Publications

Background There is a strong tradition for withdrawing medical equipment from patients receiving end-of-life (EOL) care. However, the introduction of new noninvasive medical equipment could be acceptable provided that it improves the quality of care and patient comfort. Objectives We aimed to systematically map published studies of the perspectives, attitudes and experiences of health care professionals, dying patients, and their next of kin, about introducing noninvasive medical technology in end-of-life care. Design We conducted a scoping review. On January 27, 2025, systematic searches of five databases were carried out (Medline (EBSCO), CINAHL, Embase, Academic Search Elite, and Cochrane Library CDSR). Inclusion criteria were empirical studies published in English or Scandinavian languages. We searched for qualitative, quantitative or mixed method studies focusing on perspectives on, experiences of and attitudes toward introducing noninvasive medical technologies in end-of-life care from the view of health care professionals, dying patients, and their next of kin. Three pairs of researchers independently assessed potential eligibility and data extraction. Data would be summarized qualitatively. Results The searches yielded 3288 unique articles, 3194 of which were excluded following an initial screening. Among the remaining 94 articles, none satisfied our inclusion criteria. Thus, we found no empirical articles addressing perspectives on, attitudes toward, or experiences with the use of noninvasive medical technology in end-of-life care. Conclusions Empirical research exploring the perspectives on, attitudes toward, and experiences of, and use of noninvasive medical technology in end-of-life care is needed. This is important for understanding when and how it is feasible, useful, and ethical to introduce such technologies in the terminal phase of palliative care.

Magneto/electroencephalography (M/EEG) studies of dreaming are an essential paradigm in the investigation of neurocognitive processes of human consciousness during sleep, but they are limited by the number of observations that can be collected per study. Dream research also involves substantial methodological and conceptual variability, which poses problems for the integration of results. To address these issues, here we present the DREAM database—an expanding collection of standardized datasets on human sleep M/EEG combined with dream report data—with an initial release comprising 20 datasets, 505 participants, and 2643 awakenings. Each awakening consists, at minimum, of sleep M/EEG ( ≥ 20 s, ≥100 Hz, ≥2 electrodes) up to the time of waking and a standardized dream report classification of the subject’s experience during sleep. We observed that reports of conscious experiences can be predicted with objective features extracted from EEG recordings in both Rapid Eye Movement (REM) and non-REM (NREM) sleep. We also provide several examples of analyses, showcasing the database’s high potential in paving the way for new research questions at a scale beyond the capacity of any single research group. The authors present a multicenter database to investigate the neural correlates of dreaming, including physiological, behavioral and experiential data. This database could boost the research on the mechanisms of dreaming in humans and the signatures of consciousness.

Patients undergoing general anesthesia are often assumed to be unconscious. However, it is known that conscious experiences in the form of dreams occur even in unresponsive states induced by anesthetics. Here, we recorded resting state electroencephalography (EEG) as well as EEG combined with TMS perturbations in 20 healthy participants during propofol sedation. Participants were repeatedly awoken from deep sedation and asked immediately whether they had experiences just before waking up and what they experienced. Out of the 52 attempted awakenings in this study, 24 produced reports of having had an experience, while there were 5 reports of no experience. In the remaining 23 attempts, the subject was either unarousable or the report too incoherent to provide information about their experience prior to awakening. We then tested whether two different consciousness measures based on EEG complexity—the state transitions perturbational complexity index (PCIst) and single-channel Lempel-Ziv complexity (LZc)—differed between awakenings with and without experience. While our study confirms earlier findings that the EEG complexity measures significantly decrease from the awake state to the sedated state, we find no evidence that these measures differ between periods associated with reports of dreaming and non-dreaming, within the sedated state. A few interpretations and limitations are discussed.

Background: Developing and testing methods for reliably measuring the state of consciousness of individuals is important for both basic research and clinical purposes. In recent years, several promising measures of consciousness, grounded in theoretical developments, have been proposed. However, the degrees to which these measures are affected by changes in brain activity that are not related to changes in the degree of consciousness has not been well tested. In this study, we examined whether several of these measures are modulated by the loading of cognitive resources. Methods: We recorded electroencephalography (EEG) from 12 participants in two conditions: (1) while passively attending to sensory stimuli related to the measures and (2) during increased cognitive load consisting of a demanding working memory task. We investigated whether a set of proposed objective EEG-based measures of consciousness differed between the passive and the cognitively demanding conditions. Results: The P300b event-related potential (sensitive to conscious awareness of deviance from an expected pattern in auditory stimuli) was significantly affected by concurrent performance on a working memory task, whereas various measures based on signal diversity of spontaneous and perturbed EEG were not. Conclusion: Because signal diversity-based measures of spontaneous or perturbed EEG are not sensitive to the degree of cognitive load, we suggest that these measures may be used in clinical situations where attention, sensory processing, or command following might be impaired.

