A state-space model of the burst suppression ratio

Processed electroencephalography: impact of patient age and surgical position on intraoperative processed electroencephalogram monitoring of burst-suppression

We previously reported that processed EEG underestimated the amount of burst suppression compared to off-line visual analysis. We performed a follow-up study to evaluate the reasons for the discordance. Forty-five patients were monitored intraoperatively with processed EEG. A computer algorithm was used to convert the SedLine® (machine)-generated burst suppression ratio into a raw duration of burst suppression. The reference standard was a precise off-line measurement by two neurologists. We measured other potential variables that may affect machine accuracy such as age, surgery position, and EEG artifacts. Overall, the median duration of bust suppression for all study subjects was 15.4 min (Inter-quartile Range [IQR] = 1.0-20.1) for the machine vs. 16.1 min (IQR = 0.3-19.7) for the neurologists' assessment; the 95% limits of agreement fall within - 4.86 to 5.04 s for individual 30-s epochs. EEG artifacts did not affect the concordance between the two methods. For patients in prone surgical position, the machine estimates had significantly lower overall sensitivity (0.86 vs. 0.97; p = 0.038) and significantly wider limits of agreement ([- 4.24, 3.82] seconds vs. [- 1.36, 1.13] seconds, p = 0.001) than patients in supine position. Machine readings for younger patients (age < 65 years) had higher sensitivity (0.96 vs 0.92; p = 0.021) and specificity (0.99 vs 0.88; p = 0.007) for older patients. The duration of burst suppression estimated by the machine generally had good agreement compared with neurologists' estimation using a more precise off-line measurement. Factors that affected the concordance included patient age and position during surgery, but not EEG artifacts.

Neural signatures of α2-Adrenergic agonist-induced unconsciousness and awakening by antagonist

How the brain dynamics change during anesthetic-induced altered states of consciousness is not completely understood. The α2-adrenergic agonists are unique. They generate unconsciousness selectively through α2-adrenergic receptors and related circuits. We studied intracortical neuronal dynamics during transitions of loss of consciousness (LOC) with the α2-adrenergic agonist dexmedetomidine and return of consciousness (ROC) in a functionally interconnecting somatosensory and ventral premotor network in non-human primates. LOC, ROC and full task performance recovery were all associated with distinct neural changes. The early recovery demonstrated characteristic intermediate dynamics distinguished by sustained high spindle activities. Awakening by the α2-adrenergic antagonist completely eliminated this intermediate state and instantaneously restored awake dynamics and the top task performance while the anesthetic was still being infused. The results suggest that instantaneous functional recovery is possible following anesthetic-induced unconsciousness and the intermediate recovery state is not a necessary path for the brain recovery.

Monochromatic Blue Light Activates Suprachiasmatic Nucleus Neuronal Activity and Promotes Arousal in Mice Under Sevoflurane Anesthesia

Background: Monochromatic blue light (MBL), with a wavelength between 400–490 nm, can regulate non-image-forming (NIF) functions of light in the central nervous system. The suprachiasmatic nucleus (SCN) in the brain is involved in the arousal-promoting response to blue light in mice. Animal and human studies showed that the responsiveness of the brain to visual stimuli is partly preserved under general anesthesia. Therefore, this study aimed to investigate whether MBL promotes arousal from sevoflurane anesthesia via activation of the SCN in mice.

Methods: The induction and emergence time of sevoflurane anesthesia under MBL (460 nm and 800 lux) exposure was measured. Cortical electroencephalograms (EEGs) were recorded and the burst-suppression ratio (BSR) was calculated under MBL during sevoflurane anesthesia. The EEGs and local field potential (LFP) recordings with or without locally electrolytic ablated bilateral SCN were used to further explore the role of SCN in the arousal-promoting effect of MBL under sevoflurane anesthesia. Immunofluorescent staining of c-Fos was conducted to reveal the possible downstream mechanism of SCN activation.

