General anaesthesia reduces the uniqueness of brain connectivity across individuals and across species.

The human brain is characterised by idiosyncratic patterns of spontaneous thought, rendering each brain uniquely identifiable from its neural activity. However, deep general anaesthesia suppresses subjective experience. Does it also suppress what makes each brain unique? Here we used functional MRI under the effects of the general anaesthetics sevoflurane and propofol to determine whether anaesthetic-induced unconsciousness diminishes the uniqueness of the human brain: both with respect to the brains of other individuals, and the brains of another species. We report that under anaesthesia individual brains become less self-similar and less distinguishable from each other. Loss of distinctiveness is highly organised: it co-localises with the archetypal sensory-association axis, correlating with genetic and morphometric markers of phylogenetic differences between humans and other primates. This effect is more evident at greater anaesthetic depths, reproducible across sevoflurane and propofol, and reversed upon recovery. Providing convergent evidence, we show that under anaesthesia the functional connectivity of the human brain becomes more similar to the macaque brain. Finally, anaesthesia diminishes the match between spontaneous brain activity and meta-analytic brain patterns aggregated from the NeuroSynth engine. Collectively, the present results reveal that anaesthetised human brains are not only less distinguishable from each other, but also less distinguishable from the brains of other primates, with specifically human-expanded regions being the most affected by anaesthesia.

In vivo mapping of pharmacologically induced functional reorganization onto the human brain's neurotransmitter landscape.

To understand how pharmacological interventions can exert their powerful effects on brain function, we need to understand how they engage the brain's rich neurotransmitter landscape. Here, we bridge microscale molecular chemoarchitecture and pharmacologically induced macroscale functional reorganization, by relating the regional distribution of 19 neurotransmitter receptors and transporters obtained from positron emission tomography, and the regional changes in functional magnetic resonance imaging connectivity induced by 10 different mind-altering drugs: propofol, sevoflurane, ketamine, lysergic acid diethylamide (LSD), psilocybin, N,N-Dimethyltryptamine (DMT), ayahuasca, 3,4-methylenedioxymethamphetamine (MDMA), modafinil, and methylphenidate. Our results reveal a many-to-many mapping between psychoactive drugs' effects on brain function and multiple neurotransmitter systems. The effects of both anesthetics and psychedelics on brain function are organized along hierarchical gradients of brain structure and function. Last, we show that regional co-susceptibility to pharmacological interventions recapitulates co-susceptibility to disorder-induced structural alterations. Collectively, these results highlight rich statistical patterns relating molecular chemoarchitecture and drug-induced reorganization of the brain's functional architecture.

Criticality Creates a Functional Platform for Network Transitions Between Internal and External Processing Modes in the Human Brain.

Continuous switching between internal and external modes in the brain appears important for generating models of the self and the world. However, how the brain transitions between these two modes remains unknown. We propose that a large synchronization fluctuation of brain networks, emerging only near criticality (i.e., a balanced state between order and disorder), spontaneously creates temporal windows with distinct preferences for integrating the network's internal information or for processing external stimuli. Using a computational model, electroencephalography (EEG) analysis, and functional magnetic resonance imaging (fMRI) analysis during alterations of consciousness in humans, we report that synchronized and incoherent networks, respectively, bias toward internal and external information with specific network configurations. In the brain network model and EEG-based network, the network preferences are the most prominent at criticality and in conscious states associated with the bandwidth 4-12 Hz, with alternating functional network configurations. However, these network configurations are selectively disrupted in different states of consciousness such as general anesthesia, psychedelic states, minimally conscious states, and unresponsive wakefulness syndrome. The network preference for internal information integration is only significant in conscious states and psychedelic states, but not in other unconscious states, suggesting the importance of internal information integration in maintaining consciousness. The fMRI co-activation pattern analysis shows that functional networks that are sensitive to external stimuli-such as default mode, dorsal attentional, and frontoparietal networks-are activated in incoherent states, while insensitive networks, such as global activation and deactivation networks, are dominated in highly synchronized states. We suggest that criticality produces a functional platform for the brain's capability for continuous switching between two modes, which is crucial for the emergence of consciousness.

