Biology subjects

Cofre, R.

Publications and source records attributed to Cofre, R..

At least 19 recordsLinked to original sources

bioRxiv2026

High-order brain interactions distinguish wakefulness, anaesthesia, and recovery induced by deep brain stimulation

Understanding how consciousness depends on large-scale brain interactions is key for both the neuroscience of consciousness and clinical translation. However, it requires moving beyond classical pairwise descriptions of functional connectivity, which cannot capture the collective dependencies emerging across multiple brain regions. Here, we use multivariate information theory measures to characterize how higher-order interactions re-organize across states of consciousness. Specifically, we apply O-information to resting-state fMRI data from non-human primates to quantify whether multiregional brain dynamics are dominated by synergistic or redundant information sharing. We analyse two complementary datasets: (i) wakefulness and anaesthesia-induced loss of consciousness using different molecular agents (propofol, sevoflurane, ketamine), and (ii) the recovery of consciousness driven by central thalamic deep brain stimulation during propofol anaesthesia, indexed by behavioural responsiveness. We identify optimal regional subsets whose O-information robustly discriminates conscious from non-responsive states under two complementary optimization polarities. The first captures elevated redundancy in conscious scans that decreases under anaesthesia, providing robust discrimination and placing high-voltage central-thalamus stimulation closer to wakefulness. The second captures a synergy-to-redundancy transition, prominent in multi-anaesthesia conditions but context-dependent across datasets. Discrimination performance depends on interaction order: redundancy-based signatures improve with increasing subset size, whilst synergy-based signatures peak at low orders. Higher-order informational features significantly outperform pairwise functional connectivity, particularly for synergistic signatures which remain invisible to correlations. These findings demonstrate that consciousness is reflected in the reconfiguration of higher-order interaction structures, with distinct informational substrates requiring multivariate characterization beyond pair-wise connectivity.

neuroscience↗

bioRxiv2026

Reorganization of Human Brain Waves Across Diverse States of Consciousness

Brain waves are ubiquitous phenomena of human brain activity. As they propagate, they coordinate neural communication, shaping conscious perception. Understanding how brain waves unfold across space and time is thus critical for uncovering the neural mechanisms that support and suppress consciousness. Here, we analyzed data from the Human Connectome Project alongside multiple independent human datasets of various states of consciousness collected during non-rapid eye movement sleep, propofol anesthesia, and psychedelic states produced by lysergic acid diethylamide, N,N-dimethyltryptamine, psilocybin, nitrous oxide, and ketamine. We then applied complex principal component analysis to map spatiotemporal propagation patterns of blood oxygen level-dependent activity across the human brain, under these diverse states of consciousness. We identified four dominant motifs of wave propagation: a global synchronized wave supporting unimodal-transmodal propagation, an anti-correlated unimodal-transmodal wave, an anti-correlated task-positive/task-negative wave, and an anti-correlated visual-somatomotor wave. Among them, the global wave exhibited the most pronounced state-dependent reconfiguration: in diminished states (sleep and anesthesia), the time needed for the wave to propagate across brain regions consistently increased and the distribution of regional contributions to the waves power became more spatially concentrated and heterogeneous across individuals, indicating slower, more fragmented, and less stereotyped dynamics. In contrast, propagation duration decreased under psychedelic states, reflecting accelerated global wave dynamics alongside a trend towards more spatially distributed and uniform regional contributions, consistent with a more integrated global wave propagation pattern. Beyond this global mode, diminished states slowed propagation primarily along the unimodal-transmodal axis, whereas psychedelic states selectively accelerated propagation along the task-positive/task-negative axis. Together, our findings reveal that diminished (sleep and anesthesia) and psychedelic states alter the spatiotemporal structure of wave propagation across the brain in opposite and distinct ways, providing a unifying account of how macroscale brain dynamics are dynamically reshaped under pharmacological and endogenous perturbations of consciousness.

neuroscience↗

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.

