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1.  Biased binomial assessment of cross-validated estimation of classification accuracies illustrated in diagnosis predictions 
NeuroImage : Clinical  2014;4:687-694.
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.
•We assess the influence of cross-validation on significance of classification results.•Classification of random data did not follow binomial distribution.•The permutation test was unaffected by the cross-validation scheme.•Results are illustrated on real-data from BCI and fMRI studies.
PMCID: PMC4053638  PMID: 24936420
classification; cross-validation; binomial; permutation test
2.  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.
PMCID: PMC4274966  PMID: 25566035
disorders of consciousness; breathing; sniffing; vegetative state; unresponsive wakefulness syndrome; minimally conscious state; diagnosis; brain-computer interface
3.  Common resting brain dynamics indicate a possible mechanism underlying zolpidem response in severe brain injury 
eLife  2013;2:e01157.
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.
eLife digest
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.
PMCID: PMC3833342  PMID: 24252875
Consciousness; central thalamus; striatum; GABA-A; arousal; anesthesia; Human
4.  Electroencephalographic profiles for differentiation of disorders of consciousness 
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.
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.
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.
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 ( and scripts used for creation of the presented profiles (attached to this article).
PMCID: PMC3819687  PMID: 24143892
Electroencephalography; Matching Pursuit; Disorders of consciousness; Minimally conscious state; Vegetative state; Locked-in syndrome
5.  Dynamic Change of Global and Local Information Processing in Propofol-Induced Loss and Recovery of Consciousness 
PLoS Computational Biology  2013;9(10):e1003271.
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.
Author Summary
One of the most elusive aspects of the human brain is the neural fingerprint of the subjective feeling of consciousness. While a growing body of experimental evidence is starting to address this issue, to date we are still hard pressed to answer even basic questions concerning the nature of consciousness in humans as well as other species. In the present study we follow a recent theoretical construct according to which the crucial factor underlying consciousness is the modality with which information is exchanged across different parts of the brain. In particular, we represent the brain as a network of regions exchanging information (as is typically done in a comparatively young branch of mathematics referred to as graph theory), and assess how different levels of consciousness induced by anesthetic agent affect the quality of information exchange across regions of the network. Overall, our findings show that what makes the state of propofol-induced loss of consciousness different from all other conditions (namely, wakefulness, light sedation, and consciousness recovery) is the fact that all regions of the brain appear to be functionally further apart, reducing the efficiency with which information can be exchanged across different parts of the network.
PMCID: PMC3798283  PMID: 24146606
6.  Changes in Effective Connectivity by Propofol Sedation 
PLoS ONE  2013;8(8):e71370.
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.
PMCID: PMC3747149  PMID: 23977030
8.  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.
PMCID: PMC3852001  PMID: 24367328
electroencephalogram; development; sleep; consciousness; approximate entropy
9.  Connectivity changes underlying spectral EEG changes during propofol-induced loss of consciousness 
The mechanisms underlying anesthesia-induced loss of consciousness remain a matter of debate. Recent electrophysiological reports suggest that while initial propofol infusion provokes an increase in fast rhythms (from beta to gamma range), slow activity (delta to alpha) rises selectively during loss of consciousness. Dynamic causal modeling was used to investigate the neural mechanisms mediating these changes in spectral power in humans. We analyzed source-reconstructed data from frontal and parietal cortices during normal wakefulness, propofol-induced mild sedation and loss of consciousness. Bayesian model selection revealed that the best model for explaining spectral changes across the three states involved changes in cortico-thalamic interactions. Compared to wakefulness, mild sedation was accounted for by an increase in thalamic excitability, which did not further increase during loss of consciousness. In contrast, loss of consciousness per se was accompanied by a decrease in backward cortico-cortical connectivity from frontal to parietal cortices, while thalamo-cortical connectivity remained unchanged. These results emphasize the importance of recurrent cortico-cortical communication in the maintenance of consciousness and suggest a direct effect of propofol on cortical dynamics.
PMCID: PMC3366913  PMID: 22593076
10.  Connectivity changes underlying spectral EEG changes during propofol-induced loss of consciousness 
The Journal of Neuroscience  2012;32(20):7082-7090.
The mechanisms underlying anesthesia-induced loss of consciousness remain a matter of debate. Recent electrophysiological reports suggest that while initial propofol infusion provokes an increase in fast rhythms (from beta to gamma range), slow activity (delta to alpha) rises selectively during loss of consciousness. Dynamic causal modeling was used to investigate the neural mechanisms mediating these changes in spectral power in humans. We analyzed source-reconstructed data from frontal and parietal cortices during normal wakefulness, propofol-induced mild sedation and loss of consciousness. Bayesian model selection revealed that the best model for explaining spectral changes across the three states involved changes in cortico-thalamic interactions. Compared to wakefulness, mild sedation was accounted for by an increase in thalamic excitability, which did not further increase during loss of consciousness. In contrast, loss of consciousness per se was accompanied by a decrease in backward cortico-cortical connectivity from frontal to parietal cortices, while thalamo-cortical connectivity remained unchanged. These results emphasize the importance of recurrent cortico-cortical communication in the maintenance of consciousness and suggest a direct effect of propofol on cortical dynamics.
