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

Most of what we know about interactions between bacterioplankton and eukaryotic plankton has come from snapshots taken during phytoplankton blooms, dramatic events in which algae multiply rapidly and then collapse. Those studies gave the impression that bacterial and algal communities link up mainly during these bursts of productivity. But whether such coupling holds up under the ordinary, day-to-day variability of the seasons—and what bacterial traits might sustain it—remained open questions. To find out, a team led by Xiao Ma and Jia Luo of the South China Sea Institute of Oceanology sampled coastal seawater over a full annual cycle, tracking how microbial communities changed from February through November.

The study’s design hinged on a crucial distinction in marine microbiology: the separation of particle-attached prokaryotes, those living on surfaces larger than three micrometers, from free-living prokaryotes, those drifting in the water between 0.2 and three micrometers. The researchers combined three complementary approaches: profiling communities using 16S and 18S rRNA gene sequencing to catalog bacteria and eukaryotes respectively, building statistical association networks to map which organisms tend to co-occur, and applying genome-resolved metagenomics to reconstruct the actual genetic blueprints of the bacteria involved.

The first major finding concerned diversity. The diversity of particle-attached bacteria rose and fell in tight synchrony with the diversity of eukaryotic plankton, showing a statistically significant positive correlation with a Pearson’s correlation coefficient of 0.459. Free-living bacteria, by contrast, maintained comparatively stable diversity through the year and showed only a weak, statistically insignificant coupling to eukaryotic diversity, with a coefficient of just 0.194. In other words, when the eukaryotic plankton community shifted with the seasons, it was the particle-attached bacteria that shifted with them, while their free-living counterparts marched to a more independent rhythm.

Network analysis reinforced this picture on a much grander scale. When the researchers mapped the statistical associations between bacterial and eukaryotic taxa, the particle-attached communities produced more than 2.5 million positive links with eukaryotes, against only about 15,000 negative links. The free-living networks, while still substantial, yielded fewer positive associations—roughly 1.45 million—and slightly more negative ones. This imbalance, with positive associations overwhelmingly dominating in the particle-attached world, points to recurrent facilitation: bacteria on particles and eukaryotic plankton repeatedly appearing together in ways that suggest mutual benefit rather than competition, and doing so consistently across the seasonal succession of species.

What might underlie this facilitation? Functional comparisons of gene content offered clues. The particle-attached communities were enriched in seven KEGG pathways, standardized categories of metabolic function, hinting at a broader capacity for carbon and nitrogen processing, enhanced energy conservation, and an increased ability to biosynthesize antibiotic- and toxin-like secondary metabolites. These chemical weapons may help particle dwellers defend their cramped, contested territories against rivals, while their expanded metabolic repertoire allows them to exploit the rich but chemically complex organic matter that particles provide.

The most striking evidence came from the reconstruction of genomes. From the metagenomic data, the team recovered 120 high-quality metagenome-assembled genomes, or MAGs, each exceeding ninety percent completeness with less than five percent contamination. The particle-attached genomes were significantly larger than their free-living counterparts, carrying more genetic real estate. When the researchers compared phylogenetically matched pairs—close relatives of the same bacterial lineages, one attached to particles and one free-living—the particle-attached members consistently showed expansions in genes for chemotaxis, the ability to swim toward chemical cues; biofilm formation, the construction of sticky surface communities; secretion systems that move molecules across cell envelopes; polymer-processing enzymes that break down complex organic matter; respiratory flexibility that allows energy generation under varying conditions; and detoxification functions for surviving chemical stress.

These near-neighbor comparisons carry an evolutionary message. Because the paired genomes are closely related, the differences between them are unlikely to reflect ancient lineage history and instead point to habitat-driven genomic divergence. Life on a particle, the authors argue, imposes persistent selection in a patchy, competitive microhabitat. Particles are islands of opportunity: rich in nutrients but crowded with competitors and short-lived in the water column. Bacteria that colonize them are favored if they can find the particles quickly, cling to them, dismantle their polymers, outcompete neighbors chemically, and adapt their metabolism to fluctuating oxygen and energy availability. The genes enabling these behaviors are costly to maintain, so free-living lineages that never encounter such pressures tend to lose or never acquire them.