neuroscience↗

bioRxiv2025

A Mesoscale Framework for Psychedelic Drug Action in the Human Brain

The mechanism of psychedelic drug action is a dynamic area of neuroscience, with two major lines of investigation: (1) laboratory studies at the molecular and cellular level, and (2) human neuroimaging studies of functional brain networks. Despite considerable progress, there remains insufficient understanding of the link between molecular/cellular substrates of psychedelics and the whole-brain network effects that result. Here we report a study of psychedelic action that focuses on the intermediate spatial scale of local brain regions (<1cm3). We analyzed the effects of classical psychedelics (dimethyltryptamine [DMT], lysergic acid diethylamide [LSD], psilocybin) and non-classical psychedelics (nitrous oxide, ketamine) in humans using functional magnetic resonance imaging. We found that all five drugs reduced regional homogeneity, that is, they disrupted local synchrony, in small-scale brain regions; this disruption occurred extensively in cortical regions and sparsely in subcortical regions. Dynamic analysis of both regional homogeneity and global functional connectivity showed an inverse pattern, with large-scale functional connectivity being enhanced as local synchrony declined. We then conducted dominance analysis to assess the contribution of various neurotransmitter receptors to changes in regional homogeneity. DMT, LSD, and psilocybin showed the 5-HT receptors as the most dominant association; by contrast, regional homogeneity changes attributable to both nitrous oxide and ketamine were most strongly associated with the NMDA receptor. Both neuronal (including interneurons) and non-neuronal cell types were linked to psychedelic-induced changes in synchrony at the level of local brain regions. These data, across five drugs from two drug classes, provide evidence that a diverse set of molecular and cellular events lead to a common outcome of disrupted synchrony in local brain regions, which in turn mediate drug-specific changes in global functional connectivity.

neuroscience↗

bioRxiv2025

Simulated 5-HT2A receptor activation accounts for the high complexity of brain activity during psychedelic states

Serotonergic psychedelics, such as LSD, psilocybin, and DMT, have strong effects on human brain activity, yet their mechanisms of action are only partially understood. Here, we present a biophysically-based mean-field model that integrates cellular and network-level details to simulate the effects of these compounds at different spatial scales. By incorporating the brain-wide distribution of 5-HT2A receptors, our model mechanistically links receptor activation through reducing leak membrane potassium conductances, based on electrophysiological data. Our simulations reveal that this microscopic perturbation leads to the emergence of a brain state characterised by asynchronous irregular dynamics with increased firing rates and alterations in spectral power, in particular reduction of alpha-frequency bands, consistent with empirical findings. This change in dynamics is accompanied by an increase in spontaneous complexity, as quantified by the Lempel-Ziv complexity index, as observed experimentally. Furthermore, our model accurately replicates experimental findings regarding the Perturbational Complexity Index (PCI), demonstrating that PCI does not increase significantly by psychedelic drug administration. This crucial dissociation, where spontaneous complexity and spectral power are affected while perturbational complexity is preserved, highlights the distinct neurophysiological substrates underlying different metrics in psychedelic states. Our multiscale model provides a robust, mechanistic framework for understanding how psychedelics modulate global brain activity.

neuroscience↗

bioRxiv2025

Comprehensive profiling of anaesthetised brain dynamics across phylogeny

Intrinsic dynamics of neuronal circuits shape information processing. Combining neuroimaging with causal perturbation offers the opportunity to understand how local dynamics mediate the link between neurobiology and functional repertoire. We compile a unique dataset of multi-scale neural activity during wakefulness and anaesthetic-induced suppression of information processing encompassing human, macaque, marmoset, mouse, zebrafish and nematode. Applying massive feature extraction, we comprehensively characterise local neural dynamics across >6,000 time-series features. Using dynamics as a common space for cross-species comparison reveals a conserved dynamical profile of anaesthesia across species, characterised by shorter intrinsic timescales of neural activity and dampened interregional synchrony. This dynamical regime is experimentally reversed in vivo by deep-brain stimulation of the macaque centromedian thalamus, restoring behavioural responsiveness. Spatially, this conserved dynamical phenotype covaries with conserved transcriptional profiles of excitatory and inhibitory neurotransmission across human, macaque, marmoset and mouse cortex. Biophysical modelling provides a mechanistic link between the macroscale dynamical phenotype of anaesthesia, and microscale effects of key molecular targets on the timescales of synaptic excitation and inhibition. Altogether, comprehensive dynamical phenotyping reveals a shared neural endpoint of anaesthesia: across species and scales, anaesthetics induce spatio-temporal isolation of local neural activity.

