Back

Phase-dependent closed-loop intersectional short-pulse stimulation reduces seizure duration: From computational modeling to clinical application

Barcsai, L.; Forgo, N.; Somogyvari, Z.; Hazi, V.; Furuglyas, K.; Huszar-Kis, M.; Chadaide, Z.; Rafi, P.; Laszlovszky, T.; Eross, L.; Berenyi, A.

2026-08-23 neuroscience
10.64898/2026.08.19.745828 bioRxiv
Show abstract

Drug-resistant epilepsy affects one-third of patients with persistent seizures despite optimal therapy. Intersectional short-pulse (ISP) stimulation is a novel transcranial electrical stimulation technique designed to deliver temporally precise, spatially targeted modulation of pathological brain activity. Here, we combined computational modeling with measurements in a rat epilepsy model and in patients with epilepsy to map the relationship between stimulation phase and seizure attenuation. In silico simulations of epileptiform networks showed that ISP stimulation significantly shortened seizure duration, with efficacy strongly depending on the phase of delivery. Phase-targeted stimulation during the rising phase and around the peaks (~45-90{degrees}) of the seizure oscillations led to the greatest reduction in seizure length. In rodents, ISP decreased seizure duration by 42.4% and shortened generalized seizure segments by 58.3%. In humans, stimulation reduced seizure length by 60.9% compared to control seizures. Phase dependence was evident across models and species, with a prominent efficacy window in the rising-to-peak portion of the ictal oscillation and model-specific secondary windows. These findings show that phase-targeted ISP can substantially shorten seizures and support phase-resolved stimulation as a precision-neuromodulation approach for epilepsy.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.