Results: Unlike the lack of effect on the induction time, MBL shortened the emergence time and the EEG recordings showed cortical arousal during the recovery period. MBL resulted in a significant decrease in BSR and a marked increase in EEG power at all frequency bands except for the spindle band during 2.5% sevoflurane anesthesia. MBL exposure under sevoflurane anesthesia enhances the neuronal activity of the SCN. These responses to MBL were abolished in SCN lesioned (SCNx) mice. MBL evoked a high level of c-Fos expression in the prefrontal cortex (PFC) and lateral hypothalamus (LH) compared to polychromatic white light (PWL) under sevoflurane anesthesia, while it exerted no effect on c-Fos expression in the ventrolateral preoptic area (VLPO) and locus coeruleus (LC) c-Fos expression.

Conclusions: MBL promotes behavioral and electroencephalographic arousal from sevoflurane anesthesia via the activation of the SCN and its associated downstream wake-related nuclei. The clinical implications of this study warrant further study.

Spectral Content of Electroencephalographic Burst-Suppression Patterns May Reflect Neuronal Recovery in Comatose Post-Cardiac Arrest Patients

PURPOSE

To assess the potential biologic significance of variations in burst-suppression patterns (BSPs) after cardiac arrest in relation to recovery of consciousness. In the context of recent theoretical models of BSP, bursting frequency may be representative of underlying network dynamics; discontinuous activation of membrane potential during impaired cellular energetics may promote neuronal rescue.

METHODS

We reviewed a database of 73 comatose post-cardiac arrest patients who underwent therapeutic hypothermia to assess for the presence of BSP and clinical outcomes. In a subsample of patients with BSP (n = 14), spectral content of burst and suppression periods were quantified using multitaper method.

RESULTS

Burst-suppression pattern was seen in 45/73 (61%) patients. Comparable numbers of patients with (31.1%) and without (35.7%) BSP regained consciousness by the time of hospital discharge. In addition, in two unique cases, BSP initially resolved and then spontaneously reemerged after completion of therapeutic hypothermia and cessation of sedative medications. Both patients recovered consciousness. Spectral analysis of bursts in all patients regaining consciousness (n = 6) showed a prominent theta frequency (5-7 Hz) feature, but not in age-matched patients with induced BSP who did not recover consciousness (n = 8).

CONCLUSIONS

The prognostic implications of BSP after hypoxic brain injury may vary based on the intrinsic properties of the underlying brain state itself. The presence of theta activity within bursts may index potential viability of neuronal networks underlying recovery of consciousness; emergence of spontaneous BSP in some cases may indicate an innate neuroprotective mechanism. This study highlights the need for better characterization of various BSP patterns after cardiac arrest.

Variability in pharmacologically-induced coma for treatment of refractory status epilepticus

Objective

To characterize the amount of EEG suppression achieved in refractory status epilepticus (RSE) patients treated with pharmacologically-induced coma (PIC).

Methods

We analyzed EEG recordings from 35 RSE patients between 21–84 years-old who received PIC that target burst suppression and quantified the amount of EEG suppression using the burst suppression probability (BSP). Then we measured the variability of BSPs with respect to a reference level of BSP 0.8 ± 0.15. Finally, we also measured the variability of BSPs with respect to the amount of intravenous anesthetic drugs (IVADs) received by the patients.

Results

Patients remained in the reference BSP range for only 8% (median, interquartile range IQR [0, 29] %) of the total time under treatment. The median time with BSP below the reference range was 84% (IQR [37, 100] %). BSPs in some patients drifted significantly over time despite constant infusion rates of IVADs. Similar weight-normalized infusion rates of IVADs in different patients nearly always resulted in distinct BSPs (probability 0.93 (IQR [0.82, 1.0]).

