Mental imagery for brain-computer interface control and communication in non-responsive individuals

BACKGROUND

People who survive severe brain damage may eventually develop a prolonged consciousness disorder. Others can regain full consciousness but remain unable to speak or move because of the severity of the lesions, as for those with locked-in syndrome (LIS). Brain-computer interface techniques can be useful to disentangle these states by detecting neurophysiological correlates of conscious processing of information to enable communication with these individuals after the diagnosis.

OBJECTIVE

The goal of our study was to evaluate with a user-centered design approach the usability of a mental imagery task to detect signs of voluntary information processing and enabling communication in a group of severely disabled individuals.

METHODS

Five individuals with LIS participated in the study. Participants were instructed to imagine hand, arm or feet movements during electroencephalography (EEG) to detect patterns of event-related synchronization/desynchronization associated with each task. After the user-centered design, usability was evaluated (i.e., efficiency, effectiveness and satisfaction).

RESULTS

Two participants achieved significant levels of accuracy in 2 different tasks. The associated workload and levels of satisfaction perceived by the users were moderate and were mainly related to the time demand of the task.

CONCLUSION

Results showed lack of effectiveness of the task to detect voluntary brain activity and thus detect consciousness or communicate with non-responsive individuals. The application must be modified to be sufficiently satisfying for the intended end-users and suggestions are made in this regard.

Increased cerebral responses to salient transitions between alternating stimuli in chronic migraine with medication overuse headache and during migraine attacks

Introduction

In a previous study exploring central pain modulation with heterotopic stimuli in healthy volunteers, we found that transitions between sustained noxious and innocuous thermal stimulations on the foot activated the “salience matrix”. Knowing that central sensory processing is abnormal in migraine, we searched in the present study for possible abnormalities of these salient transitional responses in different forms of migraine and at different time points of the migraine cycle.

Methods

Participants of both sexes, mostly females, took part in a conditioned pain modulation experiment: Migraineurs between (n = 14) and during attacks (n = 5), chronic migraine patients with medication overuse headache (n = 7) and healthy volunteers (n = 24). To evoke the salience response, continuous noxious cold or innocuous warm stimulations were alternatively applied on the right foot. Cerebral blood oxygenation level dependent responses were recorded with fMRI.

Results

Switching between the two stimulations caused a significant transition response in the “salience matrix” in all subject groups (effect of the condition). Moreover, some group effects appeared on subsequent post-hoc analyses. Augmented transitional blood oxygenation level dependent responses in the motor cortex and superior temporal sulcus were found in two patient groups compared to healthy controls: chronic migraine with medication overuse headache patients and migraineurs recorded during an attack. In chronic migraine with medication overuse headache patients, salience-related responses were moreover greater in the premotor cortex, supplementary motor area, lingual gyrus and dorso-medial prefrontal cortex and other “salience matrix” areas, such as the anterior cingulate and primary somatosensory cortices.

Conclusion

This study shows salience-related hyperactivation of affective and motor control areas in chronic migraine with medication overuse headache patients and, to a lesser extent, in episodic migraine patients during an attack. The greater extension of exaggerated blood oxygenation level dependent responses to unspecific salient stimuli in chronic migraine with medication overuse headache than during a migraine attack could be relevant for headache chronification.

Reproducible grey matter patterns index a multivariate, global alteration of brain structure in schizophrenia and bipolar disorder

Schizophrenia is a severe mental disorder characterized by numerous subtle changes in brain structure and function. Machine learning allows exploring the utility of combining structural and functional brain magnetic resonance imaging (MRI) measures for diagnostic application, but this approach has been hampered by sample size limitations and lack of differential diagnostic data. Here, we performed a multi-site machine learning analysis to explore brain structural patterns of T1 MRI data in 2668 individuals with schizophrenia, bipolar disorder or attention-deficit/ hyperactivity disorder, and healthy controls. We found reproducible changes of structural parameters in schizophrenia that yielded a classification accuracy of up to 76% and provided discrimination from ADHD, through it lacked specificity against bipolar disorder. The observed changes largely indexed distributed grey matter alterations that could be represented through a combination of several global brain-structural parameters. This multi-site machine learning study identified a brain-structural signature that could reproducibly differentiate schizophrenia patients from controls, but lacked specificity against bipolar disorder. While this currently limits the clinical utility of the identified signature, the present study highlights that the underlying alterations index substantial global grey matter changes in psychotic disorders, reflecting the biological similarity of these conditions, and provide a roadmap for future exploration of brain structural alterations in psychiatric patients.

Toward an Attention-Based Diagnostic Tool for Patients With Locked-in Syndrome

Electroencephalography (EEG) has been proposed as a supplemental tool for reducing clinical misdiagnosis in severely brain-injured populations helping to distinguish conscious from unconscious patients. We studied the use of spectral entropy as a measure of focal attention in order to develop a motor-independent, portable, and objective diagnostic tool for patients with locked-in syndrome (LIS), answering the issues of accuracy and training requirement. Data from 20 healthy volunteers, 6 LIS patients, and 10 patients with a vegetative state/unresponsive wakefulness syndrome (VS/UWS) were included. Spectral entropy was computed during a gaze-independent 2-class (attention vs rest) paradigm, and compared with EEG rhythms (delta, theta, alpha, and beta) classification. Spectral entropy classification during the attention-rest paradigm showed 93% and 91% accuracy in healthy volunteers and LIS patients respectively. VS/UWS patients were at chance level. EEG rhythms classification reached a lower accuracy than spectral entropy. Resting-state EEG spectral entropy could not distinguish individual VS/UWS patients from LIS patients. The present study provides evidence that an EEG-based measure of attention could detect command-following in patients with severe motor disabilities. The entropy system could detect a response to command in all healthy subjects and LIS patients, while none of the VS/UWS patients showed a response to command using this system.