Dynamic Patterns of Global Brain Communication Differentiate Conscious From Unconscious Patients After Severe Brain Injury.

The neurophysiology of the subjective sensation of being conscious is elusive; therefore, it remains controversial how consciousness can be recognized in patients who are not responsive but seemingly awake. During general anesthesia, a model for the transition between consciousness and unconsciousness, specific covariance matrices between the activity of brain regions that we call patterns of global brain communication reliably disappear when people lose consciousness. This functional magnetic imaging study investigates how patterns of global brain communication relate to consciousness and unconsciousness in a heterogeneous sample during general anesthesia and after brain injury. First, we describe specific patterns of global brain communication during wakefulness that disappear during propofol (n = 11) and sevoflurane (n = 14) general anesthesia. Second, we search for these patterns in a cohort of unresponsive wakeful patients (n = 18) and unmatched healthy controls (n = 20) in order to evaluate their potential use in clinical practice. We found that patterns of global brain communication characterized by high covariance in sensory and motor areas or low overall covariance and their dynamic change were strictly associated with intact consciousness in this cohort. In addition, we show that the occurrence of these two patterns is significantly related to activity within the frontoparietal network of the brain, a network known to play a crucial role in conscious perception. We propose that this approach potentially recognizes consciousness in the clinical routine setting.

Brain network integration dynamics are associated with loss and recovery of consciousness induced by sevoflurane.

The dynamic interplay of integration and segregation in the brain is at the core of leading theoretical accounts of consciousness. The human brain dynamically alternates between a sub-state where integration predominates, and a predominantly segregated sub-state, with different roles in supporting cognition and behaviour. Here, we combine graph theory and dynamic functional connectivity to compare resting-state functional MRI data from healthy volunteers before, during, and after loss of responsiveness induced with different concentrations of the inhalational anaesthetic, sevoflurane. We show that dynamic states characterised by high brain integration are especially vulnerable to general anaesthesia, exhibiting attenuated complexity and diminished small-world character. Crucially, these effects are reversed upon recovery, demonstrating their association with consciousness. Higher doses of sevoflurane (3% vol and burst-suppression) also compromise the temporal balance of integration and segregation in the human brain. Additionally, we demonstrate that reduced anticorrelations between the brain's default mode and executive control networks dynamically reconfigure depending on the brain's state of integration or segregation. Taken together, our results demonstrate that the integrated sub-state of brain connectivity is especially vulnerable to anaesthesia, in terms of both its complexity and information capacity, whose breakdown represents a generalisable biomarker of loss of consciousness and its recovery.

Machine learning for a combined electroencephalographic anesthesia index to detect awareness under anesthesia.

Spontaneous electroencephalogram (EEG) and auditory evoked potentials (AEP) have been suggested to monitor the level of consciousness during anesthesia. As both signals reflect different neuronal pathways, a combination of parameters from both signals may provide broader information about the brain status during anesthesia. Appropriate parameter selection and combination to a single index is crucial to take advantage of this potential. The field of machine learning offers algorithms for both parameter selection and combination. In this study, several established machine learning approaches including a method for the selection of suitable signal parameters and classification algorithms are applied to construct an index which predicts responsiveness in anesthetized patients. The present analysis considers several classification algorithms, among those support vector machines, artificial neural networks and Bayesian learning algorithms. On the basis of data from the transition between consciousness and unconsciousness, a combination of EEG and AEP signal parameters developed with automated methods provides a maximum prediction probability of 0.935, which is higher than 0.916 (for EEG parameters) and 0.880 (for AEP parameters) using a cross-validation approach. This suggests that machine learning techniques can successfully be applied to develop an improved combined EEG and AEP parameter to separate consciousness from unconsciousness.

