Improving the clinical assessment of consciousness with advances in electrophysiological and neuroimaging techniques

In clinical neurology, a comprehensive understanding of consciousness has been regarded as an abstract concept - best left to philosophers. However, times are changing and the need to clinically assess consciousness is increasingly becoming a real-world, practical challenge. Current methods for evaluating altered levels of consciousness are highly reliant on either behavioural measures or anatomical imaging. While these methods have some utility, estimates of misdiagnosis are worrisome (as high as 43%) - clearly this is a major clinical problem. The solution must involve objective, physiologically based measures that do not rely on behaviour. This paper reviews recent advances in physiologically based measures that enable better evaluation of consciousness states (coma, vegetative state, minimally conscious state, and locked in syndrome). Based on the evidence to-date, electroencephalographic and neuroimaging based assessments of consciousness provide valuable information for evaluation of residual function, formation of differential diagnoses, and estimation of prognosis.     

Practical measures of integrated information for time-series data

A recent measure of 'integrated information', Φ(DM), quantifies the extent to which a system generates more information than the sum of its parts as it transitions between states, possibly reflecting levels of consciousness generated by neural systems. However, Φ(DM) is defined only for discrete Markov systems, which are unusual in biology; as a result, Φ(DM) can rarely be measured in practice. Here, we describe two new measures, Φ(E) and Φ(AR), that overcome these limitations and are easy to apply to time-series data. We use simulations to demonstrate the in-practice applicability of our measures, and to explore their properties. Our results provide new opportunities for examining information integration in real and model systems and carry implications for relations between integrated information, consciousness, and other neurocognitive processes. However, our findings pose challenges for theories that ascribe physical meaning to the measured quantities.     

Dynamic Change of Global and Local Information Processing in Propofol-Induced Loss and Recovery of Consciousness

Whether unique to humans or not, consciousness is a central aspect of our experience of the world. The neural fingerprint of this experience, however, remains one of the least understood aspects of the human brain. In this paper we employ graph-theoretic measures and support vector machine classification to assess, in 12 healthy volunteers, the dynamic reconfiguration of functional connectivity during wakefulness, propofol-induced sedation and loss of consciousness, and the recovery of wakefulness. Our main findings, based on resting-state fMRI, are three-fold. First, we find that propofol-induced anesthesia does not bear differently on long-range versus short-range connections. Second, our multi-stage design dissociated an initial phase of thalamo-cortical and cortico-cortical hyperconnectivity, present during sedation, from a phase of cortico-cortical hypoconnectivity, apparent during loss of consciousness. Finally, we show that while clustering is increased during loss of consciousness, as recently suggested, it also remains significantly elevated during wakefulness recovery. Conversely, the characteristic path length of brain networks (i.e., the average functional distance between any two regions of the brain) appears significantly increased only during loss of consciousness, marking a decrease of global information-processing efficiency uniquely associated with unconsciousness. These findings suggest that propofol-induced loss of consciousness is mainly tied to cortico-cortical and not thalamo-cortical mechanisms, and that decreased efficiency of information flow is the main feature differentiating the conscious from the unconscious brain.     

The sense of consciousness

I propose that consciousness might be understood as the property of a system that functions as a sense in the biological meaning of that term. The theory assumes that, as a complex system, the sense of consciousness is not a fixed structure but implies structure with variations and that it evolved, as many new functions do, through the integration of simpler systems. The recognized exteroceptive and enteroceptive senses provide information about the organism's environment and about the organism itself that are important to adaptation. The sense of consciousness provides information about the brain and thus about the organism and its environment. It senses other senses and processes in the brain, selecting and relating components into a form that "makes sense"-where making sense is defined as being useful to the organism in its adaptation to the environment. The theory argues that this highly adaptive organizing function evolved with the growing complexity of the brain and that it might have helped resolve discrepancies created at earlier stages. Neural energies in the brain that are the input to the sense of consciousness, along with the processing subsystem of which they are a part, constitute the base of consciousness. Consciousness itself is an emergent effect of an organizing process achieved through the sense of consciousness. The sense of consciousness thus serves an organizing function although it is not the only means of organization in the brain. Its uniqueness lies in the character of the organization it creates with consciousness as a property of that organization. The paper relates the theory to several general conceptions-interactionism, epiphenomenalism and identity theory-and illustrates a number of testable hypotheses. Viewing consciousness as a property of a sense provides a degree of conceptual integration. Much of what we know about the evolution and role of the conventionally recognized senses should help us understand the evolution and role of the sense of consciousness, and of consciousness itself.     

How to study consciousness scientifically.

