Byeon, Kyoungseob; Park, Hyunjin; Park, Shinwon; Cluce, Jon; Mehta, Kahini P.; Cieslak, Matthew C.; Cui, Zaixu; Hong, Seokjun; Chang, Catie E.; Smallwood, Jonathan M.; Satterthwaite, Theodore Daniel; Milham, Michael Peter; & Xu, Ting. (2026).听.听Nature Communications, 17(1), 1012.听
The brain undergoes major structural and functional changes from childhood through adolescence. Research suggests that neurodevelopment happens in a hierarchical way, meaning different brain regions and networks mature at different rates. However, less is known about how the brain鈥檚 intrinsic spatiotemporal propagations鈥攑atterns showing how activity spreads across the brain over time鈥攄evelop during this period. This study examined how these activity patterns change from childhood to early adulthood.
Using a recently developed method that measures time-lagged dynamic propagations, the researchers analyzed how brain activity travels along three major axes of brain organization: the sensory鈥揳ssociation (S-A) axis, which connects basic sensory regions to higher-order thinking areas; the task-positive to default network (TP-D) axis, which reflects shifts between attention-focused networks and the default mode network active during rest and internal thought; and the somatomotor鈥搗isual (SM-V) axis, which links movement and visual processing regions. The results showed that these propagation patterns gradually become more adult-like over development. As children mature, they spend more time in S-A and TP-D propagation states, while the occurrence of SM-V propagation states decreases.
Importantly, top-down propagations along the S-A axis鈥攎eaning activity flowing from higher-order cognitive regions to sensory regions鈥攊ncreased with age and were better predictors of cognitive performance than bottom-up propagations, which flow from sensory areas upward. These findings were replicated in two independent datasets, the Human Connectome Project Development cohort and the Nathan Kline Institute Rockland Sample, supporting the robustness and generalizability of the results. Overall, the study provides new insight into how large-scale functional brain dynamics develop during youth and how these changes support cognitive abilities.
Fig. 1: Spatiotemporal propagation patterns and their neurodevelopmental change from children to early adulthood.
A聽The first three propagation patterns derived from the reference cohort (HCP-A), represent group-level reference propagation patterns. Each row displays a full propagation cycle for the recurring spatiotemporal patterns: sensorimotor to association (S-A), task-positive to default mode networks (TP-D), and somatomotor to visual networks (SM-V). The patterns are depicted through their temporal phase cycle, ranging from 0 to 2蟺.听B聽Explained variance ratios of the first six propagation patterns from CPCA. The light blue line represents the youth cohort (HCP-D) and the dark line represents the reference adult cohort (HCP-A).听C聽Between-cohort similarity matrix showing the pairwise Pearson鈥檚 correlation of the propagation patterns across youth (HCP-D) and adult (HCP-A) propagation patterns. We also confirmed cross-cohort similarity using HCP Young Adult cohort (N鈥�=鈥�892, age 21-35, Figure.听)].听D聽Reliability of propagation patterns, assessed by the discriminability for HCP-D and HCP-A cohorts.听E聽Age-related similarity of propagation patterns to adult reference. Dots represent the spatial correlations of the propagation pattern between individuals in the youth cohort and the group-level adult reference. The regression line illustrates the developmental trend across age. Age effect was assessed using a Spearman correlation, with聽p聽values adjusted for multiple comparisons using the false-discovery-rate (FDR) correction. Significant age-related increases were observed for the S-A (pFDR <0.001), TP-D (pFDR <0.001) and SM-V (pFDR = 0.002) propagation patterns. Statistical significance is denoted by asterisks (*: pFDR <0.05).听F聽Age prediction using the first three dynamic patterns. A combination of the first three dominant propagation patterns in the PLSR model predicts age with a Spearman鈥檚 correlation 蟻 of 0.80 and a mean absolute error (MAE) of 1.87 years.