Individualized Target Selection of Closed-Loop Electrical Stimulation for the Treatment of Spontaneous Temporal Lobe Epilepsy
Closed-loop neuromodulation holds significant promise for treating refractory epilepsy, but the lack of specificity and individualization considerably limits its clinical efficacy. Given the inherent complexity of epilepsy, which involves multiple brain regions and significant interindividual variability, a network-guided, personalized approach is essential. This study aims to develop precise, individualized neuromodulation strategies by leveraging unique brain network characteristics. Using a closed-loop system in chronic temporal lobe epilepsy (cTLE) rats, continuous neural signals were analyzed to identify optimal stimulation targets via the Granger causality (GC) method. Results showed that brain network connectivity remained stable in the short term but changed significantly over time. GC-guided stimulation effectively reduced seizure duration, enhancing $\theta $ and $\alpha $ frequency band activity while suppressing $\gamma $ activity. Additionally, targeted stimulation briefly inhibited interictal spikes and suppressed high-frequency oscillations during seizures. These findings highlight the potential for personalized neuromodulation to improve epilepsy treatment outcomes and deepen understanding of its underlying mechanisms.
Authors
- Yufang Yang (ORCID: https://orcid.org/0009-0008-2467-9149)
- Kedi Xu (ORCID: https://orcid.org/0000-0002-9059-3860)
- Haoqi Ni
- Chang Wang (ORCID: https://orcid.org/0000-0003-0460-8390)
- Jianmin Zhang (ORCID: https://orcid.org/0000-0001-7659-5096)
- Yuting Sun (ORCID: https://orcid.org/0000-0002-2592-1290)
- Junming Zhu
- Yanjie Xing
Institutions
- Ministry of Education (NZ)
- Second Affiliated Hospital of Zhejiang University (CN)
Publication Details
- Journal
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1109/tnsre.2025.3614867
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 0.00