Network Properties in Transitions of Consciousness during Propofol-induced Sedation

Low-density EEG-based Functional Connectivity Discriminates Minimally Conscious State plus from minus

OBJECTIVE

Within the continuum of consciousness, patients in a Minimally Conscious State (MCS) may exhibit high-level behavioral responses (MCS+) or may not (MCS-). The evaluation of residual consciousness and related classification is crucial to propose tailored rehabilitation and pharmacological treatments, considering the inherent differences among groups in diagnosis and prognosis. Currently, differential diagnosis relies on behavioral assessments posing a relevant risk of misdiagnosis. In this context, EEG offers a non-invasive approach to model the brain as a complex network. The search for discriminating features could reveal whether behavioral responses in post-comatose patients have a defined physiological background. Additionally, it is essential to determine whether the standard behavioral assessment for quantifying responsiveness holds physiological significance.

METHODS

In this prospective observational study, we investigated whether low-density EEG-based graph metrics could discriminate MCS+/- patients by enrolling 57 MCS patients (MCS-: 30; males: 28). At admission to intensive rehabilitation, 30 min resting-state closed-eyes EEG recordings were performed together with consciousness diagnosis following international guidelines. After EEG preprocessing, graphs' metrics were estimated using different connectivity measures, at multiple connection densities and frequency bands (α,θ,δ). Metrics were also provided to cross-validated Machine Learning (ML) models with outcome MCS+/-.

RESULTS

A lower level of brain activity integration was found in the MCS- group in the α band. Instead, in the δ band MCS- group presented an higher level of clustering (weighted clustering coefficient) respect to MCS+. The best-performing solution in discriminating MCS+/- through the use of ML was an Elastic-Net regularized logistic regression with a cross-validation accuracy of 79% (sensitivity and specificity of 74% and 85% respectively).

CONCLUSION

Despite tackling the MCS+/- differential diagnosis is highly challenging, a daily-routine low-density EEG might allow to differentiate across these differently responsive brain networks.

SIGNIFICANCE

Graph-theoretical features are shown to discriminate between these two neurophysiologically similar conditions, and may thus support the clinical diagnosis.

Elevating understanding: Linking high-altitude hypoxia to brain aging through EEG functional connectivity and spectral analyses

High-altitude hypoxia triggers brain function changes reminiscent of those in healthy aging and Alzheimer's disease, compromising cognition and executive functions. Our study sought to validate high-altitude hypoxia as a model for assessing brain activity disruptions akin to aging. We collected EEG data from 16 healthy volunteers during acute high-altitude hypoxia (at 4,000 masl) and at sea level, focusing on relative changes in power and aperiodic slope of the EEG spectrum due to hypoxia. Additionally, we examined functional connectivity using wPLI, and functional segregation and integration using graph theory tools. High altitude led to slower brain oscillations, that is, increased δ and reduced α power, and flattened the 1/f aperiodic slope, indicating higher electrophysiological noise, akin to healthy aging. Notably, functional integration strengthened in the θ band, exhibiting unique topographical patterns at the subnetwork level, including increased frontocentral and reduced occipitoparietal integration. Moreover, we discovered significant correlations between subjects' age, 1/f slope, θ band integration, and observed robust effects of hypoxia after adjusting for age. Our findings shed light on how reduced oxygen levels at high altitudes influence brain activity patterns resembling those in neurodegenerative disorders and aging, making high-altitude hypoxia a promising model for comprehending the brain in health and disease.

Propofol-mediated Unconsciousness Disrupts Progression of Sensory Signals through the Cortical Hierarchy

A critical component of anesthesia is the loss of sensory perception. Propofol is the most widely used drug for general anesthesia, but the neural mechanisms of how and when it disrupts sensory processing are not fully understood. We analyzed local field potential and spiking recorded from Utah arrays in auditory cortex, associative cortex, and cognitive cortex of nonhuman primates before and during propofol-mediated unconsciousness. Sensory stimuli elicited robust and decodable stimulus responses and triggered periods of stimulus-related synchronization between brain areas in the local field potential of Awake animals. By contrast, propofol-mediated unconsciousness eliminated stimulus-related synchrony and drastically weakened stimulus responses and information in all brain areas except for auditory cortex, where responses and information persisted. However, we found stimuli occurring during spiking Up states triggered weaker spiking responses than in Awake animals in auditory cortex, and little or no spiking responses in higher order areas. These results suggest that propofol's effect on sensory processing is not just because of asynchronous Down states. Rather, both Down states and Up states reflect disrupted dynamics.

Evaluation of putative signatures of consciousness using specific definitions of responsiveness, connectedness, and consciousness

BACKGROUND

Understanding the neural correlates of consciousness has important ramifications for the theoretical understanding of consciousness and for clinical anaesthesia. A major limitation of prior studies is the use of responsiveness as an index of consciousness. We identified a collection of measures derived from unresponsive subjects and more specifically their association with consciousness (any subjective experience) or connectedness (specific experience of environmental stimuli).

METHODS

Using published data generated through the UNderstanding Consciousness Connectedness and Intra-Operative Unresponsiveness Study (NCT03284307), we evaluated 10 previously published resting-state EEG-based measures that were derived using unresponsiveness as a proxy for unconsciousness. Measures were tested across dexmedetomidine and propofol sedation and natural sleep. These markers represent the complexity, connectivity, cross-frequency coupling, graph theory, and power spectrum measures.

RESULTS

Although many of the proposed markers were associated with consciousness per se (reported subjective experience), none were specific to consciousness alone; rather, each was also associated with connectedness (i.e. awareness of the environment). In addition, multiple markers showed no association with consciousness and were associated only with connectedness. Of the markers tested, loss of normalised-symbolic transfer entropy (front to back) was associated with connectedness across all three experimental conditions, whereas the transition from disconnected consciousness to unconsciousness was associated with significant decreases in permutation entropy and spectral exponent (P<0.05 for all conditions).

CONCLUSIONS

None of the proposed EEG-based neural correlates of unresponsiveness corresponded solely to consciousness, highlighting the need for a more conservative use of the term (un)consciousness when assessing unresponsive participants.

CLINICAL TRIAL REGISTRATION

NCT03284307.

Development of EEG connectivity from preschool to school-age children

Introduction

Many studies have collected normative developmental EEG data to better understand brain function in early life and associated changes during both aging and pathology. Higher cognitive functions of the brain do not normally stem from the workings of a single brain region that works but, rather, on the interaction between different brain regions. In this regard studying the connectivity between brain regions is of great importance towards understanding higher cognitive functions and its underlying mechanisms.

Methods

In this study, EEG data of children (N = 253; 3-10 years old; 113 females, 140 males) from pre-school to schoolage was collected, and the weighted phase delay index and directed transfer function method was used to find the electrophysiological indicators of both functional connectivity and effective connectivity. A general linear model was built between the indicators and age, and the change trend of electrophysiological indicators analyzed for age.

Results

The results showed an age trend for the functional and effective connectivity of the brain of children.

Discussion

The results are of importance in understanding normative brain development and in defining those conditions that deviate from typical growth trajectories.

Medial Septal Glutamatergic Neurons Modulate States of Consciousness during Sevoflurane Anesthesia in Mice

BACKGROUND

Multiple neural structures involved in maintaining wakefulness have been found to promote arousal from general anesthesia. The medial septum is a critical region that modulates arousal behavior. This study hypothesized that glutamatergic neurons in the medial septum play a crucial role in regulating states of consciousness during sevoflurane general anesthesia.

METHODS

Adult male mice were used in this study. The effects of sevoflurane anesthesia on neuronal activity were determined by fiber photometry. Lesions and chemogenetic manipulations were used to study the effects of the altered activity of medial septal glutamatergic neurons on anesthesia induction, emergence, and sensitivity to sevoflurane. Optogenetic stimulation was used to observe the role of acute activation of medial septal glutamatergic neurons on cortical activity and behavioral changes during sevoflurane-induced continuous steady state of general anesthesia and burst suppression state.

RESULTS

The authors found that medial septal glutamatergic neuronal activity decreased during sevoflurane anesthesia induction and recovered in the early period of emergence. Chemogenetic activation of medial septal glutamatergic neurons prolonged the induction time (mean ± SD, hM3Dq-clozapine N-oxide vs. hM3Dq-saline, 297.5 ± 60.1 s vs. 229.4 ± 29.9 s, P < 0.001, n = 11) and decreased the emergence time (53.2 ± 11.8 s vs. 77.5 ± 33.5 s, P = 0.025, n = 11). Lesions or chemogenetic inhibition of these neurons produced the opposite effects. During steady state of general anesthesia and deep anesthesia-induced burst suppression state, acute optogenetic activation of medial septal glutamatergic neurons induced cortical activation and behavioral emergence.

