Data-driven individualized stimulation target selection based on multi-site neurostimulator in seizure control.

Neuromodulation is an alternative treatment option to antiepileptic drugs and surgery for refractory epilepsy. Nevertheless, its therapeutic effects still have room for improvement. The optimal parameters and strategies for stimulation remain largely unknown. Moreover, it is difficult to obtain sufficient clinical data and experiments have been constrained by current neuromodulation devices. Therefore, it is essential to explore the influence of stimulus parameters on the therapeutic effects in animal experiments. In this work, we developed a miniaturized neuromodulator suitable for animals’ long-term signal recording and multi-channel neuromodulation. Then, we applied a data-driven approach to optimizing the stimulation target in pilocarpine-treated epilepsy rats. Using the Granger Causality method, brain sites with the highest causal outflow at seizure onset were identified as the key target for stimulation. Our results primarily proved that individual differences exist in the selection of optimal target and target stimulation is effective in reducing seizure duration. This may offer a new perspective for updating the neuromodulation system and developing new strategies for stimulation.