Human consciousness is supported by dynamic complex patterns of brain signal coordination

Abstract

Adopting the framework of brain dynamics as a cornerstone of human consciousness, we determined whether dynamic signal coordination provides specific and generalizable patterns pertaining to conscious and unconscious states after brain damage. A dynamic pattern of coordinated and anticoordinated functional magnetic resonance imaging signals characterized healthy individuals and minimally conscious patients. The brains of unresponsive patients showed primarily a pattern of low interareal phase coherence mainly mediated by structural connectivity, and had smaller chances to transition between patterns. The complex pattern was further corroborated in patients with covert cognition, who could perform neuroimaging mental imagery tasks, validating this pattern’s implication in consciousness. Anesthesia increased the probability of the less complex pattern to equal levels, validating its implication in unconsciousness. Our results establish that consciousness rests on the brain’s ability to sustain rich brain dynamics and pave the way for determining specific and generalizable fingerprints of conscious and unconscious states.

Hierarchical disruption in the cortex of anesthetized monkeys as a new signature of consciousness loss

“…light versus deep anesthesia). Moreover, higher values of ignition are associated with richer brain dynamics, while lower values relate to structural driven dynamics, as shown in previous reports (Barttfeld et al, 2015;Uhrig et al, 2018;Demertzi et al, 2019). Our results (Figure 1) are in line with these previous studies, and additionally indicate that the spatial and temporal dimensions of hierarchical organization change under anesthesia, while keeping different types of the same graded non-uniform hierarchy through all conditions.…”

“…Conversely, during anesthesia-induced loss of consciousness, the resting-state brain activity is shifted toward a poor repertoire of rigid functional patterns with higher similarity to structural connectivity. A finding that was generalized to different anesthetic agents (Uhrig et al, 2018) and also applied to classify different categories of chronic loss of consciousness (Demertzi et al, 2019). This dynamical disruption at long-distance networks might be the common fingerprint of all different types of loss of consciousness (anesthesia-induced, injuries-induced loss of consciousness and sleep).…”

Neural signatures of loss of consciousness and its recovery by thalamic stimulation

“…We found that it returned to cortex a higher-frequency awake-like state, restoring consciousness. Thus, propofol likely renders unconsciousness by disrupting intracortical communication through enhanced down-states and loss of the higher frequency coherence thought to integrate cortical information 3,4,7,[19][20][21][22][23][24] .…”

EEG power spectra and subcortical pathology in chronic disorders of consciousness

“…Electroencephalography (EEG) and magnetic resonance imaging (MRI) are increasingly employed to monitor patient neurological status 1, 2 , residual cognitive function [3][4][5] , state of awareness [6][7][8][9] , and potential for recovery [10][11][12][13] in patients with a disorder of consciousness (DOC; i.e., Coma, Vegetative State, VS; Minimally Conscious State 'minus,' MCS-; Minimally Conscious State 'plus,' MCS+; 14,15 ). In the context of bedside EEG, analysis of the magnitude of oscillations at different frequencies (i.e., power spectrum analysis), has shown capable of differentiating DOC patients from patients with severe neurocognitive disorder but no disorder of consciousness 16 , as well as clinical categories of chronic DOC (i.e., VS, MCS), with depth of impairment being correlated with slower, and larger amplitude, oscillations 17,18 .…”

“…Then, the selected EEG activity was analyzed by dividing it into 90 non-overlapping 2 s segments. Absolute total power and relative power were evaluated in the delta (1-4 Hz), theta (4-8 Hz), alpha(8)(9)(10)(11)(12)(13), beta(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and gamma (>30 Hz) bands, and averaged within each EEG channel.MRI data acquisition and processingNeuroimaging data were obtained with a 3T MR scanner (Achieva, Philips Healthcare BV, Best, NL) equipped with a 32-channel head coil. The MRI protocol comprised a high resolution 3D-TFE T1-weighted sequence (185 sagittal slices, TR = 9.781 ms, TE = 4.6 ms, FOV = 240 × 240 mm 2 , voxel size = 1 × 1 × 1 mm 3 , flip angle = 8 • ).…”