Recovery of cortical effective connectivity and recovery of consciousness in vegetative patients

tDCS-EEG for Predicting Outcome in Patients With Unresponsive Wakefulness Syndrome

Objectives

We aimed to assess the role of transcranial direct current stimulation (tDCS) combined with electroencephalogram (EEG) for predicting prognosis in UWS cases.

Methods

This was a historical control study that enrolled 85 patients with UWS. The subjects were assigned to the control (without tDCS) and tDCS groups. Conventional treatments were implemented in both the control and tDCS groups, along with 40 multi-target tDCS sessions only in the tDCS group. Coma Recovery Scale-Revised (CRS-R) was applied at admission. The non-linear EEG index was evaluated after treatment. The modified Glasgow Outcome Scale (mGOS) was applied 12 months after disease onset.

Results

The mGOS improvement rate in the tDCS group (37.1%) was higher than the control value (22.0%). Linear regression analysis revealed that the local and remote cortical networks under unaffected pain stimulation conditions and the remote cortical network under affected pain stimulation conditions were the main relevant factors for mGOS improvement. Furthermore, the difference in prefrontal-parietal cortical network was used to examine the sensitivity of prognostic assessment in UWS patients. The results showed that prognostic sensitivity could be increased from 54.5% (control group) to 84.6% (tDCS group).

Conclusions

This study proposes a tDCS-EEG protocol for predicting the prognosis of UWS. With multi-target tDCS combined with EEG, the sensitivity of prognostic assessment in patients with UWS was improved. The recovery might be related to improved prefrontal-parietal cortical networks of the unaffected hemisphere.

Identification and verification of a 'true' TMS evoked potential in TMS-EEG

The concurrent combination of transcranial magnetic stimulation and electroencephalography (TMS-EEG) can unveil functional neural mechanisms with applications in basic and clinical research. In particular, TMS-evoked potentials (TEPs) potentially allow studying excitability and connectivity of the cortex in a causal manner that is not easily or non-invasively attainable with other neuroimaging techniques. The TEP waveform is obtained by isolating the EEG responses phase-locked to the time of TMS application. The intended component in a TEP waveform is the cortical activation by the TMS-induced electric current, free of instrumental and physiological artifact sources. This artifact-free cortical activation can be referred to as 'true' TEP. However, due to many unwanted auxiliary effects of TMS, the interpretation of 'true' TEPs has not been free of controversy. This paper reviews the most recent understandings of 'true' TEPs and their application. In the first part of the paper, TEP components are defined according to recommended methodologies. In the second part, the verification of 'true' TEP is discussed along with its sensitivity to brain-state, age, and disease. The various proposed origins of TEP components are then presented in the context of existing literature. Throughout the paper, lessons learned from the past TMS-EEG studies are highlighted to guide the identification and interpretation of 'true' TEPs in future studies.

Transcutaneous auricular vague nerve stimulation improved brain connection activity on patients of disorders of consciousness: a pilot study

OBJECTIVE

To evaluate the clinical effect of transcutaneous auricular vagus nerve nerve stimulation (taVNS) on disorders of consciousness (DOC) patients with Coma Recovery Scale-Revised (CRS-R) and cerebral cortex activity by electroencephalogram (EEG) detection.

METHODS

Randomized controlled methods were used to evaluate the clinical effect of taVNS on patients with DOC. Twelve patients with initial CRS-R of 6-10 were randomly divided into the treatment group of taVNS and control group of transcutaneous non-auricular vague nerve stimulation (tnVNS). According to clinical diagnosis, the treatment group was divided into vegetative state (VS) group and minimally conscious state (MCS) group.

RESULTS

The energy of delta and beta bands is positively correlated with the brain activity of patients. taVNS has different regulatory effects on patients with different conscious States. In taVNS group, the energy of delta band in local brain regions changed significantly. Significant changes in brain connection activity were limited to local brain regions. While in patients with MCS in the taVNS group, delta and beta band energy significantly changed in multiple brain regions and cross-brain connection activity also changed significantly.

CONCLUSION

These findings suggest that taVNS may be a related extra method for arousing patients' awakening by improving brain connection activity. And the effect is remarkable in MCS patients.

State-dependent effects of neural stimulation on brain function and cognition

Invasive and non-invasive brain stimulation methods are widely used in neuroscience to establish causal relationships between distinct brain regions and the sensory, cognitive and motor functions they subserve. When combined with concurrent brain imaging, such stimulation methods can reveal patterns of neuronal activity responsible for regulating simple and complex behaviours at the level of local circuits and across widespread networks. Understanding how fluctuations in physiological states and task demands might influence the effects of brain stimulation on neural activity and behaviour is at the heart of how we use these tools to understand cognition. Here we review the concept of such 'state-dependent' changes in brain activity in response to neural stimulation, and consider examples from research on altered states of consciousness (for example, sleep and anaesthesia) and from task-based manipulations of selective attention and working memory. We relate relevant findings from non-invasive methods used in humans to those obtained from direct electrical and optogenetic stimulation of neuronal ensembles in animal models. Given the widespread use of brain stimulation as a research tool in the laboratory and as a means of augmenting or restoring brain function, consideration of the influence of changing physiological and cognitive states is crucial for increasing the reliability of these interventions.

Flowchart for Implementing Advanced Imaging and Electrophysiology in Patients With Disorders of Consciousness: To fMRI or Not to fMRI?

The American Academy of Neurology and the European Academy of Neurology have recognized, for the first time, the value of advanced neuroimaging and electrophysiology techniques (AIEs) in the context of diagnosing patients with a disorder of consciousness (DOC). This recognition is part of an important agenda of promoting evidence-based competency in the management of patients with DOC. Nonetheless, considering that these techniques (and the required knowledge) are seldom available outside of advanced medical centers, it is important to provide physicians with a framework for balancing risks and benefits and deciding, on a single patient basis, whether AIEs are suitable. This issue is all the more urgent considering that family members are increasingly aware of the use of AIEs in patients with DOC, pressure for these assessments is likely to increase in the context of ethical and clinical imperatives to meet standards of care, and pathways for reimbursement for such assessments in DOC are yet to be established. The new guidelines, however, provide no guiding principle for physicians to decide when such assessments are appropriate, a limitation that impedes their wide adoption. We address this important gap by proposing an easy to use algorithmic flowchart that is based on the new guidelines and can be used to determine the appropriateness of AIEs for any given patient with DOC and ensure that evidence-based best practices are being followed. We also provide a brief context for understanding the main categories of AIEs available to clinicians, their advantages, and their limitations.

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

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

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

Application of fast perturbational complexity index to the diagnosis and prognosis for disorders of consciousness

OBJECTIVE

Diagnosis and prognosis of patients with disorders of consciousness (DOC) is a challenge for neuroscience and clinical practice. Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) is an effective tool to measure the level of consciousness. However, a scientific and accurate method to quantify TMS-evoked activity is still lacking. This study applied fast perturbational complexity index (PCIst) to the diagnosis and prognosis of DOC patients.

METHODS

TMS-EEG data of 30 normal healthy participants (NOR) and 181 DOC patients were collected. The PCIst was used to assess the time-space complexity of TMS-evoked potentials (TEP). We selected parameters of PCIst in terms of data length, data delay, sampling rate and frequency band. In addition, we collected Coma Recovery Scale-Revised (CRS-R) values for 114 DOC patients after one year. Finally, we trained the classification and regression model.

