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Optimization of epilepsy surgery through virtual resections on individual structural brain networks
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 impa...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8463605/ https://www.ncbi.nlm.nih.gov/pubmed/34561483 http://dx.doi.org/10.1038/s41598-021-98046-0 |
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author | Nissen, Ida A. Millán, Ana P. Stam, Cornelis J. van Straaten, Elisabeth C. W. Douw, Linda Pouwels, Petra J. W. Idema, Sander Baayen, Johannes C. Velis, Demetrios Van Mieghem, Piet Hillebrand, Arjan |
author_facet | Nissen, Ida A. Millán, Ana P. Stam, Cornelis J. van Straaten, Elisabeth C. W. Douw, Linda Pouwels, Petra J. W. Idema, Sander Baayen, Johannes C. Velis, Demetrios Van Mieghem, Piet Hillebrand, Arjan |
author_sort | Nissen, Ida A. |
collection | PubMed |
description | 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 | Online Article Text |
id | pubmed-8463605 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-84636052021-09-27 Optimization of epilepsy surgery through virtual resections on individual structural brain networks Nissen, Ida A. Millán, Ana P. Stam, Cornelis J. van Straaten, Elisabeth C. W. Douw, Linda Pouwels, Petra J. W. Idema, Sander Baayen, Johannes C. Velis, Demetrios Van Mieghem, Piet Hillebrand, Arjan Sci Rep Article 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. Nature Publishing Group UK 2021-09-24 /pmc/articles/PMC8463605/ /pubmed/34561483 http://dx.doi.org/10.1038/s41598-021-98046-0 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Nissen, Ida A. Millán, Ana P. Stam, Cornelis J. van Straaten, Elisabeth C. W. Douw, Linda Pouwels, Petra J. W. Idema, Sander Baayen, Johannes C. Velis, Demetrios Van Mieghem, Piet Hillebrand, Arjan Optimization of epilepsy surgery through virtual resections on individual structural brain networks |
title | 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_short | 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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8463605/ https://www.ncbi.nlm.nih.gov/pubmed/34561483 http://dx.doi.org/10.1038/s41598-021-98046-0 |
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