Altered resting state effective connectivity in long-standing vegetative state patients: an EEG study

Resting-state EEG microstates as electrophysiological biomarkers in post-stroke disorder of consciousness

Introduction

Ischemic stroke patients commonly experience disorder of consciousness (DOC), leading to poorer discharge outcomes and higher mortality risks. Therefore, the identification of applicable electrophysiological biomarkers is crucial for the rapid diagnosis and evaluation of post-stroke disorder of consciousness (PS-DOC), while providing supportive evidence for cerebral neurology.

Methods

In our study, we conduct microstate analysis on resting-state electroencephalography (EEG) of 28 post-stroke patients with awake consciousness and 28 patients with PS-DOC, calculating the temporal features of microstates. Furthermore, we extract the Lempel-Ziv complexity of microstate sequences and the delta/alpha power ratio of EEG on spectral. Statistical analysis is performed to examine the distinctions in features between the two groups, followed by inputting the distinctive features into a support vector machine for the classification of PS-DOC.

Results

Both groups obtain four optimal topographies of EEG microstates, but notable distinctions are observed in microstate C. Within the PS-DOC group, there is a significant increase in the mean duration and coverage of microstates B and C, whereas microstate D displays a contrasting trend. Additionally, noteworthy variations are found in the delta/alpha ratio and Lempel-Ziv complexity between the two groups. The integration of the delta/alpha ratio with microstates' temporal and Lempel-Ziv complexity features demonstrates the highest performance in the classifier (Accuracy = 91.07%).

Discussion

Our results suggest that EEG microstates can provide insights into the abnormal brain network dynamics in DOC patients post-stroke. Integrating the temporal and Lempel-Ziv complexity microstate features with spectral features offers a deeper understanding of the neuro mechanisms underlying brain damage in patients with DOC, holding promise as effective electrophysiological biomarkers for diagnosing PS-DOC.

Understanding, detecting, and stimulating consciousness recovery in the ICU

Coma is a medical and socioeconomic emergency. Although underfunded, research on coma and disorders of consciousness has made impressive progress. Lesion-network-mapping studies have delineated the precise brainstem regions that consistently produce coma when damaged. Functional neuroimaging has revealed how mechanisms like "communication through coherence" and "inhibition by gating" work in synergy to enable cortico-cortical processing and how this information transfer is disrupted in brain injury. On the cellular level, break-down of intracellular communication between the layer 5 pyramidal cell soma and the apical dendritic part impairs dendritic information integration, with up-stream effects on microcircuits in local neuronal populations and on large-scale fronto-parietal networks, which correlates with loss of consciousness. A breakthrough in clinical concepts occurred when fMRI, and later EEG, studies revealed that 15% of clinically unresponsive patients in acute and chronic settings are in fact awake and aware, as shown by their command following abilities revealed by brain activation during motor and locomotion imagery tasks. This condition is now termed "cognitive motor dissociation." Furthermore, epidemiological data on coma were literally non-existent until recently because of difficulties related to case ascertainment with traditional methods, but crowdsourcing of family observations enabled the first estimates of how frequent coma is in the general population (pooled annual incidence of 201 coma cases per 100,000 population in the UK and the USA). Diagnostic guidelines on coma and disorders of consciousness by the American Academy of Neurology and the European Academy of Neurology provide ambitious clinical frameworks to accommodate these achievements. As for therapy, a broad range of medical and non-medical treatment options is now being tested in increasingly larger trials; in particular, amantadine and transcranial direct current stimulation appear promising in this regard. Major international initiatives like the Curing Coma Campaign aim to raise awareness for coma and disorders of consciousness in the public, with the ultimate goal to make more brain-injured patients recover consciousness after a coma. To highlight all these accomplishments, this paper provides a comprehensive overview of recent progress and future challenges related to understanding, detecting, and stimulating consciousness recovery in the ICU.

Ultra-high-resolution time-frequency analysis of EEG to characterise brain functional connectivity with the application in Alzheimer's disease

OBJECTIVE

This study aims to explore the potential of high-resolution brain functional connectivity based on electroencephalogram (EEG), a non-invasive low-cost technique, to be translated into a long-overdue biomarker and a diagnostic method for Alzheimer's disease (AD).

