Abstract

Evidence suggests that brain network dynamics are a key determinant of brain function and dysfunction. Here we propose a new framework to assess the dynamics of brain networks based on recurrence analysis. Our framework uses recurrence plots and recurrence quantification analysis to characterize dynamic networks. For resting-state magnetoencephalographic dynamic functional networks (dFNs), we have found that functional networks recur more quickly in people with epilepsy than in healthy controls. This suggests that recurrence of dFNs may be used as a biomarker of epilepsy. For stereo electroencephalography data, we have found that dFNs involved in epileptic seizures emerge before seizure onset, and recurrence analysis allows us to detect seizures. We further observe distinct dFNs before and after seizures, which may inform neurostimulation strategies to prevent seizures. Our framework can also be used for understanding dFNs in healthy brain function and in other neurological disorders besides epilepsy.

Highlights

  • IntroductionModel and study brain structure and function, it is useful to define brain networks (Bassett & Sporns, 2017; Fornito, Zalesky, & Bullmore, 2016)

  • The brain is a complex dynamic system

  • We studied the dynamics of functional networks inferred from resting-state MEG data and tested whether dynamic functional networks (dFN) from people with epilepsy differ from healthy controls

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Summary

Introduction

Model and study brain structure and function, it is useful to define brain networks (Bassett & Sporns, 2017; Fornito, Zalesky, & Bullmore, 2016). Two main types of brain networks have been considered: structural and functional networks (Bullmore & Sporns, 2009; Sporns, 2013). Functional networks are inferred from statistical dependencies between neural signals recorded from different brain regions. The statistical dependencies between signals are not stationary, making functional networks time-dependent on short time scales (tens or hundreds of milliseconds; Sporns, 2013). Epilepsy, which will be the focus of this study, has been considered to be a dynamical disease of the brain (da Silva et al, 2003), and dFNs have been useful for characterizing the epileptic brain (Lehnertz et al, 2014)

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