Repertoire of mesoscopic cortical activity is not reduced during anesthesia

Cortical recurrence supports resilience to sensory variance in the primary visual cortex

Our daily endeavors occur in a complex visual environment, whose intrinsic variability challenges the way we integrate information to make decisions. By processing myriads of parallel sensory inputs, our brain is theoretically able to compute the variance of its environment, a cue known to guide our behavior. Yet, the neurobiological and computational basis of such variance computations are still poorly understood. Here, we quantify the dynamics of sensory variance modulations of cat primary visual cortex neurons. We report two archetypal neuronal responses, one of which is resilient to changes in variance and co-encodes the sensory feature and its variance, improving the population encoding of orientation. The existence of these variance-specific responses can be accounted for by a model of intracortical recurrent connectivity. We thus propose that local recurrent circuits process uncertainty as a generic computation, advancing our understanding of how the brain handles naturalistic inputs.

A scoping review for building a criticality-based conceptual framework of altered states of consciousness

The healthy conscious brain is thought to operate near a critical state, reflecting optimal information processing and high susceptibility to external stimuli. Conversely, deviations from the critical state are hypothesized to give rise to altered states of consciousness (ASC). Measures of criticality could therefore be an effective way of establishing the conscious state of an individual. Furthermore, characterizing the direction of a deviation from criticality may enable the development of treatment strategies for pathological ASC. The aim of this scoping review is to assess the current evidence supporting the criticality hypothesis, and the use of criticality as a conceptual framework for ASC. Using the PRISMA guidelines, Web of Science and PubMed were searched from inception to February 7th 2022 to find articles relating to measures of criticality across ASC. N = 427 independent papers were initially found on the subject. N = 378 were excluded because they were either: not related to criticality; not related to consciousness; not presenting results from a primary study; presenting model data. N = 49 independent papers were included in the present research, separated in 7 sub-categories of ASC: disorders of consciousness (DOC) (n = 5); sleep (n = 13); anesthesia (n = 18); epilepsy (n = 12); psychedelics and shamanic state of consciousness (n = 4); delirium (n = 1); meditative state (n = 2). Each category included articles suggesting a deviation of the critical state. While most studies were only able to identify a deviation from criticality without being certain of its direction, the preliminary consensus arising from the literature is that non-rapid eye movement (NREM) sleep reflects a subcritical state, epileptic seizures reflect a supercritical state, and psychedelics are closer to the critical state than normal consciousness. This scoping review suggests that, though the literature is limited and methodologically inhomogeneous, ASC are characterized by a deviation from criticality, though its direction is not clearly reported in a majority of studies. Criticality could become, with more extensive research, an effective and objective way to characterize ASC, and help identify therapeutic avenues to improve criticality in pathological brain states. Furthermore, we suggest how anesthesia and psychedelics could potentially be used as neuromodulation techniques to restore criticality in DOC.

Cortical neuronal assemblies coordinate with EEG microstate dynamics during resting wakefulness

The disruption of cortical assembly activity has been associated with anesthesia-induced loss of consciousness. However, the relationship between cortical assembly activity and the variations in consciousness associated with natural vigilance states remains unclear. Here, we address this by performing vigilance state-specific clustering analysis on 2-photon calcium imaging data from the sensorimotor cortex in combination with global electroencephalogram (EEG) microstate analysis derived from multi-EEG signals obtained over widespread cortical locations. We report no difference in the structure of assembly activity during quiet wakefulness (QW), non-rapid eye movement sleep (NREMs), or REMs, despite the latter two vigilance states being associated with significantly reduced levels of consciousness relative to QW. However, we describe a significant coordination between global EEG microstate dynamics and general local cortical assembly activity during periods of QW, but not sleep. These results suggest that the coordination of cortical assembly activity with global brain dynamics could be a key factor of sustained conscious experience.

Brain Complexities and Anesthesia: Their Meaning and Measurement

A complex system is often associated with emergence of new phenomena from the interactions between the system's components. General anesthesia reduces brain complexity and so inhibits the emergence of consciousness. An understanding of complexity is necessary for the interpretation of brain monitoring algorithms. Complexity indices capture the "difficulty" of understanding brain activity over time and/or space. Complexity-entropy plots reveal the types of complexity indices and their balance of randomness and structure. Lempel-Ziv complexity is a common index of temporal complexity for single-channel electroencephalogram containing both power spectral and nonlinear effects, revealed by phase-randomized surrogate data. Computing spatial complexities involves forming a connectivity matrix and calculating the complexity of connectivity patterns. Spatiotemporal complexity can be estimated in multiple ways including temporal or spatial concatenation, estimation of state switching, or integrated information. This article illustrates the concept and application of various complexities by providing working examples; a website with interactive demonstrations has also been created.