Conclusion

This study quantitatively identified high variability in the amount of EEG suppression achieved in clinical practice when treating RSE patients. While some of this variability may arise from clinicians purposefully deviating from clinical practice guidelines, our results show that the high variability also arises in part from significant inter- and intra- individual pharmacokinetic/pharmacodynamic variation. Our results indicate that the delicate balance between maintaining sufficient EEG suppression in RSE patients and minimizing IVAD exposure in clinical practice is challenging to achieve. This may affect patient outcomes and confound studies seeking to determine an optimal amount of EEG suppression for treatment of RSE. Therefore, our analysis points to the need for developing an alternative paradigm, such as vigilant anesthetic management as happens in operating rooms, or closed-loop anesthesia delivery, for investigating and providing induced-coma therapy to RSE patients.

Physostigmine and Methylphenidate Induce Distinct Arousal States During Isoflurane General Anesthesia in Rats

BACKGROUND

Although emergence from general anesthesia is clinically treated as a passive process driven by the pharmacokinetics of drug clearance, agents that hasten recovery from general anesthesia may be useful for treating delayed emergence, emergence delirium, and postoperative cognitive dysfunction. Activation of central monoaminergic neurotransmission with methylphenidate has been shown to induce reanimation (active emergence) from general anesthesia. Cholinergic neurons in the brainstem and basal forebrain are also known to promote arousal. The objective of this study was to test the hypothesis that physostigmine, a centrally acting cholinesterase inhibitor, induces reanimation from isoflurane anesthesia in adult rats.

METHODS

The dose-dependent effects of physostigmine on time to emergence from a standardized isoflurane general anesthetic were tested. It was then determined whether physostigmine restores righting during continuous isoflurane anesthesia. In a separate group of rats with implanted extradural electrodes, physostigmine was administered during continuous inhalation of 1.0% isoflurane, and the electroencephalogram changes were recorded. Finally, 2.0% isoflurane was used to induce burst suppression, and the effects of physostigmine and methylphenidate on burst suppression probability (BSP) were tested.

RESULTS

Physostigmine delayed time to emergence from isoflurane anesthesia at doses ≥0.2 mg/kg (n = 9). During continuous isoflurane anesthesia (0.9% ± 0.1%), physostigmine did not restore righting (n = 9). Blocking the peripheral side effects of physostigmine with the coadministration of glycopyrrolate (a muscarinic antagonist that does not cross the blood-brain barrier) produced similar results (n = 9 each). However, during inhalation of 1.0% isoflurane, physostigmine shifted peak electroencephalogram power from δ (<4 Hz) to θ (4-8 Hz) in 6 of 6 rats. During continuous 2.0% isoflurane anesthesia, physostigmine induced large, statistically significant decreases in BSP in 6 of 6 rats, whereas methylphenidate did not.

CONCLUSIONS

Unlike methylphenidate, physostigmine does not accelerate time to emergence from isoflurane anesthesia and does not restore righting during continuous isoflurane anesthesia. However, physostigmine consistently decreases BSP during deep isoflurane anesthesia, whereas methylphenidate does not. These findings suggest that activation of cholinergic neurotransmission during isoflurane anesthesia produces arousal states that are distinct from those induced by monoaminergic activation.

Robust control of burst suppression for medical coma

OBJECTIVE

Medical coma is an anesthetic-induced state of brain inactivation, manifest in the electroencephalogram by burst suppression. Feedback control can be used to regulate burst suppression, however, previous designs have not been robust. Robust control design is critical under real-world operating conditions, subject to substantial pharmacokinetic and pharmacodynamic parameter uncertainty and unpredictable external disturbances. We sought to develop a robust closed-loop anesthesia delivery (CLAD) system to control medical coma.

APPROACH

We developed a robust CLAD system to control the burst suppression probability (BSP). We developed a novel BSP tracking algorithm based on realistic models of propofol pharmacokinetics and pharmacodynamics. We also developed a practical method for estimating patient-specific pharmacodynamics parameters. Finally, we synthesized a robust proportional integral controller. Using a factorial design spanning patient age, mass, height, and gender, we tested whether the system performed within clinically acceptable limits. Throughout all experiments we subjected the system to disturbances, simulating treatment of refractory status epilepticus in a real-world intensive care unit environment.

