Grey and white matter volumes in early childhood: A comparison of voxel-based morphometry pipelines

Abstract

Early childhood is an important period of sensory, motor, cognitive and socio-emotional maturation, yet relatively little is known about the brain changes specific to this period. Voxel-based morphometry (VBM) is a technique to estimate regional brain volumes from magnetic resonance (MR) images. The default VBM processing pipeline can be customized to increase accuracy of segmentation and normalization, yet the impact of customizations on analyses in young children are not clear. Here, we assessed the impact of different preprocessing steps on T1-weighted MR images from typically developing children in two separate cohorts. Data were processed with the Computational Anatomy Toolbox (CAT12), using seven different VBM pipelines with distinct combinations of tissue probability maps (TPMs) and DARTEL templates created using the Template-O-Matic, and CerebroMatic. The first cohort comprised female children aged 3.9–7.9 years (N = 62) and the second included boys and girls aged 2.7–8 years (N = 74). We found that pipelines differed significantly in their tendency to classify voxels as grey or white matter and the conclusions about some age effects were pipeline-dependent. Our study helps to both understand age-associations in grey and white matter volume across early childhood and elucidate the impact of VBM customization on brain volumes in this age range.

Gray Matter Volume in the Developing Frontal Lobe and Its Relationship With Executive Function in Late Childhood and Adolescence: A Community-Based Study

“…Our well-trained physician did not find any abnormalities from motion and/or other artifacts on T1-weighted images. The procedure for preprocessing T1-weighted images included manual reorientation to the anterior commissure, gray matter segmentation based on a standard tissue probability map provided from SPM, creation of the study-specific template, spatial normalization with DARTEL to normalize individual images to the DARTEL template (34,60,61), modulation to adjust for volume signal changes during spatial normalization, and spatial smoothing of the gray matter partitions with a Gaussian kernel of 8-mm full width at half maximum. After preprocessing, values of rGMVs were extracted by averaging the values at each frontal region, according to the automated anatomical labeling atlas (62).…”

“…Although abnormality in the neurocognitive development of the frontal lobe adversely influences the everyday activities of children and adolescents, most of the previous research was conducted in clinical populations (e.g., ADHD children, schizophrenia patients) or focused on certain age ranges (e.g., 2-5 years of age) or developmental periods (e.g., early childhood) (16,33,34). Additionally, there is not enough converging evidence on the relationship between the structural maturation of the frontal lobe and the different domains of EFs during the transition between late childhood and adolescence.…”

Differences in Cerebellar Volume as a Diagnostic and Prognostic Biomarker in Children and Adolescents With Epilepsy of Unknown Etiology

“…21 Very small areas can be precisely measured. 22 Well-known studies, such as the ENIGMA cross-sectional study, have mainly analyzed the entire volume of the cerebrum or specific regions thereby. 23 Data on volume differences in the cerebellum, especially in children and adolescents with epilepsy, are available to a limited extent.…”

“…21 Very small areas can be precisely measured. 22 Well-known studies, such as the ENIGMA cross-sectional study, have mainly analyzed the entire volume of the cerebrum or specific regions thereby. 23 …”

Resting state functional networks in 1-to-3-year-old typically developing children

“…Generally, these studies have indicated that brain structure and functional networks are already in place by the second year of life, undergoing reorganization and fine-tuning beyond two years of age. Cortical and subcortical gray matter volumes, in particular, grow most rapidly in the first year of life and peak around age 2 years ( Gilmore et al, 2012 ; Haynes et al, 2020 ; Knickmeyer et al, 2008 ; Pfefferbaum et al, 1994 ). This rapid gray matter volume growth likely reflects continuous synapse formation in the first years of life, albeit with variable rates across different brain regions ( Huttenlocher and Dabholkar, 1997 ).…”