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.

"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.

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.

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.

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.

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]. / Interfaces cerveau-ordinateur, locked-in syndrome et troubles de la conscience.

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.

Complexity of Multi-Dimensional Spontaneous EEG Decreases during Propofol Induced General Anaesthesia.

Emerging neural theories of consciousness suggest a correlation between a specific type of neural dynamical complexity and the level of consciousness: When awake and aware, causal interactions between brain regions are both integrated (all regions are to a certain extent connected) and differentiated (there is inhomogeneity and variety in the interactions). In support of this, recent work by Casali et al (2013) has shown that Lempel-Ziv complexity correlates strongly with conscious level, when computed on the EEG response to transcranial magnetic stimulation. Here we investigated complexity of spontaneous high-density EEG data during propofol-induced general anaesthesia. We consider three distinct measures: (i) Lempel-Ziv complexity, which is derived from how compressible the data are; (ii) amplitude coalition entropy, which measures the variability in the constitution of the set of active channels; and (iii) the novel synchrony coalition entropy (SCE), which measures the variability in the constitution of the set of synchronous channels. After some simulations on Kuramoto oscillator models which demonstrate that these measures capture distinct 'flavours' of complexity, we show that there is a robustly measurable decrease in the complexity of spontaneous EEG during general anaesthesia.

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.

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.

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.

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.

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.