Biology subjects

Sanz Perl, Y.

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

At least 37 records · Page 2Linked to original sources

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

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.

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bioRxiv2025

The cost of cognition: Measuring the energy consumption of non-equilibrium computation

In biological systems, survival is predicated on an animal being able to perform computations quickly on a minimal energy budget. What is the energy consumption of non-equilibrium brain computation, i.e., what is the cost of cognition? Previous literature has estimated the metabolic cost using neuroimaging measures of glucose consumption but complementary to these findings, here we directly estimate the computational costs by combining the new field of stochastic thermodynamics with whole-brain modelling. We developed the COCO (COst of COgnition) framework using an analytical expression quantifying the links between energy cost, non-equilibrium and information processing for any given brain state measured with neuroimaging. Importantly, this key relationship also holds at the level of individual brain regions. We used this to quantify the benefits of information processing on the highly anatomically, interconnected hierarchical systems of the brain. Crucially, in empirical neuroimaging data we demonstrate that the human brain uses significantly less energy overall than other mammals (including non-human primates and mice), suggestive of an evolutionary optimisation of the effectiveness of computation. Focusing on the cost of cognition, using large-scale human neuroimaging data of 970 healthy human participants, we show that the resting state uses significantly less energy that seven different cognitive tasks. Furthermore, different kinds of tasks require different amounts of non-equilibrium, information processing and energy consumption. We found that tasks requiring more distributed computation also use more energy. Overall, these results directly quantify the cost of cognition, i.e., the non-equilibrium and energetic demands of information processing, allowing a deeper understanding of how the brain compute in a way that is far more energy efficient than current generations of digital computers and artificial intelligence.

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bioRxiv2025

Evolution's boldest trick: Neurotransmission modulated whole-brain computation captures full task repertoire

The perhaps most important unsolved problem in neuroscience is how the brain survives in a complex world by performing a rich repertoire of computation on a minimal energy budget. The brain is much better at adapting to the multiplicity of stimuli and outcomes than current generations of computers, artificial neural deep learning and reservoir model architectures. Yet, at first glance the brain appears to use a fixed anatomical architecture to perform the necessary huge variety of computations. But evolutions boldest trick is that in fact the brains effective connectivity is constantly being updated through neuromodulation to allow the rich repertoire of computation. Inspired by this, we created a whole-brain model using empirical neurotransmitter maps modulating the underlying local regional dynamics. This NEMO (neurotransmission modulated) whole-brain model is able to flexibly compute the full task repertoire and associated functional connectivity of the neuroimaging data from 971 healthy participants. For each individual we defined a measure of brain computability as the fitting of the NEMO whole-brain model to all tasks performed by the individual. Importantly, brain computability correlates with both behavioural performance on individual tasks and with a general behavioural measure of intelligence. Overall, our proposed unifying NEMO framework offers a natural way to sculpt different brain dynamics in a fixed brain architecture to compute the rich repertoire of tasks required for surviving and thriving.

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bioRxiv2025

An engine for systematic discovery of cause-effect relationships between brain structure and function

Characterising how perturbations of brain architecture influence brain function is essential to understand the origins of brain dysfunction, and devise potential avenues of treatment. Here we introduce a computational engine for systematic causal discovery of the functional consequences of altering network architecture and local biophysics in the brain. We integrate multimodal anatomical and functional neuroimaging to implement over 2, 000 in-silico brains, and provide mechanistic insight into the functional consequences of local lesions, global wiring, and empirically-derived maps of regional cytoarchitecture and chemoarchitecture. We comprehensively assess how each manipulation of brain macrostructure reshapes spatial and temporal signal coordination, information dynamics, and functional hierarchy--as well as spontaneous co-activation of meta-analytic cognitive circuits, and > 6, 000 dimensions of local neural dynamics. Our computational model systematically identifies which features of brain architecture have overlapping or antagonistic causal influence over each dimension of brain function, and how functional properties are traded off against each other across disorders and neuromodulation. We find that regions functional vulnerability to lesions in silico recapitulates their vulnerability to neurodevelopmental and psychiatric in vivo, along a core-periphery organisation. We provide convergent evidence that the brains wiring diagram is finely tuned to favour the hierarchical integration of information. Notably, our model successfully recapitulates known empirical results that have not been modelled before, including desynchronisation and flattening of the brains functional hierarchy induced by psychedelic 5HT2A agonists. To catalyse future discoveries, we make this resource freely available to the neuroscience community through an interactive website (https://systematic-causal-mapping.up.railway.app/), where users can interrogate our systematic database of simulated cause-effect relationships. Altogether, we provide a powerful computational engine to predict the functional consequences of experimental or clinical interventions, and drive neuroscientific hypothesis-generation.

