Multi-scale periodicities in the functional brain networks of patients with epilepsy and their effect on seizure detection

Abstract

Highlights-We have examined the long-term characteristics of EEG functional brain networks and their correlations to seizure onset -We show periodicities over multiple time scales in network summative properties (degree, efficiency, clustering coefficient) -We also show that, in addition to average network properties, the same periodicities exist in network topology using a novel measure (graph edit distance), suggesting that specific connectivity patterns recur over time -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 seizureinduced modulations -For the first time to our knowledge, we demonstrate that seizure onset occurs preferentially at specific phases of the network periodic components, particularly for shorter periodicities (around 3 and 5 hours) -These correlations were nearly absent when examining univariate properties (EEG signal power), suggesting that network-based measures rather than EEG signal-based measures are more tightly coupled with seizure onset -Our findings suggest that seizure detection and prediction algorithms may benefit significantly by taking into account these longer-term variations in brain network properties -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 All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/221036 doi: bioRxiv preprint first posted online 3 AbstractThe 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 periods ranging between around one and 24 hours...

Graph Theoretical Characteristics of EEG-Based Functional Brain Networks in Patients With Epilepsy: The Effect of Reference Choice and Volume Conduction

“…For all patients, the instantaneous phases at the onset of the seizure were collected and the obtained phase distribution were subsequently investigated using CircStat which is a Matlab (Math works, Natick MA) toolbox related to circular statistics (Berens, 2009 ). For more details, the reader is referred to Mitsis et al ( 2018 ). To investigate whether phase values at seizure onset times were distributed uniformly around the circle from 0 to 2π, we applied the Rayleigh test with the null hypothesis ( H 0 ) being that the population is distributed uniformly around the circle.…”

“…Using a unique dataset of long-term (days) continuous scalp EEG recordings in patients with epilepsy, we recently showed that the summative properties (degree, efficiency, clustering coefficient) and topology of the resulting functional brain networks exhibit robust long-term periodicities in addition to the well-known circadian 24 h period (Anastasiadou et al, 2016 ; Mitsis et al, 2018 ). Our results demonstrated that brain network periodicities (particularly around 3 and 5 h) are strongly correlated to seizure onset.…”

Temporal dynamics of functional networks in long-term infant scalp EEG

“…We found that functional networks were stronger during sleep than wakefulness, and that they were less clustered when the subject was awake (Burroughs et al, 2014;Kuhnert et al, 2010;Mitsis et al, 2017). The networks were significantly stronger during sleep when compared to wakefulness in all but one subject ( Figure 1B), however, we note that the effects of thresholding networks, even with a proportional threshold, is an active area of research and will require further investigation (Chapeton et al, 2017;Garrison et al, 2015).…”

“…In addition to imparting knowledge of how physiological functional networks modulate throughout the day and within waking and sleep states, this will facilitate understanding of changes in network topology due to pediatric diseases such as epilepsy and autism (Righi et al, 2014;Shrey et al, 2018). Seizure forecasting in epilepsy has largely relied on prediction of seizure onset with several minutes of data, but it has been shown that modulations in functional networks due to physiological processes such as waking and sleeping can mask "pre-seizure" changes (Kuhnert et al, 2010;Mitsis et al, 2017;Schelter et al, 2011). Accounting for these physiological fluctuations in seizure prediction models may improve their accuracy and ultimately improve care for patients suffering from epilepsy.…”

“…For example, long-term functional network changes have been used to predict seizure onset (Anastasiadou et al, 2016;Baud et al, 2018;Karoly et al, 2017;Kuhnert et al, 2010), and functional network properties were better predictors of seizure onset than signal power or other signal properties (Mitsis et al, 2017). Additionally, the physiological changes in the functional networks associated with the time of day have a far greater impact than changes in the networks due to seizure onset, highlighting the need for studies to account for these physiological changes when creating seizure prediction models (Kuhnert et al, 2010;Mitsis et al, 2017;Schelter, Feldwisch-Drentrup, Ihle, Schulze-Bonhage, & Timmer, 2011). Therefore, to quantify normal physiological changes in brain networks over long time scales, we measured EEG-based functional connectivity in long-term recordings from healthy infants.…”