The neurosciences have advanced to the point that we can now treat consciousness as a scientific problem like any other. The problem is to explain how brain processes cause consciousness and how consciousness is realized in the brain. Progress is impeded by a number of philosophical mistakes, and the aim of this paper is to remove nine of those mistakes: (i) consciousness cannot be defined; (ii) consciousness is subjective but science is objective; (iii) brain processes cannot explain consciousness; (iv) the problem of ''qualia'' should be set aside; (v) consciousness is epiphenomenal; (vi) consciousness has no evolutionary function; (vii) a causal account of consciousness is necessarily dualistic; (viii) science is reductionistic, so a scientific account of consciousness would show it reducible to something else; and (ix) an account of consciousness must be an information processing account.     

Perturbations in dynamical models of whole-brain activity dissociate between the level and stability of consciousness

Consciousness transiently fades away during deep sleep, more stably under anesthesia, and sometimes permanently due to brain injury. The development of an index to quantify the level of consciousness across these different states is regarded as a key problem both in basic and clinical neuroscience. We argue that this problem is ill-defined since such an index would not exhaust all the relevant information about a given state of consciousness. While the level of consciousness can be taken to describe the actual brain state, a complete characterization should also include its potential behavior against external perturbations. We developed and analyzed whole-brain computational models to show that the stability of conscious states provides information complementary to their similarity to conscious wakefulness. Our work leads to a novel methodological framework to sort out different brain states by their stability and reversibility, and illustrates its usefulness to dissociate between physiological (sleep), pathological (brain-injured patients), and pharmacologically-induced (anesthesia) loss of consciousness.     

Information processing in brain microtubules

Models of the mind are based on the idea that neuron microtubules can perform computation. From this point of view, information processing is the fundamental issue for understanding the brain mechanisms that produce consciousness. The cytoskeleton polymers could store and process information through their dynamic coupling mediated by mechanical energy. We analyze the problem of information transfer and storage in brain microtubules, considering them as a communication channel. We discuss the implications of assuming that consciousness is generated by the subneuronal process.     

A psychophysiological approach toward the problem of consciousness

Psychophysiological analysis of the relationship between the level of consciousness and the brain activation permitted an identification of three levels of consciousness at the transition from wakefulness to sleep. A change in the intensity and quality of involuntarily mental processes served as an index of the level of consciousness according to subjects' self-reports. It was found that a certain EEG profile corresponds to each level of consciousness under study. Data obtained showed that the lowest level of activation corresponds to a state in which a subject notes an inhibition of "internal speech", "failure of thoughts". At this case the dynamics of the EEG high frequency rhythm changes which is possibly related to the mechanism of transmission of cognitive information.     

EEG frequency: amplitude characteristics at different levels of consciousness

Relationship was studied between the level of consciousness and the level of the brain activation. State of transition from wakefulness to drowsiness was used as a model of gradual decrease in the level of consciousness. A change in the intensity and quality of appearing involuntarily mental processes served as an index of the level of consciousness as determined by subjects' self-reports. It was found that a certain EEG profile corresponded to each level of consciousness under study. The data obtained showed that the lowest level of activation corresponded to a state in which a subject develops inhibition of "internal speech", "failure of thoughts". In this case, the dynamics of the EEG high frequency rhythm changes was possibly related to the mechanism of transmission of cognitive information.     

Thalamic deep brain stimulation paradigm to reduce consciousness: Cortico-striatal dynamics implicated in mechanisms of consciousness

Anesthetic manipulations provide much-needed causal evidence for neural correlates of consciousness, but non-specific drug effects complicate their interpretation. Evidence suggests that thalamic deep brain stimulation (DBS) can either increase or decrease consciousness, depending on the stimulation target and parameters. The putative role of the central lateral thalamus (CL) in consciousness makes it an ideal DBS target to manipulate circuit-level mechanisms in cortico-striato-thalamic (CST) systems, thereby influencing consciousness and related processes. We used multi-microelectrode DBS targeted to CL in macaques while recording from frontal, parietal, and striatal regions. DBS induced episodes of abnormally long, vacant staring with low-frequency oscillations here termed vacant, perturbed consciousness (VPC). DBS modulated VPC likelihood in a frequency-specific manner. VPC events corresponded to decreases in measures of neural complexity (entropy) and integration (Φ*), proposed indices of consciousness, and substantial changes to communication in CST circuits. During VPC, power spectral density and coherence at low frequencies increased across CST circuits, especially in thalamo-parietal and cortico-striatal pathways. Decreased consciousness and neural integration corresponded to shifts in cortico-striatal network configurations that dissociated parietal and subcortical structures. Overall, the features of VPC and implicated networks were similar to those of absence epilepsy. As this same multi-microelectrode DBS method–but at different stimulation frequencies–can also increase consciousness in anesthetized macaques, it can be used to flexibly address questions of consciousness with limited confounds, as well as inform clinical investigations of other consciousness disorders.     

