Tracking brain states under general anesthesia by using global coherence analysis

Cimenser, Aylin; Purdon, Patrick L.; Pierce, Eric T.; Walsh, J.; Salazar-Gomez, Andres F.; Harrell, P. Grace; Tavares-Stoeckel, Casie; Habeeb, Kathleen; Brown, Emery N.

doi:10.1073/pnas.1017041108

Cited by 227 publications

(284 citation statements)

References 29 publications

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“…3. The neuronal network consists of cortical pyramidal neurons and interneurons that produce neuronal oscillations on timescales commensurate with the EEG observed during general anesthesia (19,20). To this network, we add dynamics associated with ATP consumption, in the form of the sodium-ATP pump, which is thought to be the major consumer of cerebral ATP (29,33).…”

Section: Methodsmentioning

confidence: 99%

A neurophysiological–metabolic model for burst suppression

Ching

Purdon

Vijayan

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

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Burst suppression is an electroencepholagram (EEG) pattern in which high-voltage activity alternates with isoelectric quiescence. It is characteristic of an inactivated brain and is commonly observed at deep levels of general anesthesia, hypothermia, and in pathological conditions such as coma and early infantile encephalopathy. We propose a unifying mechanism for burst suppression that accounts for all of these conditions. By constructing a biophysical computational model, we show how the prevailing features of burst suppression may arise through the interaction between neuronal dynamics and brain metabolism. In each condition, the model suggests that a decrease in cerebral metabolic rate, coupled with the stabilizing properties of ATP-gated potassium channels, leads to the characteristic epochs of suppression. Consequently, the model makes a number of specific predictions of experimental and clinical relevance.B urst suppression-an electroencephalogram (EEG) pattern in which high voltage activity (burst) and flatline (suppression) periods alternate systematically but quasiperiodically (almost periodic but with variations in inter-and intra-burst duration) (1)-is a state of profound brain inactivation. It is frequently observed in deep general anesthesia (2). It is also observed in a range of pathological conditions including hypothermia (3-5), hypoxic-ischemic trauma/coma (6), and the so-called Ohtahara syndrome (7,8), a type of early infantile encephalopathy. These etiologies indicate that the burst suppression pattern represents a low-order dynamic mechanism that persists in the absence of higher-level brain activity. Indeed, the fact that many different conditions produce similar brain activity suggests that there may be a common pathway to the state of brain inactivation and may indicate fundamental properties of the brain's arousal circuits (2).The basic features of the burst suppression phenomenon have been established through a variety of EEG studies that are reviewed in ref. 9. The two most notable of these features are the spatial homogeneity of bursts and the quasi-periodic nature of the suppression. Later research has been directed at describing the phenomenon in vivo and in the specific context of general anesthesia. In particular, the early work of Steriade et al. (10) helped establish the neural correlates of burst suppression, including the differential participation of cortical and subcortical cell types. More recent studies (11,12) have suggested that burst suppression is associated with enhanced excitability in cortical networks. These studies implicate extracellular calcium as a correlate for the switches between burst and suppression.Despite the findings of these studies, a unifying mechanism for burst suppression-one that explains its prevailing features and also accounts for its range of etiologies-has not been proposed. Indeed, the existing characterizations of burst suppression in pathological situations are highly limited. Computational models offer a means of synthesizing the availabl...

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Section: Methodsmentioning

confidence: 99%

A neurophysiological–metabolic model for burst suppression

Ching

Purdon

Vijayan

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

259

326

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“…2B). To minimize spatial blurring in this and subsequent analyses, we used a nearest-neighbor Laplacian reference, in which the Laplacian is calculated by taking each channel and subtracting the average of the nearest neighbors (24). During induction, gamma (25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40) and beta (13-24Hz) power increased significantly above baseline levels during the 30 min before LOC-when P clicks < P verbal -and remained elevated during the unconscious period ( Fig.…”