CONCLUSIONS

The study findings reveal that activation of medial septal glutamatergic neurons has arousal-promoting effects during sevoflurane anesthesia in male mice. The activation of these neurons prolongs the induction and accelerates the emergence of anesthesia.

EDITOR’S PERSPECTIVE

Trends in medical care utilization in patients with cancer: An analysis of real-world data in a tertiary hospital in Korea, 2014-2019

BACKGROUND

Rising costs of cancer treatments challenge even areas with universal health coverage. There's a need to assess current medical care utilization trends among patients with cancer to guide public health policy, resource allocation, and set informed healthcare goals.

METHODS

We analyzed the latest trends in medical care utilization by cancer patients in four areas-drugs, radiation therapy (RT), surgery, and diagnostic procedures-using clinical databases extracted from electronic medical records of a tertiary hospital in Korea between 2014 and 2019. Compound adjusted growth rates (CAGR) were computed to capture the annual growth over the study period.

RESULTS

A total of 74,285 cancer patients were identified, with 40.3% (29,962), 14.2% (10,577), 31.1% (23,066), and 92.6% (68,849) of patients having received at least one anticancer agent, RT, surgery, and diagnostic procedure, respectively, over the period. We observed a 1.7-fold increase in the use of targeted · immune-oncology agents (from 6.8% to 11.6%) and a 21-fold increase (from 3.0% in 2014 to 65.7%) in intensity-modulated RT (IMRT) use over the period. In contrast, we observed a continuous decrease in the proportion of patients who underwent surgical treatment from 12.2% in 2014 to 10.9% in 2019. This decrease was particularly noticeable in patients with colon cancer (from 28.5% to 24.2%) and liver cancer (from 4.1% to 2.9%).

CONCLUSION

From 2014 to 2019, there was a significant rise in the use of targeted · immune-oncology agents and IMRT, alongside a decline in surgeries. While targeted · immune-oncology agents and IMRT may offer promising outcomes, their financial impact and potential for overuse necessitate careful oversight and long-term cost-effectiveness studies.

Electroencephalographic Signature of Out-of-Body Experiences Induced by Virtual Reality: A Novel Methodological Approach

Out-of-body experiences (OBEs) are subjective experiences of seeing one's own body and the environment from a location outside the physical body. They can arise spontaneously or in specific conditions, such as during the intake of dissociative drug. Given its unpredictable occurrence, one way to empirically study it is to induce subjective experiences resembling an OBE using technology such as virtual reality. We employed a complex multisensory method of virtual embodiment in a virtual reality scenario with seven healthy participants to induce virtual OBE-like experiences. Participants performed two conditions in a randomly determined order. For both conditions, the participant's viewpoint was lifted out of the virtual body toward the ceiling of the virtual room, and real body movements were (visuo-tactile ON condition) or were not (visuo-tactile OFF condition) translated into movements on the virtual body below-the latter aiming to maintain a feeling of connection with the virtual body. A continuous 128-electrode EEG was recorded. Participants reported subjective experiences of floating in the air and of feeling high up in the virtual room at a strong intensity, but a weak to moderate feeling of being "out of their body" in both conditions. The EEG analysis revealed that this subjective experience was associated with a power shift that manifested in an increase of delta and a decrease of alpha relative power. A reduction of theta complexity and an increase of beta-2 connectivity were also found. This supports the growing body of evidence revealing a prominent role of delta activity during particular conscious states.

Analogous cortical reorganization accompanies entry into states of reduced consciousness during anesthesia and sleep

Theories of consciousness suggest that brain mechanisms underlying transitions into and out of unconsciousness are conserved no matter the context or precipitating conditions. We compared signatures of these mechanisms using intracranial electroencephalography in neurosurgical patients during propofol anesthesia and overnight sleep and found strikingly similar reorganization of human cortical networks. We computed the "effective dimensionality" of the normalized resting state functional connectivity matrix to quantify network complexity. Effective dimensionality decreased during stages of reduced consciousness (anesthesia unresponsiveness, N2 and N3 sleep). These changes were not region-specific, suggesting global network reorganization. When connectivity data were embedded into a low-dimensional space in which proximity represents functional similarity, we observed greater distances between brain regions during stages of reduced consciousness, and individual recording sites became closer to their nearest neighbors. These changes corresponded to decreased differentiation and functional integration and correlated with decreases in effective dimensionality. This network reorganization constitutes a neural signature of states of reduced consciousness that is common to anesthesia and sleep. These results establish a framework for understanding the neural correlates of consciousness and for practical evaluation of loss and recovery of consciousness.

Rapid thalamocortical network switching mediated by cortical synchronization underlies propofol-induced EEG signatures: a biophysical model

Propofol-mediated unconsciousness elicits strong alpha/low-beta and slow oscillations in the electroencephalogram (EEG) of patients. As anesthetic dose increases, the EEG signal changes in ways that give clues to the level of unconsciousness; the network mechanisms of these changes are only partially understood. Here, we construct a biophysical thalamocortical network involving brain stem influences that reproduces transitions in dynamics seen in the EEG involving the evolution of the power and frequency of alpha/low-beta and slow rhythm, as well as their interactions. Our model suggests that propofol engages thalamic spindle and cortical sleep mechanisms to elicit persistent alpha/low-beta and slow rhythms, respectively. The thalamocortical network fluctuates between two mutually exclusive states on the timescale of seconds. One state is characterized by continuous alpha/low-beta-frequency spiking in thalamus (C-state), whereas in the other, thalamic alpha spiking is interrupted by periods of co-occurring thalamic and cortical silence (I-state). In the I-state, alpha colocalizes to the peak of the slow oscillation; in the C-state, there is a variable relationship between an alpha/beta rhythm and the slow oscillation. The C-state predominates near loss of consciousness; with increasing dose, the proportion of time spent in the I-state increases, recapitulating EEG phenomenology. Cortical synchrony drives the switch to the I-state by changing the nature of the thalamocortical feedback. Brain stem influence on the strength of thalamocortical feedback mediates the amount of cortical synchrony. Our model implicates loss of low-beta, cortical synchrony, and coordinated thalamocortical silent periods as contributing to the unconscious state.NEW & NOTEWORTHY GABAergic anesthetics induce alpha/low-beta and slow oscillations in the EEG, which interact in dose-dependent ways. We constructed a thalamocortical model to investigate how these interdependent oscillations change with propofol dose. We find two dynamic states of thalamocortical coordination, which change on the timescale of seconds and dose-dependently mirror known changes in EEG. Thalamocortical feedback determines the oscillatory coupling and power seen in each state, and this is primarily driven by cortical synchrony and brain stem neuromodulation.

Surgical outcomes and factors associated with postoperative complications of colorectal cancer in a Colombian Caribbean population: Results from a regional referral hospital

INTRODUCTION

Colorectal cancer is the most common malignant neoplasm of the gastrointestinal tract. Its incidence and mortality vary markedly at a global level. Assessing the epidemiological behavior of this condition allows reevaluating diagnostic, therapeutic and prognostic options, based on new findings. In Colombia, few studies have correlated variables associated with surgical and oncological outcomes in this type of cancer. Then, the aim of this study was to evaluate the surgical outcomes and factors associated with postoperative complications of colorectal cancer in a Colombian Caribbean Population.

METHODS

Retrospective cross-sectional study, including patients with a histopathological diagnosis of colorectal cancer who underwent open or laparoscopic surgery, during a period of two years (2018-2020), from a regional referral hospital. Clinical history variables were collected. Frequencies and prevalence ratios were calculated.

RESULTS

A total of 84 patients were finally included. Adenocarcinoma of non-special type with advanced clinical stages was the most prevalent (72.6%). Rectal neoplasia (45.2%) was the most frequent anatomical subsite, followed by proximal colon (p = 0.026). The anatomical subsite of the neoplasm, intraoperative complication (PR 1.38; 95% CI, 1.21-1.59, p = 0.001) and intensive care stay (PR 1.062; 95% CI, 1.01-1.12, p = 0.048) were associated with postoperative outcome.

CONCLUSIONS

The anatomical subsite of the neoplasm location, the presence of intraoperative complications and the stay in intensive care may be associated with the surgical and oncological outcome of individuals with colon cancer from the Colombian Caribbean region.

