study how the evolving
 connections among brain regions as reflected at the network level are
 related to maternal education and biological sex of the child and also how
 they are associated with cognitive development. Our methodology is based
 on applying local Fréchet regression to longitudinal neuroimaging data
 acquired from the RESONANCE cohort, a cohort of healthy children (245
 females and 309 males) ranging in age from 9 weeks to 10 years. Our
 findings reveal that sustained highly coordinated volume growth across
 brain regions is associated with lower maternal education and lower
 cognitive development. This suggests that higher neurocognitive
 performance levels in children are associated with increased variability
 of regional growth patterns as children age.
description: For all MR image acquisition, children under 4 years of age were
 scanned during natural and non-sedated sleep and older children were
 imaged whilst watching a movie or other video. Our imaging protocol
 included relaxometry, multi-shell diffusion, resting-state connectivity,
 and magnetic resonance spectroscopy acquisitions in addition to the
 anatomical data. As a result, depending on child compliance (sleeping
 and/or motion), high-quality anatomical data were not collected or
 available for every child at every scan time-point. Following data
 acquisition, scans were inspected to ensure there were no motion-related
 artifacts and image blurring and ghosting. T1-weighted anatomical data
 were acquired on a 3T Siemens Trio scanner with a 12-channel head RF
 array. T1-weighted magnetization-prepared rapid acquisition gradient echo
 anatomical data were acquired with an isotropic voxel volume of
 1.2×1.2×1.2 mm<sup>3</sup>, resampled to 0.9×0.9×0.9
 mm<sup>3</sup>. Sequence-specific parameters were: TE = 6.9
 ms; TR = 16 ms; inversion preparation time = 950 ms; flip angle = 15
 degrees; BW = 450 Hz/Pixel. The acquisition matrix and field of view were
 varied according to child head size in order to maintain a constant voxel
 volume and spatial resolution across all ages. Using a multistep
 registration procedure, a series of age-specific anatomical T1-weighted
 templates were created corresponding to 3, 6, 9, 12, 15, 18, 21, 24, 30,
 36, 42, 48, 60, 72, 84, 96 and 108-month ages. At least 10 boys and 10
 girls were included in each template. An overall study template was then
 created from these age templates, which was aligned to the MNI152
 template. Each child’s anatomical T1-weighted image was transformed into
 MNI space by first aligning to their age-appropriate template and then
 applying the pre-computed transformation to MNI space, with the calculated
 individual forward and reverse transformations saved and used for the
 volumetric analysis described below. All template creation and image
 alignment were performed using a 3D nonlinear approach with
 cross-correlation and mutual information cost functions. We then applied
 the Desikan-Killiany-Tourville (DKT) cortical labeling protocol,
 FreeSurfer's wmparc and aseg non-cortical (plus white matter) labels
 through Mindboggle, resulting in volumetric output from 96 brain regions.
 Five regions with very small volumes were excluded: left inferior lateral
 ventricle, left vessel, right inferior lateral ventricle, left basal
 forebrain, and right basal forebrain.