Does sleep deprivation alter functional EEG networks in children with focal epilepsy?

Eric Van Diessen,Kees P J Braun,Cornelis J Stam,Floor E Jansen,Willem M Otte

doi:10.3389/fnsys.2014.00067

Eric Van Diessen, Kees P J Braun + Show 3 more

Open Access

https://doi.org/10.3389/fnsys.2014.00067

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Abstract

Electroencephalography (EEG) recordings after sleep deprivation increase the diagnostic yield in patients suspected of epilepsy if the routine EEG remains inconclusive. Sleep deprivation is associated with increased interictal EEG abnormalities in patients with epilepsy, but the exact mechanism is unknown. In this feasibility study, we used a network analytical approach to provide novel insights into this clinical observation. The aim was to characterize the effect of sleep deprivation on the interictal functional network organization using a unique dataset of paired routine and sleep deprivation recordings in patients and controls. We included 21 children referred to the first seizure clinic of our center with suspected new onset focal epilepsy in whom a routine interictal and a sleep deprivation EEG (SD-EEG) were performed. Seventeen children, in whom the diagnosis of epilepsy was excluded, served as controls. For both time points weighted functional networks were constructed based on interictal artifact free time-series. Routine and sleep deprivation networks were characterized at different frequency bands using minimum spanning tree (MST) measures (leaf number and diameter) and classical measures of integration (path length) and segregation (clustering coefficient). A significant interaction was found for leaf number and diameter between patients and controls after sleep deprivation: patients showed a shift toward a more path-like MST network whereas controls showed a shift toward a more star-like MST network. This shift in network organization after sleep deprivation in patients is in accordance with previous studies showing a more regular network organization in the ictal state and might relate to the increased epileptiform abnormalities found in patients after sleep deprivation. Larger studies are needed to verify these results. Finally, MST measures were more sensitive in detecting network changes as compared to the classical measures of integration and segregation.

Highlights

An interictal electroencephalogram (EEG) is routinely acquired in patients suspected of epilepsy to support the clinical diagnosis
STATISTICAL ANALYSIS First, we explored the effect of sleep deprivation in each group separately by comparing relative power spectra, network and minimum spanning tree (MST) measures from routine EEG and sleep deprivation EEG (SD-EEG) recordings for each frequency band with a paired t-test
An EEG after sleep deprivation is often performed in patients suspected of epilepsy when the standard EEG recording is inconclusive

Summary

Introduction

An interictal electroencephalogram (EEG) is routinely acquired in patients suspected of epilepsy to support the clinical diagnosis. The interictal EEG recording often lacks epileptiform abnormalities or is insufficient to determine classification of the epilepsy syndrome. Converging evidence exists that SD-EEG recordings improve the detection of epileptiform abnormalities and help to determine classification of the epilepsy syndrome, independently of the presence of sleep during the EEG recording (Ellingson et al, 1984; Fountain et al, 1998). Network analysis reduces complex systems, such as the brain, to a collection of nodes (brain regions) and edges (connections between brain areas). From these networks several measures can be inferred to characterize global changes and efficiency in network organization: the clustering coefficient (a measure of segregation) and path length (a measure of integration).

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