This paper presents Integrated Information Theory (IIT) 4.0. IIT aims to account for the properties of experience in physical (operational) terms. It identifies the essential properties of experience (axioms), infers the necessary and sufficient properties that its substrate must satisfy (postulates), and expresses them in mathematical terms. In principle, the postulates can be applied to any system of units in a state to determine whether it is conscious, to what degree, and in what way. IIT offers a parsimonious explanation of empirical evidence, makes testable predictions concerning both the presence and the quality of experience, and permits inferences and extrapolations. IIT 4.0 incorporates several developments of the past ten years, including a more accurate formulation of the axioms as postulates and mathematical expressions, the introduction of a unique measure of intrinsic information that is consistent with the postulates, and an explicit assessment of causal relations. By fully unfolding a system’s irreducible cause–effect power, the distinctions and relations specified by a substrate can account for the quality of experience.

In a recent electroencephalography (EEG) sleep study inspired by complexity theories of consciousness, we found that multi-channel signal diversity progressively decreased from wakefulness to slow wave sleep, but failed to find any significant difference between dreaming and non-dreaming awakenings within the same sleep stage (NREM2). However, we did find that multi-channel Lempel-Ziv complexity (LZC) measured over the posterior cortex increased with more perceptual ratings of NREM2 dream experience along a thought-perceptual axis. In this follow-up study, we re-tested our previous findings, using a slightly different approach. Partial sleep-deprivation was followed by evening sleep experiments, with repeated awakenings and immediate dream reports. Participants reported whether they had been dreaming, and were asked to rate how diverse, vivid, perceptual, and thought-like the contents of their dreams were. High density (64 channel) EEG was recorded throughout the experiment, and mean single-channel LZC was calculated for each 30 s sleep epoch. LZC progressively decreased with depth of non-REM sleep. Surprisingly, estimated marginal mean LZC was slightly higher for NREM1 than for wakefulness, but the difference did not remain significant after adjusting for multiple comparisons. We found no significant difference in LZC between dream and non-dream awakenings, nor any significant relationship between LZC and subjective ratings of dream experience, within the same sleep stage (NREM2). The failure to reproduce our own previous finding of a positive correlation between posterior LZC and more perceptual dream experiences, or to find any other correlation between brain signal complexity and subjective experience within NREM2 sleep, raises the question of whether EEG LZC is really a reliable correlate of richness of experience as such, within the same sleep stage.

The target article misrepresents the foundations of integrated information theory (IIT) and ignores many essential publications. It, thus, falls to this lead commentary to outline the axioms and postulates of IIT and correct major misconceptions. The commentary also explains why IIT starts from phenomenology and why it predicts that only select physical substrates can support consciousness. Finally, it highlights that IIT’s account of experience – a cause–effect structure quantified by integrated information – has nothing to do with “information transfer.”

In the field of consciousness science, there is a tradition to categorize certain states such as slow-wave non-REM sleep and deep general anesthesia as “unconscious”. While this categorization seems reasonable at first glance, careful investigations have revealed that it is not so simple. Given that (1) behavioral signs of (un-)consciousness can be unreliable, (2) subjective reports of (un-)consciousness can be unreliable, and, (3) states presumed to be unconscious are not always devoid of reported experience, there are reasons to reexamine our traditional assumptions about “states of unconsciousness”. While these issues are not novel, and may be partly semantic, they have implications both for scientific progress and clinical practice. We suggest that focusing on approaches that provide a more pragmatic and nuanced characterization of different experimental conditions may promote clarity in the field going forward, and help us build stronger foundations for future studies.

