中国电气工程学报（英文） ›› 2023, Vol. 9 ›› Issue (1): 61-70.doi: 10.23919/CJEE.2023.000012

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  • 收稿日期:2023-02-04 修回日期:2023-02-19 接受日期:2023-02-27 出版日期:2023-03-25 发布日期:2023-04-06

Interictal Electrophysiological Source Imaging Based on Realistic Epilepsy Head Model in Presurgical Evaluation: A Prospective Study*

Ruowei Qu1, Zhaonan Wang1, Shifeng Wang2, Le Wang3, Alan Wang4, Guizhi Xu1,*   

  1. 1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, China;
    2. Tianjin Universal Medical Imaging Diagnostic Center, Tianjin 300110, China;
    3. Department of Functional Neurosurgery, Huanhu Hospital, Tianjin 300350, China;
    4. Auckland Bioengineering Institute, The University of Auckland, Grafton, Auckland 1010, New Zealand
  • Received:2023-02-04 Revised:2023-02-19 Accepted:2023-02-27 Online:2023-03-25 Published:2023-04-06
  • Contact: *E-mail: gzxu@hebut.edu.cn
  • About author:Ruowei Qu received her B.S. degree from the School of Science, University of Science and Technology Beijing, Beijing, China in 2012. She finished her Ph.D. in Electrical Engineering from Hebei University of Technology, Tianjin, China in 2020 and currently working as a Post Doctor at this university. During the year of 2015 to 2016, she was a Visiting Scholar at Department of Computational Neurology in University of Pittsburg, USA. She is currently working on the epilepsy zone localization problem based on multi-model medical data.
    Zhaonan Wang received her B.S. degree at Shandong University of Traditional Chinese Medicine in 2017. She is currently pursuing a master degree in Hebei University of Technology, Tianjin, China. Her research interests include epilepsy zone localization and electrophysiological source imaging.
    Shifeng Wang received his B.S. degree in Medicine from Tianjin Medical University in 2007. He is currently studying for a master degree at the Department of Nuclear Medicine, Cancer hospital of Tianjin Medical University, Tianjin, China. His research interests include multimodal imaging evaluation of epilepsy and PET imaging of systemic tumor.
    Le Wang received his B.S. degree from the School of Medicine, Hebei North University, Zhangjiakou, China, in 2008 and received his M.S. in Oncology from Tianjin Medical University, Tianjin, China, in 2011. He finished his M.D. in Surgery from Tianjin Medical University, Tianjin, China, in 2022 and currently working as a Doctor-in-Charge at neurosurgery of Tianjin Huanhu Hospital, Tianjin, China. He is currently working on the epilepsy zone localization problem and epileptic networks based on multimodal medical data.
    Alan Wang is a Principal Investigator and Associate Professor at Auckland University. He has more than ten years of research experience in bioengineering informatics and integrated medicine, especially in advancing the role of medical informatics in health care. His research interests include bioengineering, data informatics, neurocomputing, and biomedical statistics and simulation. He has developed medical data analytics methods for mobile health and personalized diagnosis and prognosis based on intelligent computing theories. He has experience analyzing huge cohorts of patient data with applications of early diagnosis, disease understanding, and effective treatment of patients with different disorders. He serves as an Editorial Board Member and an Active Reviewer for several international journals.
    Guizhi Xu was born in 1962. She received a Ph.D. degree from the School of Electrical Engineering, Hebei University of Technology, Tianjin, China in 2002. She is currently a Professor, Doctoral Advisor and the Dean of School of Electrical Engineering, Hebei University of Technology, Tianjin, China. She is also the Head of Key Subjects at the Provincial Level of Biomedical Engineering, a Professor of Meta-optics of Hebei University of Technology, and the Head of the National Top-quality Course of Engineering Electromagnetic Field. She published more than 90 academic papers retrieved by SCI and Ei, and published 3 monographs. She presided over 1 key project of the National Natural Science Foundation, 3 projects of the National Natural Science Foundation and one pre-research project of the Ministry of General Equipment, and completed 2 key projects of the National Natural Science Foundation in cooperation with Tsinghua University and the Fourth Military Medical University. She achieved Hebei Science and Technology Outstanding Contribution Award 1, Hebei Natural Science Second Prize and Third Prize 1, Hebei Science and Technology Progress Second Prize and Third Prize 1, and Hebei Excellent Teaching Achievement Second Prize 3. She achieved the honorary titles of the First Famous Teaching Teacher in Hebei Province, the Outstanding Young and Middle-Aged Experts in Hebei Province, the Outstanding Young and Middle-Aged Backbone Teachers in Hebei Province, the Advanced Individuals in Hebei Province, and the Outstanding Communist Party Members in Hebei Province's education system.
  • Supported by:
    *National Key R&D Program of China (2022YFC2402203) and the Key R&D Program of Hebei (21372002D).

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Abstract: Invasive techniques are becoming increasingly important in the presurgical evaluation of epilepsy. Adopting the electrophysiological source imaging (ESI) of interictal scalp electroencephalography (EEG) to localize the epileptogenic zone remains a challenge. The accuracy of the preoperative localization of the epileptogenic zone is key to curing epilepsy. The T1 MRI and the boundary element method were used to build the realistic head model. To solve the inverse problem, the distributed inverse solution and equivalent current dipole (ECD) methods were employed to locate the epileptogenic zone. Furthermore, a combination of inverse solution algorithms and Granger causality connectivity measures was evaluated. The ECD method exhibited excellent focalization in lateralization and localization, achieving a coincidence rate of 99.02% (p<0.05) with the stereo electroencephalogram. The combination of ECD and the directed transfer function led to excellent matching between the information flow obtained from intracranial and scalp EEG recordings. The ECD inverse solution method showed the highest performance and could extract the discharge information at the cortex level from noninvasive low-density EEG data. Thus, the accurate preoperative localization of the epileptogenic zone could reduce the number of intracranial electrode implantations required.

Key words: Epilepsy, epileptogenic zone, realistic head model, functional brain connectivity, ESI