Computational Modelling and Electrical Stimulation for Epilepsy Surgery

Jurgen Hebbink

Research output: ThesisPhD Thesis - Research UT, graduation UTAcademic

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Abstract

Epilepsy surgery may provide a cure for patients with focal epilepsy. Where epilepsy surgery was traditionally focused on removing pathological cortex in the anatomic sense, recent developments suggests that epilepsy is also a brain network disease. Computational models for epilepsy offer a framework to study the joint effect of local, intrinsic epileptogenicity and network interaction and allow to incorporate patient-specific information. Single pulse electrical stimulation (SPES) might be an interesting technique to obtain this patient-specific information. SPES evokes early responses, representing connectivity, and delayed responses which are a biomarker for epileptogenicity. In this thesis the added value of combining computational network models and SPES for epilepsy surgery is investigated.

First, we study the effects of surgery on the seizure rate in simple, small, computational network models. Removal of normal populations located at a crucial spot in the network, is typically more effective in reducing seizure rate than removing a hyperexcitable population.

Second, we compare connectivity probed using SPES with two traditional methods for connectivity. Strong connections in the cross-correlation network form more or less a subset of the SPES network, while Granger causality and SPES networks are related more weakly. Connectivity known to exist between two major hubs in the language circuit, is only found in SPES networks.

Next, we study how SPES may trigger delayed responses (DRs) using a neural mass model. This model suddenly generates large, non-linear responses, mimicking DRs, when input to a neural mass falls below a threshold. In combination with noisy background input this threshold explains the typical stochastic appearance of DRs. Using slow-fast analysis we reveal the origin of this sudden transition.

Computational network models offer an interesting framework to explore effects of epilepsy surgery. SPES networks are of interest to use in these models as they incorporate more physiological long-range connections compared to two traditional methods. Delayed responses to SPES are a candidate marker to tune local excitability in these models, with the advantage that they can be probed actively. In conclusion, SPES and patient-specific computational network models form a promising combination that have great potential to improve epilepsy surgery.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • University of Twente
Supervisors/Advisors
  • van Gils, Stephanus A., Supervisor
  • Leijten, Frans S.S., Supervisor
  • Meijer, Hil Gaétan Ellart, Supervisor
  • Huiskamp, Geertjan J.M., Advisor
Thesis sponsors
Award date4 Sep 2019
Place of PublicationEnschede
Publisher
Print ISBNs978-90-365-4832-8
DOIs
Publication statusPublished - 2019

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Electric Stimulation
Epilepsy
Seizures
Partial Epilepsy
Brain Diseases
Causality
Population
Language
Biomarkers

Cite this

Hebbink, Jurgen. / Computational Modelling and Electrical Stimulation for Epilepsy Surgery. Enschede : University of Twente, 2019. 148 p.
@phdthesis{0c4a64a6026e4f5d9c14b5b30a32be9b,
title = "Computational Modelling and Electrical Stimulation for Epilepsy Surgery",
abstract = "Epilepsy surgery may provide a cure for patients with focal epilepsy. Where epilepsy surgery was traditionally focused on removing pathological cortex in the anatomic sense, recent developments suggests that epilepsy is also a brain network disease. Computational models for epilepsy offer a framework to study the joint effect of local, intrinsic epileptogenicity and network interaction and allow to incorporate patient-specific information. Single pulse electrical stimulation (SPES) might be an interesting technique to obtain this patient-specific information. SPES evokes early responses, representing connectivity, and delayed responses which are a biomarker for epileptogenicity. In this thesis the added value of combining computational network models and SPES for epilepsy surgery is investigated.First, we study the effects of surgery on the seizure rate in simple, small, computational network models. Removal of normal populations located at a crucial spot in the network, is typically more effective in reducing seizure rate than removing a hyperexcitable population.Second, we compare connectivity probed using SPES with two traditional methods for connectivity. Strong connections in the cross-correlation network form more or less a subset of the SPES network, while Granger causality and SPES networks are related more weakly. Connectivity known to exist between two major hubs in the language circuit, is only found in SPES networks. Next, we study how SPES may trigger delayed responses (DRs) using a neural mass model. This model suddenly generates large, non-linear responses, mimicking DRs, when input to a neural mass falls below a threshold. In combination with noisy background input this threshold explains the typical stochastic appearance of DRs. Using slow-fast analysis we reveal the origin of this sudden transition.Computational network models offer an interesting framework to explore effects of epilepsy surgery. SPES networks are of interest to use in these models as they incorporate more physiological long-range connections compared to two traditional methods. Delayed responses to SPES are a candidate marker to tune local excitability in these models, with the advantage that they can be probed actively. In conclusion, SPES and patient-specific computational network models form a promising combination that have great potential to improve epilepsy surgery.",
author = "Jurgen Hebbink",
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language = "English",
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Hebbink, J 2019, 'Computational Modelling and Electrical Stimulation for Epilepsy Surgery', Doctor of Philosophy, University of Twente, Enschede. https://doi.org/10.3990/1.9789036548328

Computational Modelling and Electrical Stimulation for Epilepsy Surgery. / Hebbink, Jurgen.

Enschede : University of Twente, 2019. 148 p.

Research output: ThesisPhD Thesis - Research UT, graduation UTAcademic

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AB - Epilepsy surgery may provide a cure for patients with focal epilepsy. Where epilepsy surgery was traditionally focused on removing pathological cortex in the anatomic sense, recent developments suggests that epilepsy is also a brain network disease. Computational models for epilepsy offer a framework to study the joint effect of local, intrinsic epileptogenicity and network interaction and allow to incorporate patient-specific information. Single pulse electrical stimulation (SPES) might be an interesting technique to obtain this patient-specific information. SPES evokes early responses, representing connectivity, and delayed responses which are a biomarker for epileptogenicity. In this thesis the added value of combining computational network models and SPES for epilepsy surgery is investigated.First, we study the effects of surgery on the seizure rate in simple, small, computational network models. Removal of normal populations located at a crucial spot in the network, is typically more effective in reducing seizure rate than removing a hyperexcitable population.Second, we compare connectivity probed using SPES with two traditional methods for connectivity. Strong connections in the cross-correlation network form more or less a subset of the SPES network, while Granger causality and SPES networks are related more weakly. Connectivity known to exist between two major hubs in the language circuit, is only found in SPES networks. Next, we study how SPES may trigger delayed responses (DRs) using a neural mass model. This model suddenly generates large, non-linear responses, mimicking DRs, when input to a neural mass falls below a threshold. In combination with noisy background input this threshold explains the typical stochastic appearance of DRs. Using slow-fast analysis we reveal the origin of this sudden transition.Computational network models offer an interesting framework to explore effects of epilepsy surgery. SPES networks are of interest to use in these models as they incorporate more physiological long-range connections compared to two traditional methods. Delayed responses to SPES are a candidate marker to tune local excitability in these models, with the advantage that they can be probed actively. In conclusion, SPES and patient-specific computational network models form a promising combination that have great potential to improve epilepsy surgery.

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