Closed-Loop Transcranial Ultrasound Stimulation Based on Deep Learning Effectively Suppresses Epileptic Seizures in Mice

Abstract

Transcranial ultrasound stimulation is a non-invasive neuromodulation technique characterized by its high spatial resolution and penetration depth, and it has shown an inhibitory effect on epilepsy. However, current applications predominantly employ open-loop transcranial ultrasound stimulation, which lacks the capacity to dynamically respond to seizures. In the present study, we designed and implemented a closed-loop transcranial ultrasound stimulation (CTUS) system comprising a signal acquisition module, a signal preprocessing module, a deep learning network model-based epileptic signal recognition module, and an ultrasound stimulation module to enable real-time detection and ultrasound intervention in the hippocampus of penicillin-induced epileptic mice. The results indicated that the CTUS system could accurately identify epileptic signals, significantly reduce the seizure firing rate, decrease the power intensity and phase-amplitude coupling, and enhance the sample entropy. These findings demonstrated that the deep learning-based CTUS system was efficient in suppressing seizures in mice.