RESULTS

1) PCIst shows the differences among NOR, minimally consciousness state (MCS) and unresponsive wakefulness syndrome (UWS) and has low computational cost. 2) Optimal parameters of data length and delay after TMS are 300ms and 101-300ms. Significant differences of PCIst at 5-8Hz and 9-12Hz bands are found among NOR, MCS and UWS groups. PCIst still works when TEP is down-sampled to 250 Hz. 3) PCIst at 9-12Hz shows the highest performance in diagnosis and prognosis of DOC.

CONCLUSIONS

This study confirms that PCIst can quantify the level of consciousness. PCIst is a potential measure for the diagnosis and prognosis of DOC patients.

A structured ICA-based process for removing auditory evoked potentials

Transcranial magnetic stimulation (TMS)-evoked potentials (TEPs), recorded using electroencephalography (EEG), reflect a combination of TMS-induced cortical activity and multi-sensory responses to TMS. The auditory evoked potential (AEP) is a high-amplitude sensory potential-evoked by the "click" sound produced by every TMS pulse-that can dominate the TEP and obscure observation of other neural components. The AEP is peripherally evoked and therefore should not be stimulation site specific. We address the problem of disentangling the peripherally evoked AEP of the TEP from components evoked by cortical stimulation and ask whether removal of AEP enables more accurate isolation of TEP. We hypothesized that isolation of the AEP using Independent Components Analysis (ICA) would reveal features that are stimulation site specific and unique individual features. In order to improve the effectiveness of ICA for removal of AEP from the TEP, and thus more clearly separate the transcranial-evoked and non-specific TMS-modulated potentials, we merged sham and active TMS datasets representing multiple stimulation conditions, removed the resulting AEP component, and evaluated performance across different sham protocols and clinical populations using reduction in Global and Local Mean Field Power (GMFP/LMFP) and cosine similarity analysis. We show that removing AEPs significantly reduced GMFP and LMFP in the post-stimulation TEP (14 to 400 ms), driven by time windows consistent with the N100 and P200 temporal characteristics of AEPs. Cosine similarity analysis supports that removing AEPs reduces TEP similarity between subjects and reduces TEP similarity between stimulation conditions. Similarity is reduced most in a mid-latency window consistent with the N100 time-course, but nevertheless remains high in this time window. Residual TEP in this window has a time-course and topography unique from AEPs, which follow-up exploratory analyses suggest could be a modulation in the alpha band that is not stimulation site specific but is unique to individual subject. We show, using two datasets and two implementations of sham, evidence in cortical topography, TEP time-course, GMFP/LMFP and cosine similarity analyses that this procedure is effective and conservative in removing the AEP from TEP, and may thus better isolate TMS-evoked activity. We show TEP remaining in early, mid and late latencies. The early response is site and subject specific. Later response may be consistent with TMS-modulated alpha activity that is not site specific but is unique to the individual. TEP remaining after removal of AEP is unique and can provide insight into TMS-evoked potentials and other modulated oscillatory dynamics.

Consciousness explained or described?

Consciousness is an unusual phenomenon to study scientifically. It is defined as a subjective, first-person phenomenon, and science is an objective, third-person endeavor. This misalignment between the means—science—and the end—explaining consciousness—gave rise to what has become a productive workaround: the search for ‘neural correlates of consciousness’ (NCCs). Science can sidestep trying to explain consciousness and instead focus on characterizing the kind(s) of neural activity that are reliably correlated with consciousness. However, while we have learned a lot about consciousness in the bargain, the NCC approach was not originally intended as the foundation for a true explanation of consciousness. Indeed, it was proposed precisely to sidestep the, arguably futile, attempt to find one. So how can an account, couched in terms of neural correlates, do the work that a theory is supposed to do: explain consciousness? The answer is that it cannot, and in fact most modern accounts of consciousness do not pretend to. Thus, here, we challenge whether or not any modern accounts of consciousness are in fact theories at all. Instead we argue that they are (competing) laws of consciousness. They describe what they cannot explain, just as Newton described gravity long before a true explanation was ever offered. We lay out our argument using a variety of modern accounts as examples and go on to argue that at least one modern account of consciousness, attention schema theory, goes beyond describing consciousness-related brain activity and qualifies as an explanatory theory.

The role of plasticity in the recovery of consciousness

Disorders of consciousness (DOCs), i.e., coma, vegetative state, and minimally conscious state are the consequences of a severe brain injury that disrupts the brain ability to generate consciousness. Recovery from DOCs requires functional and structural changes in the brain. The sites where these plastic changes take place vary according to the pathophysiology of the DOC. The ascending reticular activating system of the brainstem and its complex connections with the thalamus and cortex are involved in the pathophysiology of coma. Subcortical structures, such as the striatum and globus pallidus, together with thalamocortical and corticothalamic projections, the basal forebrain, and several networks among different cortical areas are probably involved in vegetative and minimally conscious states. Some mechanisms of plasticity that allegedly operate in each of these sites to promote recovery of consciousness will be discussed in this chapter. While some mechanisms of plasticity work at a local level, others produce functional changes in complex neuronal networks, for example by entraining neuronal oscillations. The specific mechanisms of brain plasticity represent potential targets for future treatments aiming to restore consciousness in patients with severe DOCs.

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.

Frontal and parietal lobes play crucial roles in understanding the disorder of consciousness: A perspective from electroencephalogram studies

Background

Electroencephalogram (EEG) studies have established many characteristics relevant to consciousness levels of patients with disorder of consciousness (DOC). Although the frontal and parietal brain regions were often highlighted in DOC studies, their electro-neurophysiological roles in constructing human consciousness remain unclear because of the fragmented information from literatures and the complexity of EEG characteristics.

Methods

Existing EEG studies of DOC patients were reviewed and summarized. Relevant findings and results about the frontal and parietal regions were filtered, compared, and concluded to clarify their roles in consciousness classification and outcomes. The evidence covers multi-dimensional EEG characteristics including functional connectivity, non-linear dynamics, spectrum power, transcranial magnetic stimulation-electroencephalography (TMS-EEG), and event-related potential.

Results and conclusion

Electroencephalogram characteristics related to frontal and parietal regions consistently showed high relevance with consciousness: enhancement of low-frequency rhythms, suppression of high-frequency rhythms, reduction of dynamic complexity, and breakdown of networks accompanied with decreasing consciousness. Owing to the limitations of EEG, existing studies have not yet clarified which one between the frontal and parietal has priority in consciousness injury or recovery. Source reconstruction with high-density EEG, machine learning with large samples, and TMS-EEG mapping will be important approaches for refining EEG awareness locations.

Toward a coherent structuration of disorders of consciousness expertise at a country scale: A proposal for France

Probing consciousness and cognitive abilities in non-communicating patients is one of the most challenging diagnostic issues. A fast growing medical and scientific literature explores the various facets of this challenge, often coined under the generic expression of 'Disorders of Consciousness' (DoC). Crucially, a set of independent converging results demonstrated both (1) the diagnostic and prognostic importance of this expertise, and (2) the need to combine behavioural measures with brain structure and activity data to improve diagnostic and prognostication accuracy as well as potential therapeutic intervention. Thus, probing consciousness in DoC patients appears as a crucial activity rich of human, medical, economic and ethical consequences, but this activity needs to be organized in order to offer this expertise to each concerned patient. More precisely, diagnosis of consciousness differs in difficulty across patients: while a minimal set of data can be sufficient to reach a confident result, some patients need a higher level of expertise that relies on additional behavioural and brain activity and brain structure measures. In order to enable this service on a systematic mode, we present two complementary proposals in the present article. First, we sketch a structuration of DoC expertise at a country-scale, namely France. More precisely, we suggest that a 2-tiers network composed of local (Tier-1) and regional (Tier-2) centers backed by distant electronic databases and algorithmic centers could optimally enable the systematic implementation of DoC expertise in France. Second, we propose to create a national common register of DoC patients in order to better monitor this activity, to improve its performance on the basis of nation-wide collected evidence, and to promote rational decision-making.

