Network Neuroscience

Cerebral cortical networks in the mammalian brain exhibit a non-random organization in which reciprocal projections, although widespread, are systematically asymmetric in strength: feedforward connections are consistently stronger than their feedback counterparts, particularly in sensory cortices. This "no-strong-loops" principle is thought to prevent runaway excitation and maintain stability, yet its actual computational impact remains unclear. Here, we use computational analysis and modeling to show that connectivity asymmetry supports high working-memory capacity, whereas increasing reciprocity reduces memory capacity and representational diversity in reservoir-computing models of recurrent neural networks. We systematically examine synthetic architectures inspired by mammalian cortical connectivity and find that sparse, modular, and hierarchical networks achieve superior performance, relative to random, small-world, or core-periphery graphs, but only when reciprocity is constrained. Validated on directed mammalian (macaque, marmoset, rat, and mouse) connectomes, these results indicate that restricting reciprocal motifs yields functional benefits in sparse networks, consistent with an evolutionary strategy for stable, efficient information processing in the brain. These findings suggest a biologically-inspired design principle for artificial neural systems.

Linear state-space models have been shown to effectively reproduce large-scale brain dynamics. We applied this approach to resting-state fMRI data acquired from 20 mice, focusing on the systems Jacobian matrix, i.e. the effective connectivity, and specifically on its component encoding nonzero-lag interactions: the differential covariance matrix. Within this matrix, we concentrated on the off-diagonal component (dC-Cov), which reflect endogenous time-lagged correlations. Our aim was to identify a decomposition of the Jacobian matrix that facilitates its interpretation from a mechanistic perspective. Since the dC-Cov captures the rotational component of signal trajectories, we employed Schur decomposition to extract 2D rotational modes, each characterized by a pair of orthogonal vectors, and an associated angular frequency. This provides a more generative formulation of the modeling framework, thereby reducing the interpretability gap between this approach and connectome-based network models of coupled neural masses. Within this framework, the precision matrix governs the coupling between different Schur modes, while we hypothesize that the dC-Cov reflects spatial constraints imposed by inter-regional distances. By examining the relationship between dC-Cov and structural constraints imposed by the spatial placement of brain areas, we found a consistent alignment between the faster Schur modes across mice and the leading eigenvectors of the structural distance matrix.

The computational study of epileptic seizure dynamics has primarily focused on the identification of seizure onset zones and propagation pathways. Here, we present a network dynamical model implemented on the empirically measured mesoscale mouse brain network that reveals previously unresolved organizational principles underlying seizure propagation. Rather than the conventional assumption of a single dominant pathway to synchronization, the model reveals multiple competing pathways to synchronization with distinct dynamical properties including transition propensity, recruitment speed, spatial coverage, synchronization stability and transition kinetics. Consequently, node ablation does not uniformly suppress synchronization across pathways, but instead selectively alters pathway occupancy, producing non-trivial alterations to seizure dynamics with potential implications for resection and network-targeted intervention strategies. Biologically, the olfactory and limbic sub-networks emerge as key mesoscale regulators of synchronization dynamics, with olfactory recruitment preferentially constraining global synchronization while limbic-driven pathways preferentially support seizure generalization. More broadly, these findings extend transient explosive synchronization theory by demonstrating that synchronization in biologically constrained networks may emerge through competing mesoscale recruitment programs rather than a single transition process. Together, these findings introduce a new conceptual framework for seizure propagation, suggesting that pathological synchronization emerges not through a single dominant route, but through competing mesoscale dynamical pathways whose accessibility depends on both network architecture and ongoing network state. Significance statementEpileptic seizure propagation is conventionally understood as progressing through a dominant pathway that recruits increasingly larger portions of the brain into pathological synchronization. Using a network dynamical model implemented on the empirical mesoscale mouse connectome, we show that seizure-like synchronization instead emerges through multiple competing pathways with distinct spatial and temporal characteristics. These pathways differ in their propensity for generalization, synchronization stability and sensitivity to node perturbation, such that network interventions selectively reshape pathway accessibility rather than uniformly suppressing seizure dynamics. Our findings introduce a new framework for understanding seizure propagation, identify mesoscale mechanisms linking network architecture to synchronization dynamics and suggest that competing synchronization pathways may represent an important organizing principle in complex brain networks. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=97 SRC="FIGDIR/small/730931v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@7c4076org.highwire.dtl.DTLVardef@16c0961org.highwire.dtl.DTLVardef@1dbd8a0org.highwire.dtl.DTLVardef@6b06de_HPS_FORMAT_FIGEXP M_FIG C_FIG

