Integrated information theory (IIT) 4.0: Formulating the properties of phenomenal existence in physical terms.

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.

Are we really unconscious in "unconscious" states? Common assumptions revisited.

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.

IIT, half masked and half disfigured.

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."

Validation of a new approach for distinguishing anesthetized from awake state in patients using directed transfer function applied to raw EEG.

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.

Changes in measures of consciousness during anaesthesia of one hemisphere (Wada test).

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".

Increased signal diversity/complexity of spontaneous EEG, but not evoked EEG responses, in ketamine-induced psychedelic state in humans.

How and to what extent electrical brain activity reflects pharmacologically altered states and contents of consciousness, is not well understood. Therefore, we investigated whether 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 experience. High-density 62-channel EEG was used to record spontaneous brain activity and responses evoked by transcranial magnetic stimulation (TMS) in 10 healthy volunteers before and during 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 EEG responses to 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). Although no significant difference was found in TMS-evoked complexity (PCI) between the sub-anaesthetic ketamine condition and normal wakefulness, all measures of spontaneous EEG signal diversity (LZc, ACE, SCE) showed significantly increased values in the sub-anaesthetic ketamine condition. This increase in signal diversity 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. The results suggest that PCI and spontaneous signal diversity may reflect distinct, complementary aspects of changes in brain properties related to altered states of consciousness: the brain's capacity for information integration, assessed by PCI, might be indicative of the brain's ability to sustain consciousness, while spontaneous complexity, as measured by EEG signal diversity, may be indicative of the complexity of conscious content. Thus, sub-anaesthetic ketamine may increase the complexity of the conscious content and the brain activity underlying it, while the level or general capacity for consciousness remains largely unaffected.

Sleep deprivation differentially affects subcomponents of cognitive control.

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.

Distinguishing Anesthetized from Awake State in Patients: A New Approach Using One Second Segments of Raw EEG.

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.