Permutation entropy in intraoperative ECoG of brain tumour patients in awake tumour surgery- a robust parameter to separate consciousness from unconsciousness.

Awake craniotomies represent an essential opportunity in the case of lesions in eloquent areas. Thus, optimal surveillance of the patient during different stages of sedation, as well as the detection of seizure activity during brain surgery, remains difficult, as skin electrodes for electroencephalographic (EEG) analysis are not applicable in most cases. We assessed the applicability of ECoG to monitor different stages of sedation, as well as the influence of different patient characteristics, such as tumour volume, size, entity, and age or gender on permutation entropy (PeEn). We conducted retrospective analysis of the ECoG data of 16 patients, who underwent awake craniotomies because of left-sided brain tumours at our centre between 2014 and 2016. PeEn could be easily calculated and compared using frontal and parietal cortical electrodes. A comparison of PeEn scores showed significantly higher values in awake patients than in patients under anaesthesia (p ≤ 0.004) and significantly higher ones in the state of transition than under general anaesthesia (p = 0.023). PeEn scores in frontal and parietal leads did not differ significantly, making them both applicable for continuous surveillance during brain surgery. None of the following clinical characteristics showed significant correlation with PeEn scores: tumour volume, WHO grade, first or recurrent tumour, gender, and sex. Being 50 years or older led to significantly lower values in parietal leads but not in frontal leads. ECoG and a consecutive analysis of PeEn are feasible and suitable for the continuous surveillance of patients during awake craniotomies. Hence, the analysis is not influenced by patients' clinical characteristics.

Changes in Whole Brain Dynamics and Connectivity Patterns during Sevoflurane- and Propofol-induced Unconsciousness Identified by Functional Magnetic Resonance Imaging.

BACKGROUND: A key feature of the human brain is its capability to adapt flexibly to changing external stimuli. This capability can be eliminated by general anesthesia, a state characterized by unresponsiveness, amnesia, and (most likely) unconsciousness. Previous studies demonstrated decreased connectivity within the thalamus, frontoparietal, and default mode networks during general anesthesia. We hypothesized that these alterations within specific brain networks lead to a change of communication between networks and their temporal dynamics. METHODS: We conducted a pooled spatial independent component analysis of resting-state functional magnetic resonance imaging data obtained from 16 volunteers during propofol and 14 volunteers during sevoflurane general anesthesia that have been previously published. Similar to previous studies, mean z-scores of the resulting spatial maps served as a measure of the activity within a network. Additionally, correlations of associated time courses served as a measure of the connectivity between networks. To analyze the temporal dynamics of between-network connectivity, we computed the correlation matrices during sliding windows of 1 min and applied k-means clustering to the matrices during both general anesthesia and wakefulness. RESULTS: Within-network activity was decreased in the default mode, attentional, and salience networks during general anesthesia (P < 0.001, range of median changes: -0.34, -0.13). Average between-network connectivity was reduced during general anesthesia (P < 0.001, median change: -0.031). Distinct between-network connectivity patterns for both wakefulness and general anesthesia were observed irrespective of the anesthetic agent (P < 0.001), and there were fewer transitions in between-network connectivity patterns during general anesthesia (P < 0.001, median number of transitions during wakefulness: 4 and during general anesthesia: 0). CONCLUSIONS: These results suggest that (1) higher-order brain regions play a crucial role in the generation of specific between-network connectivity patterns and their dynamics, and (2) the capability to interact with external stimuli is represented by complex between-network connectivity patterns.

Relationship of critical dynamics, functional connectivity, and states of consciousness in large-scale human brain networks.