A method for independent component graph analysis of resting-state fMRI

INTRODUCTION

Independent component analysis (ICA) has been extensively used for reducing task-free BOLD fMRI recordings into spatial maps and their associated time-courses. The spatially identified independent components can be considered as intrinsic connectivity networks (ICNs) of non-contiguous regions. To date, the spatial patterns of the networks have been analyzed with techniques developed for volumetric data.

OBJECTIVE

Here, we detail a graph building technique that allows these ICNs to be analyzed with graph theory.

METHODS

First, ICA was performed at the single-subject level in 15 healthy volunteers using a 3T MRI scanner. The identification of nine networks was performed by a multiple-template matching procedure and a subsequent component classification based on the network "neuronal" properties. Second, for each of the identified networks, the nodes were defined as 1,015 anatomically parcellated regions. Third, between-node functional connectivity was established by building edge weights for each networks. Group-level graph analysis was finally performed for each network and compared to the classical network.

RESULTS

Network graph comparison between the classically constructed network and the nine networks showed significant differences in the auditory and visual medial networks with regard to the average degree and the number of edges, while the visual lateral network showed a significant difference in the small-worldness.

CONCLUSIONS

This novel approach permits us to take advantage of the well-recognized power of ICA in BOLD signal decomposition and, at the same time, to make use of well-established graph measures to evaluate connectivity differences. Moreover, by providing a graph for each separate network, it can offer the possibility to extract graph measures in a specific way for each network. This increased specificity could be relevant for studying pathological brain activity or altered states of consciousness as induced by anesthesia or sleep, where specific networks are known to be altered in different strength.

Cognitive Processing in Non-Communicative Patients: What Can Event-Related Potentials Tell Us?

Event-related potentials (ERP) have been proposed to improve the differential diagnosis of non-responsive patients. We investigated the potential of the P300 as a reliable marker of conscious processing in patients with locked-in syndrome (LIS). Eleven chronic LIS patients and 10 healthy subjects (HS) listened to a complex-tone auditory oddball paradigm, first in a passive condition (listen to the sounds) and then in an active condition (counting the deviant tones). Seven out of nine HS displayed a P300 waveform in the passive condition and all in the active condition. HS showed statistically significant changes in peak and area amplitude between conditions. Three out of seven LIS patients showed the P3 waveform in the passive condition and five of seven in the active condition. No changes in peak amplitude and only a significant difference at one electrode in area amplitude were observed in this group between conditions. We conclude that, in spite of keeping full consciousness and intact or nearly intact cortical functions, compared to HS, LIS patients present less reliable results when testing with ERP, specifically in the passive condition. We thus strongly recommend applying ERP paradigms in an active condition when evaluating consciousness in non-responsive patients.

Electromyographic decoding of response to command in disorders of consciousness

OBJECTIVE

To propose a new methodology based on single-trial analysis for detecting residual response to command with EMG in patients with disorders of consciousness (DOC), overcoming the issue of trial dependency and decreasing the influence of a patient's fluctuation of vigilance or arousal over time on diagnostic accuracy.

METHODS

Forty-five patients with DOC (18 with vegetative/unresponsive wakefulness syndrome [VS/UWS], 22 in a minimally conscious state [MCS], 3 who emerged from MCS [EMCS], and 2 with locked-in syndrome [LIS]) and 20 healthy controls were included in the study. Patients were randomly instructed to either move their left or right hand or listen to a control command ("It is a sunny day") while EMG activity was recorded on both arms.

RESULTS

Differential EMG activity was detected in all MCS cases displaying reproducible response to command at bedside on multiple assessments, even though only 6 of the 14 individuals presented a behavioral response to command on the day of the EMG assessment. An EMG response was also detected in all EMCS and LIS patients, and 2 MCS patients showing nonreflexive movements without command following at the bedside. None of the VS/UWS presented a response to command with this method.

CONCLUSIONS

This method allowed us to reliably distinguish between different levels of consciousness and could potentially help decrease diagnostic errors in patients with motor impairment but presenting residual motor activity.

Large-scale signatures of unconsciousness are consistent with a departure from critical dynamics

Loss of cortical integration and changes in the dynamics of electrophysiological brain signals characterize the transition from wakefulness towards unconsciousness. In this study, we arrive at a basic model explaining these observations based on the theory of phase transitions in complex systems. We studied the link between spatial and temporal correlations of large-scale brain activity recorded with functional magnetic resonance imaging during wakefulness, propofol-induced sedation and loss of consciousness and during the subsequent recovery. We observed that during unconsciousness activity in frontothalamic regions exhibited a reduction of long-range temporal correlations and a departure of functional connectivity from anatomical constraints. A model of a system exhibiting a phase transition reproduced our findings, as well as the diminished sensitivity of the cortex to external perturbations during unconsciousness. This framework unifies different observations about brain activity during unconsciousness and predicts that the principles we identified are universal and independent from its causes.

Propofol-Induced Frontal Cortex Disconnection: A Study of Resting-State Networks, Total Brain Connectivity, and Mean BOLD Signal Oscillation Frequencies

Propofol is one of the most commonly used anesthetics in the world, but much remains unknown about the mechanisms by which it induces loss of consciousness. In this resting-state functional magnetic resonance imaging study, we examined qualitative and quantitative changes of resting-state networks (RSNs), total brain connectivity, and mean oscillation frequencies of the regional blood oxygenation level-dependent (BOLD) signal, associated with propofol-induced mild sedation and loss of responsiveness in healthy subjects. We found that detectability of RSNs diminished significantly with loss of responsiveness, and total brain connectivity decreased strongly in the frontal cortex, which was associated with increased mean oscillation frequencies of the BOLD signal. Our results suggest a pivotal role of the frontal cortex in propofol-induced loss of responsiveness.

Correlation between resting state fMRI total neuronal activity and PET metabolism in healthy controls and patients with disorders of consciousness

INTRODUCTION

The mildly invasive 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) is a well-established imaging technique to measure 'resting state' cerebral metabolism. This technique made it possible to assess changes in metabolic activity in clinical applications, such as the study of severe brain injury and disorders of consciousness.

