Network Neuroscience

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.

The routing of information across the brains structural network is central to its wide range of functional capabilities. However, the mechanisms underlying information routing in complex brain networks, particularly between regions that do not share a direct anatomical connection, remain poorly understood. Neural mass models (NMMs), a computational modelling framework capable of capturing complex neural dynamics across scales, can potentially be used to study the dynamical and network bases of these vital polysynaptic routing processes. In this study, we investigate polysynaptic signalling in three widely used NMMs, obeying Ornstein-Uhlenbeck, Stuart-Landau, and Jansen-Rit dynamics, by tracking the propagation of a discrete, focal, high-amplitude perturbation across the underlying network. We find that polysynaptic propagation emerges in all tested NMMs when configured within dynamical regimes that effectively enhance the persistence of perturbations. We also find distinct parameter domains that maximise signal propagation to directly connected regions and to those separated from the source by at least two hops. Finally, we benchmark in silico stimulus propagation in the brain network against an empirical dataset of direct electrical stimulation trials, to explore the relative capabilities of the NMMs in capturing signal propagation to connected versus unconnected regions. This analysis highlights the significance of dynamical repertoire in capturing stimulus propagation outcomes. Overall, this study provides insights into how dynamical and network features shape signal propagation over complex brain networks.

Functional magnetic resonance imaging (fMRI) data are inherently complex, characterized by high dimensionality, intricate inter-regional dependencies, and substantial individual variability across experimental paradigms. Traditional linear mixed models (LMMs) provide a principled framework that models population-level fixed effects while estimating variance components arising from subject-level random effects; however, they often fail to adequately capture nonlinear relationships inherent in neuroimaging data. To address these limitations, we introduce the nonlinear mixed model (NMM) approach, an innovative extension of the LMM framework that integrates neural networks to flexibly model complex fixed-effect relationships while preserving the random-effects structure to account for individual differences. NMM advances fMRI analysis by: (1) identifying robust functional connectivity (FC) patterns consistently observed across multiple paradigms; (2) leveraging SHapley Additive exPlanations (SHAP) analysis to provide post-hoc interpretability of the nonlinear fixed effects, quantifying how age, sex, and paradigm contribute to predicted FC and how these effects are distributed across large-scale brain networks; and (3) using subject-specific random effects as neural fingerprints that not only show systematic variability across attention and default mode systems but also predict standardized cognitive scores, demonstrating biological relevance. Applied to the Philadelphia Neurodevelopmental Cohort (PNC) across emotion, n-back, and resting-state paradigms, NMM achieved superior model fit relative to classical LMMs, as evidenced by lower mean squared error (MSE) in predicting FC. This framework offers a statistically rigorous and practically explainable approach for modeling large-scale FC from modest covariates while explicitly separating population-level effects from stable individual variability in functional brain organization.

Conventional connectome edge weights, such as number of streamlines (NOS) or diffusion tensor imaging (DTI) metrics, lack specificity to microstructural details which may hold relevance for macroscale brain organisation. Since biophysical diffusion modelling offers greater specificity to microstructure, we investigated whether parameters from the Standard Model of diffusion in white matter provide informative alternatives for connectome weights - namely the intra-axonal signal fraction (f) and perpendicular extra-axonal diffusivity [Formula], as proxies of axonal density and myelination, respectively. Using diffusion MRI data from healthy adults, we constructed structural networks at four parcellation scales, weighted by f, [Formula], NOS, fractional anisotropy (FA) and radial diffusivity (RD). While all weights reproduced expected small-world properties, only [Formula] and normalised NOS captured non-random properties of local organisation across all spatial scales. We then correlated each weighted connectome with resting-state fMRI functional connectivity and intracranial measurements of conduction velocity. At the whole-brain level, although NOS gave strongest coupling with fMRI functional connectivity, only [Formula] exhibited significant structure-function coupling across all spatial scales and modalities. At the regional level, [Formula] and RD gave highest consistency in structure-function coupling across spatial scales. Thus, connectome weights derived from [Formula] capture meaningful aspects of brain network organisation with functional relevance.