neuroscience↗

bioRxiv2024

Benchmarking macaque gene expression for horizontal and vertical translation

The spatial patterning of gene expression shapes cortical organization and emergent function. Advances in spatial transcriptomics make it possible to comprehensively map cortical gene expression in both humans and model organisms. The macaque is a particularly valuable model organism, due to its evolutionary similarity with the human. The translational potential of macaque gene expression rests on the assumption that it is a good proxy for spatial patterns of corresponding proteins (vertical translation) and for spatial patterns of ortholog human genes (horizontal translation). Here we systematically benchmark the spatial distribution of gene expression in the macaque cortex against (a) cortical receptor density in the macaque and (b) cortical gene expression in the human. We find that there is moderate cortex-wide correspondence between gene expression and protein density in the macaque, which is improved by considering layer-specific gene expression. We find greater correspondence between orthologous gene expression in the macaque and human. Inter-species correspondence of gene expression exhibits systematic regional heterogeneity, with greater correspondence in unimodal than transmodal cortex, mapping onto patterns of evolutionary cortical expansion. We extend these results to additional micro-architectural features using macaque immunohistochemistry and T1w:T2w ratio, and replicate them using macaque RNA-seq and human RNA-seq gene expression. Collectively, the present results showcase both the potential and limitations of macaque spatial transcriptomics as an engine of translational discovery within and across species.

neuroscience↗

bioRxiv2024

Transcranial direct current stimulation modulates primate brain dynamics across states of consciousness.

The resting primate brain is traversed by spontaneous functional connectivity patterns that show striking differences between conscious and unconscious states. Transcranial direct current stimulation, a non-invasive neuromodulatory technique, can improve signs of consciousness in disorders of consciousness; however, can it influence both conscious and unconscious dynamic functional connectivity? We investigated the modulatory effect of prefrontal cortex (PFC) transcranial direct current stimulation (tDCS) on brain dynamics in awake and anesthetized non-human primates using functional MRI. In awake macaques receiving either anodal or cathodal tDCS, we found that cathodal stimulation robustly disrupted the repertoire of functional connectivity patterns, increased structure-function correlation, decreased Shannon entropy, and favored transitions towards anatomically-based patterns. Under deep sedation, anodal tDCS significantly altered brain pattern distribution and reduced structure-function correlation. The prefrontal stimulation also modified dynamic connectivity arrangements typically associated with consciousness and unconsciousness. Our findings offer compelling evidence that PFC tDCS induces striking modifications in the fMRI-based dynamic organization of the brain across different states of consciousness. This study contributes to an enhanced understanding of tDCS neuromodulation mechanisms and has important clinical implications for disorders of consciousness.

neuroscience↗

bioRxiv2023

Dynamical structure-function correlations provide robust and generalizable signatures of consciousness in humans

Resting-state functional magnetic resonance imaging evolves through a repertoire of functional connectivity patterns which might reflect ongoing cognition, as well as the contents of conscious awareness. We investigated whether the dynamic exploration of these states can provide robust and generalizable markers for the state of consciousness in human participants, across loss of consciousness induced by general anaesthesia or slow wave sleep. By clustering transient states of functional connectivity, we demonstrated that brain activity during unconsciousness is dominated by a recurrent pattern primarily mediated by structural connectivity and with a reduced capacity to transition to other patterns. Our results provide evidence supporting the pronounced differences between conscious and unconscious brain states in terms of whole-brain dynamics; in particular, the maintenance of rich brain dynamics measured by entropy is a critical aspect of conscious awareness. Collectively, our results may have significant implications for our understanding of consciousness and the neural basis of human awareness, as well as for the discovery of robust signatures of consciousness that are generalizable among different brain conditions.

neuroscience↗

bioRxiv2023

Local Orchestration of Global Functional Patterns Supporting Loss and Restoration of Consciousness in the Primate Brain

AbstractA central challenge of neuroscience is to elucidate how the orchestration of brain function is modulated by different states of consciousness. Here, we investigate the link between distributed structural and functional brain organisation in functional MRI signals of non-human primates, through bi-directional causal manipulations of consciousness. During varying levels of propofol, sevoflurane, or ketamine anaesthesia, and subsequent restoration of responsiveness by deep brain stimulation of the central thalamus, we investigate how loss of consciousness impacts distributed patterns of structure-function organisation across scales. Combining the specificity of electrical stimulation with global fMRI coverage of the entire cortex, we report that distributed brain activity under anaesthesia is increasingly constrained by brain structure across scales, coinciding with anaesthetic-induced collapse of multiple dimensions of hierarchical cortical organisation. Crucially, we show that these distributed signatures of anaesthetic-induced loss of consciousness are observed across different anaesthetics, and they are reversed by electrical stimulation of the central thalamus, coinciding with recovery of behavioural markers of consciousness during propofol anaesthesia. No such effects were observed upon stimulation of a control anatomical site, ventral lateral thalamus, demonstrating specificity. Through causal manipulations of consciousness that integrate pharmacology and electrical intracranial stimulation of the thalamus, our results identify global signatures of consciousness that are under local causal control by specific nuclei of the thalamus. Overall, the present work broadens our understanding of the link between brain network organisation and distributed function in supporting consciousness, and the interplay between local and global functional architecture.