Recent modeling and empirical studies support the hypothesis that large-scale brain networks function near a critical state. Similar functional connectivity patterns derived from resting state empirical data and brain network models at criticality provide further support. However, despite the strong implication of a relationship, there has been no principled explanation of how criticality shapes the characteristic functional connectivity in large-scale brain networks. Here, we hypothesized that the network science concept of partial phase locking is the underlying mechanism of optimal functional connectivity in the resting state. We further hypothesized that the characteristic connectivity of the critical state provides a theoretical boundary to quantify how far pharmacologically or pathologically perturbed brain connectivity deviates from its critical state, which could enable the differentiation of various states of consciousness with a theory-based metric. To test the hypothesis, we used a neuroanatomically informed brain network model with the resulting source signals projected to electroencephalogram (EEG)-like sensor signals with a forward model. Phase lag entropy (PLE), a measure of phase relation diversity, was estimated and the topography of PLE was analyzed. To measure the distance from criticality, the PLE topography at a critical state was compared with those of the EEG data from baseline consciousness, isoflurane anesthesia, ketamine anesthesia, vegetative state/unresponsive wakefulness syndrome, and minimally conscious state. We demonstrate that the partial phase locking at criticality shapes the functional connectivity and asymmetric anterior-posterior PLE topography, with low (high) PLE for high (low) degree nodes. The topographical similarity and the strength of PLE differentiates various pharmacologic and pathologic states of consciousness. Moreover, this model-based EEG network analysis provides a novel metric to quantify how far a pharmacologically or pathologically perturbed brain network is away from critical state, rather than merely determining whether it is in a critical or non-critical state.

Spectral Dynamics of Resting State fMRI Within the Ventral Tegmental Area and Dorsal Raphe Nuclei in Medication-Free Major Depressive Disorder in Young Adults.

Background: Dorsal raphe nucleus (DRN) and ventral tegmental area (VTA) are major brainstem monamine nuclei consisting of serotonin and dopamine neurons respectively. Animal studies show that firing patterns in both nuclei are altered when animals exhibit depression like behaviors. Functional MRI studies in humans have shown reduced VTA activation and DRN connectivity in depression. This study for the first time aims at investigating the functional integrity of local neuronal firing concurrently in both the VTA and DRN in vivo in humans using spectral analysis of resting state low frequency fluctuation fMRI. Method: A total of 97 medication-free subjects-67 medication-free young patients (ages 18-30) with major depressive disorder and 30 closely matched healthy controls were included in the study to detect aberrant dynamics in DRN and VTA. For the investigation of altered localized dynamics we conducted power spectral analysis and above this spectral cross correlation between the two groups. Complementary to this, spectral dependence of permutation entropy, an information theoretical measure, was compared between groups. Results: Patients displayed significant spectral slowing in VTA vs. controls (p = 0.035, corrected). In DRN, spectral slowing was less pronounced, but the amount of slowing significantly correlated with 17-item Hamilton Depression Rating scores of depression severity (p = 0.038). Signal complexity as assessed via permutation entropy showed spectral alterations inline with the results on spectral slowing. Conclusion: Our results indicate that altered functional dynamics of VTA and DRN in depression can be detected from regional fMRI signal. On this basis, impact of antidepressant treatment and treatment response can be assessed using these markers in future studies.

The validity of linear and non-linear heart rate metrics as workload indicators of emergency physicians.