Human behavioral and mental health outcomes arise from interactions among genetic, environmental, and neurobiological systems. Existing frameworks often model these components jointly, but many treat variables independently or use static representations. This limits their ability to capture system-level dynamics and changes over time. To address this, we developed DynoSys, a unified framework that integrates these signals using three layers: predictive models, relationship exploration models, and mechanism-oriented explanation models. Building on this framework, we introduce DynoSys 2.0, a graph-based temporal modeling approach inspired by the free-energy principle by Karl Friston. In this framework, each individual is represented as a dynamic graph that evolves over time. We hypothesize that healthy development and adverse mental health outcomes correspond to different system states and trajectories. Using longitudinal data from the Adolescent Brain Cognitive Development (ABCD) Study, we construct time-indexed graphs that integrate polygenic risk scores (PRS), multi-domain environmental features, and neuroimaging-derived representations. We study six phenotypes: externalizing behavior, internalizing behavior, and sub-stance use initiation (alcohol, nicotine, cannabis, and any substance). In these graphs, nodes represent domain-level features, and edges capture relationships derived from data-driven feature selection and temporal dependencies. We model graph evolution using recurrent neural networks and graph-temporal learning methods. We also define system-level measures, including graph energy and state transitions, to quantify dynamic patterns. Our results show that DynoSys 2.0 can model behavioral development using longitudinal multi-domain data. The framework achieved meaningful prediction for both continuous behavioral symptoms and substance-use initiation outcomes, but performance differed by outcome type. Externalizing behavior was predicted more accurately than internalizing behavior, and alcohol and any substance initiation showed stronger prediction than cannabis and nicotine initiation. Graph-derived energy measures showed clearer separation for high-versus low-symptom externalizing and internalizing groups, suggesting that continuous behavioral symptoms may be linked to different latent system states over time. Overall, DynoSys 2.0 provides a flexible framework for studying behavioral risk as a dynamic developmental process, while rare-event prediction and detailed graph-level interpretation require further work.

Brain networks coordinate distributed neuronal assemblies to support cognition. Spike-timing-dependent plasticity (STDP) and neuronal oscillations are key substrates for state-gated learning rules that shape network coupling and cognitive operations; nonetheless, how STDP mechanisms interact with neuronal oscillations is largely unexplored in humans. Cortico-cortical paired associative stimulation (ccPAS) provides a non-invasive system-level model of associative timing rules by pairing dual-site transcranial magnetic stimulation (TMS) across axonally connected regions with an inter-stimulus interval matched to pathway conduction. Here we: 1) synthesize ccPAS applications and barriers to brain-state-coupled implementation in cognitive networks; 2) provide an actionable roadmap for real-time state estimation, targeting, and dual-site parameter selection; and 3) demonstrate a novel implementation of theta-phase-locked fronto-parietal (FP) ccPAS with concurrent EEG in adult human participants. We tested whether ccPAS delivered at the positive phase of ongoing theta (POS) induces distinct changes in evoked EEG activity and FP connectivity compared to phase-uncoupled ccPAS (RAND) and phase-locked single-site prefrontal (PREF) controls. At the evoked level, POS produced a fronto-central polarity reversal of the canonical N45 component and a right parieto-temporal negativity relative to both controls. At the network level, POS induced frequency-specific reconfigurations in post-intervention connectivity beyond either control ingredient alone. Together, these changes in evoked activity and rapid network reconfiguration provide the first empirical evidence consistent with phase-gated STDP in humans--whereby oscillatory phase gates cortical excitability and modulates STDP efficacy--emerging as short-term network-level expression. Future work will assess long-term plasticity by tracking connectivity at later time points and testing for concomitant behavioral effects. SignificanceThe real-time brain state critically shapes how plasticity mechanisms are expressed in response to brain stimulation. This article provides a forward-looking synthesis of the scientific and technical challenges associated with ccPAS--an STDP induction model in the human cortex--and outlines the steps required to advance it toward real-time brain-state-coupled implementation. To our knowledge, this is the first application of brain-state-coupled ccPAS within a cognitive network. By personalizing stimulation to the individuals ongoing neural state, this approach may reduce variability, limit off-target effects, and enhance plasticity induction. Ultimately--by modulating network-level function in a brain-state-dependent manner--this technique could augment therapeutic outcomes in disorders marked by network dysfunction such as ADHD, Alzheimers disease, and major depressive disorder, potentially maximizing efficacy in patients unresponsive to existing treatments.