Graph approaches for analysis of brain connectivity during dexmedetomidine sedation

Sedation is commonly used to relieve fear and anxiety during procedures. Dexmedetomidine (DEX), approved by the US Food and Drug Administration in 1999 for short-term sedation, is a selective alpha2-adrenoreceptor agonist. The use of DEX is increasing due to minimal respiratory depression and easy and quick awakening from sedation. Its sedative mechanisms are suggested to be related to changes in the interaction between brain regions. In this study, we used graph theory to investigate whether the altered network connection is associated with sedation. Electroencephalogram (EEG) recordings of 32 channels were acquired during awake and DEX-induced sedation for 20 participants. We extracted EEG epochs from the awake and the DEX sedation state. Using the graph theory, we compared the changes in the network connection parameters with the awake state. We observed that the slopes in 1/f dynamics, which indicate overall brain network characteristics, were greater during DEX-induced sedation compared to the awake state, suggesting a transition towards a random network behavior. In addition, network connections from the perspective of information processing were significantly disturbed in the alpha frequency band, unlike other frequency bands augmenting network connections. The alpha frequency band plays a prominent role in the function and interaction of cognitive activities. These results collectively indicate that changes in the brain network critical to cognition during DEX administration may also be related to the mechanism of sedation.

Haplotypic variants of COVID-19 related genes are associated with blood pressure and metabolites levels

The genetic association of coronavirus disease 2019 (COVID-19) with its complications has not been fully understood. This study aimed to identify variants and haplotypes of candidate genes implicated in COVID-19 related traits by combining the literature review and pathway analysis. To explore such genes, the protein-protein interactions and relevant pathways of COVID-19-associated genes were assessed. A number of variants on candidate genes were identified from Genome-wide association studies (GWASs) which were associated with COVID-19 related traits (p ˂ 10^-6 ). Haplotypic blocks were assessed using haplotypic structures among the 1000 Genomes Project (r²  ≥ 0.8, D' ≥ 0.8). Further functional analyses were performed on the selected variants. The results demonstrated that a group of variants in ACE and AGT genes were significantly correlated with COVID-19 related traits. Three haplotypes were identified to be involved in the blood metabolites levels and the development of blood pressure. Functional analyses revealed that most GWAS index variants were expression quantitative trait loci and had transcription factor binding sites, exonic splicing enhancers or silencer activities. Furthermore, the proxy haplotype variants, rs4316, rs4353, rs4359, and three variants, namely rs2493133, rs2478543, and rs5051, were associated with blood metabolite and systolic blood pressure, respectively. These variants exerted more regulatory effects compared with other GWAS variants. The present study indicates that the genetic variants and candidate haplotypes of COVID-19 related genes are associated with blood pressure and blood metabolites. However, further observational studies are warranted to confirm these results.

Prognostication after cardiac arrest: how EEG and evoked potentials may improve the challenge

About 80% of patients resuscitated from CA are comatose at ICU admission and nearly 50% of survivors are still unawake at 72 h. Predicting neurological outcome of these patients is important to provide correct information to patient's relatives, avoid disproportionate care in patients with irreversible hypoxic-ischemic brain injury (HIBI) and inappropriate withdrawal of care in patients with a possible favorable neurological recovery. ERC/ESICM 2021 algorithm allows a classification as "poor outcome likely" in 32%, the outcome remaining "indeterminate" in 68%. The crucial question is to know how we could improve the assessment of both unfavorable but also favorable outcome prediction. Neurophysiological tests, i.e., electroencephalography (EEG) and evoked-potentials (EPs) are a non-invasive bedside investigations. The EEG is the record of brain electrical fields, characterized by a high temporal resolution but a low spatial resolution. EEG is largely available, and represented the most widely tool use in recent survey examining current neuro-prognostication practices. The severity of HIBI is correlated with the predominant frequency and background continuity of EEG leading to "highly malignant" patterns as suppression or burst suppression in the most severe HIBI. EPs differ from EEG signals as they are stimulus induced and represent the summated activities of large populations of neurons firing in synchrony, requiring the average of numerous stimulations. Different EPs (i.e., somato sensory EPs (SSEPs), brainstem auditory EPs (BAEPs), middle latency auditory EPs (MLAEPs) and long latency event-related potentials (ERPs) with mismatch negativity (MMN) and P300 responses) can be assessed in ICU, with different brain generators and prognostic values. In the present review, we summarize EEG and EPs signal generators, recording modalities, interpretation and prognostic values of these different neurophysiological tools. Finally, we assess the perspective for futures neurophysiological investigations, aiming to reduce prognostic uncertainty in comatose and disorders of consciousness (DoC) patients after CA.

Paradoxical markers of conscious levels: Effects of propofol on patients in disorders of consciousness

Human consciousness is widely understood to be underpinned by rich and diverse functional networks, whose breakdown results in unconsciousness. Candidate neural correlates of anesthetic-induced unconsciousness include: (1) disrupted frontoparietal functional connectivity; (2) disrupted brain network hubs; and (3) reduced spatiotemporal complexity. However, emerging counterexamples have revealed that these markers may appear outside of the state they are associated with, challenging both their inclusion as markers of conscious level, and the theories of consciousness that rely on their evidence. In this study, we present a case series of three individuals in disorders of consciousness (DOC) who exhibit paradoxical brain responses to exposure to anesthesia. High-density electroencephalographic data were recorded from three patients with unresponsive wakefulness syndrome (UWS) while they underwent a protocol of propofol anesthesia with a targeted effect site concentration of 2 μg/ml. Network hubs and directionality of functional connectivity in the alpha frequency band (8–13 Hz), were estimated using the weighted phase lag index (wPLI) and directed phase lag index (dPLI). The spatiotemporal signal complexity was estimated using three types of Lempel-Ziv complexity (LZC). Our results illustrate that exposure to propofol anesthesia can paradoxically result in: (1) increased frontoparietal feedback-dominant connectivity; (2) posterior network hubs; and (3) increased spatiotemporal complexity. The case examples presented in this paper challenge the role of functional connectivity and spatiotemporal complexity in theories of consciousness and for the clinical evaluation of levels of human consciousness.

A double-blind, Randomized controlled trial on glucose-lowering EFfects and safety of adding 0.25 or 0.5 mg lobeglitazone in type 2 diabetes patients with INadequate control on metformin and dipeptidyl peptidase-4 inhibitor therapy: REFIND study

AIMS

To compare the efficacy and safety of adding low-dose lobeglitazone (0.25 mg/day) or standard-dose lobeglitazone (0.5 mg/day) to patients with type 2 diabetes mellitus (T2DM) with inadequate glucose control on metformin and dipeptidyl peptidase (DPP4) inhibitor therapy.

MATERIALS AND METHODS

In this phase 4, multicentre, double-blind, randomized controlled, non-inferiority trial, patients with T2DM insufficiently controlled by metformin and DPP4 inhibitor combination therapy were randomized to receive either low-dose or standard-dose lobeglitazone. The primary endpoint was non-inferiority of low-dose lobeglitazone in terms of glycaemic control, expressed as the difference in mean glycated haemoglobin levels at week 24 relative to baseline values and compared with standard-dose lobeglitazone, using 0.5% non-inferiority margin.

RESULTS

At week 24, the mean glycated haemoglobin levels were 6.87 ± 0.54% and 6.68 ± 0.46% in low-dose and standard-dose lobeglitazone groups, respectively (p = .031). The between-group difference was 0.18% (95% confidence interval 0.017-0.345), showing non-inferiority of the low-dose lobeglitazone. Mean body weight changes were significantly greater in the standard-dose group (1.36 ± 2.23 kg) than in the low-dose group (0.50 ± 1.85 kg) at week 24. The changes in HOMA-IR, lipid profile and liver enzyme levels showed no significant difference between the groups. Overall treatment-emergent adverse events (including weight gain, oedema and hypoglycaemia) occurred more frequently in the standard-dose group.

CONCLUSIONS

Adding low-dose lobeglitazone to metformin and DPP4 inhibitor combination resulted in a non-inferior glucose-lowering outcome and fewer adverse events compared with standard-dose lobeglitazone. Therefore, low-dose lobeglitazone might be one option for individualized strategy in patients with T2DM.

2020 International brain-computer interface competition: A review

The brain-computer interface (BCI) has been investigated as a form of communication tool between the brain and external devices. BCIs have been extended beyond communication and control over the years. The 2020 international BCI competition aimed to provide high-quality neuroscientific data for open access that could be used to evaluate the current degree of technical advances in BCI. Although there are a variety of remaining challenges for future BCI advances, we discuss some of more recent application directions: (i) few-shot EEG learning, (ii) micro-sleep detection (iii) imagined speech decoding, (iv) cross-session classification, and (v) EEG(+ear-EEG) detection in an ambulatory environment. Not only did scientists from the BCI field compete, but scholars with a broad variety of backgrounds and nationalities participated in the competition to address these challenges. Each dataset was prepared and separated into three data that were released to the competitors in the form of training and validation sets followed by a test set. Remarkable BCI advances were identified through the 2020 competition and indicated some trends of interest to BCI researchers.

