Quentin Noirhomme

Breakdown of within- and between-network Resting State Functional Magnetic Resonance Imaging Connectivity during Propofol-induced Loss of Consciousness

Background:Mechanisms of anesthesia-induced loss of consciousness remain poorly understood. Resting-state functional magnetic resonance imaging allows investigating whole-brain connectivity changes during pharmacological modulation of the level of consciousness. Methods:Low-frequency spontaneous blood oxygen level-dependent fluctuations were measured in 19 healthy volunteers during wakefulness, mild sedation, deep sedation with clinical unconsciousness, and subsequent recovery of consciousness. Results:Propofol-induced decrease in consciousness linearly correlates with decreased corticocortical and thalamocortical connectivity in frontoparietal networks (i.e., default- and executive-control networks). Furthermore, during propofol-induced unconsciousness, a negative correlation was identified between thalamic and cortical activity in these networks. Finally, negative correlations between default network and lateral frontoparietal cortices activity, present during wakefulness, decreased proportionally to propofol-induced loss of consciousness. In contrast, connectivity was globally preserved in low-level sensory cortices, (i.e., in auditory and visual networks across sedation stages). This was paired with preserved thalamocortical connectivity in these networks. Rather, waning of consciousness was associated with a loss of cross-modal interactions between visual and auditory networks. Conclusions:Our results shed light on the functional significance of spontaneous brain activity fluctuations observed in functional magnetic resonance imaging. They suggest that propofol-induced unconsciousness could be linked to a breakdown of cerebral temporal architecture that modifies both within- and between-network connectivity and thus prevents communication between low-level sensory and higher-order frontoparietal cortices, thought to be necessary for perception of external stimuli. They emphasize the importance of thalamocortical connectivity in higher-order cognitive brain networks in the genesis of conscious perception.

Functional connectivity in the default network during resting state is preserved in a vegetative but not in a brain dead patient

Recent studies on spontaneous fluctuations in the functional MRI blood oxygen level‐dependent (BOLD) signal in awake healthy subjects showed the presence of coherent fluctuations among functionally defined neuroanatomical networks. However, the functional significance of these spontaneous BOLD fluctuations remains poorly understood. By means of 3 T functional MRI, we demonstrate absent cortico‐thalamic BOLD functional connectivity (i.e. between posterior cingulate/precuneal cortex and medial thalamus), but preserved cortico‐cortical connectivity within the default network in a case of vegetative state (VS) studied 2.5 years following cardio‐respiratory arrest, as documented by extensive behavioral and paraclinical assessments. In the VS patient, as in age‐matched controls, anticorrelations could also be observed between posterior cingulate/precuneus and a previously identified task‐positive cortical network. Both correlations and anticorrelations were significantly reduced in VS as compared to controls. A similar approach in a brain dead patient did not show any such long‐distance functional connectivity. We conclude that some slow coherent BOLD fluctuations previously identified in healthy awake human brain can be found in alive but unaware patients, and are thus unlikely to be uniquely due to ongoing modifications of conscious thoughts. Future studies are needed to give a full characterization of default network connectivity in the VS patients population. Hum Brain Mapp, 2009.

Two distinct neuronal networks mediate the awareness of environment and of self

Evidence from functional neuroimaging studies on resting state suggests that there are two distinct anticorrelated cortical systems that mediate conscious awareness: an “extrinsic” system that encompasses lateral fronto-parietal areas and has been linked with processes of external input (external awareness), and an “intrinsic” system which encompasses mainly medial brain areas and has been associated with internal processes (internal awareness). The aim of our study was to explore the neural correlates of resting state by providing behavioral and neuroimaging data from healthy volunteers. With no a priori assumptions, we first determined behaviorally the relationship between external and internal awareness in 31 subjects. We found a significant anticorrelation between external and internal awareness with a mean switching frequency of 0.05 Hz (range: 0.01–0.1 Hz). Interestingly, this frequency is similar to BOLD fMRI slow oscillations. We then evaluated 22 healthy volunteers in an fMRI paradigm looking for brain areas where BOLD activity correlated with “internal” and “external” scores. Activation of precuneus/posterior cingulate, anterior cingulate/mesiofrontal cortices, and parahippocampal areas (“intrinsic system”) was linearly linked to intensity of internal awareness, whereas activation of lateral fronto-parietal cortices (“extrinsic system”) was linearly associated with intensity of external awareness.

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 (from delta to alpha range) 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 corticothalamic interactions. Compared with 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 corticocortical connectivity from frontal to parietal cortices, while thalamocortical connectivity remained unchanged. These results emphasize the importance of recurrent corticocortical communication in the maintenance of consciousness and suggest a direct effect of propofol on cortical dynamics.

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. Hum Brain Mapp, 2012.

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.

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.

Registration and real-time visualization of transcranial magnetic stimulation with 3-D MR images

This paper describes a method for registering and visualizing in real-time the results of transcranial magnetic stimulations (TMS) in physical space on the corresponding anatomical locations in MR images of the brain. The method proceeds in three main steps. First, the patient scalp is digitized in physical space with a magnetic-field digitizer, following a specific digitization pattern. Second, a registration process minimizes the mean square distance between those points and a segmented scalp surface extracted from the magnetic resonance image. Following this registration, the physician can follow the change in coil position in real-time through the visualization interface and adjust the coil position to the desired anatomical location. Third, amplitude of motor evoked potentials can be projected onto the segmented brain in order to create functional brain maps. The registration has subpixel accuracy in a study with simulated data, while we obtain a point to surface root-mean-square error of 1.17/spl plusmn/0.38 mm in a 24 subject study.

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.

Reanalysis of “Bedside detection of awareness in the vegetative state: a cohort study.”

