Biology subjects

Deco, G.

Publications and source records attributed to Deco, G..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

A generalized scaling law to characterize coordinated brain activity

Complex ecological systems obey universal scaling laws such as Taylors law, which links population variance to the sample mean. Whether other complex systems with integrated and segregated functional networks--such as the human brain--follow analogous scaling laws, and how these laws behave under synchronized activity, remains unknown. Progress has been hindered because neural signals at all measurement scales contain both positive and negative values, whereas Taylors law was developed for non-negative ecological data. Here we introduce a generalized spatial scaling law that replaces the sample mean with the root-mean-square (RMS) of detrended activity, extending Taylor-like scaling to signed multivariate time series in neural and other complex systems. Within this framework, we quantify synchrony using a dedicated metric of functional coordination and show analytically, and via numerical simulations using multivariate Poisson, binomial, uniform and gamma distributions, that the scaling exponent is inversely related to synchrony. Applying this approach to human fMRI data from three large lifespan cohorts (N = 840, ages 18-88) reveals distinct age-related trajectories of the scaling exponent, with healthy aging characterized by a substantial synchrony-induced reduction in the exponent. The synchrony-scaling relationship remains stable during resting-state activity but progressively shifts during naturalistic tasks across the lifespan, and is particularly strong in limbic, subcortical, and cerebellar subnetworks, indicating preservation at subnetwork levels. Finally, individuals with ADHD exhibit disrupted synchrony-scaling coupling, highlighting the potential clinical and translational utility of this generalized scaling metric.

neuroscience↗

bioRxiv2025

Thermodynamics of consciousness: A non-invasive perturbational framework

The quest for reliable and objective measures of consciousness is critical in basic and clinical neuroscience. Across species, the Perturbational Complexity Index (PCI) has emerged as a robust empirical marker by directly perturbing the brain, yet its underlying principles of physics are not fully understood. Here, we bridge this gap by introducing a non-invasive framework based on generative whole-brain models of non-equilibrium brain dynamics. Using these models, we identified violations of the Fluctuation-Dissipation Theorem (FDT) in humans and rodents across wakefulness, anesthesia, and disorders of consciousness. Mirroring the patterns observed with PCI, we found decreased FDT violations in unresponsive disorders of consciousness and anesthesia compared to conscious conditions. This reveals a close link between PCI and non-equilibrium dynamics in spontaneous brain signals, grounding PCI in fundamental principles of physics. Overall, this framework offers new complementary, non-invasive, model-based avenues for understanding the nature of consciousness and for developing objective tools to assess its loss and recovery in health and disease. It also provides a principled foundation for discovering novel strategies to restore consciousness.

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bioRxiv2025

Disruption of Striatal Intrinsic Ignition in Huntington's Disease: an In-Silico Perturbational Approach to Emulate Disease Progression and Recovery

Huntingtons disease (HD) is a neurodegenerative disorder caused by a single gene that severely affects motor, cognitive, and behavioral functions and is ultimately fatal. While disruptions in the structural integrity of the striatum are well documented, less is known about how the underlying functional impairments relate to the disease. We developed a large-scale modeling framework based on intrinsic ignition to uncover functional anomalies during HD progression. We first measured how much a spontaneous event in one brain region influences the rest of the brain, then systematically perturbed the activity of the striatum to predict the empirical changes in functional connectivity (FC) by re-optimizing the model parameters and conducting in-silico experiments. Results showed that disruption of the activity dynamics in the caudate, and to some extent the putamen, are the key factors that explain a substantial amount of the variance in FC alterations during HD progression. We then assessed the ability to recover whole-brain functional activity by driving the caudate and putamen activity towards a healthy-like regime and found that, by tuning the local bifurcation parameter of caudate and putamen, it is possible to recover whole-brain healthy functional activity, albeit moderately. Surprisingly, although the functional disruptions in the caudate better explained the alterations in FC, the recovery of the dynamics was more effective in the putamen. Overall, our results suggest that the disruptions of the dynamics in the striatum can explain the gross functional connectivity changes in HD. Our study demonstrates that computational modeling is an important tool to reveal the underlying patterns behind the observed data and provides an opportunity to test interventional mechanisms in silico.

neuroscience↗

bioRxiv2025

FDTest: Fluctuation-Dissipation Theorem as a Test for Memory Effects in Brain Dynamics

