The research, led by Golia Shafiei and colleagues including Theodore Satterthwaite and Russell Shinohara, drew on two large independent neuroimaging datasets of young people: the Philadelphia Neurodevelopmental Cohort-derived HCPD sample with 565 participants and the Healthy Brain Network sample with 729 participants, all between 8 and 22 years of age. By analyzing resting-state functional MRI recordings, in which participants simply lie still while spontaneous brain activity is measured, the team estimated the intrinsic timescale of cortical regions across the entire brain in each individual.

The intrinsic timescale is extracted from the temporal autocorrelation of spontaneous neural signals, a statistical fingerprint of how slowly or rapidly a region’s activity fluctuates on its own, without any external task. Regions with short timescales behave like rapid-fire relays, discarding information quickly and responding to the immediate moment. Regions with long timescales act more like accumulators, holding onto incoming signals and integrating them over extended windows. This measure has been validated across species and imaging modalities, and it consistently reveals the same cortical hierarchy: sensorimotor cortex at the fast end and association cortex at the slow end.

What the new study adds is the developmental dimension. When the researchers mapped how intrinsic timescales change with age across the two youth cohorts, they found that maturation did not proceed uniformly across the cortex. Instead, developmental changes were organized along what neuroscientists call the sensorimotor-association axis, a fundamental gradient of cortical organization that stretches from primary sensory and motor areas at one pole to higher-order association regions such as the prefrontal and parietal cortex at the other. The developmental patterns in timescale recapitulated this axis with striking fidelity.

In practical terms, this means that the brain’s temporal architecture matures hierarchically. The fastest, most concrete processing systems in sensorimotor cortex reach their mature timescale profiles earlier, while the protracted remodeling of temporal windows continues longest in association cortex, the regions responsible for the most abstract and integrative cognitive functions. This mirrors a well-established principle of brain development, in which evolutionarily older, primary systems consolidate early and phylogenetically newer, higher-order systems mature last, sometimes not until the third decade of life.

A critical strength of the study lies in its replication strategy. Developmental neuroimaging is notoriously vulnerable to sample-specific artifacts, scanner differences, and motion confounds, so findings from a single cohort often fail to generalize. By requiring that the developmental pattern appear consistently in both the HCPD and HBN samples, which were collected with different protocols at different sites, the authors substantially raised the evidentiary bar. The convergence across these two independent youth datasets indicates that the hierarchical maturation of intrinsic timescale is a robust biological phenomenon rather than a statistical fluke of one dataset.

To test whether the observed changes were genuinely developmental rather than a general property of brain organization at any age, the team turned to a third dataset: the Human Connectome Project young adult sample, comprising 973 healthy adults between 22 and 37 years old. The analysis of this adult cohort underscored the specificity of the youth findings. While the canonical sensorimotor-to-association hierarchy of timescales is clearly present in adults, the age-related developmental gradients along that axis seen in children and adolescents were not present in adulthood. In other words, the hierarchical sculpting of temporal windows is an active process of youth, and by the early twenties it has largely stabilized.

This stabilization carries conceptual weight for developmental neuroscience. The adult cortex maintains its hierarchical timescale organization, with short windows in sensorimotor areas and long windows in association areas, but that organization functions as a fixed scaffold rather than a moving target. The youth data reveal the period during which that scaffold is actively constructed. The findings therefore suggest a convergence between two major descriptive frameworks of cortical organization: the intrinsic timescale hierarchy documented across species and modalities, and the sensorimotor-association axis that has emerged as a unifying principle of cortical variation and development. The same geometric axis along which cortical properties vary appears to be the axis along which the brain’s temporal dynamics mature.

The implications extend beyond basic science. Intrinsic timescale is increasingly studied as a marker of neural computation, and atypical development of temporal processing windows has been implicated in neurodevelopmental conditions characterized by differences in cortical maturation. Establishing a normative, replicated developmental trajectory for this measure in more than 1,200 young people provides a principled reference against which deviations can be measured. Because the measure is derived from widely available resting-state fMRI, it could eventually serve as a noninvasive index of hierarchical brain maturation, complementing structural measures such as cortical thickness and myelination that follow similar sensorimotor-to-association developmental gradients.

