Different States Of Consciousness Research Articles

Content-state dimensions characterize different types of neuronal markers of consciousness.

Identifying the neuronal markers of consciousness is key to supporting the different scientific theories of consciousness. Neuronal markers of consciousness can be defined to reflect either the brain signatures underlying specific conscious content or those supporting different states of consciousness, two aspects traditionally studied separately. In this paper, we introduce a framework to characterize markers according to their dynamics in both the "state" and "content" dimensions. The 2D space is defined by the marker's capacity to distinguish the conscious states from non-conscious states (on the x-axis) and the content (e.g. perceived versus unperceived or different levels of cognitive processing on the y-axis). According to the sign of the x- and y-axis, markers are separated into four quadrants in terms of how they distinguish the state and content dimensions. We implement the framework using three types of electroencephalography markers: markers of connectivity, markers of complexity, and spectral summaries. The neuronal markers of state are represented by the level of consciousness in (i) healthy participants during a nap and (ii) patients with disorders of consciousness. On the other hand, the neuronal markers of content are represented by (i) the conscious content in healthy participants' perception task using a visual awareness paradigm and (ii) conscious processing of hierarchical regularities using an auditory local-global paradigm. In both cases, we see separate clusters of markers with correlated and anticorrelated dynamics, shedding light on the complex relationship between the state and content of consciousness and emphasizing the importance of considering them simultaneously. This work presents an innovative framework for studying consciousness by examining neuronal markers in a 2D space, providing a valuable resource for future research, with potential applications using diverse experimental paradigms, neural recording techniques, and modeling investigations.

Covert Consciousness in Acute Brain Injury Revealed by Automated Pupillometry and Cognitive Paradigms

BackgroundIdentifying covert consciousness in intensive care unit (ICU) patients with coma and other disorders of consciousness (DoC) is crucial for treatment decisions, but sensitive low-cost bedside markers are missing. We investigated whether automated pupillometry combined with passive and active cognitive paradigms can detect residual consciousness in ICU patients with DoC.MethodsWe prospectively enrolled clinically low-response or unresponsive patients with traumatic or nontraumatic DoC from ICUs of a tertiary referral center. Age-matched and sex-matched healthy volunteers served as controls. Patients were categorized into clinically unresponsive (coma or unresponsive wakefulness syndrome) or clinically low-responsive (minimally conscious state or better). Using automated pupillometry, we recorded pupillary dilation to passive (visual and auditory stimuli) and active (mental arithmetic) cognitive paradigms, with task-specific success criteria (e.g., ≥ 3 of 5 pupillary dilations on five consecutive mental arithmetic tasks).ResultsWe obtained 699 pupillometry recordings at 178 time points from 91 ICU patients with brain injury (mean age 60 ± 13.8 years, 31% women, and 49.5% nontraumatic brain injuries). Recordings were also obtained from 26 matched controls (59 ± 14.8 years, 38% women). Passive paradigms yielded limited distinctions between patients and controls. However, active paradigms enabled discrimination between different states of consciousness. With mental arithmetic of moderate complexity, ≥ 3 pupillary dilations were seen in 17.8% of clinically unresponsive patients and 50.0% of clinically low-responsive patients (odds ratio 4.56, 95% confidence interval 2.09–10.10; p < 0.001). In comparison, 76.9% healthy controls responded with ≥ 3 pupillary dilations (p = 0.028). Results remained consistent across sensitivity analyses using different thresholds for success. Spearman’s rank analysis underscored the robust association between pupillary dilations during mental arithmetic and consciousness levels (rho = 1, p = 0.017). Notably, one behaviorally unresponsive patient demonstrated persistent command-following behavior 2 weeks before overt signs of awareness, suggesting prolonged cognitive motor dissociation.ConclusionsAutomated pupillometry combined with mental arithmetic can identify cognitive efforts, and hence covert consciousness, in ICU patients with acute DoC.

Quantitative electroencephalogram in term neonates under different sleep states.

