Chan, S. Y.; Huang, P.; Teh, A. L.; Naaz, A.; Chuah, J. S. M.; Ngoh, Z. M.; Lee, J.; Manahan, A. M. A.; Lim, X. Y. H.; Fortier, M. V.; Zhou, J. H.; Yeo, B. T. T.; Chong, Y. S.; Gluckman, P.; Eriksson, J.; Dorajoo, R.; Wang, D.; Meaney, M. J.; Tan, A. P.

BrainAge models hold promise as a clinical biomarker for developmental brain health, especially in childhood when there is the potential for early intervention. To distinguish between normative developmental variance and pathological divergence, BrainAge models should reflect the dynamic and diverse neurodevelopmental processes that occur in distinct developmental windows across childhood. We utilized multi-modal neuroimaging data from three pediatric cohorts covering ages 4 to 13 years (n = 1005, 2126 scans), split into Train and Test datasets. Twelve sex-stratified BrainAge models were built stratified by type and different combinations of neuroimaging features. Model types were 'Full-Span' models covering the full age range, and 'Phase-Specific' models split into early- and late-childhood. We first compared BrainAge estimates in the Test dataset amongst our candidate models, then benchmarked the best-performing model against published pre-trained models and DNA-based biological age measures. Our findings show that a BrainAge model that was phase-specific and consisted of both structural and functional features (cortical thickness, subcortical volumes, and functional network integration measures) showed good prediction of age and best distinguished between healthy and symptomatic subgroups. We present a proof-of-concept for developmental models supporting building BrainAge models of higher temporal resolution that align to different childhood developmental phases.