MAIN RESULTS

In 5400 simulations, CLAD behavior remained within specifications. Transient behavior after a step in target BSP from 0.2 to 0.8 exhibited a rise time (the median (min, max)) of 1.4 [1.1, 1.9] min; settling time, 7.8 [4.2, 9.0] min; and percent overshoot of 9.6 [2.3, 10.8]%. Under steady state conditions the CLAD system exhibited a median error of 0.1 [-0.5, 0.9]%; inaccuracy of 1.8 [0.9, 3.4]%; oscillation index of 1.8 [0.9, 3.4]%; and maximum instantaneous propofol dose of 4.3 [2.1, 10.5] mg kg(-1). The maximum hourly propofol dose was 4.3 [2.1, 10.3] mg kg(-1) h(-1). Performance fell within clinically acceptable limits for all measures.

SIGNIFICANCE

A CLAD system designed using robust control theory achieves clinically acceptable performance in the presence of realistic unmodeled disturbances and in spite of realistic model uncertainty, while maintaining infusion rates within acceptable safety limits.

The human burst suppression electroencephalogram of deep hypothermia

OBJECTIVE

Deep hypothermia induces 'burst suppression' (BS), an electroencephalogram pattern with low-voltage 'suppressions' alternating with high-voltage 'bursts'. Current understanding of BS comes mainly from anesthesia studies, while hypothermia-induced BS has received little study. We set out to investigate the electroencephalogram changes induced by cooling the human brain through increasing depths of BS through isoelectricity.

METHODS

We recorded scalp electroencephalograms from eleven patients undergoing deep hypothermia during cardiac surgery with complete circulatory arrest, and analyzed these using methods of spectral analysis.

RESULTS

Within patients, the depth of BS systematically depends on the depth of hypothermia, though responses vary between patients except at temperature extremes. With decreasing temperature, burst lengths increase, and burst amplitudes and lengths decrease, while the spectral content of bursts remains constant.

CONCLUSIONS

These findings support an existing theoretical model in which the common mechanism of burst suppression across diverse etiologies is the cyclical diffuse depletion of metabolic resources, and suggest the new hypothesis of local micro-network dropout to explain decreasing burst amplitudes at lower temperatures.

SIGNIFICANCE

These results pave the way for accurate noninvasive tracking of brain metabolic state during surgical procedures under deep hypothermia, and suggest new testable predictions about the network mechanisms underlying burst suppression.

Propofol and sevoflurane induce distinct burst suppression patterns in rats

Burst suppression is an EEG pattern characterized by alternating periods of high-amplitude activity (bursts) and relatively low amplitude activity (suppressions). Burst suppression can arise from several different pathological conditions, as well as from general anesthesia. Here we review current algorithms that are used to quantify burst suppression, its various etiologies, and possible underlying mechanisms. We then review clinical applications of anesthetic-induced burst suppression. Finally, we report the results of our new study showing clear electrophysiological differences in burst suppression patterns induced by two common general anesthetics, sevoflurane and propofol. Our data suggest that the circuit mechanisms that generate burst suppression activity may differ among general anesthetics.

Graded defragmentation of cortical neuronal firing during recovery of consciousness in rats

State-dependent neuronal firing patterns reflect changes in ongoing information processing and cortical function. A disruption of neuronal coordination has been suggested as the neural correlate of anesthesia. Here, we studied the temporal correlation patterns of ongoing spike activity, during a stepwise reduction of the volatile anesthetic desflurane, in the cerebral cortex of freely moving rats. We hypothesized that the recovery of consciousness from general anesthesia is accompanied by specific changes in the spatiotemporal pattern and correlation of neuronal activity. Sixty-four contact microelectrode arrays were chronically implanted in the primary visual cortex (contacts spanning 1.4-mm depth and 1.4-mm width) for recording of extracellular unit activity at four steady-state levels of anesthesia (8-2% desflurane) and wakefulness. Recovery of consciousness was defined as the regaining of the righting reflex (near 4%). High-intensity firing (HI) periods were segmented using a threshold (200-ms) representing the minimum in the neurons' bimodal interspike interval histogram under anesthesia. We found that the HI periods were highly fragmented in deep anesthesia and gradually transformed to a near-continuous firing pattern at wakefulness. As the anesthetic was withdrawn, HI periods became longer and increasingly correlated among the units both locally and across remote recording sites. Paradoxically, in 4 of 8 animals, HI correlation was also high at the deepest level of anesthesia (8%) when local field potentials (LFP) were burst-suppressed. We conclude that recovery from desflurane anesthesia is accompanied by a graded defragmentation of neuronal activity in the cerebral cortex. Hypersynchrony during deep anesthesia is an exception that occurs only with LFP burst suppression.