Section: Dynamics Of the Eeg Spectrum Covary With Changes In Probabilmentioning

confidence: 99%

“…A variety of EEG patterns are known to arise during GA maintained by both GABA A receptor-specific and ether-derived anesthetics. These EEG patterns include increases in frontal EEG power (20)(21)(22)(23)(24), a shift in EEG power toward lower frequencies (25), changes in coherence (22,26), and burst suppression and isoelectricity (27). However, the relationship between these or other EEG patterns…”

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confidence: 99%

Electroencephalogram signatures of loss and recovery of consciousness from propofol

Purdon

Pierce

Mukamel

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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849

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Significance Anesthesiologists reversibly manipulate the brain function of nearly 60,000 patients each day, but brain-state monitoring is not an accepted practice in anesthesia care because markers that reliably track changes in level of consciousness under general anesthesia have yet to be identified. We found specific behavioral and electrophysiological changes that mark the transition between consciousness and unconsciousness induced by propofol, one of the most commonly used anesthetic drugs. Our results provide insights into the mechanisms of propofol-induced unconsciousness and establish EEG signatures of this brain state that could be used to monitor the brain activity of patients receiving general anesthesia.

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“…[1][2][3][4][5] For example, the dynamic configuration of the functional network is flexible during a learning task, which a)…”

Section: Introductionmentioning

confidence: 99%

Spectral properties of the temporal evolution of brain network structure

Wang

Zhang

et al. 2015

Chaos: An Interdisciplinary Journal of Nonlinear Science

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The temporal evolution properties of the brain network are crucial for complex brain processes. In this paper, we investigate the differences in the dynamic brain network during resting and visual stimulation states in a task-positive subnetwork, task-negative subnetwork, and whole-brain network. The dynamic brain network is first constructed from human functional magnetic resonance imaging data based on the sliding window method, and then the eigenvalues corresponding to the network are calculated. We use eigenvalue analysis to analyze the global properties of eigenvalues and the random matrix theory (RMT) method to measure the local properties. For global properties, the shifting of the eigenvalue distribution and the decrease in the largest eigenvalue are linked to visual stimulation in all networks. For local properties, the short-range correlation in eigenvalues as measured by the nearest neighbor spacing distribution is not always sensitive to visual stimulation. However, the long-range correlation in eigenvalues as evaluated by spectral rigidity and number variance not only predicts the universal behavior of the dynamic brain network but also suggests non-consistent changes in different networks. These results demonstrate that the dynamic brain network is more random for the task-positive subnetwork and whole-brain network under visual stimulation but is more regular for the task-negative subnetwork. Our findings provide deeper insight into the importance of spectral properties in the functional brain network, especially the incomparable role of RMT in revealing the intrinsic properties of complex systems. The temporal evolution of brain network structure is crucial to revealing brain functions at different brain states, such as learning, memory, and visual stimulation. The sliding window method is effective for the construction of a dynamic brain network, and complex network analysis has been widely used to characterize these networks. Recently, the spectral property analysis of complex networks has attracted increasing attention based on its ability to measure more intrinsic properties of networks than complex network analysis. In particular, the dynamic brain network constructed from human electroencephalogram (EEG) data has been successfully studied by applying random matrix theory (RMT), which aims to identify the spectral fluctuation properties of networks. However, little is known about the spectral properties of the dynamic brain network based on human functional magnetic resonance imaging (fMRI) data at different brain states, and how spectral properties reflect changes in the dynamic brain network must be explained in greater detail. Here, we addressed these issues and investigated the spectral properties of a dynamic brain network constructed from human fMRI data. We show that the global and local properties of the eigenvalue of the brain network are effective for evaluating the changes in the dynamic brain network caused by visual stimulation. However, the differences between global properties...

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Tracking brain states under general anesthesia by using global coherence analysis

Cited by 227 publications

References 29 publications

A neurophysiological–metabolic model for burst suppression

A neurophysiological–metabolic model for burst suppression

Electroencephalogram signatures of loss and recovery of consciousness from propofol

Spectral properties of the temporal evolution of brain network structure

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