Network Neuroscience

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.

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.

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.

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.

Individual brains are unique in structure and function. Functional differences are captured by neural fingerprints, which reflect individual differences in behavior and cognition as well as group-level changes related to neurodegenerative diseases. Most research efforts so far have focused on fingerprints com-prising full functional connectomes. However, the high dimensionality of the connectomes can increase computational load and impede performance of machine learning methods in potential applications. A low-dimensional alternative that retains individual features of the full connectomes would thus be beneficial. The present study employed latent-noise Bayesian Reduced Rank Regression (lnBRRR) to learn low-dimensional latent spaces that capture individual features in functional connectivity and power spectral density data derived from MEG recordings. LnBRRR performance was assessed with low training set sizes (N=20-44), and against principal component analysis and linear discriminant analysis. Model performance was also assessed with task data, and the solutions were compared across task conditions with cosine similarity to establish whether individual features are altered by different cognitive processes. LnBRRR captured generalizable individual patterns already at N=20 but N=30-35 was needed to reach optimal test accuracies and to prevent potential overfitting. The model also achieved comparable performance to the alternative models. Latent fingerprints derived from task data attained comparable performance to resting-state latent fingerprints, and lnBRRR solutions were shown to generalize across conditions. Additionally, the model solutions for power spectral density data were discovered to be notably similar, yet differently rotated, over task conditions, suggesting that similar patterns of individual features were captured by the model regardless of the task condition. Altogether, the present results highlight lnBRRR as a potential tool for neuroimaging data analysis and demonstrate that individual differences in power spectral density are largely intrinsic and unaffected by varying cognitive processes.

Naturalistic cognition emerges from coordinated interactions among distributed brain systems operating across multiple representational scales. Characterizing this organization remains challenging because cognitively relevant information is embedded within high-dimensional neural activity. Here, we apply Multisubject Archetypal Analysis (MS-AA) [1] to naturalistic fMRI data collected during intact narrative listening, word-scrambled audio, and rest to investigate how condition-relevant information is distributed across archetypal representations. We examine both spatial and temporal formulations of MS-AA as complementary views of naturalistic brain activity. Across analyses, decoding performance consistently followed the hierarchy intact > word-scrambled > rest, indicating that archetypal representations preserve meaningful condition-related structure. Top-m decoding analyses further revealed that this information is highly compressible: relatively small subsets of archetypes frequently recovered substantial fractions of full-model decoding performance. Spatial AA exhibited a stable sparse-decoding regime that persisted across representational scales. Across a broad range of matched component ratios, approximately 5-15 archetypes consistently captured disproportionate amounts of condition-relevant information. These same sparse subsets also organized subjects into condition-aligned clusters more strongly than expected from random archetype subsets, with the strongest joint decoding-clustering effects occurring repeatedly within an intermediate representational regime (K {approx}50 - 88). Network over-representation analyses revealed that informative archetypes were not isolated canonical networks but distributed mixtures of interacting systems. Across the highest-performing decoding- clustering configurations, default mode and frontoparietal systems were consistently overrepresented relative to network size, whereas visual and limbic systems were underrepresented. Together, these findings suggest that the archetypal motifs most informative for distinguishing cognitive states are sparse, distributed subnetworks enriched for higher-order association systems. More broadly, the results demonstrate that MS-AA provides a useful framework for studying the compressibility, geometry, and multiscale organization of cognitive brain states.