Spatio-Spectral Feature Representation for Motor Imagery Classification Using Convolutional Neural Networks

Convolutional neural networks (CNNs) have recently been applied to electroencephalogram (EEG)-based brain-computer interfaces (BCIs). EEG is a noninvasive neuroimaging technique, which can be used to decode user intentions. Because the feature space of EEG data is highly dimensional and signal patterns are specific to the subject, appropriate methods for feature representation are required to enhance the decoding accuracy of the CNN model. Furthermore, neural changes exhibit high variability between sessions, subjects within a single session, and trials within a single subject, resulting in major issues during the modeling stage. In addition, there are many subject-dependent factors, such as frequency ranges, time intervals, and spatial locations at which the signal occurs, which prevent the derivation of a robust model that can achieve the parameterization of these factors for a wide range of subjects. However, previous studies did not attempt to preserve the multivariate structure and dependencies of the feature space. In this study, we propose a method to generate a spatiospectral feature representation that can preserve the multivariate information of EEG data. Specifically, 3-D feature maps were constructed by combining subject-optimized and subject-independent spectral filters and by stacking the filtered data into tensors. In addition, a layer-wise decomposition model was implemented using our 3-D-CNN framework to secure reliable classification results on a single-trial basis. The average accuracies of the proposed model were 87.15% (±7.31), 75.85% (±12.80), and 70.37% (±17.09) for the BCI competition data sets IV_2a, IV_2b, and OpenBMI data, respectively. These results are better than those obtained by state-of-the-art techniques, and the decomposition model obtained the relevance scores for neurophysiologically plausible electrode channels and frequency domains, confirming the validity of the proposed approach.

Brain Activity Characteristics of Patients With Disorders of Consciousness in the EEG Resting State Paradigm: A Review

The assessment of the level of consciousness in disorders of consciousness (DoC) is still one of the most challenging problems in contemporary medicine. Nevertheless, based on the multitude of studies conducted over the last 20 years on resting states based on electroencephalography (EEG) in DoC, it is possible to outline the brain activity profiles related to both patients without preserved consciousness and minimally conscious ones. In the case of patients without preserved consciousness, the dominance of low, mostly delta, frequency, and the marginalization of the higher frequencies were observed, both in terms of the global power of brain activity and in functional connectivity patterns. In turn, the minimally conscious patients revealed the opposite brain activity pattern—the characteristics of higher frequency bands were preserved both in global power and in functional long-distance connections. In this short review, we summarize the state of the art of EEG-based research in the resting state paradigm, in the context of providing potential support to the traditional clinical assessment of the level of consciousness.

Alternation of EEG Characteristics During Transcutaneous Acupoint Electrical Stimulation-Induced Sedation

Recent studies have shown that applying acupuncture during general anesthesia can reduce the dosage of anesthetics. Hence, it is speculated that acupuncture may have a sedative effect. However, existing studies employed acupuncture in combination with anesthetics, which makes determine acupuncture's role in producing sedation difficult. In this work, we investigated the sedative effect of acupuncture by using transcutaneous acupoint electrical stimulation (TAES) at bilateral Zusanli (ST36), Shenmen (HT7) and Sanyinjiao (SP6). Using a cross-over design, 2 separate sessions, that are, the resting and TAES sessions, were conducted for each subject. The sedative effect was quantified by using the bispectral index (BIS). The difference in brain activities between resting and TAES sessions was investigated by analyzing the simultaneously recorded EEG signals. Our results showed that a statistically significant difference in BIS values existed between resting and TAES sessions, which suggested that TAES alone was capable of inducing observable sedation. Using power spectrum analysis, we showed that TAES-induced sedation was accompanied by a reduction in alpha band power and an increment in delta band power. Permutation entropy was lower during the TAES session, which suggested that TAES reduced the complexity of the EEG signal. Moreover, a significant reduction in the global strength of brain functional connections was observed during TAES. These findings suggest that TAES alone can induce observable sedative effects, and this sedation effect is accompanied by changes in brain activities that have shown to be correlated with consciousness.

EEG functional connectivity is sensitive for nitrogen narcosis at 608 kPa

Divers commonly breathe air, containing nitrogen. Nitrogen under hyperbaric conditions is a narcotic gas. In dives beyond a notional threshold of 30 m depth (405 kPa) this can cause cognitive impairment, culminating in accidents due to poor decision making. Helium is known to have no narcotic effect. This study explored potential approaches to developing an electroencephalogram (EEG) functional connectivity metric to measure narcosis produced by nitrogen at hyperbaric pressures. Twelve human participants (five female) breathed air and heliox (in random order) at 284 and 608 kPa while recording 32-channel EEG and psychometric function. The degree of spatial functional connectivity, estimated using mutual information, was summarized with global efficiency. Air-breathing at 608 kPa (experienced as mild narcosis) caused a 35% increase in global efficiency compared to surface air-breathing (mean increase = 0.17, 95% CI [0.09–0.25], p = 0.001). Air-breathing at 284 kPa trended in a similar direction. Functional connectivity was modestly associated with psychometric impairment (mixed-effects model r² = 0.60, receiver-operating-characteristic area, 0.67 [0.51–0.84], p = 0.02). Heliox breathing did not cause a significant change in functional connectivity. In conclusion, functional connectivity increased during hyperbaric air-breathing in a dose-dependent manner, but not while heliox-breathing. This suggests sensitivity to nitrogen narcosis specifically.

Electroencephalography in delirium assessment: a scoping review

Background

Delirium is a common disorder affecting around 31% of patients in the intensive care unit (ICU). Delirium assessment scores such as the Confusion Assessment Method (CAM) are time-consuming, they cannot differentiate between different types of delirium and their etiologies, and they may have low sensitivities in the clinical setting. While today, electroencephalography (EEG) is increasingly being applied to delirious patients in the ICU, a lack of clear cut EEG signs, leads to inconsistent assessments.

Methods

We therefore conducted a scoping review on EEG findings in delirium. One thousand two hundred thirty-six articles identified through database search on PubMed and Embase were reviewed. Finally, 33 original articles were included in the synthesis.

Results

EEG seems to offer manifold possibilities in diagnosing delirium. All 33 studies showed a certain degree of qualitative or quantitative EEG alterations in delirium. Thus, normal routine (rEEG) and continuous EEG (cEEG) make presence of delirium very unlikely. All 33 studies used different research protocols to at least some extent. These include differences in time points, duration, conditions, and recording methods of EEG, as well as different patient populations, and diagnostic methods for delirium. Thus, a quantitative synthesis and common recommendations are so far elusive.

Conclusion

Future studies should compare the different methods of EEG recording and evaluation to identify robust parameters for everyday use. Evidence for quantitative bi-electrode delirium detection based on increased relative delta power and decreased beta power is growing and should be further pursued. Additionally, EEG studies on the evolution of a delirium including patient outcomes are needed.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-022-02557-w.

A neural circuit from the paraventricular thalamus to the bed nucleus of the stria terminalis for the regulation of states of consciousness during sevoflurane anesthesia in mice

BACKGROUND

The neural circuitry underlying sevoflurane-induced modulation of consciousness is poorly understood. We hypothesized that the paraventricular thalamus-bed nucleus of the stria terminalis pathway plays an important role in regulating states of consciousness during sevoflurane anesthesia.

METHODS

Rabies-virus-based transsynaptic tracing techniques were employed to reveal the neural pathway from the paraventricular thalamus to the bed nucleus of the stria terminalis. We investigated the role of this pathway in sevoflurane anesthesia induction, maintenance and emergence using chemogenetic and optogenetic methods combined with cortical electroencephalogram (EEG) recordings. Both male and female mice were used in our study.

RESULTS

Both GABAergic and glutamatergic neurons in the bed nucleus of the stria terminalis receive paraventricular thalamus glutamatergic projections. Chemogenetic inhibition of paraventricular thalamus glutamatergic neurons prolonged the sevoflurane anesthesia emergence time (mean ± SD, hM4D-CNO vs. mCherry-CNO, 281 ± 88 vs. 172 ± 48 s, p < 0.001, n = 24) and decreased the induction time (101 ± 32 vs. 136 ± 34 s, p = 0.002, n = 24) as well as the EC50 for the loss or recovery of the righting reflex under sevoflurane anesthesia (mean [95% confidence interval]; MACLORR, 1.16 [1.12 to 1.20] vs. 1.49 [1.46 to 1.53] vol%, p < 0.001, n = 20; MACRORR, 0.95 [0.86 to 1.03] vs. 1.34 [1.29 to 1.40] vol%, p < 0.001, n = 20). Similar results were observed during suppression of the paraventricular thalamus- bed nucleus stria terminalis pathway. Optogenetic activation of this pathway produced the opposite effects. Additionally, transient stimulation of this pathway efficiently induced behavioral arousal during continuous steady-state general anesthesia with sevoflurane and reduced the depth of anesthesia during sevoflurane-induced burst suppression.

CONCLUSIONS

In mice, axonal projections from the paraventricular thalamic neurons to the bed nucleus of the stria terminalis contribute to regulating states of consciousness during sevoflurane anesthesia.

Quantifying arousal and awareness in altered states of consciousness using interpretable deep learning

Consciousness can be defined by two components: arousal (wakefulness) and awareness (subjective experience). However, neurophysiological consciousness metrics able to disentangle between these components have not been reported. Here, we propose an explainable consciousness indicator (ECI) using deep learning to disentangle the components of consciousness. We employ electroencephalographic (EEG) responses to transcranial magnetic stimulation under various conditions, including sleep (n = 6), general anesthesia (n = 16), and severe brain injury (n = 34). We also test our framework using resting-state EEG under general anesthesia (n = 15) and severe brain injury (n = 34). ECI simultaneously quantifies arousal and awareness under physiological, pharmacological, and pathological conditions. Particularly, ketamine-induced anesthesia and rapid eye movement sleep with low arousal and high awareness are clearly distinguished from other states. In addition, parietal regions appear most relevant for quantifying arousal and awareness. This indicator provides insights into the neural correlates of altered states of consciousness.

The authors propose an explainable consciousness indicator using deep learning to quantify arousal and awareness under sleep, anesthesia, and in patients with disorders of consciousness.

How general anesthetics work: from the perspective of reorganized connections within the brain

General anesthesia is critical for various procedures and surgeries. Despite the widespread use of anesthetics, their precise mechanisms remain poorly understood. Anesthetics inevitably act on the brain, primarily through the modulation of target receptors. Even if the action is specific to an individual neuron, however, long-range effects can occur due to the tremendous interconnectedness of neuronal activity. The strength of this connectivity can be understood using mathematical models that allow for the study of neuronal connectivity dynamics. These models also allow researchers to develop hypotheses on the candidate mechanisms of action of different types of anesthesia. This review highlights the theoretical background associated with the study of the mechanisms of action of anesthetics. We propose a candidate framework that describes how anesthetics act on the brain and consciousness in general.

Propofol-induced Unresponsiveness Is Associated with a Brain Network Phase Transition

Background:

The wakeful brain can easily access and coordinate a large repertoire of different states—dynamics suggestive of “criticality.” Anesthesia causes loss of criticality at the level of electroencephalogram waveforms, but the criticality of brain network connectivity is less well studied. The authors hypothesized that propofol anesthesia is associated with abrupt and divergent changes in brain network connectivity for different frequencies and time scales—characteristic of a phase transition, a signature of loss of criticality.

Methods:

As part of a previously reported study, 16 volunteers were given propofol in slowly increasing brain concentrations, and their behavioral responsiveness was assessed. The network dynamics from 31-channel electroencephalogram data were calculated from 1 to 20 Hz using four phase and envelope amplitude–based functional connectivity metrics that covered a wide range of time scales from milliseconds to minutes. The authors calculated network global efficiency, clustering coefficient, and statistical complexity (using the Jensen–Shannon divergence) for each functional connectivity metric and compared their findings with those from an in silico Kuramoto network model.

Results:

The transition to anesthesia was associated with critical slowing and then abrupt profound decreases in global network efficiency of 2 Hz power envelope metrics (from mean ± SD of 0.64 ± 0.15 to 0.29 ± 0.28 absolute value, P < 0.001, for medium; and from 0.47 ± 0.13 to 0.24 ± 0.21, P < 0.001, for long time scales) but with an increase in global network efficiency for 10 Hz weighted phase lag index (from 0.30 ± 0.20 to 0.72 ± 0.06, P < 0.001). Network complexity decreased for both the 10 Hz hypersynchronous (0.44 ± 0.13 to 0.23 ± 0.08, P < 0.001), and the 2 Hz asynchronous (0.73 ± 0.08 to 0.40 ± 0.13, P < 0.001) network states. These patterns of network coupling were consistent with those of the Kuramoto model of an order–disorder phase transition.

Conclusions:

Around loss of behavioral responsiveness, a small increase in propofol concentrations caused a collapse of long time scale power envelope connectivity and an increase in 10 Hz phase-based connectivity—suggestive of a brain network phase transition.

Temporospatial electroencephalographic analysis of brain network dynamics over a wide range of frequencies and time scales in 16 volunteers receiving slowly increasing concentrations of propofol revealed that transition to unresponsiveness was associated with a sudden rise in alpha frequency network phase synchrony anteriorly, but also a transient surge and then loss of network coupling over long (tens of seconds) time scales. Deep anesthesia was characterized by alpha waveform hypersynchrony and slow-wave power envelope dissynchrony across the whole cortex. These observations suggest that propofol anesthesia is associated with a constellation of changes in network connectivity across frequencies and time scales that are signatures of sharp and sudden transitions in the behavior of networks.

Cyclophosphamide-induced testicular oxidative-inflammatory injury is accompanied by altered immunosuppressive indoleamine 2, 3-dioxygenase in Wister rats: Influence of dietary quercetin

This study evaluated the role of quercetin against cyclophosphamide-induced distortion of rat testicular function. Adult rats were administered cyclophosphamide (100 mg/kg), quercetin (50 mg/kg) and in combination for seven days. Cyclophosphamide caused a significant increase in the activities of indoleamine 2, 3-dioxygenases (IDO), tryptophan 2, 3-dioxygenase (TDO), myeloperoxidase (MPO), and elevated the concentrations of interleukin 6 (IL-6) and interferon-γ (IFN-γ). Cyclophosphamide increased testis malondialdehyde (MDA) concentrations but depleted superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GSH-Px) and glutathione (GSH). However, quercetin co-administration significantly (p < 0.05) prevented the increased values of IDO, TDO, MPO, IL-6, IFN-γ, MDA, SOD, CAT, GSH-Px and GSH compared with control rats. Also, quercetin co-treatment significantly increased serum testosterone, follicle-stimulating hormone (FSH), prolactin, luteinizing hormone (LH), activities of testicular 3β-hydroxysteroid dehydrogenase (3 β-HSD), 17β-hydroxysteroid dehydrogenase (17 β-HSD) as well as sperm count, motility and viability but reduced abnormal sperm morphology. Quercetin exposure alone did not alter any of the parameters evaluated relative to control. Thus, quercetin protected the testes against cyclophosphamide-induced alterations in immunosuppressive IDO/TDO activities elicited by oxidative-inflammatory mediators.

Amniotic stromal stem cell-loaded hydrogel repairs cardiac tissue in infarcted rat hearts via paracrine mediators

The use of stem cells to repair the heart after a myocardial infarction (MI) remains promising, yet clinical trials over the past 20 years suggest that cells fail to integrate into the native tissue, resulting in limited improvements in cardiac function. Here, we demonstrate the cardioprotective potential of a composite inserting human amniotic stromal mesenchymal stem cells (ASMCs) in a chitosan and hyaluronic acid (C/HA) based hydrogel in a rat MI model. Mechanical characterization of the C/HA platform indicated a swift elastic conversion at 40°C and a rapid sol-gel transition time at 37°C. Cell viability assay presented active and proliferating AMSCs in the C/HA. The ASMCs + C/HA injected composite significantly increased left ventricular ejection fraction, fractional shortening, and neovessel formation. The encapsulated AMSCs were abundantly detected in the infarcted myocardium 6 weeks post-administration and co-expressed cardiac proteins and notably proliferative markers. Proteomic profiling revealed that extracellular vesicles released from hypoxia preconditioned ASMCs contained proteins involved in cytoprotection, angiogenesis, cardiac differentiation and non-canonical Wnt-signaling. Independent activation of non-canonical Wnt-signaling pathways in ASMCs induced cardiogenesis. Despite a low injected cellular density at baseline, the encapsulated AMSCs were abundantly retained and increased cardiac function. Furthermore, the C/HA hydrogel provided an active milieu for the AMSCs to proliferate, co-express cardiac proteins, and induce new vessel formation. Hence, this novel composite of AMSCs + C/HA scaffold is a conceivable candidate that could restore cardiac function and reduce remodeling.