Abstract It remains unclear how specific cortical regions contribute to the brain’s overall capacity for consciousness. Clarifying this could help distinguish between theories of consciousness. Here, we investigate the association between markers of regionally specific (de)activation and the brain’s overall capacity for consciousness. We recorded electroencephalographic responses to cortical electrical stimulation in six rats and computed Perturbational Complexity Index state-transition (PCIST), which has been extensively validated as an index of the capacity for consciousness in humans. We also estimated the balance between activation and inhibition of specific cortical areas with the ratio between high and low frequency power from spontaneous electroencephalographic activity at each electrode. We repeated these measurements during wakefulness, and during two levels of ketamine anaesthesia: with the minimal dose needed to induce behavioural unresponsiveness and twice this dose. We found that PCIST was only slightly reduced from wakefulness to light ketamine anaesthesia, but dropped significantly with deeper anaesthesia. The high-dose effect was selectively associated with reduced high frequency/low frequency ratio in the posteromedial cortex, which strongly correlated with PCIST. Conversely, behavioural unresponsiveness induced by light ketamine anaesthesia was associated with similar spectral changes in frontal, but not posterior cortical regions. Thus, activity in the posteromedial cortex correlates with the capacity for consciousness, as assessed by PCIST, during different depths of ketamine anaesthesia, in rats, independently of behaviour. These results are discussed in relation to different theories of consciousness.

While psychedelic agents are known to have powerful, but largely unexplained, effects on contents of consciousness, there is an increasing interest in the potential clinical usefulness of such drugs for therapy, and legalization is discussed in some countries. Thus, it is relevant to study the effects of psychedelic compounds not only on experience, but also on behavioral performance.Seven healthy participants performed a motor response inhibition task before, during, and after sub-anesthetic doses of intravenously administered ketamine. The infusion rate was individually adjusted to produce noticeable subjective psychedelic effects.We observed no statistically significant impact of sub-anesthetic ketamine on reaction times, omission errors, or post error slowing, relative to the preceding drug-free condition. However, we did observe significant correlations between performance impairment and self-reported, subjective altered states of consciousness, specifically experience of “anxiety” and “complex imagery.”Considering the limited number of participants and large variation in strength of self-reported experiences, further studies with wider ranges of ketamine doses and behavioral tasks are needed to determine the presence and strength of potential behavioral effects.

Several theories link consciousness to complex cortical dynamics, as suggested by comparison of brain signal diversity between conscious states and states where consciousness is lost or reduced. In particular, Lempel-Ziv complexity, amplitude coalition entropy and synchrony coalition entropy distinguish wakefulness and REM sleep from deep sleep and anesthesia, and are elevated in psychedelic states, reported to increase the range and vividness of conscious contents. Some studies have even found correlations between complexity measures and facets of self-reported experience. As suggested by integrated information theory and the entropic brain hypothesis, measures of differentiation and signal diversity may therefore be measurable correlates of consciousness and phenomenological richness. Inspired by these ideas, we tested three hypotheses about EEG signal diversity related to sleep and dreaming. First, diversity should decrease with successively deeper stages of non-REM sleep. Second, signal diversity within the same sleep stage should be higher for periods of dreaming vs. non-dreaming. Third, specific aspects of dream contents should correlate with signal diversity in corresponding cortical regions. We employed a repeated awakening paradigm in sleep deprived healthy volunteers, with immediate dream report and rating of dream content along a thought-perceptual axis, from exclusively thought-like to exclusively perceptual. Generalized linear mixed models were used to assess how signal diversity varied with sleep stage, dreaming and thought-perceptual rating. Signal diversity decreased with sleep depth, but was not significantly different between dreaming and non-dreaming, even though there was a significant positive correlation between Lempel-Ziv complexity of EEG recorded over the posterior cortex and thought-perceptual ratings of dream contents.

We test whether a measure based on the directed transfer function (DTF) calculated from short segments of electroencephalography (EEG) time-series can be used to monitor the state of the patients also during sevoflurane anesthesia as it can for patients undergoing propofol anesthesia. We collected and analyzed 25-channel EEG from 7 patients (3 females, ages 41–56 years) undergoing surgical anesthesia with sevoflurane, and quantified the sensor space directed connectivity for every 1-s epoch using DTF. The resulting connectivity parameters were compared to corresponding parameters from our previous study (n = 8, patients anesthetized with propofol and remifentanil, but otherwise using a similar protocol). Statistical comparisons between and within studies were done using permutation statistics, a data driven algorithm based on the DTF-parameters was employed to classify the epochs as coming from awake or anesthetized state. According to results of the permutation tests, DTF-parameter topographies were significantly different between the awake and anesthesia state at the group level. However, the topographies were not significantly different when comparing results computed from sevoflurane and propofol data, neither in the awake nor in anesthetized state. Optimizing the algorithm for simultaneously having high sensitivity and specificity in classification yielded an accuracy of 95.1% (SE = 0.96%), with sensitivity of 98.4% (SE = 0.80%) and specificity of 94.8% (SE = 0.10%). These findings indicate that the DTF changes in a similar manner when humans undergo general anesthesia caused by two distinct anesthetic agents with different molecular mechanisms of action.

