The core methodological innovation is called integrated effective connectivity, or iEC. Effective connectivity refers to the directed influence one brain region exerts over another, a quantity that ordinary functional connectivity, which merely measures statistical co-fluctuation, cannot provide. Estimating directed connections from fMRI has long been a contentious enterprise, with dozens of competing algorithms, from Granger causality and vector autoregressive models to LiNGAM-style structural equation methods and dynamic causal modeling, each resting on different assumptions and each prone to distinctive failure modes. Rather than betting on a single technique, the team integrated nine such algorithms into a single consensus estimate. Each algorithm’s output was weighted by optimized coefficients determined through Bayesian optimization, with the weights tuned so that the combined directed network best reproduced the statistical and dynamic fingerprints of the empirical fMRI data, including static functional connectivity and the temporal variability of connectivity over time.

Crucially, the researchers did not rely on internal consistency alone. They validated the framework against ground truth in two ways. First, using whole-brain simulations built on a Hopf oscillator model, they generated synthetic directed networks, ran the iEC pipeline on the simulated activity, and tested whether the method could recover the known directed structure. The full integrated framework outperformed the best individual algorithm, achieving median correlations of roughly 0.75 with ground truth at finer parcellations and about 0.65 at coarser ones. Second, and more compellingly biologically, they turned to the macaque monkey, where decades of invasive tract-tracing experiments have quantified the laminar origins of interareal projections. The fraction of labeled neurons, or FLN, and the supragranular labeled neurons ratio, or SLN, provide a histological signature of whether a connection is feedforward or feedback. The team applied iEC to resting-state fMRI from macaques in the PRIME-DE repository and found that the directed connectivity estimates aligned with these anatomical markers of directionality, suggesting that the method recovers something real about how signals travel between cortical areas.

With validation in hand, the group applied the framework to resting-state fMRI data from hundreds of participants in the Human Connectome Project S1200 release, using both the Schaefer-100 parcellation and the finer multi-modal MMP-360 atlas. From the consensus directed network, they computed a hierarchy value for each cortical parcel, essentially a measure of how far upstream or downstream a region sits in the global flow of signals. The result was strikingly orderly. Hierarchy levels increased monotonically along the principal axis of cortical organization that runs from sensorimotor cortex through association areas to paralimbic regions, the same trajectory captured by the well-known sensorimotor-to-transmodal gradient. Primary sensory and motor areas, which are densely myelinated and dominated by the granular koniocortex cytoarchitectural type, sat at the bottom of the flow hierarchy, while agranular and dysgranular limbic and paralimbic cortices sat at the top.

This ordering matters because it matches an independent line of biological theory. The structural model of laminar connectivity, developed through the work of Henry Kennedy, Nikolaus Krich and others, predicts the direction of cortical signaling from the laminar distribution of neurons that send and receive interareal projections. Areas whose projections originate predominantly in deep layers and terminate in granular layers are feedforward senders; areas with the opposite pattern are feedback receivers. The iEC-derived hierarchy recovered exactly this predicted organization in the human brain, without any laminar information being supplied, and it also tracked inverted myelination and cortical type annotations derived from histological atlases, including the Campbell cytoarchitectonic map and modern myeloarchitectonic reconstructions. In other words, a purely functional, data-driven measurement of directed signal flow in humans recapitulates a century of microanatomical knowledge about cortical architecture.

The most surprising finding, however, is that this hierarchy is not static. When the team applied the framework to data collected under different conditions, including movie-watching from the StudyForrest dataset and a tonic-pain paradigm, the shape of the cortical hierarchy changed systematically with the participant’s attentional and processing state. During externally oriented processing, such as watching a film, the hierarchy flattened: the distinction between lower and higher levels of the flow hierarchy became compressed, and signal flow became more distributed and less strictly ranked. During internally focused conditions, including rest and the sustained pain state, the hierarchy steepened, with greater engagement of interoceptive regions that monitor the body’s internal milieu. The investigators interpret this steepening as reflecting the increased weight of bodily and self-referential signals in the cortical conversation when attention turns inward.

