Journal of Neurology

, Volume 266, Issue 4, pp 844–859 | Cite as

Transient seizure onset network for localization of epileptogenic zone: effective connectivity and graph theory-based analyses of ECoG data in temporal lobe epilepsy

  • Ye Ren
  • Fengyu Cong
  • Tapani Ristaniemi
  • Yuping Wang
  • Xiaoli LiEmail author
  • Ruihua ZhangEmail author
Original Communication



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.


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.


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.


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.


Adaptive directed transfer function Graph metric Brain connectivity Seizure onset zone Epileptogenic zone 



This research is supported by the Ph.D. grant of the Faculty of Information Technology in the University of Jyväskylä, Beijing Science & Technology Commission in Tongzhou District no. KJ2015CX004 and Beijing Municipal Science & Technology Commission no. Z161100002616001.

Compliance with ethical standards

Conflicts of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Ethical approval

This research has been approved by the Ethics Committees of Xuanwu Hospital, Capital Medical University and Luhe Hospital, Capital Medical University, and it has been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Geriatric Medicine, Beijing Luhe HospitalCapital Medical UniversityBeijingChina
  2. 2.Faculty of Information TechnologyUniversity of JyväskyläJyvaskylaFinland
  3. 3.School of Biomedical Engineering, Faculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianChina
  4. 4.Department of Neurology, Xuanwu HospitalCapital Medical UniversityBeijingChina
  5. 5.Beijing Key Laboratory of NeuromodulationBeijingChina
  6. 6.Center of EpilepsyBeijing Institute for Brain DisordersBeijingChina
  7. 7.State Key Laboratory of Cognitive Neuroscience and LearningIDG/McGovern Institute for Brain Research, Beijing Normal UniversityBeijingChina

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