University of Twente Student Theses

Login

The influence of feedforward inhibition on spontaneous and evoked activity of coupled neural mass models

Jansen Klomp, L.F. (2021) The influence of feedforward inhibition on spontaneous and evoked activity of coupled neural mass models.

[img] PDF
16MB
Abstract:Epilepsy is a common neurological disorder affecting between 0.4-1% of the population. When anti-epileptic drugs are not viable and a focus from which seizures arise can be assigned, removing the epileptogenic zone through surgery is an option. This type of surgery currently has a 40-80% success rate, depending on the center, cohort and epilepsy and surgery characteristics. Therefore, it is desirable to improve the methods used for this surgery. A way to improve epilepsy surgery is to construct patient-specific computational models that can help predict the outcome of surgery. In recent years, the notion has emerged that epilepsy must be seen as a network disorder rather than a localized disorder. Hence, the constructed computational model should include the connections between different areas of the brain. During the workup to surgery, Single Pulse Electrical Stimulation (SPES) can be used to monitor connectivity in the brain. During SPES, monophasic electrical pulses (0.2 Hz, 1 ms, 4-8 mA) are applied to electrodes placed on the brain. These pulses can evoke responses in other areas of the brain. Early responses to the stimulations (ERs, <100 ms) can be linked to connectivity of the brain. Delayed responses (DRs, >100 ms) are seen as biomarkers of the epileptogenic cortex. In literature it has been shown that it is possible to simulate both the early and delayed responses to SPES in a network of just two neural mass models using feedforward inhibition. This study showed that disinhibition led to DRs but did not consider reciprocal connections nor larger networks. In this thesis, we first investigate the influence of feedforward inhibition on spontaneous activity in a network of two reciprocally coupled neural mass models through simulations and a bifurcation analysis. We show that feedforward inhibition has a significant effect on the spontaneous activity of coupled neural mass models and the generation of activity associated with epilepsy. Moreover, a key step in developing a patient-specific neural mass model is to reproduce the patients' ERs and DRs through stimulation within the model. Hence, we want to understand better how stimulation-induced activity propagates through the network. In this thesis we investigate evoked responses in small networks through simulations to find the influence of specific parameters on evoked activity. We use an evolutionary algorithm to fit parameters for networks of 12 nodes. The results show that it is possible to fit desired ERs and part of the desired DRs in small networks of neural mass models. Moreover, optimizing parameters shows that most desired ERs and some desired DRs can be fitted in patient-specific networks based on clinical data collected at the University Medical Centre Utrecht. These models may later be used to study the onset and propagation of seizures, and possibly assist in delineating epileptiform tissue, thus improving epilepsy surgery.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:31 mathematics, 44 medicine
Programme:Applied Mathematics MSc (60348)
Link to this item:http://purl.utwente.nl/essays/88502
Export this item as:BibTeX
EndNote
HTML Citation
Reference Manager

 

Repository Staff Only: item control page