Peculiarities of interhemisphere interaction at different levels of consciousness

Transition from wakefulness to drowsiness was used as a model of a gradual decrease in the consciousness level. Subjects' self-reported changes in the contents of consciousness, namely: the intensity and quality of involuntary mental processes, served as an indicator of the consciousness level. Each observed level of consciousness was shown to be characterised by a certain EEG profile. It was also shown that the state in which the subject signals of a slowdown of the "internal speech" or the "lapse of thoughts" corresponds to the lowest level of activation. In this case, the interhemisphere connections involving the main mechanism of cognitive information transmission, i.e. the EEG high-frequency rhythms, becomes suppressed.     

Complexity of Multi-Dimensional Spontaneous EEG Decreases during Propofol Induced General Anaesthesia

Emerging neural theories of consciousness suggest a correlation between a specific type of neural dynamical complexity and the level of consciousness: When awake and aware, causal interactions between brain regions are both integrated (all regions are to a certain extent connected) and differentiated (there is inhomogeneity and variety in the interactions). In support of this, recent work by Casali et al (2013) has shown that Lempel-Ziv complexity correlates strongly with conscious level, when computed on the EEG response to transcranial magnetic stimulation. Here we investigated complexity of spontaneous high-density EEG data during propofol-induced general anaesthesia. We consider three distinct measures: (i) Lempel-Ziv complexity, which is derived from how compressible the data are; (ii) amplitude coalition entropy, which measures the variability in the constitution of the set of active channels; and (iii) the novel synchrony coalition entropy (SCE), which measures the variability in the constitution of the set of synchronous channels. After some simulations on Kuramoto oscillator models which demonstrate that these measures capture distinct ‘flavours’ of complexity, we show that there is a robustly measurable decrease in the complexity of spontaneous EEG during general anaesthesia.     

EEG-based automatic classification of 'awake' versus 'anesthetized' state in general anesthesia using Granger Causality

Background

General anesthesia is a reversible state of unconsciousness and depression of reflexes to afferent stimuli induced by administration of a “cocktail” of chemical agents. The multi-component nature of general anesthesia complicates the identification of the precise mechanisms by which anesthetics disrupt consciousness. Devices that monitor the depth of anesthesia are an important aide for the anesthetist. This paper investigates the use of effective connectivity measures from human electrical brain activity as a means of discriminating between ‘awake’ and ‘anesthetized’ state during induction and recovery of consciousness under general anesthesia.

Methodology/Principal Findings

Granger Causality (GC), a linear measure of effective connectivity, is utilized in automated classification of ‘awake’ versus ‘anesthetized’ state using Linear Discriminant Analysis and Support Vector Machines (with linear and non-linear kernel). Based on our investigations, the most characteristic change of GC observed between the two states is the sharp increase of GC from frontal to posterior regions when the subject was anesthetized, and reversal at recovery of consciousness. Features derived from the GC estimates resulted in classification of ‘awake’ and ‘anesthetized’ states in 21 patients with maximum average accuracies of 0.98 and 0.95, during loss and recovery of consciousness respectively. The differences in linear and non-linear classification are not statistically significant, implying that GC features are linearly separable, eliminating the need for a complex and computationally expensive non-linear classifier. In addition, the observed GC patterns are particularly interesting in terms of a physiological interpretation of the disruption of consciousness by anesthetics. Bidirectional interaction or strong unidirectional interaction in the presence of a common input as captured by GC are most likely related to mechanisms of information flow in cortical circuits.

Conclusions/Significance

GC-based features could be utilized effectively in a device for monitoring depth of anesthesia during surgery.     

Information integration: its relevance to brain function and consciousness

A proper understanding of cognitive functions cannot be achieved without an understanding of consciousness, both at the empirical and at the theoretical level. This paper argues that consciousness has to do with a system's capacity for information integration. In this approach, every causal mechanism capable of choosing among alternatives generates information, and information is integrated to the extent that it is generated by a system above and beyond its parts. The set of integrated informational relationships generated by a complex of mechanisms--its quale--specify both the quantity and the quality of experience. As argued below, depending on the causal structure of a system, information integration can reach a maximum value at a particular spatial and temporal grain size. It is also argued that changes in information integration reflect a system's ability to match the causal structure of the world, both on the input and the output side. After a brief review suggesting that this approach is consistent with several experimental and clinical observations, the paper concludes with some prospective remarks about the relevance of understanding information integration for analyzing cognitive function, both normal and pathological.     