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.

In task-oriented teams, long-term coordination among specialized roles may contribute to shared patterns of cognition and behavior, yet little is known about how such experience is reflected in brain functional organization. Here, we examined whether cross-individual differences in whole-brain functional connectivity were associated with court position and team membership in professional volleyball players. In the resting-state and naturalistic volleyball game-viewing conditions, we analyzed dyadic functional connectivity differences to test whether effects of shared position and team were evident across intrinsic and contextually engaged brain states, controlling for differences in playing time and performance-related statistics. We found that same-position players showed smaller functional connectivity differences. These effects were most prominent and widespread across brain networks during game viewing, whereas at rest they were specific to the somatomotor network. Team membership was also associated with smaller functional connectivity differences during game viewing, although position x team interactions varied across networks after covariate adjustment. A complementary machine learning classifier further showed that shared position could be predicted from intersubject differences in functional connectivity with accuracy exceeding a frequency-based baseline. Together, these findings suggest that shared role-specific and team-based experience may contribute to structured similarity in functional brain organization within a real-world team setting.

IntroductionWhile global topological properties of brain networks reach relative maturity early in development, functional reconfigurations at the regional level continue throughout adolescence to support cognitive maturation. However, regional age and sex-specific developmental patterns of functional reconfiguration remain incompletely understood. MethodsWe analyzed resting-state fMRI data from 528 participants aged 5-21 years from the Human Connectome Project in Development. Three regional graph-theory metrics (betweenness centrality, hub score, and local efficiency) were computed for each individuals functional network. Cognition was measured using NIH toolbox. Parallel factor analysis was employed to decompose an individual x region x metric array into factors representing distinct developmental properties in the full sample and separately for males and females. Brain-cognition associations were examined in developmental subgroups (<13, 13-18, >18 years). ResultsThree factors emerged, characterizing visual, multimodal integration, and higher-order factors. Across development, metrics capturing network integration (betweenness centrality and hubness) showed general stability, while metrics capturing segregation (local efficiency) presented distinct peaks, particularly in the visual factor. Females showed earlier peaks and declines in higher-order factor, while males exhibited greater variability and protracted maturation in multimodal and higher-order factors. Brain-cognition associations were modest with early childhood and crystallized cognition composites showed small negative correlations with hub score in entire sample (r=-0.212) and local efficiency in males aged <13 years (r=-0.215). ConclusionFindings highlight nonlinear, sex-specific functional reconfiguration at region-level during childhood and adolescence, underscoring the importance of sex-stratified analyses in developmental and providing a crucial foundation for future investigations of developmental disorders.

BackgroundWhile resting-state fMRI demonstrated that brain networks are spatially dynamic (expanding, shrinking, and changing complexity over time), understanding the transient spatial network interactions that remain poorly characterized is critical for revealing the mechanisms underlying brain disorders. MethodsWe introduce DESINE (Dynamic Estimation of Spatially Interactive Networks), a novel framework using joint density distributions (2D histograms) of voxel-wise activity to quantify 4D spatial network interactions across sliding windows. We analysed transient deviations from the average functional state using root-mean-square error (RMSE) and mean absolute deviation (MAD), and characterized recurring interaction patterns using k-means clustering. We applied DESINE to 91 network pairs (14 networks) in a cohort of 508 subjects (315 healthy controls; 193 patients with schizophrenia, SZ). ResultsSZ is characterized by a significantly "constrained dynamic repertoire" of network interactions. SZ patients showed markedly lower means and standard deviations for both RMSE and MAD metrics across network pairs, particularly in regions of high activity, indicating systematic rigidity. Cluster analysis revealed significant alterations in state affinity metrics, suggesting a global breakdown in the brains capacity to preserve diverse, high-fidelity spatial configurations. Critically, these interaction metrics were associated with cognitive performance, symptom scores on the positive and negative syndrome scale, and chlorpromazine equivalent drug scores. ConclusionsThis work introduces DESINE as a global, voxel-agnostic framework for characterizing time-varying spatial interactions. Our findings highlight spatial rigidity as a fundamental feature of psychopathology, suggesting that the inability to express a diverse range of spatial interactions is a factor underlying cognitive deficits in schizophrenia.