Bihemispheric sensorimotor oscillatory network states determine cortical responses to transcranial magnetic stimulation

BACKGROUND

Brain responses to external stimuli vary with fluctuating states of neuronal activity. Previous work has demonstrated effects of phase and power of the ongoing local sensorimotor μ-alpha-oscillation on responses to transcranial magnetic stimulation (TMS) of motor cortex (M1). However, M1 is part of a distributed network, and the effects of oscillatory activity in this network on TMS-evoked EEG responses (TEPs) have not been explored.

OBJECTIVES

To determine the effects of oscillatory activity in the bihemispheric sensorimotor network on TEPs.

METHODS

31 healthy subjects received single-pulse TMS of the left M1 hand area during EEG recording. Ongoing bihemispheric sensorimotor cortex oscillatory states were reconstructed from the EEG directly preceding TMS, and inferred by a data-driven method combining a multivariate autoregressive model and a Hidden Markov model. TEP amplitudes (P25, N45, P70, N100 and P180) were then compared between different bihemispheric sensorimotor cortex oscillatory states.

RESULTS

Four bihemispheric sensorimotor cortex oscillatory states were identified, with different interhemispheric expressions of theta and alpha oscillations. High alpha-power states in the stimulated sensorimotor cortex increased P25 amplitude. Alpha power in the alpha-alpha state (stimulated - non-stimulated hemisphere) correlated in both hemispheres with N45 amplitude. Theta power in the alpha-theta state correlated in the non-stimulated hemisphere with P70 amplitude.

CONCLUSIONS

Bihemispheric sensorimotor cortex oscillatory states contribute to TEPs, with a relevance shift from stimulated to non-stimulated M1 from P25 over N45 to P70. This significantly extends previous findings: not only ongoing local oscillations but distributed network oscillatory states determine cortical responsiveness to external stimuli.

Spatio-temporal analysis of EEG features during consciousness recovery in patients with disorders of consciousness

OBJECTIVE

As consciousness recovery is not only dynamic but also involves interactions between various brain regions, elucidating the mechanism of recovery requires tracking cortical activity in spatio-temporal dimensions.

METHODS

We tracked the cortical activities of 40 patients (mean age: 54.38 years; 28 males; 21 patients with minimally conscious states) with disorders of consciousness, and collected a total of 156 electroencephalographic signals. We investigated the longitudinal changes in EEG nonlinear dynamic features (i.e., approximate entropy, sample entropy, and Lempel-Ziv complexity) and relative wavelet energy along with consciousness recovery.

RESULTS

Global EEG features showed a non-monotonic trend during consciousness recovery (P < 0.05). When the level of consciousness of patients was transferred to a minimally conscious state from an unresponsive wakefulness syndrome/ vegetative state, an inflection point appeared in the EEG features. The EEG feature change trends between the injured and uninjured areas were dissimilar (P < 0.05). Importantly, the degree of dissimilarity increased non-monotonically across the levels of consciousness (P < 0.05).

CONCLUSIONS

EEG recovery was non-monotonic and dissimilar in spatio-temporal dimensions, with an inflection point.

SIGNIFICANCE

These findings further clarify the process of consciousness recovery and provide assistance in exploring the mechanism of consciousness recovery.

Insights of neurophysiology on unconscious state using combined transcranial magnetic stimulation and electroencephalography: A systematic review

Unconscious state has been investigated in numerous studies so far, but pathophysiology of this state is not fully understood. Recently, combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG) has been developed to allow for non-invasive assessment of neurophysiology in the cerebral cortex. We conducted a systematic literature search for TMS-EEG studies on human unconscious state using PubMed with cross-reference and manual searches. The initial search yielded 137 articles, and 19 of them were identified as relevant, including one article found by manual search. This review included 10 studies for unresponsive wakefulness syndrome (UWS), 9 for minimally conscious states (MCS), 5 for medication-induced unconscious states, and 6 for natural non-rapid eye movement states. These studies analyzed TMS-evoked potential to calculate perturbational complexity index (PCI) and OFF-periods. In particular, PCI was found to be a potentially useful marker to differentiate between UWS and MCS. This review demonstrated that TMS-EEG could represent a promising neuroscientific tool to investigate various unconscious states. Further TMS-EEG research may help elucidate the neural basis of unconscious state.

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.

A Precision Medicine Framework for Classifying Patients with Disorders of Consciousness: Advanced Classification of Consciousness Endotypes (ACCESS)

BACKGROUND

Consciousness in patients with brain injury is traditionally assessed based on semiological evaluation at the bedside. This classification is limited because of low granularity, ill-defined and rigid nomenclatures incompatible with the highly fluctuating nature of consciousness, failure to identify specific brain states like cognitive motor dissociation, and neglect for underlying biological mechanisms. Here, the authors present a pragmatic framework based on consciousness endotypes that combines clinical phenomenology with all essential physiological and biological data, emphasizing recovery trajectories, therapeutic potentials and clinical feasibility.

METHODS

The Neurocritical Care Society's Curing Coma Campaign identified an international group of experts who convened in a series of online meetings between May and November 2020 to discuss and propose a novel framework for classifying consciousness.

RESULTS

The expert group proposes Advanced Classification of Consciousness Endotypes (ACCESS), a tiered multidimensional framework reflecting increasing complexity and an aspiration to consider emerging and future approaches. Tier 1 is based on clinical phenotypes and structural imaging. Tier 2 adds functional measures including EEG, PET and functional MRI, that can be summarized using the Arousal, Volition, Cognition and Mechanisms (AVCM) score (where "Volition" signifies volitional motor responses). Finally, Tier 3 reflects dynamic changes over time with a (theoretically infinite) number of physiologically distinct states to outline consciousness recovery and identify opportunities for therapeutic interventions.

CONCLUSIONS

Whereas Tiers 1 and 2 propose an approach for low-resource settings and state-of-the-art expertise at leading academic centers, respectively, Tier 3 is a visionary multidimensional consciousness paradigm driven by continuous incorporation of new knowledge while addressing the Curing Coma Campaign's aspirational goals.

Persistent vegetative state: an overview

Background

Disorder of consciousness diagnosis, especially when is classified as persistent vegetative state (without misestimating the other diagnosis classifications), in the intensive care is an important diagnosis to evaluate and treat. Persistent vegetative state diagnosis is a challenge in the daily clinical practice because the diagnosis is made mainly based upon the clinical history and the patient behavior observation. There are some specific criteria for this diagnosis, and this could be very tricky when the physician is not well trained.

Main body

We made a literature review regarding the persistent vegetative state diagnosis, clinical features, management, prognosis, and daily medical practice challenges while considering the bioethical issues and the family perspective about the patient status. The objective of this overview is to provide updated information regarding this clinical state’s features while considering the current medical literature available.

Conclusions

Regardless of the currently available guidelines and literature, there is still a lot of what we do not know about the persistent vegetative state. There is a lack of evidence regarding the optimal diagnosis and even more, about how to expect a natural history of this disorder of consciousness. It is important to recall that the patients (despite of their altered mental state diagnosis) should always be treated to avoid some of the intensive care unit long-stance complications.

