Frontoparietal network dynamics impairments in juvenile myoclonic epilepsy revealed by MEG energy landscape

Abstract

Juvenile myoclonic epilepsy (JME) is a form of idiopathic generalized epilepsy affecting brain activity. It is unclear to what extent JME leads to abnormal network dynamics across functional networks. Here, we proposed a method to characterise network dynamics in MEG resting-state data, combining a pairwise maximum entropy model (pMEM) and the associated energy landscape analysis. Fifty-two JME patients and healthy controls underwent a resting-state MEG recording session. We fitted the pMEM to the oscillatory power envelopes in theta (4-7 Hz), alpha (8-13 Hz), beta (15-25 Hz) and gamma (30-60 Hz) bands in three source-localised resting-state networks: the frontoparietal network (FPN), the default mode network (DMN), and the sensorimotor network (SMN). The pMEM provided an accurate fit to the MEG oscillatory activity in both patient and control groups, and allowed estimation of the occurrence probability of each network state, with its regional activity and pairwise regional co-activation constrained by empirical data. We used energy values derived from the pMEM to depict an energy landscape of each network, with a higher energy state corresponding to a lower occurrence probability. When comparing the energy landscapes between groups, 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, numerical simulation of the fitted pMEM showed that the proportion of time the FPN was occupied within the basins of characteristic energy minima was shortened in JME patients. These network alterations were confirmed by a significant leave-one-out classification of individual participants based on a support vector machine employing the energy values of pMEM as features. Our findings suggested that JME patients had altered multi-stability in selective functional networks and frequency bands in the frontoparietal cortices.\n\nHighlightsO_LIAn energy landscape analysis characterises the dynamics of MEG oscillatory activity\nC_LIO_LIPatients with JME exhibit fewer local minima of the energy in their energy landscapes\nC_LIO_LIJME affects the network dynamics in the frontoparietal network.\nC_LIO_LIEnergy landscape measures allow good single-patient classification.\nC_LI