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The cognitive functions of human and non-human primates rely on the dynamic interplay of distributed neural assemblies. As such, it seems unlikely that cognition can be supported by macroscopic brain dynamics at the proximity of thermodynamic equilibrium. We confirmed this hypothesis by investigating electrocorticography data from non human primates undergoing different states of unconsciousness (sleep, and anesthesia with propofol, ketamine, and ketamine plus medetomidine), and funcional magnetic resonance imaging data from humans, both during deep sleep and under propofol anesthesia. Systematically, all states of reduced consciousness unfolded at higher proximity to equilibrium dynamics than conscious wakefulness, as demonstrated by entropy production and the curl of probability flux in phase space. Our results establish non-equilibrium macroscopic brain dynamics as a robust signature of consciousness, opening the way for the characterization of cognition and awareness using tools from statistical mechanics.

The dynamic core hypothesis posits that consciousness is correlated with simultaneously integrated and differentiated assemblies of transiently synchronized brain regions. We represented time-dependent functional interactions using dynamic brain networks, and assessed the integrityof the dynamic core by means of the flexibility and largest multilayer module of these networks. As a first step, we constrained parameter selection using a newly developed benchmark for module detection in heterogeneous temporal networks. Next, we applied a multilayer modularity maximization algorithm to dynamic brain networks computed from functional magnetic resonance imaging (fMRI) data acquired during deep sleep and under propofol anesthesia. We found that unconsciousness reconfigured network flexibility and reduced the size of the largest spatiotemporal module, which we identified with the dynamic core. Our results present a first characterization of modular brain network dynamics during states of unconsciousness measured with fMRI, adding support to the dynamic core hypothesis of human consciousness.

We consider the problem of encoding pairwise correlations between coupled dynamical systems in a low-dimensional latent space based on few distinct observations. We used variational autoencoders (VAE) to embed temporal correlations between coupled nonlinear oscillators that model brain states in the wake-sleep cycle into a two-dimensional manifold. Training a VAE with samples generated using two different parameter combinations resulted in an embedding that represented the whole repertoire of collective dynamics, as well as the topology of the underlying connectivity network. We first followed this approach to infer the trajectory of brain states measured from wakefulness to deep sleep from the two endpoints of this trajectory; next, we showed that the same architecture was capable of representing the pairwise correlations of generic Landau-Stuart oscillators coupled by complex network topology

Global brain activity self-organizes into discrete patterns characterized by distinct behavioral observables and modes of information processing. The human thalamocortical system is a densely connected network where local neural activation reciprocally influences coordinated collective dynamics. We introduce a semi-empirical model to investigate the relationship between regional activation and long-range functional connectivity in the different brain states visited during the natural wake-sleep cycle. Our model combines functional magnetic resonance imaging (fMRI) data, in vivo estimates of structural connectivity, and anatomically-informed priors that constrain the independent variation of regional activation. As expected, priors based on functionally coherent networks resulted in the best fit between empirical and simulated brain activity. We show that sleep progressively divided the cortex into regions presenting opposite dynamical behavior: frontoparietal regions approached a bifurcation towards local oscillatory dynamics, while sensorimotor regions presented stable dynamics governed by noise. In agreement with human electrophysiological experiments, sleep onset induced subcortical deactivation and uncoupling, which was subsequently reversed for deeper stages. Finally, we introduced external forcing of variable intensity to simulate external perturbations, and identifiedthe key regionsespecially relevant for the recovery of wakefulness from deep sleep. Our model represents sleep as a state where long-range decoupling and regional deactivation coexist with the latent capacity for a rapid transition towards wakefulness. The mechanistic insights provided by our simulations allow the in silico parametric exploration of such transitions in terms of external perturbations, with potential applications for the control of physiological and pathological brain states.

We explore a combinatorial framework which efficiently quantifies the asymmetries between minima and maxima in local fluctuations of time series. We firstly showcase its performance by applying it to a battery of synthetic cases. We find rigorous results on some canonical dynamical models (stochastic processes with and without correlations, chaotic processes) complemented by extensive numerical simulations for a range of processes which indicate that the methodology correctly distinguishes different complex dynamics and outperforms state of the art metrics in several cases. Subsequently, we apply this methodology to real-world problems emerging across several disciplines including cases in neurobiology, finance and climate science. We conclude that differences between the statistics of local maxima and local minima in time series are highly informative of the complex underlying dynamics and a graph-theoretic extraction procedure allows to use these features for statistical learning purposes.