BackgroundThis study examines a competition-based model (C-model) designed to capture the temporal dynamics of successive brain microstates derived from electroencephalography (EEG) recordings during eyes-open conditions. The analyzed data were obtained from a public repository comprising microstate sequences from 60 sessions of a single subject [1]. When applied to microstate dynamics, the C-model posits a stochastic competition among neural circuits underlying the expression of individual microstates. MethodsThe model is formulated at a conceptual level (computational level in Marrs framework) and employs a geometric distribution to account for the long right tail of microstate duration distributions, interpreted as the probability of "failure" of the currently active microstate to persist. To account for the short-lived left tail, the model incorporates a transient increase in the stability of the currently active network, or equivalently, a temporary decrease in the activation probability of competing microstates (refractory period). ResultsThe model provides a good fit to the microstate duration distributions across all 60 sessions. One third of sessions showed microstate identity sequential dependency with respect to the previous microstates. DiscussionThese results suggest that the C-model captures key aspects of microstate temporal structure. Moreover, because microstate probabilities can be modulated by psychophysiological conditions--including the influence of previously active networks--the model may serve as a building block for more comprehensive neurobiological frameworks of neural and behavioral dynamics. In such frameworks, microstate sequences could emerge from structured competition and flow among neural networks supporting microstate expression.

Predicting post-stroke aphasia severity remains challenging, in part because language outcomes reflect not only focal cortical damage but also widespread disruption of structural and functional networks. Computational models of large-scale cortical dynamics offer a principled way to infer these network-level consequences from patient-specific lesions. Here, we present and evaluate REWIRED, a lesion-informed cortical dynamics framework designed to simulate individualized alterations in large-scale brain network organization after stroke. We first evaluated whether simulation-derived functional connectivity captured patient-specific variation in empirical functional connectivity beyond lesion burden and structural disconnection alone. We then developed a multiscale feature set combining lesion volume, lesion distribution patterns, probabilistic disconnectome metrics, and simulation-derived measures of functional connectivity and effective information flow (EIF). Finally, using a nested support vector regression (SVR) framework in a separate dataset, we tested whether simulation-derived features improve prediction of chronic aphasia severity, measured by the Western Aphasia Battery - Revised Aphasia Quotient (WAB-AQ), beyond lesion-distribution and structural-connectivity predictors. Simulation-derived functional connectivity significantly predicted empirical functional connectivity beyond local lesion burden and structural disconnection alone. With respect to WAB-AQ prediction, lesion-based (Set 1) and disconnectome-based (Set 2a) features alone yielded modest accuracy. Adding simulation-derived features (Set 2b) produced substantial gains, and the full feature set (Set 3) achieved the best performance (RMSE = 14.5; r = 0.83), reaching accuracy that is competitive with recent multimodal neuroimaging approaches, despite relying solely on lesion-distribution inputs. EIF measures were consistently selected as top predictors, indicating that disruptions in interregional communication patterns carry behaviorally relevant information not captured by structural features alone. These results support REWIRED as a framework for linking structural injury to distributed network dysfunction and behavioral outcomes. By integrating lesion information with large-scale cortical dynamics modeling, REWIRED provides a foundation for future individualized modeling of recovery and rehabilitation.

