External brain stimulation is a promising tool for investigating and altering cognitive processes, with potential clinical applications to the restoration of dysfunctional neural dynamics. In line with experimental observations, we study how the effects of stimulation crucially depend on the ongoing dynamics of the brain, at the local level of the stimulated region but also of global coordinated brain activity. Specifically, we use connectome-based whole-brain computational modeling to explore how the effects of single-pulse stimulation to different regions strongly depend on both the phase of regional oscillatory activity and on the transiently occurring network of functional connectivity at the time of the applied stimulation. Importantly, we show that stimulation has not only state-dependent effects but can also induce global state switching. Lastly, predicting the effect of stimulation by using machine learning shows that functional network-aware measures (i.e., knowledge of either a discrete state of functional connectivity or of a detailed functional connectivity matrix) can increase the performance by up to 40%. Our results suggest that a fine characterization of intrinsic functional connectivity dynamics is essential for improving the reliability of exogenous stimulation.

Dynamic functional connectivity (dFC) is ubiquitously observed in the brain, but why functional networks should remain dynamic even at rest is unclear. We asked whether temporal reconfiguration becomes advantageous when keeping a functional link active is costly. Modeling resting-state dFC as a temporal communication network, we show that empirical dFC outperforms equal-cost static architectures by increasing the reach and speed of information spreading in sparse regimes. Unlike more randomized temporal null models, however, it also preserves strong local cohesiveness, temporal clustering, rapid return of information to its source, and high neighborhood retention. Empirical dFC therefore achieves a compromise between large-scale integration and transient local segregation. This compromise is not explained by generic temporal variability, nor by partially frozen null models with persistent templates. A connectome-based mean-field model reproduces several key features, including high spatial and temporal clustering and strong integrative and segregative performance, but remains more stable over time than the empirical data. Our results indicate that empirical dFC reflects a structured regime of controlled persistence and renewal, in which local neighborhoods are maintained long enough to support transient recirculation before broader network-wide spreading occurs. Dynamic functional connectivity thus appears to be a resource-efficient solution to competing communication demands.

Abstract Neurodegeneration progressively removes synapses and neurons, yet neural circuits can retain stable collective dynamics despite substantial structural loss. Which structural principles confer this resilience remained unclear. Using large-scale spiking networks spanning empirical and synthetic microcircuit architectures, we systematically compared synaptic and neuronal modes of degeneration under controlled pruning strategies. We found that resilience was not determined by connectivity loss alone, but by how inhibitory gain was embedded within circuit architecture. Networks in which inhibitory neurons occupied structurally central positions robustly maintained health-like firing rates, levels of synchrony, and informational bandwidth across degeneration stages, whereas architectures lacking such embedding exhibited amplified dynamical disruption. Across regimes, the evolution of activity was organized by a compact set of weight-aware structural descriptors that generalized across network sizes and classes, with total effective synaptic coupling providing a dominant organizing axis. These results identified inhibitory architecture as a mechanistic determinant of circuit resilience and provided a predictive framework linking structural degeneration to collective dynamics.

Abstract We present a methodological framework for analyzing multifrequency dynamic functional connectivity (dFC) in electrophysiological recordings. The approach characterizes not only the magnitude of network reconfiguration over time but also whether these changes are spatially random or, instead, spatially organized in ways that drive a slower reconfiguration of modular structure. We define a generative null model of multiscale connectivity fluctuations that differ in their degree of spatiotemporal organization, and we describe dFC flows through the joint assessment of (a) instantaneous reconfiguration speed and (b) the extent and quality of ongoing modular reorganization. Different combinations of these features delineate distinct “flow styles,” ranging from more liquid to more frozen dynamics. As a case study, we apply this framework to stereo-electroencephalography recordings from epileptic patients. We identify transitions between dynamic “allegiance states,” whose flow styles closely mirror those of the null model. Seizure onset is associated with a pronounced slowing of dFC speed, while a specific postictal regime combines low speed with highly frozen allegiance and aligns most strongly with clinician-annotated aphasia. These pilot results suggest that temporal multiplex network analyses can reveal transient, frequency-specific network regimes linked to symptom expression and offers a generalizable tool for dissecting fast network dynamics in intracranial recordings.

