Spectral signatures of reorganised brain networks in disorders of consciousness

Perturbational complexity index in assessing responsiveness to rTMS treatment in patients with disorders of consciousness: a cross-over randomized controlled trial study

BACKGROUND

Disorders of Consciousness (DoC) caused by severe brain injuries represent a challenging clinical entity, which is easy to misdiagnosis and lacks effective treatment options. Repetitive Transcranial Magnetic Stimulation (rTMS) is a non-invasive neuroelectric stimulation method that shows promise in improving consciousness for DoC, especially in minimally conscious state (MCS). However, there is little evidence of its effectiveness, especially in RCT studies.

METHODS

Twenty MCS patients participated in a double-blind, randomized, crossover, sham-controlled clinical study to evaluate the safety and efficacy of rTMS for MCS. Subjects were randomized into two groups: one group received rTMS-active for 10 consecutive days (n = 10), and the other group received rTMS-sham for 10 consecutive days (n = 10). After a 10-day washout period, the two groups were crossed over and received the opposite treatment. the rTMS protocol consisted of 2,000 pulses per day in the left dorsolateral prefrontal cortex (L-DLPFC), sent at 10 Hz. The stimulation intensity was 90% of the resting motor threshold. Coma Recovery Scale Revised (CRS-R), the main evaluation index, was evaluated before and after each phase in a double-blind manner. Meanwhile RS-EEG and TMS-EEG data were acquired and relative alpha power (RAP), and perturbational complexity index based on state transitions (PCIst) were caculated.

RESULTS

One-way ANOVA revealed significantly higher scores in rTMS-active treatment compared to rTMS-sham across various measures, including CRS-R total score, RAP, PCIst (all P < 0.05). Among the 20 MCS patients, 7 (35%) were identified as responders following rTMS treatment. Compared to rTMS-sham, responder scores for CRS-R, RAP, and PCIst (all P < 0.05) were significantly elevated after rTMS-active treatment. Conversely, there was no significant difference observed in non-responders. Furthermore, post-hoc analysis revealed that baseline PCIst was significantly higher in responders than non-responders. Upon a 6-month follow-up, CRS-R scores significantly increased in all 20 patients (P = 0.026). However, the responder group exhibited a more favorable prognosis compared to the non-responder group (P = 0.031).

CONCLUSIONS

Applying 10 Hz rTMS to L-DLPFC significantly increased consciousness level in MCS patients. PCIst is a neurophysiological index that has the potential to evaluate and predict therapeutic efficacy.

TRIAL REGISTRATION

www.

CLINICALTRIALS

gov , identifier: NCT05187000.

EEG signature of near-death-like experiences during syncope-induced periods of unresponsiveness

During fainting, disconnected consciousness may emerge in the form of dream-like experiences. Characterized by extra-ordinary and mystical features, these subjective experiences have been associated to near-death-like experiences (NDEs-like). We here aim to assess brain activity during syncope-induced disconnected consciousness by means of high-density EEG monitoring. Transient loss of consciousness and unresponsiveness were induced in 27 healthy volunteers through hyperventilation, orthostasis, and Valsalva maneuvers. Upon awakening, subjects were asked to report memories, if any. The Greyson NDE scale was used to evaluate the potential phenomenological content experienced during the syncope-induced periods of unresponsiveness. EEG source reconstruction assessed cortical activations during fainting, which were regressed out with subjective reports collected upon recovery of normal consciousness. We also conducted functional connectivity, graph-theoretic and complexity analyses. High quality high-density EEG data were obtained in 22 volunteers during syncope and unresponsiveness (lasting 22±8 s). NDE-like features (Greyson NDE scale total score ≥7/32) were apparent for eight volunteers and characterized by higher activity in delta, theta and beta2 bands in temporal and frontal regions. The richness of the NDE-like content was associated with delta, theta and beta2 bands cortical current densities, in temporal, parietal and frontal lobes, including insula, right temporoparietal junction, and cingulate cortex. Our analyses also revealed a higher complexity and that networks related to delta, theta, and beta2 bands were characterized by a higher overall connectivity paralleled by a higher segregation (i.e., local efficiency) and a higher integration (i.e., global efficiency) for the NDE-like group compared to the non-NDE-like group. Fainting-induced NDE-like episodes seem to be sustained by surges of neural activity representing promising markers of disconnected consciousness.

A neurophysiological profiling of the heartbeat-evoked potential in severe acquired brain injuries: A focus on unconsciousness

Unconsciousness in severe acquired brain injury (sABI) patients occurs with different cognitive and neural profiles. Perturbational approaches, which enable the estimation of proxies for brain reorganization, have added a new avenue for investigating the non-behavioural diagnosis of consciousness. In this prospective observational study, we conducted a comparative analysis of the topological patterns of heartbeat-evoked potentials (HEP) between patients experiencing a prolonged disorder of consciousness (pDoC) and patients emerging from a minimally consciousness state (eMCS). A total of 219 sABI patients were enrolled, each undergoing a synchronous EEG-ECG resting-state recording, together with a standardized consciousness diagnosis. A number of graph metrics were computed before/after the HEP (Before/After) using the R-peak on the ECG signal. The peak value of the global field power of the HEP was found to be significantly higher in eMCS patients with no difference in latency. Power spectrum was not able to discriminate consciousness neither Before nor After. Node assortativity and global efficiency were found to vary with different trends at unconsciousness. Lastly, the Perturbational Complexity Index of the HEP was found to be significantly higher in eMCS patients compared with pDoC. Given that cortical elaboration of peripheral inputs may serve as a non-behavioural determinant of consciousness, we have devised a low-cost and translatable technique capable of estimating causal proxies of brain functionality with an endogenous, non-invasive stimulus. Thus, we present an effective means to enhance consciousness assessment by incorporating the interaction between the autonomic nervous system (ANS) and central nervous system (CNS) into the loop.

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.

Modulation of brain activity in brain-injured patients with a disorder of consciousness in intensive care with repeated 10-Hz transcranial alternating current stimulation (tACS): a randomised controlled trial protocol

INTRODUCTION

Therapeutic interventions for disorders of consciousness lack consistency; evidence supports non-invasive brain stimulation, but few studies assess neuromodulation in acute-to-subacute brain-injured patients. This study aims to validate the feasibility and assess the effect of a multi-session transcranial alternating current stimulation (tACS) intervention in subacute brain-injured patients on recovery of consciousness, related brain oscillations and brain network dynamics.

METHODS AND ANALYSES

The study is comprised of two phases: a validation phase (n=12) and a randomised controlled trial (n=138). Both phases will be conducted in medically stable brain-injured adult patients (traumatic brain injury and hypoxic-ischaemic encephalopathy), with a Glasgow Coma Scale score ≤12 after continuous sedation withdrawal. Recruitment will occur at the intensive care unit of a Level 1 Trauma Centre in Montreal, Quebec, Canada. The intervention includes a 20 min 10 Hz tACS at 1 mA intensity or a sham session over parieto-occipital cortical sites, repeated over five consecutive days. The current's frequency targets alpha brain oscillations (8-13 Hz), known to be associated with consciousness. Resting-state electroencephalogram (EEG) will be recorded four times daily for five consecutive days: pre and post-intervention, at 60 and 120 min post-tACS. Two additional recordings will be included: 24 hours and 1-week post-protocol. Multimodal measures (blood samples, pupillometry, behavioural consciousness assessments (Coma Recovery Scale-revised), actigraphy measures) will be acquired from baseline up to 1 week after the stimulation. EEG signal analysis will focus on the alpha bandwidth (8-13 Hz) using spectral and functional network analyses. Phone assessments at 3, 6 and 12 months post-tACS, will measure long-term functional recovery, quality of life and caregivers' burden.

ETHICS AND DISSEMINATION

Ethical approval for this study has been granted by the Research Ethics Board of the CIUSSS du Nord-de-l'Île-de-Montréal (Project ID 2021-2279). The findings of this two-phase study will be submitted for publication in a peer-reviewed academic journal and submitted for presentation at conferences. The trial's results will be published on a public trial registry database (ClinicalTrials.gov).

TRIAL REGISTRATION NUMBER

NCT05833568.

