ANR-20-NEUC-0005-01 -Brainstim, by the European Union's Horizon 2020 research and innovation programme under grant agreement No. 945539 (SGA3) Human Brain Project and VirtualBrainCloud No. 826421.

At rest, mammalian brains display a rich complex spatiotemporal behavior, which is reminiscent of healthy brain function and has provided nuanced understandings of several major neurological conditions. Despite the increasingly detailed phenomenological documentation of the brain’s resting state, its principle underlying causes remain unknown. To establish causality, we link structurally defined features of a brain network model to neural activation patterns and their variability. For the mouse, we use a detailed connectome-based model and simulate the resting state dynamics for neural sources and whole brain imaging signals (Blood-Oxygen-Level-Dependent (BOLD), Electroencephalography (EEG)). Under conditions of near-criticality, characteristic neuronal cascades form spontaneously and propagate through the network. The largest neuronal cascades produce short-lived but robust co-fluctuations at pairs of regions across the brain. During these co-activation episodes, long-lasting functional networks emerge giving rise to epochs of stable resting state networks correlated in time. Sets of neural cascades are typical for a resting state network, but different across. We experimentally confirm the existence and stability of functional connectivity epochs comprising BOLD co-activation bursts in mice (N=19). We further demonstrate the leading role of the neuronal cascades in a simultaneous EEG/fMRI data set in humans (N=15), explaining a large part of the variability of functional connectivity dynamics. We conclude that short-lived neuronal cascades are a major robust dynamic component contributing to the organization of the slowly evolving spontaneous fluctuations in brain dynamics at rest.

Changes in extracellular ion concentrations are known to modulate neuronal excitability and play a major role in controlling the neuronal firing rate, not just during the healthy homeostasis, but also in pathological conditions such as epilepsy. The microscopic molecular mechanisms of field effects are understood, but the precise correspondence between the microscopic mechanisms of ion exchange in the cellular space of neurons and the macroscopic behavior of neuronal populations remains to be established. We derive a mean field model of a population of Hodgkin–Huxley type neurons. This model links the neuronal intra- and extra-cellular ion concentrations to the mean membrane potential and the mean synaptic input in terms of the synaptic conductance of the locally homogeneous mesoscopic network and can describe various brain activities including multi-stability at resting states, as well as more pathological spiking and bursting behaviors, and depolarizations. The results from the analytical solution of the mean field model agree with the mean behavior of numerical simulations of large-scale networks of neurons. The mean field model is analytically exact for non-autonomous ion concentration variables and provides a mean field approximation in the thermodynamic limit, for locally homogeneous mesoscopic networks of biophysical neurons driven by an ion exchange mechanism. These results may provide the missing link between high-level neural mass approaches which are used in the brain network modeling and physiological parameters that drive the neuronal dynamics.

Spontaneously fluctuating brain activity patterns emerge at rest and relate to brain functional networks involved in task conditions. Despite detailed descriptions of the spatio-temporal brain patterns, our understanding of their generative mechanism is still incomplete. Using a combination of computational modeling and dynamical systems analysis we provide a complete mechanistic description in terms of the constituent entities and the productive relation of their causal activities leading to the formation of a resting state manifold via the network connectivity. We demonstrate that the symmetry breaking by the connectivity creates a characteristic flow on the manifold, which produces the major empirical data features including spontaneous high amplitude co-activations, neuronal cascades, spectral cortical gradients, multistability and characteristic functional connectivity dynamics. The understanding of the brain's resting state manifold is fundamental for the construction of task-specific flows and manifolds used in theories of brain function such as predictive coding.

Decades of research have advanced our understanding of the biophysical mechanisms underlying consciousness. However, an overarching framework bridging between models of consciousness and the large-scale organization of spontaneous brain activity is still missing. Based on the observation that spontaneous brain activity dynamically switches between epochs of segregation and large-scale integration of information, we hypothesize a brain-state dependence of conscious access, whereby the presence of either segregated or integrated states marks distinct modes of information processing. We first review influential works on the neuronal correlates of consciousness, spontaneous resting-state brain activity and dynamical system theory. Then, we propose a test experiment to validate our hypothesis that conscious access occurs in aperiodic cycles, alternating windows where new incoming information is collected but not experienced, to punctuated short-lived integration events, where conscious access to previously collected content occurs. In particular, we suggest that the integration events correspond to neuronal avalanches, which are collective bursts of neuronal activity ubiquitously observed in electrophysiological recordings. If confirmed, the proposed framework would link the physics of spontaneous cortical dynamics, to the concept of ignition within the global neuronal workspace theory, whereby conscious access manifest itself as a burst of neuronal activity.

The functional organization of the brain is usually presented with a back-to-front gradient of timescales, reflecting regional specialization with sensory areas (back) processing information faster than associative areas (front), which perform information integration. However, cognitive processes require not only local information processing but also coordinated activity across regions. Using magnetoencephalography recordings, we find that the functional connectivity at the edge level (between two regions) are also characterized by a back-to-front gradient of timescales following that of the regional gradient. Unexpectedly, we demonstrate a reverse front-to-back gradient when non-local interactions are prominent. Thus, the timescales are dynamic and can switch between back-to-front and front-to-back patterns.

Behavioral responses are brain-state dependent and rely on the coordinated activity of brain areas. Their exchange of information within the network can be represented as functional links establishing a spatiotemporal pattern, evolving over time and scales. Network neuroscience offers useful tools that may guide our understanding of these patterns organisation. Here we used source-reconstructed Magnetoencephalography signals from a large healthy cohort, and estimated a link-specific time-scale as the information decay-time. Their distribution allowed the identification of two anatomically distinct and functionally coherent networks. Rapidly-decaying links define a fast-updating networK (FUN), which includes regions typically involved in the processing of external stimuli. Slow-decaying links define a slow-updating network (SUN), which comprises hierarchically higher regions, classically involved in the integration of such stimuli. Finally, we show that only a subset of the brain regions belong to both subnetworks, which we name the multi-storage core (MSC). The MSC is hypothesized to play a role in the communication between the (otherwise) temporally segregated subnetworks.

Neural bases of consciousness have been explored through many different paradigms and the notion of complexity emerged as a unifying framework to characterize conscious experience. To date, the perturbational complexity index (PCI), rooted on information theory, shows high performance in assessing consciousness. However, the mechanisms underpinning this perturbational complexity remain unclear as well as its counterpart in spontaneous activity. In the present study, we explore brain responsiveness and resting-state activity through large-scale brain modeling and prove that complexity and consciousness are directly associated with a fluid dynamical regime. This fluidity is reflected in the dynamic functional connectivity, and other metrics drawn from dynamical systems theory and manifolds can capture such dynamics in synthetic data. We then validate our findings on a cohort of 15 subjects under anesthesia and wakefulness and show that measures of fluidity on spontaneous activity can distinguish consciousness in agreement with perturbational complexity.