Real-time closed-loop control in a rodent model of medically induced coma using burst suppression

BACKGROUND

A medically induced coma is an anesthetic state of profound brain inactivation created to treat status epilepticus and to provide cerebral protection after traumatic brain injuries. The authors hypothesized that a closed-loop anesthetic delivery system could automatically and precisely control the electroencephalogram state of burst suppression and efficiently maintain a medically induced coma.

METHODS

In six rats, the authors implemented a closed-loop anesthetic delivery system for propofol consisting of: a computer-controlled pump infusion, a two-compartment pharmacokinetics model defining propofol's electroencephalogram effects, the burst-suppression probability algorithm to compute in real time from the electroencephalogram the brain's burst-suppression state, an online parameter-estimation procedure and a proportional-integral controller. In the control experiment each rat was randomly assigned to one of the six burst-suppression probability target trajectories constructed by permuting the burst-suppression probability levels of 0.4, 0.65, and 0.9 with linear transitions between levels.

RESULTS

In each animal the controller maintained approximately 60 min of tight, real-time control of burst suppression by tracking each burst-suppression probability target level for 15 min and two between-level transitions for 5-10 min. The posterior probability that the closed-loop anesthetic delivery system was reliable across all levels was 0.94 (95% CI, 0.77-1.00; n = 18) and that the system was accurate across all levels was 1.00 (95% CI, 0.84-1.00; n = 18).

CONCLUSION

The findings of this study establish the feasibility of using a closed-loop anesthetic delivery systems to achieve in real time reliable and accurate control of burst suppression in rodents and suggest a paradigm to precisely control medically induced coma in patients.

Real-time segmentation of burst suppression patterns in critical care EEG monitoring

OBJECTIVE

Develop a real-time algorithm to automatically discriminate suppressions from non-suppressions (bursts) in electroencephalograms of critically ill adult patients.

METHODS

A real-time method for segmenting adult ICU EEG data into bursts and suppressions is presented based on thresholding local voltage variance. Results are validated against manual segmentations by two experienced human electroencephalographers. We compare inter-rater agreement between manual EEG segmentations by experts with inter-rater agreement between human vs automatic segmentations, and investigate the robustness of segmentation quality to variations in algorithm parameter settings. We further compare the results of using these segmentations as input for calculating the burst suppression probability (BSP), a continuous measure of depth-of-suppression.

RESULTS

Automated segmentation was comparable to manual segmentation, i.e. algorithm-vs-human agreement was comparable to human-vs-human agreement, as judged by comparing raw EEG segmentations or the derived BSP signals. Results were robust to modest variations in algorithm parameter settings.

CONCLUSIONS

Our automated method satisfactorily segments burst suppression data across a wide range adult ICU EEG patterns. Performance is comparable to or exceeds that of manual segmentation by human electroencephalographers.

SIGNIFICANCE

Automated segmentation of burst suppression EEG patterns is an essential component of quantitative brain activity monitoring in critically ill and anesthetized adults. The segmentations produced by our algorithm provide a basis for accurate tracking of suppression depth.