OBJECTIVE

We assessed the possibility of creating functional MRI activity maps, which could estimate the relative levels of activity in FDG-PET cerebral metabolic maps. If no metabolic absolute measures can be extracted, our approach may still be of clinical use in centers without access to FDG-PET. It also overcomes the problem of recognizing individual networks of independent component selection in functional magnetic resonance imaging (fMRI) resting state analysis.

METHODS

We extracted resting state fMRI functional connectivity maps using independent component analysis and combined only components of neuronal origin. To assess neuronality of components a classification based on support vector machine (SVM) was used. We compared the generated maps with the FDG-PET maps in 16 healthy controls, 11 vegetative state/unresponsive wakefulness syndrome patients and four locked-in patients.

RESULTS

The results show a significant similarity with ρ = 0.75 ± 0.05 for healthy controls and ρ = 0.58 ± 0.09 for vegetative state/unresponsive wakefulness syndrome patients between the FDG-PET and the fMRI based maps. FDG-PET, fMRI neuronal maps, and the conjunction analysis show decreases in frontoparietal and medial regions in vegetative patients with respect to controls. Subsequent analysis in locked-in syndrome patients produced also consistent maps with healthy controls.

CONCLUSIONS

The constructed resting state fMRI functional connectivity map points toward the possibility for fMRI resting state to estimate relative levels of activity in a metabolic map.

"Look at my classifier's result": Disentangling unresponsive from (minimally) conscious patients

Given the fact that clinical bedside examinations can have a high rate of misdiagnosis, machine learning techniques based on neuroimaging and electrophysiological measurements are increasingly being considered for comatose patients and patients with unresponsive wakefulness syndrome, a minimally conscious state or locked-in syndrome. Machine learning techniques have the potential to move from group-level statistical results to personalized predictions in a clinical setting. They have been applied for the purpose of (1) detecting changes in brain activation during functional tasks, equivalent to a behavioral command-following test and (2) estimating signs of consciousness by analyzing measurement data obtained from multiple subjects in resting state. In this review, we provide a comprehensive overview of the literature on both approaches and discuss the translation of present findings to clinical practice. We found that most studies struggle with the difficulty of establishing a reliable behavioral assessment and fluctuations in the patient's levels of arousal. Both these factors affect the training and validation of machine learning methods to a considerable degree. In studies involving more than 50 patients, small to moderate evidence was found for the presence of signs of consciousness or good outcome, where one study even showed strong evidence for good outcome.

Hypnosis modulates behavioural measures and subjective ratings about external and internal awareness

In altered subjective states, the behavioural quantification of external and internal awareness remains challenging due to the need for reports on the subjects' behalf. With the aim to characterize the behavioural counterpart of external and internal awareness in a modified subjective condition, we used hypnosis during which subjects remain fully responsive. Eleven right-handed subjects reached a satisfactory level of hypnotisability as evidenced by subjective reports on arousal, absorption and dissociation. Compared to normal wakefulness, in hypnosis (a) participants' self-ratings for internal awareness increased and self-ratings for external awareness decreased, (b) the two awareness components tended to anticorrelate less and the switches between external and internal awareness self-ratings were less frequent, and (c) participants' reaction times were higher and lapses in key presses were more frequent. The identified imbalance between the two components of awareness is considered as of functional relevance to subjective (meta)cognition, possibly mediated by allocated attentional properties brought about by hypnosis. Our results highlight the presence of a cognitive counterpart in resting state, indicate that the modified contents of awareness are measurable behaviourally, and provide leverage for investigations of more challenging altered conscious states, such as anaesthesia, sleep and disorders of consciousness.

[Brain-computer interfaces, Locked-In syndrome, and disorders of consciousness]

Detecting signs of consciousness in patients with severe brain injury constitutes a real challenge for clinicians. The current gold standard in clinical diagnosis is the behavioral scale relying on motor abilities, which are often impaired or nonexistent in these patients. In this context, brain-computer interfaces (BCIs) could offer a potential complementary tool to detect signs of consciousness whilst bypassing the usual motor pathway. In addition to complementing behavioral assessments and potentially reducing error rate, BCIs could also serve as a communication tool for paralyzed but conscious patients, e.g., suffering from Locked-In Syndrome. In this paper, we report on recent work conducted by the Coma Science Group on BCI technology, aiming to optimize diagnosis and communication in patients with disorders of consciousness and Locked-In syndrome.

Performance of a tactile P300 speller for healthy people and severely disabled patients

P300 based Brain-Computer Interfaces (BCIs) for communication are well known since many years. Most of them use visual stimuli to elicit evoked potentials because it is easy to integrate a high number of different classes into the paradigm. Nevertheless, a BCI that depends on visual stimuli is sometimes not feasible due to the presence of visual impairment in patients with severe brain injuries. In this case, it could be possible to use auditory or somatosensory stimulation. In this publication a vibrotactile P300 based BCI is introduced. Two different approaches were tested: a first approach using two stimulators and a second one that utilizes three stimulators for emitting the stimuli. The two paradigms were tested on 16 users: A group of ten healthy users and a second group comprising of 6 patients suffering Locked-In Syndrome. The control accuracy was calculated for both groups and both approaches, proving the feasibility of the device, not only for healthy people but also in severely disabled patients. In a second step we evaluated the influence of the number of stimuli on the accuracy. It was shown that in many cases the maximum accuracy was already reached with a small number of stimuli, this could be used in future tests to speed up the Information transfer rate.

Detection of response to command using voluntary control of breathing in disorders of consciousness

BACKGROUND

Detecting signs of consciousness in patients in a vegetative state/unresponsive wakefulness syndrome (UWS/VS) or minimally conscious state (MCS) is known to be very challenging. Plotkin et al. (2010) recently showed the possibility of using a breathing-controlled communication device in patients with locked in syndrome. We here aim to test a breathing-based "sniff controller" that could be used as an alternative diagnostic tool to evaluate response to command in severely brain damaged patients with chronic disorders of consciousness (DOC).