Neural oscillations are fundamental to brain function and cognition. Conventional analyses often rely on predefined frequency bands to assess power modulations, which may obscure finer-grained spectral variability. In this study, we focused on frequency rather than power to investigate whether the natural frequency of each brain region, typically observed at rest, represents a stable intrinsic property or dynamically reconfigures during cognitive processing. We analysed magnetoencephalography (MEG) data from the Human Connectome Project (HCP) across motor execution, working memory, and language processing tasks. Using a multivariate, data-driven spectral clustering approach, we mapped natural frequencies on a voxel-by-voxel basis without imposing predefined bands or regional boundaries. Results indicated that, while the global spatial organization of natural frequencies remained largely preserved during task engagement, specific cortical regions exhibited systematic, task-dependent shifts. In the sensorimotor cortices, the typical resting frequency of [~]24 Hz decreased to [~]6 Hz during movement preparation and at movement onset, and shifted to high-beta rhythms ([~]30 Hz) following hand movement. Increased working memory demands accelerated parieto-occipital alpha/beta activity (from [~]11/16 Hz to [~]13/20 Hz) and recruited high-gamma oscillations (60 to 80 Hz) in medial temporal regions. Finally, arithmetic processing elicited a [~]5 to 15 Hz increase within the beta/gamma ranges across frontoparietal networks relative to semantic comprehension. Taken together, these findings demonstrate that natural frequencies reflect a hybrid architecture: globally stable, yet locally flexible in response to cognitive demands. Moreover, our results suggest that cognitive engagement tends to accelerate neural rhythms in functionally specialized regions, providing a more nuanced understanding of the spectral architecture of human brain function beyond conventional power- and band-based metrics.

The thalamus is essential for learning, dynamically engaging with other subcortical and cerebral cortex regions throughout the learning process. Here, the thalamus serves as a critical connector hub and synchroniser within the thalamocortical system of the brain. However, whilst higher order thalamic nuclei are known to be particularly important for this process, the exact contributions of individual higher order and first order thalamic nuclei, alongside their individual involvement with cortical networks and subcortical regions, remains unexplored within the initial phase of learning. In light of this, we analysed fMRI data obtained within a paradigm which is designed to examine initial learning processes within feedback-driven stimulus-response learning, in order to explore thalamic contributions. We investigated dynamic learning-related functional connectivity alterations between various thalamic nuclei with other subcortical regions and cortical networks. Our results show that the initial phase of learning was associated with: (1) decreasing functional connectivity between thalamic nuclei and frontoparietal and cingulo-opercular networks, (2) increasing functional connectivity between thalamic nuclei with default mode and salience networks, (3) decreasing functional connectivity between thalamic nuclei and the putamen, and (4) decreasing functional connectivity amongst higher order thalamic nuclei. Furthermore (5) these dynamic alterations were associated primarily by mediodorsal thalamus. Altogether, these results indicate that higher order thalamic nuclei play a crucial role within initial learning and in the generation of novel goal-directed behaviour. This was demonstrated through enhanced functional connectivity with selected cortical networks which drive goal-directed behaviour, alongside decreased functional connectivity with striatal regions which drive motor selectivity.

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.

Frontoparietal connector hubs are thought to support information integration across the brain, but this role has largely been inferred from static connectivity, leaving unclear how computational processes shape inter-regional connectivity during behavior. Here, we address this question using a model-based functional connectivity approach in human fMRI data. Thirty-Eight participants (males and females) performed a task requiring the integration of sensory evidence with an internally maintained state belief to guide behavior. We developed a computational model that combines these information sources into an integrated representation and generates distinct variables at successive stages of integration: uncertainty before choice (entropy), the inferred task representation guiding action (task belief), and feedback-driven updating (task inference error). We then tested how these variables modulate the connectivity of frontoparietal connector hubs. Entropy increased coupling between hubs and regions encoding task-relevant inputs and outputs during cue processing, suggesting enhanced communication under uncertainty. During task selection, task belief selectively modulated hub connectivity with motor regions according to the selected task. During feedback, task inference errors increased coupling with regions supporting task-relevant inputs and internal state, while reducing coupling with motor regions, consistent with updating internal representations. Together, our findings show that frontoparietal connector hubs implement integrative control by using an integrated representation to generate distinct computational signals that selectively and dynamically reconfigure inter-regional communication. SignificanceFlexible behavior depends on combining different kinds of information, but how the brain coordinates this integration remains unclear. The frontoparietal cortex is well positioned to support this process because it is broadly connected with many other systems. Here, we combined a computational model with functional MRI to test how integrating information changes patterns of functional connectivity. We find that a common set of signals is associated with dissociable changes in how frontoparietal regions couple with systems involved in perception, action, and internal updating. These findings reveal that integration generates multiple control signals that dynamically reconfigure brain-wide interactions to support goal-directed behavior.