We introduce a novel recurrent neural network (RNN) approach to account for temporal dynamics and dependencies in brain networks observed via functional magnetic resonance imaging (fMRI). Our approach directly parameterizes temporal dynamics through recurrent connections, which can be used to formulate blind source separation with a conditional (rather than marginal) independence assumption, which we call RNN-ICA. This formulation enables us to visualize the temporal dynamics of both first order (activity) and second order (directed connectivity) information in brain networks that are widely studied in a static sense, but not well-characterized dynamically. RNN-ICA predicts dynamics directly from the recurrent states of the RNN in both task and resting state fMRI. Our results show both task-related and group-differentiating directed connectivity., Comment: Accepted to Frontiers of Neuroscience

Loss of cortical integration and changes in the dynamics of electrophysiological brain signals characterize the transition from wakefulness towards unconsciousness. The common mechanism underlying these observations remains unknown. In this study we arrive at a basic model, which explains these empirical observations based on the theory of phase transitions in complex systems. We studied the link between spatial and temporal correlations of large-scale brain activity recorded with functional magnetic resonance imaging during wakefulness, propofol-induced sedation and loss of consciousness, as well as during the subsequent recovery. We observed that during unconsciousness activity in frontal and thalamic regions exhibited a reduction of long-range temporal correlations and a departure of functional connectivity from the underlying anatomical constraints. These changes in dynamics and anatomy-function coupling were correlated across participants, suggesting that temporal complexity and an efficient exploration of anatomical connectivity are inter-related phenomena. A model of a system exhibiting a phase transition reproduced our findings, as well as the diminished sensitivity of the cortex to external perturbations during unconsciousness. This theoretical framework unifies different empirical observations about brain activity during unconsciousness and predicts that the principles we identified are universal and independent of the causes behind loss of awareness., Comment: to appear in Journal of the Royal Society Interface

Large efforts are currently under way to systematically map functional connectivity between all pairs of millimeter-scale brain regions using big volumes of neuroimaging data. Functional magnetic resonance imaging (fMRI) can produce these functional connectomes, however, large amounts of data and lengthy computation times add important overhead to this task. Previous work has demonstrated that fMRI data admits a sparse representation in the form a discrete point-process containing sufficient information for the efficient estimation of functional connectivity between all pairs of voxels. In this work we validate this method, by replicating results obtained with standard whole-brain voxel-wise linear correlation matrices in two datasets. In the first one (n=71) we study the changes in node strength (a measure of network centrality) during deep sleep. The second is a large database (n=1147) of subjects in which we look at the age-related reorganization of the voxel-wise network of functional connections. In both cases it is shown that the proposed method compares well with standard techniques, despite requiring of the order of 1 % of the original fMRI time series. Overall, these results demonstrate that the proposed approach allows efficient fMRI data compression and a subsequent reduction of computation times.

Spontaneous fluctuations of the BOLD (Blood Oxygen Level-Dependent) signal, measured with fMRI (functional Magnetic Resonance Imaging), display a rich and neurobiologically relevant functional connectivity structure. This structure is usually revealed using time averaging methods, which prevent the detection of functional connectivity changes over time. In this work we studied the electrophysiological correlates of dynamical BOLD functional connectivity fluctuations, by means of long (approx. 50 min) joint electroencephalographic (EEG) and fMRI recordings. We identified widespread positive and negative correlations between EEG spectral power and fMRI BOLD connectivity fluctuations in a network of 90 cortical and subcortical regions. In particular, increased alpha (8-12 Hz) and beta (15-30 Hz) power were related to decreased functional connectivity, whereas gamma (30-60 Hz) power correlated positively with BOLD connectivity between specific brain regions. Furthermore, these patterns were altered for subjects undergoing vigilance changes, with an involvement of the slow delta (0.4 - 4 Hz) band in localized positive correlations. Finally, graph theoretical indices of network structure also exhibited sharp changes over time, with average path length correlating positively with alpha power extracted from central and frontal electrodes. Our results strongly suggest that non-stationary BOLD functional connectivity has a neurophysiological origin. Positive correlations of BOLD connectivity with gamma can be interpreted as increased average binding over relatively long periods of time, possibly due to spontaneous cognition occurring during rest. Negative correlations with alpha suggest functional inhibition of local and long-range connectivity, associated with an idling state of the brain., Comment: 39 pages, 10 figures