Background In the Wada test, one hemisphere is selectively anaesthetised by unilateral intracarotid injection of a fast-acting anaesthetic agent. This gives a unique opportunity to observe the functions and physiological activity of one hemisphere while anaesthetising the other, allowing direct comparisons between brain states and hemispheres that are not possible in any other setting. Aim To test whether potential measures of consciousness would be affected by selective anaesthesia of one hemisphere, and reliably distinguish the states of the anesthetised and non-anesthetised hemispheres. Methods We analysed EEG data from 7 patients undergoing Wada-tests in preparation for neurosurgery and computed several measures reported to correlate with the state of consciousness: power spectral density, functional connectivity, and measures of signal diversity. These measures were compared between conditions (normal rest vs. unilateral anaesthesia) and hemispheres (injected vs. non-injected), and used with a support vector machine to classify the state and site of injection objectively from individual patient’s recordings. Results Although brain function, assessed behaviourally, appeared to be substantially altered only on the injected side, we found large bilateral changes in power spectral density for all frequency bands tested, and functional connectivity changed significantly both between and within both hemispheres. Surprisingly, we found no statistically significant differences in the measures of signal diversity between hemispheres or states, for the group of 7 patients, although 4 of the individual patients showed a significant decrease in signal diversity on the injected side. Nevertheless, including signal diversity measures improved the classification results, indicating that these measures carry at least some non-redundant information about the condition and injection site. We propose that several of these results may be explained by conduction of activity, via the corpus callosum, from the injected to the contralateral hemisphere and vice versa, without substantially affecting the function of the receiving hemisphere, thus reflecting what we call “cross-state unreceptiveness”.

Integrated information theory (IIT) proposes a measure of integrated information, termed Phi (Φ), to capture the level of consciousness of a physical system in a given state. Unfortunately, calculating Φ itself is currently possible only for very small model systems and far from computable for the kinds of system typically associated with consciousness (brains). Here, we considered several proposed heuristic measures and computational approximations, some of which can be applied to larger systems, and tested if they correlate well with Φ. While these measures and approximations capture intuitions underlying IIT and some have had success in practical applications, it has not been shown that they actually quantify the type of integrated information specified by the latest version of IIT and, thus, whether they can be used to test the theory. In this study, we evaluated these approximations and heuristic measures considering how well they estimated the Φ values of model systems and not on the basis of practical or clinical considerations. To do this, we simulated networks consisting of 3–6 binary linear threshold nodes randomly connected with excitatory and inhibitory connections. For each system, we then constructed the system’s state transition probability matrix (TPM) and generated observed data over time from all possible initial conditions. We then calculated Φ, approximations to Φ, and measures based on state differentiation, coalition entropy, state uniqueness, and integrated information. Our findings suggest that Φ can be approximated closely in small binary systems by using one or more of the readily available approximations (r > 0.95) but without major reductions in computational demands. Furthermore, the maximum value of Φ across states (a state-independent quantity) correlated strongly with measures of signal complexity (LZ, rs = 0.722), decoder-based integrated information (Φ*, rs = 0.816), and state differentiation (D1, rs = 0.827). These measures could allow for the efficient estimation of a system’s capacity for high Φ or function as accurate predictors of low- (but not high-)Φ systems. While it is uncertain whether the results extend to larger systems or systems with other dynamics, we stress the importance that measures aimed at being practical alternatives to Φ be, at a minimum, rigorously tested in an environment where the ground truth can be established.

STUDY OBJECTIVES Although sleep deprivation has long been known to negatively affect cognitive performance, the exact mechanisms through which it acts and what cognitive domains are affected most is still disputed. The current study provides a theory-driven approach to examine and explain the detrimental effects of sleep loss with a focus on attention and cognitive control.

METHODS Twenty-four participants (12 females; age: 24 ± 3 years) completed the experiment that involved laboratory-controlled over-night sleep deprivation and two control conditions, namely, a normally rested night at home and a night of sleep in the laboratory. Using a stop signal task in combination with electroencephalographic recordings, we dissociated different processes contributing to task performance such as sustained attention, automatic or bottom-up processing, and strategic or top-down control. At the behavioral level, we extracted reaction times, response accuracy, and markers of behavioral adjustments (post-error and post-stop slowing), whereas at the neural level event-related potentials (ERP) found in context of response inhibition (N2/P3) and error monitoring (ERN/Pe) were obtained.