Intrinsic neural timescales represent the characteristic duration over which information is maintained in neuronal circuits. Evidence suggests that neural timescales vary systematically across the cortical hierarchy, with shorter timescales in primary sensory areas and longer timescales in higher-order association regions. In previous studies, hierarchy has been defined categorically, anatomically, or from the principal gradient of resting-state fMRI functional connectivity derived using diffusion map embedding (DME). Here, we assign hierarchical position to individual human intracranial electroencephalography (iEEG) recording sites by projecting their MNI coordinates onto this embedding space, derived from Human Connectome Project resting-state fMRI data. We estimated neural timescales from resting-state iEEG recordings in adult neurosurgical patients (n=46, 25 female) by extracting the aperiodic component of the local field potential power spectrum using spectral parameterization. Timescales increased monotonically with hierarchical position and associated with two region of interest (ROI)-level measures of network topology derived from DME of participants' iEEG functional connectivity: ROIs with stronger mean functional connectivity exhibited longer timescales, as did ROIs functioning as hubs, defined by proximity to the center of embedding space. Finally, timescales varied with sleep stage, with slowest values during NREM and fastest during wake and REM. The hierarchical gradient present during wake and N1 was no longer detected during REM, N2, and N3 sleep, driven by a selective increase in timescales at lower levels of the hierarchy. This work presents a novel metric of hierarchy that can be applied to iEEG data, establishes a direct link between neural timescales, cortical hierarchy, and network topology in human iEEG, and demonstrates that this hierarchical organization is dynamically modulated by brain state.

A central challenge in neuroscience is understanding how the human brain is organised to support optimal functioning and adaptability. One approach to characterise complex brain dynamics is by artificially perturbing whole-brain models. Here, we asked whether whole-brain organisation under perturbation in major depressive disorder (MDD) changes after intervention with psilocybin and escitalopram. First, we built whole-brain models of pre- and post-treatment resting-state functional magnetic resonance imaging (fMRI) and obtained an initial generative effective connectivity (GEC) matrix for each individual. Then, we employed systematic and local artificial perturbations across intensities, re-optimised each model to create a response GEC (GECr), and assessed the extent of brain reorganisation by quantifying the brain network reconfiguration index (NRI). Our results showed that the global brain NRI increases with psilocybin and decreases with escitalopram. Across sessions and interventions, higher global NRI was related with localised perturbations in brain areas orchestrating the brain's hierarchical dynamics. Traditional approaches complemented our investigation. Our findings suggest distinct neural changes following each treatment for MDD. The increase in brain reorganisation under perturbation following psilocybin is consistent with greater brain flexibility and changeability, whereas the decrease following escitalopram suggests more stabilised brain dynamics. Overall, perturbation-induced brain NRI may represent a useful approach for uncovering neural changes following different interventions for depression.

Brain function is deeply embedded within multiscale structural architecture. Conventional studies predominantly utilize pairwise connectivity networks to investigate structure-function relationships. However, this low-dimensional perspective overlooks multi-region collaborations for complex cognition. Consequently, whether and how anatomy constrains such higher-order functional networks remains unresolved. To address this pivotal question, we utilize an information-theoretic O-information approach to characterize higher-order functional interactions (HOIs). By reconstructing individual-level HOIs from multimodal structural networks, we directly validate the structural constraint on HOIs. The resulting reconstruction coefficients are defined as structural-functional constraint strength (SFCS), serving as a quantitative vehicle to decipher how anatomy shapes these higher-order networks. SFCS uncovers a highly heterogeneous structural constraint landscape across data modalities, spatial regions, and informational interaction modes. Crucially, individualized SFCS robustly predicts multi-domain cognitive phenotypes, showing higher sensitivity for informant-reported than patient-reported assessments. Finally, we show that this landscape undergoes pathological, mode-specific reorganization in Alzheimers disease. Cross-scale alignment with spatial transcriptomics further demonstrates that this macroscale network remodeling is coupled with microscale metabolic and regulatory gene pathways. Collectively, our findings not only validate the structural constraint on higher-order functional networks but also decipher its precise underlying mechanisms. This constraint paradigm plays a pivotal role in shaping diverse cognitive capabilities, while its pathological disruption in Alzheimers disease highlights the potential of SFCS as a biomarker for tracking neurodegenerative network impairments.

