Biology subjects

Tagliazucchi, E.

Publications and source records attributed to Tagliazucchi, E..

At least 37 records · Page 2Linked to original sources

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↗

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

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

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

Acute effects of psilocybin on the dynamics of gaze fixations during visual aesthetic perception

RationaleSerotonergic psychedelics are remarkable for their capacity to induce variable yet reproducible modifications to human consciousness The most salient acute effects of these compounds include perceptual alterations, predominantly in the visual domain, yet to date these alterations have been mostly documented only by subjective reports. ObjectivesWe used eye-tracking to quantify the effects of low vs. high doses of psilocybin mushrooms on the eye movements that underlie the exploration of complex visual stimuli, focusing on the particular case of aesthetic perception. ResultsFollowing a double-blind placebo-controlled design under semi-naturalistic conditions, we demonstrated that high doses of psilocybin result in a more local visual exploration of paintings, and thus in a less entropic fixation probability distribution. Participants reported heightened emotional response and state of flow under the high dose condition. ConclusionsThese findings are consistent with an effect of psilocybin on gaze fixation mediated by altered perception of low-level visual information, such as textures, shapes and colors. Our work also highlights the possibility of investigating psychedelics by addressing their effect on behavior under complex naturalistic conditions, which contributes to maintaining subject engagement while also increasing the ecological validity of the findings.

animal behavior and cognition↗

bioRxiv2023

Contribution of image statistics and semantics in local vs. distributed EEG decoding of rapid serial visual presentation

Spatio-temporal patterns of evoked brain activity contain information that can be used to decode and categorize the semantic content of visual stimuli. This procedure can be biased by statistical regularities which can be independent from the concepts that are represented in the stimuli, prompting the need to dissociate between the contributions of image statistics and semantics to decoding accuracy. We trained machine learning models to distinguish between concepts included in the THINGS-EEG dataset using electroencephalography (EEG) data acquired during a rapid serial visual presentation protocol. After systematic univariate feature selection in the temporal and spatial domains, we constructed simple models based on local signals which superseded the accuracy of more complex classifiers based on distributed patterns of information. Simpler models were characterized by their sensitivity to biases in the statistics of visual stimuli, with some of them preserving their accuracy after random replacement of the training dataset while maintaining the overall statistics of the images. We conclude that model complexity impacts on the sensitivity to confounding factors regardless of performance; therefore, the choice of EEG features for semantic decoding should ideally be informed by the underlying neurobiological mechanisms.

neuroscience↗

bioRxiv2023

Gaming expertise induces meso-scale brain plasticity and efficiency mechanisms as revealed by whole-brain modeling

Video games are a valuable tool for studying the effects of training and neural plasticity on the brain. However, the underlaying mechanisms related to plasticity-induced brain structural changes and their impact in brain dynamics are unknown. Here, we used a semi-empirical whole-brain model to study structural neural plasticity mechanisms linked to video game expertise. We hypothesized that video game expertise is associated with neural plasticity-mediated changes in structural connectivity that manifest at the meso-scale level, resulting in a more segregated functional network topology. To test this hypothesis, we combined structural connectivity data of StarCraft II video game players (VGPs, n = 31) and non-players (NVGPs, n = 31), with generic fMRI data from the Human Connectome Project and computational models, with the aim of generating simulated fMRI recordings. Graph theory analysis on simulated data was performed during both resting-state conditions and external stimulation. VGPs simulated functional connectivity was characterized by a meso-scale integration, with increased local connectivity in frontal, parietal and occipital brain regions. The same analyses at the level of structural connectivity showed no differences between VGPs and NVGPs. Regions that increased their connectivity strength in VGPs are known to be involved in cognitive processes crucial for task performance such as attention, reasoning, and inference. In-silico stimulation suggested that differences in FC between VGPs and NVGPs emerge in noisy contexts, specifically when the noisy level of stimulation is increased. This indicates that the connectomes of VGPs may facilitate the filtering of noise from stimuli. These structural alterations drive the meso-scale functional changes observed in individuals with gaming expertise. Overall, our work sheds light into the mechanisms underlying structural neural plasticity triggered by video game experiences.