RESULTS It was found that 24 hr of sleep deprivation resulted in declined sustained attention and reduced P300 and Pe amplitudes, demonstrating a gradual breakdown of top-down control. In contrast, N200 and ERN as well as the stop-signal reaction time showed higher resilience to sleep loss signifying the role of automatic processing.

CONCLUSIONS These results support the notion that sleep deprivation is more detrimental to cognitive functions that are relatively more dependent on mental effort and/or cognitive capacity, as opposed to more automatic control processes.

Objective How and to what extent electrical brain activity is affected in pharmacologically altered states of consciousness, where it is mainly the phenomenological content rather than the level of consciousness that is altered, is not well understood. An example is the moderately psychedelic state caused by low doses of ketamine. Therefore, we investigated whether and how measures of evoked and spontaneous electroencephalographic (EEG) signal diversity are altered by sub-anaesthetic levels of ketamine compared to normal wakefulness, and how these measures relate to subjective assessments of consciousness. Methods High-density electroencephalography (EEG, 62 channels) was used to record spontaneous brain activity and responses evoked by transcranial magnetic stimulation (TMS) in 10 healthy volunteers before and after administration of sub-anaesthetic doses of ketamine in an open-label within-subject design. Evoked signal diversity was assessed using the perturbational complexity index (PCI), calculated from the global EEG responses to local TMS perturbations. Signal diversity of spontaneous EEG, with eyes open and eyes closed, was assessed by Lempel Ziv complexity (LZc), amplitude coalition entropy (ACE), and synchrony coalition entropy (SCE). Results Although no significant difference was found in the index of TMS-evoked complexity (PCI) between the sub-anaesthetic ketamine condition and normal wakefulness, all the three measures of spontaneous EEG signal diversity showed significantly increased values in the sub-anaesthetic ketamine condition. This increase in signal diversity also correlated with subjective assessment of altered states of consciousness. Moreover, spontaneous signal diversity was significantly higher when participants had eyes open compared to eyes closed, both during normal wakefulness and during influence of sub-anaesthetic ketamine doses. Conclusion The results suggest that PCI and spontaneous signal diversity may be complementary and potentially measure different aspects of consciousness. Thus, our results seem compatible with PCI being indicative of the brain’s ability to sustain consciousness, as indicated by previous research, while it is possible that spontaneous EEG signal diversity may be indicative of the complexity of conscious content. The observed sensitivity of the latter measures to visual input seems to support such an interpretation. Thus, sub-anaesthetic ketamine may increase the complexity of both the conscious content (experience) and the brain activity underlying it, while the level, degree, or general capacity of consciousness remains largely unaffected.

Objective: The objective of this study was to test whether properties of 1-s segments of spontaneous scalp EEG activity can be used to automatically distinguish the awake state from the anesthetized state in patients undergoing general propofol anesthesia. Methods: Twenty five channel EEG was recorded from 10 patients undergoing general intravenous propofol anesthesia with remifentanil during anterior cervical discectomy and fusion. From this, we extracted properties of the EEG by applying the Directed Transfer Function (DTF) directly to every 1-s segment of the raw EEG signal. The extracted properties were used to develop a data-driven classification algorithm to categorize patients as “anesthetized” or “awake” for every 1-s segment of raw EEG. Results: The properties of the EEG signal were significantly different in the awake and anesthetized states for at least 8 of the 25 channels (p < 0.05, Bonferroni corrected Wilcoxon rank-sum tests). Using these differences, our algorithms achieved classification accuracies of 95.9%. Conclusion: Properties of the DTF calculated from 1-s segments of raw EEG can be used to reliably classify whether the patients undergoing general anesthesia with propofol and remifentanil were awake or anesthetized. Significance: This method may be useful for developing automatic real-time monitors of anesthesia.

An agent’s actions can be influenced by external factors through the inputs it receives from the environment, as well as internal factors, such as memories or intrinsic preferences. The extent to which an agent’s actions are “caused from within”, as opposed to being externally driven, should depend on its sensor capacity as well as environmental demands for memory and context-dependent behavior. Here, we test this hypothesis using simulated agents (“animats”), equipped with small adaptive Markov Brains (MB) that evolve to solve a perceptual-categorization task under conditions varied with regards to the agents’ sensor capacity and task difficulty. Using a novel formalism developed to identify and quantify the actual causes of occurrences (“what caused what?”) in complex networks, we evaluate the direct causes of the animats’ actions. In addition, we extend this framework to trace the causal chain (“causes of causes”) leading to an animat’s actions back in time, and compare the obtained spatio-temporal causal history across task conditions. We found that measures quantifying the extent to which an animat’s actions are caused by internal factors (as opposed to being driven by the environment through its sensors) varied consistently with defining aspects of the task conditions they evolved to thrive in.