METHODS

Twenty-five DOC patients were included. Patients' resting breathing-amplitude was measured during a 5 min resting condition. Next, they were instructed to end the presentation of a music sequence by sniffing vigorously. An automated detection of changes in breathing amplitude (i.e., >1.5 SD of resting) ended the music and hence provided positive feedback to the patient.

RESULTS

None of the 11 UWS/VS patients showed a sniff-based response to command. One out of 14 patients with MCS was able to willfully modulate his breathing pattern to answer the command on 16/19 trials (accuracy 84%). Interestingly, this patient failed to show any other motor response to command.

DISCUSSION

We here illustrate the possible interest of using breathing-dependent response to command in the detection of residual cognition in patients with DOC after severe brain injury.

Cerebral responses and role of the prefrontal cortex in conditioned pain modulation: an fMRI study in healthy subjects

The mechanisms underlying conditioned pain modulation (CPM) are multifaceted. We searched for a link between individual differences in prefrontal cortex activity during multi-trial heterotopic noxious cold conditioning and modulation of the cerebral response to phasic heat pain. In 24 healthy female subjects, we conditioned laser heat stimuli to the left hand by applying alternatively ice-cold or lukewarm compresses to the right foot. We compared pain ratings with cerebral fMRI BOLD responses. We also analyzed the relation between CPM and BOLD changes produced by the heterotopic cold conditioning itself, as well as the impact of anxiety and habituation of cold-pain ratings. Specific cerebral activation was identified in precuneus and left posterior insula/SII, respectively, during early and sustained phases of cold application. During cold conditioning, laser pain decreased (n=7), increased (n=10) or stayed unchanged (n=7). At the individual level, the psychophysical effect was directly proportional to the cold-induced modulation of the laser-induced BOLD response in left posterior insula/SII. The latter correlated with the BOLD response recorded 80s earlier during the initial 10-s phase of cold application in anterior cingulate, orbitofrontal and lateral prefrontal cortices. High anxiety and habituation of cold pain were associated with greater laser heat-induced pain during heterotopic cold stimulation. The habituation was also linked to the early cold-induced orbitofrontal responses. We conclude that individual differences in conditioned pain modulation are related to different levels of prefrontal cortical activation by the early part of the conditioning stimulus, possibly due to different levels in trait anxiety.

Volitional electromyographic responses in disorders of consciousness

OBJECTIVE

The aim of the study was to validate the use of electromyography (EMG) for detecting responses to command in patients in vegetative state/unresponsive wakefulness syndrome (VS/UWS) or in minimally conscious state (MCS).

METHODS

Thirty-eight patients were included in the study (23 traumatic, 25 patients >1 year post-onset), 10 diagnosed as being in VS/UWS, eight in MCS- (no response to command) and 20 in MCS+ (response to command). Eighteen age-matched controls participated in the experiment. The paradigm consisted of three commands (i.e. 'Move your hands', 'Move your legs' and 'Clench your teeth') and one control sentence (i.e. 'It is a sunny day') presented in random order. Each auditory stimulus was repeated 4 times within one block with a stimulus-onset asynchrony of 30 seconds.

RESULTS

Post-hoc analyses with Bonferroni correction revealed that EMG activity was higher solely for the target command in one patient in permanent VS/UWS and in three patients in MCS+.

CONCLUSION

The use of EMG could help clinicians to detect conscious patients who do not show any volitional response during standard behavioural assessments. However, further investigations should determine the sensitivity of EMG as compared to neuroimaging and electrophysiological assessments.

An independent SSVEP-based brain-computer interface in locked-in syndrome

OBJECTIVE

Steady-state visually evoked potential (SSVEP)-based brain-computer interfaces (BCIs) allow healthy subjects to communicate. However, their dependence on gaze control prevents their use with severely disabled patients. Gaze-independent SSVEP-BCIs have been designed but have shown a drop in accuracy and have not been tested in brain-injured patients. In the present paper, we propose a novel independent SSVEP-BCI based on covert attention with an improved classification rate. We study the influence of feature extraction algorithms and the number of harmonics. Finally, we test online communication on healthy volunteers and patients with locked-in syndrome (LIS).

APPROACH

Twenty-four healthy subjects and six LIS patients participated in this study. An independent covert two-class SSVEP paradigm was used with a newly developed portable light emitting diode-based 'interlaced squares' stimulation pattern.

MAIN RESULTS

Mean offline and online accuracies on healthy subjects were respectively 85 ± 2% and 74 ± 13%, with eight out of twelve subjects succeeding to communicate efficiently with 80 ± 9% accuracy. Two out of six LIS patients reached an offline accuracy above the chance level, illustrating a response to a command. One out of four LIS patients could communicate online.

SIGNIFICANCE

We have demonstrated the feasibility of online communication with a covert SSVEP paradigm that is truly independent of all neuromuscular functions. The potential clinical use of the presented BCI system as a diagnostic (i.e., detecting command-following) and communication tool for severely brain-injured patients will need to be further explored.

Biased binomial assessment of cross-validated estimation of classification accuracies illustrated in diagnosis predictions

Multivariate classification is used in neuroimaging studies to infer brain activation or in medical applications to infer diagnosis. Their results are often assessed through either a binomial or a permutation test. Here, we simulated classification results of generated random data to assess the influence of the cross-validation scheme on the significance of results. Distributions built from classification of random data with cross-validation did not follow the binomial distribution. The binomial test is therefore not adapted. On the contrary, the permutation test was unaffected by the cross-validation scheme. The influence of the cross-validation was further illustrated on real-data from a brain-computer interface experiment in patients with disorders of consciousness and from an fMRI study on patients with Parkinson disease. Three out of 16 patients with disorders of consciousness had significant accuracy on binomial testing, but only one showed significant accuracy using permutation testing. In the fMRI experiment, the mental imagery of gait could discriminate significantly between idiopathic Parkinson's disease patients and healthy subjects according to the permutation test but not according to the binomial test. Hence, binomial testing could lead to biased estimation of significance and false positive or negative results. In our view, permutation testing is thus recommended for clinical application of classification with cross-validation.

