Human Brain Development: a cross-sectional and longitudinal study integrating multiple neuromorphological features

Abstract

Brain maturation studies typically examine relationships linking a single morphometric feature with aspects of cognition, behavior, age, or other demographic characteristics. However, the coordinated spatiotemporal arrangement of morphological features across development and their associations with behavior are unclear. Here, we examine covariation across multiple cortical features (cortical thickness [CT], surface area [SA], local gyrification index [GI], and mean curvature [MC]) using magnetic resonance images from the long-running National Institute of Mental Health developmental cohort (ages 5-25). Neuroanatomical covariance was examined using non-negative matrix factorization (NMF), which decomposes covariance resulting in a parts-based representation. Cross-sectionally, we identified six components of covariation which demonstrate differential contributions of CT, GI, and SA in hetero- vs. unimodal areas. We sought to use this technique longitudinally to examine covariance in rates of change, which highlighted a preserved SA in unimodal areas and changes in CT and GI in heteromodal areas. Using behavioral partial least squares (PLS), we identified a single latent variable (LV; 96 % covariance explained) that recapitulated patterns of reduced CT, GI, and SA that are generally related to older age, with limited contributions of IQ and SES. Longitudinally, PLS revealed three LVs that demonstrated a nuanced developmental pattern that highlighted a higher rate of maturational change in SA and CT in higher IQ and SES females. This novel characterization of brain maturation provides an important understanding of the interdependencies between morphological measures, their coordinated development, and their relationship to biological sex, cognitive ability, and the resources of the local environment.

Mapping human brain charts cross-sectionally and longitudinally

“…Demographic, neuroimaging, developmental, and lifestyle factors are known sources of nonage-related variability ( 10 – 13 ). MAEs in predicting individualized change were significantly higher in males relative to females and across all models examined for GMV and SA but not for CTh and FA ( SI Appendix , Fig.…”

“…Another major challenge presented by cross-sectional normative models is distinguishing age-related variability from nonage-related determinants of change. Different constellations of characteristics attributable to individuals, such as neuroimaging confounds, interscan interval, and lifestyle factors are known sources of nonage-related variability ( 10 – 13 ). Such variability may explain why person-specific rates of change in common MRI measures (e.g., cortical volume and thickness) ascertained directly from longitudinal measurements can often depart from group-level age-related brain trends—e.g., upward instead of downward slopes in gray matter volume (GMV) with advancing age ( 14 ).…”