98 research outputs found
Emergence of spatially heterogeneous burst suppression in a neural field model of electrocortical activity
Burst suppression in the electroencephalogram (EEG) is a well-described phenomenon that occurs during deep anesthesia, as well as in a variety of congenital and acquired brain insults. Classically it is thought of as spatially synchronous, quasi-periodic bursts of high amplitude EEG separated by low amplitude activity. However, its characterization as a “global brain state” has been challenged by recent results obtained with intracranial electrocortigraphy. Not only does it appear that burst suppression activity is highly asynchronous across cortex, but also that it may occur in isolated regions of circumscribed spatial extent. Here we outline a realistic neural field model for burst suppression by adding a slow process of synaptic resource depletion and recovery, which is able to reproduce qualitatively the empirically observed features during general anesthesia at the whole cortex level. Simulations reveal heterogeneous bursting over the model cortex and complex spatiotemporal dynamics during simulated anesthetic action, and provide forward predictions of neuroimaging signals for subsequent empirical comparisons and more detailed characterization. Because burst suppression corresponds to a dynamical end-point of brain activity, theoretically accounting for its spatiotemporal emergence will vitally contribute to efforts aimed at clarifying whether a common physiological trajectory is induced by the actions of general anesthetic agents. We have taken a first step in this direction by showing that a neural field model can qualitatively match recent experimental data that indicate spatial differentiation of burst suppression activity across cortex
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
Identification of A Neural Mass Model of Burst Suppression
Burst suppression includes alternating patterns of silent and fast spike activities in neuronal activities observable (in micro or macro scale) electro-physiological recordings. Biological models of burst suppression are given as dynamical systems with slow and fast states. The aim of this paper is to give a method to identify parameters of a mesoscopic model of burst suppression that can provide insights into study underlying generators of intracranial electroencephalogram (iEEG) data. An optimisation technique based upon a genetic algorithm (GA) is employed to find feasible model parameters to replicate burst patterns in the iEEG data with paroxysmal transitions. Then, a continuous-discrete unscented Kalman filter (CD-UKF) is used to infer hidden states of the model and to enhance the identification results from the GA. The results show promise in finding the model parameters of a partially observed mesoscopic model of burst suppression
General Anesthetic Conditions Induce Network Synchrony and Disrupt Sensory Processing in the Cortex
General anesthetics are commonly used in animal models to study how sensory
signals are represented in the brain. Here, we used two-photon (2P) calcium
activity imaging with cellular resolution to investigate how neuronal activity
in layer 2/3 of the mouse barrel cortex is modified under the influence of
different concentrations of chemically distinct general anesthetics. Our
results show that a high isoflurane dose induces synchrony in local neuronal
networks and these cortical activity patterns closely resemble those observed
in EEG recordings under deep anesthesia. Moreover, ketamine and urethane also
induced similar activity patterns. While investigating the effects of deep
isoflurane anesthesia on whisker and auditory evoked responses in the barrel
cortex, we found that dedicated spatial regions for sensory signal processing
become disrupted. We propose that our isoflurane-2P imaging paradigm can serve
as an attractive model system to dissect cellular and molecular mechanisms
that induce the anesthetic state, and it might also provide important insight
into sleep-like brain states and consciousness
Etiology of Burst Suppression EEG Patterns
Burst-suppression electroencephalography (EEG) patterns of electrical activity, characterized by intermittent high-power broad-spectrum oscillations alternating with isoelectricity, have long been observed in the human brain during general anesthesia, hypothermia, coma and early infantile encephalopathy. Recently, commonalities between conditions associated with burst-suppression patterns have led to new insights into the origin of burst-suppression EEG patterns, their effects on the brain, and their use as a therapeutic tool for protection against deleterious neural states. These insights have been further supported by advances in mechanistic modeling of burst suppression. In this Perspective, we review the origins of burst-suppression patterns and use recent insights to weigh evidence in the controversy regarding the extent to which burst-suppression patterns observed during profound anesthetic-induced brain inactivation are associated with adverse clinical outcomes. Whether the clinical intent is to avoid or maintain the brain in a state producing burst-suppression patterns, monitoring and controlling neural activity presents a technical challenge. We discuss recent advances that enable monitoring and control of burst suppression
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.National Institutes of Health (U.S.) (Grant DP2-OD006454)National Institutes of Health (U.S.) (Grant DP1-OD003646)National Institutes of Health (U.S.) (Grant TR01-GM104948
Population based models of cortical drug response: insights from anaesthesia
A great explanatory gap lies between the molecular pharmacology of psychoactive agents and the neurophysiological changes they induce, as recorded by neuroimaging modalities. Causally relating the cellular actions of psychoactive compounds to their influence on population activity is experimentally challenging. Recent developments in the dynamical modelling of neural tissue have attempted to span this explanatory gap between microscopic targets and their macroscopic neurophysiological effects via a range of biologically plausible dynamical models of cortical tissue. Such theoretical models allow exploration of neural dynamics, in particular their modification by drug action. The ability to theoretically bridge scales is due to a biologically plausible averaging of cortical tissue properties. In the resulting macroscopic neural field, individual neurons need not be explicitly represented (as in neural networks). The following paper aims to provide a non-technical introduction to the mean field population modelling of drug action and its recent successes in modelling anaesthesia
Predictive Agent-Based Crowd Model Design Using Decentralized Control Systems
As a complex system, crowd dynamics emerge bottom-up from the local interactions between pedestrians as component subsystems. This article proposes a predictive agent-based crowd simulation model to analyze the outcomes of emergency evacuation scenarios taking into account collisions between pedestrians, smoke, fire sprinklers, and exit indicators. The crowd model is based on a decentralized control system structure, where each pedestrian agent is governed through a deliberative-reactive control architecture. The simulation model for evacuation includes a routing-based control system for dynamic-guided evacuation. A design case illustrates the modeling process. Results show that the crowd simulation model based on agent autonomy and local interactions is able to generate higher level crowd dynamics through emergence.publishedVersio
State-Dependent Cortical Network Dynamics
Neuropsychiatric illness represents a major health burden in the United States with a paucity of effective treatment. Many neuropsychiatric illnesses are network disorders, exhibiting aberrant organization of coordinated activity within and between brain areas. Cortical oscillations, arising from the synchronized activity of groups of neurons, are important in mediating both local and long-range communication in the brain and are particularly affected in neuropsychiatric diseases. A promising treatment approach for such network disorders entails ‘correcting’ abnormal oscillatory activity through non-invasive brain stimulation. However, we lack a clear understanding of the functional role of oscillatory activity in both health and disease. Thus, basic science and translational work is needed to elucidate the role of oscillatory activity and other network dynamics in neuronal processing and behavior. Organized activity in the brain occurs at many spatial and temporal scales, ranging from the millisecond duration of individual action potentials to the daily circadian rhythm. The studies comprising this dissertation focused on organization in cortex at the time scale of milliseconds, assessing local field potential and spiking activity, and contribute to understanding (1) the effects of non-invasive brain stimulation on behavioral responses, (2) network dynamics within and across cortical areas during different states, and (3) how oscillatory activity organizes spiking activity locally and long-range during sustained attention. Taken together, this work provides insight into the physiological organization of network dynamics and can provide the basis for future rational design of non-invasive brain stimulation treatments.Doctor of Philosoph
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