APPROACH

The paper proposes a novel ultra-high-resolution time-frequency nonlinear cross-spectrum method to construct a promising biomarker of AD pathophysiology. Specifically, using the peak frequency estimated from a Revised Hilbert-Huang Transformation cross-spectrum as a biomarker, the Support Vector Machine classifier is used to distinguish AD from healthy controls (HC).

MAIN RESULTS

With the combinations of the proposed biomarker and machine learning, we achieved a promising accuracy of 89%. The proposed method performs better than the wavelet cross-spectrum and other functional connectivity measures in the temporal or frequency domain, particularly in the Full, Delta and Alpha bands. Besides, a novel visualisation approach developed from topography is introduced to represent the brain functional connectivity, with which the difference between AD and HCs can be clearly displayed. The interconnections between posterior and other brain regions are obviously affected in AD.

SIGNIFICANCE

Those findings imply that the proposed RHHT approach could better track dynamic and nonlinear functional connectivity information, paving the way for the development of a novel diagnostic approach.

A Novel Approach for Segment-Length Selection Based on Stationarity to Perform Effective Connectivity Analysis Applied to Resting-State EEG Signals

Connectivity among different areas within the brain is a topic that has been notably studied in the last decade. In particular, EEG-derived measures of effective connectivity examine the directionalities and the exerted influences raised from the interactions among neural sources that are masked out on EEG signals. This is usually performed by fitting multivariate autoregressive models that rely on the stationarity that is assumed to be maintained over shorter bits of the signals. However, despite being a central condition, the selection process of a segment length that guarantees stationary conditions has not been systematically addressed within the effective connectivity framework, and thus, plenty of works consider different window sizes and provide a diversity of connectivity results. In this study, a segment-size-selection procedure based on fourth-order statistics is proposed to make an informed decision on the appropriate window size that guarantees stationarity both in temporal and spatial terms. Specifically, kurtosis is estimated as a function of the window size and used to measure stationarity. A search algorithm is implemented to find the segments with similar stationary properties while maximizing the number of channels that exhibit the same properties and grouping them accordingly. This approach is tested on EEG signals recorded from six healthy subjects during resting-state conditions, and the results obtained from the proposed method are compared to those obtained using the classical approach for mapping effective connectivity. The results show that the proposed method highlights the influence that arises in the Default Mode Network circuit by selecting a window of 4 s, which provides, overall, the most uniform stationary properties across channels.

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.

Brain functional and effective connectivity based on electroencephalography recordings: A review

Functional connectivity and effective connectivity of the human brain, representing statistical dependence and directed information flow between cortical regions, significantly contribute to the study of the intrinsic brain network and its functional mechanism. Many recent studies on electroencephalography (EEG) have been focusing on modeling and estimating brain connectivity due to increasing evidence that it can help better understand various brain neurological conditions. However, there is a lack of a comprehensive updated review on studies of EEG-based brain connectivity, particularly on visualization options and associated machine learning applications, aiming to translate those techniques into useful clinical tools. This article reviews EEG-based functional and effective connectivity studies undertaken over the last few years, in terms of estimation, visualization, and applications associated with machine learning classifiers. Methods are explored and discussed from various dimensions, such as either linear or nonlinear, parametric or nonparametric, time-based, and frequency-based or time-frequency-based. Then it is followed by a novel review of brain connectivity visualization methods, grouped by Heat Map, data statistics, and Head Map, aiming to explore the variation of connectivity across different brain regions. Finally, the current challenges of related research and a roadmap for future related research are presented.

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.

Modeling temporal dynamics of face processing in youth and adults

A hierarchical model of temporal dynamics was examined in adults (n = 34) and youth (n = 46) across the stages of face processing during the perception of static and dynamic faces. Three ERP components (P100, N170, N250) and spectral power in the mu range were extracted, corresponding to cognitive stages of face processing: low-level vision processing, structural encoding, higher-order processing, and action understanding. Youth and adults exhibited similar yet distinct patterns of hierarchical temporal dynamics such that earlier cognitive stages predicted later stages, directly and indirectly. However, latent factors indicated unique profiles related to behavioral performance for adults and youth and age as a continuous factor. The application of path analysis to electrophysiological data can yield novel insights into the cortical dynamics of social information processing.