Self-organized criticality as a framework for consciousness: A review study

Objective

No current model of consciousness is univocally accepted on either theoretical or empirical grounds, and the need for a solid unifying framework is evident. Special attention has been given to the premise that self-organized criticality (SOC) is a fundamental property of neural system. SOC provides a competitive model to describe the physical mechanisms underlying spontaneous brain activity, and thus, critical dynamics were proposed as general gauges of information processing representing a strong candidate for a surrogate measure of consciousness. As SOC could be a neurodynamical framework, which may be able to bring together existing theories and experimental evidence, the purpose of this work was to provide a comprehensive overview of progress of research on SOC in association with consciousness.

Methods

A comprehensive search of publications on consciousness and SOC published between 1998 and 2021 was conducted. The Web of Science database was searched, and annual number of publications and citations, type of articles, and applied methods were determined.

Results

A total of 71 publications were identified. The annual number of citations steadily increased over the years. Original articles comprised 50.7% and reviews/theoretical articles 43.6%. Sixteen studies reported on human data and in seven studies data were recorded in animals. Computational models were utilized in n = 12 studies. EcoG data were assessed in n = 4 articles, fMRI in n = 4 studies, and EEG/MEG in n = 10 studies. Notably, different analytical tools were applied in the EEG/MEG studies to assess a surrogate measure of criticality such as the detrended fluctuation analysis, the pair correlation function, parameters from the neuronal avalanche analysis and the spectral exponent.

Conclusion

Recent studies pointed out agreements of critical dynamics with the current most influencing theories in the field of consciousness research, the global workspace theory and the integrated information theory. Thus, the framework of SOC as a neurodynamical parameter for consciousness seems promising. However, identified experimental work was small in numbers, and a heterogeneity of applied analytical tools as a surrogate measure of criticality was observable, which limits the generalizability of findings.

Electrophysiological Activity and Brain Network During Recovery of Propofol Anesthesia: A Stereoelectroencephalography-Based Analysis in Patients With Intractable Epilepsy—An Exploratory Research

Background: The oscillations and interactions between different brain areas during recovery of consciousness (ROC) from anesthesia in humans are poorly understood. Reliable stereoelectroencephalography (SEEG) signatures for transitions between unconsciousness and consciousness under anesthesia have not yet been fully identified.

Objective: This study was designed to observe the change of electrophysiological activity during ROC and construct a ROC network based on SEEG data to describe the network property of cortical and deep areas during ROC from propofol-induced anesthetic epileptic patients.

Methods: We analyzed SEEG data recorded from sixteen right-handed epileptic patients during ROC from propofol anesthesia from March 1, 2019, to December 31, 2019. Power spectrum density (PSD), correlation, and coherence were used to describe different brain areas' electrophysiological activity. The clustering coefficient, characteristic path length, modularity, network efficiency, degrees, and betweenness centrality were used to describe the network changes during ROC from propofol anesthesia. Statistical analysis was performed using MATLAB 2016b. The power spectral data from different contacts were analyzed using a one-way analysis of variance (ANOVA) test with Tukey's post-hoc correction. One sample t-test was used for the analysis of network property. Kolmogorov-Smirnov test was used to judge data distribution. Non-normal distribution was analyzed using the signed rank-sum test.

Result: From the data of these 16 patients, 10 cortical, and 22 deep positions were observed. In this network, we observed that bilateral occipital areas are essential parts that have strong links with many regions. The recovery process is different in the bilateral cerebral cortex. Stage B (propofol 3.0-2.5 μg/ml) and E (propofol 1.5 μg/ml-ROC) play important roles during ROC exhibiting significant changes. The clustering coefficient gradually decreases with the recovery from anesthesia, and the changes mainly come from the cortical region. The characteristic path length and network efficiency do not change significantly during the recovery from anesthesia, and the changes of network modularity and clustering coefficient are similar. Deep areas tend to form functional modules. The left occipital lobe, the left temporal lobe, bilateral amygdala are essential nodes in the network. Some specific cortical regions (i.e., left angular gyrus, right angular gyrus, right temporal lobe, left temporal lobe, and right angular gyrus) and deep regions (i.e., right amygdala, left cingulate gyrus, right insular lobe, right amygdala) have more significant constraints on other regions.

Conclusion: We verified that the bilateral cortex's recovery process is the opposite, which is not found in the deep regions. Significant PSD changes were observed in many areas at the beginning of stop infusion and near recovery. Our study found that during the ROC process, the modularity and clustering coefficient of the deep area network is significantly improved. However, the changes of the bilateral cerebral cortex were different. Power spectrum analysis shows that low-frequency EEG in anesthesia recovery accounts for a large proportion. The changes of the bilateral brain in the process of anesthesia recovery are different. The clustering coefficient gradually decreased with the recovery from anesthesia, and the changes mainly came from the cortical region. The characteristic path length and network efficiency do not change significantly during the recovery from anesthesia, and the changes of network modularity and clustering coefficient were similar. During ROC, the left occipital lobe, the left temporal lobe, bilateral amygdala were essential nodes in the network. The findings of the current study suggest SEEG as an effective tool for providing direct evidence of the anesthesia recovery mechanism.