Functional connectivity (FC) from resting-state fMRI captures temporal correlations between brain regions but cannot reveal the direction of neural signalling. Determining effective connectivity, the influence of one neural system over another, is essential for understanding cortical hierarchy and its energetic constraints. We extend Metabolic Connectivity Mapping (MCM; Riedl et al., 2016), a biologically grounded framework that infers directionality by integrating FC with glucose metabolism measured via [18F]fluorodeoxyglucose positron emission tomography ([18F]FDG PET). MCM builds on the principle that postsynaptic neurons consume more energy than presynaptic ones (Attwell and Laughlin, 2001; Attwell and Gibb, 2005), linking higher local metabolism to afferent input. Here, we present a new whole-cortex implementation that estimates directed connectivity directly from inter-regional energy ratios, enabling application to multimodal and fMRI-only datasets using an average cerebral metabolic rate of glucose (CMRGlc) map. The model reproduces hierarchical signalling within visual and sensorimotor systems and identifies novel directional asymmetries along sensory-cognitive gradients. MCM-derived metrics correlate with independent biological markers, including mitochondrial density (Mosharov et al., 2025) and cortical cytoarchitecture indexed by cell layer profiles (Amunts and Zilles, 2015; Wagstyl et al., 2020). By decomposing functional connectivity into metabolically constrained directed and undirected components, this framework bridges the gap between statistical connectivity and neuroenergetic mechanisms. Our results position MCM as a scalable and biologically interpretable model for inferring directed brain connectivity from human neuroimaging data.

Structural connectivity (SC) data are crucial for brain network analysis, but SC-based machine learning often suffers from limited data availability, hindering model generalization and robustness. Although data augmentation using deep generative models has attracted increasing attention, it remains unclear how different models capture the complex topological features of SC data. To clarify the learning characteristics of deep generative models for SC generation, this study compares three representative models: variational autoencoder (VAE), Wasserstein GAN with gradient penalty (WGAN-GP), and denoising diffusion probabilistic models (DDPM). We systematically evaluated these models using both synthetic datasets with known characteristics and real-world SC data. Generation quality was assessed using graph-theoretic metric comparisons and visual inspection of the generated adjacency matrices. WGAN-GP showed relatively stable performance across datasets and metrics, without severe performance degradation across evaluation settings. In contrast, VAE and DDPM performed well in specific aspects but were more sensitive to data characteristics. These findings suggest that WGAN-GP may serve as the most balanced baseline for future SC data augmentation studies, whereas VAE and DDPM may be useful depending on the target application and structural properties of interest. Furthermore, because all models struggled to fully reproduce strict global constraints such as planarity, our results suggest that standard generative models may be insufficient to capture the complex topological features of SC data. This highlights the importance of incorporating the desired structural properties into the training or generation process.