Study of Human Tacit Knowledge Based on Electroencephalogram Signal Characteristics

Tacit knowledge is the kind of knowledge that is difficult to transfer to another person by means of writing it down or verbalizing it. In the mineral grinding process, the proficiency of the operators depends on the tacit knowledge gained from their experience and training rather than on knowledge learned from a handbook. This article proposed a method combining the electroencephalogram (EEG) signals and the industrial process to detect the proficiency of the operators in the mineral grinding process to reveal the effect of tacit knowledge on the functional cortical connection. The functional brain networks of operators were established based on partial direct coherence and directed transfer function of EEG, and the multi-classifiers were used with the graph-theoretic indexes of the FBNs as input to distinguish the trained operators (Hps) from the non-trained operators (Lps). The results showed that the brain networks of Hps had a better connectivity than those of Lps (p < 0.01), and the accuracy of classification was up to 94.2%. Our studies confirm that based on the performance of EEG features and the combination of industrial operational operation and cognitive processes, the proficiency of the operators can be detected.

EEG Assessment in Patients With Disorders of Consciousness: Aims, Advantages, Limits, and Pitfalls

This study presents a brief review of literature exploring simple EEG-polygraphic examinations and procedures that can be carried out at a patient's bedside. These include EEG with a common electrode array and sleep evaluation. The review briefly discusses more complex analytical techniques, such as the application of advanced EEG signal processing methods developed by our research group, to define what type of consistent markers are suitable for clinical use or to better understand complex patient conditions. These advanced analytical techniques aim to detect relevant EEG-based markers that could be useful in evaluating patients and predicting outcomes. These data could contribute to future developments in research.

Altered inter-frequency dynamics of brain networks in disorder of consciousness

OBJECTIVE

Growing evidence have linked disorders of consciousness (DOC) with the changes in frequency-specific functional networks. However, the alteration of inter-frequency dynamics in brain networks remain largely unknown. In this study, we investigated the network integration and segregation across frequency bands in a multiplex network framework.

APPROACH

Resting-state EEG data were recorded and analysed from 19 patients in minimally conscious state, 35 patients in unresponsive wakefulness syndrome (UWS) and 23 healthy controls. Frequency-based multiplex (cross-frequency) networks were reconstructed by integrating the five frequency-specific networks. Multiplex graph metrics, named multiplex participation coefficient and multiplex clustering coefficient, were employed to assess the network topology of subjects with different levels of consciousness.

MAIN RESULTS

Results revealed DOC networks, compared to those of healthy controls, may work at a less optimal point (closer to complete disorder) with increased integration and decreased segregation considering inter-frequency dynamics. Both metrics show increased spatial and temporal variability with the consciousness levels. Moreover, significant correlation can be found between the alteration of cross-frequency networks in DOC patients and their behavioural performance at both local and global scales.

SIGNIFICANCE

These findings may contribute to the development of EEG network study and benefit our understanding of the processes of consciousness and their pathophysiology for DOC.

Decoding the circuitry of consciousness: From local microcircuits to brain-scale networks

Identifying the physiological processes underlying the emergence and maintenance of consciousness is one of the most fundamental problems of neuroscience, with implications ranging from fundamental neuroscience to the treatment of patients with disorders of consciousness (DOCs). One major challenge is to understand how cortical circuits at drastically different spatial scales, from local networks to brain-scale networks, operate in concert to enable consciousness, and how those processes are impaired in DOC patients. In this review, we attempt to relate available neurophysiological and clinical data with existing theoretical models of consciousness, while linking the micro- and macrocircuit levels. First, we address the relationships between awareness and wakefulness on the one hand, and cortico-cortical and thalamo-cortical connectivity on the other hand. Second, we discuss the role of three main types of GABAergic interneurons in specific circuits responsible for the dynamical reorganization of functional networks. Third, we explore advances in the functional role of nested oscillations for neural synchronization and communication, emphasizing the importance of the balance between local (high-frequency) and distant (low-frequency) activity for efficient information processing. The clinical implications of these theoretical considerations are presented. We propose that such cellular-scale mechanisms could extend current theories of consciousness.