Electroencephalogram (EEG) can be used to assess depth of consciousness, but interpreting EEG can be challenging, especially in neonates whose EEG undergo rapid changes during the perinatal course. EEG can be processed into quantitative EEG (QEEG), but limited data exist on the range of QEEG for normal term neonates during wakefulness and sleep, baseline information that would be useful to determine changes during sedation or anesthesia. We aimed to determine the range of QEEG in neonates during awake, active sleep and quiet sleep states, and identified the ones best at discriminating between the three states. Normal neonatal EEG from 37 to 46weeks were analyzed and classified as awake, quiet sleep, or active sleep. After processing and artifact removal, total power, power ratio, coherence, entropy, and spectral edge frequency (SEF) 50 and 90 were calculated. Descriptive statistics were used to summarize the QEEG in each of the three states. Receiver operating characteristic (ROC) curves were used to assess discriminatory ability of QEEG. 30 neonates were analyzed. QEEG were different between awake vs asleep states, but similar between active vs quiet sleep states. Entropy beta, delta2 power %, coherence delta2, and SEF50 were best at discriminating awake vs active sleep. Entropy beta had the highest AUC-ROC ≥ 0.84. Entropy beta, entropy delta1, theta power %, and SEF50 were best at discriminating awake vs quiet sleep. All had AUC-ROC ≥ 0.78. In active sleep vs quiet sleep, theta power % had highest AUC-ROC > 0.69, lower than the other comparisons. We determined the QEEG range in healthy neonates in different states of consciousness. Entropy beta and SEF50 were best at discriminating between awake and sleep states. QEEG were not as good at discriminating between quiet and active sleep. In the future, QEEG with high discriminatory power can be combined to further improve ability to differentiate between states of consciousness.

An in-depth analysis of parameter settings and probability distributions of specific ordinal patterns in the Shannon permutation entropy during different states of consciousness in humans

As electrical activity in the brain has complex and dynamic properties, the complexity measure permutation entropy (PeEn) has proven itself to reliably distinguish consciousness states recorded by the EEG. However, it has been shown that the focus on specific ordinal patterns instead of all of them produced similar results. Moreover, parameter settings influence the resulting PeEn value. We evaluated the impact of the embedding dimension m and the length of the EEG segment on the resulting PeEn. Moreover, we analysed the probability distributions of monotonous and non-occurring ordinal patterns in different parameter settings. We based our analyses on simulated data as well as on EEG recordings from volunteers, obtained during stable anaesthesia levels at defined, individualised concentrations. The results of the analysis on the simulated data show a dependence of PeEn on different influencing factors such as window length and embedding dimension. With the EEG data, we demonstrated that the probability P of monotonous patterns performs like PeEn in lower embedding dimension (m = 3, AUC = 0.88, [0.7, 1] in both), whereas the probability P of non-occurring patterns outperforms both methods in higher embedding dimensions (m = 5, PeEn: AUC = 0.91, [0.77, 1]; P(non-occurring patterns): AUC = 1, [1, 1]). We showed that the accuracy of PeEn in distinguishing consciousness states changes with different parameter settings. Furthermore, we demonstrated that for the purpose of separating wake from anaesthesia EEG solely pieces of information used for PeEn calculation, i.e., the probability of monotonous patterns or the number of non-occurring patterns may be equally functional.

Neural coding of autonomic functions in different states of consciousness

Detecting signs of residual neural activity in patients with altered states of consciousness is a crucial issue for the customization of neurorehabilitation treatments and clinical decision-making. With this large observational prospective study, we propose an innovative approach to detect residual signs of consciousness via the assessment of the amount of autonomic information coded within the brain. The latter was estimated by computing the mutual information (MI) between preprocessed EEG and ECG signals, to be then compared across consciousness groups, together with the absolute power and an international qualitative labeling. One-hundred seventy-four patients (73 females, 42%) were included in the study (median age of 65 years [IQR = 20], MCS +: 29, MCS −: 23, UWS: 29). Electroencephalography (EEG) information content was found to be mostly related to the coding of electrocardiography (ECG) activity, i.e., with higher MI (p < 0.05), in Unresponsive Wakefulness Syndrome and Minimally Consciousness State minus (MCS −). EEG-ECG MI, besides clearly discriminating patients in an MCS − and +, significantly differed between lesioned areas (sides) in a subgroup of unilateral hemorrhagic patients. Crucially, such an accessible and non-invasive measure of residual consciousness signs was robust across electrodes and patient groups. Consequently, exiting from a strictly neuro-centric consciousness detection approach may be the key to provide complementary insights for the objective assessment of patients' consciousness levels and for the patient-specific planning of rehabilitative interventions.