A central challenge in neuroscience is to understand how the brain flexibly balances local and distributed information processing to support diverse cognitive and conscious states. We hypothesize that a key signature of this balance is the presence of memory effects, which arise when a brain regions future activity depends not only on its current state, but also on past information fed back from the wider network. Here we introduce the FDTest, a method for assessing local memory effects in multidimensional systems by measuring violations of a generalized Fluctuation-dissipation theorem (FDT). We first apply this framework to whole-brain models fitted to human neuroimaging data, showing that the brains memory structure reflects its underlying connectivity. We then extend the analysis to individualized models of subjects during wakefulness and deep sleep. Memory effects are consistently stronger in wakefulness, indicating richer inter-regional dependencies and more integrated dynamics. These findings establish local memory as a dynamical marker of brain state and position the FDTest as a principled tool for probing the hidden structure of neural dynamics in both models and experiments.

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bioRxiv2025

Modeling Hierarchical Brain Dynamics Outperforms Hormonal Biomarkers in Predicting Menstrual Cycle Phases

Hormonal fluctuations across the menstrual cycle influence large-scale brain dynamics, yet the underlying neurobiological mechanisms remain poorly understood. In this study, 60 nat-urally cycling women were scanned using resting-state fMRI during the early follicular, pre-ovulatory, and mid-luteal phases. We then applied a thermodynamics-inspired framework to explore the functional hierarchical organization of whole-brain dynamics across these phases. First, we found that brain dynamics are significantly modulated by estradiol, progesterone, and age across multiple resting-state networks. Second, to elucidate underlying mechanisms, we es-timated generative effective connectivity (GEC) matrices using whole-brain models and trained support vector machine classifiers to predict menstrual phases. These model-based biomarkers outperformed traditional functional connectivity and hormone measures in classifying men-strual cycle phases. These findings reveal that menstrual cycle-related changes modulate the hierarchical reorganization of brain dynamics, highlighting the potential of model-based ap-proaches to advance womens brain health research.

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bioRxiv2025

Large Scale Functional and Effective Connectivity Alterations cross the Huntington's Disease Integrated Staging System

Huntingtons disease (HD) is a progressive neurodegenerative disease with severe motor, cognitive and behavioral symptoms. There is a recent impetus to develop treatments that slow progression before clinical signs emerge. Such early interventions require biomarkers sensitive to the very earliest HD progression. Here we applied cutting-edge fMRI analysis on data collected in the Track-On HD study to evaluate whether functional and effective connectivity obtained from model-free and model-based approaches can produce useful biomarkers of HD progression. We analyzed data from 231 participants with up to three annual visits each and created five groups comprising normative controls and four HD groups according to the Huntingtons Disease Integrated Staging System (HD-ISS). We found significant differences across the HD-ISS stages but not in their longitudinal change. Specifically, we found attenuated functional and effective connectivity in the caudate nucleus in HD-ISS-2, and this effect extended to other corticostriatal connections in HD-ISS-3. Overall, most of the alterations were only evident in advanced HD stages, and we did not observe widespread alterations in cortical connectivity and graph topography. Although HD-ISS groups did not differ in the amount of in-scanner motion, we did find measures of functional and effective connectivity to be sensitive to motion. We conclude that fMRI can indeed capture attenuation of cortico-striatal functional and effective connectivity across HD progression. This is the first study to investigate fMRI alterations across the recently created HD-ISS stages within a novel framework of signal flow across the whole brain.

neuroscience↗

bioRxiv2025

The Turbulent Brain: a toolbox to compute the turbulent dynamical behaviour of whole-brain activity

Brain disorders remain one of the greatest challenges in neuroscience. Computational precision medicine offers a promising framework to reveal the mechanistic principles governing both healthy and pathological brain dynamics. In this context, we present The Turbulent Brain (TTB) toolbox, an open-source MATLAB platform that implements and disseminates the turbulence framework for immediate and widespread application in neuroscience research. The toolbox combines model-free and model-based analyses through an intuitive graphical interface, enabling customizable workflows across different parcellations and preprocessing pipelines. Our toolbox builds on recent advances demonstrating that turbulence physics can effectively capture the complex spatiotemporal dynamics of brain activity as a high-dimensional system operating far from equilibrium. This framework allows researchers to explore how fundamental mechanisms are modulated across distinct brain states and neurological and psychiatric disorders, including disorders of consciousness and major depressive disorder. As an illustrative application case, we used TTB to analyse fMRI data from 176 individuals from the Human Connectome Project during resting state and movie watching, showing that movie watching, as compared to resting state, significantly reduces information transmission in terms of turbulence measures, with stronger reductions observed during Creative Commons movie watching compared to Hollywood movies. In summary, the TTB toolbox establishes an accessible framework providing a methodological bridge between nonlinear physics and computational neuroscience, offering new tools for investigating psychiatric conditions and altered states of consciousness.

