Prediction of Pharmacoresistance in Drug-Naïve Temporal Lobe Epilepsy Using Ictal EEGs Based on Convolutional Neural Network.
10.1007/s12264-025-01350-2
- Author:
Yiwei GONG
1
;
Zheng ZHANG
2
;
Yuanzhi YANG
1
;
Shuo ZHANG
1
;
Ruifeng ZHENG
2
;
Xin LI
2
;
Xiaoyun QIU
1
;
Yang ZHENG
3
;
Shuang WANG
4
;
Wenyu LIU
5
;
Fan FEI
1
;
Heming CHENG
1
;
Yi WANG
1
;
Dong ZHOU
5
;
Kejie HUANG
6
;
Zhong CHEN
7
;
Cenglin XU
8
Author Information
1. Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, College of Pharmaceutical Sciences, The Second Affiliated Hospital of Zhejiang Chinese Medical University (Xinhua Hospital), Zhejiang Chinese Medical University, Hangzhou, 310053, China.
2. College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310058, China.
3. Department of Neurology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, 310006, China.
4. Epilepsy Center, School of Medicine, Second Affiliated Hospital, Zhejiang University, Hangzhou, 310009, China.
5. Department of Neurology, West China Hospital of Sichuan University, Chengdu, 610041, China.
6. College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310058, China. huangkejie@zju.edu.cn.
7. Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, College of Pharmaceutical Sciences, The Second Affiliated Hospital of Zhejiang Chinese Medical University (Xinhua Hospital), Zhejiang Chinese Medical University, Hangzhou, 310053, China. chenzhong@zju.edu.cn.
8. Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, College of Pharmaceutical Sciences, The Second Affiliated Hospital of Zhejiang Chinese Medical University (Xinhua Hospital), Zhejiang Chinese Medical University, Hangzhou, 310053, China. xucenglin5zz@zju.edu.cn.
- Publication Type:Journal Article
- Keywords:
EEG;
Pharmacoresistance;
Precision medicine;
Prediction;
Temporal lobe epilepsy
- MeSH:
Epilepsy, Temporal Lobe/diagnosis*;
Animals;
Drug Resistant Epilepsy/drug therapy*;
Electroencephalography/methods*;
Rats;
Anticonvulsants/pharmacology*;
Neural Networks, Computer;
Male;
Humans;
Phenytoin/pharmacology*;
Adult;
Disease Models, Animal;
Female;
Rats, Sprague-Dawley;
Young Adult;
Convolutional Neural Networks
- From:
Neuroscience Bulletin
2025;41(5):790-804
- CountryChina
- Language:English
-
Abstract:
Approximately 30%-40% of epilepsy patients do not respond well to adequate anti-seizure medications (ASMs), a condition known as pharmacoresistant epilepsy. The management of pharmacoresistant epilepsy remains an intractable issue in the clinic. Its early prediction is important for prevention and diagnosis. However, it still lacks effective predictors and approaches. Here, a classical model of pharmacoresistant temporal lobe epilepsy (TLE) was established to screen pharmacoresistant and pharmaco-responsive individuals by applying phenytoin to amygdaloid-kindled rats. Ictal electroencephalograms (EEGs) recorded before phenytoin treatment were analyzed. Based on ictal EEGs from pharmacoresistant and pharmaco-responsive rats, a convolutional neural network predictive model was constructed to predict pharmacoresistance, and achieved 78% prediction accuracy. We further found the ictal EEGs from pharmacoresistant rats have a lower gamma-band power, which was verified in seizure EEGs from pharmacoresistant TLE patients. Prospectively, therapies targeting the subiculum in those predicted as "pharmacoresistant" individual rats significantly reduced the subsequent occurrence of pharmacoresistance. These results demonstrate a new methodology to predict whether TLE individuals become resistant to ASMs in a classic pharmacoresistant TLE model. This may be of translational importance for the precise management of pharmacoresistant TLE.