A vibrotactile p300-based brain-computer interface for consciousness detection and communication

Brain-computer interface (BCI) has been used for many years for communication in severely disabled patients. BCI based on electrophysiological signals has enabled communication, using auditory or visual stimuli to elicit event-related potentials (ERPs). The aim of this study was to determine whether patients with locked-in syndrome (LIS) could elicit a P300 wave, using a vibrotactile oddball paradigm for establishing somatosensory BCI-based communication. Six chronic LIS patients performed 2 electroencephalography (EEG)-based vibrotactile P300 oddball tasks. After a simple mental counting task of the target stimuli, participants were instructed to answer 5 questions by counting the vibration on either the right wrist for "yes" or the left wrist for "no." All participants were able to elicit a P300 wave using the vibrotactile oddball paradigm BCI task. In the counting task, 4 patients got accuracies of 100% (average above chance). In the communication task, one patient achieved 100% accuracy (average above chance). We have shown the feasibility of eliciting a P300 response using vibrotactile stimulation in patients with LIS. The present study provides evidence that this approach can be used for EEG-based BCI communications in this patient group. This is the first study to prove the feasibility of a BCI based on somatosensory (vibratory) stimulation in a group of brain-injured patients. Furthermore, this approach could be used for the detection of consciousness in non-communicating patients due to severe brain injuries.

Automated analysis of background EEG and reactivity during therapeutic hypothermia in comatose patients after cardiac arrest

Visual analysis of electroencephalography (EEG) background and reactivity during therapeutic hypothermia provides important outcome information, but is time-consuming and not always consistent between reviewers. Automated EEG analysis may help quantify the brain damage. Forty-six comatose patients in therapeutic hypothermia, after cardiac arrest, were included in the study. EEG background was quantified with burst-suppression ratio (BSR) and approximate entropy, both used to monitor anesthesia. Reactivity was detected through change in the power spectrum of signal before and after stimulation. Automatic results obtained almost perfect agreement (discontinuity) to substantial agreement (background reactivity) with a visual score from EEG-certified neurologists. Burst-suppression ratio was more suited to distinguish continuous EEG background from burst-suppression than approximate entropy in this specific population. Automatic EEG background and reactivity measures were significantly related to good and poor outcome. We conclude that quantitative EEG measurements can provide promising information regarding current state of the patient and clinical outcome, but further work is needed before routine application in a clinical setting.

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.

Electroencephalogram approximate entropy influenced by both age and sleep

The use of information-based measures to assess changes in conscious state is an increasingly popular topic. Though recent results have seemed to justify the merits of such methods, little has been done to investigate the applicability of such measures to children. For our work, we used the approximate entropy (ApEn), a measure previously shown to correlate with changes in conscious state when applied to the electroencephalogram (EEG), and sought to confirm whether previously reported trends in adult ApEn values across wake and sleep were present in children. Besides validating the prior findings that ApEn decreases from wake to sleep (including wake, rapid eye movement (REM) sleep, and non-REM sleep) in adults, we found that previously reported ApEn decreases across vigilance states in adults were also present in children (ApEn trends for both age groups: wake > REM sleep > non-REM sleep). When comparing ApEn values between age groups, adults had significantly larger ApEn values than children during wakefulness. After the application of an 8 Hz high-pass filter to the EEG signal, ApEn values were recalculated. The number of electrodes with significant vigilance state effects dropped from all 109 electrodes with the original 1 Hz filter to 1 electrode with the 8 Hz filter. The number of electrodes with significant age effects dropped from 10 to 4. Our results support the notion that ApEn can reliably distinguish between vigilance states, with low-frequency sleep-related oscillations implicated as the driver of changes between vigilance states. We suggest that the observed differences between adult and child ApEn values during wake may reflect differences in connectivity between age groups, a factor which may be important in the use of EEG to measure consciousness.

Multiple fMRI system-level baseline connectivity is disrupted in patients with consciousness alterations

INTRODUCTION

In healthy conditions, group-level fMRI resting state analyses identify ten resting state networks (RSNs) of cognitive relevance. Here, we aim to assess the ten-network model in severely brain-injured patients suffering from disorders of consciousness and to identify those networks which will be most relevant to discriminate between patients and healthy subjects.

METHODS

300 fMRI volumes were obtained in 27 healthy controls and 53 patients in minimally conscious state (MCS), vegetative state/unresponsive wakefulness syndrome (VS/UWS) and coma. Independent component analysis (ICA) reduced data dimensionality. The ten networks were identified by means of a multiple template-matching procedure and were tested on neuronality properties (neuronal vs non-neuronal) in a data-driven way. Univariate analyses detected between-group differences in networks' neuronal properties and estimated voxel-wise functional connectivity in the networks, which were significantly less identifiable in patients. A nearest-neighbor "clinical" classifier was used to determine the networks with high between-group discriminative accuracy.

RESULTS

Healthy controls were characterized by more neuronal components compared to patients in VS/UWS and in coma. Compared to healthy controls, fewer patients in MCS and VS/UWS showed components of neuronal origin for the left executive control network, default mode network (DMN), auditory, and right executive control network. The "clinical" classifier indicated the DMN and auditory network with the highest accuracy (85.3%) in discriminating patients from healthy subjects.

CONCLUSIONS

FMRI multiple-network resting state connectivity is disrupted in severely brain-injured patients suffering from disorders of consciousness. When performing ICA, multiple-network testing and control for neuronal properties of the identified RSNs can advance fMRI system-level characterization. Automatic data-driven patient classification is the first step towards future single-subject objective diagnostics based on fMRI resting state acquisitions.

