Biology subjects

Deco, G.

Publications and source records attributed to Deco, G..

At least 73 records · Page 4Linked to original sources

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.

neuroscience↗

bioRxiv2024

Time-resolved coupling between connectome harmonics and subjective experience under the psychedelic DMT

Exploring the intricate relationship between brains structure and function, and how this affects subjective experience is a fundamental pursuit in neuroscience. Psychedelic substances offer a unique insight into the influences of specific neurotransmitter systems on perception, cognition and consciousness. Specifically, their impact on brain function propagates across the structural connectome -- a network of white matter pathways linking different regions. To comprehensively grasp the effects of psychedelic compounds on brain function, we used a theoretically rigorous framework known as connectome harmonic decomposition. This framework provides a robust method to characterize how brain function intricately depends on the organized network structure of the human connectome. We show that the connectome harmonic repertoire under DMT is reshaped in line with other reported psychedelic compounds - psilocybin, LSD and ketamine. Furthermore, we show that the repertoire entropy of connectome harmonics increases under DMT, as with those other psychedelics. Importantly, we demonstrate for the first time that measures of energy spectrum difference and repertoire entropy of connectome harmonics indexes the intensity of subjective experience of the participants in a time-resolved manner reflecting close coupling between connectome harmonics and subjective experience.

neuroscience↗

bioRxiv2024

Multilevel irreversibility reveals higher-order organisation of non-equilibrium interactions in human brain dynamics

Information processing in the human brain can be modelled as a complex dynamical system operating out of equilibrium with multiple regions interacting nonlinearly. Yet, despite extensive study of the global level of non-equilibrium in the brain, quantifying the irreversibility of interactions among brain regions at multiple levels remains an unresolved challenge. Here, we present the Directed Multiplex Visibility Graph Irreversibility framework, a method for analysing neural recordings using network analysis of time-series. Our approach constructs directed multi-layer graphs from multivariate time-series where information about irreversibility can be decoded from the marginal degree distributions across the layers, which each represents a variable. This framework is able to quantify the irreversibility of every interaction in the complex system. Applying the method to magnetoencephalography recordings during a long-term memory recognition task, we quantify the multivariate irreversibility of interactions between brain regions and identify the combinations of regions which showed higher levels of non-equilibrium in their interactions. For individual regions, we find higher irreversibility in cognitive versus sensorial brain regions whilst for pairs, strong relationships are uncovered between cognitive and sensorial pairs in the same hemisphere. For triplets and quadruplets, the most non-equilibrium interactions are between cognitive-sensorial pairs alongside medial regions. Finally, for quintuplets, our analysis finds higher irreversibility when the prefrontal cortex is included in the interaction. Combining these results, we show that multilevel irreversibility offers unique insights into the higher-order, hierarchical organisation of neural dynamics and presents a new perspective on the analysis of brain network dynamics.

biophysics↗

bioRxiv2024

LSD flattens the hierarchy of directed information flow in fast whole-brain dynamics

Psychedelics are serotonergic drugs that profoundly alter consciousness, yet their neural mechanisms are not fully understood. A popular theory, RElaxed Beliefs Under pSychedelics (REBUS), posits that psychedelics flatten the hierarchy of information flow in the brain. Here, we investigate hierarchy based on the imbalance between sending and receiving brain signals, as determined by directed functional connectivity. We measure directed functional hierarchy in a magnetoencephalography (MEG) dataset of 16 healthy human participants who were administered a psychedelic dose (75 micrograms, intravenous) of lysergic acid diethylamide (LSD) under four different conditions. LSD diminishes the asymmetry of directed connectivity when averaged across time. Additionally, we demonstrate that machine learning classifiers distinguish between LSD and placebo more accurately when trained on one of our hierarchy metrics than when trained on traditional measures of functional connectivity. Taken together, these results indicate that LSD weakens the hierarchy of directed connectivity in the brain by increasing the balance between senders and receivers of neural signals.

