Human Brain Development: a cross-sectional and longitudinal study integrating multiple neuromorphological features
biorxiv(2022)
摘要
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.
Significance The complex anatomy of the cortical sheet is best characterized using multiple morphometric characteristics. We expanded on recent developments in matrix factorization to identify spatial patterns of covariance across the cortical sheet. Using a large, well-characterized dataset, we examined the differential contributions of neuroanatomical features to cortical covariation in a single analytical framework using both cross-sectional and longitudinal data. We identified dominant modes of covariance between cortical morphometric features and their coordinated pattern of change, demonstrating sexually differentiated patterns and a strong association with variability in age, socioeconomic status, and cognitive ability. This novel characterization of cortical morphometry provides an important understanding of the interdependencies between neuroanatomical measures in the brain and behavioral development context.
### Competing Interest Statement
The authors have declared no competing interest.
* bPLS
: behavioral Partial Least Squares
C
: Component
CT
: Cortical Thickness
CV
: Cortical Volume
GI
: Local Gyrification Index
GMV
: Gray Matter volume
ICA
: Independent Component Analysis
IQ
: Intelligence Quotient
LC
: Longitudinal Component
LV
: Latent Variable
MC
: Cortical Mean Curvature
NIMH
: National Institute of Mental Health
NMF
: Non-negative Matrix Factorization
OPNMF
: Orthogonal Projective Non-negative Matrix Factorization
PCA
: Principal Component Analysis
PLS
: Partial Least Squares
SA
: Cortical Surface Area
SES
: Childhood Socioeconomic Status
sMRI
: Structural Magnetic resonance imaging
T
: Tesla
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