Common resting brain dynamics indicate a possible mechanism underlying zolpidem response in severe brain injury

Zolpidem produces paradoxical recovery of speech, cognitive and motor functions in select subjects with severe brain injury but underlying mechanisms remain unknown. In three diverse patients with known zolpidem responses we identify a distinctive pattern of EEG dynamics that suggests a mechanistic model. In the absence of zolpidem, all subjects show a strong low frequency oscillatory peak ∼6–10 Hz in the EEG power spectrum most prominent over frontocentral regions and with high coherence (∼0.7–0.8) within and between hemispheres. Zolpidem administration sharply reduces EEG power and coherence at these low frequencies. The ∼6–10 Hz activity is proposed to arise from intrinsic membrane properties of pyramidal neurons that are passively entrained across the cortex by locally-generated spontaneous activity. Activation by zolpidem is proposed to arise from a combination of initial direct drug effects on cortical, striatal, and thalamic populations and further activation of underactive brain regions induced by restoration of cognitively-mediated behaviors.

DOI:http://dx.doi.org/10.7554/eLife.01157.001

Some individuals who experience severe brain damage are left with disorders of consciousness. While they can appear to be awake, these individuals lack awareness of their surroundings and cannot respond to events going on around them. Few treatments are available, but a minority of patients show striking improvements in speech, alertness and movement in response to the sleeping pill zolpidem.

Although the idea of a sleeping pill increasing consciousness is paradoxical, it is possible that in patients with impaired consciousness, zolpidem reduces the activity of an area of the brain that would otherwise inhibit activity in other regions of the brain. However, the precise mechanisms by which zolpidem increases consciousness in these patients, and the reasons why only a minority of individuals respond, are unknown.

Now, Williams et al. have used electrodes attached to the scalp to measure changes in brain activity in three patients known to respond to zolpidem. These measurements showed that before the drug was taken, there were two important differences between the brain activity of the patients and that of healthy subjects: first, the patients showed brain waves of a lower frequency than any seen in healthy subjects; second, these brain waves were much more synchronized than brain activity in healthy individuals. After taking zolpidem, this synchronicity was reduced and all of the patients also showed an increase in higher frequency brain waves.

Based on the effects of zolpidem on electrical activity throughout the brain, Williams et al. propose a new model to explain the therapeutic action of the drug in some minimally conscious patients. If the correlation between brain waves and zolpidem response holds up in future studies, this relation could be used to predict which patients might benefit from the drug. A better understanding of these processes should also help us to understand, diagnose and develop new treatments for disorders of consciousness.

DOI:http://dx.doi.org/10.7554/eLife.01157.002

Thalamus, brainstem and salience network connectivity changes during propofol-induced sedation and unconsciousness

In this functional magnetic resonance imaging study, we examined the effect of mild propofol sedation and propofol-induced unconsciousness on resting state brain connectivity, using graph analysis based on independent component analysis and a classical seed-based analysis. Contrary to previous propofol research, which mainly emphasized the importance of connectivity in the default mode network (DMN) and external control network (ECN), we focused on the salience network, thalamus, and brainstem. The importance of these brain regions in brain arousal and organization merits a more detailed examination of their connectivity response to propofol. We found that the salience network disintegrated during propofol-induced unconsciousness. The thalamus decreased connectivity with the DMN, ECN, and salience network, while increasing connectivity with sensorimotor and auditory/insular cortices. Brainstem regions disconnected from the DMN with unconsciousness, while the pontine tegmental area increased connectivity with the insulae during mild sedation. These findings illustrate that loss of consciousness is associated with a wide variety of decreases and increases of both cortical and subcortical connectivity. It furthermore stresses the necessity of also examining resting state connectivity in networks representing arousal, not only those associated with awareness.

Electroencephalographic profiles for differentiation of disorders of consciousness

Background

Electroencephalography (EEG) is best suited for long-term monitoring of brain functions in patients with disorders of consciousness (DOC). Mathematical tools are needed to facilitate efficient interpretation of long-duration sleep-wake EEG recordings.

Methods

Starting with matching pursuit (MP) decomposition, we automatically detect and parametrize sleep spindles, slow wave activity, K-complexes and alpha, beta and theta waves present in EEG recordings, and automatically construct profiles of their time evolution, relevant to the assessment of residual brain function in patients with DOC.

Results

Above proposed EEG profiles were computed for 32 patients diagnosed as minimally conscious state (MCS, 20 patients), vegetative state/unresponsive wakefulness syndrome (VS/UWS, 11 patients) and Locked-in Syndrome (LiS, 1 patient). Their interpretation revealed significant correlations between patients’ behavioral diagnosis and: (a) occurrence of sleep EEG patterns including sleep spindles, slow wave activity and light/deep sleep cycles, (b) appearance and variability across time of alpha, beta, and theta rhythms. Discrimination between MCS and VS/UWS based upon prominent features of these profiles classified correctly 87% of cases.

Conclusions

Proposed EEG profiles offer user-independent, repeatable, comprehensive and continuous representation of relevant EEG characteristics, intended as an aid in differentiation between VS/UWS and MCS states and diagnostic prognosis. To enable further development of this methodology into clinically usable tests, we share user-friendly software for MP decomposition of EEG (http://braintech.pl/svarog) and scripts used for creation of the presented profiles (attached to this article).

Dynamic change of global and local information processing in propofol-induced loss and recovery of consciousness

Whether unique to humans or not, consciousness is a central aspect of our experience of the world. The neural fingerprint of this experience, however, remains one of the least understood aspects of the human brain. In this paper we employ graph-theoretic measures and support vector machine classification to assess, in 12 healthy volunteers, the dynamic reconfiguration of functional connectivity during wakefulness, propofol-induced sedation and loss of consciousness, and the recovery of wakefulness. Our main findings, based on resting-state fMRI, are three-fold. First, we find that propofol-induced anesthesia does not bear differently on long-range versus short-range connections. Second, our multi-stage design dissociated an initial phase of thalamo-cortical and cortico-cortical hyperconnectivity, present during sedation, from a phase of cortico-cortical hypoconnectivity, apparent during loss of consciousness. Finally, we show that while clustering is increased during loss of consciousness, as recently suggested, it also remains significantly elevated during wakefulness recovery. Conversely, the characteristic path length of brain networks (i.e., the average functional distance between any two regions of the brain) appears significantly increased only during loss of consciousness, marking a decrease of global information-processing efficiency uniquely associated with unconsciousness. These findings suggest that propofol-induced loss of consciousness is mainly tied to cortico-cortical and not thalamo-cortical mechanisms, and that decreased efficiency of information flow is the main feature differentiating the conscious from the unconscious brain.

