Energy landscape of resting magnetoencephalography reveals fronto-parietal network impairments in epilepsy

Dominik Krzemiński, Naoki Masuda, Khalid Hamandi, Krish D. Singh, Bethany Routley, Jiaxiang Zhang

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Juvenile myoclonic epilepsy (JME) is a form of idiopathic generalized epilepsy. It is yet unclear to what extent JME leads to abnormal network activation patterns. Here, we characterized statistical regularities in magnetoencephalograph (MEG) resting-state networks and their differences between JME patients and controls by combining a pairwise maximum entropy model (pMEM) and novel energy landscape analyses for MEG. First, we fitted the pMEM to the MEG oscillatory power in the front-oparietal network (FPN) and other resting-state networks, which provided a good estimation of the occurrence probability of network states. Then, we used energy values derived from the pMEM to depict an energy landscape, with a higher energy state corresponding to a lower occurrence probability. JME patients showed fewer local energy minima than controls and had elevated energy values for the FPN within the theta, beta, and gamma bands. Furthermore, simulations of the fitted pMEM showed that the proportion of time the FPN was occupied within the basins of energy minima was shortened in JME patients. These network alterations were highlighted by significant classification of individual participants employing energy values as multivariate features. Our findings suggested that JME patients had altered multistability in selective functional networks and frequency bands in the fronto-parietal cortices.

Original languageEnglish
Pages (from-to)374-396
Number of pages23
JournalNetwork Neuroscience
Volume4
Issue number2
DOIs
Publication statusPublished - 2019
Externally publishedYes

Keywords

  • Energy landscape
  • Juvenile myoclonic epilepsy
  • Maximum entropy model
  • MEG
  • Resting-state networks

ASJC Scopus subject areas

  • Neuroscience(all)
  • Computer Science Applications
  • Artificial Intelligence
  • Applied Mathematics

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