Local cortical dynamics of burst suppression in the anaesthetized brain

Burst suppression is an electroencephalogram pattern that consists of a quasi-periodic alternation between isoelectric ‘suppressions’ lasting seconds or minutes, and high-voltage ‘bursts’. It is characteristic of a profoundly inactivated brain, occurring in conditions including hypothermia, deep general anaesthesia, infant encephalopathy and coma. It is also used in neurology as an electrophysiological endpoint in pharmacologically induced coma for brain protection after traumatic injury and during status epilepticus. Classically, burst suppression has been regarded as a ‘global’ state with synchronous activity throughout cortex. This assumption has influenced the clinical use of burst suppression as a way to broadly reduce neural activity. However, the extent of spatial homogeneity has not been fully explored due to the challenges in recording from multiple cortical sites simultaneously. The neurophysiological dynamics of large-scale cortical circuits during burst suppression are therefore not well understood. To address this question, we recorded intracranial electrocorticograms from patients who entered burst suppression while receiving propofol general anaesthesia. The electrodes were broadly distributed across cortex, enabling us to examine both the dynamics of burst suppression within local cortical regions and larger-scale network interactions. We found that in contrast to previous characterizations, bursts could be substantially asynchronous across the cortex. Furthermore, the state of burst suppression itself could occur in a limited cortical region while other areas exhibited ongoing continuous activity. In addition, we found a complex temporal structure within bursts, which recapitulated the spectral dynamics of the state preceding burst suppression, and evolved throughout the course of a single burst. Our observations imply that local cortical dynamics are not homogeneous, even during significant brain inactivation. Instead, cortical and, implicitly, subcortical circuits express seemingly different sensitivities to high doses of anaesthetics that suggest a hierarchy governing how the brain enters burst suppression, and emphasize the role of local dynamics in what has previously been regarded as a global state. These findings suggest a conceptual shift in how neurologists could assess the brain function of patients undergoing burst suppression. First, analysing spatial variation in burst suppression could provide insight into the circuit dysfunction underlying a given pathology, and could improve monitoring of medically-induced coma. Second, analysing the temporal dynamics within a burst could help assess the underlying brain state. This approach could be explored as a prognostic tool for recovery from coma, and for guiding treatment of status epilepticus. Overall, these results suggest new research directions and methods that could improve patient monitoring in clinical practice.

A brain-machine interface for control of burst suppression in medical coma

Burst suppression is an electroencephalogram (EEG) marker of profound brain inactivation and unconsciousness and consists of bursts of electrical activity alternating with periods of isoelectricity called suppression. Burst suppression is the EEG pattern targeted in medical coma, a drug-induced brain state used to help recovery after brain injuries and to treat epilepsy that is refractory to conventional drug therapies. The state of coma is maintained manually by administering an intravenous infusion of an anesthetic, such as propofol, to target a pattern of burst suppression on the EEG. The coma often needs to be maintained for several hours or days, and hence an automated system would offer significant benefit for tight control. Here we present a brain-machine interface (BMI) for automatic control of burst suppression in medical coma that selects the real-time drug infusion rate based on EEG observations and can precisely control the burst suppression level in real time in rodents. We quantify the burst suppression level using the burst suppression probability (BSP), the brain's instantaneous probability of being in the suppressed state, and represent the effect of the anesthetic propofol on the BSP using a two-dimensional linear compartment model that we fit in experiments. We compute the BSP in real time from the EEG segmented into a binary time-series by deriving a two-dimensional state-space algorithm. We then derive a stochastic controller using both a linear-quadratic-regulator strategy and a model predictive control strategy. The BMI can promptly change the level of burst suppression without overshoot or undershoot and maintains precise control of time-varying target levels of burst suppression in individual rodents in real time.

Real-time segmentation and tracking of brain metabolic state in ICU EEG recordings of burst suppression

We provide a method for estimating brain metabolic state based on a reduced-order model of EEG burst suppression. The model, derived from previously suggested biophysical mechanisms of burst suppression, describes important electrophysiological features and provides a direct link to cerebral metabolic rate. We design and fit the estimation method from EEG recordings of burst suppression from a neurological intensive care unit and test it on real and synthetic data.