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Emergence of spatially heterogeneous burst suppression in a neural field model of electrocortical activity.

Bojak I, Stoyanov ZV, Liley DT - Front Syst Neurosci (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Systems Neuroscience Research Group, School of Systems Engineering, University of Reading Reading, UK.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

(A) Imposed concentration of isoflurane (red curve), and the he response (blue curve) at the cortical location indicated by black arrows in the snapshot panels below. Different plateaus of concentration are labeled “C,” “D,” “E,” and “F.” Arrows point to the central times of the corresponding time series shown below. (B) PSDs of he averaged over the entire grid and normed to unit area for plateaus “C” (blue), “D” (green), “E” (red), and “F” (cyan). The motion of the alpha peak to lower frequencies persists qualitatively into the burst suppression phase “E” at much increased power. (C1) Snapshot of the he activity of the cortical surface at 0 MAC isoflurane. The size of he is indicated by both height and color, cf. the color bar. A black arrow shows the position from which the corresponding time series were recorded. (C2) Time series of he (blue) and Γee (green) over the 10 s of the “C” plateau. Regular alpha rhythms in he and slow Γee oscillations around the standard value Γ0ee can be seen. (D1) Snapshot at 0.5 MAC. (D2) Time series of the “D” plateau. The oscillations of he have larger amplitude at a lower average. The slow Γee oscillations now occur at an elevated level. (E1) Snapshot at 1 MAC. Burst suppression patterns have emerged and move across the cortical surface. (E2) Time series of the “E” plateau. Burst suppression is apparent both in he and Γee, with a rapid drop in Γee caused by the strongest he oscillations. An animation of this simulation is provided as Movie 1 in the Supplementary Material.
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Figure 2: (A) Imposed concentration of isoflurane (red curve), and the he response (blue curve) at the cortical location indicated by black arrows in the snapshot panels below. Different plateaus of concentration are labeled “C,” “D,” “E,” and “F.” Arrows point to the central times of the corresponding time series shown below. (B) PSDs of he averaged over the entire grid and normed to unit area for plateaus “C” (blue), “D” (green), “E” (red), and “F” (cyan). The motion of the alpha peak to lower frequencies persists qualitatively into the burst suppression phase “E” at much increased power. (C1) Snapshot of the he activity of the cortical surface at 0 MAC isoflurane. The size of he is indicated by both height and color, cf. the color bar. A black arrow shows the position from which the corresponding time series were recorded. (C2) Time series of he (blue) and Γee (green) over the 10 s of the “C” plateau. Regular alpha rhythms in he and slow Γee oscillations around the standard value Γ0ee can be seen. (D1) Snapshot at 0.5 MAC. (D2) Time series of the “D” plateau. The oscillations of he have larger amplitude at a lower average. The slow Γee oscillations now occur at an elevated level. (E1) Snapshot at 1 MAC. Burst suppression patterns have emerged and move across the cortical surface. (E2) Time series of the “E” plateau. Burst suppression is apparent both in he and Γee, with a rapid drop in Γee caused by the strongest he oscillations. An animation of this simulation is provided as Movie 1 in the Supplementary Material.

Mentions: We explore the influence of isoflurane on the model in a long simulation run presented in Figure 2. The entire simulation also has been animated as Movie 1, included in the Supplementary Material. In Figure 2A we show the time course of the isoflurane concentration that we have imposed. First the system is run free of anesthesia (0 MAC) for 10 s. We call this the first plateau in the following. The equilibrium values of the system are used as initial conditions. Hence there are no transient dynamics, which allows us to estimate a power spectral density (PSD) from the he time series. Then we increase the concentration linearly to 0.5 MAC (equivalent to 0.1215 mM or 0.585% inspired at normal body temperature) over 10 s, and keep the system at this concentration for another 10 s. This second plateau corresponds to a light anesthesia state, without burst suppression, and again we can estimate a PSD here. Next we increase the concentration linearly to 1.0 MAC (equivalent to 0.243 mM or 1.17% inspired), and keep the system there for 40 s. This third plateau corresponds to a state of deep anesthesia, with burst suppression, and we can estimate a PSD here as well. After that, we increase the isoflurane concentration again for 10 s to 1.5 MAC (equivalent to 0.3645 mM or 1.755% inspired), and maintain it at this value for 10 s. Bursting is abolished at this fourth plateau, and we compute another PSD here. Finally, we raise the concentration for another 20 s up to 2.5 MAC (equivalent to 0.6075 mM or 2.925% inspired). This demonstrates that the system has finally returned to a regime without bursting.