PMCID: PMC3366913  PMID: 22593076
11.  Electrophysiological correlates of behavioural changes in vigilance in vegetative state and minimally conscious state 
Brain  2011;134(8):2222-2232.
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.
PMCID: PMC3155704  PMID: 21841201
sleep; vegetative state; minimally conscious state; consciousness; EEG
12.  Resting-state EEG study of comatose patients: a connectivity and frequency analysis to find differences between vegetative and minimally conscious states 
Functional Neurology  2012;27(1):41-47.
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.
PMCID: PMC3812750  PMID: 22687166
connectivity; disorders of consciousness; imaginary part of coherency; minimally conscious state; phase lag index; vegetative state
13.  Granger Causality Analysis of Steady-State Electroencephalographic Signals during Propofol-Induced Anaesthesia 
PLoS ONE  2012;7(1):e29072.
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.
PMCID: PMC3252303  PMID: 22242156
14.  Automated EEG entropy measurements in coma, vegetative state/unresponsive wakefulness syndrome and minimally conscious state  
Functional Neurology  2011;26(1): 25 - 30 .
Monitoring the level of consciousness in brain-injured patients with disorders of consciousness is crucial as it provides diagnostic and prognostic information. Behavioral assessment remains the gold standard for assessing consciousness but previous studies have shown a high rate of misdiagnosis. This study aimed to investigate the usefulness of electroencephalography (EEG) entropy measurements in differentiating unconscious (coma or vegetative) from minimally conscious patients.
Left fronto-temporal EEG recordings (10-minute resting state epochs) were prospectively obtained in 56 patients and 16 age-matched healthy volunteers. Patients were assessed in the acute (≤1 month post-injury; n=29) or chronic (>1 month post-injury; n=27) stage. The etiology was traumatic in 23 patients. Automated online EEG entropy calculations (providing an arbitrary value ranging from 0 to 91) were compared with behavioral assessments (Coma Recovery Scale-Revised) and outcome.
EEG entropy correlated with Coma Recovery Scale total scores (r=0.49). Mean EEG entropy values were higher in minimally conscious (73±19; mean and standard deviation) than in vegetative/unresponsive wakefulness syndrome patients (45±28). Receiver operating characteristic analysis revealed an entropy cut-off value of 52 differentiating acute unconscious from minimally conscious patients (sensitivity 89% and specificity 90%). In chronic patients, entropy measurements offered no reliable diagnostic information. EEG entropy measurements did not allow prediction of outcome.
User-independent time-frequency balanced spectral EEG entropy measurements seem to constitute an interesting diagnostic – albeit not prognostic – tool for assessing neural network complexity in disorders of consciousness in the acute setting. Future studies are needed before using this tool in routine clinical practice, and these should seek to improve automated EEG quantification paradigms in order to reduce the remaining false negative and false positive findings.
PMCID: PMC3814509  PMID: 21693085
coma ;  EEG entropy ;  electroencephalography ;  minimally conscious state ;  unresponsive wakefulness syndrome ;  vegetative state
15.  Resting state activity in patients with disorders of consciousness  
Functional Neurology  2011;26(1): 37 - 43 .
Recent advances in the study of spontaneous brain activity have demonstrated activity patterns that emerge with no task performance or sensory stimulation; these discoveries hold promise for the study of higher-order associative network functionality. Additionally, such advances are argued to be relevant in pathological states, such as disorders of consciousness (DOC), i.e., coma, vegetative and minimally conscious states. Recent studies on resting state activity in DOC, measured with functional magnetic resonance imaging (fMRI) techniques, show that functional connectivity is disrupted in the task-negative or the default mode network. However, the two main approaches employed in the analysis of resting state functional connectivity data (i.e., hypothesis-driven seed-voxel and data-driven independent component analysis) present multiple methodological difficulties, especially in non-collaborative DOC patients. Improvements in motion artifact removal and spatial normalization are needed before fMRI resting state data can be used as proper biomarkers in severe brain injury. However, we anticipate that such developments will boost clinical resting state fMRI studies, allowing for easy and fast acquisitions and ultimately improve the diagnosis and prognosis in the absence of DOC patients’ active collaboration in data acquisition.
PMCID: PMC3814510  PMID: 21693087
coma ;  consciousness ;  default network ;  functional magnetic resonance imaging ;  resting state ;  spontaneous activity
16.  fMRI Artefact Rejection and Sleep Scoring Toolbox 
We started writing the “fMRI artefact rejection and sleep scoring toolbox”, or “FA𝕊T”, to process our sleep EEG-fMRI data, that is, the simultaneous recording of electroencephalographic and functional magnetic resonance imaging data acquired while a subject is asleep. FA𝕊T tackles three crucial issues typical of this kind of data: (1) data manipulation (viewing, comparing, chunking, etc.) of long continuous M/EEG recordings, (2) rejection of the fMRI-induced artefact in the EEG signal, and (3) manual sleep-scoring of the M/EEG recording. Currently, the toolbox can efficiently deal with these issues via a GUI, SPM8 batching system or hand-written script. The tools developed are, of course, also useful for other EEG applications, for example, involving simultaneous EEG-fMRI acquisition, continuous EEG eye-balling, and manipulation. Even though the toolbox was originally devised for EEG data, it will also gracefully handle MEG data without any problem. “FA𝕊T” is developed in Matlab as an add-on toolbox for SPM8 and, therefore, internally uses its SPM8-meeg data format. “FA𝕊T” is available for free, under the GNU-GPL.
