Abstract
Transcranial direct current stimulation (tDCS) is a noninvasive technique that uses constant, low-intensity direct current to regulate brain activities. Clinical studies have shown that cathode-tDCS (c-tDCS) is effective in reducing seizure frequency in patients with epilepsy. Due to the heterogeneity and patient specificity of seizures, patient-specific epilepsy networks are increasingly important in exploring the regulatory role of c-tDCS. In this study, we first set the left hippocampus, para-hippocampus, and amygdala as the epileptogenic zone (EZ), and the left inferior temporal cortex and ventral temporal cortex as the initial propagation zone (PZ) to establish a large-scale epilepsy network model. Then we set tDCS cathode locations according to the maximum average energy of the simulated EEG signals and systematically study c-tDCS inhibitory effects on the propagation of epileptic activity. The results show that c-tDCS is effective in suppressing the propagation of epileptic activity. Further, to consider the patient specificity, we set specific EZ and PZ according to the clinical diagnosis of 6 patients and establish patient-specific epileptic networks. We find that c-tDCS can suppress the propagation of abnormal activity in most patient-specific epileptic networks. However, when the PZ is widely distributed in both hemispheres, the treatment effect of c-tDCS is not satisfactory. Hence, we propose dual-cathode tDCS. For epilepsy models with a wide distribution of PZ, it can inhibit the propagation of epileptiform activity in other nodes except EZ and PZ without increasing the tDCS current strength. Our results provide theoretical support for the treatment of epilepsy with tDCS.
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This work was supported by the National Natural Science Foundation of China (Grant Nos. 12202027, 11932003, 12272092, 11972115) and the China Postdoctoral Science Foundation (Grant No. 2021TQ0025).
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Yu, Y., Fan, Y., Han, F. et al. Transcranial direct current stimulation inhibits epileptic activity propagation in a large-scale brain network model. Sci. China Technol. Sci. 66, 3628–3638 (2023). https://doi.org/10.1007/s11431-022-2341-x
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DOI: https://doi.org/10.1007/s11431-022-2341-x