neuroscience↗

bioRxiv2023

Emergence of High-Order Functional Hubs in the Human Brain

Network theory is often based on pairwise relationships between nodes, which is not necessarily realistic for modeling complex systems. Importantly, it does not accurately capture non-pairwise interactions in the human brain, often considered one of the most complex systems. In this work, we develop a multivariate signal processing pipeline to build high-order networks from time series and apply it to resting-state functional magnetic resonance imaging (fMRI) signals to characterize high-order communication between brain regions. We also propose connectivity and signal processing rules for building uniform hypergraphs and argue that each multivariate interdependence metric could define weights in a hypergraph. As a proof of concept, we investigate the most relevant three-point interactions in the human brain by searching for high-order "hubs" in a cohort of 100 individuals from the Human Connectome Project. We find that, for each choice of multivariate interdependence, the high-order hubs are compatible with distinct systems in the brain. Additionally, the high-order functional brain networks exhibit simultaneous integration and segregation patterns qualitatively observable from their high-order hubs. Our work hereby introduces a promising heuristic route for hypergraph representation of brain activity and opens up exciting avenues for further research in high-order network neuroscience and complex systems.

neuroscience↗

bioRxiv2022

Neural mass modelling for the masses: Democratising access to whole-brain biophysical modelling with FastDMF

Different whole-brain models constrained by neuroimaging data have been developed during the last years to investigate causal hypotheses related to brain mechanisms. Among these, the Dynamic Mean Field (DMF) model is a particularly attractive model, combining a biophysically realistic single-neuron model that is scaled up via a mean-field approach and multimodal imaging data. Despite these favourable features, an important barrier for a widespread usage of the DMF model is that current implementations are computationally expensive - to the extent that the model often becomes unfeasible when no high-performance computing infrastructure is available. Furthermore, even when such computing structure is available, current implementations can only support simulations on brain parcellations that consider less than 100 brain regions. To remove these barriers, here we introduce a user-friendly and computationally-efficient implementation of the DMF model, which we call FastDMF, with the goal of making biophysical whole-brain modelling accessible to neuroscientists worldwide. By leveraging a suit of analytical and numerical advances - including a novel estimation of the feedback inhibition control parameter, and a Bayesian optimisation algorithm - the FastDMF circumvents various computational bottlenecks of previous implementations. An evaluation of the performance of the FastDMF showed that it can attain a significantly faster performance than previous implementations while reducing the memory consumption by several orders of magnitude. Thanks to these computational advances, FastDMF makes it possible to increase the number of simulated regions by one order of magnitude: we found good agreement between empirical and simulated functional MRI data parcellated at two different spatial scales (N=90 and N=1000 brain regions). These advances open the way to the widespread use of biophysically grounded whole-brain models for understanding the interplay among anatomy, function and brain dynamics in health and disease, and to provide mechanistic explanations of recent results obtained from empirical fine-grained neuroimaging data sets, such as turbulence or connectome harmonics. HighlightsO_LIWe present the FastDMF, a user-friendly and computationally efficient implementation of the Dynamic Mean Field model for simulations of whole-brain dynamics. C_LIO_LIUsing analytical and numerical tools, we develop a novel estimation of the feedback inhibition control based on the structural connectivity, bypassing an important computational bottleneck. C_LIO_LIThe FastDMF is coupled with a Bayesian Optimization algorithm significantly reducing the number of simulations required to fit the FastDMF to empirical neuroimaging data. C_LIO_LIOur advances open the possibility of simulating thousands of brain regions in a biophysically grounded whole-brain model. C_LI

neuroscience↗

bioRxiv2022

Whole-brain modeling explains the context-dependent effects of cholinergic neuromodulation