neuroscience↗

bioRxiv2025

Slow waves generation and propagation in a model of brain lesions

Slow waves (SWs), the hallmark of non-rapid eye movement (NREM) sleep, reflect the periodic occurrence of transient silent periods in cortical neurons (Down states). During NREM, SWs and Down states physiologically disrupt large-scale network interactions. Since early EEG studies, SWs have also been observed in awake patients after brain injury. Emerging evidence indicates that these intrusions of sleep-like activity interfere with ongoing network activity and contribute to motor and cognitive deficits; yet, the mechanisms governing the generation and spread of post-lesional SWs remain unclear. Here, we extend a neural mass model of EEG to capture transitions between wake-like and sleep-like dynamics and embed it in connectome-based networks with virtual lesions. This model supports that local disfacilitation, topology-dependent propagation, and synchrony-dependent amplification throughout the connectome are sufficient to produce post-lesional SWs. These mechanisms reproduce the spatial gradients of post-lesional SWs seen in patients and identify actionable targets for neuromodulation and rehabilitation.

neuroscience↗

bioRxiv2025

Brain function in language and associated networks in non- or minimally verbal children

Language is universal in humans, develops robustly in infancy, and is rarely absent entirely in aphasia following strokes. Non- or minimally verbal children, in whom language has not developed by school-age in either production or comprehension, thus provide an unparalleled window into the human brain when it is deprived of language function. Yet insights from functional MRI are absent. Here we report results from a first study of intrinsic connectivity in 9 non- or minimally verbal children with autism spectrum disorder (nvASD) scanned under sedation with propofol along with 8 typically developing children (scanned awake), using resting-state (rs) fMRI. We targeted both functional (FC) and anatomically constrained, generative effective connectivity (GEC) in the speech and language networks, the insula and associated networks, and the hippocampus. Identical analyses were applied to an independent rsMRI dataset of healthy adults scanned both under propofol and while awake, to evaluate sedation confounds. NvASDs compared to their neurotypical peers showed a widespread pattern of hypoconnectivity in auditory speech perception, frontotemporal, and semantic processing regions, which extended further to the insula, and the hippocampus. GEC results selectively replicated these patterns, which correlated with autism diagnostic observation schedule (ADOS) behavioral scores within the nvASD group. This hypoconnectivity pattern extended neither to the adults scanned under propofol, nor to the visual cortex used as control region in nvASD, suggesting that this pattern of results could not be explained by sedation. Together, this first evidence from intrinsic connectivity reveals a broad pattern of underconnectivity across key cognitive networks, which provides a neural correlate for the significant breakdown of language-related cognitive functions in this population.

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bioRxiv2025

Restoring signatures of consciousness by thalamic stimulation in a whole-brain model of an anesthetized nonhuman primate

Treatment options for Disorders of Consciousness (DoC) are limited due to insufficient understanding of the underlying neurobiological mechanisms. Two primary strategies for characterizing DoC and assessing treatment efficacy are in vivo experiments with animal models, and in silico computational models. We combined both approaches by creating a whole-brain model tailored to the experimental functional magnetic resonance imaging (fMRI) data of a single anesthetized macaque. It was previously reported in an in vivo experiment that anesthesia-induced loss of consciousness was partially reversed by specific electrical stimulation of the thalamic central nuclei. The in silico model reproduced the brain dynamics underlying the restoration of consciousness, providing a potential explanation for the transition between these brain states as continuous trajectories unfolding in a low-dimensional space. Our results demonstrate that whole-brain computational models reproduce the spatiotemporal properties of fMRI recordings during loss of consciousness and during its recovery induced by electrical stimulation, enabling computational exploration of perturbation-based interventions to potentially personalize treatment and aid recovery of consciousness in DoC patients.

neuroscience↗

bioRxiv2025

Disrupted hierarchical organization in disorders of consciousness revealed by fluctuation-dissipation deviations

