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A computational study of stimulus driven epileptic seizure abatement.

Taylor PN, Wang Y, Goodfellow M, Dauwels J, Moeller F, Stephani U, Baier G - PLoS ONE (2014)

Bottom Line: Active brain stimulation to abate epileptic seizures has shown mixed success.However, several factors can impact success in such a bistable setting.For the deterministic (noise-free) case, we show how the success of response to stimuli depends on the amplitude and phase of the SW cycle, in addition to the direction of the stimulus in state space.

View Article: PubMed Central - PubMed

Affiliation: School of Computing Science, Newcastle University, Newcastle upon Tyne, United Kingdom.

ABSTRACT
Active brain stimulation to abate epileptic seizures has shown mixed success. In spike-wave (SW) seizures, where the seizure and background state were proposed to coexist, single-pulse stimulations have been suggested to be able to terminate the seizure prematurely. However, several factors can impact success in such a bistable setting. The factors contributing to this have not been fully investigated on a theoretical and mechanistic basis. Our aim is to elucidate mechanisms that influence the success of single-pulse stimulation in noise-induced SW seizures. In this work, we study a neural population model of SW seizures that allows the reconstruction of the basin of attraction of the background activity as a four dimensional geometric object. For the deterministic (noise-free) case, we show how the success of response to stimuli depends on the amplitude and phase of the SW cycle, in addition to the direction of the stimulus in state space. In the case of spontaneous noise-induced seizures, the basin becomes probabilistic introducing some degree of uncertainty to the stimulation outcome while maintaining qualitative features of the noise-free case. Additionally, due to the different time scales involved in SW generation, there is substantial variation between SW cycles, implying that there may not be a fixed set of optimal stimulation parameters for SW seizures. In contrast, the model suggests an adaptive approach to find optimal stimulation parameters patient-specifically, based on real-time estimation of the position in state space. We discuss how the modelling work can be exploited to rationally design a successful stimulation protocol for the abatement of SW seizures using real-time SW detection.

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Related in: MedlinePlus

Comparison between clinical and simulated EEG.The clinical (left) and simulated (right) EEG are compared in various properties, such as the long-term time series (a,b), and seizure waveforms (c,d).
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pone-0114316-g002: Comparison between clinical and simulated EEG.The clinical (left) and simulated (right) EEG are compared in various properties, such as the long-term time series (a,b), and seizure waveforms (c,d).

Mentions: Fig. 2 (a) shows a clinical recording of a typical SWD seizure from a single EEG electrode. There is an apparently spontaneous transition from a normal irregular background state to an abnormal seizure state with large amplitude regular oscillations. The seizure stops abruptly after about 11 seconds and is followed by continued normal background activity. To account for this paroxysmal dynamics, we use the minimal model (Equation 4) of thalamo-cortical interactions. The model describes the temporal evolution of the state of four variables corresponding to the activity of populations of (i) cortical pyramidal neurons (), (ii) cortical inhibitory interneurons (), (iii) thalamo-cortical neurons (), and (iv) inhibitory (thalamic) reticular neurons () [25] (see section Model and Methods for details). The model can account for the background state of normal activity and the rhythmic SW state of abnormal activity. Parameters are set such that the background state coexists with the SW state in the absence of noisy input. The addition of noise (simulating e.g. irregular subcortical input to the cortex) results in irregular background activity and occasional noise-induced transitions to large-amplitude SW rhythms. Fig. 2 (b) shows a simulated time series for comparison with the clinical recording Fig. 2(a) . In this setting the simulated paroxysms have durations between 10–15 seconds which is common for clinical absence seizures in humans [23]. Fig. 2(c) shows a zoom into the EEG seizure state and the morphology of the SW waveform with a duration of approximately 300 msec. A zoom into the simulated seizure dynamics (Fig. 2 (d)) reveals qualitative similarity of the SW complex, its large amplitude and a duration of about 300 msec. The model thus correctly reproduces the proposed mechanism of a dynamical setting where the background state and the seizure state coexist, and are in close vicinity to each other such that noisy input induces sudden transitions to the seizure state and back again [21].