Lateral frontoparietal effective connectivity differentiates and predicts state of consciousness in a cohort of patients with traumatic disorders of consciousness

Neuroimaging studies have suggested an important role for the default mode network (DMN) in disorders of consciousness (DoC). However, the extent to which DMN connectivity can discriminate DoC states-unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS)-is less evident. Particularly, it is unclear whether effective DMN connectivity, as measured indirectly with dynamic causal modelling (DCM) of resting EEG can disentangle UWS from healthy controls and from patients considered conscious (MCS+). Crucially, this extends to UWS patients with potentially "covert" awareness (minimally conscious star, MCS*) indexed by voluntary brain activity in conjunction with partially preserved frontoparietal metabolism as measured with positron emission tomography (PET+ diagnosis; in contrast to PET- diagnosis with complete frontoparietal hypometabolism). Here, we address this gap by using DCM of EEG data acquired from patients with traumatic brain injury in 11 UWS (6 PET- and 5 PET+) and in 12 MCS+ (11 PET+ and 1 PET-), alongside with 11 healthy controls. We provide evidence for a key difference in left frontoparietal connectivity when contrasting UWS PET- with MCS+ patients and healthy controls. Next, in a leave-one-subject-out cross-validation, we tested the classification performance of the DCM models demonstrating that connectivity between medial prefrontal and left parietal sources reliably discriminates UWS PET- from MCS+ patients and controls. Finally, we illustrate that these models generalize to an unseen dataset: models trained to discriminate UWS PET- from MCS+ and controls, classify MCS* patients as conscious subjects with high posterior probability (pp > .92). These results identify specific alterations in the DMN after severe brain injury and highlight the clinical utility of EEG-based effective connectivity for identifying patients with potential covert awareness.

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.

EEG connectivity and network analyses predict outcome in patients with disorders of consciousness - A systematic review and meta-analysis

Outcome prediction in prolonged disorders of consciousness (DOC) remains challenging. This can result in either inappropriate withdrawal of treatment or unnecessary prolongation of treatment. Electroencephalography (EEG) is a cheap, portable, and non-invasive device with various opportunities for complex signal analysis. Computational EEG measures, such as EEG connectivity and network metrics, might be ideal candidates for the investigation of DOC, but their capacity in prognostication is still undisclosed. We conducted a meta-analysis aiming to compare the prognostic power of the widely used clinical scale, Coma Recovery Scale-Revised - CRS-R and EEG connectivity and network metrics. We found that the prognostic power of the CRS-R scale was moderate (AUC: 0.67 (0.60-0.75)), but EEG connectivity and network metrics predicted outcome with significantly (p = 0.0071) higher accuracy (AUC:0.78 (0.70-0.86)). We also estimated the prognostic capacity of EEG spectral power, which was not significantly (p = 0.3943) inferior to that of the EEG connectivity and graph-theory measures (AUC:0.75 (0.70-0.80)). Multivariate automated outcome prediction tools seemed to outperform clinical and EEG markers.

Slow-wave brain connectivity predicts executive functioning and group belonging in socially vulnerable individuals

Important efforts have been made to describe the neural and cognitive features of healthy and clinical populations. However, the neural and cognitive features of socially vulnerable individuals remain largely unexplored, despite their proneness to developing neurocognitive disorders. Socially vulnerable individuals can be characterised as socially deprived, having a low socioeconomic status, suffering from chronic social stress, and exhibiting poor social adaptation. While it is known that such individuals are likely to perform worse than their peers on executive function tasks, studies on healthy but socially vulnerable groups are lacking. In the current study, we explore whether neural power and connectivity signatures can characterise executive function performance in healthy but socially vulnerable individuals, shedding light on the impairing effects that chronic stress and social disadvantages have on cognition. We measured resting-state electroencephalography and executive functioning in 38 socially vulnerable participants and 38 matched control participants. Our findings indicate that while neural power was uninformative, lower delta and theta phase synchrony are associated with worse executive function performance in all participants, whereas delta phase synchrony is higher in the socially vulnerable group compared to the control group. Finally, we found that delta phase synchrony and years of schooling are the best predictors for belonging to the socially vulnerable group. Overall, these findings suggest that exposure to chronic stress due to socioeconomic factors and a lack of education are associated with changes in slow-wave neural connectivity and executive functioning.

How well do neural signatures of resting-state EEG detect consciousness? A large-scale clinical study

The assessment of consciousness states, especially distinguishing minimally conscious states (MCS) from unresponsive wakefulness states (UWS), constitutes a pivotal role in clinical therapies. Despite that numerous neural signatures of consciousness have been proposed, the effectiveness and reliability of such signatures for clinical consciousness assessment still remains an intense debate. Through a comprehensive review of the literature, inconsistent findings are observed about the effectiveness of diverse neural signatures. Notably, the majority of existing studies have evaluated neural signatures on a limited number of subjects (usually below 30), which may result in uncertain conclusions due to small data bias. This study presents a systematic evaluation of neural signatures with large-scale clinical resting-state electroencephalography (EEG) signals containing 99 UWS, 129 MCS, 36 emergence from the minimally conscious state, and 32 healthy subjects (296 total) collected over 3 years. A total of 380 EEG-based metrics for consciousness detection, including spectrum features, nonlinear measures, functional connectivity, and graph-based measures, are summarized and evaluated. To further mitigate the effect of data bias, the evaluation is performed with bootstrap sampling so that reliable measures can be obtained. The results of this study suggest that relative power in alpha and delta serve as dependable indicators of consciousness. With the MCS group, there is a notable increase in the phase lag index-related connectivity measures and enhanced functional connectivity between brain regions in comparison to the UWS group. A combination of features enables the development of an automatic detector of conscious states.

Acoustic-electric trigeminal-nerve stimulation enhances functional connectivity in patients with disorders of consciousness

AIM

Disruption of functional brain connectivity is thought to underlie disorders of consciousness (DOC) and recovery of impaired connectivity is suggested as an indicator of consciousness restoration. We recently found that rhythmic acoustic-electric trigeminal-nerve stimulation (i.e., musical stimulation synchronized to electrical stimulation of the trigeminal nerve) in the gamma band can improve consciousness in patients with DOC. Here, we investigated whether these beneficial stimulation effects are mediated by alterations in functional connectivity.

METHODS

Sixty-three patients with DOC underwent 5 days of gamma, beta, or sham acoustic-electric trigeminal-nerve stimulation. Resting-state electroencephalography was measured before and after the stimulation and functional connectivity was assessed using phase-lag index (PLI).

RESULTS

We found that gamma stimulation induces an increase in gamma-band PLI. Further characterization revealed that the enhancing effect is (i) specific to the gamma band (as we observed no comparable change in beta-band PLI and no effect of beta-band acoustic-electric stimulation or sham stimulation), (ii) widely spread across the cortex, and (iii) accompanied by improvements in patients' auditory abilities.

CONCLUSION

These findings show that gamma acoustic-electric trigeminal-nerve stimulation can improve resting-state functional connectivity in the gamma band, which in turn may be linked to auditory abilities and/or consciousness restoration in DOC patients.

Identifying ADHD and subtypes through microstates analysis and complex networks

The diagnosis of attention-deficit hyperactivity disorder (ADHD) is based on the health history and on the evaluation of questionnaires to identify symptoms. This evaluation can be subjective and lengthy, especially in children. Therefore, a biomarker would be of great value to assist mental health professionals in the process of diagnosing ADHD. Event-related potential (ERP) is one of the most informative and dynamic methods of monitoring cognitive processes. Previous works suggested that specific sets of ERP-microstates are selectively affected by ADHD. This paper proposes a new methodology for the ERP-microstate analysis and identification of ADHD patients based on complex networks to model the microstate topographic maps. The analysis of global and local features of ERP-microstate networks revealed topological differences between ADHD and healthy control. The classification using a neural network with a single hidden layer resulted in an average accuracy of 99.72% in binary classification and 99.31% in the classification of ADHD subtypes. The results were compared to the power band spectral densities and the energy of wavelet coefficients. The temporal features of ERP-microstates, such as frequency of occurrence, duration, coverage, and transition probabilities, were also evaluated for comparison proposes. Overall, the selected topological features of ERP-microstate networks derived from the proposed method performed significantly better classification results. The results suggest that topological features of ERP-microstate networks are promising to identify ADHD and its subtypes with a neural network model compared to power band spectrum density, wavelet transform, and temporal features of ERP-microstates.

Cognitive Motor Dissociation: Gap Analysis and Future Directions

BACKGROUND

Patients with disorders of consciousness who are behaviorally unresponsive may demonstrate volitional brain responses to motor imagery or motor commands detectable on functional magnetic resonance imaging or electroencephalography. This state of cognitive motor dissociation (CMD) may have prognostic significance.

METHODS

The Neurocritical Care Society's Curing Coma Campaign identified an international group of experts who convened in a series of monthly online meetings between September 2021 and April 2023 to examine the science of CMD and identify key knowledge gaps and unmet needs.

RESULTS

The group identified major knowledge gaps in CMD research: (1) lack of information about patient experiences and caregiver accounts of CMD, (2) limited epidemiological data on CMD, (3) uncertainty about underlying mechanisms of CMD, (4) methodological variability that limits testing of CMD as a biomarker for prognostication and treatment trials, (5) educational gaps for health care personnel about the incidence and potential prognostic relevance of CMD, and (6) challenges related to identification of patients with CMD who may be able to communicate using brain-computer interfaces.

CONCLUSIONS

To improve the management of patients with disorders of consciousness, research efforts should address these mechanistic, epidemiological, bioengineering, and educational gaps to enable large-scale implementation of CMD assessment in clinical practice.

Structural and Functional Neuroanatomy of Core Consciousness: A Primer for Disorders of Consciousness Clinicians

Understanding the structural and functional neuroanatomy of core consciousness (ie, wakefulness and awareness) is an asset to clinicians caring for persons with disorders of consciousness. This article provides a primer on the structural and functional neuroanatomy of wakefulness and awareness. The neuroanatomical structures supporting these elements of core consciousness functions are reviewed first, after which brief description of the clinically evaluable relationships between disruption of these structures and disorders of consciousness (ie, brain-behavior relationships) are outlined. Consideration of neuroanatomy at the mesoscale (ie, the mesocircuit hypothesis) as well as in relation to several large-scale neural networks is offered.

The EEG complexity, information integration and brain network changes in minimally conscious state patients during general anesthesia

Objective.General anesthesia (GA) can induce reversible loss of consciousness. Nonetheless, the electroencephalography (EEG) characteristics of patients with minimally consciousness state (MCS) during GA are seldom observed.Approach.We recorded EEG data from nine MCS patients during GA. We used the permutation Lempel-Ziv complexity (PLZC), permutation fluctuation complexity (PFC) to quantify the type I and II complexities. Additionally, we used permutation cross mutual information (PCMI) and PCMI-based brain network to investigate functional connectivity and brain networks in sensor and source spaces.Main results.Compared to the preoperative resting state, during the maintenance of surgical anesthesia state, PLZC decreased (p< 0.001), PFC increased (p< 0.001) and PCMI decreased (p< 0.001) in sensor space. The results for these metrics in source space are consistent with sensor space. Additionally, node network indicators nodal clustering coefficient (NCC) (p< 0.001) and nodal efficiency (NE) (p< 0.001) decreased in these two spaces. Global network indicators normalized average path length (Lave/Lr) (p< 0.01) and modularity (Q) (p< 0.05) only decreased in sensor space, while the normalized average clustering coefficient (Cave/Cr) and small-world index (σ) did not change significantly. Moreover, the dominance of hub nodes is reduced in frontal regions in these two spaces. After recovery of consciousness, PFC decreased in the two spaces, while PLZC, PCMI increased. NCC, NE, and frontal region hub node dominance increased only in the sensor space. These indicators did not return to preoperative levels. In contrast, global network indicatorsLave/LrandQwere not significantly different from the preoperative resting state in sensor space.Significance.GA alters the complexity of the EEG, decreases information integration, and is accompanied by a reconfiguration of brain networks in MCS patients. The PLZC, PFC, PCMI and PCMI-based brain network metrics can effectively differentiate the state of consciousness of MCS patients during GA.

EEG Changes during Propofol Anesthesia Induction in Vegetative State Patients Undergoing Spinal Cord Stimulation Implantation Surgery

OBJECTIVE

To compare the EEG changes in vegetative state (VS) patients and non-craniotomy, non-vegetative state (NVS) patients during general anesthesia with low-dose propofol and to find whether it affects the arousal rate of VS patients.

METHODS

Seven vegetative state patients (VS group: five with traumatic brain injury, two with ischemic-hypoxic VS) and five non-craniotomy, non-vegetative state patients (NVS group) treated in the Department of Neurosurgery, Peking University International Hospital from January to May 2022 were selected. All patients were induced with 0.5 mg/kg propofol, and the Bispectral Index (BIS) changes within 5 min after administration were observed. Raw EEG signals and perioperative EEG signals were collected and analyzed using EEGLAB in the MATLAB software environment, time-frequency spectrums were calculated, and EEG changes were analyzed using power spectrums.

RESULTS

There was no significant difference in the general data before surgery between the two groups (p > 0.05); the BIS reduction in the VS group was significantly greater than that in the NVS group at 1 min, 2 min, 3 min, 4 min, and 5 min after 0.5 mg/kg propofol induction (p < 0.05). Time-frequency spectrum analysis showed the following: prominent α band energy around 10 Hz and decreased high-frequency energy in the NVS group, decreased high-frequency energy and main energy concentrated below 10 Hz in traumatic brain injury VS patients, higher energy in the 10-20 Hz band in ischemic-hypoxic VS patients. The power spectrum showed that the brain electrical energy of the NVS group was weakened R5 min after anesthesia induction compared with 5 min before induction, mainly concentrated in the small wave peak after 10 Hz, i.e., the α band peak; the energy of traumatic brain injury VS patients was weakened after anesthesia induction, but no α band peak appeared; and in ischemic-hypoxic VS patients, there was no significant change in low-frequency energy after anesthesia induction, high-frequency energy was significantly weakened, and a clear α band peak appeared slightly after 10 Hz. Three months after the operation, follow-up visits were made to the VS group patients who had undergone SCS surgery. One patient with traumatic brain injury VS was diagnosed with MCS-, one patient with ischemic-hypoxic VS had increased their CRS-R score by 1 point, and the remaining five patients had no change in their CRS scores.

CONCLUSIONS

Low doses of propofol cause great differences in the EEG of different types of VS patients, which may be the unique response of damaged nerve cell residual function to propofol, and these weak responses may also be the basis of brain recovery.

The Characteristics of Electroencephalogram Signatures in Minimally Conscious State Patients Induced by General Anesthesia

OBJECTIVE

General anesthesia (GA) is necessary for surgery, even for patients in a minimally conscious state (MCS). The characteristics of the electroencephalogram (EEG) signatures of the MCS patients under GA are still unclear.

METHODS

The EEG during GA were recorded from 10 MCS patients undergoing spinal cord stimulation surgery. The power spectrum, phase-amplitude coupling (PAC), the diversity of connectivity, and the functional network were investigated. Long term recovery was assessed by the Coma Recovery Scale-Revised at one year after the surgery, and the characteristics of the patients with good or bad prognosis status were compared.

RESULTS

For the four MCS patients with good prognostic recovery, slow oscillation (0.1-1 Hz) and the alpha band (8-12 Hz) in the frontal areas increased during the maintenance of a surgical state of anesthesia (MOSSA), and "peak-max" and "trough-max" patterns emerged in frontal and parietal areas. During MOSSA, the six MCS patients with bad prognosis demonstrated: increased modulation index, reduced diversity of connectivity (from mean±SD of 0.877 ± 0.003 to 0.776 ± 0.003, p < 0.001), reduced function connectivity significantly in theta band (from mean±SD of 1.032 ± 0.043 to 0.589 ± 0.036, p < 0.001, in prefrontal-frontal; and from mean±SD of 0.989 ± 0.043 to 0.684 ± 0.036, p < 0.001, in frontal-parietal) and reduced local and global efficiency of the network in delta band.

CONCLUSIONS

A bad prognosis in MCS patients is associated with signs of impaired thalamocortical and cortico-cortical connectivity - as indicated by inability to produce inter-frequency coupling and phase synchronization. These indices may have a role in predicting the long-term recovery of MCS patients.

Differences in generation and maintenance between ictal and interictal generalized spike-and-wave discharges in childhood absence epilepsy: A magnetoencephalography study

PURPOSE

Childhood absence epilepsy (CAE) is characterized by impaired consciousness and distinct electroencephalogram (EEG) patterns. However, interictal epileptiform discharges (IEDs) do not lead to noticeable symptoms. This study examines the disparity between ictal and interictal generalized spike-and-wave discharges (GSWDs) to determine the mechanisms behind CAE and consciousness.

METHODS

We enrolled 24 patients with ictal and interictal GSWDs in the study. The magnetoencephalography (MEG) data were recorded before and during GSWDs at a sampling rate of 6000 Hz and analyzed across six frequency bands. The absolute and relative spectral power were estimated with the Minimum Norm Estimate (MNE) combined with the Welch technique. All the statistical analyses were performed using paired-sample tests.

RESULTS

During GSWDs, the right lateral occipital cortex indicated a significant difference in the theta band (5-7 Hz) with stronger power (P = 0.027). The interictal group possessed stronger spectral power in the delta band (P < 0.01) and weaker power in the alpha band (P < 0.01) as early as 10 s before GSWDs in absolute and relative spectral power. Additionally, the ictal group revealed enhanced spectral power inside the occipital cortex in the alpha band and stronger spectral power in the right frontal regions within beta (15-29 Hz), gamma 1 (30-59 Hz), and gamma 2 (60-90 Hz) bands.

CONCLUSIONS

GSWDs seem to change gradually, with local neural activity changing even 10 s before discharge. During GSWDs, visual afferent stimulus insensitivity could be related to the impaired response state in CAE. The inhibitory signal in the low-frequency band can shorten GSWD duration, thereby achieving seizure control through inhibitory effect strengthening.

Cerebral electrometabolic coupling in disordered and normal states of consciousness

We assess cerebral integrity with cortical and subcortical FDG-PET and cortical electroencephalography (EEG) within the mesocircuit model framework in patients with disorders of consciousness (DoCs). The mesocircuit hypothesis proposes that subcortical activation facilitates cortical function. We find that the metabolic balance of subcortical mesocircuit areas is informative for diagnosis and is associated with four EEG-based power spectral density patterns, cortical metabolism, and α power in healthy controls and patients with a DoC. Last, regional electrometabolic coupling at the cortical level can be identified in the θ and α ranges, showing positive and negative relations with glucose uptake, respectively. This relation is inverted in patients with a DoC, potentially related to altered orchestration of neural activity, and may underlie suboptimal excitability states in patients with a DoC. By understanding the neurobiological basis of the pathophysiology underlying DoCs, we foresee translational value for diagnosis and treatment of patients with a DoC.

EEG fractal dimensions predict high-level behavioral responses in minimally conscious patients

Objective.Brain-injured patients may enter a state of minimal or inconsistent awareness termed minimally conscious state (MCS). Such patient may (MCS+) or may not (MCS-) exhibit high-level behavioral responses, and the two groups retain two inherently different rehabilitative paths and expected outcomes. We hypothesized that brain complexity may be treated as a proxy of high-level cognition and thus could be used as a neural correlate of consciousness.Approach.In this prospective observational study, 68 MCS patients (MCS-: 30; women: 31) were included (median [IQR] age 69 [20]; time post-onset 83 [28]). At admission to intensive rehabilitation, 30 min resting-state closed-eyes recordings were performed together with consciousness diagnosis following international guidelines. The width of the multifractal singularity spectrum (MSS) was computed for each channel time series and entered nested cross-validated interpretable machine learning models targeting the differential diagnosis of MCS±.Main results.Frontal MSS widths (p< 0.05), as well as the ones deriving from the left centro-temporal network (C3:p= 0.018, T3:p= 0.017; T5:p= 0.003) were found to be significantly higher in the MCS+ cohort. The best performing solution was found to be the K-nearest neighbor model with an aggregated test accuracy of 75.5% (median [IQR] AuROC for 100 executions 0.88 [0.02]). Coherently, the electrodes with highest Shapley values were found to be Fz and Cz, with four out the first five ranked features belonging to the fronto-central network.Significance.MCS+ is a frequent condition associated with a notably better prognosis than the MCS-. High fractality in the left centro-temporal network results coherent with neurological networks involved in the language function, proper of MCS+ patients. Using EEG-based interpretable algorithm to complement differential diagnosis of consciousness may improve rehabilitation pathways and communications with caregivers.

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.

Event-Related Spectral Perturbation, Inter Trial Coherence, and Functional Connectivity in motor execution: A comparative EEG study of old and young subjects

INTRODUCTION

The motor-related bioelectric brain activity of healthy young and old subjects was studied to understand the effect of aging on motor execution. A visually cued finger tapping movement paradigm and high-density EEG were used to examine the time and frequency characteristics.

METHODS

Twenty-two young and 22 healthy elderly adults participated in the study. Repeated trials of left and right index finger movements were recorded with a 128-channel EEG. Event-Related Spectral Perturbation (ERSP), Inter Trial Coherence (ITC), and Functional Connectivity were computed and compared between the age groups.

RESULTS

An age-dependent theta and alpha band ERSP decrease was observed over the frontal-midline area. Decrease of beta band ERSP was found over the ipsilateral central-parietal regions. Significant ITC differences were found in the delta and theta bands between old and young subjects over the contralateral parietal-occipital areas. The spatial extent of increased ITC values was larger in old subjects. The movement execution of older subjects showed higher global efficiency in the delta and theta bands, and higher local efficiency and node strengths in the delta, theta, alpha, and beta bands.

CONCLUSION

As functional compensation of aging, elderly motor networks involve more nonmotor, parietal-occipital, and frontal areas, with higher global and local efficiency, node strength. ERSP and ITC changes seem to be sensitive and complementary biomarkers of age-related motor execution.

Multidimensional assessment of heartbeat-evoked responses in disorders of consciousness

Because consciousness does not necessarily translate into overt behaviour, detecting residual consciousness in noncommunicating patients remains a challenge. Bedside diagnostic methods based on EEG are promising and cost-effective alternatives to detect residual consciousness. Recent evidence showed that the cortical activations triggered by each heartbeat, namely, heartbeat-evoked responses (HERs), can detect through machine learning the presence of minimal consciousness and distinguish between overt and covert minimal consciousness. In this study, we explore different markers to characterize HERs to investigate whether different dimensions of the neural responses to heartbeats provide complementary information that is not typically found under standard event-related potential analyses. We evaluated HERs and EEG average non-locked to heartbeats in six types of participants: healthy state, locked-in syndrome, minimally conscious state, vegetative state/unresponsive wakefulness syndrome, comatose and brain-dead patients. We computed a series of markers from HERs that can generally separate the unconscious from the conscious. Our findings indicate that HER variance and HER frontal segregation tend to be higher in the presence of consciousness. These indices, when combined with heart rate variability, have the potential to enhance the differentiation between different levels of awareness. We propose that a multidimensional evaluation of brain-heart interactions could be included in a battery of tests to characterize disorders of consciousness. Our results may motivate further exploration of markers in brain-heart communication for the detection of consciousness at the bedside. The development of diagnostic methods based on brain-heart interactions may be translated into more feasible methods for clinical practice.

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.

Towards modern post-coma care based on neuroscientific evidence

Background

Understanding the mechanisms underlying human consciousness is pivotal to improve the prognostication and treatment of severely brain-injured patients. Consciousness remains an elusive concept and the identification of its neural correlates is an active subject of research, however recent neuroscientific advances have allowed scientists to better characterize disorders of consciousness. These breakthroughs question the historical nomenclature and our current management of post-comatose patients.

Method

This review examines the contribution of consciousness neurosciences to the current clinical management of severe brain injury. It investigates the major impact of consciousness disorders on healthcare systems, the scientific frameworks employed to identify their neural correlates and how evidence-based data from neuroimaging research have reshaped the landscape of post-coma care in recent years.

Results

Our increased ability to detect behavioral and neurophysiological signatures of consciousness has led to significant changes in taxonomy and clinical practice. We advocate for a multimodal framework for the management of severely brain-injured patients based on precision medicine and evidence-based decisions, integrating epidemiology, health economics and neuroethics.

Conclusions

Major progress in brain imaging and clinical assessment have opened the door to a new era of post-coma care based on standardized neuroscientific evidence. We highlight its implications in clinical applications and call for improved collaborations between researchers and clinicians to better translate findings to the bedside.

Altered brain functional connectivity in vegetative state and minimally conscious state

Objectives

The pathological mechanism for a disorder of consciousness (DoC) is still not fully understood. Based on traditional behavioral scales, there is a high rate of misdiagnosis for subtypes of DoC. We aimed to explore whether topological characterization may explain the pathological mechanisms of DoC and be effective in diagnosing the subtypes of DoC.

Methods

Using resting-state functional magnetic resonance imaging data, the weighted brain functional networks for normal control subjects and patients with vegetative state (VS) and minimally conscious state (MCS) were constructed. Global and local network characteristics of each group were analyzed. A support vector machine was employed to identify MCS and VS patients.

Results

The average connection strength was reduced in DoC patients and roughly equivalent in MCS and VS groups. Global efficiency, local efficiency, and clustering coefficients were reduced, and characteristic path length was increased in DoC patients (p < 0.05). For patients of both groups, global network measures were not significantly different (p > 0.05). Nodal efficiency, nodal local efficiency, and nodal clustering coefficient were reduced in frontoparietal brain areas, limbic structures, and occipital and temporal brain areas (p < 0.05). The comparison of nodal centrality suggested that DoC causes reorganization of the network structure on a large scale, especially the thalamus. Lobal network measures emphasized that the differences between the two groups of patients mainly involved frontoparietal brain areas. The accuracy, sensitivity, and specificity of the classifier for identifying MCS and VS patients were 89.83, 78.95, and 95%, respectively.

Conclusion

There is an association between altered network structures and clinical symptoms of DoC. With the help of network metrics, it is feasible to differentiate MCS and VS patients.

Aberrant temporal correlations of ongoing oscillations in disorders of consciousness on multiple time scales

Changes in neural oscillation amplitude across states of consciousness has been widely reported, but little is known about the link between temporal dynamics of these oscillations on different time scales and consciousness levels. To address this question, we analyzed amplitude fluctuation of the oscillations extracted from spontaneous resting-state EEG recorded from the patients with disorders of consciousness (DOC) and healthy controls. Detrended fluctuation analysis (DFA) and measures of life-time and waiting-time were employed to characterize the temporal structure of EEG oscillations on long time scales (1-20 s) and short time scales (< 1 s), in groups with different consciousness states: patients in minimally conscious state (MCS), patients with unresponsive wakefulness syndrome (UWS) and healthy subjects. Results revealed increased DFA exponents that implies higher long-range temporal correlations (LRTC), especially in the central brain area in alpha and beta bands. On short time scales, declined bursts of oscillations were also observed. All the metrics exhibited lower individual variability in the UWS or MCS group, which may be attributed to the reduced spatial variability of oscillation dynamics. In addition, the temporal dynamics of EEG oscillations showed significant correlations with the behavioral responsiveness of patients. In summary, our findings shows that loss of consciousness is accompanied by alternation of temporal structure in neural oscillations on multiple time scales, and thus may help uncover the mechanism of underlying neuronal correlates of consciousness.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09852-9.

A systematic approach to brain dynamics: cognitive evolution theory of consciousness

The brain integrates volition, cognition, and consciousness seamlessly over three hierarchical (scale-dependent) levels of neural activity for their emergence: a causal or 'hard' level, a computational (unconscious) or 'soft' level, and a phenomenal (conscious) or 'psyche' level respectively. The cognitive evolution theory (CET) is based on three general prerequisites: physicalism, dynamism, and emergentism, which entail five consequences about the nature of consciousness: discreteness, passivity, uniqueness, integrity, and graduation. CET starts from the assumption that brains should have primarily evolved as volitional subsystems of organisms, not as prediction machines. This emphasizes the dynamical nature of consciousness in terms of critical dynamics to account for metastability, avalanches, and self-organized criticality of brain processes, then coupling it with volition and cognition in a framework unified over the levels. Consciousness emerges near critical points, and unfolds as a discrete stream of momentary states, each volitionally driven from oldest subcortical arousal systems. The stream is the brain's way of making a difference via predictive (Bayesian) processing. Its objective observables could be complexity measures reflecting levels of consciousness and its dynamical coherency to reveal how much knowledge (information gain) the brain acquires over the stream. CET also proposes a quantitative classification of both disorders of consciousness and mental disorders within that unified framework.

Aperiodic brain activity and response to anesthesia vary in disorders of consciousness

In the human electroencephalogram (EEG), oscillatory power peaks co-exist with non-oscillatory, aperiodic activity. Although EEG analysis has traditionally focused exclusively on oscillatory power, recent investigations have shown that the aperiodic EEG component can distinguish conscious wakefulness from sleep and anesthetic-induced unconsciousness. This study investigates the aperiodic EEG component of individuals in a disorder of consciousness (DOC); how it changes in response to exposure to anesthesia; and how it relates to the brain's information richness and criticality. High-density EEG was recorded from 43 individuals in a DOC, with 16 of these individuals undergoing a protocol of propofol anesthesia. The aperiodic component was defined by the spectral slope of the power spectral density. Our results demonstrate that the EEG aperiodic component is more informative about the participants' level of consciousness than the oscillatory component, especially for patients that suffered from a stroke. Importantly, the pharmacologically induced change in the spectral slope from 30-45 Hz positively correlated with individual's pre-anesthetic level of consciousness. The pharmacologically induced loss of information-richness and criticality was associated with individual's pre-anesthetic aperiodic component. During exposure to anesthesia, the aperiodic component was correlated with 3-month recovery status for individuals with DOC. The aperiodic EEG component has been historically neglected; this research highlights the necessity of considering this measure for the assessment of individuals in DOC and future research that seeks to understand the neurophysiological underpinnings of consciousness.

Olfactory response is a potential sign of consciousness: electroencephalogram findings

Objective

This study aimed to explore whether olfactory response can be a sign of consciousness and represent higher cognitive processing in patients with disorders of consciousness (DoC) using clinical and electroencephalogram data.

Methods

Twenty-eight patients with DoC [13 vegetative states (VS)/unresponsive wakefulness syndrome (UWS) and 15 minimally conscious states (MCS)] were divided into two groups: the presence of olfactory response (ORES) group and the absence of olfactory response (N-ORES) group according to behavioral signs from different odors, i.e., vanillin, decanoic acid, and blank stimuli. We recorded an olfactory task-related electroencephalogram (EEG) and analyzed the relative power and functional connectivity at the whole-brain level in patients with DoC and healthy controls (HCs). After three months, the outcomes of DoC patients were followed up using the coma recovery scale-revised (CRS-R).

Results

A significant relationship was found between olfactory responses and the level of consciousness (χ²(1) = 6.892, p = 0.020). For olfactory EEG, N-ORES patients showed higher theta functional connectivity than ORES patients after stimulation with vanillin (p = 0.029; p = 0.027). Patients with N-ORES showed lower alpha and beta relative powers than HCs at the group level (p = 0.019; p = 0.033). After three months, 62.5% (10/16) of the ORES patients recovered consciousness compared to 16.7% (2/12) in the N-ORES group. The presence of olfactory response was significantly associated with an improvement in consciousness (χ²(1) = 5.882, p = 0.023).

Conclusion

Olfactory responses should be considered signs of consciousness. The differences in olfactory processing between DoC patients with and without olfactory responses may be a way to explore the neural correlates of olfactory consciousness in these patients. The olfactory response may help in the assessment of consciousness and may contribute to therapeutic orientation.

Effect of Visual Feedback on Behavioral Control and Functional Activity During Bilateral Hand Movement

Previous researches state vision as a vital source of information for movement control and more precisely for accurate hand movement. Further, fine bimanual motor activity may be associated with various oscillatory activities within distinct brain areas and inter-hemispheric interactions. However, neural coordination among the distinct brain areas responsible to enhance motor accuracy is still not adequate. In the current study, we investigated task-dependent modulation by simultaneously measuring high time resolution electroencephalogram (EEG), electromyogram (EMG) and force along with bi-manual and unimanual motor tasks. The errors were controlled using visual feedback. To complete the unimanual tasks, the participant was asked to grip the strain gauge using the index finger and thumb of the right hand thereby exerting force on the connected visual feedback system. Whereas the bi-manual task involved finger abduction of the left index finger in two contractions along with visual feedback system and at the same time the right hand gripped using definite force on two conditions that whether visual feedback existed or not for the right hand. Primarily, the existence of visual feedback for the right hand significantly decreased brain network global and local efficiency in theta and alpha bands when compared with the elimination of visual feedback using twenty participants. Brain network activity in theta and alpha bands coordinates to facilitate fine hand movement. The findings may provide new neurological insight on virtual reality auxiliary equipment and participants with neurological disorders that cause movement errors requiring accurate motor training. The current study investigates task-dependent modulation by simultaneously measuring high time resolution electroencephalogram, electromyogram and force along with bi-manual and unimanual motor tasks. The findings show that visual feedback for right hand decreases the force root mean square error of right hand. Visual feedback for right hand decreases local and global efficiency of brain network in theta and alpha bands.

Optimizing the modulation paradigm of transcutaneous auricular vagus nerve stimulation in patients with disorders of consciousness: A prospective exploratory pilot study protocol

Background

Transcutaneous auricular vagus nerve stimulation (taVNS) is a non-invasive neuromodulation technique. Several studies have reported the effectiveness of taVNS in patients with disorders of consciousness (DOC); however, differences in the modulation paradigm have led to inconsistent treatment outcomes.

Methods/design

This prospective exploratory trial will include 15 patients with a minimally conscious state (MCS) recruited according to the coma recovery scale-revised (CRS-R). Each patient will receive 5 different frequencies of taVNS (1, 10, 25, 50, and 100 Hz); sham stimulation will be used as a blank control. The order of stimulation will be randomized, and the patients' CRS-R scores and resting electroencephalography (EEG) before and after stimulation will be recorded.

Discussion

The overall study of taVNS used in treating patients with DOC is still in the preliminary stage of exploration. Through this experiment, we aim to explore the optimal stimulation frequency parameters of taVNS for the treatment of DOC patients. Furthermore, we expect to achieve a stable improvement of consciousness in DOC patients by continuously optimizing the neuromodulation paradigm of taVNS for the treatment of DOC patients.

Clinical trial registration

https://www.chictr.org.cn/index.aspx, identifier ChiCTR 2200063828.

Statistical learning in patients in the minimally conscious state

When listening to speech, cortical activity can track mentally constructed linguistic units such as words, phrases, and sentences. Recent studies have also shown that the neural responses to mentally constructed linguistic units can predict the outcome of patients with disorders of consciousness (DoC). In healthy individuals, cortical tracking of linguistic units can be driven by both long-term linguistic knowledge and online learning of the transitional probability between syllables. Here, we investigated whether statistical learning could occur in patients in the minimally conscious state (MCS) and patients emerged from the MCS (EMCS) using electroencephalography (EEG). In Experiment 1, we presented to participants an isochronous sequence of syllables, which were composed of either 4 real disyllabic words or 4 reversed disyllabic words. An inter-trial phase coherence analysis revealed that the patient groups showed similar word tracking responses to real and reversed words. In Experiment 2, we presented trisyllabic artificial words that were defined by the transitional probability between words, and a significant word-rate EEG response was observed for MCS patients. These results suggested that statistical learning can occur with a minimal conscious level. The residual statistical learning ability in MCS patients could potentially be harnessed to induce neural plasticity.

Not with a “zap” but with a “beep”: measuring the origins of perinatal experience

When does the mind begin? Infant psychology is mysterious in part because we cannot remember our first months of life, nor can we directly communicate with infants. Even more speculative is the possibility of mental life prior to birth. The question of when consciousness, or subjective experience, begins in human development thus remains incompletely answered, though boundaries can be set using current knowledge from developmental neurobiology and recent investigations of the perinatal brain. Here, we offer our perspective on how the development of a sensory perturbational complexity index (sPCI) based on auditory ("beep-and-zip"), visual ("flash-and-zip"), or even olfactory ("sniff-and-zip") cortical perturbations in place of electromagnetic perturbations ("zap-and-zip") might be used to address this question. First, we discuss recent studies of perinatal cognition and consciousness using techniques such as functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and, in particular, magnetoencephalography (MEG). While newborn infants are the archetypal subjects for studying early human development, researchers may also benefit from fetal studies, as the womb is, in many respects, a more controlled environment than the cradle. The earliest possible timepoint when subjective experience might begin is likely the establishment of thalamocortical connectivity at 26 weeks gestation, as the thalamocortical system is necessary for consciousness according to most theoretical frameworks. To infer at what age and in which behavioral states consciousness might emerge following the initiation of thalamocortical pathways, we advocate for the development of the sPCI and similar techniques, based on EEG, MEG, and fMRI, to estimate the perinatal brain's state of consciousness.

Quantitative analysis of early-stage EEG reactivity predicts awakening and recovery of consciousness in patients with severe brain injury

BACKGROUND

Decisions of withdrawal of life-sustaining therapy for patients with severe brain injury are often based on prognostic evaluations such as analysis of electroencephalography (EEG) reactivity (EEG-R). However, EEG-R usually relies on visual assessment, which requires neurophysiological expertise and is prone to inter-rater variability. We hypothesised that quantitative analysis of EEG-R obtained 3 days after patient admission can identify new markers of subsequent awakening and consciousness recovery.

METHODS

In this prospective observational study of patients with severe brain injury requiring mechanical ventilation, quantitative EEG-R was assessed using standard 11-lead EEG with frequency-based (power spectral density) and functional connectivity-based (phase-lag index) analyses. Associations between awakening in the intensive care unit (ICU) and reactivity to auditory and nociceptive stimulations were assessed with logistic regression. Secondary outcomes included in-ICU mortality and 3-month Coma Recovery Scale-Revised (CRS-R) score.

RESULTS

Of 116 patients, 86 (74%) awoke in the ICU. Among quantitative EEG-R markers, variation in phase-lag index connectivity in the delta frequency band after noise stimulation was associated with awakening (adjusted odds ratio=0.89, 95% confidence interval: 0.81-0.97, P=0.02 corrected for multiple tests), independently of age, baseline severity, and sedation. This new marker was independently associated with improved 3-month CRS-R (adjusted β=-0.16, standard error 0.075, P=0.048), but not with mortality (adjusted odds ratio=1.08, 95% CI: 0.99-1.18, P=0.10).

CONCLUSIONS

An early-stage quantitative EEG-R marker was independently associated with awakening and 3-month level of consciousness in patients with severe brain injury. This promising marker based on functional connectivity will need external validation before potential integration into a multimodal prognostic model.

Electroencephalogram-Based Brain Connectivity Analysis in Prolonged Disorders of Consciousness

Background

Prolonged disorders of consciousness (pDOC) are common in neurology and place a heavy burden on families and society. This study is aimed at investigating the characteristics of brain connectivity in patients with pDOC based on quantitative EEG (qEEG) and extending a new direction for the evaluation of pDOC.

Methods

Participants were divided into a control group (CG) and a DOC group by the presence or absence of pDOC. Participants underwent magnetic resonance imaging (MRI) T1 three-dimensional magnetization with a prepared rapid acquisition gradient echo (3D-T1-MPRAGE) sequence, and video EEG data were collected. After calculating the power spectrum by EEG data analysis tool, DTABR ((δ + θ)/(α + β) ratio), Pearson's correlation coefficient (Pearson r), Granger's causality, and phase transfer entropy (PTE), we performed statistical analysis between two groups. Finally, receiver operating characteristic (ROC) curves of connectivity metrics were made.

Results

The proportion of power in frontal, central, parietal, and temporal regions in the DOC group was lower than that in the CG. The percentage of delta power in the DOC group was significantly higher than that in the CG, the DTABR in the DOC group was higher than that in the CG, and the value was inverted. The Pearson r of the DOC group was higher than that of CG. The Pearson r of the delta band (Z = -6.71, P < 0.01), theta band (Z = -15.06, P < 0.01), and alpha band (Z = -28.45, P < 0.01) were statistically significant. Granger causality showed that the intensity of directed connections between the two hemispheres in the DOC group at the same threshold was significantly reduced (Z = -82.43, P < 0.01). The PTE of each frequency band in the DOC group was lower than that in the CG. The PTE of the delta band (Z = -42.68, P < 0.01), theta band (Z = -56.79, P < 0.01), the alpha band (Z = -35.11, P < 0.01), and beta band (Z = -63.74, P < 0.01) had statistical significance.

Conclusion

Brain connectivity analysis based on EEG has the advantages of being noninvasive, convenient, and bedside. The Pearson r of DTABR, delta, theta, and alpha bands, Granger's causality, and PTE of the delta, theta, alpha, and beta bands can be used as biological markers to distinguish between pDOC and healthy people, especially when behavior evaluation is difficult or ambiguous; it can supplement clinical diagnosis.

Disrupted relationship between intrinsic neural timescales and alpha peak frequency during unconscious states - A high-density EEG study

Our brain processes the different timescales of our environment's temporal input stochastics. Is such a temporal input processing mechanism key for consciousness? To address this research question, we calculated measures of input processing on shorter (alpha peak frequency, APF) and longer (autocorrelation window, ACW) timescales on resting-state high-density EEG (256 channels) recordings and compared them across different consciousness levels (awake/conscious, ketamine and sevoflurane anaesthesia, unresponsive wakefulness, minimally conscious state). We replicate and extend previous findings of: (i) significantly longer ACW values, consistently over all states of unconsciousness, as measured with ACW-0 (an unprecedented longer version of the well-know ACW-50); (ii) significantly slower APF values, as measured with frequency sliding, in all four unconscious states. Most importantly, we report a highly significant correlation of ACW-0 and APF in the conscious state, while their relationship is disrupted in the unconscious states. In sum, we demonstrate the relevance of the brain's capacity for input processing on shorter (APF) and longer (ACW) timescales - including their relationship - for consciousness. Albeit indirectly, e.g., through the analysis of electrophysiological activity at rest, this supports the mechanism of temporo-spatial alignment to the environment's temporal input stochastics, through relating different neural timescales, as one key predisposing factor of consciousness.

Neural complexity is a common denominator of human consciousness across diverse regimes of cortical dynamics

What is the common denominator of consciousness across divergent regimes of cortical dynamics? Does consciousness show itself in decibels or in bits? To address these questions, we introduce a testbed for evaluating electroencephalogram (EEG) biomarkers of consciousness using dissociations between neural oscillations and consciousness caused by rare genetic disorders. Children with Angelman syndrome (AS) exhibit sleep-like neural dynamics during wakefulness. Conversely, children with duplication 15q11.2-13.1 syndrome (Dup15q) exhibit wake-like neural dynamics during non-rapid eye movement (NREM) sleep. To identify highly generalizable biomarkers of consciousness, we trained regularized logistic regression classifiers on EEG data from wakefulness and NREM sleep in children with AS using both entropy measures of neural complexity and spectral (i.e., neural oscillatory) EEG features. For each set of features, we then validated these classifiers using EEG from neurotypical (NT) children and abnormal EEGs from children with Dup15q. Our results show that the classification performance of entropy-based EEG biomarkers of conscious state is not upper-bounded by that of spectral EEG features, which are outperformed by entropy features. Entropy-based biomarkers of consciousness may thus be highly adaptable and should be investigated further in situations where spectral EEG features have shown limited success, such as detecting covert consciousness or anesthesia awareness.

EEG-based methods for recovery prognosis of patients with disorders of consciousness: A systematic review

OBJECTIVE

Disorders of consciousness (DoC) are acquired conditions of severely altered consciousness. Electroencephalography (EEG)-derived biomarkers have been studied as clinical predictors of consciousness recovery. Therefore, this study aimed to systematically review the methods, features, and models used to derive prognostic EEG markers in patients with DoC in a rehabilitation setting.

METHODS

We conducted a systematic literature search of EEG-based strategies for consciousness recovery prognosis in five electronic databases.

RESULTS

The search resulted in 2964 papers. After screening, 15 studies were included in the review. Our analyses revealed that simpler experimental settings and similar filtering cut-off frequencies are preferred. The results of studies were categorised by extracting qualitative and quantitative features. The quantitative features were further classified into evoked/event-related potentials, spectral measures, entropy measures, and graph-theory measures. Despite the variety of methods, features from all categories, including qualitative ones, exhibited significant correlations with DoC prognosis. Moreover, no agreement was found on the optimal set of EEG-based features for the multivariate prognosis of patients with DoC, which limits the computational methods applied for outcome prediction and correlation analysis to classical ones. Nevertheless, alpha power, reactivity, and higher complexity metrics were often found to be predictive of consciousness recovery.

CONCLUSIONS

This study's findings confirm the essential role of qualitative EEG and suggest an important role for quantitative EEG. Their joint use could compensate for their reciprocal limitations.

SIGNIFICANCE

This study emphasises the need for further efforts toward guidelines on standardised EEG analysis pipeline, given the already proven role of EEG markers in the recovery prognosis of patients with DoC.

Is frontoparietal electroencephalogram activity related to the level of functional disability in patients emerging from a minimally conscious state? A preliminary study

Objective

When regaining consciousness, patients who emerge from a minimally conscious state (EMCS) present with different levels of functional disability, which pose great challenges for treatment. This study investigated the frontoparietal activity in EMCS patients and its effects on functional disability.

Materials and methods

In this preliminary study, 12 EMCS patients and 12 healthy controls were recruited. We recorded a resting-state scalp electroencephalogram (EEG) for at least 5 min for each participant. Each patient was assessed using the disability rating scale (DRS) to determine the level of functional disability. We analyzed the EEG power spectral density and sensor-level functional connectivity in relation to the patient’s functional disability.

Results

In the frontoparietal region, EMCS patients demonstrated lower relative beta power (P < 0.01) and higher weighted phase lag index (wPLI) values in the theta (P < 0.01) and gamma (P < 0.01) bands than healthy controls. The frontoparietal theta wPLI values of EMCS patients were positively correlated with the DRS scores (r_s = 0.629, P = 0.029). At the whole-brain level, EMCS patients only had higher wPLI values in the theta band (P < 0.01) than healthy controls. The whole-brain theta wPLI values of EMCS patients were also positively correlated with the DRS scores (r_s = 0.650, P = 0.022). No significant difference in the power and connectivity between the frontoparietal region and the whole brain in EMCS patients was observed.

Conclusion

EMCS patients still experience neural dysfunction, especially in the frontoparietal region. However, the theta connectivity in the frontoparietal region did not increase specifically. At the level of the whole brain, the same shift could also be seen. Theta functional connectivity in the whole brain may underlie different levels of functional disability.

Therapeutic Neuromodulation toward a Critical State May Serve as a General Treatment Strategy

Brain disease has become one of this century’s biggest health challenges, urging the development of novel, more effective treatments. To this end, neuromodulation represents an excellent method to modulate the activity of distinct neuronal regions to alleviate disease. Recently, the medical indications for neuromodulation therapy have expanded through the adoption of the idea that neurological disorders emerge from deficits in systems-level structures, such as brain waves and neural topology. Connections between neuronal regions are thought to fluidly form and dissolve again based on the patterns by which neuronal populations synchronize. Akin to a fire that may spread or die out, the brain’s activity may similarly hyper-synchronize and ignite, such as seizures, or dwindle out and go stale, as in a state of coma. Remarkably, however, the healthy brain remains hedged in between these extremes in a critical state around which neuronal activity maneuvers local and global operational modes. While it has been suggested that perturbations of this criticality could underlie neuropathologies, such as vegetative states, epilepsy, and schizophrenia, a major translational impact is yet to be made. In this hypothesis article, we dissect recent computational findings demonstrating that a neural network’s short- and long-range connections have distinct and tractable roles in sustaining the critical regime. While short-range connections shape the dynamics of neuronal activity, long-range connections determine the scope of the neuronal processes. Thus, to facilitate translational progress, we introduce topological and dynamical system concepts within the framework of criticality and discuss the implications and possibilities for therapeutic neuromodulation guided by topological decompositions.

Functional Connectivity Increases in Response to High-Definition Transcranial Direct Current Stimulation in Patients with Chronic Disorder of Consciousness

Highlights

Functional connectivity induced by HD-tDCS in DLPFC has different trends in CRS-R score improvers and non-improvers.

An increase in theta PLV in the left frontal–parietooccipital region was significantly associated with CRS-R changes.

DOC patients with increased PLV of the alpha band in the intra-bifrontal region have a better prognosis than those without.

Abstract

High-definition transcranial direct current stimulation (HD-tDCS) has been shown to play an important role in improving consciousness in patients with disorders of consciousness (DOCs), but its neuroelectrophysiological evidence is still lacking. To better explain the electrophysiological mechanisms of the effects of HD-tDCS on patients with DOCs, 22 DOC patients underwent 10 anodal HD-tDCS sessions of the left dorsolateral prefrontal cortex (DLPFC). This study used the Coma Recovery Scale-Revised (CRS-R) to assess the level of consciousness in DOC patients. According to whether the CRS-R score increased before and after stimulation, DOC patients were divided into a responsive group and a non-responsive group. By comparing the differences in resting-state EEG functional connectivity between different frequency bands and brain regions, as well as the relationship between functional connectivity values and clinical scores, the electrophysiological mechanism of the clinical effects of HD-tDCS was further explored. The change of the phase locking value (PLV) on the theta frequency band in the left frontal–parietooccipital region was positively correlated with the change in the CRS-R scores. As the number of interventions increased, we observed that in the responsive group, the change in PLV showed an upward trend, and the increase in the PLV appeared in the left frontal–parietooccipital region at 4–8 Hz and in the intra-bifrontal region at 8–13 Hz. In the non-responsive group, although the CRS-R scores did not change after stimulation, the PLV showed a downward trend, and the decrease in the PLV appeared in the intra-bifrontal region at 8–13 Hz. In addition, at the three-month follow-up, patients with increased PLV in the intra-bifrontal region at 8–13 Hz after repeated HD-tDCS stimulation had better outcomes than those without. Repeated anodal stimulation of the left DLPFC with HD-tDCS resulted in improved consciousness in some patients with DOCs. The increase in functional connectivity in the brain regions may be associated with the improvement of related awareness after HD-tDCS and may be a predictor of better long-term outcomes.

Accuracy of EEG Biomarkers in the Detection of Clinical Outcome in Disorders of Consciousness after Severe Acquired Brain Injury: Preliminary Results of a Pilot Study Using a Machine Learning Approach

Accurate outcome detection in neuro-rehabilitative settings is crucial for appropriate long-term rehabilitative decisions in patients with disorders of consciousness (DoC). EEG measures derived from high-density EEG can provide helpful information regarding diagnosis and recovery in DoC patients. However, the accuracy rate of EEG biomarkers to predict the clinical outcome in DoC patients is largely unknown. This study investigated the accuracy of psychophysiological biomarkers based on clinical EEG in predicting clinical outcomes in DoC patients. To this aim, we extracted a set of EEG biomarkers in 33 DoC patients with traumatic and nontraumatic etiologies and estimated their accuracy to discriminate patients’ etiologies and predict clinical outcomes 6 months after the injury. Machine learning reached an accuracy of 83.3% (sensitivity = 92.3%, specificity = 60%) with EEG-based functional connectivity predicting clinical outcome in nontraumatic patients. Furthermore, the combination of functional connectivity and dominant frequency in EEG activity best predicted clinical outcomes in traumatic patients with an accuracy of 80% (sensitivity = 85.7%, specificity = 71.4%). These results highlight the importance of functional connectivity in predicting recovery in DoC patients. Moreover, this study shows the high translational value of EEG biomarkers both in terms of feasibility and accuracy for the assessment of DoC.

The strength of weak integrated information theory

The integrated information theory of consciousness (IIT) is divisive: while some believe it provides an unprecedentedly powerful approach to address the 'hard problem', others dismiss it on grounds that it is untestable. We argue that the appeal and applicability of IIT can be greatly widened if we distinguish two flavours of the theory: strong IIT, which identifies consciousness with specific properties associated with maxima of integrated information; and weak IIT, which tests pragmatic hypotheses relating aspects of consciousness to broader measures of information dynamics. We review challenges for strong IIT, explain how existing empirical findings are well explained by weak IIT without needing to commit to the entirety of strong IIT, and discuss the outlook for both flavours of IIT.

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.

High-Definition Transcranial Direct Current Stimulation of the Dorsolateral Prefrontal Cortex Modulates the Electroencephalography Rhythmic Activity of Parietal Occipital Lobe in Patients With Chronic Disorders of Consciousness

Background

Disorders of consciousness (DOC) are a spectrum of pathologies affecting one’s ability to interact with the external world. At present, High-Definition Transcranial Direct Current Stimulation (HD-tDCS) is used in many patients with DOC as a non-invasive treatment, but electrophysiological research on the effect of HD-tDCS on patients with DOC is limited.

Objectives

To explore how HD-tDCS affects the cerebral cortex and examine the possible electrophysiological mechanisms underlying the effects of HD-tDCS on the cerebral cortex.

Methods

A total of 19 DOC patients were assigned to HD-tDCS stimulation. Each of them underwent 10 anodal HD-tDCS sessions of the left dorsolateral prefrontal cortex (DLPFC) over 5 consecutive days. Coma Recovery Scale-Revision (CRS-R) scores were recorded to evaluate the consciousness level before and after HD-tDCS, while resting-state electroencephalography (EEG) recordings were obtained immediately before and after single and multiple HD-tDCS stimuli. Depending on whether the CRS-R score increased after stimulation, we classified the subjects into responsive (RE) and non-responsive (N-RE) groups and compared the differences in power spectral density (PSD) between the groups in different frequency bands and brain regions, and also examined the relationship between PSD values and CRS-R scores.

Results

For the RE group, the PSD value of the parieto-occipital region increased significantly in the 6–8 Hz frequency band after multiple stimulations by HD-tDCS. After a single stimulation, an increase in PSD was observed at 10–13 and 13–30 Hz. In addition, for all subjects, a positive correlation was observed between the change in PSD value in the parieto-occipital region at 10–13 and 6–8 Hz frequency band and the change in CRS-R score after a single stimulation.

Conclusion

Repeated anodal HD-tDCS of the left DLPFC can improve clinical outcomes in patients with DOC, and HD-tDCS-related increased levels of consciousness were associated with increased parieto-occipital PSD.

Neural Correlates of Non-ordinary States of Consciousness in Pranayama Practitioners: The Role of Slow Nasal Breathing

The modulatory effect of nasal respiration on integrative brain functions and hence consciousness has recently been unambiguously demonstrated. This effect is sustained by the olfactory epithelium mechanical sensitivity complemented by the existence of massive projections between the olfactory bulb and the prefrontal cortex. However, studies on slow nasal breathing (SNB) in the context of contemplative practices have sustained the fundamental role of respiratory vagal stimulation, with little attention to the contribution of the olfactory epithelium mechanical stimulation. This study aims at disentangling the effects of olfactory epithelium stimulation (proper of nasal breathing) from those related to respiratory vagal stimulation (common to slow nasal and mouth breathing). We investigated the psychophysiological (cardio-respiratory and electroencephalographic parameters) and phenomenological (perceived state of consciousness) aftereffects of SNB (epithelium mechanical – 2.5 breaths/min) in 12 experienced meditators. We compared the nasal breathing aftereffects with those observed after a session of mouth breathing at the same respiratory rate and with those related to a resting state condition. SNB induced (1) slowing of electroencephalography (EEG) activities (delta-theta bands) in prefrontal regions, (2) a widespread increase of theta and high-beta connectivity complemented by an increase of phase-amplitude coupling between the two bands in prefrontal and posterior regions belonging to the Default Mode Network, (3) an increase of high-beta networks small-worldness. (4) a higher perception of being in a non-ordinary state of consciousness. The emerging scenario strongly suggests that the effects of SNB, beyond the relative contribution of vagal stimulation, are mainly ascribable to olfactory epithelium stimulation. In conclusion, slow Pranayama breathing modulates brain activity and hence subjective experience up to the point of inducing a non-ordinary state of consciousness.

STXBP1 Syndrome Is Characterized by Inhibition-Dominated Dynamics of Resting-State EEG

STXBP1 syndrome is a rare neurodevelopmental disorder caused by heterozygous variants in the STXBP1 gene and is characterized by psychomotor delay, early-onset developmental delay, and epileptic encephalopathy. Pathogenic STXBP1 variants are thought to alter excitation-inhibition (E/I) balance at the synaptic level, which could impact neuronal network dynamics; however, this has not been investigated yet. Here, we present the first EEG study of patients with STXBP1 syndrome to quantify the impact of the synaptic E/I dysregulation on ongoing brain activity. We used high-frequency-resolution analyses of classical and recently developed methods known to be sensitive to E/I balance. EEG was recorded during eyes-open rest in children with STXBP1 syndrome (n = 14) and age-matched typically developing children (n = 50). Brain-wide abnormalities were observed in each of the four resting-state measures assessed here: (i) slowing of activity and increased low-frequency power in the range 1.75–4.63 Hz, (ii) increased long-range temporal correlations in the 11–18 Hz range, (iii) a decrease of our recently introduced measure of functional E/I ratio in a similar frequency range (12–24 Hz), and (iv) a larger exponent of the 1/f-like aperiodic component of the power spectrum. Overall, these findings indicate that large-scale brain activity in STXBP1 syndrome exhibits inhibition-dominated dynamics, which may be compensatory to counteract local circuitry imbalances expected to shift E/I balance toward excitation, as observed in preclinical models. We argue that quantitative EEG investigations in STXBP1 and other neurodevelopmental disorders are a crucial step to understand large-scale functional consequences of synaptic E/I perturbations.

Mapping the Unconscious Brain: Insights From Advanced Neuroimaging

SUMMARY

Recent advances in neuroimaging have been a preeminent factor in the scientific effort to unravel mechanisms of conscious awareness and the pathophysiology of disorders of consciousness. In the first part of this review, we selectively discuss operational models of consciousness, the biophysical signal that is measured using different imaging modalities, and knowledge on disorders of consciousness that has been gleaned with each neuroimaging modality. Techniques considered include diffusion-weighted imaging, diffusion tensor imaging, different types of nuclear medicine imaging, functional MRI, magnetoencephalography, and the combined transcranial magnetic stimulation-electroencephalography approach. In the second part of this article, we provide an overview of how advanced neuroimaging can be leveraged to support neurological prognostication, the use of machine learning to process high-dimensional imaging data, potential applications in clinical practice, and future directions.