Memory reactivations during sleep: a neural basis of dream experiences?

Newly encoded memory traces are spontaneously reactivated during sleep. Since their discovery in the 1990s, these memory reactivations have been discussed as a potential neural basis for dream experiences. New results from animal and human research, as well as from the rapidly growing field of sleep and dream engineering, provide essential insights into this question, and reveal both strong parallels and disparities between the two phenomena. We suggest that, although memory reactivations may contribute to subjective experiences across different states of consciousness, they are not likely to be the primary neural basis of dreaming. We identify important limitations in current research paradigms and suggest novel strategies to address this question empirically.

Stability of mental motor-imagery classification in EEG depends on the choice of classifier model and experiment design, but not on signal preprocessing.

Modern consciousness research has developed diagnostic tests to improve the diagnostic accuracy of different states of consciousness via electroencephalography (EEG)-based mental motor imagery (MI), which is still challenging and lacks a consensus on how to best analyse MI EEG-data. An optimally designed and analyzed paradigm must detect command-following in all healthy individuals, before it can be applied in patients, e.g., for the diagnosis of disorders of consciousness (DOC). We investigated the effects of two important steps in the raw signal preprocessing on predicting participant performance (F1) and machine-learning classifier performance (area-under-curve, AUC) in eight healthy individuals, that are based solely on MI using high-density EEG (HD-EEG): artifact correction (manual correction with vs. without Independent Component Analysis [ICA]), region of interest (ROI; motor area vs. whole brain), and machine-learning algorithm (support-vector machine [SVM] vs. k-nearest neighbor [KNN]). Results revealed no significant effects of artifact correction and ROI on predicting participant performance (F1) and classifier performance (AUC) scores (all ps > 0.05) in the SVM classification model. In the KNN model, ROI had a significant influence on the classifier performance [F(1,8.939) = 7.585, p = 0.023]. There was no evidence for artifact correction and ROI selection changing the prediction of participants performance and classifier performance in EEG-based mental MI if using SVM-based classification (71-100% correct classifications across different signal preprocessing methods). The variance in the prediction of participant performance was significantly higher when the experiment started with a resting-state compared to a mental MI task block [X2(1) = 5.849, p = 0.016]. Overall, we could show that classification is stable across different modes of EEG signal preprocessing when using SVM models. Exploratory analysis gave a hint toward potential effects of the sequence of task execution on the prediction of participant performance, which should be taken into account in future studies.

Seekers of the Face: Secrets of the Idra Rabba (the Great Assembly) of the Zohar by Melila Hellner-Eshed