The impact of artifact removal approaches on TMS-EEG signal

Transcranial magnetic stimulation (TMS)-evoked potentials (TEPs) allow one to assess cortical excitability and effective connectivity in clinical and basic research. However, obtaining clean TEPs is challenging due to the various TMS-related artifacts that contaminate the electroencephalographic (EEG) signal when the TMS pulse is delivered. Different preprocessing approaches have been employed to remove the artifacts, but the degree of artifact reduction or signal distortion introduced in this phase of analysis is still unknown. Knowing and controlling this potential source of uncertainty will increase the inter-rater reliability of TEPs and improve the comparability between TMS-EEG studies. The goal of this study was to assess the variability in TEP waveforms due to of the use of different preprocessing pipelines. To accomplish this aim, we preprocessed the same TMS-EEG data with four different pipelines and compared the results. The dataset was obtained from 16 subjects in two identical recording sessions, each session consisting of both left dorsolateral prefrontal cortex and left inferior parietal lobule stimulation at 100% of the resting motor threshold. Considerable differences in TEP amplitudes and global mean field power (GMFP) were found between the preprocessing pipelines. Topographies of TEPs from the different pipelines were all highly correlated (ρ>0.8) at latencies over 100 ms. By contrast, waveforms at latencies under 100 ms showed a variable level of correlation, with ρ ranging between 0.2 and 0.9. Moreover, the test-retest reliability of TEPs depended on the preprocessing pipeline. Taken together, these results take us to suggest that the choice of the preprocessing approach has a marked impact on the final TEP, and that further studies are needed to understand advantages and disadvantages of the different approaches.

Impact of GABAA and GABAB Inhibition on Cortical Dynamics and Perturbational Complexity during Synchronous and Desynchronized States

Quantitative estimations of spatiotemporal complexity of cortical activity patterns are used in the clinic as a measure of consciousness levels, but the cortical mechanisms involved are not fully understood. We used a version of the perturbational complexity index (PCI) adapted to multisite recordings from the ferret (either sex) cerebral cortex in vitro (sPCI) to investigate the role of GABAergic inhibition in cortical complexity. We studied two dynamical states: slow-wave activity (synchronous state) and desynchronized activity, that express low and high causal complexity respectively. Progressive blockade of GABAergic inhibition during both regimes revealed its impact on the emergent cortical activity and on sPCI. Gradual GABAA receptor blockade resulted in higher synchronization, being able to drive the network from a desynchronized to a synchronous state, with a progressive decrease of complexity (sPCI). Blocking GABAB receptors also resulted in a reduced sPCI, in particular when in a synchronous, slow wave state. Our findings demonstrate that physiological levels of inhibition contribute to the generation of dynamical richness and spatiotemporal complexity. However, if inhibition is diminished or enhanced, cortical complexity decreases. Using a computational model, we explored a larger parameter space in this relationship and demonstrate a link between excitatory/inhibitory balance and the complexity expressed by the cortical network.SIGNIFICANCE STATEMENT The spatiotemporal complexity of the activity expressed by the cerebral cortex is a highly revealing feature of the underlying network's state. Complexity varies with physiological brain states: it is higher during awake than during sleep states. But it also informs about pathologic states: in disorders of consciousness, complexity is lower in an unresponsive wakefulness syndrome than in a minimally conscious state. What are the network parameters that modulate complexity? Here we investigate how inhibition, mediated by either GABAA or GABAA receptors, influences cortical complexity. And we do this departing from two extreme functional states: a highly synchronous, slow-wave state, and a desynchronized one that mimics wakefulness. We find that there is an optimal level of inhibition in which complexity is highest.

Effects of rTMS on the brain: is there value in variability?

The ability of repetitive transcranial magnetic stimulation (rTMS) to non-invasively induce neuroplasticity in the human cortex has opened exciting possibilities for its application in both basic and clinical research. Changes in the amplitude of motor evoked potentials (MEPs) elicited by single-pulse transcranial magnetic stimulation has so far provided a convenient model for exploring the neurophysiology of rTMS effects on the brain, influencing the ways in which these stimulation protocols have been applied therapeutically. However, a growing number of studies have reported large inter-individual variability in the mean MEP response to rTMS, raising legitimate questions about the usefulness of this model for guiding therapy. Although the increasing application of different neuroimaging approaches has made it possible to probe rTMS-induced neuroplasticity outside the motor cortex to measure changes in neural activity that impact other aspects of human behaviour, the high variability of rTMS effects on these measurements remains an important issue for the field to address. In this review, we seek to move away from the conventional facilitation/inhibition dichotomy that permeates much of the rTMS literature, presenting a non-standard approach for measuring rTMS-induced neuroplasticity. We consider the evidence that rTMS is able to modulate an individual's moment-to-moment variability of neural activity, and whether this could have implications for guiding the therapeutic application of rTMS.

Neuromodulatory therapies for patients with prolonged disorders of consciousness

Background:

Reviving patients with prolonged disorders of consciousness (DOCs) has always been focused and challenging in medical research. Owing to the limited effectiveness of available medicine, recent research has increasingly turned towards neuromodulatory therapies, involving the stimulation of neural circuits. We summarised the progression of research regarding neuromodulatory therapies in the field of DOCs, compared the differences among different studies, in an attempt to explore optimal stimulation patterns and parameters, and analyzed the major limitations of the relevant studies to facilitate future research.

Methods:

We performed a search in the PubMed database, using the concepts of DOCs and neuromodulation. Inclusion criteria were: articles in English, published after 2002, and reporting clinical trials of neuromodulatory therapies in human patients with DOCs.

Results:

Overall, 187 published articles met the search criteria, and 60 articles met the inclusion criteria. There are differences among these studies regarding the clinical efficacies of neurostimulation techniques for patients with DOCs, and large-sample studies are still lacking.

Conclusions:

Neuromodulatory techniques were used as trial therapies for DOCs wherein their curative effects were controversial. The difficulties in detecting residual consciousness, the confounding effect between the natural course of the disease and therapeutic effect, and the heterogeneity across patients are the major limitations. Large-sample, well-designed studies, and innovations for both treatment and assessment are anticipated in future research.

Outcome Prediction in Unresponsive Wakefulness Syndrome and Minimally Conscious State by Non-linear Dynamic Analysis of the EEG

Objectives: This study aimed to investigate the role of non-linear dynamic analysis (NDA) of the electroencephalogram (EEG) in predicting patient outcome in unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS).

Methods: This was a prospective longitudinal cohort study. A total of 98 and 64 UWS and MCS cases, respectively, were assessed. During admission, EEGs were acquired under eyes-closed and pain stimulation conditions. EEG nonlinear indices, including approximate entropy (ApEn) and cross-ApEn, were calculated. The modified Glasgow Outcome Scale (mGOS) was employed to assess functional prognosis 1 year following brain injury.

Results: The mGOS scores were improved in 25 (26%) patients with UWS and 42 (66%) with MCS. Under the painful stimulation condition, both non-linear indices were lower in patients with UWS than in those with MCS. The frontal region, periphery of the primary sensory area (S1), and forebrain structure might be the key points modulating disorders of consciousness. The affected local cortical networks connected to S1 and unaffected distant cortical networks connecting S1 to the prefrontal area played important roles in mGOS score improvement.

Conclusions: NDA provides an objective assessment of cortical excitability and interconnections of residual cortical functional islands. The impaired interconnection of the residual cortical functional island meant a poorer prognosis. The activation in the affected periphery of the S1 and the increase in the interconnection of affected local cortical areas around the S1 and unaffected S1 to the prefrontal and temporal areas meant a relatively favorable prognosis.