In the first months postterm, human infants are traditionally viewed as passive to their environment, unable to focus or sustain attention on specific objects. Here, we asked whether 4-mo-old infants could engage their attention strongly enough in a visual task to block the perception of a subsequent interesting stimulus—a phenomenon known as attentional blink, which in adults reflects a serial processing bottleneck for accessing a central workspace. We presented three successive visual events: a central teddy bear (T1) followed by a lateralized face and scrambled face (T2) at varying stimulus onset asynchrony (SOAs: 400, 800, and 1,200 ms), then the same face reappeared after 1,600 ms (T3). We monitored saccades toward the face, pupil size, and electroencephalic (EEG) responses to the flickering background. Our behavioral and EEG results showed that infants missed the lateralized face at the shortest SOA (400 ms). At 800 ms, detection occurred only when the face appeared in the left hemifield, while detection in the right hemifield required 1,200 ms, suggesting a hemispheric asymmetry in face processing. Furthermore, at SOAs where T2 should be visible, a trial-by-trial metric based on event-related variability revealed that the depth of attentional engagement to T1 predicted access to T2, despite identical visual input. These findings support the presence of a global workspace in early infancy, though with slower dynamics.

Ongoing brain activity displays rich temporal variability associated with efficient cognition, with functional connectivity (FC) continually reconfiguring over time. The resulting functional connectivity dynamics (FCD) specifically show complex, fat-tailed statistics that alternate between persistent epochs and faster reconfiguration transients. While nonlinear whole-brain models tuned nearby a critical point have reproduced some aspects of FCD, they fall short of capturing its full temporal complexity. We propose that slow fluctuations in arousal offer a biologically plausible mechanism for exploring critical regimes in large-scale brain dynamics and thus enrich FCD. Using a connectome-based model of coupled cortical populations, we identified phase boundaries where system dynamics transition between regimes of faster or slower FCD. We then phenomenologically incorporated arousal changes, modeling them as stochastic fluctuations in key parameters such as cortical excitability, input gain, and noise amplitude. This explicitly time-dependent formulation enables the system to roam dynamically across regime boundaries, flexibly tuning its distance from critical transition lines and producing intermittent transitions that mirror the stochastic evolution observed in empirical FCD. Fitting these models to human resting-state fMRI and performing model comparison, we find that arousal-driven models more accurately reproduce the distinctive quantitative features of FCD, with the greatest improvements coming from the previously poorly accounted fat-tailed portions of the distributions. Together, these results suggest that arousal fluctuations-likely mediated by changes in neuromodulatory tone-shape the brain's attractor landscape over time, expanding the repertoire of accessible functional network states and providing a mechanistic basis for the complexity of spontaneous functional dynamics.

Neural oscillations at distinct frequency bands facilitate communication within and between neural populations. While single-frequency oscillations are well-characterized, the simultaneous emergence of slow (beta) and fast (gamma) oscillations within the same network remains unclear. Here, we demonstrate that multi-frequency oscillations naturally arise when the ratio of inhibitory-to-excitatory synaptic strength falls within a specific regime using a biologically plausible Izhikevich model. We show that this regime maximizes both information capacity and transmission efficiency, suggesting an optimal balance for neural communication. Deviations from this range lead to single-frequency oscillations and reduced communication efficiency, mirroring disruptions observed in neurological disorders. These findings provide mechanistic insight into how the brain leverages multiple oscillatory frequencies for efficient information processing and suggest a potential biomarker for impaired neural communication.

Abstract The claustrum is a broadly connected subcortical structure proposed to coordinate distributed cortical activity. Claustral neurons are recruited during synchronized brain states and contribute to sleep-dependent memory consolidation, primarily through effects on cortical dynamics. Yet the claustrum also innervates the subicular complex, a major hippocampal output node, raising the possibility that it may regulate both cortical state and hippocampo-cortical dialogue during sleep. Here, we identify a projection-defined population of claustral neurons targeting the subicular complex, CLAsc, that is preferentially recruited during slow-wave sleep and tracks cortical and subicular sleep dynamics. Optogenetic activation of CLAsc neurons during post-learning sleep enhanced spatial memory consolidation without detectable changes in ripple occurrence or slow-oscillation-spindle coupling. Instead, CLAsc activation imposed a stereotyped cortical-subicular motif: a brief gamma-rich Up state followed by a coordinated Down state across prefrontal, retrosplenial and subicular regions. Within this gamma-rich Up state, interareal coherence increased, gamma bursts became synchronized, and lagged directed interactions were transiently reorganized, including an enhanced prefrontal-to-subicular component. Together, these findings identify the claustrum as a state-dependent coordinator that transforms ongoing cortical and hippocampal-output activity into coordinated network transitions during sleep, providing a circuit mechanism through which distributed brain states may support memory consolidation.