A computational study of stimulus driven epileptic seizure abatement.

Taylor PN, Wang Y, Goodfellow M, Dauwels J, Moeller F, Stephani U, Baier G - PLoS ONE (2014)

Comparison between clinical and simulated EEG.The clinical (left) and simulated (right) EEG are compared in various properties, such as the long-term time series (a,b), and seizure waveforms (c,d).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4273970&req=5

pone-0114316-g002: Comparison between clinical and simulated EEG.The clinical (left) and simulated (right) EEG are compared in various properties, such as the long-term time series (a,b), and seizure waveforms (c,d).
Mentions: Fig. 2 (a) shows a clinical recording of a typical SWD seizure from a single EEG electrode. There is an apparently spontaneous transition from a normal irregular background state to an abnormal seizure state with large amplitude regular oscillations. The seizure stops abruptly after about 11 seconds and is followed by continued normal background activity. To account for this paroxysmal dynamics, we use the minimal model (Equation 4) of thalamo-cortical interactions. The model describes the temporal evolution of the state of four variables corresponding to the activity of populations of (i) cortical pyramidal neurons (), (ii) cortical inhibitory interneurons (), (iii) thalamo-cortical neurons (), and (iv) inhibitory (thalamic) reticular neurons () [25] (see section Model and Methods for details). The model can account for the background state of normal activity and the rhythmic SW state of abnormal activity. Parameters are set such that the background state coexists with the SW state in the absence of noisy input. The addition of noise (simulating e.g. irregular subcortical input to the cortex) results in irregular background activity and occasional noise-induced transitions to large-amplitude SW rhythms. Fig. 2 (b) shows a simulated time series for comparison with the clinical recording Fig. 2(a) . In this setting the simulated paroxysms have durations between 10–15 seconds which is common for clinical absence seizures in humans [23]. Fig. 2(c) shows a zoom into the EEG seizure state and the morphology of the SW waveform with a duration of approximately 300 msec. A zoom into the simulated seizure dynamics (Fig. 2 (d)) reveals qualitative similarity of the SW complex, its large amplitude and a duration of about 300 msec. The model thus correctly reproduces the proposed mechanism of a dynamical setting where the background state and the seizure state coexist, and are in close vicinity to each other such that noisy input induces sudden transitions to the seizure state and back again [21].

Bottom Line: Active brain stimulation to abate epileptic seizures has shown mixed success.However, several factors can impact success in such a bistable setting.For the deterministic (noise-free) case, we show how the success of response to stimuli depends on the amplitude and phase of the SW cycle, in addition to the direction of the stimulus in state space.

View Article: PubMed Central - PubMed

Affiliation: School of Computing Science, Newcastle University, Newcastle upon Tyne, United Kingdom.

ABSTRACT
Active brain stimulation to abate epileptic seizures has shown mixed success. In spike-wave (SW) seizures, where the seizure and background state were proposed to coexist, single-pulse stimulations have been suggested to be able to terminate the seizure prematurely. However, several factors can impact success in such a bistable setting. The factors contributing to this have not been fully investigated on a theoretical and mechanistic basis. Our aim is to elucidate mechanisms that influence the success of single-pulse stimulation in noise-induced SW seizures. In this work, we study a neural population model of SW seizures that allows the reconstruction of the basin of attraction of the background activity as a four dimensional geometric object. For the deterministic (noise-free) case, we show how the success of response to stimuli depends on the amplitude and phase of the SW cycle, in addition to the direction of the stimulus in state space. In the case of spontaneous noise-induced seizures, the basin becomes probabilistic introducing some degree of uncertainty to the stimulation outcome while maintaining qualitative features of the noise-free case. Additionally, due to the different time scales involved in SW generation, there is substantial variation between SW cycles, implying that there may not be a fixed set of optimal stimulation parameters for SW seizures. In contrast, the model suggests an adaptive approach to find optimal stimulation parameters patient-specifically, based on real-time estimation of the position in state space. We discuss how the modelling work can be exploited to rationally design a successful stimulation protocol for the abatement of SW seizures using real-time SW detection.

Show MeSH
Related in: MedlinePlus