neuroscience↗

bioRxiv2024

Beyond cortical geometry: brain dynamics shaped by long-range connections

A fundamental topological principle is that the container always shapes the content. In neuroscience, this translates into how the brain anatomy shapes brain dynamics. From neuroanatomy, the topology of the mammalian brain can be approximated by local connectivity, accurately described by an exponential distance rule (EDR). The compact, folded geometry of the cortex is shaped by this local connectivity and the geometric harmonic modes can reconstruct much of the functional dynamics. However, this ignores the fundamental role of the rare long-range cortical connections, crucial for improving information processing in the mammalian brain, but not captured by local cortical folding and geometry. Here we show the superiority of harmonic modes combining rare long-range connectivity with EDR (EDR+LR) in capturing functional dynamics (specifically long-range functional connectivity and task-evoked brain activity) compared to geometry and EDR representations. Importantly, the orchestration of dynamics is carried out by a more efficient manifold made up of a low number of fundamental EDR+LR modes. Our results show the importance of rare long-range connectivity for capturing the complexity of functional brain activity through a low-dimensional manifold shaped by fundamental EDR+LR modes. Significance StatementExplaining how structure of the brain gives rise to its emerging dynamics is a primary pursuit in neuroscience. We describe a fundamental anatomical constraint that emphasises the key role of rare long-range connections in explaining functional organisation of the brain in terms of spontaneous and task-evoked activity. Specifically, this constraint unifies brain geometry and local connectivity through the Exponential Distance Rule while considering the long-range exceptions to this local connectivity as derived from the structural connectome. In addition, when using this structural information, we show that the task-evoked brain activity is described by a low-dimensional manifold of several modes suggesting that less is more for the efficient information processing in the brain.

neuroscience↗

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.

neuroscience↗

bioRxiv2024

Whole-Brain Dynamics Disruptions in the Progression of Alzheimer's Disease: Understanding the Influence of Amyloid-Beta and Tau

Alzheimers disease (AD) progressively alters brain structure and function, yet the associated changes in large-scale brain network dynamics remain poorly understood. We applied the intrinsic ignition framework to resting-state functional MRI (rs-fMRI) data from AD patients, individuals with mild cognitive impairment (MCI), and cognitively healthy controls (HC) to elucidate how AD shapes intrinsic brain activity. We assessed node-metastability at the whole-brain level and in 7 canonical resting-state networks (RSNs). Our results revealed a progressive decline in dynamical complexity across the disease continuum. HC exhibited the highest node-metastability, whereas it was substantially reduced in MCI and AD patients. The cortical hierarchy of information processing was also disrupted, indicating that rich-club hubs may be selectively affected in AD progression. Furthermore, we used linear mixed-effects models to evaluate the influence of Amyloid-{beta} (A{beta}) and tau pathology on brain dynamics at both regional and whole-brain levels. We found significant associations between both protein burdens and alterations in node-metastability. Lastly, a machine learning classifier trained on brain dynamics, A{beta}, and tau burden features achieved high accuracy in discriminating between disease stages. Together, our findings highlight the progressive disruption of intrinsic ignition across whole-brain and RSNs in AD and support the use of node-metastability in conjunction with proteinopathy as a novel framework for tracking disease progression.

neuroscience↗

bioRxiv2024

Individual trajectories for recovery of neocortical activity in disorders of consciousness

The evolution from disturbed brain activity to physiological brain rhythms can precede recovery in patients with disorders of consciousness (DoC). Accordingly, intriguing questions arise: What are the pathophysiological factors responsible for disrupted brain rhythms in patients with DoC, and are there potential pathways for individual patients with DoC to return to normal brain rhythms? We addressed these questions at the individual subject level using biophysical simulations based on electroencephalography (EEG). The main findings are that unconscious patients exhibit a loss of excitatory corticothalamic synaptic strength. Synaptic plasticity in this excitatory corticothalamic circuitry fosters physiological brain rhythms in the selection of patients with DoC. The extent to which this occurred was correlated with cerebral glucose uptake. The current findings emphasize the importance of excitatory thalamocortical activity in reestablishing normal brain rhythms after brain injury and show that biophysical modelling of the corticothalamic circuitry could help select patients that might be potentially receptive to treatment and undergo plasticity.