Changes in effective connectivity by propofol sedation

Mechanisms of propofol-induced loss of consciousness remain poorly understood. Recent fMRI studies have shown decreases in functional connectivity during unconsciousness induced by this anesthetic agent. Functional connectivity does not provide information of directional changes in the dynamics observed during unconsciousness. The aim of the present study was to investigate, in healthy humans during an auditory task, the changes in effective connectivity resulting from propofol induced loss of consciousness. We used Dynamic Causal Modeling for fMRI (fMRI-DCM) to assess how causal connectivity is influenced by the anesthetic agent in the auditory system. Our results suggest that the dynamic observed in the auditory system during unconsciousness induced by propofol, can result in a mixture of two effects: a local inhibitory connectivity increase and a decrease in the effective connectivity in sensory cortices.

Electrophysiological investigations of brain function in coma, vegetative and minimally conscious patients

Electroencephalographic activity in the context of disorders of consciousness is a swiss knife like tool that can evaluate different aspects of cognitive residual function, detect consciousness and provide a mean to communicate with the outside world without using muscular channels. Standard recordings in the neurological department offer a first global view of the electrogenesis of a patient and can spot abnormal epileptiform activity and therefore guide treatment. Although visual patterns have a prognosis value, they are not sufficient to provide a diagnosis between vegetative state/unresponsive wakefulness syndrome (VS/UWS) and minimally conscious state (MCS) patients. Quantitative electroencephalography (qEEG) processes the data and retrieves features, not visible on the raw traces, which can then be classified. Current results using qEEG show that MCS can be differentiated from VS/UWS patients at the group level. Event Related Potentials (ERP) are triggered by varying stimuli and reflect the time course of information processing related to the stimuli from low-level peripheral receptive structures to high-order associative cortices. It is hence possible to assess auditory, visual, or emotive pathways. Different stimuli elicit positive or negative components with different time signatures. The presence of these components when observed in passive paradigms is usually a sign of good prognosis but it cannot differentiate VS/UWS and MCS patients. Recently, researchers have developed active paradigms showing that the amplitude of the component is modulated when the subject's attention is focused on a task during stimulus presentation. Hence significant differences between ERPs of a patient in a passive compared to an active paradigm can be a proof of consciousness. An EEG-based brain-computer interface (BCI) can then be tested to provide the patient with a communication tool. BCIs have considerably improved the past two decades. However they are not easily adaptable to comatose patients as they can have visual or auditory impairments or different lesions affecting their EEG signal. Future progress will require large databases of resting state-EEG and ERPs experiment of patients of different etiologies. This will allow the identification of specific patterns related to the diagnostic of consciousness. Standardized procedures in the use of BCIs will also be needed to find the most suited technique for each individual patient.

DTI BASED STRUCTURAL DAMAGE CHARACTERIZATION FOR DISORDERS OF CONSCIOUSNESS

MRI Diffusion Tensor Imaging (DTI) has been recently proposed as a highly discriminative measurement to detect structural damages in Disorders of Consciousness patients (Vegetative State/Unresponsive Wakefulness Syndrome-(VS/UWS) and Minimally Consciousness State-MCS). In the DTI analysis, certain tensor features are often used as simplified scalar indices to represent these alterations. Those characteristics are mathematically and statistically more tractable than the full tensors. Nevertheless, most of these quantities are based on a tensor diffusivity estimation, the arithmetic average among the different strengths of the tensor orthogonal directions, which is supported on a symmetric linear relationship among the three directions, an unrealistic assumption for severely damaged brains. In this paper, we propose a new family of scalar quantities based on Generalized Ordered Weighted Aggregations (GOWA) to characterize morphological damages. The main idea is to compute a tensor diffusitivity estimation that captures the deviations in the water diffusivity associated to damaged tissue. This estimation is performed by weighting and combining differently each tensor orthogonal strength. Using these new scalar quantities we construct an affine invariant DTI tensor feature using regional tissue histograms. An evaluation of these new scalar quantities on 48 patients (23 VS/UWS and 25 MCS) was conducted. Our experiments demonstrate that this new representation outperforms state-of-the-art tensor based scalar representations for characterization and classification problems.

Brain-computer interfacing in disorders of consciousness

BACKGROUND

Recent neuroimaging research has strikingly demonstrated the existence of covert awareness in some patients with disorders of consciousness (DoC). These findings have highlighted the potential for the development of simple brain-computer interfaces (BCI) as a diagnosis in behaviourally unresponsive patients.

OBJECTIVES

This study here reviews current EEG-based BCIs that hold potential for assessing and eventually assisting patients with DoC. It highlights key areas for further development that might eventually make their application feasible in this challenging patient group.

METHODS

The major types of BCIs proposed in the literature are considered, namely those based on the P3 potential, sensorimotor rhythms, steady state oscillations and slow cortical potentials. In each case, a brief overview of the relevant literature is provided and then their relative merits for BCI applications in DoC are considered.

RESULTS

A range of BCI designs have been proposed and tested for enabling communication in fully conscious, paralysed patients. Although many of these have potential applicability for patients with DoC, they share some key challenges that need to be overcome, including limitations of stimulation modality, feedback, user training and consistency.

CONCLUSION

Future work will need to address the technical and practical challenges facing reliable implementation at the patient's bedside.