Integration and segregation are two fundamental principles of brain organization. The brain manages the transitions and balance between different functional segregated or integrated states through neuromodulatory systems. Recently, computational and experimental studies suggest a pro-segregation effect of cholinergic neuromodulation. Here, we studied the effects of the cholinergic system on brain functional connectivity using both empirical fMRI data and computational modeling. First, we analyzed the effects of nicotine on functional connectivity and network topology in healthy subjects during resting-state conditions and during an attentional task. Then, we employed a whole-brain neural mass model interconnected using a human connectome to simulate the effects of nicotine and investigate causal mechanisms for these changes. The drug effect was modeled decreasing both the global coupling and local feedback inhibition parameters, consistent with the known cellular effects of acetylcholine. We found that nicotine incremented functional segregation in both empirical and simulated data, and the effects are context-dependent: observed during the task, but not in the resting state. In-task performance correlates with functional segregation, establishing a link between functional network topology and behavior. Furthermore, we found in the empirical data that the regional density of the nicotinic acetylcholine 4{beta}2 correlates with the decrease in functional nodal strength by nicotine during the task. Our results confirm that cholinergic neuromodulation promotes functional segregation in a context-dependent fashion, and suggest that this segregation is suited for simple visual-attentional tasks.

neuroscience↗

bioRxiv2021

High-order functional interactions in ageing explained via alterations in the connectome in a whole-brain model

The human brain generates a rich repertoire of spatio-temporal activity patterns, which support a wide variety of motor and cognitive functions. These patterns of activity change with age in a multi-factorial manner. One of these factors is the variations in the brains connectomics that occurs along the lifespan. However, the precise relationship between high-order functional interactions and connnectomics, as well as their variations with age are largely unknown, in part due to the absence of mechanistic models that can efficiently map brain connnectomics to functional connectivity in aging. To investigate this issue, we have built a neurobiologically-realistic whole-brain computational model using both anatomical and functional MRI data from 161 participants ranging from 10 to 80 years old. We show that the age differences in high-order functional interactions can be largely explained by variations in the connectome. Based on this finding, we propose a simple neurodegeneration model that is representative of normal physiological aging. As such, when applied to connectomes of young participant it reproduces the age-variations that occur in the high-order structure of the functional data. Overall, these results begin to disentangle the mechanisms by which structural changes in the connectome lead to functional differences in the ageing brain. Our model can also serve as a starting point for modelling more complex forms of pathological ageing or cognitive deficits. Author summaryModern neuroimaging techniques allow us to study how the human brains anatomical architecture (a.k.a. structural connectome) changes under different conditions or interventions. Recently, using functional neuroimaging data, we have shown that complex patterns of interactions between brain areas change along the lifespan, exhibiting increased redundant interactions in the older population. However, the mechanisms that underlie these functional differences are still unclear. Here, we extended this work and hypothesized that the variations of functional patterns can be explained by the dynamics of the brains anatomical networks, which are known to degenerate as we age. To test this hypothesis, we implemented a whole-brain model of neuronal activity, where different brain regions are anatomically wired using real connectomes from 161 participants with ages ranging from 10 to 80 years old. Analyzing different functional aspects of brain activity when varying the empirical connectomes, we show that the increased redundancy found in the older group can indeed be explained by precise rules affecting anatomical connectivity, thus emphasizing the critical role that the brain connectome plays for shaping complex functional interactions and the efficiency in the global communication of the human brain.

neuroscience↗

bioRxiv2020

Scalable and accurate automated method for neuronal ensemble detection in spiking neural networks

We propose a novel, scalable, and accurate automated method for detecting neuronal ensembles from a population of spiking neurons. Our approach offers a simple yet powerful tool to study ensemble activity. It allows the participation of neurons in different ensembles, has few parameters to tune and is computationally efficient. We used spike trains of retinal ganglion cells obtained from multi-electrode array recordings under a simple ON-OFF light stimulus to test our method. We found a consistent stimuli-evoked ensemble activity intermingled with spontaneously active ensembles and irregular activity. Our results suggest that the early visual system activity is already organized in clearly distinguishable functional ensembles. To validate the performance and generality of our method, we generated synthetic data, where we found that our method accurately detects neuronal ensembles for a wide range of simulation parameters. Additionally, we found that our method outperforms current alternative methodologies. Finally, we provide a Graphic User Interface, which aims to facilitate our methods use by the scientific community. Author summaryNeuronal ensembles are strongly interconnected groups of neurons that tend to fire together (Hebb 1949). However, even when this concept was proposed more than 70 years ago, only recent advances in multi-electrode arrays and calcium imaging, statistical methods, and computing power have made it possible to record and analyze multiple neurons activities spiking simultaneously, providing a unique opportunity to study how groups of neurons form ensembles spontaneously and under different stimuli scenarios. Using our method, we found that retinal ganglion cells show a consistent stimuli-evoked ensemble activity, and, when validated with synthetic data, the method shows good performance by detecting the number of ensembles, the activation times, and the core-cells for a wide range of firing rates and number of ensembles accurately.

