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Analysis of the optimal target node to reduce seizure-like discharge in networks |
| Luyao Yan(闫璐瑶)1, Honghui Zhang(张红慧)1,2,†, and Zhongkui Sun(孙中奎)1,‡ |
1 School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an 710129, China;
2 MIIT Key Laboratory of Dynamics and Control of Complex Systems, Xi'an 710129, China |
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Abstract Network approaches have been widely accepted to guide surgical strategy and predict outcome for epilepsy treatment. This study starts with a single oscillator to explore brain activity, using a phenomenological model capable of describing healthy and epileptic states. The ictal number of seizures decreases or remains unchanged with increasing the speed of oscillator excitability and in each seizure, there is an increasing tendency for ictal duration with respect to the speed. The underlying reason is that the strong excitability speed is conducive to reduce transition behaviors between two attractor basins. Moreover, the selection of the optimal removal node is estimated by an indicator proposed in this study. Results show that when the indicator is less than the threshold, removing the driving node is more possible to reduce seizures significantly, while the indicator exceeds the threshold, the epileptic node could be the removal one. Furthermore, the driving node is such a potential target that stimulating it is obviously effective in suppressing seizure-like activity compared to other nodes, and the propensity of seizures can be reduced 60$%$ with the increased stimulus strength. Our results could provide new therapeutic ideas for epilepsy surgery and neuromodulation.
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Received: 25 December 2023
Revised: 23 February 2024
Accepted manuscript online: 13 March 2024
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PACS:
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87.19.le
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(EEG and MEG)
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87.19.xm
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(Epilepsy)
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| Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 12072265, 12272295, and 11972288). |
Corresponding Authors:
Honghui Zhang, Zhongkui Sun
E-mail: haozhucy@nwpu.edu.cn;dynsun@126.com
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Cite this article:
Luyao Yan(闫璐瑶), Honghui Zhang(张红慧), and Zhongkui Sun(孙中奎) Analysis of the optimal target node to reduce seizure-like discharge in networks 2024 Chin. Phys. B 33 058703
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