Reviewed by: Seekers of the Face: Secrets of the Idra Rabba (the Great Assembly) of the Zohar by Melila Hellner-Eshed Nathaniel Berman Melila Hellner-Eshed. Seekers of the Face: Secrets of the Idra Rabba (the Great Assembly) of the Zohar. Translated by Raphael Dascalu. Stanford, CA: Stanford University Press, 2021. 480 pp. “It is time to act for God; they have violated your Torah” (Ps 119:126). It is with this verse’s urgent exhortation that the Zoharic sage Shimon bar Yoḥai announces the project of the Idra Rabba, the “Great Assembly”—the subject of Hellner-Eshed’s magnificent book. This project is nothing less than the tikkun, the “configuration,” of the divine personae. The psalmist’s exhortation, probably intended as a plea for God to act, becomes the Zoharic theurgist’s aspiration to act upon the divine realm. Moreover, Hellner-Eshed asserts, it is only through such a theurgical project that “religion” itself can be “healed” (4)—in our own time, as much as in the thirteenth century of the Zoharic writers. The Idra Rabba is perhaps the most influential composition of the Zoharic literature, largely through its impact on the sixteenth-century Lurianic revision of Kabbalah. Yet its extravagant anthropomorphism has also rendered it inaccessible for many, especially for those under the sway of Maimonideanism. Undaunted, Hellner-Eshed seeks to do more than merely make this composition legible to modern readers. Rather, she seeks to reveal its spiritual depth, imaginative power, and narrative virtuosity—and to enable us to experience, or at least glimpse, its vital stakes. Hellner-Eshed is a renowned teacher and scholar of the Zoharic literature. Her skill in bringing to life the most recondite texts is evident on every page. Indeed, the experiential dimension was central to her own methodology. The Idra [End Page 185] required her “to experience different states of consciousness,” as well as to “internalize a mythic language with a unique syntax, grammar, and inner landscapes” (5). Only thus, she suggests, can a reader engage fully with this strange text. In “configuring” the divine personae, the Idra elaborately depicts their eyes, noses, hair, beards, brains, torsos, sexual organs, and so on. A “surreal—even psychedelic” (4) atmosphere emerges as the text engages in both minute examination of anatomical details and hyperbolic magnification of fantastical dimensions—the latter exemplified by the “120 million worlds” dwelling within the “skull” of the divine persona called ʿAttiqa Qaddisha, the Holy Ancient One. The Idra interweaves these descriptions with theosophical symbolism, midrashic allusions, and biblical verses. “Configuration” is a translation of tikkun particularly apt for Hellner-Eshed’s approach—for the malaise which requires “healing” is what we can call the “disfiguration” of the divine. This disfiguration takes two principal forms. The first is the denial that the divine has a “face” in any sense but the most allegorical. Maimonides’s “profound influence in articulating a radically abstract theology” provoked the Idra’s “bold refutation,” with “personal and fantastical accounts of the Divine” (43). The second is the constriction of the divine face to an exclusively male, nationally specific potentate, the “King of the cosmos and the God of Israel” (69). For Hellner-Eshed, the Idra is impelled by the need to “heal” (22) this constricted divine face, that of Zeʿeir Anpin, the “Small Countenance” or “Short-Tempered One”—as well as the Judaism associated with it, “devoted to its dualistic, legal, systematic, and ethical aspects” (69). Zeʿeir is both the “omnipotent God of the Hebrew Bible” and a “distressed figure, a deficient and diminished deity” (52–53). Nevertheless, the Idra does not reject this deity but seeks to integrate him into a richer divine life. This “reconfiguration” requires bringing Zeʿeir in relation to two other divine personae: ʿAttiqa Qaddisha, “the divine as a primordial and undifferentiated being” (69), and “the Queen,” a female divine persona. Zeʿeir is a lesser progeny of ʿAttiqa, the latter also called the “Elongated Countenance” or “Patient One.” Standing alone, Zeʿeir possesses a “dualistic consciousness” and an excess of judgment (76). By contrast, ʿAttiqa is a “non-dual Divinity” (6) of pure compassion. ʿAttiqa has the power to “heal” his progeny, particularly through his...

Consistency of the anesthesia consciousness index versus the bispectral index during laparoscopic gastrointestinal surgery with sevoflurane anesthesia: A prospective multi-center randomized controlled clinical study.

This study aimed to compare the consistency of anesthesia consciousness index (Ai) with that of bispectral index (BIS) in monitoring the depth of anesthesia (DOA) during sevoflurane anesthesia, to reveal the optimal cutoff values in different states of consciousness, and explore the stability of DOA monitoring during intraoperative injurious stimulation. We enrolled 145 patients (97 men and 48 women) from 10 medical centers. General anesthesia was induced using intravenous anesthetics and maintained with sevoflurane. Ai and BIS values were recorded. The mean difference between the Ai and BIS was-0.1747 (95% confidence interval, -0.6660 to 0.3166; p = 0.4857). The regression equation of Ai and BIS from the Deming regression analysis was y = 5.6387 + 0.9067x (y is BIS, x is Ai), and the slope and intercept were statistically significant. Meanwhile, the receiver operating characteristic curve analysis of anesthesia-induced unconsciousness, loss of consciousness, and recovery of consciousness revealed that the accuracy of Ai and BIS were similar. In addition, the optimal cutoff values of the different states of consciousness were not sensitive to age, and both Ai and BIS had no correlation with hemodynamics. We conclude that Ai and BIS show no systematic deviation in readings with high consistency, similar accuracy, and good stability; these insights provide more data for clinical application.