neuroscience↗

bioRxiv2020

Cholinergic neuromodulation of inhibitory interneurons facilitates functional integration in whole-brain models

Segregation and integration are two fundamental principles of brain structural and functional organization. Neuroimaging studies have shown that the brain transits between different functionally segregated and integrated states, and neuromodulatory systems have been proposed as key to facilitate these transitions. Although computational models have reproduced the effect of neuromodulation at the whole-brain level, the role of local inhibitory circuits and their cholinergic modulation has not been studied. In this article, we consider a Jansen & Rit whole-brain model in a network interconnected using a human connectome, and study the influence of the cholinergic and noradrenergic neuromodulatory systems on the segregation/integration balance. In our model, a newly introduced local inhibitory feedback enables the integration of whole-brain activity, and its modulation interacts with the other neuromodulatory influences to facilitate the transit between different functional states. Moreover, the new proposed model is able to reproduce an inverted-U relationship between noradrenergic modulation and network integration. Our work proposes a new possible mechanism behind segregation and integration in the brain.

neuroscience↗

bioRxiv2020

A mechanistic model of the neural entropy increaseelicited by psychedelic drugs

Psychedelic drugs, including lysergic acid diethylamide (LSD) and other agonists of the serotonin 2A receptor (5HT2A-R), induce drastic changes in subjective experience, and provide a unique opportunity to study the neurobiological basis of consciousness. One of the most notable neurophysiological signatures of psychedelics, increased entropy in spontaneous neural activity, is thought to be of relevance to the psychedelic experience, encoding both acute alterations in consciousness and mediating long-term effects. However, no clear mechanistic explanation for this entropic phenomenon has been put forward so far. We sought to do this here by building upon a recent whole-brain model of serotonergic neuromodulation, to study the entropic effects of 5HT2A-R activation. Our results reproduce the overall entropy increase observed in previous experiments in vivo, providing the first model-based explanation for this phenomenon. We also found that entropy changes were not uniform across the brain: entropy increased in some regions and decreased in others, suggesting a topographical reconfiguration mediated by 5HT2A-R activation. Interestingly, at the whole-brain level, this reconfiguration was not well explained by 5HT2A-R density, but related closely to the topological properties of the brains anatomical connectivity. These results help us understand the mechanisms underlying the psychedelic state and, more generally, the pharmacological modulation of whole-brain activity.

neuroscience↗

bioRxiv2020

High-order interdependencies in the aging brain

Brain interdependencies can be studied either from a structural/anatomical perspective ("structural connectivity", SC) or by considering statistical interdependencies ("functional connectivity", FC). Interestingly, while SC is typically pairwise (white-matter fibers start in a certain region and arrive at another), FC is not; however, most FC analyses focus only on pairwise statistics and neglect high-order interactions. A promising tool to study high-order interdependencies is the recently proposed O-Information, which can quantify the intrinsic statistical synergy and redundancy in groups of three or more interacting variables. In this paper we used the O-Information to investigate how high-order statistical interdependencies are affected by age. For this, we analised functional magnetic resonance imaging (fMRI) data at rest obtained from 164 healthy participants, ranging from 10 to 80 years old. Our results show that older subjects (age ranging from 60 to 80 years) exhibit a higher predominance of redundant dependencies than younger subjects; moreover, this effect seems to be pervasive, taking place at all interaction orders. Additionally, we found that these effects are highly heterogeneous across brain regions, and suggest the existence of a "redundancy core" formed by the prefrontal and motor cortices - thus involving functions such as working memory, executive and motor functions. Our methodology to assess high-order interdependencies in fMRI data has unlimited applications. The code to calculate these metrics is freely available.

neuroscience↗