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

Antunes Lopes, Marinho, Krzemiński, Dominik, Khalid, Hamandi, Singh, Krish D., Masuda, Naoki, Terry, John R. and Zhang, Jiaxiang 2021. A computational biomarker of juvenile myoclonic epilepsy from resting-state MEG. Clinical Neurophysiology 132 (4) , pp. 922-927. 10.1016/j.clinph.2020.12.021

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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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Psychology
Cardiff University Brain Research Imaging Centre (CUBRIC)
Publisher: Elsevier
ISSN: 1388-2457
Funders: Wellcome Trust
Date of First Compliant Deposit: 4 January 2021
Date of Acceptance: 18 December 2020
Last Modified: 26 Feb 2021 12:15
URI: http://orca.cf.ac.uk/id/eprint/137235

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