neuroscience↗

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.

neuroscience↗

bioRxiv2024

Generative whole-brain dynamics models from healthy subjects predict functional alterations in stroke at the level of individual patients

Computational whole-brain models describe the resting activity of each brain region based on a local model, inter-regional functional interactions, and a structural connectome that specifies the strength of inter-regional connections. Strokes damage the healthy structural connectome that forms the backbone of these models and produce large alterations in inter-regional functional interactions. These interactions are typically measured by correlating the timeseries of activity between two brain regions, so-called resting functional connectivity. We show that adding information about the structural disconnections produced by a patients lesion to a whole-brain model previously trained on structural and functional data from a large cohort of healthy subjects predicts the resting functional connectivity of the patient about as well as fitting the model directly to the patients data. Furthermore, the model dynamics reproduce functional connectivity-based measures that are typically abnormal in stroke patients as well as measures that specifically isolate these abnormalities. Therefore, although whole-brain models typically involve a large number of free parameters, the results show that even after fixing those parameters, the model reproduces results from a population very different than the population on which the model was trained. In addition to validating the model, these results show that the model mechanistically captures relationships between the anatomical structure and functional activity of the human brain.

neuroscience↗

bioRxiv2023

Whole-brain modelling supports the use of serotonergic psychedelics for the treatment of disorders of consciousness

Disorders of consciousness (DoC) are a challenging and complex group of neurological conditions characterised by absent or impaired awareness. The current range of therapeutic options for DoC patients is limited, offering few non-invasive pharmacological alternatives. This situation has sprung a growing interest in the development of novel treatments, such as the proposal to study the efficacy of 5HT2A receptor agonists (also known as psychedelics) to restore impaired consciousness. Given the ethical implications of exploring novel compounds in non-communicative individuals, we assessed in silico their effects in the whole-brain dynamics of DoC patients. We embedded the whole-brain activity of patients in a low-dimensional space, and then used this representation to visualise the effects of simulated neuromodulation across a range of receptors representing potential drug targets. Our findings show that activation of serotonergic and opioid receptors shifted brain dynamics of DoC patients towards patterns typically seen in conscious and awake individuals, and that this effect was mediated by the brain-wide density of activated receptors. These results showcase the role of whole-brain models in the discovery of novel pharmacological treatments for neuropsychiatric conditions, while also supporting the feasibility of accelerating the recovery of consciousness with serotonergic psychedelics.

neuroscience↗

bioRxiv2023

Brain network connectivity underlying remission in early psychosis: a whole brain model approach

BackgroundAlterations in brain connectivity occur early during psychosis and underlie the clinical manifestations of the illness as well as patient functioning and outcome. After a first episode of psychosis (FEP), different trajectories are possible and best described by the clinical-staging model that places the patient along a continuum of conditions: from non-remitting chronic symptoms to full-remission, often followed by relapses. However, little is known about the differences in brain connectivity that could underlie these differences in clinical outcome. MethodsIn this study, we included resting-state fMRI and DSI data from a cohort of 128 healthy controls (HC) and 88 patients with early psychosis (EP) stratified based on their ability to remit after the FEP. In particular we focused on differences between stage IIIb,c remitting-relapsing (EP3R) and stage IIIa non-remitting (EP3NR) patients. We investigated alterations in resting-state functional connectivity (FC), and combined information derived from fMRI and DSI into generative whole-brain models of each condition to explore the underlying mechanisms. ResultsOpposite alterations in FC could be found in patients as compared to HC, depending on their stage. In non-remitting patients (EP3NR), we observed a reduction of FC, aligned with the reduced structural connectivity found in previous studies, while remitting-relapsing patients (EP3R) showed increased FC, potentially indicating a relevant compensatory mechanism. By means of a whole-brain network model, we showed that in HC a subset of areas is characterized by increased stability to prevent an oversynchronisation of the network, while in EP3 patients such property is lost. This alteration was more relevant in the EP3R than in EP3NR patients, probably indicating a compensatory response to the reduced effective conductivity (global coupling) highlighted by the model in both EP3 conditions as compared to controls. ConclusionsThese findings highlight the significance of categorizing patients into subgroups based on the progression of their psychotic disorders, providing insights into the factors contributing to heterogeneity in functional alterations. They enhance our understanding of the interplay between structural and functional properties, shedding light on the mechanisms of psychosis emergence, remission and progression, with potential implications for future therapeutic advancements.