Possible Levels of Animal Consciousness with Reference to Grey Parrots (Psittacus erithacus)

Researchers often study nonhuman abilities by assuming theirsubjects form representations about perceived stimuli and thenprocess such information; why then would consciousness be required,and, if required, at what level? Arguments about nonhuman consciousnessrange from claims of levels comparable to humans to refutationof any need to study such phenomena. We suggest that (a) speciesexhibit different levels attuned to their ecological niches,and (b) animals, within their maximum possible level, exhibitdifferent extents of awareness appropriate to particular situations,much like humans (presumably conscious) who often act withoutconscious awareness of factors controlling their behavior. Wepropose that, to engage in complex information processing, animalslikely exhibit perceptual consciousness sensu Natsoulas (1978),i.e., are aware of what is being processed. We discuss theseissues and provide examples suggesting perceptual consciousness.     

Measures of entropy and complexity in altered states of consciousness

Quantification of complexity in neurophysiological signals has been studied using different methods, especially those from information or dynamical system theory. These studies have revealed a dependence on different states of consciousness, and in particular that wakefulness is characterized by a greater complexity of brain signals, perhaps due to the necessity for the brain to handle varied sensorimotor information. Thus, these frameworks are very useful in attempts to quantify cognitive states. We set out to analyze different types of signals obtained from scalp electroencephalography (EEG), intracranial EEG and magnetoencephalography recording in subjects during different states of consciousness: resting wakefulness, different sleep stages and epileptic seizures. The signals were analyzed using a statistical (permutation entropy) and a deterministic (permutation Lempel–Ziv complexity) analytical method. The results are presented in complexity versus entropy graphs, showing that the values of entropy and complexity of the signals tend to be greatest when the subjects are in fully alert states, falling in states with loss of awareness or consciousness. These findings were robust for all three types of recordings. We propose that the investigation of the structure of cognition using the frameworks of complexity will reveal mechanistic aspects of brain dynamics associated not only with altered states of consciousness but also with normal and pathological conditions.     

In search for neurophysiological criteria of altered consciousness

Neurophysiological approaches to brain mechanisms of consciousness are discussed. The concept of spatial synchronization of nervous processes developed by M.N. Livanov is applied to neurophysiological analysis of higher brain functions. However, the spatial synchronization of brain potentials is only a condition for information processing and does not represent it as such. This imposes restrictions on conclusions about the neural mechanisms of consciousness. It is more adequate to use the concept of spatial synchronization in views of consciousness as a psychophysiological level along with sub- and superconsciousness in three-level structure of mind according to P.V. Simonov. Forms of consciousness interaction with other levels concern the problem of altered consciousness and may be reflected in various patterns of spatial organization of brain potentials.     

Does consciousness overflow cognitive access? Novel insights from the new phenomenon of attribute amnesia

The moment we open our eyes, we experience a rich and detailed visual world, but the amount of information available to report is rather limited. This dissociation relates to a major debate regarding the nature of visual consciousness. The overflow argument suggests that our conscious experience is quite rich and far beyond what can be reported, standing in sharp contrast to the nooverflow argument that visual consciousness is severely impoverished and limited to what can be reported. In this paper, we systematically reviewed existing evidence in favor of the overflow argument, including studies of several variations of the iconic memory paradigm and the divided attention paradigm, as well as studies of neural correlates of consciousness. Simultaneously,we expounded some critical objections and alternative interpretations to such evidence, as well as some opposing evidence.Finally, we introduced a series of our recent studies based on a striking phenomenon of attribute amnesia, which we believe could provide new insight into the overflow view of visual consciousness.     

基于听觉刺激的意识障碍患者脑功能连通性研究

目的 目前对意识障碍（DOC）患者的分级评估仍是相关领域的重点和难点。因效性网络可以通过时间序列间的因果关系直观地反映信息传递方向,帮助人们更好地理解患者大脑不同区域之间的信息交互作用。本文结合脑电图和因效性网络探讨听觉刺激下无反应觉醒综合征（VS）患者与最低意识状态（MCS）患者的脑功能连通性差异。方法 共纳入23例DOC患者,采集并分析唤名刺激下的脑电信号,通过多元格兰杰因果方法构建脑功能网络,利用脑网络节点度、聚类系数、全局效率以及因果流向性等参量从脑区之间协同工作的角度对比研究听觉刺激下不同意识水平患者的网络特征。结果 唤名刺激下MCS患者的脑功能连通性强于VS患者,且呈现出因果流向差异,MCS与VS患者四个脑区的信息传递方向均不相同。结论 唤名听觉刺激下MCS患者的信息传递能力强于VS患者;与VS患者相比MCS患者为因果源的电极通道数增多,对其他脑区的信息输出增多。本研究可为DOC患者意识水平的分级评估提供一定的理论依据。