Role of neurorehabilitative treatment using transcranial magnetic stimulation in disorders of consciousness

Disorders of consciousness (DOC) result from brain injuries that cause functional changes in vigilance, awareness and behaviour. It is important to correctly diagnose DOC so that the most appropriate rehabilitation treatments can be initiated. Several studies in DOC patients have demonstrated that repetitive transcranial magnetic stimulation (rTMS) has an important role to play in the recovery of consciousness as highlighted by monitoring clinical scale scores. Although studies indicate that rTMS can be used to aid recovery, it is not combined with other rehabilitative cognitive treatments. As of December 2018, there have been no studies published that combined DOC cognitive rehabilitation with TMS. This current review describes the use of rTMS as a form of non-invasive brain stimulation, as distinct from its use as a tool to investigate residual cortical activity, in terms of its possible therapeutic effects including cognitive rehabilitation. Literature searches were undertaken to identify all relevant studies. The available evidence suggests that rTMS may have an important role to play in in monitoring brain function during recovery and making other intensive rehabilitation treatments more effective, such as sensorial stimulations and cognitive training in patients after a severe acquired brain injury. Further research is required to establish the usefulness of rTMS treatment in DOC rehabilitation.

Real-Time Artifacts Reduction during TMS-EEG Co-Registration: A Comprehensive Review on Technologies and Procedures

Transcranial magnetic stimulation (TMS) excites neurons in the cortex, and neural activity can be simultaneously recorded using electroencephalography (EEG). However, TMS-evoked EEG potentials (TEPs) do not only reflect transcranial neural stimulation as they can be contaminated by artifacts. Over the last two decades, significant developments in EEG amplifiers, TMS-compatible technology, customized hardware and open source software have enabled researchers to develop approaches which can substantially reduce TMS-induced artifacts. In TMS-EEG experiments, various physiological and external occurrences have been identified and attempts have been made to minimize or remove them using online techniques. Despite these advances, technological issues and methodological constraints prevent straightforward recordings of early TEPs components. To the best of our knowledge, there is no review on both TMS-EEG artifacts and EEG technologies in the literature to-date. Our survey aims to provide an overview of research studies in this field over the last 40 years. We review TMS-EEG artifacts, their sources and their waveforms and present the state-of-the-art in EEG technologies and front-end characteristics. We also propose a synchronization toolbox for TMS-EEG laboratories. We then review subject preparation frameworks and online artifacts reduction maneuvers for improving data acquisition and conclude by outlining open challenges and future research directions in the field.

Auditory Event-Related "Global Effect" Predicts Recovery of Overt Consciousness

Objective: To explore whether the presence of an event-related potential (ERP) "global effect" (GE+)-that corresponds to a correlate of conscious processing in the local-global auditory task-predicts behaviorally overt consciousness recovery in a large cohort of patients suffering from disorders of consciousness (DOC). Methods: We conducted a prospective study on all DOC patients evaluated during the 2009-2018 period. Behavioral examination included Coma Recovery Scale-Revised (CRS-R) scores and bedside high-density EEG recordings. Consciousness recovery was evaluated at 6 months by a structured phone interview. The predictive value of a GE+ was calculated both on survivors and on all patients. Results: A total of 236 patients with a documented outcome and technically valid EEG recordings could be included. Among them, 66 patients had a GE+ status (28%). Presence of GE+ predicted behaviorally overt consciousness recovery in survivors with high specificity (Sp = 84%) and high positive predictive value (PPV = 80%) but with low sensitivity (Se = 35%) and low negative predictive value (NPV = 42%). Positive likelihood ratio (LR+) of GE+ was superior to LR+ of initial clinical status and of ERP effect indexing unconscious auditory processing [local effect (LE)]. Interpretation: Our results demonstrate that the presence of a bedside ERP GE+ is highly predictive of behaviorally overt consciousness recovery in DOC patients, regardless of the delay, of behavioral status, and of the etiology of brain dysfunction. However, the absence of this effect is not a reliable predictor of negative outcome. This study provides Class III evidence that the presence of an ERP "global effect" predicts consciousness recovery in DOC patients.

Impaired consciousness due to injury of the ascending reticular activating system in a patient with bilateral pontine infarction: A case report

The ascending reticular activating system (ARAS) is known to play an essential role in maintaining arousal and consciousness. In this report, we describe the case of a patient with impaired consciousness due to injury of the ARAS after bilateral pontine infarction. A 73-year-old female patient presented with anterior chest pain to the Emergency Department of our university hospital. She was diagnosed with chronic stable angina pectoris, three-vessel disease, and chronic total occlusion of the left anterior descending artery by coronary angiography and received conservative treatment. After five days, she showed deep drowsy mentality and brain MRI revealed bilateral paramedian pontine infarction. Four weeks after the pontine infarction, she showed severely impaired consciousness, with a Glasgow Coma Scale score of 7 (eye-opening: 2, best verbal response: 2, and best motor response: 3). Coma Recovery Scale-Revised score was 10 (auditory function: 2, visual function: 3, motor function: 2, verbal function: 2, communication: 0, and arousal: 1). Results of diffusion tensor tractography (DTT) for the ARAS showed decreased neural connectivity in the left lower dorsal ARAS, both lower ventral ARAS, and both upper ARAS. To the best of our knowledge, this is the first report of injury to the ARAS in bilateral pontine infarction diagnosed by DTT. We presume that our report would provide clinicians a better understanding of the mechanism of impaired consciousness in patients with pontine infarction.

Assessing the state of consciousness for individual patients using complex, statistical stimuli

Patients with prolonged disorders of consciousness (PDOC) are often unable to communicate their state of consciousness. Determining the latter is essential for the patient's care and prospects of recovery. Auditory stimulation in combination with neural recordings is a promising technique towards an objective assessment of conscious awareness. Here, we investigated the potential of complex, acoustic stimuli to elicit EEG responses suitable for classifying multiple subject groups, from unconscious to responding. We presented naturalistic auditory textures with unexpectedly changing statistics to human listeners. Awake, active listeners were asked to indicate the change by button press, while all other groups (awake passive, asleep, minimally conscious state (MCS), and unresponsive wakefulness syndrome (UWS)) listened passively. We quantified the evoked potential at stimulus onset and change in stimulus statistics, as well as the complexity of neural response during the change of stimulus statistics. On the group level, onset and change potentials classified patients and healthy controls successfully but failed to differentiate between the UWS and MCS groups. Conversely, the Lempel-Ziv complexity of the scalp-level potential allowed reliable differentiation between UWS and MCS even for individual subjects, when compared with the clinical assessment aligned to the EEG measurements. The accuracy appears to improve further when taking the latest available clinical diagnosis into account. In summary, EEG signal complexity during onset and changes in complex acoustic stimuli provides an objective criterion for distinguishing states of consciousness in clinical patients. These results suggest EEG-recordings as a cost-effective tool to choose appropriate treatments for non-responsive PDOC patients.

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

We test whether a measure based on the directed transfer function (DTF) calculated from short segments of electroencephalography (EEG) time-series can be used to monitor the state of the patients also during sevoflurane anesthesia as it can for patients undergoing propofol anesthesia. We collected and analyzed 25-channel EEG from 7 patients (3 females, ages 41–56 years) undergoing surgical anesthesia with sevoflurane, and quantified the sensor space directed connectivity for every 1-s epoch using DTF. The resulting connectivity parameters were compared to corresponding parameters from our previous study (n = 8, patients anesthetized with propofol and remifentanil, but otherwise using a similar protocol). Statistical comparisons between and within studies were done using permutation statistics, a data driven algorithm based on the DTF-parameters was employed to classify the epochs as coming from awake or anesthetized state. According to results of the permutation tests, DTF-parameter topographies were significantly different between the awake and anesthesia state at the group level. However, the topographies were not significantly different when comparing results computed from sevoflurane and propofol data, neither in the awake nor in anesthetized state. Optimizing the algorithm for simultaneously having high sensitivity and specificity in classification yielded an accuracy of 95.1% (SE = 0.96%), with sensitivity of 98.4% (SE = 0.80%) and specificity of 94.8% (SE = 0.10%). These findings indicate that the DTF changes in a similar manner when humans undergo general anesthesia caused by two distinct anesthetic agents with different molecular mechanisms of action.

