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

Marinho A. Lopes; Dominik Krzemiński; Khalid Hamandi; Krish D. Singh; Naoki Masuda; John R. Terry; Jiaxiang Zhang

doi:10.1016/j.clinph.2020.12.021

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

Marinho A. Lopes^*, Dominik Krzemiński, Khalid Hamandi, Krish D. Singh, Naoki Masuda, John R. Terry, Jiaxiang Zhang

^*Corresponding author for this work

Metabolism and Systems Science

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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.

Original language	English
Pages (from-to)	922-927
Number of pages	6
Journal	Clinical Neurophysiology
Volume	132
Issue number	4
Early online date	4 Feb 2021
DOIs	https://doi.org/10.1016/j.clinph.2020.12.021
Publication status	Published - Apr 2021

Bibliographical note

Publisher Copyright:
© 2021 International Federation of Clinical Neurophysiology

Keywords

Biomarker
Epilepsy diagnosis
Functional connectivity
Juvenile myoclonic epilepsy
MEG
Phenomenological model

ASJC Scopus subject areas

Sensory Systems
Neurology
Clinical Neurology
Physiology (medical)

Access to Document

10.1016/j.clinph.2020.12.021Licence: Creative Commons: Attribution (CC BY)

LopesM2021computationalFinal published version, 689 KBLicence: Creative Commons: Attribution (CC BY)

EPSRC Centre for Predictive Modelling in Healthcare
Terry, J. (Principal Investigator)
Engineering & Physical Science Research Council
1/01/20 → 31/01/21
Project: Research Councils

Cite this

@article{c525c360d04f43cbafcb08d2f34e071b,

title = "A computational biomarker of juvenile myoclonic epilepsy from resting-state MEG",

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.",

keywords = "Biomarker, Epilepsy diagnosis, Functional connectivity, Juvenile myoclonic epilepsy, MEG, Phenomenological model",

author = "Lopes, {Marinho A.} and Dominik Krzemi{\'n}ski and Khalid Hamandi and Singh, {Krish D.} and Naoki Masuda and Terry, {John R.} and Jiaxiang Zhang",

note = "Publisher Copyright: {\textcopyright} 2021 International Federation of Clinical Neurophysiology",

year = "2021",

month = apr,

doi = "10.1016/j.clinph.2020.12.021",

language = "English",

volume = "132",

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AU - Lopes, Marinho A.

AU - Krzemiński, Dominik

AU - Hamandi, Khalid

AU - Singh, Krish D.

AU - Masuda, Naoki

AU - Terry, John R.

AU - Zhang, Jiaxiang

PY - 2021/4

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N2 - 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.

AB - 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.

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

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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Projects

EPSRC Centre for Predictive Modelling in Healthcare

Cite this