A computational biomarker of juvenile myoclonic epilepsy from resting-state MEG. Issue 4 (April 2021)

• Record Type: Journal Article
• Authors: Lopes, Marinho A.; Krzemiński, Dominik; Hamandi, Khalid; Singh, Krish D.; Masuda, Naoki; Terry, John R.; Zhang, Jiaxiang
• Is Part Of: Clinical neurophysiology. Volume 132:Issue 4(2021)
• Page Start: 922
• Page End: 927
• Publication Date: 2021-04
• Subjects: Epilepsy diagnosis -- Juvenile myoclonic epilepsy -- Biomarker -- MEG -- Functional connectivity -- Phenomenological model; Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2020.12.021
• Languages: English
• ISSNs: 1388-2457

Abstract

Highlights: Computational modelling is combined with MEG to differentiate people with juvenile myoclonic epilepsy from healthy controls. Brain network ictogenicity (BNI) was found higher in people with juvenile myoclonic epilepsy relative to healthy controls. BNI's classification accuracy in our cohort was 73%. 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.