Toward Improving Diagnostic Strategies in Chronic Disorders of Consciousness: An Overview on the (Re-)Emergent Role of Neurophysiology

The differential diagnosis of patients with Disorder of Consciousness (DoC), in particular in the chronic phase, is significantly difficult. Actually, about 40% of patients with unresponsive wakefulness syndrome (UWS) and the minimally conscious state (MCS) are misdiagnosed. Indeed, only advanced paraclinical approaches, including advanced EEG analyses, can allow achieving a more reliable diagnosis, that is, discovering residual traces of awareness in patients with UWS (namely, functional Locked-In Syndrome (fLIS)). These approaches aim at capturing the residual brain network models, at rest or that may be activated in response to relevant stimuli, which may be appropriate for awareness to emerge (despite their insufficiency to generate purposeful motor behaviors). For this, different brain network models have been studied in patients with DoC by using sensory stimuli (i.e., passive tasks), probing response to commands (i.e., active tasks), and during resting-state. Since it can be difficult for patients with DoC to perform even simple active tasks, this scoping review aims at summarizing the current, innovative neurophysiological examination methods in resting state/passive modality to differentiate and prognosticate patients with DoC. We conclude that the electrophysiologically-based diagnostic procedures represent an important resource for diagnosis, prognosis, and, therefore, management of patients with DoC, using advance passive and resting state paradigm analyses for the patients who lie in the “greyzones” between MCS, UWS, and fLIS.

COALIA: A Computational Model of Human EEG for Consciousness Research

Understanding the origin of the main physiological processes involved in consciousness is a major challenge of contemporary neuroscience, with crucial implications for the study of Disorders of Consciousness (DOC). The difficulties in achieving this task include the considerable quantity of experimental data in this field, along with the non-intuitive, nonlinear nature of neuronal dynamics. One possibility of integrating the main results from the experimental literature into a cohesive framework, while accounting for nonlinear brain dynamics, is the use of physiologically-inspired computational models. In this study, we present a physiologically-grounded computational model, attempting to account for the main micro-circuits identified in the human cortex, while including the specificities of each neuronal type. More specifically, the model accounts for thalamo-cortical (vertical) regulation of cortico-cortical (horizontal) connectivity, which is a central mechanism for brain information integration and processing. The distinct neuronal assemblies communicate through feedforward and feedback excitatory and inhibitory synaptic connections implemented in a template brain accounting for long-range connectome. The EEG generated by this physiologically-based simulated brain is validated through comparison with brain rhythms recorded in humans in two states of consciousness (wakefulness, sleep). Using the model, it is possible to reproduce the local disynaptic disinhibition of basket cells (fast GABAergic inhibition) and glutamatergic pyramidal neurons through long-range activation of vasoactive intestinal-peptide (VIP) interneurons that induced inhibition of somatostatin positive (SST) interneurons. The model (COALIA) predicts that the strength and dynamics of the thalamic output on the cortex control the local and long-range cortical processing of information. Furthermore, the model reproduces and explains clinical results regarding the complexity of transcranial magnetic stimulation TMS-evoked EEG responses in DOC patients and healthy volunteers, through a modulation of thalamo-cortical connectivity that governs the level of cortico-cortical communication. This new model provides a quantitative framework to accelerate the study of the physiological mechanisms involved in the emergence, maintenance and disruption (sleep, anesthesia, DOC) of consciousness.

Computational Methods for Resting-State EEG of Patients With Disorders of Consciousness

Patients who survive brain injuries may develop Disorders of Consciousness (DOC) such as Coma, Vegetative State (VS) or Minimally Conscious State (MCS). Unfortunately, the rate of misdiagnosis between VS and MCS due to clinical judgment is high. Therefore, diagnostic decision support systems aiming to correct any differentiation between VS and MCS are essential for the characterization of an adequate treatment and an effective prognosis. In recent decades, there has been a growing interest in the new EEG computational techniques. We have reviewed how resting-state EEG is computationally analyzed to support differential diagnosis between VS and MCS in view of applicability of these methods in clinical practice. The studies available so far have used different techniques and analyses; it is therefore hard to draw general conclusions. Studies using a discriminant analysis with a combination of various factors and reporting a cut-off are among the most interesting ones for a future clinical application.

