Marinho A Lopes1, Dominik Krzemiński2, Khalid Hamandi3, Krish D Singh2, Naoki Masuda4, John R Terry5, Jiaxiang Zhang2. 1. Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff CF24 4HQ, United Kingdom. Electronic address: m.lopes@exeter.ac. 2. Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff CF24 4HQ, United Kingdom. 3. Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff CF24 4HQ, United Kingdom; The Welsh Epilepsy Unit, Department of Neurology, University Hospital of Wales, Cardiff CF14 4XW, United Kingdom. 4. Department of Mathematics, University at Buffalo, State University of New York, USA; Computational and Data-Enabled Science and Engineering Program, University at Buffalo, State University of New York, USA. 5. EPSRC Centre for Predictive Modelling in Healthcare, University of Birmingham, Birmingham, United Kingdom; Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Edgbaston, United Kingdom; Institute for Metabolism and Systems Research, University of Birmingham, Edgbaston, United Kingdom.
Abstract
OBJECTIVE: For people with idiopathic generalized epilepsy, functional networks derived from their resting-state scalp electrophysiological recordings have shown an inherent higher propensity to generate seizures than those from healthy controls when assessed using the concept of brain network ictogenicity (BNI). Herein we tested whether the BNI framework is applicable to resting-state magnetoencephalography (MEG) from people with juvenile myoclonic epilepsy (JME). METHODS: The BNI framework consists in deriving a functional network from apparently normal brain activity, placing a mathematical model of ictogenicity into the network and then computing how often such network generates seizures in silico. We considered data from 26 people with JME and 26 healthy controls. RESULTS: We found that resting-state MEG functional networks from people with JME are characterized by a higher propensity to generate seizures (i.e., higher BNI) than those from healthy controls. We found a classification accuracy of 73%. CONCLUSIONS: The BNI framework is applicable to MEG and was capable of differentiating people with epilepsy from healthy controls. SIGNIFICANCE: The BNI framework may be applied to resting-state MEG to aid in epilepsy diagnosis.
OBJECTIVE: For people with idiopathic generalized epilepsy, functional networks derived from their resting-state scalp electrophysiological recordings have shown an inherent higher propensity to generate seizures than those from healthy controls when assessed using the concept of brain network ictogenicity (BNI). Herein we tested whether the BNI framework is applicable to resting-state magnetoencephalography (MEG) from people with juvenile myoclonic epilepsy (JME). METHODS: The BNI framework consists in deriving a functional network from apparently normal brain activity, placing a mathematical model of ictogenicity into the network and then computing how often such network generates seizures in silico. We considered data from 26 people with JME and 26 healthy controls. RESULTS: We found that resting-state MEG functional networks from people with JME are characterized by a higher propensity to generate seizures (i.e., higher BNI) than those from healthy controls. We found a classification accuracy of 73%. CONCLUSIONS: The BNI framework is applicable to MEG and was capable of differentiating people with epilepsy from healthy controls. SIGNIFICANCE: The BNI framework may be applied to resting-state MEG to aid in epilepsy diagnosis.
Authors: Selim R Benbadis; Sándor Beniczky; Edward Bertram; Stephanie MacIver; Solomon L Moshé Journal: Epileptic Disord Date: 2020-04-01 Impact factor: 1.819
Authors: Petroula Laiou; Eleftherios Avramidis; Marinho A Lopes; Eugenio Abela; Michael Müller; Ozgur E Akman; Mark P Richardson; Christian Rummel; Kaspar Schindler; Marc Goodfellow Journal: Front Neurol Date: 2019-10-01 Impact factor: 4.003
Authors: Marinho A Lopes; Leandro Junges; Luke Tait; John R Terry; Eugenio Abela; Mark P Richardson; Marc Goodfellow Journal: Clin Neurophysiol Date: 2019-11-22 Impact factor: 3.708
Authors: Maher A Quraan; Cornelia McCormick; Melanie Cohn; Taufik A Valiante; Mary Pat McAndrews Journal: PLoS One Date: 2013-07-26 Impact factor: 3.240
Authors: Marinho A Lopes; Mark P Richardson; Eugenio Abela; Christian Rummel; Kaspar Schindler; Marc Goodfellow; John R Terry Journal: Front Neurol Date: 2018-03-01 Impact factor: 4.003