Towards New Diagnostic Approaches in Disorders of Consciousness: A Proof of Concept Study on the Promising Use of Imagery Visuomotor Task

Background: advanced paraclinical approaches using functional neuroimaging and electroencephalography (EEG) allow identifying patients who are covertly aware despite being diagnosed as unresponsive wakefulness syndrome (UWS). Bedside detection of covert awareness employing motor imagery tasks (MI), which is a universally accepted clinical indicator of awareness in the absence of overt behavior, may miss some of these patients, as they could still have a certain level of awareness. We aimed at assessing covert awareness in patients with UWS using a visuomotor-guided motor imagery task (VMI) during EEG recording. Methods: nine patients in a minimally conscious state (MCS), 11 patients in a UWS, and 15 healthy individuals (control group—CG) were provided with an VMI (imagine dancing while watching a group dance video to command), a simple-MI (imagine squeezing their right hand to command), and an advanced-MI (imagine dancing without watching a group dance video to command) to detect command-following. We analyzed the command-specific EEG responses (event-related synchronization/desynchronization—ERS/ERD) of each patient, assessing whether these responses were appropriate, consistent, and statistically similar to those elicited in the CG, as reliable markers of motor imagery. Results: All patients in MCS, all healthy individuals and one patient in UWS repeatedly and reliably generated appropriate EEG responses to distinct commands of motor imagery with a classification accuracy of 60–80%. Conclusions: VMI outperformed significantly MI tasks. Therefore, patients in UWS may be still misdiagnosed despite a rigorous clinical assessment and an appropriate MI assessment. It is thus possible to suggest that motor imagery tasks should be delivered to patients with chronic disorders of consciousness in visuomotor-aided modality (also in the rehabilitation setting) to greatly entrain patient’s participation. In this regard, the EEG approach we described has the clear advantage of being cheap, portable, widely available, and objective. It may be thus considered as, at least, a screening tool to identify the patients who deserve further, advanced paraclinical approaches.

Multiplex and Multilayer Network EEG Analyses: A Novel Strategy in the Differential Diagnosis of Patients with Chronic Disorders of Consciousness

The deterioration of specific topological network measures that quantify different features of whole-brain functional network organization can be considered a marker for awareness impairment. Such topological measures reflect the functional interactions of multiple brain structures, which support the integration of different sensorimotor information subtending awareness. However, conventional, single-layer, graph theoretical analysis (GTA)-based approaches cannot always reliably differentiate patients with Disorders of Consciousness (DoC). Using multiplex and multilayer network analyses of frequency-specific and area-specific networks, we investigated functional connectivity during resting-state EEG in 17 patients with Unresponsive Wakefulness Syndrome (UWS) and 15 with Minimally Conscious State (MCS). Multiplex and multilayer network metrics indicated the deterioration and heterogeneity of functional networks and, particularly, the frontal-parietal (FP), as the discriminant between patients with MCS and UWS. These data were not appreciable when considering each individual frequency-specific network. The distinctive properties of multiplex/multilayer network metrics and individual frequency-specific network metrics further suggest the value of integrating the networks as opposed to analyzing frequency-specific network metrics one at a time. The hub vulnerability of these regions was positively correlated with the behavioral responsiveness, thus strengthening the clinically-based differential diagnosis. Therefore, it may be beneficial to adopt both multiplex and multilayer network analyses when expanding the conventional GTA-based analyses in the differential diagnosis of patients with DoC. Multiplex analysis differentiated patients at a group level, whereas the multilayer analysis offered complementary information to differentiate patients with DoC individually. Although further studies are necessary to confirm our preliminary findings, these results contribute to the issue of DoC differential diagnosis and may help in guiding patient-tailored management.

Decreased Evoked Slow-Activity After tDCS in Disorders of Consciousness

Due to life-saving medical advances, the diagnosis and treatment of disorders of consciousness (DOC) has become a more commonly occurring clinical issue. One recently developed intervention option has been non-invasive transcranial direct current stimulation. This dichotomy of patient responders may be better understood by investigating the mechanism behind the transcranial direct current stimulation (tDCS) intervention. The combination of transcranial magnetic stimulation and electroencephalography (TMS-EEG) has been an important diagnostic tool in DOC patients. We therefore examined the neural response using TMS-EEG both before and after tDCS in seven DOC patients (four diagnosed as in a minimally conscious state and three with unresponsive wakefulness syndrome). tDCS was applied over the dorsolateral prefrontal cortex, while TMS pulses were applied to the premotor cortex. None of the seven patients showed relevant behavioral change after tDCS. We did, however, find that the overall evoked slow activity was reduced following tDCS intervention. We also found a positive correlation between the strength of the slow activity and the amount of high-frequency suppression. However, there was no significant pre-post tDCS difference in high frequencies. In the resting-state EEG, we observed that both the incidence of slow waves and the positive slope of the wave were affected by tDCS. Taken together, these results suggest that the tDCS intervention can reduce the slow-wave activity component of bistability, but this may not directly affect high-frequency activity. We hypothesize that while reduced slow activity may be necessary for the recovery of neural function, especially consciousness, this alone is insufficient.

Managing disorders of consciousness: the role of electroencephalography

Disorders of consciousness (DOC) are an important but still underexplored entity in neurology. Novel electroencephalography (EEG) measures are currently being employed for improving diagnostic classification, estimating prognosis and supporting medicolegal decision-making in DOC patients. However, complex recording protocols, a confusing variety of EEG measures, and complicated analysis algorithms create roadblocks against broad application. We conducted a systematic review based on English-language studies in PubMed, Medline and Web of Science databases. The review structures the available knowledge based on EEG measures and analysis principles, and aims at promoting its translation into clinical management of DOC patients.

Brain network motif topography may predict emergence from disorders of consciousness: a case series

Neuroimaging methods have improved the accuracy of diagnosis in patients with disorders of consciousness (DOC), but novel, clinically translatable methods for prognosticating this population are still needed. In this case series, we explored the association between topographic and global brain network properties and prognosis in patients with DOC. We recorded high-density electroencephalograms in three patients with acute or chronic DOC, two of whom also underwent an anesthetic protocol. In these two cases, we compared functional network motifs, network hubs and power topography (i.e. topographic network properties), as well as relative power and graph theoretical measures (i.e. global network properties), at baseline, during exposure to anesthesia and after recovery from anesthesia. We also compared these properties to a group of healthy, conscious controls. At baseline, the topographic distribution of nodes participating in alpha motifs resembled conscious controls in patients who later recovered consciousness and high relative power in the delta band was associated with a negative outcome. Strikingly, the reorganization of network motifs, network hubs and power topography under anesthesia followed by their return to a baseline patterns upon recovery from anesthesia, was associated with recovery of consciousness. Our findings suggest that topographic network properties measured at the single-electrode level might provide more prognostic information than global network properties that are averaged across the brain network. In addition, we propose that the brain network's capacity to reorganize in response to a perturbation is a precursor to the recovery of consciousness in DOC patients.