The study also highlights the value of large-scale, multi-cohort collaboration in developmental science. With authors spanning institutions and datasets including the Human Connectome Project development and young adult phases and the Healthy Brain Network, the work exemplifies a shift toward reproducible, population-scale characterization of the developing brain. The picture that emerges is one of elegant temporal choreography: the brain builds its hierarchy of processing windows from the bottom of the sensorimotor-association axis upward during youth, and once the architecture is complete, it holds steady, providing the stable temporal foundation on which adult cognition operates.

Subject of Research: Developmental maturation of intrinsic neural timescales along the sensorimotor-association cortical axis in youth

Article Title: Intrinsic timescale develops hierarchically from sensorimotor to association cortex in youth and stabilizes in adulthood

Article References: Shafiei, G., Bagautdinova, J., Sydnor, V. J., Bassett, D. S., Barch, D. M., Cieslak, M., Fan, Y., Flook, E., Franco, A. R., Kiar, G., Luo, A. C., Milham, M. P., Parkes, L., Salo, T., Somerville, L. H., Tong, T. T., Shinohara, R. T., & Satterthwaite, T. D. (2026). Intrinsic timescale develops hierarchically from sensorimotor to association cortex in youth and stabilizes in adulthood. PLOS Biology, 24(9), e3004037. https://doi.org/10.1371/journal.pbio.3004037

Image Credits: AI Generated

DOI: 10.1371/journal.pbio.3004037

Keywords: intrinsic timescale, brain development, cortex, sensorimotor-association axis, resting-state fMRI, adolescence, neuroimaging, hierarchical maturation, association cortex, PLOS Biology, autocorrelation, prefrontal cortex

The cortical hierarchy is one of the organizing principles of modern neuroscience. At its lower tiers sit primary sensory and motor areas, which handle raw inputs from the eyes, ears, and body. Moving up the hierarchy, regions become progressively less tied to immediate sensation and more engaged in abstraction, prediction, language, and executive control, culminating in association cortices such as the prefrontal and parietal networks. Neuroscientists have mapped this gradient in detail using functional imaging, showing that higher-order regions integrate information from many lower-order sources. What has remained less clear is how the brain’s physical cabling supports this flow, and whether individual differences in that cabling relate to differences in how people think and reason.

White matter provides the anatomical substrate for that communication. Composed largely of axons wrapped in myelin, a fatty sheath that accelerates electrical signaling, white matter tracts form the brain’s long-distance infrastructure. Techniques such as diffusion magnetic resonance imaging allow researchers to infer the orientation and coherence of these fibers in living brains by tracking the movement of water molecules through tissue. Measures derived from these scans, including fractional anisotropy and related diffusion metrics, serve as indirect indicators of tract organization, myelination, and fiber density. In the new work, the researchers applied such methods to map how white matter pathways connect regions across successive levels of the cortical hierarchy.

The central finding is that the tracts most strongly associated with cognitive performance are not confined to any single level of the hierarchy. Instead, they thread through it, linking early sensory areas to intermediate association regions and onward to the most abstract frontal territories. This pattern suggests that efficient long-range communication across hierarchical levels, rather than the strength of any isolated hub, may be a key anatomical ingredient of higher cognition. The result aligns with a growing body of evidence that intelligence and related abilities depend on the coordinated activity of distributed networks, and that the brain’s wiring diagram constrains how effectively those networks can synchronize.

The notion of cognitive diversity is central to the study’s framing. Rather than ranking individuals on a single scale of ability, the researchers emphasize the many dimensions along which human cognition varies: some people excel at verbal reasoning, others at spatial manipulation, working memory, or cognitive control. The analysis indicates that distinct patterns of white matter organization across the cortical hierarchy relate to these different profiles. In other words, the anatomical substrate of cognition is not a single pipeline but a heterogeneous set of pathways whose varying configurations may give rise to the rich variety of mental strengths seen in the population.

