University of Twente Student Theses


Burst-suppression in next-generation neural mass models and EEG

Lip, S.G.J. (2023) Burst-suppression in next-generation neural mass models and EEG.

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Abstract:Burst-suppression (BS) is a typical electroencephalogram pattern seen in patients during postanoxic coma. It is characterised by an alternation of bursts of activity and iso-electricity. Two types exist, BS with identical bursts (BSIB) and BS with non-identical bursts (BSNIB). The mechanisms underlying these two types are poorly understood. Neuronal network models are adequate tools for investigating mechanisms underlying observed behaviour of neuronal populations. We implemented a spiking neural network of Quadratic-Integrate-and-Fire neurons and derived its exact neural mass equivalent. In these models, we identified bursting dynamics and investigated the effect of network architecture, spike-frequency adaptation (SFA) and inhibition on the similarity of bursts within a simulation. A fold-fold burster exists in the neural mass model and the spiking neural network. Introducing sparser coupling in the spiking neural network caused more similar bursts within a model simulation. Neuron-individual SFA has a large effect on burst similarity. The main conclusion is that SFA in the spiking neural network and the neural mass model are not equivalent.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:31 mathematics, 42 biology, 44 medicine
Programme:Applied Mathematics MSc (60348)
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