Neural signatures of heterogeneity in risk-taking and strategic consistency

People display a high degree of heterogeneity in risk-taking behaviour, but this heterogeneity remains poorly understood. Here, we use a neural trait approach to examine if task-independent, brain-based differences can help uncover the sources of heterogeneity in risky decision-making. We extend prior research in two key ways. First, we disentangled risk-taking and strategic consistency using novel measures afforded by the Balloon Analogue Risk Task. Second, we applied a personality neuroscience framework to explore why personality traits are typically only weakly related to risk-taking behaviour. We regressed participants' (N = 104) source localized resting-state electroencephalographic activity on risk-taking and strategic consistency. Results revealed that higher levels of resting-state delta-band current density (reflecting reduced cortical activation) in the left dorsal anterior cingulate cortex and the left dorsolateral prefrontal cortex were associated with increased risk-taking and decreased strategic consistency, respectively. These results suggest that heterogeneity in risk-taking behaviour is associated with neural dispositions related to sensitivity to the risk of loss, whereas heterogeneity in strategic consistency is associated with neural dispositions related to strategic decision-making. Finally, extraversion, neuroticism, openness, and self-control were broadly associated with both of the identified neural traits, which in turn mediated indirect associations between personality traits and behavioural measures. These results provide an explanation for the weak direct relationships between personality traits and risk-taking behaviour, supporting a personality neuroscience framework of traits and decision-making.

Consciousness and complexity: a consilience of evidence

Over the last years, a surge of empirical studies converged on complexity-related measures as reliable markers of consciousness across many different conditions, such as sleep, anesthesia, hallucinatory states, coma, and related disorders. Most of these measures were independently proposed by researchers endorsing disparate frameworks and employing different methods and techniques. Since this body of evidence has not been systematically reviewed and coherently organized so far, this positive trend has remained somewhat below the radar. The aim of this paper is to make this consilience of evidence in the science of consciousness explicit. We start with a systematic assessment of the growing literature on complexity-related measures and identify their common denominator, tracing it back to core theoretical principles and predictions put forward more than 20 years ago. In doing this, we highlight a consistent trajectory spanning two decades of consciousness research and provide a provisional taxonomy of the present literature. Finally, we consider all of the above as a positive ground to approach new questions and devise future experiments that may help consolidate and further develop a promising field where empirical research on consciousness appears to have, so far, naturally converged.

Different underlying mechanisms for high and low arousal in probabilistic learning in humans

Humans are uniquely capable of adapting to highly changing environments by updating relevant information and adjusting ongoing behaviour accordingly. Here we show how this ability -termed cognitive flexibility- is differentially modulated by high and low arousal fluctuations. We implemented a probabilistic reversal learning paradigm in healthy participants as they transitioned towards sleep or physical extenuation. The results revealed, in line with our pre-registered hypotheses, that low arousal leads to diminished behavioural performance through increased decision volatility, while performance decline under high arousal was attributed to increased perseverative behaviour. These findings provide evidence for distinct patterns of maladaptive decision-making on each side of the arousal inverted u-shaped curve, differentially affecting participants' ability to generate stable evidence-based strategies, and introduces wake-sleep and physical exercise transitions as complementary experimental models for investigating neural and cognitive dynamics.

EEG p-adic quantum potential accurately identifies depression, schizophrenia and cognitive decline

No diagnostic or predictive instruments to help with early diagnosis and timely therapeutic intervention are available as yet for most neuro-psychiatric disorders. A quantum potential mean and variability score (qpmvs), to identify neuropsychiatric and neurocognitive disorders with high accuracy, based on routine EEG recordings, was developed. Information processing in the brain is assumed to involve integration of neuronal activity in various areas of the brain. Thus, the presumed quantum-like structure allows quantification of connectivity as a function of space and time (locality) as well as of instantaneous quantum-like effects in information space (non-locality). EEG signals reflect the holistic (nonseparable) function of the brain, including the highly ordered hierarchy of the brain, expressed by the quantum potential according to Bohmian mechanics, combined with dendrogram representation of data and p-adic numbers. Participants consisted of 230 participants including 28 with major depression, 42 with schizophrenia, 65 with cognitive impairment, and 95 controls. Routine EEG recordings were used for the calculation of qpmvs based on ultrametric analyses, closely coupled with p-adic numbers and quantum theory. Based on area under the curve, high accuracy was obtained in separating healthy controls from those diagnosed with schizophrenia (p<0.0001), depression (p<0.0001), Alzheimer's disease (AD; p<0.0001), and mild cognitive impairment (MCI; p<0.0001) as well as in differentiating participants with schizophrenia from those with depression (p<0.0001), AD (p<0.0001) or MCI (p<0.0001) and in differentiating people with depression from those with AD (p<0.0001) or MCI (p<0.0001). The novel EEG analytic algorithm (qpmvs) seems to be a useful and sufficiently accurate tool for diagnosis of neuropsychiatric and neurocognitive diseases and may be able to predict disease course and response to treatment.

Cross-participant prediction of vigilance stages through the combined use of wPLI and wSMI EEG functional connectivity metrics

Functional connectivity (FC) metrics describe brain inter-regional interactions and may complement information provided by common power-based analyses. Here, we investigated whether the FC-metrics weighted Phase Lag Index (wPLI) and weighted Symbolic Mutual Information (wSMI) may unveil functional differences across four stages of vigilance-wakefulness (W), NREM-N2, NREM-N3, and REM sleep-with respect to each other and to power-based features. Moreover, we explored their possible contribution in identifying differences between stages characterized by distinct levels of consciousness (REM+W vs. N2+N3) or sensory disconnection (REM vs. W). Overnight sleep and resting-state wakefulness recordings from 24 healthy participants (27 ± 6 years, 13F) were analyzed to extract power and FC-based features in six classical frequency bands. Cross-validated linear discriminant analyses (LDA) were applied to investigate the ability of extracted features to discriminate (1) the four vigilance stages, (2) W+REM vs. N2+N3, and (3) W vs. REM. For the four-way vigilance stages classification, combining features based on power and both connectivity metrics significantly increased accuracy relative to considering only power, wPLI, or wSMI features. Delta-power and connectivity (0.5-4 Hz) represented the most relevant features for all the tested classifications, in line with a possible involvement of slow waves in consciousness and sensory disconnection. Sigma-FC, but not sigma-power (12-16 Hz), was found to strongly contribute to the differentiation between states characterized by higher (W+REM) and lower (N2+N3) probabilities of conscious experiences. Finally, alpha-FC resulted as the most relevant FC-feature for distinguishing among wakefulness and REM sleep and may thus reflect the level of disconnection from the external environment.

Alterations in coordinated EEG activity precede the development of seizures in comatose children

OBJECTIVE

We aimed to test the hypothesis that computational features of the first several minutes of EEG recording can be used to estimate the risk for development of acute seizures in comatose critically-ill children.

METHODS

In a prospective cohort of 118 comatose children, we computed features of the first five minutes of artifact-free EEG recording (spectral power, inter-regional synchronization and cross-frequency coupling) and tested if these features could help identify the 25 children who went on to develop acute symptomatic seizures during the subsequent 48 hours of cEEG monitoring.

RESULTS

Children who developed acute seizures demonstrated higher average spectral power, particularly in the theta frequency range, and distinct patterns of inter-regional connectivity, characterized by greater connectivity at delta and theta frequencies, but weaker connectivity at beta and low gamma frequencies. Subgroup analyses among the 97 children with the same baseline EEG background pattern (generalized slowing) yielded qualitatively and quantitatively similar results.

CONCLUSIONS

These computational features could be applied to baseline EEG recordings to identify critically-ill children at high risk for acute symptomatic seizures.

SIGNIFICANCE

If confirmed in independent prospective cohorts, these features would merit incorporation into a decision support system in order to optimize diagnostic and therapeutic management of seizures among comatose children.