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bioRxiv2025

Perturbing whole-brain models of brain hierarchy: an application for depression following pharmacological treatment

Neural representation can extend beyond localised activity to encompass global patterns, where information is distributed across brain networks in a hierarchical manner. Recent research suggests that the hierarchy of causal influences shaping these patterns can serve as a signature of distinct brain states, with implications for neuropsychiatric disorders. Here, we first delve into how whole-brain models, guided by the Thermodynamics of Mind framework, can estimate the brain hierarchy of specific brain states, and how perturbations of such models can study the in-silico transitions to other states represented by static functional connectivity. We then show an application for major depressive disorder, where different brain hierarchical reconfigurations have been found following psilocybin and escitalopram treatments. We build whole-brain models of depressed patients before and after psilocybin and escitalopram interventions, and we carry a dynamic sensitivity analysis to explore the susceptibility of brain states and their drivability to healthier states. We show that susceptibility is on average reduced by escitalopram and increased by psilocybin, and that both treatments succeed in promoting healthier transitions. These results align with the post-treatment window of plasticity opened by serotonergic psychedelics, as well as with the similar clinical efficacy of both drugs observed in clinical trials. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=193 HEIGHT=200 SRC="FIGDIR/small/631011v1_ufig1.gif" ALT="Figure 1"> View larger version (56K): org.highwire.dtl.DTLVardef@7f1b46org.highwire.dtl.DTLVardef@d7a883org.highwire.dtl.DTLVardef@783e39org.highwire.dtl.DTLVardef@3d334d_HPS_FORMAT_FIGEXP M_FIG C_FIG We apply whole-brain models of brain hierarchy based on the Thermodynamics of Mind framework to investigate state transitions in depression. Dynamic sensitivity analysis explores how psilocybin and escitalopram affect susceptibility and drivability to healthier states. Results show that psilocybin increases susceptibility, while escitalopram reduces it, with both enabling optimal transitions. This pipeline demonstrates the promise of in-silico approaches to inform neurostimulation protocols, potentially enhancing or complementing antidepressant therapies.

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bioRxiv2024

Collapse of directed functional hierarchy under classical serotonergic psychedelics

Despite divergent behavioral and phenomenological profiles, both psychedelic states and reduced states of consciousness have been associated with a flattening of the brains functional hierarchy. To address this apparent paradox, we developed a more specific definition of hierarchy based on the proximity of the brain to thermodynamic equilibrium and then applied it to investigate the changes to the functional hierarchy elicited by three classical serotonergic psychedelics - psilocybin, lysergic acid diethylamide, and dimethyltryptamine. We found that all three psychedelics consistently induced a global reduction in the functional hierarchy. In contrast to the flattening of the functional hierarchy observed during loss of consciousness, psychedelics displaced the brain towards equilibrium while simultaneously increasing the complexity of neural activity, indicating a unique mechanism linked to specific changes in the configuration and differentiation of resting-state networks. This work showcases how metrics based on statistical mechanics can be used for the specific characterization of different global brain states, contributing to the understanding of consciousness as a collective process emerging from complex neural interactions.

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bioRxiv2024

Nonequilibrium dynamics elicited as the origin of perturbative complexity

Assessing the level of consciousness someone is in, is not a trivial question and physicians have to rely on behavioural evaluations instead of quantifiable metrics. Many studies have empirically investigated measures related to the complexity elicited after the brain is stimulated to quantify and assess the level of consciousness across different states. Here we hypothesized that the level of non-equilibrium dynamics of the unperturbed brain already contains the information needed to know how the system will react to an external stimulus. We created personalized whole-brain models fitted to resting state fMRI data recorded in participants in different states of reduced consciousness (such as deep sleep and disorders of consciousness) to infer the effective connections underlying their brain dynamics. We then measured the out-of-equilibrium nature of the unperturbed brain by evaluating the level of asymmetry of the inferred connectivity, the time irreversibility in each model and compared this with the elicited complexity generated after in silico perturbations. Crucially, we found that states of reduced consciousness had a lower level of asymmetry in their effective connectivities compared to control subjects, as well as a lower level of irreversibility in their simulated dynamics, and a lower complexity. We demonstrated that the asymmetry in the underlying connections drives the nonequilibrium state of the system and in turn the differences in complexity as a response to the external stimuli.