Clinical and advanced neurophysiology in the prognostic and diagnostic evaluation of disorders of consciousness: review of an IFCN-endorsed expert group

The analysis of spontaneous EEG activity and evoked potentialsis a cornerstone of the instrumental evaluation of patients with disorders of consciousness (DoC). Thepast few years have witnessed an unprecedented surge in EEG-related research applied to the prediction and detection of recovery of consciousness after severe brain injury,opening up the prospect that new concepts and tools may be available at the bedside. This paper provides a comprehensive, critical overview of bothconsolidated and investigational electrophysiological techniquesfor the prognostic and diagnostic assessment of DoC.We describe conventional clinical EEG approaches, then focus on evoked and event-related potentials, and finally we analyze the potential of novel research findings. In doing so, we (i) draw a distinction between acute, prolonged and chronic phases of DoC, (ii) attempt to relate both clinical and research findings to the underlying neuronal processes and (iii) discuss technical and conceptual caveats.The primary aim of this narrative review is to bridge the gap between standard and emerging electrophysiological measures for the detection and prediction of recovery of consciousness. The ultimate scope is to provide a reference and common ground for academic researchers active in the field of neurophysiology and clinicians engaged in intensive care unit and rehabilitation.

Neural signs and mechanisms of consciousness: Is there a potential convergence of theories of consciousness in sight?

Various theories for the neural basis of consciousness have been proposed, suggesting a diversity of neural signs and mechanisms. We ask to what extent this diversity is real, or whether many theories share the same basic ideas with a potential for convergence towards a more unified theory of the neural basis of consciousness. For that purpose, we review and compare the various neural signs, measures, and mechanisms proposed in the different theories. We demonstrate that different theories focus on neural signs and measures of distinct aspects of neural activity including stimulus-related, prestimulus, and resting state activity as well as on distinct features of consciousness. Therefore, the various mechanisms proposed in the different theories may, in part, complement each other. Together, we provide insight into the shared basis and convergences (and, in part, discrepancies) of the different theories of consciousness. We conclude that the different theories concern distinct aspects of both neural activity and consciousness which, as we suppose, may be integrated and nested within the brain's overall temporo-spatial dynamics.

Autonomic responses to emotional linguistic stimuli and amplitude of low-frequency fluctuations predict outcome after severe brain injury

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Acute DOC patients with favourable outcome show preserved event-related electrodermal response.

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Acute DOC patients showed reduced fALFF in the posterior cingulate cortex.

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Event-related electrodermal activity correlated with the fALFFs in the PCC in the acute phase.

An accurate prognosis on the outcome of brain-injured patients with disorders of consciousness (DOC) remains a significant challenge, especially in the acute stage. In this study, we applied a multiple-technique approach to provide accurate predictions on functional outcome after 6 months in 15 acute DOC patients. Electrophysiological correlates of implicit cognitive processing of verbal stimuli and data-driven voxel-wise resting-state fMRI signals, such as the fractional amplitude of low-frequency fluctuations (fALFF), were employed. Event-related electrodermal activity, an index of autonomic activation, was recorded in response to emotional words and pseudo-words at baseline (T0). On the same day, patients also underwent a resting-state fMRI scan. Six months later (T1), patients were classified as outcome-negative and outcome-positive using a standard functional outcome scale. We then revisited the baseline measures to test their predictive power for the functional outcome measured at T1. We found that only outcome-positive patients had an earlier, higher autonomic response for words compared to pseudo-words, a pattern similar to that of healthy awake controls. Furthermore, DOC patients showed reduced fALFF in the posterior cingulate cortex (PCC), a brain region that contributes to autonomic regulation and awareness. The event-related electrodermal marker of residual cognitive functioning was found to have a significant correlation with residual local neuronal activity in the PCC. We propose that a residual autonomic response to cognitively salient stimuli, together with a preserved resting-state activity in the PCC, can provide a useful prognostic index in acute DOC.

Transcranial magnetic stimulation-evoked connectivity reveals modulation effects of repetitive transcranial magnetic stimulation on patients with disorders of consciousness

Several studies have investigated possible role of repetitive transcranial magnetic stimulation (rTMS) in patients with disorder of consciousness (DOC). But the details of patients' brain responses to the rTMS are yet to be disclosed. The aim of the study is to explore the neural electrical responses of DOC patients to rTMS modulation. DOC Patients [14 vegetative state, seven minimally conscious state (MCS)] and healthy subjects were enrolled and received one session of rTMS. The TMS-electroencephalogram was recorded at before and immediately after rTMS stimulation. TMS-evoked potentials as well as TMS-evoked connectivity were proposed to capture the effective connectivity alteration induced by rTMS. Significant changes of TMS-evoked potential were found in the healthy group but not in DOC patients. TMS-evoked connectivity was significantly enhanced by the rTMS in healthy and MCS groups. In addition, the enhancement was positively correlated with patients' Coma Recovery Scale-Revised scores. Global synchrony of the TMS-evoked connectivity matrix significantly enhanced by rTMS in the control and MCS groups but not in vegetative state patients. Furthermore, after rTMS stimulation, the similarity of TMS-evoked connectivity patterns between pairwise patients was significantly raised in MCS patients. But no significant changes were found in vegetative state patients. TMS-evoked connectivity reveals that rTMS can effectively modulate effective connectivity of MCS patients, but no evidence of changes in vegetative state patients.

Subcortical atrophy correlates with the perturbational complexity index in patients with disorders of consciousness

BACKGROUND

The complexity of neurophysiological brain responses to direct cortical stimulation, referred to as the perturbational complexity index (PCI), has been shown able to discriminate between consciousness and unconsciousness in patients surviving severe brain injury as well as several other conditions (e.g., wake, dreamless sleep, sleep and ketamine dreaming, anesthesia).

OBJECTIVE

This study asks whether, in patients with a disorder of consciousness (DOC), the complexity of the neurophysiological response to cortical stimulation is preferentially associated with atrophy within specific brain structures.

METHODS

We perform a retrospective analysis of 40 DOC patients and correlate their maximal PCI to MR-based measurements of cortical thinning and subcortical atrophy.

RESULTS

PCI was systematically and inversely associated with the degree of local atrophy within the globus pallidus, a region previously linked to electrocortical and behavioral arousal. Conversely, we fail to detect any association between variance in cortical ribbon thickness and PCI.

CONCLUSION

These findings corroborate the previously reported association between pallidal atrophy and low behavioral arousal and suggest that this region's role in maintaining the overall balance of excitation and inhibition may critically affect the emergence of complex cortical interactions in chronic disorders of consciousness. This finding thus also suggests a target for potential neuromodulatory intervention in DOC patients.

Recovery from stroke: current concepts and future perspectives

Stroke is a leading cause of acquired, permanent disability worldwide. Although the treatment of acute stroke has been improved considerably, the majority of patients to date are left disabled with a considerable impact on functional independence and quality of life. As the absolute number of stroke survivors is likely to further increase due to the demographic changes in our aging societies, new strategies are needed in order to improve neurorehabilitation. The most critical driver of functional recovery post-stroke is neural reorganization. For developing novel, neurobiologically informed strategies to promote recovery of function, an improved understanding of the mechanisms enabling plasticity and recovery is mandatory. This review provides a comprehensive survey of recent developments in the field of stroke recovery using neuroimaging and non-invasive brain stimulation. We discuss current concepts of how the brain reorganizes its functional architecture to overcome stroke-induced deficits, and also present evidence for maladaptive effects interfering with recovery. We demonstrate that the combination of neuroimaging and neurostimulation techniques allows a better understanding of how brain plasticity can be modulated to promote the reorganization of neural networks. Finally, neurotechnology-based treatment strategies allowing patient-tailored interventions to achieve enhanced treatment responses are discussed. The review also highlights important limitations of current models, and finally closes with possible solutions and future directions.

