Optimization of epilepsy surgery through virtual resections on individual structural brain networks

Abstract The success of epilepsy surgery in patients with refractory epilepsy depends upon correct identification of the epileptogenic zone (EZ) and an optimal choice of the resection area. In this study we developed individualized computational models based upon structural brain networks to explore...

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Autores principales:	Ida A. Nissen, Ana P. Millán, Cornelis J. Stam, Elisabeth C. W. van Straaten, Linda Douw, Petra J. W. Pouwels, Sander Idema, Johannes C. Baayen, Demetrios Velis, Piet Van Mieghem, Arjan Hillebrand
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/39efc0e6af774a4f9ef9ce928e2d7242
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spelling	oai:doaj.org-article:39efc0e6af774a4f9ef9ce928e2d72422021-12-02T18:48:01ZOptimization of epilepsy surgery through virtual resections on individual structural brain networks10.1038/s41598-021-98046-02045-2322https://doaj.org/article/39efc0e6af774a4f9ef9ce928e2d72422021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98046-0https://doaj.org/toc/2045-2322Abstract The success of epilepsy surgery in patients with refractory epilepsy depends upon correct identification of the epileptogenic zone (EZ) and an optimal choice of the resection area. In this study we developed individualized computational models based upon structural brain networks to explore the impact of different virtual resections on the propagation of seizures. The propagation of seizures was modelled as an epidemic process [susceptible-infected-recovered (SIR) model] on individual structural networks derived from presurgical diffusion tensor imaging in 19 patients. The candidate connections for the virtual resection were all connections from the clinically hypothesized EZ, from which the seizures were modelled to start, to other brain areas. As a computationally feasible surrogate for the SIR model, we also removed the connections that maximally reduced the eigenvector centrality (EC) (large values indicate network hubs) of the hypothesized EZ, with a large reduction meaning a large effect. The optimal combination of connections to be removed for a maximal effect were found using simulated annealing. For comparison, the same number of connections were removed randomly, or based on measures that quantify the importance of a node or connection within the network. We found that 90% of the effect (defined as reduction of EC of the hypothesized EZ) could already be obtained by removing substantially less than 90% of the connections. Thus, a smaller, optimized, virtual resection achieved almost the same effect as the actual surgery yet at a considerably smaller cost, sparing on average 27.49% (standard deviation: 4.65%) of the connections. Furthermore, the maximally effective connections linked the hypothesized EZ to hubs. Finally, the optimized resection was equally or more effective than removal based on structural network characteristics both regarding reducing the EC of the hypothesized EZ and seizure spreading. The approach of using reduced EC as a surrogate for simulating seizure propagation can suggest more restrictive resection strategies, whilst obtaining an almost optimal effect on reducing seizure propagation, by taking into account the unique topology of individual structural brain networks of patients.Ida A. NissenAna P. MillánCornelis J. StamElisabeth C. W. van StraatenLinda DouwPetra J. W. PouwelsSander IdemaJohannes C. BaayenDemetrios VelisPiet Van MieghemArjan HillebrandNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-18 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ida A. Nissen Ana P. Millán Cornelis J. Stam Elisabeth C. W. van Straaten Linda Douw Petra J. W. Pouwels Sander Idema Johannes C. Baayen Demetrios Velis Piet Van Mieghem Arjan Hillebrand Optimization of epilepsy surgery through virtual resections on individual structural brain networks
description	Abstract The success of epilepsy surgery in patients with refractory epilepsy depends upon correct identification of the epileptogenic zone (EZ) and an optimal choice of the resection area. In this study we developed individualized computational models based upon structural brain networks to explore the impact of different virtual resections on the propagation of seizures. The propagation of seizures was modelled as an epidemic process [susceptible-infected-recovered (SIR) model] on individual structural networks derived from presurgical diffusion tensor imaging in 19 patients. The candidate connections for the virtual resection were all connections from the clinically hypothesized EZ, from which the seizures were modelled to start, to other brain areas. As a computationally feasible surrogate for the SIR model, we also removed the connections that maximally reduced the eigenvector centrality (EC) (large values indicate network hubs) of the hypothesized EZ, with a large reduction meaning a large effect. The optimal combination of connections to be removed for a maximal effect were found using simulated annealing. For comparison, the same number of connections were removed randomly, or based on measures that quantify the importance of a node or connection within the network. We found that 90% of the effect (defined as reduction of EC of the hypothesized EZ) could already be obtained by removing substantially less than 90% of the connections. Thus, a smaller, optimized, virtual resection achieved almost the same effect as the actual surgery yet at a considerably smaller cost, sparing on average 27.49% (standard deviation: 4.65%) of the connections. Furthermore, the maximally effective connections linked the hypothesized EZ to hubs. Finally, the optimized resection was equally or more effective than removal based on structural network characteristics both regarding reducing the EC of the hypothesized EZ and seizure spreading. The approach of using reduced EC as a surrogate for simulating seizure propagation can suggest more restrictive resection strategies, whilst obtaining an almost optimal effect on reducing seizure propagation, by taking into account the unique topology of individual structural brain networks of patients.
format	article
author	Ida A. Nissen Ana P. Millán Cornelis J. Stam Elisabeth C. W. van Straaten Linda Douw Petra J. W. Pouwels Sander Idema Johannes C. Baayen Demetrios Velis Piet Van Mieghem Arjan Hillebrand
author_facet	Ida A. Nissen Ana P. Millán Cornelis J. Stam Elisabeth C. W. van Straaten Linda Douw Petra J. W. Pouwels Sander Idema Johannes C. Baayen Demetrios Velis Piet Van Mieghem Arjan Hillebrand
author_sort	Ida A. Nissen
title	Optimization of epilepsy surgery through virtual resections on individual structural brain networks
title_short	Optimization of epilepsy surgery through virtual resections on individual structural brain networks
title_full	Optimization of epilepsy surgery through virtual resections on individual structural brain networks
title_fullStr	Optimization of epilepsy surgery through virtual resections on individual structural brain networks
title_full_unstemmed	Optimization of epilepsy surgery through virtual resections on individual structural brain networks
title_sort	optimization of epilepsy surgery through virtual resections on individual structural brain networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/39efc0e6af774a4f9ef9ce928e2d7242
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Optimization of epilepsy surgery through virtual resections on individual structural brain networks

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