Structural connectivity defines the network architecture supporting large-scale brain dynamics, yet how this network constrains the temporal statistics of signals defined on it remains poorly understood. Prior work has reported associations between intrinsic timescales of resting-state fMRI and structural connectivity strength, but it is unclear which signal components primarily drive this relationship. Here, we adopt a graph signal processing framework to analyze intrinsic temporal properties of networked brain signals. Regional Blood Oxygenation Level Dependent (BOLD) activity is modeled as a graph signal supported on the structural connectome and decomposed via graph spectral filtering into low-frequency (structure-coupled) and high-frequency (structure-decoupled) components. Using diffusion MRI-derived structural connectivity and resting-state fMRI from 100 unrelated participants of the Human Connectome Project, intrinsic timescales are quantified using relatively low-frequency power and related to node-wise structural connectivity strength while controlling for regional volume. We show that intrinsic timescales derived from structure-coupled signals exhibit robust positive associations with structural connectivity strength at both group and inter-individual levels, whereas structure-decoupled signals display substantially weaker coupling. Notably, slow structure-decoupled dynamics are preferentially expressed in higher-order association cortex. Graph-spectral null models further demonstrate that these effects critically depend on the empirical organization of the structural network. Together, these results establish a graph-spectral interpretation of structure-timescale coupling, showing that network topology selectively constrains the temporal statistics of graph-smooth neural activity. Author SummaryA fundamental question in network neuroscience is how the brains structural connectivity shapes the temporal dynamics of functional activity. Previous studies have shown that brain regions with stronger anatomical connectivity tend to exhibit slower intrinsic activity fluctuations, but the functional signal components responsible for this relationship remain unclear. Here, we combine graph signal processing with analyses of intrinsic BOLD timescales to separate resting-state activity into structure-coupled and structure-decoupled components. Using multimodal neuroimaging data from the Human Connectome Project, we show that the association between structural connectivity strength and intrinsic timescales is primarily driven by structure-coupled, graph-smooth activity. In contrast, structure-decoupled dynamics exhibit substantially weaker dependence on anatomical connectivity, although transmodal association cortex retains selective structural influences. These findings provide new insight into how anatomical networks shape temporal processing in the human brain and suggest that intrinsic timescales emerge through distinct modes of interaction between structural constraints and functional dynamics.

Analyses of neuroimaging data increasingly leverage the distribution of neurotransmitter receptors derived from Positron Emission Tomography (PET) to bridge the gap between micro- and macro-scale brain function. However, these receptor maps are highly spatially overlapping which can give rise to interpretive and analytical challenges. Here, we systematically investigate the impact of spatial collinearity among PET maps in the context of Receptor-Enriched Analysis of functional Connectivity by Targets (REACT), a method that uses receptor maps as spatial regressors to derive subject-level molecular-enriched functional connectivity networks. Exhaustive combinatorial analysis across 19 receptor and transporter maps showed that collinearity scales rapidly with the number of receptors modelled simultaneously, and that this was relatively stable across parcellation scales, reflecting the intrinsic organisation of neurotransmitter systems. Using test-retest fMRI data from the Human Connectome Project, we demonstrate that modelling greater numbers of receptors degrades the reliability of molecular-enriched networks derived from conventional multivariate REACT models, and that collinearity among receptor maps drives this degradation. An alternative univariate approach, in which each receptor is modelled independently, yielded more reliable networks and, when applied to a within-subjects study of LSD compared to placebo, better recovered the role of the 5HT-2A receptor in LSDs neural effects. These findings identify spatial collinearity as a fundamental constraint on multivariate molecular-enriched network estimation and support univariate modelling as a more robust default for this class of analysis.

The human structural connectome, most commonly derived from diffusion-weighted imaging (DWI) and tractography, provides a macroscopic description of whole-brain wiring and serves as the structural foundation of network neuroscience, large-scale brain simulations, and personalized digital brain twins. However, tractography-derived connectomes are fundamentally limited by their inability to distinguish afferent from efferent connections, yielding networks that are undirected and therefore blind to the hierarchical organization imposed by the directionality of anatomical connections. In this study, we introduce a directed human structural connectome (DHSC) by transferring tracer-derived projection patterns from macaque to human using cross-species connectivity blueprints. Topological analysis of the DHSC manifests biological plausibility, a small-world network organization, and a directionality-based hierarchy, which offer the hierarchical organization of human brain networks. In the context of brain dynamics, the introduction of directionality reshapes the propagation and persistence of sensory inputs. DHSC also best captures the empirical spatiotemporal dynamics of stimulus-evoked brain activity. The findings demonstrate that anatomical directionality is a critical determinant of large-scale brain organization and dynamics. This provides evidence that directed connectome may offer potential advantages in large-scale simulations of the human brain. The resulting DHSC, along with all related analyses and data are openly available.

