Biology subjects

Sanz Perl, Y.

Publications and source records attributed to Sanz Perl, Y..

At least 19 recordsLinked to original sources

bioRxiv2026

Serotonergic psychedelics delay resting state neurovascular coupling dynamics

Psychedelics are increasingly explored as tools to investigate consciousness and cognition, and as potential treatments for mood disorders. While neuroimaging has been central to characterizing their mechanisms of action, the interpretation of these studies may be confounded by the neurovascular effects elicited by these drugs. Here, we investigated the modulation of resting state neurovascular coupling by the serotonergic psychedelics LSD and DMT. Applying a blind deconvolution algorithm to blood-oxygen-level-dependent (BOLD) signals, we found that both drugs increased the estimated hemodynamic delays in regions overlapping with the distribution of serotonin receptors implicated in psychedelic action and vascular regulation. Deconvolution enhanced the detection of psychedelic-induced changes in whole-brain functional connectivity, suggesting that vascular effects obscure connectivity changes instead of generating spurious results. Finally, a biophysical whole-brain model linked these effects to increased flow-dependent feedback and slower dissipation of the vasoactive drive. These findings underscore the need to account for vascular effects in future psychedelic neuroimaging studies.

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bioRxiv2026

Whole-brain receptor markers and hub excitabilities determine acute and long-term outcomes in stroke

Following a stroke, changes in receptor systems have been observed that may influence the excitability of affected brain regions and drive recovery trajectories. Translating results from animal studies to patients remains a major challenge, and while imaging modalities have provided a wealth of findings, a mechanistic link between local receptor systems and observed imaging data is still missing. To bridge this gap, we built patient-specific digital twins with their own neuromodulatory profiles: we integrated 19 receptor and transporter density maps into a mechanistic whole-brain model, and estimated their influence on cortical excitability for each stroke patient and how these systems subsequently explain recovery trajectories. We found that distinct receptor profiles modulated excitability in perilesional versus non-perilesional tissue. Favourable acute motor outcomes were most strongly explained by increased inhibition mediated by non-perilesional GABA-A receptors, together with enhanced excitability driven by cannabinoid, histaminergic, and noradrenergic systems. For acute language outcomes, we identified receptor signatures that, together with motor findings, formed two cross-domain clusters linking excitability to either better or poorer performance. Remarkably, structurally highly connected hubs played a key role: their acute excitability explained cognitive and language deficits and predicted language recovery at one year. Together, our results establish a mechanistic link between receptor-specific modulation of cortical excitability and behavioural recovery, highlighting potential targets for pharmacological and non-invasive therapeutic interventions.

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bioRxiv2026

Spiral brain dynamics track the temporal unfolding of subjective intensity during the N,N-dimethyltryptamine experience

Psychedelic compounds such as N,N-dimethyltryptamine (DMT) induce rapid and profound changes in conscious experience, requiring neuroimaging measures that capture how brain dynamics evolve across space and time. Emerging evidence suggests that rotational (vortex-like) dynamics may contribute to the coordination of large-scale neural activity. Here, we introduce two vorticity-based metrics; spiral vorticity (SV) and spatial spiral vorticity dispersion (SSVD), to quantify the rotational organization and spatial heterogeneity of large-scale cortical phase dynamics. Using fMRI data acquired under placebo and DMT in a within-subject design, we assessed these measures globally, across resting-state networks, and regionally to examine their relationship to subjective intensity. DMT increased SV and SSVD and reduced temporal irreversibility, indicating stronger rotational organization, greater spatial heterogeneity, and altered directional structure of cortical dynamics. Effects were non-uniform across functional systems, with the largest changes in default mode, visual, frontoparietal, and dorsal attention networks. Both vorticity measures tracked the temporal evolution of subjective intensity globally, while network-level analyses revealed spatially heterogeneous brain-experience coupling. Spatiotemporally resolved analyses further showed that the strongest reorganization of spiral dynamics emerged during offset and recovery rather than at peak subjective intensity. These findings identify spiral dynamics as a time-resolved neural signature of the DMT experience. By quantifying the rotational structure and spatial heterogeneity of cortical phase flow, vorticity-based measures provide a framework for linking large-scale brain dynamics to evolving subjective experience.