The promoting effect of vagus nerve stimulation on Lempel-Ziv complexity index of consciousness

Recent studies suggest that vagus nerve stimulation (VNS) promotes cognitive and behavioral restoration after traumatic brain injuries. As vagus nerve has wide effects over the brain and visceral organs, stimulation of the sensory/visceral afferents might have a therapeutic potential to modulate the level of consciousness. One of the most important challenges in studying consciousness is objective evaluation of the consciousness level. Brain complexity that can be measured through Lempel-Ziv complexity (LZC) index was used as a novel mathematical approach for objective measurement of consciousness. The main goal of our study was to examine the effects of VNS on LZC index of consciousness. In this study, we did VNS on the anesthetized rats, and simultaneously LFPs recording was performed in two different cortical areas of primary somatosensory (S₁) or visual (V₁) cortex. LZC and the amplitude of slow waves were computed during different periods of VNS. We found that the LZC index during VNS period was significantly higher in both of the cortical areas of S₁ and V₁. Slow-wave activity decreased during VNS in S₁, while there was no significant change in V₁. Our findings showed that VNS can augment the consciousness level, and LZC index is a more sensitive parameter for detecting the level of consciousness.

Cell-type-specific imaging of neurotransmission reveals a disrupted excitatory-inhibitory cortical network in isoflurane anaesthesia

BACKGROUND

Despite the fundamental clinical significance of general anaesthesia, the cortical mechanism underlying anaesthetic-induced loss of consciousness (aLOC) remains elusive.

METHODS

Here, we measured the dynamics of two major cortical neurotransmitters, gamma-aminobutyric acid (GABA) and glutamate, through in vivo two-photon imaging and genetically encoded neurotransmitter sensors in a cell type-specific manner in the primary visual (V1) cortex.

FINDINGS

We found a general decrease in cortical GABA transmission during aLOC. However, the glutamate transmission varies among different cortical cell types, where in it is almost preserved on pyramidal cells and is significantly reduced on inhibitory interneurons. Cortical interneurons expressing vasoactive intestinal peptide (VIP) and parvalbumin (PV) specialize in disinhibitory and inhibitory effects, respectively. During aLOC, VIP neuronal activity was delayed, and PV neuronal activity was dramatically inhibited and highly synchronized.

INTERPRETATION

These data reveal that aLOC resembles a cortical state with a disrupted excitatory-inhibitory network and suggest that a functional inhibitory network is indispensable in the maintenance of consciousness.

FUNDING

This work was supported by the grants of the National Natural Science Foundation of China (grant nos. 81620108012 and 82030038 to H.D. and grant nos. 31922029, 61890951, and 61890950 to J.H.).

Muting, not fragmentation, of functional brain networks under general anesthesia

Changes in resting-state functional connectivity (rs-FC) under general anesthesia have been widely studied with the goal of identifying neural signatures of consciousness. This work has commonly revealed an apparent fragmentation of whole-brain network structure during unconsciousness, which has been interpreted as reflecting a break-down in connectivity and a disruption of the brain's ability to integrate information. Here we show, by studying rs-FC under varying depths of isoflurane-induced anesthesia in nonhuman primates, that this apparent fragmentation, rather than reflecting an actual change in network structure, can be simply explained as the result of a global reduction in FC. Specifically, by comparing the actual FC data to surrogate data sets that we derived to test competing hypotheses of how FC changes as a function of dose, we found that increases in whole-brain modularity and the number of network communities - considered hallmarks of fragmentation - are artifacts of constructing FC networks by thresholding based on correlation magnitude. Taken together, our findings suggest that deepening levels of unconsciousness are instead associated with the increasingly muted expression of functional networks, an observation that constrains current interpretations as to how anesthesia-induced FC changes map onto existing neurobiological theories of consciousness.