The brain requires coordination among different regions to execute cognitive tasks, which may involve both positive- and negative-correlations. The topology of these correlations may indicate the mechanism underlying brain functioning in a given state. Here, we study changes in the functional connectomes (FCs) of both the positive and negative-correlations across various cognitive task states relative to the resting state, using publicly available electroencephalographic (EEG) data. Considering the EEG-specific topographical cortical regions as topographical modules (TMs), we find that the FC comprising positive correlations (G+) is modular. In contrast, networks of negative-correlations (G-) are anti-modular, with more connections between TMs than within them, and are associated with improved overall topological efficiency. These functional networks also show variability across frequency bands and brain states. In the low-frequency delta band, resting states exhibit higher modularity and anti-modularity than task states; in contrast, in the high-frequency Gamma band, modularity and anti-modularity are much higher during task states than in the resting state. The k-core analysis of all networks further reveals differences: G+ is more hierarchical and robust than G- across all states. Moreover, the task-state networks are always more hierarchical than the resting-state networks across all frequency bands. In the high-frequency gamma band, they are also significantly more robust than the resting-state networks. These networks also differ in the topology of their innermost core constituents: the innermost core regions of G+ are randomly connected and spatially localized, mostly in posterior brain regions across subjects, in the high-frequency gamma band. Whereas those in G- are spatially de-localized, cover the extreme anterior and extreme posterior brain regions, and remain anti-modular in all the frequency bands. Overall, our analysis reveals the presence of an anti-modular organization of functionally specialized TMs alongside their modular organization and points to task- and resting-state differences in their topologies.

Functional connectivity in network neuroscience is traditionally characterized using time-averaged correlations between brain regions. While these summaries capture stable large-scale organization, they do not fully reflect the temporal structure of moment-to-moment interactions. Here, we investigate how the order of interaction used to represent brain dynamics shapes the organization recovered from neural data. We compare three interaction representations of fMRI dynamics: regional activation (node time series), pairwise co-fluctuations (edge time series), and higher-order triplet interactions (triangle time series); within a common topological framework using Mapper from topological data analysis (TDA). Across task and resting-state data, Mapper representations derived from pairwise co-fluctuations more distinctly segregate task conditions than activation-based or higher-order representations. This organization reflects structured coordination patterns beyond activation polarity and is driven by high-amplitude interaction events. Beyond task states, modularity quality computed across all Mapper representations is highest for edge time series and selectively associated with stable individual differences: higher modularity relates to higher conscientiousness and lower internalizing and externalizing symptom dimensions. Together, these findings suggest that behaviorally relevant information is reflected in the topology of moment-to-moment brain interactions. Topological analysis of interaction-level dynamics therefore provides a complementary and interpretable framework for linking large-scale neural coordination to cognition, personality, and mental health.

Amyotrophic lateral sclerosis (ALS) is increasingly recognized as a multisystem neurodegenerative disorder in which motor-neuron degeneration is accompanied by widespread alterations in cortical dynamics. Among its most reproducible neurophysiological signatures is cortical hyperexcitability, yet how this local excitability imbalance shapes distributed whole-brain activity remains poorly understood. Here, we combined source-reconstructed resting-state MEG data, tractography-informed whole-brain modeling, and simulation-based inference to investigate whether ALS-related alterations in large-scale brain dynamics can be mechanistically explained by changes in cortical excitability. First, we characterized empirical brain dynamics using complementary features spanning regional activity amplitude and variability, functional connectivity, and avalanche-based metrics. These analyses revealed significant alterations in ALS patients relative to healthy controls, as well as associations with clinical impairment and disease staging. To mechanistically interpret these changes, we employed a reduced Wong-Wang whole-brain model in which local recurrent excitation modulates emergent large-scale neural dynamics. Simulations showed that increasing excitability systematically reproduced the empirical dynamical signatures observed in ALS. We then applied a simulation-based inference framework to estimate latent excitability parameters directly from empirical observations. Whole-brain model inversion revealed increased excitability in ALS patients compared with controls. The recovered excitability parameter was associated with disease staging, supporting its clinical relevance as a model-derived descriptor of ALS progression. Finally, by extending the model to estimate frontal and non-frontal excitability separately, we found that ALS-related alterations were predominantly associated with increased frontal excitability, whereas non-frontal regions appeared comparatively less affected. The recovered parameters related to disease staging. Together, these findings provide a mechanistic framework linking altered large-scale brain dynamics in ALS to selective cortical hyperexcitability, explaining how local excitability changes can give rise to global network reorganization. More broadly, they show how computational model inversion can recover latent multiscale pathophysiological processes from empirical neural recordings, offering a non-perturbative alternative to complex experimental paradigms typically required to causally probe local-to-global mechanisms.