Brain network motifs are markers of loss and recovery of consciousness

Motifs are patterns of inter-connections between nodes of a network, and have been investigated as building blocks of directed networks. This study explored the re-organization of 3-node motifs during loss and recovery of consciousness. Nine healthy subjects underwent a 3-h anesthetic protocol while 128-channel electroencephalography (EEG) was recorded. In the alpha (8-13 Hz) band, 5-min epochs of EEG were extracted for: Baseline; Induction; Unconscious; 30-, 10- and 5-min pre-recovery of responsiveness; 30- and 180-min post-recovery of responsiveness. We constructed a functional brain network using the weighted and directed phase lag index, on which we calculated the frequency and topology of 3-node motifs. Three motifs (motifs 1, 2 and 5) were significantly present across participants and epochs, when compared to random networks (p < 0.05). The topology of motifs 1 and 5 changed significantly between responsive and unresponsive epochs (p-values < 0.01; Kendall's W = 0.664 (motif 1) and 0.529 (motif 5)). Motif 1 was constituted of long-range chain-like connections, while motif 5 was constituted of short-range, loop-like connections. Our results suggest that anesthetic-induced unconsciousness is associated with a topological re-organization of network motifs. As motif topological re-organization may precede (motif 5) or accompany (motif 1) the return of responsiveness, motifs could contribute to the understanding of the neural correlates of consciousness.

Neural Decoding of Imagined Speech and Visual Imagery as Intuitive Paradigms for BCI Communication

Brain-computer interface (BCI) is oriented toward intuitive systems that users can easily operate. Imagined speech and visual imagery are emerging paradigms that can directly convey a user's intention. We investigated the underlying characteristics that affect the decoding performance of these two paradigms. Twenty-two subjects performed imagined speech and visual imagery of twelve words/phrases frequently used for patients' communication. Spectral features were analyzed with thirteen-class classification (including rest class) using EEG filtered in six frequency ranges. In addition, cortical regions relevant to the two paradigms were analyzed by classification using single-channel and pre-defined cortical groups. Furthermore, we analyzed the word properties that affect the decoding performance based on the number of syllables, concrete and abstract concepts, and the correlation between the two paradigms. Finally, we investigated multiclass scalability in both paradigms. The high-frequency band displayed a significantly superior performance to that in the case of any other spectral features in the thirteen-class classification (imagined speech: 39.73 ± 5.64%; visual imagery: 40.14 ± 4.17%). Furthermore, the performance of Broca's and Wernicke's areas and auditory cortex was found to have improved among the cortical regions in both paradigms. As the number of classes increased, the decoding performance decreased moderately. Moreover, every subject exceeded the confidence level performance, implying the strength of the two paradigms in BCI inefficiency. These two intuitive paradigms were found to be highly effective for multiclass communication systems, having considerable similarities between each other. The results could provide crucial information for improving the decoding performance for practical BCI applications.

Reducing Perioperative Neurocognitive Disorders (PND) Through Depth of Anesthesia Monitoring: A Critical Review

General anesthesia has been administered for over 150 years, and in that time, has become progressively safer. Improvements in outcomes have been driven by multiple advances, including the use of non-invasive monitors to assess cardiovascular and respiratory status. More recent advances have included the development and use of monitors to measure neurologic status by means of “processed” electroencephalography (pEEG), wherein the frontal EEG signal is analyzed by proprietary algorithms to produce a dimensionless number (scaled from 0 to 100), wherein low values are associated with deepening levels of sedation that progresses to loss of consciousness. Such monitors have been shown to enable anesthetic titration so as to expedite emergence and early recovery, and their use is advocated for the prevention of intraoperative awareness in the setting of administration of total intravenous anesthesia and neuromuscular blockade. Whether their use can minimize, or prevent, longer term adverse events is a matter of debate. In this narrative review of the most recent literature, we provide an assessment on the use of pEEG monitors in the prevention of a notable, and important, postoperative adverse outcome – delirium – in elderly patients. As we will discuss, the existing data do not support its routine use for the prevention of postoperative delirium in this, or any other, patient population.

Prediction of Memory Retrieval Performance Using Ear-EEG Signals

Many studies have explored brain signals during the performance of a memory task to predict later remembered items. However, prediction methods are still poorly used in real life and are not practical due to the use of electroencephalography (EEG) recorded from the scalp. Ear-EEG has been recently used to measure brain signals due to its flexibility when applying it to real world environments. In this study, we attempt to predict whether a shown stimulus is going to be remembered or forgotten using ear-EEG and compared its performance with scalp-EEG. Our results showed that there was no significant difference between ear-EEG and scalp-EEG. In addition, the higher prediction accuracy was obtained using a convolutional neural network (pre-stimulus: 74.06%, on-going stimulus: 69.53%) and it was compared to other baseline methods. These results showed that it is possible to predict performance of a memory task using ear-EEG signals and it could be used for predicting memory retrieval in a practical brain-computer interface.

End-to-End Automatic Sleep Stage Classification Using Spectral-Temporal Sleep Features

Sleep disorder is one of many neurological diseases that can affect greatly the quality of daily life. It is very burdensome to manually classify the sleep stages to detect sleep disorders. Therefore, the automatic sleep stage classification techniques are needed. However, the previous automatic sleep scoring methods using raw signals are still low classification performance. In this study, we proposed an end-to-end automatic sleep staging framework based on optimal spectral-temporal sleep features using a sleep-edf dataset. The input data were modified using a bandpass filter and then applied to a convolutional neural network model. For five sleep stage classification, the classification performance 85.6% and 91.1% using the raw input data and the proposed input, respectively. This result also shows the highest performance compared to conventional studies using the same dataset. The proposed framework has shown high performance by using optimal features associated with each sleep stage, which may help to find new features in the automatic sleep stage method.Clinical Relevance- The proposed framework would help to diagnose sleep disorders such as insomnia by improving sleep stage classification performance.

Predicting Motor Imagery Performance From Resting-State EEG Using Dynamic Causal Modeling

Motor imagery-based brain–computer interfaces (MI-BCIs) send commands to a computer using the brain activity registered when a subject imagines—but does not perform—a given movement. However, inconsistent MI-BCI performance occurs in variations of brain signals across subjects and experiments; this is considered to be a significant problem in practical BCI. Moreover, some subjects exhibit a phenomenon referred to as “BCI-inefficiency,” in which they are unable to generate brain signals for BCI control. These subjects have significant difficulties in using BCI. The primary goal of this study is to identify the connections of the resting-state network that affect MI performance and predict MI performance using these connections. We used a public database of MI, which includes the results of psychological questionnaires and pre-experimental resting-state taken over two sessions on different days. A dynamic causal model was used to calculate the coupling strengths between brain regions with directionality. Specifically, we investigated the motor network in resting-state, including the dorsolateral prefrontal cortex, which performs motor planning. As a result, we observed a significant difference in the connectivity strength from the supplementary motor area to the right dorsolateral prefrontal cortex between the low- and high-MI performance groups. This coupling, measured in the resting-state, is significantly stronger in the high-MI performance group than the low-MI performance group. The connection strength is positively correlated with MI-BCI performance (Session 1: r = 0.54; Session 2: r = 0.42). We also predicted MI performance using linear regression based on this connection (r-squared = 0.31). The proposed predictors, based on dynamic causal modeling, can develop new strategies for improving BCI performance. These findings can further our understanding of BCI-inefficiency and help BCI users to lower costs and save time.

Optogenetic Activation of Afferent Pathways in Brain Slices and Modulation of Responses by Volatile Anesthetics

Anesthetics influence consciousness in part via their actions on thalamocortical circuits. However, the extent to which volatile anesthetics affect distinct cellular and network components of these circuits remains unclear. Ex vivo brain slices provide a means by which investigators may probe discrete components of complex networks and disentangle potential mechanisms underlying the effects of volatile anesthetics on evoked responses. To isolate potential cell type- and pathway-specific drug effects in brain slices, investigators must be able to independently activate afferent fiber pathways, identify non-overlapping populations of cells, and apply volatile anesthetics to the tissue in aqueous solution. In this protocol, methods to measure optogenetically-evoked responses to two independent afferent pathways to neocortex in ex vivo brain slices are described. Extracellular responses are recorded to assay network activity and targeted whole-cell patch clamp recordings are conducted in somatostatin- and parvalbumin-positive interneurons. Delivery of physiologically relevant concentrations of isoflurane via artificial cerebral spinal fluid to modulate cellular and network responses is described.

Frequency dependent functional brain reorganization in anesthesia is specific to drug concentration

The differential effects of general anesthesia on brain activity in terms of drug selection, concentration and combination remain to be elucidated. Using fMRI, it has been shown that increasing doses of sevoflurane is associated with progressive breakdown in brain functional connectivity, while EEG studies have shown that higher activity in the delta band is associated with unconsciousness. Despite these promising results, the band- specific neural substrates of brain changes which occur during sevoflurane anesthesia have not yet been investigated. To this end, we employ high-density EEG-based brain connectivity estimates and graph theoretical analysis in a protocol of progressive sevoflurane administration (conditions: baseline, 1.1%, 2.1%, 2.8%, recovery), both at a global (whole-brain) and at a local (sensor-specific) level in 12 healthy subjects (7 males, mean age 25 ± 4.7 years). We show a statistically significant dependence of global strength, clustering coefficient and efficiency on sevoflurane concentration in the slow delta, beta 1 and beta 2 bands. Interestingly, high and low-frequency bands behaved in an opposite manner as a function of condition. We also found significant band*condition interactive effects in clustering coefficient, efficiency and strength both on local and global scales.