Taken together, the network and genomic results support what the researchers describe as a niche-based interpretation of plankton ecology. Eukaryote-derived particles act as persistent microhabitat interfaces—tiny, ephemeral worlds that increase the heterogeneity of the seemingly uniform ocean and act as ecological filters, selecting for a particle-adapted interaction toolkit among bacteria. Because eukaryotic plankton continuously generate these particles through feeding, excretion, and decay, the bacterial communities that specialize in them remain coupled to their eukaryotic hosts year-round, regardless of whether a bloom is underway. This reframing matters because the ocean’s biological carbon pump—the process by which organic carbon is transported from the surface to the deep sea—depends heavily on particles, and the microbes attached to them determine how much carbon is recycled versus exported. Understanding that the bacteria on these particles are not passive hitchhikers but genetically specialized, persistently coupled partners adds a new dimension to models of marine food webs and carbon cycling, and suggests that the seasonal rhythm of plankton communities is underwritten by a far more intimate bacterial partnership than previously appreciated.

Subject of Research: Particle-mediated coupling between prokaryotic and eukaryotic plankton communities in coastal seawater

Article Title: Networks and genome-resolved analyses reveal persistent particle-mediated coupling between prokaryotes and eukaryotic plankton

Article References: Ma, X., Luo, J., Wu, Y., Dai, S., Wang, M., & Li, C. (2026). Networks and genome-resolved analyses reveal persistent particle-mediated coupling between prokaryotes and eukaryotic plankton. Microbiome. https://doi.org/10.1186/s40168-026-02528-0

Image Credits: AI Generated

DOI: 10.1186/s40168-026-02528-0

Keywords: particle-attached prokaryotes, free-living prokaryotes, eukaryotic plankton, association networks, genome-resolved metagenomics, metagenome-assembled genomes, marine microbiome, South China Sea, seasonal succession, chemotaxis, biofilm formation, biological carbon pump

The research, led by Xiaoyu Xu and Zaixu Cui of the Chinese Institute for Brain Research in Beijing, together with colleagues at Peking University Sixth Hospital, Stanford University and other institutions, took aim at a fundamental problem in pediatric psychiatry. ADHD affects a substantial share of school-age children worldwide, yet no validated biomarkers exist for tracking symptom trajectories or guiding treatment selection in youth. Clinicians must largely rely on trial and error when choosing between medications, and families often wait weeks or months to learn whether a prescription is working. The team asked whether the developing brain’s structural connections could supply the missing prognostic information.

Their approach rested on the idea of normative growth charts, familiar from pediatrics, where a child’s height and weight are compared against population curves to flag unusual development. The researchers applied the same logic to the brain’s wiring diagram. Using diffusion magnetic resonance imaging, which traces the bundles of nerve fibers that connect distant brain regions, they built normative age-related trajectories of white-matter structural connectivity from a large longitudinal developmental cohort comprising 6,687 scans from typically developing youths and 1,114 scans from youths with ADHD. They then quantified, for every individual with ADHD, how much each connection deviated from the trajectory expected for that person’s age. An independent replication cohort of 355 typically developing and 477 ADHD participants allowed the team to confirm that their findings were not an artifact of a single dataset.

The first major result was that youths with ADHD showed pronounced deviations in structural connectivity, and those deviations were not distributed randomly across the brain. Instead, they clustered overwhelmingly at the association end of what neuroscientists call the sensorimotor–association connectional axis, a gradient that runs from regions devoted to basic sensation and movement to the higher-order association cortices that support attention, executive control and self-regulation. These association networks are precisely the circuits implicated in ADHD symptoms, and they are also the slowest-maturing parts of the brain, continuing to develop well into adolescence and early adulthood. The findings echo an influential earlier report that ADHD involves a delay in cortical maturation, but extend it from the gray matter of the cortex to the white-matter highways that link cortical networks together.

The study then probed how these deviations evolve. A subset of higher-order association connections showed ADHD-specific reductions in deviation with age, changes that went beyond typical developmental patterns and could not be explained by ordinary maturation. Critically, these converging trajectories statistically mediated the age-related decline in ADHD symptoms observed across development, suggesting a mechanistic account of why many children appear to grow out of the disorder. When the researchers followed individuals across two years, they found that within-person decreases in deviation tracked symptom improvement over the same interval, linking individual brain maturation to individual clinical course in a way that cross-sectional group comparisons never could.