Dynamic Reconfiguration, Fragmentation, and Integration of Whole-Brain Modular Structure across Depths of Unconsciousness

General anesthetics are routinely used to induce unconsciousness, and much is known about their effects on receptor function and single neuron activity. Much less is known about how these local effects are manifest at the whole-brain level nor how they influence network dynamics, especially past the point of induced unconsciousness. Using resting-state functional magnetic resonance imaging (fMRI) with nonhuman primates, we investigated the dose-dependent effects of anesthesia on whole-brain temporal modular structure, following loss of consciousness. We found that higher isoflurane dose was associated with an increase in both the number and isolation of whole-brain modules, as well as an increase in the uncoordinated movement of brain regions between those modules. Conversely, we found that higher dose was associated with a decrease in the cohesive movement of brain regions between modules, as well as a decrease in the proportion of modules in which brain regions participated. Moreover, higher dose was associated with a decrease in the overall integrity of networks derived from the temporal modules, with the exception of a single, sensory-motor network. Together, these findings suggest that anesthesia-induced unconsciousness results from the hierarchical fragmentation of dynamic whole-brain network structure, leading to the discoordination of temporal interactions between cortical modules.

Reduced Repertoire of Cortical Microstates and Neuronal Ensembles in Medically Induced Loss of Consciousness

Medically induced loss of consciousness (mLOC) during anesthesia is associated with a macroscale breakdown of brain connectivity, yet the neural microcircuit correlates of mLOC remain unknown. To explore this, we applied different analytical approaches (t-SNE/watershed segmentation, affinity propagation clustering, PCA, and LZW complexity) to two-photon calcium imaging of neocortical and hippocampal microcircuit activity and local field potential (LFP) measurements across different anesthetic depths in mice, and to micro-electrode array recordings in human subjects. We find that in both cases, mLOC disrupts population activity patterns by generating (1) fewer discriminable network microstates and (2) fewer neuronal ensembles. Our results indicate that local neuronal ensemble dynamics could causally contribute to the emergence of conscious states.

Role of Network Science in the Study of Anesthetic State Transitions

The heterogeneity of molecular mechanisms, target neural circuits, and neurophysiologic effects of general anesthetics makes it difficult to develop a reliable and drug-invariant index of general anesthesia. No single brain region or mechanism has been identified as the neural correlate of consciousness, suggesting that consciousness might emerge through complex interactions of spatially and temporally distributed brain functions. The goal of this review article is to introduce the basic concepts of networks and explain why the application of network science to general anesthesia could be a pathway to discover a fundamental mechanism of anesthetic-induced unconsciousness. This article reviews data suggesting that reduced network efficiency, constrained network repertoires, and changes in cortical dynamics create inhospitable conditions for information processing and transfer, which lead to unconsciousness. This review proposes that network science is not just a useful tool but a necessary theoretical framework and method to uncover common principles of anesthetic-induced unconsciousness.

Long-Range Temporal Correlations of Patients in Minimally Conscious State Modulated by Spinal Cord Stimulation

Spinal cord stimulation (SCS) has been shown to improve the consciousness levels of patients with disorder of consciousness (DOC). However, the underlying mechanisms of SCS remain poorly understood. This study recorded resting-state electroencephalograms (EEG) from 16 patients with minimally conscious state (MCS), before and after SCS, and investigated the mechanisms of SCS on the neuronal dynamics in MCS patients. Detrended fluctuation analysis (DFA), combined with surrogate data method, was employed to measure the long-range temporal correlations (LRTCs) of the EEG signals. A surrogate data method was utilized to acquire the genuine DFA exponents (GDFAE) reflecting the genuine LRTCs of brain activity. We analyzed the GDFAE in four brain regions (frontal, central, posterior, and occipital) at five EEG frequency bands [delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), and gamma (30-45 Hz)]. The GDFAE values ranged from 0.5 to 1, and showed temporal and spatial variation between the pre-SCS and the post-SCS states. We found that the channels with GDFAE spread wider after SCS. This phenomenon may indicate that more cortical areas were engaged in the information integration after SCS. In addition, the GDFAE values increased significantly in the frontal area at delta, theta, and alpha bands after SCS. At the theta band, a significant increase in GDFAE was observed in the occipital area. No significant change was found at beta or gamma bands in any brain region. These findings show that the enhanced LRTCs after SCS occurred primarily at low-frequency bands in the frontal and occipital regions. As the LRTCs reflect the long-range temporal integration of EEG signals, our results indicate that information integration became more "complex" after SCS. We concluded that the brain activities at low-frequency oscillations, particularly in the frontal and occipital regions, were improved by SCS.

Neural Correlates of Unconsciousness in Large-Scale Brain Networks

The biological basis of consciousness is one of the most challenging and fundamental questions in 21st century science. A related pursuit aims to identify the neural correlates and causes of unconsciousness. We review current trends in the investigation of physiological, pharmacological, and pathological states of unconsciousness at the level of large-scale functional brain networks. We focus on the roles of brain connectivity, repertoire, graph-theoretical techniques, and neural dynamics in understanding the functional brain disconnections and reduced complexity that appear to characterize these states. Persistent questions in the field, such as distinguishing true correlates, linking neural scales, and understanding differential recovery patterns, are also addressed.