Disorders of consciousness (DoC) are associated with large scale alterations in brain dynamics, yet the structural factors that constrain these changes remain unclear. Here, we investigate how the topology of the structural connectome shapes the sensitivity of brain dynamics to perturbation using a whole brain computational model constrained by diffusion MRI derived connectivity. We systematically probed the effects of node removal and targeted modulation of local excitation/inhibition balance on dynamic functional connectivity, quantifying dynamical richness via transitions between recurrent connectivity states and jump length distributions in functional connectivity space. We show that a node's integration within the structural connectome, quantified using a spectral integration measure, strongly predicts its impact on global brain dynamics. Lesions to highly integrative hubs drive the system toward low complexity dynamical regimes resembling those observed in DoC, particularly posterior medial regions such as the precuneus and posterior cingulate cortex. Analogously, increasing excitability in these regions restores healthy like dynamics in silico. In contrast, perturbations to weakly integrated regions have limited global effects. These results demonstrate that generic features of structural connectivity constrain whole brain dynamical stability and help explain why damage to specific hubs disproportionately disrupts conscious brain activity.

Diffusion MRI (dMRI) tractography is widely used to estimate structural connectivity (SC) between brain regions in vivo, but it lacks directional information about white matter pathways. Here, we introduce a computational framework to infer directionality by first combining dMRI-derived SC with gene co-expression gradients, then fitting a structure-function model based on the Lyapunov equation. We found that our model successfully predicts ground-truth neuron-to-neuron synaptic connectivity in the nematode, C. elegans, as well as tracer-derived region-to-region directionality in both mouse and macaque. Then, we infer directionality across 770 healthy young adults from the Human Connectome Project (HCP), finding interdigitated sink/source network architecture across the brain and biologically plausible feedback/feedforward pathways in primary sensory areas. Finally, we show how a directional SC implies a new form of directed functional connectivity we term "angular flow" (AF). Our AF measure both correlates with causal functional connectivity metrics and explains the principal gradient of undirected functional connectivity as the net-flow through SC from sensory areas to multimodal areas. By revealing the link between genetic expression, neuronal directionality, and brain function, our approach unlocks significant potential to study directed SC and AF in humans across both health and disease.

Understanding the principles governing large-scale functional organization of the brain remains a central challenge in systems neuroscience. Despite convergent findings, substantial variability across analytical approaches suggests that functional networks may not admit a unique partitioning. Here, we propose that this variability reflects an intrinsic property of the connectome itself: its organization may be fundamentally multi-modal rather than singular. To test this hypothesis, we employ a Bayesian generative modeling framework based on stochastic block models, enabling principled comparison of competing organizational principles and characterization of the full posterior distribution over network partitions. Applying this framework to resting-state fMRI data in mice, we find that a non-degree-corrected hierarchical architecture provides the most parsimonious description of the functional connectome. Importantly, the inferred posterior landscape is not dominated by a single configuration, but instead comprises multiple distinct and co-dominant organizational schemes. At the mesoscale, these hierarchical communities are anatomically grounded yet systematically reorganize canonical resting-state networks: primary sensory systems remain cohesive, whereas higher-order association networks are fractionated into multiple interacting sub-circuits. This global structural variation is driven by structured variability at the community level, where integrative systems exhibit variable regional affiliations while sensory systems act as structurally stable anchors. Together, these findings suggest that the resting-state connectome is best described as a distribution over alternative, yet co-dominant, organizational configurations. This perspective reconciles inconsistencies across previous studies and supports a view of brain organization as inherently degenerate, providing a latent repertoire of network configurations that may underlie adaptive information routing and dynamic functional reconfiguration.