The most clinically provocative findings concerned treatment. Using data from youths treated with either atomoxetine or methylphenidate, the two most widely prescribed ADHD medications, the team tested whether baseline structural connectivity deviations could predict response to a 12-week course of treatment. The answer was strikingly specific. Deviations predicted response to atomoxetine, a norepinephrine reuptake inhibitor whose effects are concentrated in prefrontal association circuits, but not to methylphenidate, a stimulant whose primary mechanism centers on dopamine signaling in striatal reward pathways. Follow-up imaging further revealed that treatment itself was associated with reductions in deviation, hinting that effective medication may nudge wayward white-matter development back toward the normative curve. Together, these results identify structural connectivity deviation as a developmental biomarker with prognostic relevance, supporting precision care through symptom monitoring and treatment stratification.

Technically, the study represents a synthesis of several modern neuroimaging and statistical methods. Diffusion MRI data were preprocessed and reconstructed with tools including QSIPrep and MRtrix3, with anatomically constrained tractography and multi-tissue constrained spherical deconvolution used to estimate the strength of each white-matter connection. Cortical parcellations derived from functional connectivity provided a common map of brain regions organized along the sensorimotor–association axis. Normative trajectories were modeled with generalized additive models for location, scale and shape, the same statistical machinery used to construct World Health Organization child growth standards, and deviation was quantified as the distance between an individual’s connectivity and the population curve. Longitudinal scanner effects were harmonized with longitudinal ComBat, and mediation analysis, mixed-effects models and structural equation modeling tied the deviations to symptom change.

The scale of the evidence base deserves emphasis. Prior studies of white matter in ADHD have often compared groups of a few dozen participants and produced inconsistent results, a pattern documented in meta-analyses of more than one hundred diffusion imaging studies. By anchoring deviation estimates in a normative cohort of thousands and replicating them in an independent cohort, the researchers sidestepped the case-control designs that have long limited interpretation. The normative modeling framework they used was developed specifically to understand heterogeneity in clinical cohorts, recognizing that each patient’s brain tells an individual story that average group differences obscure. The method also parallels the construction of lifespan brain charts published in recent years, extending that approach from brain volume to the connectome and from typically developing populations to clinical prediction.

The implications reach beyond ADHD. The sensorimotor–association axis has emerged in recent work as a general organizing principle of cortical development and function, and deviations along this axis have been linked to autism and other neurodevelopmental conditions. If individual deviation from normative development can forecast symptoms and treatment response in ADHD, the same logic may apply to other childhood psychiatric disorders that similarly lack biomarkers. The researchers have released their analysis code publicly, and the ABCD dataset underlying much of the work is available to qualified investigators, which should accelerate independent validation. Limitations remain: the medication analyses were observational, deviations were measured from diffusion imaging with inherent biases in tractography, and clinical deployment would require streamlined acquisition and standardized norms across scanner platforms.

Still, the study sketches a plausible near future in which a child newly diagnosed with ADHD undergoes a brief MRI session, their white-matter wiring is compared against a growth chart of the developing connectome, and the resulting deviation profile informs whether atomoxetine is likely to succeed, how their symptoms are likely to evolve over adolescence, and whether their brain is already converging toward the normative trajectory. For a disorder that has been defined almost entirely by behavior since it was first described more than a century ago, the prospect of a measurable, mechanistic, individualized biomarker drawn from the brain’s structural wiring marks a genuine turning point, one that could move pediatric psychiatry from reactive adjustment of prescriptions toward genuinely predictive, precision-guided care.

Subject of Research: Developmental deviations of association-network structural connectivity as predictive biomarkers of ADHD symptoms and treatment response in youth

Article Title: Developmental deviations of association-network structural connectivity in youths with ADHD predict symptom and treatment outcomes

Article References: Xu, X., Fu, Z., Xu, H., Zhang, K., Cong, J., Yang, H., Li, Y., Zhao, Y., Zheng, X., Keller, A. S., Xu, T., Chen, R., Cai, W., Cao, Q., Yang, L., & Cui, Z. (2026). Developmental deviations of association-network structural connectivity in youths with ADHD predict symptom and treatment outcomes. Nature Biomedical Engineering. https://doi.org/10.1038/s41551-026-01779-4

Image Credits: AI Generated

DOI: 10.1038/s41551-026-01779-4

Keywords: ADHD, structural connectivity, white matter, diffusion MRI, normative modeling, brain development, association networks, atomoxetine, methylphenidate, biomarkers, precision medicine, pediatric psychiatry