Cruse and colleagues reported1 that a new electroencephalography (EEG)-based tool was able to show that 3 out of 16 vegetative state (VS) patients performed a motor imagery task requiring language and short-term memory. This finding, if confirmed, has major implications for diagnosis and care of severely brain-injured patients. We were concerned about the method’s validity because of the difficulty of the task, and its critical reliance on certain statistical assumptions. To allow us to test the validity of the method, Cruse and colleagues graciously supplied their data and analysis software. Below we show that the patient data do not meet the statistical assumptions made in Cruse et al., likely because of the presence of various artifacts (Table). We then show that when the data are re-analyzed by methods that do not depend on these model assumptions, there is no evidence for task performance in the patients. Table Overview of analyses and findings. To begin, we examine the EEG data itself. The normals have findings typical of healthy adults (Figure 1A, left): rhythmicity in the alpha range (~10 Hz) with minimal eye-blink and muscle artifact. In contrast, the patients’ EEG (Figure 1A, right) is dominated by 1–4 Hz activity, as is typical of severe brain dysfunction, deep sleep or anesthesia2. Frequency-domain representation (Figure 1B) confirms these findings. It also reveals that the patient’s EEG has significant muscle artifact3 that fluctuates block-to-block. Figure 1 Time and frequency domain representations of the EEG of a typical normal (N2) and patient (P13) who had similar classification rates in Cruse et al. (75% and 78%, respectively; Webappendix for methods). A. Laplacian-montaged EEG of the first trial of ... To determine whether subjects performed motor imagery, Cruse and colleagues used a multivariate method (Support Vector Machine; SVM)4,5 to differentiate EEG signals recorded while subjects were asked to imagine moving their hand, vs. their toes. SVM is a powerful technique, but, without a gold-standard for task performance, the validity hinges on the appropriateness of the statistical model.6 As detailed below, the statistical model used in Cruse et al. did not account for relationships between adjacent blocks, or correlations between trials within a block. For calculation of accuracy (how often the SVM correctly classified trials as “hand” vs. “toe”), the Cruse et al. methods did not take into account the possibility of slow variations across blocks, as their approach always classified pairs of neighbouring blocks (e.g., hand and toe block 1, but never hand block 1 and toe block 4). We modified their analysis to use these alternative pairings for cross-validation6 (Webappendix). In two of the positive patients (Webappendix Figure 1), accuracy decreased to chance (P1), or worse-than-chance (P12) as the test-block-pairs were further apart. This drop in accuracy implies that idiosyncratic relationships between adjacent blocks contributed substantially to SVM performance in these subjects. For calculation of significance, Cruse and colleagues calculated p-values using a binomial distribution for the number of correct trials, an approach that assumes that each trial is an independent assay. We found that this assumption does not hold in the patients. First, frequency domain representation of the EEG (Figure 1B; Webappendix) reveals a lack of independence: data from individual trials are more nearly matched within a block than across blocks. Second, we applied the Cruse et al. analysis separately to all time points of the trials. For patients, we found that worse-than-chance classification occurred substantially more often than expected from binomial statistics. This excess of outliers implies that trials are correlated (Webappendix and Webappendix Figure 2). We next show that when the SVM results are re-analyzed with a statistical approach that takes into account the correlations mentioned above (Webappendix and Webappendix Table 1 for full details), there is no statistical evidence of a task-related signal. To take into account correlations between blocks, we defined accuracy using all block-pairs as test components6, rather than restricting consideration to adjacent block pairs. To account for dependence among trials, we determined significance via a permutation test that recognized the block design. With this approach, positive normals remained significant, but only one patient (P13) remained significant (p=0·0286; lowest possible p-value with 4 blocks). We further note that even for random data, a classifier would be expected to yield 1 in 20 positive subjects at p≤0.05. We therefore corrected for multiple comparisons via the False-Discovery Rate (FDR)7; normals remained significant but none of the patients were significant at p≤0·05. Finally, we applied an independent approach that asked whether there was a significant difference between task and rest periods, using univariate statistics (i.e., separate tests for each frequency and channel of the EEG; methods in Webappendix and8; Webappendix Figures 3 and 4). Normals showed the expected task-related changes in motor imagery tasks (decreases in EEG power from 7–30 Hz, especially over the motor cortices contralateral to the imagined limb movement; p≤0. 05 after FDR correction)9,10. None of the 16 patients had significant changes identified by this measure. This emphasizes that even if we were to accept the ‘positive’ patient classifications of Cruse et al. as different from chance, the EEG signals lack the expected physiological changes associated with motor imagery (in contrast to the suggestion made by Cruse and colleagues in connection with their Figure 2). In sum, we found that the method of Cruse et al. is not valid because the patient data do not meet the assumptions of their statistical model. Specifically, the model does not allow for correlations between nearby trials and blocks, which are likely induced by fluctuating artifact and arousal state; when these factors are taken into account, there is no statistical evidence for task performance in patients. Importantly, the model of Cruse et al. generally suffices for normals, where there is minimal artifact contamination. These findings cast doubt about conclusions drawn from this method, both in Cruse et al., and a more recent study11. SVM and related methods are useful tools, particularly in EEG analysis for Brain-Computer Interface (BCI)10,12. In BCI applications, subjects can confirm task performance and the consequences of classifier failure are limited to reduced device performance. But in the diagnostic setting (e.g., determination of consciousness, genomic diagnosis of cancer13,14), classifier failure can misinform clinical decision making, with major consequences for patients and families. Given this, and the ease of dissemination of EEG technology, standards of demonstration of validity need to be high. Our analysis suggests that the approach of Cruse et al. falls short of this standard. Finally, we wish to emphasize the importance of data sharing. This analysis would not have been possible without full access to the original data and code.15