These state-dependent shifts resonate with a growing body of literature on hierarchical reorganization. Previous work by Morten Kringelbach, Gustavo Deco and colleagues reported a flattening of brain hierarchy during naturalistic movie-watching compared with rest, and studies of cortical gradients have shown that the principal sensorimotor-to-association axis compresses or expands depending on task demands, development and clinical condition. What the new study adds is directional information: it is not merely that the amplitude of regional activity or the strength of correlations changes, but that the very direction in which signals propagate across the cortex reorganizes. This distinction between correlation and directed influence has been a persistent bottleneck for human connectomics, and the iEC framework offers one route past it, at least at the macroscale resolution that fMRI affords.

The technical safeguards the team employed deserve emphasis, because the credibility of any effective connectivity result hinges on them. The researchers showed that most constituent algorithms were robust to hemodynamic response function variability, and that the integrated estimate remained highly stable before and after blind deconvolution of region-specific hemodynamic lag, with correlations of 0.91 in macaque and 0.87 in human data. They demonstrated that including negative, inhibitory-flavored connections improved hierarchy estimation in higher-order cortical areas, where excitation-inhibition balance is thought to shift systematically along the cortical gradient. They also verified that the Hopf whole-brain model used for simulation adequately reproduced both static and dynamic features of empirical brain activity in both species, lending legitimacy to the synthetic ground truths used for training. All code and processed derivatives are publicly available through the authors’ GitHub repository, allowing independent replication of the entire pipeline.

The implications reach well beyond basic systems neuroscience. Because the method yields a directed, individually estimable network, it opens the door to quantifying atypical signal flow in clinical populations. Prior work has already documented compressed cortical hierarchy in autism and in schizophrenia using undirected gradient measures; a directional framework could clarify whether such compression reflects altered feedforward propagation, exaggerated feedback, or both. Similarly, conditions characterized by aberrant interoception and bodily awareness, from chronic pain to anxiety disorders, might be reframed in terms of state-dependent steepening of the cortical hierarchy, providing a mechanistically grounded target for interventions. The methodological benchmarking literature, including recent large-scale comparisons of connectivity mapping approaches, has highlighted how fragile many directed-connectivity estimates are; a validated, integrated estimator that survives adversarial testing represents a meaningful step toward reliability.

Cautions remain. Effective connectivity inferred from fMRI is still an indirect inference, filtered through the vascular hemodynamic response, and the authors themselves note that one macaque-fMRI-tested algorithm, multivariate Granger causality, had to be excluded from human analyses owing to its sensitivity to hemodynamic variability. The tonic-pain dataset is restricted and not publicly available, and the framework’s assumptions, though tested in simulation, ultimately rest on the fidelity of macroscale models. Yet the convergence achieved here, across species, across algorithms, across parcellations and against histological ground truth, is difficult to dismiss. If the findings hold up under independent replication, the picture that emerges is of a human cortex whose direction of information flow obeys the laminar logic discovered in animal anatomy, but whose hierarchical geometry bends to the demands of the moment, flatter when the world commands attention and steeper when the mind turns toward itself.

Subject of Research: Directed signal flow hierarchy and its state-dependent reorganization in the human cerebral cortex

Article Title: State-dependent signal flow hierarchy in the human cerebral cortex

Article References: Oh, Y., Ann, Y., Lee, J.-J., Ito, T., Froudist-Walsh, S., Paquola, C., Milham, M., Spreng, R. N., Margulies, D., Bernhardt, B., Woo, C.-W., & Hong, S.-J. (2026). State-dependent signal flow hierarchy in the human cerebral cortex. Nature Neuroscience. https://doi.org/10.1038/s41593-026-02389-8

Image Credits: AI Generated

DOI: 10.1038/s41593-026-02389-8

Keywords: effective connectivity, cerebral cortex, cortical hierarchy, fMRI, feedforward feedback, signal flow, interoception, connectomics, laminar connectivity, brain states, neuroimaging, macaque validation

The research team set out to test whether the dense web of feedforward and feedback projections linking neocortical areas functions as more than a pipeline for transmitting already-computed results. Prior work had established the anatomical scaffolding — every pair of connected cortical areas exchanges projections in both directions, and feedback fibers often numerically dominate their feedforward counterparts. But functional studies frequently treated these reciprocal loops as static wiring diagrams, as if the connections were fixed channels whose influence never changed. The new experiments challenged that assumption directly, asking whether the strength and direction of communication between areas shifts on fast timescales depending on what the network is doing and what the animal is perceiving.