neuroscience↗

bioRxiv2023

Brain network dynamics in transitions of consciousness reorganize according to task engagement

Substantial changes in behavior, physiology, and brain function occur when alertness decreases 1- 5. These changes in brain function involve increased synchronization between cortical areas 6,7 as well as alterations in sensory processing pathways and networks connecting the thalamus and cortex 5,8-11. Cognitive tasks engage overlapping functional networks with sensory pathways facilitating information processing 12,13, and thalamocortical and corticocortical networks supporting task performance 14,15. Frontoparietal circuits play a crucial role in cognitive tasks 16 and states of decreased consciousness 17. To develop an integrated framework of consciousness and cognition, it is important to understand how fluctuations in alertness and cognitive processing interact in these shared circuits 18. Our hypothesis is that during periods of low alertness, individuals who actively maintain task engagement would recruit additional frontoparietal and sensory processing networks, while thalamocortical dynamics that typically change during sleep onset would remain unaffected. Our findings demonstrated that as alertness decreased, passively listening to auditory tones led to increased synchronization in the parietal lobe, whereas actively performing an auditory task resulted in increased long-range frontoparietal synchronization. During decreasing alertness, passive listening (but not active task engagement) was associated with widespread increased synchronization between the thalamus and cortex. In contrast, active task engagement (but not passive listening) led to increased synchronization between the auditory cortex and the rest of the brain. These results reveal the functional mechanisms of the brains flexible reorganization during transitions of consciousness when individuals are actively engaged in cognitive processes.

neuroscience↗

bioRxiv2022

Low-dimensional organization of global brain states of reduced consciousness

Brain states are frequently represented using a unidimensional scale measuring the richness of subjective experience (level of consciousness). This description assumes a mapping between the high-dimensional space of whole-brain configurations and the trajectories of brain states associated with changes in consciousness, yet this mapping and its properties remain unknown. We combined whole-brain modelling, data augmentation and deep learning for dimensionality reduction to determine a mapping representing states of consciousness in a low-dimensional space, where distances parallel similarities between states. An orderly trajectory from wakefulness to brain injured patients is revealed in a latent space whose coordinates represent metrics related to functional modularity and structure-function coupling, both increasing alongside loss of consciousness. Finally, we investigated the effects of model perturbations, providing geometrical interpretation for the stability and reversibility of states. We conclude that conscious awareness depends on functional patterns encoded as a low-dimensional trajectory within the vast space of brain configurations.

neuroscience↗

bioRxiv2022

Model-based whole-brain perturbational landscape of neurodegenerative diseases

The treatment of neurodegenerative diseases is hindered by lack of interventions capable of steering multimodal whole-brain dynamics towards patterns indicative of preserved brain health. To address this problem, we combined deep learning with a model capable of reproducing whole-brain functional connectivity in patients diagnosed with Alzheimers disease (AD) and behavioral variant frontotemporal dementia (bvFTD). These models included disease-specific atrophy maps as priors to modulate local parameters, revealing increased stability of hippocampal and insular dynamics as signatures of brain atrophy in AD and bvFTD, respectively. Using variational autoencoders, we visualized different pathologies and their severity as the evolution of trajectories in a low-dimensional latent space. Finally, we perturbed the model to reveal key AD- and bvFTD-specific regions to induce transitions from pathological to healthy brain states. Overall, we obtained novel insights on disease progression and control by means of external stimulation, while identifying dynamical mechanisms that underlie functional alterations in neurodegeneration.