Brain responsivity provides an individual readout for motor recovery after stroke

Promoting the recovery of motor function and optimizing rehabilitation strategies for stroke patients is closely associated with the challenge of individual prediction. To date, stroke research has identified critical pathophysiological neural underpinnings at the cellular level as well as with regard to network reorganization. However, in order to generate reliable readouts at the level of individual patients and thereby realize translation from bench to bedside, we are still in a need for innovative methods. The combined use of transcranial magnetic stimulation (TMS) and EEG has proven powerful to record both local and network responses at an individual's level. To elucidate the potential of TMS-EEG to assess motor recovery after stroke, we used neuronavigated TMS-EEG over ipsilesional primary motor cortex (M1) in 28 stroke patients in the first days after stroke. Twenty-five of these patients were reassessed after >3 months post-stroke. In the early post-stroke phase (6.7 ± 2.5 days), the TMS-evoked EEG responses featured two markedly different response morphologies upon TMS to ipsilesional M1. In the first group of patients, TMS elicited a differentiated and sustained EEG response with a series of deflections sequentially involving both hemispheres. This response type resembled the patterns of bilateral activation as observed in the healthy comparison group. By contrast, in a subgroup of severely affected patients, TMS evoked a slow and simplified local response. Quantifying the TMS-EEG responses in the time and time-frequency domain revealed that stroke patients exhibited slower and simple responses with higher amplitudes compared to healthy controls. Importantly, these patterns of activity changes after stroke were not only linked to the initial motor deficit, but also to motor recovery after >3 months post-stroke. Thus, the data revealed a substantial impairment of local effects as well as causal interactions within the motor network early after stroke. Additionally, for severely affected patients with absent motor evoked potentials and identical clinical phenotype, TMS-EEG provided differential response patterns indicative of the individual potential for recovery of function. Thereby, TMS-EEG extends the methodological repertoire in stroke research by allowing the assessment of individual response profiles.

A Graph Signal Processing Approach to Study High Density EEG Signals in Patients with Disorders of Consciousness

Graph signal processing (GSP) is a novel approach to analyse multi-dimensional neuroimaging data, constraining functional measures by structural characteristics in a single framework (i.e. graph signals). In this approach, functional time series are assigned to the vertices of the underlying weighted graph and GSP analysis is performed in each time point of the signal. Here we used GSP to study local brain connectivity changes in patients with disorders of consciousness based on resting state high density electroencephalography (hdEEG) recordings. Total variation of the graph signals is a measure of signal smoothness over the underlying graph. In this study, we constructed the underlying graph based on the geometrical distances between each electrode pairs in such a way that local smoothness of the signal can be studied. Total variation analysis in α-band showed that in the pathological states of altered consciousness, local short range communication of brain regions in this frequency band is stronger than in healthy states which shows that information is segregated in local regions in patients with disorders of consciousness.

Non-invasive brain intervention techniques used in patients with disorders of consciousness

Aim of the study: With the development of emergency medicine and intensive care technology, the number of people who survive with disorders of consciousness (DOC) has dramatically increased. The diagnosis and treatment of such patients have attracted much attention from the medical community. From the latest evidence-based guidelines, non-invasive brain intervention (NIBI) techniques may be valuable and promising in the diagnosis and conscious rehabilitation of DOC patients.Methods: This work reviews the studies on NIBI techniques for the assessment and intervention of DOC patients.Results: A large number of studies have explored the application of NIBI techniques in DOC patients. The NIBI techniques include transcranial magnetic stimulation, transcranial electric stimulation, music stimulation, near-infrared laser stimulation, focused shock wave therapy, low-intensity focused ultrasound pulsation and transcutaneous auricular vagus nerve stimulation.Conclusions: NIBI techniques present numerous advantages such as being painless, safe and inexpensive; having adjustable parameters and targets; and having broad development prospects in treating DOC patients.

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.

Individualized perturbation of the human connectome reveals reproducible biomarkers of network dynamics relevant to cognition

Large-scale brain networks are often described using resting-state functional magnetic resonance imaging (fMRI). However, the blood oxygenation level-dependent (BOLD) signal provides an indirect measure of neuronal firing and reflects slow-evolving hemodynamic activity that fails to capture the faster timescale of normal physiological function. Here we used fMRI-guided transcranial magnetic stimulation (TMS) and simultaneous electroencephalography (EEG) to characterize individual brain dynamics within discrete brain networks at high temporal resolution. TMS was used to induce controlled perturbations to individually defined nodes of the default mode network (DMN) and the dorsal attention network (DAN). Source-level EEG propagation patterns were network-specific and highly reproducible across sessions 1 month apart. Additionally, individual differences in high-order cognitive abilities were significantly correlated with the specificity of TMS propagation patterns across DAN and DMN, but not with resting-state EEG dynamics. Findings illustrate the potential of TMS-EEG perturbation-based biomarkers to characterize network-level individual brain dynamics at high temporal resolution, and potentially provide further insight on their behavioral significance.

European Academy of Neurology guideline on the diagnosis of coma and other disorders of consciousness

BACKGROUND AND PURPOSE

Patients with acquired brain injury and acute or prolonged disorders of consciousness (DoC) are challenging. Evidence to support diagnostic decisions on coma and other DoC is limited but accumulating. This guideline provides the state-of-the-art evidence regarding the diagnosis of DoC, summarizing data from bedside examination techniques, functional neuroimaging and electroencephalography (EEG).

METHODS

Sixteen members of the European Academy of Neurology (EAN) Scientific Panel on Coma and Chronic Disorders of Consciousness, representing 10 European countries, reviewed the scientific evidence for the evaluation of coma and other DoC using standard bibliographic measures. Recommendations followed the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. The guideline was endorsed by the EAN.

RESULTS

Besides a comprehensive neurological examination, the following suggestions are made: probe for voluntary eye movements using a mirror; repeat clinical assessments in the subacute and chronic setting, using the Coma Recovery Scale - Revised; use the Full Outline of Unresponsiveness score instead of the Glasgow Coma Scale in the acute setting; obtain clinical standard EEG; search for sleep patterns on EEG, particularly rapid eye movement sleep and slow-wave sleep; and, whenever feasible, consider positron emission tomography, resting state functional magnetic resonance imaging (fMRI), active fMRI or EEG paradigms and quantitative analysis of high-density EEG to complement behavioral assessment in patients without command following at the bedside.

CONCLUSIONS

Standardized clinical evaluation, EEG-based techniques and functional neuroimaging should be integrated for multimodal evaluation of patients with DoC. The state of consciousness should be classified according to the highest level revealed by any of these three approaches.

Near-Death Experience as a Probe to Explore (Disconnected) Consciousness

Forty-five years ago, the first evidence of near-death experience (NDE) during comatose state was provided, setting the stage for a new paradigm for studying the neural basis of consciousness in unresponsive states. At present, the state of consciousness associated with NDEs remains an open question. In the common view, consciousness is said to disappear in a coma with the brain shutting down, but this is an oversimplification. We argue that a novel framework distinguishing awareness, wakefulness, and connectedness is needed to comprehend the phenomenon. Classical NDEs correspond to internal awareness experienced in unresponsive conditions, thereby corresponding to an episode of disconnected consciousness. Our proposal suggests new directions for NDE research, and more broadly, consciousness science.