Electroencephalography quadratic phase self-coupling correlates with consciousness states and restoration in patients with disorders of consciousness

OBJECTIVE

The objective of this study was to explore the role for quadratic phase coupling within electroencephalography (EEG) oscillations in the diagnosis of consciousness and consciousness restoration for disorders of consciousness (DOC).

METHODS

Fifty-one DOC patients were enrolled in this study. For each patient, a Coma Recovery Scale-Revised (CRS-R) score and 20-min resting-state EEG were recorded. Consciousness recovery was assessed with a CRS-R score at a three-month follow-up. Twenty healthy subjects were included as controls. General harmonic wavelet transform-based bicoherence was used to quantify the quadratic phase coupling characteristics of the EEG oscillations.

RESULTS

Quadratic phase self-coupling (QPSC) at the delta (QPSC_delta), theta (QPSC_theta) and alpha (QPSC_alpha) bands were closely correlated with patient CRS-R scores. Particularly, the QPSC_theta value could significantly differentiate between vegetative state (VS) patients, minimally conscious state (MCS) patients and healthy control subjects. As compared to VS patients, patients with MCS had a lower QPSC_theta value on the left as well as a higher QPSC_alpha value in right frontal regions. The frontal QPSC_theta value showed significant differences between recovered and unrecovered patients.

CONCLUSION

QPSC characteristics could differentiate between consciousness states and show a predictive ability for the recovery of consciousness in DOC patients.

SIGNIFICANCE

Changes in QPSC accompany consciousness injury and restoration in DOC patients. A QPSC assessment is helpful in the diagnosis and prognosis of DOC patients.

Intrinsic network reactivity differentiates levels of consciousness in comatose patients

OBJECTIVE

We devise a data-driven framework to assess the level of consciousness in etiologically heterogeneous comatose patients using intrinsic dynamical changes of resting-state Electroencephalogram (EEG) signals.

METHODS

EEG signals were collected from 54 comatose patients (GCS ⩽ 8) and 20 control patients (GCS > 8). We analyzed the EEG signals using a new technique, termed Intrinsic Network Reactivity Index (INRI), that aims to assess the overall lability of brain dynamics without the use of extrinsic stimulation. The proposed technique uses three sigma EEG events as a trigger for ensuing changes to the directional derivative of signals across the EEG montage.

RESULTS

The INRI had a positive relationship with GCS and was significantly different between various levels of consciousness. In comparison, classical band-limited power analysis did not show any specific patterns correlated to GCS.

CONCLUSIONS

These findings suggest that reaching low variance EEG activation patterns becomes progressively harder as the level of consciousness of patients deteriorate, and provide a quantitative index based on passive measurements that characterize this change.

SIGNIFICANCE

Our results emphasize the role of intrinsic brain dynamics in assessing the level of consciousness in coma patients and the possibility of employing simple electrophysiological measures to recognize the severity of disorders of consciousness (DOC).

EEG and autonomic responses to nociceptive stimulation in disorders of consciousness

Since behavioral responses to external stimuli of patients presenting disorders of consciousness (DoC) are often difficult to qualify, covert physiological correlates of responsivity are deemed as potentially valuable tools to help assessment procedures. While noxious stimuli seem good candidates to explore DoC patients' responsivity, autonomic and electrophysiological correlates of pain detection in DoC patients are still debated. This research aims at investigating autonomic and cortical activation as covert measure of residual somatosensory and nociceptive processes in patients in vegetative state. Twenty-one patients received touch- and pain-related stimulations while autonomic and cortical measures were recorded, with minimal stress for them. Results showed an increased frontal and parietal activation in response to both touch and pain stimuli. Pain-related stimulation was however associated with greater delta parietal response, lower left frontal activation, and increased electrodermal and heart rate measures. Present findings suggest that both somatic stimulations could induce measurable central responses, which might mirror basic attention orientation and perceptual processes. Nonetheless, the nociceptive stimulation in particular seemed to induce a more consistent and informative pattern of covert response even if we used a mild pain-induction procedure.