neuroscience↗

bioRxiv2022

Data-driven discovery of canonical large-scale brain dynamics

Human behavior and cognitive function correlate with complex patterns of spatio-temporal brain dynamics, which can be simulated using computational models with different degrees of biophysical realism. We used a data-driven optimization algorithm to determine and classify the types of local dynamics that enable the reproduction of different observables derived from functional magnetic resonance recordings. The phase space analysis of the resulting equations revealed a predominance of stable spiral attractors, which optimized the similarity with the empirical data in terms of the synchronization, metastability, and functional connectivity dynamics. For stable limit cycles, departures from harmonic oscillations improved the fit in terms of functional connectivity dynamics. Eigenvalue analyses showed that the proximity to Hopf bifurcations improved the accuracy of the simulation for wakefulness, while deep sleep was associated with increased stability. Our results provide testable predictions that constrain the landscape of suitable biophysical models, while supporting noise-driven dynamics close to a bifurcation as a canonical mechanism underlying the complex fluctuations that characterize endogenous brain activity.

neuroscience↗

bioRxiv2022

Masked target visibility is selectively impaired by 20 Hz transcranial alternating current stimulation

It has been proposed that both conscious and unconscious perception are associated with a feedforward sweep of oscillatory activity in the gamma band (>40 Hz), while conscious perception also requires recurrent feedback via beta band (~20 Hz) oscillations. To investigate the causal relationship between these oscillations and (un)conscious visual perception, we assessed the effect of transcranial alternating current stimulation (tACS) in the gamma (40 Hz) and beta (20 Hz) bands on the objective and subjective visibility of targets in a metacontrast backward masking task. We expected that 40hz-tACS would affect both the correct categorization of the target (i.e. objective visibility) and the reports of conscious perception (i.e. subjective visibility). Moreover, we expected that 20Hz-tACS would selectively affect the subjective visibility. Our results showed that target visibility was selectively compromised by 20Hz-tACS but, in contrast to our hypothesis, this effect was specific to objective visibility. Although the power of local beta oscillations increased after 20Hz-tACS, inter-areal beta synchrony could have nevertheless been impaired, a possibility that should be investigated in the future by means of source reconstructed high density electroencephalography recordings. In summary, we provided evidence supporting that 20Hz-tACS is capable of modulating target visibility, suggesting a possible a causal link between synchrony in this frequency band and visual perception. Future studies could build upon this result by investigating other forms of stimulation and other model organisms, further contributing to our knowledge of how conscious access causally depends on brain oscillations.

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

Natural language signatures of psilocybin microdosing

Serotonergic psychedelics are being studied as novel treatments for mental health disorders and as facilitators of improved well-being, mental function and creativity. Recent studies have found mixed results concerning the effects of low doses of psychedelics ("microdosing") on these domains. However, microdosing is generally investigated using instruments designed to assess larger doses of psychedelics, which might lack sensitivity and specificity for this purpose. Following a double-blind and placebo-controlled experimental design, we explored natural language as a resource to identify speech produced under the acute effects of psilocybin microdoses, focusing on variables known to be affected by higher doses: verbosity, semantic variability and sentiment scores. Except for semantic variability, these metrics presented significant differences between a typical active microdose of 0.5 g of psilocybin mushrooms and an inactive placebo condition. Moreover, machine learning classifiers trained using these metrics were capable of distinguishing between conditions with high accuracy (AUC{approx}0.8). Our results constitute first proof that low doses of serotonergic psychedelics can be identified from unconstrained natural speech, with potential for widely applicable, affordable, and ecologically valid monitoring of microdosing schedules.

pharmacology and toxicology↗

bioRxiv2021

Unifying turbulent dynamics framework distinguishes different brain states

Recently, significant advances have been made by identifying the levels of synchronicity of the underlying dynamics of a given brain state. This research has demonstrated that unconscious dynamics tend to be more synchronous than those found in conscious states, which are more asynchronous. Here we go beyond this dichotomy to demonstrate that the different brain states are always underpinned by spatiotemporal chaos but with dissociable turbulent dynamics. We investigated human neuroimaging data from different brain states (resting state, meditation, deep sleep, and disorders of consciousness after coma) and were able to distinguish between them using complementary model-free and model-based measures of turbulent information transmission. Our model-free approach used recent advances describing a measure of information cascade across spatial scales using tools from turbulence theory. Complementarily, our model-based approach used exhaustive in silico perturbations of whole-brain models fitted to the empirical neuroimaging data, which allowed us to study the information encoding capabilities of the brain states. Overall, the current framework demonstrates that different levels of turbulent dynamics are fundamental for describing and differentiating between brain states.

