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ORIGINAL RESEARCH article
Front. Neurol.
Sec. Epilepsy
Volume 15 - 2024 |
doi: 10.3389/fneur.2024.1402004
Predicting radiofrequency thermocoagulation surgical outcomes in refractory focal epilepsy patients using functional coupled neural mass model
Provisionally accepted- 1 School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- 2 Guangdong Provincial Key Laboratory of Sensor Technology and Biomedical Instrument, School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
- 3 Department of Functional Neurosurgery, First People's Hospital of Foshan, Foshan, Guangdong Province, China
The success rate of achieving seizure freedom after radiofrequency thermocoagulation surgery for patients with refractory focal epilepsy is about 20%-40%. This study aims to enhance the prediction of surgical outcomes based on preoperative decisions through network model simulation, providing a reference for clinicians to validate and optimize surgical plans.Twelve patients with epilepsy who underwent radiofrequency thermocoagulation were retrospectively reviewed in this study. A coupled model based on model subsets of the neural mass model was constructed by calculating partial directed coherence as the coupling matrix from stereoelectroencephalography (SEEG) signals. Multi-channel time-varying model parameters of excitation and inhibitions were identified by fitting the real SEEG signals with the coupled model.Further incorporating these model parameters, the coupled model virtually removed contacts destroyed in radiofrequency thermocoagulation or selected randomly. Subsequently, the coupled model after virtual surgery was simulated.The identified excitatory and inhibitory parameters showed significant difference before and after seizure onset (P < 0.05), and the trends of parameter changes aligned with the seizure process.Additionally, excitatory parameters of epileptogenic contacts were higher than that of nonepileptogenic contacts, and opposite findings were noticed for inhibitory parameters. The simulated signals of postoperative models to predict surgical outcomes yielded an area under the curve (AUC) of 83.33% and an accuracy of 91.67%.The multi-channel coupled model proposed in this study with physiological characteristics showed a desirable performance for preoperatively predicting patients' prognoses.
Keywords: coupled neural mass model, Stereoelectroencephalography, focal epilepsy, functional connectivity, Parameter identification
Received: 16 Mar 2024; Accepted: 12 Aug 2024.
Copyright: © 2024 Cai, Lin, Wang and Luo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yaoxin Lin, Department of Functional Neurosurgery, First People's Hospital of Foshan, Foshan, 528300, Guangdong Province, China
Guofu Wang, Department of Functional Neurosurgery, First People's Hospital of Foshan, Foshan, 528300, Guangdong Province, China
Jie Luo, School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
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