PMCID: PMC3063413  PMID: 21461381
17.  Default network connectivity reflects the level of consciousness in non-communicative brain-damaged patients 
Brain  2009;133(1):161-171.
The ‘default network’ is defined as a set of areas, encompassing posterior-cingulate/precuneus, anterior cingulate/mesiofrontal cortex and temporo-parietal junctions, that show more activity at rest than during attention-demanding tasks. Recent studies have shown that it is possible to reliably identify this network in the absence of any task, by resting state functional magnetic resonance imaging connectivity analyses in healthy volunteers. However, the functional significance of these spontaneous brain activity fluctuations remains unclear. The aim of this study was to test if the integrity of this resting-state connectivity pattern in the default network would differ in different pathological alterations of consciousness. Fourteen non-communicative brain-damaged patients and 14 healthy controls participated in the study. Connectivity was investigated using probabilistic independent component analysis, and an automated template-matching component selection approach. Connectivity in all default network areas was found to be negatively correlated with the degree of clinical consciousness impairment, ranging from healthy controls and locked-in syndrome to minimally conscious, vegetative then coma patients. Furthermore, precuneus connectivity was found to be significantly stronger in minimally conscious patients as compared with unconscious patients. Locked-in syndrome patient’s default network connectivity was not significantly different from controls. Our results show that default network connectivity is decreased in severely brain-damaged patients, in proportion to their degree of consciousness impairment. Future prospective studies in a larger patient population are needed in order to evaluate the prognostic value of the presented methodology.
PMCID: PMC2801329  PMID: 20034928
Default mode; fMRI; coma; vegetative state; minimally conscious state
18.  Brain Connectivity in Pathological and Pharmacological Coma 
Recent studies in patients with disorders of consciousness (DOC) tend to support the view that awareness is not related to activity in a single brain region but to thalamo-cortical connectivity in the frontoparietal network. Functional neuroimaging studies have shown preserved albeit disconnected low-level cortical activation in response to external stimulation in patients in a “vegetative state” or unresponsive wakefulness syndrome. While activation of these “primary” sensory cortices does not necessarily reflect conscious awareness, activation in higher-order associative cortices in minimally conscious state patients seems to herald some residual perceptual awareness. PET studies have identified a metabolic dysfunction in a widespread frontoparietal “global neuronal workspace” in DOC patients including the midline default mode network (“intrinsic” system) and the lateral frontoparietal cortices or “extrinsic system.” Recent studies have investigated the relation of awareness to the functional connectivity within intrinsic and extrinsic networks, and with the thalami in both pathological and pharmacological coma. In brain damaged patients, connectivity in all default network areas was found to be non-linearly correlated with the degree of clinical consciousness impairment, ranging from healthy controls and locked-in syndrome to minimally conscious, vegetative, coma, and brain dead patients. Anesthesia-induced loss of consciousness was also shown to correlate with a global decrease in cortico-cortical and thalamo-cortical connectivity in both intrinsic and extrinsic networks, but not in auditory, or visual networks. In anesthesia, unconsciousness was also associated with a loss of cross-modal interactions between networks. These results suggest that conscious awareness critically depends on the functional integrity of thalamo-cortical and cortico-cortical frontoparietal connectivity within and between “intrinsic” and “extrinsic” brain networks.
PMCID: PMC3010745  PMID: 21191476
disorders of consciousness; connectivity; anesthesia; fMRI; PET; coma; default mode network; consciousness
19.  Modern Electrophysiological Methods for Brain-Computer Interfaces 
Modern electrophysiological studies in animals show that the spectrum of neural oscillations encoding relevant information is broader than previously thought and that many diverse areas are engaged for very simple tasks. However, EEG-based brain-computer interfaces (BCI) still employ as control modality relatively slow brain rhythms or features derived from preselected frequencies and scalp locations. Here, we describe the strategy and the algorithms we have developed for the analysis of electrophysiological data and demonstrate their capacity to lead to faster accurate decisions based on linear classifiers. To illustrate this strategy, we analyzed two typical BCI tasks. (1) Mu-rhythm control of a cursor movement by a paraplegic patient. For this data, we show that although the patient received extensive training in mu-rhythm control, valuable information about movement imagination is present on the untrained high-frequency rhythms. This is the first demonstration of the importance of high-frequency rhythms in imagined limb movements. (2) Self-paced finger tapping task in three healthy subjects including the data set used in the BCI-2003 competition. We show that by selecting electrodes and frequency ranges based on their discriminative power, the classification rates can be systematically improved with respect to results published thus far.
PMCID: PMC2233873  PMID: 18288256

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