This symposium will present diverse examples from the recent surge in neuroscientific consciousness research, across multiple levels, from subcellular, neuronal mechanisms to systems neuroscience and computational approaches. While various research approaches often address different aspects or mechanistic levels of consciousness, they may contribute complementary elements to a more complete understanding of the properties, nature, and origin of conscious experience. The presentations will discuss subcellular mechanisms of reportable sensory perception and unconsciousness induced by general anesthesia, computational approached to consciousness, and an adversarial collaboration for testing contrasting predictions from different theories of consciousness. Speakers Matthew Larkum, Humboldt-Universität zu Berlin, Germany Björn Merker, Uppsala University, Sweden Johan Frederik Storm, University of Oslo, Norway

After decades of progress in cognitive neuroscience, consciousness still eludes our understanding. Are scientific theories of consciousness even possible? If so, how close have we gotten so far? This symposium event will entertain these questions through the lens of integrated information theory (IIT), a leading theory of consciousness. First, the theory and its development will be put in historical context and the fundamentals of IIT introduced. Next, we will explain how the general approach of IIT sets it apart from other theories and how IIT has inspired clinically useful tools and applications. We will end with a panel discussion and audience Q&A covering IIT and consciousness more broadly. Speakers Jeremiah Hendren, Bjørn E. Juel, Matteo Grasso

This workshop will explore the state-of-the-art applications of Brain-Computer Interfaces (BCI) in general anesthesia monitoring. BCIs offer the potential to improve patient care by providing direct neural feedback, giving clinicians real-time insights into brain activity during anesthesia through EEG-based tools to monitor awareness and the depth of anesthesia. We will focus on integrating BCIs in clinical settings, emphasizing how they can enhance anesthesia precision and enable more individualized care. Following brief introductions and discussions of relevant research methodologies and empirical findings (i.e., neural markers of awareness, machine learning tools, experimental protocols to study depth of anesthesia, etc.), we aim to open a dialogue on the implications of BCIs in the operating room and their potential to transform general anesthesia practice. In particular, better monitoring through BCIs could significantly reduce the incidence of intraoperative awareness, a rare but serious complication where patients regain consciousness during surgery. Participants will gain an understanding of the technical challenges, ethical considerations, and potential clinical benefits of employing BCIs for anesthesia monitoring. The workshop will provide a platform for cross-disciplinary discussion, inviting contributions from experts in neurotechnology, anesthesiology, and clinical practice to advance the state of knowledge in this rapidly evolving field.

A general problem that hinders progress in our field is the lack of consensus on what would count as scientific explanation of consciousness. A solution to this could be to find a set of minimal explanatory and methodological requirements for satisfactory scientific theories of consciousness (ST). We propose an initial set of such requirements, argue for the necessity of each requirement, and invite attendees to discuss how the list may be amended. The explanatory requirements are; (i) Presence/absence of experience; ST must explain and predict whether or not a physical system (including non-neural, non-biological systems) is having a conscious experience in a given moment, and provide reasons for why any proposed measurable property accounts for the presence/absence of experience; (ii) Content of experience; ST must explain and predict what it is like to be any conscious system, provide a one-to-one mapping between phenomenology and measurable properties of a system, explain how subsystems contribute to the content of experience (e.g. contributing visual experience, tactile experience, etc.), and why those subsystems contribute that content rather than other contents; The methodological requirements are; (iii) Start from, and end with, phenomenology; ST must be grounded in properties of the experience itself, allow for using first-person data (gathered from subjective report, introspection, self-reflection, etc.), and provide an explanation for the subjective aspects of consciousness rather than functions or physical properties that accompany them; (iv) Behavior/stimulus independent; ST must measure the properties that account for experience in a way that does not rely solely on behavior / stimuli to determine the presence or contents of experience; (v) Validation in undisputed cases; ST must be validated in conditions where both the presence and content of the subject’s consciousness are undisputed and accessible via subjective report (e.g. in awake healthy human subjects in a natural setting); (vi) Falsifiable; ST must in principle be falsifiable, at least in conditions where both the presence and contents of the subject’s consciousness are undisputed.