We show that sensorimotor behavior can be reliably predicted from single-trial EEG oscillations fluctuating in a coordinated manner across brain regions, frequency bands and movement time epochs. We define high-dimensional oscillatory portraits to capture the interdependence between basic oscillatory elements , quantifying oscillations occurring in single-trials at specific frequencies, locations and time epochs. We find that the general structure of the element-interdependence networks (effective connectivity) remains stable across task conditions, reflecting an intrinsic coordination architecture and responds to changes in task constraints by subtle but consistently distinct topological reorganizations. Trial categories are reliably and significantly better separated using oscillatory portraits, than from the information contained in individual oscillatory elements, suggesting an inter-element coordination-based encoding. Furthermore, single-trial oscillatory portrait fluctuations are predictive of fine trial-to-trial variations in movement kinematics. Remarkably, movement accuracy appears to be reflected in the capacity of the oscillatory coordination architecture to flexibly update as an effect of movement-error integration.

Accurately identifying individuals from brain activity—functional fingerprinting—is a powerful tool for understanding individual variability and detecting brain disorders. Most current approaches rely on functional connectivity (FC), which measures pairwise correlations between brain regions. However, FC is limited in capturing the higher-order, multiscale structure of brain organization. Here, we propose a novel fingerprinting method based on homological scaffolds, a topological representation derived from persistent homology of resting-state fMRI data. Using data from the Human Connectome Project ( n = 100), we show that scaffold-based fingerprints achieve near-perfect identification accuracy (∼ 100%), outperforming FC-based methods (90%), and remain robust across preprocessing pipelines, atlas choices, and even with drastically shortened scan durations. Unlike FC, in which fingerprinting features localize within networks, scaffolds derive their discriminative power from inter-network connections, revealing the existence of individual mesoscale organizational signatures. Finally, we show that scaffolds act as bridges between redundancy and synergy, by balancing redundancy along high-FC border edges with high synergy across the topological voids that the cycles define. These findings establish topological scaffolds as a powerful tool for capturing individual variability, revealing that unique signatures of brain organization are encoded in the interplay between mesoscale network integration and information dynamics. ### Competing Interest Statement The authors have declared no competing interest. European Research Council, https://ror.org/0472cxd90, 101171380, 101120085, 101208090

Functional multiplexing is a signature of higher-order brain regions. Beyond single-unit mixed selectivity, the six-layered cortical microcircuit has been proposed as an optimal substrate for such parallel computations. Recurrent connections within and across layers allow cortical columns to retain incoming information for some time and integrate it into stable latent representations, thereby acting as functional units. The key question is whether the multiplexing capacity arises from specialized processing within individual layers or the collective coordination of multi-layer activity patterns. Here, we analyzed laminar recordings from the motor cortex of macaque monkeys performing a complex delayed match-to-sample task. We identified laminarly distributed, behaviorally specific subspaces that captured the encoding of distinct task-related variables. These subspaces, spanning the entire column and expressed as coordinated activity patterns, were functionally reused to encode the same variable over time and flexibly recycled to encode new ones. Subtle variations of laminar weights gave rise to multiple coexistent laminar coding subspaces, enabling multiplexing at the columnar level. Task-related information propagated across layers in temporally organized trajectories that transiently localized in superficial or deep layers at distinct trial epochs. These organized laminar trajectories were consistently observed across recording sites, but exhibited site-specific propagation patterns. The activity of the population on the other hand lacked structured dynamics. Thus, laminar trajectories of information emerged atop a background of spatially and temporally unspecific activity-fluctuations. ### Competing Interest Statement The authors have declared no competing interest. EU Horizon 2020 Marie Skłodowska - Curie Actions grant, In2PrimateBrains - 956669 FLAG-ERA grant PrimCorNet, ANR-19-HBPR- 0005 ANR FunSy grant, ANR-25-CE45-2766 Neuroschool end-of-phd grant from the French government under the “France 2030” investment plan managed by the French National Research Agency and from Excellence Initiative of AixMarseille University

Visual gamma entrainment using sensory stimuli (vGENUS) is a promising non-invasive therapeutic approach for Alzheimer's disease (AD), showing efficacy in improving memory function. However, its mechanisms of action remain poorly understood. Using young AppNL-F/MAPT double knock-in (dKI) mice, a model of early AD, we examined brain dynamics alterations before amyloid plaque onset. High-density EEG recordings and metrics from fields outside neuroscience were used to assess brain dynamics fluidity-a measure of the brain's ability to transition between activity states. We revealed that dKI mice exhibit early, awake state-specific reductions in brain dynamics fluidity associated with cognitive deficits in complex memory tasks. Daily vGENUS sessions over 2 weeks restored brain dynamics fluidity and rescued memory deficits in dKI mice. Importantly, these effects built up during the stimulation protocol and persisted after stimulation ended, suggesting long-term modulation of brain function. Based on these results, we propose a "brain dynamics repair" mechanism for vGENUS that goes beyond current amyloid-centric hypotheses. This dual insight-that brain dynamics are both a target for repair and a potential diagnostic tool-provides new perspectives on early Alzheimer's disease pathophysiology.

