DECODING BRAIN DYNAMICS VIA CROSS-NETWORK NON-LINEAR TRAJECTORIES

Abstract

Infant brain networks mature rapidly and nonlinearly, yet cross-network comparisons of developmental trajectories remain rare. We introduce cross-network non-linear trajectory characterization (CNTC), which fits cubic polynomials to age-related change in three spatial measures--network-averaged spatial similarity (NASS), network strength, and network size--derived from resting-state fMRI. Using 137 scans from 74 neurotypical infants spanning 0-6 months of age, we estimated networks via group ICA, modeled age trajectories within each network, and then contrasted coefficients across networks. Across seven canonical systems, trajectories showed robust age effects. Cross-network differences were dominated by linear terms, with selective quadratic effects and few cubic effects. Visual and cerebellar systems exhibited the steepest NASS slopes and pronounced increases in strength and size, whereas motor/attention changes were more gradual. CNTC provides a compact, coefficient-level summary of inter-network maturation and a basis for benchmarking atypical development in future work.