Fig. 1: Study design overview and methodological approach.

a We included structural and functional fMRI images from 591 typically developing subjects aged 8–21 years from the HCP-D dataset. First, task activation maps were integrated into the gradient-weighted Markov Random Field (gwMRF) model to constrain group-level parcellations. Then, individualized homologous functional parcellations (IHFPs) were constructed using a variational Bayes expectation-maximization (VBEM) algorithm^21,28, with areal matching procedures aligned to reference parcellations. For more details, please see Supplementary Fig. 2. b Resting-state functional connectivity can provide a criterion for characterizing functional development. Then, we leverage GAMLSS to calculate the developmental trajectories of areal- and network-level functional connectivity and quantify the whole-brain distribution of its mean strength and growth rate. c We developed prediction models based on the within-system and between-system functional connectivity of IHFPs to predict adolescent cognitive behaviors. These models were compared to state-of-the-art individualized functional parcellations proposed by Kong et al. (noted as Kong2021)²¹. We selected 6 cognitive behaviors that stable and well-performed documented in previous studies^28,40,41, including reading, inhibition, vocabulary, working memory, fluid cognition, and crystallized cognition in predict models.