Homeostatic dynamics, hysteresis and synchronization in a low-dimensional model of burst suppression

Propagation of transient explosive synchronization in a mesoscale mouse brain network model of epilepsy

Generalized epileptic attacks, which exhibit widespread disruption of brain activity, are characterized by recurrent, spontaneous, and synchronized bursts of neural activity that self-initiate and self-terminate through critical transitions. Here we utilize the general framework of explosive synchronization (ES) from complex systems science to study the role of network structure and resource dynamics in the generation and propagation of seizures. We show that a combination of resource constraint and adaptive coupling in a Kuramoto network oscillator model can reliably generate seizure-like synchronization activity across different network topologies, including a biologically derived mesoscale mouse brain network. The model, coupled with a novel algorithm for tracking seizure propagation, provides mechanistic insight into the dynamics of transition to the synchronized state and its dependence on resources; and identifies key brain areas that may be involved in the initiation and spatial propagation of the seizure. The model, though minimal, efficiently recapitulates several experimental and theoretical predictions from more complex models and makes novel experimentally testable predictions.

Correlation between burst suppression and postoperative delirium in elderly patients: a prospective study

OBJECTIVE

To explore the correlation between intraoperative burst suppression (BS) and postoperative delirium (POD) in elderly patients, and provide more ideas for reducing POD in clinical.

METHODS

Ninety patients, aged over 60 years, who underwent lumbar internal fixation surgery in our hospital were selected. General information of patients was obtained and informed consent was signed during preoperative visits. Patients were divided into burst suppression (BS) group and non-burst suppression (NBS) group by intraoperative electroencephalogram monitoring. Intraoperative systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), and heart rate (HR) were recorded, and the variation and minimum value were obtained by calculating. Hemoglobin (HGB), C-reactive protein (CRP), system immune inflammatory index (SII) at 24 and 72 h after surgery, the incidence of postoperative adverse reactions, postoperative hospital stay, and total cost were recorded after operation. POD assessment was performed using CAM within 7 days after surgery or until discharge. SPSS25.0 was used for statistical analysis.

RESULTS

Compared with the NBS group, the number of elderly patients with high frailty level in BS group was more (P = 0.048). There is correlation between BS and POD (OR: 4.954, 95%CI 1.034-23.736, P = 0.045), and most of the POD patients in BS group behave as hyperactive type.

CONCLUSION

The occurrence of intraoperative BS is associated with POD, and elderly patients with frailty are more likely to have intraoperative BS. BS can be used as a predictor of POD.

Mechanisms underlying pathological cortical bursts during metabolic depletion

Cortical activity depends upon a continuous supply of oxygen and other metabolic resources. Perinatal disruption of oxygen availability is a common clinical scenario in neonatal intensive care units, and a leading cause of lifelong disability. Pathological patterns of brain activity including burst suppression and seizures are a hallmark of the recovery period, yet the mechanisms by which these patterns arise remain poorly understood. Here, we use computational modeling of coupled metabolic-neuronal activity to explore the mechanisms by which oxygen depletion generates pathological brain activity. We find that restricting oxygen supply drives transitions from normal activity to several pathological activity patterns (isoelectric, burst suppression, and seizures), depending on the potassium supply. Trajectories through parameter space track key features of clinical electrophysiology recordings and reveal how infants with good recovery outcomes track toward normal parameter values, whereas the parameter values for infants with poor outcomes dwell around the pathological values. These findings open avenues for studying and monitoring the metabolically challenged infant brain, and deepen our understanding of the link between neuronal and metabolic activity.

Quiescence during burst suppression and postictal generalized EEG suppression are distinct patterns of activity

OBJECTIVE

Periods of low-amplitude electroencephalographic (EEG) signal (quiescence) are present during both anesthetic-induced burst suppression (BS) and postictal generalized electroencephalographic suppression (PGES). PGES following generalized seizures induced by electroconvulsive therapy (ECT) has been previously linked to antidepressant response. The commonality of quiescence during both BS and PGES motivated trials to recapitulate the antidepressant effects of ECT using high doses of anesthetics. However, there have been no direct electrographic comparisons of these quiescent periods to address whether these are distinct entities.

METHODS

We compared periods of EEG quiescence recorded from two human studies: BS induced in 29 healthy adult volunteers by isoflurane general anesthesia and PGES in 11 patients undergoing right unilateral ECT for treatment-resistant depression. An automated algorithm allowed detection of EEG quiescence based on a 10-microvolt amplitude threshold. Spatial, spectral, and temporal analyses compared quiescent epochs during BS and PGES.

RESULTS

The median (interquartile range) voltage for quiescent periods during PGES was greater than during BS (1.81 (0.22) microvolts vs 1.22 (0.33) microvolts, p < 0.001). Relative power was greater for quiescence during PGES than BS for the 1-4 Hz delta band (p < 0.001), at the expense of power in the theta (4-8 Hz, p < 0.001), beta (13-30 Hz, p = 0.04) and gamma (30-70 Hz, p = 0.006) frequency bands. Topographic analyses revealed that amplitude across the scalp was consistently higher for quiescent periods during PGES than BS, whose voltage was within the noise floor.

CONCLUSIONS

Quiescent epochs during PGES and BS have distinct patterns of EEG signals across voltage, frequency, and spatial domains.

SIGNIFICANCE

Quiescent epochs during PGES and BS, important neurophysiological markers for clinical outcomes, are shown to have distinct voltage and frequency characteristics.