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bioRxiv2024

Long-term effects of psilocybin on dynamic and effectivity connectivity of fronto-striatal-thalamic circuits

Psilocybin has been shown to induce fast and sustained improvements in mental well-being across various populations, yet its long-term mechanisms of action are not fully understood. Initial evidence suggests that longitudinal functional and structural brain changes implicate fronto-striatal-thalamic (FST) circuitry, a broad system involved in goal-directed behavior and motivational states. Here, we apply empirical methods and computational modeling to resting-state fMRI data from a within-subject longitudinal psilocybin trial in psychedelic-naive healthy volunteers. We first show increases in FST dynamic activity four weeks after a full dose of psilocybin. We then proceed to mechanistically account for these increased dynamics, by showing that reduced structural constraints underlie increased FST dynamic activity post psilocybin. Further, we show that these reduced structural constraints come along with increased bottom-up and reduced top-down modulation of FST circuits. While cortical reductions in top-down modulation are linked to regional 5-HT2A receptor availability, increased information outflow via subcortical and limbic regions relate to local D2 receptor availability. Together, these findings show that increased FST flexibility weeks after psilocybin administration is linked to serotonergic-mediated decreases in top-down information flow and dopaminergic-mediated increases in bottom-up information flow. This long-term functional re-organization of FST circuits may represent a common mechanism underling the potential clinical efficacy of psilocybin across various neuropsychiatric disorders including substance abuse, major depression, and anorexia. Significance StatementFronto-striatal-thalamic systems, which underlie motivation and reward, go through profound functional and structural changes following psilocybin administration. We leveraged longitudinal fMRI data from a within-subject psilocybin trial in psychedelic-naive healthy participants to show that psilocybin increases fronto-striatal-thalamic dynamic activity as well as flexibility four weeks after dosing. Computational modeling revealed that this increased flexibility is mechanistically caused by reduced structural constraints on functional dynamics. Further long-term changes included increased bottom-up and reduced top-down information flow mediated by the serotonergic and dopaminergic systems. This long-term functional re-organization of fronto-striatal-thalamic circuits may reflect a common mechanism underlying clinical symptoms improvements across diagnostic groups, such as increased openness, improved well-being, and reductions in anhedonia, apathy, and substance craving.

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bioRxiv2024

Competitive interactions shape brain dynamics and computation across species

Adaptive cognition relies on cooperation across anatomically distributed brain circuits. However, specialised neural systems are also in constant competition for limited processing resources. How does the brains network architecture enable it to balance these cooperative and competitive tendencies? Here we use computational whole-brain modelling to examine the dynamical and computational relevance of cooperative and competitive interactions in the mammalian connectome. Across human, macaque, and mouse we show that the architecture of the models that most faithfully reproduce brain activity, consistently combines modular cooperative interactions with diffuse, long-range competitive interactions. The model with competitive interactions consistently outperforms the cooperative-only model, with excellent fit to both spatial and dynamical properties of the living brain, which were not explicitly optimised but rather emerge spontaneously. Competitive interactions in the effective connectivity produce greater levels of synergistic information and local-global hierarchy, and lead to superior computational capacity when used for neuromorphic computing. Altogether, this work provides a mechanistic link between network architecture, dynamical properties, and computation in the mammalian brain.

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bioRxiv2024

Whole-brain dynamics and hormonal shifts throughout women's lifespan: From reproductive stages to menopausal transition and beyond

Neuroimaging studies have identified significant age-related disruptions in whole-brain dynamics, yet the influence of womens reproductive stages and associated hormonal shifts remains underexplored. This study leverages resting-state fMRI data from the Human Connectome Project in Aging to examine brain dynamics through five reproductive stages: reproductive, late reproductive, perimenopause, early postmenopause, and late postmenopause. Our results indicate that the late reproductive stage is characterized by the highest dynamical complexity across whole-brain and resting-state networks, while brain dynamics significantly decline at menopause onset. Additionally, we employ machine learning classifiers using two approaches: (1) brain dynamics alone and (2) brain dynamics combined with follicle-stimulating hormone (FSH) and estradiol (brain-hormone model). Both models accurately distinguished reproductive stages, but the brain-hormone model outperformed the brain dynamics model. Key predictors included decreased estradiol, increased FSH, and altered brain dynamics in later life stages. These results offer a framework for assessing brain health across womens reproductive lifespan.