Structural covariance networks (SCNs) represent spatial patterns of covariation in brain morphology, often as a network of connections between nodes representing correlations in grey matter volume or cor- tical thickness measured by magnetic resonance imaging (MRI). SCNs have been suggested to reveal differences in functional organisation that are reflected in coordinated alterations to brain structure, and often these differences are sought in graph-theoretic measures such as the degree of clustering, segregation into distinct modules, or the characteristic path length between nodes. A common practice is to calculate SCNs for groups of interest, and use permutation testing to determine if they are significantly different for the measure of interest. However, the statistical validity of group comparisons using SCN-derived graph measures remains poorly understood. Here, we systematically evaluate the reliability of SCN estimation and downstream graph-theoretic anal- yses using structural MRI data from the Human Connectome Project ( = 1,096). We use simulations to show the effects of sample size and atlas dimensionality on SCN reliability. Using bootstrapping to characterise the distribution of SCN graph measures, we establish that small sample sizes systematically bias graph-theoretic measures including clustering, characteristic path length and modularity. Finally, we use simulations based on extrema from the bootstrapping distribution to characterise the statistical power and false discovery rate (FDR) for graph-theoretic between-group comparisons of SCNs, showing that at small sample sizes ( [≤] 30) permutation testing is no better than chance. These findings suggest that many significant SCN group differences, particularly those using small sam- ples and high-dimensional parcellations, may reflect sampling noise rather than true biological differences. We recommend that future SCN studies use larger samples, coarser parcellations, and explicitly evaluate reliability before interpreting group differences.

Brain tumors disrupt neural connectivity, but the nature of this disruption depends on tumor growth biology. Here, we analyze pre-operative structural connectivity (SC), functional connectivity (FC), and generalized effective connectivity (GEC) in 14 meningioma patients, 10 glioma patients, and 10 matched controls to characterize how extra-axial and intra-axial tumors differentially affect brain networks. We introduce FC resilience, the relative preservation of functional connectivity in structurally damaged regions, and find that meningioma patients exhibit significantly higher FC resilience than glioma patients, with SC-dominant damage and preserved neural activity in damaged regions. Glioma patients show balanced SC-FC damage and degraded neural activity, consistent with infiltrative destruction of both white matter and neural substrate. Connectivity damage is not localized to the tumor vicinity and is non-randomly distributed across functional networks, with distinct propagation patterns: glioma SC damage clusters along white matter pathways, while meningioma SC damage preferentially targets Limbic and Default networks. Network topology analysis reveals that more segregated functional and effective connectivity, particularly higher modularity, predicts FC resilience in meningioma patients but not in glioma patients, while structural connectivity topology shows no predictive value. Non-equilibrium dynamics, quantified via the Fluctuation-Dissipation Theorem, are elevated in damaged regions of meningioma patients, serving as a dynamical marker of structural damage rather than an independent compensatory mechanism. Clinically, higher FC resilience in glioma patients is associated with worse cognitive outcomes, suggesting that preserved FC without an intact neural substrate does not reflect genuine functional preservation. These findings demonstrate that the interpretation of functional connectivity resilience depends fundamentally on tumor type and its underlying growth biology.

A central question in neuroscience is how the brains structural connectivity gives rise to its emergent, correlated dynamics. These large-scale dynamical correlations underlie functional networks that support cognitive functions. Here, we identify coupling correlation--the similarity between the input connectivity profiles of brain regions--as a key structural determinant of macroscopic neural dynamical correlation. Using dynamical mean-field theory (DMFT) and numerical simulations of random neural network models, we demonstrate that coupling correlation quantitatively governs dynamical correlation. The functional form of this structure-function mapping is dictated by the eigenvalue spectrum of the coupling correlation matrix: networks with bulk eigenspectra exhibit an exact linear relationship, whereas biologically plausible long-tailed spectra yield an approximately linear mapping except when the magnitude of coupling correlation approaches unity. Particularly, a long-tailed spectrum is necessary to reproduce the appropriate magnitude and size-invariance of coupling correlations observed in empirical data, thereby sustaining non-vanishing dynamical correlations that may support brain function in large systems. The theoretical prediction of approximate linearity is consistently validated using empirical datasets that include both structural coupling and neural dynamics in humans, mice, and Drosophila. Together, these results provide a mechanistic and quantitative framework linking macroscopic brain network structure to emergent neural dynamics--an essential step toward a theory of structure-function relationship in the brain. Significance StatementHow the brains wiring gives rise to its coordinated activity is a fundamental unsolved problem in neuroscience. Prior work has identified correlations between structural and functional connectivity, but these relationships lacked a mechanistic, first-principles explanation. Here, we derive an analytical framework using Dynamical Mean-Field Theory and random neural network models to show that a single structural statistic--coupling correlation, the similarity between the input connectivity profiles of brain regions--linearly and causally determines the magnitude of correlated neural dynamics. We further show that a long-tailed eigenvalue spectrum in biological structural connectivity is necessary to sustain the strong, size-invariant functional correlations observed across species. Validated in humans, mice, and Drosophila using multiple imaging and connectome modalities, this principle may provide a quantitative bridge between structural connectomics and emergent brain dynamics, with implications extending to a broad class of complex networked systems.