Suočiti učenika sa njegovom senkom znači pokazati mu sopstveno svetlo - na primerima iz nastave matematike

The greatest wealth begins within. The most difficult thing is to make a change within yourself, but it is the most profitable in the long run. Working on yourself is a serious and demanding job, it is a lifelong learning that never ends. The world in which each of the students lives, primarily depends on how they present it to themselves, which is conditioned by the individual characteristics of the students. The shadow child and the sun child are metaphors for different states of consciousness. All the beauty and diversity of teaching is made of light and shadows, while the main role of the teacher is to transform all the shadows of the students into light. This paper talks about the fact that the most important role of the teacher is to form his students into beings who are persistent, dedicated, committed and awaken in them self-confidence, self-respect, self-discipline and self-evaluation. All of the above is presented on concrete examples from mathematics classes. Once the student has mastered the skills and tools listed, he can apply that knowledge in any life situation. In this way, each of the students becomes truly rich and successful in any area of life - in the mind, in emotions, in the relationships they build with other people, in communication, finances and future profession.

The complexity of the stream of consciousness

Typical consciousness can be defined as an individual-specific stream of experiences. Modern consciousness research on dynamic functional connectivity uses clustering techniques to create common bases on which to compare different individuals. We propose an alternative approach by combining modern theories of consciousness and insights arising from phenomenology and dynamical systems theory. This approach enables a representation of an individual’s connectivity dynamics in an intrinsically-defined, individual-specific landscape. Given the wealth of evidence relating functional connectivity to experiential states, we assume this landscape is a proxy measure of an individual’s stream of consciousness. By investigating the properties of this landscape in individuals in different states of consciousness, we show that consciousness is associated with short term transitions that are less predictable, quicker, but, on average, more constant. We also show that temporally-specific connectivity states are less easily describable by network patterns that are distant in time, suggesting a richer space of possible states. We show that the cortex, cerebellum and subcortex all display consciousness-relevant dynamics and discuss the implication of our results in forming a point of contact between dynamical systems interpretations and phenomenology.

SPECIAL ISSUE: Do Biofeedback, Neurofeedback, and Neuromeditation Have a Role in Psychedelic-Assisted Therapies?

SPECIAL ISSUE: Do Biofeedback, Neurofeedback, and Neuromeditation Have a Role in Psychedelic-Assisted Therapies?

Effective Connectivity of Functionally Anticorrelated Networks Under Lysergic Acid Diethylamide

BackgroundClassic psychedelic-induced ego dissolution involves a shift in the sense of self and a blurring of the boundary between the self and the world. A similar phenomenon is identified in psychopathology and is associated with the balance of anticorrelated activity between the default mode network, which directs attention inward, and the salience network, which recruits the dorsal attention network to direct attention outward. MethodsTo test whether changes in anticorrelated networks underlie the peak effects of lysergic acid diethylamide (LSD), we applied dynamic causal modeling to infer effective connectivity of resting-state functional magnetic resonance imaging scans from a study of 25 healthy adults who were administered 100 μg of LSD or placebo. ResultsWe found that inhibitory effective connectivity from the salience network to the default mode network became excitatory, and inhibitory effective connectivity from the default mode network to the dorsal attention network decreased under the peak effect of LSD. ConclusionsThe effective connectivity changes we identified may reflect diminution of the functional anticorrelation between resting-state networks that may be a key neural mechanism of LSD and underlie ego dissolution. Our findings suggest that changes to the sense of self and subject-object boundaries across different states of consciousness may depend upon the organized balance of effective connectivity of resting-state networks.

Complexity of Brain Dynamics as a Correlate of Consciousness in Anaesthetized Monkeys.