Brain Network Studies in Chronic Disorders of Consciousness: Advances and Perspectives

Neuroimaging has opened new opportunities to study the neural correlates of consciousness, and provided additional information concerning diagnosis, prognosis, and therapeutic interventions in patients with disorders of consciousness. Here, we aim to review neuroimaging studies in chronic disorders of consciousness from the viewpoint of the brain network, focusing on positron emission tomography, functional MRI, functional near-infrared spectroscopy, electrophysiology, and diffusion MRI. To accelerate basic research on disorders of consciousness and provide a panoramic view of unconsciousness, we propose that it is urgent to integrate different techniques at various spatiotemporal scales, and to merge fragmented findings into a uniform "Brainnetome" (Brain-net-ome) research framework.

Topographical assessment of neurocortical connectivity by using directed transfer function and partial directed coherence during meditation

Due to the presence of nonlinearity and volume conduction in electroencephalography (EEG), sometimes it's challenging to find out the actual brain network from neurodynamical alteration. In this paper, two well-known time-frequency brain connectivity measures, namely partial directed coherence (PDC) and directed transfer function (DTF), have been applied to evaluate the performance analysis of EEG signals obtained during meditation. These measures are implemented to the multichannel meditation EEG data to get the directed neural information flow. Mostly the assessment of PDC and DTF is entirely subjective and there are probabilities to have erroneous connectivity estimation. To avoid the subjective evaluation, the performance results are compared in terms of absolute energy, signal-to-noise ratio (SNR) and relative SNR (R-SNR) scale. In most of the cases, the PDC result is found to be more efficient than DTF. The limitation of DTF and PDC in terms of the time-varying multivariate autoregressive (MVAR) model is highlighted. The time-varying MVAR model can track the neurodynamical changes better than any other method. In the present study, we would like to show that the PDC-based connectivity gives a better understanding of the non-symmetric relation in EEG obtained during Kriya Yoga meditation in comparison to DTF. However, it needs to be investigated further to warrant this claim.

Global structural integrity and effective connectivity in patients with disorders of consciousness

BACKGROUND

Previous studies have separately reported impaired functional, structural, and effective connectivity in patients with disorders of consciousness (DOC). The perturbational complexity index (PCI) is a transcranial magnetic stimulation (TMS) derived marker of effective connectivity. The global fractional anisotropy (FA) is a marker of structural integrity. Little is known about how these parameters are related to each other.

OBJECTIVE

We aimed at testing the relationship between structural integrity and effective connectivity.

METHODS

We assessed 23 patients with severe brain injury more than 4 weeks post-onset, leading to DOC or locked-in syndrome, and 14 healthy subjects. We calculated PCI using repeated single pulse TMS coupled with high-density electroencephalography, and used it as a surrogate of effective connectivity. Structural integrity was measured using the global FA, derived from diffusion weighted imaging. We used linear regression modelling to test our hypothesis, and computed the correlation between PCI and FA in different groups.

RESULTS

Global FA could predict 74% of PCI variance in the whole sample and 56% in the patients' group. No other predictors (age, gender, time since onset, behavioural score) improved the models. FA and PCI were correlated in the whole population (r = 0.86, p < 0.0001), the patients, and the healthy subjects subgroups.

CONCLUSION

We here demonstrated that effective connectivity correlates with structural integrity in brain-injured patients. Increased structural damage level decreases effective connectivity, which could prevent the emergence of consciousness.

Task-free spectral EEG dynamics track and predict patient recovery from severe acquired brain injury

For some patients, coma is followed by a state of unresponsiveness, while other patients develop signs of awareness. In practice, detecting signs of awareness may be hindered by possible impairments in the patient's motoric, sensory, or cognitive abilities, resulting in a substantial proportion of misdiagnosed disorders of consciousness. Task-free paradigms that are independent of the patient's sensorimotor and neurocognitive abilities may offer a solution to this challenge. A limitation of previous research is that the large majority of studies on the pathophysiological processes underlying disorders of consciousness have been conducted using cross-sectional designs. Here, we present a study in which we acquired a total of 74 longitudinal task-free EEG measurements from 16 patients (aged 6–22 years, 12 male) suffering from severe acquired brain injury, and an additional 16 age- and education-matched control participants. We examined changes in amplitude and connectivity metrics of oscillatory brain activity within patients across their recovery. Moreover, we applied multi-class linear discriminant analysis to assess the potential diagnostic and prognostic utility of amplitude and connectivity metrics at the individual-patient level. We found that over the course of their recovery, patients exhibited nonlinear frequency band-specific changes in spectral amplitude and connectivity metrics, changes that aligned well with the metrics' frequency band-specific diagnostic value. Strikingly, connectivity during a single task-free EEG measurement predicted the level of patient recovery approximately 3 months later with 75% accuracy. Our findings show that spectral amplitude and connectivity track patient recovery in a longitudinal fashion, and these metrics are robust pathophysiological markers that can be used for the automated diagnosis and prognosis of disorders of consciousness. These metrics can be acquired inexpensively at bedside, and are fully independent of the patient's neurocognitive abilities. Lastly, our findings tentatively suggest that the relative preservation of thalamo-cortico-thalamic interactions may predict the later reemergence of awareness, and could thus shed new light on the pathophysiological processes that underlie disorders of consciousness.

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Using behavioral criteria, disorders of consciousness are often misdiagnosed

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We probed the diagnostic and prognostic value of task-free spectral EEG metrics

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Metrics changed non-linearly across recovery and predicted level of consciousness

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EEG connectivity predicted the level of patient recovery with 75% accuracy

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These metrics are fully independent of the patient's neurocognitive abilities

A Review of Resting-State Electroencephalography Analysis in Disorders of Consciousness

Recently, neuroimaging technologies have been developed as important methods for assessing the brain condition of patients with disorders of consciousness (DOC). Among these technologies, resting-state electroencephalography (EEG) recording and analysis has been widely applied by clinicians due to its relatively low cost and convenience. EEG reflects the electrical activity of the underlying neurons, and it contains information regarding neuronal population oscillations, the information flow pathway, and neural activity networks. Some features derived from EEG signal processing methods have been proposed to describe the electrical features of the brain with DOC. The computation of these features is challenging for clinicians working to comprehend the corresponding physiological meanings and then to put them into clinical applications. This paper reviews studies that analyze spontaneous EEG of DOC, with the purpose of diagnosis, prognosis, and evaluation of brain interventions. It is expected that this review will promote our understanding of the EEG characteristics in DOC.

Abnormalities in Dynamic Brain Activity Caused by Mild Traumatic Brain Injury Are Partially Rescued by the Cannabinoid Type-2 Receptor Inverse Agonist SMM-189

Mild traumatic brain injury (mTBI) can cause severe long-term cognitive and emotional deficits, including impaired memory, depression, and persevering fear, but the neuropathological basis of these deficits is uncertain. As medial prefrontal cortex (mPFC) and hippocampus play important roles in memory and emotion, we used multi-site, multi-electrode recordings of oscillatory neuronal activity in local field potentials (LFPs) in awake, head-fixed mice to determine if the functioning of these regions was abnormal after mTBI, using a closed-skull focal cranial blast model. We evaluated mPFC, hippocampus CA1, and primary somatosensory/visual cortical areas (S1/V1). Although mTBI did not alter the power of oscillations, it did cause increased coherence of θ (4-10 Hz) and β (10-30 Hz) oscillations within mPFC and S1/V1, reduced CA1 sharp-wave ripple (SWR)-evoked LFP activity in mPFC, downshifted SWR frequencies in CA1, and enhanced θ-γ phase-amplitude coupling (PAC) within mPFC. These abnormalities might be linked to the impaired memory, depression, and persevering fear seen after mTBI. Treatment with the cannabinoid type-2 (CB2) receptor inverse agonist SMM-189 has been shown to mitigate functional deficits and neuronal injury after mTBI in mice. We found that SMM-189 also reversed most of the observed neurophysiological abnormalities. This neurophysiological rescue is likely to stem from the previously reported reduction in neuron loss and/or the preservation of neuronal function and connectivity resulting from SMM-189 treatment, which appears to stem from the biasing of microglia from the proinflammatory M1 state to the prohealing M2 state by SMM-189.

Transcranial Alternating Current Stimulation in Patients with Chronic Disorder of Consciousness: A Possible Way to Cut the Diagnostic Gordian Knot?

