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Modelling the effect of cardiac arrest on neural activity using an energy-dependent neural mass model based on ion concentrations.

Ligtenstein, S.L.B. (2020) Modelling the effect of cardiac arrest on neural activity using an energy-dependent neural mass model based on ion concentrations.

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Abstract:Background: Significant changes in rhythm are visible on electroencephalography (EEG) recordings during cardiac arrest. Our aim is to improve the understanding of underlying pathophysiological mechanisms causing these EEG changes using computational modelling. To couple the computational outcomes to clinical EEG recordings, we use a neural mass model. Hence, the primary objective of this study is to construct and analyse an energy-dependent neural mass model based on ion concentrations to examine the effect of energy depletion during cardiac arrest on neural activity. Methods: We constructed an ion-based neural mass model comprising an excitatory and inhibitory neural population and excitatory external input. Energy (adenosine triphosphate (ATP)) dependency was accomplished by including ATP-dependent Na+-K+-ATPase (NKA) and an adapted firing rate function for the two neural populations. In addition, a network of coupled spiking single cells was constructed to which the outcome of the neural mass model was compared. NKAs were included in this second model as well, offering the possibility to tune the strength of these pumps during simulation. We studied the responses of both models to an excitatory external input, to different levels of ATP deprivation and to recovery after deprivation and restoration of the ATP supply. We also investigated a mechanism to promote this recovery. Furthermore, the outcome of both models was compared to the EEG signal of a patient recorded during cardiac arrest. Lastly, both models were analysed to gain more insight into the dynamic behaviour of the models. Results: Periodic behaviour is observed in both models as a response to transient external input indicating normal behaviour of the models under physiological conditions. The strength of the external input determines the qualitative dynamics of the models. The response of the models to ATP deprivation depends on the depth of this deprivation. ATP levels below a critical point lead to depolarization of the membrane potential, which is visible on the simulated EEG signals as a large slow wave. Based on the real EEG signal, the hypothesis was to observe slowing of the simulated EEG signals during ATP deprivation. However, the frequency of the periodic behaviour initially increased in both models. In case of total ATP cessation, this was followed by EEG suppression. Besides, the membrane potentials of the network model recovered after ATP deprivation only if voltage-gated sodium channels were blocked. The membrane potentials of the neural mass model recovered as soon as ATP levels increased, even without blocking voltage-gated sodium channels. Conclusion: The newly constructed models provide a unique extension in the field of computational neuroscience by including NKAs and ATP dependency at a macrosopic level. Both models show similar behaviour as other computational models. However, complete ATP cessation results in changes of the EEG rhythm in both models which do not correspond to the changes seen in the real EEG. We recommend further research to extend the models e.g. by incorporating more ATP-dependent processes, to improve the understanding of the EEG changes visible during cardiac arrest. Although there is still a gap between the models and clinical use, this study provides a starting point for future models to investigate and understand pathophysiological mechanisms of neural diseases caused by ATP deprivation and/or imbalance of ion concentrations.
Item Type:Essay (Master)
Faculty:TNW: Science and Technology
Subject:31 mathematics, 44 medicine
Programme:Technical Medicine MSc (60033)
Link to this item:http://purl.utwente.nl/essays/81048
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