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bioRxiv2024

Modeling disorders of consciousness at the patient level reveals the network's influence on the diagnosis vs the local node parameters role in prognosis

The study of disorders of consciousness (DoC) is very complex because patients suffer from a wide variety of lesions, affected brain mechanisms, different severity of symptoms, and are unable to communicate. Combining neuroimaging data and mathematical modeling can help us quantify and better describe some of these alterations. The goal of this study is to provide a new analysis and modeling pipeline for fMRI data leading to new diagnosis and prognosis biomarkers at the individual patient level. To do so, we project patients fMRI data into a low-dimension latent-space. We define the latent spaces dimension as the smallest dimension able to maintain the complexity, non-linearities, and information carried by the data, according to different criteria that we detail in the first part. This dimensionality reduction procedure then allows us to build biologically inspired latent whole-brain models that can be calibrated at the single-patient level. In particular, we propose a new model inspired by the regulation of neuronal activity by astrocytes in the brain. This modeling procedure leads to two types of model-based biomarkers (MBBs) that provide novel insight at different levels: (1) the connectivity matrices bring us information about the severity of the patients diagnosis, and, (2) the local node parameters correlate to the patients etiology, age and prognosis. Altogether, this study offers a new data processing framework for resting-state fMRI which provides crucial information regarding DoC patients diagnosis and prognosis. Finally, this analysis pipeline could be applied to other neurological conditions.

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bioRxiv2024

Reconfiguration of Functional Brain Hierarchy in Schizophrenia

The multidimensional nature of schizophrenia requires a comprehensive exploration of the functional and structural brain networks. While prior research has provided valuable insights into these aspects, our study goes a step further to investigate the reconfiguration of the hierarchy of brain dynamics, which can help understand how brain regions interact and coordinate in schizophrenia. We applied an innovative thermodynamic framework, which allows for a quantification of the degree of functional hierarchical organization by analysing resting state fMRI-data. Our findings reveal increased hierarchical organization at the whole-brain level and within specific resting-state networks in individuals with schizophrenia, which correlated with negative symptoms, positive formal thought disorder and apathy. Moreover, using a machine learning approach, we showed that hierarchy measures allow a robust diagnostic separation between healthy controls and schizophrenia patients. Thus, our findings provide new insights into the nature of functional connectivity anomalies in schizophrenia, suggesting that they could be caused by the breakdown of the functional orchestration of brain dynamics.

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bioRxiv2024

A virtual clinical trial of psychedelics to treat patients with disorders of consciousness

Disorders of consciousness (DoC), including the unresponsive wakefulness syndrome (UWS) and the minimally conscious state (MCS), have limited treatment options. Recent research suggests that psychedelic drugs, known for their complexity-enhancing properties, could be promising treatments for DoC. This study uses whole-brain computational models to explore this potential. We created individualised models for DoC patients, optimised with empirical fMRI and diffusion-weighted imaging (DWI) data, and simulated the administration of LSD and psilocybin. We used an in-silico perturbation protocol to distinguish between different states of consciousness, including DoC, anaesthesia, and the psychedelic state, and assess the dynamical stability of the brains of DoC patients pre- and post-psychedelic simulation. Our findings indicate that LSD and psilocybin shift DoC patients brains closer to criticality, with a greater effect in MCS patients. In UWS patients, the treatment response correlates with structural connectivity, while in MCS patients, it aligns with baseline functional connectivity. This virtual clinical trial lays a computational foundation for using psychedelics in DoC treatment and highlights the future role of computational modelling in drug discovery and personalised medicine.