Unresponsive wakefulness syndrome (UWS) is a chronic disorder of consciousness (DOC) characterized by a lack of awareness and purposeful motor behaviors, owing to an extensive brain connectivity impairment. Nevertheless, some UWS patients may retain residual brain connectivity patterns, which may sustain a covert awareness, namely functional locked-in syndrome (fLIS). We evaluated the possibility of bringing to light such residual neural networks using a non-invasive neurostimulation protocol. To this end, we enrolled 15 healthy individuals and 26 DOC patients (minimally conscious state-MCS- and UWS), who underwent a γ-band transcranial alternating current stimulation (tACS) over the right dorsolateral prefrontal cortex. We measured the effects of tACS on power and partial-directed coherence within local and long-range cortical networks, before and after the protocol application. tACS was able to specifically modulate large-scale cortical effective connectivity and excitability in all the MCS participants and some UWS patients, who could be, therefore, considered as suffering from fLIS. Hence, tACS could be a useful approach in supporting a DOC differential diagnosis, depending on the level of preservation of the cortical large-scale effective connectivity.

The contribution of TMS-EEG coregistration in the exploration of the human cortical connectome

Recent developments in neuroscience have emphasised the importance of integrated distributed networks of brain areas for successful cognitive functioning. Our current understanding is that the brain has a modular organisation in which segregated networks supporting specialised processing are linked through a few long-range connections, ensuring processing integration. Although such architecture is structurally stable, it appears to be flexible in its functioning, enabling long-range connections to regulate the information flow and facilitate communication among the relevant modules, depending on the contingent cognitive demands. Here we show how insights brought by the coregistration of transcranial magnetic stimulation and electroencephalography (TMS-EEG) integrate and support recent models of functional brain architecture. Moreover, we will highlight the types of data that can be obtained through TMS-EEG, such as the timing of signal propagation, the excitatory/inhibitory nature of connections and causality. Last, we will discuss recent emerging applications of TMS-EEG in the study of brain disorders.

Significance of multiple neurophysiological measures in patients with chronic disorders of consciousness

OBJECTIVE

The aim of this study was to verify the value of multiple neurophysiological tests in classifying disorders of consciousness (DOCs) in patients in a chronic vegetative or minimal consciousness state categorised on the basis of the Coma Recovery Scale (CRS).

METHODS

The study included 142 patients, all of whom underwent long (18h) EEG-polygraphic recordings including one night. The EEG was scored using the Synek scale and sleep patterns using an arbitrary scale. Absolute total power and relative EEG power were evaluated in different frequency bands. Multimodal evoked potentials (EPs), including auditory event-related potentials, were also evaluated and scored.

RESULTS

The most information came from the combined multimodal EPs and sleep EEG scores. A two-step cluster analysis based on the collected information allowed a satisfactory evaluation of DOC severity. Spectral EEG properties seemed to be significantly related to DOC classes and CRS scores, but did not seem to make any significant additional contribution to DOC classification.

CONCLUSIONS

Multiple electrophysiological evaluations based on EEG, sleep polygraphic recordings and multimodal EPs are helpful in assessing DOC severity and residual functioning in patients with chronic DOCs.

SIGNIFICANCE

Simple electrophysiological measures that can be easily applied at patients' bedsides can significantly contribute to the recognition of DOC severity in chronic patients surviving a severe brain injury.

Directed information transfer in scalp electroencephalographic recordings: insights on disorders of consciousness

The neural mechanisms underlying electrophysiological changes observed in patients with disorders of consciousness following a coma remain poorly understood. The aim of this study is to investigate the mechanisms underlying the differences in spontaneous electroencephalography (EEG) between patients in vegetative/unresponsive wakefulness syndrome, minimally conscious state, emergence of the minimally conscious state and age-matched healthy control subjects. Forty recordings of spontaneous scalp EEG were performed in 27 patients who were comatose on admission, and on healthy controls. Multivariate Granger causality and transfer entropy were applied to the data. Distinctive patterns of putative bottlenecks of information were associated with each conscious state. Healthy controls are characterized by a greater amount of synergetic contributions from duplets of variables. In conclusion a novel set of measures was tested to get a new insight on the pattern of information transfer in a network of scalp electrodes in patients with disorders of consciousness.