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Paper: PS-2B.1
Session: Poster Session 2B
Location: H Fl├Ąche 1.OG
Session Time: Sunday, September 15, 17:15 - 20:15
Presentation Time:Sunday, September 15, 17:15 - 20:15
Presentation: Poster
Publication: 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Paper Title: MEG energy landscape abnormalities in juvenile myoclonic epilepsy
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
DOI: https://doi.org/10.32470/CCN.2019.1256-0
Authors: Dominik Krzeminski, Cardiff University, United Kingdom; Naoki Masuda, University of Bristol, United Kingdom; Khalid Hamandi, Krish Singh, Jiaxiang Zhang, Cardiff University, United Kingdom
Abstract: Juvenile myoclonic epilepsy (JME) is a network disorder affecting brain activity and connectivity. However, it is unclear whether JME leads to widespread abnormalities in the network dynamics across different functional networks. Here, we used a pairwise maximum entropy model (pMEM) and energy landscape analysis to characterize network dynamics in MEG resting-state data and its abnormalities in JME. We fitted the pMEM to the MEG oscillatory power in three functional networks: the default mode network (DMN), the frontoparietal network (FPN) and the sensorimotor network (SMN). The pMEM provided an accurate fit to the MEG data in both patient and control groups. We then used pMEM-derived energy values to depict an energy landscape of each network, with a higher energy state corresponding to a lower occurrence probability. JME patients exhibited a lower number of local energy minima than controls, and had elevated energy values in the theta, beta and gamma-band of FPN oscillatory activity as well as the beta-band DMN activity, but not in the SMN. Our findings suggested that JME patients had impaired multi-stability in selective functional networks and frequency bands in the frontoparietal cortices.