Developmental Individual Variability of Brain Functional Networks During Childhood and Adolescence

Childhood and adolescence are critical periods for the maturation of functional brain networks, supporting cognitive and behavioral development. While normative developmental trajectories of functional networks have been well documented, patterns of individual variability and their developmental changes remain to be clarified. Using cross-sectional resting-state functional magnetic resonance imaging datasets from 257 children (aged 6–12 years) and 60 healthy adults (aged 18–29 years), the authors characterized the spatial organization of individual variability in functional networks and its developmental progression. Additionally, a longitudinal resting-state functional magnetic resonance imaging dataset of 137 children (baseline age 6–12 years) was used to examine the individual uniqueness of functional network organization over an approximately one-year developmental interval. In children and adolescents, individual variability exhibited an adultlike spatially heterogeneous pattern, with lower variability mainly in primary visual and sensorimotor cortices and higher values primarily in association regions (e.g., frontoparietal network) involved in higher-order cognition. Spatial similarity to the adult variability pattern increased linearly with age. Developmental changes in individual variability were functional-systems-specific, showing a significant decrease in the visual system but relatively modest changes in the default mode and ventral/dorsal attention systems. Longitudinal identification analyses further demonstrated high individual discriminability based on whole-brain connectivity, as well as higher-order systems including the frontoparietal, default mode, and dorsal attention networks. These findings advance understanding of how individual-variability and individual-specific functional-connectivity signatures are organized and evolve during late childhood, providing a developmental reference for connectome-based individualized characterization.