To address the question, the researchers combined high-density electrophysiology with targeted circuit manipulations in animal models, recording simultaneously from multiple neocortical areas while subjects performed perceptual tasks. Rather than analyzing activity in each area in isolation, the team focused on the interaction terms — the moment-by-moment statistical relationships between spiking patterns in different regions. These relationships revealed a constantly reconfiguring dialogue. During the earliest phase of stimulus processing, feedforward signals dominated, consistent with the classical view of information flowing up a sensory hierarchy. But within hundreds of milliseconds, as the network settled on an interpretation, feedback and lateral influences grew dramatically, and activity across areas began to align toward a shared pattern — the neural signature of consensus formation.

The technical heart of the study lies in how the authors quantified this alignment. Using multielectrode arrays positioned across connected cortical fields, they tracked dimensionality-reduced activity trajectories in each area and measured how those trajectories converged or diverged over time. Their analyses showed that consensus was not achieved by one area imposing its answer on another. Instead, the reciprocal projections acted like a bidirectional negotiation channel: each area’s output pushed its partners toward its own state, while simultaneously being pulled toward the states of those partners. When the team modeled this interaction mathematically, the dynamics resembled consensus algorithms used in distributed computing and swarm robotics, in which individual agents repeatedly exchange estimates and update their own state as a weighted average of their neighbors’ — a process that provably converges on collective agreement.

What makes the biological version remarkable is that the weights of that averaging are not fixed. Perturbation experiments demonstrated that the influence one area exerts on another depends on the internal state of both regions at the moment of communication. When a downstream area was highly engaged — for example, during periods of heightened attention or just before a behavioral choice — its feedback projections reshaped upstream activity far more powerfully than during passive viewing. Conversely, when the researchers transiently silenced one node of the reciprocal loop, the consensus process did not collapse; the remaining areas re-balanced their exchanges, partially compensating for the lost input. This redundancy reveals a network that is robust by design, with agreement emerging from the architecture of mutual influence rather than from any single commanding region.

The findings carry major implications for how neuroscientists understand cortical hierarchy. For decades, the dominant framework organized neocortical areas into a ladder: primary sensory cortex at the bottom, association cortex in the middle, and prefrontal regions issuing commands at the top. The new evidence suggests that hierarchy is only half the story. Even areas separated by many synaptic steps behave less like superiors and subordinates and more like participants in an ongoing committee meeting, each contributing evidence and revising its own verdict in light of what its partners report. Consensus, on this view, is not decreed at the top of the hierarchy — it is computed continuously across the entire network, and behavior reflects the outcome of that distributed vote.

The timing of the consensus process also aligns closely with behavior. The researchers found that the moment when activity across cortical areas converged onto a shared trajectory predicted, on a trial-by-trial basis, the moment the animal committed to a perceptual decision. Trials in which the reciprocal dialogue resolved quickly produced fast, confident choices; trials marked by prolonged disagreement — visible as sustained divergence between regional activity patterns — produced slower responses and greater variability. This tight coupling between inter-areal negotiation and decision time suggests that the subjective experience of certainty may correspond, at the circuit level, to the successful resolution of the cortical consensus process itself.

Beyond perception, the work offers a fresh lens on disorders in which cortical communication is thought to go awry. Conditions such as schizophrenia and autism have long been associated with altered functional connectivity between brain regions, and theories of psychosis propose that perception breaks down when distant cortical areas stop agreeing on a shared model of reality. The new framework makes such ideas concrete: if consensus is an active, dynamically weighted computation carried out by reciprocal projections, then genetic or developmental disruptions that alter those projections could shift how easily cortical networks converge, producing both the sensory anomalies and the decision-making irregularities observed clinically. Similarly, age-related declines in white matter integrity could degrade the bandwidth of the negotiation channel, explaining why perceptual decisions become slower and more variable with advancing age.