Dynamic functional connectivity (dFC) -- the time-varying re-configuration of brain network interactions -- has become a widely adopted method for studying how neural dynamics reflect ongoing cognition. Yet a fundamental question remains unresolved: can dFC reliably track when a person is cognitively engaged, and if not, why does it fail? Here, we address this question through a large-scale benchmark of seven widely used dFC methods, evaluating how well each predicts task presence across 16 fMRI datasets encompassing over 1,500 participants and 28 distinct experimental settings, complemented by realistic simulated data. Across experimental data, dFC-based tracking of cognitive engagement was unreliable in many cases: most method-experiment combinations performed near chance, and no single method succeeded across all contexts. This failure, however, was not uniform. Both experimental and simulated data showed that decoding performance varied systematically with three interacting factors -- experimental design, data quality, and the choice of dFC method -- rather than depending on dFC features alone. Critically, we identify specific experimental design conditions associated with more reliable tracking: paradigms with longer, more regular task blocks and fewer task-rest transitions were substantially more decodable, while data quality independently influenced performance across methods. These findings offer actionable principles for when dFC can -- and cannot -- be expected to serve as a reliable marker of underlying cognitive states.

Neural oscillations are thought to play a central role in encoding and transmitting cognitive information across large-scale brain networks, yet the relative contributions of phase synchrony and amplitude co-modulations to distributed coding remain unclear. A key obstacle is the absence of tools that can simultaneously quantify task-relevant information in the frequency domain and disentangle its phase and amplitude components across pairwise and higher-order interactions. Here, we introduce a spectral partial information decomposition framework (named NeOPID) for quantifying information about cognitive variables in power and phase contributions, and to quantify redundant and synergistic information in brain relations, from pairwise to higher-order interactions. We validated the approach on Kuramoto and Stuart-Landau oscillator networks, including a whole-brain model constrained by macaque anatomical connectivity. NeOPID accurately recovers ground-truth encoding schemes and reveals that phase relations and amplitude co-modulations act as complementary coding channels with both redundant and synergistic components. NeOPID further extends this decomposition to higher-order functional interactions enabling the characterization of how cognitive information is collectively distributed across multiple oscillatory edges via redundant and synergistic encoding. To illustrate biological applicability, we applied NeOPID to local field potentials (LFPs) recorded from the macaque fronto-parietal network during a working memory task. In this dataset, NeOPID identified beta-band amplitude co-modulations as the primary carrier of stimulus information, and revealed that higher-order phase interactions exhibit both redundant and synergistic structure during the memory delay. These results establish NeOPID as a principled tool for dissecting the informational architecture about cognitive processes of oscillatory brain networks.

The primary objective of system neuroscience is to understand the functional mapping and its causation in the dynamics of the brain network. Some experimental and methodological studies suggest that functional modularity and its hierarchical information processing in the brain network are crucial to understanding the functional role of task-specific or state-specific information flow in the brain. However, because most of the established techniques for detecting effective network structures in the neuroscience research field are strongly based on the "Granger causality" perspective, existing causal discovery methods specified for brain network analysis cannot identify the causal hierarchy in the modular network in the brain due to spurious correlation issues and indistinguishability of causal direction under the Gaussianity of observational noise in a linear system. To address the issues, we developed a causal discovery method for synchronous neural dynamics, called the Jacobian-informed linear non-Gaussian acyclic model, "j-VAR-LiNGAM", by incorporating the information of the Jacobian matrix determined from a phase-coupled oscillator model estimated from observed neural data into the VAR-LiNGAM algorithms. The method was validated by showing that it could extract causal ordering in both synthetic data and empirical neural observed data. Moreover, by analyzing the observed neural oscillatory signals obtained from mice and humans, we confirmed that our method identified causally hierarchical structures in the brain, which aligned with the neurophysiological interpretations. These findings suggested that our proposed method can reveal the neural basis of hierarchical information processing in the brain network.