Prediction of Event Related Potential Speller Performance Using Resting-State EEG

Event-related potential (ERP) speller can be utilized in device control and communication for locked-in or severely injured patients. However, problems such as inter-subject performance instability and ERP-illiteracy are still unresolved. Therefore, it is necessary to predict classification performance before performing an ERP speller in order to use it efficiently. In this study, we investigated the correlations with ERP speller performance using a resting-state before an ERP speller. In specific, we used spectral power and functional connectivity according to four brain regions and five frequency bands. As a result, the delta power in the frontal region and functional connectivity in the delta, alpha, gamma bands are significantly correlated with the ERP speller performance. Also, we predicted the ERP speller performance using EEG features in the resting-state. These findings may contribute to investigating the ERP-illiteracy and considering the appropriate alternatives for each user.

Analysis of brain connectivity during nitrous oxide sedation using graph theory

Nitrous oxide, the least potent inhalation anesthetic, is widely used for conscious sedation. Recently, it has been reported that the occurrence of anesthetic-induced loss of consciousness decreases the interconnection between brain regions, resulting in brain network changes. However, few studies have investigated these changes in conscious sedation using nitrous oxide. Therefore, the present study aimed to use graph theory to analyze changes in brain networks during nitrous oxide sedation. Participants were 20 healthy volunteers (10 men and 10 women, 20–40 years old) with no history of systemic disease. We acquired electroencephalogram (EEG) recordings of 32 channels during baseline, nitrous oxide inhalation sedation, and recovery. EEG epochs from the baseline and the sedation state (50% nitrous oxide) were extracted and analyzed with the network connection parameters of graph theory. Analysis of 1/f dynamics, revealed a steeper slope while in the sedation state than during the baseline. Network connectivity parameters showed significant differences between the baseline and sedation state, in delta, alpha1, alpha2, and beta2 frequency bands. The most pronounced differences in functional distance during nitrous oxide sedation were observed in the alpha1 and alpha2 frequency bands. Change in 1/f dynamics indicates that changes in brain network systems occur during nitrous oxide administration. Changes in network parameters imply that nitrous oxide interferes with the efficiency of information integration in the frequency bands important for cognitive processes and attention tasks. Alteration of brain network during nitrous oxide administration may be associated to the sedative mechanism of nitrous oxide.

Cortical functional connectivity indexes arousal state during sleep and anesthesia

Disruption of cortical connectivity likely contributes to loss of consciousness (LOC) during both sleep and general anesthesia, but the degree of overlap in the underlying mechanisms is unclear. Both sleep and anesthesia comprise states of varying levels of arousal and consciousness, including states of largely maintained conscious experience (sleep: N1, REM; anesthesia: sedated but responsive) as well as states of substantially reduced conscious experience (sleep: N2/N3; anesthesia: unresponsive). Here, we tested the hypotheses that (1) cortical connectivity will exhibit clear changes when transitioning into states of reduced consciousness, and (2) these changes will be similar for arousal states of comparable levels of consciousness during sleep and anesthesia. Using intracranial recordings from five adult neurosurgical patients, we compared resting state cortical functional connectivity (as measured by weighted phase lag index, wPLI) in the same subjects across arousal states during natural sleep [wake (WS), N1, N2, N3, REM] and propofol anesthesia [pre-drug wake (WA), sedated/responsive (S), and unresponsive (U)]. Analysis of alpha-band connectivity indicated a transition boundary distinguishing states of maintained and reduced conscious experience in both sleep and anesthesia. In wake states WS and WA, alpha-band wPLI within the temporal lobe was dominant. This pattern was largely unchanged in N1, REM, and S. Transitions into states of reduced consciousness N2, N3, and U were characterized by dramatic changes in connectivity, with dominant connections shifting to prefrontal cortex. Secondary analyses indicated similarities in reorganization of cortical connectivity in sleep and anesthesia. Shifts from temporal to frontal cortical connectivity may reflect impaired sensory processing in states of reduced consciousness. The data indicate that functional connectivity can serve as a biomarker of arousal state and suggest common mechanisms of LOC in sleep and anesthesia.

Atypical gamma functional connectivity pattern during light sleep in children with attention deficit hyperactivity disorder

INTRODUCTION

We examined functional connectivity analyses in electroencephalograms (EEGs) of patients with attention deficit hyperactivitydisorder (ADHD) and in those of typically developing children (TDC) to uncover neurobiological abnormalities.

METHODOLOGY

We enrolled 31 children with ADHD (mean age 11.1 years; 23 boys) and 17 sex-, age-, and intelligence-matched TDC to undergo 19-channel EEGs during light sleep. We estimated functional connectivity using the phase lag index (PLI) and coherence measurements that capture the synchronization of EEG signals and graphed metrics with GRETNA. We also performed continuous performance tests (CPTs) on the children and obtained answered questionnaires on ADHD and autism spectrum disorder.

RESULTS

The central-to-posterior gamma PLI was lower in children with ADHD than that in TDC. The other PLI frequency bands and all coherence frequency bands were not statistically different between both groups. Individuals with high hyperactivity scores on questionnaires and low reaction times (SDs) on CPT had low motor and occipital pairs of gamma PLIs. Graph metrics showed no differences between the groups.

CONCLUSIONS

The difference in averaged gamma PLI (especially with motor and occipital pairs) between groups was more suitable for diagnosis than the averaged coherence. Lower averaged gamma PLIs reflected more severe ADHD symptoms. A prospective study with more controlled conditions is warranted to determine if gamma-band PLI can be used as an auxiliary tool for ADHD diagnosis.

Graph Theoretical Analysis of Cortical Networks based on Conscious Experience

The aim of the study was to investigate differences in cortical networks based on the state of consciousness. Five subjects performed a serial-awakening paradigm with electroencephalography (EEG) recordings. We considered four states of consciousness: (1) non-rapid eye movement (NREM) sleep with no conscious experience, (2) NREM sleep with conscious experience, (3) rapid eye movement (REM) sleep with conscious experience, and (4) wakefulness. We applied graph theoretical analysis to explore the cortical connectivity and network properties in five frequency bands. Connectivity between EEG channels was evaluated with the weighted phase lag index (wPLI). The characteristic path length, transitivity, and clustering coefficient were computed to evaluate functional integration and segregation of the associated brain network. There were no significant differences in wPLI among the four states of consciousness. In the beta band, functional integration in wakefulness was higher than in NREM sleep. Regarding functional segregation, in the theta band, transitivity and clustering coefficient in NREM sleep with no conscious experience were stronger than in wakefulness or REM sleep, but clustering in the beta band showed an opposite effect. The observed differences may be related to cortical bistability and add to previously observed neural correlates of consciousness.

Leveraging Coupled Interaction for Multimodal Alzheimer's Disease Diagnosis

As the population becomes older worldwide, accurate computer-aided diagnosis for Alzheimer's disease (AD) in the early stage has been regarded as a crucial step for neurodegeneration care in recent years. Since it extracts the low-level features from the neuroimaging data, previous methods regarded this computer-aided diagnosis as a classification problem that ignored latent featurewise relation. However, it is known that multiple brain regions in the human brain are anatomically and functionally interlinked according to the current neuroscience perspective. Thus, it is reasonable to assume that the extracted features from different brain regions are related to each other to some extent. Also, the complementary information between different neuroimaging modalities could benefit multimodal fusion. To this end, we consider leveraging the coupled interactions in the feature level and modality level for diagnosis in this paper. First, we propose capturing the feature-level coupled interaction using a coupled feature representation. Then, to model the modality-level coupled interaction, we present two novel methods: 1) the coupled boosting (CB) that models the correlation of pairwise coupled-diversity on both inconsistently and incorrectly classified samples between different modalities and 2) the coupled metric ensemble (CME) that learns an informative feature projection from different modalities by integrating the intrarelation and interrelation of training samples. We systematically evaluated our methods with the AD neuroimaging initiative data set. By comparison with the baseline learning-based methods and the state-of-the-art methods that are specially developed for AD/MCI (mild cognitive impairment) diagnosis, our methods achieved the best performance with accuracy of 95.0% and 80.7% (CB), 94.9% and 79.9% (CME) for AD/NC (normal control), and MCI/NC identification, respectively.