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Laiou P, Avramidis E, Lopes MA, Abela E, Müller M, Akman OE, Richardson MP, Rummel C, Schindler K, Goodfellow M. Quantification and Selection of Ictogenic Zones in Epilepsy Surgery. Front Neurol 2019; 10:1045. [PMID: 31632339 PMCID: PMC6779811 DOI: 10.3389/fneur.2019.01045] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Accepted: 09/16/2019] [Indexed: 01/23/2023] Open
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.
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Affiliation(s)
- Petroula Laiou
- Living Systems Institute, University of Exeter, Exeter, United Kingdom
- EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
| | | | - Marinho A. Lopes
- Living Systems Institute, University of Exeter, Exeter, United Kingdom
- EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
- Wellcome Trust Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, United Kingdom
| | - Eugenio Abela
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Michael Müller
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Department of Neurology, Inselspital, University Hospital Bern, Bern, Switzerland
| | - Ozgur E. Akman
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
| | - Mark P. Richardson
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Christian Rummel
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Kaspar Schindler
- Department of Neurology, Inselspital, University Hospital Bern, Bern, Switzerland
| | - Marc Goodfellow
- Living Systems Institute, University of Exeter, Exeter, United Kingdom
- EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
- Wellcome Trust Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, United Kingdom
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