The study also points toward new therapeutic and technological directions. Brain–computer interfaces, which decode intentions from cortical activity, currently rely heavily on signals from small numbers of areas; a consensus-based framework suggests that reading out the converged state of a reciprocal network, rather than any single region’s output, could yield more stable and accurate decoders. On the basic science side, the results generate testable predictions about the synaptic and neuromodulatory mechanisms that control the dynamic weights of inter-areal communication — questions the authors and their colleagues are already pursuing. In the meantime, the study reframes a familiar picture of the brain. The neocortex, with its patchwork of specialized areas, now appears less like an assembly line and more like a parliament: a legislature of parallel experts whose reciprocal connections, updated moment by moment, allow the mind to speak with one voice.

Subject of Research: Dynamic reciprocal connectivity and consensus formation between neocortical areas during perception and decision-making

Article Title: Reciprocal connections dynamically build consensus between neocortical areas

Article References: Javadzadeh, M., Schimel, M., Hofer, S. B., Ahmadian, Y., & Hennequin, G. (2026). Reciprocal connections dynamically build consensus between neocortical areas. Nature Neuroscience. https://doi.org/10.1038/s41593-026-02437-3

Image Credits: AI Generated

DOI: 10.1038/s41593-026-02437-3

Keywords: neocortex, reciprocal connections, cortical hierarchy, consensus dynamics, feedforward and feedback, perceptual decision-making, neural circuits, functional connectivity, Nature Neuroscience, systems neuroscience, Reciprocal, connections

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

The key advance is a new way to quantify diversity in individual neurons’ response profiles. Rather than treating selectivity variability as qualitative “noise,” they define α-diversity, a measure grounded in the participation ratio of response profiles in the full space of regression coefficients. In other words, α-diversity captures how many effective dimensions of neuron-to-condition tuning are “occupied” by the observed response structure, integrating both amplitude differences across variables and broader organization in feature space.

The data reveal two recurring modes of structure. Some neurons show uneven selectivity, where encoding is dominated by a subset of task variables or conditions. Others exhibit categorical selectivity, where neurons cluster into functional groupings. These patterns can be illustrated in example regions: one area shows an elongated profile driven mainly by whisking, while another region distributes selectivity more uniformly across dimensions.

Crucially, α-diversity is linked to representational geometry across the cortex. Regions with higher α-diversity exhibit greater population dimensionality, providing more axes along which conditions can be separated. In the study’s empirical tests, α-diversity correlates with an increase in the number of independent conditions (Spearman’s ρ = 0.73), suggesting that richer single-neuron diversity scales up to more expressive population codes.

The authors also show that higher α-diversity aligns with less clustering in the α-diversity space, using a silhouette-score analysis (Spearman’s ρ = −0.76). This provides a geometric interpretation: when response profiles span more effective dimensions, the neural representation is less forced into a few tight groups, enabling better separability.

To connect structure to function, the study directly ties α-diversity to decoding performance. Using a cross-validated linear classifier, they demonstrate that response diversity predicts how many dichotomies of conditions can be decoded. The message is practical and theoretical at once: single-neuron tuning diversity improves population-level readouts by increasing the dimensionality available to linear decision boundaries.

The result reframes “rarely categorical” neural coding as a feature of cortical computation. Along the hierarchy, representations become more separable not by enforcing rigid neuron groups, but by spreading selectivity across many effective dimensions—making linear separation increasingly capable.

Subject of Research: Neuroscience / Neural coding and population representations
Article Title: Rarely categorical, highly separable representations along the cortical hierarchy.
Article References: Posani, L., Wang, S., Muscinelli, S.P. et al. Nature (2026). https://doi.org/10.1038/s41586-026-10668-4
Image Credits: AI Generated
DOI: https://doi.org/10.1038/s41586-026-10668-4
Keywords: neural selectivity, α-diversity, cortical hierarchy, representational dimensionality, linear separability, neural decoding