BACKGROUND: It has been shown that linear and non-linear heart rate variability (HRV) metrics are suitable to assess workload of anesthetists administering anesthesia. In pre-hospital emergency care, these parameters have not yet been evaluated. We hypothesized that heart rate (HR) and HRV metrics discriminate between differing workload levels of an emergency physician. METHODS: Electrocardiograms were obtained from 13 emergency physicians. Mean HR, ten linear and seven non-linear HRV metrics were analyzed. For each sortie, four different levels of workload were defined. Mixed-effects models and the area under the receiver operating characteristics curve (AUC) were used to test and quantify the HR and HRV metrics' ability to discriminate between levels of workload. This was conducted for mean HR and each HRV metric as well as for groups of metrics (time domain vs. frequency domain vs. non-linear metrics). RESULTS: The non-linear HRV metric Permutation entropy (PeEn) discriminated best between the time before the alarm and primary patient care (AUC = 0.998, 1st rank of 18 HRV metrics). In contrast, AUC of the mean HR was low (0.558, 17th rank). In the multivariable approach, the non-linear HRV metrics provided a higher AUC (0.998) compared to the frequency domain (0.677) and to the time domain metrics (0.680). CONCLUSION: Non-linear heart rate metrics and, specifically, PeEn provided good validity for the assessment of different levels of a physician's workload in the setting of pre-hospital emergency care. In contradiction to earlier findings, the physicians' mean HR was not a valid marker of workload.

Simultaneous EEG-PET-fMRI measurements in disorders of consciousness: an exploratory study on diagnosis and prognosis.

Previous studies could demonstrate that functional magnetic resonance imaging (fMRI), fludeoxyglucose positron emission tomography (FDG-PET), and electroencephalography (EEG) measures contain information about patients suffering from disorders of consciousness (DOC) and thus improve the clinical diagnosis. Additionally, the technical modalities were able to predict the outcome of patients. However, most studies lack proven reproducibility in a clinical setting. We here applied a standardized combined EEG/fMRI/FDG-PET measurement to a cohort of 20 patients suffering from DOC and focused on parameters that have been demonstrated to contain information about diagnosis and prognosis of these patients. We evaluated EEG band power, fMRI connectivity in networks associated with consciousness and sensory networks, as well as absolute glucose uptake in the brain as potential markers of preserved consciousness or favorable outcome. Acquired data were analyzed by a principal component analysis to identify the most important markers in a hypothesis-free manner. These were then analyzed with statistical group comparisons. Absolute FDG-PET could prove that glucose metabolism in the occipital lobe is significantly higher in minimally conscious than in vegetative state patients. Delta band power showed to be prognostic marker for a favorable outcome. We conclude that absolute FDG-PET is a suitable tool to evaluate the level consciousness in DOC patients. Additionally, we propose delta band power as marker of a favorable outcome in DOC patients. We suggest that these findings promote a standardized technical evaluation of DOC patients to improve diagnosis and prognosis.

Coherence of BOLD signal and electrical activity in the human brain during deep sevoflurane anesthesia.

INTRODUCTION: Changes in neural activity induce changes in functional magnetic resonance (fMRI) blood oxygenation level dependent (BOLD) signal. Commonly, increases in BOLD signal are ascribed to cellular excitation. OBJECTIVE: The relationship between electrical activity and BOLD signal in the human brain was probed on the basis of burst suppression EEG. This condition includes two distinct states of high and low electrical activity. METHODS: Resting-state simultaneous EEG and BOLD measurements were acquired during deep sevoflurane anesthesia with burst suppression EEG in nineteen healthy volunteers. Afterwards, fMRI volumes were assigned to one of the two states (burst or suppression) as defined by the EEG. RESULTS: In the frontal, parietal and temporal lobes as well as in the basal ganglia, BOLD signal increased after burst onset in the EEG and decreased after onset of EEG suppression. In contrast, BOLD signal in the occipital lobe was anticorrelated to electrical activity. This finding was obtained consistently in a general linear model and in raw data. CONCLUSIONS: In human brains exhibiting burst suppression EEG induced by sevoflurane, the positive correlation between BOLD signal and electrical brain activity could be confirmed in most gray matter. The exceptional behavior of the occipital lobe with an anticorrelation of BOLD signal and electrical activity might be due to specific neurovascular coupling mechanisms that are pronounced in the deeply anesthetized brain.

Denervation versus pre- and postsynaptic muscle immobilization: Effects On acetylcholine- and muscle-specific tyrosine kinase receptors.

