Functional connectivity MRI, the technique at the heart of the study, exploits the fact that spatially distant brain regions fluctuate together in their activity even when a person is simply resting with eyes open. These temporally coherent fluctuations reveal the brain’s intrinsic architecture: distinct functional systems that support sensorimotor, cognitive, and affective processes. In adults, this organization has been characterized extensively, both in group averages and in densely sampled individuals. But questions about how these networks mature through childhood and adolescence have persisted, partly because pediatric neuroimaging is notoriously difficult.

Children move in the scanner. Head motion induces systematic artifacts in functional connectivity estimates, and variable data quality has long complicated efforts to establish what is real signal and what is noise. Subcortical structures present their own challenges, including reduced temporal signal-to-noise ratio, small anatomical size, partial volume effects, and sensitivity to preprocessing choices. As a result, many developmental studies have focused on the cortex alone, leaving the organization of cortico-subcortical loops—which connect the cortex with the basal ganglia, thalamus, and cerebellum through recurrent circuits supporting integrated processing—less systematically characterized in children.

The new study tackled these obstacles with scale and statistical discipline. The team divided the ABCD baseline sample into two demographically matched halves: a discovery cohort of 3,624 children and a replication cohort of 3,692 children, all aged 9 to 10 years, matched across ten demographic variables including site location, age, sex, ethnicity, grade, parental education, handedness, family income, and exposure to anesthesia. Family members were kept together in the same set to avoid confounding. Strict motion exclusion criteria were applied: only children with at least 600 frames—eight minutes—of low-motion data (framewise displacement below 0.2 millimeters) were included, with a minimum of five contiguous retained frames. The imaging data themselves were collected across 21 U.S. sites on Siemens Prisma, Philips, and GE 3 Tesla scanners, with 20 minutes of eyes-open resting-state fMRI per child.

After preprocessing with the ABCD-HCP pipeline—which included anatomical normalization, cortical surface parcellation, distortion correction, nuisance regression, band-pass filtering, and respiratory motion correction—the researchers computed pairwise correlations among all 59,412 cortical vertices for each participant and averaged them within each cohort. They then applied the Infomap community-detection algorithm, a graph-theoretical method originally developed for mapping communities in social and biological networks, across a range of edge densities from 0.1 to 5 percent. Communities were assigned functional identities by template matching against canonical network templates using the Jaccard index, with a consensus identity selected at the sparsest threshold where a reliable match was found. For subcortical voxels, each voxel in the basal ganglia, thalamus, and cerebellum was assigned to the cortical network with which it showed the strongest correlation, in a winner-take-all fashion.

The reproducibility results were striking. Vertex-wise functional connectivity between the discovery and replication cohorts correlated at r = 0.99, and parcel-level matrices showed the same near-perfect agreement. Network topography was similarly consistent: normalized mutual information (NMI), a metric ranging from 0 for no agreement to 1 for perfect agreement, reached 0.90 in the cortex, 0.87 in the basal ganglia and thalamus, and 0.89 in the cerebellum, all statistically significant under spatial permutation testing that rotated network labels on a spherical surface 1,000 times. The maps recovered all the canonical systems previously described in adults, including the default-mode, frontoparietal, cingulo-opercular, salience, dorsal attention, ventral attention, somatomotor, visual, and auditory networks.

The large sample also confirmed classic anatomical motifs with unprecedented statistical power. On the medial wall of the cortex, the team observed a foot-hand-foot somatomotor arrangement consistent with the known somatotopy of the supplementary motor area, a pattern first described through stimulation studies in the 1950s. The parietal operculum showed a hand-face topography matching the established organization of secondary somatosensory cortex. In the subcortex, the posterior putamen and central thalamus displayed topographically organized somatomotor assignments, and the lateral geniculate nucleus of the thalamus appeared exactly where visual relay anatomy predicts it. In the cerebellum, the characteristic dorsal and ventral representations of motor cortex were evident, reflecting the well-documented doubled somatomotor topography of the motor cerebellum. Every one of these features replicated independently across the two halves of the sample.

