Abstract

The task of automated epileptic seizure detection and prediction by using non-invasive measurements such as scalp EEG signals or invasive, intracranial recordings, has been at the heart of epilepsy studies for at least three decades. By far, the most common approach for tackling this problem is to examine short-length recordings around the occurrence of a seizure - normally ranging between several seconds and up to a few minutes before and after the epileptic event - and identify any significant changes that occur before or during the event. An inherent assumption in these studies is the presence of a relatively constant EEG activity in the interictal period, which is presumably interrupted by the occurrence of a seizure. Here, we examine this assumption by using long-duration scalp EEG data (ranging between 21 and 94 hours) in patients with epilepsy, based on which we construct functional brain networks. Our results suggest that not only these networks vary over time, but they do so in a periodic fashion, exhibiting multiple peaks at periods ranging between around one and 24 hours. The effects of seizure onset on the functional brain network properties were found to be considerably smaller in magnitude compared to the changes due to the inherent periodic cycles of these networks. Importantly, the properties of the identified network periodic components (instantaneous phase, particularly that of short-term periodicities around 3 and 5 h) were found to be strongly correlated to seizure onset. These correlations were found to be largely absent between EEG signal periodicities and seizure onset, suggesting that higher specificity may be achieved by using network-based metrics. In turn, this suggests that to achieve more robust seizure detection and/or prediction, the evolution of the underlying longer term functional brain network periodic variations should be taken into account.\n\nHighlights- We have examined the long-term characteristics of EEG functional brain networks and their correlations to seizure onset\n\n- We show periodicities over multiple time scales in network summative properties (degree, efficiency, clustering coefficient)\n\n- We also show that, in addition to average network properties, similar periodicities exist in network topology using a novel measure based on the graph edit distance, suggesting that specific connectivity patterns recur over time\n\n- These periodic patterns were preserved when we corrected for the effects of volume conduction and were found to be of much larger magnitude compared to seizure-induced modulations\n\n- For the first time to our knowledge, we demonstrate that seizure onset occurs preferentially at specific phases of network periodic components that were consistently observed across subjects, particularly for shorter periodicities (around 3 and 5 hours)\n\n- These correlations between the phase of network periodic components and seizure onset were nearly absent when examining univariate properties (EEG signal power), suggesting that network-based measures are more tightly coupled with seizure onset compared to EEG signal-based measures\n\n- Our findings suggest that seizure detection and prediction algorithms may benefit significantly by taking into account longer-term variations in brain network properties\n\n- As we show strong evidence that shorter network-based periodicities (3-5 hours) are tightly coupled with seizure onset, our results pave the way for further investigation into the pathophysiology of seizure generation mechanisms beyond the well-known effects of circadian rhythms