neuroscience↗

bioRxiv2023

Identifying the seizure onset zone with intracranial EEG: A systematic comparison between power and connectivity-based biomarkers

BackgroundIctal stereo-encephalography (sEEG) biomarkers for seizure onset zone (SOZ) localization can be classified depending on whether they target abnormalities in signal power or functional connectivity between signals, and they may depend on the frequency band and time window at which they are estimated. New methodThis work aimed to compare and optimize the performance of a power and a connectivity-based biomarkers to identify SOZ contacts from ictal sEEG recordings. To do so, we used a previously introduced power-based measure, the normalized mean activation (nMA), which quantifies the ictal average power activation. Similarly, we defined the normalized mean strength (nMS), to quantify the ictal mean functional connectivity of every contact with the rest. The optimal frequency bands and time windows were selected based on optimizing AUC and F2-score. ResultsThe analysis was performed on a dataset of 67 seizures from 10 patients with pharmacoresistant temporal lobe epilepsy. Our results suggest that the power-based biomarker generally performs better for the detection of SOZ than the connectivity-based one. However, an equivalent performance level can be achieved when both biomarkers are independently optimized. Optimal performance was achieved in the beta and lower-gamma range for the power biomarker and in the higher-gamma range for connectivity, both using a 30 s period after seizure onset. ConclusionsThe results of this study highlight the importance of this optimization step over frequency and time windows when comparing different SOZ discrimination biomarkers. This information should be considered when training SOZ classifiers on retrospective patients data for clinical applications.

neuroscience↗

bioRxiv2023

Whole-brain modelling of low-dimensional manifold modes reveals organising principle of brain dynamics

The discovery of resting state networks shifted the focus from the role of local regions in cognitive tasks to the ongoing spontaneous dynamics in global networks. Recently, efforts have been invested to reduce the complexity of brain activity recordings through the application of nonlinear dimensionality reduction algorithms. Here, we investigate how the interaction between these networks emerges as an organising principle in human cognition. We combine deep variational auto-encoders with computational modelling to construct a dynamical model of brain networks fitted to the whole-brain dynamics measured with functional magnetic resonance imaging (fMRI). Crucially, this allows us to infer the interaction between these networks in resting state and 7 different cognitive tasks by determining the effective functional connectivity between networks. We found a high flexible reconfiguration of task-driven network interaction patterns and we demonstrate that can be used to classify different cognitive tasks. Importantly, compared to using all the nodes in a parcellation, we obtain better results by modelling the dynamics of interacting networks in both model and classification performance. These findings show the key causal role of manifolds as a fundamental organising principle of brain function, providing evidence that interacting networks are the computational engines brain during cognitive tasks.

neuroscience↗

bioRxiv2023

The meditative brain: State and trait changes in harmonic complexity for long-term mindfulness meditators

Meditation is an ancient practice that is shown to yield benefits for cognition, emotion regulation and human flourishing. In the last two decades, there has been a surge of interest in extracting the neural correlates of meditation, in particular of mindfulness meditation. Yet, these efforts have been mostly limited to the analysis of certain regions or networks of interest and a clear understanding of meditation-induced changes in the whole-brain dynamics has been lacking. Here, we investigate meditation-induced changes in brain dynamics using a novel connectome-specific harmonic decomposition method. Specifically, utilising the connectome harmonics as brain states - elementary building blocks of complex brain dynamics - we study the immediate (state) and long-term (trait) effects of mindfulness meditation in terms of the energy, power and complexity of the repertoire of these harmonic brain states. Our results reveal increased power, energy and complexity of the connectome harmonic repertoire and demonstrate that meditation alters brain dynamics in a frequency selective manner. Remarkably, the frequency-specific alterations observed in meditation are reversed in resting state in group-wise comparison revealing for the first time the long-term (trait) changes induced by meditation. These findings also provide evidence for the entropic brain hypothesis in meditation and provide a novel understanding of state and trait changes in brain dynamics induced by mindfulness meditation revealing the unique connectome harmonic signatures of the meditative brain.