Brain connectivity in disorders of consciousness

The last 10 years witnessed a considerable increase in our knowledge of brain function in survivors to severe brain injuries with disorders of consciousness (DOC). At the same time, a growing interest developed for the use of functional neuroimaging as a new diagnostic tool in these patients. In this context, particular attention has been devoted to connectivity studies-as these, more than measures of brain metabolism, may be more appropriate to capture the dynamics of large populations of neurons. Here, we will review the pros and cons of various connectivity methods as potential diagnostic tools in brain-damaged patients with DOC. We will also discuss the relevance of the study of the level versus the contents of consciousness in this context.

Granger causality analysis of steady-state electroencephalographic signals during propofol-induced anaesthesia

Changes in conscious level have been associated with changes in dynamical integration and segregation among distributed brain regions. Recent theoretical developments emphasize changes in directed functional (i.e., causal) connectivity as reflected in quantities such as 'integrated information' and 'causal density'. Here we develop and illustrate a rigorous methodology for assessing causal connectivity from electroencephalographic (EEG) signals using Granger causality (GC). Our method addresses the challenges of non-stationarity and bias by dividing data into short segments and applying permutation analysis. We apply the method to EEG data obtained from subjects undergoing propofol-induced anaesthesia, with signals source-localized to the anterior and posterior cingulate cortices. We found significant increases in bidirectional GC in most subjects during loss-of-consciousness, especially in the beta and gamma frequency ranges. Corroborating a previous analysis we also found increases in synchrony in these ranges; importantly, the Granger causality analysis showed higher inter-subject consistency than the synchrony analysis. Finally, we validate our method using simulated data generated from a model for which GC values can be analytically derived. In summary, our findings advance the methodology of Granger causality analysis of EEG data and carry implications for integrated information and causal density theories of consciousness.

Resting-state EEG study of comatose patients: a connectivity and frequency analysis to find differences between vegetative and minimally conscious states

The aim of this study was to look for differences in the power spectra and in EEG connectivity measures between patients in the vegetative state (VS/UWS) and patients in the minimally conscious state (MCS). The EEG of 31 patients was recorded and analyzed. Power spectra were obtained using modern multitaper methods. Three connectivity measures (coherence, the imaginary part of coherency and the phase lag index) were computed. Of the 31 patients, 21 were diagnosed as MCS and 10 as VS/UWS using the Coma Recovery Scale-Revised (CRS-R). EEG power spectra revealed differences between the two conditions. The VS/UWS patients showed increased delta power but decreased alpha power compared with the MCS patients. Connectivity measures were correlated with the CRS-R diagnosis; patients in the VS/UWS had significantly lower connectivity than MCS patients in the theta and alpha bands. Standard EEG recorded in clinical conditions could be used as a tool to help the clinician in the diagnosis of disorders of consciousness.

Electrophysiological correlates of behavioural changes in vigilance in vegetative state and minimally conscious state

The existence of normal sleep in patients in a vegetative state is still a matter of debate. Previous electrophysiological sleep studies in patients with disorders of consciousness did not differentiate patients in a vegetative state from patients in a minimally conscious state. Using high-density electroencephalographic sleep recordings, 11 patients with disorders of consciousness (six in a minimally conscious state, five in a vegetative state) were studied to correlate the electrophysiological changes associated with sleep to behavioural changes in vigilance (sustained eye closure and muscle inactivity). All minimally conscious patients showed clear electroencephalographic changes associated with decreases in behavioural vigilance. In the five minimally conscious patients showing sustained behavioural sleep periods, we identified several electrophysiological characteristics typical of normal sleep. In particular, all minimally conscious patients showed an alternating non-rapid eye movement/rapid eye movement sleep pattern and a homoeostatic decline of electroencephalographic slow wave activity through the night. In contrast, for most patients in a vegetative state, while preserved behavioural sleep was observed, the electroencephalographic patterns remained virtually unchanged during periods with the eyes closed compared to periods of behavioural wakefulness (eyes open and muscle activity). No slow wave sleep or rapid eye movement sleep stages could be identified and no homoeostatic regulation of sleep-related slow wave activity was observed over the night-time period. In conclusion, we observed behavioural, but no electrophysiological, sleep wake patterns in patients in a vegetative state, while there were near-to-normal patterns of sleep in patients in a minimally conscious state. These results shed light on the relationship between sleep electrophysiology and the level of consciousness in severely brain-damaged patients. We suggest that the study of sleep and homoeostatic regulation of slow wave activity may provide a complementary tool for the assessment of brain function in minimally conscious state and vegetative state patients.

Identifying the default-mode component in spatial IC analyses of patients with disorders of consciousness

OBJECTIVES

Recent fMRI studies have shown that it is possible to reliably identify the default-mode network (DMN) in the absence of any task, by resting-state connectivity analyses in healthy volunteers. We here aimed to identify the DMN in the challenging patient population of disorders of consciousness encountered following coma.

EXPERIMENTAL DESIGN

A spatial independent component analysis-based methodology permitted DMN assessment, decomposing connectivity in all its different sources either neuronal or artifactual. Three different selection criteria were introduced assessing anticorrelation-corrected connectivity with or without an automatic masking procedure and calculating connectivity scores encompassing both spatial and temporal properties. These three methods were validated on 10 healthy controls and applied to an independent group of 8 healthy controls and 11 severely brain-damaged patients [locked-in syndrome (n = 2), minimally conscious (n = 1), and vegetative state (n = 8)].

PRINCIPAL OBSERVATIONS

All vegetative patients showed fewer connections in the default-mode areas, when compared with controls, contrary to locked-in patients who showed near-normal connectivity. In the minimally conscious-state patient, only the two selection criteria considering both spatial and temporal properties were able to identify an intact right lateralized BOLD connectivity pattern, and metabolic PET data suggested its neuronal origin.

CONCLUSIONS

When assessing resting-state connectivity in patients with disorders of consciousness, it is important to use a methodology excluding non-neuronal contributions caused by head motion, respiration, and heart rate artifacts encountered in all studied patients.