The use of anaesthesia is a fundamental tool in the investigation of consciousness. Anesthesia procedures allow to investigate different states of consciousness from sedation to deep anesthesia within controlled scenarios. In this study we use information quantifiers to measure the complexity of electrocorticogram recordings in monkeys. We apply these metrics to compare different stages of general anesthesia for evaluating consciousness in several anesthesia protocols. We find that the complexity of brain activity can be used as a correlate of consciousness. For two of the anaesthetics used, propofol and medetomidine, we find that the anaesthetised state is accompanied by a reduction in the complexity of brain activity. On the other hand we observe that use of ketamine produces an increase in complexity measurements. We relate this observation with increase activity within certain brain regions associated with the ketamine used doses. Our measurements indicate that complexity of brain activity is a good indicator for a general evaluation of different levels of consciousness awareness, both in anesthetized and non anesthetizes states.

FV 8 Quantifying consciousness in healthy adults using EEG phase coherence

Background: Phase coherence (PC) is an extensively researched parameter for functional connectivity (FC) based on EEG data [1]. Functional connectivity quantifies the temporal interdependency between activity of spatially separated brain regions [2]. It can help to describe different brain states. Objective: This study investigates PC in healthy adults in wakefulness and non-REM sleep. We aim to evaluate, whether PC distinguishes different states of consciousness, and we use sleep as an example. Methods: We measured resting state EEG in 53 healthy volunteers (20–40 y, 35 females). For three-minute segments per sleep stage, we extracted the phase of the Hilbert transform and calculated the PC. As PC values were not normally distributed, we used Wilcoxon signed-rank tests for statistical analysis considering the different frequency bands (delta, theta, alpha, beta) and sleep stages (W, N1, N2, N3). Results: Alpha-frequency PC was significantly higher in W than in N1, N2, N3 (Fig. 1). In contrast, delta coupling increased with sleep depth. It was lower in wakefulness and light sleep (N1, N2) than during deep sleep (Fig. 1). Within the beta- and theta-frequency bands PC did not differ significantly between stages. Conclusions: Functional connectivity, as measured by PC, differed between sleep stages in a frequency-specific manner – corresponding to sleep stage-specific dominant frequencies as observed in clinical EEG. Sleep-like PC patterns different to those observed during wakefulness might serve to identify reduced consciousness in pathologic conditions like epilepsy or neurodegenerative diseases.

Dynamic reconfiguration of human brain networks across altered states of consciousness

Consciousness is supported by rich neuronal dynamics to orchestrate behaviors and conscious processing can be disrupted by general anesthetics. Previous studies suggested that dynamic reconfiguration of large-scale functional network is critical for learning and higher-order cognitive function. During altered states of consciousness, how brain functional networks are dynamically changed and reconfigured at the whole-brain level is still unclear. To fill this gap, using multilayer network approach and functional magnetic resonance imaging (fMRI) data of 21 healthy subjects, we investigated the dynamic network reconfiguration in three different states of consciousness: wakefulness, dexmedetomidine-induced sedation, and recovery. Applying time-varying community detection algorithm, we constructed multilayer modularity networks to track and quantify dynamic interactions among brain areas that span time and space. We compared four high-level network features (i.e., switching, promiscuity, integration, and recruitment) derived from multilayer modularity across the three conditions. We found that sedation state is primarily characterized by increased switching rates as well as decreased integration, representing a whole-brain pattern with higher modular dynamics and more fragmented communication; such alteration can be mostly reversed after the recovery of consciousness. Thus, our work can provide additional insights to understand the modular network reconfiguration across different states of consciousness and may provide some clinical implications for disorders of consciousness.

Anomalous Cognition

Objective: The aim of this study was to assess the results of all meta-analyses on anomalous cognition conducted between 1989 and 2021 in order to find moderators associated with greater effect sizes. Method: We included all meta-analyses of studies related to anomalous cognition published up to 2021. Results: Our dataset, accumulated over more than 80 years of investigation, refers to 11 meta-analyses related to six different states of consciousness. The evidence clearly shows that anomalous cognition seems possible and its effects can be enhanced by using a combination of some non-ordinary or altered states of consciousness (e.g., dreaming, ganzfeld, etc.), coupled with free-response procedures, or neurophysiological dependent variables. These conditions facilitate an alternative form of cognition seemingly unconstrained by the known biological characteristics of the sense organs and the brain. Conclusion: The accumulated evidence expands our understanding of the mind-brain relation and the nature of the human mind.