An agent’s actions can be influenced by external factors through the inputs it receives from the environment, as well as internal factors, such as memories or intrinsic preferences. The extent to which an agent’s actions are “caused from within”, as opposed to being externally driven, should depend on its sensor capacity as well as environmental demands for memory and context-dependent behavior. Here, we test this hypothesis using simulated agents (“animats”), equipped with small adaptive Markov Brains (MB) that evolve to solve a perceptual-categorization task under conditions varied with regards to the agents’ sensor capacity and task difficulty. Using a novel formalism developed to identify and quantify the actual causes of occurrences (“what caused what?”) in complex networks, we evaluate the direct causes of the animats’ actions. In addition, we extend this framework to trace the causal chain (“causes of causes”) leading to an animat’s actions back in time, and compare the obtained spatio-temporal causal history across task conditions. We found that measures quantifying the extent to which an animat’s actions are caused by internal factors (as opposed to being driven by the environment through its sensors) varied consistently with defining aspects of the task conditions they evolved to thrive in.

How does our subjective experience emerge from the brain? How does consciousness relate to the physical world? Where does consciousness arise? These questions are currently of great interest for neuroscience worldwide. The Human Brain Project (HBP) is organising a conference gathering world-leading experts in this area of consciousness research. The conference will focus on fundamentals and theory, computational models, and clinical-societal implications of consciousness research. As society needs to be scientifically and culturally prepared to face the emerging questions on consciousness, an approach with the broadest scope is needed; diverse theoretical frameworks, brain anatomy, physiology, and chemistry across scales and species, detailed and large-scale computer simulations, deep learning, neuromorphic computing, robotics, clinical neurology, anaesthesiology, psychology, behavioural, computational, and philosophical analysis must interact and blend on a single infrastructure.

The nature of consciousness is widely regarded as one of the great challenges in science. Over the past decade there has been substantive empirical and theoretical progress in this field. This symposium presents results from this recent surge in consciousness research. Consciousness research is developing rapidly. Using evidence from brain injury in patients and physiological and behavioral studies in humans and related animals (single neuron, fMRI, EEG, TMS, intracranial recordings), the symposium will highlight how different conscious states and contents arise in the brain. Speakers will discuss different experimental approaches and theoretical frameworks as well as the medical and ethical relevance of this area. Melanie Boly will review evidence of frequent dissociation between consciousness and responsiveness in patients with brain damage. She will present recent evidence that after ruling out confounds, the anatomical neural correlates of consciousness are primarily localized to a posterior cortical hot zone, rather than to a fronto-parietal network involved in task performance and report, and discuss the potential clinical applications of these findings. Marcello Massimimi will describe the rationale and validation of the perturbational complexity index (PCI), a theory-driven empirical metric designed to gauge the brain’s capacity for integrated information. He will show how this index can be employed at the bedside to assess and stratify unresponsive patients, independently of sensory processing and motor/executive functions, and will highlight physiopathological implications. Melanie Wilke will discuss how to disentangle conscious perception from decision making and visuomotor processes. Drawing conclusions from electrophysiological and fMRI experiments in monkeys and humans, she will address which signals and brain regions continue to correlate with conscious perception without the requirement of a behavioral report. She will also show consequences of parietal and thalamic pulvinar perturbations on conscious perception versus visuomotor decisions. Finally, she will discuss how to avoid or control for report-related confounds in future studies of conscious perception. Cyriel Pennartz will focus on the theoretical delineation of requirements for animal brains capable of sustaining consciousness. Next, he will review recent advances in uncovering neuronal population correlates of visual stimulus detection, which is considered an important component of sensory awareness. Finally, he will zoom out from visual cortex to larger interconnected neural systems, probed with multi-area ensemble recordings to investigate changes in local and long-range functional connectivity across the sleep-wake cycle.

Integrated information theory (IIT) argues that the substrate of consciousness is a maximally irreducible complex of units. Together, subsets of the complex specify a cause-effect structure, composed of distinctions and their relations, which accounts in full for the quality of experience. The feeling of a specific experience is also its meaning for the subject, which is thus defined intrinsically, regardless of whether the experience occurs in a dream or is triggered by processes in the environment. Here we extend IIT’s framework to characterize the relationship between intrinsic meaning, extrinsic stimuli, and causal processes in the environment, illustrated using a simple model of a sensory hierarchy. We argue that perception should be considered as a structured interpretation, where a stimulus from the environment acts merely as a trigger for the complex’s state and the structure is provided by the complex’s intrinsic connectivity. We also propose that perceptual differentiation - the richness and diversity of structures triggered by representative sequences of stimuli - quantifies the meaningfulness of different environments to a complex. In adaptive systems, this reflects the”matching”between intrinsic meanings and causal processes in an environment.

