Hierarchical individual variation and socioeconomic impact on personalized functional network topography in children

The spatial layout of large-scale functional brain networks exhibits considerable inter-individual variability, especially in the association cortex. Research has demonstrated a link between early socioeconomic status (SES) and variations in both brain structure and function, which are further associated with cognitive and mental health outcomes. However, the extent to which SES is associated with individual differences in personalized functional network topography during childhood remains largely unexplored.

We used a machine learning approach-spatially regularized non-negative matrix factorization (NMF)-to delineate 17 personalized functional networks in children aged 9-10 years, utilizing high-quality functional MRI data from 6001 participants in the Adolescent Brain Cognitive Development study. Partial least square regression approach with repeated random twofold cross-validation was used to evaluate the association between the multivariate pattern of functional network topography and three SES factors, including family income-to-needs ratio, parental education, and neighborhood disadvantage.

We found that individual variations in personalized functional network topography aligned with the hierarchical sensorimotor-association axis across the cortex. Furthermore, we observed that functional network topography significantly predicted the three SES factors from unseen individuals. The associations between functional topography and SES factors were also hierarchically organized along the sensorimotor-association cortical axis, exhibiting stronger positive associations in the higher-order association cortex. Additionally, we have made the personalized functional networks publicly accessible.

These results offer insights into how SES influences neurodevelopment through personalized functional neuroanatomy in childhood, highlighting the cortex-wide, hierarchically organized plasticity of the functional networks in response to diverse SES backgrounds.