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bioRxiv2026

Brain dynamics predict oral contraceptive treatment duration

Oral contraceptives are used by millions of women worldwide, yet their cumulative effects on the female brain remain poorly understood. We analyzed resting-state fMRI from 192 women (never, current, and past users) using brain-dynamics metrics that quantify information transfer across spatiotemporal scales. Duration of use was associated with a progressive, age-independent modulation of brain dynamics: prolonged use was related to restricted local synchronization but enhanced information flow across scales. Machine learning predicted individual duration of use from brain dynamics, and node-level classifiers distinguished users from never-users based on a spatially distributed cortical pattern spanning multiple functional networks, most clearly detectable in long-term users. In past users, these associations were inverted in sign, scaling with prior duration rather than returning to the never-user baseline, suggesting active reorganization following cessation. Together, these results indicate that cumulative oral contraceptive exposure acts as a neuromodulator of information processing.

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bioRxiv2026

Flattened brain hierarchy and increased EEG complexity during unusual bodily experiences and out-of-body experiences

Experiences in which the sense of the body becomes distorted offer a unique window into how the brain constructs the bodily self. Despite their clinical relevance in conditions such as depersonalization and body-image disorders, large-scale brain dynamics underlying unusual bodily experiences (UBEs) remain poorly understood, and no prior study has examined them systematically across wakefulness and sleep. Here, we analyzed a high-density EEG dataset including 36 UBE episodes, such as floating sensations, distorted body boundaries, and out-of-body experiences, recorded across rapid eye-movement (REM) sleep, light sleep, sleep arousals, and wakefulness during meditation (N=20). We quantified the temporal irreversibility of neural dynamics, a marker of cortical hierarchical organization, and complemented these analyses with whole-brain signal complexity measures following a within-subject design. UBEs were consistently associated with a global reduction in neural irreversibility and cortical hierarchical differentiation relative to non-UBEs and wakefulness, along with a concurrent increase in whole-brain complexity to levels comparable to wakefulness. Together, these findings suggest that UBEs reflect a neural state combining high informational richness with a transient flattening of cortical hierarchy, a profile that resembles that observed in psychedelic states. Key pointsO_LIUBEs are associated with a global flattening of brain hierarchy measured by EEG. C_LIO_LIUBEs are associated with increased global EEG complexity, resembling the psychedelic state. C_LI

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bioRxiv2026

Divergent changes in perturbation-induced brain reconfiguration following depression treatment with psilocybin and escitalopram

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 brains 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.

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bioRxiv2026

Distinct brain responses to psilocybin and escitalopram in depression captured by the Fluctuation-Dissipation Theorem

In recent decades, the psychedelic psilocybin has been studied as a potential treatment for major depressive disorder (MDD), offering an alternative to traditional antidepressants. However, the brain changes underlying the clinical effects of different interventions remain unclear. Here, we investigated the effects of psilocybin and a conventional antidepressant, escitalopram, from the double-blind randomised controlled trial (DB-RCT) -NCT03429075- on the brains hierarchical organisation. Using pre- and post-treatment resting-state functional magnetic resonance imaging (fMRI) we built whole-brain models and obtained a generative effective connectivity (GEC) matrix for each patient. Based on the GEC, we measured the level of non-equilibrium brain dynamics by quantifying the deviation from the fluctuation-dissipation theorem (FDT) and performed complementary analysis on brain segregation and asymmetry. Our results showed opposite reconfigurations of the hierarchical non-equilibrium brain dynamics following each treatment. Additionally, baseline measures effectively distinguished responders from non-responders within each treatment. These findings suggest that the deviation of the FDT may serve as a marker for differentiating the effects of psilocybin and escitalopram in MDD treatment, overall, contributing to the understanding of therapeutic mechanisms of depression.

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bioRxiv2026

The blueprint of human functional architecture shifts from cognition to anatomy during perturbations of consciousness

Consciousness and cognition arise from the ongoing interactions between brain regions. Synchronous fluctuations of fMRI signals may indicate that two brain regions perform similar cognitive functions, but neural interactions are also constrained by anatomical connectivity and regions molecular, cytoarchitectonic, and metabolic profiles. Here we disentangle the respective contributions of ongoing cognition and multimodal neurobiological constraints in shaping functional connectivity. We jointly contextualise haemodynamic FC against eight distinct multimodal representations of the human connectome: (i) structural connectivity from diffusion tractography; (ii) spatial embedding; (iii) similarity of transcriptional profiles from gene expression; (iv) similarity of receptor profiles from Positron Emission Tomography; (v) laminar profile similarity from histology; (vi) correlated electrophysiological activity from magnetoencephalography; (vii) correlated metabolic activity from PET glucose uptake; (viii) coordinated activation across 123 cognitive operations from the NeuroSynth meta-analytic engine. We demonstrate that cognitive co-activation is the dominant predictor of inter-regional fMRI synchrony in the awake human brain, even when quantified using intracranial electrical stimulation. Crucially, this predominance of cognitive co-activation for shaping functional connectivity is systematically obliterated across five datasets of pharmacological and pathological perturbations of consciousness (chronic disorders of consciousness; anaesthesia with sevoflurane, propofol, or ketamine) when cognition is disconnected from the environment or altogether abolished. Altogether, we show that multimodal predictors of functional architecture shift away from cognitive co-activation and toward anatomicalmolecular constraints during pharmacological and pathological perturbations of consciousness.

