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Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence
Petroula Laiou
Andrea Biondi
Elisa Bruno
Pedro F. Viana
Joel S. Winston
Zulqarnain Rashid
Yatharth Ranjan
Pauline Conde
Callum Stewart
Shaoxiong Sun
Yuezhou Zhang
Amos Folarin
Richard Dobson
Andreas Schulze-Bonhage
Matthias Dümpelmann
Mark P. Richardson
出版
Universität
, 2022
URL
http://books.google.com.hk/books?id=K-aazwEACAAJ&hl=&source=gbs_api
註釋
Abstract: Epilepsy is one of the most common neurological disorders, characterized by the occurrence of repeated seizures. Given that epilepsy is considered a network disorder, tools derived from network neuroscience may confer the valuable ability to quantify the properties of epileptic brain networks. In this study, we use well-established brain network metrics (i.e., mean strength, variance of strength, eigenvector centrality, betweenness centrality) to characterize the temporal evolution of epileptic functional networks over several days prior to seizure occurrence. We infer the networks using long-term electroencephalographic recordings from 12 people with epilepsy. We found that brain network metrics are variable across days and show a circadian periodicity. In addition, we found that in 9 out of 12 patients the distribution of the variance of strength in the day (or even two last days) prior to seizure occurrence is significantly different compared to the corresponding distributions on all previous days. Our results suggest that brain network metrics computed fromelectroencephalographic recordings could potentially be used to characterize brain network changes that occur prior to seizures, and ultimately contribute to seizure warning systems