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bioRxiv2024

Complex harmonics reveal low-dimensional manifolds of critical brain dynamics

The brain needs to perform time-critical computations to ensure survival. A potential solution lies in the non-local, distributed computation at the whole-brain level made possible by criticality and amplified by the rare long-range connections found in the brains unique anatomical structure. This non-locality can be captured by the mathematical structure of Schrodingers wave equation, which is at the heart of the novel CHARM (Complex Harmonics decomposition) framework that performs the necessary dimensional manifold reduction able to extract non-locality in critical spacetime brain dynamics. Using a large neuroimaging dataset of over 1,000 people, CHARM captured the critical, non-local/long-range nature of brain dynamics and the underlying mechanisms were established using a precise whole-brain model. Equally, CHARM revealed the significantly different critical dynamics of wakefulness and sleep. Overall, CHARM is a promising theoretical framework for capturing the low-dimensionality of the complex network dynamics observed in neuroscience and provides evidence that networks of brain regions rather than individual brain regions are the key computational engines of critical brain dynamics.

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bioRxiv2024

Dynamics of EEG Microstates Change Across the Spectrum of Disorders of Consciousness

As a response to the environment and internal signals, brain networks reorganize on a sub-second scale. To capture this reorganization in patients with disorders of consciousness and understand their residual brain activity, we investigated the dynamics of electroencephalography (EEG) microstates. We analyze EEG microstate markers to quantify the periods of semi-stable topographies and the large-scale cortical networks they may reflect. To achieve this, EEG samples are clustered into four groups and then fit back into each time sample. We then obtain a time series of maps with different frequencies of occurrence and duration. One such occurrence of a map with a given duration is called a microstate. The goal of this work is to study the dynamics of these topographical patterns across patients with disorders of consciousness. Using the microstate time series, we calculate static and dynamic markers. In contrast to the static, the dynamic metrics depend on the specific temporal sequences of the maps. The static measure Ratio of Total Time covered (RTT) shows differences between healthy controls and patients, however, no differences were observed between the groups of patients. In contrast, some dynamic markers capture inter-patient group differences. The dynamic markers we investigated are Mean Microstate Durations (MMD), Microstate Duration Variances (MDV), Microstate Transition Matrices (MTM), and Entropy Production (EP). The MMD and MDV decrease with the state of consciousness, whereas the MTM non-diagonal transitions and EP increase. In other words, DoC patients have slower and closer to equilibrium (time-reversible) brain dynamics. In conclusion, static and dynamic EEG microstate metrics differ across consciousness levels, with the latter capturing the subtitler differences between groups of patients with disorders of consciousness. Abbreviated summaryThis study investigates EEG microstate dynamics in patients with disorders of consciousness (DoC) to understand residual brain activity and network reorganization. Entropy production, in addition to static markers, differs between patients and healthy controls, whereas the rest of the dynamic markers show differences across patient groups.

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bioRxiv2024

The fluctuation-dissipation theorem and the discovery of distinctive off-equilibrium signatures of brain states

The brain is able to sustain many different states as shown by the daily natural transitions between wakefulness and sleep. Yet, the underlying complex dynamics of these brain states are essentially in non-equilibrium. Here, we develop a thermodynamical formalism based on the off-equilibrium extension of the fluctuation-dissipation theorem (FDT) together with a whole-brain model. This allows us to investigate the non-equilibrium dynamics of different brain states and more specifically to apply this formalism to wakefulness and deep sleep brain states. We show that the off-equilibrium thermodynamical signatures of brain states are significantly different in terms of the overall level of differential and integral violation of FDT. Furthermore, the framework allows for a detailed understanding of how different brain regions and networks are contributing to the off-equilibrium signatures in different brain states. Overall, this framework shows great promise for characterising and differentiating any brain state in health and disease.

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bioRxiv2024

DMT induces a transient destabilization of whole-brain dynamics

The transition towards the brain state induced by psychedelic drugs is frequently neglected in favor of a static description of their acute effects. We used a time-dependent whole-brain model to reproduce large-scale brain dynamics measured with fMRI from 15 volunteers under 20 mg intravenous N,N-Dimethyltryptamine (DMT), a short-acting psychedelic. To capture its transient effects, we parametrized the proximity to a global bifurcation using a pharmacokinetic equation. Simulated perturbations revealed a transient of heightened reactivity concentrated in fronto-parietal regions and visual cortices, correlated with serotonin 5HT2a receptor density, the primary target of psychedelics. These advances suggest a mechanism to explain key features of the psychedelic state and also predicts that the temporal evolution of these features aligns with pharmacokinetics. Our results contribute to understanding how psychedelics introduce a transient where minimal perturbations can achieve a maximal effect, shedding light on how short psychedelic episodes may extend an overarching influence over time.

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