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bioRxiv2026

Beyond where: When and how brain stimulation drives state transitions

Brain stimulation is increasingly used to treat neurological and psychiatric conditions, but stimulation sites and timing are typically chosen based on a priori assumptions from prior studies, not individualised mechanisms. Here, we investigate why some brain regions respond more to stimulation, when they respond best, and whether optimal targets for brain-state transitions are defined by functional anatomy (e.g. an auditory region in a resting-to-listening task transition), by system dynamics, or a combination of both. To do so, we build subject-specific whole-brain Hopf models fitted to MEG data, separating signals into five canonical frequency bands. We find that regions with a smaller oscillation radius and greater temporal variability respond more strongly to stimulation. Moreover, responsiveness was highly dependent on state and frequency band, with slower frequencies exhibiting phase-driven responsiveness, while faster ones depending more on network synchrony. Importantly, optimal nodes for transitioning between brain states are not defined by functional labels but rather by their dynamical regime. These results emphasise the value of mechanistic, personalised models for identifying when and where to stimulate the brain, and may help inform more precise brain-stimulation strategies.

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bioRxiv2026

Acute perilesional excitability explains long-term motor recovery after stroke

Stroke is a leading cause of disability, and changes in neuronal excitability in perilesional regions may critically influence patients recovery. While reduced excitability is often associated with impaired recovery, increased excitability following injury may support reparative mechanisms. Although animal studies have shown how synaptic transmission adapts after stroke, a mechanistic understanding of how these excitability changes in the perilesional area relate to recovery in acute patients remains unclear. Here, we apply patient-specific (N = 96) computational whole-brain models to infer regional excitability, focusing on both perilesional and non-perilesional sites. Specifically, we estimate the firing sensitivity of local excitatory neuronal populations. Our findings indicate large inter-subject variability, with patients displaying both relative perilesional hypo- and hyper-excitability. Notably, perilesional excitability emerges as a robust predictor (P = 0.002) of motor recovery one year after the stroke, but not of acute post-stroke motor impairment, emphasizing its significance in specifically shaping long-term recovery. This synaptic modulation of excitability exhibits a strong correlation with gamma-aminobutyric acid A (GABA-A) receptor density distributions before the stroke, providing a potential biological substrate. These findings highlight the subject-specific nature of perilesional excitability, positioning it as a compelling target for personalized interventions to optimize post-stroke motor recovery.

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bioRxiv2026

The impact of homeostatic inhibitory plasticity in a generative biophysical model

A main characteristic of biological systems is their capacity to dynamically adapt to environmental changes. In the brain, synaptic plasticity enables the strengthening or weakening of connections between neurons, allowing neural circuits to adapt based on experience, learning, and environmental changes. Yet, it is homeostatically regulated such that it avoids excessive proliferation of synaptic contacts. These mechanisms can be studied with large-scale models of brain activity. Here, we embed a biologically grounded inhibitory-homeostatic plasticity rule into the Dynamic Mean Field (DMF) model, creating a Homeostatic Dynamic Mean Field (HDMF) model that dynamically tunes local excitation-inhibition balance. Convergence of excitatory firing rates is reached by mapping a large range of coupling strength to parameters of inhibitory synapses. The HDMF reproduces statistical observables of brain activity as well as the original DMF, and can sustain neuromodulatory perturbations without overhead computations. The HDMF can generate unprecedented sleep-like slow-wave activity, which can also coexist with wake-like asynchronous dynamics, permitting to model dissociated states of consciousness such as parasomnias. Together, these results show that a single homeostatic rule broadens the stability and expressiveness of the DMF, providing a unified platform for studying how local adaptive processes shape the diverse global dynamics of the human brain.

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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

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.

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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.

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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.

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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.

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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.

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