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.

Traumatic brain injury (TBI) profoundly disrupts hippocampal network dynamics, triggering persistent alterations in oscillatory activity that underlie cognitive deficits and increased susceptibility to post-traumatic epilepsy. Characterizing these alterations quantitatively remains challenging: the resulting signals are nonlinear, non-stationary, and exhibit complex multiscale structure that conventional spectral metrics fail to resolve. Ordinal-pattern information-theoretic quantifiers offer a principled, model-free alternative for probing such dynamics. In this work we apply permutation entropy (PE), statistical complexity (SC), Fisher information (FI), and permutation Lempel-Ziv complexity (PLZC) to hippocampal local field potentials (LFPs) recorded over 21 days in a rodent controlled cortical impact model of TBI, across five experimental groups under distinct pharmacological conditions. Embedding signal trajectories in the SC-PE, FI-PE, and PLZC-PE information planes reveals group- and time-dependent dynamical signatures in the theta (4-8 Hz) and high-frequency oscillation (80-200 Hz) bands, exposing state transitions invisible to spectral analysis. Unsupervised dimensionality reduction (UMAP) combined with HDBSCAN clustering further delineates distinct regions of dynamical state space associated with injury progression and pharmacological modulation. We additionally applied the Ordinal Modulation Index (OMI), an ordinal-based measure of theta-HFO cross-frequency coupling, which captures treatment-dependent reorganization of phase-amplitude coordination. These results establish ordinal-pattern analysis as a sensitive and interpretable framework for tracking the nonlinear reorganization of hippocampal dynamics following TBI.

Diffusion MRI (dMRI) tractography provides a non-invasive method for mapping whole-brain structural connectivity. However, its application is limited by substantial false-positive and false-negative connections. While deep learning based methods have shown promise in improving tractography, most rely on training data derived from conventional dMRI tractography, therefore inheriting the same limitations. Here, we introduce FiberLM, an attention-based Transformer model for mouse brain tractography. The model was trained using a whole-brain streamline dataset based on viral tracer data from the Allen Mouse Brain Connectivity Atlas (AMBCA), allowing the model to learn the properties of both local and long-range axonal trajectories through self-attention. FiberLM was applied to predict anatomically plausible axonal trajectories from ex vivo high-resolution mouse brain dMRI data. Quantitative evaluations demonstrated that FiberLM significantly reduced false-positive and false-negative connections, improved spatial agreement with tracer-defined pathways, and generated whole-brain connectomes that more closely approximated AMBCA results compared to conventional tractography. These findings suggest FiberLM as a potential tool for accurate reconstruction of mouse brain structural connectomics.

The emergence of psychiatric symptoms during adolescence is increasingly hypothesized as arising from maturational imbalance across brain systems. However, this concept largely lacks quantitative grounding, which requires measuring fine-grained imbalance patterns that accurately capture acceleration or delay relative to normative developmental trajectories of brain regions. To address this gap, we leverage predictive normative modeling to learn models that predict chronological age from regional multivariate functional connectivity patterns. We demonstrate that these region-specific models are highly generalizable across independent cohorts and capture greater developmental effects than traditional functional connectivity metrics. From these models, we then derive a region-wise Relative Maturity (RM) index that quantifies individualized, region-specific deviations from normative development. Rigorous cross-cohort and longitudinal evaluations across four datasets show that RM maps are reproducible, subject-specific fingerprints of neurodevelopmental imbalance. These fingerprints are organized along continuous, low-dimensional axes aligned with intrinsic functional gradients and can predict dimensions of psychopathological vulnerability. Together, our findings establish RM as a robust, sensitive, and generalizable framework for quantifying individual vulnerability to psychopathology through system-level patterns of developmental imbalance.