Quantification and Selection of Ictogenic Zones in Epilepsy Surgery
dc.contributor.author | Laiou, P | |
dc.contributor.author | Avramidis, E | |
dc.contributor.author | Lopes, MA | |
dc.contributor.author | Abela, E | |
dc.contributor.author | Müller, M | |
dc.contributor.author | Akman, OE | |
dc.contributor.author | Richardson, MP | |
dc.contributor.author | Rummel, C | |
dc.contributor.author | Schindler, K | |
dc.contributor.author | Goodfellow, M | |
dc.date.accessioned | 2019-11-20T16:11:21Z | |
dc.date.issued | 2019-10-01 | |
dc.description.abstract | Network models of brain dynamics provide valuable insight into the healthy functioning of the brain and how this breaks down in disease. A pertinent example is the use of network models to understand seizure generation (ictogenesis) in epilepsy. Recently, computational models have emerged to aid our understanding of seizures and to predict the outcome of surgical perturbations to brain networks. Such approaches provide the opportunity to quantify the effect of removing regions of tissue from brain networks and thereby search for the optimal resection strategy. Here, we use computational models to elucidate how sets of nodes contribute to the ictogenicity of networks. In small networks we fully elucidate the ictogenicity of all possible sets of nodes and demonstrate that the distribution of ictogenicity across sets depends on network topology. However, the full elucidation is a combinatorial problem that becomes intractable for large networks. Therefore, we combine computational models with a genetic algorithm to search for minimal sets of nodes that contribute significantly to ictogenesis. We demonstrate the potential applicability of these methods in practice by identifying optimal sets of nodes to resect in networks derived from 20 individuals who underwent resective surgery for epilepsy. We show that they have the potential to aid epilepsy surgery by suggesting alternative resection sites as well as facilitating the avoidance of brain regions that should not be resected. | en_GB |
dc.description.sponsorship | Medical Research Council (MRC) | en_GB |
dc.description.sponsorship | Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
dc.description.sponsorship | Wellcome Trust | en_GB |
dc.description.sponsorship | National Institute for Health Research (NIHR) | en_GB |
dc.description.sponsorship | Swiss League Against Epilepsy | en_GB |
dc.description.sponsorship | European Union Horizon 2020 | en_GB |
dc.identifier.citation | Vol. 10, article 1045 | en_GB |
dc.identifier.doi | 10.3389/fneur.2019.01045 | |
dc.identifier.grantnumber | MR/K013998/1 | en_GB |
dc.identifier.grantnumber | EP/N014391/1 | en_GB |
dc.identifier.grantnumber | EP/P021417/1 | en_GB |
dc.identifier.grantnumber | WT105618MA | en_GB |
dc.identifier.grantnumber | MR/N026063/1 | en_GB |
dc.identifier.grantnumber | EP/N017846/1 | en_GB |
dc.identifier.grantnumber | EP/N014391/1 | en_GB |
dc.identifier.grantnumber | EP/P020259/1 | en_GB |
dc.identifier.grantnumber | 75088 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/39637 | |
dc.language.iso | en | en_GB |
dc.publisher | Frontiers Media | en_GB |
dc.rights | © 2019 Laiou, Avramidis, Lopes, Abela, Müller, Akman, Richardson, Rummel, Schindler and Goodfellow. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. | en_GB |
dc.subject | epilepsy surgery | en_GB |
dc.subject | brain networks | en_GB |
dc.subject | ictogenesis | en_GB |
dc.subject | graph theory | en_GB |
dc.subject | optimization | en_GB |
dc.subject | genetic algorithm | en_GB |
dc.title | Quantification and Selection of Ictogenic Zones in Epilepsy Surgery | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2019-11-20T16:11:21Z | |
dc.description | This is the final version. Available on open access from Frontiers Media via the DOI in this record | en_GB |
dc.description | Data Availability Statement: The synthetic networks are available upon request. | en_GB |
dc.identifier.eissn | 1664-2295 | |
dc.identifier.journal | Frontiers in Neurology | en_GB |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2019-09-16 | |
exeter.funder | ::Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
exeter.funder | ::Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2019-10-01 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2019-11-20T16:07:53Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2019-11-20T16:11:24Z | |
refterms.panel | B | en_GB |
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Except where otherwise noted, this item's licence is described as © 2019 Laiou, Avramidis, Lopes, Abela, Müller, Akman, Richardson, Rummel, Schindler and Goodfellow. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.