This paper presents a framework for modelling the topography of whole-brain connectivity in resting-state functional MRI. The aim is to disentangle functional segregation, which manifests as abrupt changes in connectivity, from so-called gradients, i.e., smooth variations in connectivity across the brain. Our core assumption is that functional segregation leads to low-rank structure in the dense (point-to-point) connectome, whereas connectivity gradients imply a sparse and non-low-rank structure in the dense connectome. Our method thus decomposes the connectome into low-rank and sparse components, enabling the integration of local-nonlinear and global-linear embedding strategies. We show that this hybrid model approximates the empirical dense connectome more effectively than purely low-rank or purely gradient approaches. We also find that connectivity gradients derived from this model exhibit strong correspondence with task-based topographic maps. We hope that this approach can provide insight into the organisational principles of brain regions where gradients remain poorly characterised.

Brain oscillations emerge from nonlinear interactions across anatomically connected neural populations, alternating between transiently coordinated and desynchronised states. Transcranial alternating current stimulation (tACS) can modulate these dynamics, but most work has focused on frequency and amplitude, leaving waveform shape comparatively unexplored. Here we used a whole-brain model of delay-coupled Stuart-Landau oscillators constrained by empirical human structural connectivity to determine how sinusoidal, square, triangular, sawtooth and pulsed stimulation reshape spontaneous alpha-band activity. All waveforms were applied at the same frequency and amplitude to the posterior parieto-occipital regions. Network responses were quantified using the Kuramoto order parameter, spectral entropy and metastable oscillatory modes of transient alpha bursts. Sinusoidal and pulsed stimulation produced the strongest effects, increasing global synchrony while reducing metastability and spectral entropy, consistent with a transition from a fluctuation-rich regime to a coherent and spectrally concentrated state. These waveforms also transformed intermittent alpha bursts into prolonged or near-continuous oscillatory episodes. In contrast, square, triangular and sawtooth stimulation reduced synchrony while largely preserving metastability, producing weaker and more fragmented modulation. These findings identify waveform shape as a key determinant of rhythmic stimulation effects and a principled parameter for neuromodulation design.

Introduction Understanding the mechanisms of human neuromaturation constitutes one of the fundamental questions of neuroscience. While it is well described that large-scale brain maturation is initiated within sensorimotor brain regions and progresses to associative cortex, the underlying developmental neurobiology remains to be fully characterized. Animal models have indicated that cortical inhibitory upregulation might be a driver of neurodevelopment. To investigate the hypothesis that cortical inhibitory upregulation plays a similar role in human neuromaturation, we developed a The Virtual Brain (TVB) based computational model (TVB-Child) to explore potential mechanisms of human neurodevelopment. Material and method We created neurodevelopmental dynamic brain network models capturing neurobiological maturation by using the large-scale brain simulator TVB and fitting brain network models to developmental functional MRI (fMRI) from the Human Connectome Project-Development (HCP-D) data set with 640 subjects with an age range of 6-21 years. Age-dependent trajectories in the fMRI data set were first analyzed by combined group-ICA/Dual Regression extracting subject-specific resting-state networks (RSN). Maturational topographical and topological redistribution of these networks were analyzed by linear and non-linear regression of RSN size and degree and strength centrality. Brain network models were fitted to the fMRI functional connectivity obtained from the HCP-D data set. Hypothesizing that cortical inhibition is a driver of neuromaturation, we analyzed spatiotemporal inhibition parameter gradients in the dynamic brain network model for the hypothesized significant correlations with fMRI RSN maturational trajectories. Results While during development frontoparietal (FP) and default mode network (DMN) grew and exhibited an increase in both degree and strength centrality, becoming dominant network hubs, the attention network underwent network pruning with a decrease in size and node degree. The primary sensory network changed little. For the fitted brain network models, we obtained a high degree of reproduction with correlation coefficients between empirical and simulated functional connectivities ranging between 0.80 and 0.95. Values of the feed forward inhibition model parameter wijFFI representing the strength of regional feedforward inhibitory input exhibited the most significant increase with age within the FP and DMN networks. A less pronounced, but significant, age-dependent increase of the inhibitory parameter values were seen in attention networks and no change within primary sensory networks. Conclusion Our study shows that high order (FP, DMN), attention and primary sensory networks exhibit distinct topographical and topological maturation trajectories. Moreover, brain network modeling revealed RSN-specific age-dependent inhibition trajectories, indicating that the model is able to reproduce and thus support candidate mechanisms of neurodevelopment.

Gaming addiction may be contagious through social interaction. In team competitive video games, individuals of varying addiction levels are often paired. This raises the question of whether, and through what mechanisms, one individuals addiction level is affected by other players. Methodologically, addressing this question requires understanding both what happens between brains and within individual brains during a gaming session. To this end, we used HYPER-NESS (Hyper Brain Network Estimation via Source Separation), a novel analytical framework to decompose and weight the distinct contributions of inter-brain and intra-brain processes to social brain networks. We applied this framework to a hyperscanning fNIRS dataset where dyads were scanned while playing a video game together against experimenters. We found that inter-brain and intra-brain contributions to the social brain networks were differentially associated with changes in game reward and social reward sensitivity, depending on the addiction level of ones partner. Granger causality analysis of both social and individual brain networks revealed influences from the high-addiction partner to low-addiction partner. Furthermore, significant cross-frequency coupling was found selectively between low-addiction players, supporting the idea that this form of inter-brain interactions underpins the joint processing of task-relevant rewards. To our knowledge, these findings provide the first neural-level account for the hypothesis that gaming addiction propagates though dyadic social interaction.

Advanced meditation offers a powerful lens for investigating consciousness and for understanding how sustained training may contribute to human flourishing. In the spirit of neurophenomenology, we combine first-person reports with model-free empirical analyses and formal whole-brain modeling to investigate the mechanisms underlying advanced meditative states and minimal phenomenal experience (MPE). Specifically, we focus on jh[a]na meditation, a type of advanced concentrative absorption meditation (ACAM-J). Advanced practitioners accessed the eight ACAM-J states during ultra-high-field 7T functional magnetic resonance imaging. For each state, we first characterize empirical functional connectivity and then build a mechanistic whole-brain model that reproduces brain activity by modeling the dynamical regimes of different brain networks. We found that the later ACAM-J states, taken here as candidates for MPE, show increased large-scale functional integration and a shift of functional network dynamics toward near-critical working points. The default mode network (DMN) exhibits the largest shift, from a distant noise-driven regime during the control condition to near-critical dynamics during ACAM-J. We also observed that the trajectory of ACAM-J states is non-linear, with prominent reconfigurations at key meditative milestones. Our results suggest that MPE, as instantiated in later ACAM-J states, corresponds to a globally susceptible state where near-critical dynamics dominate. We interpret this near-critical regime as a form of "openness", in which constrained and differentiated patterns of brain activity give way to greater flexibility. In particular, increased DMN susceptibility is correlated with broader attention and reduced narrative thought, consistent with a more flexible mode of self-related processing. In this context, advanced meditation provides a powerful model for studying how sustained contemplative practice can profoundly shape brain dynamics and provide a window into core aspects of consciousness.

Personalized whole-brain modeling aims to transform treatment planning for neurological disorders by enabling patient-specific simulations of brain network dynamics. Neural mass models (NMMs) offer a tractable compromise between biophysical detail and computational cost and can be directly linked to macroscopic observables such as EEG. However, scaling NMMs to whole-brain networks with realistic connectivity, conduction delays, and cortical surface resolution--and fitting them to individual patient data--imposes computational demands that existing frameworks cannot meet at clinically relevant timescales. Here we introduce BraiNN, a JAX-based Python framework for large-scale neural mass modeling that achieves speedups of up to two to three orders of magnitude over existing tools by leveraging GPU/TPU-accelerated, XLA-compiled array computation. BraiNN combines a region-level Jansen-Rit network with a subject-specific cortical surface mesh of coupled neural mass models and biophysically grounded EEG forward modeling via reciprocity-based lead fields. Its fully differentiable computational graph enables a hybrid personalization pipeline that pairs Bayesian optimization for global parameter exploration with gradient-based refinement, completing EEG-driven spectral fitting of an eight-dimensional parameter space in approximately 2-3 hours on a single consumer GPU--compared to multiple days with conventional neural mass modeling software. Numerical verification against established benchmarks confirms that BraiNN faithfully reproduces canonical synchronization and bifurcation dynamics of Jansen-Rit networks. By reducing the time requirements for personalizing a high-detail whole-brain surface model from days to a few hours on consumer-grade hardware, BraiNN brings personalized brain network modeling closer to practical use in clinical contexts. We anticipate that BraiNN will serve as a foundation for patient-specific digital twins and EEG-guided neuromodulation planning.

Capturing naturalistic behavioral dynamics is essential for understanding social interaction in ecologically valid settings. Existing investigations of naturalistic social interaction rely on time-aggregated analysis methods better suited for task-based experiments, which lose the complex, moment-to-moment dynamics exhibited in naturalistic settings. The emerging field of topological data analysis (TDA) provides new tools to characterize fine-grained dynamics in time-series data that cannot be captured by time-averaged methods. The present work utilizes Temporal Mapper, a recently developed TDA specifically tailored to analyzing dynamical systems. Temporal Mapper characterizes complex temporal dynamics as transition networks, where nodes are stable states and edges are transitions between states. Originally designed for human neural time series analysis, here we demonstrate the utility of Temporal Mapper to capture rich animal postural dynamics during naturalistic social interaction. We utilized an existing dataset with 12 video recording sessions of two domestic ferrets (Mustela putorius furo) during naturalistic interaction and tracked the postures of animals during social interaction. Ferrets were chosen due to their strong social-cognitive skills and rich postural dynamics for investigating social behavior via posture estimation. Temporal Mapper was then used to represent the postural dynamics as transition networks for each recording session. Here, we found that posture states are significantly smaller and more widespread during active social interaction compared to non-social activities. Additionally, the number of sequential postural states before transitioning to new behaviors is more consistent during active social interaction than non-social activities. Together, our findings suggest that social activity has a broad range of unstable postural states arranged in consistent sequences. Our method, Temporal Mapper, allows for network structure analysis of complex naturalistic data, applicable for characterizing rich dynamics in different species, scales, and paradigms.

Functional MRI studies have traditionally focused on gray matter, whereas white-matter BOLD signals have often been treated as weak or artifactual. Recent work suggests that white-matter BOLD fluctuations contain reproducible functional information, but most gray-to-white matter analyses rely on marginal functional connectivity, which cannot separate pairwise coupling from shared variance among distributed cortical systems. Here, we used a multivariate cortical encoding framework to test whether spontaneous white-matter BOLD activity can be predicted from distributed cortical gray-matter activity and whether this predictive structure reveals organization beyond marginal connectivity. Resting-state fMRI data from 81 Human Connectome Project young adult participants were analyzed using a strict white-matter mask with no overlap with cortical predictors. For each white-matter voxel, time series from 400 Schaefer cortical parcels were used to predict held-out white-matter BOLD signals with nested leave-one-run-out ridge regression. Cortical activity modestly but reliably predicted white-matter BOLD dynamics, demonstrating consistent cross-validated prediction accuracy across a broad spatial extent of the white matter. Ridge beta fingerprints strongly recapitulated marginal functional connectivity fingerprints, indicating a shared functional backbone, but their first gradients diverged reproducibly. This beta-FC divergence axis organized FC-adjusted prediction residuals and remained robust after controlling for gray-matter proximity, mask-boundary distance, white-matter prevalence, temporal signal variability, spatial coordinates, and spatial autocorrelation. The high-divergence end showed relatively low marginal FC but high FC-adjusted prediction residuals and was enriched for posterior thalamic/optic-radiation and posterior corona-radiata anatomy. These findings suggest that multivariate cortical encoding reveals a tract-organized dimension of white-matter functional coupling not captured by pairwise connectivity alone.