The study also extended its reach to a recently discovered system: the Somato-Cognitive Action Network, or SCAN, first described in 2023 as a network interdigitated with effector-specific motor regions and implicated in integrating motor control with cognitive and goal-directed processes. Using a manual seed-mapping approach with two independent raters and conservative connectivity thresholds around the 96th to 98th percentile, the team identified SCAN’s characteristic distributed topography in the child data. A split-half analysis yielded an NMI of 0.88, providing novel evidence that SCAN is a reproducible feature of large-scale functional organization in children, though the authors caution that automated approaches remain variable in their sensitivity to the network’s ventral and integrative components.

When the child maps were compared with adult data, the picture became more nuanced. To ensure comparable sample sizes, the researchers subsampled 1,000 children from each ABCD cohort, sex-matched to the 1,000 HCP adults aged 22 to 37. Mantel tests confirmed robust adult-child correspondence in both the rank ordering and the absolute strength of functional connections: Spearman correlations of roughly 0.80 and Pearson correlations of roughly 0.85 between HCP and each ABCD subsample. But these cross-age values were consistently lower than the near-identical agreement between the two child cohorts (Spearman r = 0.998). Community structure showed moderate adult-child similarity (NMI around 0.62 to 0.63) compared with 0.88 between child partitions, and Dice coefficients quantifying spatial overlap of individual networks exceeded spatially constrained null expectations for every network, with sensorimotor networks showing the highest overlap and association networks the lowest.

A graph-theoretical measure called the participation coefficient, which indexes how broadly a region’s connections span multiple networks, revealed further developmental nuance. Adults showed higher participation in dorsal attention, somatomotor-hand, somatomotor-foot, and visual networks, suggesting that in maturity these systems engage in more distributed cross-network communication, potentially reflecting greater integration with higher-order association and control systems. Children, by contrast, showed relatively higher participation in the auditory network. The authors interpret the lower participation of sensory and motor systems in children as possibly reflecting a more modular organization, with these systems remaining relatively segregated from higher-order networks during late childhood. The researchers emphasize that these differences should be read as descriptive features of network architecture rather than definitive markers of developmental progression, since the metric is sensitive to parcellation boundaries and cross-dataset acquisition differences.

The benchmark carries practical weight beyond basic neuroscience. By establishing a stable, reproducible reference for functional network organization at ages 9 to 10, the framework gives researchers a foundation for longitudinal analyses as additional waves of ABCD imaging data become available, helping distinguish true within-subject developmental change from measurement variability. It may also aid the detection of atypical network organization underlying emerging cognitive, emotional, or behavioral difficulties in conditions such as attention-deficit/hyperactivity disorder, autism spectrum disorder, mood disorders, and psychotic-spectrum disorders. The authors note that group averages necessarily smooth over individual variability, which is substantial in higher-order association regions, and that population-level and individualized mapping approaches should be treated as complementary. As the ABCD cohort matures, this benchmark will serve as the yardstick against which the adolescent remodeling of the brain’s functional architecture can finally be measured.

Subject of Research: Population-scale mapping of functional brain network organization in childhood and its similarity to adult network architecture

Article Title: Benchmarking functional brain network organization in childhood and its similarity to adults

Article References: Ali, S. A., Demeter, D. V., Baim, A. R., Koithan, E. M., Feigelis, M., Zreik, S., Ahern, J., Chang, S. E., Park, S., Gordon, E. M., Marek, S., & Greene, D. J. (2026). Benchmarking functional brain network organization in childhood and its similarity to adults. Frontiers in Neuroimaging, 5, Article 1818662. https://doi.org/10.3389/fnimg.2026.1818662

Image Credits: AI Generated

DOI: 10.3389/fnimg.2026.1818662

Keywords: brain networks, functional connectivity, childhood development, resting-state fMRI, ABCD Study, Human Connectome Project, neuroimaging, somatomotor networks, association networks, Somato-Cognitive Action Network, graph theory, brain maturation