Emergence of spatially heterogeneous burst suppression in a neural field model of electrocortical activity.

Bojak I, Stoyanov ZV, Liley DT - Front Syst Neurosci (2015)

(A) Imposed concentration of isoflurane (red curve), and the he response (blue curve) at the cortical location indicated by black arrows in the snapshot panels below. Different plateaus of concentration are labeled “C,” “D,” “E,” and “F.” Arrows point to the central times of the corresponding time series shown below. (B) PSDs of he averaged over the entire grid and normed to unit area for plateaus “C” (blue), “D” (green), “E” (red), and “F” (cyan). The motion of the alpha peak to lower frequencies persists qualitatively into the burst suppression phase “E” at much increased power. (C1) Snapshot of the he activity of the cortical surface at 0 MAC isoflurane. The size of he is indicated by both height and color, cf. the color bar. A black arrow shows the position from which the corresponding time series were recorded. (C2) Time series of he (blue) and Γee (green) over the 10 s of the “C” plateau. Regular alpha rhythms in he and slow Γee oscillations around the standard value Γ0ee can be seen. (D1) Snapshot at 0.5 MAC. (D2) Time series of the “D” plateau. The oscillations of he have larger amplitude at a lower average. The slow Γee oscillations now occur at an elevated level. (E1) Snapshot at 1 MAC. Burst suppression patterns have emerged and move across the cortical surface. (E2) Time series of the “E” plateau. Burst suppression is apparent both in he and Γee, with a rapid drop in Γee caused by the strongest he oscillations. An animation of this simulation is provided as Movie 1 in the Supplementary Material.
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4341547&req=5

Figure 2: (A) Imposed concentration of isoflurane (red curve), and the he response (blue curve) at the cortical location indicated by black arrows in the snapshot panels below. Different plateaus of concentration are labeled “C,” “D,” “E,” and “F.” Arrows point to the central times of the corresponding time series shown below. (B) PSDs of he averaged over the entire grid and normed to unit area for plateaus “C” (blue), “D” (green), “E” (red), and “F” (cyan). The motion of the alpha peak to lower frequencies persists qualitatively into the burst suppression phase “E” at much increased power. (C1) Snapshot of the he activity of the cortical surface at 0 MAC isoflurane. The size of he is indicated by both height and color, cf. the color bar. A black arrow shows the position from which the corresponding time series were recorded. (C2) Time series of he (blue) and Γee (green) over the 10 s of the “C” plateau. Regular alpha rhythms in he and slow Γee oscillations around the standard value Γ0ee can be seen. (D1) Snapshot at 0.5 MAC. (D2) Time series of the “D” plateau. The oscillations of he have larger amplitude at a lower average. The slow Γee oscillations now occur at an elevated level. (E1) Snapshot at 1 MAC. Burst suppression patterns have emerged and move across the cortical surface. (E2) Time series of the “E” plateau. Burst suppression is apparent both in he and Γee, with a rapid drop in Γee caused by the strongest he oscillations. An animation of this simulation is provided as Movie 1 in the Supplementary Material.
Mentions: We explore the influence of isoflurane on the model in a long simulation run presented in Figure 2. The entire simulation also has been animated as Movie 1, included in the Supplementary Material. In Figure 2A we show the time course of the isoflurane concentration that we have imposed. First the system is run free of anesthesia (0 MAC) for 10 s. We call this the first plateau in the following. The equilibrium values of the system are used as initial conditions. Hence there are no transient dynamics, which allows us to estimate a power spectral density (PSD) from the he time series. Then we increase the concentration linearly to 0.5 MAC (equivalent to 0.1215 mM or 0.585% inspired at normal body temperature) over 10 s, and keep the system at this concentration for another 10 s. This second plateau corresponds to a light anesthesia state, without burst suppression, and again we can estimate a PSD here. Next we increase the concentration linearly to 1.0 MAC (equivalent to 0.243 mM or 1.17% inspired), and keep the system there for 40 s. This third plateau corresponds to a state of deep anesthesia, with burst suppression, and we can estimate a PSD here as well. After that, we increase the isoflurane concentration again for 10 s to 1.5 MAC (equivalent to 0.3645 mM or 1.755% inspired), and maintain it at this value for 10 s. Bursting is abolished at this fourth plateau, and we compute another PSD here. Finally, we raise the concentration for another 20 s up to 2.5 MAC (equivalent to 0.6075 mM or 2.925% inspired). This demonstrates that the system has finally returned to a regime without bursting.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Systems Neuroscience Research Group, School of Systems Engineering, University of Reading Reading, UK.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus