A computational biomarker of juvenile myoclonic epilepsy from resting-state MEG.


Journal

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
ISSN: 1872-8952
Titre abrégé: Clin Neurophysiol
Pays: Netherlands
ID NLM: 100883319

Informations de publication

Date de publication:
04 2021
Historique:
received: 15 05 2020
revised: 07 12 2020
accepted: 18 12 2020
pubmed: 27 2 2021
medline: 28 8 2021
entrez: 26 2 2021
Statut: ppublish

Résumé

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). 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. 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%. The BNI framework is applicable to MEG and was capable of differentiating people with epilepsy from healthy controls. The BNI framework may be applied to resting-state MEG to aid in epilepsy diagnosis.

Identifiants

pubmed: 33636607
pii: S1388-2457(21)00031-6
doi: 10.1016/j.clinph.2020.12.021
pmc: PMC7992031
pii:
doi:

Substances chimiques

Biomarkers 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

922-927

Subventions

Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 204824/Z/16/Z
Pays : United Kingdom
Organisme : Wellcome Trust
ID : WT105618MA
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 104943/Z/14/Z
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/K005464/1
Pays : United Kingdom

Informations de copyright

Copyright © 2021 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest JT is co-founder and Director of Neuronostics.

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Auteurs

Marinho A Lopes (MA)

Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff CF24 4HQ, United Kingdom. Electronic address: m.lopes@exeter.ac.

Dominik Krzemiński (D)

Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff CF24 4HQ, United Kingdom.

Khalid Hamandi (K)

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.

Krish D Singh (KD)

Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff CF24 4HQ, United Kingdom.

Naoki Masuda (N)

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.

John R Terry (JR)

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.

Jiaxiang Zhang (J)

Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff CF24 4HQ, United Kingdom.

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