Evaluating consciousness levels after coma remains clinically challenging, and probing the brains functional hierarchy offers model-based biomarkers of brain states. We characterize the hierarchy loss in disorders of consciousness (DoC) via departures from non-equilibrium dynamics. Irreversible, directed interactions are indexed by deviation from the fluctuation- dissipation theorem (FDT), computed from individualized whole-brain models fit to fMRI from controls and patients in minimally conscious state (MCS) or unresponsive wakefulness syndrome (UWS). Global and resting-state network dynamics in DoC were closer to equilibrium than in controls, decreasing stepwise with decreasing levels of consciousness. Mapping site-specific hierarchical drive over the system revealed disruptions within default-mode network components (e.g., medial and dorsolateral superior frontal gyrus) and subcortical hubs (e.g., thalamus, pallidum and putamen) differentiating between all groups. Recovery of near-control hierarchy in the visual network differentiated MCS from UWS, whereas multiple limbic areas showed similar abnormalities across both DoC groups. Together, these results identify non-equilibrium dynamics as a signature of conscious capacity and stablish FDT deviation as a principled, model-based hierarchy measure that can be operationalised for clinical stratification and monitoring, opening avenues for targeted in silico intervention planing.

neuroscience↗

bioRxiv2025

Quantum-like dynamics in the human brain

Emerging new research indicates evidence of quantum-like (QL) probability laws, including interference effects, in non-quantum physical systems using coupled oscillators. This can produce QL states which can compute in a QL fashion. Given the success of using coupled oscillators for human whole-brain modelling, we investigate the possibility of QL dynamics in the human brain. Here, we investigate how the special topology of human brain anatomy together with QL bits can promote the rich dynamic repertoire necessary for human advanced cognition. We systematically changed the level of QL processing in a whole-brain model. We found the QL regime provided the best whole-brain model fit to large-scale human empirical neuroimaging data. Extraordinarily, at this optimum point we found significantly lower energy consumption than for the non-QL networks. Mechanistically, this implies that the significantly larger whole-brain spectral gap for QL networks offers a backbone to the functional metastability needed to provide the necessary dynamical regime for efficient computation. The underlying QL spectral gaps amplify through interference the metastability and richness of repertoire of the human brain. Overall, we found that the special topology of the human brain promotes QL information processing.

neuroscience↗

bioRxiv2025

The turbulent brain: Modelling vortex interactions for understanding human cognition

The human brain needs distributed, time-critical computation to efficiently solve complex problems. Turbulence provides such highly efficient spacetime information processing and transmission across wide-spread brain networks, yet we have been missing a mechanistic understanding of the interactions of turbulent vortices underlying human cognition. Here, we build the first whole-brain model of turbulent vortices as defined by the levels of local synchronisation in brain signals quantifying turbulent interactions in vortex space. Specifically, using large-scale human neuroimaging data, we found that the interactions of turbulent vortices is an excellent framework for understanding cognition and brain computation. It is not only useful for distinguishing the detailed spacetime dynamics of rest and cognition - but can even distinguish between subtle sub-components of cognitive tasks, where manipulation of vortices can be shown to change cognition. Overall, this whole-brain framework creates a natural vortex space for the brain computation underlying cognition, as well as potentially providing novel ways of controlling turbulent interactions in disease.

neuroscience↗

bioRxiv2025

Whole-Brain Models of Minimal Phenomenal Experience: Approaching Criticality through Jhana Meditation

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.

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bioRxiv2025

Pre-stimulus Brain States Predict and Control Variability in Stimulation Responses

Does the ongoing brain state determine how it responds to localized electrical stimulation? This fundamental question has major implications for neuroscience and medicine, as stimulation outcomes remain highly variable even when identical parameters (how) and target sites (where) are used. Such unpredictability undermines reproducibility, limits clinical reliability, and forces current protocols to rely on empirical trial-and-error rather than principled, evidence-based strategies. Mounting evidence suggests that the brains state prior to stimulation-a crucial when factor-shapes responses, yet the most reliable predictive markers remain unknown. Here, we systematically characterize the links between pre-stimulus (spontaneous) activity and post-stimulus (evoked) responses using simultaneous high-density EEG and stereotactic EEG from 36 epilepsy patients across[~] 320 sessions (>10,000 individual stimulations). We show that large-scale neural dynamics robustly predict stimulation outcomes, with a subset of measures-particularly network synchronization, functional connectivity, and spatiotemporal signal diversity-consistently forecasting responses across sessions. Whole-brain activity enhanced prediction compared to local assessments, and predictability varied across networks, being strongest in sensorimotor and visual regions. These findings establish a quantitative framework for state-dependent brain stimulation: by timing interventions to optimal pre-stimulus states, variability can be reduced and reproducibility enhanced. Our results directly address the fundamental question of where and when to stimulate, providing a pathway toward evidence-based protocols with improved therapeutic precision.