It remains unclear how specific cortical regions contribute to the brain’s overall capacity for consciousness. Clarifying this could help distinguish between theories of consciousness. Here, we investigate the association between markers of regionally specific (de)activation and the brain’s overall capacity for consciousness. We recorded electroencephalographic (EEG) responses to cortical electrical stimulation in 6 rats, and computed Perturbational Complexity Index state-transition (PCIST), which has been extensively validated as an index of the capacity for consciousness in humans. We also estimated the balance between activation and inhibition of specific cortical areas with the ratio between high and low frequency power (HF/LF) from spontaneous EEG activity at each electrode. We repeated these measurements during wakefulness, and under the influence of ketamine anaesthesia at two doses: the minimal dose needed to induce behavioural unresponsiveness and twice this dose. We found that PCIST was only slightly reduced from wakefulness to light ketamine anaesthesia, but dropped significantly down with deeper anaesthesia. The high-dose effect was selectively associated with reduced HF/LF ratio in the posteromedial cortex, which strongly correlated with PCIST. Conversely, behavioural unresponsiveness induced by light ketamine anaesthesia, was associated with similar spectral changes in frontal, but not posterior cortical regions. These findings seem to support the claim that the posteromedial cortex may play a primary role for the capacity for consciousness. Such region-specific associations between cortical activation and the overall capacity for consciousness must be accounted for by theories of consciousness.

Knowledge of which brain properties are required for consciousness is essential for improving clinical diagnostics and therapy as well as for investigating consciousness per se. The search for such properties has yielded many methods and measures for distinguishing conscious and apparently unconscious brain states. Here, we present a systematic literature review of 255 electroencephalography (EEG)-based measures of consciousness in humans. We show that measures based on signal diversity and event related potentials appear to be the most consistent. Specifically, spectral entropy, Lempel Ziv complexity, and spectral edge frequency, seem most practical, consistent, and reproducible. However, since most studies did not collect current or retrospective subjective reports about experiences, the states of consciousness were usually assessed from behavior. Hence, the ground truth about presence or absence of phenomenological experience in such cases is often uncertain, thus limiting the conclusions that can be drawn. We provide detailed overviews of general categories and specific measures of consciousness, to serve as a basis for further studies and future development.

Background Developing and testing methods for reliably assessing states of consciousness in humans is important for both basic research and clinical purposes. Several potential measures, partly grounded in theoretical developments, have been proposed, and some of them seem to reliably distinguish between conscious and unconscious brain states. However, the degrees to which these measures may also be affected by changes in brain activity or conditions that can occur within conscious brain states have rarely been tested. In this study we test whether several of these measures are modulated by attentional load and related use of cognitive resources. Methods We recorded EEG from 12 participants while they passively received three types of stimuli: (1) transcranial magnetic stimulation (TMS) pulses (for measuring perturbational complexity), (2) auditory stimuli (for detection of auditory pattern deviants), or (3) audible clicks from a clock (spontaneous EEG, for measures of signal diversity and functional connectivity). We investigated whether the measures significantly differed between the passive condition and a attentional and cognitively demanding working memory task. Results Our results showed that in the attention-based auditory P3b ERP measure (global auditory pattern deviant) was significantly affected by increased attentional and cognitive load, while the various measures based on spontaneous and perturbed EEG were not affected. Conclusion Measures of conscious state based on complexity, diversity, and effective connectivity, are not affected by attentional and cognitive load, suggesting that these measures can be used to test both for the presence and absence of consciousness.

Can we know whether someone is conscious by looking solely at the activity of their brain? If so, what is it about the patterns of their brain activity that is particular for when they are having some conscious experience? Perhaps there are markers in the brain activity that are always observable when someone is conscious, but that are never observable when they are unconscious? In an attempt to answer such questions, we obtained measurements of electrical brain activity from patients and healthy volunteers in both conscious and unconscious states. Using these measurements, we tried to find particular markers in the recorded brain activity that differed predictably between conscious and unconscious states, and that could be used to precisely and objectively classify individuals as conscious or unconscious. We found that several of the markers we tested could be used to accurately classify the state of consciousness in patients and healthy controls. Hopefully, the work presented here will become useful for tools for objective monitoring of the conscious state in patients, and be informative for expanding our scientific understanding of the phenomenon consciousness. Our research was funded by grants from the Norwegian Research Council (NRC: 262950/F20 and 214079/F20) and the European Union’s Horizon 2020 Framework Programme for Research and Innovation (Specific Grant Agreement No. 720270 (Human Brain Project SGA1) and 785907 (Human Brain Project SGA2)).