Neurological pathologies as e.g. Alzheimer’s Disease or Multiple Sclerosis are often associated to neurodegenerative processes affecting the strength and the transmission speed of long-range inter-regional fiber tracts. Such degradation of Structural Connectivity impacts on large-scale brain dynamics and the associated Functional Connectivity, eventually perturbing network computations and cognitive performance. Functional Connectivity however is not bound to merely mirror Structural Connectivity, but rather reflects the complex coordinated dynamics of many regions. Here, using analytical characterizations of toy models and computational simulations connectome-base whole-brain models, we predict that suitable modulations of regional dynamics could precisely compensate for the effects of structural degradation, as if the original Structural Connectivity strengths and speeds of conduction were effectively restored. The required dynamical changes are widespread and aspecific (i.e. they do not need to be restricted to specific regions) so that they could be potentially implemented via neuromodulation or pharmacological therapy, globally shifting regional excitability and/or excitation/inhibition balance. Computational modelling and theory thus suggest that, in the future therapeutic interventions could be designed to “repair brain dynamics” rather than structure to boost functional connectivity without having to block or revert neurodegenerative processes. AUTHOR SUMMARY Neurological disorders affect Structural Connectivity, i.e. the wiring infrastructure interlinking distributed brain regions. Here we propose that the resulting disruptions in Functional Connectivity, i.e. inter-regional coordination and information sharing, could be compensated by modifying local dynamics so to effectively emulate the restoration of Structural Connectivity (but through a suitable “software patch” rather than by repairing the “hardware”). For simple toy models involving a few regions we can achieve an analytical understanding of how structural and dynamical changes jointly control Functional Connectivity. We then show that the concept of “effective connectome change” via modulation of dynamics robustly extend also to simulation of large-scale models embedding realistic whole-brain connectivity. We thus forecast that novel therapeutic strategies could be devised, targeting dynamics rather than neurodegenerative mechanisms. ### Competing Interest Statement The authors have declared no competing interest.

The hippocampus and entorhinal cortex exhibit rich oscillatory patterns critical for cognitive functions. In the hippocampal region CA1, specific gamma-frequency oscillations, timed at different phases of the ongoing theta rhythm, are hypothesized to facilitate the integration of information from varied sources and contribute to distinct cognitive processes. Here, we show that gamma elements -a multidimensional characterization of transient gamma oscillatory episodes- occur at any frequency or phase relative to the ongoing theta rhythm across all CA1 layers in male mice. Despite their low power and stochastic-like nature, individual gamma elements still carry behavior-related information and computational modeling suggests that they reflect neuronal firing. Our findings challenge the idea of rigid gamma sub-bands, showing that behavior shapes ensembles of irregular gamma elements that evolve with learning and depend on hippocampal layers. Widespread gamma diversity, beyond randomness, may thus reflect complexity, likely functional but invisible to classic average-based analyses.

The mechanisms that cause aphasia as a transient post-seizure symptom in epileptic patients are yet unknown. We analyse intracranial EEG (sEEG) recordings of patients suffering from pharmaco-resistant epilepsy with postictal aphasia. We study the Functional Connectivity (FC) between different cortical sites in a time- and frequency-resolved manner, representing each recording as a time-varying, multilayer network (dynamic multiplex). We studied in particular: the rate of overall reconfiguration of links from one frame to the next, or dynamic Functional Connectivity (dFC) speed; and the stability of network modules through time, by means of a dynamic modular Allegiance (dA) analysis. The combination of these two approaches allows identifying states of “Functional Connectivity flow” (beyond connectivity states), defined as epochs in which network reconfiguration occurs with comparable speed and degree of spatio-temporal coordination. Our unsupervised analyses reveal then that high-frequency dFC is slowed down in a long postictal phase lasting well beyond the ictal episodes themselves. Furthermore, a pathological state of slow and poorly structured network flow consistently co-occurs with episodes of aphasia symptoms annotated by the clinicians. In conclusion, our multiplex network dynamics description cast light on functional mechanisms of postictal cognitive dysfunction at the level of individual patients. ### Competing Interest Statement The authors have declared no competing interest.