neuroscience↗

bioRxiv2025

Spectral patterns of MEG oscillatory coupling emerge from meta-stable dynamics with small coupling delays

Functional connectivity (FC) is a fundamental mechanism of neural communication, connecting distinct oscillating populations. Oscillatory networks exhibit heterogeneity across frequencies and coupling modes whose origins are not well understood, but have been suggested to involve a complex interplay of critical-like dynamics and structure-function coupling. We here utilized structural connectivity (SC) to tune a whole-brain computational model of delay-coupled damped oscillators near a Hopf bifurcation to match oscillations and FC as observed in resting-state magnetoencephalography (MEG) data. We assessed two forms of oscillation-based FC from empirical and model data, namely phase synchronization (PS) and amplitude coupling (AC). We found that both oscillations and FC best matched with empirical observations in a meta-stable regime which was characterized by small delays, realistic oscillation lifetimes, and FC with intermediate strength and high variability. How well MEG FC patterns were matched by the model varied between frequency bands and best fits were observed for high-alpha and beta-band networks. These fits could partially, but not fully, be explained by correlations with SC, implicating that both structure-function coupling and critical-like metastable dynamics underlie empirical FC, and the contributions of these mechanisms varies between different frequency bands in MEG data.

neuroscience↗

bioRxiv2025

Inception: Simulating Personalized Long-Term Recovery in Disorders of Consciousness using Whole-Brain Computational Perturbations

Advancements in the treatment of Disorders of Consciousness have seen significant progress with per-turbative techniques and pharmacological therapies. Despite their potential, the underlying mechanisms of their variable efficacy remain poorly understood. To address this challenge, recent studies have utilised whole-brain modelling to simulate in silico perturbations. However, existing models focus exclusively on system behaviour during active stimulations, leaving unexplored how the brain dynamics evolve in post-acute and long-term stages, crucial in the recovery of consciousness. Here, we introduce Inception, a novel personalized approach to in silico perturbation modelling. We use a whole-brain models to simulate the perturbations and to unravel information about the long term effects of the proposed intervention. Applied to fMRI data from patients in a minimally conscious state and an unresponsive wakefulness state, our approach effectively simulates the transition to a healthy state, generating perturbed data closely resembling healthy brain activity. Moreover, we show that Inception enhances patient classification through machine learning, outperforming functional connectivity-based approaches. Finally, we investigate the correlation between perturbation responses and brain neuroreceptors, proposing that Inception might capture the long-term effects of pharmacological interventions in Disorders of Consciousness treatment.

neuroscience↗

bioRxiv2025

The synergistic interactions of low-dimensional brain modes

Neuroimaging techniques produce vast amounts of data, capturing brain activity in a high-dimensional space. However, brain dynamics are consistently shown to reside in a rather lower-dimensional space, which contains relevant information for cognition and behavior. This dimensionality reduction reflects distinct types of interactions between brain regions, such as redundancy -shared neural information distributed across regions- and synergy, where information emerges only when regions are considered collectively. Significant efforts have been devoted to developing linear and non-linear algorithms to reveal these low-dimensional dynamics, often termed "brain modes." Here, we apply various dimensionality reduction techniques to resting-state functional magnetic resonance imaging (fMRI) data from 100 healthy participants to examine how synergistic interactions in brain dynamics are preserved by these techniques. We first demonstrate that biologically informed brain parcellation modulates and preserves synergy-dominated interactions. Next, we show that synergy among low-dimensional modes enhances functional-connectivity reconstruction: nonlinear autoencoders not only achieve the lowest reconstruction error but also maximally preserve synergy, outperforming principal component analysis, diffusion maps, and Laplacian eigenmodes. Finally, we confirm previous results suggesting that global signal regression helps to identify synergistic interactions between regions. Our findings establish synergy preservation as a complementary criterion to reconstruction accuracy, highlighting autoencoders as a nonlinear tool for uncovering synergistic low-dimensional brain modes from high-dimensional neuroimaging data.

neuroscience↗