Integrated Information Theory (IIT) has become nowadays the most sensible general theory of consciousness. In addition to very important statements, it opens the door for an abstract (mathematical) formulation of the theory. Given a mechanism in a particular state, IIT identifies a conscious experience with a conceptual structure, an informational object which exists, is composed of identified parts, is informative, integrated and maximally irreducible. This paper introduces a space-time continuous version of the concept of integrated information. To this aim, a graph and a dynamical systems treatment is used to define, for a given mechanism in a state for which a dynamics is settled, an Informational Structure, which is associated to the global attractor at each time of the system. By definition, the informational structure determines all the past and future behavior of the system, possesses an informational nature and, moreover, enriches all the points of the phase space with cause-effect power by means of its associated Informational Field. A detailed description of its inner structure by invariants and connections between them allows to associate a transition probability matrix to each informational structure and to develop a measure for the level of integrated information of the system.

Electroencephalography (EEG) monitors -by either intrusive or noninvasive electrodes-time and frequency variations and spectral content of voltage fluctuations or waves, known as brain rhythms, which in some way uncover activity during both rest periods and specific events in which the subject is under stimulus. This is a useful tool to explore brain behavior, as it complements imaging techniques that have a poorer temporal resolution. We here approach the understanding of EEG data from first principles by numerical simulating and studying a networked model of excitatory and inhibitory neurons which generates a variety of comparable waves. In fact, we thus numerically reproduce oscillatory behavior similar to α, β, γ and other rhythms as observed by EEG recordings, and identify the details of the respectively involved complex phenomena, including a precise relationship between an input and the collective response to it. It ensues the potentiality of our model to better understand actual brain oscillatory activity in normal and pathological situations, and we also describe kind of stochastic resonance phenomena which could be useful to locate main qualitative changes of brain activity in (e.g.) humans.

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Contemplative neuroscience has increasingly explored meditation using neuroimaging. However, the brain mechanisms underlying meditation remain elusive. Here, we implemented a causal mechanistic framework to explore the spatiotemporal dynamics of expert meditators during meditation and rest. We first applied a model-free approach by defining a probabilistic metastable substate (PMS) space for each state, consisting of different probabilities of occurrence from a repertoire of dynamic patterns. Different brain signatures were mainly found in the triple-network model (i.e., the executive control, salience, and default-mode networks). Moreover, we implemented a model-based approach by adjusting the PMS of the resting state to a whole-brain model, which enabled us to explorein silicoperturbations to transition to the meditation state. Consequently, we assessed the sensitivity of different brain areas regarding their perturbability and their mechanistic local-global effects. Using a synchronous protocol, we successfully transitioned from the resting state to the meditative state by shifting areas mainly from the somatomotor and dorsal attention networks. Overall, our work reveals distinct whole-brain dynamics in meditation compared to rest, and how the meditation state can be induced with localized artificial perturbations. It motivates future work regarding meditation as a practice in health and as a potential therapy for brain disorders.

The self-organising global dynamics underlying brain states emerge from complex recursive nonlinear interactions between interconnected brain regions. Until now, most efforts of capturing the causal mechanistic generating principles have supposed underlying stationarity, being unable to describe the non-stationarity of brain dynamics, i.e. time-dependent changes. Here, we present a novel framework able to characterise brain states with high specificity, precisely by modelling the time-dependent dynamics. Through describing a topological structure associated to the brain state at each moment in time (its attractor or ‘information structure’), we are able to classify different brain states by using the statistics across time of these structures hitherto hidden in the neuroimaging dynamics. Proving the strong potential of this framework, we were able to classify resting-state BOLD fMRI signals from two classes of post-comatose patients (minimally conscious state and unresponsive wakefulness syndrome) compared with healthy controls with very high precision.

The self-organising global dynamics underlying brain states emerge from complex recursive nonlinear interactions between interconnected brain regions. Until now, all efforts of capturing the causal mechanistic generating principles have proven elusive, since they have been unable to describe the non-stationarity of brain dynamics, i.e. time-dependent changes. Here, we present a novel framework able to characterise brain states with high specificity, precisely by modelling the time-dependent dynamics. Through describing the topological structure of the brain at each moment in time (its ‘information structure’), we are able to classify different brain states by using the statistics across time of these exact ‘information structures’ hitherto hidden in the neuroimaging dynamics. Proving the strong potential of this framework, we were able to classify the neuroimaging data from two classes of comatose patients (minimally conscious state and unresponsive wakefulness syndrome) compared with healthy controls with very high precision.

The self-organising global dynamics underlying brain states emerge from complex recursive nonlinear interactions between interconnected brain regions. Until now, all efforts of capturing the causal mechanistic generating principles have proven elusive, since they have been unable to describe the non-stationarity of brain dynamics, i.e. time-dependent changes. Here, we present a novel framework able to characterise brain states with high specificity, precisely by modelling the time-dependent dynamics. Through describing the topological structure of the brain at each moment in time (its ‘information structure’), we are able to classify different brain states by using the statistics across time of these exact ‘information structures’ hitherto hidden in the neuroimaging dynamics. Proving the strong potential of this framework, we were able to classify the neuroimaging data from two classes of comatose patients (minimally conscious state and unresponsive wakefulness syndrome) compared with healthy controls with very high precision.

The self-organising global dynamics underlying brain states emerge from complex recursive nonlinear interactions between interconnected brain regions. Until now, all efforts of capturing the causal mechanistic generating principles have proven elusive, since they have been unable to describe the non-stationarity of brain dynamics, i.e. time-dependent changes. Here, we present a novel framework able to characterise brain states with high specificity, precisely by modelling the time-dependent dynamics. Through describing the topological structure of the brain at each moment in time (its ‘information structure’), we are able to classify different brain states by using the statistics across time of these exact ‘information structures’ hitherto hidden in the neuroimaging dynamics. Proving the strong potential of this framework, we were able to classify the neuroimaging data from two classes of comatose patients (minimally conscious state and unresponsive wakefulness syndrome) compared with healthy controls with very high precision.