Neuroanatomical assessment of biological maturity

Abstract

Structural MRI allows unparalleled in vivo study of the anatomy of the developing human brain. For more than two decades, MRI research has revealed many new aspects of this multifaceted maturation process, significantly augmenting scientific knowledge gathered from postmortem studies. Postnatal brain development is notably protracted and involves considerable changes in cerebral cortical, subcortical, and cerebellar structures, as well as significant architectural changes in white matter fiber tracts (see [12]). Although much work has described isolated features of neuroanatomical development, it remains a critical challenge to characterize the multidimensional nature of brain anatomy, capturing different phases of development among individuals. Capitalizing on key advances in multisite, multimodal MRI, and using cross-validated nonlinear modeling, we demonstrate that developmental brain phase can be assessed with much greater precision than has been possible using other biological measures, accounting for more than 92% of the variance in age. Further, our composite metric of morphology, diffusivity, and signal intensity shows that the average difference in phase among children of the same age is only about 1 year, revealing for the first time a latent phenotype in the human brain for which maturation timing is tightly controlled.

Fig. 1

Individual measures derived from the T1-weighted imaging protocol. Example measures derived from the segmentation of the T1-weighted volume are plotted for 885 subjects as a function of age: total cortical area in square millimeters by thousands; mean cortical thickness in millimeters; volume of the left hippocampus in cubic millimeters by thousands; and volume of the right thalamus in cubic millimeters by thousands. Colors correspond to different sites and scanners. Symbol size represents subject sex (larger = female, smaller = male). A spline-fit curve (solid line) with 5% and 95% prediction intervals (dashed lines) are also shown.

Fig. 2

Individual measures derived from the T2- and diffusion-weighted imaging protocols. Example measures derived from the T2- and diffusion-weighted protocols are plotted for 885 subjects as a function of age: T2-normalized (T2N) signal intensity of left uncinate fibers; apparent diffusion coefficient (ADC) of the corpus callosum; ADC of the right superior longitudinal fasciculus (SLF); and fractional anisotropy (FA) of the left caudate nucleus. Colors correspond to different sites and scanners. Symbol size represents subject sex (larger = female, smaller = male). A spline-fit curve (solid line) with 5% and 95% prediction intervals (dashed lines) are also shown.

Fig. 3

Multimodal quantitative anatomical prediction of age. For 885 individuals, estimated brain age is plotted as a function of actual chronological age. Colors correspond to different sites and scanners. Symbol size represents subject sex (larger = female, smaller = male). A spline-fit curve (solid line) with 5% and 95% prediction intervals (dashed lines) are also shown.

Fig. 4

Age-varying contributions of different imaging measures to the prediction of age. The relative contributions of separate morphological, diffusivity, and signal intensity measures within different brain structures are plotted as a function of age. Colors correspond to measure and structure type (dark blue = T1 cortical area; green = T1 cortical thickness; red = T1 subcortical volumes; light blue = diffusion (FA/ADC) within white matter tracts; dark pink = diffusion (FA/ADC) within subcortical ROIs; gold = T2 signal intensity within white matter tracts; black = T2 signal intensity within subcortical ROIs). Contributions are computed as units of the proportion of total explained variance.