Non-Linear Trajectories of Brain Network Development in Infants: Insights from Resting-State fMRI

Motivation: Understanding non-linear brain development in infants is crucial, addressing the gap in comparing network-specific trajectories to reveal distinct growth patterns. Goal(s): The study aimed to systematically characterize and compare non-linear trajectories in various brain networks in neurotypically developing infants to identify unique developmental patterns. Approach: Using a longitudinal dataset of 137 resting-state fMRI scans from 74 infants, a cubic model was applied across five spatial metrics in 13 networks to capture linear and non-linear growth. Results: The analysis revealed significant non-linear growth differences, with unique cubic and quadratic trajectories across networks, underscoring varied maturation timelines and interactions. Impact: This study advances understanding of infant brain development by revealing distinct growth patterns across networks, which can inform early detection of atypical neurodevelopment. Future research may explore how these trajectories correlate with cognitive and behavioral outcomes, enhancing early intervention strategies.