neuroscience↗

bioRxiv2023

Navigating Pubertal Goldilocks: The Optimal Pace for Hierarchical Brain Organization

Adolescence is a timed process with an onset, tempo, and duration. Nevertheless, the temporal dimension, especially the pace of maturation, remains an insufficiently studied aspect of developmental progression. This study focuses on the modifications due to the different timings of developmental shifts during adolescence and addresses the impact of adolescent maturation on brain development. To reveal potential relationships between pubertal pace and the advancement of brain organisation, we analyse the connection between skeletal age-based maturation stages and hierarchical organisation in the temporal dynamics of resting-state EEG recordings (alpha frequency range). By adopting skeletal maturity as a proxy for pubertal progress and employing entropy production to measure hierarchical brain organisation, our findings indicate that an average maturational trajectory optimally aligns with cerebral hierarchical order. Adaptive developmental plasticity may not fully compensate for accelerated or decelerated timelines, potentially increasing the risk of behavioural problems and psychiatric disorders consequent to such alterations.

neuroscience↗

bioRxiv2023

Harmonic decomposition of spacetime (HADES) framework characterises the spacetime hierarchy of the DMT brain state

The human brain is a complex system, whose activity exhibits flexible and continuous reorganisation across space and time. The decomposition of whole-brain recordings into harmonic modes has revealed a repertoire of gradient-like activity patterns associated with distinct brain functions. However, the way these activity patterns are expressed over time with their changes in various brain states remains unclear. In this study, we develop the Harmonic Decomposition of Spacetime (HADES) framework that characterises how different harmonic modes defined in space are expressed over time, and, as a proof-of-principle, demonstrate the sensitivity and robustness of this approach to specific changes induced by the serotonergic psychedelic N,N-Dimethyltryptamine (DMT) in healthy participants. HADES demonstrates significant decreases in contributions across most low-frequency harmonic modes in the DMT-induced brain state. When normalizing the contributions by condition (DMT and non-DMT), we detect a decrease specifically in the second functional harmonic, which represents the uni- to transmodal functional hierarchy of the brain, supporting the hypothesis that functional hierarchy is changed in psychedelics. Moreover, HADES dynamic spacetime measures of fractional occupancy, life time and latent space provide a precise description of the significant changes of the spacetime hierarchical organization of brain activity in the psychedelic state.

neuroscience↗

bioRxiv2023

Functional hierarchies in brain dynamics characterized by signal reversibility in ferret cortex

Brain signal irreversibility has been shown to be a promising approach to study neural dynamics. Nevertheless, the relation with cortical hierarchy and the influence of different electrophysiological features is not completely understood. In this study, we recorded local field potentials (LFPs) during spontaneous behavior, including awake and sleep periods, using custom micro-electrocorticographic (ECoG) arrays implanted in ferrets. In contrast to humans, ferrets remain less time in each state across the sleep-wake cycle. We deployed a diverse set of metrics in order to measure the levels of complexity of the different behavioral states. In particular, brain irreversibility, which allows us to quantify the level of non-equilibrium captured by the arrow of time of the signal, revealed the hierarchical organization of the ferrets cortex. We found different signatures of irreversibility and functional hierarchy of large-scale dynamics in three different brain states (active awake, quiet awake, and deep sleep), showing a lower level of irreversibility in the deep sleep stage, compared to the other. Irreversibility also allowed us to disentangle the influence of different brain regions and frequency bands in this process, showing a predominance of the parietal area and the theta band. Furthermore, when inspecting the embedded dynamic through a Hidden Markov Model, the deep sleep stage was revealed to have a lower switching rate and lower entropy production. These results suggest functional hierarchies in organization that can be revealed through thermodynamic features and information theory metrics.

neuroscience↗