Transient seizure onset network for localization of epileptogenic zone : effective connectivity and graph theory-based analyses of ECoG data in temporal lobe epilepsy
Ren, Y., Cong, F., Ristaniemi, T., Wang, Y., Li, X., & Zhang, R. (2019). Transient seizure onset network for localization of epileptogenic zone : effective connectivity and graph theory-based analyses of ECoG data in temporal lobe epilepsy. Journal of Neurology, 266(4), 844-859. https://doi.org/10.1007/s00415-019-09204-4
Julkaistu sarjassa
Journal of NeurologyPäivämäärä
2019Oppiaine
TietotekniikkaMonitieteinen aivotutkimuskeskusHyvinvoinnin tutkimuksen yhteisöMathematical Information TechnologyCentre for Interdisciplinary Brain ResearchSchool of WellbeingTekijänoikeudet
© Springer-Verlag GmbH Germany, part of Springer Nature 2019
Objective:
Abnormal and dynamic epileptogenic networks cause difficulties for clinical epileptologists in the localization of the seizure onset zone (SOZ) and the epileptogenic zone (EZ) in preoperative assessments of patients with refractory epilepsy. The aim of this study is to investigate the characteristics of time-varying effective connectivity networks in various non-seizure and seizure periods, and to propose a quantitative approach for accurate localization of SOZ and EZ.
Methods:
We used electrocorticogram recordings in the temporal lobe and hippocampus from seven patients with temporal lobe epilepsy to characterize the effective connectivity dynamics at a high temporal resolution using the full-frequency adaptive directed transfer function (ffADTF) measure and five graph metrics, i.e., the out-degree (OD), closeness centrality (CC), betweenness centrality (BC), clustering coefficient (C), and local efficiency (LE). The ffADTF effective connectivity network was calculated and described in five frequency bands (δ, θ, α, β, and γ) and five seizure periods (pre-seizure, early seizure, mid-seizure, late seizure, and post-seizure). The cortical areas with high values of graph metrics in the transient seizure onset network were compared with the SOZ and EZ identified by clinical epileptologists and the results of epilepsy resection surgeries.
Results:
Origination and propagation of epileptic activity were observed in the high time resolution ffADTF effective connectivity network throughout the entire seizure period. The seizure-specific transient seizure onset ffADTF network that emerged at seizure onset time remained for approximately 20–50 ms with strong connections generated from both SOZ and EZ. The values of graph metrics in the SOZ and EZ were significantly larger than that in the other cortical areas. More cortical areas with the highest mean of graph metrics were the same as the clinically determined SOZ in the low-frequency δ and θ bands and in Engel Class I patients than in higher frequency α, β, and γ bands and in Engel Class II and III patients. The OD and C were more likely to localize the SOZ and EZ than CC, BC, and LE in the transient seizure onset network.
Conclusion:
The high temporal resolution ffADTF effective connectivity analysis combined with the graph theoretical analysis helps us to understand how epileptic activity is generated and propagated during the seizure period. The newly discovered seizure-specific transient seizure onset network could be an important biomarker and a promising tool for more precise localization of the SOZ and EZ in preoperative evaluations.
...
Julkaisija
Springer Berlin HeidelbergISSN Hae Julkaisufoorumista
0340-5354Asiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/28888513
Metadata
Näytä kaikki kuvailutiedotKokoelmat
Lisenssi
Samankaltainen aineisto
Näytetään aineistoja, joilla on samankaltainen nimeke tai asiasanat.
-
Network Entropy for the Sequence Analysis of Functional Connectivity Graphs of the Brain
Zhang, Chi; Cong, Fengyu; Kujala, Tuomo; Liu, Wenya; Liu, Jia; Parviainen, Tiina; Ristaniemi, Tapani (MDPI, 2018)Dynamic representation of functional brain networks involved in the sequence analysis of functional connectivity graphs of the brain (FCGB) gains advances in uncovering evolved interaction mechanisms. However, most of the ... -
One Dimensional Convolutional Neural Networks for Seizure Onset Detection Using Long-term Scalp and Intracranial EEG
Wang, Xiaoshuang; Wang, Xiulin; Liu, Wenya; Chang, Zheng; Kärkkäinen, Tommi; Cong, Fengyu (Elsevier, 2021)Epileptic seizure detection using scalp electroencephalogram (sEEG) and intracranial electroencephalogram (iEEG) has attracted widespread attention in recent two decades. The accurate and rapid detection of seizures not ... -
Channel Increment Strategy-Based 1D Convolutional Neural Networks for Seizure Prediction Using Intracranial EEG
Wang, Xiaoshuang; Zhang, Chi; Kärkkäinen, Tommi; Chang, Zheng; Cong, Fengyu (Institute of Electrical and Electronics Engineers (IEEE), 2023)The application of intracranial electroencephalogram (iEEG) to predict seizures remains challenging. Although channel selection has been utilized in seizure prediction and detection studies, most of them focus on the ... -
One-Dimensional Convolutional Neural Networks Combined with Channel Selection Strategy for Seizure Prediction Using Long-Term Intracranial EEG
Wang, Xiaoshuang; Zhang, Guanghui; Wang, Ying; Yang, Lin; Liang, Zhanhua; Cong, Fengyu (World Scientific, 2022)Seizure prediction using intracranial electroencephalogram (iEEG) has attracted an increasing attention during recent years. iEEG signals are commonly recorded in the form of multiple channels. Many previous studies generally ... -
One and Two Dimensional Convolutional Neural Networks for Seizure Detection Using EEG Signals
Wang, Xiaoshuang; Ristaniemi, Tapani; Cong, Fengyu (IEEE, 2020)Deep learning for the automated detection of epileptic seizures has received much attention during recent years. In this work, one dimensional convolutional neural network (1D-CNN) and two dimensional convolutional neural ...
Ellei toisin mainittu, julkisesti saatavilla olevia JYX-metatietoja (poislukien tiivistelmät) saa vapaasti uudelleenkäyttää CC0-lisenssillä.