In patients with epilepsy, seizures are associated with pathological neural synchronization. However, the preictal period preceding a seizure often exhibits reduced spatial synchronization compared to normal cognition. This observation aligns with the concept of the brain as a complex dynamical system, where a reduction in dimensionality and resilience can precede a phase transition. The Critical Brain Hypothesis suggests a connection between the loss of healthy scale-free behavior and various disorders, including epilepsy. Our study investigates preictal changes by utilizing network features, such as mean node degree and mean clustering coefficient, derived from thresholded correlation matrices of patient intracranial electrocorticographic electrode data. We observed a suppression of intermittent high-synchronization periods within the feature space during the minutes leading up to seizure onset. This constriction of the explored hypervolume in the preictal state indicates a breakdown in the brains ability to maintain normal coherence. We use these preictal changes to predict the probability of seizure onset using a Support Vector Machine algorithm. These discrete predictions can then be combined into real-time continuous seizure risk forecasts via Bayesian updating. This innovative and computationally lightweight approach has the potential to significantly improve upon static predictions, providing opportunities for more adaptable, quantitative, and interpretable tools for managing seizures.

Comprehensive biomarkers of multiple sclerosis (MS) capable of simultaneously diagnosing the condition, capturing symptom severity and predicting treatment efficacy remain elusive. Although several studies have highlighted the pivotal role played by demyelinating lesions in determining MS structural pathology, their relationship with symptom severity is limited. Here, we combined personalized computational brain modeling with magnetoencephalography (MEG) recordings from 17 MS patients and 20 healthy controls (CTR) to derive personalized brain network excitability parameters, which we tested as MS biomarkers. Personalized parameters discriminated between CTR and MS participants with high accuracy, also classifying between progressing and remitting MS patients. Notably, they also predicted MS clinical scales across multiple domains. In all clinical tasks, personalized parameters consistently outperformed standard clinical measures and total lesion loads. Together, these results highlight the potential of personalized brain modelling in deriving integrative MS biomarkers, capable of simultaneously identifying the condition, classifying MS subtypes and predicting symptom severity. d brain modelling in deriving integrative MS biomarkers, capable of simultaneously identifying the condition, classifying between MS subtypes and predicting the severity of symptomatology.

While Blood Oxygenation Level-Dependent (BOLD) fMRI remains the gold standard for mapping functional brain networks with MRI, its vascular origins inherently conflate haemodynamic effects with neural activity, limiting its sensitivity in white matter (WM) or its interpretation in neurovascular diseases. Apparent Diffusion Coefficient (ADC) fMRI offers an alternative, diffusion-based contrast that is theoretically more sensitive to neuromorphological coupling and therefore more specific to neuronal activation, though investigated primarily during task-based conditions. This study aimed to comprehensively evaluate the efficacy of isotropic ADC-fMRI in detecting established resting-state networks (RSNs) and to extend this methodology to the investigation of grey-to-white matter (GM-WM) functional connectivity. Our analyses revealed a gradient of ADC detectability shaped by the degree of static functional cohesion and structural tethering of each network. The visual and somatomotor networks, being both highly segregated and strongly anchored to underlying structural pathways, yielded the most robust detection. The default mode network (DMN) and dorsal attention network (DAN) reached group-level significance but with lower effect sizes, and their detection proved fragile across analytical approaches. The frontoparietal network (FPN) and salience network (SAN), whose functional identity is defined by dynamic cross-network reconfiguration, did not reach significance. This gradient partially mirrors the established hierarchy of network segregation observed in BOLD, while further suggesting that ADC sensitivity depends on the structural grounding of each network. Furthermore, ADC demonstrated superior sensitivity to GM-WM functional coupling compared to BOLD. GM-WM functional connectivity profiles derived from ADC were significantly more aligned with underlying structural WM architecture across subjects. Taken together, these findings position isotropic ADC-fMRI as a viable complementary modality to BOLD, offering a more direct window into the neural and structural foundations of brain connectivity.