neuroscience↗

bioRxiv2021

On the edge of criticality: strength-dependent perturbation unveils delicate balance between fluctuation and oscillation in brain dynamics

Despite decades of research, there is still a lack of understanding of the role and generating mechanisms of the ubiquitous fluctuations and oscillations found in recordings of brain dynamics. Here, we used a strength-dependent perturbative framework to provide a causal mechanistic description of how human brain function is perched at the delicate balance between fluctuation and oscillation. Applying local strength-dependent perturbations and subsequently measuring the perturbative complexity index clearly demonstrates that the overall balance of brain dynamics is shifted towards fluctuations for providing much needed flexibility. Importantly, stimulation in the fluctuation regime modulates specific resting state network, thus providing a mechanistic explanation of experimentally reported brain dynamics. Furthermore, this framework generates specific, testable empirical predictions for human stimulation studies using strength-dependent rather than constant perturbation. Overall, the strength-dependent perturbative framework demonstrates how the human brain is poised on the edge of criticality, between fluctuations to oscillations, allowing for maximal flexibility.

neuroscience↗

bioRxiv2021

Temporal irreversibility of neural dynamics as a signature of consciousness

Even though the fundamental laws of physics are the same when the direction of time is inverted, dissipative systems evolve in the preferred temporal direction indicated by the thermodynamic arrow of time. The fundamental nature of this temporal asymmetry led us to hypothesize its presence in the neural activity evoked by conscious perception of the physical world, and thus its covariance with the level of conscious awareness. Inspired by recent developments in stochastic thermodynamics, we implemented a data-driven and model-free deep learning framework to decode the temporal inversion of electrocorticography signals acquired from non-human primates. Brain activity time series recorded during conscious wakefulness could be distinguished from their inverted counterparts with high accuracy, both using frequency and phase information. However, classification accuracy was reduced for data acquired during deep sleep and under ketamine-induced anesthesia; moreover, the predictions obtained from multiple independent neural networks were less consistent for sleep and anesthesia than for conscious wakefulness. Finally, the analysis of feature importance scores highlighted transitions between slow ({approx}20 Hz) and fast frequencies (> 40 Hz) as the main contributors to the temporal asymmetry observed during conscious wakefulness. Our results show that a preferred temporal direction is simultaneously manifest in the neural activity evoked by conscious mentation and in the phenomenology of the passage of time, establishing common ground to tackle the relationship between brain and subjective experience.

neuroscience↗

bioRxiv2021

Deep learning the arrow of time in brain activity: characterising brain-environment behavioural interactions in health and disease

The complex intrinsic and extrinsic forces from the body and environment push the brain into non-equilibrium. The arrow of time, central to thermodynamics in physics, is a hallmark of non-equilibrium and serves to distinguish between reversible and non-reversible dynamics in any system. Here, we use a deep learning Temporal Evolution NETwork (TENET) framework to discover the asymmetry in the flow of events, arrow of time, in human brain signals, which provides a quantification of how the brain is driven by the interplay of the environment and internal processes. Specifically, we show in large-scale HCP neuroimaging data from a thousand participants that the levels of non-reversibility/non-equilibrium change across time and cognitive state with higher levels during tasks than when resting. The level of non-equilibrium also differentiates brain activity during the seven different cognitive tasks. Furthermore, using the large-scale UCLA neuroimaging dataset of 265 participants, we show that the TENET framework can distinguish with high specificity and sensitivity resting state in control and different neuropsychiatric diseases (schizophrenia, bipolar disorders and ADHD) with higher levels of non-equilibrium found in health. Overall, the present thermodynamics-based machine learning framework provides vital new insights into the fundamental tenets of brain dynamics for orchestrating the interactions between behaviour and brain in complex environments.

neuroscience↗