Controlling extended criticality via modular connectivity

Criticality has been conjectured as an integral part of neuronal network dynamics. Operating at a critical threshold requires precise parameter tuning and a corresponding mechanism remains an open question. Recent studies have suggested that topological features observed in brain networks give rise to a Griffiths phase, leading to power-law scaling in brain activity dynamics and the operational benefits of criticality in an extended parameter region. Motivated by growing evidence of neural correlates of different states of consciousness, we investigate how topological changes affect the expression of a Griffiths phase. We analyze the activity decay in modular networks using a susceptible-infected-susceptible propagation model and find that we can control the extension of the Griffiths phase by altering intra- and intermodular connectivity. We find that by adjusting system parameters, we can counteract changes in critical behavior and maintain a stable critical region despite changes in network topology. Our results give insight into how structural network properties affect the emergence of a Griffiths phase and how its features are linked to established topological network metrics. We discuss how those findings could contribute to an understanding of the changes in functional brain networks.

Automatic Sensory Predictions: A Review of Predictive Mechanisms in the Brain and Their Link to Conscious Processing.

The human brain has the astonishing capacity of integrating streams of sensory information from the environment and forming predictions about future events in an automatic way. Despite being initially developed for visual processing, the bulk of predictive coding research has subsequently focused on auditory processing, with the famous mismatch negativity signal as possibly the most studied signature of a surprise or prediction error (PE) signal. Auditory PEs are present during various consciousness states. Intriguingly, their presence and characteristics have been linked with residual levels of consciousness and return of awareness. In this review we first give an overview of the neural substrates of predictive processes in the auditory modality and their relation to consciousness. Then, we focus on different states of consciousness - wakefulness, sleep, anesthesia, coma, meditation, and hypnosis - and on what mysteries predictive processing has been able to disclose about brain functioning in such states. We review studies investigating how the neural signatures of auditory predictions are modulated by states of reduced or lacking consciousness. As a future outlook, we propose the combination of electrophysiological and computational techniques that will allow investigation of which facets of sensory predictive processes are maintained when consciousness fades away.

Comparison of synchrosqueezing transform to alternative methods for time-frequency analysis of TMS-evoked EEG oscillations

BackgroundTranscranial magnetic stimulation combined with electroencephalography (TMS-EEG) has become a powerful tool to assess cortical properties, such as cortical oscillation. An accurate and robust time–frequency analysis tool with high resolution would facilitate the understanding of TMS-evoked oscillations. MethodsThe synchrosqueezing transform (SST), the Hilbert-Huang transform (HHT) and the Morlet wavelet transform (MWT) were used to analyze TMS-evoked oscillations. Firstly, we generated simulation data, and compared the performance of three methods to analyze the time–frequency characteristics of simulation data; then, we collected TMS-EEG data from normal people (NOR), patients with minimally conscious state (MCS) and vegetative state (VS). SST was used to analysis time–frequency characteristics of TMS-evoked oscillations in different states of consciousness. In addition, relative power (RP) and spectral entropy (SeEn) were calculated based on SST results. ResultsSimulation results showed that SST detected rhythm characteristics of instantaneous signal and background signal more completely. Results of NOR group showed that SST could more accurately detect TMS-evoked oscillations with high frequency resolution than HHT and MWT. The main frequency of TMS-evoked oscillations for DOC patients (MCS: 11.73 ± 1.94 Hz; VS: 2.06 ± 0.25 Hz) was different from that of NOR (22.79 ± 1.42 Hz) based on SST. RP and SeEn further verified the differences of the main frequency of TMS-evoked oscillations between NOR and DOC patients. ConclusionsThe results suggest that SST is a robust analytical method and outperforms HHT and MWT in studying TMS-evoked oscillations. The main frequency of TMS-evoked oscillations of DOC was lower than that of NOR.