INTRODUCTION: Functional immobility of the diaphragm by mechanical ventilation impairs neuromuscular transmission and may result in ventilator-induced diaphragmatic dysfunction. We compared 3 diaphragmatic immobilization models with respect to their effects on expression of adult and fetal acetylcholine receptors (AChRs), muscle-specific receptor tyrosine kinase (MuSK), and muscle fiber morphology. METHODS: Diaphragms of rats were immobilized by either: (1) phrenicotomy; (2) presynaptic tetrodotoxin nerve blockade; or (3) postsynaptic polyethylene orthosis. AChR subtypes and MuSK were quantified by Western blot and immunohistochemistry. Muscle fiber morphology was evaluated by hematoxylin-eosin staining. RESULTS: Adult AChRs remained unchanged, whereas fetal AChRs and MuSK were upregulated in all models. Denervation induced the strongest changes in muscle morphology. CONCLUSIONS: Each diaphragm immobilization model led to severe morphologic and postsynaptic receptor changes. Postsynaptic polyethylene orthosis, a new model with an intact and functioning motor unit, best reflects the clinical picture of a functionally immobilized diaphragm. Muscle Nerve 55: 101-108, 2017.

Neural Correlates of Sevoflurane-induced Unconsciousness Identified by Simultaneous Functional Magnetic Resonance Imaging and Electroencephalography.

BACKGROUND: The neural correlates of anesthetic-induced unconsciousness have yet to be fully elucidated. Sedative and anesthetic states induced by propofol have been studied extensively, consistently revealing a decrease of frontoparietal and thalamocortical connectivity. There is, however, less understanding of the effects of halogenated ethers on functional brain networks. METHODS: The authors recorded simultaneous resting-state functional magnetic resonance imaging and electroencephalography in 16 artificially ventilated volunteers during sevoflurane anesthesia at burst suppression and 3 and 2 vol% steady-state concentrations for 700 s each to assess functional connectivity changes compared to wakefulness. Electroencephalographic data were analyzed using symbolic transfer entropy (surrogate of information transfer) and permutation entropy (surrogate of cortical information processing). Functional magnetic resonance imaging data were analyzed by an independent component analysis and a region-of-interest-based analysis. RESULTS: Electroencephalographic analysis showed a significant reduction of anterior-to-posterior symbolic transfer entropy and global permutation entropy. At 2 vol% sevoflurane concentrations, frontal and thalamic networks identified by independent component analysis showed significantly reduced within-network connectivity. Primary sensory networks did not show a significant change. At burst suppression, all cortical networks showed significantly reduced functional connectivity. Region-of-interest-based thalamic connectivity at 2 vol% was significantly reduced to frontoparietal and posterior cingulate cortices but not to sensory areas. CONCLUSIONS: Sevoflurane decreased frontal and thalamocortical connectivity. The changes in blood oxygenation level dependent connectivity were consistent with reduced anterior-to-posterior directed connectivity and reduced cortical information processing. These data advance the understanding of sevoflurane-induced unconsciousness and contribute to a neural basis of electroencephalographic measures that hold promise for intraoperative anesthesia monitoring.

EEG entropy measures indicate decrease of cortical information processing in Disorders of Consciousness.

OBJECTIVE: Clinical assessments that rely on behavioral responses to differentiate Disorders of Consciousness are at times inapt because of some patients' motor disabilities. To objectify patients' conditions of reduced consciousness the present study evaluated the use of electroencephalography to measure residual brain activity. METHODS: We analyzed entropy values of 18 scalp EEG channels of 15 severely brain-damaged patients with clinically diagnosed Minimally-Conscious-State (MCS) or Unresponsive-Wakefulness-Syndrome (UWS) and compared the results to a sample of 24 control subjects. Permutation entropy (PeEn) and symbolic transfer entropy (STEn), reflecting information processes in the EEG, were calculated for all subjects. Participants were tested on a modified active own-name paradigm to identify correlates of active instruction following. RESULTS: PeEn showed reduced local information content in the EEG in patients, that was most pronounced in UWS. STEn analysis revealed altered directed information flow in the EEG of patients, indicating impaired feed-backward connectivity. Responses to auditory stimulation yielded differences in entropy measures, indicating reduced information processing in MCS and UWS. CONCLUSIONS: Local EEG information content and information flow are affected in Disorders of Consciousness. This suggests local cortical information capacity and feedback information transfer as neural correlates of consciousness. SIGNIFICANCE: The utilized EEG entropy analyses were able to relate to patient groups with different Disorders of Consciousness.