Adiabatic dynamic causal modelling

This technical note introduces adiabatic dynamic causal modelling, a method for inferring slow changes in biophysical parameters that control fluctuations of fast neuronal states. The application domain we have in mind is inferring slow changes in variables (e.g., extracellular ion concentrations or synaptic efficacy) that underlie phase transitions in brain activity (e.g., paroxysmal seizure activity). The scheme is efficient and yet retains a biophysical interpretation, in virtue of being based on established neural mass models that are equipped with a slow dynamic on the parameters (such as synaptic rate constants or effective connectivity). In brief, we use an adiabatic approximation to summarise fast fluctuations in hidden neuronal states (and their expression in sensors) in terms of their second order statistics; namely, their complex cross spectra. This allows one to specify and compare models of slowly changing parameters (using Bayesian model reduction) that generate a sequence of empirical cross spectra of electrophysiological recordings. Crucially, we use the slow fluctuations in the spectral power of neuronal activity as empirical priors on changes in synaptic parameters. This introduces a circular causality, in which synaptic parameters underwrite fast neuronal activity that, in turn, induces activity-dependent plasticity in synaptic parameters. In this foundational paper, we describe the underlying model, establish its face validity using simulations and provide an illustrative application to a chemoconvulsant animal model of seizure activity.

Electroencephalographic features of discontinuous activity in anesthetized infants and children

Background

Discontinuous electroencephalographic activity in children is thought to reflect brain inactivation. Discontinuity has been observed in states of pathology, where it is predictive of adverse neurological outcome, as well as under general anesthesia. Though in preterm-infants discontinuity reflects normal brain development, less is known regarding its role in term children, particularly in the setting of general anesthesia. Here, we conduct a post-hoc exploratory analysis to investigate the spectral features of discontinuous activity in children under general anesthesia.

Methods

We previously recorded electroencephalography in children less than forty months of age under general anesthesia (n = 65). We characterized the relationship between age, anesthetic depth, and discontinuous activity, and used multitaper spectral methods to compare the power spectra of subjects with (n = 35) and without (n = 30) discontinuous activity. In the subjects with discontinuous activity, we examined the amplitude and power spectra associated with the discontinuities and analyzed how these variables varied with age.

Results

Cumulative time of discontinuity was associated with increased anesthetic depth and younger age. In particular, age-matched children with discontinuity received higher doses of propofol during induction as compared with children without discontinuity. In the tens of seconds preceding the onset of discontinuous activity, there was a decrease in high-frequency power in children four months and older that could be visually observed with spectrograms. During discontinuous activity, there were distinctive patterns of amplitude, spectral edge, and power in canonical frequency bands that varied with age. Notably, there was a decline in spectral edge in the seconds immediately following each discontinuity.

Conclusion

Discontinuous activity in children reflects a state of a younger or more deeply anesthetized brain, and characteristic features of discontinuous activity evolve with age and may reflect neurodevelopment.

Isoflurane but Not Halothane Prevents and Reverses Helpless Behavior: A Role for EEG Burst Suppression?

BACKGROUND

The volatile anesthetic isoflurane may exert a rapid and long-lasting antidepressant effect in patients with medication-resistant depression. The mechanism underlying the putative therapeutic actions of the anesthetic have been attributed to its ability to elicit cortical burst suppression, a distinct EEG pattern with features resembling the characteristic changes that occur following electroconvulsive therapy. It is currently unknown whether the antidepressant actions of isoflurane are shared by anesthetics that do not elicit cortical burst suppression.

METHODS

In vivo electrophysiological techniques were used to determine the effects of isoflurane and halothane, 2 structurally unrelated volatile anesthetics, on cortical EEG. The effects of anesthesia with either halothane or isoflurane were also compared on stress-induced learned helplessness behavior in rats and mice.

RESULTS

Isoflurane, but not halothane, anesthesia elicited a dose-dependent cortical burst suppression EEG in rats and mice. Two hours of isoflurane, but not halothane, anesthesia reduced the incidence of learned helplessness in rats evaluated 2 weeks following exposure. In mice exhibiting a learned helplessness phenotype, a 1-hour exposure to isoflurane, but not halothane, reversed escape failures 24 hours following burst suppression anesthesia.

CONCLUSIONS

These results are consistent with a role for cortical burst suppression in mediating the antidepressant effects of isoflurane. They provide rationale for additional mechanistic studies in relevant animal models as well as a properly controlled clinical evaluation of the therapeutic benefits associated with isoflurane anesthesia in major depressive disorder.

Network dynamics in the healthy and epileptic developing brain

Electroencephalography (EEG) allows recording of cortical activity at high temporal resolution. EEG recordings can be summarized along different dimensions using network-level quantitative measures, such as channel-to-channel correlation, or band power distributions across channels. These reveal network patterns that unfold over a range of different timescales and can be tracked dynamically. Here we describe the dynamics of network state transitions in EEG recordings of spontaneous brain activity in normally developing infants and infants with severe early infantile epileptic encephalopathies (n = 8, age: 1–8 months). We describe differences in measures of EEG dynamics derived from band power, and correlation-based summaries of network-wide brain activity. We further show that EEGs from different patient groups and controls may be distinguishable on a small set of the novel quantitative measures introduced here, which describe dynamic network state switching. Quantitative measures related to the sharpness of switching from one correlation pattern to another show the largest differences between groups. These findings reveal that the early epileptic encephalopathies are associated with characteristic dynamic features at the network level. Quantitative network-based analyses like the one presented here may in the future inform the clinical use of quantitative EEG for diagnosis.