Methodologically, the study draws on large-scale neuroimaging datasets in which hundreds to thousands of participants undergo diffusion imaging alongside extensive behavioral testing. This combination allows researchers to correlate tract-level anatomical measures with performance across multiple cognitive domains while controlling for confounds such as age, sex, and overall brain size. Statistical models in such analyses typically account for the fact that neighboring tracts share biological influences, and modern approaches increasingly test whether findings replicate across independent samples. The emphasis on hierarchical positioning, rather than simple regional labels, represents a methodological refinement: instead of asking whether a named tract predicts a named test, the authors asked whether connectivity spanning particular hierarchical distances predicts cognitive outcomes.

The findings carry implications for several long-standing debates. One concerns the neural basis of general intelligence, often indexed by the tendency of performance across diverse cognitive tests to correlate. Network-based accounts propose that a highly connected brain, with efficient communication among distributed regions, supports the flexible integration that demanding tasks require. The new evidence that white matter tracts span the hierarchy in a way that tracks cognitive diversity lends anatomical weight to that proposal, suggesting that the architecture of interregional communication is where some of the variance in human cognitive ability is physically realized.

A second implication concerns development and plasticity. White matter continues to mature well into adulthood, with myelination proceeding in a hierarchical fashion, from primary sensory tracts toward frontal pathways, over years and decades. If hierarchical connectivity supports cognitive diversity, then developmental changes in white matter may help explain why cognitive profiles shift across the lifespan, and why adolescence and early adulthood, periods of ongoing frontal myelination, are marked by gains in abstract reasoning and executive function. The study’s framework also offers a lens on conditions in which white matter integrity is disrupted, where atypical hierarchical connectivity may contribute to differences in cognitive function.

The researchers and outside commentators alike caution against overinterpreting the results. Diffusion imaging provides indirect measures of microstructure, and the relationship between diffusion metrics and the underlying biology of axons and myelin remains an active area of technical debate. Correlational findings in healthy adults cannot establish causation, and cognitive abilities reflect the interplay of genetics, environment, education, and experience alongside brain structure. The authors frame their contribution as a step toward an anatomical account of cognitive variation, one that must be integrated with functional imaging, genetic data, and longitudinal designs before its full significance can be judged.

Even with those caveats, the study adds a compelling piece to the picture of the human brain as a hierarchically organized communication network. By showing that the same white matter infrastructure carries signals from the senses to the heights of abstraction, and that the organization of that infrastructure varies meaningfully from person to person, the work underscores a principle increasingly central to neuroscience: to understand how minds differ, one must look not only at where the brain is active, but at how its regions are wired together across the full span of the cortical hierarchy.

Subject of Research: The role of anatomical white matter tracts spanning the cortical hierarchy in supporting individual differences in cognition

Article Title: Anatomical white matter tracts span the cortical hierarchy to support cognitive diversity

Article References: Bagautdinova, J., Shafiei, G., Luo, A. C., Pecsok, M. K., Salo, T., Alexander-Bloch, A. F., Bassett, D. S., Gardner, M. E., Gur, R. E., Gur, R. C., Mackey, A. P., Meisler, S. L., Misic, B., Moore, T. M., Roalf, D. R., Shinohara, R. T., Sydnor, V. J., Tong, T. T., Yeh, F.-C., … Satterthwaite, T. D. (2026). Anatomical white matter tracts span the cortical hierarchy to support cognitive diversity. Nature Human Behaviour. https://doi.org/10.1038/s41562-026-02559-5

Image Credits: AI Generated

DOI: 10.1038/s41562-026-02559-5

Keywords: white matter, cortical hierarchy, cognitive diversity, diffusion MRI, myelination, brain connectivity, neuroimaging, intelligence, association cortex, cognitive ability, brain networks, human cognition