Conceptual framework of the study

(a) Consciousness is a continuum and can be explored with drug-induced coma of various depths (Xenon, Propofol > Ketamine > Wakefulness). We hypothesize a correspondence between the variations in complexity found with PCI and the dynamics of spontaneous activity across the spectrum of consciousness. (b) We sketch various patterns of spatio-temporal activity reflecting changes in perturbational complexity from left to right. In (c) we show the conceptual shapes of corresponding manifolds of brain activity responsible for different sizes of the functional repertoire (number of wells) and associated with consciousness. (d) The brain is modeled as a network of neural masses coupled by an empirical connectome. This whole-brain model serves as a platform to simulate resting state activity (bottom left) and cortical stimulation (top left, example of firing rate time series with applied stimulus). Dynamical properties of the simulations are studied and compared with data features of human empirical recordings of spontaneous activity (bottom right, EEG during wakefulness and under three anesthetics) and stimulation (top right, TMS-EEG protocol performed in the same conditions).

Dynamical regimes and associated metrics

(a) Examples of firing rate times series of the model depending on the strength of interaction between nodes (global coupling, G) and noise intensity (sigma). On the left column, the interactions are weak (G=0.27, sigma-bottom=0.022, sigma-top=0.056) and the activity is sparse. On the right column, connections are tighter (G=0.65, sigma-bottom=0.022, sigma-top=0.056) and a stable coactivation pattern appears in an ordered fashion. In the middle (bottom) global coupling and noise are at optimal values (G=0.56, sigma=0.036) and allow the emergence of structured patterns (coactivation cascades) of different sizes and durations. (b) Analysis of in-silico stimulation revealed that the same dynamical regime around the optimal point reaches the highest change in complexity (color scale: max(sPCI) in parameter space G, sigma). (c) Results of the four resting state metrics that we studied across dynamical regimes i.e in the parameter space of the model. (c, top left) results of the fluidity of the spontaneous firing rate activity (Variance(dFC) with a sliding window of 3s and 1s step-size). In (c, top right) we show the results of bursting potential assessed by the fastest change in the residual sum of squares across sources’ membrane potential. (c, bottom left) presents the size of the functional repertoire defined by the number of unique configurations of binarized firing rate activity (with a threshold at r=0.7). (c, bottom right) Lempel-Ziv complexity of binarized firing rate activity (with a threshold at r=0.7).

Resting-state metrics on EEG during anesthesia and wakefulness

Subject-specific distributions of (a) fluidity, (b) bursting capacity, (c) Lempel-Ziv complexity and (d) the size of the functional repertoire, between wakefulness (blue) and anesthesia (red). From left to right in each panel : Ketamine group, Propofol group and Xenon group. Values and distributions (kernel density estimations) were obtained by randomly sampling with replacement a minute of signal within each subject’s recording (50 samples drawn per subject per condition, 1 point per sample). Fluidity was calculated on the full recordings for each subject.

Predictive power of resting-state metrics and PCI

(a) Crossplots between the PCI obtained experimentally during a TMS-EEG protocole and each metric on spontaneous recordings (functional repertoire, complexity, fluidity and bursting potential). Complexity and the size of the functional repertoire were normalized by the length of the recording in minutes. (b) Classification accuracy of a Support Vector Machine classifier with a linear kernel to distinguish either between anesthesia and wakefulness (downward orange triangles) or between conscious report and no report (upward blue triangles). Dashed lines represent the benchmark performances achieved by PCI classification (100% for consciousness and 87% for anesthesia).