title: Longitudinal regression analysis of spatial-temporal growth patterns of geometrical diffusion measures in early postnatal brain development with diffusion tensor imaging.
abstract: Although diffusion tensor imaging (DTI) has provided substantial insights into early brain development, most DTI studies based on fractional anisotropy (FA) and mean diffusivity (MD) may not capitalize on the information derived from the three principal diffusivities (e.g. eigenvalues). In this study, we explored the spatial and temporal evolution of white matter structures during early brain development using two geometrical diffusion measures, namely, linear (Cl) and planar (Cp) diffusion anisotropies, from 71 longitudinal datasets acquired from 29 healthy, full-term pediatric subjects. The growth trajectories were estimated with generalized estimating equations (GEE) using linear fitting with logarithm of age (days). The presence of the white matter structures in Cl and Cp was observed in neonates, suggesting that both the cylindrical and fanning or crossing structures in various white matter regions may already have been formed at birth. Moreover, we found that both Cl and Cp evolved in a temporally nonlinear and spatially inhomogeneous manner. The growth velocities of Cl in central white matter were significantly higher when compared to peripheral, or more laterally located, white matter: central growth velocity Cl=0.0465±0.0273/log(days), versus peripheral growth velocity Cl=0.0198±0.0127/log(days), p<10⁻⁶. In contrast, the growth velocities of Cp in central white matter were significantly lower than that in peripheral white matter: central growth velocity Cp=0.0014±0.0058/log(days), versus peripheral growth velocity Cp=0.0289±0.0101/log(days), p<10⁻⁶. Depending on the underlying white matter site which is analyzed, our findings suggest that ongoing physiologic and microstructural changes in the developing brain may exert different effects on the temporal evolution of these two geometrical diffusion measures. Thus, future studies utilizing DTI with correlative histological analysis in the study of early brain development are warranted.
authors.affiliation: Dept. of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. yasheng_chen@med.unc.edu
authors.forename: Yasheng
authors.initials: Y
authors.lastname: Chen
authors.forename: Hongyu
authors.initials: H
authors.lastname: An
authors.forename: Hongtu
authors.initials: H
authors.lastname: Zhu
authors.forename: Valerie
authors.initials: V
authors.lastname: Jewells
authors.forename: Diane
authors.initials: D
authors.lastname: Armao
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
authors.forename: John H
authors.initials: JH
authors.lastname: Gilmore
authors.forename: Weili
authors.initials: W
authors.lastname: Lin
mesh_terms: D000465:Algorithms; D016880:Anisotropy; D001921:Brain / Q000033:anatomy & histology* / Q000254:growth & development*; D001931:Brain Mapping; D002675:Child, Preschool; D056324:Diffusion Tensor Imaging; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007223:Infant; D007231:Infant, Newborn; D016014:Linear Models; D008137:Longitudinal Studies; D008297:Male; D012044:Regression Analysis