Electromyographic permutation entropy quantifies diaphragmatic denervation and reinnervation.

Spontaneous reinnervation after diaphragmatic paralysis due to trauma, surgery, tumors and spinal cord injuries is frequently observed. A possible explanation could be collateral reinnervation, since the diaphragm is commonly double-innervated by the (accessory) phrenic nerve. Permutation entropy (PeEn), a complexity measure for time series, may reflect a functional state of neuromuscular transmission by quantifying the complexity of interactions across neural and muscular networks. In an established rat model, electromyographic signals of the diaphragm after phrenicotomy were analyzed using PeEn quantifying denervation and reinnervation. Thirty-three anesthetized rats were unilaterally phrenicotomized. After 1, 3, 9, 27 and 81 days, diaphragmatic electromyographic PeEn was analyzed in vivo from sternal, mid-costal and crural areas of both hemidiaphragms. After euthanasia of the animals, both hemidiaphragms were dissected for fiber type evaluation. The electromyographic incidence of an accessory phrenic nerve was 76%. At day 1 after phrenicotomy, PeEn (normalized values) was significantly diminished in the sternal (median: 0.69; interquartile range: 0.66-0.75) and mid-costal area (0.68; 0.66-0.72) compared to the non-denervated side (0.84; 0.78-0.90) at threshold p<0.05. In the crural area, innervated by the accessory phrenic nerve, PeEn remained unchanged (0.79; 0.72-0.86). During reinnervation over 81 days, PeEn normalized in the mid-costal area (0.84; 0.77-0.86), whereas it remained reduced in the sternal area (0.77; 0.70-0.81). Fiber type grouping, a histological sign for reinnervation, was found in the mid-costal area in 20% after 27 days and in 80% after 81 days. Collateral reinnervation can restore diaphragm activity after phrenicotomy. Electromyographic PeEn represents a new, distinctive assessment characterizing intramuscular function following denervation and reinnervation.

Monitoring depth of anesthesia utilizing a combination of electroencephalographic and standard measures.

BACKGROUND: For decades, monitoring depth of anesthesia was mainly based on unspecific effects of anesthetics, for example, blood pressure, heart rate, or drug concentrations. Today, electroencephalogram-based monitors promise a more specific assessment of the brain function. To date, most approaches were focused on a "head-to-head" comparison of either electroencephalogram- or standard parameter-based monitoring. In the current study, a multimodal indicator based on a combination of both electro encephalographic and standard anesthesia monitoring parameters is defined for quantification of "anesthesia depth." METHODS: Two hundred sixty-three adult patients from six European centers undergoing surgery with general anesthesia were assigned to 1 of 10 anesthetic combinations according to standards of the enrolling hospital. The anesthesia multimodal index of consciousness was developed using a data-driven approach, which maps standard monitoring and electroencephalographic parameters into an output indicator that separates different levels of anesthesia from awake to electroencephalographic burst suppression. Obtained results were compared with either a combination of standard monitoring parameters or the electroencephalogram-based bispectral index. RESULTS: The anesthesia multimodal index of consciousness showed prediction probability (P(K)) of 0.96 (95% CI, 0.95 to 0.97) to separate different levels of anesthesia (wakefulness to burst suppression), whereas the bispectral index had significantly lower PK of 0.80 (0.76 to 0.81) at corrected threshold P value of less than 0.05. At the transition between consciousness and unconsciousness, anesthesia multimodal index of consciousness yielded a PK of 0.88 (0.85 to 0.91). CONCLUSION: A multimodal integration of both standard monitoring and electroencephalographic parameters may more precisely reflect the level of anesthesia compared with monitoring based on one of these aspects alone.

Fronto-parietal connectivity is a non-static phenomenon with characteristic changes during unconsciousness.

BACKGROUND: It has been previously shown that loss of consciousness is associated with a breakdown of dominating fronto-parietal feedback connectivity as assessed by electroencephalogram (EEG) recordings. Structure and strength of network connectivity may change over time. Aim of the current study is to investigate cortico-cortical connectivity at different time intervals during consciousness and unconsciousness. For this purpose, EEG symbolic transfer entropy (STEn) was calculated to indicate cortico-cortical information transfer at different transfer times. METHODS: The study was performed in 15 male volunteers. 29-channel EEG was recorded during consciousness and propofol-induced unconsciousness. EEG data were analyzed by STEn, which quantifies intensity and directionality of the mutual information flow between two EEG channels. STEn was computed over fronto-parietal channel pair combinations (10 s length, 0.5-45 Hz total bandwidth) to analyze changes of intercortical directional connectivity. Feedback (fronto → parietal) and feedforward (parieto → frontal) connectivity was calculated for transfer times from 25 ms to 250 ms in 5 ms steps. Transfer times leading to maximum directed interaction were identified to detect changes of cortical information transfer (directional connectivity) induced by unconsciousness (p<0.05). RESULTS: The current analyses show that fronto-parietal connectivity is a non-static phenomenon. Maximum detected interaction occurs at decreased transfer times during propofol-induced unconsciousness (feedback interaction: 60 ms to 40 ms, p = 0.002; feedforward interaction: 65 ms to 45 ms, p = 0.001). Strength of maximum feedback interaction decreases during unconsciousness (p = 0.026), while no effect of propofol was observed on feedforward interaction. During both consciousness and unconsciousness, intensity of fronto-parietal interaction fluctuates with increasing transfer times. CONCLUSION: Non-stationarity of directional connectivity may play a functional role for cortical network communication as it shows characteristic changes during propofol-induced unconsciousness.

Non-stationarity of EEG during wakefulness and anaesthesia: advantages of EEG permutation entropy monitoring.

Monitors evaluating the electroencephalogram (EEG) to determine depth of anaesthesia use spectral analysis approaches for analysis windows up to 61.5 s as well as additional smoothing algorithms. Stationary EEG is required to reliably apply the index algorithms. Because of rapid physiological changes, artefacts, etc., the EEG may not always fulfil this requirement. EEG analysis using permutation entropy (PeEn) may overcome this issue, since PeEn can also be applied to practically nonstationary EEG. One objective was to determine the duration of EEG sequences that can be considered stationary at different anaesthetic levels. The second, more important objective was to test the reliability of PeEn to reflect the anaesthetic levels for short EEG segments. EEG was recorded from 15 volunteers undergoing sevoflurane and propofol anaesthesia at different anaesthetic levels and for each group 10 data sets were included. EEG stationarity was evaluated for EEG sample lengths from 4 to 116 s for each level. PeEn was calculated for these sequences using different parameter settings and analysis windows from 2 to 60 s. During wakefulness EEG can only be considered stationary for sequences up to 12 s. With increasing anaesthetic level the probability and duration of stationary EEG increases. PeEn is able to reliably separate consciousness from unconsciousness for EEG segments as short as 2 s. Especially during wakefulness a conflict between stationary EEG sequence durations and methods used for monitoring may exist. PeEn does not require stationarity and functions for EEG sequences as short as 2 s. These promising results seem to support the application of non-linear parameters, such as PeEn, to depth of anaesthesia monitoring.